bioengineering
Article
Profile of a Multivariate Observation under Destructive
Sampling—A Monte Carlo Approach to a Case of Spina Bifida
Tianyuan Guan 1,2, * , Rigwed Tatu 3 , Koffi Wima 2 , Marc Oria 3 , Jose L. Peiro 3 , Chia-Ying Lin 4
and Marepalli. B. Rao 2, *
1
2
3
4
*
College of Public Health, Kent State University, Kent, OH 44242, USA
Division of Biostatistics and Bioinformatics, University of Cincinnati, Cincinnati, OH 45221, USA
The Center for Fetal and Placental Research, Pediatric General and Thoracic Surgery Division,
Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA; oriaalmc@ucmail.uc.edu (M.O.)
Orthopedic Surgery, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA;
linc9@ucmail.uc.edu
Correspondence: tiguan5@kent.edu (T.G.); raomb@ucmail.uc.edu (M.B.R.); Tel.: +1-330-672-4788 (T.G.);
Tel.: +1-513-558-3602 (M.B.R.)
Abstract: A biodegradable hybrid polymer patch was invented at the University of Cincinnati to
cover gaps on the skin over the spinal column of a growing fetus, characterized by the medical
condition spina bifida. The inserted patch faces amniotic fluid (AF) on one side and cerebrospinal
fluid on the other side. The goal is to provide a profile of the roughness of a patch over time at 0, 4, 8,
12, and 16 weeks with a 95% confidence band. The patch is soaked in a test tube filled with either
amniotic fluid (AF) or phosphate-buffered saline (PBS) in the lab. If roughness is measured at any
time point for a patch, the patch is destroyed. Thus, it is impossible to measure roughness at all weeks
of interest for any patch. It is important to assess the roughness of a patch because the rougher the
patch is, the faster the skin grows under the patch. We use a model-based approach with Monte Carlo
simulations to estimate the profile over time with a 95% confidence band. The roughness profiles
are similar with both liquids. The profile can be used as a template for future experiments on the
composition of patches.
Citation: Guan, T.; Tatu, R.; Wima, K.;
Oria, M.; Peiro, J.L.; Lin, C.-Y.; Rao,
Keywords: birth defects; hybrid polymer patches; destructive sampling; multivariate normal distribution
M.B. Profile of a Multivariate
Observation under Destructive
Sampling—A Monte Carlo Approach
to a Case of Spina Bifida.
Bioengineering 2024, 11, 249.
https://doi.org/10.3390/
bioengineering11030249
Academic Editor: Yu Kuang
Received: 8 February 2024
Revised: 28 February 2024
Accepted: 1 March 2024
Published: 3 March 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
1. Introduction
Spina bifida (SB) is a congenital neural tube defect that occurs during the early stages
of fetal development, which is characterized by the incomplete closure of the neural tube,
resulting in a range of spinal cord abnormalities [1]. The neural tube normally forms early
in pregnancy, closing around the fourth week of conception [2]. However, in cases of spina
bifida, this closure is incomplete, with amniotic fluid entering the fetus and cerebrospinal
fluid seeping out of the fetus, resulting in structural defects in the spinal cord that can
significantly impact the quality of life and well-being of the patients [3]. The three main
types of spina bifida are spina bifida occulta, meningocele, and myelomeningocele, with
varying degrees of severity [4]. Spina bifida occulta is the mildest form, involving a small
gap in the spine with no visible protrusion [5]. Meningocele involves a sac of cerebrospinal
fluid protruding through the opening, while myelomeningocele is the most severe form,
where the spinal cord and its protective covering protrude outside the body [6–8]. Spina
bifida can range from being soft to causing a disability. Symptoms depend on where on
the spine the opening is located and how large the gap is. More serious symptoms happen
when the spinal cord and nerves are involved. The precise etiologies of spina bifida are
complex and multifactorial, involving genetic and environmental factors [9]. Factors such
as folic acid deficiency, certain medications, and maternal health conditions may contribute
to the occurrence of spina bifida [9,10].
Bioengineering 2024, 11, 249. https://doi.org/10.3390/bioengineering11030249
https://www.mdpi.com/journal/bioengineering
Bioengineering 2024, 11, 249
2 of 9
In the United States, about 1500 infants, or 1 in every 2700 births, are born with spina
bifida every year [11]. It is a neural tube defect that frequently occurs in families. Spina
bifida occurs because of an abnormality in the development of the spinal cord that occurs
in the first trimester of pregnancy. Treatment of spina bifida varies based on the severity
and type of the condition, and it includes several methods [12]. If the condition is detected
early, fetoscopy is a good option for rectifying the problem, having been proven to be safer
and more beneficial than traditional surgery [13–15]. The latest technology used in the
minimally invasive fetoscope prenatal surgery involves deploying a coiled patch through a
trocar, expanding the patch at the surgical site, and using tissue sealants or sutures [16]. If
the condition is not detected in a timely fashion, the baby will live with the condition with
several physical and mental ailments like paralysis and bowel and bladder dysfunction [13].
When necessary, early treatment for spina bifida involves surgery. However, surgery does
not always completely restore lost function. Ideally, early screening and diagnosis can
reduce the likelihood of damage to the baby.
2. Materials and Methods
The Cincinnati Children’s Hospital Medical Center, in cooperation with the Biomedical
Engineering Department at the University of Cincinnati, had developed a polymeric patch
to protect the defect site and prevent fluid transfer. The patch designed is biocompatible,
watertight, self-expanding, and biodegradable. It is a hybrid of poly (L-lactic acid) (PLA)
and poly(ε-caprolactone) (PCL) polymers in a 4:1 ratio. PLA degrades quickly, while PCL
degrades slowly. Through a series of experiments, it was found that the hybrid patch
degrades by an average of ~20% by weight in 16 weeks. Further studies will be conducted
in animal models to track degradation beyond 16 weeks.
The patch has been experimented successfully on rats. One major advantage of
the hybrid patch is that it is not necessary to perform a second surgery to remove the
patch. The patch is patented (the patch for spina bifida repair is under U.S. Patent No.
WO/2018/067811), and the details about the patch were reported in their previous research [16–18].
The next item on their research agenda was to examine the properties of the hybrid
polymer patch in a simulated fetal environment. Inside the womb, the patch faces amniotic
fluid (AF) on one side and physiological (cerebrospinal) fluid on the other side. The
physiological fluid is chemically represented by phosphate-buffered saline (PBS). Amniotic
fluid discarded from fetal surgeries at Cincinnati Children’s Hospital Medical Center
was used for experimental purposes with IRB permission (CCHMCIRB#2017-2414). The
designed patches are placed in test tubes either soaked in AF or PBS. One of the tasks is to
measure patch roughness over a span of time. The reason for measuring roughness is to
assess how good the patch is at absorbing nutrients. The higher the degree of roughness
is, the stronger the nutrients latch onto the patch, and the speedier the natural skin covers
the gap. To measure the roughness of a patch, the patch is subjected to a process, which is
destructive. Once the measurement is obtained, the patch is no longer usable. Consequently,
how roughness evolves over time cannot be assessed. Despite this acute difficulty in
obtaining the requisite data, it is hoped that the assessment over time could be possible in
some way. We are addressing this issue in the paper.
The data we have on hand are destructive. All the roughness measurements come
from different patches. The goal is to develop a profile of roughness using this destructive
data. This problem is common in pharmaceutical drug testing [19–21]. One common
research problem in pharmacokinetics is obtaining a profile of how much of a drug remains
in the blood. A researcher injects a specific drug at 0 h into a mouse and examines how
much drug is left in the bloodstream at several different hours. At any hour of interest,
a mouse has to be sacrificed in order to determine the amount of drug in the blood. It is
impossible to measure the amount of drug left in the blood for several hours for a single
mouse. Pharmaceutical researchers implement the so-called “Sacrifice Design” to collect
data [19–21]. The classical complete data design where each animal is sampled for analysis
Bioengineering 2024, 11, 249
3 of 9
once per time point is usually only applicable for larger animals. In the case of rats and mice,
where blood sampling is restricted, the batch design or the serial sacrifice design needs
to be considered. In serial sacrifice designs, only one sample is taken from each animal.
The design involves injecting the drug into 10 animals, for example. At one hour, two
animals are sacrificed to measure their drug content. At two hours, another two animals are
sacrificed to measure their drug content. This is repeated at 4, 10, 12, and 24 h. We will never
have data at all hours of interest for any animal [22–25]. Our data, in spirit, are similar to
the data from the sacrifice design. Monte Carlo methods can be used to recover profile data
from the destructive data [25,26]. The Monte Carlo method is a generic name for recovering
information from partial data by simulations. In this paper, we introduce an innovative
Monte Carlo method to generate profiles of patches from destructive data. Our method is
model-based. We pursued two types of Monte Carlo simulations to generate profiles with
confidence bands. In one, it was a conditional profile conditioned on information at the
16th week. In the other, it was an unconditional profile covering the entire time span. The
details are provided in the Materials and Method Section 2.2.
2.1. Experimental Details
Twelve patches were placed in separate test tubes soaked in AF and kept in a shaker.
Another twelve patches were placed in separate test tubes soaked in PBS in a shaker.
Roughness was measured on an additional three patches as baseline measurements. At
four weeks, three patches from AF test tubes were removed and roughness was measured.
As has been pointed out, these patches were not reusable. The same process is repeated at
eight weeks, twelve weeks, and sixteen weeks. The same is repeated for PBS patches. The
data are reproduced in Table 1. Summary statistics are provided in Table 2.
Table 1. Roughness measurements by fluid and time.
Roughness
Week
Baseline
0
0
0
4
4
4
8
8
8
12
12
12
16
16
16
139
122
132
AF
PBS
223
267
217
245
269
257
265
283
285
306
247
320
177
202
212
185
198
205
167
217
248
224
198
229
Table 2. Mean and standard deviation (SD) of roughness by fluid and time.
Baseline
Week
0
4
8
12
16
AF
Mean
SD
131
8.54
PBS
Mean
SD
Mean
SD
235.67
257
277.67
291
27.3
12
11.02
38.74
197
196
210.67
217
18.03
10.15
40.87
16.64
Bioengineering 2024, 11, 249
4 of 9
The roughness of a patch rises over time on average, no matter whether the patch was
soaked in amniotic fluid or phosphate-buffered saline. Our goal was to build a profile of the
roughness of a patch soaked either in AF or PBS at 0, 4, 8, and 12 weeks given X5 . We use a
model-oriented endeavor to build the profiles. The method is outlined and implemented in
Section 2.2.
2.2. Statistical Methods
For any hybrid polymer patch, let X1 = roughness at zero weeks. After the patch is
dipped in AF (or PBS), let X2 = roughness at four weeks, X3 = roughness at eight weeks,
X4 = roughness at twelve weeks, and X5 = roughness at sixteen weeks.
Technically, the vector (X1 , X2 , X3 , X4 , X5 ) is not observable in its entirety for any patch.
This means, for example, if X1 is observed for a patch, X2 , X3 , X4 , and X5 are not observable.
In the experiment, three measurements were obtained on each Xi independently from a
total of 15 patches. Let (µ1 , µ2 , µ3 , µ4 , µ5 ) be the population mean vector of (X1 , X2 , X3 ,
X4 , X5 ). The homogeneity of the means was tested by the ANOVA (analysis of variance)
method. The null hypothesis of homogeneity of means was rejected for patches soaked
in AF (p < 0.001). The homogeneity of population variances was tested by the Bartlett test
(p = 0.247). The hypothesis of homogeneity of variances was not rejected. An estimate of
the common variance was given as 517. The normality and homoskedasticity were checked
out to be valid (Wilk–Shapiro test: p = 0.705). Similar results hold for patches soaked in
PBS (homogeneity of means: p = 0.005; normality and homoscedasticity: Wilk–Shapiro
Test: p = 0.364; homogeneity of variances: Bartlett test: p = 0.253). Estimate of the common
variance = 490.
Each Xi can be taken to be normally distributed. It is reasonable to assume that (X1 ,
X2 , X3 , X4 , X5 )~MVN5 (µ, Σ) with mean vector µT = (µ1 , µ2 , µ3 , µ4 , µ5 ) = (µ(1) , µ5 ) and
dispersion matrix
σ12
ρσ2 σ1
Σ=
ρσ3 σ1
ρσ4 σ1
ρσ5 σ1
ρσ1 σ2
σ22
ρσ3 σ2
ρσ4 σ2
ρσ5 σ2
ρσ1 σ3
ρσ2 σ3
σ32
ρσ4 σ3
ρσ5 σ3
ρσ1 σ4
ρσ2 σ4
ρσ3 σ4
σ42
ρσ5 σ4
ρσ1 σ5
ρσ2 σ5
Σ11
=
ρσ3 σ5
Σ
21
ρσ4 σ5
Σ12
,
Σ22
σ52
where Σ11 is the dispersion matrix of (X1 , X2 , X3 , X4 ), and Σ22 = (σ52 ). The entity µ(1) is the
mean vector of (X1 , X2 , X3 , X4 ). The way we have partitioned the mean vector and the
dispersion matrix is influenced by the following conditional distribution. The acronym
MVN stands for multivariate normal distribution.
The Xi s are equi-correlated with common correlation coefficient ρ. The dispersion
matrix is positive if −1/4 < ρ < 1. We have chosen the simple model because it is a
reasonable way to build a conditional profile of roughness. We can also handle the
conditional probability.
Pr(−a ≤ X1 − µ1 ≤ a, −b ≤ X2 − µ2 ≤ b, −c ≤ X3 − µ3 ≤ c, −d ≤ X4 − µ4 ≤ d|X5 ),
which will be helpful for building a prediction band. Even though we know the conditional
distribution of X1 , X2 , X3 , X4 given X5 , under this model, calculating the conditional probability is extremely difficult. It involves evaluating a four-dimensional integral. However,
the distribution can be simulated so that the joint probability can be estimated. This is the
gist of the Monte Carlo simulations.
The conditional joint distribution of
−1
Σ21 ,
(X1 , X2 , X3 , X4 ) |X5 ∼ MVN4 λ, Σ11 − Σ12 Σ22
Bioengineering 2024, 11, 249
5 of 9
σ1 /σ5
µ1
σ2 /σ5
µ2
−1
+ Σ12 Σ22
( X5 − µ 5 ) =
µ3 + ρσ3 /σ5 (X5 − µ5 ) and
σ4 /σ5
µ4
where λ = µ(1)
−1
Σ11 − Σ12 Σ22
Σ21 = Σ11
σ1
σ2
− ρ2
σ3 σ1
σ2
σ3
σ4 .
σ4
The conditional dispersion matrix is also equi-correlated with correlation ρ/(1 + ρ).
The conditional variance of Xi |X5 is (1 − ρ2 ) × σi2 . The conditional variance is less now,
and the correlation is also less if ρ > 0.
Our strategy now works out as follows:
1.
2.
3.
4.
5.
Given X5 , simulate the joint distribution of (X1 , X2 , X3 , X4 ). This requires knowledge
of the conditional mean and conditional dispersion matrix.
We need µi s, which can be estimated using the individual data on Xi s.
We need σi s, which can be estimated using the individual data on Xi s.
The correlation coefficient ρ glues the means, variances, and joint distribution. There
was no way we can estimate the correlation coefficient using the marginal data we
have. We performed simulations by assuming the value of ρ = 0.0 (0.1) 0.9.
We conducted Monte Carlo simulations. For each choice of ρ and fluid, Steps 1 through
4 were repeated one thousand times. The average of (X1 , X2 , X3 , X4 ) s was the desired
profile. The 95% band surrounding the mean was built using the following inequality:
Pr(−a1 ≤ X1 − µ1 ≤ a1 , −a2 ≤ X2 − µ2 ≤ a2 , −a3 ≤ X3 − µ3 ≤ a3 , −a4 ≤ X4 − µ4 ≤ a4 | X5 )
≥ ∏4i=1 Pr(−ai ≤ X1 − µ1 ≤ ai | X5 )
See Dykstra [27] and Tong [28,29].
Each marginal probability was set at 0.95ˆ0.25 and solved for a. The band was conservative. Simulations were carried out and the results were reported in Section 3.
For the unconditional profile, we took each Xi to be normally distributed. It was
reasonable to assume that (X1 , X2 , X3 , X4 , X5 )~MVN5 (µ, Σ) with mean vector µT = (µ1 , µ2 ,
µ3 , µ4 , µ5 ) and dispersion matrix
1
ρ
Σ = σ2
ρ
ρ
ρ
ρ
1
ρ
ρ
ρ
ρ
ρ
1
ρ
ρ
ρ
ρ
ρ
1
ρ
ρ
ρ
ρ
.
ρ
1
Each Xi was assumed to have the same variance. This was justified by the ANOVA
procedure carried out in Section 2.2. This model was the classic equi-correlated normal
distribution, which means there was the same variance and correlation (ρ) between any
two Xi and Xj . We took the liberty in assuming equi-correlation. This assumption allowed
us build a profile of roughness overtime and a 95% confidence band of the profile. We
chose the simple model because this was a reasonable way to build a profile of roughness.
The goal now was to find a number a such that:
Pr(−a ≤ X1 − µ1 ≤ a, −a ≤ X2 − µ2 ≤ a, −a ≤ X3 − µ3 ≤ a, −a ≤ X4 − µ4 ≤ a, a ≤ X5 − µ5 ≤ a) = 0.95.
This probability was a function of the means µ1 , µ2 , µ3 , µ4 , µ5 , σ2 , and ρ. We used
estimates of means and common variance in the equation. We experimented with several
choices of correlation for the band. We chose ρ = 0.5 for which the length of each interval
2 × a was minimum. The calculation of the probability was daunting. We resorted to Monte
Bioengineering 2024, 11, 249
6 of 9
Carlo simulations. The multivariate normal distribution was simulated one thousand times
to determine a for our choice of ρ [30].
Bioengineering 2024, 11, x FOR PEER REVIEW
6 of 10
3. Results
3.1. Conditional Profile
3. Results
We set X5 to equal the average of observed X5 . For AF, X5 = 291 and for PBS, X5 = 217.
3.1.
Profile
The Conditional
Monte Carlo
average profile remained more or less the same across a whole range of ρ
s. WeWe
calculated
average
profile of
at observed
ρ = 0.6 forXeach
set X5 tothe
equal
the average
5. Forfluid.
AF, X5 = 291 and for PBS, X5 = 217.
For
amniotic
fluid,
the
band
is
narrow
at
0,
8,
and
The band
is very
wide
The Monte Carlo average profile remained more or less 12
theweeks.
same across
a whole
range
of
at
12
weeks.
The
variances
in
the
marginal
data
very
strongly
influence
the
width
of
the
ρ s. We calculated the average profile at ρ = 0.6 for each fluid.
bands (Figure 1).
200
100
150
Roughness
250
300
Profile of Roughness of Hybrid Patches +
95% Prediction Band
Amniotic Fluid
Phosphate Buffer Saline
0
4
8
12
16
Week No.
Figure 1.
1. Conditional
Conditional profile
profile of
of roughness
roughness at
at zero,
zero, four,
four, eight,
eight, and
and twelve
twelve weeks
weeks given
given roughness
roughness at
at
Figure
sixteen
weeks
+
95%
prediction
band.
sixteen weeks + 95% prediction band.
3.2. Unconditional Profile
For amniotic fluid, the band is narrow at 0, 8, and 12 weeks. The band is very wide at
The unconditional
stable
across
This was
the result
of the model
12 weeks.
The variancesprofiles
in the were
marginal
data
verytime.
strongly
influence
the width
of the
we
assumed.
The
vertical
width
was
constant
across
time.
The
width
was
wider
for AF
bands (Figure 1).
than for PBS.
3.2. Unconditional Profile
4. Discussion and Conclusions
The unconditional profiles were stable across time. This was the result of the model
Estimating roughness profile of a patch at 0, 4, 8, and 12 weeks given information
we assumed. The vertical width was constant across time. The width was wider for AF
on roughness at 16 weeks seemed to be hopeless with the data we had on hand. Our
than for PBS.
data were destructive in the sense that once roughness was measured on a patch, the
patch was no longer useable. We overcame the difficulties by following a model-based
4. Discussion and Conclusions
approach. We assumed that the roughness measurements on a patch have a multivariate
Estimating
roughness
profile
of a patch
at 0, 4, 8,equal.
and 12By
weeks
givento
information
on
normal
distribution
with all
pairwise
correlations
resorting
Monte Carlo
roughness
at
16
weeks
seemed
to
be
hopeless
with
the
data
we
had
on
hand.
Our
data
simulations [19–21], we were able to build the required profile.
were destructive in the sense that once roughness was measured on a patch, the patch was
no longer useable. We overcame the difficulties by following a model-based approach. We
assumed that the roughness measurements on a patch have a multivariate normal distribution with all pairwise correlations equal. By resorting to Monte Carlo simulations [19–
21], we were able to build the required profile.
Bioengineering 2024, 11, x FOR PEER REVIEW
Bioengineering 2024, 11, x FOR PEER REVIEW
Bioengineering 2024, 11, 249
7 of 10
7 of 10
7 of 9
The means of roughness were rising over the weeks no matter in what fluid the
Thewere
means
of roughness
were
rising
overclear
the weeks
no
matter
in what
fluid the
patches
soaked
in. However,
there
was
pattern
among
standard
The means
of roughness
were rising
over no
the weeks
no matter
in what
fluid deviations.
the patches
patches
were
soaked
in.
However,
there
was
no
clear
pattern
among
standard
deviations.
The
deviations
of roughness
were
thepattern
largest among
at 16 weeks
for AF
and 12 weeks
werestandard
soaked in.
However,
there was no
clear
standard
deviations.
The
The
standard
deviations
of roughness
were
theatlargest
at 16the
weeks
forofAF
and 12 weeks
for
PBS.
Since
we were
conditioning
thethe
profile
16
weeks,
roughness
atPBS.
the
standard
deviations
of roughness
were
largest
at 16
weeks
forprofile
AF and
12
weeks for
for
PBS. Sincefor
weAF
were
conditioning
the profile at over
16 weeks,
the profile
of roughness
at the
other
provided
a steady
theprofile
weeks.
band
for
Since weeks
we were conditioning
the
profile behavior
at 16 weeks, the
ofHowever,
roughnessthe
at the
other
other
for AF
a steady
behavior over
the weeks.
However,
the band
for
PBS
atweeks
12
wasprovided
very
wide,
reflecting
present
atthe
12 band
weeks
forPBS
PBS.
weeks
forweeks
AF provided
a steady
behavior unusual
over thevariation
weeks. However,
for
at
PBS at
12 unconditional
weeks was very
wide,was
reflecting
unusual
at 12
weeks for PBS.
The
stable over
timevariation
for
both present
fluids.
The
stability
12 weeks
was very wide,profile
reflecting
unusual
variation
present
at 12 weeks
for PBS.was due
The unconditional
was over
stable
overThe
time
for both fluids.
The stability
was due
to a constant
variance inprofile
roughness
time.
assumption
of variance
was permissible
The unconditional
profile
was stable
over time
for both fluids.
The stability
was due
to
a
constant
variance
in
roughness
over
time.
The
assumption
of
variance
was
permissible
to a constant
variance in
roughnessjustified
over time.
The assumption
of variance
permissible
because
the ANOVA
procedure
it. According
to Figures
2 andwas
3, the
vertical
because
the
ANOVA
procedure
justified
it.
According
to Figures
and
the vertical
because
procedure
justified
it.
According
to Figures
2 and2 3,
the3,vertical
width
width
ofthe
theANOVA
profiles was
approximately
100.
width
of the profiles
was approximately
of the profiles
was approximately
100. 100.
Figure 2. Unconditional profile of roughness at zero, four, eight, twelve and sixteen weeks + 95%
Figure 2.
2. Unconditional
Unconditionalprofile
profile of
of roughness
roughness at
at zero,
zero, four,
four, eight,
eight, twelve
twelve and
and sixteen
sixteen weeks
weeks ++ 95%
95%
Figure
prediction
band for AF.
prediction band for AF.
prediction
Figure 3.
3. Unconditional
Unconditional profile
profile of
of roughness
roughness at
at zero,
zero, four,
four, eight,
eight, twelve
twelve and
and sixteen
sixteen weeks
weeks ++ 95%
95%
Figure
Figure
3.
Unconditional
profile
of
roughness
at
zero,
four,
eight,
twelve
and
sixteen
weeks
+
95%
prediction band
band for
for PBS.
PBS.
prediction
prediction band for PBS.
This is the first study that develops a biodegradable hybrid patch to cover the gap
This
is of
thea first
study
that
develops
a biodegradable
hybrid
patchserve
to cover
the
gap in
in the
skin
fetus.
Thethat
roughness
measurements
andhybrid
the profile
as athe
template
This
is
the
first
study
develops
a
biodegradable
patch
to
cover
gap
in
the
skin ofresearch.
a fetus. The
roughness
measurements
and the creating
profile serve
as a patch
template
for
for
future
Future
research
work
would
involve
a
hybrid
with
the skin of a fetus. The roughness measurements and the profile serve as a template fora
Bioengineering 2024, 11, 249
8 of 9
different composition of polymer patches. The roughness of the new patch can then be
compared with our roughness analysis of the hybrid patch.
Inferring the joint behavior of the variables based on marginal behavior is fraught with
difficulties. The assumption of equi-correlation seems to be very strong, and its validity is
difficult to assess with the limited data available. There are some limitations to our study.
There is considerable variation in the marginal data. It seems that no fluid is preferable
to the other. Small sample size could be a reason. Sample sizes are typically small in
biomedical research. We have informed the core researcher of the need for a reasonable
number of samples for a clear understanding of the evolution of roughness over time. In
addition, our study is a single-institution study. More trustworthy conclusions could be
drawn from a multi-institutional study.
Examining properties of hybrid patches is a natural line of research following the core
experiment of covering gaps on the skin of a fetus. In this paper, we laid out how to build a
profile using destructive data. The methodology we used is applicable to study other properties. We also plan to explore and improve the methodology to overcome assumptions.
The overarching research goal is to understand how roughness of hybrid patches
evolves overtime when the patch is immersed either in AF or PBS. The profile we developed
can be used as a template for future experiments on the composition of patches. The
experimentalists, as it stands now, understand that amniotic fluid exposure has a higher
effect on the surface roughness of the patch.
Author Contributions: Conceptualization, T.G. and M.B.R.; methodology, T.G.; software, T.G. and
K.W.; validation, T.G., R.T. and M.B.R.; formal analysis, T.G.; investigation, M.O., J.L.P. and C.-Y.L.;
resources, M.O., J.L.P. and C.-Y.L.; data curation, R.T., M.O., J.L.P., C.-Y.L. and T.G.; writing—original
draft preparation, T.G.; writing, M.B.R. and T.G.; visualization, M.B.R.; supervision, M.B.R.; project
administration, T.G.; funding acquisition, T.G. and M.B.R. All authors have read and agreed to the
published version of the manuscript.
Funding: This research was funded by “Bioengineering Research Grant, grant number NIH/NINDS
R01NS103992” and “The APC was funded by University of Cincinnati, Library”.
Institutional Review Board Statement: The animal study protocol was approved by the Institutional Review Board (or Ethics Committee) of the University of Cincinnati (protocol code CCHMCIRB#2017-2414).
Informed Consent Statement: Not applicable.
Data Availability Statement: Reported in Table 1.
Acknowledgments: We thank Melodie Fickenscher at the Advanced Materials Characterization
Center (University of Cincinnati) for help with surface roughness characterization. We also thank
the Bioengineering Research Grant (NIH/NINDS R01NS103992), Ohio’s Third Frontier Technology
Validation and Start-up Fund (TVSF), and the Cincinnati Children’s Innovation Fund for the financial
support. This publication was made possible in part by support from the Kent State University Open
Access Publishing Fund. We sincerely appreciate the College of Public Health at Kent State University
for providing facilities to carry out research of the paper. The first author immensely grateful to Mu
Guan for sustaining throughout her life so far.
Conflicts of Interest: The authors declare no conflicts of interest.
References
1.
2.
3.
4.
5.
Iskandar, B.J.; Finnell, R.H. Spina Bifida. N. Engl. J. Med. 2022, 387, 444–450. [CrossRef] [PubMed]
Avagliano, L.; Massa, V.; George, T.M.; Qureshy, S.; Bulfamante, G.; Finnell, R.H. Overview on Neural Tube Defects: From
Development to Physical Characteristics. Birth Defects Res. 2018, 111, 1455–1467. [CrossRef] [PubMed]
Hassan, A.-E.S.; Du, Y.; Lee, S.Y.; Wang, A.; Farmer, D.L. Spina Bifida: A Review of the Genetics, Pathophysiology and Emerging
Cellular Therapies. J. Dev. Biol. 2022, 10, 22. [CrossRef] [PubMed]
About Spina Bifida. Available online: https://www.nichd.nih.gov/health/topics/spinabifida/conditioninfo (accessed on 23
January 2024).
How Do Healthcare Providers Diagnose Spina Bifida? Available online: https://www.nichd.nih.gov/health/topics/spinabifida/
conditioninfo/diagnose (accessed on 23 January 2024).
4.
About Spina Bifida. Available online: https://www.nichd.nih.gov/health/topics/spinabifida/conditioninfo (accessed on 23 January 2024).
5.
How Do Healthcare Providers Diagnose Spina Bifida? Available online: https://www.nichd.nih.gov/health/topics/spinabiBioengineering 2024, 11, 249
9 of 9
fida/conditioninfo/diagnose (accessed on 23 January 2024).
6.
Song, R.B.; Glass, E.N.; Kent, M. Spina Bifida, Meningomyelocele, and Meningocele. Vet. Clin. N. Am. Small Anim. Pract. 2016,
46, 327–345. https://doi.org/10.1016/j.cvsm.2015.10.007.
Del E.N.;
Magno,
S.; Gandini,
Pisoni,
L.; Menchetti, M.;and
Foglia,
A.; Ródenas,
Surgical
Outcomes
of Six
Bulldogs
7.
Muñiz,
L.M.;
6.
Song,
R.B.;
Glass,
Kent,
M. Spina G.;
Bifida,
Meningomyelocele,
Meningocele.
Vet. S.
Clin.
N. Am.
Small Anim.
Pract.
2016,
with
Spinal
Lumbosacral
Meningomyelocele
or
Meningocele.
Vet.
Surg.
2019,
49,
200–206.
https://doi.org/10.1111/vsu.13342.
46, 327–345. [CrossRef] [PubMed]
8.
Piatt, J.H.
Treatment
of S.;
Myelomeningocele:
A L.;
Review
of Outcomes
Neurosurgical
Considerations
among
7.
Muñiz,
L.M.;
Del Magno,
Gandini, G.; Pisoni,
Menchetti,
M.; Foglia,and
A.; Continuing
Ródenas, S. Surgical
Outcomes
of Six Bulldogs
with
Adults.Lumbosacral
J. Neurosurg.Meningomyelocele
2010, 6, 515–525. https://doi.org/10.3171/2010.9.peds10266.
Spinal
or Meningocele. Vet. Surg. 2019, 49, 200–206. [CrossRef] [PubMed]
9.
Copp,J.H.
A.J.;
Adzick, of
N.S.;
Chitty, L.S.; Fletcher,
J.Μ.; of
Holmbeck,
Shaw, G.M.Neurosurgical
Spina Bifida. Considerations
Nat. Rev. Dis. Primers
1,
8.
Piatt,
Treatment
Myelomeningocele:
A Review
OutcomesG.N.;
and Continuing
among 2015,
Adults.
https://doi.org/10.1038/nrdp.2015.7.
J.15007.
Neurosurg.
2010, 6, 515–525. [CrossRef] [PubMed]
10. Copp,
Bibbins-Domingo,
Grossman,
D.C.;Fletcher,
Curry, S.J.;
Davidson,
K.W.;
Epling,
García,
Kemper,
A.H.; Kurth,
9.
A.J.; Adzick,K.;
N.S.;
Chitty, L.S.;
J.M.;
Holmbeck,
G.N.;
Shaw,J.W.;
G.M.
Spina F.;
Bifida.
Nat.A.R.;
Rev. Krist,
Dis. Primers
2015,
A.;15007.
Landefeld,
C.S.; et al. Folic Acid Supplementation for the Prevention of Neural Tube Defects. JAMA 2017, 317, 183.
1,
[CrossRef]
https://doi.org/10.1001/jama.2016.19438.
10. Bibbins-Domingo,
K.; Grossman, D.C.; Curry, S.J.; Davidson, K.W.; Epling, J.W.; García, F.; Kemper, A.R.; Krist, A.H.; Kurth, A.;
11. Landefeld,
Spina Bifida
and
for Disease
Control
and JAMA
Prevention.
Available
online:
C.S.; etData
al. Folic
AcidStatistics|CDC.
Supplementation Centers
for the Prevention
of Neural
Tube Defects.
2017, 317,
183. [CrossRef]
https://www.cdc.gov/ncbddd/spinabifida/data.html
(accessed
on 23and
January
2024). Available online: https://www.cdc.gov/
11. Spina
Bifida Data and Statistics|CDC. Centers for Disease
Control
Prevention.
12. ncbddd/spinabifida/data.html
Spina Bifida—Diagnosis and (accessed
Treatment—Mayo
Clinic.
on 23 January
2024). Available online: https://www.mayoclinic.org/diseases-conditions/spina-bifida/diagnosis-t…
(accessed
on 23
January
2024).
12. Spina
Bifida—Diagnosis and Treatment—Mayo Clinic.
Available
online:
https://www.mayoclinic.org/diseases-conditions/
Johnson,
M.P.; Howell, L.J.; Farrell, J.A.; Dabrowiak, M.E.;
13. spina-bifida/diagnosis-treatment/drc-20377865
Adzick, N.S.; Thom, E.; Spong, C.Y.; Brock, J.W.;
Burrows,
(accessed
on P.K.;
23 January
2024).
Sutton, L.N.;
et al. AE.;Randomized
ofJ.W.;
Prenatal
versus
Postnatal
of Myelomeningocele.
Engl. J. Med.
2011,
364,
13. Adzick,
N.S.; Thom,
Spong, C.Y.; Trial
Brock,
Burrows,
P.K.;
Johnson,Repair
M.P.; Howell,
L.J.; Farrell, J.A.;N.Dabrowiak,
M.E.;
Sutton,
993–1004.
L.N.;
et al. https://doi.org/10.1056/nejmoa1014379.
A Randomized Trial of Prenatal versus Postnatal Repair of Myelomeningocele. N. Engl. J. Med. 2011, 364, 993–1004.
14. [CrossRef]
Moldenhauer, J.S.; Soni, S.; Rintoul, N.E.; Spinner, S.S.; Khalek, N.; Martinez-Poyer, J.; Flake, A.W.; Hedrick, H.L.; Peranteau,
Myelomeningocele
Repair:
The Post-MOMS
Experience at
Children’s
HospitalH.L.;
of Philadelphia.
W.H.; Rendon,J.S.;
N.; et
al. Fetal
14. Moldenhauer,
Soni,
S.; Rintoul,
N.E.; Spinner,
S.S.; Khalek,
N.; Martinez-Poyer,
J.; the
Flake,
A.W.; Hedrick,
Peranteau,
Fetal Diagn.
Ther.
235–240.
https://doi.org/10.1159/000365353.
W.H.;
Rendon,
N.;2014,
et al. 37,
Fetal
Myelomeningocele
Repair: The Post-MOMS Experience at the Children’s Hospital of Philadelphia.
15. Fetal
Cortés,
M.S.;Ther.
Chmait,
Lapa, D.A.;
Belfort, M.A.; Carreras, E.; Miller, J.L.; Brawura-Biskupski-Samaha, R.; González, G.S.;
Diagn.
2014,R.H.;
37, 235–240.
[CrossRef]
Report ofG.S.;
the
Gielchinsky,
Yamamoto,
M.; etD.A.;
al. Experience
of 300
Cases of
Fetoscopic
Open Spina Bifida Repair:
15. Cortés,
M.S.; Y.;
Chmait,
R.H.; Lapa,
Belfort, M.A.;
Carreras,
E.;Prenatal
Miller, J.L.;
Brawura-Biskupski-Samaha,
R.; González,
InternationalY.;Fetoscopic
Tube
Defect of
Repair
Consortium.
Am.
J. Obstet.
Gynecol.
2021, Repair:
225, 678.e1–678.e11.
Gielchinsky,
Yamamoto,Neural
M.; et al.
Experience
300 Cases
of Prenatal
Fetoscopic
Open
Spina Bifida
Report of the
https://doi.org/10.1016/j.ajog.2021.05.044.
International
Fetoscopic Neural Tube Defect Repair Consortium. Am. J. Obstet. Gynecol. 2021, 225, 678.e1–678.e11. [CrossRef]
16. Tatu,
Tatu, R.;
R.; Oria, M.; Pulliam, S.; Signey,
Signey, L.;
L.; Rao,
Rao, M.B.;
M.B.; Peiró, J.L.; Lin, C. Using Poly(L-lactic Acid) and Poly(Ɛ-caprolactone)
Blends
16.
Poly( -caprolactone) Blends
to Fabricate Self-expanding, Watertight and Biodegradable Surgical Patches for Potential Fetoscopic
Fetoscopic Myelomeningocele
Myelomeningocele Repair.
Repair.
Biomed. Mater.
Mater. Res. Part B Appl. Biomater. 2018, 107, 295–305. https://doi.org/10.1002/jbm.b.34121.
J. Biomed.
[CrossRef]
17. Oria, M.;
M.; Tatu,
Tatu, R.;
R.; Lin,
Lin, C.;
C.; Peiró,
Peiró, J.L.
J.L.In
InVivo
VivoEvaluation
Evaluationof
ofNovel
NovelPLA/PCL
PLA/PCL Polymeric Patch in Rats for Potential
Potential Spina
Spina Bifida
Bifida
17.
Coverage. J. Surg. Res. 2019, 242, 62–69. https://doi.org/10.1016/j.jss.2019.04.035.
[CrossRef]
Acid) and
and Poly(ε-Caprolactone)
Poly(ε-Caprolactone) Patches by
18. Tatu, R.;
R.; Oria,
Oria, M.;
M.; Rao,
Rao, M.B.;
M.B.; Peiró,
Peiró, J.L.;
J.L.; Lin,
Lin, C.
C. Biodegradation
Biodegradation of
of Poly(l-Lactic
Poly(l-Lactic Acid)
18.
Human Amniotic
Amniotic Fluid
Fluid in
in an
an in-Vitro
in-Vitro Simulated
Simulated Fetal
Fetal Environment.
Environment. Sci.
Sci. Rep. 2022, 12, 3950. https://doi.org/10.1038/s41598-022Human
[CrossRef]
07681-8.P.L. A Brief Introduction to Monte Carlo Simulation. Clin. Pharmacokinet. 2001, 40, 15–22. [CrossRef]
19. Bonate,
19. Martins,
Bonate, M.T.;
P.L. Lourenço,
A BriefF.R.Introduction
to Monte forCarlo
2001,
15–22.
20.
Measurement Uncertainty
<905> Simulation.
Uniformity of Clin.
DosagePharmacokinet.
Units Tests Using
Monte40,
Carlo
and
https://doi.org/10.2165/00003088-200140010-00002.
Bootstrapping
Methods—Uncertainties Arising from Sampling and Analytical Steps. J. Pharm. Biomed. Anal. 2024, 238, 115857.
20. [CrossRef]
Martins, M.T.; Lourenço, F.R. Measurement Uncertainty for <905> Uniformity of Dosage Units Tests Using Monte Carlo and
Bootstrapping
Methods—Uncertainties
Arising
from Sampling
andInteractive
AnalyticalProtein-Ligand
Steps. J. Pharm.Modeling.
Biomed. Anal.
238, 115857.
21. Lecina,
D.; Gilabert,
J.F.; Guallar, V. Adaptive
Simulations,
towards
Sci. 2024,
Rep. 2017,
7, 8466.
https://doi.org/10.1016/j.jpba.2023.115857.
[CrossRef]
22.
Randomization
tests forSimulations,
assessing thetowards
equalityInteractive
of area under
curves for studies
usingSci.
destructive
sampling.
21. Bailer,
Lecina,A.J.;
D.; Ruberg,
Gilabert,S.J.
J.F.;
Guallar, V. Adaptive
Protein-Ligand
Modeling.
Rep. 2017,
7, 8466.
J.https://doi.org/10.1038/s41598-017-08445-5.
Appl. Toxicol. 1996, 16, 391–395. [CrossRef]
23.
D.J.;Ruberg,
Hsuan, F.;
R.; Soper, K.tests
A method
for estimating
and testing
under
the for
curve
in serial
sacrifice,
batch,samand
22. Holder,
Bailer, A.J.;
S.J.Dixit,
Randomization
for assessing
the equality
of areaarea
under
curves
studies
using
destructive
complete
dataToxicol.
designs.1996,
J. Biopharm.
Stat. https://doi.org/10.1007/BF01062139.
1999, 9, 451–464. [CrossRef] [PubMed]
pling. J. Appl.
16, 391–395.
24.
Jaki,F.;T.Dixit,
Estimation
of AUC
0 tofor
infinity
in serial
J. Pharmacokinet.
Pharmacodyn.
2005,
23. Wolfsegger,
Holder, D.J.;M.J.;
Hsuan,
R.; Soper,
K. Afrom
method
estimating
andsacrifice
testing designs.
area under
the curve in serial
sacrifice, batch,
32,
[CrossRef]
[PubMed]
and757–766.
complete
data designs.
J. Biopharm. Stat. 1999, 9, 451–464. https://doi.org/10.1081/BIP-100101187.
N. Biomarker
Analysis
in Clinical
Trials from
with R,
1stinfinity
ed.; CRC
Boca Raton,
FL, USA;
Taylor & Francis
Group: Abingdon,
25.
24. Rabbee,
Wolfsegger,
M.J.; Jaki,
T. Estimation
of AUC
0 to
in Press:
serial sacrifice
designs.
J. Pharmacokinet.
Pharmacodyn.
2005, 32,
UK,
2020.https://doi.org/10.1007/s10928-005-0044-0.
[CrossRef]
757–766.
26.
R.Y.; Kroese,
D.P.in
Simulation
and the
Method,
3rd
ed.;Raton,
John Wiley
& Sons:
USA,
2016.
Analysis
Clinical Trials
withMonte
R, 1st Carlo
ed.; CRC
Press:
Boca
FL, USA;
TaylorHoboken,
& Francis NJ,
Group:
Abing25. Rubinstein,
Rabbee, N. Biomarker
[CrossRef]
don, UK, 2020. https://doi.org/10.1201/9780429428371.
27.
R.L.R.Y.;
Product
Inequalities
Involvingand
the the
Multivariate
Normal
Distribution.
J. Am.
Stat. &
Assoc.
1980,
75, 646–650.
[CrossRef]
26. Dykstra,
Rubinstein,
Kroese,
D.P. Simulation
Monte Carlo
Method,
3rd ed; John
Wiley
Sons:
Hoboken,
NJ, USA,
2016.
28. Tong,
Y.L. Some Probability Inequalities of Multivariate Normal and Multivariate t. J. Am. Stat. Assoc. 1970, 65, 1243–1247.
https://doi.org/10.1002/9781118631980.
27. [CrossRef]
Dykstra, R.L. Product Inequalities Involving the Multivariate Normal Distribution. J. Am. Stat. Assoc. 1980, 75, 646–650.
29. Tong,
Y.L. Probability Inequalities in Multivariate Distributions. J. Am. Stat. Assoc. 1982, 77, 690. [CrossRef]
https://doi.org/10.1080/01621459.1980.10477526.
30.
B.D.Some
Ohio Library
and Inequalities
Information of
Network.
In Stochastic
Wiley: New
NY, USA,
28.1243–1247.
28. Ripley,
Tong, Y.L.
Probability
Multivariate
NormalSimulation;
and Multivariate
t. J. York,
Am. Stat.
Assoc.1987;
1970,p.65,
https://doi.org/10.1080/01621459.1970.10481159.
29. Tong, Y.L. Probability
Inequalities
in Multivariate
Distributions.
J. Am. Stat.
1982, 77, 690.
Disclaimer/Publisher’s
Note:
The statements,
opinions
and data contained
in Assoc.
all publications
arehttps://doi.org/10.2307/2287749.
solely those of the individual
30.
Ripley,
B.D.;
Ohio
Library
and
Information
Network.
Stochastic
Simulation;
Wiley:
New
York,
NY,responsibility
USA, 1987; p.for
28.any injury to
author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim
people or property resulting from any ideas, methods, instructions or products referred to in the content.
bioengineering
Article
Reliability of Systematic and Targeted Biopsies versus
Prostatectomy
Tianyuan Guan 1, * , Abhinav Sidana 2 and Marepalli B. Rao 3
1
2
3
*
College of Public Health, Kent State University, Kent, OH 44240, USA
Division of the Biological Sciences, The University of Chicago, 5841 S Maryland Avenue,
Chicago, IL 60637, USA; abhinavsidana@uchicago.edu
Division of Biostatistics and Bioinformatics, University of Cincinnati, Cincinnati, OH 45219, USA;
marepalli.rao@uc.edu
Correspondence: tiguan5@kent.edu; Tel.:+1-330-672-7488
Abstract: Systematic Biopsy (SBx) has been and continues to be the standard staple for detecting
prostate cancer. The more expensive MRI guided biopsy (MRITBx) is a better way of detecting cancer.
The prostatectomy can provide an accurate condition of the prostate. The goal is to assess how
reliable SBx and MRITBx are vis à vis prostatectomy. Graded Gleason scores are used for comparison.
Cohen’s Kappa index and logistic regression after binarization of the graded Gleason scores are some
of the methods used to achieve our goals. Machine learning methods, such as classification trees,
are employed to improve predictability clinically. The Cohen’s Kappa index is 0.31 for SBx versus
prostatectomy, which means a fair agreement. The index is 0.34 for MRITBx versus prostatectomy,
which again means a fair agreement. A direct comparison of SBx versus prostatectomy via binarized
graded scores gives sensitivity 0.83 and specificity 0.50. On the other hand, a direct comparison of
MRITBx versus prostatectomy gives sensitivity 0.78 and specificity 0.67, putting MRITBx on a higher
level of accuracy. The SBx and MRITBx do not yet match the findings of prostatectomy completely,
but they are useful. We have developed new biomarkers, considering other pieces of information
from the patients, to improve the accuracy of SBx and MRITBx. From a clinical point of view, we
provide a prediction model for prostatectomy Gleason grades using classification tree methodology.
Citation: Guan, T.; Sidana, A.; Rao,
M.B. Reliability of Systematic and
Targeted Biopsies versus
Keywords: prostate cancer; systematic biopsy; targeted biopsy; ROC curve; area under the curve;
sensitivity; specificity; logistic regression; biomarker; machine learning methods
Prostatectomy. Bioengineering 2023,
10, 1395. https://doi.org/10.3390/
bioengineering10121395
1. Introduction
Academic Editors: Junwei Shi,
Wensi Tao, Eriko Katsuta and
Yu-Chang Tyan
Received: 14 November 2023
Revised: 2 December 2023
Accepted: 5 December 2023
Published: 6 December 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Detection of cancer in the prostate gland is fraught with difficulties. The character of
prostate cancer is different from cancers in other organs. Some segments of the prostate
are cancerous, other segments benign, and the rest metastatic. Systematic biopsy (SBx), in
which needles are inserted into the prostate to extract tissue, is commonly used to obtain
information from the prostate in the form of a Gleason score. The biopsy may completely
miss the cancerous part of the prostate. Multiparametric magnetic resonance imaging
(mpMRI) targeted biopsy (MRITBx) is being accepted as a more reliable screening test for
the detection of cancer [1–3]. The needles are guided using MRI. The test could miss cancer.
There is a need to assess how reliable these biopsies are. For assessing reliability, one needs
a definitive procedure. It is prostatectomy, in which the prostate is removed and examined.
However, prostatectomy is not a gold standard. If the prostate is removed, there is no gland
left to treat. It will be a boon if one has data on patients with information on both SBx and
prostatectomy, and MRITBx and prostatectomy. We are bestowed with such a boon.
There are research papers proposing new ways of detecting prostate cancer [4–12],
most of them using machine learning methods. We do not know how reliable these
methods of detection are. They do not have the benefit of definitive procedures of detection
to compare with.
Bioengineering 2023, 10, 1395. https://doi.org/10.3390/bioengineering10121395
https://www.mdpi.com/journal/bioengineering
Bioengineering 2023, 10, 1395
2 of 14
One cannot see how one can develop a gold standard procedure without removing
the prostate. Of course, removing the prostate is a treatment which is not common. There is
a gap in how to examine the reliability of any detection procedure vis-à-vis prostatectomy.
We are filling this gap by focusing on the biopsies, SBx and MRITBx. The department of
Urology at the University of Cincinnati has been collecting data on patients who came for
prostate screening for many, many, years. One segment of the data has information on the
results of SBx, MRITBx, and prostatectomy. This is our core data for the assessment of the
screening tests vis-à-vis prostatectomy.
A summary of acronyms is provided below:
SBx: Systematic Biopsy
MRITBx: Multiparametric Magnetic Resonance Imaging Targeted Biopsy
PSA: Prostate-specific Antigen
DRE: Digital Rectal Examination
ROC: Receiver Operating Characteristic
HCsys: The Systematic Biopsy Gleason Grades binarized, analogously, Grades 1, 2,
3 versus Grades 4, 5
HCpros: The prostatectomy Biopsy Gleason Grades binarized, analogously,
Grades 1, 2, 3 versus Grades 4, 5
HCTa: The Targeted Biopsy Gleason Grades binarized, analogously, with Grades
1, 2, 3 (0) versus Grades 4, 5
2. Materials and Methods
Our study was approved by the University of Cincinnati institutional review board
(UC IRB: 2018-4010). The data have information on Gleason scores from patients on SBx,
MRITBx, prostatectomy and many other covariates. Besides demographic details, the data
have information on PSA (prostate-specific antigen), prostate volume, DRE (digital rectal
examination), and family history. We performed a retrospective study of patients with
newly diagnosed status of prostate cancer at UC Health between September 2014 and April
2020. The final cohort had 597 patients for analysis. For our analysis, we included those
patients with data on prostatectomy, SBx, and MRITBx. The final size of one of the data
sets is 235, with information on both SBx and prostatectomy results along with covariates.
The other data set has a size of 104, with information on both MRITBx and prostatectomy
along with covariates.
Patient demographic, clinical, and pathological data were recorded. The Gleason
scores are categorized into 5 Grades (1, 2, 3, 4, 5) [13], as described in Table 1.
Table 1. Gleason Scores and Grade Groups.
Risk Group
Gleason Grade
Gleason Score
Low/Very Low
Grade 1
Gleason Score ≤ 6
Intermediate
(Favorable/Unfavorable)
Grade 2
Gleason Score 7 (3 + 4)
Grade 3
Gleason Score 7 (4 + 3)
Grade 4
Gleason Score 8
Grade 5
Gleason Score 9–10
High/Very High
The workflow of our research is presented below:
a.
b.
c.
Grade 1, 2, 3, 4 or 5, as per SBx in comparison to Grade 1, 2, 3, 4 or 5 as per
prostatectomy (Cohen’s Kappa)
Grade 1, 2, 3, 4 or 5, as per MRITBx in comparison to Grade 1, 2, 3, 4 or 5 as per
prostatectomy (Cohen’s Kappa)
Grade 1 vs. Grade 2, 3, 4 or 5 as per SBx in comparison to Grade 1 vs. Grade 2, 3, 4 or
5 as per prostatectomy (Logistic Regression)
Bioengineering 2023, 10, 1395
3 of 14
d.
e.
f.
g.
Grade 1 vs. Grade 2, 3, 4 or 5 as per MRITBx in comparison to Grade 1 vs. Grade 2, 3,
4, 5 as per prostatectomy (Logistic Regression)
Grade 1 or 2 vs. Grade 3, 4, 5 as per SBx in comparison to Grade 1 or 2 vs. Grade 3, 4,
5 as per prostatectomy (Logistic Regression)
Grade 1 or 2 vs. Grade 3, 4, 5 as per MRITBx in comparison to Grade 1 or 2 vs. Grade
3, 4, 5 as per prostatectomy (Logistic Regression)
Grade 1 or 2 vs. Grade 3, 4, 5 as per MRITBx in comparison to Grade 1 or 2 vs. Grade
3, 4, 5 as per prostatectomy (Classification Tree)
Significant cancer was defined as a Gleason score ≥ 7. Several methods were employed
to contrast the Gleason grades as per prostatectomy and SBx on one hand, and prostatectomy and MRITBx on the other hand. The Kappa statistic [14,15] is calculated to assess the
degree of agreement between the Gleason grades of SBx and prostatectomy, MRITBx and
prostatectomy. For other contrasts, the Gleason grades are binarized in several different
ways. At each type of binarization, SBx is assessed vis-à-vis prostatectomy in terms of
sensitivity and specificity. In a similar way, MRITBx is assessed vis-à-vis prostatectomy. A
logistic regression model is fitted to each of the binarized Gleason grades of prostatectomy
with several predictors, including the corresponding binarized Gleason grades of SBx. We
then developed a biomarker out of the logistic regression model and assessed its utility
for prediction by calculating the area under the ROC (Receiver Operating Characteristic)
curve of the biomarker. The Youden method [16,17] is used to put forward a diagnostic
(screening) test. The sensitivity and specificity of the diagnostic test are calculated to assess
the effectiveness of the diagnostic test. An identical methodology is used for binarized
Gleason grades of prostatectomy, with several predictors including the corresponding
binarized Gleason grades of MRITBx.
Following Table 1, there are two ways to binarize the Gleason grades. One simple
and natural way is to take the binarized levels to be A = {1} and B = {2, 3, 4, 5}. Another
way is to take A = {1, 2, 3} and B = {4, 5} [13]. The binarization allowed us to fit logistic
regression models with the two ways of binarizing prostatectomy Gleason grades. In each
model, we take the binarized prostatectomy Gleason grades as the outcome variable and the
corresponding binarized SBx Gleason grades as the principal predictor. In the first method
of binarization, there are only 4 cases in the A = {1} group. The logistic regression model
fitting is not advisable [18,19]. Therefore, we focused on the binarization of the Gleason
grades into A = {1, 2, 3} and B = {4, 5}. We have employed the cross-validation method
(LOOCV: leave one out cross-validation) on the model comparing high cancers vs. not high
cancers using the predictors age, race, prostate volume, PSA, and high cancers vs. not high
cancers, as per SBx. In the same context, we have employed the K-fold cross-validation
method. A screening marker is developed from the regression model and its utility is
assessed for the screening test by its ROC curve. A screening test is laid out with a cut
point determined by the Youden method [16,17], along with its sensitivity and specificity.
Similar pursuits are carried out with MRITBx. Statistical analysis was performed by using
the computing software R 4.3.0 (R Core Team, 2017) [20]. The Kruskal–Wallis rank sum test
was used to compare the medians of continuous variables. Pearson’s Chi-squared test and
Fisher’s exact test were used to compare proportions of categorical variables. A crowning
achievement was to employ machine learning methods to develop a prediction model on
three fronts. On one front, the response variable is taken to be the categorical variable, the
Gleason grades of prostatectomy. On the other two fronts, the response variable is taken
to be the binarized Gleason grades of prostatectomy, binarized in two different ways as
enunciated above.
3. Results
The focus was on comparing the diagnoses stemming from SBx vs. prostatectomy on
one hand, and MRITBx vs. prostatectomy on the other hand. The first step in the analysis
was to make overall comparisons via the Kappa Statistic. The second step in the analysis
was to make comparisons with respect to cancer vs. no cancer. The third step in the analysis
Bioengineering 2023, 10, 1395
4 of 14
was to make comparisons with respect to high cancers vs. not high cancers. Ultimately,
we developed biomarkers to discriminate high cancers vs. not high cancers built on SBx
and MRITBx by including additional predictors. We showed that these biomarkers are
highly accurate, with more than 90% accuracy. The key methodology we used was the
logistic regression model. The work was supplemented by the machine leaning method,
classification tree.
3.1. Kappa Statistic
The data covers the period from September 2014 to April 2020 with a cohort of
597 patients. An SBx Gleason score was determined for every patient in the study. The data
reports Gleason scores: benign, 3 + 3, 3 + 4, 4 + 3, 4 + 4, 4 + 5, 5 + 4, and 5 + 5. The scores are
categorized into 5 grades: benign or 3 + 3 = Grade 1; 3 + 4 = Grade 2; 4 + 3 = Grade 3; 4 + 4,
3 + 5 or 5 + 3 = Grade 4; 4 + 5, 5 + 5, or 5 + 4 = Grade 5. A prostatectomy was performed on
only 235 patients. Only 104 patients received MRITBx. The following table (Table 2) shows
the Gleason grades along with the frequencies.
Table 2. Gleason Grades by Biopsy.
Grades
SBx
Prostatectomy
MRITBx
Prostatectomy
1
2
3
4
5
Total
41
103
43
20
28
235
4
133
68
6
24
235
24
48
11
5
16
104
3
60
32
3
6
104
This table is alarming. Suppose diagnosis was made based on SBx. As per the
prostatectomy diagnosis, only 4 out of 235 fall into Grade 1. On the other hand, as per the
SBx diagnosis, 41 out of 235 fall into Grade 1. For a substantial number of patients, cancer
diagnosis was missed out by SBx.
Suppose the diagnosis was made based on MRITBx. As per the prostatectomy diagnosis, only 3 out of 104 fall into Grade 1. On the other hand, as per the MRITBx diagnosis,
24 out of 104 fall into Grade 1. For a substantial number of patients, cancer diagnosis was
missed out by MRITBx.
We evaluated how close the agreement is between Gleason grades of prostatectomy
and SBx overall. The Cohen’s Kappa [14,15] is 0.31, which means a fair agreement, with a
95% CI [0.23, 0.39]. For prostatectomy vs. MRITBx, the index is 0.34, which also means a
fair agreement, with a 95% CI [0.23, 0.45]. On a binary level, a value of Kappa greater than
0.75 is considered as an excellent agreement, whereas lower than 0.4 is treated as a poor
agreement [14,15].
A simple diagnostic test was developed following the data in Table 1. To discriminate
the graded score ≥ 2 from the graded score = 1 truly, we used the following screening test.
Test is positive if the graded score ≥ 2 under SBx.
Test is negative if the graded score = 1 under SBx.
To assess the effectiveness of the test versus prostatectomy, we used Table 3.
Table 3. Cross tabulation of SBx versus prostatectomy.
Diagnosed Condition by SBx
Prostatectomy
(True Condition)
Grade ≥ 2
Grade = 1
Grade ≥ 2
Grade = 1
192
2
38
2
231
4
Marginal
194
41
235
Marginal
Bioengineering 2023, 10, 1395
5 of 14
Sensitivity of the test = 192/231 = 0.83.
Specificity of the test = 2/4 = 0.50.
We can use MRITBx for a screening test to discriminate the graded score ≥ 2 from the
graded score = 1. The relevant screening test was given by:
Test is positive if the graded score ≥ 2 under MRITBx.
Test is negative if the graded score = 1 under MRITBx.
To assess the effectiveness of the test versus prostatectomy, we used Table 4.
Table 4. Cross tabulation of MRITBx versus prostatectomy.
Diagnosed Condition by MRITBx
Prostatectomy
(True Condition)
Grade ≥ 2
Grade = 1
Grade ≥ 2
Grade = 1
79
1
22
2
101
3
Marginal
80
24
104
Marginal
Sensitivity of the test = 79/101 = 0.78.
Specificity of the test = 2/3 = 0.67.
Overall, MRITBx is a better procedure compared with SBx.
We embarked on improving the diagnostic test based on SBx by including some
information on the patients. We fitted a logistic regression model with the response variable
binarized prostatectomy (Level 1—prostatectomy positive: 2, 3, 4, 5; Level 2—prostatectomy
negative: 1), and predictors: age, race, prostate volume, PSA, DRE and family history of
prostate cancer. The number of prostatectomy negatives is only 4, which is less than 10%
of the total size of the sample 235. Logistic regression for these binarized grades is not
recommended [18,19]. We desisted including the results from this data analysis exercise.
We binarized the grades in a different way: detect high/very high cancers from
not high/very high cancers [21]. We fit a logistic regression model with the binarized
response variable (Level 1—prostatectomy high/very high cancers: grades 4, 5 versus
Level 2—prostatectomy not high/very high cancers: grades 1, 2, 3), and predictors: age,
race, prostate volume, PSA, DRE and family history. We fitted two separate logistic
regression models. In one, we included the corresponding binarized SBx Gleason grades.
In the other, we included the corresponding binarized MRITBx Gleason grades.
3.2. SBx Gleason Grades Binarized as a Predictor in the Model
Two sets of logistic regression models were run. In one set, the predictors were age,
race, prostate volume, PSA, and HCsys (The systematic biopsy Gleason grades binarized,
analogously, Grades 1, 2, 3 versus Grades 4, 5). The model fit was good with the ratio
of residual deviance and degrees of freedom less than 1 (p-value = 1). The significant
predictors were PSA (p-value = 0.0465) and Hcsys (p-value < 0.0001). The implication is
that SBx coupled with PSA is a good predictor of the true condition (prostatectomy: Grades
1, 2, 3 vs. Grades 4, 5). The output is given in Appendix A.
We developed a biomarker based on predictors to discriminate the levels of Hcpros.
The biomarker is the logit of the model, i.e.,
Logit = −4.503 + 0.01 ∗ Age + 0.999 ∗ Race (Caucasian) + (−0.127) ∗ Race (Other )
+ 2.982 ∗ HCsys + (−0.0033) ∗ Volume + 0.015 ∗ PSA
The Logit can be computed for a patient with information on age, race, Hcsys, prostate
volume, and PSA. The result is the biomarker value of the patient.
The race, Black, was the baseline of race. With the parameters of the model estimated,
the logit was computable for everyone in the study. The summary statistics of the logit by
the levels of Hcpros (1 = Grades 4, 5 and 0 = Grades 1, 2, 3) were tabulated in Table 5.
𝐿𝑜𝑔𝑖𝑡 = −4.503 + 0.01 ∗ 𝐴𝑔𝑒 + 0.999 ∗ 𝑅𝑎𝑐𝑒 (𝐶𝑎𝑢𝑐𝑎𝑠𝑖𝑎𝑛) + (−0.127) ∗ 𝑅𝑎𝑐𝑒 (𝑂𝑡ℎ𝑒𝑟)
+ 2.982 ∗ 𝐻𝐶𝑠𝑦𝑠 + (−0.0033) ∗ 𝑉𝑜𝑙𝑢𝑚𝑒 + 0.015 ∗ 𝑃𝑆𝐴
The Logit can be computed for a patient with information on age, race, Hcsys, pro
tate volume, and PSA. The result is the biomarker value of the patient. 6 of 14
The race, Black, was the baseline of race. With the parameters of the model estimate
the logit was computable for everyone in the study. The summary statistics of the logit b
the levels of Hcpros (1 = Grades 4, 5 and 0 = Grades 1, 2, 3) were tabulated in Table 5.
Bioengineering 2023, 10, 1395
Table 5. Summary Statistics of the biomarker (based on SBx and other predictors) by the levels of
Hcpros.
Table 5. Summary Statistics of the biomarker (based on SBx and other predictors) by the levels
Hcpros.
III
Levels of
Max
Min
I Quartile
Mean
Median
Quartile
Hcpros
Levels of Hcpros Min
I Quartile
Mean
Median
III Quartile
Max
1
0
−3.413 1
−16.7760
−0.933
−3.413 0.0838
−0.933
−3.842
−16.776 −2.934
−3.842
−
0.693
0.0838
−
3.205
−2.934
0.201
−0.693
−
2.789
−3.205
1.079
0.201
1.756
−2.789
1.079
1.756
logitlevel
values
the level
of Hcsys were
generally
higher
than0.those
The logit valuesThe
of the
1 ofofHcsys
were1 generally
higher
than those
of level
The of level
The
Kernel
density
curves
(Figure
1)
attest
to
this
phenomenon.
This
density
Kernel density curves (Figure 1) attest to this phenomenon. This density curves indicatecurves ind
cate how good
biomarker
logit is
to discriminate
highhigh
cancers
vs. not high cancers.
how good the biomarker
logitthe
is to
discriminate
high
cancers vs. not
cancers.
Figure 1. KernelFigure
Density
of the Biomarker
(based
on SBx(based
and other
predictors)
Cancer by Canc
1. Curves
Kernel Density
Curves of the
Biomarker
on SBx
and otherby
predictors)
Levels.
Levels.
In Figure 1, the
curve
associated
with
level 1 with
(high/very
cancers)
ofcancers)
In density
Figure 1,
the density
curve
associated
level 1 high
(high/very
high
Hcpros is on theHcpros
right side
theright
curveside
associated
with associated
level 0 (notwith
high/very
cancers). high ca
is onofthe
of the curve
level 0 high
(not high/very
This was an indication
thatwas
the biomarker
would
a good
discriminator
high
cers). This
an indication
thatbethe
biomarker
would of
behigh/very
a good discriminator
Bioengineering 2023, 10, x FOR PEER REVIEW
7 of 15
cancers versus high/very
not high/very
high cancers.
ROC curve
associated
withROC
the biomarker
high cancers
versus The
not high/very
high
cancers. The
curve associated wi
is given in Figure
the2.
biomarker is given in Figure 2.
Figure2.
2.ROC
ROCcurve
curveof
ofthe
thebiomarker
biomarker(based
(basedon
onSBx
SBxand
andother
otherpredictors).
predictors).
Figure
The area under the curve (AUC) was 87.5% with a 95% confidence interval 80.8% to
94.2%. The arrow pointed to our choice of the cut point −2.722 (Youden Method) with the
specificity, 0.838 and the sensitivity, 0.833.
Bioengineering 2023, 10, 1395
7 of 14
The area under the curve (AUC) was 87.5% with a 95% confidence interval 80.8% to
94.2%. The arrow pointed to our choice of the cut point −2.722 (Youden Method) with the
specificity, 0.838 and the sensitivity, 0.833.
In view of Table 5, a diagnostic test for discriminating the levels of Hcpros has the
following format.
Test is positive indicating high/very high cancers if biomarker ≥ c, Test is negative
indicating not high/very high cancers if biomarker < c, for some c.
Our choice of c was governed by the following optimality principle. Minimize (1 −
sensitivityc )2 + (1 − specificityc )2 with respect to c. The number 1 in the expression refers
to the sensitivity and specificity of SBx. For each choice of c, sensitivityc and specificityc are
the sensitivity and specificity, respectively, associated with the cutpoint c. Our optimization
foray gave us c = −2.722 with the sensitivity, 83.3% and the specificity, 83.8%. Thus the
biomarker based on SBx, and other predictors were a better choice than the one based on
SBx alone.
Diagnostic Test based on SBx
Test is positive (indicating high cancer) if Logit = −4.503 + 0.01 ∗ Age + 0.999∗ Race
(Caucasian) + (−0.127) ∗ Race (Other ) + 2.982 ∗ HCsys + (−0.003) ∗ Volume + 0.015 ∗ PSA
≥ −2.722.
Test is negative (indicating not high cancer) if Logit = −4.503 + 0.01 ∗ Age + 0.999∗ Race
(Caucasian) + (−0.127) ∗ Race (Other ) + 2.982∗Hcsys + (−0.003) ∗ Volume + 0.015 ∗ PSA
< −2.722.
3.3. MRITBx Gleason Grade Binarized as a Predictor in the Model
In another set, the predictors were age, race, prostate volume, PSA, and HCTa. The MRI
targeted biopsy Gleason grades are binarized, HCTa (the targeted biopsy Gleason grades
binarized, analogously, with Grades 1, 2, 3 (0) versus Grades 4, 5 (1). The model fit was
good with the ratio of residual deviance and degrees of freedom less than 1 (p-value = 1).
The significant predictors were PSA (p-value = 0.0229) and HCTa (p-value < 0.0001). The
implication was that MRITBx, coupled with PSA, was a good predictor of the true condition
(prostatectomy: Grades 1, 2, 3 vs. Grades 4, 5). The output is given in Appendix B.
We developed a biomarker based on predictors to discriminate the levels of Hcpros.
The biomarker is the logit of the model, i.e.,
Logit = −0.246 + 0.007 ∗ Age + 0.196 ∗ Race (Caucasion) + 1.873 ∗ Race (Other )
+2.565 ∗ HCTa + (−0.01) ∗ volume + 0.143 ∗ PSA
The Race, Black, was the baseline of race. With the parameters of the model estimated,
the logit was computable for everyone in the study. The summary statistics of logit by the
levels of Hcpros (1 = Grades 4, 5 and 0 = Grades 1, 2, 3) were tabulated in Table 6.
Table 6. Summary Statistics of the biomarker (based on MRITBx and other predictors) by the levels
of Hcpros.
Levels of
Hcpros
Min
I Quartile
Mean
Median
III
Quartile
Max
1
0
−3.811
−24.531
−1.137
−19.515
0.499
−4.045
−0.559
−8.649
0.3428
−3.353
1.288
1.463
The logit values of level 1 of HCpros were generally higher than those of level 0. The
Kernel density curves (Figure 3) attest to this phenomenon.
Levels of Hcpros
Min
1
−3.811
0
−24.531
Bioengineering 2023, 10, 1395
I Quartile
−1.137
−19.515
Mean
0.499
−4.045
Median
−0.559
−8.649
III Quartile
0.3428
−3.353
Max
1.288
1.463
The logit values of level 1 of HCpros were generally higher than those of level 80.ofThe
14
Kernel density curves (Figure 3) attest to this phenomenon.
Figure 3. Kernel Density Curves of the Biomarker (based on MRITBx and other predictors) by Cancer
Figure 3. Kernel Density Curves of the Biomarker (based on MRITBx and other predictors) by CanLevels.
cer Levels.
In Figure 3, the density curve associated with level 1 of HCpros is on the right side of
In Figure 3, the density curve associated with level 1 of HCpros is on the right side of
the curve associated with level 0. This is an indication that the biomarker will be a good
the curve associated with level 0. This is an indication that the biomarker will be9 aofgood
Bioengineering 2023, 10, x FOR PEER REVIEW
15
discriminator of high/very high cancers versus not high/very high cancers. The ROC
discriminator of high/very high cancers versus not high/very high cancers. The ROC curve
curve associated with the biomarker was given in Figure 4.
associated with the biomarker was given in Figure 4.
Figure4.
4.ROC
ROCcurve
curveof
ofthe
thebiomarker
biomarker(based
(basedon
onMRITBx
MRITBxand
andother
otherpredictors).
predictors).
Figure
The area under the curve (AUC) is 0.922% with a 95% confidence interval 83% to 1.
The area under the curve (AUC) is 0.922% with a 95% confidence interval 83% to 1.
The arrow pointed to the choice of the cut point −2.204 with the specificity, 0.9759 and the
The arrow pointed to the choice of the cut point −2.204 with the specificity, 0.9759 and the
sensitivity, 0.9. In view of Table 6, a diagnostic test for discriminating the levels of HCpros
sensitivity, 0.9. In view of Table 6, a diagnostic test for discriminating the levels of HCpros
has the following format.
has the following format.
Test is positive indicating high/very high cancers if biomarker ≥ c; Test is negative
Test is positive indicating high/very high cancers if biomarker ≥ c; Test is negative
indicating not high/very high cancers if biomarker < c, for some c.
indicating not high/very high cancers if biomarker < c, for some c.
Our choice of c was governed by the following optimality principle. Minimize
Our choice of2 c was governed by2 the following optimality principle. Minimize (1
(1 − sensitivity
c ) + (1 − specificityc ) with respect to c. The number 1 in the expres−sensitivityc)2 + (1 −specificityc)2 with respect to c. The number 1 in the expression refers
sion refers to the sensitivity and specificity of MRITBx. For each choice of c, sensitivityc
to the sensitivity and specificity of MRITBx. For each choice of c, sensitivityc and specificand specificityc are the sensitivity and specificity, respectively, associated with the cutpoint
ity
areoptimization
the sensitivity
andgave
specificity,
respectively,
associated93.3%
with and
the specificity,
cutpoint c.42.6%.
Our
c. cOur
foray
us c = −2.975
with a sensitivity,
optimization foray gave us c = −2.975 with a sensitivity, 93.3% and specificity, 42.6%. Thus,
the biomarker based on MRITBx and other predictors was a better choice than the one
based on MRITBx alone. Further, the biomarker based on MRITBx and other predictors
was a better choice than the one based on SBx and other predictors.
Diagnostic test based on MRITBx
Bioengineering 2023, 10, 1395
9 of 14
Thus, the biomarker based on MRITBx and other predictors was a better choice than the
one based on MRITBx alone. Further, the biomarker based on MRITBx and other predictors
was a better choice than the one based on SBx and other predictors.
Diagnostic test based on MRITBx
Test is positive (indicating high cancer) if Logit = −0.246 + 0.007 ∗ Age + 0.196 ∗
Race (Caucasion) + 1.873 ∗ Race (Other ) + 2.565 ∗ HCTa + (−0.01) ∗ volume + 0.143 ∗ PSA
≥ −2.975
Test is negative (indicating not high cancer) if Logit = −0.246 + 0.007 ∗ Age + 0.196 ∗
Race (Caucasion) + 1.873 ∗ Race (Other ) + 2.565 ∗ HCTa + (−0.01) ∗ volume + 0.143 ∗ PSA
< −2.975
3.4. SBx as a Predictor in Classification Tree
We developed a classification tree with the outcome variable as the binarized prosta10 of 15
tectomy Gleason grades with the levels A = {1, 2, 3} and B = {4, 5}. The SBx is binarized
correspondingly as a predictor. Additional predictors are included in the tree. The tree is
produced in Figure 5.
Bioengineering 2023, 10, x FOR PEER REVIEW
Figure 5. Classification Tree of Prostatectomy vs. SBx.
Figure 5. Classification Tree of Prostatectomy vs. SBx.
The tree is used as a prediction model. The tree has four terminal nodes. The preThe
is usedasasfollows:
a prediction
model. The
tree has
four
terminal
dictiontree
proceeds
if systematic
Gleason
grade
= 1,
2, or 3, nodes.
classifyThe
thepredicsubject’s
tion
proceeds asGleason
follows:grade
if systematic
grade =Gleason
1, 2, orgrade
3, classify
subject’s
prostatectomy
as 1, 2, orGleason
3; if systematic
= 4 orthe
5 and
prostate
volume less than
28, classify
the
Gleason
grade
as 54 and
or 5;prostate
if systemprostatectomy
Gleason
grade as
1, subject’s
2, or 3; if prostatectomy
systematic Gleason
grade
= 4 or
atic Gleason
grade
4 or 5, prostate
volume
greater thanGleason
or equalgrade
to 28, as
and
than
volume
less than
28, =classify
the subject’s
prostatectomy
4 PSA
or 5; less
if sys14, classify
thegrade
subject’s
prostatectomy
Gleason
gradethan
as 1,or
2,equal
or 3; iftosystematic
Gleason
tematic
Gleason
= 4 or
5, prostate volume
greater
28, and PSA
less
grade
4 or 5, prostate
volume
greater than Gleason
or equal to
28, and
PSA
or equal
than
14, =classify
the subject’s
prostatectomy
grade
as 1,
2, greater
or 3; if than
systematic
to
14,
classify
the
subject’s
prostatectomy
Gleason
grade
as
4
or
5.
The
misclassification
Gleason grade = 4 or 5, prostate volume greater than or equal to 28, and PSA greater than
of to
the14,
tree
is 23/235
= 9.8% orprostatectomy
the accuracy of
the treegrade
is 91.2%.
orrate
equal
classify
the subject’s
Gleason
as 4 or 5. The misclassification rate of the tree is 23/235 = 9.8% or the accuracy of the tree is 91.2%.
3.5. MRITBx as a Predictor in Classification Tree
We developed
a classification
tree with
3.5. MRITBx
as a Predictor
in Classification
Tree the outcome variable as the binarized prostatectomy
Gleason
grades
with
the
levels
A
= the
{1, 2,outcome
3} and Bvariable
= {4, 5}.as
The
is binarized
We developed a classification tree with
theMRITBx
binarized
prostacorrespondingly
as
a
predictor.
Additional
predictors
are
included
in
the
tree.
The tree is
tectomy Gleason grades with the levels A = {1, 2, 3} and B = {4, 5}. The MRITBx is binarized
produced in Figure
6.
correspondingly
as a predictor.
Additional predictors are included in the tree. The tree is
produced in Figure 6.
3.5. MRITBx as a Predictor in Classification Tree
Bioengineering 2023, 10, 1395
We developed a classification tree with the outcome variable as the binarized prostatectomy Gleason grades with the levels A = {1, 2, 3} and B = {4, 5}. The MRITBx is binarized
correspondingly as a predictor. Additional predictors are included in the tree. The tree is
10 of 14
produced in Figure 6.
Figure 6. Classification Tree of prostatectomy vs. MRITBx.
Figure 6. Classification Tree of prostatectomy vs. MRITBx.
The tree is used as a prediction model. The tree has four terminal nodes. The prediction
proceeds as follows: if PSA less than 18, classify the subject’s prostatectomy Gleason grade
as 1, 2, or 3; if PSA greater than or equal to 18, and prostate volume less than 37, classify
the subject’s prostatectomy Gleason grade as 1, 2 or 3; if PSA greater than or equal to
18, and prostate volume greater than or equal to 50, classify the subject’s prostatectomy
Gleason grade as 1, 2 or 3; if PSA greater than or equal to 18, prostate volume greater than
or equal to 37 and less than 50, the subject’s prostatectomy Gleason grade as 4 or 5. The
misclassification rate of the tree is 25/235 = 10.6% or the accuracy of the tree is 89.4%. The
predictor HCTa is not present in the tree at all. There is a reason behind this. The column
HCTa has 131 missing values. Among the non-missing values, there are only 9 cases of high
cancers, which constitutes less than 10% of the total number of subjects. In other words,
the tree is built based on predictors, not including HCTa. This defeats our goal of making
HCTa the main predictor.
4. Discussion
There are several studies devoted to diagnosis of prostate cancer by prostatectomy [19–23].
A number of studies compare the efficacy of systematic biopsy (SBx) with other types of
biopsies [24–27], none of which is a gold standard. Prostatectomy is accurate but cannot be
a gold standard procedure. We have data on patients with information from SBx, MRITBx,
and prostatectomy. This data provides a way to examine the efficacy of SBx vis-à-vis
prostatectomy and that of MRITBx vis-à-vis prostatectomy. Such data enable us to develop
a biomarker to discriminate high risk cancer (Grades 4, 5) and not high risk cancer (Grades
1, 2, 3). We showed that SBx with the predictor PSA is a better discriminator of high cancers
and not high cancers, with the area under the ROC curve 87.5%, the sensitivity, 83.3%
and the specificity, 83.8%, than the one just based on SBx alone (Cohen’s Kappa = 0.34,
sensitivity = 73.3% and specificity = 87.3%). We showed that MRITBx with the predictor
PSA is a better discriminator of high cancers and not high cancers with the area under the
ROC curve 92.6%, the sensitivity, 93.3% and the specificity, 42.6%, than the one just based
on MRITBx alone (Cohen’s Kappa = 0.16, sensitivity = 77.8% and specificity = 85.3%).
When prostatectomy Gleason grades and SBx Gleason grades are binarized with
A = {1, 2, 3} and B = {4, 5}. The accuracy of the classification tree for discriminating high
cancers and not high cancers is 91.2% when binarized systematic Gleason grades, PSA,
and prostate volume were used as predictors. When prostatectomy Gleason grades and
targeted Gleason grades are binarized with A = {1, 2, 3} and B = {4, 5}. The accuracy of
the classification tree for discriminating high cancers and not high cancers is 89.4% when
Bioengineering 2023, 10, 1395
11 of 14
binarized targeted Gleason grades, PSA, and prostate volume were used as predictors.
However, the targeted Gleason grades are not present in the tree because over 55% of its
data is missing. The tree in Figure 5 is built on the predictors PSA and prostate volume.
Some limitations in our study: when prostatectomy Gleason grades and SBx Gleason
grades are binarized with A = {1} and B = {2, 3, 4, 5}, logistic regression and classification
tree fail to explain prostatectomy Gleason grades because there are too few cases of A
= {1}. The output is not reliable because there are only 4 cases with A = {1} among the
prostatectomy grades. For the validity of logistic regression model, the frequency of A = {1}
or B = {2, 3, 4, 5} should be at least 10% of the data [28,29].
To assess the accuracy of SBx and MRITBx, we need substantial data in each of the
prostatectomy Gleason grades. The current data spanned January 2014 to March 2020. We
are accumulating data from March 2020 onwards. We hope to have comprehensive data in
the future to be able to assess accuracies.
5. Conclusions
This is the first time the efficacy of SBx vis-à-vis prostatectomy and that of MRITBx
vis-à-vis prostatectomy were examined. We have developed biomarkers to discriminate
high cancers and not high cancers using six-year clinical records data from the Urology Department at UCHealth. From our analysis, discriminating high cancers and not high cancers
based on SBx by the logistic regression model, as well as the classification tree paradigm
has an accuracy around 90% (Figures 5 and 6 on Classification Tree). MRITBx has better
accuracy compared with SBx when we use a logistic regression model (Figures 2 and 4 on
AUC). The models take information from additional predictors besides SBx and MRITBx.
There are some limitations to our study. In the first place, our study used data from a single
institution only. More trustworthy conclusions could be drawn from a multi-institutional
study. Secondly, the potential for selection bias and a possible lack of powered analysis
associated with the retrospective nature of the study must be noted. Lastly, we do not have
enough data to discriminate cancer (Gleason Grade 2, 3, 4, 5) versus low cancer (Gleason
Grade 1). Finally, we showed that the biomarker based on SBx and other predictors is a
better discriminator of high cancers versus not high cancers than the one based on SBx
alone. A similar conclusion holds for the biomarker based on MRITBx and other predictors.
The following are the biomarkers for detecting high cancers versus not high cancers.
The sensitivities and specificities are reported along with the areas under their ROC curves.
Biomarker based on SBx:
Logit = −4.503 + 0.01 ∗ Age + 0.999 ∗ Race (Caucasian) + (−0.127)
∗ Race (Other ) + 2.982 ∗ HCsys + (−0.0033) ∗ Volume + 0.015
∗ PSA
With specificity is 0.838 and sensitivity is 0.833.
Biomarker based on MRITBx:
Logit = −0.246 + 0.007 ∗ Age + 0.196 ∗ Race (Caucasion) + 1.873 ∗ Race (Other )
+2.565 ∗ HCTa + (−0.01) ∗ volume + 0.143 ∗ PSA
with specificity is 0.9759 and sensitivity is 0.9.
The diagnostic procedures based on SBx and MRITBx are not reliable for detecting
cancer vs. no cancer. However, the procedures are excellent in detecting high cancer vs.
not high cancer. The logistic regression model contrasting high cancers vs. not high cancers
based on age, race, prostate volume, PSA and high cancers vs. not high cancers as per
SBx has an accuracy of 84%. The cross-validation method as per LOOCV corroborated the
accuracy with its own accuracy calculation at 86%.The K-fold cross-validation method put
accuracy at 87% [30]. This is the main message coming from our paper.
If the high cancer determination is based on biopsies, the current practice is that high
cancer is present if the Gleason Score is greater than or equal to 8. We have pointed out
Bioengineering 2023, 10, 1395
12 of 14
that this is not a good judgment. We can improve the diagnosis if we take into account age,
race, prostate volume, and PSA.
Some recent literature on cancer detection has focused on machine learning methods [31,32]. From our perspective, we would like to assess how good these methods are
vis-à-vis prostatectomy, if only we have data.
Author Contributions: Conceptualization, T.G. and M.B.R.; methodology, M.B.R.; software, T.G.; validation, T.G. and M.B.R.; formal analysis, T.G.; investigation, A.S.; resources, A.S.; data curation, A.S.;
writing—original draft preparation, T.G.; writing—review and editing, M.B.R. and A.S.; visualization,
T.G.; supervision, M.B.R. and A.S.; project administration, M.B.R. All authors have read and agreed
to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: The study was conducted in accordance with the Declaration
of Helsinki and approved by the Institutional Review Board of University of Cincinnati (protocol
code: UC IRB: 2018-4010).
Informed Consent Statement: Informed consent was obtained from all subjects involved in the
study.
Data Availability Statement: Data is available on request.
Acknowledgments: We sincerely appreciate the College of Public Health at Kent State University for
providing the facilities to carry out research of the paper. The first author is immensely grateful to
Mu Guan for sustaining me throughout my life so far. This publication was made possible in part by
support from the Kent State University Open Access Publishing Fund.
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A
Logistic regression: High Cancer vs. Not High Cancer
Outcome variable: prostatectomy-High Cancer vs. Not High Cancer (HCpros)
Main predictor: SBx- High Cancer vs. Not High Cancer (HCsys)
Output
Call:
glm(formula = HCpros ~ Age + Race + HCsys + Volume + PSA, family = binomial,
data = P2023C)
Deviance Residuals:
Min
1Q
Median
3Q
Max
−1.9573 −0.3392 −0.3153 −0.1977 2.6249
Coefficients:
Estimate
Std. Error z value Pr(>|z|)
(Intercept)
−4.503e+00 2.285e+00 −1.971
0.0487 *
Age
9.951e−03
3.542e−02 0.281
0.7787
RaceCaucasian 9.985e−01
5.389e−01 1.853
0.0639.
RaceOther
−1.269e+01 1.072e+03 −0.012
0.9906
HCsys
2.982e+00
5.012e−01 5.950
2.67e−09 ***
Volume
−3.309e−03 1.160e−02 −0.285 0.7754
PSA
1.480e−02
7.435e−03 1.991
0.0465 *
--Signif. codes:
0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 177.27 on 226 degrees of freedom
Residual deviance: 121.75 on 220 degrees of freedom
(8 observations deleted due to missingness)
AIC: 135.75
Bioengineering 2023, 10, 1395
13 of 14
Appendix B
Logistic regression: High Cancer vs. Not High Cancer
Outcome variable: prostatectomy-High Cancer vs. Not High Cancer (HCpros)
Main predictor: MRITBx- High Cancer vs. Not High Cancer (HCTa)
Output
Call:
glm(formula = HCpros ~ Age + Race + HCTa + Volume + PSA, family = binomial,
data = P2023C)
Deviance Residuals:
Min
1Q
Median
3Q
Max
−1.82815 −0.25549 −0.17894 −0.00001 2.76879
Coefficients:
Estimate
Std. Error
z value Pr(>|z|)
(Intercept)
−2.455e+01 2.911e+03
−0.008 0.99327
Age
7.186e−03
6.467e−02
0.111
0.91152
RaceCaucasian 1.957e+01
2.911e+03
0.007
0.99464
RaceOther
1.873e+00
1.227e+04
0.000
0.99988
HCTa
2.565e+00
9.257e−01 2.771
0.00558 **
Volume
−1.038e−02 2.319e−02 −0.447 0.65463
PSA
1.425e−01
6.264e−02
2.275
0.02289 *
--Signif. codes:
0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 61.065 on 102 degrees of freedom
Residual deviance: 35.451 on 96 degrees of freedom
(132 observations deleted due to missingness)
AIC: 49.451
References
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
Hoge, C.; Maynor, S.; Sidana, A.; Guan Tianyuan Rao, M.B.; Naffouje, R.; Verma, S. A comparison of cancer detection rates
between template systematic biopsies obtained using magnetic resonance imaging-ultrasound fusion machine and freehand
transrectal ultrasound-guided systematic biopsies. J. Endourol. 2020, 3, 154–196. [CrossRef]
Kaneko, M.; Sugano, D.; Lebastchi, A.H.; Duddalwar, V.; Nabhani, J.; Haiman, C.; Gill, I.S.; Cacciamani, G.E.; Abreu, A.L.
Techniques and Outcomes of MRI-TRUS Fusion Prostate Biopsy. Curr. Urol. Rep. 2021, 22, 27. [CrossRef]
National Library of Medicine. Available online: https://www.ncbi.nlm.nih.gov/books/NBK556081/ (accessed on 8 May 2023).
Cao, R.; Bajgiran, A.M.; Mirak, S.A.; Shakeri, S.; Zhong, X.; Enzmann, D.; Raman, S.; Sung, K. Joint Prostate Cancer Detection and
Gleason Score Prediction in Mp-MRI via FocalNet. Med. Imaging 2019, 38, 2496–2506. [CrossRef] [PubMed]
Vente, C.d.; Vos, P.; Hosseinzadeh, M.; Pluim, J.; Veta, M. Deep Learning Regression for Prostate Cancer Detection and Grading in
Bi-Parametric MRI. Biomed. Eng. 2021, 68, 374–383. [CrossRef]
Larsen, L.K.; Jakobsen, J.S.; Abdul-Al, A.; Guldberg, P. Noninvasive Detection of High Grade Prostate Cancer by DNA Methylation
Analysis of Urine Cells Captured by Microfiltration. J. Urol. 2018, 200, 749–757. [CrossRef] [PubMed]
Lih, T.-S.M.; Dong, M.; Mangold, L.; Partin, A.; Zhang, H. Urinary Marker Panels for Aggressive Prostate Cancer Detection. Sci.
Rep. 2022, 12, 14837. [CrossRef] [PubMed]
Sayyadi, N.; Justiniano, I.; Wang, Y.; Zheng, X.; Zhang, W.; Jiang, L.; Polikarpov, D.M.; Willows, R.D.; Gillatt, D.; Campbell, D.;
et al. Detection of Rare Prostate Cancer Cells in Human Urine Offers Prospect of Non-Invasive Diagnosis. Sci. Rep. 2022, 12,
18452. [CrossRef] [PubMed]
Da Silva, L.M.; Pereira, E.M.; Salles, P.G.; Godrich, R.; Ceballos, R.; Kunz, J.D.; Casson, A.; Viret, J.; Chandarlapaty, S.; Gil Ferreira,
C.; et al. Independent Real-World Application of a Clinical-Grade Automated Prostate Cancer Detection System. J. Pathol. 2021,
254, 147–158. [CrossRef]
Yoo, S.; Gujrathi, I.; Haider, M.A.; Khalvati, F. Prostate Cancer Detection using Deep Convolutional Neural Networks. Sci. Rep.
2019, 9, 19518. [CrossRef]
Hao, R.; Namdar, K.; Liu, L.; Haider, M.A.; Khalvati, F. A Comprehensive Study of Data Augmentation Strategies for Prostate
Cancer Detection in Diffusion-Weighted MRI Using Convolutional Neural Networks. J. Digit. Imaging 2021, 34, 862–876.
[CrossRef]
Bioengineering 2023, 10, 1395
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
14 of 14
Lorusso, V.; Kabre, B.; Pignot, G.; Branger, N.; Pacchetti, A.; Thomassin-Piana, J.; Brunelle, S.; Nicolai, N.; Musi, G.; Salem, N.;
et al. External Validation of the Computerized Analysis of TRUS of the Prostate with the ANNA/C-TRUS System: A Potential
Role of Artificial Intelligence for Improving Prostate Cancer Detection. World J. Urol. 2023, 41, 619–625. [CrossRef]
Prostate Conditions Education Council. Available online: https://www.prostateconditions.org/about-prostate-conditions/
prostate-cancer/newly-diagnosed/gleason-score (accessed on 8 May 2023).
Rosner, B. Fundamentals of Biostatistics, 6th ed; Thomson-Brooks/Cole: Belmont, CA, USA, 2006; pp. 434–437.
Toutenburg, H.; Fleiss, J.L. Statistical Methods for Rates and Proportions, 3rd ed.; John Wiley & Sons: New York, NY, USA, 1973;
pp. 610–617.
Reiser, B.; Faraggi, D.; Fluss, R. Estimation of the Youden Index and Its Associated Cutoff Point. Biom. J. 2005, 47, 458–472.
Martínez-Camblor, P.; Pardo-Fernández, J.C. The Youden Index in the Generalized Receiver Operating Characteristic Curve
Context. Int. J. Biostat. 2019, 15, 20180060. [CrossRef]
Cancer Research, UK. Available online: https://www.cancerresearchuk.org/about-cancer/prostate-cancer/stages/gr… (accessed on 8 May 2023).
Sekhoacha, M.; Riet, K.; Motloung, P.; Gumenku, L.; Adegoke, A.; Mashele, S. Prostate Cancer Review: Genetics, Diagnosis,
Treatment Options, and Alternative Approaches. Molecules 2022, 27, 5730. [CrossRef]
Nguyen-Nielsen, M.; Borre, M. Diagnostic and Therapeutic Strategies for Prostate Cancer. Semin. Nucl. Med. 2016, 46, 484–490.
[CrossRef]
Costello, A.J. Considering the Role of Radical Prostatectomy in 21st Century Prostate Cancer Care. Nat. Rev. Urol. 2020, 17,
177–188. [CrossRef] [PubMed]
Sussman, J.; Haj-Hamed, M.; Talarek, J.; Verma, S.; Sidana, A. How Does a Prebiopsy Mri Approach for Prostate Cancer Diagnosis
Affect Prostatectomy Upgrade Rates? Urol. Oncol. 2021, 39, 784. [CrossRef] [PubMed]
Autorino, R.; Porpiglia, F. Recent advances in prostate cancer: Diagnosis, patient selection and minimally invasive treatment.
Minerva Urol. E Nefrol. 2015, 67, 197–200.
Rebello, R.J.; Oing, C.; Knudsen, K.E.; Loeb, S.; Johnson, D.C.; Reiter, R.E.; Gillessen, S.; Van der Kwast, T.; Bristow, R.G. Prostate
Cancer. Nat. Rev. Dis. Primers 2021, 7, 1. [CrossRef] [PubMed]
Goel, S.; Shoag, J.; Groß, M.; Robinson, B.; Khani, F.; Nelson, B.B.; Margolis, D.; Hu, J.C. Concordance between Biopsy and radical
Prostatectomy Pathology in the era of Targeted Biopsy: A systematic review and meta-analysis. Eur. Urol. Oncol. 2020, 3, 10–20.
[CrossRef] [PubMed]
Ma, Z.; Wang, X.; Zhang, W.; Gao, K.; Wang, L.; Qian, L.; Mu, J.; Zheng, Z.; Cao, X. Developing a predictive model for clinically
significant prostate cancer by combining age, PSA density, and mpMRI. World J. Surg. Oncol. 2023, 21, 83. [CrossRef]
O’Connor, L.P.; Wang, A.Z.; Yerram, N.K.; Lebastchi, A.H.; Ahdoot, M.; Gurram, S.; Zeng, J.; Mehralivand, S.; Harmon, S.; Merino,
M.J.; et al. Combined MRI-targeted Plus Systematic Confirmatory Biopsy Improves Risk Stratification for Patients Enrolling on
Active Surveillance for Prostate Cancer. Urology 2022, 144, 164–170. [CrossRef]
Vittinghoff, E.; McCulloch, C.E. Relaxing the Rule of Ten Events per Variable in Logistic and Cox Regression. Am. J. Epidemiol.
2007, 165, 710–718. [CrossRef]
Van Smeden, M.; Moons, K.G.M.; Groot, J.A.H.; Eijkemans, M.J.C.; Reitsma, J.B.; Collins, G.S.; Altman, D.G. Sample size for
binary logistic prediction models: Beyond events per variable criteria. Stat. Methods Med. Res. 2019, 28, 2455–2474. [CrossRef]
Kassambara, A. Practical Guide to Cluster Analysis in R: Unsupervised Machine Learning; STHDA: Marseille, France, 2017; Chapter 2;
pp. 10–23.
Sun, Y.; Fang, J.; Shi, Y.; Li, H.; Wang, J.; Xu, J.; Zhang, B.; Liang, L. Machine Learning Based on Radiomics Features Combing
B-Mode Transrectal Ultrasound and Contrast-Enhanced Ultrasound to Improve Peripheral Zone Prostate Cancer Detection.
Abdom. Radiol. 2023, 1–10. [CrossRef]
Michaely, H.J.; Aringhieri, G.; Cioni, D.; Neri, E. Current Value of Biparametric Prostate MRI with Machine-Learning or DeepLearning in the Detection, Grading, and Characterization of Prostate Cancer: A Systematic Review. Diagnostics 2022, 12, 799.
[CrossRef]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual
author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to
people or property resulting from any ideas, methods, instructions or products referred to in the content.
International Journal of
Molecular Sciences
Article
Heterotypic Stressors Unmask Behavioral Influences of PMAT
Deficiency in Mice
Brady L. Weber , Marissa M. Nicodemus , Allianna K. Hite, Isabella R. Spalding, Jasmin N. Beaver,
Lauren R. Scrimshaw , Sarah K. Kassis, Julie M. Reichert , Matthew T. Ford , Cameron N. Russell
Elayna M. Hallal and T. Lee Gilman *
,
Department of Psychological Sciences, Brain Health Research Institute, Healthy Communities Research Institute,
Kent State University, Kent, OH 44240, USA
* Correspondence: lgilman1@kent.edu
Citation: Weber, B.L.; Nicodemus,
M.M.; Hite, A.K.; Spalding, I.R.;
Beaver, J.N.; Scrimshaw, L.R.; Kassis,
S.K.; Reichert, J.M.; Ford, M.T.;
Abstract: Certain life stressors having enduring physiological and behavioral consequences, in
part by eliciting dramatic signaling shifts in monoamine neurotransmitters. High monoamine levels can overwhelm selective transporters like the serotonin transporter. This is when polyspecific
transporters like plasma membrane monoamine transporter (PMAT, Slc29a4) are hypothesized to
contribute most to monoaminergic signaling regulation. Here, we employed two distinct counterbalanced stressors—fear conditioning and swim stress—in mice to systematically determine how
reductions in PMAT function affect heterotypic stressor responsivity. We hypothesized that male
heterozygotes would exhibit augmented stressor responses relative to female heterozygotes. Decreased PMAT function enhanced context fear expression, an effect unexpectedly obscured by a sham
stress condition. Impaired cued fear extinction retention and enhanced context fear expression in
males were conversely unmasked by a sham swim condition. Abrogated corticosterone levels in
male heterozygotes that underwent swim stress after context fear conditioning did not map onto any
measured behaviors. In sum, male heterozygous mouse fear behaviors proved malleable in response
to preceding stressor or sham stress exposure. Combined, these data indicate that reduced male
PMAT function elicits a form of stress-responsive plasticity. Future studies should assess how PMAT
is differentially affected across sexes and identify downstream consequences of the stress-shifted
corticosterone dynamics.
Russell, C.N.; et al. Heterotypic
Stressors Unmask Behavioral
Keywords: stress; mice; fear conditioning; swim; corticosterone; sex differences; behavior
Influences of PMAT Deficiency in
Mice. Int. J. Mol. Sci. 2023, 24, 16494.
https://doi.org/10.3390/
ijms242216494
Academic Editor: Giuliano
Ciarimboli
Received: 8 September 2023
Revised: 16 November 2023
Accepted: 16 November 2023
Published: 18 November 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
1. Introduction
Under stressful environmental conditions, signaling patterns of monoamine neurotransmitters like dopamine and serotonin change dramatically [1–4]. The duration and
magnitude of monoamine neurotransmitter signaling are regulated, in part, by transportermediated uptake. Monoamine neurotransmitter transporters are broadly categorized into
two groups based upon their transport capacity (high or low) and their selectivity for
substrates (see reviews [5–7]). Historically, the most well-studied monoamine transporters
are those that have relatively high substrate selectivity and lower transport capacity. These
include norepinephrine (Slc6a2), dopamine (Slc6a3), and serotonin (Slc6a4) transporters.
In contrast, monoamine transporters that have higher capacity for substrate transport
but are less selective about the substrates they transport include the organic cation transporters (Slc22a1, Slc22a2, Slc22a3; also known as OCT1, OCT2, and OCT3, respectively)
and plasma membrane monoamine transporter (PMAT, Slc29a4). PMAT preferentially
transports dopamine and serotonin over other monoamine neurotransmitters like norepinephrine or histamine [8] (see review [9]). Thus, PMAT function likely impacts dopamine
and serotonin signaling, particularly during high signaling periods like stressful environmental conditions.
Int. J. Mol. Sci. 2023, 24, 16494. https://doi.org/10.3390/ijms242216494
https://www.mdpi.com/journal/ijms
Int. J. Mol. Sci. 2023, 24, 16494
2 of 25
Previous studies in mice show that constitutive reductions in, or loss of, PMAT function
affect behavioral responses to stressful environmental conditions, such as a swim stress [10]
or tail suspension test [11]. Moreover, these behavioral responses were sex-specific. Mice
constitutively lacking OCT2 exhibit augmented behavioral responses to both acute (both
sexes used, but sex differences not analyzed; [12]) and chronic (males only; [13]) stressors.
In contrast, male mice constitutively deficient in, or lacking, OCT3 exhibited no changes in
the resident–intruder test nor in Morris water maze performance [14]. Findings with the
latter test suggest that OCT3 does not affect spatial learning or memory processes, whereas
the outcomes with both tests indicate that OCT3 is not involved in behavioral responses to
stressors (aggressive encounters or water immersion).
Surprisingly few evaluations have examined how PMAT or the OCTs contribute to
learning and memory processes. Moreover, no studies have assessed fear conditioning
either in PMAT or OCT knockout mice or after administration of the broad OCT + PMAT
inhibitor, decynium-22. Beyond the Morris water maze study mentioned earlier, a couple of groups have assessed conditioned place preference (CPP)—a form of classical
conditioning—in OCT3 or PMAT knockout mice. Gautron’s group observed no influence of OCT3 knockout on amphetamine CPP (sex(es) not stated; [15]), whereas Daws’
group reported that males (but not females) lacking OCT3 had attenuated amphetamine
CPP [16]. The latter group used a dose half that of the former group, which may explain
some of the discrepancies between findings. In contrast, Daws’ group found that females
(but not males) deficient in PMAT exhibited enhanced amphetamine CPP. Thus, some evidence exists that sex-specific influences of these understudied transporters could influence
learning and memory processes. Still, because amphetamine affects monoamine signaling,
disentangling the effects of PMAT/OCT deficiency upon responses to amphetamine from
those upon learning and memory is difficult to do.
We evaluated how PMAT deficiency influences classical conditioning in the absence
of any drug exposure in the present investigation. We accomplished this using both contextual and cued fear conditioning paradigms in conjunction with exposure to a second,
different form of stressor—swim stress. Contextual and cued fear conditioning preferentially engage activity within the dorsal hippocampus and amygdala [17–19] (see reviews [20–22]), whereas swim stress predominantly increases hypothalamus and amygdala
activity [23–25]. Here, we assessed directional influences of these two different stressor formats—context/cued fear conditioning before/after swim stress—by evaluating
stress-responsive behaviors specific to each paradigm (freezing; swimming, climbing, immobility; respectively) and circulating blood corticosterone levels as a proxy index of stress
persistence. Because we have previously observed sex-specific stressor responses in PMATdeficient mice [10,11], these studies were likewise performed in mice of both biological
sexes. Finally, we intentionally used only wild-type (+/+) and heterozygous (+/−) PMAT
mice, given potential translational relevance to humans with functional reductions in PMAT
resulting from common polymorphisms [26–29].
We hypothesized that attenuated PMAT function in heterozygous mice would enhance behavioral responses to both initial and secondary stressors due to reduced clearance
of elevated dopamine and serotonin. Further, we hypothesized that male heterozygotes
would exhibit augmented behavioral and physiological stressor responses relative to females, given previous indications of such in male PMAT-deficient mice [11], plus literature
evidence suggesting overall sex differences in stressor responsivity in mice [30,31].
2. Results
2.1. Fear Behavior
2.1.1. Phase 1
Because this is the first report of fear conditioning in PMAT-deficient mice, we began
with Phase 1 experiments (Figure 1), which involved first performing fear conditioning
followed four weeks thereafter by swim stress. This allowed for the initial identification of
any influences of PMAT deficiency upon fear processing, independent of prior swim stress
2. Results
2.1. Fear Behavior
2.1.1. Phase 1
Int. J. Mol. Sci. 2023, 24, 16494
Because this is the first report of fear conditioning in PMAT-deficient mice, we began
3 of 25
with Phase 1 experiments (Figure 1), which involved first performing fear conditioning
followed four weeks thereafter by swim stress. This allowed for the initial identification
of any influences of PMAT deficiency upon fear processing, independent of prior swim
stress exposure.
As expected,
there no
were
no significant
interactions
with swim
conexposure.
As expected,
there were
significant
interactions
with swim
stress stress
condition,
dition,
nor
any
main
effects
of
swim,
for
Phase
1
cued
(Supplemental
Tables
S1
and
S2)1
nor any main effects of swim, for Phase 1 cued (Supplemental Tables S1 and S2) and Phase
and Phase
1 context (Supplemental
Tables
S3 and S4)
experiments
across sexes.graphed
Consecontext
(Supplemental
Tables S3 and S4)
experiments
across
sexes. Consequently,
quently,
data
wereswim
collapsed
across
swim upon
condition
to focus
uponand
thegenotype,
effects of
data
weregraphed
collapsed
across
condition
to focus
the effects
of time
timethe
andinteractions
genotype, and
the as
interactions
and
thereof
applicable.thereof as applicable.
1. Experimental
Experimentalmanipulations
manipulations
and
variables
for study.
Experimental
manipulations
inFigure 1.
and
variables
for study.
Experimental
manipulations
involved
volved
cued
fear conditioning,
fear conditioning,
and stress
swim (yellow
stress (yellow
compartment,
left
cued
fear
conditioning,
contextcontext
fear conditioning,
and swim
compartment,
left side).
side). fear
Cued
fear conditioning
(top, yellow
compartment)
involved
cued
fear training
in Context
A
Cued
conditioning
(top, yellow
compartment)
involved
cued fear
training
in Context
A (visible
(visible light, grid floor, patterned background, ethanol scent) on Day 0, followed by cued fear exlight, grid floor, patterned background, ethanol scent) on Day 0, followed by cued fear expression
pression testing and cued extinction training on Day 2 in Context B (infrared light, smooth floor, no
testing and cued extinction training on Day 2 in Context B (infrared light, smooth floor, no background,
background, Windex scent). On Day 4, testing of extinction retention occurred in Context B, then
Windex
scent). On
Day 4,
testing
of extinction
occurred intesting
Context
B, thenimmediately
Day 5 involved
Day 5 involved
testing
mice
in Context
A for retention
context expression
followed
by
testing
mice
in
Context
A
for
context
expression
testing
followed
immediately
by
cued
fear
cued fear renewal testing. Note that for Day 5, the graphic shows the different conditions
forrenewal
context
testing.
Note
that for
Day105,min)
the graphic
shows
different
conditions
context
expression
expression
testing
(left;
and cued
fearthe
renewal
testing
(right; for
5 min)
for clarity,
but testing
that in
(left;
10 min)
fear renewal
testing
5 min)15for
clarity,
butsession.
that in Context
practice fear
these
tests
practice
theseand
testscued
occurred
within the
same(right;
continuous
min
testing
conditioning (bottom
left,same
yellow
compartment)
contextContext
fear training
in Context A
on Dayleft,
0,
occurred
within the
continuous
15 mininvolved
testing session.
fear conditioning
(bottom
with
testing
occurring
in
Context
A
on
Day
2.
Swim
stress
involved
a
6
min
inescapable
immersion
yellow compartment) involved context fear training in Context A on Day 0, with testing occurring in
in room A
temperature
waterstress
(bottom
right,ayellow
compartment).
Variables
involved
plasma memContext
on Day 2. Swim
involved
6 min inescapable
immersion
in room
temperature
water
brane monoamine transporter (PMAT, Slc29a4) genotype, sex, swim condition, and the timeline of
(bottom right, yellow compartment). Variables involved plasma membrane monoamine transporter
stressor exposure (Phase 1 or 2) (green compartment, right side). Wild-type (+/+) or heterozygous
(PMAT, Slc29a4) genotype, sex, swim condition, and the timeline of stressor exposure (Phase 1 or 2)
(+/−) mice of both sexes were used (bottom left, green compartment). Phase 1 (top right, green com(green
compartment,
right side).
(+/+)
heterozygous
(+/−) mice
of both
sexes were
partment)
involved exposing
miceWild-type
to either cued
or or
context
fear conditioning,
followed
4 weeks
later
used
(bottom
left,
green
compartment).
Phase
1
(top
right,
green
compartment)
involved
exposing
by swim stress; 2 h after swim stress, blood was collected for serum corticosterone analyses.
Phase
mice
to either
orcompartment)
context fear conditioning,
followed
weeksfollowed
later by 4swim
stress;
2 heither
after
2 (bottom
right,cued
green
had mice undergo
swim4 stress,
weeks
later by
swim
stress,
blood
collected for
analyses.
cued or
context
fearwas
conditioning;
2 hserum
after corticosterone
the last test (Day
5, cued;Phase
Day 22,(bottom
context),right,
bloodgreen
was
collected
for
serum
corticosterone
analyses.
compartment) had mice undergo swim stress, followed 4 weeks later by either cued or context fear
conditioning; 2 h after the last test (Day 5, cued; Day 2, context), blood was collected for serum
Phase 1 Cuedanalyses.
Females
corticosterone
In Phase 1 cued females, there was an expected main effect of time for training
Phase 1 Cued Females
(F(3.122,109.266) = 183.4, p < 0.001, partial η2 = 0.840), extinction retention testing (F(9.774,342.097) =
1 partial
cued females,
there
was an
expected
main
effect of =time
forp training
8.024,Inp Phase
< 0.001,
η2 = 0.186),
context
fear
expression
(F(6.926,242.403)
5.297,
< 0.001,
2 = 0.840), extinction retention testing (F
(F
=
183.4,
p
<
0.001,
partial
η
2
(3.122,109.266)
partial
η = 0.131), and cued fear renewal (F(3.712,129.923) = 12.40, p < 0.001, partial(9.774,342.097)
η2 = 0.262)
2 = 0.186), context fear expression (F
=
8.024, p < 0.001,
partial
= 5.297,
p < 0.001,
(Supplemental
Table
S1; ηFigure
2A,C–E). While there were no(6.926,242.403)
interactions
with, nor
main
2
partial η = 0.131), and cued fear renewal (F(3.712,129.923) = 12.40, p < 0.001, partial η2 = 0.262)
(Supplemental Table S1; Figure 2A,C–E). While there were no interactions with, nor main
effects of, genotype for any of these, there was a significant time × genotype interaction
(F(9.728,340.487) = 3.865, p < 0.001, partial η2 = 0.099) in Phase 1 cued females for cued
expression testing and extinction training (Supplemental Table S1; Figure 2B). Post hoc
testing reflects that female heterozygous mice exhibited a temporally distinct pattern of
cued fear expression and cued fear extinction from wild-type females at multiple time
Int. J. Mol. Sci. 2023, 24, 16494
interactions with, or main effects of, genotype at any stage (Supplemental Table S2; Figure
2F–J). The anticipated main effect of time was present for all stages in Phase 1 cued males
(training, F(3.156,170.433) = 205.7, p < 0.001, partial η2 = 0.792; cued expression testing and extinction training, F(10.594,572.057) = 9.786, p < 0.001, partial η2 = 0.153; extinction retention testing, F(10.125,536.64) = 11.09, p < 0.001, partial η2 = 0.173; context fear expression, F(10.889,533.558)
=
4 of 25
11.58, p < 0.001, partial η2 = 0.191; cued fear renewal, F(3.82,187.198) = 11.17, p < 0.001, partial η2
= 0.186) (Supplemental Table S2; Figure 2F–J). Combined, these data indicate that PMAT
deficiency was largely without consequence on cued fear processing in males, whereas it
points
Thison
appears
to be the result
of female
heterozygotes displaying delayed
had a (Figure
modest2B).
impact
cued extinction
learning
in females.
cued fear extinction relative to female wild types.
Figure2.2. Phase
Phase 11 cued
mice,
collapsed
across
swim
condition.
Figure
cued fear
fear conditioning
conditioningininfemale
femaleand
andmale
male
mice,
collapsed
across
swim
condiFemale (A–E) wild types are represented by teal solid lines, and female heterozygotes are repretion. Female (A–E) wild types are represented by teal solid lines, and female heterozygotes are
sented by blue dashed lines. Male (F–J) wild types are represented by orange solid lines, and male
represented by blue dashed lines. Male (F–J) wild types are represented by orange solid lines, and
heterozygotes are represented by yellow dashed lines. Fear conditioning commenced 4 weeks prior
male
heterozygotes
are represented
byMice
yellow
dashed
lines.
commenced
4 weeks
to swim
stress exposure
for Phase 1.
were
trained
on Fear
Day 0conditioning
in Context A,
then two days
later
prior
exposure
for Phase
1. Mice
trained ontesting
Day 0 in
Context
A, then
days
(Dayto
2),swim
mice stress
were placed
in Context
B for
cued were
fear expression
as well
as cued
fear two
extinction
later
(Day Two
2), mice
placedmice
in Context
B forthe
cued
fear expression
testing
well as cued
fear
training.
dayswere
thereafter,
underwent
identical
procedure
for theaspurposes
of testing
extinctiontraining.
retention
(Day
4; C,H).
One mice
day later
(Day 5),
mice
were procedure
placed in Context
A and were
extinction
Two
days
thereafter,
underwent
the
identical
for the purposes
of
tested extinction
for contextretention
fear expression
followed
bymice
testing
cued
fear renewal
(E,J).
Data
testing
(Day 4;(D,I)
C,H).
One dayimmediately
later (Day 5),
were
placed
in Context
A and
are percent
time
spentfear
freezing
for each
30 sfollowed
period indicated.
Databywere
analyzed
within
each (E,J).
trainwere
tested for
context
expression
(D,I)
immediately
testing
cued fear
renewal
ing/testing
stage
and
within
each
sex
using
two-way
repeated-measures
ANOVAs
(PMAT
genotype
Data are percent time spent freezing for each 30 s period indicated. Data were analyzed within
each training/testing stage and within each sex using two-way repeated-measures ANOVAs (PMAT
genotype × time) and Holm–Šídák post hoc testing and are graphed as mean ± 95% confidence
interval. * p = 0.027, * p = 0.042, * p = 0.016 (left to right, panel B); * p = 0.021 (panel F); ** p = 0.002
(panel B); indicate the difference between heterozygous and wild type within the same sex at the
indicated timepoint.
Phase 1 Cued Males
Unlike females that underwent Phase 1 cued procedures, Phase 1 cued males had
no interactions with, or main effects of, genotype at any stage (Supplemental Table S2;
Figure 2F–J). The anticipated main effect of time was present for all stages in Phase 1
cued males (training, F(3.156,170.433) = 205.7, p < 0.001, partial η2 = 0.792; cued expression
testing and extinction training, F(10.594,572.057) = 9.786, p < 0.001, partial η2 = 0.153; extinction
retention testing, F(10.125,536.64) = 11.09, p < 0.001, partial η2 = 0.173; context fear expression,
F(10.889,533.558) = 11.58, p < 0.001, partial η2 = 0.191; cued fear renewal, F(3.82,187.198) = 11.17,
p < 0.001, partial η2 = 0.186) (Supplemental Table S2; Figure 2F–J). Combined, these data
indicate that PMAT deficiency was largely without consequence on cued fear processing in
males, whereas it had a modest impact on cued extinction learning in females.
Int. J. Mol. Sci. 2023, 24, 16494
5 of 25
Phase 1 Context Females
Females assigned to the Phase 1 context condition exhibited no interactions of
time × genotype, and no main effect of genotype, but the expected main effect of time during context fear training (F(3.626,123.285) = 125.3, p < 0.001, partial η2 = 0.787) (Supplemental
Table S3; Figure 3A). Similarly, testing of context fear expression in these females revealed
a significant main effect of time (F(9.138,310.686) = 8.874, p < 0.001, partial η2 = 0.207) (Supplemental Table S3; Figure 3B). A non-significant trend of genotype (F(1,34) = 2.936, p = 0.096,
Int. J. Mol. Sci. 2023, 24, x FOR PEER REVIEW
6 of 27
partial η2 = 0.079) was noted for females during context fear testing, but pairwise comparisons did not indicate any select time points that differed significantly across genotype.
Figure
Phase11context
context fear conditioning
and
male
mice,
collapsed
across
swimswim
condition.
Figure
3.3.Phase
conditioningininfemale
female
and
male
mice,
collapsed
across
condiFemale
(A,B)
wildwild
typestypes
are represented
by tealby
solid
female
are represented
tion.
Female
(A,B)
are represented
teallines,
solidand
lines,
andheterozygotes
female heterozygotes
are represented
blue dashed
lines.(C,D)
Male wild
(C,D)types
wildare
types
are represented
by solid
orange
solid
lines,
and
by blueby
dashed
lines. Male
represented
by orange
lines,
and
male
male
heterozygotes
are represented
by yellow
dashed
lines.conditioning
Fear conditioning
commenced
weeks
heterozygotes
are represented
by yellow
dashed
lines. Fear
commenced
4 weeks4prior
prior
to swim
stress
exposure
for Phase
1. Mice
were trained
0 in Context
A Two
(A,C).
Two
days
to swim
stress
exposure
for Phase
1. Mice
were trained
on Dayon0 Day
in Context
A (A,C).
days
later
later (Day 2), mice were placed back in Context A to test for context fear expression (B,D). Data are
percent time spent freezing for each 30 s period following the foot shock (A,C) or every 30 s of testing
(B,D). Data were analyzed within each training/testing stage and within each sex using two-way
repeated-measures ANOVAs (PMAT genotype × time) and Holm–Šídák post hoc testing and are
graphed as mean ±95% confidence interval. * p = 0.039 indicates a difference between heterozygous
and wild type within the same sex at the indicated time point.
Int. J. Mol. Sci. 2023, 24, 16494
6 of 25
(Day 2), mice were placed back in Context A to test for context fear expression (B,D). Data are percent
time spent freezing for each 30 s period following the foot shock (A,C) or every 30 s of testing (B,D).
Data were analyzed within each training/testing stage and within each sex using two-way repeatedmeasures ANOVAs (PMAT genotype × time) and Holm–Šídák post hoc testing and are graphed as
mean ± 95% confidence interval. * p = 0.039 indicates a difference between heterozygous and wild
type within the same sex at the indicated time point.
Phase 1 Context Males
Males that underwent context fear conditioning were similar to females in that only
a main effect of time was detected for training (F(3.226,125.803) = 12.92, p < 0.001, partial
η2 = 0.249) (Supplemental Table S4; Figure 3C). Unlike females, however, context fear
testing in males showed main effects of both genotype (F(1,39) = 4.555, p = 0.039, partial
η2 = 0.105) and time (F(9.546,372.282) = 13.94, p < 0.001, partial η2 = 0.263) (Supplemental
Table S4; Figure 3D). Heterozygous males exhibited increased context fear expression
compared to wild-type males (Figure 3D; Supplemental Table S15 and Supplemental
Figure S2), suggesting that typical PMAT function could selectively suppress the expression
of context fear in males.
2.1.2. Phase 2
After determining how reductions in PMAT function impact cued and context fear
processing in the absence of any preceding stressors, we next evaluated how heterotypic
stressor exposure interacted with PMAT function and sex. To do this, mice underwent
cued or context fear conditioning procedures identical to those in Phase 1, except these
procedures occurred four weeks after a swim stressor.
Phase 2 Cued Females
Repeated-measures ANOVAs of females in the Phase 2 cued condition indicated that
there were no three-way time × genotype × swim interactions in any of the five stages:
training, cued expression testing and extinction training, extinction retention testing, context fear expression, or cued fear renewal (Supplemental Table S5; Figure 4). There were
also no time × genotype interactions across these five stages, nor were any main effects of
genotype or swim detected (Supplemental Table S5; Figure 4). The first four stages all had
the expected main effect of time (training, F(3.605,147.811) = 159.1, p < 0.001, partial η2 = 0.795;
cued expression testing and extinction training, F(8.741,358.393) = 6.623, p < 0.001, partial
η2 = 0.139; extinction retention testing, F(8.864,310.252) = 11.15, p < 0.001, partial η2 = 0.242;
context fear expression, F(10.762,441.257) = 5.923, p < 0.001, partial η2 = 0.126) (Supplemental Table S5; Figure 4A–D,F–I). Cued fear renewal was the only stage with a significant
time × swim interaction (F(3.57,139.219) = 3.592, p < 0.001, partial η2 = 0.0.84) (Supplemental
Table S5; Figure 4E,J). With pairwise comparisons, we determined that this appeared to
be driven by reduced extinction of cued fear renewal over time in mice that previously
underwent a swim stressor (Figure 4J). This was most prominent in heterozygous females,
reaching significance for cued fear in response to the final tone between heterozygous
females that had a swim stressor compared to heterozygous females that did not undergo
swim stress (Figure 4J). This partially mirrors the apparent impaired cued fear extinction
learning exhibited by Phase 1 cued females on testing Day 2.
Int. J. Mol. Sci. 2023, 24, 16494
4E,J). With pairwise comparisons, we determined that this appeared to be driven by reduced extinction of cued fear renewal over time in mice that previously underwent a swim
stressor (Figure 4J). This was most prominent in heterozygous females, reaching significance for cued fear in response to the final tone between heterozygous females that had a
swim stressor compared to heterozygous females that did not undergo swim stress (Figof 25
ure 4J). This partially mirrors the apparent impaired cued fear extinction learning 7exhibited by Phase 1 cued females on testing Day 2.
Figure4.4.Phase
Phase2 2cued
cuedfear
fearconditioning
conditioningininfemale
femalemice,
mice,separated
separatedbyby
swim
condition.Female
Femalewild
wild
Figure
swim
condition.
types
are
represented
by
teal
solid
lines,
and
female
heterozygotes
are
represented
by
blue
dashed
types are represented by teal solid lines, and female heterozygotes are represented by blue dashed
lines.Fear
Fearconditioning
conditioningoccurred
occurred4 4weeks
weeksafter
afterswim
swimstress
stressexposure
exposure
for
Phase
Micewere
weretrained
trained
lines.
for
Phase
2.2.Mice
on Day 0 in Context A (A,F). Two days later (Day 2), mice were placed in Context B (B,G); this served
on Day 0 in Context A (A,F). Two days later (Day 2), mice were placed in Context B (B,G); this
as cued fear expression testing as well as cued fear extinction training. Two days thereafter, mice
served as cued fear expression testing as well as cued fear extinction training. Two days thereafter,
underwent the identical procedure for the purposes of testing extinction retention (Day 4; C,H). One
mice
themice
identical
for the purposes
retention
(Day
4; C,H).
dayunderwent
later (Day 5),
wereprocedure
placed in Context
A to test of
fortesting
contextextinction
fear expression
(D,I)
then
immeOne
day
later
(Day
5),
mice
were
placed
in
Context
A
to
test
for
context
fear
expression
(D,I)
then30
diately thereafter tested for cued fear renewal (E,J). Data are percent time spent freezing for each
immediately
thereafter
tested
cued fear
renewal
(E,J). Data are percent
time
spenteach
freezing
for
s period indicated.
Data
werefor
analyzed
within
each training/testing
stage and
within
sex, using
3-way
× PMAT
genotype
× swim condition)
each
30 srepeated-measures
period indicated. ANOVAs
Data were(time
analyzed
within
each training/testing
stage and
and pairwise
within
each sex, using 3-way repeated-measures ANOVAs (time × PMAT genotype × swim condition)
and pairwise comparisons with Bonferroni correction, and are graphed as mean ± 95% confidence
interval. C indicates p = 0.021 difference between no-swim and swim conditions within the same sex
and genotype (heterozygous) at the indicated time point.
Phase 2 Cued Males
Phase 2 cued males likewise showed no three-way time × genotype × swim interactions, nor any two-way interactions across all five fear behavior testing stages (Supplemental Table S6; Figure 5). No main effect of swim was detected at any stage, whereas significant main effects of time occurred for all stages (training, F(2.700,97.197) = 123.1, p < 0.001,
partial η2 = 0.774; cued expression testing and extinction training, F(8.933,267.996) = 10.55,
p < 0.001, partial η2 = 0.260; extinction retention testing, F(7.79,296.005) = 8.583, p < 0.001,
partial η2 = 0.184; context fear expression, F(9.633,356.405) = 5.218, p < 0.001, partial η2 = 0.124;
cued fear renewal, F(3.835,130.383) = 24.30, p < 0.001, partial η2 = 0.417) (Supplemental Table S6;
Figure 5). Significant main effects of genotype were found for Phase 2 cued males for both
extinction retention testing (F(1,38) = 6.914, p = 0.012, partial η2 = 0.154) (Figure 5C,H) and
context fear expression (F(1,37) = 4.175, p = 0.048, partial η2 = 0.101) (Figure 5D,I), the latter
reflecting what was found for Phase 1 context males (Figure 3D), but not context testing in
Phase 1 cued males (Figure 2I). Pairwise comparisons highlight that, within the no-swim
condition, heterozygous males in Phase 2 cued extinction retention testing exhibited impaired retention of extinction training relative to wild types (Figure 5C). Put another way,
Int. J. Mol. Sci. 2023, 24, 16494
Significant main effects of genotype were found for Phase 2 cued males for both extinction
retention testing (F(1,38) = 6.914, p = 0.012, partial η2 = 0.154) (Figure 5C,H) and context fear
expression (F(1,37) = 4.175, p = 0.048, partial η2 = 0.101) (Figure 5D,I), the latter reflecting
what was found for Phase 1 context males (Figure 3D), but not context testing in Phase 1
cued males (Figure 2I). Pairwise comparisons highlight that, within the no-swim condi8 of 25
tion, heterozygous males in Phase 2 cued extinction retention testing exhibited impaired
retention of extinction training relative to wild types (Figure 5C). Put another way, heterozygous males that did not first undergo swim stress displayed persistent fear in response
heterozygous
males
thatpredicted
did not first
undergo
swim stress
displayed
persistent
in
to tones that no
longer
foot shock,
indicating
that they
did not
retain thefear
extincresponse
to tones
that
noundergone
longer predicted
footDay
shock,
indicating
that they
did not retain
the
tion learning
they
had
on testing
2. Further,
pairwise
comparisons
suggest
extinction
learning they
undergone
testing Day
2. Further,
pairwise
comparisons
that the genotype
effecthad
in context
fear on
expression
appears
to be mostly
driven
by males
suggest
that
the
genotype
effect
in
context
fear
expression
appears
to
be
mostly
driven
by
in the no-swim condition (Figure 5D), despite the absence of any significant swim
effects
males
in
the
no-swim
condition
(Figure
5D),
despite
the
absence
of
any
significant
swim
or interactions. These Phase 2 findings provide further support for a sex-dependent role
effects
or interactions.
These
Phase 2 findings
provide
furtherfear
support
a sex-dependent
of intact
PMAT function
attenuating
expression
of context
and for
additionally
suggest
role
of
intact
PMAT
function
attenuating
expression
of
context
fear
and
additionally
suggest
that PMAT might typically function to facilitate retention of cued extinction in males.
that PMAT might typically function to facilitate retention of cued extinction in males.
Figure5.5.Phase
Phase2 2cued
cuedfear
fearconditioning
conditioningininmale
malemice,
mice,separated
separatedbybyswim
swimcondition.
condition.Male
Malewild
wild
Figure
types are represented by orange solid lines, and male heterozygotes are represented by yellow
types are represented by orange solid lines, and male heterozygotes are represented by yellow
dashed lines. Fear conditioning occurred 4 weeks after swim stress exposure for Phase 2. Mice
were trained on Day 0 in Context A (A,F), then two days later (Day 2) placed in Context B (B,G),
where they underwent cued fear expression testing as well as cued fear extinction training. Two days
thereafter, mice underwent the identical procedure for the purposes of testing extinction retention
(Day 4; C,H). One day later (Day 5), mice were placed in Context A for context fear expression (D,I).
Immediately after context fear expression testing, mice were tested for cued fear renewal (E,J). Data
are percent time spent freezing for each 30 s period indicated. Data were analyzed within each
training/testing stage and within each sex, using 3-way repeated-measures ANOVAs (time × PMAT
genotype × swim condition) and pairwise comparisons with Bonferroni correction, and are graphed
as mean ± 95% confidence interval. * p = 0.023, * p = 0.015 (left to right, panel C); * p = 0.021,
* p = 0.029,* p = 0.026, * p = 0.023, * p = 0.043, * p = 0.046, * p = 0.015 (left to right, panel D); * p = 0.039
(panel H); ** p = 0.002 (panel C); ** p = 0.008 (panel H) indicate difference between heterozygous
and wild type within the same sex at the indicated time point. C p = 0.025, C p = 0.019 (left to right,
panel G); C p = 0.031 (panel H); C p = 0.019, C p = 0.035, C p = 0.013 (left to right, panel I); CC p = 0.009
(panel I); C p = 0.015 (panel J) indicates difference between no-swim and swim conditions within
the same sex and genotype (indicated by color; black for wild-type, grey for heterozygous) at the
indicated time point.
Int. J. Mol. Sci. 2023, 24, 16494
9 of 25
Phase 2 Context Females
No three- or two-way interactions were found for females in the Phase 2 context
27
during either training or testing, and the only main effects were those10ofoftime
2
(training, F(3.029,102.984) = 76.52, p < 0.001, partial η = 0.692; testing, F(9.209,276.265) = 8.773,
p < 0.001, partial η2 = 0.226) (Supplemental Table S7, Figure 6).
Int. J. Mol. Sci. 2023, 24, x FOR PEER REVIEW
condition
Figure
conditioning in
infemale
femalemice,
mice,separated
separated
swim
condition.
Female
Figure6.6.Phase
Phase22context
context fear conditioning
byby
swim
condition.
Female
wild
wild
types
are represented
by solid
teal solid
and female
heterozygotes
are represented
bydashed
blue
types
are represented
by teal
lines,lines,
and female
heterozygotes
are represented
by blue
dashed
lines.conditioning
Fear conditioning
occurred
4 weeks
after stress
swim exposure
stress exposure
for Phase
Micetrained
were
lines. Fear
occurred
4 weeks
after swim
for Phase
2. Mice2.were
trained on Day 0 in Context A (A,C). Two days later (Day 2), mice were placed back in Context A to
on Day 0 in Context A (A,C). Two days later (Day 2), mice were placed back in Context A to test
test for context fear expression (B,D). Data were analyzed within each training/testing stage and
for context fear expression (B,D). Data were analyzed within each training/testing stage and within
within each sex, using 3-way repeated-measures ANOVAs (time × PMAT genotype × swim condieachand
sex,pairwise
using 3-way
repeated-measures
ANOVAs
(time ×Data
PMAT
× swim
and
tion)
comparisons
with Bonferroni
correction.
aregenotype
percent time
spentcondition)
freezing for
pairwise
comparisons
with
Bonferroni
correction.
Data
are
percent
time
spent
freezing
for
each
30
each 30 s period following the foot shock (A,C) or every 30 s of testing (B,D). Data are graphed as s
period
following
the foot
shock (A,C) or every 30 s of testing (B,D). Data are graphed as mean ± 95%
mean
±95%
confidence
interval.
confidence interval.
Phase 2 Context Males
Males in the Phase 2 context similarly had no significant three-way interactions for
training or testing. No two-way interactions were found for training. Training did exhibit
the anticipated main effect of time (F(3.284,108.38) = 66.40, p < 0.001, partial η2 = 0.668). Neither
time × genotype nor time × swim interactions were significant for testing (Supplemental
Table S8; Figure 7). A non-significant trend was observed for swim × genotype (F(1,33) =
2
Int. J. Mol. Sci. 2023, 24, 16494
10 of 25
Phase 2 Context Males
Males in the Phase 2 context similarly had no significant three-way interactions for
training or testing. No two-way interactions were found for training. Training did exhibit the anticipated main effect of time (F(3.284,108.38) = 66.40, p < 0.001, partial η2 = 0.668).
Neither time × genotype nor time × swim interactions were significant for testing (Supplemental
Table S8; Figure 7). A non-significant trend was observed for swim
Int. J. Mol. Sci. 2023, 24, x FOR PEER REVIEW
11 of 27× genotype
(F(1,33) = 3.400, p = 0.074, partial η2 = 0.093) during context fear testing in males, as was a
significant main effect of time (F(8.625,284.634) = 14.87, p < 0.001, partial η2 = 0.311) (Supplemental
Table
S8; Figure
7B,D). Pairwise
comparisons
that
Figure
7B,D).
Pairwise
comparisons
suggest that
context fear suggest
expression
wascontext
loweredfear
by expression
was lowered
previous
swim
stress
exposure
wild
types,
while swim stress
previous
swim by
stress
exposure
in wild
types,
while in
swim
stress
in heterozygous
males in heterozygous males
their
context (Figure
fear expression
(Figure
7B,D).
These changes
increased
theirincreased
context fear
expression
7B,D). These
changes
in context
fear ex- in context
pression
across genotypes
from an earlier
in males
appear
to be the
inverse
of the inverse
fear expression
across genotypes
fromstressor
an earlier
stressor
in males
appear
to be
what
is observed
for context
fear expression
in Phasein
2 males
underwent
cued fear cued fear
of what
is observed
for context
fear expression
Phasethat
2 males
that underwent
conditioning
(Figure
5). In5).
other
swim
stressswim
hid genotype
differences
in conconditioning
(Figure
Inwords,
other prior
words,
prior
stress hid
genotype
differences in
text fear in Phase 2 cued males, but prior swim stress made more apparent genotype difcontext fear in Phase 2 cued males, but prior swim stress made more apparent genotype
ferences in context fear in Phase 2 context males. Though complex in directionality and
differences in context fear in Phase 2 context males. Though complex in directionality and
circumstance, overall these fear behavior data indicate that reductions in PMAT function
circumstance,
overall these
fear effects
behavior
dataversus
indicate
that reductions
PMAT function
result
in more prominent
behavioral
in males
females.
Further, the in
present
result
in
more
prominent
behavioral
effects
in
males
versus
females.
Further,
findings suggest that the form of fear conditioning and stressor history can sex-specifically the present
findings
the form
of fear conditioning
and stressor history can sex-specifically
enhance
orsuggest
mask thethat
influence
of diminished
PMAT function.
enhance or mask the influence of diminished PMAT function.
Figure 7. Phase 2 context fear conditioning in male mice, separated by swim condition. Male wild
Figure 7. Phase 2 context fear conditioning in male mice, separated by swim condition. Male wild
types are represented by orange solid lines, and male heterozygotes are represented by yellow
types are
represented
by orange
solid4 weeks
lines, and
represented
yellow dashed
dashed
lines.
Fear conditioning
occurred
aftermale
swimheterozygotes
stress exposure are
for Phase
2. Mice by
were
trained on Day 0 in Context A (A,C). Two days later (Day 2), mice were placed back in Context A to
test for context fear expression (B,D). Data are percent time spent freezing for each 30 s period following the foot shock (A,C) or every 30 s of testing (B,D). Data were analyzed within each
Int. J. Mol. Sci. 2023, 24, 16494
11 of 25
lines. Fear conditioning occurred 4 weeks after swim stress exposure for Phase 2. Mice were trained
on Day 0 in Context A (A,C). Two days later (Day 2), mice were placed back in Context A to test for
context fear expression (B,D). Data are percent time spent freezing for each 30 s period following the
foot shock (A,C) or every 30 s of testing (B,D). Data were analyzed within each training/testing stage
and within each sex, using 3-way repeated-measures ANOVAs (time × PMAT genotype × swim
condition) and pairwise comparisons with Bonferroni correction, and are graphed as mean ± 95%
confidence interval. * p = 0.034 (panel B); * p = 0.048, * p = 0.013 (left to right, panel D); ** p = 0.004
(panel D) indicate difference between heterozygous and wild type within the same sex at the indicated
time point. C p = 0.030, C p = 0.035, C p = 0.011 (left to right, panel D) indicate difference between
no-swim and swim conditions within the same sex and genotype (indicated by color; black for
wild-type, grey for heterozygous) at the indicated time point.
2.2. Serum Corticosterone
Blood was collected from all mice 2 h after their last test to measure serum corticosterone levels. For Phase 1 mice, this was 2 h after swim stress; for Phase 2 mice, this was
2 h after the context testing and cued fear renewal test. This 2 h time point was to capture
the descending limb of the corticosterone curve to assess how elevated corticosterone
levels remained after the established 30 min peak post-stressor [32–36]. This 2 h time point
was the focus here because no sex nor genotype differences were detected in PMAT mice
when serum corticosterone was measured 30 min following a single acute swim stressor
(Supplemental Table S14 and Supplemental Figure S1). All serum corticosterone levels
were log-transformed (hereafter referred to as “cort”) to ensure normal distribution, as
previously reported [37–40].
2.2.1. Phase 1 Cued
Analyzing Phase 1 cued cort levels via three-way ANOVA indicated no significant
three- or two-way interactions (Supplemental Table S9; Figure 8). The only significant
main effect was one of sex (F(1,82) = 17.77, p < 0.001, partial η2 = 0.178)) (Supplemental
Table S9). Post hoc testing indicated that male wild-type mice in the no-swim condition
exhibited significantly lower cort levels than female wild-type no-swim mice (Figure 8A,C).
Conversely, in the swim condition, male heterozygous mice had lower cort levels than
female heterozygotes (Figure 8A,C). Such sex differences in descending cort levels have
been reported previously [36,41–44] (see review [45]).
2.2.2. Phase 1 Context
Unlike Phase 1 cued cort levels, there was a significant three-way interaction of
genotype × sex × swim (F(1,72) = 6.029, p = 0.016, partial η2 = 0.077) (Supplemental Table
S9, Figure 8). Pairwise comparisons showed that male heterozygotes in the swim condition
had significantly lower cort levels than male wild types in the same condition (Figure 8D).
Male wild types that underwent swim stress also had significantly higher cort levels than
male wild types not exposed to swim stress (Figure 8D). Several sex differences were
also detected; specifically, cort levels for males were lower than females for all swimgenotype combinations except swam wild types (Figure 8B,D). These data could indicate
that prior context fear conditioning augments cort elevations to subsequent acute stressors
in wild-type males but that reduced PMAT function dampens this response.
2.2.3. Phase 2 Cued
Similar to Phase 1 cued, no significant three- nor two-way interactions were detected
in cort levels for Phase 2 cued (Supplemental Table S9). As with Phase 1 cued, the only
significant main effect in Phase 2 cued was of sex (F(1,79) = 17.40, p < 0.001, partial η2 = 0.180)
(Supplemental Table S9; Figure 9). Unlike Phase 1 cued cort levels though, pairwise
comparisons indicated no specific differences between individual groups (Figure 9A,C).
The absence of any swim effect for Phase 2 cued mice indicates that an acute swim stress
Int. J. Mol. Sci. 2023, 24, 16494
12 of 25
Int. J. Mol. Sci. 2023, 24, x FOR PEER REVIEW
13 of 27
4 weeks prior to undergoing cued fear conditioning was not impactful enough to alter cort
responses in the long term.
Figure 8. Log-transformed corticosterone levels in mice from Phase 1. Mice in Phase 1 had blood
Figure 8. Log-transformed corticosterone levels in mice from Phase 1. Mice in Phase 1 had blood
collected2 2hhfollowing
followingswim
swimstress
stresstotomeasure
measureserum
serumcorticosterone
corticosteronelevels.
levels.Female
Female(A,B)
(A,B)wild
wildtypes
types
collected
are
represented
by
teal
squares,
and
female
heterozygotes
are
represented
by
blue
diamonds.
Male
are represented by teal squares, and female heterozygotes are represented by blue diamonds. Male
(C,D)
wild
types
are
represented
by
orange
squares,
and
male
heterozygotes
are
represented
by
(C,D) wild types are represented by orange squares, and male heterozygotes are represented by
yellow
diamonds.
Phase
1
cued
data
(A,C)
and
Phase
1
context
data
(B,D)
are
graphed
in
columns
yellow diamonds. Phase 1 cued data (A,C) and Phase 1 context data (B,D) are graphed in columns
separated
separatedby
bysex.
sex.Data
Dataare
arelog-transformed
log-transformedserum
serumcorticosterone
corticosteronelevels,
levels,showing
showingindividual
individualdata
data
points.
Data
were
analyzed
within
each
Phase
and
form
of
fear
conditioning
(cued
or
context)
by a
points. Data were analyzed within each Phase and form of fear conditioning (cued
or context)
3-way
ANOVA
(PMAT (PMAT
genotype
× sex × swim
condition)
Holm–Šídák
post hoc tests.
Horizontal
by a 3-way
ANOVA
genotype
× sex
× swim and
condition)
and Holm–Šídák
post
hoc tests.
lines are shown as the mean, with vertical lines as ±95% confidence interval. *** p = 0.001 (panel D)
Horizontal lines are shown as the mean, with vertical lines as ±95% confidence interval. *** p = 0.001
indicates difference between heterozygous and wild type within the same sex and same swim con(panel D) indicates difference between heterozygous and wild type within the same sex and same
dition. ✣✣✣ p < 0.001
(panel D) indicates difference between no-swim and swim conditions within
swim condition. CCC p < 0.001 (panel D) indicates difference between no-swim and swim conditions
the same sex and genotype. p = 0.008, p = 0.014 (left to right, panel C); p < 0.001, p <
= 0.008, p = 0.014 (left to right, panel C); p < 0.001,
within
thep same
and
0.001,
< 0.001sex
(left
to genotype.
right, panel D)p indicate
difference between sexes within the same geno p < 0.001, p < 0.001 (left to right, panel D) indicate difference between sexes within the
type and swim condition.
same genotype and swim condition.
2.2.2. Phase 1 Context
2.2.4. Phase 2 Context
Unlike Phase 1 cued cort levels, there was a significant three-way interaction of genNo significant three-way interaction of genotype × sex × swim (Supplemental Table S9)
otype × sex × swim (F(1,72) = 6.029, p = 0.016, partial η2 = 0.077) (Supplemental Table S9,
was detected. Though genotype × sex and sex × swim interactions were not significant, a
Figure 8). Pairwise comparisons showed that male heterozygotes in the swim condition
genotype × swim interaction was significant for Phase 2 context (F(1,63) = 4.377, p = 0.040,
had significantly
lower cort levels than male wild types in the same condition (Figure 8D).
partial η2 = 0.065) (Supplemental Table S9). As with Phase 2 cued though, post hoc testing
Male wild types that underwent swim stress also had significantly higher cort levels than
indicated no specific differences between any two groups (Figure 9B,D). Considering these
male
toof
swim
stress
(Figure
Several sex
weretesting
also
datawild
alongtypes
withnot
the exposed
cort levels
Phase
2 cued
mice,8D).
it is possible
thatdifferences
the prolonged
detected;
specifically,
cort
levels
for
males
were
lower
than
females
for
all
swim-genotype
for cued fear conditioning obscured any lasting cort regulatory changes. In contrast, context
combinations except swam wild types (Figure 8B,D). These data could indicate that prior
context fear conditioning augments cort elevations to subsequent acute stressors in wildtype males but that reduced PMAT function dampens this response.
2.2.3. Phase 2 Cued
Int. J. Mol. Sci. 2023, 24, 16494
Similar to Phase 1 cued, no significant three- nor two-way interactions were detected
in cort levels for Phase 2 cued (Supplemental Table S9). As with Phase 1 cued, the only
significant main effect in Phase 2 cued was of sex (F(1,79) = 17.40, p < 0.001, partial η2 = 0.180)
(Supplemental Table S9; Figure 9). Unlike Phase 1 cued cort levels though, pairwise com13 of 25
parisons indicated no specific differences between individual groups (Figure 9A,C). The
absence of any swim effect for Phase 2 cued mice indicates that an acute swim stress 4
weeks prior to undergoing cued fear conditioning was not impactful enough to alter cort
fear conditioning’s more concise timeline could have facilitated a glimpse into the impact
responses in the long term.
of PMAT genotype upon cort levels in Phase 1 following an earlier acute stressor.
Figure 9. Log-transformed corticosterone levels in mice from Phase 2. Mice in Phase 2 had blood
Figure 9. Log-transformed corticosterone levels in mice from Phase 2. Mice in Phase 2 had blood
collected2 2h hfollowing
followingcontext
contextfear
feartesting
testingand
andcued
cuedfear
fearrenewal
renewaltotomeasure
measureserum
serumcorticosterone
corticosterone
collected
levels.
Female
(A,B)
wild
types
are
represented
by
teal
squares,
and
female
heterozygotes
are
levels. Female (A,B) wild types are represented by teal squares, and female heterozygotes are reprepresented
by
blue
diamonds.
Male
(C,D)
wild
types
are
represented
by
orange
squares,
and
male
resented by blue diamonds. Male (C,D) wild types are represented by orange squares, and male
heterozygotes
yellow diamonds.
diamonds.Phase
Phase22cued
cueddata
data
(A,C)
and
Phase
2 context
heterozygotesare
arerepresented
represented by yellow
(A,C)
and
Phase
2 context
data
data
(B,D)
are
graphed
in
columns
separated
by
sex.
Data
are
log-transformed
serum
corticosterone
(B,D) are graphed in columns separated by sex. Data are log-transformed serum corticosterone levels,
levels,
showing
individual
data points.
Dataanalyzed
were analyzed
each and
phase
andofform
fear conshowing
individual
data points.
Data were
withinwithin
each phase
form
fear of
conditioning
ditioning
(cued
or
context)
by
a
3-way
ANOVA
(PMAT
genotype
×
sex
×
swim
condition).
Horizon(cued or context) by a 3-way ANOVA (PMAT genotype × sex × swim condition). Horizontal lines
tal lines are shown as the mean, with vertical lines as ±95% confidence interval.
are shown as the mean, with vertical lines as ±95% confidence interval.
2.2.4.
PhaseStress
2 Context
2.3. Swim
No
three-way
of genotype
× sex ×toswim
(Supplemental
Table
Forsignificant
both Phases
1 and interaction
2, only those
mice assigned
the swim
stress condition
S9)
was detected.
Though
× sex
and sexto× the
swim
interactions
werewere
not significant,
underwent
a 6 min
swimgenotype
stress. Mice
assigned
no-swim
condition
transported
a to
genotype
× swim
was
significant
Phase
context (F(1,63)
= 4.377,
= 0.040,in
the same
roominteraction
and treated
the
same as for
mice
that 2underwent
swim
(i.e.,pplaced
2
clean ηcages
half-on
heating pads)
but
were
not swam.
stresses
were
videopartial
= 0.065)
(Supplemental
Table
S9).
As with
Phase 2 All
cuedswim
though,
post hoc
testing
recorded,no
then
later hand-scored
offline byany
twotwo
blinded
observers
quantify
swimming,
indicated
specific
differences between
groups
(Figureto9B,D).
Considering
climbing,
and immobility
behaviors
subsequent
analyses.
Additionally,
latency
to the
these
data along
with the cort
levels offor
Phase
2 cued mice,
it is possible
that the
prolonged
first bout of immobility (i.e., “latency”) and the number of fecal boli (Supplemental Table
S16, Supplemental Figure S4) were analyzed.
2.3.1. Phase 1 Cued
Mice that underwent cued fear conditioning 4 weeks prior to swim stress exhibited no
significant interactions between genotype × sex, nor any main effects of either genotype
or sex (Supplemental Table S10). Accordingly, no significant post hoc tests were observed
Int. J. Mol. Sci. 2023, 24, 16494
14 of 25
Int. J. Mol. Sci. 2023, 24, x FOR PEER REVIEW
16 of 27
either (Figure 10A,C). This aligns with the absence of any cort level differences detected in
Phase 1 cued mice (Figure 8A,C).
Figure 10. Behaviors during the swim stressor in Phase 1 mice. Mice in Phase 1 assigned to swim
Figure 10. Behaviors during the swim stressor in Phase 1 mice. Mice in Phase 1 assigned to swim
stressexperienced
experienced an
an acute
4 weeks
after
undergoing
Cued
(A,C)
or Context
(B,D)
stress
acute66min
minswim
swimstressor
stressor
4 weeks
after
undergoing
Cued
(A,C)
or Context
fear conditioning.
Female
(A,B) (A,B)
wild types
are represented
by tealby
squares,
and female
heterozygotes
(B,D)
fear conditioning.
Female
wild types
are represented
teal squares,
and female
heterare represented
by blue diamonds.
Male (C,D)Male
wild(C,D)
types wild
are represented
by orange squares,
and
ozygotes
are represented
by blue diamonds.
types are represented
by orange
squares,
and male heterozygotes
are represented
by yellow diamonds.
Data
are the
of time
male heterozygotes
are represented
by yellow diamonds.
Data are the
amount
of amount
time in seconds
inspent
seconds
spent swimming,
immobile,
or of
the
amount
of time
untilofthe
first bout(i.e.,
of
swimming,
immobile, or
climbing;or
orclimbing;
the amount
time
until the
first bout
immobility
immobility
(i.e.,were
latency).
Data by
were
analyzed
by a 2-way
(PMAT
× sex)
within
latency). Data
analyzed
a 2-way
ANOVA
(PMATANOVA
genotype
× sex)genotype
within each
phase
and
each
and
form of fear(cued
conditioning
(cued
context) comparisons
and pairwise with
comparisons
with
Bonferformphase
of fear
conditioning
or context)
andorpairwise
Bonferroni
correction.
roni correction. Horizontal lines are shown as the mean, with vertical lines as ±95% confidence
inHorizontal lines are shown as the mean, with vertical lines as ±95% confidence interval. p = 0.003
terval. p = 0.003 (panel D) indicates difference between sexes within the same genotype and swim
(panel D) indicates difference between sexes within the same genotype and swim behavior.
behavior.
2.3.2. Phase 1 Context
2.3.3. Phase 2 Cued
Mice that were swam 4 weeks after undergoing context fear conditioning had a signifiPhase 2, ×
mice
swim
stress 4behaviors
weeks before
fear=conditioning.
Mice
that
cantIn
genotype
sexunderwent
interactionain
swimming
(F(1,36)
5.572, p = 0.024,
partial
2
went
through
cued
fear
conditioning
after
swim
stress
had
no
significant
differences
in
η = 0.134) (Supplemental Table S11). Post hoc testing indicated that male heterozygotes
swimming
(Supplemental
Table
S12). Though
no significant
or main
effects
displayed significantly
more
swimming
behavior
than femaleinteractions
heterozygotes
(Figure
10D).
were
detected
for either
or climbing,behavioral
both had non-significant
trends
for genDespite
the absence
of immobility
any genotype-specific
changes observed
in swim
be2 = 0.071; climbing, F(1,42) = 3.024, p =
otype
(immobility,
F
(1,42)
=
3.221,
p
=
0.080,
partial
η
haviors, cort measurements indicate that heterotypic stressor exposure across the 4 week
0.089,
partial η2timeframe
= 0.067) (Supplemental
Table
S12). Latency,impact
while not
having
a significant
experimental
did indeed have
a physiological
in males
that
appears to
genotype
×
sex
interaction,
did
have
a
significant
main
effect
of
genotype
(F
(1,42)
= 4.679, p
be moderated by PMAT deficiency (Figure 8B,D).
2
= 0.036, partial η = 0.100) (Supplemental Table S12; Figure 11A,C). Post hoc testing demon2.3.3. Phase
2 Cued
strated
that male
heterozygotes that went through swim stress 4 weeks before cued fear
conditioning
less immobility,
more
climbing
behavior,
than male wild
In Phasedisplayed
2, mice underwent
a swimand
stress
4 weeks
before
fear conditioning.
Mice
types
that
went
through
the
same
procedures
(Figure
11C).
The
male-specific
influences
that went through cued fear conditioning after swim stress had no significant differences
of
PMAT
function onTable
swimS12).
stressThough
behavior
the largely
consistent
trend
inreduced
swimming
(Supplemental
noreflect
significant
interactions
or main
efobserved
here,
wherefor
male
behavior
and physiology
were
than females.
fects were
detected
either
immobility
or climbing,
bothmore
had affected
non-significant
trends for
genotype (immobility, F(1,42) = 3.221, p = 0.080, partial η2 = 0.071; climbing, F(1,42) = 3.024,
2.3.4. Phase 2 Context
Four weeks before going through Phase 2 context fear conditioning, mice were subjected to swim stress. Across all four measures of behavior, there was no significant
Int. J. Mol. Sci. 2023, 24, x FOR PEER REVIEW
Int. J. Mol. Sci. 2023, 24, 16494
17 of 27
15 of 25
genotype × sex interaction and no main effect of genotype, but there was a significant main
effect of sex (swimming,
F(1,34) = 4.996, p = 0.032, partial η2 = 0.128; immobility, F(1,34) = 10.09,
2 = 0.067) (Supplemental Table S12). Latency, while not having a
p
=
0.089,
partial
η
2
p = 0.003, partial η = 0.229; climbing, F(1,34) = 5.611, p = 0.024, partial η2 = 0.142; latency, F(1,34)
genotype
interaction,
did haveTable
a significant
main
effect
of hoc
genotype
2 = 0.356)
=significant
18.76, p < 0.001,
partial×ηsex
(Supplemental
S13; Figure
11B,D).
Post
tests
2 = 0.100) (Supplemental Table S12; Figure 11A,C). Post
(F
=
4.679,
p
=
0.036,
partial
η
(1,42)
emphasized significantly less time spent immobile, and an accompanying increase in lahoc testing demonstrated that male heterozygotes that went through swim stress 4 weeks
tency, in males of both genotypes compared to females of the same genotype (Figure
before cued fear conditioning displayed less immobility, and more climbing behavior, than
11B,D). Why these sex differences were not also present in Phase 2 cued mice (Figure
male wild types that went through the same procedures (Figure 11C). The male-specific
11A,C) is not clear, though it could be attributable to the somewhat greater variability
influences of reduced PMAT function on swim stress behavior reflect the largely consistent
observed in Phase 2 cued females versus Phase 2 context females.
trend observed here, where male behavior and physiology were more affected than females.
Figure 11. Behaviors during the swim stressor in Phase 2 mice. Mice in Phase 2 assigned to swim stress
Figure 11. Behaviors during the swim stressor in Phase 2 mice. Mice in Phase 2 assigned to swim
experienced an acute 6 min swim stressor 4 weeks before undergoing Cued (A,C) or Context (B,D)
stress experienced an acute 6 min swim stressor 4 weeks before undergoing Cued (A,C) or Context
fear conditioning.
Female
(A,B)(A,B)
wild types
are represented
by tealby
squares,
and female
heterozygotes
(B,D)
fear conditioning.
Female
wild types
are represented
teal squares,
and female
heterare
represented
by
blue
diamonds.
Male
(C,D)
wild
types
are
represented
by
orange
and
ozygotes are represented by blue diamonds. Male (C,D) wild types are representedsquares,
by orange
male
heterozygotes
are
represented
by
yellow
diamonds.
Data
are
the
amount
of
time
in
seconds
squares, and male heterozygotes are represented by yellow diamonds. Data are the amount of time
inspent
seconds
spent swimming,
immobile,
or of
thetime
amount
of time
until of
the
first bout (i.e.,
of
swimming,
immobile, or
climbing;ororclimbing;
the amount
until the
first bout
immobility
immobility
(i.e.,were
latency).
Data by
were
analyzed
by a 2-way
ANOVA
(PMAT
× sex)
within
latency). Data
analyzed
a 2-way
ANOVA
(PMAT
genotype
× sex)genotype
within each
phase
and
each
and
form of fear(cued
conditioning
(cued
orpairwise
context) and
pairwise comparisons
withcorrection.
Bonferformphase
of fear
conditioning
or context)
and
comparisons
with Bonferroni
roni
correction.
*
p
=
0.049,
*
p
=
0.043
(left
to
right,
panel
C)
indicate
difference
between
heterozygous
* p = 0.049, * p = 0.043 (left to right, panel C) indicate difference between
heterozygous
and wild type
and wild type within the same sex for the same swim
0.032,
p = 0.030,
= 0.006,
p behavior.
p p==0.030,
p p
within
the
same
sex
for
the
same
swim
behavior.
=
0.032,
p
=
0.006,
= 0.003
p = 0.003 (left to right, panel D) indicate difference between sexes within the same genotype and
(left to right, panel D) indicate difference between sexes within the same genotype and swim behavior.
swim behavior.
2.3.4. Phase 2 Context
3. Discussion
Four weeks before going through Phase 2 context fear conditioning, mice were sub3.1.
Summary
of Fear
Behavior
Findings
jected
to swim
stress.
Across
all four measures of behavior, there was no significant
genotype
× sextointeraction
andassessment
no main effect
of reduced
genotype,
but there
wasimpacts
a significant
In addition
being the first
of how
PMAT
function
clasmain
effect of sexto(swimming,
F(1,34) = 4.996,
p = 0.032,
partial
η2 = 0.128;are
immobility,
sical
conditioning
an aversive stimulus,
the present
findings
additionally
an inauF(1,34)foray
= 10.09,
= 0.003, partial
η2 = 0.229;
climbing,between
F(1,34) =PMAT
5.611,function
p = 0.024,
gural
intopsystematically
examining
interactions
andpartial
het2
2 = 0.356) (Supplemental Table S13;
η
=
0.142;
latency,
F
=
18.76,
p
<
0.001,
partial
η
erotypic stressor exposure.
(1,34) Previously, we noticed that sequential brief stressors in male
Figure 11B,D). Post
tests emphasized
significantly
less time
spent immobile,
an
PMAT-deficient
micehoc
altered
behavior [11].
Consequently,
we sought
to exploreand
this
accompanying increase in latency, in males of both genotypes compared to females of the
Int. J. Mol. Sci. 2023, 24, 16494
16 of 25
same genotype (Figure 11B,D). Why these sex differences were not also present in Phase 2
cued mice (Figure 11A,C) is not clear, though it could be attributable to the somewhat
greater variability observed in Phase 2 cued females versus Phase 2 context females.
3. Discussion
3.1. Summary of Fear Behavior Findings
In addition to being the first assessment of how reduced PMAT function impacts
classical conditioning to an aversive stimulus, the present findings additionally are an
inaugural foray into systematically examining interactions between PMAT function and
heterotypic stressor exposure. Previously, we noticed that sequential brief stressors in male
PMAT-deficient mice altered behavior [11]. Consequently, we sought to explore this phenomenon in more depth, while simultaneously assessing how functional PMAT reductions
affect fear-processing measures. Here, we observed that diminished PMAT expression
shifts the time course of cued fear expression and cued extinction training in females, while
augmenting the expression of context fear in males. Notably, PMAT function appeared
to be without substantive impact upon acquisition of cued or context fear conditioning.
This allows conclusions about how the function of PMAT moderates expression of fear
to be made independent of any concerns regarding confounds of acquisition. Certainly,
though, the contribution of PMAT to the consolidation of aversive memories remains to be
clearly defined.
3.1.1. Sex-Specific Impacts of PMAT Function on Fear Behavior
Previously, we and others have found sex-specific effects of PMAT deficiency on
behavior [10,11,16] (see review [5]). Similar outcomes were found here. With the exception
of cued fear expression and extinction training in females, the broad theme in the present
findings was that females were largely unaffected by PMAT reductions or heterotypic stress
exposure in their fear behavior. In this singular instance of fear behavior differences in
female Phase 1 cued heterozygotes, it appears that reduced PMAT function in females
impedes cued fear extinction, resulting in this interaction of genotype with time, the latter
of which is required for the process of extinction to be detected.
In contrast to females, decreased PMAT function in males enhanced context fear
expression prior to any preceding stress exposures (i.e., in Phase 1). This remained true
for Phase 2 PMAT-deficient males that encountered a brief swim stressor 4 weeks before
being trained in context fear conditioning. Combined, these data suggest an overall effect
of reduced PMAT function on context fear expression in males, in contrast to the minor but
significant interaction between reduced PMAT function and cued fear extinction within
females. Put another way, functional PMAT in males appears to exert broader effects on
expression of learned fear, whereas in females, influences of functional PMAT may be more
restricted to specific instances of initial extinction learning.
Also counter to our hypothesis, Phase 2 cued PMAT-deficient males exhibited behavior that mostly mirrored that of behavior by pre-swim males in Phase 1 cued. In other
words, swim stress exposure did not alter male mouse fear behaviors independent of genotype. Paradoxically, male Phase 2 context PMAT-deficient mice that experienced a sham
procedure (no-swim) instead of a swim stress exhibited reduced context fear expression
relative to their wild-type counterparts. Adding to the confusion were Phase 2 cued male
heterozygous no-swim mice that exhibited enhanced context fear (like Phase 1 context
males) preceded by impaired cued extinction retention.
3.1.2. Hypotheses and Next Steps—Fear Behavior
While initially perplexing, we hypothesize that these data indicate that typical PMAT
function might usually obscure enduring effects of modestly arousing experiences—such as
those of no-swim mice being temporarily relocated for a sham swim. In contrast, exposure
to an overt, albeit brief, swim stressor might induce enough of a neurophysiological
perturbation to obscure this slight sensitivity present in heterozygotes. Moreover, the
Int. J. Mol. Sci. 2023, 24, 16494
17 of 25
observed behavioral changes in fear expression and retention were mostly selective to
males, suggesting a sex hormone component [46–48]. Indeed, recent studies are beginning
to parse apart the molecular underpinnings of PMAT’s sex-specific functions [49,50]. Future
experiments to test the long-term effects of arousing experiences at different intervals and
durations could facilitate testing this hypothesis, as would gonadectomy experiments to
determine if these are activational or organizational effects of sex hormones (or independent
of sex hormones). To better characterize the nuances of cued fear expression and extinction
learning in females, overtraining of females, combined with longer extinction training trials
(e.g., presenting 30 tones instead of 15), could be useful.
3.2. Summary of Log-Transformed Corticosterone (Cort) Findings
Evaluating cort levels 2 h following the last behavioral test (swim, Phase 1; context
testing and cued fear renewal, Phase 2) helped determine how PMAT deficiency interacted
with prior stressor exposure to influence the return of cort levels to baseline. This was the
focus here given previous evidence that reduced or ablated PMAT function had no impact
on cort levels 30 min after an acute swim stressor (Supplemental Figure S1); a time point
accepted to be the peak of cort response to acute stress [32–35].
Overall, both Phase 1 cued and Phase 2 cued cort levels only exhibited significant sex
differences, a known phenomenon where female mice typically have higher cort levels than
males [41,51,52]. In contrast, Phase 1 context cort levels indicated that the return of cort
levels to baseline following a swim stressor was influenced by the combination of swim, sex,
and genotyping. Phase 2 context cort levels revealed a parallel interaction between swim
and genotype. Thus, it could be that context fear is better suited for studying heterotypic
stressor exposure than the more protracted cued fear conditioning paradigm, when used in
combination with a swim stressor.
3.2.1. Sex-Specific Impacts of PMAT Function and Stressor Exposure on Cort Levels
Expected sex effects [41,51,52] were observed in Phase 1 mice. Phase 1 cued mice
exhibited no other effects, suggesting that their prior cued fear conditioning experiences
did not manifest in cort level dynamics after mice were swam. In contrast to Phase 1
cued mice, Phase 1 context mice displayed the most robust interaction between the three
variables of genotype, sex, and swim. Cort levels in male Phase 1 context wild-type swam
mice had not returned to baseline, reflected in their non-swam counterparts, whereas male
heterozygotes displayed cort levels similar to those of non-swam males independent of
genotype. This indicates that the prior exposure of male heterozygotes to context fear
conditioning may have either improved the mice’s ability to regulate HPA axis activation
and return to baseline more quickly or that they exhibited a blunted cort response to the
swim stressor.
Like Phase 1 cued, Phase 2 cued cort levels displayed the anticipated differences across
sexes [41,51,52] but were without any other remarkable features. Because testing of fear
expression, in the absence of any unconditioned stimulus, is by its nature less evocative of
a stressor, the absence of prominent sex-, genotype- and swim-specific differences is not
necessarily surprising for Phase 2 cued cort levels. Additionally, the five-day-long cued
fear conditioning procedure may have led to some physiological habituation across all
Phase 2 cued mice, dampening cort levels by the time the fifth day of testing arrived.
Only for Phase 2 context were sex differences not observed, and instead a genotype × swim
interaction predominated. The loss of this sex difference in Phase 2 context could be due to
the larger variability in cort levels but also could suggest a lingering effect of prior context
fear conditioning on the physiological regulation of cort.
3.2.2. Hypotheses and Next Steps—Cort Levels
While intriguing, these cort level differences (or lack thereof) do not map onto either
fear or swim behavior, indicating that these physiological changes are likely exerting
other influences that were not captured by the present study. Nonetheless, the significant
Int. J. Mol. Sci. 2023, 24, 16494
18 of 25
interaction term detected for Phase 1 context suggests that the pairing of context fear
conditioning and swim stress—in that order—might be more informative when optimized.
Extended studies examining timelines of cort levels following an acute swim stressor that
occurs 4 weeks after context fear conditioning would help answer our hypothesis that
male heterozygous mice have cort levels that are returning to baseline faster, rather than
exhibiting a blunted response. Alternatively, given the absence of concordance between
cort levels and behavior here, future investigations could query what behaviors these cort
levels do map onto, including risk assessment [53], depressive-like behavior [54], or social
interaction [38] as possibilities.
3.3. Summary of Swim Behavior Findings
The swim behaviors exhibited by mice after (Phase 1) or before (Phase 2) fear conditioning were only modestly different across Phases. Phase 1 cued swim behavior revealed
no effects or interactions of genotype and sex, whereas Phase 2 cued swim behaviors exhibited a genotype effect on latency plus non-significant trends for genotype in time spent
immobile and climbing. Once again, Phase 1 context revealed sex-specific genotype effects,
further supporting the combination of context fear testing followed by swim stress as a
useful directional combination for uncovering the effects of heterotypic stress exposure and
its interactions with genotypes related to stress responsivity. Phase 2 context, in contrast,
was consistent only in a pervasive sex effect.
3.3.1. Sex-Specific Impacts of PMAT Function on Swim Behavior
A swimming-specific genotype × sex interaction was found for Phase 1 context
mice, where male heterozygotes swam more than female heterozygotes. Otherwise, prior
exposure to either cued or context fear conditioning did not drastically alter swim behaviors.
When Phase 2 swim behaviors were evaluated, 4 weeks before those mice would ever
experience any form of fear conditioning, there was an unanticipated disparity in the overall
patterns observed. Genotype effects were more prominent in Phase 2 cued mice, whereas
sex effects dominated in Phase 2 context mice. Previously, we observed sex × genotype
interactions during swim stress [10]. Because of the timing of the swim stressors in the
current study though, differences between Phase 2 cued and Phase 2 context should
not exist, particularly considering that the same two blinded observers scored all swim
behaviors after all behavior testing had concluded. Thus, the findings noted for these
should be interpreted with caution.
Mirroring findings for fear behavior and cort levels, swim behavior differences were
largely specific to males. Phase 1 context swimming behavior moved in opposing directions
between the sexes of heterozygous mice. Male Phase 2 cued heterozygotes showed less
immobility and a corresponding increase in climbing behaviors, though the same was not
observed for male Phase 2 context heterozygotes. Across genotypes in Phase 2 context,
males exhibited less immobility and greater latencies to first immobility than females.
Again though, this was inexplicably not replicated in Phase 2 cued mice. As with fear
behavior, swim behaviors in both phases did not map onto cort levels, suggesting that
PMAT function influences other physiological processes that drive swim behaviors, likely
serotonin signaling [3,55,56], among others.
3.3.2. Hypotheses and Next Steps—Swim Behavior
The differences in swim stress behavior in Phase 2 mice is disconcerting. Though we
assigned mice to swim/no-swim and cued/context conditions using systematic randomization, and took care to have all swim behaviors scored by two blinded observers, we
obtained data that did not replicate in our own hands. One adjustment we have considered,
after hand-scoring of swim behaviors was completed and we discovered these perplexing
outcomes, is to merely use the acute swim as a stressor and to not attribute much meaning
to the behaviors that can be scored from it. Debate about the utility and interpretation of
swim stress (also known as forced swim test) persists [57–59], with data suggesting that it
Int. J. Mol. Sci. 2023, 24, 16494
19 of 25
is a better test of coping style, and it may best be suited for eliciting physiological stress
responses (e.g., increasing circulating corticosterone). Indeed, both as a standalone inducer
of acute cort increases and as a tool combined with preceding context fear conditioning to
look at heterotypic stressor responses in a physiological manner, the swim stress has, at
least in our hands, been consistently reliable for these purposes.
3.4. Limitations
The present study supports the contributions of PMAT function to behavioral and physiological heterotypic stress responses. Limitations of the study include the aforementioned
differences in swim behavior prior to any conditioning exposure, a consequence of quantifying swim behaviors later than would have been optimal. Additionally, the discordance
between cort changes and behavioral shifts indicates that alternative physiologic/behavior
measures might have instead been better suited to detect concordance [38,53,54]. The
absence of a complete time course of cort levels is another limitation but would have either
resulted in a potentially confounding stress source or the use of more mice than we could
ethically justify for the purpose of this investigation. In hindsight, focusing specifically
on context fear conditioning and swim stress, in that order, and incorporating instead a
behavioral measure of appetitive learning (e.g., lever pressing for a food reinforcer), might
have provided better insight into the behavioral consequences of PMAT function upon heterotypic stress responsivity. Additionally, given the unreliability of commercial antibodies
against PMAT, plus the absence of any selective PMAT inhibitors, it has been challenging
to determine the level of PMAT protein expression—let alone PMAT function—in PMAT
heterozygous mice. Consequently, the translatability of the present findings is hindered by
this presently unobtainable information.
3.5. Overview
Nonetheless, the present findings provide important information upon which future
experiments can be based to better focus efforts on understanding PMAT’s roles. Such
studies should take a deeper look at learning and memory processes and explore both behavioral and molecular changes occurring from PMAT reductions and stressor encounters.
Investigations employing orchidectomies could also provide insight into the organizational
and/or activational interactions of sex hormones with PMAT function. This is only the
second study to date to use classical conditioning In PMAT mice [16], and the first to employ
fear conditioning, so more remains to be learned in this domain, including long-term memory, cue discrimination, and generalization, among other parameters. Moreover, expanding
studies into evaluations of PMAT’s role in operant (rather than classical) conditioning
procedures could be informative. And, as always with PMAT, the development of drugs
that selectively inhibit this transporter would be a tremendous boon to understanding the
functional influences of this protein.
4. Materials and Methods
4.1. Animals
Adult (≥90 days old) male and female PMAT-deficient mice maintained on a C57BL/6J
background and bred in-house were used for all experiments. This line of mice was
developed by Dr. Joanne Wang’s lab at the University of Washington [60]. Our PMATdeficient colony is maintained in accordance with a material transfer agreement between
the University of Washington and Kent State University. Males and females were run
through all experiments separately; if both sexes were run on the same day, all males were
always run before any females. All mice were group-housed (2–5 per cage) within the
same sex on 7090 Teklad Sani-chip bedding (Envigo, East Millstone, NJ, USA). Mice had
ad libitum access to LabDiet 5001 rodent laboratory chow (LabDiet, Brentwood, MO, USA)
and drinking water. The vivarium was maintained at 22 ± 1 ◦ C, on a 12:12 light:dark cycle,
with lights on at 07:00. All procedures adhered to the National Research Council’s Guide
Int. J. Mol. Sci. 2023, 24, 16494
20 of 25
for the Care and Use of Laboratory Animals, 8th Ed. [61], and were approved by the Kent
State University Institutional Animal Care and Use Committee.
4.2. Genotyping
On postnatal day 21 (P21), mice were weaned, and 2 mm ear punches were collected
for DNA extraction. Extensive details regarding buffer compositions, and procedures for
DNA extraction, PCR (including primer sequences), and agarose gel electrophoresis, are
published [10,60], including in an open-access journal [11].
4.3. Fear Conditioning
Mice underwent either contextual fear training or cued fear training (Figure 1). Regardless of the type of training, all mice were trained in ‘Context A’ in chambers made by
Coulbourn Instruments (7 in D × 7 in W × 12 in H; Allentown, PA, USA). These chambers
consisted of two opposite clear acrylic walls and two opposite aluminum panel walls. In
Context A, the chamber contained a metal shock grid floor, had a blue dotted pattern
hung behind one of the clear acrylic walls, was illuminated with visible light, and was
cleaned with 70% ethanol as a scent cue. Sound-attenuating enclosures surrounded each
separate chamber, and every chamber had a camera mounted at the top to record behavior.
FreezeFrame (v. 5.201, Coulbourn Instruments) software was used to quantify freezing
behavior in real time. Freezing behavior is defined as the absence of all movement except
that required for breathing. Testing commenced 48 h after training for both contextual
and cued fear conditioning paradigms. Mice were brought directly from the vivarium to
the fear behavior room on every day of testing and training in a designated individual
transport cage. Differences between context and cued fear conditioning paradigms are
described below.
4.4. Cued Fear Conditioning
Following a 2 min baseline, training for cued fear involved five tone–shock pairings,
with each 4 kHz, 30 s tone co-terminating with a 1 s, 0.8 mA scrambled mild foot shock.
Inter-tone intervals (ITIs) of 90 s were used, and the entire training duration including
baseline lasted 11 min. Percent freezing was measured for each 30 s period when a tone
was played; this was graphed as cued fear training. Testing for cued fear began 48 h after
training (Figure 1) and involved three stages, none of which included shocks. The first
stage was for cued fear expression and cued fear extinction training; this included a 2 min
baseline followed by fifteen 30 s, 4 kHz tone presentations separated by 30 s ITIs [62]. The
second stage of testing began 48 h after the first testing stage. This had the exact same
structure as the first stage of testing, but the purpose was to evaluate cued fear extinction
retention, plus further cued fear extinction training. The first and second stages of testing
occurred in ‘Context B’. Context B had a smooth acrylic floor, no pattern, was illuminated
only with infrared light, and was cleaned with Windex® (SC Johnson, Racine, WI, USA) as
the scent cue. The third and final stage of testing occurred 24 h after the second stage. This
third stage of testing occurred in Context A and contained two portions. First, behavior
was observed in Context A for 10 min in the absence of any tones, to evaluate contextual
fear expression and extinction. Then, the second portion began immediately at the 10 min
point by presentation of five 30 s, 4 kHz tones separated by 30 s ITIs to assess cued fear
renewal [63–65].
4.5. Context Fear Conditioning
Following a 2 min baseline, training for context fear involved pseudorandom delivery
of five 1 s, 0.8 mA scrambled mild foot shocks delivered at 137, 186, 229, 285, and 324 s.
The entire training duration including baseline lasted 6 min (Figure 1). Percent freezing
was measured for each 30 s period—averaged across six 5 s bins—that followed each foot
shock, starting with the first 5 s bin that did not include the foot shock. This was graphed
as context fear training. Testing for context fear occurred 48 h after training, in Context A.
Int. J. Mol. Sci. 2023, 24, 16494
21 of 25
Testing lasted for 10 min; freezing from min 2 through 6 was averaged to assess contextual
fear expression [37,66] (Supplemental Figures S2 and S3). The full time course of the testing
period was evaluated to determine contextual fear expression and extinction. No shocks
were administered during testing.
4.6. Swim Stress
Mice were moved to a holding room approximately 30 ft away from the swim stress
testing room a minimum of 1 h prior to test commencement to acclimate. Control (“no
swim”) mice were included in every cohort. These mice experienced a sham stressor,
involving moving them to the holding room, acclimating, then being moved to individual
transport cages during the ‘test’ period and put half-on a heating pad. Mice that did
undergo a swim stress test were, after the acclimation period, brought in an individual
transport cage directly to the swim stress testing room and immediately (and gently)
placed in a tank of water (26 cm radius × 36.8 cm high) that was between 22.5 and
24.0 ◦ C. This swim stress lasted for 6 min, and the entirety was recorded with a digital
video camera for offline hand scoring of behaviors (Solomon Coder v. beta 19.08.02;
https://solomon.andraspeter.com/, accessed on 16 November 2023). Fresh water was
used for every single mouse, and the tank was rinsed thoroughly between each mouse. An
experimenter, remaining silent and still, watched each entire swim in real time to ensure
that no mouse was ever at risk of becoming submerged below the water’s surface. At the
test end, mice were immediately (and gently) removed from the water, hand-dried with
clean paper towels, and then placed in an individual transport cage half-on a heating pad.
Mice remained half-on heating pads in their individual transport cages for at least 15 min,
or until their fur was completely dry, whichever came second.
4.7. Study Phases
Two phases were conducted for this study, each with separate mice (Figure 1). The
numbers of mice within each subgroup (Phase, fear conditioning type and stage, swim
vs. no-swim, sex, genotype) range between n = 7 and 17; specific numbers for each
subgroup in Figures 2–11 are detailed in Supplemental Table S17. Phase 1 involved mice
first undergoing context or cued fear conditioning, followed 4 weeks after the last fear test
by swim stress. Phase 2 was the reverse, with mice first undergoing a swim stress, then
4 weeks later commencing either cued or context fear conditioning. No-swim mice were
used as controls in both Phases 1 and 2. All mice underwent fear conditioning, because we
have previously published on swim stress behavior in the absence of fear conditioning or
any other stressor [10]. This approach, combined with our within-subjects design for each
phase, was to minimize the number of mice used in accordance with the three Rs [67].
4.8. Tissue Collection
Tissue was collected 2 h after swim stress (or placement half-on heating pad, for noswim controls) for Phase 1 and 2 h after the final fear test for Phase 2 (Figure 1). Previously,
we observed no differences in serum corticosterone levels 30 min after swim stress (see
Supplemental Figure S1), the time point at which corticosterone peaks following an acute
stressor. Given this information, plus our experimental design of heterotypic stressors
spaced 4 weeks apart, we intentionally evaluated corticosterone levels 2 h after the last
behavioral test for each phase. This allowed us to determine if the descending limb of the
corticosterone curve was impacted by PMAT deficiency, biological sex, stressor history,
or any interaction thereof. Just prior to tissue collection, mice were briefly anesthetized
with isoflurane, then rapidly decapitated to obtain trunk blood. Ears were also collected
at this time for reverification of genotype. Blood was allowed to clot at room temperature
(20 ± 2 ◦ C) for 30 min, then it was spun in a tabletop centrifuge at 3500 rpm and 4 ◦ C for
30 min. Serum supernate was collected and placed in a clean tube, then serum and ears were
frozen and stored at −80 ◦ C until analyses. Serum corticosterone levels were quantified
using corticosterone ELISA kits (ADI-900-097, Enzo Life Sciences, Inc., Farmingdale, NY,
Int. J. Mol. Sci. 2023, 24, 16494
22 of 25
USA). Log transformation of serum corticosterone levels was performed prior to analyses
to correct for the typical skewness of these data [38–40].
4.9. Data Graphing & Statistical Analyses
Data were graphed using GraphPad Prism (v 10.0.2 (171); GraphPad Software, San
Diego, CA, USA), showing the mean ±95% confidence interval (CI), plus individual data
points when not showing repeated measures data. Data were analyzed with GraphPad
Prism and IBM SPSS Statistics (v 29.0.1.0 (171), IBM, Armonk, NY, USA). Significance
thresholds were set a priori at p < 0.05, and non-significant trends (p < 0.10) were only
examined if their corresponding partial η2 > 0.060. Analyses were performed within each
phase and each form of fear conditioning (e.g., Phase 1 cued, Phase 2 context, etc.). Repeated measures data were analyzed within each training/testing stage and within each
sex, using 3-way repeated-measures ANOVAs (time × PMAT genotype × swim condition) and pairwise comparisons with Bonferroni correction, or 2-way repeated-measures
ANOVAs (PMAT genotype × time) and Holm–Šídák post hoc testing. Greenhouse–Geisser
corrections were employed for within-subjects analyses. Average contextual fear expression
(minutes 2 through 6) was analyzed in Phase 1 by a 2-way ANOVA (PMAT genotype × sex;
because no effects of swim were detected, so data were collapsed across swim condition),
and in Phase 2 by a 3-way ANOVA (PMAT genotype × sex × swim condition), all with
Holm–Šídák post hoc tests (Supplemental Table S15 and Figures S2 and S3). Measurements
of serum corticosterone were analyzed within each phase and form of fear conditioning
(cued or context) by a 3-way ANOVA (PMAT genotype × sex × swim condition) and
Holm–Šídák post hoc tests. Swim measures were analyzed by a 2-way ANOVA (PMAT
genotype × sex) within each phase and form of fear conditioning (cued or context) and
pairwise comparisons with Bonferroni correction. Some data loss occurred for the following reasons: software malfunctions (e.g., file did not save); equipment malfunction (e.g.,
camera was not displaying real-time images); operator error (e.g., chamber door left open
by the experimenter, and mouse departed chamber). Additionally, some mouse behavior
indicated impairments in fear learning or excessive unconditioned fear. Exclusion criteria
were as follows: (1) freezing > 75% in any 5 (context) or 30 (cued) s bin prior to the first
mild foot shock being administered; or (2) freezing < 25% for the first five tones (first stage
of cued fear testing in Context B), for every 30 s bin of testing (context fear testing), or for
all five tones of cued fear testing in Context A (i.e., cued fear renewal). Specific details of
all instances are in the Supplementary Materials. The criterion to exclude outliers was a
priori assigned as >5 standard deviations ± mean.
Supplementary Materials: The following supporting information can be downloaded at: https:
//www.mdpi.com/article/10.3390/ijms242216494/s1. References [37,38,38,39,66] are cited in the
Supplementary Materials.
Author Contributions: Conceptualization: B.L.W. and T.L.G.; data curation, B.L.W. and T.L.G.; formal
analysis, T.L.G.; funding acquisition, T.L.G.; investigation, B.L.W., M.M.N., A.K.H., I.R.S., J.N.B.,
L.R.S., S.K.K., J.M.R., M.T.F., C.N.R., E.M.H. and T.L.G.; methodology, T.L.G.; project administration,
B.L.W. and T.L.G.; resources, T.L.G.; supervision, B.L.W. and T.L.G.; validation, B.L.W., M.M.N.,
A.K.H., I.R.S., J.N.B., and T.L.G.; visualization, B.L.W. and T.L.G.; writing—original draft, B.L.W.
and T.L.G.; writing—review and editing, B.L.W., M.M.N., A.K.H., I.R.S., J.N.B., L.R.S., S.K.K., J.M.R.,
M.T.F., C.N.R., E.M.H. and T.L.G. All authors have read and agreed to the published version of
the manuscript.
Funding: This work was supported by a 2017 NARSAD Young Investigator Grant (26249) from the
Brain & Behavior Research Foundation, and Vital Projects Fund, Inc., to T.L.G.; a National Institute
of Mental Health grant R15 MH118705 to T.L.G.; and support from Kent State University, including
its Brain Health Research Institute and University Research Council. This publication was made
possible in part by support from the Kent State University Open Access Publishing Fund.
Int. J. Mol. Sci. 2023, 24, 16494
23 of 25
Institutional Animal Care and Use Statement: All procedures adhered to the National Research
Council’s Guide for the Care and Use of Laboratory Animals, 8th Ed. [61], and were approved by
the Kent State University Institutional Animal Care and Use Committee (protocols 486 AJ 19-10,
approved 29 August 2019; and 536 LG 22-14, approved 25 August 2022).
Data Availability Statement: All data will be made publicly available at the time of publication
through OSF: https://osf.io/qwev3/?view_only=2426baca6f634d06a5fb40100573c54b, accessed on
16 November 2023.
Acknowledgments: The authors gratefully acknowledge the assistance of research assistants Aliyah
Ross, Anna Anello, and Kaden Ruffin, as well as unrivaled veterinary care by Stan Dannemiller,
DACLAM. The authors gratefully appreciate Joanne Wang, and the University of Washington for the
material transfer agreement allowing the lab to use PMAT-deficient mice. Biorender.com (Toronto,
ON, Canada) was used to create Figure 1.
Conflicts of Interest: The authors declare no conflict of interest.
References
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
Puglisi-Allegra, S.; Kempf, E.; Schleef, C.; Cabib, S. Repeated Stressful Experiences Differently Affect Brain Dopamine Receptor
Subtypes. Life Sci. 1991, 48, 1263–1268. [CrossRef]
Finlay, J.M.; Zigmond, M.J.; Abercrombie, E.D. Increased Dopamine and Norepinephrine Release in Medial Prefrontal Cortex
Induced by Acute and Chronic Stress: Effects of Diazepam. Neuroscience 1995, 64, 619–628. [CrossRef]
Fujino, K.; Yoshitake, T.; Inoue, O.; Ibii, N.; Kehr, J.; Ishida, J.; Nohta, H.; Yamaguchi, M. Increased Serotonin Release in Mice
Frontal Cortex and Hippocampus Induced by Acute Physiological Stressors. Neurosci. Lett. 2002, 320, 91–95. [CrossRef]
Matuszewich, L.; Filon, M.E.; Finn, D.A.; Yamamoto, B.K. Altered Forebrain Neurotransmitter Responses to Immobilization
Stress Following 3,4-Methylenedioxymethamphetamine. Neuroscience 2002, 110, 41–48. [CrossRef]
Weber, B.L.; Beaver, J.N.; Gilman, T.L. Summarizing Studies Using Constitutive Genetic Deficiency to Investigate Behavioural
Influences of Uptake 2 Monoamine Transporters. Basic Clin. Pharmacol. Toxicol. 2023, 133, 439–458. [CrossRef] [PubMed]
Daws, L.C. Organic Cation Transporters in Psychiatric Disorders. In Handbook of Experimental Pharmacology; Springer: Cham,
Switzerland, 2021; Chapter 9; pp. 215–239. [CrossRef]
Koepsell, H. Organic Cation Transporters in the Central Nervous System. In Handbook of Experimental Pharmacology; Springer:
Cham, Switzerland, 2021; Chapter 1; pp. 1–39. [CrossRef]
Duan, H.; Wang, J. Selective Transport of Monoamine Neurotransmitters by Human Plasma Membrane Monoamine Transporter
and Organic Cation Transporter 3. J. Pharmacol. Exp. Ther. 2010, 335, 743–753. [CrossRef] [PubMed]
Bönisch, H. Substrates and Inhibitors of Organic Cation Transporters (OCTs) and Plasma Membrane Monoamine Transporter
(PMAT) and Therapeutic Implications. In Handbook of Experimental Pharmacology; Springer: Cham, Switzerland, 2021; Chapter 5;
pp. 119–167. [CrossRef]
Gilman, T.L.; George, C.M.; Vitela, M.; Herrera-Rosales, M.; Basiouny, M.S.; Koek, W.; Daws, L.C. Constitutive Plasma Membrane
Monoamine Transporter (PMAT, Slc29a4) Deficiency Subtly Affects Anxiety-like and Coping Behaviours. Eur. J. Neurosci. 2018,
48, 1706–1716. [CrossRef]
Beaver, J.N.; Weber, B.L.; Ford, M.T.; Anello, A.E.; Kassis, S.K.; Gilman, T.L. Uncovering Functional Contributions of PMAT
(Slc29a4) to Monoamine Clearance Using Pharmacobehavioral Tools. Cells 2022, 11, 1874. [CrossRef]
Bacq, A.; Balasse, L.; Biala, G.; Guiard, B.; Gardier, A.M.; Schinkel, A.; Louis, F.; Vialou, V.; Martres, M.-P.; Chevarin, C.; et al.
Organic Cation Transporter 2 Controls Brain Norepinephrine and Serotonin Clearance and Antidepressant Response. Mol.
Psychiatr. 2012, 17, 926–939. [CrossRef] [PubMed]
Couroussé, T.; Bacq, A.; Belzung, C.; Guiard, B.; Balasse, L.; Louis, F.; Guisquet, A.-M.L.; Gardier, A.M.; Schinkel, A.H.; Giros, B.;
et al. Brain Organic Cation Transporter 2 Controls Response and Vulnerability to Stress and GSK3β Signaling. Mol. Psychiatr.
2015, 20, 889–900. [CrossRef]
Wultsch, T.; Grimberg, G.; Schmitt, A.; Painsipp, E.; Wetzstein, H.; Breitenkamp, A.F.S.; Gründemann, D.; Schömig, E.; Lesch,
K.-P.; Gerlach, M.; et al. Decreased Anxiety in Mice Lacking the Organic Cation Transporter 3. J. Neural. Transm. 2009, 116,
689–697. [CrossRef]
Vialou, V.; Balasse, L.; Callebert, J.; Launay, J.; Giros, B.; Gautron, S. Altered Aminergic Neurotransmission in the Brain of Organic
Cation Transporter 3-deficient Mice. J. Neurochem. 2008, 106, 1471–1482. [CrossRef]
Clauss, N.J.; Koek, W.; Daws, L.C. Role of Organic Cation Transporter 3 and Plasma Membrane Monoamine Transporter in the
Rewarding Properties and Locomotor Sensitizing Effects of Amphetamine in Male andFemale Mice. Int. J. Mol. Sci. 2021, 22,
13420. [CrossRef]
Logue, S.F.; Paylor, R.; Wehner, J.M. Hippocampal Lesions Cause Learning Deficits in Inbred Mice in the Morris Water Maze and
Conditioned-Fear Task. Behav. Neurosci. 1997, 111, 104–113. [CrossRef]
Huff, N.C.; Rudy, J.W. The Amygdala Modulates Hippocampus-Dependent Context Memory Formation and Stores Cue-Shock
Associations. Behav. Neurosci. 2004, 118, 53–62. [CrossRef] [PubMed]
Int. J. Mol. Sci. 2023, 24, 16494
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
24 of 25
Zelikowsky, M.; Hersman, S.; Chawla, M.K.; Barnes, C.A.; Fanselow, M.S. Neuronal Ensembles in Amygdala, Hippocampus, and
Prefrontal Cortex Track Differential Components of Contextual Fear. J. Neurosci. 2014, 34, 8462–8466. [CrossRef]
Maren, S. Pavlovian Fear Conditioning as a Behavioral Assay for Hippocampus and Amygdala Function: Cautions and Caveats.
Eur. J. Neurosci. 2008, 28, 1661–1666. [CrossRef]
Farrell, M.R.; Sengelaub, D.R.; Wellman, C.L. Sex Differences and Chronic Stress Effects on the Neural Circuitry Underlying Fear
Conditioning and Extinction. Physiol. Behav. 2013, 122, 208–215. [CrossRef] [PubMed]
Chaaya, N.; Battle, A.R.; Johnson, L.R. An Update on Contextual Fear Memory Mechanisms: Transition between Amygdala and
Hippocampus. Neurosci. Biobehav. Rev. 2018, 92, 43–54. [CrossRef] [PubMed]
Yanagida, S.; Motomura, K.; Ohashi, A.; Hiraoka, K.; Miura, T.; Kanba, S. Effect of Acute Imipramine Administration on the
Pattern of Forced Swim-Induced c-Fos Expression in the Mouse Brain. Neurosci. Lett. 2016, 629, 119–124. [CrossRef]
Duncan, G.E.; Inada, K.; Farrington, J.S.; Koller, B.H.; Moy, S.S. Neural Activation Deficits in a Mouse Genetic Model of NMDA
Receptor Hypofunction in Tests of Social Aggression and Swim Stress. Brain Res. 2009, 1265, 186–195. [CrossRef]
Liu, Y.F.; Bertram, K.; Perides, G.; McEwen, B.S.; Wang, D. Stress Induces Activation of Stress-activated Kinases in the Mouse
Brain. J. Neurochem. 2004, 89, 1034–1043. [CrossRef] [PubMed]
Dawed, A.Y.; Zhou, K.; van Leeuwen, N.; Mahajan, A.; Robertson, N.; Koivula, R.; Elders, P.J.M.; Rauh, S.P.; Jones, A.G.; Holl,
R.W.; et al. Variation in the Plasma Membrane Monoamine Transporter (PMAT, Encoded in SLC29A4) and Organic Cation
Transporter 1 (OCT1, Encoded in SLC22A1) and Gastrointestinal Intolerance to Metformin in Type 2 Diabetes: An IMI DIRECT
Study. Diabetes Care 2019, 42, dc182182. [CrossRef] [PubMed]
Moeez, S.; Khalid, S.; Shaeen, S.; Khalid, M.; Zia, A.; Gul, A.; Niazi, R.; Khalid, Z. Clinically Significant Findings of High-Risk
Mutations in Human SLC29A4 Gene Associated with Diabetes Mellitus Type 2 in Pakistani Population. J. Biomol. Struct. Dyn.
2021, 40, 12660–12673. [CrossRef]
Christensen, M.M.H.; Brasch-Andersen, C.; Green, H.; Nielsen, F.; Damkier, P.; Beck-Nielsen, H.; Brosen, K. The Pharmacogenetics
of Metformin and Its Impact on Plasma Metformin Steady-State Levels and Glycosylated Hemoglobin A1c. Pharmacogenet. Genom.
2011, 21, 837–850. [CrossRef]
Pérez-Gómez, N.; Fernández-Ortega, M.D.; Elizari-Roncal, M.; Santos-Mazo, E.; de la Maza-Pereg, L.; Calvo, S.; Alcaraz, R.;
Sanz-Solas, A.; Vinuesa, R.; Saiz-Rodríguez, M. Identification of Clinical and Pharmacogenetic Factors Influencing Metformin
Response in Type 2 Diabetes Mellitus. Pharmacogenomics 2023, 24, 651–663. [CrossRef]
Yohn, N.L.; Blendy, J.A. Adolescent Chronic Unpredictable Stress Exposure Is a Sensitive Window for Long-Term Changes in
Adult Behavior in Mice. Neuropsychopharmacology 2017, 42, 1670–1678. [CrossRef]
Sillivan, S.E.; Joseph, N.F.; Jamieson, S.; King, M.L.; Chévere-Torres, I.; Fuentes, I.; Shumyatsky, G.P.; Brantley, A.F.; Rumbaugh,
G.; Miller, C.A. Susceptibility and Resilience to Posttraumatic Stress Disorder–like Behaviors in Inbred Mice. Biol. Psychiatry 2017,
82, 924–933. [CrossRef]
Romeo, R.D.; Bellani, R.; Karatsoreos, I.N.; Chhua, N.; Vernov, M.; Conrad, C.D.; McEwen, B.S. Stress History and Pubertal
Development Interact to Shape Hypothalamic-Pituitary-Adrenal Axis Plasticity. Endocrinology 2006, 147, 1664–1674. [CrossRef]
[PubMed]
Romeo, R.D.; Karatsoreos, I.N.; McEwen, B.S. Pubertal Maturation and Time of Day Differentially Affect Behavioral and
Neuroendocrine Responses Following an Acute Stressor. Horm. Behav. 2006, 50, 463–468. [CrossRef]
Hare, B.D.; Beierle, J.A.; Toufexis, D.J.; Hammack, S.E.; Falls, W.A. Exercise-Associated Changes in the Corticosterone Response to Acute Restraint Stress: Evidence for Increased Adrenal Sensitivity and Reduced Corticosterone Response Duration.
Neuropsychopharmacology 2014, 39, 1262–1269. [CrossRef] [PubMed]
McClennen, S.J.; Cortright, D.N.; Seasholtz, A.F. Regulation of Pituitary Corticotropin-Releasing Hormone-Binding Protein
Messenger Ribonucleic Acid Levels by Restraint Stress and Adrenalectomy. Endocrinology 1998, 139, 4435–4441. [CrossRef]
[PubMed]
Alele, P.E.; Devaud, L.L. Sex Differences in Steroid Modulation of Ethanol Withdrawal in Male and Female Rats. J. Pharmacol. Exp.
Ther. 2007, 320, 427–436. [CrossRef] [PubMed]
Beaver, J.N.; Weber, B.L.; Ford, M.T.; Anello, A.E.; Ruffin, K.M.; Kassis, S.K.; Gilman, T.L. Generalization of Contextual Fear Is
Sex-Specifically Affected by High Salt Intake. PLoS ONE 2023, 18, e0286221. [CrossRef]
Gilman, T.L.; George, C.M.; Andrade, M.A.; Mitchell, N.C.; Toney, G.M.; Daws, L.C. High Salt Intake Lowers Behavioral Inhibition.
Front. Behav. Neurosci. 2019, 13, 271. [CrossRef]
Uarquin, D.G.; Meyer, J.S.; Cardenas, F.P.; Rojas, M.J. Effect of Overcrowding on Hair Corticosterone Concentrations in Juvenile
Male Wistar Rats. J. Am. Assoc. Lab. Anim. Sci. 2016, 55, 749–755.
Teilmann, A.C.; Kalliokoski, O.; Sørensen, D.B.; Hau, J.; Abelson, K.S.P. Manual versus Automated Blood Sampling: Impact of
Repeated Blood Sampling on Stress Parameters and Behavior in Male NMRI Mice. Lab. Anim. 2014, 48, 278–291. [CrossRef]
Albrechet-Souza, L.; Schratz, C.L.; Gilpin, N.W. Sex Differences in Traumatic Stress Reactivity in Rats with and without a History
of Alcohol Drinking. Biol. Sex Differ. 2020, 11, 27. [CrossRef] [PubMed]
Palumbo, M.C.; Dominguez, S.; Dong, H. Sex Differences in Hypothalamic–Pituitary–Adrenal Axis Regulation after Chronic
Unpredictable Stress. Brain Behav. 2020, 10, e01586. [CrossRef]
Shors, T.J.; Chua, C.; Falduto, J. Sex Differences and Opposite Effects of Stress on Dendritic Spine Density in the Male Versus
Female Hippocampus. J. Neurosci. 2001, 21, 6292–6297. [CrossRef] [PubMed]
Int. J. Mol. Sci. 2023, 24, 16494
44.
45.
46.
47.
48.
49.
50.
51.
52.
53.
54.
55.
56.
57.
58.
59.
60.
61.
62.
63.
64.
65.
66.
67.
25 of 25
Aoki, M.; Shimozuru, M.; Kikusui, T.; Takeuchi, Y.; Mori, Y. Sex Differences in Behavioral and Corticosterone Responses to Mild
Stressors in ICR Mice Are Altered by Ovariectomy in Peripubertal Period. Zool. Sci. 2010, 27, 783–789. [CrossRef] [PubMed]
Goel, N.; Workman, J.L.; Lee, T.T.; Innala, L.; Viau, V. Comprehensive Physiology. Compr. Physiol. 2021, 4, 1121–1155. [CrossRef]
Aikey, J.L.; Nyby, J.G.; Anmuth, D.M.; James, P.J. Testosterone Rapidly Reduces Anxiety in Male House Mice (Mus Musculus).
Horm. Behav. 2002, 42, 448–460. [CrossRef] [PubMed]
van Honk, J.; Peper, J.S.; Schutter, D.J.L.G. Testosterone Reduces Unconscious Fear but Not Consciously Experienced Anxiety:
Implications for the Disorders of Fear and Anxiety. Biol. Psychiatry 2005, 58, 218–225. [CrossRef] [PubMed]
Chen, L.-S.; Tzeng, W.-Y.; Chuang, J.-Y.; Cherng, C.G.; Gean, P.-W.; Yu, L. Roles of Testosterone and Amygdaloid LTP Induction in
Determining Sex Differences in Fear Memory Magnitude. Horm. Behav. 2014, 66, 498–508. [CrossRef]
Wei, R.; Gust, S.L.; Tandio, D.; Maheux, A.; Nguyen, K.H.; Wang, J.; Bourque, S.; Plane, F.; Hammond, J.R. Deletion of Murine
Slc29a4 Modifies Vascular Responses to Adenosine and 5-hydroxytryptamine in a Sexually Dimorphic Manner. Physiol. Rep. 2020,
8, e14395. [CrossRef]
Gu, Y.; Zhang, N.; Zhu, S.; Lu, S.; Jiang, H.; Zhou, H. Estradiol Reduced 5-HT Reuptake by Downregulating the Gene Expression
of Plasma Membrane Monoamine Transporter (PMAT, Slc29a4) through Estrogen Receptor β and the MAPK/ERK Signaling
Pathway. Eur. J. Pharmacol. 2022, 924, 174939. [CrossRef]
Daviu, N.; Andero, R.; Armario, A.; Nadal, R. Sex Differences in the Behavioural and Hypothalamic–Pituitary–Adrenal Response
to Contextual Fear Conditioning in Rats. Horm. Behav. 2014, 66, 713–723. [CrossRef]
Xia, J.; Wang, H.; Zhang, C.; Liu, B.; Li, Y.; Li, K.; Li, P.; Song, C. The Comparison of Sex Differences in Depression-like Behaviors
and Neuroinflammatory Changes in a Rat Model of Depression Induced by Chronic Stress. Front. Behav. Neurosci. 2023, 16,
1059594. [CrossRef]
Rodgers, R.J.; Haller, J.; Holmes, A.; Halasz, J.; Walton, T.J.; Brain, P.F. Corticosterone Response to the Plus-Maze High Correlation
with Risk Assessment in Rats and Mice. Physiol. Behav. 1999, 68, 47–53. [CrossRef]
Kokras, N.; Krokida, S.; Varoudaki, T.Z.; Dalla, C. Do Corticosterone Levels Predict Female Depressive-like Behavior in Rodents?
J. Neurosci. Res. 2021, 99, 324–331. [CrossRef]
Porsolt, R.D.; Bertin, A.; Blavet, N.; Deniel, M.; Jalfre, M. Immobility Induced by Forced Swimming in Rats: Effects of Agents
Which Modify Central Catecholamine and Serotonin Activity. Eur. J. Pharmacol. 1979, 57, 201–210. [CrossRef]
Ehlinger, D.G.; Commons, K.G. Cav1.2 L-Type Calcium Channels Regulate Stress Coping Behavior via Serotonin Neurons.
Neuropharmacology 2019, 144, 282–290. [CrossRef] [PubMed]
Koolhaas, J.M.; de Boer, S.F.; Coppens, C.M.; Buwalda, B. Neuroendocrinology of Coping Styles: Towards Understanding the
Biology of Individual Variation. Front. Neuroendocrin. 2010, 31, 307–321. [CrossRef]
Commons, K.G.; Cholanians, A.B.; Babb, J.A.; Ehlinger, D.G. The Rodent Forced Swim Test Measures Stress-Coping Strategy, Not
Depression-Like Behavior. ACS Chem. Neurosci. 2017, 8, 955–960. [CrossRef] [PubMed]
Trunnell, E.R.; Carvalho, C.D.P.O. The Forced Swim Test Has Poor Accuracy for Identifying Novel Antidepressants. Drug Discov.
Today 2021, 26, 2898–2904. [CrossRef] [PubMed]
Duan, H.; Wang, J. Impaired Monoamine and Organic Cation Uptake in Choroid Plexus in Mice with Targeted Disruption of the
Plasma Membrane Monoamine Transporter (Slc29a4) Gene. J. Biol. Chem. 2013, 288, 3535–3544. [CrossRef]
National Research Council. Guide for the Care and Use of Laboratory Animals, 8th ed.; The National Academies Press: Washington,
DC, USA, 2011. [CrossRef]
Gilman, T.L.; DaMert, J.P.; Meduri, J.D.; Jasnow, A.M. Grin1 Deletion in CRF Neurons Sex-Dependently Enhances Fear, Sociability,
and Social Stress Responsivity. Psychoneuroendocrinology 2015, 58, 33–45. [CrossRef] [PubMed]
Radford, K.D.; Berman, R.Y.; Jaiswal, S.; Kim, S.Y.; Zhang, M.; Spencer, H.F.; Choi, K.H. Enhanced Fear Memories and Altered
Brain Glucose Metabolism (18F-FDG-PET) Following Subanesthetic Intravenous Ketamine Infusion in Female Sprague–Dawley
Rats. Int. J. Mol. Sci. 2022, 23, 1922. [CrossRef]
Wang, C.-M.; Zhang, Y.-F.; Lin, Z.-Q.; Cai, Y.-F.; Fu, X.-Y.; Lin, Z.-H. Pre-Extinction Activation of Hippocampal AMPK Prevents
Fear Renewal in Mice. Pharmacol. Res. 2020, 161, 105099. [CrossRef]
Jasnow, A.M.; Ehrlich, D.E.; Choi, D.C.; Dabrowska, J.; Bowers, M.E.; McCullough, K.M.; Rainnie, D.G.; Ressler, K.J. Thy1Expressing Neurons in the Basolateral Amygdala May Mediate Fear Inhibition. J. Neurosci. 2013, 33, 10396–10404. [CrossRef]
[PubMed]
Lynch, J.F.; Winiecki, P.; Gilman, T.L.; Adkins, J.M.; Jasnow, A.M. Hippocampal GABAB(1a) Receptors Constrain Generalized
Contextual Fear. Neuropsychopharmacology 2017, 42, 914–924. [CrossRef] [PubMed]
Russell, W.; Burch, R. The Principles of Humane Experimental Technique. Nature 1959, 184, 1675–1676. [CrossRef]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual
author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to
people or property resulting from any ideas, methods, instructions or products referred to in the content.
antibiotics
Article
Pseudouridine Synthase RsuA Confers a Survival Advantage to
Bacteria under Streptomycin Stress
Sudeshi M. Abedeera † , Kumudie S. Jayalath † , Jiale Xie
and Sanjaya C. Abeysirigunawardena *
, Rushdhi M. Rauff
Department of Chemistry and Biochemistry, Kent State University, 1175 Risman Dr., Kent, OH 44242, USA;
asudeshi@kent.edu (S.M.A.); kjayalat@kent.edu (K.S.J.); jxie10@kent.edu (J.X.); mmohame1@kent.edu (R.M.R.)
* Correspondence: sabeysir@kent.edu
† These authors contributed equally to this work.
Abstract: Bacterial ribosome small subunit rRNA (16S rRNA) contains 11 nucleotide modifications
scattered throughout all its domains. The 16S rRNA pseudouridylation enzyme, RsuA, which
modifies U516, is a survival protein essential for bacterial survival under stress conditions. A
comparison of the growth curves of wildtype and RsuA knock-out E. coli strains illustrates that RsuA
renders a survival advantage to bacteria under streptomycin stress. The RsuA-dependent growth
advantage for bacteria was found to be dependent on its pseudouridylation activity. In addition, the
role of RsuA as a trans-acting factor during ribosome biogenesis may also play a role in bacterial
growth under streptomycin stress. Furthermore, circular dichroism spectroscopy measurements and
RNase footprinting studies have demonstrated that pseudouridine at position 516 influences helix
18 structure, folding, and streptomycin binding. This study exemplifies the importance of bacterial
rRNA modification enzymes during environmental stress.
Keywords: ribosome; pseudouridine; pseudouridine synthase; streptomycin; helix18
Citation: Abedeera, S.M.; Jayalath,
1. Introduction
K.S.; Xie, J.; Rauff, R.M.;
Post-transcriptional ribonucleotide modifications are scattered in various biologically
relevant RNAs in all three kingdoms of life. Currently, more than 170 post-transcriptional
ribonucleotide modifications have been observed in nature. Although RNA nucleotide
modifications are implicated in influencing local RNA structure and stability, RNA–RNA
and RNA–protein interactions, and RNA folding and dynamics, the exact biological role of
many RNA nucleotide modifications is unknown [1,2]. However, several known ribosomal
RNA (rRNA) modifications are related to antibiotic resistance in bacteria. Additionally,
several rRNA modification enzymes in bacteria are also correlated with antibiotic resistance [3–7]. There are 11 nucleotide modifications in bacterial small ribosomal subunit
rRNA (16S rRNA) [1]. Out of the 10 nucleotide modification enzymes responsible for
16S rRNA modifications, functional mutants of KsgA and RsmG caused antibiotic resistance in bacteria [7,8]. In addition to their role in antibiotic resistance in bacteria, these
two enzymes influence 30S ribosome subunit assembly. Interestingly, however, methyltransferase activity is not required for the role of KsgA as a ribosome assembly factor [6],
suggesting a possible link between ribosome assembly and antibiotic resistance in bacteria.
Ribosomal small subunit pseudouridine synthase A (RsuA) is responsible for the single
pseudouridine modification located at position 516 (E. coli numbering) of 16S helix 18 [9–11].
RsuA belongs to the RsuA family of pseudouridine synthases. It contains a core domain
that carries the catalytic site and a peripheral domain that is required for its binding to
rRNA [12]. Like many rRNA modification enzymes, RsuA preferably binds to a ribosome
assembly intermediate [9]. The presence of ribosomal protein uS17 is advantageous for
both RsuA binding and its activity [13]. Although RsuA is among many rRNA modification
Abeysirigunawardena, S.C.
Pseudouridine Synthase RsuA
Confers a Survival Advantage to
Bacteria under Streptomycin Stress.
Antibiotics 2023, 12, 1447. https://
doi.org/10.3390/antibiotics12091447
Academic Editor: Marc Maresca
Received: 18 July 2023
Revised: 28 August 2023
Accepted: 8 September 2023
Published: 14 September 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Antibiotics 2023, 12, 1447. https://doi.org/10.3390/antibiotics12091447
https://www.mdpi.com/journal/antibiotics
Antibiotics 2023, 12, x FOR PEER REVIEW
Antibiotics 2023, 12, 1447
2 of 16
2 of 15
modification enzymes and ribosomal proteins classified as redundant proteins under norenzymes and ribosomal proteins classified as redundant proteins under normal growth
mal growth
conditions
[10], itbeen
has recently
been
as a survival
protein
that plays
conditions
[10],
it has recently
identified
as aidentified
survival protein
that plays
a crucial
role
a
crucial
role
in
the
survival
of
bacteria
under
various
environmental
stress
conditions
in the survival of bacteria under various environmental stress conditions [14]. Due to the
[14]. Due
to theto
ability
RsuA
bind
closer to the
streptomycin
site
function
ability
of RsuA
bind of
closer
toto
the
streptomycin
binding
site andbinding
function
asand
a “survival
as
a
“survival
protein”,
this
study
examines
the
capability
of
the
RsuA
protein
to
influence
protein”, this study examines the capability of the RsuA protein to influence streptomycin
streptomycin
tolerance
in bacteria.
In addition,
the role of
pseudouridine
in 16S
tolerance
in bacteria.
In addition,
the role
of pseudouridine
(Ψ516)
in 16S helix(Ψ516)
18 structure
helix
18 structurebinding
and streptomycin
binding is also discussed.
and
streptomycin
is also discussed.
Results
2.2.Results
2.1.
2.1.RsuA
RsuAProvides
ProvidesaaSurvival
SurvivalAdvantage
AdvantagetotoBacteria
Bacteriaunder
underStreptomycin
StreptomycinStress
Stress
Streptomycin
binds
to
a
pocket
in
the
30S
ribosome
that
is
created
by ribosomal
Streptomycin binds a pocket in the 30S ribosome that is created by ribosomal
proprotein
uS12
helices
44 (Figure
1) and
interferes
its decoding
tein uS12
andand
16S16S
helices
18, 18,
27, 27,
andand
44 (Figure
1) and
interferes
withwith
its decoding
funcfunction.
Streptomycin-resistance
mutations
are found
in rRNA,
16S rRNA,
ribosomal
protein
tion. Streptomycin-resistance
mutations
are found
in 16S
ribosomal
protein
uS12,
uS12,
and rRNA
methyltransferase
RsmG [7,15–20].
However,
streptomycinand rRNA
methyltransferase
enzymeenzyme
RsmG [7,15–20].
However,
streptomycin-resistance
resistance
are not reported
in the
RsuA
that preferably
to a 30S assembly
mutationsmutations
are not reported
in the RsuA
that
preferably
binds to binds
a 30S assembly
intermeintermediate
with
an
extended
helix
18
[13].
diate with an extended helix 18 [13].
Figure
Figure1.1.Streptomycin
Streptomycinbinds
bindsclose
closetoto16S
16Shelix
helix18.
18.(a)
(a)16S
16Shelix
helix18
18model
modelRNAs
RNAsused
usedininthis
thisresearch
research
are
binds to
tothe
thepocket
pocketformed
formedbybyribosomal
ribosomal
protein
uS12
(yellow)
areshown.
shown. (b)
(b) Streptomycin binds
protein
uS12
(yellow)
andand
16S
helices
1 (green),
18 18
(blue),
27 27
(cyan),
and
44 44
(pink)
as as
observed
in in
the
X-ray
16S
helices
1 (green),
(blue),
(cyan),
and
(pink)
observed
the
X-raycrystal
crystalstructure
structureof
30S
ribosome
(PDB
ID ID
4V50).
BlueBlue
and and
red spheres
represent
Ψ516Ψ516
and strepofstreptomycin-bound
streptomycin-bound
30S
ribosome
(PDB
4V50).
red spheres
represent
and
tomycin,
respectively..
streptomycin, respectively.
Wehypothesized
hypothesizedthat
thatbacteria
bacteriaare
areless
lessresistant
resistanttotostreptomycin
streptomycinininthe
theabsence
absenceofof
We
RsuA,
perhaps
due
to
its
role
as
a
survival
protein.
To
test
this
hypothesis,
the
growthofof
RsuA, perhaps due to its role as a survival protein. To test this hypothesis, the growth
wildtype
(Wt)
and
RsuA
knock-out
(ΔRsuA)
strains
of
E.
coli
under
streptomycin
stress
wildtype (Wt) and RsuA knock-out (∆RsuA) strains of E. coli under streptomycin stress
werecompared.
compared.The
The
growth
wildtype
(Wt)
RsuA
knock-out
(ΔRsuA)
strains
were
growth
of of
wildtype
(Wt)
andand
RsuA
knock-out
(∆RsuA)
strains
of E. of
coliE.
coli after
18inoculation
h of inoculation
varying
streptomycin
concentrations
µg/mL)
after
18 h of
underunder
varying
streptomycin
concentrations
(0–200(0–200
µg/mL)
was
was recorded.
Approximately,
a 2-fold
decrease
ICstreptomycin
50 of streptomycin
was observed
recorded.
Approximately,
a 2-fold
decrease
in theinICthe
was observed
for
50 of
∆RsuA
E. coliE.strain
(5.9 ± 1.3
compared
with that
for Wt
coliWt
(12.0
1.2(12.0
µg/mL),
for ΔRsuA
coli strain
(5.9µg/mL)
± 1.3 µg/mL)
compared
with
thatE.for
E. ±
coli
± 1.2
suggesting
that RsuAthat
plays
a supportive
role in therole
survival
of E. coli of
under
streptomycin
µg/mL), suggesting
RsuA
plays a supportive
in the survival
E. coli
under strepstress
(Figure
2).
tomycin stress (Figure 2).
The study of bacterial growth kinetics can provide an idea of the mechanism by which
RsuA provides a survival advantage to cells experiencing streptomycin stress. Bacterial
growth curves were obtained at three different streptomycin concentrations based on
the IC50 values for both Wt and ∆RsuA strains (Figure 3). Wt E. coli strain illustrates
a significant drop in the log phase growth rates as the streptomycin concentration is
increased (Supplementary Table S1). The asymptote of growth curves is also significantly
decreased in response to increasing streptomycin concentrations (Figure 3a). Interestingly,
the growth of Wt E. coli under 11 and 13.5 µg/mL streptomycin was recovered after
~600 min. However, in contrast, the growth curves obtained for the ∆RsuA E. coli strain
did not demonstrate a second log phase at the same higher streptomycin concentrations
(Figure 3b). The comparison of the growth curves for the Wt and ∆RsuA strains shows that
Antibiotics 2023, 12, 1447
3 of 15
the Wt E. coli strain possesses an ability to tolerate streptomycin that is lacking in the ∆RsuA
strain, suggesting an ability on the part of RsuA to grant E. coli cells a survival advantage.
The recovery of cell growth after 600 min (10 h) is likely due to the depleted effective
concentration of streptomycin. However, the ∆RsuA strain cannot recover after 10 h. The
existence of a subpopulation of bacterial cells that can survive streptomycin stress in the
presence of protein RsuA is a plausible explanation for this observation. The decrease in the
bacterial growth rates of the ∆RsuA strain compared with the Wt strain in the presence of
streptomycin is perhaps due to the decreased translation rates of ribosomes lacking Ψ516.
These observations suggest that the presence of RsuA renders a survival advantage for Wt
Antibiotics 2023, 12, x FOR PEER REVIEW
3 of 1
E. coli under streptomycin stress, especially at higher streptomycin concentrations (11 and
13.5 µg/mL).
Figure2.2. RsuA
RsuAincreases
increasesthe
theresistance
resistance
toward
streptomycin.
normalized
cell growth
Figure
toward
streptomycin.
TheThe
normalized
cell growth
for for W
(black
squares)
and
ΔRsuA
Wt
(black
squares)
and
∆RsuA(red
(redsquares)
squares)E.E.coli
colistrains
strainsare
are plotted
plotted at
at varying
varying streptomycin
streptomycin concen
(µg/mL) for
forWt
Wtand
and ΔRsuA
trations (0–200
µg/mL).
The The
insetinset
shows
thethe
corresponding
concentrations
(0–200
µg/mL).
shows
correspondingIC
IC50
values (µg/mL)
50 values
E. coli strains.
∆RsuA
E. coli strains.
RsuA
was supplemented
the ∆RsuA
E. coli
strain
using aan
protein
The study
of bacterialinto
growth
kinetics
can
provide
idea overexpression
of the mechanism by
plasmid to ensure that observed differences in bacterial growth are RsuA dependent.
which RsuA provides a survival advantage to cells experiencing streptomycin stress. Bac
Wildtype RsuA overexpression plasmid (∆RsuA + wtRsuA) was transformed into ∆RsuA
terial growth curves were obtained at three different streptomycin concentrations based
E. coli cells. We then performed similar bacterial growth assays, as previously explained,
on thea IC
values
for(IPTG)
both concentration.
Wt and ΔRsuA
3). Wt E. coli
strain
illustrates a
under
50 50
µM
inducer
Thestrains
50 µM (Figure
IPTG concentration
is the
minimal
significant
drop in the
log phase
growth rates
as theisstreptomycin
concentration
is in
inducer
concentration
at which
an overexpression
of RsuA
visible in an SDS-PAGE
gel
creased (Supplementary
S1). growth
The asymptote
growth
is also
(Supplementary
Figure S1).Table
Bacterial
curves forofthe
Wt E. curves
coli strain
weresignificantly
not
significantly
altered
by adding
50 µM of streptomycin
IPTG (Supplementary
Figure S2).
The initial
decreased in
response
to increasing
concentrations
(Figure
3a). Interest
short
andof
extended
stationary
phase
Wtµg/mL
and ∆RsuA
E. coli strains
not
ingly,log
thephase
growth
Wt E. coli
under 11
andin
13.5
streptomycin
waswere
recovered
afte
observed
in
the
RsuA-supplemented
∆RsuA
E.
coli
(Figure
3a,c).
Such
differences
may
~600 min. However, in contrast, the growth curves obtained for the ΔRsuA E. coli strain
arise from the inability to control RsuA expression levels after induction with IPTG. This
did not demonstrate a second log phase at the same higher streptomycin concentration
may be because bacterial ribosome biogenesis is negatively impacted in the presence of
(Figure 3b). The comparison of the growth curves for the Wt and ΔRsuA strains show
RsuA in high concentrations. The RsuA-dependent changes in bacterial growth may be
that
the
Wt E.
strain possesses
an ability
to be
tolerate
streptomycin
thathas
is yet
lacking
due to
either
its coli
pseudouridylation
activity
or may
a function
of RsuA, and
to in the
ΔRsuA
strain,For
suggesting
ability
the part
RsuA to
grant E.factor
coli cells
a surviva
be
discovered.
instance, an
RsuA
may on
function
as aof
ribosome
assembly
during
advantage.
The
recovery
of
cell
growth
after
600
min
(10
h)
is
likely
due
to
the
antibiotic stress. To investigate the role of Ψ516 in streptomycin resistance, a functionaldepleted
mutant
of RsuA
was overexpressed
in ∆RsuAHowever,
E. coli. An overexpression
plasmid
encoding
effective
concentration
of streptomycin.
the ΔRsuA strain
cannot
recover afte
a10
catalytically
inactive RsuA
(∆RsuA + mutRsuA)
generated
by site-directed
mutagenesis
h. The existence
of a subpopulation
of bacterial
cells
that can survive
streptomycin
was
transformed
into ∆RsuA
E. coli [10].
Theisgrowth
curves explanation
obtained at higher
streptomycin
stress
in the presence
of protein
RsuA
a plausible
for this
observation. The
concentrations (11 and 13.5 µg/mL) for ∆RsuA + mutRsuA E. coli showed a significant loss
decrease in the bacterial growth rates of the ΔRsuA strain compared with the Wt strain in
of bacterial growth at the end of 24 h, compared with ∆RsuA E. coli strain with wildtype
the presence of streptomycin is perhaps due to the decreased translation rates of ribo
RsuA being overexpressed (Figure 3c,d). Although the shape of the two growth curves
somes
lacking
Ψ516. and
These
observations
suggest
that
the presence
of RsuA
renders a sur
for
∆RsuA
+ wtRsuA
∆RsuA
+ mutRsuA
E. coli
strains
looked similar
at higher
vival advantage for Wt E. coli under streptomycin stress, especially at higher streptomycin
concentrations (11 and 13.5 µg/mL).
Antibiotics 2023, 12, 1447
4 of 15
streptomycin concentrations, the growth rate after 600 min was found to be higher for
4 of 16
E. coli containing wtRsuA. This observation suggests that pseudouridylation
at
position 516 of 16S rRNA can influence the bacterial growth rate.
Antibiotics 2023, 12, x FOR PEER REVIEW
∆RsuA
Figure 3. RsuA influences bacterial growth kinetics during streptomycin stress. Bacterial growth
curves
curves obtained
obtained for
for (a) wildtype
wildtype (Wt)
(Wt) E. coli and (b) RsuA knock-out (ΔRsuA)
(∆RsuA) strain
strain of
of E. coli, and
RsuA
wildtype
RsuA
(ΔRsuA
+ wtRsuA)
and and
(d) mutant
RsuARsuA
(ΔRRsuA knock-out
knock-outstrain
strainexpressing
expressing(c)(c)
wildtype
RsuA
(∆RsuA
+ wtRsuA)
(d) mutant
suA
+
mutRsuA)
in
the
background
(50
µM
IPTG),
at
varying
streptomycin
concentrations
from
0–
(∆RsuA + mutRsuA) in the background (50 µM IPTG), at varying streptomycin concentrations from
13.5 µg/mL, are shown. The average of the biological triplicates is shown.
0–13.5 µg/mL, are shown. The average of the biological triplicates is shown.
RsuAInfluences
was supplemented
into theduring
ΔRsuA
E. Ribosome
coli strainBiogenesis
using a protein overexpression
2.2. RsuA
rRNA Maturation
30S
plasmid
to ensure
that observed
differences
in bacterial
growth
are factors
RsuA dependent.
Several
rRNA modification
enzymes
can also
function as
assembly
in addition
Wildtype
RsuA
overexpression
plasmid
(ΔRsuA
+
wtRsuA)
was
transformed
into
to their enzymatic activity. The absence of these enzymes causes ribosome assembly ΔRsuA
defects
E.
coli
cells.
then performed
similar
growth
assays,
asinfluence
previously
explained,
and
leads
toWe
bacterial
growth defects.
Thebacterial
hypothesis
that RsuA
can
bacterial
ribounder
a 50 µM inducer
Theratios
50 µM
concentration
is the
minisome assembly
has been(IPTG)
tested concentration.
by comparing the
of IPTG
16S and
17S rRNA for
wildtype
mal
concentration
which
an overexpression
RsuA is visible
in an
SDS-PAGE
and inducer
∆RsuA E.
coli strains inatthe
presence
and absence ofofstreptomycin
stress.
Radiolabeled
gel
(Supplementary
Figure
S1).complementary
Bacterial growth
curves
for to
the16S
Wtand
E. coli
DNA
oligonucleotide
primers
and
specific
17Sstrain
RNAwere
werenot
ansignificantly
by adding
50 µM
of IPTG
Figure
The initial short
nealed to thealtered
total RNA
extracted
from
these (Supplementary
two strains. Native
gel S2).
electrophoresis
was
log
phase and
extended
phase
in Wt and
ΔRsuA
coli strains
were
not obperformed
to separate
the stationary
rRNA–primer
complexes
from
the freeE.primer.
Bands
correspondserved
in
the
RsuA-supplemented
ΔRsuA
E.
coli
(Figure
3a,c).
Such
differences
may
arise
ing to rRNA–primer complexes were quantified, and 17S/16S fractions were calculated
from
the
inability
to
control
RsuA
expression
levels
after
induction
with
IPTG.
This
may
(Figure 4, Supplementary Figure S3). During the early growth stages, the 17S composition
be
because
bacterial
is negatively
impacted
in the presence
RsuA
was
found to
be veryribosome
high for biogenesis
both wildtype
and ∆RsuA
E. coli strains
(Figure 4).ofAs
the
in
high concentrations.
Thethe
RsuA-dependent
changes as
in expected.
bacterial growth
maythe
be ∆RsuA
due to
bacterial
growth increased,
17S/16S ratio decreased
In addition,
either its pseudouridylation activity or may be a function of RsuA, and has yet to be discovered. For instance, RsuA may function as a ribosome assembly factor during antibiotic
stress. To investigate the role of Ψ516 in streptomycin resistance, a functional mutant of
RsuA was overexpressed in ΔRsuA E. coli. An overexpression plasmid encoding a
Antibiotics 2023, 12, 1447
RNA were annealed to the total RNA extracted from these two strains. Native gel electrophoresis was performed to separate the rRNA–primer complexes from the free primer.
Bands corresponding to rRNA–primer complexes were quantified, and 17S/16S fractions
were calculated (Figure 4, Supplementary Figure S3). During the early growth stages, the
17S composition was found to be very high for both wildtype and ΔRsuA E.5 of
coli
15 strains
(Figure 4). As the bacterial growth increased, the 17S/16S ratio decreased as expected. In
addition, the ΔRsuA bacterial strain showed a higher 17S/16S ratio at 4 h and 8 h (Figure
4).
Unfortunately,
however,
the 17S/16S
17S/16S ratio
ratioatat44hhand
and88hh(Figure
for bacteria
under streptomybacterial
strain showed
a higher
4). Unfortunately,
however,
the 17S/16S
ratio at 4 hdue
andto
8 hthe
forlower
bacteria
under
streptomycin
was
not stages.
cin
stress was
not determined
cell
growth
observedstress
at the
early
determined
due
to
the
lower
cell
growth
observed
at
the
early
stages.
At
the
stationary
At the stationary phase, the ratio was similar for both wildtype and ΔRsuA E. coli strains
phase,streptomycin
the ratio was similar
both wildtype
∆RsuA
E. coliwith
strains
streptomycin
under
stress,forsimilar
to whatand
was
observed
nounder
streptomycin.
stress, similar to what was observed with no streptomycin.
Figure 4.
4. The
The 17S/16S
(Wt)
andand
RsuA
knock-out
(∆RsuA)
strainsstrains
of E. coli
Figure
17S/16S ratio
ratiofor
forwildtype
wildtype
(Wt)
RsuA
knock-out
(ΔRsuA)
ofinE.the
coli in the
presence
and
absence
of
streptomycin
(Sm)
stress.
Error
bars
represent
the
SD
of
triplicates.
presence and absence of streptomycin (Sm) stress. Error bars represent the SD of triplicates.
2.3. The Ψ516 Modification Influences the h18 Structure
2.3. The
Modification
Influences
h18donor
Structure
TheΨ516
presence
of an extra
hydrogenthe
bond
(N1H) and unique stacking properties
presence ofthe
an ability
extra hydrogen
bond
donor
(N1H)
and
unique[20–23].
stacking
properties
giveThe
pseudouridine
to influence
RNA
structure
and
stability
Many
previous
studies have
changes
in RNA
give
pseudouridine
theillustrated
ability topseudouridine-dependent
influence RNA structurestructural
and stability
[20–23].
Many preupon studies
changeshave
in environmental
conditions such as pH, Mg2+ structural
concentration,
and the
vious
illustrated pseudouridine-dependent
changes
in presRNA upon
ence
of
small
molecules
[24,25].
Such
pseudouridine-induced
changes
in
RNA
structure
changes in environmental conditions such as pH, Mg2+ concentration, and the presence of
and stability are sensitive to the location of the pseudouridine in the RNA sequence [26].
small molecules [24,25]. Such pseudouridine-induced changes in RNA structure and staΨ516 is located near the sharp bend of the 16S helix 18 formed in the pseudoknotted
bility
sensitive
to the location
of the
in the
sequence [26].
nativeare
helix
18 structure.
The presence
ofpseudouridine
several Mg2+ ions
thatRNA
are coordinated
to he-Ψ516 is
located
neartothe
sharp
bend
thehas
16Sled
helix
18hypothesis
formed inthat
the pseudouridine
pseudoknotted
native
lix 18 close
Ψ516
(Figure
5a)of[27]
to the
plays
a helix
vital role in the folding of helix 18 to its native form with the addition of Mg2+ . To test
pseudouridine-dependent changes in helix 18 folding, circular dichroism (CD) experiments
were performed for two helix 18 upper hairpin loop model RNAs with pseudouridine and
uridine at position 516 (Figure 1a). The usage of model RNAs presents many advantages
in studying the structure and stability of RNA. The modular nature of RNA structure can
justify the use of short model RNAs in various biophysical studies. In addition, antibiotic
binding to ribosomal RNA and many other RNAs, such as RRE RNA, is studied using
model RNAs.
The h18-Ψ model RNA comprises E. coli 16S helix 18 residues 511 through 540 with a
pseudouridine at position 516, whereas the unmodified counterpart, h18-U model RNA,
has the pseudouridine at position 516 replaced by uridine. CD spectra were obtained for
both h18-Ψ and h18-U constructs at varying magnesium concentrations to track structural
changes during helix 18 folding. Both of the helix 18 RNAs give rise to CD spectra typical
for RNAs. In the absence of Mg2+ ions, both helix 18 model RNAs give rise to a dichroic
maximum at approximately 266 nm in the CD spectra. However, the molar ellipticity value
at the spectral peak at 266 nm for the modified helix 18 RNA is significantly higher than
that for the unmodified helix 18 model RNA (Figure 5b). Such changes can arise from the
changes in stacking interactions. As expected, subtle changes in the CD spectra have been
observed with an increase in magnesium ion concentration, showing the ability of both
RNAs to fold in the presence of magnesium (Figure 5c,d). For both helix 18 model RNAs
used in this research, shoulders in the CD spectra are observed at ~260 nm, suggesting
structural heterogeneity. Although the shapes of CD spectra for both the modified and the
Antibiotics 2023, 12, 1447
than that for the unmodified helix 18 model RNA (Figure 5b). Such changes can arise from
the changes in stacking interactions. As expected, subtle changes in the CD spectra have
been observed with an increase in magnesium ion concentration, showing the ability of
both RNAs to fold in the presence of magnesium (Figure 5c,d). For both helix 18 model
RNAs used in this research, shoulders in the CD spectra are observed at ~2606nm,
sugof 15
gesting structural heterogeneity. Although the shapes of CD spectra for both the modified
and the unmodified RNAs are found to be similar, contrasting Mg2+-dependent folding
profiles
were observed
the two
the presence
the modification,
shoulder
2+ -dependent
unmodified
RNAs arefor
found
to beRNAs.
similar,Incontrasting
Mgof
foldingthe
profiles
2+ was added up to 1 mM.
in the
CD
spectra
(at
260
nm)
of
h18
RNA
decreased
as
the
Mg
were observed for the two RNAs. In the presence of the modification, the shoulder in
However,
when the
concentration
increased
beyond
1 mM,
the shoulder
started
the CD spectra
(at Mg
2602+nm)
of h18 RNAwas
decreased
as the
Mg2+ was
added
up to 1 mM.
2+
However, when
the 5c,e).
Mg A
concentration
was increased
beyond
1 mM, in
theall
shoulder
started dereappearing
(Figure
similar folding
profile was
observed
three repeats,
2+ for each
reappearing
(Figure
5c,e). Aatsimilar
profile
was(Figure
observed
in However,
all three repeats,
spite
the change
in ellipticity
no Mgfolding
repeat
5b).
for the un2+ for each repeat (Figure 5b). However, for the
despite
the
change
in
ellipticity
at
no
Mg
modified helix 18 model RNA, only a gradual decrease in the molar ellipticity was obunmodified helix 18 model RNA, only a gradual decrease in the molar ellipticity was
served,
and only at 260 nm (Figure 5d,f). These results suggest that the change in 16S helix
observed, and only at 260 nm (Figure 5d,f). These results suggest that the change in
18 structure at increasing concentrations of Mg2+ is sensitive
to the presence of pseudour16S helix 18 structure at increasing concentrations of Mg2+ is sensitive to the presence of
idine
modification
at positionat516.
pseudouridine
modification
position 516.
Figure
5. The
Ψ516modification
modification influences
the
folding
of helix
18. (a)
helix
in the1830S
Figure
5. The
Ψ516
influences
the
folding
of helix
18.The
(a)16S
The
16S18helix
inX-ray
the 30S X2+ present near
2+
crystal
structure
(PDB
ID:
4V50)
is
shown.
Blue
spheres
represent
Mg
helix
18.
Ψ516helix
is 18.
ray crystal structure (PDB ID: 4V50) is shown. Blue spheres represent Mg present near
shown in magenta. (b) A comparison of circular dichroism (CD) spectra for h18-Ψ (green) and h18-U
(blue) model RNAs. CD spectra of (c) h18-Ψ and (d) h18-U at various magnesium concentrations
are shown. The inset shows the change in the 250–270 nm wavelength region of each spectrum.
Changes in molar ellipticity with [Mg2+ ] for (e) h18-Ψ and (f) h18-U are shown. Standard deviation
of triplicates is shown as error bars.
RNase T1 footprinting was performed to identify regions of helix 18 that change with
the addition of Mg2+ , especially in a pseudouridine-dependent manner. RNase T1 cleaves
the phosphodiester bond at the 50 -end of unpaired guanines in RNA. We carried out RNase
T1 footprinting for both the h18-Ψ (Figure 6b) and h18-U RNA constructs at 0–25 mM Mg2+
concentrations (Figure 6a). At all Mg2+ concentrations tested, 50 -phosphodiester bonds of
G521 and G524 located at helix 18 upper hairpin region were cleaved by RNase T1. The
fraction of RNase cleavage at each guanine was obtained and compared with the intensity
of the full-length or intact RNA band. The fraction of RNase cleavage was found to be
higher for G524 compared with G521, indicating that G524 shows lesser tertiary interactions
than that of G521 (Figure 6b–e). Surprisingly, however, G530, known to be unpaired and
projected towards the helix 44 in the 30S X-ray crystal structure, and its neighboring
guanine, G529, were protected from RNase cleavage (Figure 6a). Such deviation from the
expected cleavage pattern could arise from the previously predicted alternate base pairing
in the 530 loop [27–29]. Similar to CD spectrometric measurements, RNase T1 cleavage at
G521 and G524 increased up to 1 mM Mg2+ and decreased for concentrations higher than
1 mM. In addition to RNase T1 cleavages at G524 and G521 observed in modified helix 18,
the unmodified counterpart is also cleaved at G515 and G517 (Figure 6a). The ability of
Antibiotics 2023, 12, 1447
7 of 15
pseudouridine at 516 (Ψ516) to stabilize local structures may be the reason for the absence
of RNase T1 cleavage at G515 and G517 for modified helix 18 RNA. Furthermore, the
relative RNase cleavage for G524 and G521 was higher for unmodified helix 18, indicating
a less structured helix 18 structure. The pattern of RNase cleavage at G524 was similar in
both unmodified and pseudouridylated helix 18 model RNAs. The RNase cleavage at G515
slightly decreases with the addition of Mg2+ , suggesting slight differences in structures of
modified and unmodified model RNAs near the pseudouridylation site. Interestingly, the
cleavage at G521 prompted an increase in unmodified helix 18 RNAs with increasing Mg2+
concentrations, whereas a decrease in metal ions may restore the stability that was absent
Antibiotics 2023, 12, x FOR PEER REVIEW
8 of T1
16
due to the lack of modification (Figure 6g). However, a significant change in RNase
2+
digestion at G517 was not observed as the concentration of Mg was increased (Figure 6f).
changes
local
RNA
structure.
(a) Radiograph
showing
RNaseRNase
T1 digesFigure 6. Ψ516
Ψ516modification
modification
changes
local
RNA
structure.
(a) Radiograph
showing
T1
tion pattern
of h18-Ψ
(left(left
panel)
andand
h18-U
(right
panel)
to
digestion
pattern
of h18-Ψ
panel)
h18-U
(right
panel)model
modelRNAs.
RNAs.Bands
Bands corresponding
corresponding to
RNase T1
T1cleavages
cleavagesatatG524,
G524,G521,
G521,G517,
G517,
and
G515
shown
in blue,
green,
purple,
teal arRNase
and
G515
areare
shown
in blue,
green,
purple,
and and
teal arrows,
rows, respectively.
The relative
cleavage
atguanine
each guanine
of h18-Ψ
(b,c)h18-U
and h18-U
compared
respectively.
The relative
cleavage
at each
of h18-Ψ
(b,c) and
(d–g) (d–g)
compared
with
with full-length intact RNA at various
Mg2+ concentrations (mM) are plotted.
2+
full-length intact RNA at various Mg concentrations (mM) are plotted.
2.4. Ψ516
Ψ516 Modification Increases Streptomycin Binding to Helix 18
18
Streptomycin binds to a pocket formed by the 16S helices 1, 18, 27, and 44 and ribosomal small subunit binding protein uS12
uS12 (Figure
(Figure 1b).
1b). The phosphate backbone residues
G526
h18
upper
hairpin
loop
form
contacts
withwith
streptomycin
[30,31].
With
G526 and
andG527
G527ininthe
the
h18
upper
hairpin
loop
form
contacts
streptomycin
[30,31].
the
ability
of pseudouridine
to influence
helix 18
structure
and folding,
we hypothesized
With
the ability
of pseudouridine
to influence
helix
18 structure
and folding,
we hypoththat
streptomycin
could bind
to bind
h18-Ψ
h18-U
varying
affinity. affinity.
CD spectroscopy
esized
that streptomycin
could
toand
h18-Ψ
andwith
h18-U
with varying
CD specwas
used
to
monitor
any
structural
changes
of
helix
18
upon
the
addition
of
streptomycin
troscopy was used to monitor any structural changes of helix 18 upon the addition of
to
h18-Ψ and to
h18-U
model
RNAs.model
Furthermore,
we used CDwe
spectroscopic
changes to
streptomycin
h18-Ψ
and h18-U
RNAs. Furthermore,
used CD spectroscopic
determine
binding
affinities
of
streptomycin
to
each
helix
18
model
RNA.
Streptomycin
changes to determine binding affinities of streptomycin to each helix 18 model RNA.
2+ concentration.
was
titrated against
h18-Ψ against
and h18-U
constructs
at pH
7.0 and 4atmM
Streptomycin
was titrated
h18-Ψ
and h18-U
constructs
pHMg
7.0 and
4 mM Mg2+
A
redshift in the
maximum
for both
RNAs was
upon
additionupon
of strepconcentration.
Apeak
redshift
in the peak
maximum
for observed
both RNAs
wasthe
observed
the
addition of streptomycin (Figure 7a). At very high concentrations of streptomycin, the molar ellipticity decreased drastically, perhaps due to the non-specific binding of the antibiotic to RNA. The fraction of RNA bound to streptomycin was calculated using the change
in molar ellipticity at 265 nm. The equilibrium dissociation constants (Kds) for streptomy-
Antibiotics 2023, 12, 1447
8 of 15
tomycin (Figure 7a). At very high concentrations of streptomycin, the molar ellipticity
decreased drastically, perhaps due to the non-specific binding of the antibiotic to RNA. The
fraction of RNA bound to streptomycin was calculated using the change in molar ellipticity
Antibiotics 2023, 12, x FOR PEER REVIEW
at 265 nm. The equilibrium dissociation constants (Kd s) for streptomycin binding with
h18-Ψ and h18-U constructs were found to be 23 ± 3 µM and 63 ± 1 µM, respectively
(Figure 7b).
Figure 7.7.
Ψ516
modification
increases
the affinitythe
of streptomycin
helix 18. (a) CDto
spectra
Figure
Ψ516
modification
increases
affinity of to
streptomycin
helixobtained
18. (a) CD sp
for the h18-Ψ model RNA, at various streptomycin concentrations (0–3 mM) are shown. Experiments
tained for the h18-Ψ model RNA, at various streptomycin concentrations (0–3 mM) are sh
were performed at 30 ◦ C in CD buffer (20 mM potassium cacodylate pH 7.0, 15 mM KCl, 4 mM
periments were performed at 30 °C in CD buffer (20 mM potassium cacodylate pH 7.0, 15 m
MgCl2 ). (b) The fraction of RNA complexed with streptomycin at each streptomycin concentration is
4 mM MgCl2). (b) The fraction of RNA complexed with streptomycin at each streptomycin
shown. The dissociation constants (Kd s) of streptomycin binding for h18-Ψ and h18-U model RNAs
tration
is shown.
The dissociation
constants
ds) of streptomycin binding for h18-Ψ an
were
obtained
by least-square
fitting of binding
curves to(K
binding
isotherm. All the experiments were
model
RNAs
were
obtained
by
least-square
fitting
of binding curves to binding isotherm
undertaken in triplicate to confirm the reproducibility.
experiments were undertaken in triplicate to confirm the reproducibility.
The increased binding affinity of streptomycin to helix 18 RNA (3-fold) in the presence
of pseudouridine modification at position 516 suggests that pseudouridine preferably
According to the X-ray crystal structure of the 30S ribosomal subunit bound t
stabilizes a structure of helix 18 that favors streptomycin binding. On the other hand,
tomycin,
themay
antibiotic
molecule
C526
and G527
in the
h18 upper
streptomycin
bind to two
differentinteracts
regions ofwith
the helix
depending
on the
presence
[31].
RNase T1 footprinting was performed for h18-Ψ and h18-U RNAs in the pre
of pseudouridine.
According totothe
X-ray crystal
structure
of the
30S ribosomal
bound to
streptomycin
determine
the
regions
of helix
18 thatsubunit
are affected
bystreptostreptomyc
mycin,
the
antibiotic
molecule
interacts
with
C526
and
G527
in
the
h18
upper
hairpin
[31].
ing (Supplementary Figure S4). Unlike in the presence of increasing magnesium
i
RNase T1 footprinting was performed for h18-Ψ and h18-U RNAs in the presence of strepposure
to
RNase
T1
increased
with
the
addition
of
streptomycin
for
all
four
expos
tomycin to determine the regions of helix 18 that are affected by streptomycin binding
nines in unmodified
helix
18 in
RNA
(Figureof8c–f).
A similar
trendions,
wasexposure
also observed
(Supplementary
Figure S4).
Unlike
the presence
increasing
magnesium
two
exposed
guanines
the
modified
helix 18 RNA
(Figure
8a,b). guanines
However, the p
to RNase
T1 increased
withinthe
addition
of streptomycin
for all
four exposed
in
unmodified
helix
18
RNA
(Figure
8c–f).
A
similar
trend
was
also
observed
for
the two
age change in exposure to RNase T1 for G521 and G524 in the presence
of strept
exposed guanines in the modified helix 18 RNA (Figure 8a,b). However, the percentage
slightly increased in the modified RNA compared with its unmodified counterpar
change in exposure to RNase T1 for G521 and G524 in the presence of streptomycin slightly
dition,
tightly
bound
to G515 counterpart.
and G517 compared
increasedstreptomycin
in the modifiedwas
RNA
compared
withclose
its unmodified
In addition,with G
G521
in thewas
unmodified
helix
18toRNA
(Figure
In the
helixin18 RNA
streptomycin
tightly bound
close
G515 and
G517 8c–f).
compared
withmodified
G524 and G521
the unmodified
helix 18
RNA (Figure
8c–f).
InG524
the modified
helix 18
RNA,
streptomycin
is helix
tomycin
is bound
tighter
to G521
and
compared
with
the
unmodified
bound
tighter
to
G521
and
G524
compared
with
the
unmodified
helix
18
RNA
(Figure
8a,d,
(Figure 8a,d, Supplementary Figure S5). Similar to the previously observed X-ray
Supplementary Figure S5). Similar to the previously observed X-ray crystallography data,
lography
data, these results indicate that streptomycin interacts with the helix 1
these results indicate that streptomycin interacts with the helix 18 upper hairpin loop
hairpin
loop
region
only whenisΨ516
modification
is present.
Although
streptom
region only
when
Ψ516 modification
present.
Although streptomycin
interacts
with the
teracts
with
the
helix 18are
RNA,
are closer
unmodified
helix
18 unmodified
RNA, those interactions
closerthose
to the interactions
helix 18 lower stem
region. to the
lower stem region.
Antibiotics 2023, 12, 1447
Antibiotics 2023, 12, x FOR PEER REVIEW
9 of 15
10 of 16
Figure8.8. Streptomycin
Streptomycin interacts
ofof
thethe
h18
model
RNAs
with
contrasting
Figure
interactswith
withdifferent
differentregions
regions
h18
model
RNAs
with
contrasting
affinities. Relative
Relative cleavage
ofof
h18-Ψ
and
(c)(c)
G524,
(d)(d)
G521,
(e) G517,
andand
affinities.
cleavageat
at(a)
(a)G524
G524and
and(b)
(b)G521
G521
h18-Ψ
and
G524,
G521,
(e) G517,
(f) G515 of h18-U model RNAs at various concentrations of streptomycin are shown. Error bars
(f) G515 of h18-U model RNAs at various concentrations of streptomycin are shown. Error bars
shown on graphs represent the standard deviation of three replicates.
shown on graphs represent the standard deviation of three replicates.
Discussion
3.3.Discussion
Inbacteria,
bacteria, the
the nucleotide
a set
of of
unique
modification
In
nucleotidemodification
modificationprocess
processrequires
requires
a set
unique
modification
enzymes
[1].
The
roles
of
many
rRNA
modification
enzymes
and
their
respective
enzymes [1]. The roles of many rRNA modification enzymes and their respective nucleonucleotide
tide modifications
aretoyet
be discovered.
Interestingly,
bacteria
survive
without
modifications
are yet
beto
discovered.
Interestingly,
bacteria
can can
survive
without
many
many ribosome
modification
enzymes
under
optimal
growth
conditions
[32].
addition,riboribosome
modification
enzymes
under
optimal
growth
conditions
[32].
In In
addition,
ribosomes
lacking
nucleotide
modifications
catalyze
peptide
formation
[33].Interestingly,
Interestsomes
lacking
nucleotide
modifications
cancan
catalyze
peptide
formation
[33].
ingly,
however,
several
studies
show
that
rRNA
nucleotide
modifications
influence
the
however, several studies show that rRNA nucleotide modifications influence the efficiency
efficiency of ribosome assembly and its peptidyltransferase activity [33–35].
of ribosome assembly and its peptidyltransferase activity [33–35].
This research clearly illustrates a growth disadvantage for E. coli cells lacking RsuA
This research clearly illustrates a growth disadvantage for E. coli cells lacking RsuA
under streptomycin stress. Similar to previous bacterial growth studies for RsuA deletion
under streptomycin stress. Similar to previous bacterial growth studies for RsuA deletion
strains, no significant growth defects were observed in the absence of streptomycin [36].
strains, no significant growth defects were observed in the absence of streptomycin [36].
The increased lag time for the growth of persistent bacteria in the presence of streptomyThe increased lag time for the growth of persistent bacteria in the presence of streptomycin
cin suggests that bacteria are able to build mechanisms by which to tolerate streptomycin
suggests that bacteria are able to build mechanisms by which to tolerate streptomycin [37].
[37]. However, the lack of RsuA may influence these tolerance mechanisms. The increase
However,
the lack of RsuA may influence these tolerance mechanisms. The increase in
in the growth lag in bacterial strains with RsuA present is likely due to the slow producthe
growth
lag in bacterial
strains
with
RsuA present
is likely
to the slow
production
tion of specialized
ribosomes
capable
of fighting
antibiotic
stress,due
as suggested
previously
ofbyspecialized
ribosomes
capable
of
fighting
antibiotic
stress,
as
suggested
previously
Gorini et al. [38]. It is also possible that the production of fully assembled ribosomes is by
Gorini
[38]. Itperhaps
is also due
possible
the production
fully assembled
ribosomes
stalledet
inal.
bacteria,
to thethat
decreased
ribosomalofprotein
pool. In addition,
the is
stalled
in
bacteria,
perhaps
due
to
the
decreased
ribosomal
protein
pool.
In
addition,
existing 70S ribosomes may operate with an altered translation rate. Bacteria lacking RsuA the
existing 70S ribosomes may operate with an altered translation rate. Bacteria lacking RsuA
resulted in slow growth rates, and the asymptote at the stationary phase decreased with
the increasing concentration of streptomycin. These observations suggest that bacteria
Antibiotics 2023, 12, 1447
10 of 15
lacking RsuA may synthesize ribosomes with reduced translation elongation rates [39].
Interestingly, our RNase T1 footprinting assays illustrate that the Ψ516 changes the confirmation of G530, which is known to influence mRNA decoding, also suggesting the
ability of pseudouridylation at position 516 to influence translation rates. In addition, the
lack of pseudouridylation can decrease the translation rates of streptomycin-bound 70S
ribosomes even further compared with its presence, as is evident from the slow growth
rates in the exponential growth phase. The universally conserved A1492 and A1493 of
16S helix 44 and G530 of 16S helix 18 undergo conformational changes during the decoding
process [40]. The inability of G530 to undergo conformational changes due to streptomycin
binding at the tip of helix 18 leads to translation defects. Our data illustrates that the lack of
pseudouridylation can cause structural changes in the 530 loop. In addition, the stability of
helix 18 and its flexibility may also decrease in the absence of pseudouridylation. However,
the binding of streptomycin to helix 18 lacking the pseudouridylation may trap helix 18
in a non-native conformation that disfavors ribosome function. Therefore, the structural
changes caused by the lack of pseudouridine may generate ribosomes that are less tolerant
to streptomycin. In addition to changes in translation rate, pseudouridylation and the
presence of RsuA can influence ribosome biogenesis. The increase in 17S observed in this
research may be correlated to the deficiencies in ribosome assembly and 30S assembly
intermediates produced in the absence of RsuA may have a weaker tendency to be matured.
Previous studies have shown the ability of the RsuA to influence the binding of S17 and,
hence, influence the binding of late-binding proteins such as S12.
Streptomycin interacts with the 530 loop of 16S helix 18 [31]. Interestingly, streptomycin binding does not cause structural changes in helix 18 in fully modified native
ribosomes [31]. However, the pioneering work undertaken by Powers and Noller illustrates that ribosomes with different helix 18 structures have contrasting affinities to
streptomycin [41]. It is also likely that the local structural changes caused by the pseudouridine modification can alter the binding orientation of streptomycin to helix 18. Our
study shows that the presence of pseudouridine at position 516 changes the stability of
streptomycin–helix 18 complexes. Unexpectedly, streptomycin binds to helix 18 model
RNA with pseudouridine tighter than the unmodified counterpart. The change in the
streptomycin affinity to unmodified helix 18 model RNA compared with its modified
counterpart may have resulted from the binding of streptomycin to two different regions
of the two helix 18 model RNAs. RNase T1 footprinting experiments confirm the contrasting streptomycin binding modes for both model RNAs. However, our experiments
with model RNAs may not sufficiently represent the binding affinity of streptomycin to
70S ribosomes due to the lack of interactions with the neighboring helices and protein
uS12. The slower growth rates for the ∆RsuA E. coli strain supplemented with the RsuA
functional mutant compared with wildtype E. coli strain also supports the idea that the
pseudouridylation function of RsuA is also critical for the streptomycin resistance in bacteria. Interestingly, in M. tuberculosis, A514C and C517U mutations located in 16S helix 18
show resistance towards streptomycin, whereas, in T. thermophilus, C507A and G524U
mutants are found to be streptomycin-dependent mutants [42–44]. All of these mutations
can influence helix 18 pseudoknot stability. Furthermore, both the RsmG and uS12 that bind
to the pseudoknotted helix 18 also carry streptomycin-resistance mutations. Similarly, the
absence of pseudouridylation at position 516 likely influences the pseudoknot formation.
Pseudouridylation at position 516 can influence the accurate positioning of G530 at the 30S
decoding center by folding helix 18 to its pseudoknotted native structure. On the other
hand, the lack of Ψ516 can also influence the accurate binding of ribosomal protein uS12
and modification enzyme RsmG that binds to pseudoknotted helix 18 [27,45] and carries
streptomycin-resistance mutations. Any defects in the binding of uS12 and RsmG, and the
lack of RsmG methyltransferase activity may also lead to slow translation rates.
Some growth defects observed in E. coli lacking RsuA were absent when catalytically
inactive RsuA is overexpressed, indicating that the RsuA enzyme has a unique role in streptomycin resistance in addition to Ψ516. It is likely that the lack of nucleotide modification
Antibiotics 2023, 12, 1447
11 of 15
and their respective modification enzymes produce unique sub-optimally active ribosomes
that are not sturdy enough to survive under various cellular stress conditions. Different
subpopulations of ribosomes can exist in different stages of growth as well as various
environmental stress conditions [14,46–49]. MazEF is the most studied toxin–antitoxin
(TA) system in E. coli that is triggered under stress conditions. MazEF-based cell death
is considered a population phenomenon under stress conditions where it still results in
the survival of a small subpopulation of cells. MazF leads to the selective synthesis of
about 10% of total cellular proteins. The majority of the population has the expression of
“death proteins”, while a small subpopulation will have “survival proteins” expressed [14].
Interestingly, RsuA is one of the survival proteins [14,47]. An increase in the bacterial
growth lag in the presence of inactive protein similar to that of the wildtype strain suggests
that the presence of the RsuA protein is equally important for streptomycin resistance as its
pseudouridylase activity. RsuA may stabilize the binding of uS17 and hence influence the
binding kinetics of late-binding proteins such as uS5 and uS12 [13]. The absence of RsuA
through its alleged role as an assembly factor can deplete the pool of newly assembled
ribosomes by slowing down the assembly process.
In summary, pseudouridylation of E. coli 16S rRNA at position 516 is likely to be
critical for the structural integrity of helix 18 and its dynamics during mRNA decoding and
may significantly influence bacterial growth in the presence of antibiotics that bind to its
vicinity. In addition, it is also likely that the presence of RsuA, regardless of its activity, can
influence the tolerance against streptomycin.
4. Materials and Methods
4.1. Experimental Details Preparation of Helix 18 RNA
This study used two chemically synthesized 30 nt long 16S helix 18 (residues 511–540;
E. coli numbering) model RNAs. The h18-Ψ model RNA contained a pseudouridine
residue at position 516, whereas the h18-U model RNA contained uridine at position 516.
These two model RNA oligonucleotides were purchased from Horizon Discovery in the
20 -ACE-protected form. RNAs were deprotected by incubating them for 30 min at 60 ◦ C in
a 20 -deprotection buffer (100 mM acetic acid pH 3.4–3.8, adjusted with TEMED) provided
by Horizon Discovery. Deprotected model RNAs were then dried in vacufuge and stored
at −20 ◦ C until further use. RNA stocks for CD experiments were made by dissolving
the deprotected aliquots in TE (1 M Tris–HCl pH 7.5 and 0.5 M EDTA pH 8.0) buffer.
The concentrations of RNA stock solutions were calculated using A260 absorbance and
appropriate extinction coefficients provided by the manufacturer. RNA samples used for
RNase T1 footprinting experiments were radiolabeled at the 50 -end with 32 P isotope using
standard T4-polynucleotide kinase (PNK)-based end-labeling protocols. The radiolabeled
RNAs were then purified using 16% denaturing polyacrylamide gel. RNA samples were
electrophoresed for 1 h at 15W per gel. An autoradiograph was developed to identify the
band representing the full-length RNA. The identified full-length RNA band was exercised,
and the radiolabeled RNA was extracted onto 1 mL of TEN buffer (10 mM Tris–HCl at
pH 7.5, 1 mM Na2 EDTA at pH 8.0, 250 mM NaCl) using the freeze–thaw method. Extracted
RNAs were precipitated and dried before use.
4.2. Circular Dichroism (CD) Spectroscopy
CD spectra (200–320 nm) for model RNAs were obtained using a JASCO J-810 circular
dichroism spectropolarimeter equipped with a water bath to control the temperature.
Model RNA samples (5 µM) were resuspended in 500 µL of CD buffer (50 mM KCl, 20 mM
sodium cacodylate, 0.5 mM EDTA, pH 7.6 adjusted with 1 M HCl). Three scans performed
at 30 ◦ C were averaged for each Mg2+ (0–25 mM) or streptomycin concentration (0–0.5 mM).
The averaged CD spectra were then smoothed using the Savitsky–Golay algorithm (n = 15).
All CD experiments were performed in triplicate to ensure reproducibility. The fraction of
model RNAs bound to streptomycin was calculated using CD at 265 nm and plotted against
streptomycin concentrations. Equilibrium dissociation constants (Kd s) for streptomycin–RNA
Antibiotics 2023, 12, 1447
12 of 15
complexes were obtained by least-square fitting of the fraction-bound versus streptomycin
concentration curves of the binding isotherms, using the Origin program.
4.3. RNase Footprinting Experiments
RNase T1 or RNase A footprints for the h18-Ψ and h18-U RNAs were obtained at various
Mg2+ concentrations (0 mM–25 mM) or streptomycin concentrations (0 mM–0.5 mM). Each
RNase T1/A digestion reaction was carried out by incubating 15 pmols of h18-Ψ/U RNAs
and 1 µg of E. coli tRNA with 0.02 U of enzyme for 5 min at room temperature. Magnesium
or streptomycin concentrations were adjusted with MgCl2 stock solutions (0–50 mM) or
streptomycin sulfate stock solutions (0–2.5 mM), respectively. Digestion reactions were
stopped by adding 1 mM aurin tricarboxylic acid and an equal volume of buffered formamide. Digestion products were run on a 16% polyacrylamide sequencing gel at 55W
for 45 min. An autoradiograph was developed, and the intensities of RNase T1 cuts were
quantified using the ImageJ software. The intensity of each band was normalized for
the total intensity of the lane. The relative streptomycin cleavage versus streptomycin
concentration curves was fitted to the binding isotherm as described in the previous section
to obtain Kd for individual interaction.
4.4. Growth Inhibition Assays
The RsuA knock-out (∆RsuA) strain and the respective wildtype strain of E. coli (E. coli
K-12 BW25113: rrnB3 ∆lacZ4787 hsdR514 ∆(araBAD)567 ∆(rhaBAD)568 rph-1) were treated
with varying concentrations of streptomycin ranging from 0–200 µg/mL in LB medium.
They were grown at 37 ◦ C for 18 h, and absorbance (at 600 nm) was measured with a
SpectraMax4 spectrometer. The data were obtained in independent biological triplicates,
and the average of the absorbance values (normalized) was plotted against (streptomycin)
(µg/mL) and fitted with Origin to Equation 1 to determine the IC50 value.
4.5. Generation of Recombinant pCA24N Vector Encoding Catalytically Inactive RsuA
The recombinant pCA24N vector containing the coding sequence for wildtype RsuA
was purchased from the ASKA collection [50]. The RsuA functional mutant (D102N) was
generated by site-directed mutagenesis using a Q5 site-directed mutagenesis kit purchased
from NEB. The forward primer (50 -GGGGCGGTTGAATATTGATACC-30 ) and reverse
primer (50 -GCCGCATGCAGT TTCCAC-30 ) were purchased from IDT. The mutated plasmids were transformed first into E. coli DH5α competent cells. Plasmid DNA was extracted
using QIAprep Spin Miniprep Kit (Qiagen Inc., USA) following the manufacturer-suggested
protocol. The mutation was confirmed by DNA sequencing (Eurofins Genomics Inc., USA).
4.6. Generation of ∆RsuA + wtRsuA and ∆RsuA + mutRsuA E. coli Strains
Competent cells from the E. coli strain ∆RsuA were prepared and transformed with
recombinant pCA24N vector containing the coding sequence for wildtype RsuA (wtRsuA) and catalytically inactive RsuA (mutRsuA) to generate the ∆RsuA + wtRsuA, and
∆RsuA + mutRsuA E. coli strains, respectively. Transformed cells were then plated on LB
agar plates containing 50 µg/mL chloramphenicol and incubated overnight at 37 ◦ C. A
single colony from each plate was used to prepare a glycerol stock of ∆RsuA + wtRsuA
and ∆RsuA + mutRsuA E. coli. The expression of wtRsuA and mutRsuA (28 kDa) in E. coli
(∆RsuA) was confirmed by overexpression in the presence of 0–1 mM IPTG for 8 h followed
by SDS-PAGE. Protein bands in the SDS-PAGE gels were visualized using coomassie staining. The overexpressed RsuA band intensity increased with the IPTG concentration. These
gels were used to determine the IPTG concentration that gives the expected minimum
level of RsuA overexpression. The selected IPTG concentration was used to obtain the
growth curves for ∆RsuA + wtRsuA, and ∆RsuA + mutRsuA E. coli strains at varying
streptomycin concentrations.
Antibiotics 2023, 12, 1447
13 of 15
4.7. Growth Curve Analysis
The growth curves for wildtype (Wt), ∆RsuA, ∆RsuA + wtRsuA, and ∆RsuA + mutRsuA
E. coli strains were obtained at 4 selected streptomycin concentrations from 0, 6, 11, and
13.5 µg/mL. Bacterial cultures from respective E. coli strains were started as a 100X dilution
from an overnight culture in fresh LB media and were grown at 37 ◦ C. The absorbance
was recorded at different time intervals up to 24 h with a SpectraMax4 spectrometer.
All experiments were undertaken in triplicate, and the average absorbance values for
each streptomycin concentration were plotted against time (minutes). IPTG was added
to the growth medium to a final concentration of 50 µM to obtain growth curves of the
∆RsuA + wtRsuA and ∆RsuA + mutRsuA E. coli strains.
4.8. Non-Denaturing Gel Assays
Bacterial pellets were collected from wildtype (Wt) and ∆RsuA strains of bacteria in
the presence and absence of streptomycin (13.5 µg/mL) at 4, 8, 15, and 24 h of growth. Total
RNA was extracted from each bacterial pellet using TRIzol Max Bacterial RNA Isolation
Kit (Ambion Inc., Austin, TX, USA). Two DNA primers (50 -AGTCTGGACCGTGTCTC-30 ,
50 -GAA TTAAACTTCGTAATGAATTAC-30 ) that bind to mature 16S rRNA sequence and
17S leader sequence, respectively, were designed and purchased from Integrated DNA
Technologies Inc. The 32 P-labelled DNA primer (1.5 pmol) was mixed with total RNA
(2 µg) in HK buffer (80 mM HEPES, 330 mM KCl), and the primer was annealed to rRNA
by heating at 50 ◦ C for 1 h and slow-cooling to room temperature. The samples were
mixed with the non-denaturing loading buffer and run in an 8% non-denaturing PAGE
gel. The intensities of the gel bands after phosphorimaging were measured to obtain
the relative amounts of total rRNA (16S + 17S rRNA), 17S rRNA, and 16S rRNA (total
RNA—17S rRNA). The total RNA, 17S rRNA, and 16S rRNA were plotted to investigate
the influence of RsuA deletion for rRNA processing and, thereby, ribosome assembly.
5. Conclusions
Pseudouridylation enzyme RsuA and its activity can influence the growth of bacteria
under streptomycin stress. The ability of Ψ516 to influence 16S helix 18 folding and
streptomycin binding is partly the cause of streptomycin tolerance in bacteria. In addition,
RsuA may function as a ribosome assembly factor that plays a role during streptomycin
stress that is yet to be discovered.
Supplementary Materials: The following supporting information can be downloaded at: https://
www.mdpi.com/article/10.3390/antibiotics12091447/s1. Figure S1. RsuA expression level is visible
on SDS PAGE gels at 0.05 mM inducer; Figure S2. The growth of E. coli is not significantly affected
by the addition of IPTG; Figure S3. Promer hybridization assay to determine 17S/16S rRNA ratio;
Figure S4. RNase T1 footprinting of helix 18 model RNAs in the presence of streptomycin; Figure S5.
Streptomycin binding to helix 18 model RNAs with different affinities; Table S1: Growth rates of
E. coli strains at various streptomycin concentrations.
Author Contributions: Conceptualization, S.C.A.; methodology, S.M.A. and K.S.J.; validation, S.C.A.,
S.M.A. and K.S.J.; formal analysis, S.M.A. and K.S.J.; investigation, S.M.A., K.S.J., J.X. and R.M.R.;
resources, S.C.A.; data curation, S.C.A.; writing—original draft preparation, S.M.A. and K.S.J.;
writing—review and editing, S.C.A., S.M.A. and K.S.J.; visualization, S.C.A., S.M.A. and K.S.J.;
supervision, S.C.A.; project administration, S.C.A.; funding acquisition, S.C.A. All authors have read
and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Data is available upon request.
Acknowledgments: We would like to thank Robert Twieg for sharing his spectropolarimeter with us.
Antibiotics 2023, 12, 1447
14 of 15
Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design
of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or
in the decision to publish the results.
References
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
Chow, C.S.; Lamichhane, T.N.; Mahto, S.K. Expanding the Nucleotide Repertoire of the Ribosome with Post-Transcriptional
Modifications. ACS Chem. Biol. 2007, 2, 610–619. [CrossRef] [PubMed]
Helm, M. Post-transcriptional nucleotide modification and alternative folding of RNA. Nucleic Acids Res. 2006, 34, 721–733.
[CrossRef] [PubMed]
Douthwaite, S.; Fourmy, D.; Yoshizawa, S. Fine-Tuning of RNA Functions by Modification and Editing, 2nd ed.; Grosjean, H., Ed.;
Springer: Berlin/Heidelberg, Germany, 2005; Volume 12, pp. 285–307.
Lázaro, E.; Rodriguez-Fonseca, C.; Porse, B.; Ureña, D.; Garrett, R.A.; Ballesta, J.P.G. A Sparsomycin-resistant Mutant of
Halobacterium salinarium Lacks a Modification at Nucleotide U2603 in the Peptidyl Transferase Centre of 23 S rRNA. J. Mol. Biol.
1996, 261, 231–238. [CrossRef] [PubMed]
Toh, S.M.; Mankin, A.S. An indigenous post-transcriptional modification in the ribosomal peptidyl transferase center confers
resistance to an array of protein synthesis inhibitors. J. Mol. Biol. 2008, 380, 593–597. [CrossRef] [PubMed]
Boehringer, D.; O’Farrell, H.C.; Rife, J.P.; Ban, N. Structural insights into methyltransferase KsgA function in 30S ribosomal
subunit biogenesis. J. Biol. Chem. 2012, 287, 10453–10459. [CrossRef]
Okamoto, S.; Tamaru, A.; Nakajima, C.; Nishimura, K.; Tanaka, Y.; Tokuyama, S.; Suzuki, Y.; Ochi, K. Loss of a conserved
7-methylguanosine modification in 16S rRNA confers low-level streptomycin resistance in bacteria. Mol. Microbiol. 2007, 63,
1096–1106. [CrossRef]
Helser, T.L.; Davies, J.E.; Dahlberg, J.E. Mechanism of Kasugamycin Resistance in Escherichia coli. Nat. New Biol. 1972, 235, 6–9.
[CrossRef]
Wrzesinski, J.; Bakin, A.; Nurse, K.; Lane, B.G.; Ofengand, J. Purification, cloning, and properties of the 16S RNA pseudouridine
516 synthase from Escherichia coli. Biochemistry 1995, 34, 8904–8913. [CrossRef]
Conrad, J.; Niu, L.; Rudd, K.; Lane, B.G.; Ofengand, J. 16S ribosomal RNA pseudouridine synthase RsuA of Escherichia coli:
Deletion, mutation of the conserved Asp102 residue, and sequence comparison among all other pseudouridine synthases. RNA
1999, 5, 751–763. [CrossRef]
Bakin, A.; Kowalak, J.A.; McCloskey, J.A.; Ofengand, J. The single pseudouridine residue in Escherichia coli 16S RNA is located at
position 516. Nucleic Acids Res. 1994, 22, 3681–3684. [CrossRef]
Sivaraman, J.; Sauvé, V.; Larocque, R.; Stura, E.A.; Schrag, J.D.; Cygler, M.; Matte, A. Structure of the 16S rRNA pseudouridine
synthase RsuA bound to uracil and UMP. Nat. Struct. Mol. Biol. 2002, 9, 353–358. [CrossRef] [PubMed]
Jayalath, K.; Frisbie, S.; To, M.; Abeysirigunawardena, S. Pseudouridine Synthase RsuA Captures an Assembly Intermediate that
Is Stabilized by Ribosomal Protein S17. Biomolecules 2020, 10, 841. [CrossRef] [PubMed]
Amitai, S.; Kolodkin-Gal, I.; Hananya-Meltabashi, M.; Sacher, A.; Engelberg-Kulka, H. Escherichia coli MazF Leads to the
Simultaneous Selective Synthesis of Both “Death Proteins” and “Survival Proteins”. PLoS Genet. 2009, 5, e1000390. [CrossRef]
[PubMed]
Funatsu, G.; Wittmann, H.G. Ribosomal proteins. 33. Location of amino-acid replacements in protein S12 isolated from
Escherichia coli mutants resistant to streptomycin. J. Mol. Biol. 1972, 68, 547–550. [CrossRef] [PubMed]
Melancon, P.; Lemieux, C.; Brakier-Gingras, L. A mutation in the 530 loop of Escherichia coli 16S ribosomal RNA causes resistance
to streptomycin. Nucleic Acids Res. 1988, 16, 9631–9639. [CrossRef]
Frattali, A.L.; Flynn, M.K.; de Stasio, E.A.; Dah´lberg, A.E. Effects of mutagenesis of C912 in the streptomycin binding region of
Escherichia coli 16S ribosomal RNA. Biochim. Biophys. Acta 1990, 1050, 27–33. [CrossRef]
Pinard, R.; Payant, C.; Melançon, P.; Brakier-Gingras, L. The 5’ proximal helix of 16S rRNA is involved in the binding of
streptomycin to the ribosome. FASEB J. 1993, 7, 173–176. [CrossRef]
Nishimura, K.; Johansen, S.K.; Inaoka, T.; Hosaka, T.; Tokuyama, S.; Tahara, Y.; Okamoto, S.; Kawamura, F.; Douthwaite, S.;
Ochi, K. Identification of the RsmG Methyltransferase Target as 16S rRNA Nucleotide G527 and Characterization of Bacillus
subtilis rsmG Mutants. J. Bacteriol. 2007, 189, 6068–6073. [CrossRef]
Powers, T.; Noller, H.F. Selective perturbation of G530 of 16 S rRNA by translational miscoding agents and a streptomycindependence mutation in protein S12. J. Mol. Biol. 1994, 235, 156–172. [CrossRef]
Hall, K.B.; McLaughlin, L.W. Properties of pseudouridine N1 imino protons located in the major groove of an A-form RNA
duplex. Nucleic Acids Res. 1992, 20, 1883–1889. [CrossRef]
Arnez, J.G.; Steitz, T.A. Crystal Structure of Unmodified tRNAGln Complexed with Glutaminyl-tRNA Synthetase and ATP
Suggests a Possible Role for Pseudo-Uridines in Stabilization of RNA Structure. Biochemistry 1994, 33, 7560–7567. [CrossRef]
[PubMed]
Davis, D.R. Stabilization of RNA stacking by pseudouridine. Nucleic Acids Res. 1995, 23, 5020–5026. [CrossRef]
Abeysirigunawardena, S.C.; Chow, C.S. pH-dependent structural changes of helix 69 from Escherichia coli 23S ribosomal RNA.
RNA 2008, 14, 782–792. [CrossRef] [PubMed]
Antibiotics 2023, 12, 1447
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
46.
47.
48.
49.
50.
15 of 15
Sakakibara, Y.; Abeysirigunawardena, S.C.; Duc, A.-C.E.; Dremann, D.N.; Chow, C.S. Ligand- and pH-Induced Conformational
Changes of RNA Domain Helix 69 Revealed by 2-Aminopurine Fluorescence. Angew. Chem. Int. Ed. Engl. 2012, 51, 12095–12098.
[CrossRef] [PubMed]
Meroueh, M.; Grohar, P.J.; Qiu, J.; SantaLucia, J., Jr.; Scaringe, S.A.; Chow, C.S. Unique structural and stabilizing roles for
the individual pseudouridine residues in the 1920 region of Escherichia coli 23S rRNA. Nucleic Acids Res. 2000, 28, 2075–2083.
[CrossRef] [PubMed]
Wimberly, B.; Brodersen, D.; Clemons, W.; Morgan-Warren, R.; Carter, A.; Vonrhein, C.; Hartsch, T.; Ramakrishnan, V. Structure of
the 30S Ribosomal Subunit. Nature 2000, 407, 327–339. [CrossRef]
Adilakshmi, T.; Ramaswamy, P.; Woodson, S.A. Protein-independent Folding Pathway of the 16S rRNA 50 Domain. J. Mol. Biol.
2005, 351, 508–519. [CrossRef]
Woese, C.R.; Magrum, L.J.; Gupta, R.; Siegel, R.B.; Stahl, D.A.; Kop, J.; Crawford, N.; Brosius, J.; Gutell, R.; Hogan, J.J.; et al.
Secondary structure model for bacterial 16S ribosomal RNA: Phylogenetic, enzymatic, and chemical evidence. Nucleic Acids Res.
1980, 8, 2275–2293. [CrossRef]
Carter, A.P.; Clemons, W.M.; Brodersen, D.E.; Morgan-Warren, R.J.; Wimberly, B.T.; Ramakrishnan, V. Functional insights from the
structure of the 30S ribosomal subunit and its interactions with antibiotics. Nature 2000, 407, 340–348. [CrossRef]
Demirci, H.; Murphy, F.; Murphy, E.; Gregory, S.T.; Dahlberg, A.E.; Jogl, G. A structural basis for streptomycin-induced misreading
of the genetic code. Nat. Commun. 2013, 4, 1355. [CrossRef]
O’Connor, M.; Leppik, M.; Remme, J. Pseudouridine-Free Escherichia coli Ribosomes. J. Bacteriol. 2021, 200, e00540-17. [CrossRef]
Green, R.; Noller, H.F. In vitro complementation analysis localizes 23S rRNA post-transcriptional modifications that are required
for Escherichia coli 50S ribosomal subunit assembly and function. RNA 1996, 2, 1011–1021. [PubMed]
Tollervey, D.; Lehtonen, H.; Jansen, R.; Kern, H.; Hurt, E.C. Temperature-sensitive mutations demonstrate roles for yeast fibrillarin
in pre-rRNA processing, pre-rRNA methylation, and ribosome assembly. Cell 1993, 72, 443–457. [CrossRef] [PubMed]
Krzyzosiak, W.; Denman, R.; Nurse, K.; Hellmann, W.; Boublik, M.; Gehrke, C.W.; Agris, P.F.; Ofengand, J. In vitro synthesis of
16S ribosomal RNA containing single base changes and assembly into a functional 30S ribosome. Biochemistry 1987, 26, 2353–2364.
[CrossRef] [PubMed]
Baba, T.; Ara, T.; Hasegawa, M.; Takai, Y.; Okumura, Y.; Baba, M.; Datsenko, K.A.; Tomita, M.; Wanner, B.L.; Mori, H. Construction
of Escherichia coli K-12 in-frame, single-gene knock-out mutants: The Keio collection. Mol. Syst. Biol. 2006, 2, 2006.0008. [CrossRef]
Li, B.; Qiu, Y.; Shi, H.; Yin, H. The importance of lag time extension in determining bacterial resistance to antibiotics. Analyst 2016,
141, 3059–3067. [CrossRef] [PubMed]
Garvin, R.T.; Rosset, R.; Gorini, L. Ribosomal assembly influenced by growth in the presence of streptomycin. Proc. Natl. Acad.
Sci. USA 1973, 70, 2762–2766. [CrossRef] [PubMed]
Dai, X.; Zhu, M.; Warren, M.; Balakrishnan, R.; Patsalo, V.; Okano, H.; Williamson, J.R.; Fredrick, K.; Wang, Y.-P.; Hwa, T.
Reduction of translating ribosomes enables Escherichia coli to maintain elongation rates during slow growth. Nat. Microbiol. 2016,
2, 16231. [CrossRef]
Powers, T.; Noller, H.F. A functional pseudoknot in 16S ribosomal RNA. EMBO J. 1991, 10, 2203–2214. [CrossRef]
Ogle, J.M.; Ramakrishnan, V. Structural insights into translational fidelity. Annu. Rev. Biochem. 2005, 74, 129–177. [CrossRef]
GC, K.; Gyawali, P.; Balci, H.; Abeysirigunawardena, S. Ribosomal RNA Methyltransferase RsmC Moonlights as an RNA
Chaperone. ChemBioChem 2020, 21, 1885–1892. [CrossRef] [PubMed]
Meier, A.; Kirschner, P.; Bange, F.C.; Vogel, U.; Böttger, E.C. Genetic alterations in streptomycin-resistant Mycobacterium
tuberculosis: Mapping of mutations conferring resistance. Antimicrob. Agents Chemother. 1994, 38, 228–233. [CrossRef] [PubMed]
Gregory, S.T.; Cate, J.H.D.; Dahlberg, A.E. Streptomycin-resistant and streptomycin-dependent mutants of the extreme thermophile Thermus thermophilus. J. Mol. Biol. 2001, 309, 333–338. [CrossRef] [PubMed]
Gregory, S.T.; Carr, J.F.; Dahlberg, A.E. A Mutation in the Decoding Center of Thermus thermophilus 16S rRNA Suggests a Novel
Mechanism of Streptomycin Resistance. J. Bacteriol. 2005, 187, 2200–2202. [CrossRef]
Abedeera, S.M.; Hawkins, C.M.; Abeysirigunawardena, S.C. RsmG forms stable complexes with premature small subunit rRNA
during bacterial ribosome biogenesis. RSC Adv. 2020, 10, 22361–22369. [CrossRef]
Byrgazov, K.; Vesper, O.; Moll, I. Ribosome heterogeneity: Another level of complexity in bacterial translation regulation. Curr.
Opin. Microbiol. 2013, 16, 133–139. [CrossRef]
Vesper, O.; Amitai, S.; Belitsky, M.; Byrgazov, K.; Kaberdina, A.C.; Engelberg-Kulka, H.; Moll, I. Selective translation of leaderless
mRNAs by specialized ribosomes generated by MazF in Escherichia coli. Cell 2011, 147, 147–157. [CrossRef]
Kaberdina, A.; Szaflarski, W.; Nierhaus, K.; Moll, I. An Unexpected Type of Ribosomes Induced by Kasugamycin: A Look into
Ancestral Times of Protein Synthesis? Mol. Cell 2009, 33, 227–236. [CrossRef]
Kitagawa, M.; Ara, T.; Arifuzzaman, M.; Ioka-Nakamichi, T.; Inamoto, E.; Toyonaga, H.; Mori, H. Complete set of ORF clones of
Escherichia coli ASKA library (A Complete Set of E. coli K-12 ORF Archive): Unique Resources for Biological Research. DNA Res.
2005, 12, 291–299. [CrossRef]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual
author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to
people or property resulting from any ideas, methods, instructions or products referred to in the content.
International Journal of
Molecular Sciences
Article
Exploring the Impact of Head Group Modifications on the
Anticancer Activities of Fatty-Acid-like Platinum(IV) Prodrugs:
A Structure–Activity Relationship Study
Man Kshetri 1 , Wjdan Jogadi 1 , Suha Alqarni 1,2 , Payel Datta 1,3 , May Cheline 1 , Arpit Sharma 1 , Tyler Betters 1 ,
Deonya Broyles 1 and Yao-Rong Zheng 1, *
1
2
3
*
Citation: Kshetri, M.; Jogadi, W.;
Alqarni, S.; Datta, P.; Cheline, M.;
Sharma, A.; Betters, T.; Broyles, D.;
Zheng, Y.-R. Exploring the Impact of
Head Group Modifications on the
Anticancer Activities of
Fatty-Acid-like Platinum(IV)
Prodrugs: A Structure–Activity
Relationship Study. Int. J. Mol. Sci.
Department of Chemistry and Biochemistry, Kent State University, 236 Integrated Sciences Building,
Kent, OH 44242, USA; salqarn3@kent.edu (S.A.); pdatta1@kent.edu (P.D.); mcheline@kent.edu (M.C.)
Department of Chemistry, University of Bisha, Bisha 67714, Saudi Arabia
Department of Chemistry, Case Western Reserve University, Cleveland, OH 44106, USA
Correspondence: yzheng7@kent.edu; Tel.: +1-330-672-2267
Abstract: We conducted the first comprehensive investigation on the impact of head group modifications on the anticancer activities of fatty-acid-like Pt(IV) prodrugs (FALPs), which are a class of
platinum-based metallodrugs that target mitochondria. We created a small library of FALPs (1–9)
with diverse head group modifications. The outcomes of our study demonstrate that hydrophilic
modifications exclusively enhance the potency of these metallodrugs, whereas hydrophobic modifications significantly decrease their cytotoxicity. To further understand this interesting structure–activity
relationship, we chose two representative FALPs (compounds 2 and 7) as model compounds: one
(2) with a hydrophilic polyethylene glycol (PEG) head group, and the other (7) with a hydrophobic hydrocarbon modification of the same molecular weight. Using these FALPs, we conducted a
targeted investigation on the mechanism of action. Our study revealed that compound 2, with hydrophilic modifications, exhibited remarkable penetration into cancer cells and mitochondria, leading
to subsequent mitochondrial and DNA damage, and effectively eradicating cancer cells. In contrast,
compound 7, with hydrophobic modifications, displayed a significantly lower uptake and weaker
cellular responses. The collective results present a different perspective, indicating that increased
hydrophobicity may not necessarily enhance cellular uptake as is conventionally believed. These
findings provide valuable new insights into the fundamental principles of developing metallodrugs.
Keywords: platinum(IV) prodrugs; structure–activity relationship; anticancer
2023, 24, 13301. https://doi.org/
10.3390/ijms241713301
Academic Editors: Manuel Aureliano,
´
Juan Llopis and Agnieszka Scibior
Received: 30 July 2023
Revised: 18 August 2023
Accepted: 23 August 2023
Published: 27 August 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
1. Introduction
Platinum-based chemotherapy has been a cornerstone of cancer treatment for several
decades [1,2]. Key agents such as cisplatin, carboplatin, and oxaliplatin have assumed
critical roles in the management of diverse malignancies, encompassing testicular, ovarian,
lung, head and neck, and colorectal cancers. These chemotherapeutic agents exert their
anticancer effects by instigating the formation of DNA cross-links, which, in turn, impede
cancer cell proliferation and elicit apoptosis [1,3,4]. Nonetheless, despite their widespread
application, the clinical utilization of platinum-based drugs is encumbered by notable
toxicity concerns, culminating in adverse effects such as nephrotoxicity, neurotoxicity, and
ototoxicity [1,2]. Furthermore, the regrettably common development of drug resistance and
the subsequent cancer relapse in patients underline an imperious necessity to explore novel
approaches to platinum-based chemotherapy [5–7]. The pursuit of such innovative strategies is envisaged to surmount the prevailing limitations and offer improved therapeutic
outcomes for cancer patients [8–35].
Fatty-acid-like Pt(IV) prodrugs (FALPs) have emerged as a promising new class of Ptbased anticancer agents that utilize innovative drug delivery strategies and cancer biology
Int. J. Mol. Sci. 2023, 24, 13301. https://doi.org/10.3390/ijms241713301
https://www.mdpi.com/journal/ijms
Int. J. Mol. Sci. 2023, 24, 13301
of such innovative strategies is envisaged to surmount the prevailing limitations and offer
improved therapeutic outcomes for cancer patients [8–35].
Fatty-acid-like Pt(IV) prodrugs (FALPs) have emerged as a promising new class of
Pt-based anticancer agents that utilize innovative drug delivery strategies and cancer
2 of 13biology to overcome the challenges associated with conventional Pt(II) drugs [8,36–41]. Designed to mimic the structure of fatty acids, these prodrugs utilize non-covalent interactions
with human
serum albumin
(HSA)
efficient drug
delivery
[36]. FALPs
have
to overcome
the challenges
associated
with for
conventional
Pt(II) drugs
[8,36–41].
Designed
demonstrated
remarkable
stability
in
whole
human
blood,
reducing
their
rate
of
reduction
to mimic the structure of fatty acids, these prodrugs utilize non-covalent interactions with
via
reducing
agents.
Furthermore,
they possess
a distinctive
action that inhuman
serum
albumin
(HSA) for efficient
drug delivery
[36].mechanism
FALPs haveof
demonstrated
remarkable
stabilityin
inmitochondria,
whole human inducing
blood, reducing
their rate
of reduction
viarelease
reduc- of
volves
accumulation
mitochondrial
damage
with the
ing
agents.
Furthermore,
they
possess
a
distinctive
mechanism
of
action
that
involves
Pt(II) payloads, and resulting in increased proapoptotic peroxidase activity and elevated
accumulation
mitochondria,
mitochondrial
the release
of activity
Pt(II)
reactive
oxygeninspecies
(ROS) inducing
levels [39].
FALPs havedamage
shownwith
potent
in vitro
payloads,
and resulting
increased
proapoptotic
peroxidase
activity
and elevated
reactive
against
a broad
range ofincancer
types
and promising
in vivo
efficacy
in various
mouse
oxygen
species
(ROS)
levels
[39].
FALPs
have
shown
potent
in
vitro
activity
against
a
models [42]. Importantly, FALPs can be readily chemically modified to alter their biologbroad
range
of
cancer
types
and
promising
in
vivo
efficacy
in
various
mouse
models
[42].
ical activities and chemical properties [37,41,43–46]. Recent studies have also demonImportantly, FALPs can be readily chemically modified to alter their biological activities
strated the potential of incorporating these novel Pt(IV) prodrugs into nanoparticles for
and chemical properties [37,41,43–46]. Recent studies have also demonstrated the potential
drug delivery using either non-covalent encapsulation or covalent conjugation based on
of incorporating these novel Pt(IV) prodrugs into nanoparticles for drug delivery using
their
amphiphilic
structures
[37]. Overall,
FALPs
represent
a highly
diverse
and unique
either
non-covalent
encapsulation
or covalent
conjugation
based
on their
amphiphilic
struc-Pt
scaffold
with
promising
mechanisms
of action
that could
servePt
asscaffold
powerful
tools
in develtures [37].
Overall,
FALPs
represent a highly
diverse
and unique
with
promising
oping
new
approaches
for
cancer
therapy.
Although
the
modification
of
FALPs
hasfor
premechanisms of action that could serve as powerful tools in developing new approaches
dominantly
centered
around
their
carboxylic
head
group,
there
has
not
been
a
comprecancer therapy. Although the modification of FALPs has predominantly centered around
hensive
exploration
ofgroup,
the effects
modifying
groups on exploration
cellular responses.
their carboxylic
head
thereof
has
not been athese
comprehensive
of the effects
This new study
focusesononcellular
exploring
the structure–activity relationship of FALP deof modifying
these groups
responses.
This
new
study focuses
onaexploring
the structure–activity
relationship
of FALP
rivatives
(1–9
in Figure
1A), with
specific emphasis
on how modifications
of the
carboxderivatives
(1–9
in
Figure
1A),
with
a
specific
emphasis
on
how
modifications
of
the
ylic head group’s hydrophobicity influence their anticancer activity and cellular responses
carboxylic
head
group’s
hydrophobicity
influence
their
anticancer
activity
and
cellular
(Figure 2A). Understanding the impact of hydrophobicity on the uptake of therapeutic
responsesis(Figure
Understanding
the factor
impactinofdrug
hydrophobicity
on[36,47–53].
the uptakeRemolecules
widely 2A).
recognized
as a crucial
development
of
therapeutic
molecules
is
widely
recognized
as
a
crucial
factor
inenhances
drug
markably, contrary to the widely accepted notion that increased hydrophobicity
development [36,47–53]. Remarkably, contrary to the widely accepted notion that inthe cellular uptake of therapeutic molecules, the primary findings of this study demoncreased hydrophobicity enhances the cellular uptake of therapeutic molecules, the primary
strate that FALPs with hydrophilic modifications exhibit exceptional penetration into canfindings of this study demonstrate that FALPs with hydrophilic modifications exhibit
cer cells and mitochondria. This, in turn, triggers a cascade of events, leading to substanexceptional penetration into cancer cells and mitochondria. This, in turn, triggers a castial
mitochondrial
and DNA
damage, and
effectively eradicating
cancer cells.
the other
cade
of events, leading
to substantial
mitochondrial
and DNA damage,
and On
effectively
hand,
increased
hydrophobicity
in the
modifications
unexpectedlyinhinders
cellular uperadicating
cancer
cells. On the other
hand,
increased hydrophobicity
the modifications
take
and
mitochondrial
accumulation,
resulting
in
weaker
cellular
responses
and
a lower
unexpectedly hinders cellular uptake and mitochondrial accumulation, resulting in weaker
incellular
vitro therapeutic
efficacy.
These
findings
provide
valuable
new
insights
into
the
fundaresponses and a lower in vitro therapeutic efficacy. These findings provide valuable
mental
principles
developing
metallodrugs.
new insights
intoof
the
fundamental
principles of developing metallodrugs.
Figure
1.1.Cytotoxicity
Pt(IV)prodrugs.
prodrugs.(A)
(A)Chemical
Chemical
structures
Figure
Cytotoxicityprofiles
profilesof
of the
the fatty-acid-like
fatty-acid-like Pt(IV)
structures
of of
thethe
50 values of the Pt compounds against human
Pt(IV)
prodrugs
(1–9)
and
cisplatin.
(B)
Table
of
IC
Pt(IV) prodrugs (1–9) and cisplatin. (B) Table of IC50 values of the Pt compounds against human
cancer
cells.
cancer
cells.
Int. J. Mol. Sci. 2023, 24, 13301
Int. J. Mol. Sci. 2023, 24, 13301
3 of 13
3 of 13
Figure
relationshipofoffatty-acid-like
fatty-acid-like
Pt(IV)
prodrugs.
Graphical
represenFigure 2.
2. Structure–activity
Structure–activity relationship
Pt(IV)
prodrugs.
(A)(A)
Graphical
representation
of
the
hydrophobicity
of
the
head
group
tuning
the
cytotoxicity
of
FALPs.
(B)
A
bar
graph
tation of the hydrophobicity of the head group tuning the cytotoxicity of FALPs. (B) A bar graph
valuesofofFALPs
FALPs
(2–8)
with
varying
levels
of hydrophobicity
in comparison
depicting
the IC
IC5050 values
depicting the
(2–8)
with
varying
levels
of hydrophobicity
in comparison
to un- to
unmodified
1 against
A2780cis
ovarian
cancer
(C) Correlation
of the
IC50 values
and
the calcumodified 1 against
A2780cis
ovarian
cancer
cells. cells.
(C) Correlation
of the IC
values
and
the
calculated
50
lated
P of
the groups
head groups
of FALPs
(2–8).
(D) curves
Killingofcurves
5 and 9A2780cis
against cells
A2780cis
Log PLog
of the
head
of FALPs
(2–8). (D)
Killing
5 and 9ofagainst
for 24cells
for
24
h.
(E)
Killing
curves
of
2
and
7
against
A2780cis
cells
for
24
h.
(F)
Live/dead
cell
assay
images
h. (E) Killing curves of 2 and 7 against A2780cis cells for 24 h. (F) Live/dead cell assay images of
of A2780cis cells treated with 2 and 7 ([Pt] = 1 µM) for 24 h. Scale bar = 100 µm.
A2780cis cells treated with 2 and 7 ([Pt] = 1 µM) for 24 h. Scale bar = 100 µm.
Results and
and Discussion
Discussion
2.2.Results
Synthesis and
with
various
head
groups.
TheThe
Synthesis
and characterization
characterizationofofFALP
FALPderivatives
derivatives
with
various
head
groups.
synthesis
of
the
Pt(IV)
prodrug
(2–8)
is
depicted
in
Figure
S1A
in
the
Supplementary
Matesynthesis of the Pt(IV) prodrug (2–8) is depicted in Figure S1A in the Supplementary Marials. Briefly,
moieties
with
different
hydrophobicities
(10–16)
were
conjugated
to to
terials.
Briefly,amino
amino
moieties
with
different
hydrophobicities
(10–16)
were
conjugated
compound
1
via
the
HATU-catalyzed
amide
bond
formation
reaction.
The
final
compounds
compound 1 via the HATU-catalyzed amide bond formation reaction. The final comwere purified
flash column
chromatography
and/or recrystallization.
The overall yieldsThe
pounds
were via
purified
via flash
column chromatography
and/or recrystallization.
were 44–78%. The synthesis of the Pt(IV) prodrug (9) was accomplished in a similar manner,
overall yields were 44–78%. The synthesis of the Pt(IV) prodrug (9) was accomplished in
as shown in Figure S1B. The conjugated Pt(IV) compounds (2–9) were characterized via
a1 similar13manner, as shown in Figure S1B. The conjugated Pt(IV) compounds (2–9) were
H and C NMR 1spectroscopy,
electrospray ionization mass spectrometry (ESI-MS), and
characterized
via H and 13C NMR spectroscopy, electrospray ionization mass spectromeHPLC, and they can be found in the Supplementary Materials (Figures S2–S9). In the 1 H
try
(ESI-MS),
HPLC,signal
and they
can
be found
in the to
Supplementary
Materials
(Figures
NMR
spectra,and
the broad
at ~6.6
ppm
corresponds
the amine groups
of the Pt(IV)
1
S2–S9).
In the
H NMR
theCH
broad
signal at ~6.6 ppm corresponds to the amine
center. The
signal
at ~2.8spectra,
ppm is the
2 group adjacent to the carbamate. The signals at
groups
of theare
Pt(IV)
center.
signal at
is thethe
CHisotopically
2 group adjacent
to signals
the carba6.8–8.6 ppm
attributed
to The
the amides
in ~2.8
2–8. ppm
In ESI-MS,
resolved
mate. The signals at 6.8–8.6 ppm are attributed to the amides in 2–8. In ESI-MS, the isotopically resolved signals agree with the theoretical value of 2–9. The HPLC analysis of
Int. J. Mol. Sci. 2023, 24, 13301
4 of 13
agree with the theoretical value of 2–9. The HPLC analysis of the final product indicated
that the purity of compounds 2–9 from the described synthetic method was >95%.
Cytotoxicity profiles of FALP derivatives with various head groups. The in vitro anticancer activity of the Pt(IV) prodrugs (1–9) was assessed using the 3-(4,5-dimethylthiazol2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. This study utilized two human cancer
cell lines, namely A2780cis and MDA-MB-231. A2780cis is an ovarian cancer cell line
known for its resistance to conventional platinum chemotherapy, making it a formidable
challenge to treat. On the other hand, MDA-MB-231 represents a triple-negative breast
cancer cell line, which is currently recognized as one of the difficult-to-treat cancer types.
The cells were treated with 1–9 or cisplatin for 24 h, and the cell viability was evaluated.
The IC50 values, which represent the concentration of the drug required to inhibit the
growth of cells by 50%, are reported in the table in Figure 1B. The results show that 2–5
have lower IC50 values compared to those of 1 and have lower cisplatin in general. For
example, in the A2780cis ovarian cancer cell line, the IC50 (2) = 0.30 ± 0.06 µM is 36 times
lower than that of cisplatin (IC50 = 109.2 ± 13.5 µM) and 3 times lower than that of 1
(IC50 = 1.00 ± 0.06 µM). Notably, head group modifications do not always increase cytotoxicity more than FALP-1. For example, 7 and 8 exhibit much lower cytotoxicity than
1. Overall, in the A2780cis ovarian cancer cell line, the IC50 (7) = 3.75 ± 0.47 µM is
three times higher than that of 1, but it is still more potent than cisplatin. Notably, the
IC50 (8) = 5.07 ± 0.71 µM is 15 times higher than that of 2. Overall, the head group modifications of FALPs result in an alternation of therapeutic effects, which is a promising way to
fine-tune the anticancer activity of this class of metallodrugs.
Structure–activity relationship of FALP derivatives with head groups of different
hydrophobicity. Our goal was to study the relationship between the structure and activity
to gain a better understanding of how modifications to the head group affect the anticancer
properties of FALP derivatives. We hypothesized that the hydrophobicity of these modifications is a key factor in determining the cytotoxicity of the compounds (Figure 2A). To test
this hypothesis, we calculated the Log P values for all the modifications with the ALOGPS
2.1 program, which ranged from −0.28 to 4.22, as shown in Figure 1B. Compounds 2–5
had head group modifications with low hydrophobicity (or high hydrophilicity), while
compounds 7 and 8 had head groups with high hydrophobicity (or low hydrophilicity).
Our results, presented in Figure 2B, indicate that hydrophilic modifications lead to lower
IC50 values and a higher potency of FALPs, while hydrophobic modifications result in
increased IC50 values and reduced anticancer activity. To better illustrate the correlation
between the hydrophobicity of the head group modifications, we plotted the calculated Log
P values against the corresponding IC50 values in Figure 2C, which clearly demonstrates
the inverse impact of hydrophobicity on the anticancer activities of FALPs in general. Additionally, we sought to determine if this observation was solely based on hydrophobicity,
so we engineered two isomers, 5 and 9. The head group modification of compound 9
was changed from amide to carbamate compared to compound 5, and interestingly, the
cytotoxicity profiles of both 5 and 9 were identical, as shown in Figures 1B and 2D. These
results suggest that the hydrophobicity of the structure plays a major role in the structure–
activity relationship. Finally, we focused on the two FALPs, 2 and 7, to illustrate this effect.
Although both 2 and 7 were very similar compounds, 2 had a hydrophilic polyethylene
glycol (PEG) modification (Log P = −0.28), while compound 7 carried a C6 hydrocarbon
chain (Log P = 4.02) of the same molecular weight. As shown in Figure 2E, compound
2 exhibited a much higher potency than 7 in a wide range of concentrations tested, and
the IC50 (7) = 3.75 ± 0.47 µM was 12 times higher than that of 2. The live/dead cell imaging assays further validated this drastic difference in their in vitro anticancer activity, as
shown in Figure 2F, where compound 2 effectively eliminated all drug-resistant A2780cis
ovarian cancer cells, while compound 7 was deemed ineffective at the tested concentration
((Pt) = 1 µM). Overall, the combined evidence points out that the hydrophobicity of the
head group modifications dictate the anticancer activities of the FALPs.
Int. J. Mol. Sci. 2023, 24, 13301
Int. J. Mol. Sci. 2023, 24, 13301
5 of 13
5 of 13 groups
Cell entry and mitochondrial accumulation of FALP derivatives with head
of different hydrophobicities. Our next objective was to gain a more detailed understanding of how modifications to the head group of FALPs affect their anticancer activities
Cellhydrophobicity.
entry and mitochondrial
of FALP
derivativesas
with
head groups
through
Cellularaccumulation
uptake is widely
recognized
a critical
factor in the
of
different
hydrophobicities.
Our
next
objective
was
to
gain
a
more
detailed
understandactivity of metallodrugs. The influence of hydrophobicity on the cellular uptake of theraing of how modifications to the head group of FALPs affect their anticancer activities
peutic molecules has been widely acknowledged, with an increase in hydrophobicity typthrough hydrophobicity. Cellular uptake is widely recognized as a critical factor in the
ically
promoting cell entry. To this end, we conducted a graphite furnace atomic absorpactivity of metallodrugs. The influence of hydrophobicity on the cellular uptake of thertion
spectroscopic
(GFAAS)
analysis
of the cellular
uptake
for FALPs
with various head
apeutic molecules has
been widely
acknowledged,
with
an increase
in hydrophobicity
group
modifications,
including
andend,
7. The
P values
of compounds
and 7,abas detertypically
promoting cell
entry. To2this
we Log
conducted
a graphite
furnace 2
atomic
mined
viaspectroscopic
GFAAS, are(GFAAS)
1.97 and analysis
2.57, respectively.
Thisuptake
indicates
that compound
7 exhibits
sorption
of the cellular
for FALPs
with various
head group
modifications,
including
2 and2.7.Surprisingly,
The Log P values
of compounds
andindicate
greater
hydrophobicity
than
compound
our results
in Figure2 3A
7, asthe
determined
via modification
GFAAS, are 1.97
2.57,
respectively.
This indicates
thatto
comthat
hydrophilic
of 2and
(496.6
± 16.09
pmol Pt/million
cells) led
an uptake
pound
7
exhibits
greater
hydrophobicity
than
compound
2.
Surprisingly,
our
results
in pmol
of over eight times greater than the hydrophobic modifications of 7 (60.03 ± 8.01
Figure 3A indicate that the hydrophilic modification of 2 (496.6 ± 16.09 pmol Pt/million
Pt/million cells). We previously discovered that mitochondria play significant roles in the
cells) led to an uptake of over eight times greater than the hydrophobic modifications of
mechanism
of action of FALPs, so we further investigated how the hydrophobicity of the
7 (60.03 ± 8.01 pmol Pt/million cells). We previously discovered that mitochondria play
head
grouproles
modifications
affectsofthe
mitochondrial
of FALPs.
Asthe
shown in
significant
in the mechanism
action
of FALPs, soaccumulation
we further investigated
how
Figure
3B,
the
mitochondrial
Pt
content
of
2
(31.7
±
5.1
pmol
Pt/million
cells)
was
hydrophobicity of the head group modifications affects the mitochondrial accumulation three
times
higher
that
7 (11.72
± 2.76
pmol Pt/million
cells).
all FALPs
of FALPs.
Asthan
shown
in of
Figure
3B, the
mitochondrial
Pt content
of 2Nevertheless,
(31.7 ± 5.1 pmol
Pt/million cells)a was
threecellular
times higher
thanand
thatmitochondrial
of 7 (11.72 ± 2.76accumulation
pmol Pt/million
cells).
demonstrated
higher
uptake
than
cisplatin,
Nevertheless,
all
FALPs
demonstrated
a
higher
cellular
uptake
and
mitochondrial
accudespite using a higher Pt concentration in the cisplatin sample. In summary, the introducmulation
than cisplatin,head
despite
usingina FALPs
higher Pt
concentration
in the cisplatin
sample. In accution
of a hydrophilic
group
promotes
cell entry
and mitochondrial
summary, the introduction of a hydrophilic head group in FALPs promotes cell entry and
mulation.
mitochondrial accumulation.
Figure 3.
3. Cellular
(A)
and
mitochondrial
accumulation
(B) of FALPs
and 7)(2
and
cisplatin
Figure
Cellularuptake
uptake
(A)
and
mitochondrial
accumulation
(B) of(2FALPs
and
7) andincisplatin
A2780cis
cells
(24 (24
h). h).
in
A2780cis
cells
Cellular Responses of FALP derivatives with head groups of different hydrophoCellular
Responses
of FALP
of different
bicity.
Based on
the observation
thatderivatives
hydrophilicwith
headhead
groupgroups
modifications
lead tohydrophoinbicity.
on the
that hydrophilic
head
group
modifications
lead to increasedBased
intracellular
Pt observation
levels, we formulated
the hypothesis
that
such modifications
would
creased
intracellular
Pt levels,
the as
hypothesis
that suchMitoSOX
modifications
result in greater
mitochondrial
and we
DNAformulated
damage, as well
increased apoptosis.
would
resulttoinassess
greater
and DNA
damage,
as γH2AX
well as levels
increased
was utilized
themitochondrial
levels of mitochondrial
ROS,
while the
were apoptosis.
analyzed to was
determine
DNA
damage.
mitochondrial
and DNA
damage
arethe
known
to levels
MitoSOX
utilized
to assess
theAs
levels
of mitochondrial
ROS,
while
γH2AX
promote
apoptosis,
Annexin
V/PI
assays
were
also
conducted
to
examine
the
apoptotic
were analyzed to determine DNA damage. As mitochondrial and DNA damage are
effects. A
cytometric
analysis
was used
to evaluate
the mitochondrial
ROS,to
γH2AX,
known
to flow
promote
apoptosis,
Annexin
V/PI
assays were
also conducted
examine the
and apoptosis in the cancer cells treated with FALPs (2 and 7) in the experiments. As
apoptotic effects. A flow cytometric analysis was used to evaluate the mitochondrial ROS,
illustrated in Figure 1A, compound 2 has a hydrophilic head group, while compound 7 has
γH2AX,
and apoptosis in the cancer cells treated with FALPs (2 and 7) in the experiments.
a hydrophobic head group. According to the flow cytometric results in Figure 4A, treatment
As
illustrated
inh)
Figure
1A, compound
has
a hydrophilic
head
group,
whiletocompound
with
2 (1 µM, 24
significantly
increased 2
the
mitochondrial
ROS
levels
compared
the
7control
has a or
hydrophobic
head
group.
According
to
the
flow
cytometric
results
in24
Figure
4A,
7. Additionally, the treatment of cisplatin at a higher concentration (10 µM,
h)
treatment
2 (1 µM, change
24 h) significantly
increased
mitochondrial
ROS with
levels comresulted in with
an insignificant
in the mitochondrial
ROSthe
levels,
which is consistent
its mechanism
of action.
treatment of the
2 (0.25
µM, 24 h)
induced
damage
in
pared
to the control
or 7.The
Additionally,
treatment
ofalso
cisplatin
at aDNA
higher
concentration
the
treated
A2780cis
cells,
as
shown
in
Figure
4B.
Furthermore,
7
at
a
higher
concentration
(10 µM, 24 h) resulted in an insignificant change in the mitochondrial ROS levels, which
µM, 24 h) with
triggered
DNA damage,
but to The
a lesser
extent than
2. Likewise,
flowinduced
is(1consistent
its mechanism
of action.
treatment
of 2 (0.25
µM, 24 our
h) also
DNA damage in the treated A2780cis cells, as shown in Figure 4B. Furthermore, 7 at a
higher concentration (1 µM, 24 h) triggered DNA damage, but to a lesser extent than 2.
Int. J. Mol. Sci. 2023, 24, 13301
Int. J. Mol. Sci. 2023, 24, 13301
6 of 13
6 of 13
Likewise, our flow cytometric analysis of Annexin V/PI showed that a larger population
cytometric analysis of Annexin V/PI showed that a larger population of cells were in the
of cells were in the late stages of apoptosis (23.3% for 2) compared to the effect of 7, which
late stages of apoptosis (23.3% for 2) compared to the effect of 7, which induced only 3.58%
induced only 3.58% of cells to undergo the late stages of apoptosis (Figure 4C). Based on
of cells to undergo the late stages of apoptosis (Figure 4C). Based on the results, it can be
the
results,
canFALP
be inferred
that(2)the
FALP
derivativehead
(2) with
a hydrophilic
group
inferred
thatitthe
derivative
with
a hydrophilic
group
modification head
induces
modification
induces
mitochondrial
and
DNA
damage
as
well
as
apoptosis
more
effecmitochondrial and DNA damage as well as apoptosis more effectively than the one (7) with
tively
than the modification.
one (7) with a hydrophobic modification.
a hydrophobic
Figure 4.
4. Cellular
Cellular responses
treated
with
FALPs
andand
cisplatin.
(A) (A)
Flow
cytometric
Figure
responsesofofA2780cis
A2780ciscells
cells
treated
with
FALPs
cisplatin.
Flow
cytometric
analysis of
of MitoSOX
with
FALPs
(2 or
for 24
analysis
MitoSOXin
inthe
theA2780cis
A2780ciscells
cellstreated
treated
with
FALPs
(2 7)
or or
7) cisplatin
or cisplatin
forh.24(B)
h. Flow
(B) Flow
cytometric
analysis
of
γH2AX
in
the
A2780cis
cells
treated
with
FALPs
(2
or
7)
or
cisplatin
for
cytometric analysis of γH2AX in the A2780cis cells treated with FALPs (2 or 7) or cisplatin 24
forh.24 h.
(C)
Flow
cytometric
analysis
of
apoptosis
in
the
A2780cis
cells
treated
with
FALPs
(2
or
7)
or
cisplatin
(C)
cytometric analysis of apoptosis in the A2780cis cells treated with FALPs (2 or 7) or cisplafor for
48 h.
tin
48 h.
3. Materials and Methods
3. Materials and Methods
General information. All reagents were purchased from Strem, Aldrich, or Alfa
General information. All reagents were purchased from Strem, Aldrich, or Alfa and
and used without further purification. Compound 1 was synthesized according to the
used
without
purification.
Compound
1 was
synthesized
according
to the literaliterature
[36].further
All reactions
were carried
out under
normal
atmospheric
conditions.
A
ture
[36].
All
reactions
were
carried
out
under
normal
atmospheric
conditions.
A Bruker
1
Bruker 400 NMR was used for NMR data acquisition (frequency: 400 M Hz for H NMR;
13 C{1 H} 400
400
was13used
for NMR
data shifts
acquisition
(frequency:
M spectra
Hz for 1were
H NMR;
100 MHz
100 NMR
MHz for
C NMR).
Chemical
in 1 H and
NMR
internally
13
1
13
1
1
13
for
C NMR).
Chemical
shifts( in
H and DMSO
C{ H}atNMR
spectra
wereCinternally
referenced
referenced
to solvent
signals
H NMR:
δ = 2.50
ppm;
NMR: DMSO
at
13C NMR: DMSO at δ = 40.45 ppm).
to
signalsThe
(1Hhigh-resolution
NMR: DMSO at
δ = spectra
2.50 ppm;
δ =solvent
40.45 ppm).
mass
of created
ions were recorded on an
The high-resolution mass spectra of created ions were recorded on an Exactive Plus mass
spectrometer (Thermo Scientific, Bremen, Germany). Analytical HPLC was conducted on
an Agilent 1100 system using C18 reverse-phase columns (Hypersil GOLD; 100 mm × 3
Int. J. Mol. Sci. 2023, 24, 13301
7 of 13
Exactive Plus mass spectrometer (Thermo Scientific, Bremen, Germany). Analytical HPLC
was conducted on an Agilent 1100 system using C18 reverse-phase columns (Hypersil
GOLD; 100 mm × 3 mm; 5 µm). Graphite furnace atomic absorption spectroscopic (GFAAS)
measurements were taken on a PinAAcle 900Z spectrometer (PerkinElmer, Shelton, CT,
USA). Fluorescence spectra were taken on a FluoroMax-3 Fluorescence spectrophotometer
(Horiba, Japan) using the software called FluorEssence. Fluorescence images were acquired
using an IX70 (Olympus, Japan) inverted epifluorescence microscope equipped with a digital CCD camera (QImaging, Surrey, BC, Canada). Images were processed, and intensities
were quantified with ImageJ software v1.53t. Live/dead cell assay was carried out using
Invitrogen (Thermo Fisher Scientific) LIVE/DEADTM Cell Viability Kit (Cat. No. L3224).
Flow cytometry was carried out on a Accuri C6 flow cytometer (Becton, Dickinson and
Company Biosciences, Lakes, NJ, USA).
Synthesis of Compound 2. An amount of 1 mL of anhydrous DMF was added to
compound 1 (70 mg; 0.10 mmol) and HATU (46 mg; 0.12 mmol) in a vial under a stream
of Ar and stirred at r.t. for 15 min. 2-(2-aminoethoxy) ethanol (28 µL; 0.28 mmol) was
added to the mixture. After 20 min of stirring at r.t., DIPEA (70 µL; 0.41 mmol) was
added. The reaction mixture was stirred in the dark at r.t. overnight, centrifuged, and
the supernatant was added into 3 mL of brine. Then, the precipitation was collected
via centrifugation, washed with water, and lyophilized overnight. Lyophilized product
was dissolved in small amount of MeOH and purified with flash chromatography. Yield:
53 mg (67%). 1 HNMR (400 MHz, DMSO-d6 ): δ: 0.856 (NHCH2(CH2)14CH3, t, 3H), 1.23
(NHCH2(CH2)14CH3, m, 28H), 2.36 (CO(CH2)2CO, m, 4H), 2.88 (NHCH2(CH2)14CH3, q,
2H), 3.19 (NHCH2CH2O, q, 2H), 3.40 (CH2CH2OCH2CH2, m, 4H), 3.49 (OCH2CH2OH, q,
2H), 6.52 (NHCH2(CH2)14CH3, t, 1H), 6.62 (NH3, m, 6H), 7.90 (CONH(CH2)2O, t, 1H);
13 C NMR(100 MHz, DMSO-d ): δ: 180.4, 172.0, 164.4, 72.6, 69.5, 60.6, 41.4, 40.0, 31.8, 30.3,
6
29.5, 29.4, 29.2, 27.0, 22.6, 14.4; HR-MS (positive mode) for [C25 H54 Cl2 N4 O7 Pt]+ : m/z calc:
787.3088, obsd: 787.3064. Purity: 99% determined via HPLC.
Synthesis of Compound 3. An amount of 1 mL of anhydrous DMF was added to
compound 1 (70 mg; 0.10 mmol) and HATU (46 mg; 0.12 mmol) in a vial under a stream of
Ar and stirred at r.t. for 15 min. 2-methoxyethylamine (24 µL; 0.28 mmol) was added to the
mixture. After 20 min of stirring at r.t., DIPEA (70 µL; 0.41 mmol) was added. The reaction
mixture was stirred in the dark at r.t. overnight, centrifuged, and the supernatant was
added into 3 mL of brine. Then, the precipitation was collected via centrifugation, washed
with water, and lyophilized overnight. Lyophilized product was dissolved in small amount
of MeOH and purified with flash chromatography. Yield: 59 mg (78%). 1 H NMR (400 MHz,
DMSO-d6): δ: 0.836 (NHCH2(CH2)14CH3, t, 3H); 1.23 (NHCH2(CH2)14CH3, m, 28H);
2.26 (CO(CH2)2CO, m, 4H), 2.85 (NHCH2(CH2)14CH3, q, 2H); 3.16 (NHCH2CH2O, 2H);
3.34 (CH2CH2OCH3, m, 4H); 3.23 (CH2CH2OCH3, s, 3H); 6.40 (NHCH2(CH2)14CH3 and
NH3, m, 7H); 7.84 (CONH, 1H); 13C NMR (400MHz, DMSO-d6 ): δ: 180.45,171.98, 164.49,
71.09, 58.33, 31.94, 31.73, 30.30, 29.48, 29.12, 26.92, 22.51, 14.36; HR-MS (positive mode)
for [C24 H52 Cl2 N4 O6 PtH]+: m/z calc: 758.2986, obsd: 758.2985. Purity: 95% determined
via HPLC.
Synthesis of Compound 4. An amount of 1 mL of anhydrous DMF was added to
compound 1 (70 mg; 0.10 mmol) and HATU (46 mg; 0.12 mmol) in a vial under a stream
of Ar and stirred at r.t. for 15 min. To the mixture, 0.5 mL anhydrous DMF solution of
aminoacetonitrile bisulfate (43 mg; 0.28 mmol) was added. After 20 min of stirring at
r.t., DIPEA (70 µL; 0.41 mmol) was added. The reaction mixture was stirred in the dark
at r.t. overnight, centrifuged, and the supernatant was added into 3 mL of brine. Then,
the precipitation was collected via centrifugation, washed with water, and lyophilized
overnight to afford yellowish-white-colored solid. Yield: 47 mg (65%). 1 H NMR (400 MHz,
DMSO-d6 ): δ: 0.856 (NHCH2(CH2)14CH3, t, 3H), 1.23 (NHCH2(CH2)14CH3, m, 28H), 2.42
(CO(CH2)2CO, m, 4H), 2.87 (NHCH2(CH2)14CH3, q, 2H), 4.10 (NHCH2CN, d, 2H), 6.51
(NHCH2(CH2)14CH3, t, 1H), 6.66 (NH3, m, 6H), 8.68 (CONHCH2CN, t, 1H); 13 C NMR
(100 MHz, DMSO-d6 ): δ: 180.2, 172.5, 170.5, 118.2, 60.8, 41.1, 31.8, 30.3, 29.5, 29.4, 29.2,
Int. J. Mol. Sci. 2023, 24, 13301
8 of 13
26.9, 22.6, 14.4; HR-MS (positive mode) for [C23 H47 Cl2 N5 O5 Pt]+ : m/z calc: 739.2677, obsd:
739.2673. Purity: 95% determined via HPLC.
Synthesis of Compound 5. An amount of 1 mL of anhydrous DMF was added to
compound 1 (70 mg; 0.1 mmol) and HATU (46 mg; 0.12 mmol) in a vial under a stream
of Ar and stirred at r.t. for 15 min. To the mixture, 0.5 mL anhydrous DMF solution
of glycine ethyl ester hydrochloride (39 mg; 0.28 mmol) was added. After 20 min of
stirring at r.t., DIPEA (70 µL; 0.41 mmol) was added. The reaction mixture was stirred
in the dark at r.t. overnight, centrifuged, and the supernatant was added into 3 mL of
brine. Then, the precipitation was collected via centrifugation, washed with water, and
lyophilized overnight to collect yellowish-white-colored solid. Yield: 47 mg (60%). 1 HNMR
(400 MHz, DMSO-d6 ): δ: 0.853 (NHCH2(CH2)14CH3, t, 3H, J = 6.9 Hz), 1.15 (COOCH2CH3,
t, 3H, J = 7.1 Hz), 1.23 (NHCH2(CH2)14CH3, m, 28H), 2.39 (CO(CH2)2CO, m, 4H), 2.87
(NHCH2(CH2)14CH3, q, 2H), 3.792 (NHCH2COOCH2, d, 2H), 4.08 (COOCH2CH3, q, 2H),
6.50 (NHCH2(CH2)14CH3, t, 1H), 6.69 (NH3, m, 6H), 8.37 (CONHCH2COOCH2, t, 1H);
13 C NMR(100 MHz, DMSO-d ): δ: 180.2, 172.4, 170.4, 60.8, 41.1, 31.6 30.3, 29.5, 29.4, 29.2,
6
26.9, 22.6, 14.6, 14.4; HR-MS (positive mode) for [C25 H52 Cl2 N4 O7 Pt]+ : m/z calc: 786.2936,
obsd: 786.2933. Purity: 98% determined via HPLC.
Synthesis of Compound 6. An amount of 1 mL of anhydrous DMF was added
to compound 1 (70 mg; 0.1 mmol) and HATU (46 mg; 0.12 mmol) in a vial under a
stream of Ar and stirred at r.t. for 15 min. To the mixture, 0.5 mL anhydrous DMF
solution of propylamine (23 µL; 0.28 mmol) was added. After 20 min of stirring at r.t.,
DIPEA (70 µL; 0.41 mmol) was added. The reaction mixture was stirred in the dark at
r.t. overnight, centrifuged, and the supernatant was added into 3 mL of brine. Then,
the precipitation was collected via centrifugation, washed with water, and lyophilized
overnight to collect yellowish-white-colored solid. Lyophilized product was dissolved
in small amount of MeOH and purified with flash chromatography. Yield: 53 mg (72%).
1 H NMR (400 MHz, DMSO-d ): δ: 0.814 (NHCH2(CH2)14CH3 and NHCH2CH2CH3 m,
6
6H); 1.23 (NHCH2(CH2)14CH3 and NHCH2CH2CH3 m, 28H); 2.25 (CO(CH2)2CO, m, 4H,
J = 7.1, 42.4 Hz), 2.87 (NHCH2(CH2)14CH3, q, 2H); 6.34 (NH and NH3, 7H); 7.73 (NH,
s, 1H); 13 C NMR (100 MHz, DMSO-d6 ): δ: 180.54,171.68, 164.46, 32.03, 31.73, 30.30, 29.49,
29.44, 29.35, 29.12, 26.93, 22.80, 14.37; HR-MS (positive mode) for [C24 H52 Cl2 N4 O5 PtH]+ :
m/z calc: 742.3037, obsd: 742.3034. Purity: 95% determined via HPLC.
Synthesis of Compound 7. An amount of 1 mL of anhydrous DMF was added to
compound 1 (70 mg; 0.1 mmol) and HATU (46 mg; 0.12 mmol) in a vial under a stream of Ar
and stirred at r.t. for 15 min. To the mixture, 0.5 mL anhydrous DMF solution of hexylamine
(37 µL; 0.28 mmol) was added. After 20 min of stirring at r.t., DIPEA (70 µL; 0.41 mmol)
was added. The reaction mixture was stirred in the dark at r.t. overnight, centrifuged,
and the supernatant was added into 3 mL of brine. Then, the precipitation was collected
via centrifugation, washed with water, and lyophilized overnight to collect yellowishwhite-colored solid. Lyophilized product was dissolved in small amount of MeOH and
purified with flash chromatography. Yield: 54 mg (69%). 1 H NMR (400 MHz, DMSO-d6 ):
δ: 0.83 (NHCH2(CH2)14CH3 and NHCH2(CH2)4CH3, 6H); 1.23 (NHCH2(CH2)14CH3
and NHCH2(CH2)4CH3, 36H); 2.24 (CO(CH2)2CO, m, 4H), 2.87 (NHCH2(CH2)14CH3,
q, 2H); 2.97 (NHCH2(CH2)4CH3, 4H); 6.48 (NH and NH3, 7H); 7.79 (NH, 1H); 13 C NMR
(100 MHz, DMSO-d6 ): δ: 180.47,171.60, 164.41, 31.99, 31.75, 31.46, 29.51, 29.16, 26.94, 26.57,
22.55, 22.52, 14.42; HR-MS (positive mode) for [C27 H58 Cl2 N4 O5 PtH]+ : m/z calc: 784.3507,
obsd: 784.3504. Purity: 95% determined via HPLC.
Synthesis of Compound 8. An amount of 1 mL of anhydrous DMF was added to
compound 1 (70 mg; 0.1 mmol) and HATU (46 mg; 0.12 mmol) in a vial under a stream
of Ar and stirred at r.t. for 15 min to obtain pale-yellow-colored solution. To the mixture,
0.5 mL anhydrous DMF solution of 1-adamantylamine (42 mg; 0.28 mmol) was added. After
20 min of stirring at R.T., DIPEA (70 µL; 0.41 mmol) was added. The reaction mixture was
stirred in the dark at r.t. overnight. The solution turned into a golden yellow color. It was
centrifuged, and the supernatant was added into 3 mL of brine. Then, the precipitation was
Int. J. Mol. Sci. 2023, 24, 13301
9 of 13
collected via centrifugation, washed with water, and lyophilized overnight. Lyophilized
product was dissolved in small amount of MeOH and purified with flash chromatography.
Yield: 56 mg (67%). 1 H NMR (400 MHz, DMSO-d6 ): δ: 0.858 (NHCH2(CH2)14CH3, t, 3H
1.23 (NHCH2(CH2)14CH3, m, 28H), 1.60 (CHCH2CH, Adamantyl, t, 6H), 1.90 (CCH2CH,
Adamantyl, d, 6H), 1.98 (CH2CH(CH2)2, Adamantyl, m, 3H), 2.31 (CO(CH2)2CO, m,
4H), 2.87 (NHCH2(CH2)14CH3, q, 2H), 6.52 (NHCH2(CH2)14CH3, t, 1H), 6.66 (NH3,
m, 6H), 7.30 (CONHC(CH2)3, s, 1H); 13 C NMR(100 MHz, DMSO-d6 ): δ: 180.7, 171.2,
164.4, 51.0, 41.5, 41.4, 36.6, 31.8, 29.5, 29.4, 29.3, 26.9, 22.6, 14.4; HR-MS (positive mode)
for [C31 H60 Cl2 N4 O5 Pt]+ : m/z calc: 834.3664, obsd: 834.3660. Purity: 96% determined
via HPLC.
Synthesis of Compound 9. To PtC16 (80 mg; 0.122 mmol) and ethyl isocyanatoacetate (16 µL; 0.14 mmol) in a vial, 1.5 mL of anhydrous DMF was added under a
stream of Ar and stirred at r.t. overnight. The product was extracted with Et2 O, washed
with H2 O, and lyophilized overnight to obtain a yellowish-white-colored solid. Yield:
42 mg (44%). 1 HNMR (400 MHz, DMSO-d6 ): δ: 0.854 (NHCH2(CH2)14CH3, t, 3H), 1.18
(COOCH2CH3, t, 3H), 1.23 (NHCH2(CH2)14CH3, m, 28H), 2.34 (CO(CH2)2CO, m, 4H),
2.97 (NHCH2(CH2)14CH3, m, 2H), 3.72 (NHCH2COOCH2, d, 2H), 4.06 (COOCH2CH3, q,
2H), 6.647 (NH3, m, 6H), 6.83 (NHCH2(CH2)14CH3, t, 1H), 7.85 (CONHCH2COOCH2, t,
1H); 13 C NMR(100 MHz, DMSO-d6 ): δ: 180.4, 171.7, 171.6, 158.4, 60.6, 41.9, 40.0, 31.9, 31.8,
30.4, 29.5, 29.3, 29.2, 27.0, 22.6, 14.6, 14.4; HR-MS (positive mode) for [C25 H52 Cl2 N4 O7 Pt]+ :
m/z calc: 786.2936, obsd: 786.2933. Purity: 95% determined via HPLC.
GFAAS analysis of Log P values for 2 and 7. The samples were first dissolved with
DMSO to create 200 µM stocks. From these stocks, 50 µL was added to a H2 O:Octanol
mixture with a 1:1 volume ratio. This mixture was vortexed for 5 min and subsequently
centrifuged for 3 min at 3000 rpm. Following centrifugation, the H2 O and octanol layers
were isolated for analysis. The Pt content in each phase was quantified using GFAAS to
calculate the Log P value.
Cell culture. A2780cis cell lines were purchased from Sigma-Aldrich and cultured in
RPMI 1640 with L-glutamine (Corning, New York, NY, USA) supplemented with 10% FBS
(Atlanta Biologicals, USA) and 1% penicillin-streptomycin (Corning). The MDA-MB-231
cell line was obtained via American Type Culture Collection, and cultured in DMEM 1 g/L
glucose, with L-glutamine and sodium pyruvate (Corning) supplemented with 10% FBS
and 1% penicillin-streptomycin (Corning). All cell lines were cultured at 37 ◦ C under an
atmosphere containing 5% CO2 . Cells were passaged upon reaching 80–90% confluence
via trypsinization and split in a 1:5 ratio.
MTT assays. Cytotoxicity profiles of compounds 1–9 and cisplatin against different
cell lines (A2780cis and MDA-MB-231) were evaluated using the MTT assays. A volume
of 100 µL of a RPMI or DMEM containing 8 × 104 cells/mL was seeded in 96-well plates.
The plates were incubated for 24 h at 37 ◦ C with 5% CO2 to allow for adherence of cells. A
volume of 50 µL of RPMI or DMEM with various concentrations of cisplatin or compounds
1–9 were added to each well of the microplates. The Pt concentrations were determined via
GFAAS. After 24 h, a volume of 30 µL of MTT (5.0 mg/mL in PBS, Alfa Aesar, Haverhill,
MA, USA) was added to each well of the microplates. After 24 h, the medium was aspirated,
and 200 µL of DMSO was added to each well. The plates were shaken gently on a shaker at
r.t. for 10 min. Then, the absorbance of purple formazan was recorded at 562 nm with a
BioTek ELx800 plate reader. IC50 values were determined using Origin software v7.0. All
experiments were performed in triplicate.
LIVE/DEAD cell viability assays. A2780cis cells were cultured in imaging disks
(MatTek, Ashland, MA, USA) at a concentration of 5 × 104 cells with 2 mL of complete
medium and incubated for 24 h at 37 ◦ C with 5% CO2 . The cells were then treated with
compound 2 or 7 ((Pt) = 1 µM) and incubated for 24 h at 37 ◦ C with 5% CO2 . Before the
assay, the cells were washed with 1 mL PBS and 1 mL dye-free RPMI to remove serum
esterase activity that is generally present in serum-supplemented growth media. A 100 µL
volume of LIVE/DEAD working solution (formed by mixing 2 µM of calcein AM and 2 µM
Int. J. Mol. Sci. 2023, 24, 13301
10 of 13
ethidium homodimer-1 in PBS) was carefully added to the disk, which was then incubated
at r.t. for 30 min. Images were acquired using an Olympus IX70 inverted epifluorescence
microscope equipped with a digital CCD camera (QImaging, Surrey, BC, Canada). Images
were processed, and intensities were quantified with ImageJ software (NIH).
GFAAS analysis of cellular platinum contents in A2780cis cells. A2780cis cells were
seeded in a 6-well plate at a concentration of 5 × 105 cells per well and incubated at
37 ◦ C with 5% CO2 overnight. Next day, the cells were treated with compound 2 or 7
((Pt) = 1 µM) or cisplatin ((Pt) = 30 µM) for 24 h at 37 ◦ C with 5% CO2 . The remaining
live cells were harvested via trypsinization and counted. The cells were then digested in
200 µL 65% HNO3 at r.t. overnight. The Pt contents in the cells were analyzed via GFAAS.
All experiments were performed in triplicate.
Measurements of mitochondrial platinum contents in A2780cis cells. A2780cis cells
were seeded on a 6-well plate and incubated at 37 ◦ C with 5% CO2 overnight. The cells
were treated with cisplatin ((Pt) = 30 µM) or compound 2 or 7 ((Pt) = 1 µM) for 24 h
at 37 ◦ C with 5% CO2 . Next, the wells were washed with PBS (1 mL) and harvested
via trypsinization (1 mL) and counted. Mitochondrial fractions were isolated using the
Thermo Scientific™ Mitochondria Isolation Kit for Mammalian Cells. The mitochondrial
fraction was then dissolved in 200 µL 65% nitric acid and shaken at 400 rpm on an Eppendorf ThermoMixer™ F1.5 at r.t. overnight. Next, the fractions were diluted 4× in water
and the platinum content was analyzed using GFAAS. All experiments were performed
in triplicate.
Flow cytometric analysis of MitoSOX. A2780cis cells were seeded in 6-well plate at a
concentration of 6 × 104 cells/mL and incubated overnight. Then, the cells were treated
with cisplatin ((Pt) = 10 µM) or compound 2 or 7 ((Pt) = 1 µM) and incubated overnight.
The medium was aspirated, and cells were washed with 1 mL PBS. Next, the cells were
incubated with 5 µM MitoSOX reagent in fresh medium for 60 min at 37 ◦ C with 5% CO2
in the dark. Cells were trypsinized and collected. The cell pellet was washed 2 times with
PBS. The cells were then re-suspended in PBS with 0.5% BSA to reach 106 cells/mL and
analyzed with BD Accuri C6 flow cytometer using FL-2 channel, and data were processed
with FlowJo v10.
Flow cytometric analysis of γH2AX. A2780cis cells were seeded in a 6-well plate at
a concentration of 4 × 105 cells/well. Cells were then incubated at 37 ◦ C with 5% CO2
for 24 h. Next, the cells were treated with compound 2 ((Pt) = 0.25 µM), 7 ((Pt) = 1 µM) or
cisplatin ((Pt) = 30 µM) and incubated for 24 h. Live cells were collected and 250 µL BD
Permeabilization solution was added to re-suspend the cells, which were then incubated
for 20 min at 4 ◦ C. Cell pellets were collected, washed twice with 1X BD Perm/Wash buffer,
and resuspended in 50 µL of buffer. Alexa 488-anti γH2AX antibody solution was then
added, and the samples were incubated in the dark for 60 min at r.t. The final cell pellets
were suspended in 500 µL of PBS with 0.5% BSA and analyzed with BD Accuri C6 flow
cytometer using FL-1 channel, and data were processed with FlowJo.
Flow cytometric analysis of apoptosis. A2780cis cells were seeded in a 6-well plate
at a concentration of 3 × 105 cells/well. Cells were then incubated at 37 ◦ C 5% CO2 for 24 h.
Next, compound 2 or 7 ((Pt) = 0.5 µM) or cisplatin ((Pt) = 7 µM) was added and incubated
for 48 h. Both live and dead cells were collected, resuspended in 1mL PBS, and counted.
A 1X binding buffer from the FITC Annexin V Apoptosis Detection Kit 1 (BD Biosciences,
Franklin Lakes, NJ, USA) was then added to reach a concentration of 106 cells/mL. An
amount of 100 µL cell solution was transferred to a fresh 2 mL Eppendorf tube, and 5 µL
of both Annexin V-FITC and PI solutions were added to cells. Cells were incubated for
15 min at r.t. in the dark and then brought to 400 µL volume by adding required volume of
binding 1X buffer. Cells were then analyzed with FL-1 and FL-3 channels on a BD Accuri
C6 flow cytometer and data were processed with FlowJo.
Int. J. Mol. Sci. 2023, 24, 13301
11 of 13
4. Conclusions
Our study represents the first comprehensive investigation of the structure–activity
relationship of FALPs. We synthesized a small library of FALPs with diverse head group
modifications and found that such modifications can greatly affect the cytotoxicity profiles
of FALPs, ranging from low to highly potent. Interestingly, a further analysis revealed
that only hydrophilic modifications led to a high potency, while hydrophobic moieties
resulted in a much lower cytotoxicity. To explore the impact of hydrophobicity on the
cytotoxicity of FALPs, we focused on two similar FALPs, one with a hydrophilic PEG head
group and the other with a hydrophobic hydrocarbon modification of the same molecular
weight. Using these model compounds, we evaluated cellular uptake and mitochondrial
accumulation through GFAAS, as well as mitochondrial and DNA damage and apoptosis
through flow cytometry. Our comprehensive findings reveal that FALPs incorporating
hydrophilic modifications can readily penetrate cancer cells and mitochondria, initiating
subsequent cellular responses that effectively eradicate cancer cells. Conversely, FALPs with
hydrophobic modifications showed a notably lower uptake and weaker cellular responses.
These combined results present an alternative perspective, differing from the conventional
belief that increased hydrophobicity invariably enhances cellular uptake. These findings
provide valuable new insights into the fundamental principles of developing metallodrugs.
It underscores the significance of developing FALPs with hydrophilic modifications, which
hold the potential to yield more potent and effective anticancer agents. This study lays the
groundwork for future research endeavors aimed at optimizing the structural design of
FALPs, with the objective of enhancing anticancer activity while minimizing side effects.
Supplementary Materials: The synthetic schemes and characterization of FALPs can be downloaded
at https://www.mdpi.com/article/10.3390/ijms241713301/s1.
Author Contributions: Conceptualization, Y.-R.Z.; synthesis and characterization of the Pt compounds, M.K., M.C., P.D., A.S., T.B. and D.B.; cell-based studies, M.K., W.J., S.A. and P.D.; writing,
M.K., W.J., S.A. and Y.-R.Z.; supervision, Y.-R.Z.; project administration, Y.-R.Z.; funding acquisition,
Y.-R.Z. All authors have read and agreed to the published version of the manuscript.
Funding: This research was funded by the R15 grant (1R15CA249712-01A1) provided by the National
Cancer Institute, NSF Award 2050873, and the Farris Family Innovation Fellowship and LaunchPad
Award provided by Kent State University.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Acknowledgments: Y.-R.Z. is thankful for the support from the Research Council of Kent State
University. W.J. is thankful for the financial support provided by the Healthy Communities Research
Initiative at Kent State University.
Conflicts of Interest: The authors declare no conflict of interest.
References
1.
2.
3.
4.
5.
6.
7.
8.
Wang, D.; Lippard, S.J. Cellular processing of platinum anticancer drugs. Nat. Rev. Drug Discov. 2005, 4, 307–320. [CrossRef]
Kelland, L. The resurgence of platinum-based cancer chemotherapy. Nat. Rev. Cancer 2007, 7, 573–584. [CrossRef] [PubMed]
Jamieson, E.R.; Lippard, S.J. Structure, recognition, and processing of cisplatin-DNA adducts. Chem. Rev. 1999, 99, 2467–2498.
[CrossRef] [PubMed]
Todd, R.C.; Lippard, S.J. Inhibition of transcription by platinum antitumor compounds. Metallomics 2009, 1, 280–291. [CrossRef]
Jordan, C.T.; Guzman, M.L.; Noble, M. Cancer Stem Cells. N. Engl. J. Med. 2006, 355, 1253–1261. [CrossRef]
Gupta, P.B.; Chaffer, C.L.; Weinberg, R.A. Cancer stem cells: Mirage or reality? Nat. Med. 2009, 15, 1010–1012. [CrossRef]
[PubMed]
Zhou, J.; Kang, Y.; Chen, L.; Wang, H.; Liu, J.; Zeng, S.; Yu, L. The Drug-Resistance Mechanisms of Five Platinum-Based Antitumor
Agents. Front. Pharmacol. 2020, 11, 343. [CrossRef]
Jogadi, W.; Zheng, Y.R. Supramolecular platinum complexes for cancer therapy. Curr. Opin. Chem. Biol. 2023, 73, 102276.
[CrossRef]
Int. J. Mol. Sci. 2023, 24, 13301
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
12 of 13
Xu, Z.; Wang, Z.; Deng, Z.; Zhu, G. Recent advances in the synthesis, stability, and activation of platinum(IV) anticancer prodrugs.
Coord. Chem. Rev. 2021, 442, 213991. [CrossRef]
Johnstone, T.C.; Suntharalingam, K.; Lippard, S.J. The Next Generation of Platinum Drugs: Targeted Pt(II) Agents, Nanoparticle
Delivery, and Pt(IV) Prodrugs. Chem. Rev. 2016, 116, 3436–3486. [CrossRef]
Konkankit, C.C.; Marker, S.C.; Knopf, K.M.; Wilson, J.J. Anticancer activity of complexes of the third row transition metals,
rhenium, osmium, and iridium. Dalton Trans. 2018, 47, 9934–9974. [CrossRef] [PubMed]
Olelewe, C.; Awuah, S.G. Mitochondria as a target of third row transition metal-based anticancer complexes. Curr. Opin. Chem.
Biol. 2023, 72, 102235. [CrossRef] [PubMed]
Vaidya, S.P.; Patra, M. Platinum glycoconjugates: “Sweet bullets” for targeted cancer therapy? Curr. Opin. Chem. Biol. 2023, 72,
102236. [CrossRef] [PubMed]
Northcote-Smith, J.; Suntharalingam, K. Targeting chemotherapy-resistant tumour sub-populations using inorganic chemistry:
Anti-cancer stem cell metal complexes. Curr. Opin. Chem. Biol. 2023, 72, 102237. [CrossRef] [PubMed]
Elie, B.T.; Fernández-Gallardo, J.; Curado, N.; Cornejo, M.A.; Ramos, J.W.; Contel, M. Bimetallic titanocene-gold phosphane
complexes inhibit invasion, metastasis, and angiogenesis-associated signaling molecules in renal cancer. Eur. J. Med. Chem. 2019,
161, 310–322. [CrossRef]
Boulet, M.H.C.; Bolland, H.R.; Hammond, E.M.; Sedgwick, A.C. Oxali(IV)Fluors: Fluorescence Responsive Oxaliplatin(IV)
Complexes Identify a Hypoxia-Dependent Reduction in Cancer Cells. J. Am. Chem. Soc. 2023, 145, 12998–13002. [CrossRef]
Momeni, B.Z.; Abd-El-Aziz, A.S. Recent advances in the design and applications of platinum-based supramolecular architectures
and macromolecules. Coord. Chem. Rev. 2023, 486, 215113. [CrossRef]
Li, H.; Cheng, S.; Zhai, J.; Lei, K.; Zhou, P.; Cai, K.; Li, J. Platinum based theranostics nanoplatforms for antitumor applications.
J. Mater. Chem. B 2023. [CrossRef]
Wang, T.; Wu, C.; Hu, Y.; Zhang, Y.; Ma, J. Stimuli-responsive nanocarrier delivery systems for Pt-based antitumor complexes: A
review. RSC Adv. 2023, 13, 16488–16511. [CrossRef]
Zhong, T.; Yu, J.; Pan, Y.; Zhang, N.; Qi, Y.; Huang, Y. Recent Advances of Platinum-Based Anticancer Complexes in Combinational
Multimodal Therapy. Adv. Healthc. Mater. 2023, e2300253. [CrossRef]
Deng, Z.; Zhu, G. Beyond mere DNA damage: Recent progress in platinum(IV) anticancer complexes containing multi-functional
axial ligands. Curr. Opin. Chem. Biol. 2023, 74, 102303. [CrossRef] [PubMed]
Li, Y.; Lin, W. Platinum-based combination nanomedicines for cancer therapy. Curr. Opin. Chem. Biol. 2023, 74, 102290. [CrossRef]
Giorgi, E.; Binacchi, F.; Marotta, C.; Cirri, D.; Gabbiani, C.; Pratesi, A. Highlights of New Strategies to Increase the Efficacy of
Transition Metal Complexes for Cancer Treatments. Molecules 2022, 28, 273. [CrossRef] [PubMed]
Zhang, C.; Kang, T.; Wang, X.; Song, J.; Zhang, J.; Li, G. Stimuli-responsive platinum and ruthenium complexes for lung cancer
therapy. Front. Pharmacol. 2022, 13, 1035217. [CrossRef] [PubMed]
Su, S.; Chen, Y.; Zhang, P.; Ma, R.; Zhang, W.; Liu, J.; Li, T.; Niu, H.; Cao, Y.; Hu, B.; et al. The role of Platinum(IV)-based antitumor
drugs and the anticancer immune response in medicinal inorganic chemistry. A systematic review from 2017 to 2022. Eur. J. Med.
Chem. 2022, 243, 114680. [CrossRef] [PubMed]
Lu, Y.; Ma, X.; Chang, X.; Liang, Z.; Lv, L.; Shan, M.; Lu, Q.; Wen, Z.; Gust, R.; Liu, W. Recent development of gold(I) and gold(III)
complexes as therapeutic agents for cancer diseases. Chem. Soc. Rev. 2022, 51, 5518–5556. [CrossRef]
Alassadi, S.; Pisani, M.J.; Wheate, N.J. A chemical perspective on the clinical use of platinum-based anticancer drugs. Dalton
Trans. 2022, 51, 10835–10846. [CrossRef]
Czarnomysy, R.; Radomska, D.; Szewczyk, O.K.; Roszczenko, P.; Bielawski, K. Platinum and Palladium Complexes as Promising
Sources for Antitumor Treatments. Int. J. Mol. Sci. 2021, 22, 8271. [CrossRef]
Anthony, E.J.; Bolitho, E.M.; Bridgewater, H.E.; Carter, O.W.L.; Donnelly, J.M.; Imberti, C.; Lant, E.C.; Lermyte, F.; Needham,
R.J.; Palau, M.; et al. Metallodrugs are unique: Opportunities and challenges of discovery and development. Chem. Sci. 2020, 11,
12888–12917. [CrossRef]
Gibson, D. Platinum(IV) anticancer agents; are we en route to the holy grail or to a dead end? J. Inorg. Biochem. 2021, 217, 111353.
[CrossRef]
Zhang, C.; Xu, C.; Gao, X.; Yao, Q. Platinum-based drugs for cancer therapy and anti-tumor strategies. Theranostics 2022, 12,
2115–2132. [CrossRef] [PubMed]
Wu, T.; Liu, J.; Liu, M.; Liu, S.; Zhao, S.; Tian, R.; Wei, D.; Liu, Y.; Zhao, Y.; Xiao, H.; et al. A Nanobody-Conjugated DNA
Nanoplatform for Targeted Platinum-Drug Delivery. Angew. Chem. Int. Ed. 2019, 58, 14224–14228. [CrossRef] [PubMed]
Kostrhunova, H.; Zajac, J.; Novohradsky, V.; Kasparkova, J.; Malina, J.; Aldrich-Wright, J.R.; Petruzzella, E.; Sirota, R.; Gibson,
D.; Brabec, V. A Subset of New Platinum Antitumor Agents Kills Cells by a Multimodal Mechanism of Action Also Involving
Changes in the Organization of the Microtubule Cytoskeleton. J. Med. Chem. 2019, 62, 5176–5190. [CrossRef] [PubMed]
Gorle, A.K.; Katner, S.J.; Johnson, W.E.; Lee, D.E.; Daniel, A.G.; Ginsburg, E.P.; von Itzstein, M.; Berners-Price, S.J.; Farrell,
N.P. Substitution-Inert Polynuclear Platinum Complexes as Metalloshielding Agents for Heparan Sulfate. Chemistry 2018, 24,
6606–6616. [CrossRef] [PubMed]
Amarsy, I.; Papot, S.; Gasser, G. Stimuli-Responsive Metal Complexes for Biomedical Applications. Angew. Chem. Int. Ed. 2022,
61, e202205900. [CrossRef]
Int. J. Mol. Sci. 2023, 24, 13301
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
46.
47.
48.
49.
50.
51.
52.
53.
13 of 13
Zheng, Y.-R.; Suntharalingam, K.; Johnstone, T.C.; Yoo, H.; Lin, W.; Brooks, J.G.; Lippard, S.J. Pt(IV) Prodrugs Designed to Bind
Non-Covalently to Human Serum Albumin for Drug Delivery. J. Am. Chem. Soc. 2014, 136, 8790–8798. [CrossRef]
Zhang, G.; Zhu, Y.; Wang, Y.; Wei, D.; Wu, Y.; Zheng, L.; Bai, H.; Xiao, H.; Zhang, Z. pH/redox sensitive nanoparticles with
platinum(iv) prodrugs and doxorubicin enhance chemotherapy in ovarian cancer. RSC Adv. 2019, 9, 20513–20517. [CrossRef]
Jayawardhana, A.M.D.S.; Stilgenbauer, M.; Datta, P.; Qiu, Z.; Mckenzie, S.; Wang, H.; Bowers, D.; Kurokawa, M.; Zheng, Y.R.
Fatty acid-like Pt(IV) prodrugs overcome cisplatin resistance in ovarian cancer by harnessing CD36. Chem. Commun. 2020, 56,
10706–10709. [CrossRef]
Jayawardhana, A.M.D.S.; Zheng, Y.R. Interactions between mitochondria-damaging platinum(IV) prodrugs and cytochrome c.
Dalton Trans. 2022, 51, 2012–2018. [CrossRef]
Awuah, S.G.; Zheng, Y.R.; Bruno, P.M.; Hemann, M.T.; Lippard, S.J. A Pt(IV) Pro-drug Preferentially Targets Indoleamine2,3-dioxygenase, Providing Enhanced Ovarian Cancer Immuno-Chemotherapy. J. Am. Chem. Soc. 2015, 137, 14854–14857.
[CrossRef]
Wei, D.; Yu, Y.; Zhang, X.; Wang, Y.; Chen, H.; Zhao, Y.; Wang, F.; Rong, G.; Wang, W.; Kang, X.; et al. Breaking the Intracellular
Redox Balance with Diselenium Nanoparticles for Maximizing Chemotherapy Efficacy on Patient-Derived Xenograft Models.
ACS Nano 2020, 14, 16984–16996. [CrossRef] [PubMed]
Miller, M.; Zheng, Y.; Suresh, G.; Pfirschke, C.; Zope, H.; Engblom, C.; Kohler, R.; Iwamoto, Y.; Yang, K.; Askevold, B.; et al.
Tumour-associated macrophages act as a slow-release reservoir of nano-therapeutic Pt(IV) pro-drug. Nat. Commun. 2015, 6, 8692.
[CrossRef] [PubMed]
Zhou, F.; Feng, B.; Yu, H.; Wang, D.; Wang, T.; Ma, Y.; Wang, S.; Li, Y. Tumor Microenvironment-Activatable Prodrug Vesicles for
Nanoenabled Cancer Chemoimmunotherapy Combining Immunogenic Cell Death Induction and CD47 Blockade. Adv. Mater.
2019, 31, e1805888. [CrossRef] [PubMed]
Kang, X.; Wang, Y.; Chen, Z.; Wu, Y.; Chen, H.; Yang, X.; Yu, C. Imidazole modified Pt(iv) prodrug-loaded multi-stage pH
responsive nanoparticles to overcome cisplatin resistance. Chem. Commun. 2020, 56, 11271–11274. [CrossRef]
Ma, J.; Wang, Q.; Huang, Z.; Yang, X.; Nie, Q.; Hao, W.; Wang, P.G.; Wang, X. Glycosylated Platinum(IV) Complexes as Substrates
for Glucose Transporters (GLUTs) and Organic Cation Transporters (OCTs) Exhibited Cancer Targeting and Human Serum
Albumin Binding Properties for Drug Delivery. J. Med. Chem. 2017, 60, 5736–5748. [CrossRef]
Abu Ammar, A.; Raveendran, R.; Gibson, D.; Nassar, T.; Benita, S. A Lipophilic Pt(IV) Oxaliplatin Derivative Enhances Antitumor
Activity. J. Med. Chem. 2016, 59, 9035–9046. [CrossRef]
Martinho, N.; Santos, T.C.B.; Florindo, H.F.; Silva, L.C. Cisplatin-Membrane Interactions and Their Influence on Platinum
Complexes Activity and Toxicity. Front. Physiol. 2018, 9, 1898. [CrossRef]
Chin, C.F.; Tian, Q.; Setyawati, M.I.; Fang, W.; Tan, E.S.; Leong, D.T.; Ang, W.H. Tuning the activity of platinum(IV) anticancer
complexes through asymmetric acylation. J. Med. Chem. 2012, 55, 7571–7582. [CrossRef]
Park, G.Y.; Wilson, J.J.; Song, Y.; Lippard, S.J. Phenanthriplatin, a monofunctional DNA-binding platinum anticancer drug
candidate with unusual potency and cellular activity profile. Proc. Natl. Acad. Sci. USA 2012, 109, 11987–11992. [CrossRef]
Arzuman, L.; Beale, P.; Yu, J.Q.; Huq, F. Monofunctional Platinum-containing Pyridine-based Ligand Acts Synergistically in
Combination with the Phytochemicals Curcumin and Quercetin in Human Ovarian Tumour Models. Anticancer Res. 2015, 35,
2783–2794.
Morstein, J.; Capecchi, A.; Hinnah, K.; Park, B.; Petit-Jacques, J.; Van Lehn, R.C.; Reymond, J.L.; Trauner, D. Medium-Chain Lipid
Conjugation Facilitates Cell-Permeability and Bioactivity. J. Am. Chem. Soc. 2022, 144, 18532–18544. [CrossRef] [PubMed]
Annunziata, A.; Imbimbo, P.; Cucciolito, M.E.; Ferraro, G.; Langellotti, V.; Marano, A.; Melchiorre, M.; Tito, G.; Trifuoggi, M.;
Monti, D.M.; et al. Impact of Hydrophobic Chains in Five-Coordinate Glucoconjugate Pt(II) Anticancer Agents. Int. J. Mol. Sci.
2023, 24, 2369. [CrossRef] [PubMed]
Desiatkina, O.; Anghel, N.; Boubaker, G.; Amdouni, Y.; Hemphill, A.; Furrer, J.; Păunescu, E. Trithiolato-bridged dinuclear
ruthenium(II)-arene conjugates tethered with lipophilic units: Synthesis and Toxoplasma gondii antiparasitic activity. J. Organomet.
Chem. 2023, 986, 122624. [CrossRef]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual
author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to
people or property resulting from any ideas, methods, instructions or products referred to in the content.
Journal of
Intelligence
Article
Metacognitive Cues, Working Memory, and Math Anxiety:
The Regulated Attention in Mathematical Problem Solving
(RAMPS) Framework
Daniel A. Scheibe * , Christopher A. Was, John Dunlosky and Clarissa A. Thompson
The Psychological Sciences, Kent State University, Kent, OH 44240, USA; cthomp77@kent.edu (C.A.T.)
* Correspondence: dscheib2@kent.edu
Abstract: Mathematical problem solving is a process involving metacognitive (e.g., judging progress),
cognitive (e.g., working memory), and affective (e.g., math anxiety) factors. Recent research encourages researchers who study math cognition to consider the role that the interaction between
metacognition and math anxiety plays in mathematical problem solving. Problem solvers can make
many metacognitive judgments during a math problem, ranging from global judgments such as,
“Do I care to solve this problem?” to minor cue-based judgments such as, “Is my current strategy
successful in making progress toward the correct solution?” Metacognitive monitoring can hinder accurate mathematical problem solving when the monitoring is task-irrelevant; however, task-relevant
metacognitive experiences can lead to helpful control decisions in mathematical problem solving
such as checking work, considering plausibility of an answer, and considering alternate strategies.
Worry and negative thoughts (i.e., math anxiety) can both interfere with the accuracy of metacognitive experiences as cues in mathematical problem solving and lead to avoidance of metacognitive
control decisions that could otherwise improve performance. The current paper briefly reviews and
incorporates prior literature with current qualitative reports (n = 673) to establish a novel framework
of regulated attention in mathematical problem solving (RAMPS).
Citation: Scheibe, Daniel A.,
Keywords: metacognition; working memory; math anxiety; mathematical problem solving; state
math anxiety; metacognitive experiences
Christopher A. Was, John Dunlosky,
and Clarissa A. Thompson. 2023.
Metacognitive Cues, Working
Memory, and Math Anxiety: The
1. Introduction
Regulated Attention in Mathematical
Why do some people seem to effortlessly solve math problems while other people
regularly run into mental roadblocks that keep them from producing solutions? Most
adults (approximately 60% of American adults reported by Handel 2016) report reasoning
with rational numbers in their daily jobs. Beyond the workplace, people of all ages use
mathematical reasoning to complete everyday tasks such as tipping at a restaurant, evaluating medical information, playing games, understanding sports statistics, and making
financial decisions. Incorporating quantitative information in decision-making is foundational to daily life (Peters 2020), numerosity is one of the most basic dimensions upon which
humans perceive the world (Dehaene 2011), and solving mathematical problems is central
to learning math (Lester and Cai 2016; Passolunghi et al. 2019). The term “mathematical
problem solving” represents a variety of similar, yet different, stimuli. Here, we operationalize mathematical problem solving as any multi-step task that involves the use and
manipulation of numerical information. Given the prevalence of mathematical problem
solving in daily life and in educational contexts, understanding individual differences that
affect mathematical problem solving is of critical importance.
The current paper explores how individual differences, such as metacognitive experiences, working memory (WM), and math anxiety (MA), are related to one another
and may predict success in mathematical problem solving. We discuss the online (i.e., in
the moment) cognitive (including WM) and metacognitive processes that are necessary
Problem Solving (RAMPS)
Framework. Journal of Intelligence 11:
117. https://doi.org/10.3390/
jintelligence11060117
Received: 1 April 2023
Revised: 16 May 2023
Accepted: 4 June 2023
Published: 11 June 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
J. Intell. 2023, 11, 117. https://doi.org/10.3390/jintelligence11060117
https://www.mdpi.com/journal/jintelligence
J. Intell. 2023, 11, 117
The current paper explores how individual differences, such as metacognitive
experiences, working memory (WM), and math anxiety (MA), are related to one another
2 of 18
and may predict success in mathematical problem solving. We discuss the online (i.e.,
in
the moment) cognitive (including WM) and metacognitive processes that are necessary
for mathematical problem solving, and how these processes are affected by MA. Then, we
introduce
a novelproblem
framework
of regulated
in mathematical
problem
for
mathematical
solving,
and howattention
these processes
are affected
by MA.solving
Then,
(RAMPS),
focusing
on
the
role
of
online
metacognitive
experiences
to clarify
the
we introduce a novel framework of regulated attention in mathematical problem
solving
previously
proposed
WM–MA
relation
(e.g.,
Ashcraft
and
Kirk
2001).
Next,
we
present
(RAMPS), focusing on the role of online metacognitive experiences to clarify the previously
qualitative,
open-ended
responses
from two
(n = 673)
elucidate
RAMPS
proposed
WM–MA
relation
(e.g., Ashcraft
andstudies
Kirk 2001).
Next,towe
presentthe
qualitative,
framework mechanism
of in-the-moment
(i.e.,
MA). framework
In the latter
open-ended
responses from
two studies mathematics
(n = 673) to anxiety
elucidate
thestate
RAMPS
half of the of
paper,
we zoom in
on the specific
online
relations
metacognitive
mechanism
in-the-moment
mathematics
anxiety
(i.e., state
MA).between
In the latter
half of the
experiences,
MA,
and
WM
in
a
five-phase
approach.
We
conclude
by
discussing
paper, we zoom in on the specific online relations between metacognitive experiences, how
MA,
themes
emerged approach.
in our qualitative
might inform
futurethat
research
and
WM that
in a five-phase
We concludedata
by discussing
how themes
emergedand
in
interventions.
our
qualitative data might inform future research and interventions.
Mathematical problem solving,
solving, broadly construed,
construed, involves cognitive (e.g., WM;
Mathematical
Peng
Peng et
et al.
al. 2016),
2016), metacognitive
metacognitive (e.g.,
(e.g., feeling of error;
error; Ackerman and Thompson 2017),
and
and affective
affective (e.g.,
(e.g., MA,
MA, Hembree
Hembree 1990)
1990) components.
components. MA is closely linked to WM
WM in
in the
the
math
cognition
literature
because
it
has
been
proposed
that
MA
works
by
disrupting
math cognition literature because it has been proposed that MA works by disrupting WM
WM
resources
is attempting
to solve
problems
(i.e., disruption
account).
resources
whenwhen
one isone
attempting
to solve
math math
problems
(i.e., disruption
account).
Thus,
Thus,
a discussion
of the
link between
and metacognition
in theof
domain
of math
a discussion
of the link
between
WM andWM
metacognition
in the domain
math would
be
would
be incomplete
without including
MA.
and
Gümü¸
(2019,
p. 122) previously
incomplete
without including
MA. Özcan
andÖzcan
Gümüş
(2019,
p.s122)
previously
proposed
proposed
a model
which mathematical
was predicted
by metacognition,
a model in
whichinmathematical
problemproblem
solvingsolving
was predicted
by metacognition,
selfself-efficacy,
motivation,
and
anxiety;
yet,
their
model
did
not
involve
the
role
of
WM,
efficacy, motivation, and anxiety; yet, their model did not involve the role of WM, and
and
metacognitive
experiences
were
operationalized
onlyasasretrospective
retrospectivejudgments
judgments (i.e.,
(i.e.,
metacognitive
experiences
were
operationalized
only
metacognitive
judgments
made
after
completing
a
task,
Dunlosky
and
Metcalfe
2009;
metacognitive judgments made after completing a task, Dunlosky and Metcalfe 2009;
Rhodes
TheRAMPS
RAMPSframework
frameworkbuilds
buildson
onprior
priorresearch
researchtoto
incorporate
role
Rhodes 2019).
2019). The
incorporate
thethe
role
of
of
WM
and
operationalizein-the-moment
in-the-momentmetacognitive
metacognitiveexperiences.
experiences.See
SeeFigure
Figure 11 for
WM
and
operationalize
for an
an
illustration
illustration of
of the
the proposed
proposed RAMPS
RAMPS framework.
framework.
Figure 1.
1. Regulated
Regulated attention
attention in
in mathematical
mathematical problem
problem solving
solving (RAMPS)
(RAMPS)framework.
framework. Note:
Note: The
The
Figure
primary
use
of
the
RAMPS
framework
is
a
reference
tool
to
discuss
the
proposed
interrelations
primary use of the RAMPS framework is a reference tool to discuss the proposed interrelations
between metacognitive experiences, MA, and WM during a math task. There are multiple recursive
between metacognitive experiences, MA, and WM during a math task. There are multiple recursive
loops within this framework; thus, it is better suited as a framework for future discussions and
loops within this framework; thus, it is better suited as a framework for future discussions and
testable models than as a testable path model.
testable models than as a testable path model.
2. The Role of Working Memory in Mathematical Problem Solving
Individuals vary in their mathematical resources and abilities; thus, what may be
an intensive tax on WM via a multi-step mathematical task for one person (i.e., mathematical problem solving) may be a matter of simple recall for another person (Schoenfeld
[1985] 2016). Just as a chess expert’s recall of a correct move based on prior experience with
that exact situation (Schneider et al. 1993) would not be considered problem solving, a math
expert in a given domain will not be said to be problem solving if the answer constitutes a
recalled answer instead of a process. Evidence of expertise is demonstrated by automatic
J. Intell. 2023, 11, 117
3 of 18
responses (recalling from long-term memory that 3 × 3 is 9) replacing algorithmic responses
(using WM resources to count 3 plus another 3 plus another 3).
Math problems vary in a wide variety of factors such as context, notation, and level
of difficulty. For example, fraction addition problems (e.g., 1/2 + 1/9 = ?), math word
problems (e.g., a bat and a ball cost $1.10 and the bat costs one dollar more than the
ball, how much does the ball cost?), and geometric proofs (e.g., prove that two circles
are concentric) are just three examples of the wide array of potential types of math that
are considered mathematical problem solving for the current paper. Research in math
cognition suggests that people have different affective reactions to different number types
as well. For example, both adults (Mielicki et al. 2022; Scheibe et al. 2023; Sidney et al.
2021) and children (Sidney et al. 2021) report disliking fractions significantly more than
other number types. It is certainly possible that differences exist in the antecedents for
predicting mathematical problem solving in one math sub-domain (e.g., fraction addition)
than another domain (e.g., geometric proofs).
Considering different types of math is particularly important because some forms
of math rely more heavily on WM resources than others. A recent meta-analysis of WM
and mathematics reported a medium correlation between the two constructs (r = 0.35;
Peng et al. 2016). Many forms of mathematical problem solving involve maintaining and
manipulating information to find a solution, similar to the attention-control theory of WM
(Engle 2002; see also Burgoyne and Engle 2020; Cowan 2017). The attention-control theory
of WM (Engle 2002) conceptualizes WM not as a number of items that can be recalled, but
the ability to inhibit task-irrelevant information and focus on task-relevant information.
Thus, differences in attention-control (sometimes termed the central executive; Baddeley
2001; Baddeley and Hitch 1974) are largely responsible for correlations between typical tests
of WM capacity and other higher-order cognitive functions (Burgoyne and Engle 2020).
Directed attention toward goals and subgoals is crucial to mathematical problem
solving. Note that mathematical problem solving goes beyond mathematics computation in
that it is a dynamic interaction between computational skills, reasoning, and metacognitive
regulation. An arithmetic computation such as 2 + 2 likely may not involve the use of
WM resources in adults, but mathematical problem solving that incorporates reasoning,
relevant information from memory, and metacognitive regulation is a process that requires
WM resources. Thus, it is unsurprising that from an individual-differences perspective,
mathematical problem solving ability is linked with WM (Ashcraft 2019; Chen and Bailey
2021; Peng et al. 2016; Widaman et al. 1989).
3. Processes Involved in Mathematical Problem Solving
Cognitive processes involve the acquisition, storage, transformation, and use of knowledge (Matlin 2013). In mathematical problem solving, cognitive processes can be defined
as the active processing and manipulation of stimuli. The RAMPS framework (see Figure 1)
considers WM to be a cognitive process and math ability to be a composite of skills based
on factors such as prior knowledge, magnitude processing (Dehaene 2011), and numeracy
(Peters 2020). Beyond cognitive processes and math ability, several factors affect mathematical problem solving (Schoenfeld [1985] 2016). Problem solvers incorporate metacognitive
judgments (Ackerman and Thompson 2017; Efklides 2006; Nelson and Narens 1990) and
come into math environments with a rich history of attitudes toward math (Mielicki et al.
2022; Sidney et al. 2021) and affective reactions, such as anxiety prior to and during math
tasks (Ashcraft 2019; Dowker et al. 2016; Hembree 1990). Mathematical problem solving
never exists in a vacuum. Relations among constructs displayed in Figure 1 are discussed
in subsequent sections.
3.1. Metacognition and Mathematical Problem Solving
Metacognition—thoughts about one’s own thoughts and cognitions (Flavell 1979)—
is studied in a variety of ways and affects many facets of everyday life (Dunlosky and
Metcalfe 2009; Rhodes 2019). The RAMPS framework builds on previous work on general
J. Intell. 2023, 11, 117
4 of 18
metacognitive frameworks (e.g., Nelson and Narens 1990) and metacognitive frameworks
in meta-reasoning (Ackerman and Thompson 2015, 2017; Efklides 2006). Meta-reasoning
is operationally defined as monitoring and control of reasoning and problem solving
(Ackerman and Thompson 2017). This definition is similar to the current definition of
mathematical problem solving; thus, models of meta-reasoning are ideal starting points
from which to create a framework of metacognition in mathematical problem solving.
Ackerman and Thompson (2015, 2017) proposed a model of meta-reasoning based on
Nelson and Narens’ (1990) seminal framework of metacognition in learning and memory
as well as Ackerman’s Diminishing Criterion Model (Ackerman 2014). Each of these
models describe metacognition as a two-facet construct involving both monitoring (i.e.,
self-assessments) and control (i.e., actions). Metacognition in math encompasses both
monitoring (e.g., “Is this solution correct?”) and control (e.g., making the deliberate choice
to check one’s work) dimensions.
Ackerman and Thompson’s (2015, 2017) model of meta-reasoning included a series of
metacognitive judgments during problem solving. These judgments (e.g., initial judgment
of solvability) map closely onto mathematical problem-solving processes due to the close
overlap between mathematical problem solving and domain-general problem solving.
In addition to judgments, problem solvers also experience less explicit metacognitive
reactions, termed “metacognitive feelings” (Efklides 2006). Metacognitive feelings are
elicited by nonconscious analytical processes (Efklides 2006; Koriat and Levy-Sadot 1999).
These feelings and emotions (i.e., affect produced while problem solving) provide people
with clues—some of which may be misleading—about the progress of cognitive processes
during a task (Efklides 2006). According to Efklides (2006), metacognitive feelings interact
with metacognitive judgments (i.e., judgments of learning, Dunlosky and Nelson 1992), to
provide people with a continuously updating sense of their likelihood to reach a satisfying
solution to the problem.
Metacognition is central to mathematical problem solving because online metacognitive experiences or “concurrent metacognition”—specific online metacognitive feelings
and judgments that interact with WM (Bellon et al. 2019; Efklides 2006; Hertzog and Dixon
1994)—occur continuously during problem solving. We argue that these metacognitive
experiences interact with MA and WM to affect control decisions such as checking one’s
work or altering one’s strategy. Such control decisions directly affect performance in math
tasks. Additionally, retrospective metacognitive judgments may affect these same factors
and interact with them to predict future iterations of mathematical problem solving (see
Path K in Figure 1).
Note that both explicit metacognitive judgments and implicit metacognitive feelings
are encompassed in “metacognitive experiences”. Metacognitive feelings represent an
important component of the RAMPS framework because solving math problems is often
an emotionally charged experience (Ashcraft 2002; Dowker et al. 2016). Although problem
solvers may not often make explicit judgments about their emotional state (e.g., “What
level of math anxiety am I experiencing at this moment?”), feelings and emotions clearly
run concurrently with the cognitive processing of mathematical stimuli. The variety of
metacognitive experiences illustrates the potential for investigating many open questions
in the domain of mathematical problem solving.
3.2. Math Anxiety and Mathematical Problem Solving
Metacognitive experiences (i.e., judgments and feelings) occur continuously during
mathematical problem solving. These metacognitive experiences not only affect control
decisions (e.g., checking one’s work or giving up), but they can also dictate changes in affect.
Carver (2003) and Carver and Scheier (1998) proposed a two-loop feedback model of affect
in problem solving that highlights how positive affect can broaden the scope of attention.
People incorporate metacognitive experiences, whether consciously or unconsciously, that
affect their online control decisions. For example if a person notices that they are struggling
with a complicated problem, they might work harder through an approach process (see
J. Intell. 2023, 11, 117
5 of 18
Carver and Scheier 1998). However, a math anxious individual will likely be more prone to
avoidance and would be likely to spend less time attempting to complete the problem than
they otherwise might have in the absence of a negative affective reaction, especially if the
individual is metacognitively aware of negative affect. People often become anxious while
solving math problems (Ashcraft 2002), so much so that MA is often likened to a specific
phobia (Ashcraft and Ridley 2005).
4. Working Memory and Math Anxiety
A consistent, moderate relation between math performance and MA is regularly cited
in math cognition literature (Barroso et al. 2021; Caviola et al. 2022; Hembree 1990; Ma 1999;
Namkung et al. 2019; Zhang et al. 2019). Seminal research on MA (e.g., Dreger and Aiken
1957; Richardson and Suinn 1972) treated MA as a stable personality construct. Similarly,
in the RAMPS framework, we consider trait MA to be a personality construct. However,
MA is also a cognitive construct (Ashcraft 2019) in that worry or fear during a math task
is an internal process that disrupts the cognitive system while problem solving (Eysenck
1992; Eysenck and Calvo 1992). The most common construct posited to mediate the math
performance–MA relation is WM (Pellizzoni et al. 2021), because MA during a math task is
posited to disrupt WM resources (Ashcraft and Kirk 2001). Thus, little debate remains in
the literature that both (a) WM is important in mathematical problem solving and (b) WM
interacts with MA in some way to predict math outcomes. However, many open questions
remain regarding this interaction. One open question is how metacognition—specifically
online metacognitive experiences—affects the WM–MA interaction. A proposed framework
is presented in Figure 1.
Of course, further research will be required on MA and WM to completely operationalize both constructs. Often in WM research, the term “working memory” is used without a
clear definition (Cowan 2017). Yet, as Cowan points out, at least nine different definitions of
WM and short-term storage currently are used in the WM literature (Cowan 2017, p. 1159).
Perhaps a main reason that the mechanism by which MA exerts its influence is yet to
be fully understood is because of the vast variability in the operationalization of related
constructs, such as WM. WM in math cognition is often referred to as both a system and a
capacity or resource (e.g., Beilock and Carr 2005; Justicia-Galiano et al. 2017; Ng and Lee
2019; Passolunghi et al. 2019; Peng et al. 2016; Ramirez et al. 2013). From our perspective, it
may be easiest to consider WM from an attention-control model (e.g., Engle 2002; Unsworth
and Engle 2007) or a multicomponent system (e.g., Baddeley 2001; Baddeley and Hitch
1974) for the purposes of considering the MA–math performance relation (see the Discussion section for an extended argument and recommendations for researchers). WM is often
referred to as a processing resource or capacity in the MA literature (e.g., Passolunghi et al.
2019); thus, we adopt an attention-control perspective on WM (e.g., Engle 2002).
4.1. The Mechanism of State Math Anxiety
Our primary focus is on the relation between WM and metacognitive experiences
in mathematical problem solving; however, because MA interacts with WM to predict
mathematical problem-solving accuracy, elucidating the mechanism of MA is relevant to
the current argument. Discussions of interventions specifically for MA are outside the
scope of the current paper (but see Barroso et al. 2021; Dowker et al. 2016; Ganley et al. 2021;
Mammarella et al. 2019; Ramirez et al. 2018; Scheibe et al. 2023), but clarifying the causes of
state MA can help explicate the relation between WM and metacognitive experiences in
mathematical problem solving. We focus on WM (as opposed to other executive functions,
Miyake et al. 2000) because WM is the postulated mechanism in the disruption account of
math anxiety; thus, WM is a central component of the RAMPS framework.
4.1.1. The Disruption Account of Math Anxiety
At least three theoretical models of MA currently exist in the math cognition literature.
The most highly cited model of MA (Ramirez et al. 2018) is the “disruption account”
J. Intell. 2023, 11, 117
6 of 18
championed by Ashcraft and colleagues (Ashcraft 2002; Ashcraft and Faust 1994; Ashcraft
and Kirk 2001; Ashcraft and Krause 2007; Faust et al. 1996). This account treats MA as a
cognitive construct and builds on prior work outside the domain of math: the processing
efficiency theory (Eysenck 1992; Eysenck and Calvo 1992). The primary tenets are that
cognitive worry is an internalized process that consumes cognitive resources during an
anxious reaction (Ashcraft 2019). Importantly, Ashcraft (2019) notes that MA can be
disruptive at a dual-task level (e.g., cognitive worry creating task-irrelevant thoughts) or at
a metacognitive level (e.g., failure to inhibit attention to worries, also creating task-irrelevant
thoughts). Note that prior work in math cognition does not clearly label the latter negative
effect of MA as metacognitive, but by the most parsimonious definition of metacognition
(i.e., thinking about thinking; Flavell 1979), directing attention to cognitive worries is
inherently metacognitive. We extend this prior work to explicitly address the differences
between cognitive worry creating a dual-task paradigm and meta-level task-irrelevant
cognitions caused by anxious reactions (see Section 4.2.2 on Phase 2: Progress Evaluations).
Because WM is a processing resource, any moderation of WM on the MA–math
performance relation would be expected to be in-the-moment (i.e., “state”) effects. Thus,
because the disruption account proposes decreased math task performance due to increased
MA through decreased WM resources (see Figure 1), this account would predict state WM
to largely, if not entirely, account for the MA–math performance relation (although see
Ashcraft 2019 for a discussion of MA as a multifaceted phenomenon). Math cognition
researchers disagree regarding the nature of the MA–math performance link in terms of
causal direction (Ashcraft 2019; Carey et al. 2016; Dowker et al. 2016; Mammarella et al.
2019; Ramirez et al. 2018). For example, one explanation is that MA causes a decrease in
math performance due to its in-the-moment effects on mathematical problem solving (the
disruption account; Ashcraft and Kirk 2001). Another explanation is that when people
are not good at math, that deficit causes MA (the deficit account; Maloney 2016). A third
explanation is that the MA–math performance link is driven by how one interprets math
situations (the interpretation account; Ramirez et al. 2018).
We address the disruption account’s state effects of MA on math performance through
WM; however, other accounts (e.g., the deficit approach and the interpretation account; see
Ashcraft 2019; Ramirez et al. 2018) may be better suited to explain how math experiences
inform trait MA. Such relations (e.g., math self-concept predicts MA; Ahmed et al. 2012)
are important in influencing trait MA and trait math ability but are outside the scope of the
current paper and thus are not modeled in Figure 1. Instead, we incorporate qualitative
data and a novel framework to argue for how competing theories of MA might exist
more harmoniously.
4.1.2. Factors Inducing State Math Anxiety
Where do online MA feelings originate? Scheibe et al. (2023)1 collected two samples
of open-ended responses about MA experiences from college students (total n = 673 independent participants). The primary aim of Scheibe et al. (2023) was to assess the efficacy of
different MA interventions (e.g., expressive writing). However, as part of those two studies,
participants answered several open-ended questions about MA, such as: “What types of
situations make you feel the most anxious about math and why?” Open-ended responses to
these questions were analyzed and coded for several different potential causes of MA. As
shown in Table 1, 46.1% of students reported that testing situations or high stakes situations
induced anxiety, 30.5% reported that social pressure or fear of embarrassment induced
anxiety, and 20.3% reported that specific number types induced anxiety.
These qualitative data provide a data-driven perspective on what factors induce
anxiety during math situations. Participants’ responses also provide insights into the
interrelations displayed in Figure 1. For example, one of the primary reasons participants
reported MA is that they were fearful of social judgment, i.e., of embarrassment due to
peer evaluation. It may be much easier to identify as “not a math person” than to put forth
one’s best effort on mathematical problem solving in social situations, make an error, and
J. Intell. 2023, 11, 117
7 of 18
“look like an idiot,” as one participant described it. Thus, it appears that one primary way
that MA might be alleviated in the future is to foster learning environments, both formal
and informal, that allow learners to be incorrect. Fear of failure appears to be a primary
motivator for MA, which often leads to math avoidance (Erickson and Heit 2015; Morsanyi
et al. 2016). Consider for example, one participant’s anecdote:
“For me, it’s being called on by a teacher. Just remembering this now, I remember
one day in elementary I had this one teacher who called on me to answer a
simple fraction problem and I didn’t know the answer to it. The teacher became
frustrated at this, and she kept demanding the right answer. Every single time, I
would guess and get the answer wrong, eventually to the point where she started
yelling at me and I started crying. I think from this point on, I just avoided
being picked on, even if I knew the answer, it really took a toll on my confidence
towards math.”
Table 1. Coding of Participants’ Open-Ended Math Anxiety Responses in (Scheibe et al. 2023).
Code
Code Definition
Examples
Prevalence
Testing
or
High Stakes
Any mention of (a) testing
situations or (b) high-stake
ramifications inducing anxiety.
“Important exams and [the] ACT
because the grade matters a lot.”
46.1%
Social
Pressure
or Embarrassment
Any mention of (a) being
watched, (b) being judged, or
(c) being embarrassed due to
social comparison
inducing anxiety.
Specific
Type
of Math
Any mention of a specific type
of math inducing anxiety (as
opposed to math anxiety as
more of a generality).
Surprise or
Lack of Preparation
Any mention of being put on
the spot to complete math or
having to do math without the
chance for proper preparation
inducing anxiety.
Time Constraints
Any mention of a specific
allotted amount of time
inducing anxiety.
“Exams. I hate tests.”
“When people depend on me or
people are watching me because I
don’t want to disappoint them.”
“When I have to express my
math abilities to others. It’s easy
to mess up, and that would
be embarrassing.”
30.5%
“Anything that requires
percentages and needs to be
quickly determined.”
“Fractions and word problems. I
have never been good at fractions
and word problems can be
very confusing.”
“When I am put on the spot
because I do my best work when I
have time to prepare and study.”
20.3%
10.4%
“Pop quizzes because it
is unexpected.”
“Anything that requires
percentages and needs to be
quickly determined.”
7.7%
“When I have to do it in a
time limit.”
Note. The codes were not mutually exclusive. That is, a participant’s answer could be coded for none of the five
codes, one of the codes, or a combination of multiple codes. An example of this overlap in the coding scheme is
included in the “Time Constraints” and “Specific Type of Math” examples. Authors DAS and CAT coded 25% of
the data with an interrater reliability of 0.95. The authors discussed the few disagreements, and author DAS did
the remaining coding based on the high initial level of agreement between coders.
Note the closing sentence of this anecdote. This participant is demonstrating clear
metacognitive control to avoid math situations due to anxiety based on prior situations.
This is just one example of how intrusive thoughts regarding fear of judgment and ensuing
embarrassment can either (a) disrupt online WM or (b) cause the problem solver to avoid
putting in effort on the problem altogether.
J. Intell. 2023, 11, 117
8 of 18
One way that the induction of MA can affect the WM-metacognitive experiences relation is that online feelings of MA (i.e., state MA) appear to often be driven by metacognitive
judgments. For example, in line with prior research on time-limited testing and anxiety
(Boaler 2014; Devine et al. 2012; Kellogg et al. 1999), qualitative evidence suggests that one
main cause of MA might be time constraints during mathematical problem solving (see
Table 1). In order for problem solvers to feel state MA due to time constraints, they must
make some judgment comparing how much time they have to complete the math task and
how much time they require to complete the task under current conditions. To illustrate this
point, consider two different time constraints. Both scenarios involve solving 20 fraction
addition problems. In scenario A, the time constraint is three hours. In scenario B, the
time constraint is 20 min. In order for the time constraint to be relevant to the problem
solver in scenario A, their average time to complete one fraction addition problem must be
6 min or more. However, to complete all problems in scenario B, the problem solver must
complete one problem per minute. Thus, it could be predicted that whether MA due to
time constraints is experienced should be a function of the problem solver’s evaluation
regarding if they have adequate time. Following this initial assessment, the astute problem
solver will likely re-evaluate their initial assessment based on their progress. For example,
in scenario A, if the problem solver completes the first fraction addition problem in 60 s
and is metacognitively aware that they are well ahead of the schedule they must maintain
to complete all problems on time, they should dismiss the time constraint as a factor, or
at least re-assess at a later point. Note, however, that if the problem solver in scenario B
completes the first problem in 60 s, that would likely induce anxiety due to being exactly
on pace, with little room for error. Thus, metacognitive judgments affect MA both at the
beginning of, and throughout mathematical problem solving.
4.2. Phase Approach to Relations between Working Memory and Metacognitive Experiences
Nelson and Narens’ (1990) seminal framework of metacognition posited that metacognition is a series of evaluations (monitoring) and decisions (control) that connect the meta
level and the object level (Ackerman and Thompson 2015). We applied this framework
to mathematical problem solving with special attention to metacognitive experiences and
the relation between MA and WM (see Figure 1). From an attention-control perspective
of WM (e.g., Engle 2002; Burgoyne and Engle 2020), WM resources are necessary for both
working through the math problem (object level) and the maintenance and updating of
progress (meta level). Metacognitive experiences cannot simply be broken into one construct in a path model (e.g., Figure 1) because these experiences vary in several aspects
including time (i.e., predictive, concurrent, and postdictive) and type of processing (i.e.,
explicit or implicit). Thus, we propose a path model (see Figure 1) that can be revised
and tested, but we also propose a 5-phase framework (see Figure 2) based on monitoring
and control processes (Nelson and Narens 1990). By combining the big picture path approach and the microanalysis of the 5-phase approach, we present a wide range of open
empirical questions.
One facet of metacognition, metacognitive monitoring, is a crucial component in
mathematical problem solving because online metacognitive judgments inform and predict whether a person will initiate, terminate, or change effort in a cognitive task (i.e.,
metacognitive control; Ackerman and Thompson 2017). Metacognition is often studied
by examining judgments given after the problem solving (cf. Özcan and Gümü¸s 2019).
These retrospective judgments are often more accurately aligned with performance than
are predictive judgments (Dunlosky and Metcalfe 2009; Rhodes 2019); thus, retrospective
judgments are often used as a, if not the, measure of metacognition in empirical studies
(Özcan and Gümü¸s 2019). Retrospective judgments are displayed in Figure 1 by Path K,
but note that Path K does not encompass all possible online metacognitive experiences. A
rich variety of meta-reasoning judgments and metacognitive control decisions coincide
with the temporal evolution of solving a cognitive task. It is this cycling of judgments
and metacognitive feedback loops (i.e., online judgments) during mathematical problem
J. Intell. 2023, 11, 117
retrospective judgments are often more accurately aligned with performance than are
predictive judgments (Dunlosky and Metcalfe 2009; Rhodes 2019); thus, retrospective
judgments are often used as a, if not the, measure of metacognition in empirical studies
(Özcan and Gümüş 2019). Retrospective judgments are displayed in Figure 1 by Path K,
but note that Path K does not encompass all possible online metacognitive experiences. A
9 of 18
rich variety of meta-reasoning judgments and metacognitive control decisions coincide
with the temporal evolution of solving a cognitive task. It is this cycling of judgments and
metacognitive feedback loops (i.e., online judgments) during mathematical problem
solving
solving that
that the
the proposed
proposed framework
framework in
in Figure
Figure 22 highlights.
highlights. An
An important
important note
note about
about
Figure
Figure 2:
2: This figure was designed
designed to apply
apply Nelson
Nelson and
and Narens’
Narens’ framework
framework to the
the path
path
model
model displayed
displayed in
in Figure
Figure 11 by
by incorporating
incorporating the
the data from
from Table
Table 1.
1. In
In other
other words,
words, traits
traits
certainly
2 applies
a
certainly affect
affect problem
problemsolvers
solvers(e.g.,
(e.g.,paths
pathsB,B,C,C,and
andDDininFigure
Figure1),1),but
butFigure
Figure
2 applies
phase
approach
to
the
online
relations
between
WM,
MA,
and
metacognitive
experiences
a phase approach to the online relations between WM, MA, and metacognitive
(i.e.,
paths E, (i.e.,
F, G,paths
and J E,
in F,
Figure
1) that
affect problem-solving
performance. Each
phase of
experiences
G, and
J in Figure
1) that affect problem-solving
performance.
Figure
2 will be
separately
with down
mention
of specific with
metacognitive
Each phase
ofbroken
Figuredown
2 will
be broken
separately
mention experiences
of specific
based
on prior experiences
work (Ackerman
Thompson
2017; Efklides
For an
example
to
metacognitive
based and
on prior
work (Ackerman
and 2006).
Thompson
2017;
Efklides
illustrate
wetopresent
the following
problem
takenthe
from
the cognitive
reflection
2006). Forthe
anphases,
example
illustrate
the phases,
we present
following
problem
taken
test
2005):
If it takes
min to
5 widgets,
how
longtowould
from(Frederick
the cognitive
reflection
test5 machines
(Frederick52005):
If make
it takes
5 machines
5 min
make it5
take
100
machines
to
make
100
widgets?
widgets, how long would it take 100 machines to make 100 widgets?
Figure 2.
2. 5-Phase
5-Phase Framework
Framework of
of Monitoring
Monitoring and
and Control
Control in
in Mathematical
Mathematical Problem
ProblemSolving.
Solving.
Figure
4.2.1.
4.2.1. Phase 1: Initial Evaluation
What
What cues
cues do people use to evaluate a problem when they are first presented with it?
Depending
Depending on
on the
the time
time constraints
constraints and
and how much problem
problem solvers
solvers are
are motivated
motivated to
to make
make
their
best
effort
on
a
problem,
a
variety
of
explicit
judgments
and
implicit
feelings
their best effort on a problem, a variety of explicit judgments and implicit feelings might
might
be
judgment displayed
displayed in Figure 2 is “Can I solve
be employed.
employed. The primary monitoring judgment
solve
this?”
judgment
can occur
explicitly
or implicitly—a
common theme
for metacognitive
this?” This
This
judgment
can occur
explicitly
or implicitly—a
common
theme for
judgments
during
mathematical
problem
solving problem
is that they
are often
automatized
metacognitive
judgments
during
mathematical
solving
is that
they are (thus,
often
becoming
not
metacognitive
by
most
definitions,
Flavell
1979;
Hacker
1998).
However,
automatized (thus, becoming not metacognitive by most definitions, Flavell 1979; Hacker
whether
an explicitwhether
questionan
to explicit
the self or
an implicit
metacognitive
feeling (Efklides
2006),
1998). However,
question
to the
self or an implicit
metacognitive
the
answer
to
this
initial
monitoring
evaluation
will
dictate
whether
the
problem
solver
feeling (Efklides 2006), the answer to this initial monitoring evaluation will dictate
attempts
the individual
believes
themself
toindividual
be entirely believes
incapablethemself
of solving
whether the
the problem.
problem If
solver
attempts the
problem.
If the
to
the problem, what use is there to try? But how do people make this initial evaluation?
According to prior research on meta-reasoning, people make an initial judgment of
solvability (Ackerman and Thompson 2017; Thompson 2009; Topolinski and Strack 2009) at
the onset of a problem. Importantly, this judgment is not only that the problem is solvable
(i.e., it is possible that an expert could solve the problem), but that the problem is solvable by
the problem solver (i.e., it is possible that that person can solve the problem; Ackerman and
Thompson 2017). We argue that people use many cues to inform this decision in the domain
of mathematical problem solving, such as: (a) feeling of familiarity, (b) initial feeling of
difficulty, and (c) math self-perceptions. Feeling of familiarity refers to the sense of previous
occurrence with a stimulus (Efklides 2006; Nelson et al. 1998). Feeling of difficulty is a
J. Intell. 2023, 11, 117
10 of 18
sense of challenge associated with a particular problem due to perceived likelihood of error,
lack of available response, or the need to invest more time or effort (Efklides 2006; Efklides
et al. 1999). Feeling of familiarity tends to be associated with positive affect and feeling of
difficulty tends to be associated with negative affect (Efklides 2006). That is, people tend to
like familiar stimuli, and dislike challenging stimuli. Finally, math self-perceptions are an
aggregated individual difference specific to the domain of math that influences people’s
motivation to interact with math stimuli (Lee 2009). These self-perceptions include math
self-concept (Ahmed et al. 2012), math self-efficacy (Pajares and Miller 1996), and math
attitudes (Mielicki et al. 2022; Sidney et al. 2021). Math self-perceptions act as a phase 1
cue. Even if the other feelings are at odds with math self-perceptions, math self-perceptions
might override them. For example, consider a person who feels that the machines and
widgets problem is both unfamiliar and difficult, yet they consider themself to be good
at math, so they evaluate it as solvable anyway, despite the conflict between the cues.
Interpretation of these feelings leads to a decision on the initial judgment of solvability,
which directly affects the control decisions in phase 1. That is, an individual who judges
that they are 100% capable of solving a problem is much more likely to not only attempt
the problem, but to attempt it with a motivated effort. At this point, the problem solver is
ready to choose a strategy and begin the problem.
4.2.2. Phase 2: Progress Evaluation
During phase 2, problem solvers attempt to start making progress toward the solution
and metacognitive and affective influences contribute. The problem solver can advance
toward the solution by engaging in appropriate mathematical steps (cognitive processes),
ideally while evaluating the efficacy of the steps (metacognitive processes) and inhibiting
task-irrelevant distractors (e.g., MA) that can interfere with WM resources necessary for
solving the problem (recall that we define WM as a processing resource of limited capacity;
Baddeley and Logie 1999).
Metacognitive processes in phase 2 center on the question, “Am I making progress
toward the solution?” (see Figure 2). Two facets involved in feelings of difficulty are
estimates of effort and estimates of time required for problem solving (Efklides 2006;
Efklides et al. 1999). Problem solvers generate feelings about these factors, whether implicit
or explicit, during phase 1 while attempting to generate a judgment of solvability. Thus, by
phase 2, problem solvers have an existing expectation for how long they feel the problem
should be taking them to solve and how much effort it should require. These estimates
during phase 1 are informed by a variety of factors specific to the problem solver (e.g., math
self-efficacy; Pajares and Miller 1996), the stimulus type (e.g., fractional components versus
whole number components; Mielicki et al. 2022), and environmental factors (e.g., time
constraints; Scheibe et al. 2023, mood; Efklides and Petkaki 2005). All of these factors are
considered, typically implicitly, and the problem solver develops feelings about appropriate
effort and timing. During phase 2, these feelings and expectations are compared to the
progress being made on the problem.
Comparison to expectations can elicit positive affect (e.g., elation, eagerness, relief,
or calmness; Carver and Scheier 1998) or negative affect (e.g., sadness, depression, fear,
or anxiety; Carver and Scheier 1998). For example, if progress toward the solution comes
quickly and easily to an individual who was expecting the problem to take a lengthy
amount of time to solve, that person may experience positive affect due to being above
their expected baseline in terms of effort and timing. The opposite is unfortunately also
true. Underperforming against expectations of effort and timing often leads to negative
affect, particularly MA. Feelings of fear or apprehension related to math stimuli (i.e., MA,
Richardson and Suinn 1972) often are paired with physiological responses (Pizzie and
Kraemer 2021), similar to other forms of anxiety (Dowker et al. 2016). Anxious responses
include hands shaking, palms sweating, heart racing, limbs bouncing, and feeling like one’s
brain is overwhelmed. These reactions are particularly important for two reasons. First, by
the disruption account of MA (Ashcraft 2019; Ashcraft and Kirk 2001; Faust et al. 1996), MA
J. Intell. 2023, 11, 117
11 of 18
depletes available WM resource by introducing task-irrelevant thoughts, thereby causing
decreased math performance. We also extend the argument of the disruption account
to posit that MA not only affects available WM resources, but MA itself, in the form of
physiological responses, is a metacognitive cue for problem solvers. That is, state MA is a
dual burden in that it directly taxes WM resources with task-irrelevant processing, but it
can also be an observable cue that may lead to further distractions from the task.
To illustrate this point, consider an individual who notices that they are struggling
with the machines and widgets problem. They thought they would probably be able to
solve it without much time or effort (phase 1 monitoring judgment), but now that they
have begun to try and solve the problem, they do not know where to begin. After several
moments of not making any progress, they notice that their leg is bouncing and their brain
suddenly feels clogged. These anxious physiological responses are an activation of the
autonomic nervous system, and although the math problem presents no physical danger,
the problem solver has a decision to make: fight or flight.
Control decisions based on monitoring evaluations of not making progress during
phase 2 include giving up or skipping (flight) the current problem (more likely with
individuals experiencing high levels of state MA; Bellon et al. 2021), or pivoting to a
different strategy (fight; Berardi-Coletta et al. 1995). If instead problem solvers evaluate that
they are making progress, they are likely to continue taking steps with their current strategy.
4.2.3. Phase 3: Intermediate Evaluation
At phase three, problem solvers generate an initial response and must decide whether
to provide that response as their answer, or continue working on the problem (see Ackerman
and Thompson 2017, Figure 1, p. 611). Prior research on meta-reasoning by Ackerman,
Thompson, and colleagues (Ackerman and Thompson 2017; see also Ackerman 2014;
Ackerman and Beller 2017; Thompson 2009; Thompson and Johnson 2014; Thompson et al.
2011, 2013) proposed different possibilities for how problem solvers develop a final answer.
Two of these possibilities are the Metacognitive Reasoning Theory (see Thompson 2009) and
the Diminishing Criterion Model (see Ackerman 2014). The Diminishing Criterion Model
(Ackerman 2014; Ackerman and Thompson 2017) informs the mechanism of how problem
solvers reach a final answer. During phase 3, problem solvers make one or more internal
evaluations about the accuracy of potential solutions (Ackerman and Thompson 2017).
These evaluations are captured by judgments of intermediate confidence. According to the
Diminishing Criterion Model (Ackerman 2014), as time passes during the problem-solving
process, problem solvers are increasingly more likely to provide a final response that they
endorse with less confidence.
Meta-reasoning research has involved tasks that are more mathematical (e.g., cognitive reflection task; Frederick 2005) and less mathematical (e.g., remote associates test;
Mednick 1962). We argue that meta-reasoning research effectively informs metacognitive
research in mathematics because math is fundamentally relational in nature (Thompson
et al. 2023). Thus, even though many people treat math differently than other academic
subjects (Erickson and Heit 2015), and it has been argued that MA might be similar to a
specific phobia (Ashcraft and Ridley 2005), the RAMPS framework is informed by several
existing parallels from prior research in neighboring domains.
4.2.4. Phase 4: Second Progress Evaluation
Phase 4 is a combination and extension of phase 2 and phase 3. This phase is similar to
phase 2 in that it involves active problem solving with monitoring components focused on
evaluations of progress. These evaluations are based on similar cues to phase 2: comparison
to expectations of ease, effort, and time required, and monitoring of physiological reactions
(e.g., MA). The same interactions between metacognitive experiences, MA, and WM that
are present in phase 2 are also present in phase 4. These are the active problem-solving
phases. Based on the metacognitive judgments and feelings in phase 4, problem solvers can
continue working on the problem based on their current strategy, change strategy again, or
J. Intell. 2023, 11, 117
12 of 18
give up. Note that phases 3 and 4 can repeat in multiple sequential loops depending on how
many different strategies the problem solver attempts prior to reaching the diminishing
criterion for confidence (Ackerman 2014). Eventually, a final answer is provided, which
takes the problem solver to phase 5.
4.2.5. Phase 5: Final Answer Evaluation
Phase 5 is all about the final answer. Problem solvers engage in several possible
solutions during problem solving that could become the final solution, but if more active
problem solving or strategy switching takes place following coming up with the solution,
such efforts would fall under phases 3 and 4; not phase 5. Phase 5 is the retrospective
counterpart to the predictive phase 1. Just as in phase 1 problem solvers make judgments
about solvability, how hard the problem might be, how prepared they are to attempt to
solve the problem, how familiar they are with the problem features, etc. Problem solvers
are capable of making explicit metacognitive judgments in phase 5 based on implicit
judgments and feelings. Common examples of retrospective metacognitive judgments are
confidence judgments (e.g., “How confident are you in your answer, from 0% = Not at all
confident to 100% = Completely confident?”; Dunlosky and Metcalfe 2009; Fitzsimmons
et al. 2020; Rhodes 2019; Scheibe et al. 2022). But what cues do participants use to make
these judgments, and why are they important?
According to Ackerman and Thompson (2017), problem solvers make judgments
including final confidence, feeling of error, and final judgment of solvability. Further
metacognitive feelings include judgment of solution correctness (Efklides 2006) and feeling
of satisfaction (Efklides 2002, 2006). Collectively, problem solvers have a sense of whether
they committed an error, they might be right, or they are certainly right (Ackerman and
Thompson 2017; Efklides 2006; Fitzsimmons et al. 2020; Gangemi et al. 2015). These feelings
are not foolproof; indeed, even though retrospective judgments are better predictors of task
accuracy than predictive judgments, they are rarely perfectly aligned with accuracy (Rhodes
2019). Problem-solvers’ feelings about their final solution may affect task performance and
more broadly their own self-perceptions.
For example, consider an individual who has spent approximately a minute trying
to solve the widgets and machines problem. That person considered multiple different
strategies and attempted the problem from multiple angles, yet is still not confident with
the solution they chose. A relevant task-specific effect might be that they have multiple
different problems to solve and will approach the next problem differently based on their
low confidence about their answer to the widgets and machine problem (see Path K in
Figure 1). If multiple low-confidence judgments are made during one session, it is also
possible that the individual will assimilate these judgments into their self-perceptions (e.g.,
“I thought I was good at math, but I did not know how to solve any of these problems, so
maybe I am not as good as I thought”). Both the task-specific and downstream implications
are discussed in future directions.
5. Conclusions and Future Directions
We proposed the RAMPS framework based on prior work on metacognition (Efklides
2006; Nelson and Narens 1990) and meta-reasoning (Ackerman and Thompson 2017). For
the remainder of the paper, we discuss why the domain of math is a logical extension of
meta-reasoning research, future extensions of the RAMPS framework, and how it could
inform future interventions.
5.1. Extending Meta-Reasoning into Mathematics
Meta-reasoning researchers have argued that the processes of thinking and reasoning
might easily be described using models of memory (Thompson and Feeney 2015, p. 7).
We argue that this logic can be extended to the domain of mathematics. Indeed, metareasoning research often overlaps with mathematical concepts (e.g., cognitive reflection;
Ackerman and Thompson 2017). The close connections between metacognitive processes
J. Intell. 2023, 11, 117
13 of 18
and meta-reasoning are likely due to an underlying factor in both: relational reasoning.
Both reasoning tasks and math tasks often involve the ability to apply rules and transfer
knowledge to novel domains. Thus, the RAMPS framework significantly relies on prior
work in meta-reasoning to draw extensions into the domain of math.
The RAMPS framework is novel in that it incorporates a path model involving state
and trait MA, as well as metacognitive experiences and WM in predicting mathematical
problem solving. We also offered a five-phase framework to zoom in on the central, yet
recursive, components of the RAMPS framework to describe the cues that problem solvers
may use. Focusing solely on retrospective judgments may provide valuable post-hoc information about problem solving, but doing so overlooks the wealth of cues and judgments
made during phases 1–4 (see Figure 2). Additionally, WM is crucial to mathematical problem solving (Ashcraft and Kirk 2001; Peng et al. 2016). The RAMPS framework adopts
an attentional-control (Engle 2002) model of WM. Indeed, we refer to the focal point of
our framework as regulated attention. Attention-control is just one of several WM models,
however (Cowan 2017), and future research should investigate the best model(s) of WM to
employ (e.g., Ng and Lee 2019) for research at the nexus of metacognition, math cognition,
and cognitive science.
5.2. Extensions, Interventions, and Future Directions
A primary aim is to propose clarifying relations between metacognitive experiences,
WM, and MA in mathematical problem solving. Theoretical contributions to elucidate
these relations are valuable; but could this work be extended to improve mathematical
problem-solving outcomes? That is, could metacognitive experiences be manipulated to
decrease state MA and thus relieve the task-irrelevant taxing of WM? These and many other
open questions should be investigated using experimental methods to explore and test
the RAMPS framework. For example, to date, light-touch MA interventions have mostly
been unsuccessful (Ganley et al. 2021; Scheibe et al. 2023). It is possible that understanding
metacognitive processes and developing interventions based on this understanding might
be a promising new frontier in MA interventions (Morsanyi et al. 2019). Future research
can manipulate the number of metacognitive experiences or draw participants’ attention to
specific metacognitive experiences during problem solving to attempt to affect participants’
task performance and task interpretation. Indeed, the recently proposed interpretation
account of MA (Ramirez et al. 2018) focuses not on the math situations or an individual’s
mathematical ability, but meta-level interpretation of math stimuli to be the cause of
anxious reactions. We posit that the RAMPS framework may help to bridge the gap and
help interdisciplinary researchers understand interrelations at the nexus of metacognitive
research, cognitive research, clinical research, and research specifically on math cognition.
Future research should target specific components of the RAMPS framework. One
way to do this is to test the RAMPS from a structural equation modeling approach. A
challenge in conducting this type of research is that operationalizing metacognitive regulation in mathematical problem solving can be difficult (Zepeda and Nokes-Malach 2023).
Researchers must develop creative designs to tap both explicit and implicit processes. Recall that what might be an explicit step-by-step process for a novice problem solver might
be an implicit recall process for an expert. Thus, individual differences in mathematical
problem-solving ability pose unique research challenges that future research should aim
to address. Additionally, future research could delve into the five-phase approach to empirically test or manipulate the cues used in mathematical problem solving. For example,
Fitzsimmons and Thompson (2022, 2023) presented participants with familiar or unfamiliar
fractions in order to manipulate the cues (e.g., familiarity) participants used to determine
their confidence with predicting where to place the fractions on a number line. This is
one of many ways to manipulate participants’ reliance on individual cues that are used
during mathematical problem solving. Similar procedures could be used to manipulate
the salience or presence of cues used in mathematical problem solving to test different
elements of the RAMPS framework.
J. Intell. 2023, 11, 117
14 of 18
Future research could also use the RAMPS framework to investigate other open
questions in math cognition, such as why women often report greater levels of MA (Devine
et al. 2012) and lower levels of confidence (Rivers et al. 2020) than do men, despite having
equal math abilities. It is possible that research methodologies inspired by the RAMPS
framework might lead to a deeper understanding of this issue. Gender differences are just
one example of a long-discussed topic in math cognition that could potentially benefit from
research derived from the RAMPS framework.
5.3. Final Thoughts
The current paper has offered a novel framework for future research at the nexus of
math cognition, WM, and metacognition. Many open questions remain in the RAMPS
framework, and many empirical studies must be conducted to test the claims we have made
herein. It is our hope that the current conceptualization of relations between WM, MA,
and metacognitive experiences during mathematical problem solving will be provocative
and facilitate future interdisciplinary work. Morsanyi et al. (2019) recently proposed
that research on metacognitive processes, MA, and WM has the potential to “significantly
expand the scope of metacognitive investigations and provide novel insights into individual
differences in the metacognitive regulation of learning and problem solving” (Morsanyi
et al. 2019, p. 147). We thoroughly endorse this view, and hope that interested readers will
join us in seeking empirical answers to the open questions.
Author Contributions: Conceptualization, D.A.S., C.A.T., C.A.W. and J.D.; methodology, D.A.S. and
C.A.T.; software, D.A.S. and C.A.T.; validation, D.A.S., C.A.W. and C.A.T.; formal analysis, D.A.S.
and C.A.T.; investigation, D.A.S. and C.A.T.; resources, D.A.S. and C.A.T.; data curation, D.A.S. and
C.A.T.; writing—original draft preparation, D.A.S., C.A.T., C.A.W. and J.D.; writing—review and
editing, D.A.S., C.A.T., C.A.W. and J.D.; visualization, D.A.S.; supervision, C.A.T., C.A.W. and J.D.;
project administration, D.A.S. and C.A.T.; funding acquisition, C.A.T. and D.A.S. All authors have
read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Data Availability Statement: The qualitative data presented in Table 1 are part of a currently embargoed dataset. They will be publicly available on OSF at the time of the publication of Scheibe
et al. (2023).
Conflicts of Interest: The authors declare no conflict of interest.
Notes
1
The qualitative data presented herein (see Table 1) were analyzed using codes generated separately from Scheibe et al. (2023) and
have not been analyzed or published in any other outlet.
References
Ackerman, Rakefet. 2014. The diminishing criterion model for metacognitive regulation of time investment. Journal of Experimental
Psychology: General 143: 1349–68. [CrossRef] [PubMed]
Ackerman, Rakefet, and Yael Beller. 2017. Shared and distinct cue utilization for metacognitive judgements during reasoning and
memorisation. Thinking and Reasoning 23: 376–408. [CrossRef]
Ackerman, Rakefet, and Valerie A. Thompson. 2015. Meta-reasoning: What can we learn from meta-memory? In Reasoning as Memory.
Edited by Aidan Feeney and Valerie Thompson. London: Psychology Press, pp. 164–82.
Ackerman, Rakefet, and Valerie A. Thompson. 2017. Meta-Reasoning: Monitoring and Control of Thinking and Reasoning. Trends in
Cognitive Sciences 21: 607–17. [CrossRef] [PubMed]
Ahmed, Wondimu, Alexander Minnaert, Hans Kuyper, and Margaretha Van der Werf. 2012. Reciprocal relationships between math
self-concept and math anxiety. Learning and Individual Differences 22: 385–89. [CrossRef]
Ashcraft, Mark H. 2002. Math Anxiety: Personal, Educational, and Cognitive Consequences. Current Directions in Psychological Science
11: 181–85. [CrossRef]
J. Intell. 2023, 11, 117
15 of 18
Ashcraft, Mark H. 2019. Models of math anxiety. In Mathematics Anxiety: What Is Known and What Is Still to Be Understood. Edited by
Irene C. Mammarella, Sara Caviola and Ann Dowker. Abingdon: Routledge, pp. 1–19.
Ashcraft, Mark H., and Michael W. Faust. 1994. Mathematics anxiety and mental arithmetic performance: An exploratory investigation.
Cognition & Emotion 8: 97–125. [CrossRef]
Ashcraft, Mark H., and Elizabeth P. Kirk. 2001. The relationships among working memory, math anxiety, and performance. Journal of
Experimental Psychology: General 130: 224–37. [CrossRef]
Ashcraft, Mark H., and Jeremy A. Krause. 2007. Working memory, math performance, and math anxiety. Psychonomic Bulletin & Review
14: 243–48. [CrossRef]
Ashcraft, Mark H., and Kelly S. Ridley. 2005. Math anxiety and its cognitive consequences: A tutorial review. In Handbook of
Mathematical Cognition. Edited by Jamie I. D. Campbell. London: Psychology Press, pp. 315–27.
Baddeley, A. 2001. Is working memory still working? American Psychologist 56: 851–64. [CrossRef]
Baddeley, Alan D., and Graham Hitch. 1974. Psychology of Learning and Motivation. Working Memory 8: 47–89. [CrossRef]
Baddeley, Alan D., and Robert H. Logie. 1999. Working memory: The multiple-component model. In Models of Working Memory:
Mechanisms of Active Maintenance and Executive Control. Edited by Akira Miyake and Priti Shah. Cambridge: Cambridge University
Press, pp. 28–61. [CrossRef]
Barroso, Connie, Colleen M. Ganley, Amanda L. McGraw, Elyssa A. Geer, Sara A. Hart, and Mia C. Daucourt. 2021. A meta-analysis of
the relation between math anxiety and math achievement. Psychological Bulletin 147: 134–68. [CrossRef] [PubMed]
Beilock, Sian L., and Thomas H. Carr. 2005. When High-Powered People Fail. Psychological Science 16: 101–5. [CrossRef]
Bellon, Elien, Wim Fias, and Bert De Smedt. 2019. More than number sense: The additional role of executive functions and
metacognition in arithmetic. Journal of Experimental Child Psychology 182: 38–60. [CrossRef]
Bellon, Elien, Wim Fias, and Bert De Smedt. 2021. Too anxious to be confident? A panel longitudinal study into the interplay of
mathematics anxiety and metacognitive monitoring in arithmetic achievement. Journal of Educational Psychology 113: 1550–64.
[CrossRef]
Berardi-Coletta, Bernadette, Linda S. Buyer, Roger L. Dominowski, and Elizabeth R. Rellinger. 1995. Metacognition and problem
solving: A process-oriented approach. Journal of Experimental Psychology: Learning, Memory, and Cognition 21: 205–23. [CrossRef]
Boaler, Jo. 2014. Research Suggests that Timed Tests Cause Math Anxiety. Teaching Children Mathematics 20: 469–74. [CrossRef]
Burgoyne, Alexander P., and Randall W. Engle. 2020. Attention Control: A Cornerstone of Higher-Order Cognition. Current Directions
in Psychological Science 29: 624–30. [CrossRef]
Carey, Emma, Francesca Hill, Amy Devine, and Dénes Szucs. 2016. The chicken or the egg? The direction of the relationship between
mathematics anxiety and mathematics performance. Frontiers in Psychology 6. [CrossRef]
Carver, Charles. 2003. Pleasure as a sign you can attend to something else: Placing positive feelings within a general model of affect.
Cognition and Emotion 17: 241–61. [CrossRef]
Carver, Charles S., and Michael F. Scheier. 1998. On the Self-Regulation of Behavior. Cambridge: Cambridge University Press.
Caviola, Sara, Enrico Toffalini, David Giofrè, Jessica Mercader Ruiz, Dénes Szucs,
˝ and Irene C. Mammarella. 2022. Math Performance
and Academic Anxiety Forms, from Sociodemographic to Cognitive Aspects: A Meta-analysis on 906,311 Participants. Educational
Psychology Review 34: 363–99. [CrossRef]
Chen, Edward H., and Drew H. Bailey. 2021. Dual-task studies of working memory and arithmetic performance: A meta-analysis.
Journal of Experimental Psychology: Learning, Memory, and Cognition 47: 220–33. [CrossRef]
Cowan, Nelson. 2017. The many faces of working memory and short-term storage. Psychonomic Bulletin & Review 24: 1158–70.
[CrossRef]
Dehaene, Stanislas. 2011. The Number Sense, 2nd ed. Oxford: Oxford University Press.
Devine, Amy, Kayleigh Fawcett, Dénes Szucs,
˝ and Ann Dowker. 2012. Gender differences in mathematics anxiety and the relation to
mathematics performance while controlling for test anxiety. Behavioral and Brain Functions 8: 33–33. [CrossRef]
Dowker, Ann, Amar Sarkar, and Chung Yen Looi. 2016. Mathematics anxiety: What have we learned in 60 Years? Frontiers in Psychology
7: 508. [CrossRef]
Dreger, Ralph Mason, and Lewis R. Aiken. 1957. The identification of number anxiety in a college population. Journal of Educational
Psychology 48: 344–51. [CrossRef]
Dunlosky, John, and Janet Metcalfe. 2009. Metacognition. Thousand Oaks: Sage Publications.
Dunlosky, John, and Thomas O. Nelson. 1992. Importance of the kind of cue for judgments of learning (JOL) and the delayed-JOL
effect. Memory & Cognition 20: 374–80. [CrossRef]
Efklides, Anastasia. 2002. Feelings and judgments as subjective evaluations of cognitive processing: How reliable are they? Psychology:
The Journal of the Hellenic Psychological Society 9: 163–84.
Efklides, Anastasia, and Chryssoula Petkaki. 2005. Effects of mood on students’ metacognitive experiences. Learning and Instruction 15:
415–31. [CrossRef]
Efklides, Anastasia. 2006. Metacognition and affect: What can metacognitive experiences tell us about the learning process? Educational
Research Review 1: 3–14. [CrossRef]
Efklides, Anastasia, Akilina Samara, and Marina Petropoulou. 1999. Feeling of difficulty: An aspect of monitoring that influences
control. European Journal of Psychology of Education 14: 461–76. [CrossRef]
Engle, Randall W. 2002. Working memory capacity as executive attention. Current Directions in Psychological Science 11: 19–23. [CrossRef]
J. Intell. 2023, 11, 117
16 of 18
Erickson, Shanna, and Evan Heit. 2015. Metacognition and confidence: Comparing math to other academic subjects. Frontiers in
Psychology 6: 742. [CrossRef]
Eysenck, Michael W. 1992. Anxiety: The Cognitive Perspective. Hove: Erlbaum.
Eysenck, Michael W., and Manuel G. Calvo. 1992. Anxiety and performance: The processing efficiency theory. Cognition and Emotion 6:
409–34. [CrossRef]
Faust, Michael W., Mark H Ashcraft, and David E. Fleck. 1996. Mathematics Anxiety Effects in Simple and Complex Addition.
Mathematical Cognition 2: 25–62. [CrossRef]
Fitzsimmons, Charles J., and Clarissa A. Thompson. 2022. Developmental differences in monitoring accuracy and cue use when
estimating whole-number and fraction magnitudes. Cognitive Development 61: 101148. [CrossRef]
Fitzsimmons, Charles J., and Clarissa A. Thompson. 2023. Why is monitoring accuracy so poor in number line estimation? The
importance of valid cues and systematic variability for U.S. college students. Metacognition and Learning 1–32. [CrossRef]
Fitzsimmons, Charles J., Clarissa A. Thompson, and Pooja G. Sidney. 2020. Confident or familiar? The role of familiarity ratings in
adults’ confidence judgments when estimating fraction magnitudes. Metacognition and Learning 15: 215–31. [CrossRef]
Flavell, John H. 1979. Metacognition and cognitive monitoring: A new area of cognitive–developmental inquiry. American Psychologist
34: 906–11. [CrossRef]
Frederick, Shane. 2005. Cognitive reflection and decision making. Journal of Economic Perspectives 19: 25–42. [CrossRef]
Gangemi, Amelia, Sacha Bourgeois-Gironde, and Francesco Mancini. 2015. Feelings of error in reasoning—In search of a phenomenon.
Thinking & Reasoning 21: 383–96. [CrossRef]
Ganley, Colleen M., Rachel A. Conlon, Amanda L. McGraw, Connie Barroso, and Elyssa A. Geer. 2021. The effect of brief anxiety
interventions on reported anxiety and math test performance. Journal of Numerical Cognition 7: 4–19. [CrossRef]
Hacker, Douglas J. 1998. Self-regulated comprehension during normal reading. In Metacognition in Educational Theory and Practice.
Edited by Douglas J. Hacker, John Dunlosky and Arthur C. Graesser. Hillsdale: Lawrence Erlbaum Associates Publishers, pp.
165–91.
Handel, Michael J. 2016. What do people do at work? Journal for Labour Market Research 49: 177–97. [CrossRef]
Hembree, Ray. 1990. The Nature, Effects, and Relief of Mathematics Anxiety. Journal for Research in Mathematics Education 21: 33.
[CrossRef]
Hertzog, Christopher, and Roger A. Dixon. 1994. Metacognitive development in adulthood and old age. In Metacognition: Knowing
about Knowing. Edited by Janet Metcalfe and Arthur Shimamura. Bradford: MIT Press, pp. 227–51.
Justicia-Galiano, José M., Eva M. Martín-Puga, Rocío Linares, and Santiago Pelegrina. 2017. Math anxiety and math performance in
children: The mediating roles of working memory and math self-concept. British Journal of Educational Psychology 87: 573–89.
[CrossRef] [PubMed]
Kellogg, Jeffry S., Derek R. Hopko, and Mark H. Ashcraft. 1999. The Effects of Time Pressure on Arithmetic Performance. Journal of
Anxiety Disorders 13: 591–600. [CrossRef]
Koriat, Asher, and Ravit Levy-Sadot. 1999. Processes underlying metacognitive judgments: Information-based and experience-based
monitoring of one’s own knowledge. In Dual-Process Theories in Social Psychology. Edited by Shelly Chaiken and Yaacov Trope.
New York: The Guilford Press, pp. 483–502.
Lee, Jihyun. 2009. Universals and specifics of math self-concept, math self-efficacy, and math anxiety across 41 PISA 2003 participating
countries. Learning and Individual Differences 19: 355–65. [CrossRef]
Lester, Frank K., and Jinfa Cai. 2016. Can Mathematical Problem Solving Be Taught? Preliminary Answers from 30 Years of Research. In
Posing and Solving Mathematical Problems. Edited by Patricio Felmer, Erkki Pehkonen, Jeremy Kilpatrick. Berlin and Heidelberg: Springer,
pp. 117–35. [CrossRef]
Ma, Xin. 1999. A Meta-Analysis of the Relationship between Anxiety toward Mathematics and Achievement in Mathematics. Journal
for Research in Mathematics Education 30: 520. [CrossRef]
Maloney, Erin A. 2016. Math anxiety: Causes, consequences, and remediation. In Handbook of Motivation at School, 2nd ed. Edited by
Kathryn R. Wentzel and David B. Miele. Abingdon: Routledge, pp. 408–23.
Mammarella, Irene C., Sara Caviola, and Ann Dowker. 2019. Concluding remarks. In Mathematics Anxiety: What Is Known and What Is
Still to Be Understood. Edited by Irene C. Mammarella, Sara Caviola and Ann Dowker. Abingdon: Routledge, pp. 211–21.
Matlin, Margaret W. 2013. Cognitive Psychology. Hoboken: Wiley.
Mednick, Sarnoff A. 1962. The associative basis of the creative process. Psychological Review 69: 220–32. [CrossRef]
Mielicki, Marta K., Lauren K. Schiller, Charles J. Fitzsimmons, Daniel Scheibe, and Clarissa A. Thompson. 2022. Perceptions of ease and
difficulty, but not growth mindset, relate to specific math attitudes. British Journal of Educational Psychology 92: e12472. [CrossRef]
Miyake, Akira, Naomi Friedman, Michael J. Emerson, Alexander H. Witzki, Amy Howerter, and Tor D. Wager. 2000. The Unity and
Diversity of Executive Functions and Their Contributions to Complex “Frontal Lobe” Tasks: A Latent Variable Analysis. Cognitive
Psychology 41: 49–100. [CrossRef] [PubMed]
Morsanyi, Kinga, Irene C. Mammarella, Dénes Szücs, Carlo Tomasetto, Caterina Primi, and Erin A. Maloney. 2016. Editorial:
Mathematical and Statistics Anxiety: Educational, Social, Developmental and Cognitive Perspectives. Frontiers in Psychology 7:
1083. [CrossRef] [PubMed]
Morsanyi, Kinga, Niamh Ní Cheallaigh, and Rakafet Ackerman. 2019. Mathematics Anxiety and Metacognitive Processes: Proposal for
a new line of inquiry. Psihologijske Teme 28: 147–69. [CrossRef]
J. Intell. 2023, 11, 117
17 of 18
Namkung, Jessica M., Peng Peng, and Xin Lin. 2019. The Relation between Mathematics Anxiety and Mathematics Performance
Among School-Aged Students: A Meta-Analysis. Review of Educational Research 89: 459–96. [CrossRef]
Nelson, Thomas O., Arie W. Kruglanski, and John Jost. 1998. Knowing thyself and others: Progress in metacognitive social psychology.
In Metacognition: Cognitive and Social Dimensions. Edited by Vincent Y. Yzerbyt, Guy Lories and Benoit Dardenne. Wallsend: Sage,
pp. 69–89.
Nelson, Thomas O., and Louis Narens. 1990. Metamemory: A Theoretical Framework and New Findings. The Psychology of Learning
and Motivation 26: 125–73. [CrossRef]
Ng, Ee Lynn, and Kerry Lee. 2019. The different involvement of working memory in math and test anxiety. In Mathematics Anxiety:
What Is Known and What Is Still to Be Understood. Edited by Irene C. Mammarella, Sara Caviola and Ann Dowker. Abingdon:
Routledge.
Özcan, Zeynep Çigdem,
˘
and Aynur Eren Gümü¸s. 2019. A modeling study to explain mathematical problem-solving performance
through metacognition, self-efficacy, motivation, and anxiety. Australian Journal of Education 63: 116–34. [CrossRef]
Pajares, Frank, and M. David Miller. 1996. Role of self-efficacy and self-concept beliefs in mathematical problem solving: A path
analysis. Journal of Educational Psychology 86: 193–203. [CrossRef]
Passolunghi, Maria Chiara, Marija Zivkovic, and Sandra Pellizzoni. 2019. Mathematics anxiety and working memory: What is the
relationship? In Mathematics Anxiety: What Is Known and What is Still to Be Understood. Edited by Irene C. Mammarella, Sara
Caviola and Ann Dowker. Abingdon: Routledge, pp. 103–25.
Pellizzoni, Sandra, Martina Fontana, and Maria Chiara Passolunghi. 2021. Exploring the effect of cool and hot EFs training in
four-year-old children. European Journal of Developmental Psychology 18: 731–46. [CrossRef]
Peng, Peng, Jessica Namkung, Marcia Barnes, and Congying Sun. 2016. A meta-analysis of mathematics and working memory:
Moderating effects of working memory domain, type of mathematics skill, and sample characteristics. Journal of Educational
Psychology 108: 455–73. [CrossRef]
Peters, Ellen. 2020. Innumeracy in the Wild: Misunderstanding and Misusing Numbers. Oxford: Oxford University Press.
Pizzie, Rachel G., and David J. M. Kraemer. 2021. The Association between Emotion Regulation, Physiological Arousal, and
Performance in Math Anxiety. Frontiers in Psychology 12: 639448. [CrossRef]
Ramirez, Gerardo, Elizabeth A. Gunderson, Susan C. Levine, and Sian L. Beilock. 2013. Math Anxiety, Working Memory, and Math
Achievement in Early Elementary School. Journal of Cognition and Development 14: 187–202. [CrossRef]
Ramirez, Gerardo, Stacy T. Shaw, and Erin A. Maloney. 2018. Math Anxiety: Past Research, Promising Interventions, and a New
Interpretation Framework. Educational Psychologist 53: 145–64. [CrossRef]
Rhodes, Matthew G. 2019. Metacognition. Teaching of Psychology 46: 168–75. [CrossRef]
Richardson, Frank C., and Richard M. Suinn. 1972. The Mathematics Anxiety Rating Scale: Psychometric data. Journal of Counseling
Psychology 19: 551–54. [CrossRef]
Rivers, Michelle L., Charles J. Fitzsimmons, Susan R. Fisk, John Dunlosky, and Clarissa A. Thompson. 2020. Gender differences in
confidence during number-line estimation. Metacognition and Learning 16: 157–78. [CrossRef]
Scheibe, Daniel A., Charles J. Fitzsimmons, Marta K. Mielicki, Jennifer M. Taber, Pooja G. Sidney, Karin Coifman, and Clarissa A.
Thompson. 2022. Confidence in COVID problem solving: What factors predict adults’ item-level metacognitive judgments on
health-related math problems before and after an educational intervention? Metacognition and Learning 17: 989–1023. [CrossRef]
Scheibe, Daniel A., Christopher A. Was, Pooja G.. Sidney, and Clarissa A. Thompson. 2023. How Does Math Anxiety Affect Math
Performance? An Experimental Two-Study Investigation into the Mechanism Driving Math Anxiety Interventions. Manuscript submitted
for publication. Kent: The Psychological Sciences, Kent State University.
Schneider, Wolfgang, Hans Gruber, Andreas Gold, and Klaus Opwis. 1993. Chess Expertise and Memory for Chess Positions in
Children and Adults. Journal of Experimental Child Psychology 56: 328–49. [CrossRef]
Schoenfeld, Alan H. 2016. Learning to Think Mathematically: Problem Solving, Metacognition, and Sense Making in Mathematics
(Reprint). Journal of Education 196: 1–38. First published 1985. [CrossRef]
Sidney, Pooja G., Clarissa A. Thompson, Charles Fitzsimmons, and Jennifer M. Taber. 2021. Children’s and Adults’ Math Attitudes Are
Differentiated by Number Type. The Journal of Experimental Education 89: 1–32. [CrossRef]
Thompson, Clarissa A., Marta K. Mielicki, Ferdinand Rivera, Charles J. Fitzsimmons, Daniel A. Scheibe, Pooja G. Sidney, Lauren K.
Schiller, Jennifer M. Taber, and Erika A. Waters. 2023. Leveraging Math Cognition to Combat Health Innumeracy. Perspectives on
Psychological Science 18: 152–77. [CrossRef]
Thompson, Valerie A. 2009. Dual-process theories: A metacognitive perspective. In In Two Minds: Dual Processes and Beyond. Edited by
Jonathan Evans and Keith Frankish. Oxford: Oxford University Press, pp. 171–95.
Thompson, Valerie A., and Aidan Feeney. 2015. Reasoning and memory: A case for integration. In Reasoning as Memory. Edited by
Aidan Feeney and Valerie A. Thompson. London: Psychology Press, pp. 1–8.
Thompson, Valerie A., and Stephen C. Johnson. 2014. Conflict, metacognition, and analytic thinking. Thinking & Reasoning 20: 215–44.
[CrossRef]
Thompson, Valerie A., Jamie A. Prowse Turner, and Gordon Pennycook. 2011. Intuition, reason, and metacognition. Cognitive
Psychology 63: 107–40. [CrossRef] [PubMed]
J. Intell. 2023, 11, 117
18 of 18
Thompson, Valerie A., Jamie A. Prowse Turner, Pennycook Gordon, Linden J. Ball, Hannah Brack, Yael Ophir, and Rakefet Ackerman.
2013. The role of answer fluency and perceptual fluency as metacognitive cues for initiating analytic thinking. Cognition 128:
237–51. [CrossRef] [PubMed]
Topolinski, Sascha, and Fritz Strack. 2009. The analysis of intuition: Processing fluency and affect in judgements of semantic coherence.
Cognition and Emotion 23: 1465–503. [CrossRef]
Unsworth, Nash, and Randall W. Engle. 2007. The nature of individual differences in working memory capacity: Active maintenance
in primary memory and controlled search from secondary memory. Psychological Review 114: 104–32. [CrossRef]
Widaman, Keith F., David C. Geary, Pierre Cormier, and Todd D. Little. 1989. A componential model for mental addition. Journal of
Experimental Psychology: Learning, Memory, and Cognition 15: 898–919. [CrossRef]
Zepeda, Cristina D., and Timothy J. Nokes-Malach. 2023. Assessing Metacognitive Regulation during Problem Solving: A Comparison
of Three Measures. Journal of Intelligence 11: 16. [CrossRef]
Zhang, Jing, Nan Zhao, and Qi-Ping Kong. 2019. The Relationship Between Math Anxiety and Math Performance: A Meta-Analytic
Investigation. Frontiers in Psychology 10: 1613. [CrossRef]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual
author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to
people or property resulting from any ideas, methods, instructions or products referred to in the content.
International Journal of
Environmental Research
and Public Health
Article
Non-Motor Symptoms after One Week of High
Cadence Cycling in Parkinson’s Disease
Sara A. Harper 1,2, * , Bryan T. Dowdell 3 , Jin Hyun Kim 3 , Brandon S. Pollock 4
and Angela L. Ridgel 3
1
2
3
4
*
Department of Medicine, Division of Gerontology, Geriatrics, and Palliative Care, University of Alabama at
Birmingham, Birmingham, AL 35205, USA
Center for Exercise Medicine, University of Alabama at Birmingham, Birmingham, AL 35205, USA
Exercise Physiology Department, Kent State University, Kent, OH 44240, USA; bdowdell@kent.edu (B.T.D.);
jkim74@kent.edu (J.H.K.); aridgel@kent.edu (A.L.R.)
Department of Exercise Science, Walsh University, North Canton, OH 44720, USA; bpollock@walsh.edu
Correspondence: saharper@uabmc.edu; Tel.: +205-934-6721
Received: 10 May 2019; Accepted: 12 June 2019; Published: 14 June 2019
Abstract: The objective was to investigate if high cadence cycling altered non-motor cognition and
depression symptoms in individuals with Parkinson’s disease (PD) and whether exercise responses
were influenced by brain-derived neurotrophic factor (BDNF) Val66Met polymorphism. Individuals
with idiopathic PD who were ≥50 years old and free of surgical procedures for PD were recruited.
Participants were assigned to either a cycling (n = 20) or control (n = 15) group. The cycling group
completed three sessions of high cadence cycling on a custom motorized stationary ergometer.
The primary outcome was cognition (attention, executive function, and emotion recognition were
assessed via WebNeuro® and global cognition via Montreal Cognitive Assessment). Depression
symptoms were assessed via Beck Depression Inventory-II. There was a main effect of time for
emotional recognition (p = 0.048), but there were no other changes in cognition or depression
symptoms. Regardless of intervention or Val66Met polymorphism, high cadence cycling does not
alter cognition or depression symptoms after three sessions in one week.
Keywords: cognition; depression; exercise; neurodegenerative disease
1. Introduction
Parkinson’s disease (PD) is a progressive neurodegenerative disorder that is characterized by both
motor and non-motor symptoms [1]. However, less may be understood regarding non-motor symptoms,
such as cognition and depression symptoms [1–3]. There are also genetic variations as determinants of
phenotype that may influence PD symptoms [4]. One gene that has been of recent interest in PD is
brain-derived neurotrophic factor (BDNF)—a common single-nucleotide polymorphism where there is
an amino-acid substitution in the prodomain of valine (Val) to methionine (Met) at codon 66 known
as Val66Met polymorphism [5]. When present, it could lead to altered BDNF distribution [6] and
decreased BDNF secretion [7]. Moreover, cascading effects could alter the BDNF regulation of synaptic
transmission and neuronal growth [8] and the support of dopaminergic neurons in the substantia
nigra [9]. Improvements in PD symptoms after exercise interventions have been associated with BDNF
neuroplastic changes [10,11]. However, there is a high amount of inter-individual variability in the
neuroplastic response to exercise [12] that may be influenced by BDNF Val66Met polymorphism.
With the prevalence of BDNF Val66Met polymorphism and non-motor symptoms, such as
cognitive dysfunction (reduced attention and concentration, executive function, emotional recognition,
and global cognition) and depression symptoms, in individuals with PD, there is a need for efficacious
Int. J. Environ. Res. Public Health 2019, 16, 2104; doi:10.3390/ijerph16122104
www.mdpi.com/journal/ijerph
Int. J. Environ. Res. Public Health 2019, 16, 2104
2 of 10
therapeutic modalities [1–3]. An aerobic exercise intervention, high cadence cycling, may be effective
in improving non-motor symptoms without the established side effects that are associated with
interventions such as PD prescribed medications, deep brain stimulation (DBS), and combined
treatments [13,14].
Aerobic exercise can lead to increases in BDNF concentrations which could, in turn, decrease
the prevalence of non-motor depression and cognitive dysfunction symptoms [15,16]. Although 86%
of individuals with chronic diseases have observed increased peripheral BDNF concentration after
exercise [17], individuals with Val66Met polymorphism may have diminished BDNF activity-dependent
secretion [18,19].
Our group has shown that dynamic, high cadence cycling interventions are beneficial for
individuals with PD motor symptoms [20,21]. This approach utilizes a motorized cycle to assist the
rider in maintaining a high pedaling cadence, which improved motor symptoms after three exercise
sessions, as described previously [20]. This intervention has a low to moderate intensity and has shown
similar improvements to high-intensity interventions [20].
Given these improvements in PD motor symptoms, high cadence cycling could be a viable exercise
intervention for improving cognitive domains and depression non-motor symptoms. Therefore, our
central hypothesis was that three sessions of high cadence cycling would positively influence cognition
and depression non-motor symptoms. A secondary hypothesis was whether cognition and depression
non-motor symptoms would increase with the presence of Val66Met BDNF polymorphism.
2. Materials and Methods
2.1. Protocol
The cohort trial compared cognition and depression in individuals with PD between a high
cadence cycling group and a no cycling control group. Prior to enrollment, all participants provided
written informed consent approved by an Institutional Review Board. All research was conducted in
accordance with the principles of the Belmont Report and was approved by the Kent State University
Institutional Review Board (IRB # 15-605). The trial was registered with the Michael J. Fox Foundation
at www.foxtrialfinder.org (trial 4345). Both groups were evaluated on Day 1 (pre-test) and returned
48 h after the third exercise visit for the cycling group (or Day 8 for the control) for post-testing.
2.2. Inclusion/Exclusion Criteria
Individuals were 50–85 years old, diagnosed with idiopathic PD, on prescribed PD-specific
medications, and were free of contraindications to exercise. Contraindications included cardiovascular
disease (heart attack, heart surgery, angioplasty, pacemaker, rhythm disturbance, heart valve disease,
heart failure, heart transplantation, and congenital heart disease), stroke, and any surgical procedures
for the treatment of PD (e.g., DBS). All individuals who met the inclusion criteria participated in a
telephone pre-screening process using an American Heart Association (AHA)/American College of
Sports Medicine (ACSM) exercise pre-participation questionnaire for Kent State University Exercise
Physiology Laboratories [22]. Individuals that were identified as high risk were excluded from study
participation. A family history of cardiovascular disease did not constitute a sufficient basis for
exclusion. Following AHA/ACSM recommendations, individuals with two or more risk factors and/or
were 80 years of age or older obtained the physician consent required by the Institutional Review Board.
2.3. Baseline Participant Characteristics
During this visit, participants’ height (DigiStad HM210D, Charder Medical, Tiachung City, Taiwan)
and weight (Physician Balance Beam scale, Health o meter® Professional, McCook, IL, USA) were
measured. In addition, years of education, current PD prescription medications, and a baseline
EQ-5D-3L quality of life questionnaire [23] were completed.
Int. J. Environ. Res. Public Health 2019, 16, 2104
3 of 10
2.4. BDNF Val66Met Polymorphism
BDNF Val66Met polymorphism was tested for through a saliva test using an Oragene DNA
collection kit (DNA Genotek® Inc., Ottawa, ON, Canada) and outsourced to GenoFind (DNA Genotek
Inc., Ottawa, ON, Canada) for analysis. The saliva samples had DNA extracted and then genotyped for
a single-nucleotide polymorphism rs6262, or Val66Met. Quality checks were performed, including the
PicoGreen analysis, Nanodrop absorbance readings, and agarose gel electrophoresis, for each sample.
Previous investigations have utilized Genotek® products to determine the BDNF allelic status for
Val66Met polymorphism [24].
2.5. Intervention
The cycling group performed three, 40 min exercise visits as described previously [20]. Participants
had a five-minute warm-up, a 30 min main-set, and a five-minute cool-down. Heart rate (HR), rating
of perceived exertion (RPE) [25], power, and torque were recorded during the exercise intervention for
the cycling group every second, separated into the warm-up, main-set, and cool-down blocks, and
reported as the average ± SD [26]. The control group did not complete any cycling in the laboratory
but were instructed to maintain normal levels of activity between the assessment visits.
2.6. Non-Motor Symptoms
2.6.1. Cognition
Neurocognitive function was assessed through WebNeuro® computer software (Brain Resource,
Ultimo, New South Wales, Australia), which provides different clinical tests for the attention, executive
function, and emotional recognition domains [27]. The attention and concentration domain involved a
digit span and continuous performance test, while the executive function domain involved a maze
task, switching of attention, verbal interference, and a go–no-go test as described previously [27,28].
Emotional recognition assesses variations in time and percent accuracy for identifying sad, disgust,
fear, anger, happy, and neutral emotions [29,30]. When a participant was logged to be re-tested, an
alternative test was presented. In addition, global cognitive function was evaluated via the Montreal
Cognitive Assessment (MoCA, Greenfield Park, Quebec, Canada) [31,32]. To prevent a learning effect,
alternative forms of MoCA were used in a counterbalanced manner. The MoCA is scored 0–30 where
<26 may indicate a mild cognitive impairment [31,32].
2.6.2. Beck Depression Inventory-II (BDI-II)
BDI-II [33] (The Psychological Corporation, San Antonio, TX, USA) was used to evaluate the
prevalence and severity of depression symptoms [34,35] in individuals with PD [36]. Twenty-one
questions were summed for a range of 0–63. The prevalence and severity of depression symptoms were
classified with the following ranges: 0–13: minimal depression symptoms; 14–19: mild depression
symptoms; 20–28: moderate depression symptoms; and 29–63: severe depression symptoms [33].
2.7. Statistical Analysis
All data were analyzed using the Statistical Package for Social Sciences software (IBM SPSS
Statistics for Windows, Version 24.0, IBM Corp., Armonk, NY, USA). The alpha was set a priori to
p ≤ 0.05. The baseline participant characteristics between groups were evaluated via independent
samples t-tests. A repeated measures analysis of variance was performed to compare the cycling
group’s physiological variables across the visits. The cognitive domains and depression symptom
(non-motor) outcome measures were analyzed via two-way repeated measures analysis of variance
comparing intervention groups over time. Val66Met BDNF polymorphism acted as a co-variate
to potentially assist in predicting the physiological exercise outcomes and non-motor symptoms.
The baseline characteristics are represented as the mean ± SD, p-value, and 95% CI. The cycling
Int. J. Environ. Res. Public Health 2019, 16, 2104
4 of 10
physiological variables are reported as the mean ± SD from each visit, p-value, and ηp2 . The non-motor
symptoms are reported as the F-value, p-value, and mean ± SD.
3. Results
Thirty-five participants completed the trial (cycling: n = 20 and control: n = 15). Two additional
participants did not complete the cycling intervention and one participant returned for the post-testing
after 8 days. Data from these participants were therefore removed from the final analysis. There was a
statistically significant difference in the body mass index at the baseline likely driven by the variance
in females by group (cycling: 9, 45% and control: 3, 20%). All remaining participant demographic
characteristics were similar (Table 1). The exercise group recruitment was advertised primarily as an
exercise research trial. Thus, recruitment may have engaged individuals participating in exercise and,
therefore, those who had a lower body mass index.
Table 1. Baseline participant characteristics.
Variable
Age, years
Gender, Female
Val66Met
Polymorphism
BMI, kg/m2
Education, years
LED, mg
EQ-5D QOL, points
QOL VAS, %
Cycling (n = 20)
Control (n = 15)
p-Value
95% CI
65.05 ± 9.13
9, 45%
64.87 ± 6.90
3, 20%
p = 0.949
p = 0.130
(−5.55, 5.92)
(−0.07, 0.58)
5, 25%
5, 33%
p = 0.602
(−0.402, 0.239)
26.15 ± 4.7
15.3 ± 2.1
532 ± 275
6.9 ± 1.8
72.58 ± 18.2
29.90 ± 4.3
15.6 ± 2.0
560 ± 557
7.7 ± 1.8
71.00 ± 14.0
p = 0.025 *
p = 0.624
p = 0.847
p = 0.222
p = 0.782
(−7.01, −0.51)
(−1.79, 1.09)
(−268.48, 325.27)
(−0.49, 2.02)
(−9.93, 13.08)
Independent samples t-tests compared cycling and control groups. Beck Depression Inventory-II (BDI-II) ranges
from 0 to 63, ≥14 indicates mild or greater depression symptoms. Montreal Cognitive Assessment (MoCA) ranges
from 0 to 30, 18–25 indicates mild cognitive impairment. Abbreviations: body mass index (BMI), Levodopa
equivalent dose (LED), EQ-5D EuroQol Quality of Life (QOL), Quality of Life Visual Analog Scale (QOL VAS). Data
indicate mean ± SD, n, or percentage. * p ≤ 0.05.
Descriptive cycling physiological variables were not statistically significant between Visits 1 and 3
(Table 2). As noted in Table 2, there was variability as noted by the standard deviations across the
outcomes across the three visits.
Table 2. Cycling physiological variables.
Variable
Visit 1
Visit 2
Visit 3
p-Value
ηp2
Cadence, rpm
Power
Torque, Nm
Heart rate, bpm
RPE, Borg 6–20
80.3 ± 3.9
5.3 ± 23.6
3.81 ± 20.19
84.5 ± 12.0
11.0 ± 2.2
79.7 ± 4.4
0.7 ± 28.0
−0.65 ± 25.13
86.0 ± 13.0
11.0 ± 2.6
78.0 ± 7.7
0.0 ± 23.7
3.34 ± 27.62
88.4 ± 13.8
11.2 ± 2.2
p = 0.811
p = 0.824
p = 0.630
p = 0.584
p = 0.566
0.019
0.068
0.057
0.042
0.100
Repeated measures analysis of variance compared cycling group visit physiological responses. Abbreviations: beats
per minute (bpm), rating of perceived exertion (RPE), Newton meters (Nm), revolutions per minute (rpm). Data
indicate mean ± SD.
Mean results for Visits 1–3 were as follows: cadence 79.3 ± 5.6 rpm, power 1.92 ± 24.78, HR
86.25 ± 12.78 bpm, torque 2.21 ± 24.26, and RPE 11.0 ± 2.2. The HR and RPE values represent a low
intensity exercise. There was a significant main effect of time for emotion recognition—F = 4.262,
p = 0.048 (pre-control 0.04 ± 0.80 and cycling −0.36 ± 1.35, post-test control −0.28 ± 0.97 and cycling
−0.48 ± 1.27). All other non-motor symptom outcomes were not significantly different—outlined in
Table 3. There was no significant interaction for either between groups or over time.
Int. J. Environ. Res. Public Health 2019, 16, 2104
5 of 10
Table 3. Outcome results.
Variable
Pre-Test
Post-Test
Statistical Results
Attention/Concentration
control 160.47 ± 30.36
cycling 167.60 ± 51.98
control 152.89 ± 47.47
cycling 164.92 ± 47.22
F = 0.164
p = 0.688
Executive Function
control 7612.75 ± 2232.06
cycling 7148.00 ± 2745.56
control 5912.83 ± 2999.25
cycling 6569.44 ± 2628.10
F = 0.400
p = 0.532
Emotional Recognition
control 0.04 ± 0.80
cycling −0.36 ± 1.35
control −0.28 ± 0.97
cycling −0.48 ± 1.27
F = 4.262
p = 0.048 *
MoCA
control 25.7 ± 3.2
cycling 25.7 ± 2.8
control 25.6 ± 3.3
cycling 25.0 ± 3.2
F = 0.614
p = 0.439
BDI-II
control 9.7 ± 7.5
cycling 9.45 ± 10.0
control 25.6 ± 3.3
cycling 25.00 ± 3.2
F = 0.837
p = 0.367
Repeated measures analysis of variance compared the cycling and control group non-motor symptoms over time.
Attention/Concentration, Executive Function, and Emotional Recognition were assessed through WebNeuro®
software. Abbreviations: Beck Depression Inventory-II (BDI-II), Montreal Cognitive Assessment (MoCA). Data
indicate mean ± SD. * p ≤ 0.05.
4. Discussion
Although the concept that aerobic exercise can be beneficial for individuals with PD has been
suggested, it was unknown if dynamic, high cadence cycling would alter non-motor symptoms.
Therefore, our purpose was to investigate if high cadence cycling altered cognition and depression
symptoms and whether potential changes are influenced by the presence of BDNF polymorphism.
Our data indicate that there was a main effect of time for a subset of cognition—emotional
recognition—regardless of intervention group or the presence of BDNF polymorphism. There
were no significant differences in any of the other cognitive domains or depression symptoms. Thus,
our overall results from this investigation do not support that novel high cadence cycling alters
cognition or depression symptoms after three 30 min sessions.
4.1. BDNF Val66Met Polymorphism Role
There were reasons to suspect that the presence of Val66Met polymorphism would influence
the non-motor symptoms. Met-allele carriers have been associated with a difficulty in the attention
and concentration and executive function domains and global functioning compared to Val-allele
carriers. Previous research suggests that the Val66Met presence was associated with more delayed
recall errors compared to Val-allele carriers [37]. Moreover, another study found that the Val66Met
group had higher verbal recall errors in the executive function domain [38]. In contrast, Foltynie and
colleagues found that individuals with Val66Met had a better executive function performance than the
Val-allele group [39]. Although our results observed no differences in attention and concentration,
executive function, or global cognitive function, previous studies have found a strong correlation
between Val66Met presence and mild cognitive impairment [40].
4.2. High Cadence Cycling Compared to High-Intensity Cycling
The current literature varies on whether acute changes in non-motor symptoms may occur with
minimal exercise sessions. Participants performed at approximately 50–60% of their age-predicted
maximal HR during the 30 min of high cadence cycling. In addition to the intensity, the duration of the
intervention could further be reviewed. The ACSM states that adults should receive at least 150 min of
moderate-intensity exercise per week [22]. In our investigation, participants exercised for 40 min for
three exercise sessions totaling 120 min of aerobic exercise for one week. Further research is needed to
investigation the ideal duration and intensity exercise to alleviate non-motor symptoms.
Int. J. Environ. Res. Public Health 2019, 16, 2104
6 of 10
4.3. Possible Explanations
Previous research suggested that non-motor symptoms are prevalent among individuals with PD
and that they may improve after aerobic exercise interventions, although this was not supported by
our findings. Emerging literature has suggested that individuals with Parkinson’s disease may have a
deficit in recognizing facial expression, or emotional recognition [41–43], hence why it was included in
the non-motor assessment. Our past high cadence cycling research has primarily focused on motor
symptom outcomes, such as rigidity and bradykinesia, in individuals with PD [20,21,44,45]. It is
suggested that high cadence cycling may increase sensory feedback; activating basal ganglia circuits to
enhance central motor processing may explain these favorable motor function results. However, it is
possible that this approach is favorable for targeting motor symptoms, not non-motor symptoms [46–48].
Non-motor symptoms of PD are regulated by multiple non-dopaminergic neurotransmitters; thus,
common levodopa and other pharmacological dopamine therapies may not address associated
neurotransmitter dysfunction [48,49]. Interestingly, reports suggest that non-motor symptoms, such as
depression, may have an “inconsistent relationship [with the] severity of motor symptoms” [50,51].
Thus, cell-based therapies that address the non-dopaminergic system may be better targeted approaches
for non-motor symptoms in PD [48].
4.4. Study Limitations
Although this study yielded some interesting findings, recruiting individuals with PD and
screening for various chronic health issues limits the implications for the PD population. All
participants were tested while on their prescribed medication in order to not hinder their quality
of life while participating in the investigation, and the timing of their medication was controlled
as previously described [20]. Consequently, this approach means that our baseline data are a
representation of the participants’ daily symptoms with PD medication. In addition, assessing BDNF
blood concentration at both time points may have reflected whether BDNF was released during
exercise [52]. Frazzita et al. observed that BDNF serum levels increased [53]. As alluded to in
the discussion, participants cycled at a low intensity—even compared to past high cadence studies.
Furthermore, many interventions are designed to achieve 150 minutes of aerobic exercise and are
longer in length [10,54,55]. Briefly, cycling intervention lengths for other cycling paradigms tend to be
three sessions a week for 8–12 weeks [10,54,55]. However, the paradigm proposed in this investigation
has produced significant changes in motor symptoms after three sessions [20]. Previous research
on improving depression symptoms in exercise interventions are varied. Research in older adults
with Alzheimer’s disease suggests that participation in exercise plus behavioral intervention for 12
weeks can improve depression symptoms. Thus, a longer intervention may yield different non-motor
symptom outcomes.
5. Conclusions
These results suggest that a short-term high cadence cycling intervention may improve emotional
recognition over time but does not improve other cognitive domains or depression symptoms for
individuals with PD. Furthermore, the Val66Met BDNF phenotype did not result in differential
responses to this exercise intervention.
6. Patents
A.L.R. Inventor on US patent 9,802,081, 10,058,736 to Kent State University.
Author Contributions: Conceptualization, A.L.R. and S.A.H.; methodology, A.L.R. and S.A.H.; analysis, S.A.H.;
investigation, S.A.H., A.L.R., B.T.D., J.H.K., and B.S.P.; resources, A.L.R.; writing—original draft preparation,
S.A.H.; writing—review and editing, all authors; supervision, A.L.R.; funding acquisition, S.A.H.
Int. J. Environ. Res. Public Health 2019, 16, 2104
7 of 10
Funding: This research was funded by Kent State University’s School of Health Sciences Small Grant, the Midwest
American College of Sports Medicine Graduate Student Research Grant, the Ohio Parkinson Foundation Northeast
Region Grant, and the National Center for Medical Rehabilitation Research (T32HD071866).
Acknowledgments: The authors would like to thank Alena Varner and other students of the Motor and Cognitive
Control Laboratory. We also would like to thank the support of the Parkinson’s disease community for their time
and research support.
Conflicts of Interest: S.A.H., B.T.D., J.H.K., and B.S.P. declare no conflict of interest. A.L.R. is Inventor on U.S.
patent 9,802,081, 10,058,736 to Kent State University.
References
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
Goldman, W.P.; Baty, J.D.; Buckles, V.D.; Sahrmann, S.; Morris, J.C. Cognitive and motor functioning in
parkinson disease: Subjects with and without questionable dementia. Arch. Neurol. 1998, 55, 674–680.
[CrossRef] [PubMed]
Aarsland, D.; Marsh, L.; Schrag, A. Neuropsychiatric symptoms in parkinson’s disease. Mov. Disord. Off. J.
Mov. Disord. Soc. 2009, 24, 2175–2186. [CrossRef] [PubMed]
Van der Kolk, N.M.; Speelman, A.D.; van Nimwegen, M.; Kessels, R.P.; IntHout, J.; Hakobjan, M.; Munneke, M.;
Bloem, B.R.; van de Warrenburg, B.P. Bdnf polymorphism associates with decline in set shifting in parkinson’s
disease. Neurobiol. Aging 2015, 36, 1605.e1–1605.e6. [CrossRef] [PubMed]
Le Couteur, D.G.; Muller, M.; Yang, M.C.; Mellick, G.D.; McLean, A.J. Age-environment and gene-environment
interactions in the pathogenesis of parkinson’s disease. Rev. Environ. Health 2002, 17, 51–64. [CrossRef]
[PubMed]
Bath, K.G.; Lee, F.S. Variant bdnf (val66met) impact on brain structure and function. Cogn. Affect. Behav.
Neurosci. 2006, 6, 79–85. [CrossRef] [PubMed]
Hwang, J.P.; Tsai, S.J.; Hong, C.J.; Yang, C.H.; Lirng, J.F.; Yang, Y.M. The val66met polymorphism of the
brain-derived neurotrophic-factor gene is associated with geriatric depression. Neurobiol. Aging 2006, 27,
1834–1837. [PubMed]
Hariri, A.R.; Goldberg, T.E.; Mattay, V.S.; Kolachana, B.S.; Callicott, J.H.; Egan, M.F.; Weinberger, D.R.
Brain-derived neurotrophic factor val66met polymorphism affects human memory-related hippocampal
activity and predicts memory performance. J. Neurosci. Off. J. Soc. Neurosci. 2003, 23, 6690–6694. [CrossRef]
McAllister, A.K.; Katz, L.C.; Lo, D.C. Neurotrophins and synaptic plasticity. Annu. Rev. Neurosci. 1999, 22,
295–318. [CrossRef]
Hyman, C.; Hofer, M.; Barde, Y.A.; Juhasz, M.; Yancopoulos, G.D.; Squinto, S.P.; Lindsay, R.M. Bdnf is a
neurotrophic factor for dopaminergic neurons of the substantia nigra. Nature 1991, 350, 230–232.
Rosenfeldt, A.B.; Rasanow, M.; Penko, A.L.; Beall, E.B.; Alberts, J.L. The cyclical lower extremity exercise
for parkinson’s trial (cycle): Methodology for a randomized controlled trial. BMC Neurol. 2015, 15, 63.
[CrossRef]
Zigmond, M.J.; Cameron, J.L.; Hoffer, B.J.; Smeyne, R.J. Neurorestoration by physical exercise: Moving
forward. Parkinsonism Relat. Disord. 2012, 18 (Suppl. 1), S147–S150. [CrossRef]
Roemmich, R.T.; Field, A.M.; Elrod, J.M.; Stegemoller, E.L.; Okun, M.S.; Hass, C.J. Interlimb coordination is
impaired during walking in persons with parkinson’s disease. Clin. Biomech. (Bristol Avon) 2013, 28, 93–97.
[CrossRef] [PubMed]
Mermillod, M.; Mondillon, L.; Rieu, I.; Devaux, D.; Chambres, P.; Auxiette, C.; Dalens, H.; Coulangeon, L.M.;
Jalenques, I.; Durif, F. Dopamine replacement therapy and deep brain stimulation of the subthalamic nuclei
induce modulation of emotional processes at different spatial frequencies in parkinson’s disease. J. Parkinson
Dis. 2014, 4, 97–110.
Mondillon, L.; Mermillod, M.; Musca, S.C.; Rieu, I.; Vidal, T.; Chambres, P.; Auxiette, C.; Dalens, H.; Marie
Coulangeon, L.; Jalenques, I.; et al. The combined effect of subthalamic nuclei deep brain stimulation and
l-dopa increases emotion recognition in parkinson’s disease. Neuropsychologia 2012, 50, 2869–2879. [CrossRef]
[PubMed]
Int. J. Environ. Res. Public Health 2019, 16, 2104
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
8 of 10
Monteiro-Junior, R.S.; Cevada, T.; Oliveira, B.R.; Lattari, E.; Portugal, E.M.; Carvalho, A.; Deslandes, A.C. We
need to move more: Neurobiological hypotheses of physical exercise as a treatment for parkinson’s disease.
Med. Hypotheses 2015, 85, 537–541. [CrossRef] [PubMed]
Tuon, T.; Valvassori, S.S.; Dal Pont, G.C.; Paganini, C.S.; Pozzi, B.G.; Luciano, T.F.; Souza, P.S.; Quevedo, J.;
Souza, C.T.; Pinho, R.A. Physical training prevents depressive symptoms and a decrease in brain-derived
neurotrophic factor in parkinson’s disease. Brain Res. Bull. 2014, 108, 106–112. [CrossRef]
Knaepen, K.; Goekint, M.; Heyman, E.M.; Meeusen, R. Neuroplasticity—Exercise-induced response of
peripheral brain-derived neurotrophic factor: A systematic review of experimental studies in human subjects.
Sports Med. (Auckland N.Z.) 2010, 40, 765–801. [CrossRef]
Chen, Z.Y.; Bath, K.; McEwen, B.; Hempstead, B.; Lee, F. Impact of genetic variant bdnf (val66met) on brain
structure and function. Novartis Found. Symp. 2008, 289, 180–188.
Egan, M.F.; Kojima, M.; Callicott, J.H.; Goldberg, T.E.; Kolachana, B.S.; Bertolino, A.; Zaitsev, E.; Gold, B.;
Goldman, D.; Dean, M.; et al. The bdnf val66met polymorphism affects activity-dependent secretion of bdnf
and human memory and hippocampal function. Cell 2003, 112, 257–269. [CrossRef]
Ridgel, A.; Phillips, R.; Walter, B.; Discenzo, F.; Loparo, K. Dynamic high-cadence cycling improves motor
symptoms in parkinson’s disease. Front. Neurol. 2015, 6, 194. [CrossRef]
Ridgel, A.L.; Walter, B.L.; Tatsuoka, C.; Walter, E.M.; Colon-Zimmermann, K.; Welter, E.; Sajatovic, M.
Enhanced exercise therapy in parkinson’s disease: A comparative effectiveness trial. J. Sci. Med. Sport Sports
Med. Aust. 2015, 19, 12–17. [CrossRef] [PubMed]
ACSM. Acsm’s Guidelines for Exercise Testing and Prescription; American College of Sports Medicine: Baltimore,
MD, USA, 2014.
Soh, S.E.; Morris, M.E.; Watts, J.J.; McGinley, J.L.; Iansek, R. Health-related quality of life in people with
parkinson’s disease receiving comprehensive care. Aust. Health Rev. Publ. Aust. Hosp. Assoc. 2016, 40,
613–618. [CrossRef] [PubMed]
Hopkins, M.E.; Davis, F.C.; Vantieghem, M.R.; Whalen, P.J.; Bucci, D.J. Differential effects of acute and regular
physical exercise on cognition and affect. Neuroscience 2012, 215, 59–68. [CrossRef] [PubMed]
Borg, G. Perceived Exertion and Pain Scales; Human Kinetics: Champaign, IL, USA, 1988.
Mohammadi-Abdar, H.; Ridgel, A.L.; Discenzo, F.M.; Loparo, K.A. Design and development of a smart
exercise bike for motor rehabilitation in individuals with parkinson’s disease. IEEE/ASME Trans. Mechatron.
2016, 21, 1650–1658. [CrossRef] [PubMed]
Silverstein, S.M.; Berten, S.; Olson, P.; Paul, R.; Willams, L.M.; Cooper, N.; Gordon, E. Development and
validation of a world-wide-web-based neurocognitive assessment battery: Webneuro. Behav. Res. Methods
2007, 39, 940–949. [CrossRef] [PubMed]
Stanek, K.M.; Strain, G.; Devlin, M.; Cohen, R.; Paul, R.; Crosby, R.D.; Mitchell, J.E.; Gunstad, J. Body mass
index and neurocognitive functioning across the adult lifespan. Neuropsychology 2013, 27, 141–151. [CrossRef]
[PubMed]
Clark, U.S.; Neargarder, S.; Cronin-Golomb, A. Specific impairments in the recognition of emotional facial
expressions in parkinson’s disease. Neuropsychologia 2008, 46, 2300–2309. [CrossRef] [PubMed]
Enrici, I.; Adenzato, M.; Ardito, R.B.; Mitkova, A.; Cavallo, M.; Zibetti, M.; Lopiano, L.; Castelli, L. Emotion
processing in parkinson’s disease: A three-level study on recognition, representation, and regulation. PLoS
ONE 2015, 10, e0131470. [CrossRef]
Chou, K.L.; Amick, M.M.; Brandt, J.; Camicioli, R.; Frei, K.; Gitelman, D.; Goldman, J.; Growdon, J.;
Hurtig, H.I.; Levin, B.; et al. A recommended scale for cognitive screening in clinical trials of parkinson’s
disease. Mov. Disord. 2010, 25, 2501–2507. [CrossRef]
Nasreddine, Z.S.; Phillips, N.A.; Bedirian, V.; Charbonneau, S.; Whitehead, V.; Collin, I.; Cummings, J.L.;
Chertkow, H. The montreal cognitive assessment, moca: A brief screening tool for mild cognitive impairment.
J. Am. Geriatr. Soc. 2005, 53, 695–699. [CrossRef]
Beck, A.T.; Steer, R.A.; Ball, R.; Ciervo, C.A.; Kabat, M. Use of the beck anxiety and depression inventories for
primary care with medical outpatients. Assessment 1997, 4, 211–219. [CrossRef] [PubMed]
Dashtipour, K.; Johnson, E.; Kani, C.; Kani, K.; Hadi, E.; Ghamsary, M.; Pezeshkian, S.; Chen, J.J. Effect of
exercise on motor and nonmotor symptoms of parkinson’s disease. Parkinson Dis. 2015, 2015, 5. [CrossRef]
[PubMed]
Int. J. Environ. Res. Public Health 2019, 16, 2104
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
46.
47.
48.
49.
50.
51.
52.
53.
9 of 10
Doose, M.; Ziegenbein, M.; Hoos, O.; Reim, D.; Stengert, W.; Hoffer, N.; Vogel, C.; Ziert, Y.; Sieberer, M.
Self-selected intensity exercise in the treatment of major depression: A pragmatic rct. Int. J. Psychiatry Clin.
Pract. 2015, 19, 266–275. [CrossRef] [PubMed]
Teixeira-Machado, L.; Araujo, F.M.; Cunha, F.A.; Menezes, M.; Menezes, T.; Melo DeSantana, J. Feldenkrais
method-based exercise improves quality of life in individuals with parkinson’s disease: A controlled,
randomized clinical trial. Altern. Ther. Health Med. 2015, 21, 8–14. [CrossRef] [PubMed]
Li, S.C.; Chicherio, C.; Nyberg, L.; von Oertzen, T.; Nagel, I.E.; Papenberg, G.; Sander, T.; Heekeren, H.R.;
Lindenberger, U.; Backman, L. Ebbinghaus revisited: Influences of the bdnf val66met polymorphism on
backward serial recall are modulated by human aging. J. Cogn. Neurosci. 2010, 22, 2164–2173. [CrossRef]
[PubMed]
Schofield, P.R.; Williams, L.M.; Paul, R.H.; Gatt, J.M.; Brown, K.; Luty, A.; Cooper, N.; Grieve, S.;
Dobson-Stone, C.; Morris, C.; et al. Disturbances in selective information processing associated with
the bdnf val66met polymorphism: Evidence from cognition, the p300 and fronto-hippocampal systems. Biol.
Psychol. 2009, 80, 176–188. [CrossRef] [PubMed]
Foltynie, T.; Cheeran, B.; Williams-Gray, C.H.; Edwards, M.J.; Schneider, S.A.; Weinberger, D.; Rothwell, J.C.;
Barker, R.A.; Bhatia, K.P. Bdnf val66met influences time to onset of levodopa induced dyskinesia in
parkinson’s disease. J. Neurol. Neurosurg. Psychiatry 2009, 80, 141–144. [CrossRef]
Guerini, F.R.; Beghi, E.; Riboldazzi, G.; Zangaglia, R.; Pianezzola, C.; Bono, G.; Casali, C.; Di Lorenzo, C.;
Agliardi, C.; Nappi, G.; et al. Bdnf val66met polymorphism is associated with cognitive impairment in italian
patients with parkinson’s disease. Eur. J. Neurol. 2009, 16, 1240–1245. [CrossRef]
Alonso-Recio, L.; Serrano-Rodriguez, J.M.; Carvajal-Molina, F.; Loeches-Alonso, A.; Martin-Plasencia, P.
Recognition of facial expression of emotions in parkinson’s disease: A theoretical review. Rev. Neurol. 2012,
54, 479–489.
Lin, C.Y.; Tien, Y.M.; Huang, J.T.; Tsai, C.H.; Hsu, L.C. Degraded impairment of emotion recognition
in parkinson’s disease extends from negative to positive emotions. Behav. Neurol. 2016, 2016, 9287092.
[CrossRef]
Mathersul, D.; Palmer, D.M.; Gur, R.C.; Gur, R.E.; Cooper, N.; Gordon, E.; Williams, L.M. Explicit identification
and implicit recognition of facial emotions: Ii. Core domains and relationships with general cognition. J. Clin.
Exp. Neuropsychol. 2009, 31, 278–291. [CrossRef]
Ridgel, A.L.; Peacock, C.A.; Fickes, E.J.; Kim, C.H. Active-assisted cycling improves tremor and bradykinesia
in parkinson’s disease. Arch. Phys. Med. Rehabil. 2012, 93, 2049–2054. [CrossRef] [PubMed]
Ridgel, A.L.; Vitek, J.L.; Alberts, J.L. Forced, not voluntary, exercise improves motor function in parkinson’s
disease patients. Neurorehabilit. Neural Repair 2009, 23, 600–608. [CrossRef] [PubMed]
Chaudhuri, K.R.; Schapira, A.H. Non-motor symptoms of parkinson’s disease: Dopaminergic
pathophysiology and treatment. Lancet Neurol. 2009, 8, 464–474. [CrossRef]
Honig, H.; Antonini, A.; Martinez-Martin, P.; Forgacs, I.; Faye, G.C.; Fox, T.; Fox, K.; Mancini, F.; Canesi, M.;
Odin, P.; et al. Intrajejunal levodopa infusion in parkinson’s disease: A pilot multicenter study of effects on
nonmotor symptoms and quality of life. Mov. Disord. 2009, 24, 1468–1474. [CrossRef] [PubMed]
Pantcheva, P.; Reyes, S.; Hoover, J.; Kaelber, S.; Borlongan, C.V. Treating non-motor symptoms of parkinson’s
disease with transplantation of stem cells. Expert Rev. Neurother. 2015, 15, 1231–1240. [CrossRef]
Tsui, A.; Isacson, O. Functions of the nigrostriatal dopaminergic synapse and the use of neurotransplantation
in parkinson’s disease. J. Neurol. 2011, 258, 1393–1405. [CrossRef] [PubMed]
Schrag, A.; Jahanshahi, M.; Quinn, N.P. What contributes to depression in parkinson’s disease? Psychol. Med.
2001, 31, 65–73. [CrossRef]
Wishart, S.; Macphee, G.J.A. Evaluation and management of the non-motor features of parkinson’s disease.
Ther. Adv. Chronic Dis. 2011, 2, 69–85. [CrossRef]
Seifert, T.; Brassard, P.; Wissenberg, M.; Rasmussen, P.; Nordby, P.; Stallknecht, B.; Adser, H.; Jakobsen, A.H.;
Pilegaard, H.; Nielsen, H.B.; et al. Endurance training enhances bdnf release from the human brain. Am. J.
Physiol. Regul. Integr. Comp. Physiol. 2010, 298, R372–R377. [CrossRef]
Frazzitta, G.; Maestri, R.; Ghilardi, M.F.; Riboldazzi, G.; Perini, M.; Bertotti, G.; Boveri, N.; Buttini, S.;
Lombino, F.L.; Uccellini, D.; et al. Intensive rehabilitation increases bdnf serum levels in parkinsonian
patients: A randomized study. Neurorehabilit. Neural Repair 2014, 28, 163–168. [CrossRef] [PubMed]
Int. J. Environ. Res. Public Health 2019, 16, 2104
54.
55.
10 of 10
Demonceau, M.; Maquet, D.; Jidovtseff, B.; Donneau, A.F.; Bury, T.; Croisier, J.L.; Crielaard, J.M.; Rodriguez
de la Cruz, C.; Delvaux, V.; Garraux, G. Effects of twelve weeks of aerobic or strength training in addition
to standard care in parkinson’s disease: A controlled study. Eur. J. Phys. Rehabil. Med. 2017, 53, 184–200.
[PubMed]
Nadeau, A.; Lungu, O.; Duchesne, C.; Robillard, M.-È.; Bore, A.; Bobeuf, F.; Plamondon, R.; Lafontaine, A.-L.;
Gheysen, F.; Bherer, L.; et al. A 12-week cycling training regimen improves gait and executive functions
concomitantly in people with parkinson’s disease. Front. Hum. Neurosci. 2017, 10, 690. [CrossRef] [PubMed]
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).
cells
Article
Uncovering Functional Contributions of PMAT (Slc29a4) to
Monoamine Clearance Using Pharmacobehavioral Tools
Jasmin N. Beaver, Brady L. Weber, Matthew T. Ford
, Anna E. Anello, Sarah K. Kassis and T. Lee Gilman *
Department of Psychological Sciences & Brain Health Research Institute, Kent State University,
Kent, OH 44242, USA; jbeave18@kent.edu (J.N.B.); bweber18@kent.edu (B.L.W.); mford27@kent.edu (M.T.F.);
aanello@kent.edu (A.E.A.); skassis@kent.edu (S.K.K.)
* Correspondence: tgilman@kent.edu
Citation: Beaver, J.N.; Weber, B.L.;
Ford, M.T.; Anello, A.E.; Kassis, S.K.;
Gilman, T.L. Uncovering Functional
Contributions of PMAT (Slc29a4) to
Monoamine Clearance Using
Pharmacobehavioral Tools. Cells 2022,
11, 1874. https://doi.org/10.3390/
cells11121874
Academic Editors: Sonja Sucic,
Lynette C. Daws, Ameya Sanjay
Kasture and Shreyas Bhat
Abstract: Plasma membrane monoamine transporter (PMAT, Slc29a4) transports monoamine neurotransmitters, including dopamine and serotonin, faster than more studied monoamine transporters,
e.g., dopamine transporter (DAT), or serotonin transporter (SERT), but with ~400–600-fold less affinity.
A considerable challenge in understanding PMAT’s monoamine clearance contributions is that no
current drugs selectively inhibit PMAT. To advance knowledge about PMAT’s monoamine uptake
role, and to circumvent this present challenge, we investigated how drugs that selectively block
DAT/SERT influence behavioral readouts in PMAT wildtype, heterozygote, and knockout mice of
both sexes. Drugs typically used as antidepressants (escitalopram, bupropion) were administered
acutely for readouts in tail suspension and locomotor tests. Drugs with psychostimulant properties
(cocaine, D-amphetamine) were administered repeatedly to assess initial locomotor responses plus
psychostimulant-induced locomotor sensitization. Though we hypothesized that PMAT-deficient
mice would exhibit augmented responses to antidepressant and psychostimulant drugs due to constitutively attenuated monoamine uptake, we instead observed sex-selective responses to antidepressant
drugs in opposing directions, and subtle sex-specific reductions in psychostimulant-induced locomotor sensitization. These results suggest that PMAT functions differently across sexes, and support
hypotheses that PMAT’s monoamine clearance contribution emerges when frontline transporters
(e.g., DAT, SERT) are absent, saturated, and/or blocked. Thus, known human polymorphisms that
reduce PMAT function could be worth investigating as contributors to varied antidepressant and
psychostimulant responses.
Keywords: sex differences; psychostimulants; antidepressants; tail suspension test; locomotor activity;
monoamine transporters; sensitization
Received: 11 May 2022
Accepted: 7 June 2022
Published: 9 June 2022
1. Introduction
Publisher’s Note: MDPI stays neutral
Two broad classes of monoamine transporters regulate the amount and duration of
extracellular monoaminergic signaling in the central nervous system (reviewed in [1–4]).
These classes are known as uptake 1 (Na+ and Cl- dependent, high affinity, and low capacity), and uptake 2 (Na+ and Cl- independent, low affinity, and high capacity) [2–9].
Many psychoactive drugs, including those used to alleviate depression and anxiety symptoms (e.g., escitalopram, bupropion), as well as stimulants that are sometimes abused
(e.g., cocaine, amphetamine), predominantly act upon uptake 1 transporters, including
dopamine and serotonin transporters (DAT, Slc6a3; SERT, Slc6a4). Uptake 2 transporters, in
contrast, lack selective inhibitors [3], making their functional contributions to monoaminergic signaling regulation challenging to study. Uptake 2 transporters are thought to function
in a compensatory fashion when uptake 1 transporters are inhibited and/or overwhelmed
with substrate, or in brain regions where uptake 1 transporters are minimally expressed [9].
Consequently, the contributions of uptake 2 transporters to monoaminergic signaling regulation are hypothesized to become prominent when uptake 1 transporters are inhibited
with regard to jurisdictional claims in
published maps and institutional affiliations.
Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Cells 2022, 11, 1874. https://doi.org/10.3390/cells11121874
https://www.mdpi.com/journal/cells
Cells 2022, 11, 1874
2 of 17
by psychoactive drugs, such as escitalopram or cocaine. Thus, to circumvent the absence
of selective uptake 2 inhibitors, we utilized mice with constitutive genetic reductions
in the uptake 2 transporter plasma membrane monoamine transporter (PMAT; Slc29a4).
In particular, we investigated how lifelong reductions in PMAT function influenced adult
behavioral responses to psychoactive drugs that primarily inhibit uptake 1 transporters,
particularly DAT and/or SERT. PMAT preferentially transports dopamine and serotonin
over other monoamine neurotransmitters, albeit with approximately 400- and 600-fold
lower affinity, respectively [10–12].
Little is known about how the functional loss of uptake 2 transporters influences
responses to psychoactive drugs. The uptake 2 transporter organic cation transporter 3
(OCT3; Slc22a3) preferentially transports histamine, norepinephrine, and epinephrine over
other monoamine neurotransmitters (e.g., dopamine, serotonin) [10]. A recent study focused
on OCT3 provided in vitro and in vivo evidence suggesting that D-amphetamine mediates
dopamine efflux via OCT3, though D-amphetamine is not a substrate for OCT3 [8]. Support
for this conclusion is found in a recent report by Angenoorth and colleagues [13] indicating
that in vitro, D-amphetamine inhibits PMAT with relatively low affinity (~72 µM), but does
not inhibit OCT3. Investigations using conditioned place preference in mice suggest that
both OCT3 and PMAT contribute to D-amphetamine-mediated reward, as indicated by
time spent in previously D-amphetamine-paired chambers [14]. However, in vivo studies
have thus far used only a single dose of D-amphetamine and have not explored any other
psychoactive drugs (e.g., cocaine), nor non-stimulant drugs (e.g., escitalopram, bupropion)
in mice with genetic reductions in an uptake 2 transporter. Moreover, though uptake
2 transporters as a whole are not well understood, PMAT remains particularly understudied
among polyspecific cation transporters that transport monoamines within the brain [4].
To evaluate the behavioral effects of psychoactive drugs in PMAT-deficient mice, two
different approaches were used. For non-stimulant psychoactive drugs (escitalopram and
bupropion), mice were subjected to tail suspension and locomotor tests to assess how the
drugs influenced antidepressant-predictive and locomotor behavior, respectively. For psychostimulants (cocaine and D-amphetamine), a psychostimulant-induced locomotor sensitization paradigm was used [15]. Overall, we hypothesized that mice with reduced (+/−)
or ablated (−/−) PMAT function would exhibit augmented behavioral responses to psychoactive drugs, as compared to PMAT wildtype controls (+/+), given a diminished ability
to compensate for pharmacologically impaired DAT/SERT function. Further, we anticipated
sex-specific effects would be observed, with females exhibiting augmented sensitization to
psychostimulants [16–18]. Sex-specific effects have been reported across PMAT genotypes
as well [14,19,20], but given limited evidence in this realm, we did not have any a priori
hypotheses regarding directionality of sex × genotype interactions for each drug.
2. Materials and Methods
2.1. Animals
Mice of both sexes were bred in-house and used for experiments at ≥90 days of age.
Mice were the offspring of heterozygote × heterozygote (+/− × +/−) crosses, and were
weaned at postnatal day 21, at which time ear punches were collected for mouse identification and genotyping. Mice constitutively deficient in PMAT were originally developed
by the lab of Dr. Joanne Wang at the University of Washington [21]. A breeding colony
was developed under a material transfer agreement (MTA) between Kent State University
and the University of Washington. Breeding colonies originated from +/− PMAT-deficient
mice on a C57BL/6J background shipped from the University of Texas Health Science
Center at San Antonio (from the lab of Dr. Lynette C. Daws) to Kent State University, the
latter with agreement from the University of Washington. Mice always had ad libitum
access to LabDiet 5001 rodent laboratory chow (LabDiet, Brentwood, MO, USA) and water,
and were housed in rooms maintained on a 12:12 light/dark cycle with lights on at 07:00.
Mice were kept on 7090 Teklad Sani-chip bedding (Envigo, East Millstone, NJ, USA), and
cages were changed weekly. Experiments were approved by the Institutional Animal
Cells 2022, 11, 1874
3 of 17
Care and Use Committee at Kent State University and adhered to the National Research
Council’s Guide for the Care and Use of Laboratory Animals, 8th Ed. [22].
2.2. Genotyping
Genomic DNA was extracted from ear punches using proteinase K (Roche, Basel,
Switzerland), dissolved to 0.077% w/v on the day of extraction within a buffer of 100 mM
Tris, 5 mM ethylenediaminetetraacetic acid (EDTA), 0.2% sodium dodecyl sulfate (SDS),
and 200 mM NaCl, pH = 8.5 [20]. Genomic DNA (3.6 µL) was analyzed via PCR in 1X
PCR buffer containing 1.74 mM MgCl2 and 34.7 µM dNTPs, with 0.20 µL of Platinum Taq
(Invitrogen, Carlsbad, CA, USA) per 22 µL reaction. Amplification of the wildtype allele,
between exons 3 and 4, and/or the knockout allele at the neomycin resistance cassette (Neo),
was performed using 0.68 µL each of 10 µM primer stocks (Integrated DNA Technologies,
Coralville, IA, USA) designed by Duan and Wang [21]: Exon 3 forward—50 CGA CTA TCT
TCA CCA CAA GTA CCC AG 30 ; Exon 4 reverse—50 GAG GCT CAT GTC AAA TAC GAT
GGA G 30 ; Neo F—50 CTT GCT CCT GCC GAG AAA GTA TC 30 ; and Neo R—50 TCA
GAA GAA CTC GTC AAG AAG GCG 30 . The following procedure was used for each
PCR: 95 ◦ C for 5 min; 34 cycles of 94 ◦ C for 30 s, 59 ◦ C for 30 s, and 72 ◦ C for 90 s; 72 ◦ C
for 5 min; hold at 4 ◦ C. To visualize PCR products, a 1% agarose gel electrophoresis was
used. Gels were run at 150 V in 1X buffer of 40 mM Tris base, 0.114% acetic acid, and 1 mM
EDTA, pH = 8.5, for 30 min. A 1 kb DNA ladder (Invitrogen) was used for DNA amplicon
size reference, with the wildtype allele presenting at 847 bp, and the knockout allele at
447 bp [21]. Genotypes of experimental animals were re-verified post-mortem.
2.3. Drugs
All doses administered were calculated based on the salt form of each drug, except
for escitalopram oxalate, which was calculated on the base form. Drugs were dissolved
in sterile-filtered 0.9% NaCl (saline; vehicle). All injections were given intraperitoneal
(ip.), at a volume of 10 mL/kg. Bupropion hydrochloride (PHR1730), escitalopram oxalate
(E4786), cocaine hydrochloride (C5776), and D-amphetamine hemisulfate salt (A5880) were
all purchased from Sigma Aldrich (St. Louis, MO, USA).
2.4. Behavior Tests
Females and males were always tested on different days. Mice were always moved in
their home cages to the testing location at least 1 h prior to injection or test commencement,
to permit acclimation. Testing always occurred during lights on. For every experimental
paradigm, no more than one mouse per sex per genotype per drug treatment per litter was
used, to minimize potential litter confounds.
2.4.1. Tail Suspension Test (TST)
Mice were injected between 09:00 and 16:00, and tested 30 min after injection. Because
no study to date has evaluated baseline behavior of PMAT-deficient mice in TST, we ran noninjected (i.e., naïve) mice alongside injected mice for TST experiments. Injected mice were
treated with vehicle (saline), 1 or 2 mg/kg escitalopram, or 4 or 8 mg/kg bupropion [23,24].
The higher doses for each drug were selected because they were reported as being the
lowest effective dose for each respective drug in TST, and the lower doses were half of
these lowest effective doses, to test our hypothesis that mice with constitutive reductions in
PMAT would exhibit greater behavioral responses to sub- or minimally effective doses of
antidepressants in the TST. For testing, the tails of mice were gently secured to metal plates
with 1” wide adhesive tape, and a loose cylindrical tube (41 mm L × 12 mm diameter)
between the adhesive and the tail base, to prevent mice from climbing up their own tails
and holding on to the plate during the test. Once tails were secured with tape to the metal
plates, the metal plates were hung on hooks in the ceilings of separate, adjacent chambers
for the 6 min test so that mice could not see each other during the test. Mice were oriented
to be suspended so that their feet faced outwards to be visible to a video camera, which was
Cells 2022, 11, 1874
4 of 17
used for recording behavior. After testing, mice were immediately released from the plates
and tape and returned to their home cages. Offline scoring of TST behavior was performed
by an observer blinded to mouse treatment and genotype. Time spent immobile and latency
to first immobility for the 6 min test were scored using Solomon Coder (v. beta 19.08.02).
2.4.2. Post-TST Locomotor Testing
Eight days after TST, mice that had already undergone TST were injected with a treatment different from that used for their TST, and tested for locomotor activity immediately
after treatment injection for 1 h. This was done to minimize the number of mice used
for experiments, in accord with the Three Rs for animal research [25]. We have previously reported on the lack of any locomotor activity differences between wildtype and
PMAT-deficient mice in the absence of any injections [20], so we did not include a naïve
condition for post-TST locomotor testing, again in accord with the Three Rs. Injections for
locomotor testing were performed between 08:30–15:30. Arenas for locomotor testing were
45.7 H × 66.0 L × 38.1 cm W. Overhead cameras were used to record locomotor activity
using ANY-Maze software (v. 7, Stoelting Co., Wood Dale, IL, USA). Distance traveled
was quantified in 5 min bins, and the two bins concurrent with when the TST occurred
post-injection (i.e., min 30–40) were analyzed to identify potential locomotor confounds when
interpreting TST results. Locomotor activity for the entire 1 h duration of this test is presented
in Supplementary Figure S1, and accompanying statistics in Supplementary Table S1.
2.4.3. Psychostimulant-Induced Locomotor Sensitization—Common Methods
Each injection day, mice were moved to the testing area at ~08:30 to acclimate to the
environment and were weighed at that time. Experimental testing began at ~10:45 each
injection day, starting with a 30 min habituation phase, followed immediately by a vehicle
(saline) injection. Starting after the saline injection, testing occurred in 10 min bouts after
each injection (1 saline injection + 4 drug injections; doses for individual drugs are detailed
below). After the last test at the last dose for each injection day, mice were returned to
their home cages and placed back in the colony until the next injection day. In addition to
cumulative distance traveled under the influence of each psychostimulant, data were also
graphed as percent change from same-sex and same-genotype cumulative distance traveled
after drug given on Day 1, to more precisely assess each psychostimulant’s induction of
locomotor sensitization across the subsequent four injection days.
2.4.3.1. Cocaine-Induced Locomotor Sensitization
Following procedures optimized by Elliot [15], mice underwent a cocaine-induced
locomotor sensitization paradigm every day for 5 consecutive days. Doses followed those
of Elliot [15]: individual doses were 5, 5, 10, and 20 mg/kg, meaning cumulative doses
of 5, 10, 20, and 40 mg/kg cocaine. Elliot demonstrated that this cumulative dosing
paradigm produced a more robust locomotor sensitization to cocaine than single 40 mg/kg
injections [15].
2.4.3.2. D-Amphetamine Induced Locomotor Sensitization
Given that sensitization to amphetamine—unlike cocaine—is optimal in mice when
there are drug-free days between injection days [26], mice were injected with amphetamine
once every 3 days, for a total of 5 injection days. Individual doses were 0.1, 0.32, 1.0, and
3.2 mg/kg, meaning cumulative doses of 0.1, 0.42, 1.42, and 4.62 mg/kg [27,28].
2.5. Statistical Analyses
Figures were generated using GraphPad Prism 9.1.1 (GraphPad Software, San Diego,
CA, USA), and statistical analyses were performed with GraphPad Prism and IBM SPSS
Statistics 28.0.0.0 (IBM, Armonk, NY, USA). The significance threshold was set a priori at
p < 0.05. During Day 1 of a cocaine experiment, after one female knockout was injected
with her second 5 mg/kg cocaine dose, the camera for her arena failed, and thus data for
Cells 2022, 11, 1874
5 of 17
this mouse was excluded due to an inability to calculate total distance traveled on Day
1, and percent change from Day 1. One male escitalopram 1 mg/kg knockout was more
than six standard deviations outside the mean of his same-sex/genotype/treatment group
for post-TST locomotor activity, and was thus excluded. Baseline conditions (naïve and
saline-treated mice in TST, saline-treated mice in post-TST locomotor, and Day 1 cumulative psychostimulant-induced locomotor responses were analyzed with a 2-way ANOVA
(genotype × sex) and Holm–Šídák post hoc testing to identify directional effects of PMAT
deficiency. All other TST, post-TST locomotor, and psychostimulant-induced locomotor
sensitization data were analyzed using a 3-way ANOVA (treatment × genotype × sex, or
day × genotype × sex) and pairwise comparisons with Bonferroni correction. For withinsubjects analyses, Greenhouse–Geisser corrections were utilized. Data were graphed as the
mean ± the standard error of the mean (SEM), or as violin plots showing individual data
points plus medians and quartiles.
3. Results
3.1. TST Behavior
Mice constitutively deficient in PMAT have not previously been tested in TST, so we
evaluated behavioral responses to TST in the absence of any injections (i.e., naïve). In these
mice, there was no interaction between genotype × sex in time spent immobile during
TST (F (2,42) = 1.42, p = 0.252, partial η2 = 0.064) (Figure 1A). There was also no effect
of sex (F (1,42) = 1.99, p = 0.166, partial η2 = 0.045), although a non-significant trend for
genotype was noted (F (2,42) = 2.45, p = 0.099, partial η2 = 0.104). Holm-Šídák’s post hoc
testing revealed that, relative to male wildtype mice, male heterozygotes (p = 0.0414) and
knockouts (p = 0.0414) exhibited significantly increased time immobile in TST, whereas no
differences relative to same-sex wildtypes were observed in females (p > 0.8). No significant
genotype × sex interaction in latency to first immobility bout in TST was detected
(F (2,42) = 0.802, p = 0.455, partial η2 = 0.037), nor were main effects of sex (F (1,42) = 1.88,
p = 0.177, partial η2 = 0.043) or genotype (F (2,42) = 0.564, p = 0.573, partial η2 = 0.026)
(Figure 1B). In contrast to naïve mice, when evaluating time immobile during TST in salineinjected mice, no significant genotype × sex interaction was observed (F (2,43) = 0.507,
p = 0.606, partial η2 = 0.023) (Figure 1C). Likewise, main effects of sex (F (1,43) = 0.089,
p = 0.767, partial η2 = 0.002) and genotype (F (2,43) = 0.737, p = 0.485, partial η2 = 0.033) were
not observed. However, while no significant genotype × sex interaction (F (2,43) = 0.700,
p = 0.502, partial η2 = 0.032) nor main effect of sex (F (1,43) = 0.796, p = 0.377, partial
η2 = 0.018) were observed in saline-injected mice for latency to first immobility bout, a significant genotype effect was found (F (2,43) = 3.548, p = 0.037, partial η2 = 0.142) (Figure 1D).
Holm-Šídák’s post hoc testing indicated that female saline-injected knockout mice exhibited significantly shorter latencies to first immobility bout relative to female saline-injected
wildtype mice (p = 0.0291).
To facilitate interpretation of TST behavioral changes in response to drug injections,
and to control for the behavioral effects of injection stress, data were graphed and analyzed as percent change from same-sex and same-genotype saline-treated mice, as done
previously [24]. Graphs are separated by sex for clarity, but analyses were performed
across sexes. Starting with percent change in TST immobility time relative to samesex and same-genotype mice injected with saline, there was no three-way interaction
of treatment × genotype × sex (F (8,212) = 0.925, p = 0.496, partial η2 = 0.034), nor was
there a treatment × genotype interaction (F (8,212) = 0.676, p = 0.713, partial η2 = 0.025).
There were, however, significant interactions of genotype × sex (F (2,212) = 3.574, p = 0.030,
partial η2 = 0.033) and treatment × sex (F (4,212) = 2.605, p = 0.037, partial η2 = 0.047)
(Figure 2A). Pairwise comparisons indicated that in male wildtypes (p < 0.001) and heterozygotes (p = 0.028), mice treated with 2 mg/kg escitalopram exhibited significantly less
immobility relative to same-sex and same-genotype saline-injected controls (Figure 2C).
Moreover, the response of male wildtypes to 2 mg/kg escitalopram was significantly
different from the response of female wildtypes to the same drug and dose (p < 0.001;
Cells 2022, 11, 1874
6 of 17
Figure 2C). For percent change in latency to the first immobility bout during TST relative
to same-sex and -genotype mice injected with saline, once again, no three-way interaction of treatment × genotype × sex (F (8,212) = 1.412, p = 0.193, partial η2 = 0.051) was
observed. Unlike with percent change in TST immobility, no interaction of treatment × sex
occurred (F (4,212) = 1.673, p = 0.157, partial η2 = 0.031). A non-significant trend for a
treatment × genotype interaction was noted (F (8,212) = 1.866, p = 0.067, partial η2 = 0.066).
As with percent change in TST immobility, a genotype × sex interaction was significant
(F (2,212) = 8.720, p < 0.001, partial η2 = 0.076). There was also a significant main effect of
treatment (F (4,212) = 11.157, p < 0.001, partial η2 = 0.174). Pairwise comparisons revealed
that, relative to saline-injected female knockouts, female knockouts injected with either
dose of escitalopram or the higher 8 mg/kg dose of bupropion exhibited significantly
greater latencies to the first immobility bout (all p < 0.001; Figure 2B). Further, female
knockouts injected with 1 mg/kg (p < 0.001) or 2 mg/kg (p = 0.025) escitalopram, or
Cells 2022, 11, x FOR PEER REVIEW 8 mg/kg bupropion (p = 0.009) displayed enhanced percent changes in latencies6 to
of the
17
first immobility bout relative to male knockouts injected with the same drug and dose
(Figure 2B).
Figure 1. Naïve and saline-injected mouse behavior in tail suspension test. Naïve (i.e., noninjected;
clear) female,
and male PMAT
wildtype
(+/+,
squares), PMAT
heterozygote
(+/−,
Figure
1. Naïve
and saline-injected
mouse
behavior
in black
tail suspension
test. Naïve
(i.e., non-ingrey diamonds),
and
PMAT
(−/−, open
mice underwent
the tail suspension
test
jected;
clear) female,
and
maleknockout
PMAT wildtype
(+/+, circles)
black squares),
PMAT heterozygote
(+/−, grey
diamonds),
PMATofknockout
(−/−,
open circles)
mice
underwent
the bout
tail suspension
test(B)
(TST),
(TST), and and
measures
time spent
immobile
(A) and
latency
to the first
of immobility
were
and
measures offline
of timeby
spent
(A) and latency
to the and
first genotype.
bout of immobility
(B)saline-treated
were deterdetermined
an immobile
observer blinded
to treatment
Likewise,
mined
offline
by
an
observer
blinded
to
treatment
and
genotype.
Likewise,
saline-treated
(yellow,
(yellow, 10 mL/kg) PMAT mice underwent TST 30 min after injection, and time spent immobile
10 mL/kg) PMAT mice underwent TST 30 min after injection, and time spent immobile (C) and
(C) and latency to the first immobility bout (D) were quantified in the same manner as naïve mice.
latency to the first immobility bout (D) were quantified in the same manner as naïve mice. Data are
Data are shown as individual points in violin plots, with horizontal lines indicating median and
shown as individual points in violin plots, with horizontal lines indicating median and quartiles. *
quartiles.
* p < 0.05 vs.
same-sex,
same
treatment
PMAT
wildtype mice.
p < 0.05 vs. same-sex,
same
treatment
PMAT
wildtype
mice.
To facilitate interpretation of TST behavioral changes in response to drug injections,
and to control for the behavioral effects of injection stress, data were graphed and analyzed as percent change from same-sex and same-genotype saline-treated mice, as done
Cells 2022, 11, 1874
0.001, partial η2 = 0.174). Pairwise comparisons revealed that, relative to saline-inj
female knockouts, female knockouts injected with either dose of escitalopram o
higher 8 mg/kg dose of bupropion exhibited significantly greater latencies to the firs
mobility bout (all p < 0.001; Figure 2B). Further, female knockouts injected with 1 m
(p < 0.001) or 2 mg/kg (p = 0.025) escitalopram, or 8 mg/kg bupropion (p = 0.009) displ
7 of 17
enhanced percent changes in latencies to the first immobility bout relative
to male kn
outs injected with the same drug and dose (Figure 2B).
Figure 2. Escitalopram- and bupropion-injected mouse behavior in tail suspension test. Behavior
Figure 2. Escitalopram- and bupropion-injected mouse behavior in tail suspension test. Beh
in the tail suspension
was normalized
to same-sex
andtosame-genotype
saline-injected saline-in
in thetest
tail (TST)
suspension
test (TST) was
normalized
same-sex and same-genotype
mice (yellow, 10 mL/kg)
to best10evaluate
injections
escitalopram
(light
blue, 1 mg/kg;
mice (yellow,
mL/kg) how
to best
evaluateofhow
injections of
escitalopram
(light dark
blue, 1 mg/kg
blue, 2 mg/kg) or
bupropion
red, 4 mg/kg;
dark
red, 8 dark
mg/kg)
behavior
in
blue,
2 mg/kg)(light
or bupropion
(light red,
4 mg/kg;
red, affected
8 mg/kg)TST
affected
TST behavior
in f
and male
(C,D)
PMAT(+/+,
wildtype
blackPMAT
squares),
PMAT heterozygote
female (A,B) and(A,B)
male (C,D)
PMAT
wildtype
black (+/+,
squares),
heterozygote
(+/−, grey (+/−, grey
monds),
and PMAT
(−/−, open
circles)
wereasgraphed
percent change
diamonds), and PMAT
knockout
(−/knockout
−, open circles)
mice.
Datamice.
wereData
graphed
percentas
change
same-sex
and same-genotype
saline-injected
forimmobile
time spent
immobile
(A,C) and laten
from the same-sexthe
and
same-genotype
saline-injected
mice for timemice
spent
(A,C)
and latency
the first bout of immobility (B,D), based on scoring offline by an observer blinded to treatmen
to the first bout of immobility (B,D), based on scoring offline by an observer blinded to treatment
genotype. Data are shown as individual points in violin plots, with horizontal lines indicatin
and genotype. Data are shown as individual points in violin plots, with horizontal lines indicating
dian and quartiles. The dashed line across all graphs indicates the mean of 100% for saline-in
median and quartiles.
The *dashed
line
all graphs
indicates
the mean of 100%
for saline-injected
controls.
p < 0.05,
***across
p < 0.001
vs. same-sex,
same-genotype
saline-injected
mice. ⟡ p < 0.05, ⟡
controls. * p < 0.05,
***
p
<
0.001
vs.
same-sex,
same-genotype
saline-injected
mice.
♦ p < 0.05,
0.01, ⟡⟡⟡ p < 0.001 vs. opposite-sex, same-genotype, same treatment mice.
♦♦ p < 0.01, ♦♦♦ p < 0.001 vs. opposite-sex, same-genotype, same treatment mice.
3.2. Post-TST Locomotor Behavior
3.2. Post-TST Locomotor
Behavior the possibility that interpretations of TST results might be confou
To evaluate
To evaluatebythelocomotor-induced
possibility that interpretations
of TST
might TST
be confounded
by subsequ
changes, mice
thatresults
underwent
testing were
locomotor-induced
micefor
that
underwent
TST testing
wereasubsequently
tested
testedchanges,
8 days later
locomotor
behavior
following
different injection
treatment
8 days later for locomotor behavior following a different injection treatment than what the
mouse received for TST. We have previously reported that there are no overall locomotor
changes from PMAT deficiency alone [20], so unlike the protocol used for TST behavior, we
did not include a naïve group here. We specifically analyzed locomotor behavior occurring
between 30–40 min post-injection, corresponding to the same time frame as when TST testing occurred (Figure 3). Locomotor data for the entire duration of the 1 h test are presented
in the Supplementary Material. Saline-injected mice similarly did not exhibit any significant interaction between genotype × sex (F (2,47) = 0.977, p = 0.384, partial η2 = 0.040),
nor were main effects of genotype (F (2,47) = 0.028, p = 0.972, partial η2 = 0.001) or sex
(F (1,47) = 1.608, p = 0.211, partial η2 = 0.033) observed (Figure 3A). Holm-Šídák’s post hoc
testing likewise did not indicate any differences (all p > 0.58). Subsequently, as for TST data,
we graphed and analyzed post-TST locomotor behavior as a percent change from salineinjected mice of the same sex and genotype, and for clarity, the sexes are graphed separately. A non-significant trend was noted for treatment × genotype × sex (F (8,233) = 1.728,
p = 0.093, partial η2 = 0.056). While there was not a significant treatment × genotype interaction (F (8,233) = 1.163, p = 0.322, partial η2 = 0.038), there were significant interactions
between treatment × sex (F (4,233) = 3.079, p = 0.017, partial η2 = 0.050) and genotype × sex
Cells 2022, 11, 1874
8 of 17
(F (2,233) = 22.366, p < 0.001, partial η2 = 0.161). Pairwise comparisons indicated that in
female wildtypes, mice treated with either dose of escitalopram or the higher 8 mg/kg
bupropion dose exhibited significantly greater locomotor activity relative to saline-injected
female wildtypes (all p = 0.002) (Figure 3B). In female knockouts, only treatment with
2 mg/kg escitalopram significantly increased locomotor activity relative to saline-treated
controls (p = 0.048). Male heterozygotes only exhibited elevated locomotor activity in
response to either dose of bupropion (both p < 0.001) relative to saline-injected controls
(Figure 3C). Across sexes for each genotype, there were several sex differences in response
to drug treatment. Female wildtypes exhibited significantly elevated locomotor activity
in response to 1 mg/kg (p = 0.002) or 2 mg/kg (p < 0.001) escitalopram relative to male
wildtypes. In contrast, male heterozygotes displayed greater locomotor activity in response
to 4 mg/kg (p = 0.031) or 8 mg/kg (p < 0.001) bupropion in comparison to female heterozyCells 2022, 11, x FOR PEER REVIEW
gotes. Locomotor responses to 1 mg/kg (p = 0.027) or 2 mg/kg (p = 0.003) escitalopram
were higher in female knockouts compared to male knockouts (Figure 3B,C).
9 of 1
Figure 3. Escitalopramand
bupropion-injected
mouse locomotor
behavior.
Locomotor
Figure
3. Escitalopramand bupropion-injected
mouse
locomotor
behavior.behavior
Locomotor behavio
the
open field
wassex
notnor
different
across
nor genotype
in saline-injected
in the open field wasin
not
different
across
genotype
in sex
saline-injected
(yellow,
10 mL/kg)(yellow,
PMAT 10 mL/kg
PMAT wildtype (+/+, black squares), PMAT heterozygote (+/−, grey diamonds), and PMAT knock
wildtype (+/+, black squares), PMAT heterozygote (+/−, grey diamonds), and PMAT knockout
out (−/−, open circles) mice (A). Locomotor data were normalized to same-sex and same-genotype
(−/−, open circles)saline-injected
mice (A). Locomotor
data
werehow
normalized
and
same-genotype
mice to best
evaluate
injections to
of same-sex
escitalopram
(light
blue, 1 mg/kg; dark blue
saline-injected mice 2tomg/kg)
best evaluate
how(light
injections
of escitalopram
blue,
1 mg/kg;
dark
blue, in female
or bupropion
red, 4 mg/kg;
dark red, 8(light
mg/kg)
affected
locomotor
behavior
(B) and
malered,
(C) PMAT
mice.dark
Data red,
were8graphed
percent change
from behavior
the same-sex
2 mg/kg) or bupropion
(light
4 mg/kg;
mg/kg)asaffected
locomotor
in and same
saline-injected
distanceas
traveled,
quantified
by the
ANY-maze
software.
female (B) and malegenotype
(C) PMAT
mice. Datamice
wereforgraphed
percentaschange
from
same-sex
and Data are
shown as individual points in violin plots, with horizontal lines indicating median and quartiles
same-genotype saline-injected mice for distance traveled, as quantified by ANY-maze software. Data
The dashed line across (B,C) indicates the mean of 100% for saline-injected controls. * p < 0.05, ** p <
are shown as individual
in violin
plots, with
horizontal lines
indicating
median
0.01, points
*** p < 0.001
vs. same-sex,
same-genotype
saline-injected
mice.
⟡ p < and
0.05,quartiles.
⟡⟡ p < 0.01, ⟡⟡⟡ p <
0.001(B,C)
vs. opposite-sex,
same-genotype,
samefor
treatment
mice.
The dashed line across
indicates the
mean of 100%
saline-injected
controls. * p < 0.05,
** p < 0.01, *** p < 0.001 vs. same-sex, same-genotype saline-injected mice. ♦ p < 0.05, ♦♦ p < 0.01,
In addition
to behavioralsame
responses
to themice.
non-stimulant drugs escitalopram and bu
♦♦♦ p < 0.001 vs. opposite-sex,
same-genotype,
treatment
propion, we also investigated how constitutive PMAT deficiency affected psychostimu
lant-induced locomotor sensitization using a cumulative dosing paradigm. First, we ex
plored locomotor sensitization to cocaine, which inhibits uptake by DAT, SERT, and the
norepinephrine transporter (NET) [29]; then we investigated locomotor sensitization to D
amphetamine, a DAT substrate that induces the DAT- and OCT-mediated efflux of dopa
mine [8].
3.3. Cocaine-Induced Locomotor Sensitization
Cells 2022, 11, x FOR PEER REVIEW
Cells 2022, 11, 1874
10 of 17
When evaluating total distance travelled across the 5 consecutive days of injections,
there was no three-way interaction of day × genotype × sex (F (5.28,116.0) = 0.175, p =90.975,
of 17
partial η2 = 0.008) (Figure 4B,C), nor two-way interactions of genotype × sex (F (2,44) =
1.089, p = 0.346, partial η2 = 0.047) or day × genotype (F (5.28,116.0) = 0.411, p = 0.850, partial
η2 = 0.018). While there was a significant two-way interaction of day × sex (F (2.64,116.0) =
In addition to behavioral responses to the non-stimulant drugs escitalopram and bupro2.973, p = 0.041, partial η2 = 0.063), there was no main effect of genotype (F (2,44) = 0.039, p
pion, we also investigated
how constitutive PMAT deficiency affected psychostimulant= 0.962, partial η2 = 0.002).
induced locomotor sensitization using a cumulative dosing paradigm. First, we exWhen cocaine data were normalized to a percentage of Day 1 for each sex-genotype
plored locomotor sensitization to cocaine, which inhibits uptake by DAT, SERT, and the
combination,
revealed
three-way
day × genotype
× sexto(F
norepinephrineanalyses
transporter
(NET)no
[29];
then we interaction
investigatedoflocomotor
sensitization
2 = 0.024) (Figure 4D,E). As expected based on pre(4.73,104.0)
=
0.546,
p
=
0.731,
partial
η
D-amphetamine, a DAT substrate that induces the DAT- and OCT-mediated efflux of
vious studies
dopamine
[8]. [16–18], there was a significant interaction between day × sex (F (2.36,104.0)
= 10.502, p < 0.001, partial η2 = 0.193). No interactions of day × genotype (F (4.73,104.0) =
1.48,Cocaine-Induced
p = 0.207, partial
η2 = 0.063)
or genotype × sex (F (2,44) = 0.234, p = 0.792, partial η2 =
3.3.
Locomotor
Sensitization
0.011)
were
there was
no main effect
of genotype
(2,44)
2.27, in
p=
The
totaldetected.
distanceLikewise,
traveled under
the influence
of cocaine
on Day 1(Fdid
not =result
2 = 0.094). Pairwise comparisons indicated that on Day
2
0.115,
partial
η
5,
female
heterozya genotype × sex interaction (F (2,44) = 0.506, p = 0.607, partial η = 0.022) (Figure 4A).
gotes
exhibited
less
(p = 0.039)
locomotor
sensitization
as compared
to sameNo
main
effect ofsignificantly
genotype was
observed
(F (2,44)
= 0.357,
p = 0.702, partial
η2 = 0.016),
but
sex wildtypes
(Figure
there
was a main
effect4D).
of sex (F (1,44) = 5.91, p = 0.019, partial η2 = 0.118).
Figure4.4.Cocaine-induced
Cocaine-inducedlocomotor
locomotorbehavior
behaviorand
andlocomotor
locomotorsensitization.
sensitization.Locomotor
Locomotorbehavior
behavior
Figure
inthe
theopen
openfield
fieldwas
wasnot
notdifferent
differentacross
acrosssex
sexnor
norgenotype
genotypeininPMAT
PMAT
wildtype
(+/+,black
blacksquares),
squares),
in
wildtype
(+/+,
PMATheterozygote
heterozygote(+/
(+/−,
circles)
mice
(A)
inin
rePMAT
−,grey
greydiamonds),
diamonds),and
and PMAT
PMAT knockout
knockout (−/−,
(−/−open
, open
circles)
mice
(A)
sponse
to
a
cumulative
dose
of
40
mg/kg
(individual
injections
of
5,
5,
10,
and
20
mg/kg
cocaine,
response to a cumulative dose of 40 mg/kg (individual injections of 5, 5, 10, and 20 mg/kg cocaine,
each 10 min apart) on Day 1 of 5 total injection days. Over 5 consecutive injection days, locomotor
each 10 min apart) on Day 1 of 5 total injection days. Over 5 consecutive injection days, locomotor
data in response to a cumulative dose of 40 mg/kg cocaine each day were graphed across days for
data in response to a cumulative dose of 40 mg/kg cocaine each day were graphed across days for
females (B) wildtypes, green squares; heterozygotes, blue diamonds; knockouts, white circles) and
females
(B)wildtypes,
wildtypes, orange
green squares;
diamonds;
knockouts,
whitewhite
circles)
and
males (C)
squares;heterozygotes,
heterozygotes,blue
yellow
diamonds;
knockouts,
circles).
males
wildtypes,
orange
heterozygotes,
yellow
diamonds; knockouts,
white
circles).
These (C)
same
locomotor
data insquares;
response
to cocaine were
also normalized
to Day 1 data
for same-sex
These
same locomotor
datato
inbest
response
to cocaine
were also
normalized
to Day
for same-sex
and same-genotype
mice,
evaluate
how repeated
cocaine
exposure
over1 5data
consecutive
days
and
same-genotype
mice,locomotor
to best evaluate
how repeated
cocaine
exposure
over
5 consecutive
elicited
cocaine-induced
sensitization
in female
(D) and
male (E)
PMAT
mice. Datadays
were
graphed
as percent change
from Day
1 cocaine-induced
for
and Data
same-genoelicited
cocaine-induced
locomotor
sensitization
in female locomotion
(D) and male
(E)same-sex
PMAT mice.
were
type mice,
quantified
byfrom
ANY-maze
software. Data in
(A) are shown
as individual
points in violin
graphed
as as
percent
change
Day 1 cocaine-induced
locomotion
for same-sex
and same-genotype
plots,aswith
horizontal
lines indicating
median
quartiles.
Data
(B–E) arepoints
graphed
as means
mice,
quantified
by ANY-maze
software.
Dataand
in (A)
are shown
as in
individual
in violin
plots, ±
SEM.
The
dashed
line
across
(D,E)
indicates
the
mean
of
100%
for
Day
1
cocaine-induced
locomotion
with horizontal lines indicating median and quartiles. Data in (B–E) are graphed as means ± SEM.
for the same-sex and same-genotype. * p < 0.05 vs. same-sex wildtype mice on same day.
The dashed line across (D,E) indicates the mean of 100% for Day 1 cocaine-induced locomotion for
the same-sex and same-genotype. * p < 0.05 vs. same-sex wildtype mice on same day.
Cells 2022, 11, 1874
10 of 17
When evaluating total distance travelled across the 5 consecutive days of injections,
there was no three-way interaction of day × genotype × sex (F (5.28,116.0) = 0.175,
p = 0.975, partial η2 = 0.008) (Figure 4B,C), nor two-way interactions of genotype × sex
(F (2,44) = 1.089, p = 0.346, partial η2 = 0.047) or day × genotype (F (5.28,116.0) = 0.411,
p = 0.850, partial η2 = 0.018). While there was a significant two-way interaction of day × sex
(F (2.64,116.0) = 2.973, p = 0.041, partial η2 = 0.063), there was no main effect of genotype
(F (2,44) = 0.039, p = 0.962, partial η2 = 0.002).
When cocaine data were normalized to a percentage of Day 1 for each sex-genotype
combination, analyses revealed no three-way interaction of day × genotype × sex
(F (4.73,104.0) = 0.546, p = 0.731, partial η2 = 0.024) (Figure 4D,E). As expected based
on previous studies [16–18], there was a significant interaction between day × sex
(F (2.36,104.0) = 10.502, p < 0.001, partial η2 = 0.193). No interactions of day × genotype
(F (4.73,104.0) = 1.48, p = 0.207, partial η2 = 0.063) or genotype × sex (F (2,44) = 0.234,
p = 0.792, partial η2 = 0.011) were detected. Likewise, there was no main effect of genotype
(F (2,44) = 2.27, p = 0.115, partial η2 = 0.094). Pairwise comparisons indicated that on Day 5,
female heterozygotes exhibited significantly less (p = 0.039) locomotor sensitization as
compared to same-sex wildtypes (Figure 4D).
3.4. Amphetamine-Induced Locomotor Sensitization
On Day 1 of amphetamine experiments, there was no genotype × sex interaction
(F (2,41) = 2.074, p = 0.139, partial η2 = 0.092), nor were there main effects of genotype
(F (2,41) = 0.207, p = 0.814, partial η2 = 0.010) or sex (F (1,41) = 0.410, p = 0.526, partial η2 = 0.010) (Figure 5A). When analyzing amphetamine-induced locomotor activity
across days, there was not a significant three-way interaction of day × genotype × sex
(F (5.68,116.5) = 0.941, p = 0.465, partial η2 = 0.044) (Figure 5B,C). We did observe an expected day × sex interaction (F (2.84,116.5) = 4.031, p = 0.010, partial η2 = 0.090), but there
was not a day × genotype interaction (F (5.68,116.5) = 0.628, p = 0.699, partial η2 = 0.030).
A trend towards significance was noted for genotype × sex (F (2,41) = 2.875, p = 0.068;
partial η2 = 0.123); for clarity, sexes are graphed separately (Figure 5B,C). There was no
main effect of genotype (F (2,41) = 0.257, p = 0.775, partial η2 = 0.012). Pairwise comparisons
found that female heterozygotes had significantly less amphetamine-induced locomotor
activity on the third day of injections (Day 7) as compared to female wildtypes (p = 0.029)
(Figure 5B).
As with cocaine, amphetamine data were normalized to Day 1 distance traveled for the
same sex and genotype, to better evaluate drug sensitization over time. When evaluating
the data in this manner, a non-significant trend was observed for day × genotype × sex
(F (5.34,109.5) = 2.041, p = 0.074, partial η2 = 0.091) (Figure 5D,E). Given that this analysis
normalizes to Day 1 for the same sex and genotype, it is not surprising that there was no
significant day × sex interaction (F (2.67,109.5) = 1.516, p = 0.218, partial η2 = 0.036). There
was also no significant day × genotype interaction (F (5.34,109.5) = 0.915, p = 0.479, partial η2 = 0.043), but a trend towards a genotype × sex interaction was noted (F (2,41) = 2.495,
p = 0.095, partial η2 = 0.108).
An expected main effect of day was detected
(F (2.67,109.5) = 186.4, p < 0.001, partial η2 = 0.82), but main effects of sex (F (1,41) = 0.912,
p = 0.345, partial η2 = 0.022) and genotype (F (2,41) = 0.610, p = 0.548, partial η2 = 0.029)
were not significant.
Cells 2022, 11, 1874
no significant day × sex interaction (F (2.67,109.5) = 1.516, p = 0.218, partial η2 = 0.036).
There was also no significant day × genotype interaction (F (5.34,109.5) = 0.915, p = 0.479,
partial η2 = 0.043), but a trend towards a genotype × sex interaction was noted (F (2,41) =
2.495, p = 0.095, partial η2 = 0.108). An expected main effect of day was detected (F
(2.67,109.5) = 186.4, p < 0.001, partial η2 = 0.82), but main effects of sex (F (1,41) = 0.912, p =
11 of 17
0.345, partial η2 = 0.022) and genotype (F (2,41) = 0.610, p = 0.548, partial η2 = 0.029) were
not significant.
Figure5.5. D-amphetamine-induced
D-amphetamine-induced locomotor
locomotorbehavior
behaviorand
andlocomotor
locomotorsensitization.
sensitization.Locomotor
Locomotor
Figure
behaviorin
inthe
theopen
openfield
fieldwas
wasnot
notdifferent
differentacross
acrosssex
sexnor
norgenotype
genotypeininPMAT
PMATwildtype
wildtype(+/+,
(+/+,black
black
behavior
squares),
PMAT
heterozygote
(+/−,
grey
diamonds),
and
PMAT
knockout
(−/−,
open
circles)
mice
squares), PMAT heterozygote (+/−, grey diamonds), and PMAT knockout (−/−, open circles)
(A) in response to a cumulative dose of 4.62 mg/kg (individual injections of 0.1, 0.32, 1.0, and 3.2
mice (A) in response to a cumulative dose of 4.62 mg/kg (individual injections of 0.1, 0.32, 1.0, and
mg/kg D-amphetamine, each 10 min apart) on Day 1 of 5 total injection days. Over 5 injection days,
3.2 mg/kg D-amphetamine, each 10 min apart) on Day 1 of 5 total injection days. Over 5 injection
each separated by 3 days, locomotor data in response to a cumulative dose of 4.62 mg/kg Ddays, each separated by 3 days, locomotor data in response to a cumulative dose of 4.62 mg/kg
D-amphetamine each day were graphed across days for females (B) wildtypes, green squares; heterozygotes, blue diamonds; knockouts, white circles) and males (C) wildtypes, orange squares;
heterozygotes, yellow diamonds; knockouts, white circles). These same locomotor data in response
to D-amphetamine were also normalized to Day 1 data for same-sex and same-genotype mice, to
best evaluate how repeated D-amphetamine exposure over 5 injection days elicited D-amphetamineinduced locomotor sensitization in female (D) and male (E) PMAT mice. Data were graphed as percent
change from Day 1 D-amphetamine-induced locomotion for same-sex and same-genotype mice, as
quantified by ANY-maze software. Data in (A) are shown as individual points in violin plots, with
horizontal lines indicating median and quartiles. Data in (B–E) are graphed as means ± SEM. The
dashed line across (D,E) indicates the mean of 100% for Day 1 D-amphetamine-induced locomotion
for the same-sex and same-genotype. * p < 0.05 vs. same-sex wildtype mice on same day.
4. Discussion
Across psychoactive compounds, our results indicate that PMAT function is sexually
dimorphic, a revelation that required perturbations in monoaminergic signaling either via
pharmacological mechanisms or by an acute stressor. These findings agree with previous
reports that behavioral and physiological consequences of PMAT deficiency emerge in a
sex-specific manner following homeostatic perturbations [19,20]. Moreover, the outcomes
observed align with current thinking that PMAT is engaged in a compensatory manner,
recruited when uptake 1 transporters are saturated and/or incapacitated but otherwise
remaining relatively quiescent, and/or exists as a substitute monoamine uptake mechanism
in brain regions where uptake 1 transporter expression is scant (e.g., cerebellum, frontal
cortex) [3,9]. Still, our results did not align with our hypotheses about PMAT-deficient
mice in many instances, both with our anticipation of enhanced behavioral responses to
the non-stimulant compounds escitalopram and bupropion, as well as with our hypothesis
that locomotor sensitization to cocaine and D-amphetamine would be augmented. Though
portions of each of these expected outcomes were supported by some data to varying
Cells 2022, 11, 1874
12 of 17
extents, nuances of sex, genotype, drug, dose, and day all contributed to create a much
more complex story than PMAT ‘merely’ serving as a catch-all for the castoffs of uptake
1 transporters.
Constitutive deficiency in PMAT sex-selectively affected TST immobility behavior in
naïve males, with both heterozygous and knockout males displaying increased immobility,
while no genotype effect was observed across genotypes in naïve females. In contrast, and
highlighting the importance of evaluating behavior in non-injected animals, TST immobility
was unaffected across sex and genotype in saline-injected mice. When assessing latency
to the first immobility bout in TST, no genotype nor sex effects were observed in naïve
mice, but saline-injected female knockouts exhibited a reduced latency relative to salineinjected female wildtypes, whereas no differences were observed across genotypes in
latency to first immobility in saline-injected males. Thus, the experience of a saline injection
stress was sufficient to affect behavioral responses to a different brief stressor—that of the
TST—and to both obscure (male PMAT-deficient) and elicit (female PMAT knockout) sexand genotype-specific responses.
Administration of non-stimulant drugs escitalopram and bupropion further emphasized sex- and genotype-specific effects, though not exactly in the manner we hypothesized.
Administration of the higher dose of escitalopram attenuated TST immobility in male
wildtype mice, similar to previous work [23,24]. However, this effect was ablated in PMAT
knockout male mice, in direct contrast to our hypothesis. Moreover these drugs, at both
their lower and higher doses, elicited no changes in TST immobility in female wildtype
mice, highlighting how doses optimized for the male sex [23] do not always translate
to the female sex. Unlike in males, where no changes in latency to the first immobility
bout occurred after drug administration, female PMAT knockouts exhibited significantly
increased latencies in response to both doses of escitalopram, as well as to the higher dose
of bupropion. Unlike what we observed with immobility times, these data align with our
hypothesis, but in a sex-selective manner. Collectively, our findings indicate that intact
PMAT function could sex-selectively counteract specific behavioral changes elicited by
escitalopram and bupropion in females, but facilitate other behavioral changes evoked by
these drugs in males.
When assessing an activity-related measure such as immobility, considerations of
potential confounds like broader effects on locomotor behavior are important. In agreement
with our previous work showing that PMAT deficiency does not impact locomotor activity
in non-injected mice [20], we observed that saline injections did not significantly affect
locomotor behavior across genotypes in both sexes. An unexpected observation was that
all but the lower dose of bupropion enhanced locomotor activity in female wildtypes, yet
these mice exhibited no significant shifts in TST immobility. Female knockouts displayed
increased locomotor activity only in response to the higher dose of escitalopram, suggesting
the heightened latency to first immobility in this specific sex, genotype, and treatment group
might be confounded by enhanced locomotion. In males, the locomotor-enhancing effects
of both bupropion doses were specific to heterozygotes, but as with female wildtypes,
there curiously was no significant change in TST measures in bupropion-treated male
heterozygotes. Combined, these data can be interpreted to suggest that—with the exception
of female knockouts given 2 mg/kg escitalopram—either there are likely not locomotor
confounds in TST behavior, or alternatively, that the locomotor effects of these drugdose-genotype-sex combinations potentially obscured reductions in TST immobility (or
augmentations in latency).
Regardless, the TST and locomotor data together provide strong evidence for PMAT
deficiency eliciting sex-specific behavioral responses to psychoactive drugs that inhibit
SERT (escitalopram) or DAT and NET (bupropion). Researchers in several labs [3,13]
have reported that bupropion has weak inhibitory action (~100 µM) at PMAT in vitro,
and that (es)citalopram does not act at PMAT at all. In our study, bupropion’s effects
were significant in female knockouts (TST latency), female wildtypes (locomotor activity),
and male heterozygotes (locomotor activity). Given the relatively low (4 and 8 mg/kg)
Cells 2022, 11, 1874
13 of 17
doses of bupropion we employed here, these effects are most likely unmasking the contribution of PMAT uptake under conditions when DAT/NET function is impaired, rather
than any (lack of) action at PMAT (see also [30]). Likewise, the behavioral influences of
escitalopram in male wildtypes (TST immobility), female wildtypes (locomotor activity),
male heterozygotes (TST immobility), and female knockouts (TST latency and locomotor
activity) illustrate how PMAT likely facilitates the uptake of monoamines when SERT
function is blocked. For example, our TST data suggest that intact PMAT function facilitates the lowered immobility induced by escitalopram in males, whereas in females
PMAT probably compensates for impaired SERT function due to escitalopram blockade by
keeping extracellular serotonin levels relatively low. Certainly, neurochemical investigations using microdialysis or voltammetric techniques would be necessary to investigate
these possibilities.
Given the relatively modest effects of non-stimulant psychoactive drugs in PMATdeficient mice, we next pursued a more heavy-handed pharmacological approach to investigate how cumulative dosing of psychostimulants that act at DAT/SERT/NET (cocaine)
or primarily DAT and NET (D-amphetamine) affected locomotor sensitization. We anticipated that repeated dosing with these more behaviorally activating drugs would elicit
clearer sex- and PMAT genotype-specific behavioral responses, but once again, our findings
did not support this hypothesis. Initial (Day 1) locomotor responses to either cocaine or
D-amphetamine revealed no influence of PMAT deficiency, nor any suggestion of sex as
a moderator. Moreover, although we observed the anticipated augmented sensitization
to both psychostimulants in females compared to males, PMAT genotype did not reliably impact locomotor sensitization to either drug. Instead, we observed quite modest
attenuations in cocaine- and D-amphetamine-induced locomotor sensitization only on a
single day for each drug (day 5 and day 7, respectively), and only in female heterozygotes.
There was also a non-significant trend (p < 0.10) for male knockouts over the final three
days of D-amphetamine-induced locomotor sensitization. Once again, these investigations illustrate how there appears to be a relationship between sex and PMAT deficiency
that only emerges after stress or uptake 1 inhibition, but further studies are necessary to
determine the mechanisms responsible for this relationship. Multiple reports agree that
cocaine does not act at PMAT, but evidence is more conflicted regarding amphetamine,
with the Sitte lab providing evidence that amphetamine has some action at hPMAT at
concentrations of ~72 uM in vitro [3,4,13]. Our cocaine findings align with these reports,
and the eventual attenuation of cocaine-induced locomotor sensitization in female PMAT
heterozygotes could indicate that PMAT’s role in psychostimulant-induced sensitization
is secondary and may be obscured in female knockouts due to constitutive compensatory
changes. Indeed, the potential minor involvement of PMAT in sensitization processes
to uptake 1-acting psychostimulants, at least in females, is suggested by our data. An
alternative interpretation, and one not mutually exclusive with the preceding statement, is
that the doses of these psychostimulants did not generate extracellular monoamine levels
sufficient enough to robustly reveal the entire contribution of PMAT to monoamine uptake,
despite these doses being literature-based [15,27,28]. One other report has observed a
modest relationship between PMAT deficiency and amphetamine-mediated locomotor
sensitization [14]; though this investigation used a single 1 mg/kg dose administered over
four injection days to either wildtypes or knockouts, in contrast to the cumulative dosing
paradigm over five injection days given to all three genotypes here.
Unlike the Day 1 psychostimulant locomotor responses, our TST and locomotor results
following escitalopram and bupropion administration suggest PMAT deficiency might
sex-selectively influence antidepressant drug responses. The effectiveness of any given
antidepressant treatment in humans is notoriously unpredictable [31–33]. This unpredictability has been attributed, among other things, to genetic polymorphisms in uptake
1 transporters [34–36]. Functional polymorphisms in the human PMAT gene (SLC29A4) can
affect treatment responses to metformin, a drug for type II diabetes management [37–39].
However, studies have yet to evaluate how SLC29A4 polymorphisms might be associated
Cells 2022, 11, 1874
14 of 17
with antidepressant treatment response in humans, or indeed any measurements of overall
mood or other mental states. This is likely a consequence, at least in part, of candidate
gene investigations falling out of favor in the literature despite lingering evidence that
some polymorphism findings are replicable [40–42]. Polymorphisms in SLC29A4, such as
rs3889348 [37,39], substantially reduce PMAT function, meaning heterozygous PMAT mice
could serve as a model for attenuated PMAT function in humans.
Indeed, much remains unexplored about PMAT function across species. The absence
of a selective pharmacological inhibitor of PMAT is a prominent roadblock to such studies,
reflecting the necessity for the indirect approach here with uptake 1 inhibitors in combination with constitutive genetic deficiency of PMAT. Compensatory development-specific
and/or lifelong upregulation of similar transporters is certainly an inherent concern when
making conclusions with such rodent models. The Wang lab, which developed these PMATdeficient mice, reported that mRNA for SERT, DAT, NET, and OCT3 were unaffected in
the adult brains of these mice, allaying such concerns [21]. Still, the compensatory protein
expression of these transporters remains a potential limitation, as does the possibility that
PMAT could influence brain development, given that PMAT mRNA has been detected
throughout mouse embryo brains [9]. The sex-specific effects observed here and previously [20], for instance, might instead be explained by compensatory upregulation of OCT3,
which unlike PMAT, can be inhibited by sex hormones such as progesterone and estradiol
(see review in [3]). Alternatively, and not mutually exclusive of the preceding possibility,
is that stress hormone release in response to injections inhibits OCT3—which could be
compensatorily upregulated in PMAT-deficient mice—thereby inducing the blockade of
OCT3 and muddying interpretations of the contributions of PMAT alone (see review by [4]).
Our current understanding of the function of uptake 2 transporters is that they predominantly serve as a backup/substitute system for uptake 1 transporters [9]. Consequently,
compensatory lifelong upregulation of other transporters may be unlikely. Indeed, it is the
very nature of these uptake 2 transporters as backups that appears to make their constitutive absence challenging to detect until monoaminergic systems are sufficiently stimulated,
such as in adult acute stress situations like TST. The brief stress of TST unmasked the
behavioral consequence of reduced PMAT function specifically in naïve males, as revealed
by their increased immobility behavior, suggesting intact PMAT function in males might
facilitate active stress coping behaviors [43–45]. Additional studies are necessary to identify
how PMAT function contributes to heterotypic stressor responsivity and to determine if the
sexually dimorphic effects of PMAT function are organizational or activational. Inclusion
of heterozygotes in future investigations will be crucial, given their biological mirroring
of recognized functional human PMAT polymorphisms. This is particularly relevant considering that the present findings support an underappreciated role for PMAT function
in sex-specific responses to drugs used in humans to influence mood, cognition, anxiety,
attention, and other mental states.
Supplementary Materials: The raw data can be downloaded as supporting information at: https:
//www.mdpi.com/article/10.3390/cells11121874/s1. Figure S1: Time course of locomotor activity in
open field 8 days after tail suspension test; Table S1: Statistical analyses of post-tails suspension test
locomotor testing.
Author Contributions: Conceptualization, T.L.G.; methodology, T.L.G.; formal analyses, J.N.B. and
T.L.G.; validation, J.N.B., M.T.F., S.K.K., A.E.A. and T.L.G.; investigation, J.N.B., B.L.W., M.T.F., S.K.K.,
A.E.A. and T.L.G.; resources, T.L.G.; data curation, J.N.B., B.L.W. and T.L.G.; writing—original draft
preparation, J.N.B. and T.L.G.; writing—review and editing, J.N.B., B.L.W. and T.L.G.; visualization,
T.L.G.; supervision, T.L.G.; project administration, T.L.G.; funding acquisition, T.L.G. All authors
have read and agreed to the published version of the manuscript.
Funding: This work was generously supported by Kent State University, the Applied Psychology
Center in the Department of Psychological Sciences at Kent State University, and by a 2017 NARSAD
Young Investigator Grant (26249) from the Brain & Behavior Research Foundation (New York, NY,
USA) and Vital Projects Fund, Inc. (USA), to T.L.G.
Cells 2022, 11, 1874
15 of 17
Institutional Review Board Statement: The animal study protocol was approved by the Institutional
Animal Care and Use Committee at Kent State University, protocol 486 AJ 19-10 2019-2022), and
adhered to the National Research Council’s Guide for the Care and Use of Laboratory Animals,
8th Ed. [22].
Informed Consent Statement: Not applicable.
Data Availability Statement: The data presented in this study are available in the Supplementary
Material.
Acknowledgments: The authors express their immense gratitude to Dr. Lynette C. Daws for feedback
on this manuscript, and profusely thank her for all the help, support, advice, and resources she has
generously provided. We thank Dr. Melodi A. Bowman for assistance and for facilitating the blind
coding of TST treatments, and Kelsey Toney for assistance in scoring the pilot data that led to the
present experiments. The authors thank Dr. Joanne Wang and the University of Washington for the
MTA that allows us to use the PMAT-deficient mice, and Dr. Lynette C. Daws for supplying founder
mice to start a PMAT mouse colony at Kent State University, with the University of Washington’s
permission. We thank the Kent Hall Animal Facility caretakers for their animal care. We thank Drs.
Michelle Doyle, Rheaclare Fraser-Spears, Georgianna G. Gould, Vanessa Minervini, and Robert W.
Seaman, Jr., for their technical expertise and moral support.
Conflicts of Interest: The authors declare no conflict of interest. The sponsors had no role in the
design, execution, interpretation, or writing of the study.
References
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
Aggarwal, S.; Mortensen, O.V. Overview of Monoamine Transporters. Curr. Protoc. Pharm. 2017, 79, 12.16.1–12.16.17. [CrossRef]
[PubMed]
Daws, L.C. Unfaithful neurotransmitter transporters: Focus on serotonin uptake and implications for antidepressant efficacy.
Pharm. Ther. 2009, 121, 89–99. [CrossRef] [PubMed]
Bönisch, H. Organic Cation Transporters in the Central Nervous System. Handb. Exp. Pharm. 2021, 266, 119–167. [CrossRef]
Maier, J.; Niello, M.; Rudin, D.; Daws, L.C.; Sitte, H.H. The Interaction of Organic Cation Transporters 1-3 and PMAT with
Psychoactive Substances. Handb. Exp. Pharmacol. 2021, 266, 199–214. [CrossRef]
Gu, H.; Wall, S.C.; Rudnick, G. Stable expression of biogenic amine transporters reveals differences in inhibitor sensitivity, kinetics,
and ion dependence. J. Biol. Chem. 1994, 269, 7124–7130. [CrossRef]
Wang, J. The plasma membrane monoamine transporter (PMAT): Structure, function, and role in organic cation disposition.
Clin. Pharm. Ther. 2016, 100, 489–499. [CrossRef]
Shirasaka, Y.; Lee, N.; Duan, H.; Ho, H.; Pak, J.; Wang, J. Interspecies comparison of the functional characteristics of plasma
membrane monoamine transporter (PMAT) between human, rat and mouse. J. Chem. Neuroanat. 2017, 83, 99–106. [CrossRef]
Mayer, F.P.; Schmid, D.; Owens, W.A.; Gould, G.G.; Apuschkin, M.; Kudlacek, O.; Salzer, I.; Boehm, S.; Chiba, P.;
Williams, P.H.; et al. An unsuspected role for organic cation transporter 3 in the actions of amphetamine. Neuropsychopharmacology
2018, 43, 2408–2417. [CrossRef]
Dahlin, A.; Xia, L.; Kong, W.; Hevner, R.; Wang, J. Expression and immunolocalization of the plasma membrane monoamine
transporter in the brain. Neuroscience 2007, 146, 1193–1211. [CrossRef]
Duan, H.; Wang, J. Selective Transport of Monoamine Neurotransmitters by Human Plasma Membrane Monoamine Transporter
and Organic Cation Transporter 3. J. Pharm. Exp. Ther. 2010, 335, 743–753. [CrossRef]
Zhang, P.; Jørgensen, T.N.; Loland, C.J.; Newman, A.H. A rhodamine-labeled citalopram analogue as a high-affinity fluorescent
probe for the serotonin transporter. Bioorg. Med. Chem. Lett. 2013, 23, 323–326. [CrossRef] [PubMed]
Verrico, C.D.; Miller, G.M.; Madras, B.K. MDMA (Ecstasy) and human dopamine, norepinephrine, and serotonin transporters:
Implications for MDMA-induced neurotoxicity and treatment. Psychopharmacology 2007, 189, 489–503. [CrossRef] [PubMed]
Angenoorth, T.J.; Stankovic, S.; Niello, M.; Holy, M.; Brandt, S.D.; Sitte, H.H.; Maier, J. Interaction Profiles of Central Nervous
System Active Drugs at Human Organic Cation Transporters 1–3 and Human Plasma Membrane Monoamine Transporter. Int. J.
Mol. Sci. 2021, 22, 12995. [CrossRef] [PubMed]
Clauss, N.J.; Koek, W.; Daws, L.C. Role of Organic Cation Transporter 3 and Plasma Membrane Monoamine Transporter in
the Rewarding Properties and Locomotor Sensitizing Effects of Amphetamine in Male and Female Mice. Int. J. Mol. Sci. 2021,
22, 13420. [CrossRef] [PubMed]
Elliot, E.E. Cocaine sensitization in the mouse using a cumulative dosing regime. Behav. Pharm. 2002, 13, 407–415. [CrossRef]
Zakharova, E.; Wade, D.; Izenwasser, S. Sensitivity to cocaine conditioned reward depends on sex and age. Pharm. Biochem. Behav.
2009, 92, 131–134. [CrossRef]
Cells 2022, 11, 1874
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
16 of 17
Milesi-Hallé, A.; McMillan, D.E.; Laurenzana, E.M.; Byrnes-Blake, K.A.; Owens, S.M. Sex differences in (+)-amphetamine- and
(+)-methamphetamine-induced behavioral response in male and female Sprague-Dawley rats. Pharm. Biochem. Behav. 2007,
86, 140–149. [CrossRef]
Becker, J.B.; McClellan, M.L.; Reed, B.G. Sex differences, gender and addiction: Sex, Gender, and Addiction. J. Neurosci. Res. 2016,
95, 136–147. [CrossRef]
Wei, R.; Gust, S.L.; Tandio, D.; Maheux, A.; Nguyen, K.H.; Wang, J.; Bourque, S.; Plane, F.; Hammond, J.R. Deletion of murine
slc29a4 modifies vascular responses to adenosine and 5-hydroxytryptamine in a sexually dimorphic manner. Physiol. Rep. 2020,
8, e14395. [CrossRef]
Gilman, T.L.; George, C.M.; Vitela, M.; Herrera-Rosales, M.; Basiouny, M.S.; Koek, W.; Daws, L.C. Constitutive plasma membrane
monoamine transporter (PMAT, Slc29a4) deficiency subtly affects anxiety-like and coping behaviours. Eur. J. Neurosci. 2018,
48, 1706–1716. [CrossRef]
Duan, H.; Wang, J. Impaired Monoamine and Organic Cation Uptake in Choroid Plexus in Mice with Targeted Disruption of the
Plasma Membrane Monoamine Transporter (Slc29a4) Gene. J. Biol. Chem. 2013, 288, 3535–3544. [CrossRef] [PubMed]
National Research Council. Guide for the Care and Use of Laboratory Animals, 8th ed.; National Academies Press: Washington, DC,
USA, 2011. [CrossRef]
Ripoll, N.; David, D.J.P.; Dailly, E.; Hascoët, M.; Bourin, M. Antidepressant-like effects in various mice strains in the tail suspension
test. Behav. Brain Res. 2003, 143, 193–200. [CrossRef]
Mitchell, N.C.; Gould, G.G.; Koek, W.; Daws, L.C. Ontogeny of SERT Expression and Antidepressant-like Response to Escitalopram in Wild-Type and SERT Mutant Mice. J. Pharm. Exp. Ther. 2016, 358, 271–281. [CrossRef] [PubMed]
Russell, W.M.S.; Burch, R.L. The Principles of Humane Experimental Technique. Nature 1959, 184, 1675–1676. [CrossRef]
Robinson, T.E.; Becker, J.B. Enduring changes in brain and behavior produced by chronic amphetamine administration: A review
and evaluation of animal models of amphetamine psychosis. Brain Res. Rev. 1986, 11, 157–198. [CrossRef]
Yates, J.W.; Meij, J.T.A.; Sullivan, J.R.; Richtand, N.M.; Yu, L. Bimodal effect of amphetamine on motor behaviors in C57BL/6 mice.
Neurosci. Lett. 2007, 427, 66–70. [CrossRef]
El-Ghundi, M.B.; Fan, T.; Karasinska, J.M.; Yeung, J.; Zhou, M.; O’Dowd, B.F.; George, S.R. Restoration of amphetamine-induced
locomotor sensitization in dopamine D1 receptor-deficient mice. Psychopharmacology 2010, 207, 599–618. [CrossRef]
Han, D.D.; Gu, H.H. Comparison of the monoamine transporters from human and mouse in their sensitivities to psychostimulant
drugs. BMC Pharm. 2006, 6, 6. [CrossRef]
Haenisch, B.; Bönisch, H. Interaction of the human plasma membrane monoamine transporter (hPMAT) with antidepressants
and antipsychotics. Naunyn-Schmiedeberg’s Arch. Pharm. 2010, 381, 33–39. [CrossRef]
Kirsch, I.; Huedo-Medina, T.B.; Pigott, H.E.; Johnson, B.T. Do Outcomes of Clinical Trials Resemble Those “Real World” Patients?
A Reanalysis of the STAR*D Antidepressant Data Set. Psychol. Conscious Theory Res. Pract. 2018, 5, 339–345. [CrossRef]
Chekroud, A.M.; Gueorguieva, R.; Krumholz, H.M.; Trivedi, M.H.; Krystal, J.H.; McCarthy, G. Reevaluating the Efficacy and
Predictability of Antidepressant Treatments: A Symptom Clustering Approach. JAMA Psychiatry 2017, 74, 370. [CrossRef]
[PubMed]
Patel, K.; Allen, S.; Haque, M.N.; Angelescu, I.; Baumeister, D.; Tracy, D.K. Bupropion: A systematic review and meta-analysis of
effectiveness as an antidepressant. Ther. Adv. Psychopharmacol. 2016, 6, 99–144. [CrossRef] [PubMed]
Reynolds, G.P.; McGowan, O.O.; Dalton, C.F. Pharmacogenomics in psychiatry: The relevance of receptor and transporter
polymorphisms. Br. J. Clin. Pharm. 2014, 77, 654–672. [CrossRef] [PubMed]
Porcelli, S.; Fabbri, C.; Serretti, A. Meta-analysis of serotonin transporter gene promoter polymorphism (5-HTTLPR) association
with antidepressant efficacy. Eur. Neuropsychopharm. 2012, 22, 239–258. [CrossRef]
Andre, K.; Kampman, O.; Illi, A.; Viikki, M.; Setälä-Soikkeli, E.; Mononen, N.; Lehtimäki, T.; Haraldsson, S.; Koivisto, P.A.;
Leinonen, E. SERT and NET polymorphisms, temperament and antidepressant response. Nord. J. Psychiatry 2015, 69, 531–538.
[CrossRef]
Christensen, M.M.; Brasch-Andersen, C.; Green, H.; Nielsen, F.; Damkier, P.; Beck-Nielsen, H.; Brosen, K. The pharmacogenetics
of metformin and its impact on plasma metformin steady-state levels and glycosylated hemoglobin A1c. Pharm. Genom. 2011,
21, 837–850. [CrossRef]
Moeez, S.; Khalid, S.; Shaeen, S.; Khalid, M.; Zia, A.; Gul, A.; Niazi, R.; Khalid, Z. Clinically significant findings of high-risk
mutations in human SLC29A4 gene associated with diabetes mellitus type 2 in Pakistani population. J. Biomol. Struct. Dyn. 2021,
1–14. [CrossRef]
Dawed, A.Y.; Zhou, K.; van Leeuwen, N.; Mahajan, A.; Robertson, N.; Koivula, R.; Elders, P.J.; Rauh, S.P.; Jones, A.G.;
Holl, R.W.; et al. Variation in the Plasma Membrane Monoamine Transporter (PMAT, Encoded in SLC29A4) and Organic
Cation Transporter 1 (OCT1, Encoded in SLC22A1) and Gastrointestinal Intolerance to Metformin in Type 2 Diabetes: An IMI
DIRECT Study. Diabetes Care 2019, 42, dc182182. [CrossRef]
Duncan, L.E.; Keller, M.C. A Critical Review of the First 10 Years of Candidate Gene-by-Environment Interaction Research in
Psychiatry. Am. J. Psychiatry 2011, 168, 1041–1049. [CrossRef]
Schinka, J.A.; Letsch, E.A.; Crawford, F.C. DRD4 and novelty seeking: Results of meta-analyses. Am. J. Med. Genet. 2002,
114, 643–648. [CrossRef]
Cells 2022, 11, 1874
42.
43.
44.
45.
17 of 17
Gizer, I.R.; Ficks, C.; Waldman, I.D. Candidate gene studies of ADHD: A meta-analytic review. Hum. Genet. 2009, 126, 51–90.
[CrossRef] [PubMed]
Commons, K.G.; Cholanians, A.B.; Babb, J.A.; Ehlinger, D.G. The Rodent Forced Swim Test Measures Stress-Coping Strategy,
Not Depression-Like Behavior. ACS Chem. Neurosci. 2017, 8, 955–960. [CrossRef] [PubMed]
Bandler, R.; Keay, K.A.; Floyd, N.; Price, J. Central circuits mediating patterned autonomic activity during active vs. passive
emotional coping. Brain Res. Bull. 2000, 53, 95–104. [CrossRef]
De Kloet, E.R.; Molendijk, M.L. Coping with the Forced Swim Stressor: Towards Understanding an Adaptive Mechanism.
Neural Plast. 2016, 2016, 6503162. [CrossRef] [PubMed]