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  + ρσ3 /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. 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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. 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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. 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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. 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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. 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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. 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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. 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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. 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