University of Florida Thesis Or Dissertation Formatting

TRANSCRIPTIONAL AND EPIGENETIC PROFILING OF THE HIPPOCAMPUS AND THE MEDIAL PREFRONTAL CORTEX: RELATIONSHIP TO AGING AND COGNITIVE DECLINE

LARA IANOV

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2017

To my spouse, Okie Ianov, and my dear friends Lana McMillan and Guido Pardi

ACKNOWLEDGMENTS

First, I would like to thank my mentor, Dr. Thomas C. Foster, for providing me the opportunity to work on an exciting area of research, along with his trust in my skills and his time and patience to mentor me. I would also like to acknowledge my committee members, Dr. Leonid Moroz, Dr. Jorg Bungert and Dr. Martha Campbell-Thompson for their guidance and suggestions in all committee meetings. In addition, Dr. Alberto Riva, deserves special recognition for his devoted support to me with a section of the data analysis shown in this dissertation. I would also like to thank Dr. Jennifer Bizon and Dr.

B. Sofia Beas for their help with the set shifting task.

Furthermore, I would also like to thank the past and present Genetics and

Genomics program coordinators including Dr. Wilfred Vermerris who admitted me into the program, Dr. Jorg Bungert and Dr. Connie Mulligan, as past coordinators, and the current coordinators, Dr. Maurice Swanson and Dr. Doug Soltis. I would also like to thank Dr. Patrick Concannon, for his support to the program and the Genetics Institute.

Finally, I would like to thank Hope Parmeter for signing me up for all classes each semester along with all student-related requirements.

Additionally, the scientific and moral support from the Foster lab family was very helpful and I am grateful to each one of you, both past and present: Dr. Ashok Kumar,

Asha Rani, Dr. Michael Guidi, Dr. Linda Bean, Dr. Brittney Yegla, Costa Kyritsopoulos,

Jolie Barter, Olga Tchigrinova and Semir Karic.

Finally, I would like to thank Okie Ianov, Lana McMillan and Guido Pardi for their constant encouragement in this chapter of my life.

TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...... 4

LIST OF TABLES ...... 8

LIST OF FIGURES ...... 9

LIST OF ABBREVIATIONS ...... 11

ABSTRACT ...... 13

CHAPTER

1 INTRODUCTION ...... 15

Goals of Current Research ...... 15 Aging and Age-Related Cognitive Decline ...... 16 Estrogen Receptors and the Hippocampus ...... 17 The Prefrontal Cortex and Cognitive Flexibility ...... 21 Transcriptomic Studies in Aging and Cognition ...... 23 DNA Methylation in Aging and the Prefrontal Cortex ...... 25

2 EPIGENETIC REGULATION OF ESTROGEN RECEPTOR ALPHA CONTRIBUTES TO AGE-RELATED DIFFERENCES IN TRANSCRIPTION ACROSS THE HIPPOCAMPAL REGIONS CA1 AND CA3 ...... 30

Background ...... 30 Materials and Methods...... 31 Animals...... 31 Surgery and Tissue Collection ...... 31 RNA Isolation and Reverse Transcription Quantitative Polymerase Chain Reaction ...... 32 Sodium Bisulfite Sequencing ...... 33 Statistical Analysis ...... 34 Results ...... 35 Region and Aging Effects on ERα mRNA Expression ...... 35 Methylation of the ERα Promoter ...... 36 Discussion ...... 38 Acknowledgement of Financial Support to Chapter 2 ...... 43

3 TRANSCRIPTIONAL PROFILE OF AGING AND COGNITION-RELATED GENES IN THE MEDIAL PREFRONTAL CORTEX ...... 49

Background ...... 49 Materials and Methods...... 51

Animals...... 51 Behavioral Studies ...... 51 Set shifting operant task ...... 51 Morris water maze ...... 55 Tissue Collection ...... 57 RNA and Library Preparation ...... 57 Reverse Transcription Quantitative Polymerase Chain Reaction ...... 58 Sequencing, Bioinformatics, and Statistical analysis ...... 58 Results ...... 61 Behavior ...... 61 Gene Expression ...... 62 Gene expression related to aging ...... 62 mPFC gene expression related to behavior ...... 65 Discussion ...... 68 Acknowledgement of Financial Support to Chapter 3 ...... 72

4 DNA METHYLOME OF AGING AND EXECUTIVE FUNCTION IN THE MEDIAL PREFRONTAL CORTEX ...... 94

Background ...... 94 Materials and Methods...... 95 Animals...... 95 Behavior and Gene Expression ...... 95 Genomic DNA Isolation, Sodium Bisulfite Conversion and Library Preparation ...... 96 Sequencing, Bioinformatics and Statistical Analysis ...... 97 Results ...... 100 DNA Methylome Profiling at CpGs of the Aging mPFC ...... 100 DNA Methylome Profiling at CpGs of Cognitive Flexibility in the mPFC ...... 104 Non-CG Methylation in the mPFC ...... 107 Non-CG methylation and aging ...... 107 Non-CG methylation and cognitive flexibility ...... 109 Discussion ...... 111 Acknowledgements to Chapter 4 ...... 115

5 CONCLUSIONS AND FUTURE DIRECTIONS ...... 145

General Conclusions...... 145 Future Directions ...... 149

APPENDIX

A SUPPLEMENTARY FIGURES TO RNA-SEQ PROFILING ...... 153

B SUPPLEMENTARY FIGURE AND TABLES TO GENOME-WIDE PROFILING OF DNA METHYLATION ...... 155

C SEQUENCING DEPTH CONSIDERATIONS FOR RNA-SEQ AND WHOLE GENOME BISULFITE SEQUENCING ...... 181

Differential Expression with RNA-seq ...... 181 Whole Genome Bisulfite Sequencing ...... 182

D THE UNITS OF NORMALIZATION FOR RNA-SEQ ...... 185

LIST OF REFERENCES ...... 188

BIOGRAPHICAL SKETCH ...... 209

LIST OF TABLES

Table page

2-1 Fisher's PLSD for CpG Sites in Esr1 promoter ...... 48

3-1 Behavioral correlations ...... 82

3-2 Age-related changes across the rat mPFC & the dorsolateral PFC of humans...... 83

3-3 Increased mPFC expression during aging...... 85

3-4 Decreased mPFC expression during aging ...... 87

3-5 Positive correlation of mPFC genes with set shift TTC ...... 89

3-6 Increased expression in the mPFC for AI vs AU...... 93

4-1 Hypermethylation of the mPFC during aging ...... 133

4-2 Hypermethylation of the mPFC during delayed shifting ...... 136

4-3 Hypomethylation of the mPFC during delayed shifting ...... 139

4-4 Promoter and Gene Body index features in the mPFC in young and aged ...... 142

4-5 Promoter and Gene Body index features in the mPFC in aged-impaired and aged-unimpaired ...... 142

4-6 Hypermethylation in CHG sites in rats with delayed shifting ...... 143

B-1 Age-related differentially methylated CpGs correlated to RNA levels ...... 156

B-2 Genes of CpGs correlated to set shifting and RNA levels ...... 162

LIST OF FIGURES

Figure page

1-1 Rat Esr1 promoter exon 1b region ...... 29

2-1 Experimental research design ...... 44

2-2 Illustration of differences in Esr1 expression ...... 45

2-3 Site specific DNA methylation of CpG sites across CA1 and CA3 regions ...... 46

2-4 ERα promoter DNA methylation is altered in regions CA1 and CA3 during aging ...... 47

3-1 Region of the mPFC and white matter collected for RNA-seq ...... 73

3-2 Performance on the visual discrimination and set shift operant tasks ...... 74

3-3 Performance on the water maze task ...... 75

3-4 Number of genes altered during aging across regions ...... 76

3-5 Heat map of age-related changes in gene expression for the mPFC ...... 77

3-6 Number of genes correlated with behavioral measures ...... 78

3-7 Impaired set shifting is associated with increased expression of genes involved in transcription regulation ...... 79

3-8 Set shifting performance for animals used in RT-qPCR validations ...... 80

3-9 Comparison between RT-qPCR and RNA-seq ...... 81

4-1 Genomic distribution of CpG sites in the aging mPFC ...... 116

4-2 Biotypes of differentially methylated CpGs in the gene body and promoter regions from the aging mPFC ...... 117

4-3 DNA methylome profile of the aging mPFC ...... 118

4-4 Diagram summarizing CpG methylation in aging mPFC ...... 119

4-5 Diagram summarizing CpG correlation to RNA in the aging mPFC ...... 120

4-6 DNA methylation of repetitive elements in the aging mPFC ...... 121

4-7 DNA methylation from the LINE and SINE families ...... 122

4-8 Genomic distribution of CpG sites relative to cognitive flexibility performance in aged rats ...... 123

4-9 Biotypes of CpG sites from gene body and promoter regions correlated to cognitive flexibility of aged rats ...... 124

4-10 DNA methylation patterns in aged rats with delayed set-shift behavior ...... 125

4-11 Pearson’s correlation between TTC scores of each aged animal and DNA methylation rates of gene clusters ...... 126

4-12 Diagram summarizing CpG methylation in aged-impaired and aged- unimpaired rats ...... 127

4-13 Diagram summarizing CpG correlation to RNA in aged-impaired and aged unimpaired rats ...... 128

4-14 Boxplots of DNA methylation in young and aged rats from the gene body and promoters in CpG, CHG and CHH sites ...... 129

4-15 Genomic distribution of CHG and CHH sites in the aging mPFC...... 130

4-16 Boxplots of DNA methylation in aged-unimpaired and aged-impaired rats from the gene body and promoters in CpG, CHG and CHH sites...... 131

4-17 Genomic distribution of CHG and CHH sites relative to cognitive flexibility performance in aged rats ...... 132

A-1 RNA-seq spike-in controls ...... 153

A-2 Heatmap of gene expression correlated to set shift performance...... 154

B-1 Pearson’s correlation of CpGs detected genome-wide across all biological replicates in the aged and young groups ...... 155

C-1 Number of reads and coverage of RNA-seq across the mPFC biological replicates ...... 183

C-2 Number of paired-end reads of WGBS across the biological replicates from the mPFC...... 184

D-1 DESeq normalization of RNA-seq across the mPFC biological replicates ...... 187

LIST OF ABBREVIATIONS

AD Alzheimer’s Disease

AI Aged-Impaired

ANOVA Analysis of Variance

AU Aged-Unimpaired bp Base Pair

BSP Bisulfite Sequencing Primers

CA1 Cornu Ammonis 1

CA3 Cornu Ammonis 3

CpG Cytosine phosphate Guanine

DAVID Database for Annotation Visualization and Integrated Discovery

DI Discrimination Index

DMAP2 Differential Methylation Analysis Pipeline 2

DNA Deoxyribonucleic Acid

DNMT DNA Methyltransferases

E2 Estradiol

ERCC External RNA Controls Consortium

ERα Estrogen Receptor α

ERβ Estrogen Receptor β

F344 Fischer 344 (rat strain used in aging studies)

FDR False Discovery Rate

Fisher’s PLSD Fisher’s Protected Least Significant Difference

GO Gene Ontology

HPC High Performance Computer

IEGs Immediate-Early Genes

LINE Long Interspersed nuclear element

LTR Long Terminal Repeat mPFC Medial Prefrontal Cortex

OVX Ovariectomy/ovariectomized

PFC Prefrontal Cortex

RNA Ribonucleic Acid

RNA-seq RNA sequencing

RT-qPCR Reverse Transcription Quantitative Polymerase Chain Reaction

Sec Seconds (unit of time)

SEM Standard Error of the Mean

SINE Short Interspersed nuclear element

TET Ten-Eleven Translocation (enzyme)

TSS Transcriptional Start Site

TTC Trials To Criterion

WCST Wisconsin Card Sorting Test

WGBS Whole Genome Bisulfite Sequencing wk Week (unit of time)

Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

TRANSCRIPTIONAL AND EPIGENETIC PROFILING OF THE HIPPOCAMPUS AND THE MEDIAL PREFRONTAL CORTEX: RELATIONSHIP TO AGING AND COGNITIVE DECLINE

Lara Ianov

May 2017

Chair: Thomas C Foster Major: Genetics and Genomics

Cognitive decline during aging and neurodegenerative disease are associated with senescence of specific brain regions including the hippocampus and the medial prefrontal cortex (mPFC). However, there is enormous variability in cognitive decline, suggesting differential aging of processes critical to memory. One of the processes which affect hippocampal function is alteration in transcription of estrogen receptors α

(ERα) which is associated with estradiol responsiveness. While differential expression of the ERα gene, Esr1, has been studied, the epigenetic mechanism is not well known.

Chapter 2 of this dissertation, investigated Esr1 expression and DNA methylation changes in the promoter exon 1b of ovariectomized rats associated with aging and estradiol deprivation time in the hippocampal subregions CA1 and CA3. The results indicate that reduced expression in CA1 relative to CA3 is linked to increased DNA methylation of the first CpG site. Further, methylation of distal CpG sites was associated with altered Esr1 expression during aging or following long-term estradiol deprivation.

Additionally, the current study expands the goal of investigating transcriptional and epigenetic alterations associated with aging and cognitive function to a genome-

wide and transcriptome level in the mPFC region of rats which were behaviorally characterized in an executive function task. Chapter 3 described the transcriptional differences linked to aging and executive function. The results suggest that aging was associated with an increase in immune-related genes and a decrease of synaptic genes. Furthermore, up regulation of genes involved in the regulation of transcription, including transcription factors that regulate the strength of excitatory and inhibitory inputs, and neural activity-related immediate-early genes was observed in aged animals that exhibit delayed set shift behavior. Finally, Chapter 4 investigated genome-wide alterations of DNA methylation in the mPFC. The results suggest that hypermethylation in aged rats was linked to genes related to GTPase activity, ion transmembrane transport and synaptic function. Finally, methylation in aged rats that exhibit delayed set shift behavior correlated to hypermethylation of synaptic and ion channel genes.

Therefore, the results from the current dissertation suggest that transcriptional and epigenetic alterations are important factors contributing to aging and cognitive function in the hippocampus and the mPFC.

CHAPTER 1 INTRODUCTION

Goals of Current Research

The current dissertation investigated the transcription and epigenetic modifications associated with aging and cognitive decline from a gene-specific approach to a genome-wide approach using next-generation sequencing. In particular, the current study focused in two regions of the brain which have a role in memory and cognitive functions that are affected by aging in humans and rodents. The first region discussed is the hippocampus, which is fundamental for episodic memory (Foster,

2012a). The next region discussed is the medial prefrontal cortex (mPFC), which is involved in several cognitive processes which fall under the general term of executive function. Specifically, executive function includes attention, cognitive flexibility, working memory, and reasoning. However, the current study focused on cognitive flexibility, which is defined as the ability to adjust responses in accord with changes in task demands (Barense et al., 2002; Bizon et al., 2012; Robbins et al., 1998).

Following the introduction to the background, the first set of studies (Chapter 2) of this dissertation, expands our knowledge concerning molecular mechanisms that regulate the expression pattern of the estrogen receptor α (ERα) by examining the potential role of DNA methylation in mediating hippocampal subregion differences in expression, as well as changes in ERα gene expression during aging or long-term estradiol (E2) deprivation. This work is followed by studies that investigated changes in transcription (Chapter 3) and DNA methylation (Chapter 4) in the mPFC, associated with aging and cognitive decline. In order to introduce the topics, a background is provided addressing the problem of aging and cognitive decline, the reasons for the

brain regions examined, and the current state of knowledge concerning DNA methylation during aging and associated with cognitive function.

Aging and Age-Related Cognitive Decline

The aging population of the U.S. is predicted to radically increase over the next few decades from 46.2 million people over the age of 65 in the latest count from 2014 to

98.2 million by the year of 2060 according to the U.S. Census Bureau (Census, 2016).

Currently, the Center for Disease Control estimates that 49-66% of people over the age of 50 suffers some form of cognitive impairment (CDC, February 2011). The increase in the number of aged individuals and the likelihood that they will experience cognitive decline highlights the importance of further studies on the mechanisms that contribute to age-related cognitive impairment. In addition, the study of cognitive impairment may also benefit additional neurocognitive areas of research including neurodegenerative diseases such as Alzheimer’s disease (AD), which has a current estimate of 5.1 million affected people with a projected increase to 13.2 million by 2050 (CDC, February 2011).

Aging is associated with selective changes in specific cognitive functions.

Impaired memory, executive function, and processing speed have been well- characterized with advancing age (Bizon et al., 2012; Foster et al., 2012). These cognitive processes depend on particular brain regions, suggesting likely brain regions for investigation. For example, impaired memory depends on proper hippocampal function, the primary cognitive deficit associated with aging and AD. Similarly, executive function depends on the mPFC, a region which is also susceptible to alterations with advancing age.

Age-related cognitive decline is not uniform, raising questions of environmental, and genetic factors that determine the age of onset and the trajectory of decline. Thus,

a major question concerns the variability in cognitive function and the relationship between memory impairments and brain aging. Brain aging is characterized by altered neural activity and reduced neuronal plasticity (i.e. senescent physiology), the accumulation of molecular damage, and a loss of homeostasis. A number of factors have been proposed to contribute to brain aging including inflammation, oxidative stress, calcium dysregulation, and hormonal response (Bean et al., 2014; Berchtold et al., 2013; Blalock et al., 2003; Nicolle et al., 2001). The sections below review how the hippocampus and the mPFC may be affected by some of the proposed factors.

Estrogen Receptors and the Hippocampus

E2 is neuroprotective and influences hippocampal anatomy and physiology in a manner that is opposite to that observed during aging (Bean et al., 2014; Foster, 2005).

Thus, the decline in E2 during menopause or weakening of the hippocampal response to E2 is thought to contribute to brain aging. Estrogen receptor α and estrogen receptor

β (ERβ) are the two primary nuclear receptors activated by E2. In turn, the activated estrogen receptors form dimers that bind to estrogen response elements within the genome to drive transcription. Previous research indicates that E2 influences the transcription of genes related to cognitive function, including genes linked to neuroprotection, synaptic plasticity, and inflammation (Adams et al., 2002; Benedusi et al., 2012; Foster, 2012b; Zhang et al., 2009). Interestingly, while ERβ is more highly expressed in the hippocampus, ERα is transcriptionally more active (Foster, 2012b;

Shughrue et al., 1997). The greater transcriptional activity by ERα is due to structural differences between the receptors. While the DNA binding domain is highly homologous between the receptors, variances in the ligand binding domain contribute to the higher binding affinity of E2 to ERα (Bean et al., 2014; Kuiper et al., 1997). Thus, ERα

homodimers are a major driving force for transcription, particularly when E2 levels are low, and ERβ can act as a dominant negative, forming heterodimers as E2 levels rise

(Foster, 2012b).

Studies investigating the role of estrogen receptors in cognition suggest that ERα is the predominant receptor involved in maintaining hippocampal health and memory function (Foster et al., 2008; Frye et al., 2007; Han et al., 2013; Qu et al., 2013). For instance, ERα knock out mice exhibit impaired cognitive function while ERβ knock out mice exhibit enhanced cognitive performance, relative to ERα knock out mice (Fugger et al., 2000; Han et al., 2013). Furthermore, the delivery of ERα by viral vector to the hippocampi of ovariectomized ERα knock out mice improved memory and cognitive performance, while viral injection of ERβ impaired spatial memory (Foster et al., 2008;

Han et al., 2013).

Importantly, the role of ERα on memory function is also suggested by studies which have investigated long-term E2 deprivation and the concept of a “critical window” for effective estrogen therapy in women around the time of menopause. The critical window hypothesis suggests that the beneficial effects of estrogen therapy to cognitive function is only observed when the therapy is initiated around the time of menopause, but not if initiated long after menopause onset (Sherwin, 2009). Similar results have been observed in ovariectomized (OVX) rodents, which demonstrated improved cognitive function in young and middle-aged animals treated with E2. In contrast, E2 treatment failed to improve cognitive function in aged rodents following long-term E2 deprivation (Bean et al., 2014; Gibbs, 2000; Markowska and Savonenko, 2002). Bean et al. investigated the hypothesis of re-opening the critical window in aged OVX rodents

after a long-term period of E2 deprivation (14 weeks) with the combination of E2 treatment and gene therapy for the up regulation of the estrogen receptors in the hippocampal subregion CA1. The study demonstrated that the control group which received E2 and green fluorescent protein did not show improvements in cognitive performance in a spatial memory task. The lack of improvement following E2 treatment indicates the closing of the therapeutic window. Similarly, E2 treatment followed by the up regulation of ERβ also failed to improve spatial memory function. However, E2 treatment and up regulation of ERα resulted in improved spatial memory in aged animals, suggesting that ERα is an important regulatory element and potential therapeutic target of the critical window (Bean et al., 2015). Thus, E2 replacement within the critical window is neuroprotective and improves performance on hippocampal dependent tasks, including spatial episodic memory (Bean et al., 2014).

Furthermore, the balance of ERα/ERβ contributes to transcription and synaptogenesis and a shift in this balance contributes to cognitive decline and the closing of the therapeutic window in aging (Bean et al., 2015; Han et al., 2013; Zhang et al., 2011). Previous studies suggest that alterations in the expression of ERα may be linked to the decrease in E2 replacement efficacy in advanced aging (Aenlle and Foster,

2010; Bean et al., 2014). For instance, a decrease of ERα positive neurons and synapses containing ERα from the hippocampus is observed during aging (Mehra et al.,

2005). Therefore, while the effects of E2 treatment and the role of ERα in cognitive function have been well studied in the hippocampus, the transcriptional and epigenetic mechanisms that mediate a shift in ERα/ERβ expression during aging and E2 deprivation time are not well understood.

DNA methylation provides one epigenetic mechanism which may influence the expression of ERα, as well as the ability of ligand activated ERα to induce transcription.

In other tissues, DNA methylation at cytosines in guanine-cytosine rich areas of the ERα gene promoter is associated with reduced ERα mRNA (Issa et al., 1994; Post et al.,

1999; Wilson et al., 2008). In the rat, the ERα gene, Esr1, contains 17 CpG sites in the promoter exon 1b region which have been linked to Esr1 transcriptional levels in a number of studies (Champagne et al., 2006; Gore et al., 2011; Kurian et al., 2010).

Specifically, the promoter from this region (promoter 1b) is of importance to the expression of Esr1 in neuronal tissue as this is the active promoter in the rat brain

(Freyschuss and Grandien, 1996), which contains 9 CpG sites upstream of the transcriptional start site followed by 8 sites downstream which are part of the exon 1 region (Figure 1-1) (Gore et al., 2011). Thus, methylation of specific CpG sites within this region is associated with the regulation of Esr1 transcription (Champagne et al.,

2006; Gore et al., 2011; Kurian et al., 2010). Importantly, DNA methylation of specific

CpG sites, rather than the cumulative level of methylation across a region, has been associated with the regulation of mRNA from other genes (Harony-Nicolas et al., 2014;

Nile et al., 2008; Weidner et al., 2014). An example of the importance of site-specific analysis is shown by a study which investigated the oxytocin receptor gene in vivo by performing association to the mRNA profile in mice, and in vitro, by the introduction of mutations to specific CpG sites which resulted in the alteration of mRNA expression

(Mamrut et al., 2013). Thus, the current dissertation focuses on the analysis of methylation at single base pair resolution. Chapter 2 of this dissertation addresses how mRNA levels of Esr1 along with DNA methylation status of the 17 CpG sites in the

promoter exon 1b region are altered across the hippocampal subregions, CA1 and CA3, and across aging and E2 deprived ovariectomized female Fischer 344 rats.

The Prefrontal Cortex and Cognitive Flexibility

Another major area of the brain which is vulnerable to age-related cognitive decline is the prefrontal cortex (PFC) of humans, non-human primates and rodents

(Bizon et al., 2012; Moore et al., 2006). Across species, the PFC is subdivided into a number of regions which have been well characterized according to function. In particular, lesion studies to the medial PFC of rodents, a region analogous to the human dorsolateral PFC, have shown disruptions to executive functions including cognitive flexibility, working memory and attention (Brown and Bowman, 2002; Freedman and

Oscar-Berman, 1986; Muir et al., 1996; Woods and Knight, 1986).

Important for the current work, cognitive flexibility has been shown to decline with age across species (Beas et al., 2013; Ianov et al., 2016b; Moore et al., 2006; Rhodes,

2004; Robbins et al., 1998). To emphasize, cognitive flexibility is the ability to shift or adapt reasoning and attention in response to a stimuli in the environment (Barense et al., 2002; Bizon et al., 2012). This ability has been classically characterized in humans by the Wisconsin Card Sorting Test (WCST). In this task, a participant is presented with a set of cards which contain different cues (color, shape of symbol, and number) which change during each trial of sorting. The participants do not receive any instructions on which choice is correct, and by trial and error, discover the initial correct pattern (e.g.: color). After a number of correct choices with the same pattern (matching based on color), the correct pattern is shifted to another dimension (e.g.: shape), such that the participant needs to shift from their response to the new correct response (Rhodes,

2004). Thus, cognitive flexibility, a function of the PFC, is quantified according to the number of trials it takes to shift to the new correct response.

The WCST has been used in a number of studies to characterize cognitive flexibility in the aging populations, and the results indicate substantial variability in performance in older individuals, relative to young (Ashendorf and McCaffrey, 2008;

Dias et al., 2015; MacPherson et al., 2002; Rhodes, 2004). Similarly, cognitive flexibility also declines in rodent models of aging. Importantly, variability in performance of aged rats is seen using a variety of set shifting tasks, including the operant chamber and digging set shifting tasks (Barense et al., 2002; Beas et al., 2013; Stefani et al., 2003).

In brief, in the operant set shifting tasks, rats are trained to press a lever signaled by a visual cue (light) to receive a food reward. Once the rats learn this initial discrimination, the cue of the task is changed to location, where the rats must choose the lever which is located always on the right or left while ignoring the location of the light to receive a food reward. This second phase of the task is the set shifting discrimination phase where the measure of the set shifting ability is determined by the number of trials to reach criterion

(Beas et al., 2013; Bizon et al., 2012). On the other hand, the digging set shifting task relies on the animal’s olfactory ability to perform the task to discriminate between two odors present in pots with different digging material, where only one odor indicates the food reward. The reward is then shifted to a specific digging material regardless of odor

(Bizon et al., 2012). However, while the digging set shifting task has successfully characterized animals in their cognitive flexibility (Barense et al., 2002), this task is not optimal for the characterization of the Fischer 344 (F344) rats, a commonly used model of aging and cognitive decline (Gallagher et al., 2011). Although age-related spatial and

executive functions have been well studied in the F344 rats (Foster and Kumar, 2007;

Guidi et al., 2015; Guidi et al., 2014), this strain has been shown to be impaired in their olfactory ability, which would lead to the incorrect characterization of the F344 rats’ cognitive flexibility function in the digging task (LaSarge et al., 2007). Thus, the operant set shifting task is a superior choice to the F344 strain, allowing the rats to complete the task in an automated chamber.

Overall, cognitive flexibility tasks in humans and rodents have identified a number of subjects in the aged cohort, which perform at levels similar to younger individuals, while the other aged subjects perform significantly worse. Thus there is enormous variability in cognitive flexibility, suggesting that neural, genetic (e.g. transcriptional levels, variation at single nucleotides etc.) or epigenetic (e.g. DNA methylation, chromatin structure, micro RNA, long non-coding RNA) may contribute to variability in performance for the aged cohorts.

Transcriptomic Studies in Aging and Cognition

Transcriptional changes within the hippocampus of aging rodents have been well studied with the use of microarray technology (Burger, 2010). While there are differences in the experimental design, transcriptional profiling studies of the hippocampal subregions have frequently identified an age-related increase in expression of inflammation and immune related genes, and decreased expression of neurogenesis and synaptic plasticity genes either at specific subregions or in the entire hippocampus (Blalock et al., 2003; Kadish et al., 2009; Rowe et al., 2007; Verbitsky et al., 2004). However, the use of microarray technology is limiting to researchers as the number of transcripts investigated are only a fraction from the whole transcriptome. On the other hand, new studies using next-generation sequencing such as RNA-seq, will

broaden previous findings by investigating additional genes not available in older technologies, including regulatory elements such as noncoding RNA. Several addition advantages exist with the use of RNA-seq including, faster timing for transcriptome profiling, lower background signals and de novo sequencing (Marguerat and Bahler,

2010). Thus, further studies of the whole transcriptome with the use of next-generation sequencing technologies are necessary to broaden our understanding of the molecular changes occurring during cognitive decline in each subregion separately.

Several recent studies have examined transcriptomic changes associated with aging in the human PFC. Notably, Erraji-Benchekroun et al. identified neuronal-enriched transcripts in the aging dorsolateral PFC, including down regulated genes functionally related to synaptic transmission, receptor activity and signal transduction. Furthermore, an up regulation of oxidative stress, DNA repair and immune response genes was also observed (Erraji-Benchekroun et al., 2005). Likewise, additional studies in the human

PFC and the rodent mPFC have also shown an age-related increase in immune and inflammation genes, suggesting that inflammatory genes may be used as aging markers

(Bordner et al., 2011; Keleshian et al., 2013; Primiani et al., 2014). However, while the aging mRNA profiles have been well characterized, little is known about the transcriptomic changes associated with age-related decline in executive function.

Notably, transcriptomic profiling of the human PFC (Brodmann’s areas 11 and 47) have been investigated in the context of aging on circadian rhythms patterns which has been shown to influence performance in executive function tasks (Chen et al., 2016; Cho et al., 2000). However, the authors of this work did not examine executive function, such that the link to mRNA expression remains to be determined. In an attempt to expand our

understanding of the role of mPFC transcription in cognitive function, Chapter 3 describes a study that characterized the transcriptional profile of the mPFC associated with aging and impaired cognitive flexibility.

DNA Methylation in Aging and the Prefrontal Cortex

DNA methylation is among the most well described epigenetic modifications which is mechanistically related to transcriptional regulation across the genome. In mammals, the modification normally occurs at the fifth carbon in cytosines. The CpG sites have been localized to different regions of the genome including promoters, gene bodies and intergenic regions. Importantly, the use of modern screening techniques such as whole genome bisulfite sequencing (WGBS) have allowed investigators to quantify the abundance levels of non-CpG methylation across species and tissues, including the dorsolateral PFC, which have revealed a high number of non-CpG sites containing methylation (Patil et al., 2014; Varley et al., 2013). It should be noted that non-CpG modifications have also been reported in additional brain areas (Brant et al.,

2014; Guo et al., 2014; Patil et al., 2014). Furthermore, while there are several non-CpG sites which exhibit methylation, the methylation levels in CpG sites are much greater than non-CpGs (Guo et al., 2014; Xie et al., 2012).

DNA methylation, in particular 5-methylcytosine in CpG context located in promoters, have been implicated to be transcriptional repressors through the inhibition of the binding of transcriptional factors and through the recruitment of methyl binding proteins which recruit additional proteins associated with heterochromatin formation

(Klose and Bird, 2006; Lardenoije et al., 2015). However, a debate still remains about the role of DNA methylation in gene body regions. In addition, another prominent epigenetic modification is 5-hydroxymethylcytosine, which is generated through the

DNA demethylation pathway by the ten-eleven translocation (TET) enzymes (Lardenoije et al., 2015). Interestingly, 5-hydroxymethylcytosine has been associated with transcription activation, suggesting that DNA methylation has a dynamic role in transcriptional regulation, however the mechanism of this modification is not well described (Al-Mahdawi et al., 2014).

In brain tissue, DNA methylation is a common modification; however, the role of this modification in cognition decline and aging is limited, particularly for the PFC

(Lardenoije et al., 2015). Similar to RNA profiling, DNA methylation associated with aging and cognitive function has been more well studied in the hippocampus (Day and

Sweatt, 2010). For example, inhibition of DNA methyltransferases (DNMT) by zebularine resulted in differential methylation in the hippocampus of the promoters of two key genes for synaptic plasticity, Bdnf (brain-derived neurotrophic factor) and Reln

(reelin) (Levenson et al., 2006). In addition, fear conditioning experiments have shown that DNMT inhibition in the hippocampus resulted in impaired memory consolidation on this task, suggesting that memory involves an active role for epigenetic modification of the methylome in regions associated with memory function (Feng et al., 2010; Miller et al., 2008; Miller and Sweatt, 2007).

On the other hand, the relationship between the DNA methylation profile of the

PFC and the age-related impairment of cognitive processes that depend on the PFC has yet to be elucidated. Importantly, few studies have examined the effect of age on

DNA methylation in the human dorsolateral PFC. One study using the Illumina Infinium

BeadChip technology screened the promoters of thousands of CpG sites across human samples ranging from fetal time points, childhood, and post childhood (Numata et al.,

2012). Indeed, the study did not focus on aging from young to middle-aged and aged individuals, but it provides a synopsis of the epigenetic changes occurring in this brain region. Overall, the study found increased methylation in postnatal tissue and an inverse correlation between DNA methylation of promoter regions and gene expression in some genes of interest which are functionally related to brain development (e.g.:

NNAT – neuronatin) and DNMT3 gene group (DNMT3A, DNMT3B and DNMT3L).

Another similar study, investigated the methylation levels of more than 27,000 CpG sites in several brain regions, including the frontal cortex, in individuals of ages 1 to 102

(Hernandez et al., 2011). The study showed that aging was positively correlated to increased methylation which included genes functionally related to transcriptional regulation and DNA binding, suggesting that this epigenetic modification may have a direct effect in the transcriptional machinery.

Furthermore, an additional study of the human PFC, investigated the expression and DNA methylation levels of synaptic genes in middle aged and aged individuals

(Keleshian et al., 2013). Among the genes assayed, BDNF, CREB and SYP

(synaptophysin) were hypermethylated in aged individuals relative to middle aged.

Furthermore, CREB also contained decreased expression levels of mRNA and protein while BDNF and SYP protein levels were reduced with age. Interestingly the study also reported the up regulation of a number of inflammatory markers (e.g.: CD11b, GFAP,

IL-1β); however the DNA methylation for the neuroinflammatory markers was not investigated. Thus, while the evidence indicates that DNA methylation is an active mechanism in the aging brain, further studies are necessary to narrow which molecular pathways may be epigenetically regulated during aging and how these modifications in

the PFC relate to impairment in executive function during aging. Chapter 4 of this work addresses the hypothesized epigenetic mechanism in the mPFC in mediating age- related cognitive decline.

Figure 1-1. Rat Esr1 promoter exon 1b region. The figure illustrates the relative location of the CpG sites present in the promoter exon 1b region according to GenBank accession number X98236. The promoter 1b region contains 9 CpG sites followed by the transcriptional start site (TSS). The exon 1 region contains 8 CpG sites. The length in base pairs (bp) from site 1 to 17 is 411bp.

CHAPTER 2 EPIGENETIC REGULATION OF ESTROGEN RECEPTOR ALPHA CONTRIBUTES TO AGE-RELATED DIFFERENCES IN TRANSCRIPTION ACROSS THE HIPPOCAMPAL REGIONS CA1 AND CA3 1

Background

Estradiol (E2) influences several biological processes that likely contribute to neuroprotection (Bean et al., 2014). The relative levels and subcellular distributions of

ERα varies across brain regions and according to the previous history of E2 exposure

(Mehra et al., 2005; Milner et al., 2001; Mitra et al., 2003; Mitterling et al., 2010).

Differences in ERα expression/activity likely contribute to regional differences in vulnerability to ischemia and oxidative stress (Merchenthaler et al., 2003; Zhang et al.,

2009). While the expression profile for ERα in the hippocampus is well characterized by age and hippocampal subregions, the molecular mechanisms that regulate estrogen receptor expression in the hippocampus are not well understood (Bean et al., 2014).

One mechanism for the regulation of gene expression is through methylation of cytosines in guanine-cytosine rich areas of the gene promoter region, termed CpG islands. In several tissues, ERα promoter methylation increases with age and is associated with decreased ERα expression and increased incidence of disease (Li et al., 2004; Post et al., 1999). Similarly, in the brain, ERα promoter methylation is associated with a decrease in ERα expression and underlies physiological and behavioral differences across the lifespan (Gore et al., 2011; Schwarz et al., 2010). We examined the DNA methylation status of the 17 CpG sites within the ERα promoter exon 1b region in ovariectomized female rats to test the hypothesis that CpG DNA methylation is an active epigenetic regulator of regional and age-related differences in

Reprinted with permission from Elsevier (Ianov et al., 2016a).

the expression of ERα mRNA, Esr1. For this study, we took advantage of regional differences in hippocampal ERα expression, with increased expression in region CA3 relative to region CA1 (Mehra et al., 2005; Rune et al., 2002), and possible auto- regulation of ERα promoter activity by E2 (Castles et al., 1997; Donaghue et al., 1999;

Pinzone et al., 2004), which may underlie effects of hormone deprivation on ERα expression (Bean et al., 2014). The results suggest that differential methylation of sites within the ERα promoter may regulate transcription of Esr1 across hippocampal regions and that DNA methylation of distal CpG sites may have a function in age-related expression changes relative to upstream sites in the promoter.

Materials and Methods

Animals

Procedures involving animal subjects have been reviewed and approved by the

Institutional Animal Care and Use Committee and were in accordance with guidelines established by the U.S. Public Health Service Policy on Humane Care and Use of

Laboratory Animals. Female Fischer 344 rats of two ages, young (3 months, n = 21) and aged (18 months, n = 22) were obtained from National Institute on Aging colony at

Charles River Laboratories, through the University of Florida Animal Care and Service facility. Animals were maintained on a 12:12 hour light schedule, and provided ad lib access to food and water.

Surgery and Tissue Collection

Ovariectomy (OVX) was performed as previously described (Bean et al., 2015;

Sharrow et al., 2002). Briefly, rats were handled for 5 min a day for at least 1 week prior to surgery. Female rats were ovariectomized under isoflurane (Piramal Healthcare) in oxygen using a VetEquip isoflurane anesthesia system. Bilateral incisions were made to

expose the ovaries which were cleared from the fat tissue and dissected out.

Subsequent to the closure of the incisions, buprenorphine (0.03mg/kg) and saline (5-

10ml) were given by subcutaneous injection. Following OVX, the food of the animals was exchanged to a casein-based chow which contains lower levels of phytoestrogens.

Three weeks (wk) following OVX (young, n=10 and aged, n=11), rats were overdosed with CO2, decapitated, and the hippocampi were immediately dissected. The same procedure was repeated for the remaining animals after a 14 week period (young, n=11 and aged, n=11) (Figure 2-1). Hippocampal regions (CA1 and CA3) were separated, placed in tubes, immediately frozen in liquid nitrogen, and stored in -80°C until processed.

RNA Isolation and Reverse Transcription Quantitative Polymerase Chain Reaction

RNA was isolated from each hippocampal region (n=5-6 per region of each age and OVX duration group) using the RNeasy Lipid Tissue Mini kit (Qiagen, catalog number: 74804) and DNase digestion was performed with the RNase-Free DNase Set

(Qiagen, catalog number: 79254). Following isolation, the concentration was measured using the NanoDrop 2000 spectrophotometer (Thermo Scientific). Reverse transcription was performed using the QuantiTect Reverse Transcription kit (Qiagen, catalog number: 205311) and quantitative polymerase chain reaction (qPCR) was completed using the TaqMan Gene Expression Assays (Esr1: Rn01640372_m1, Gapdh:

Rn01775763_g1) in a 7300 Real-Time PCR system with SDS software version 1.3.1

(Applied Biosystems). The ΔΔCT method (Livak and Schmittgen, 2001) was used to determine the relative cDNA levels and the CA1 region from young short-term rats were used as the calibrator samples.

Sodium Bisulfite Sequencing

Genomic DNA was isolated from the CA1 and CA3 areas (n=5 per age group and OVX time) using the DNeasy Blood & Tissue kit (Qiagen, catalog number: 69504).

The DNA concentration was quantified using the NanoDrop 2000 spectrophotometer and sodium bisulfite conversion was performed with the EZ DNA Methylation-Gold kit

(Zymo Research, catalog number: D5005) according to the manufacturer's directions.

The exon 1b promoter region of ERα (GenBank accession number: X98236) was amplified with the following modifications from previous reports (Champagne et al.,

2006; Kurian et al., 2010). The thermocycler parameters included an initial denaturation cycle of 5 minutes at 94°C, 40 cycles of 1 minute at 94°C (denaturing), 3 minutes at

56°C (annealing) and 1 minute at 72°C (extension), followed by a final extension cycle of 15 minutes at 72°C. The following bisulfite sequencing PCR (BSP) primers were used: outer forward 5'TAGTATATTTTGATTGTTATTTTAT3'; outer reverse

5'CTAAACAAAAAAATAAATTACTTTC3'. The PCR product was used for nested PCR with the same thermocycler parameters with the following BSP nested primers: forward

5'TTTATTTGTGGTTTATAGATATTT3' and reverse

5'ACAAAAAAAAAAAAATCAAAACAC3'. In addition, a preliminary check of bisulfite conversion efficiency of each sample was assessed by performing a separate nested

PCR reaction. The same thermocycler conditions were used with wild type sequence- specific primers, which map to the unconverted DNA sequence of the exon 1b promoter region of ERα (outer forward 5'CAGCACACTTTGACTGCCATTCTAC3'; outer reverse

5'CTAGGCAGAAAGGTAAGTTGCTTTC3'; nested forward

5'TTTATCTGTGGTTTACAGACATCT3'; nested reverse

5'ACAGAAAGAGGGAAATCAAAACAC3'). Amplification of the target sequence with

wild-type primers would indicate incomplete bisulfite conversion. All samples demonstrated complete bisulfite conversion. Unconverted DNA was used as the positive control for the wild-type primers.

The nested product (459bp) of each PCR reaction using BSP primers was cloned with the TOPO TA cloning kit for Sequencing (Life Technologies, catalog number:

K4575-J10) with the following modifications to the transformation reaction: TOP10 cells were heat-shocked for 45 seconds at 42°C and immediately transferred to ice for 7 minutes before the addition of S.O.C. medium. Positive clones were confirmed by colony PCR using nested BSP primers, and miniprep was performed on each positive clone (PureLinkquick plasmid DNA miniprep, Life Technologies, catalog number:

K2100-10). The samples were sent for Sanger sequencing at the Interdisciplinary

Center for Biotechnology Research, University of Florida. The DNA methylation status of all 17 CpG sites from each region were analyzed using BiQ analyzer (Bock et al.,

2005) retaining the default parameters. All positive clones contained conversion rates from 97-100%, and FASTA files which contained a gap in more than one CpG site were removed. After quality filtering, the average number of clones per animal/region was 10

(± SEM 2) and the average number of clones per age and OVX groups was 51 (± SEM

4). In addition, the total number of clones for each hippocampal region was 204 for CA1 and 203 for CA3. Hierarchical clustering and heatmap figures were generated in Partek

Genomics Suite 6.6 (Partek Inc.) using clones which contained at least one site methylated to illustrate the DNA methylation pattern in site 1 to sites 2-17.

Statistical Analysis

All statistical analyses were performed using StatView 5.0 (SAS Institute Inc,

NC). Analyses of variance (ANOVAs) were used to determine significant main effects

for RNA expression. For CpG methylation, repeated measures ANOVAs were employed to determine main effects of age and hippocampal region across CpG sites. Fisher’s protected least significant difference (PLSD) post hoc comparisons with p < 0.05 were employed to localize differences related to differential expression across CpG sites. For interactions of CpG sites with age, OVX duration, or hippocampal region, post hoc

ANOVAs were employed to localize differences within each site. A chi-square analysis was employed to examine the independence of methylation across CpG sites.

Results

Region and Aging Effects on ERα mRNA Expression

Figure 2-2 illustrates the expression of Esr1 associated with age (young: 3 months, aged: 18 months), OVX duration (short-term: 3 wk, long-term: 14 wk), and region (CA1 or CA3). An increase in Esr1 expression was observed in older animals

(Figure 2-2A). Similarly, expression was increased for the long-term OVX relative to short-term OVX (Figure 2-2B). Finally, the largest difference was observed as a 3 fold increase in Esr1 expression in CA3 relative to CA1 (Figure 2-2C). A three factor ANOVA for expression of Esr1 confirmed significant main effects of age [F(1,38) = 33.14, p <

0.001], OVX duration [F(1,38) = 56.0, p < 0.0001], and region [F(1,38) = 155.42, p <

0.0001]. Additionally, there was an interaction of OVX duration and age [F(1,38) =

13.31, p < 0.001], region and age F(1,38) = 9.25, p < 0.01], and a tendency (p = 0.063) for a region by OVX duration interaction.

The interaction of age and OVX duration was due to increased Esr1 expression limited to the short duration OVX (Figure 2-2D). To examine the interaction of age and

OVX duration, post hoc ANOVAs were conducted within respective OVX durations and revealed an age difference for short-term OVX [F(1,18) = 40.27, p < 0.001], with an age

by region interaction [F(1,18) = 9.52, p < 0.01]. Examination of each region indicated that, for short-term OVX, an age-related increase in Esr1 expression was observed in region CA1 [F(1,9) = 27.6, p < 0.001] and in region CA3 [F(1,9) = 24.6, p < 0.0001]

(Figure 2-2D).

Long-term OVX was associated with an increase in Esr1 expression in CA1 and

CA3 for younger animals. For older animals, long-term OVX increased expression only in region CA1 (Figure. 2-2D). To localize effects of E2 deprivation, effects of OVX duration within each region and each age group were examined. The results indicated that relative to young short-term OVX, Esr1 expression increased in CA1 [F(1,9) =

26.68, p < 0.001] and CA3 [F(1,9) = 60.6, p < 0.0001] of young long-term OVX animals

(Figure 2-2D). For aged animals, long-term OVX increased Esr1 expression [F(1,10) =

10.45, p < 0.01] in CA1 relative to aged short-term OVX rats (Figure 2-2D). Thus, it appears that Esr1 expression is increased due to age and long-term E2 deprivation. We confirmed this by comparing young short-term OVX relative to aged long-term in CA1

[F(1,9) = 100.83, p < 0.0001] and in CA3 [F(1,9) = 75.80, p < 0.0001] (Figure. 2-2D).

Methylation of the ERα Promoter

Considerable variability in methylation was observed across the 17 CpG sites of the ERα promoter region. In general, the pattern of methylation was similar across the two regions with the greatest methylation observed at site 1 and minimal methylation for sites 2-10. Modest methylation was observed for distal sites 11-17 (Figure 2-3A). A repeated measures ANOVA was conducted across the 17 DNA CpG methylation sites examining main effects of region, age, and OVX duration. As expected, a significant difference in methylation was observed across CpG sites [F(16,512) = 26.91, p <

0.0001] indicative of the considerable variability in CpG methylation. Post hoc tests

across all sites, collapsed across age, region and OVX duration, indicated the first CpG site exhibited methylation that was significantly greater than all other sites. In addition, considerable methylation was observed for sites 11, 15, and 17, which were greater than sites 2-7, 9, and 14 (Table 2-1). No significance difference was observed for any of the main effects; however, there was an interaction of CpG site and region [F(16,512) =

2.49, p < 0.005] and a CpG site by age by region interaction [F(16,512) = 2.13, p <

0.01].

ANOVAs were conducted within each site to examine the site by region interaction. Increased methylation was observed in region CA1 for site 1 [F(1,38) = 4.30, p < 0.05] and methylation was increased in region CA3 for site 14 [F(1,38) = 10.34, p <

0.005], site 15 [F(1,38) = 4.12, p < 0.05] (Figure 2-3A). Due to the higher DNA methylation ratio on site 1 relative to downstream sites, a closer examination of the clones that contained at least one site methylated in the promoter was performed by chi-square analysis between DNA methylation in site 1 to sites 2-17.

For region CA1, chi-square analysis showed no significant associations between site 1 methylation and distal CpG methylation on sites 2-17, suggesting that methylation of these sites is independent of methylation of the first site in the exon 1b promoter

(Figure 2-3B). For region CA3, site 1 methylation was independent from the other sites with the exception of a significant association to DNA methylation in site 15. The analysis showed that when site 1 is not methylated, there is a higher probability of methylation on site 15 (Χ2 = 4.662, p < 0.05).

To examine the site by age by region interaction, ANOVAs were conducted within each site and region to examine age effects (Figure 2-4A-B). A significant age-

related increase in methylation was observed for site 11 [F(1,18) = 6.49, p < 0.05] and

12 [F(1,18) = 6.09, p < 0.05] in CA3. In contrast, young animals exhibited a significant increase in methylation at site 15 [F(1,18) = 4.76, p < 0.05] in CA1 and site 17 [F(1,18) =

5.57, p < 0.05] in CA3.

Because Esr1 expression was increased in area CA1 and CA3 of young animals by long-duration OVX, age differences in DNA methylation may have been masked by effects of long-term OXV in young animals. Therefore, we separated young animals according to OVX duration and for sites 11-17, we compared young short-term OVX, young long-term OVX, and all aged animals. For region CA1 a significant group difference was observed for site 11 [F(2,17) = 3.96, p < 0.05] and site 14 [F(2,17) =

4.24, p < 0.05]. Post hoc tests indicated that in each case, methylation was increased in young short-term, relative to young long-term and aged animals (Figure 2-4C-D). For region CA3 a significant group difference was observed for site 13 [F(2,17) = 3.80, p <

0.05] and post hoc tests indicated that methylation was decreased in young short-term

OVX, relative to young long-term OVX (Figure 2-4E).

Discussion

Previous research using rat models indicate region and age-related changes in

ERα protein expression within the hippocampus, with elevated expression in region CA3 relative to CA1 and altered expression in both regions with age or E2 deprivation

(Bohacek and Daniel, 2009; Mehra et al., 2005; Zhang et al., 2011). While the effect of age on Esr1 expression is unclear (Ishunina and Swaab, 2007; Tohgi et al., 1995), Esr1 messenger levels increase following E2 deprivation (Sarvari et al., 2014), and are higher in area CA3 relative to CA1 (Rune et al., 2002). In the current study, we confirmed that Esr1 expression is elevated in region CA3 relative to region CA1,

suggesting that transcriptional regulation contributes to differential expression of the receptor across the two regions. The age difference in Esr1 expression was limited to the short-term OVX animals, with increased expression in all aged animals.

Furthermore, expression in young animals was elevated in region CA1 following long- term hormone deprivation, such that expression was similar to that of aged animals.

These results are consistent with work indicating that hormone state can regulate Esr1 expression and indicate that long-term E2 deprivation may up regulate Esr1, possibly as a compensatory mechanism for a loss of ERα activity (Han et al., 2013). Nevertheless, it should be noted that the level of CA1 Esr1 expression was consistently reduced relative to area CA3, regardless of age or OVX duration.

In other brain regions and tissues, DNA methylation of the ERα promoter is thought to contribute to differential Esr1 expression. The promoter exon 1b region was chosen for examination as this has been shown to be the active promoter in the rat brain and contains a number of CpG sites previously associated with the regulation of the Esr1 gene (Champagne et al., 2006; Freyschuss and Grandien, 1996; Kurian et al.,

2010). Considerable variability in methylation was observed across the 17 CpG sites located in this promoter region. In general, the greatest methylation was observed at site 1 and minimal methylation was observed for sites 2-10. Modest methylation was observed for sites 11-17 with sites 11, 15, and 17 exhibiting higher methylation relative to upstream sites 2-7.

DNA Methylation is involved in heritable gene silencing or gene inactivation (Bird and Wolffe, 1999; Newell-Price et al., 2000). While promoter regions that are highly methylated tend to be less transcriptionally active, the relationship between DNA

methylation and gene expression is far from clear. In the current study, the largest difference in Esr1 expression was observed across hippocampal subregions and the site of the greatest DNA methylation, site 1, also exhibited differential methylation across subregions, with increased methylation in region CA1 associated with reduced

Esr1 expression. Similarly, the increase in mRNA expression in CA1 with age was associated with decreased methylation of site 15 in CA1 and the increase in Esr1 for young long-term OVX and aged animals was associated with decreased methylation in

CA1 of sites 11 and 14, compared to young short-term OVX animals. The results indicate that for sites with the greatest methylation (i.e. site 1), variability in methylation is associated with changes in Esr1 expression. Furthermore, for more distal sites (i.e.

11, 14, 15), methylation is more modest and can be modified across the lifespan. Each of these points is addressed below.

The idea that increased methylation of site 1 is related to decreased mRNA expression is consistent with previous work in other brain regions (Kurian et al., 2010).

However, we also observed increased methylation for sites 14 and 15 in region CA3 associated with increased Esr1 expression. Similarly, Gore et al. (2011) have reported that exposure to an estrogenic endocrine disruptor increased DNA methylation at one site, identified as site 14 of the Esr1 promoter in the current study, which was associated with increased mRNA levels in the preoptic area. Thus, it should be emphasized that the variability in methylation, increasing or decreasing, only provides a correlate of mRNA expression. A mechanism through which differential DNA methylation might regulate Esr1 expression remains to be elucidated.

The idea that methylation of these distal sites can be modified across the lifespan to regulate Esr1 expression is supported by previous work, which demonstrates that maternal care and hormonal manipulations altered Esr1 expression in the medial preoptic area and amygdala, and the changes in Esr1 expression were associated with differential methylation of sites 11-16 (Champagne et al., 2006; Edelmann and Auger,

2011; Gore et al., 2011). Across hippocampal subregions there is heterogeneity in DNA methylation, gene expression, and in the transcriptional response to aging (Xu, 2015;

Zeier et al., 2011). Previous studies have highlighted distinct patterns in DNA methylation and transcription across cell types including different neuronal cell types

(Angermueller et al., 2016; Brunner et al., 2009; Kozlenkov et al., 2014; Kozlenkov et al., 2016), suggesting that variability in DNA methylation observed in the current study could be due to cell-type heterogeneity. However, it should also be noted that mRNA for

ERα has been observed in both pyramidal cells and interneurons in the hippocampus and expression is higher in region CA3 relative to CA1 (Rune et al., 2002). Regardless, it will be important for future studies to determine if the relationship of DNA methylation and Esr1 expression is specific to certain cell types.

Several transcriptional and post-translational feedback mechanisms control estrogen receptor expression (Bean et al., 2014). However, most of this work has been performed in breast cancer cell cultures and the molecular mechanisms that regulate estrogen receptor expression in the hippocampus are not well understood. In mice, functional knockout of ERα or ERβ induces a compensatory increase in hippocampal

Esr1 and Esr2 transcription, respectively, suggesting a feedback mechanism (Han et al., 2013). The current study suggests that a shift in DNA methylation, particularly for

distal sites, could be involved in feedback regulation of ERα expression. Previous studies have reported a link between ERα expression, DNA methyltransferase activity, and ERα promoter methylation during development, aging, and in disease states (Wang et al., 2012; Westberry et al., 2010; Yang et al., 2001). Methylation of the promoter may influence mRNA expression by regulating the binding of transcription factors. A number of putative transcriptional factors binding sites have been reported for the exon1b region

(Gore et al., 2011), however, only the binding of one transcriptional factor (Stat5b) has been reported to be associated with ERα methylation (Champagne et al., 2006). In addition, DNA methyltransferase interacts with transcription repressor proteins (e.g. histone deacetylase) to alter chromatin structure and the pattern of DNA methylation

(Robertson, 2002). Transcription repressors, HDAC2 and SAP18, influence Esr1 transcription (Bicaku et al., 2008; Ellison-Zelski et al., 2009) and E2 treatment decreases the expression of Hdac2 and Sap18 in the hippocampus (Aenlle et al., 2009).

Thus, altered expression of transcription repressor proteins may interact with DNA methylation as part of a feedback mechanism.

Finally, the ratio of hippocampal ERα and ERβ expression interacts with the level of E2 to influence transcription and synaptogenesis and a shift in the ERα/ERβ ratio may determine the ability of E2 to influence cognition (Bean et al., 2014; Bean et al.,

2015; Hall and McDonnell, 1999; Han et al., 2013; Pettersson et al., 2000). The increase in Esr1 and Esr2 transcription in estrogen receptor knockout mice is diminished for Esr1, but not Esr2, during aging (Han et al., 2013). It is also interesting to note that E2 treatment of aged animals increases hippocampal synaptic expression of

ERβ, but not ERα (Waters et al., 2011), suggesting differential regulation of estrogen

receptors during aging. Due to the interaction of ERα and ERβ in determining functional outcome, it will be important to map out differences in the regulation of these two estrogen receptors during aging.

In summary, transcriptional levels of Esr1 were altered across hippocampal CA1 and CA3 subregions, with increased expression in region CA3 relative to CA1. In addition, an age-related increase in expression was found in region CA1 relative to young short-term OVX rats. Furthermore, the results support the idea that DNA methylation is an active epigenetic mechanism for the regulation of Esr1 in the hippocampus, where methylation of site 1 may be the primary regulatory region for cross-regional patterns in ERα expression. Additionally, differential methylation of distal

CpG sites, 11-17, was associated with aging or E2 deprivation, suggesting that these sites are modifiable across the life span and may act as a feedback mechanism for ERα activity.

Acknowledgement of Financial Support to Chapter 2

Financial support by National Institutes of Aging Grants R01AG037984,

R37AG036800, R01AG49711, RO1AG052258, and the Evelyn F. McKnight Brain

Research Foundation is highly appreciated.

Figure 2-1. Experimental research design. Young (3 months) and aged (18 months) F344 female rats were handled for 5 minutes a day for 1 week prior to OVX surgery, and sacrificed following a short-term E2 deprivation time (3 weeks) or a long-term E2 deprivation time (14 weeks).

Figure 2-2. Illustration of differences in Esr1 expression. The CA1 region from young short-term rats were used as the calibrator sample in calculating the ΔΔCT. For A-C, each bar represents the mean (+SEM) of Esr1 expression for the relevant variable, collapsed across all other variables. Esr1 expression is increased with A) age, B) long-term OVX, and C) in region CA3 relative to region CA1. Asterisks in A-C indicated a significant (p < 0.001) main effect. D) Examination of interactions, where each bar represents the mean (+SEM) of Esr1 expression for young (n=5-6; open bars) and aged (n=6; filled bars), across regions CA1 and CA3 according to OVX duration (short-term: 3 wk, long-term: 14 wk). Asterisk indicates a significant (p < 0.05) increase in region CA1 relative to young short-term OVX CA1. Pound sign indicates a significant (p < 0.05) increase in region CA3 relative to young short-term OVX CA3. For aged animals, long-term OVX was associated with increased expression relative to aged short-term OVX for region CA1 only (α, p < 0.05).

Figure 2-3. Site specific DNA methylation of CpG sites across CA1 and CA3 regions. A) Each bar represents the mean (+SEM) DNA methylation ratio for sites 1-17 in CA1 (open bars) and CA3 (filled bars). Regional differences in CpG site methylation included increased methylation of site 1 in CA1 and sites 14 and 15 in CA3 (n=20 per region). Asterisk indicates a significant (p<0.05) increase in methylation. B) Hierarchical clustering and heatmap of the clones which contained ≥ 1 methylated site (CA1: 104 clones (51%) & CA3: 84 clones (41%).

Figure 2-4. ERα promoter DNA methylation is altered in regions CA1 and CA3 during aging. Age-related differences in CpG site methylation for A) area CA1 were observed as an increase in DNA methylation for site 15 for young animals (open bars, n=10) relative to aged animals (filled bars, n=10). B) For area CA3, site 17 contained higher methylation in young rats, and sites 11 and 12 showed higher methylation in aged rats. Due to the influence of OVX duration on transcription, young animals were separated into short-term OVX (Young- s) and long-term OVX (young-l) and compared to all aged animals for sites 11-17. Specific differences were observed in CA1 for C) site 11 and D) site 14. E) For region CA3, a difference was observed for site 13. Bars represent the mean (+SEM) of the methylation ratio. Asterisks indicate a significant (p < 0.05) increase in A and B and a significant (p < 0.05) difference from young short-term in C-E.

Table 2-1. Fisher's PLSD for CpG Sites in Esr1 promoter DNA methylation on CpG site P-Value 1 > 16 sites (2-17) <0.05 8 > 1 sites (5) <0.05 10 > 2 sites (5, 9) <0.05 11 > 8 sites (2-7, 9, 14) <0.05 12 > 3 sites (3, 5, 9) <0.05 13 > 3 sites (3-7, 9) <0.05 15 > 9 sites (2-9, 14) <0.05 16 > 4 sites (3, 5, 6, 9) <0.05 17 > 8 sites (2-7, 9, 14) <0.05

CHAPTER 3 TRANSCRIPTIONAL PROFILE OF AGING AND COGNITION-RELATED GENES IN THE MEDIAL PREFRONTAL CORTEX 2

Background

The extent to which cognition declines over the course of aging varies across individuals. Neuroimaging studies indicate that the pattern of cognitive decline is related to changes in the structure and activity of the prefrontal cortex (PFC) and hippocampus

(Dennis et al., 2008; Grady et al., 2005; Migo et al., 2015; Park and Reuter-Lorenz,

2009; Persson et al., 2006), suggesting that individual differences in cognitive aging may result from vulnerability and reorganization of these neural systems. In addition, altered white matter integrity could influence connectivity of the PFC with other brain regions (Andrews-Hanna et al., 2007; Bennett et al., 2011; Borghesani et al., 2013;

O'Sullivan et al., 2001; Pfefferbaum et al., 2005; Salat et al., 2005). The molecular mechanisms for vulnerability and adaptive reorganization during aging have been the subject of speculation (Gray and Barnes, 2015; Jackson et al., 2009; Kumar et al.,

2009; McEwen and Morrison, 2013). Previous work using microarray technology indicates that over the course of aging, the transcription of genes linked to inflammation and synaptic function increases and decreases, respectively, within a number of brain regions (Berchtold et al., 2013; Blalock et al., 2003; Bordner et al., 2011; Burger, 2010;

Cribbs et al., 2012; Erraji-Benchekroun et al., 2005; Haberman et al., 2011; Loerch et al., 2008; Primiani et al., 2014; Prolla, 2002; VanGuilder et al., 2011; Verbitsky et al.,

2004; Yuan et al., 2012; Zeier et al., 2011), suggesting possible mechanisms for variability in cognitive decline.

Reprinted with permission from Frontiers (Ianov et al., 2016b).

While it is widely thought that transcription is linked to cognitive function, there is relatively little information on the PFC transcriptional profile, which attempts to link altered gene expression to an age-related decline in behaviors that depend on the PFC.

Indeed, the PFC provides several unique challenges for examining the relationship of transcription to age-related cognitive impairment. The PFC can be divided into several sub-regions and there is a long-standing debate over the equivalence of anatomical regions within the PFC across species (Brown and Bowman, 2002; Hoover and Vertes,

2007; Preuss, 1995; Uylings and van Eden, 1990; Vertes, 2004). In addition, the PFC is involved in executive function, which encompasses a number of cognitive processes including attention, response inhibition, working memory, and mental flexibility (Bizon et al., 2012; Robbins, 1996).

In the current study, we exploit individual differences in behavior to examine the relationship between age-related changes in cognition and transcription. Young and aged rats were characterized on two tasks that are age-sensitive, including an attentional set shift task that depends on the mPFC (Brown and Bowman, 2002; Kesner and Churchwell, 2011) and on a hippocampal-dependent spatial episodic memory task

(Foster, 2012a; Foster et al., 2012). RNA sequencing (RNA-seq) was used to construct transcriptomic profiles for the mPFC, white matter, and CA1 region of the hippocampus.

Expression differences associated with aging and cognition, defined by variability in set shift or spatial memory behavior, were examined. Finally, the aging and cognition mPFC gene sets were compared to microarray data from other studies to test specific hypotheses. The results indicate that expression of immediate-early genes (IEGs) related to neural activity and synaptic plasticity decline with age in the mPFC; however,

within the group of aged animals, expression of IEGs is up regulated in animals that exhibit delayed set shift behavior.

Materials and Methods

Animals

Procedures involving animal subjects have been reviewed and approved by the

Institutional Animal Care and Use Committee and were in accordance with guidelines established by the U.S. Public Health Service Policy on Humane Care and Use of

Laboratory. Male Fischer 344 rats of two ages, young (5-6 months, n = 11) and aged

(17-22 months, n = 20) were obtained from National Institute on Aging colony (Taconic) through the University of Florida Animal Care and Service facility. Animals were maintained on a 12:12 hour light schedule, and provided ad lib access to food and water prior to the set shifting task.

Behavioral Studies

Set shifting operant task

Apparatus. Testing in the set shifting task was conducted in standard rat behavioral test chambers (30.5 X 25.4 X 30.5 cm, Coulbourn Instruments, Whitehall,

PA) with metal front and back walls, transparent Plexiglas side walls, and a floor composed of steel rods (0.4 cm in diameter) spaced 1.1 cm apart. Each test chamber was housed in a sound-attenuating cubicle, and was equipped with a recessed food pellet delivery trough located 2 cm above the floor in the center of the front wall. The trough was fitted with a photobeam to detect head entries and a 1.12 W lamp for illumination. Food rewards consisted of one 45 mg grain-based food pellet for each correct response (PJAI, Test Diet, Richmond, IN). Two retractable levers were located to the left and right of the food trough (11 cm above the floor), and a 1.12 W cue lamp

was located 3.8 cm above each lever. An additional 1.12 W house light was mounted near the top of the rear wall of the sound-attenuating cubicle. An activity monitor was positioned above each test chamber to monitor locomotor activity throughout each session. This monitor consisted of an array of infrared (body heat) detectors focused over the entire test chamber. Movement in the test chamber (in x, y, or z planes) was defined as a relative change in the infrared energy falling on the different detectors. A computer interfaced with the behavioral test chambers and equipped with Graphic State

3.01 software (Coulbourn Instruments) was used to control experiments and collect data.

Behavioral Shaping. The design of the set shifting task was based previously published methods (Beas et al., 2013; Floresco et al., 2008). Prior to the start of behavioral testing, rats were reduced to 85% of their free feeding weights over the course of five days and maintained at this weight for the duration of the experiments.

Rats were trained and tested in the same behavioral testing chamber during the course of the experiment. Rats progressed through four stages of shaping prior to the start of the set shifting task, with new stages beginning on the day immediately following completion of the previous stage. On the day prior to Shaping Stage 1, each rat was given five 45 mg food pellets in its home cage to reduce neophobia to the food reward used in the task. Shaping Stage 1 consisted of a 64-min session of magazine training, involving 38 deliveries of a single food pellet with an inter-trial interval (ITI) of 100 ± 40 sec. Shaping Stage 2 consisted of lever press training, in which a single lever (left or right, counterbalanced across groups) was extended and a press resulted in delivery of

a single food pellet. After reaching a criterion of 50 lever presses in 30 min, rats were then trained on the opposite lever using the same procedures.

Shaping Stage 3 consisted of 90 trials that were designed to train rats to press the levers after their insertion into the test chamber. Each 20 sec trial began with illumination of the house light and insertion of a single lever (either left or right, randomly selected within each pair of trials) into the test chamber where it remained for a maximum of 10 sec. A response on the lever within this time window resulted in retraction of the lever, delivery of a single food pellet, and continued illumination of the house light for an additional 4 sec. If a rat failed to respond on the lever within 10 sec, the lever was retracted and the house light turned off, and the trial was scored as an omission. Rats received at least 4 daily sessions in this stage, and were trained until reaching criterion performance of fewer than 10 omissions out of the 90 trials.

Shaping Stage 4 was designed to determine each rat’s side bias (i.e., preference for one lever over the other). Each trial consisted of multiple phases. In the first phase of a trial, the house light was illuminated and both levers were inserted into the test chamber. A response on either lever resulted in retraction of both levers and delivery of a single food pellet. In the second phase of a trial, both levers were again inserted, but only a response on the lever opposite to that chosen in the first phase resulted in food delivery. A response on the same lever chosen in the first phase (i.e., “incorrect”) resulted in the levers being retracted and the house light being extinguished. After a

“correct” response in this second phase of a trial, a new trial was initiated, whereas after an “incorrect” response, the second phase of the trial was repeated. The second phase was repeated until rats made a “correct” response. The session ended after a total of 45

completed trials. The side associated with the greatest number of total responses across this phase of testing was considered a rat’s biased side.

Visual Cue Discrimination. Following shaping stage 4, rats were trained to press the lever signaled by the illumination of a cue light over the lever. Each 20 sec trial began with illumination of one of the cue lights (left or right, randomly selected in each pair of trials). After 3 sec, the house light was illuminated and both levers were inserted into the chamber (the cue light remained illuminated while the levers were extended). A response on the lever corresponding to the cue light (a correct response) resulted in the house light remaining on for 4 sec, during which time the levers were retracted, the cue light was extinguished, and a single food pellet was delivered. A response on the opposite lever (an incorrect response) or failure to respond within 10 sec (omission) resulted in retraction of both levers and all lights being extinguished.

Rats were considered to have acquired the task upon reaching criterion performance of

8 consecutive correct trials (and at least 30 total trials, excluding omissions), with the maximum number of trials per session set at 120. Rats that failed to acquire the task within a single session (young: n = 5; aged: n = 13) received additional sessions on subsequent days.

Left/Right Discrimination (Set shift). After reaching criterion performance on the visual cue discrimination, rats were tested the next day in the set shift condition, in which the task contingencies were altered. In this condition, rats were required to ignore the visual cue and instead to consistently choose the left or right lever (whichever was not their biased side as determined in Shaping Stage 4). Hence, accurate performance required rats to “shift” their attention away from the visual cue and toward the left/right

position of the lever. Beyond the shift in reward contingencies, trials were identical in presentation to those in the visual cue discrimination (i.e., on each trial, both levers were presented; with the cue light illuminated over one lever). As in the visual cue discrimination, the location of the illuminated cue light was randomized (left or right) in each pair of trials. Rats were considered to have acquired the task upon reaching criterion performance of 8 consecutive correct trials, excluding omissions. The maximum number of trials per session was set at 120 and all rats acquired the task within a single session.

Morris water maze

Following completion of set shifting, animals were again provided ad lib access to food and water for ~7 weeks prior to testing on the water maze. Animals were trained in a black tank, 1.7 m in diameter, positioned in a well-lit room. The pool was surrounded by black walls and black curtain. For spatial training an assortment of two- and three- dimensional cues were hung on the walls and curtain. Water (27 ± 2ºC) was maintained at a level approximately 8 cm below the surface of the tank. Methods employed to assess sensory-motor deficits and impaired episodic spatial memory on the water maze have been published previously (Foster et al., 1991; Guidi et al., 2014; Kumar and

Foster, 2013). For cue and spatial tasks, training consisted of five blocks with three trials per block and training on each task was massed into a single day. Inter-trial intervals were 20s and inter-block intervals were ~15 min. Rats remained on the platform between trials and in home cages under the heat lamp after each block.

Behavioral data was acquired with Noldus EthoVision computer tracking software

(Noldus Information Technology, Leesburg, VA, USA) and included path-length and

latency to escape to the platform, platform crossing and time in the goal and opposite quadrants.

Rats were first trained on the cue discrimination version of the water escape task.

The escape platform was extended approximately 1 cm above the water level and a white Styrofoam flag was attached. For each trial, the platform position and start location were randomized. If an animal did not escape the water maze within 60 sec, the rat was gently guided to the platform. Three days following cue training, animals were trained on the spatial discrimination task. For spatial discrimination, the escape platform was hidden approximately 1.5 cm beneath the water level and remained in the same location relative to the distal cues in the room for the duration of the initial spatial training. Fifteen minutes following the end of training on block 5, a free-swim probe trial was administered as a measure of learning. For the probe trial, the platform was removed and the animal placed in the tank for 60 sec. A spatial discrimination index was computed according to the formula (G − O)/(G + O) where G and O represent the percent of time spent in the goal quadrant and quadrant opposite the goal, respectively.

Statistical Analysis of Behavior. The total numbers of trials required to achieve criterion (TTC) on the visual cue discrimination and on the left/right discrimination (set shift) were used as the indices of performance. Mean distance to find the platform during each training block for the water maze cue and spatial tasks and probe trial data, platform crossing and discrimination index, were employed to examine learning on the water maze. For the distance measures, repeated measures analyses of variance

(ANOVAs) were used to examine age and training effects. One way ANOVAs were used to examine aged effects for the water maze probe trial data and TTC measures

from the operant tasks. Fisher’s protected least significant difference comparisons, with the p-value set at 0.05, were used to localize differences.

Tissue Collection

Two weeks following water maze testing, rats were anesthetized with isoflurane

(Piramal Healthcare), decapitated and the brain was rapidly removed. The PFC was blocked into 1 mm coronal slices. The mPFC including the prelimbic and infralimbic regions were collected from 2 sections (between +5.0 to +2.5 anterior to bregma)

(Paxinos and Watson, 1986). For a subset of animals (young = 8, aged = 9), white matter was collected adjacent to the mPFC (Figure 3-1). For region CA1, the hippocampus was isolated, a 1-2 mm slice was removed from the dorsal hippocampus, and the CA1 region was dissected (Blalock et al., 2003; Zeier et al., 2011). The collected tissue was immediately frozen in liquid nitrogen and stored in -80ºC until processed.

RNA and Library Preparation

RNA was isolated from the mPFC, CA1, and white matter using the RNeasy Lipid

Tissue Mini kit (Qiagen, catalog number 74804) and DNase digestion was performed with the RNase-Free DNase Set (Qiagen, catalog number 79254). The concentration was measured with the NanoDrop 2000 spectrophotometer and the RNA integrity number (RIN) was quantified by the University of Florida Interdisciplinary Center for

Biotechnology Research using the High Sensitivity RNA Screen Tape in an Agilent 2200

Tapestation system. The average RIN across all regions was 8.02 (±SEM 0.05).

External RNA Controls Consortium (ERCC) spike-in controls (Thermo Fisher, catalog number 4456740) were added to a subset of samples and the mRNA was selected by poly-A selection with the use of the Dynabeads mRNA DIRECT Micro kit which captures

poly-A mRNA with beads containing an oligo-dT sequence (Thermo Fisher, catalog number 61021). Whole transcriptome libraries were prepared with the Ion Total RNA- seq Kit v2 (Thermo Fisher, catalog number 4475936) with the addition of the Ion Xpress barcodes for multiplex sequencing (Thermo Fisher, catalog number 4475485). The concentration of the libraries was quantified by the Qubit dsDNA HS Assay (Thermo

Fisher, catalog number Q32851) and size distribution was evaluated with the High

Sensitivity D1000 Screen Tape in the Tapestation system.

Reverse Transcription Quantitative Polymerase Chain Reaction

Reverse transcription quantitative polymerase chain reaction (RT-qPCR) was performed in a subset of samples to validate RNA-seq results. cDNA was prepared using the QuantiTect Reverse Transcription kit (Qiagen, catalog number 205311) and quantitative PCR was completed with the TaqMan Gene Expression Assays (Arc:

Rn00571208_g1, Egr1: Rn00561138_m1, Egr2: Rn00586224_m1, Egr4:

Rn00569509_g1, Fos: Rn02396759_m1, Lin7b: Rn00572781_m1, Gapdh:

Rn01775763_g1) in a 7300 Real-Time PCR system with SDS software version 1.3.1

(Applied Biosystems). The ΔΔCT method (Livak and Schmittgen, 2001) was used to determine the relative cDNA levels. Differences in the subset of RNA-seq and RT-qPCR were confirmed using t-tests between young and aged rats and between age impaired and unimpaired animals.

Sequencing, Bioinformatics, and Statistical analysis

Template preparation was performed in the Ion Chef system and sequencing was completed in the Ion Proton (Thermo Fisher). ERCC analysis was executed in the

Torrent Server with the ERCC analysis plugin. ERCC analysis per sequencing run contained R2 above 0.9 with at least 60 transcripts. Pearson’s correlation among the

ERCC’s in each biological replicate resulted in an average R value of 0.84 and an average of 86 ERCC transcripts detected (Figure A-1). Furthermore, each sample contained an average of 18.8 million reads of 131 base pair length (Figure C-1). Low quality reads were removed from the FASTQ files and the data was aligned to the rn5 genome using the two-step alignment method for Ion Proton transcriptome data with

TopHat2 and Bowtie2 in the Partek Flow servers (Partek Inc.). Gene level counts were generated from BAM files using the featureCounts function in the R package Rsubread

(Liao et al., 2014). The Rnor_5.0.78.gtf file was used for annotation and count normalization was performed with the DESeq package in R. The data for this study has been uploaded to NCBI’s Gene Expression Omnibus under the accession number:

GSE75772.

Gene filtering and initial statistical analysis was performed according to our previously published work (Aenlle and Foster, 2010; Aenlle et al., 2009; Blalock et al.,

2003; Zeier et al., 2011). Gene lists were initially filtered to remove those genes with counts of 5 or less, which resulted in the detection of over 18,000 Ensembl database genes from each area. Gene lists were further filtered such that only genes with annotation of at least one gene ontology (GO) term were considered for gene enrichment analysis. Filtering for GO terms resulted in 15,075 mPFC genes, 15,235 genes, and 15,084 white matter genes. For differential expression analysis associated with age, a statistical filter was performed in each tissue type independently, using a one-way ANOVA generated in Partek Genomics Suite 6.6, with p < 0.025 according to our previous work (Aenlle and Foster, 2010; Aenlle et al., 2009; Blalock et al., 2003;

Zeier et al., 2011). For examination of mPFC gene expression related to cognition,

Pearson’s correlations were calculated between the behavioral TTC measures for the set shifting task or discrimination index score for spatial learning and the expression of each gene in the mPFC transcriptome. Correlations were limited to aged animals in order to remove age as a confound. Due to multiple comparisons, the confidence in any single gene is low; therefore, gene enrichment analysis was performed under the assumption that changes in biological process with age or cognition would result in a shift in the expression of clusters of genes related to the biological process. For gene enrichment and functional annotation clustering analysis, data sets of genes that exhibited an increase or decrease in expression were separately submitted to the NIH database for annotation, visualization, and integrated discovery (DAVID version 6.7)

(Huang et al., 2007a; Huang et al., 2007b). Enrichment analysis was limited to gene ontology for biological processes and cellular components with the Benjamini False

Discovery Rate (FDR) p < 0.05 as a cut-off for cluster selection. Heat maps were generated in Partek Genomics Suite 6.6 and in R with gplots (3.0.1) using genes that were identified in DAVID with counts which were standardized to z-scores. In other cases, specific hypotheses were tested by comparing gene expression with previous published work using microarrays. In this case, we determined whether the previously published genes were detected by our procedures. This set of genes that were common across studies represented the total data set. Next, we used a fold change and chi squared test to determine if the genes were altered in the same direction, relative to what would be expected by chance. The number of genes that were significantly altered in the same direction was determined using one-tailed t-tests with the direction specified by the microarray studies. A false discovery rate was calculated by calculating the

number of genes expected to change in a specified direction using the formula T*p, where T = total number of genes tested and p is the significance level (0.05). The expected number of genes was divided by the number of genes that were significantly different in the predicted direction to obtain the false discovery rate.

Results

Behavior

Set shift. Young (n = 11) and aged (n = 20) animals were trained on the visual discrimination operant task followed by set shift testing. Aged animals exhibited considerable variability in performance on each task (Figure 3-2), with some aged animals performing in a range similar to young. Examination of the TTC for visual discrimination indicated no effect of age [F(1,29) = 2.15, p = 0.15] (Figure 3-2A). In contrast, an age difference in TTC was observed for the set shifting (left/right discrimination) behavior [F(1,29) = 7.95, p < 0.01], with a subset of aged rats exhibiting an increase in TTC relative to young (Figure 3-2B).

Water maze. For the cue discrimination version of the water maze task, all animals were able to find the visible platform during the 60 sec time limit during the last three trials (block 5). A repeated measures ANOVA for the cue discrimination task indicated an effect of training [F(4,116) = 7.84, p < 0.0001] and age [F(1,29) = 11.43, p

< 0.005] and an interaction of age and training [F(4,116) = 2.92, p < 0.05] due to superior performance by young animals during the final training blocks (Figure 3-3A). A repeated measures ANOVA for the spatial discrimination task indicated an effect of training [F(4,116) = 17.82, p < 0.0001] and an interaction of age and training [F(4,116) =

3.13, p < 0.05] (Figure 3-3B). The results of the probe trial indicated a decrease in platform crossings for aged animals [F(1,29) = 8.52, p < 0.01] (Figure 3-3C). No age

effect was observed for the discrimination index; however, consistent with previous reports (Blalock et al., 2003; Foster, 2012a; Guidi et al., 2014; Kumar and Foster, 2013), there was considerable variability (Figure 3-3D). For aged animals, the relationship between behavioral measures on the operant tasks (visual discrimination TTC, set shift

TTC) and on the water maze (distance to escape on block 5 of the cue task, distance to escape on block 5 of the spatial task, platform crossings, and discrimination index of the probe trial) was examined (Table 3-1). The results indicated a correlation between the distance on block 5 of the spatial task and the discrimination index (r = -0.484, p <

0.05), such that longer escape distances on the spatial task were associated with poorer discrimination index scores.

Gene Expression

Gene expression related to aging

RNA-seq libraries from the mPFC and region CA1 of the hippocampus were prepared from all animals (young = 11, aged = 20). In addition, libraries were prepared from white matter, collected from a subset of animals (young = 8, aged = 9). Gene expression data was statistically filtered for age differences using ANOVAs for each tissue type with a cut-off set at p < 0.025. The largest and smallest number of age- related genes was observed for white matter (1529 genes) and region CA1 (286 genes), with 731 genes altered in the mPFC. Although, expression changes were relatively distinct, there was some overlap. Furthermore, for genes that overlapped across any two regions, the number of up regulated genes was 2-8 times larger than genes that decreased expression (Figure 3-4).

Based on functional studies, it has been suggested that the mPFC of the rat may be analogous to the dorsolateral PFC of humans and non-human primates (Birrell and

Brown, 2000; Dias et al., 1997; Kesner and Churchwell, 2011). To determine possible expression changes in the rat mPFC that may correspond to changes observed in humans, we compared our results with a microarray study examining age-related changes in the dorsolateral (Brodmann’s area 9) PFC of humans (Erraji-Benchekroun et al., 2005). For the 414 age-related probes from the dorsolateral PFC of the original human data set, we were able to identify 318 rat genes in the mPFC and the direction of change, increasing or decreasing, was determined for the rat. From the set of 318 genes, we observed 203 genes (64%) that changed in the same direction as predicted in humans and a chi square analysis indicated that the directional changes were different than that expected by chance (x2 = 11.86, p < 0.001). One-tailed t-tests (p <

0.05 with direction specified by age-related changes in humans) were conducted to examine age differences in expression of these 318 genes. A total of 60 genes (39 decreased, 21 increased) reached significance (false discovery rate: 0.27) (Table 3-2).

The data sets for differentially expressed up regulated and down regulated genes were separately submitted to NIH DAVID for enrichment analysis based on gene ontology for biological processes and cellular components. Cluster selection cut-off was restricted to clusters with a Benjamini FDR p < 0.05. In each case, several genes were observed in multiple related clusters, which are shown below.

Increased expression. In general, age-related changes in transcription involve up regulation of genes linked to immune response, oxidative stress, and the lysosome

(Blalock et al., 2003; de Magalhaes et al., 2009; Fraser et al., 2005; Kadish et al., 2009;

Lee et al., 2000; Lipinski et al., 2010; Rowe et al., 2007; VanGuilder et al., 2011;

Verbitsky et al., 2004; Yuan et al., 2012; Zeier et al., 2011). Expression of immune

response related genes was particularly evident in the white matter and mPFC. For the

940 white matter genes, enrichment was observed for immune response (GO:0006955,

85 genes, FDR p=4.1-25), defense response (GO:0006952, 70 genes, FDR p=1.3-16), and the lysosome (GO:0005764, 33 genes, FDR p=2.2-9). Similarly, for the 344 genes that increased expression in the mPFC, gene enrichment was observed for biological processes linked to oxidation reduction (GO:0055114, 30 genes, FDR p=0.02), response to wounding (GO:0009611, 24 genes, FDR p=0.02), and adaptive immune response (GO:0002250, 10 genes, FDR p= 0.01) (Table 3-3). Increased expression in the mPFC was also observed for carboxylic and catabolic process (GO:0046395, 13 genes, FDR p=0.002) (Figure 3-5). Finally, for 182 CA1 genes, enrichment was observed for the lysosome (GO:0005764, 10 genes, FDR p=0.007). Up regulation was observed for response to wounding (GO:0009611, 11 genes, FDR p=0.38) and defense response (GO:0006952, 10 genes, FDR p=0.44); however, the clusters did not reach the FDR cut-off.

Decreased expression. Previous work indicates that aging is associated with decreased expression of genes linked to neuronal/synaptic genes (Aenlle and Foster,

2010; Berchtold et al., 2013; Blalock et al., 2003; Burger et al., 2007; Lu et al., 2004;

Primiani et al., 2014; Verbitsky et al., 2004; Zeier et al., 2011). In the case of the 589 genes that were decreased in white matter, enrichment was observed for cell division

(GO:0051301, 16 genes, FDR p=0.04). For the 104 genes that decreased in area CA1, clustering did not pass our cut-off. For the 387 mPFC genes that decreased with age, common genes were observed for clusters related to the synapse (GO:0045202, 25 genes, FDR p=1.2-4) and postsynaptic membrane (GO:0045211, 14 genes, FDR p=4.1-

4) (Table 3-4). In addition, decreased expression was observed for neuron projection

(GO:0043005, 22 genes, FDR p= 0.015) (Figure 3-5). Thus, the age-related decrease in neuronal genes was particularly evident in the mPFC. mPFC gene expression related to behavior

To examine behavioral specificity of transcriptional changes, Pearson’s correlations were run comparing expression of mPFC genes with the TTC for set shifting and visual discrimination, and the discrimination index score for the water maze.

Correlations were limited to aged animals in order to remove age as a confound and correlations were performed across all genes in the mPFC transcriptome. Using a cutoff set at p < 0.025 (r = 0.499), a total of 416 genes were correlated with behavioral flexibility. Most of the genes (73%) were positively correlated with the set shift TTC score (303 genes increasing expression with impairment), with 113 genes negatively correlated (decreased in impaired animals) (Figure A-2). A similar analysis for the visual discrimination TTC indicated many fewer mPFC genes (135 total) correlated with acquisition of the visual discrimination, with 103 mPFC genes positively correlated

(increased in animals with more trials to criteria) and 32 genes that were negatively correlated (decreased in animals with more trials to criteria). Unlike the set shift behavior, more mPFC genes exhibited decreased expression (62%) in animals with poorer spatial learning. A total of 401 mPFC genes correlated with the water maze discrimination index, with 249 genes positively correlated with the discrimination index

(decreased in animals with poor spatial learning) and 152 mPFC genes negatively correlated (increased in spatial learning impaired animals) (Figure 3-6).

Gene enrichment analysis was conducted to determine which mPFC genes and biological processes might be good markers for the age-related impairment in set shift

behavior. For mPFC genes that were negatively correlated with the set shift TTC

(decreased expression in impaired animals that delay shifting), no gene enrichment clusters were observed to pass the cut-off for cluster selection. For gene expression that positively correlated with the TTC score (increasing in animals that delayed shifting), clusters were observed for response to organic substance (GO:0010033, 34 genes, FDR p=0.01), regulation of apoptosis (GO:0042981, 27 genes, FDR p= 0.02) and the regulation of transcription (GO:0045449, 46 genes, FDR p=0.027), which contained the largest number of genes (Table 3-5). An index score describing expression of transcription regulation genes was generated for each animal by first standardizing the expression of the 46 transcription genes and the standard scores were averaged within each animal. The standard scores were plotted against the standardized TTC set shift score (r = 0.89) to illustrate the correspondence of transcription regulators with set shift behavior (Figure 3-7). Interestingly, several IEGs linked to neuronal activity (Arc, Egr1, Egr2, Egr3, Egr4, Fos, Fosb, Fosl2, Junb) were observed to increase in association with delayed set shift behavior.

Next we compared our results to a previously published microarray study that examined gene expression across rat species that differ on tests of attention (Qiu et al.,

2010). The spontaneously hypertensive-rat (SHR), a well characterized model of impaired attention, exhibiting impairment on the five-choice serial reaction time task and attentional set shift relative to the Wistar-Kyoto rat (De Bruin et al., 2003; Sagvolden et al., 2005). Using RNA-seq, we were able to detect 48 genes that were correlated with set shift behavior and were previously reported to differ in the mPFC of SHR and

Wistar-Kyoto rats (Qiu et al., 2010). In order to compare our data with the results of Qiu

et al., (2010), a mean split for the set shift TTC scores of aged animals (mean TTC =

51.7) was used to separate aged animals into those that delayed shifting and were considered aged-impaired (AI) and aged-unimpaired (AU). One-tailed t-tests were run on the 48 genes using a p < 0.05 and the direction specified by the results of Qiu et al.,

(2010). For the 26 genes that were predicted to increase, 16 genes exhibited a significant increase (false discovery rate: 0.08) (Table 3-6). Interestingly, for the 16 genes that exhibited a significant increase in expression in AI rats, these genes also exhibited decreased expression during aging with seven genes (Arc, Egr1, Egr3, Egr4,

Junb, Klf10, Nr4a3) exhibiting a significant (p < 0.05) decrease with age. Finally, for the

22 genes that were previously reported to decrease expression in SHR animals, no genes were significantly decreased in AI animals compared to AU animals.

In order to provide some validation of the findings, RT-qPCR was performed on 5

IEGs from a subset of young (n = 9), AI (n = 6), and AU (n = 6) animals. The animals were selected based on set shifting performance to insure group differences in behavior

(Figure 3-8) and an ANOVA confirmed a group difference [F(2,18) = 17.15 , p < 0.0001].

Post hoc tests confirmed that AI animals exhibited an increase in the TTC relative to young and AU animals. Figure 3-9 shows a comparison of RT-qPCR results relative to the gene counts for the same genes and animals. The genes selected were IEGs that were increased in impaired animals (Arc, Egr1, Egr2, Egr4, Fos) and the comparisons

(i.e. t-tests) were limited to confirmation of results observed for the whole data set. For both the RNA-seq and RT-qPCR measures of expression, t-tests indicated a difference in expression (p < 0.05) between the subset of AI and AU animals (Figure 3-9). In addition, Lin7b and Egr4 were expected to decrease with age, which was confirmed for

the RNA-seq and RT-qPCR using t-tests to compare the subset of young and aged animals (Figure 3-9).

Discussion

Neuroimaging indicates that the pattern of cognitive decline is related to the structure or activity of specific brain regions (Dennis et al., 2008; Grady et al., 2005;

Migo et al., 2015; Park and Reuter-Lorenz, 2009; Persson et al., 2006). Furthermore, altered white matter integrity could influence connectivity between brain regions

(Andrews-Hanna et al., 2007; Bennett et al., 2011; Borghesani et al., 2013; Lu et al.,

2011; O'Sullivan et al., 2001; Pfefferbaum et al., 2005; Salat et al., 2005). While similar biological processes were altered across regions with age, very few genes were similarly affected across regions. Dissimilarities may relate to regional differences in vulnerability. Furthermore, vulnerability to aging is influenced by environment and lifestyle such that food restriction to promote operant behavior as well as the training procedure, may have differentially modified aging processes across regions (Lee et al.,

2000; Zeier et al., 2011). Nevertheless, RNA-seq profiles confirmed that brain aging is associated with biological processes that involve increased expression of immune/defense response genes and decreased mitochondria and neuronal/synaptic genes (Berchtold et al., 2013; Blalock et al., 2003; Bordner et al., 2011; Cribbs et al.,

2012; Lu et al., 2004; Primiani et al., 2014; Prolla, 2002; VanGuilder et al., 2011;

Verbitsky et al., 2004; Zeier et al., 2011).

Age-related differences in cortical transcription are observed across species possibly due to evolutionary constraints on aging, examination of disparate brain regions, or differences in the age range examined (Berchtold et al., 2013; Cribbs et al.,

2012; Erraji-Benchekroun et al., 2005; Fraser et al., 2005; Loerch et al., 2008). The

mPFC of rats is thought to be functionally related to the dorsolateral PFC in humans and monkeys (Birrell and Brown, 2000; Dias et al., 1997; Kesner and Churchwell,

2011). Similar to the PFC of humans, we found that genes linked to excitatory and inhibitory transmitter systems decline with age in the mPFC. Significantly, age-related changes in gene expression did not predict cognition.

An important contribution of the current research was the specificity of transcriptional changes that correlate with a cognitive function that depends on the mPFC. The mPFC contributes to cognitive flexibility and spatial learning (Churchwell et al., 2010); however, no correlation was observed between set shifting and acquisition of a spatial search strategy (Barense et al., 2002; Beas et al., 2013). Transcription in the mPFC reflected this distinction in that genes correlated with the discrimination index generally decreased expression (62%) in impaired animals, while a large proportion

(73%) of genes associated with impaired set shifting exhibited increasing expression.

Specificity of mPFC transcription was also reflected in the 3-fold increase in the number of genes that correlated with set shift performance relative to visual discrimination learning. Finally, many of the activity-related IEGs that increased in impaired animals, exhibit down regulation in the mPFC during aging; and down regulation of IEGs in other brain regions is associated with cognitive impairment and decreased responsiveness

(Benloucif et al., 1997; Blalock et al., 2003; Rowe et al., 2007). The results indicate specificity of mPFC transcription with an age-related impairment of mPFC-dependent behavior.

Altered basal expression of PFC IEGs is observed across rat species that exhibit differences in executive function. Up regulation of neural activity and synaptic plasticity

genes is observed for SHR rats, which exhibit impaired set shift behavior compared to

Wistar-Kyoto rats (Qiu et al., 2010). Similarly, compared to successful aging of

LOU/C/Jall rats, aging in Wistar rats is associated with working memory deficits and an increase in PFC expression of IEGs (Arc, Egr2, Fos, Junb, and Nr4a1) (Paban et al.,

2013). Differences in transcription across rodent strains may result from genetic polymorphisms. Thus, an important finding from the current study is that individual variability in cognition, within the same species, is associated with increased basal expression of mPFC IEGs indicating that increased expression is indicative of impaired cognitive flexibility during aging.

What mechanism could increase IEG expression in animals that delay set shift behavior? Differences in IEG expression could result from differences in epigenetic regulation of transcription during aging (Hernandez et al., 2011; Peleg et al., 2010).

Indeed, set shifting behavior was associated with increased expression of genes involved in regulating histone deacetylase (Bcor, Ccdc101, Dnajb5, Kdm6b) and histone acetyltransferase (Ing3) activity. Alternatively, IEG expression is upregulated by increased neuronal activity (Ghosh et al., 1994; Guan et al., 2009; Rudenko et al.,

2013). Impaired set shift behavior was correlated with increased expression of transcription factors of inhibitory neurons (Dlx1, Npas1) and genes linked to the strength of excitatory and inhibitory inputs (Arc, Npas4) suggesting altered synaptic plasticity and increased neural activity. Interestingly, an increase in frontal cortex neural activity is observed in older humans and may relate to performance of cognitive tasks (Maillet and

Rajah, 2014; Rosano et al., 2005; Turner and Spreng, 2012).

One possible mechanism for impaired set shift performance and increased neuronal activity involves a decline in N-methyl-D-aspartate (NMDA) receptor function.

The degree of mPFC NMDA receptor hypofunction is correlated with impaired attention starting in middle-age (Guidi et al., 2015). Furthermore, blockade of NMDA receptors in this region disrupts set shift behavior (Dalton et al., 2011; Stefani and Moghaddam,

2005). The mechanism appears to involve a shift in the balance of excitatory/inhibitory synaptic input since inhibition of NMDA receptors in the PFC decreases the activity of inhibitory interneurons and increases the discharge activity of pyramidal cells

(Homayoun and Moghaddam, 2007). Thus, an age-related decline in NMDA receptor function may reduce inhibitory drive and increase expression of activity-related genes in pyramidal cells.

In summary, the results support the idea that aging is associated with an increase in expression of immune and defense response genes and a decline in synaptic and neural activity genes. Importantly, mPFC expression of IEGs related to neural activity and synaptic plasticity decline with age; however, expression is up regulated in aged animals that exhibit delayed set shift behavior. The mPFC transcriptional profile of impaired animals is in contrast to decreased IEG expression reported for the hippocampus and other brain regions during aging. The specificity of impairment on a mPFC-dependent task, associated with a particular mPFC transcriptional profile indicates that impaired executive function involves altered transcriptional regulation and neural activity/plasticity processes that are distinct from that described for impaired hippocampal function.

Acknowledgement of Financial Support to Chapter 3

Financial support by National Institutes of Aging Grants R01AG037984,

R37AG036800, R01AG49711 and the Evelyn F. McKnight Brain Research Foundation is highly appreciated.

Figure 3-1. Region of the mPFC and white matter (WM) collected for RNA-seq. The right panel provides a schematic of a coronal slice +2.7 anterior to bregma diagram as adapted from Paxinos and Watson (1986) and illustrates the region of the mPFC and white matter collected for RNA-seq. The left panel shows a coronal slice from this same region.

Figure 3-2. Performance on the visual discrimination and set shift operant tasks. Trials to criteria (TTC) are illustrated for individual aged (filled circles, n = 20) and young (open circles, n = 11) animals during performance of the A) initial visual discrimination and B) set shift tasks. Asterisk indicates that aged animals exhibited more trails to criteria for the set shift task (p < 0.01). The open bars indicate the mean TTC for each group.

Figure 3-3. Performance on the water maze task. Symbols indicate the mean (±SEM) escape path length to the escape platform during 5 training blocks on the A) cue and B) spatial discrimination tasks for young (open symbols) and aged (filled symbols) animals. Individual C) platform crossing and D) discrimination index scores for young (open symbols) and aged (filled symbols) animals. The open bars indicate the means for each group.

Figure 3-4. Number of genes altered during aging across regions. Graphic summary of the total number of genes whose expression either significantly (p < 0.025) increased (up arrow) or decreased (down arrow) in the mPFC, region CA1, white matter, and across regions.

Figure 3-5. Heat map of age-related changes in gene expression for the mPFC. Each row represents a differentially expressed gene (p < 0.025) associated with aging. Expression for each gene was converted to a standardized score and the color represents the standard deviation increasing (red) or decreasing (blue) relative to the mean (gray). The age-related gene enrichment clusters are indicated (FDR p<0.05). Top clusters: Genes that exhibit a decrease from young (left) to aged (right) animals. Bottom: Genes that exhibit an increase from young to aged animals.

Figure 3-6. Number of genes correlated with behavioral measures. Graphic summary for the number of mPFC genes whose expression either significantly increased (filled) or decreased (open) in relation to performance on the set shift and visual discrimination operant tasks, and a spatial memory task (Pearson’s correlation p < 0.025). A large number of genes exhibited increased expression associated with impaired set shifting compared to visual discrimination and spatial discrimination index (DI).

Figure 3-7. Impaired set shifting is associated with increased expression of genes involved in transcription regulation. The z-scores for the cluster of 46 transcription regulation genes were averaged for each animal. Individual mean z-scores (y-axes) are plotted relative to z-scores for set shift TTC (x- axes). The correlation is illustrated as a regression line (dashed line).

Figure 3-8. Set shifting performance for animals used in RT-qPCR validations. The animals were selected based on set shifting performance to insure group differences in behavior. The bars illustrate this difference as the mean + SEM TTC for young (n = 9) and aged animals classified as unimpaired (AU, n = 6) and impaired (AI, n = 6) on the set shift task. Asterisks indicate a significant (p < 0.05) difference relative to AI animals.

Figure 3-9. Comparison between RT-qPCR and RNA-seq. Six genes were selected for validation experiments using a subset of animals. Each panel provides the mPFC expression determined by RT-qPCR (left, ΔΔCT values) and RNA-seq (right, counts). Two-tailed t-tests confirmed increased expression of Arc, Fos, Egr1, Egr2, and Egr4 in AI, relative to AU rats. Gene expression for young animals is provided for comparison to aged animals. For two genes, Lin7b and Egr4, age differences were confirmed (***p< 0.005, **p< 0.025, *p< 0.05).

Table 3-1. Behavioral correlations (* indicates p<0.05) Visual Cue Block Spatial Discrimination Crossings TTC 5 Block 5 Index Set shift 0.168 0.115 -0.356 0.145 0.154 TTC Visual 0.245 -0.185 0.197 0.309 TTC Cue 0.29 -0.126 0.17 Block 5 Spatial -0.376 -0.484* Block 5

Crossings 0.311

Table 3-2. Age-related changes across the rat mPFC & the dorsolateral PFC of humans Gene Gene name Direction symbol Acidic leucine-rich nuclear phosphoprotein 32 family Anp32b Increased member B C4a complement component 4A Increased Chi3l1 chitinase 3-like 1 (cartilage glycoprotein-39) Increased Clu clusterin Increased Daam2 dishevelled associated activator of morphogenesis 2 Increased Enah enabled homolog Increased Fgfr1 fibroblast growth factor receptor 1 Increased Gfap glial fibrillary acidic protein Increased Hipk2 homeodomain interacting protein kinase 2 Increased Maob monoamine oxidase B Increased Map4 microtubule-associated protein 4 Increased Map7 microtubule-associated protein 7 Increased Mid1ip1 MID1 interacting protein 1 Increased Moxd1 monooxygenase, DBH-like 1 Increased Mxi1 MAX interactor 1 Increased pleckstrin homology domain containing, family B (evectins) Plekhb1 Increased member 1 Ptk2b PTK2B protein tyrosine kinase 2 beta Increased Rassf2 Ras association (RalGDS/AF-6) domain family member 2 Increased Ssfa2 sperm specific antigen 2 Increased Sun2 unc-84 homolog B (C. elegans) Increased Zcchc24 zinc finger, CCHC domain containing 24 Increased ADAM metallopeptidase with thrombospondin type 1 motif, Adamts8 Decreased 8 Agfg1 ArfGAP with FG repeats 1 Decreased calcium channel, voltage-dependent, T type, alpha 1G Cacna1g Decreased subunit calcium/calmodulin-dependent serine protein kinase Cask Decreased (MAGUK family) Cdh11 cadherin 11, type 2, OB-cadherin (osteoblast) Decreased Cdh8 cadherin 8, type 2 Decreased Cdk5 cyclin-dependent kinase 5 Decreased Crh corticotropin releasing hormone Decreased Crhr1 corticotropin releasing hormone receptor 1 Decreased Cx3cl1 chemokine (C-X3-C motif) ligand 1 Decreased Cyp26b1 cytochrome P450, family 26, subfamily B, polypeptide 1 Decreased Dcaf7 WD repeat domain 68 Decreased

Table 3-2. Continued Gene Gene name Direction symbol Dnajb5 DnaJ (Hsp40) homolog, subfamily B, member 5 Decreased Dusp14 dual specificity phosphatase 14 Decreased Edn3 endothelin 3 Decreased Egr4 early growth response 4 Decreased Eif4g1 eukaryotic translation initiation factor 4 gamma, 1 Decreased Fam131a family with sequence similarity 131, member A Decreased Fam49a family with sequence similarity 49, member A Decreased Gabra4 gamma-aminobutyric acid (GABA) A receptor, alpha 4 Decreased Gng4 guanine nucleotide binding protein (G protein), gamma 4 Decreased Grm2 glutamate receptor, metabotropic 2 Decreased Hmgcs1 3-hydroxy-3-methylglutaryl-Coenzyme A synthase 1 Decreased Htr2a 5-hydroxytryptamine (serotonin) receptor 2A Decreased Kcnf1 potassium voltage-gated channel, subfamily F, member 1 Decreased potassium voltage-gated channel, subfamily H (eag- Kcnh1 Decreased related), member 1 Lancl2 LanC lantibiotic synthetase component C-like 2 (bacterial) Decreased Large like-glycosyltransferase Decreased Lppr4 plasticity related gene 1 Decreased Mapk4 mitogen-activated protein kinase 4 Decreased Mmd monocyte to macrophage differentiation-associated Decreased Neto2 neuropilin (NRP) and tolloid (TLL)-like 2 Decreased Rprm reprimo, TP53 dependent G2 arrest mediator candidate Decreased Sel1l3 KIAA0746 protein Decreased solute carrier family 8 (sodium/calcium exchanger), Slc8a2 Decreased member 2 Sst somatostatin Decreased Sstr1 somatostatin receptor 1 Decreased ST8 alpha-N-acetyl-neuraminide alpha-2,8-sialyltransferase St8sia3 Decreased 3 Trib2 tribbles homolog 2 Decreased

Table 3-3. Increased mPFC expression during aging Gene Response to Immune Oxidation Gene Name Symbol wounding response Reduction Adam17 ADAM metallopeptidase domain 17 X X

Fc fragment of IgG, low affinity IIb, Fcgr2b X X receptor (CD32) Pou2f3 POU class 2 homeobox 3 X

RAB27A, member RAS oncogene Rab27a X X family Timp3 TIMP metallopeptidase inhibitor 3 X

Bmp6 bone morphogenetic protein 6 X

Ctsb cathepsin B X

Clu clusterin X

F11 coagulation factor XI X

complement component 1, q C1qc X X subcomponent, C chain complement component 1, q C1qb X X subcomponent C4b complement component 4B X X

Fn1 fibronectin 1 X

Gfap glial fibrillary acidic protein X

Hmox1 heme oxygenase (decycling) 1 X X

Itgb2 integrin beta 2 X

Ncf1 neutrophil cytosolic factor 1 X

platelet/endothelial cell adhesion Pecam1 X molecule 1 Pdpn podoplanin X

secreted protein, acidic, cysteine- Sparc X rich Serpina1 serpin peptidase inhibitor, clade A X

triggering receptor expressed on Treml1 X myeloid cells-like 1 Tp73 tumor protein p73 X

v-erb-b2 erythroblastic leukemia Erbb2 X viral oncogene homolog 2 Swap70 SWAP-70 protein X

Il18 interleukin 18 X

Il18bp interleukin 18 binding protein X

leukocyte immunoglobulin-like Lilrb3 X receptor 4-hydroxyphenylpyruvate Hpd X dioxygenase Acadm acyl-Coenzyme A dehydrogenase X

acyl-Coenzyme A dehydrogenase, Acadl X long-chain

Table 3-3. Continued Gene Response to Immune Oxidation Gene Name Symbol wounding response Reduction alcohol dehydrogenase, iron Adhfe1 X containing, 1 Aldh1l1 aldehyde dehydrogenase 1 family X

Aldh2 aldehyde dehydrogenase 2 family X

aldehyde dehydrogenase 6 family, Aldh6a1 X member A1 aldo-keto reductase family 1, Akr1c13 X member C13 aminoadipate-semialdehyde Aass X synthase Aifm3 apoptosis-inducing factor X

Cdo1 cysteine dioxygenase, type I X

Cybrd1 cytochrome b reductase 1 X

Dcxr dicarbonyl L-xylulose reductase X

Fmo2 flavin containing monooxygenase 2 X

hydroxyacyl-Coenzyme A Hadh X dehydrogenase hydroxyacyl-Coenzyme A Hadha X dehydrogenase hydroxysteroid (17-beta) Hsd17b4 X dehydrogenase 4 isocitrate dehydrogenase 2 Idh2 X (NADP+) Maob monoamine oxidase B X

acyl-Coenzyme A dehydrogenase Acad11 X family phytanoyl-CoA dioxygenase Phyhd1 X domain containing 1 procollagen-lysine 1, Plod1 X 2-oxoglutarate 5-dioxygenase 1 pyridine nucleotide-disulphide Pyroxd2 X oxidoreductase domain 2 Phgdh phosphoglycerate dehydrogenase X

Prodh proline dehydrogenase X

solute carrier family 14 (urea Slc14a1 X transporter) Tbxas1 thromboxane A synthase 1, platelet X

Tph1 tryptophan hydroxylase 1 X

Xdh xanthine dehydrogenase X

Table 3-4. Decreased mPFC expression during aging Gene Postsynaptic Neuron Gene Name Synapse Symbol membrane projection ankyrin repeat and sterile alpha Anks1b X X X motif domain containing 1B Clstn3 calsyntenin 3 X X

Cbln1 cerebellin 1 precursor X

Chrm2 cholinergic receptor, muscarinic 2 X X X cholinergic receptor, nicotinic, Chrna5 X X X alpha 5 cytochrome P450, family 19, Cyp19a1 X X subfamily a, polypeptide 1 Doc2a double C2-like domains, alpha X

Dnm3 dynamin 3 X X

gamma-aminobutyric acid Gabra4 X X (GABA) A receptor, alpha 4 Gad1 glutamate decarboxylase 1 X X

glutamate receptor interacting Grip1 X X protein 1 glutamate receptor, ionotropic, Grid1 X X delta 1 glutamate receptor, ionotropic, Grid2 X X delta 2 glutamate receptor, ionotropic, Grik3 X X X kainate 3 glutamate receptor, metabotropic Grm7 X X X 7 glutamate receptor, metabotropic Grm8 X X X 8 Glrb glycine receptor, beta X X

Lin7b lin-7 homolog b (C. elegans) X X

Magee1 melanoma antigen, family E, 1 X X X secretory carrier membrane Scamp1 X protein 1 similar to CG2662-PA; protein Prkaca kinase, cAMP-dependent, X

catalytic, alpha solute carrier family 2 (facilitated Slc2a3 X glucose transporter), member 3 Sv2b synaptic vesicle glycoprotein 2b X

Syt6 synaptotagmin VI X

tyrosine 3- monooxygenase/tryptophan 5- Ywhaz X monooxygenase activation protein, zeta polypeptide

Table 3-4. Continued Gene Postsynaptic Neuron Gene Name Synapse Symbol membrane projection Bace1 beta-site APP cleaving enzyme 1 X

Dcc deleted in colorectal carcinoma X

Dpysl2 dihydropyrimidinase-like 2 X

Dpysl5 dihydropyrimidinase-like 5 X

Dctn2 dynactin 2 X

Got1 glutamic-oxaloacetic transaminase 1 X Klhl1 kelch-like 1 (Drosophila) X

mitogen activated protein kinase Map2k4 X kinase 4 Kcnj12 potassium inwardly-rectifying channel X Pgr progesterone receptor X

Ptprn2 protein tyrosine phosphatase X Tacr3 tachykinin receptor 3 X

Table 3-5. Positive correlation of mPFC genes with set shift TTC Regulation Response to Gene Regulation Gene Name of organic Symbol of Apoptosis transcription substance B-cell CLL/lymphoma 6, Bcl6b X member B Bcor BCL6 co-repressor X

CCAAT/enhancer binding Cebpb X protein (C/EBP), beta CCAAT/enhancer binding Cebpd X X protein (C/EBP), delta DnaJ (Hsp40) homolog, Dnajb5 X X subfamily B, member 5 FBJ osteosarcoma Fos X X oncogene FBJ osteosarcoma Fosb X X oncogene B Jun Jun oncogene X X X Max dimerization protein Mxd3 X 3 Meis1 Meis homeobox 1 X

Nab2 Ngfi-A binding protein 2 X

Swi/SNF related matrix Smarcal1 X associated TSC22 domain family, Tsc22d3 X X member 3 coiled-coil domain Ccdc101 X containing 101 cryptochrome 2 Cry2 X (photolyase-like) cysteine-serine-rich Csrnp1 X nuclear protein 1 Dlx1 distal-less homeobox 1 X X

Egr1 early growth response 1 X X

Egr2 early growth response 2 X X

Egr3 early growth response 3 X

Egr4 early growth response 4 X

Fosl2 fos-like antigen 2 X X

hairy and enhancer of Hes3 X split 3 (Drosophila) heme oxygenase Hmox1 X X X (decycling) 1 inhibitor of growth family, Ing3 X X member 3

Table 3-5. Continued Response to Gene Regulation of Regulation Gene Name organic Symbol transcription of Apoptosis substance integrin alpha 9; Eomes X eomesodermin homolog jumonji domain Kdm6b X containing 3 jumonji domain Jmjd6 X containing 6 Junb jun B proto-oncogene X X

Mnt max binding protein X X

myocyte enhancer factor Mef2b X 2B neuronal PAS domain Npas1 X protein 1 neuronal PAS domain Npas4 X protein 4 nuclear factor of kappa Nfkbia light polypeptide gene X X X enhancer nuclear receptor Nr4a1 subfamily 4, group A, X X

member 1 nuclear receptor Nr4a2 subfamily 4, group A, X X X member 2 Pax1 paired box 1 X

period homolog 1 Per1 X (Drosophila) Rxra retinoid X receptor alpha X X X serum response factor Srf (c-fos transcription X X

factor) Sim2 single-minded homolog 2 X

Timeless timeless homolog X

transducin-like enhancer Tle3 X of split 3 Trib1 tribbles homolog 1 X X

v-maf Mafk musculoaponeurotic X

fibrosarcoma oncogene v-myc myelocytomatosis Mycn X viral related oncogene CUB and zona pellucida- Cuzd1 X like domains 1

Table 3-5. Continued Response to Gene Regulation of Regulation Gene Name organic Symbol transcription of Apoptosis substance RAS-like, estrogen- Rerg regulated, growth- X

inhibitor apolipoprotein B Apob X (including Ag(x) antigen) carboxypeptidase N, Cpn1 X polypeptide 1 cyclin-dependent kinase Cdkn1a X X inhibitor 1A Dlc1 deleted in liver cancer 1 X X

dual specificity Dusp4 X phosphatase 4 guanine nucleotide Gng7 binding protein (G X

protein), gamma 7 Hspa1b, heat shock 70kD protein X X Hspa1a 1B, 1A hydroxysteroid 11-beta Hsd11b2 X dehydrogenase 2 insulin receptor substrate Irs2 X 2 Lats2 large tumor suppressor 2 X

plasminogen activator, Plat X tissue potassium voltage-gated Kcna5 X channel,member 5 protein phosphatase 5, Ppp5c X catalytic subunit solute carrier family 6, Slc6a3 X member 3 Sphk1 sphingosine kinase 1 X X

Sts steroid sulfatase X

suppressor of cytokine Socs3 X signaling 3 vesicle-associated Vamp2 X membrane protein 2 BRCA1 associated RING Bard1 X domain 1 Bcl2-associated Bag3 X athanogene 3 GDNF family receptor Gfral X alpha like

Table 3-5. Continued Response to Gene Regulation of Regulation Gene Name organic Symbol transcription of Apoptosis substance NUAK family, SNF1-like Nuak2 X kinase, 2 apoptosis-inducing Aifm3 factor, mitochondrion- X

associated 3 cell death-inducing DNA Cidea X fragmentation factor complement component C5 X 5 glutamate receptor, Grm4 X metabotropic 4 Pim1 pim-1 oncogene X

pleckstrin homology Plekhf1 domain containing, X

family F Serinc3 serine incorporator 3 X

lymphocyte-specific Lck X protein tyrosine kinase

Table 3-6. Increased expression in the mPFC for AI vs AU AI vs AU Gene symbol Gene Name Age Fold Fold Activity regulated cytoskeletal-associated Arc 1.89 -1.19 protein Basic helix-loop-helix domain containing, Bhlhe40 1.22 -1.08 class B2 B-cell translocation gene 2, anti- Btg2 1.49 -1.28 proliferative Dusp1 Dual specificity phosphatase 1 1.45 -1.27 Egr1 Early growth response 1 1.57 -1.31 Egr2 Early growth response 2 2.79 -1.37 Egr4 Early growth response 4 1.53 -1.41 Hspa1a Heat shock 70kD protein 1A 2.12 -1.26 Hspa1b Heat shock 70kD protein 1B 1.84 -1.46 Ier5 Immediate early response 5 1.29 -1.17 Junb Jun-B oncogene 1.50 -1.36 Klf10 Krueppel-like factor 10 1.36 -1.29 Nuclear receptor subfamily 4, group A, Nr4a1 1.60 -1.23 member 1 Nuclear receptor subfamily 4, group A, Nr4a3 1.39 -1.20 member 3 Ptgs2 Prostaglandin-endoperoxide synthase 2 1.36 -1.12 Sik1 SNF1-like kinase 1.76 -1.12

CHAPTER 4 DNA METHYLOME OF AGING AND EXECUTIVE FUNCTION IN THE MEDIAL PREFRONTAL CORTEX

Background

Aging and age-related cognitive decline are associated with alterations to the brain’s transcriptional and epigenetic mechanisms linked to a number of functions including synaptic activity and inflammation (Blalock et al., 2003; Day and Sweatt, 2011;

Hernandez et al., 2011; Ianov et al., 2016b). In particular, epigenetic mechanisms such as DNA methylation and histone modifications in the hippocampus have been shown to play an important role in learning and memory (Day and Sweatt, 2011). For instance, delivery of DNA methyltransferase (DNMT) inhibitors to the CA1 region of the hippocampus resulted in differential methylation of synaptic plasticity genes reelin, Bdnf and the memory suppressor gene Pp1 (Levenson et al., 2006; Miller and Sweatt, 2007).

Interestingly, suppression of DNA methylation resulted in impairment of memory consolidation on a fear conditioning task, suggesting that DNA methylation is an active and adaptable mechanism in cognition from hippocampal-dependent memory (Day and

Sweatt, 2010).

The medial prefrontal cortex (mPFC) is another brain region which is sensitive to aging and age-related cognitive decline. Recently, we reported that aging of the rodent mPFC is characterized by down regulation of synaptic, postsynaptic, and neuron projection genes and up regulation of immune-related genes and genes involved in oxidation reduction processes (Ianov et al., 2016b). Furthermore, performance of aged animals on an executive function task was correlated with differential expression of genes associated with synaptic activity and regulation of transcription. While this study describes the mPFC transcriptional profile associated with aging and cognitive decline,

the role of DNA methylation in this region remains to be elucidated. Previous profiling studies of the aging human prefrontal cortex have reported differential methylation of

CpG sites related to brain development and the regulation of transcription (Hernandez et al., 2011; Numata et al., 2012); however, no study has investigated this epigenetic mark in the mPFC at the whole genome base pair resolution. Furthermore, the possible involvement of DNA methylation in contributing to aging of the mPFC and the decline in executive function remains to be elucidated. The current study explores the DNA methylome using whole genome bisulfite sequencing to investigate the impact of DNA methylation in aging and the mPFC’s executive function of cognitive flexibility.

Materials and Methods

Animals

Procedures involving animal subjects have been reviewed and approved by the

Institutional Animal Care and Use Committee and were in accordance with guidelines established by the U.S. Public Health Service Policy on Humane Care and Use of

Laboratory Animals. Male Fischer 344 rats of two ages, young (5-6 months, n = 10) and aged (17-22 months, n = 20) were obtained from National Institute on Aging colony

(Taconic) through the University of Florida Animal Care and Service facility. Animals were maintained on a 12:12 hour light schedule, and provided ad lib access to food and water prior to the set shifting task.

Behavior and Gene Expression

The animals were tested on a battery of behavioral tests. For the current study we focused on a task, set shifting, that depends on the mPFC function of cognitive flexibility. The set shifting task, tissue collection and RNA-seq methods have been previously published (Ianov et al., 2016b). In brief, following behavioral shaping, animals

were trained on visual discrimination, which required the animal to press the lever signaled by a light over the lever in order to obtain a reward. Upon the acquisition of visual discrimination, animals were tested in the set shifting phase, where the task parameters were changed such that the animal needed to press a lever based on location, and ignore the light, in order to obtain a reward. No age difference was observed in the ability of animals to acquire the visual discrimination. In contrast, an age difference was noted in the trials to criteria (TTC) on the set shift task. Aged animals exhibited substantial variability in their ability to shift their responding, with a subset of aged rats exhibiting an increase in TTC relative to young (Ianov et al., 2016b). In order to separate aged animals according to impairment, a mean split for the set shifting TTC score from the aged rats was performed to separate them into aged unimpaired rats

(TTC <51.7, n=11) or aged impaired rats (TTC >51.7, n=9).

Two weeks after completion of behavioral characterization, the mPFC (prelimbic and infralimbic regions) was collected and stored in -80°C until processed. Furthermore, the mRNA was enriched by poly-A selection, libraries were constructed and sequencing was performed in the Ion Proton system (Thermo Fisher). The RNA-seq data is available at the NCBI’s Gene Expression Omnibus under the accession number:

GSE75772 and the results, relating to the mPFC RNA alterations in aging and cognitive function have been reported (Ianov et al., 2016b).

Genomic DNA Isolation, Sodium Bisulfite Conversion and Library Preparation

Genomic DNA was isolated from the mPFC using the DNeasy Blood & Tissue kit

(Qiagen, catalog number: 69504). The DNA concentration was quantified using the

Qubit dsDNA HS Assay (Thermo Fisher, catalog number: Q32851) and sodium bisulfite conversion was performed with the EZ DNA Methylation-Direct kit (Zymo Research,

catalog number: D5020) according to the manufacturer's directions. Whole genome bisulfite sequencing (WGBS) libraries were constructed with the Illumina Truseq DNA

Methylation kit (Illumina, catalog number: EGMK91324) with the following modifications: following terminal tagging of bisulfite converted DNA, purification was performed with

SPRIselect reagent (Beckman Coulter, catalog number: B23317) for optimal size selection. Library size selection was completed using the SPRIselect reagent double- sided method, to remove fragments above and below the target size. The right size ratio used was 0.64 for a total of 32µl of SPRIselect. The left side ratio used was 0.75, resulting in the ratio difference of 0.11 (left side minus right side ratios). Thus, 5.5µl of

SPRIselect was used for the second selection. Following size selection, amplification of the WGBS libraries was performed with a total of 17 cycles and with the addition of a unique barcode per library for multiplex sequencing with the TruSeq DNA Methylation

Index PCR Primers (Illumina, catalog number: EGIDX81312). Successful amplification of each library was visualized with the 2% agarose SizeSelect E-Gel (Thermo Fisher, catalog number: G661002). Finally, libraries were purified using the Agencourt AMPure

XP beads (BeckMan Coulter, catalog number: A63880) following the Truseq DNA

Methylation kit directions. The concentration of the libraries was quantified by the Qubit dsDNA HS Assay and size distribution was evaluated with the High Sensitivity D1000

Screen Tape in the Tapestation system (Agilent Technologies).

Sequencing, Bioinformatics and Statistical Analysis

Paired-end sequencing of the WGBS libraries was performed with an Illumina

NextSeq 500 (2x 101bp) at the University of Florida Interdisciplinary Center for

Biotechnology Research core. Multiplex sequencing of WGBS libraries was performed with RNA-seq libraries from a collaborator (50:50 ratio from each library type) to

introduce base diversity to each sequencing cycle. Furthermore, 1% of PhiX spike-in control was added to improve the generation of base calls. On average, each biological sample contained a total of 133 million paired-end reads (Figure C-2).

The data analysis was performed using the differential methylation analysis pipeline (DMAP2) available at the University of Florida high performance computer

(HPC) clusters (Riva, 2016b). In short, DMAP2 is a new pipeline with a number of improvements from the MOABS pipeline (Sun et al., 2014) from quality control to DNA methylation calls for each biological replicate. The steps from DMAP2 which were used for the current study include: 1) read trimming and quality control which were performed with trimmomatic and FastQC (Andrews, 2010; Bolger et al., 2014). 2) Bisulfite conversion filtering of unconverted bases which was executed by the ‘cscall’ program

(Riva, 2016a). 3) Alignment to the rn5 genome using BSMAP (Xi and Li, 2009). 4) DNA methylation calling at CpG sites which was performed with the ‘cscall’ program. The methylation calling parameters were set such that each site detected per group contained an effective coverage of least 15x (minimum coverage per site = 5; minimum number of animals per group = 3). Following the specified parameters, the average genome-wide coverage per CpG site was: 75.4 (young), 73.4 (aged), 74.4 (aged- unimpaired) and 70.1 (aged-impaired). In addition, all replicates contained bisulfite conversion rates above 95%. Furthermore, in order to assess variability among biological replicates, Pearson’s correlation was performed across all CpGs in all biological replicates in the aged and young groups. This analysis shows that the range for the r values among the young animals was from 0.75 – 0.78. Aged animals contained r values from 0.75 – 0.80 (Figure B-1).

To address the abundance level of non-CG methylation, the DNA methylation calling step was also performed at CHG and CHH sites (where H represents non-G bases: A, T, or C) with ‘cscall’ at the same coverage levels as CpG. The genome-wide coverage for the detected CHG sites were: 60.7 (young), 65.3 (aged), 61.1 (aged- unimpaired) and 57.5 (aged-impaired). The genome-wide coverage for the detected

CHH sites were: 61.7 (young), 67.7 (aged), 62.4 (aged-unimpaired) and 58.9 (aged- impaired).

Following this step, DNA methylation values from each CpG site and non-CG site were extracted for each biological replicate and statistical comparisons were made for aging and for age impairment. Statistical filtering was employed to obtain gene lists for cluster analysis (Aenlle et al., 2009; Blalock et al., 2003; Ianov et al., 2016b). For differential methylation analysis associated with age, t-tests were performed between the aged and young groups, and a p-value of < 0.05 was applied as a statistical filter.

For the analysis of differential methylation associated with cognitive flexibility impairment, Pearson’s correlations were calculated between TTC scores for the set shifting task, and the DNA methylation ratios for each site. Correlations were limited to the aged animals in order to remove age as a confound. Pearson’s correlation values corresponding to a p-value < 0.05 (r = 0.444) were used as a statistical filter for the analysis. Following statistical filtering, the methylated sites were annotated with the

‘genediffmeth’ program available at the University of Florida HPC. Annotation was performed using the Rnor_5.0.78.gtf file, and all sites were annotated according to promoters and gene body (exons and introns) regions.

For gene enrichment and functional annotation clustering analysis, data sets of hypomethylated or hypermethylated genes were separately submitted to the NIH database for annotation, visualization, and integrated discovery (DAVID, version 6.8)

(Huang et al., 2007a; Huang et al., 2007b). The cutoff for cluster selection from DAVID was set to the Benjamini False Discovery Rate (FDR) p < 0.05 and the ‘Direct’ and

‘FAT’ categories were used for gene ontology (GO) annotation. The circular genome map figures were generated by Circos using the rn5 genome ideogram obtained from the University of California, Santa Cruz (UCSC) genome browser (Karolchik et al., 2004;

Krzywinski et al., 2009). Repetitive elements, identified by RepeatMasker, and CpG island locations were also acquired from the UCSC genome browser for the rn5 genome. The matrix plots were generated in R with gplots (3.0.1) and the box plots were generated with ggplot2 (2.2.1).

Results

DNA Methylome Profiling at CpGs of the Aging mPFC

Across the age groups, a total of 16,090 CpG sites were detected in promoter and gene body regions, which corresponded to 2,475 genes. The majority of identified sites (94.9%) were found in the gene body regions (Figure 4-1A). Among these sites, a statistical filter for age differences in DNA methylation (p<0.05) was applied, which resulted in a total of 485 hypermethylated CpGs, and 527 hypomethylated CpGs in aged rats. The distribution of the differentially methylated CpGs was analyzed according to genomic location, which indicated that the majority of the differentially methylated

CpGs were in introns (82.1%), followed by exons (11.6%), promoters (5.1%) and exon/intron boundaries (1.2%) (Figure 4-1B). In addition, 933 differentially methylated

CpG sites were annotated to protein coding genes (475 hypomethylated, 458

100

hypermethylated), followed by 10 long noncoding (3 hypomethylated, 7 hypermethylated), 36 short noncoding (34 hypomethylated, 2 hypermethylated) and 33 pseudogenes (15 hypomethylated, 18 hypermethylated) (Figure 4-2).

In order to determine functional relevance to the gene sets of differentially methylated CpGs, hypomethylated and hypermethylated sites were separately submitted to NIH DAVID for enrichment analysis of gene ontology terms. Among the differentially methylated CpGs, 240 hypermethylated and 182 hypomethylated genes were annotated in DAVID. Overall, age-related changes in the DNA methylome were primarily associated with hypermethylation of genes linked to GTPase activity and synaptic function. In particular, enrichment was observed for the following gene ontology clusters: regulation of GTPase activity (GO:0043087, 23 genes, FDR p=1.2-4), synapse (GO:0045202, 23 genes, FDR p=1.8-2), and ion transmembrane transport

(GO:00342202, 24 genes, FDR p=4.9-2) (Figure 4-3) (Table 4-1). Furthermore, positive regulation of molecular function (GO:0044093, 40 genes, FDR p=1.4-3) and postsynapse (GO:0098794, 19 genes, FDR p=9.2-4) GOs were also detected.

Hypomethylation was associated with a single cluster, adenyl nucleotide binding

(GO:0030554, 26 genes, FDR p=4.5-2). A summary diagram of the results from CpG site detection to GO analysis is shown in Figure 4-4.

To investigate the potential influence of DNA methylation on altered transcription associated with aging, the WGBS dataset was compared to previously published RNA- seq data from the same animals (Ianov et al., 2016b). The comparison between the datasets showed that the 12,784 detected CpGs corresponded to 1,848 genes in the

RNA-seq dataset. Pearson’s correlation identified 2,627 CpGs in which DNA

101

methylation for a gene was correlated to RNA expression (p<0.05, r=0.361), regardless of age. When the analysis was limited to the 1,012 CpG sites which were different with age, 79 differentially methylated CpGs (p<0.05) were negatively correlated to RNA expression (Table B-1). Likewise, 57 differentially methylated CpGs across the age groups were positively correlated to RNA expression (Table B-1). Among the age- relevant CpGs that negatively correlated with RNA expression, a single significant cluster was observed, glutamate receptor activity (GO:0008066, 3 genes, FDR p=1.2-2), which contained the following genes with increased methylation during aging: Grik2,

Grm5, Grm1. It is also interesting to note that an additional two genes with increased methylation correlated with decreased expression were also linked postsynaptic function from Table 4-1 (Asap1, Ppp1r9a). A summary diagram of the correlation analysis is shown in Figure 4-5.

Due to the high occurrence of CpGs in repetitive elements across mammalian genomes, we investigated the abundance of the DNA methylation in repetitive elements of the rat mPFC for the aging dataset (Cordaux and Batzer, 2009; Darby et al., 2016; Su et al., 2012). The genomic locations from repetitive elements were downloaded from the

UCSC genome browser identified by RepeatMasker for the rn5 genome which contained several classes of repetitive elements including DNA transposons, long interspersed elements (LINE), LTR (long terminal repeats), SINE (short interspersed elements), low complexity repeat, simple repeat, satellites, and RNA repeat. The locations of the repetitive elements were intersected to the chromosomal location of all

CpG sites in promoters, exons, introns and all other sites outside of intragenic regions of young and aged animals. The total number of CpG sites within repetitive elements

102

was 64,280, where the top three classes containing the most sites were: LINE (47,313

CpGs), followed by LTR (7,811 CpGs) and satellite (6,543 CpGs) (Figure 4-6A).

Furthermore, the genome-wide DNA methylation ratios between young and aged rats were similar among most repeat types, however, there was a significant difference for age in low complexity repeats and RNA repeats (p<0.05) (Figure 4-6B).

In addition, previous work in other tissues have also indicated that DNA methylation of specific families from the LINE and SINE classes have been associated with several diseases, including cancer (Igarashi et al., 2010; Richardson et al., 2015).

Therefore, we quantified DNA methylation in the most abundant families from LINE and

SINE. The L1 family from the LINE group contained the highest number of CpGs

(47,270) while the L2 family contained 36 CpGs. The SINE class was subdivided into

Alu, B2, B4 and MIR families which contained 149, 274, 284 and 45 CpGs respectively

(Figure 4-7). However, t-tests between young and aged animals with the genome-wide methylation levels among the elements were not different with aging (Figure 4-7).

Next, we investigated the abundance of repetitive elements in the 1,012 CpG sites which were located in the promoter and gene body regions and differentially methylated (p<0.05) for age. Relative to young, aged rats exhibited 344 hypomethylated

CpGs (24 in exons, 314 in introns and 6 in promoters regions) and 312 hypermethylated

CpGs within repetitive elements (11 in exons, 282 in introns and 13 in promoter regions). Hypomethylated and hypermethylated genes were separately submitted to

DAVID for each class of repetitive elements. Significant clustering were limited to LINE, which contained 264 hypermethylated sites (158 genes in DAVID) and 198 hypomethylated sites (128 genes in DAVID). Hypermethylated genes within LINE were

103

linked to postsynaptic density (GO:0014069, 10 genes, FDR p=1.3-2) and hypomethylated genes were associated with adenyl ribonucleotide binding

(GO:0032559, 22 genes, FDR p=2.3-2). The hypermethylated genes linked to postsynaptic density function were identified as the following: Ctnna2, Erbb4, Exoc4,

Grik2, Grm1, Grm5, Nlgn1, Kcnd2, Sorcs3, Snap91.

Lastly, CpG sites were annotated to regions belonging to CpG islands downloaded from the UCSC browser. Across the entire genome, we identified 2,511

CpGs which belong to 146 islands. Among these, only a small number of sites within promoter and gene bodies were differentially methylated across the age groups: 33 hypomethylated CpGs were in islands, and 9 hypermethylated sites belonged to islands. In addition, among the differentially methylated sites, no overlap between sites in CpG islands and repetitive elements were observed.

DNA Methylome Profiling at CpGs of Cognitive Flexibility in the mPFC

In order to examine DNA methylation related to cognitive function, sites were called separately for aged animals characterized as impaired or unimpaired. Similarly to the aging analysis, CpG calls were limited to sites that exhibited a minimum of 15x coverage per group (i.e. at least 3 animals in each group). The total number of CpG sites detected across the age impaired and unimpaired rats in the promoter and gene body regions was 14,696 which corresponded to 2,123 genes. Similar to the aging dataset, the majority of the sites detected (94.9%) were found in the gene body regions

(Figure 4-8A). Among these sites, Pearson’s correlation analysis was performed between the set shifting TTC scores and methylation for each CpG site to investigate the relationship between DNA methylation changes and the ability to shift responses

(p<0.05 r=0.444). The results indicated that 1,329 CpGs were positively correlated,

104

such that increased methylation was associated with delayed shifting, and 1,353 CpGs were negatively correlated with delayed shifting. The genomic distribution of CpGs, correlated to delayed shifting, was similar to that observed for age differences, with the majority of the sites being present in introns (83.3%), followed by exons (11.5%), promoters (3.5%) and exon/intron boundaries (1.7%) (Figure 4-8B). In addition, 2,547 differentially methylated CpGs sites were annotated to protein coding genes (1,285 hypomethylated, 1,262 hypermethylated), 20 long noncoding RNA genes (11 hypomethylated, 9 hypermethylated), 21 short noncoding RNA genes (8 hypomethylated, 13 hypermethylated) and 94 pseudogenes (49 hypomethylated, 45 hypermethylated) (Figure 4-9).

Gene enrichment analysis was performed to identify biological function related to differential methylation correlated with set shifting behavior. For the 1,329 CpGs that were positively correlated with delayed shifting (i.e. hypermethylated in impaired animals), 549 genes were annotated in DAVID, and for the 1,353 CpGs which were negatively correlated with delayed shifting (i.e. hypomethylated in impaired animals),

562 genes were annotated in DAVID. Hypermethylation was observed for genes linked to synapse (GO:0045202, 40 genes, FDR p=1.3-2), postsynaptic density (GO:0014069,

21 genes, FDR p=4.4-4), and ion channel activity (GO:0005216, 25 genes, FDR p=2.7-2)

(Figures 4-10 and 4-11) (Table 4-2). Additional clusters linked to cell junction

(GO:0030054, 25 genes, FDR p=7.9-3), axon terminus (GO:0043679, 14 genes, FDR p=3.8-2), dendrite (GO:0030425, 31 genes, FDR p=2.1-2) and calcium channel activity

(GO:0005262, 12 genes, FDR p=1.9-2) were also observed. Hypomethylation in delayed shifting was correlated to cellular component clusters linked to neuron part

105

(GO:0097458, 70 genes, FDR p=3.4-2), and cytoskeleton organization (GO:0007010, 51 genes, FDR p =4.7-2) (Table 4-3). A summary diagram of the results from CpG site detection among aged animals to GO analysis is shown in Figure 4-12.

Next, the results were compared to the RNA-seq dataset to examine the relationship of DNA methylation and RNA levels among the aged individuals belonging to impaired and unimpaired groups. For aged animals, across the cognitive characterized groups, 11,596 CpGs were detected, which corresponded to 1,548 genes in the RNA-seq data. Pearson’s correlation across all aged-impaired and aged- unimpaired rats resulted 1,983 CpGs which were correlated to RNA levels (p<0.05, r=0.444). However, in order to focus on set shifting performance, a statistical filter was employed, such that only CpGs sites which exhibited a correlation between DNA methylation and TTC scores (p<0.05, r=0.444), were retained for subsequent correlation with mRNA expression and gene enrichment analysis. Among the sites that were correlated with set shifting performance, 244 CpGs were negatively correlated to mRNA

(Table B-2). Likewise, 249 CpG sites were positively correlated with mRNA (Table B-2).

Cluster analysis for genes that negatively correlated with CpG methylation indicated enrichment of RNA involved in the cellular component of the somatodendritic compartment (GO:0036477, 20 genes, FDR p=2.8-2). For genes that positively correlated with CpG methylation enrichment was observed for cell adhesion

(GO:0007155, 27 genes, FDR p=4.4-2). However, many of the genes have also been linked to regulation of synaptic contacts (Cntn4, Kirrel, Nfas, Dscaml1, Ctnna2,

Cntnap5b, Cntnap5c, Efna5, Il1rapl1, Nlgn1, Phldb2, Ptk2). A summary diagram of the correlation analysis is shown in Figure 4-13.

106

Non-CG Methylation in the mPFC

In order to access the abundance of non-CG methylation in promoters and gene body regions of the mPFC, CHG and CHH sites were quantified in young and aged animals. Table 4-4 shows the total number of sites detected and the DNA methylation ratio for each context analyzed. While more sites were detected in non-CG context, the majority of the non-CG sites detected contained DNA methylation levels of less than

10%, with an overall average of less than 2.5% across all age groups (Figure 4-14 and

Table 4-4). In contrast, in CpG sites, only 3.5% of the sites in young, and 3.6% of the sites in aged animals contained ratios of less than 10%, with an overall average of 73% methylation across all sites in the promoter and gene body (Figure 4-14 and Table 4-4).

Thus, to reduce the chance of false positives, non-CG sites that contained methylation levels of less than 10% in both age groups were filtered from our analysis.

Non-CG methylation and aging

CHG sites

Analysis revealed 92 hypermethylated (p<0.05) CHG sites in aged animals

(corresponding to 61 genes) and 257 hypomethylated sites (corresponding to 164 genes) (Figure 4-15A). Gene enrichment analysis was performed to identify biological function related to differential methylation of CHG sites in aging. Among the hypomethylated genes, 126 genes were annotated in DAVID, which contained GOs linked to protein kinase activity (GO:0004672, 14 genes, FDR p=3.5-3) and regulation of

GTPase activity (GO:0043087, 14 genes, FDR p=7.2-3). Among the hypermethylated genes, 40 genes were annotated in DAVID, however, clustering did not pass our FDR cutoff.

107

First, the relationship between methylation of CHG sites and RNA levels was examined, regardless of age. Overall, in the CHG dataset, 2,413 sites detected corresponded to 750 genes in the RNA-seq dataset. Pearson’s correlation across all rats resulted in 526 sites which were correlated to RNA levels (p<0.05, r=0.361). For the

526 sites in which methylation correlated with RNA expression, only 65 sites exhibited differential methylation across age groups, with 20 differentially methylated negatively correlated to RNA, and 45 differentially methylated sites positively correlated to RNA levels. Enrichment analysis did not indicate significant clusters for RNA associated with differentially methylated CHG sites.

CHH sites

For CHH sites that exhibited methylation greater than 10% in at least one of the age groups, 225 sites were hypermethylated (p<0.05, corresponding to 110 genes) and

465 hypomethylated sites (corresponding to 242 genes) in aged rats (Figure 4-15B).

Gene enrichment analysis of differentially methylated CHH sites showed that among the hypomethylated sites, 181 genes were annotated in DAVID, however clustering did not pass our FDR cutoff. Furthermore, among the hypermethylated CHH sites, 75 genes were annotated in DAVID, but the gene list did not significantly cluster.

Examination of all CHH that exhibited methylation greater than 10%, regardless of age, indicated a total of 5,061 sites, which corresponded to 1,045 genes from the

RNA-seq dataset. Among these sites, Pearson’s correlation was performed across all young and aged rats which resulted in 1,116 sites correlated to RNA levels (p<0.05, r=0.361). Among the CHH sites which correlated to RNA expression, 48 CHH sites, which were negatively correlated to RNA, were also differentially methylated across age

108

groups (p<0.05) and 55 CHH sites, which were positively correlated to RNA, were differentially methylated during aging. Enrichment analysis did not indicate any significant clusters.

Non-CG methylation and cognitive flexibility

CHG sites

DNA methylation in non-CG context was also quantified for aged animals according to performance on the set shifting task. The average levels of DNA methylation in non-CG relative to CpG were similar to the aging dataset, where over

95% of the methylated non-CG sites contained methylation ratios of less than 10%

(Table 4-5 and Figure 4-16). Therefore, to reduce the chance of false positives, sites that contained methylation levels of less than 10% in both cognitive performance groups were filtered from our analysis. Thus, for the CHG context, 497 sites (corresponding to

276 genes) were positively correlated to TTC scores while 386 sites (corresponding to

228 genes) were negatively correlated to set shift behavior (Figure 4-17A). Gene lists of negatively and positively correlated genes were submitted to DAVID which resulted in

176 negatively correlated genes (hypomethylated for animals that delayed shifting) and

219 positively correlated genes (hypermethylated for animals that delayed shifting) annotated in the database. Among the hypermethylated CHG sites, GOs linked to kinase activity (GO:0016301, 22 genes, FDR p=5.9-3), regulation of GTPase activity

(GO:0043087, 19 genes, FDR p=5.8-3) and synapse (GO:0045202, 23 genes, FDR p=4.9-3) were observed (Table 4-6). Clustering did not pass our FDR cutoff among the hypomethylated genes.

The results were compared to the RNA-seq dataset to examine the association of DNA methylation on non-CG context to RNA levels among the aged animals. Overall,

109

in the CHG dataset, 2,467 sites detected in aged animals, which corresponded to 671 genes in the RNA-seq dataset. Pearson’s correlation across all aged rats resulted 494 sites which were correlated to RNA levels (p<0.05, r=0.444) Among the sites which correlated to RNA, 81 CHG sites which negatively correlated to RNA were also correlated to TTC scores, and 97 CHG sites which were positively correlated to RNA were also correlated to TTC scores. Enrichment analysis did not indicate significant clusters for RNA associated with differentially methylated CHG sites.

CHH sites

For DNA methylation at CHH sites, 766 CHH sites (corresponding to 364 genes) were negatively correlated to the TTC score (high methylation in animals that readily shifted), and 923 CHH sites (corresponding to 397 genes) were positively correlated in animals that delayed shifting (Figure 4-17B). The genes list of hypomethylated and hypermethylated genes which contained CHH methylation were separately submitted to

DAVID which resulted in 300 hypomethylated genes and 322 hypermethylated genes annotated in DAVID. Among the hypermethylated genes, a single cluster passed our

FDR cutoff: adenyl ribonucleotide binding (GO:0032559, 39 genes, FDR p=2.3-2).

Clustering did not pass our FDR cutoff among the hypomethylated genes.

Furthermore, analysis was performed to CHH site methylation with RNA expression which resulted in a total of 5,163 sites which corresponded to 946 genes from the RNA-seq dataset. Among these sites, Pearson’s correlation was performed across the aged rats which resulted in 1,011 CHH sites correlated to RNA levels

(p<0.05, r=0.444). Among the sites which correlated to RNA levels, 184 CHH sites which negatively correlated to RNA were also correlated to TTC scores, and 184 CHH

110

sites which were positively correlated to RNA were also correlated to TTC scores. No significant clusters were observed following enrichment analysis.

Discussion

Transcriptional studies indicate that several biological processes, including synaptic function, are altered during aging and cognitive decline (Blalock et al., 2003;

Ianov et al., 2016b; Rowe et al., 2007; Verbitsky et al., 2004). Furthermore, DNA methylation has been linked to synaptic plasticity and memory suppressor genes in the hippocampus, suggesting that DNA methylation has a role in cognitive function (Day and Sweatt, 2010; Feng et al., 2010; Lardenoije et al., 2015; Levenson et al., 2006;

Miller and Sweatt, 2007). The current study present several novel findings including the finding that the majority of DNA methylation in the mPFC is located within gene body regions. In addition, most of the variability in methylation was observed for CpG sites as opposed to CHG and CHH sites. The variability in mPFC CpG methylation was related to age and cognitive function that depends on the mPFC. Finally, CpG methylation was linked to the expression of genes within functional categories that are thought to mediate impaired cognition during aging and in age-related neurodegenerative diseases. Each of these points is discussed below.

Interestingly, the results from the current study shows that the majority of DNA methylation is located within gene body regions, particularly introns. DNA methylation at promoter regions has been well studied (Moore et al., 2013). In contrast, the mechanism and function of gene body methylation is not well known. While a number of studies have reported that gene body methylation is positively associated with transcriptional activity (Hellman and Chess, 2007; Jones, 2012; Wu et al., 2010; Yang et al., 2014), several studies have also indicated repression of expression, or no clear

111

pattern, due to evidence for the activation and repression of genes in the same dataset

(Debski et al., 2016; Guo et al., 2011; Jjingo et al., 2012; Neri et al., 2017). The current work supports the hypothesis that gene body methylation may have more than one regulatory mechanism since several differentially methylated CpGs for age or cognitive function were correlated to RNA levels, with no specific pattern between methylation and RNA expression. One study described a relationship of gene body methylation and gene expression, which followed a bell-shaped distribution, such that low and highly expressed genes contained the least amount of methylation, with mid-level genes containing the highest levels of DNA methylation (Jjingo et al., 2012). In addition, a recent study proposed that the mechanism of gene body methylation may be dependent on the type of DNMT utilized by the cell, as Dnmt3b was associated with the repression of aberrant transcription in embryonic stem cells (Neri et al., 2017). While the expression of Dnmt3b is lower in the brain, Dnmt1 and Dnmt3a are highly expressed in postmitotic neurons and are fundamental to memory and synaptic plasticity, raising the possibility that they may have a role in the mechanism of gene body methylation (Feng et al., 2010; Moore et al., 2013). Finally, previous studies have distinguished methylation patterns of glial and neurons (Lister et al., 2013). Thus, it is likely that the cell heterogeneity of the mPFC may contribute to the lack of a specific pattern in RNA expression and gene body methylation.

Several results presented in the current study touch upon the general or specific nature of DNA methylation. First, compared to CHG and CHH sites, CpG sites are much more likely to be methylated, indicating some specificity. In contrast, CpG methylation does not appear to be specific for genomic location. For aging, the distribution of

112

differential methylation across genomic location was similar to the distribution of available sites detected. Furthermore, there was no indication that the direction of differential methylation, hypermethylation versus hypomethylation, was specific for age or cognitive function, as approximately half the differentially methylated sites were hypermethylated. In contrast, differential methylation was specific for genes linked to neurons, particularly synaptic genes, and regulation of GTPase activity. It is likely that cell type specific methylation is due to ongoing transcription, which renders these sites available for methylation or demethylation.

The current study investigated changes in the DNA methylome in the mPFC, linked to senescence and impaired executive function. Indeed, the results show differential methylation of genes within functional categories that have been linked to age-related cognitive decline and diseases of aging, including synaptic function and the activity of GTPases, kinases, and ion channels genes (Berchtold et al., 2013; Bishop et al., 2010; Bossers et al., 2010; Erraji-Benchekroun et al., 2005; Pavlidis et al., 2004;

Twine et al., 2011). The changes observed supports the idea that the DNA may be dynamically modified across the life span, resulting in alterations to gene expression, which influence synaptic connectivity, and cognition (Ianov et al., 2016a; Levenson et al., 2006; Xu, 2015).

Importantly, hypermethylation of CpG sites over the course of aging, was observed for genes linked to the synapse. Furthermore, we noted that hypermethylation correlated with decreased expression of synaptic genes. Previous work in humans, monkeys, and rodents indicate that aging is associated with a decrease in expression of

RNA for synaptic genes (Berchtold et al., 2013; Blalock et al., 2003; Ianov et al., 2016b;

113

Loerch et al., 2008; Lu et al., 2004). The results of the current study are consistent with the idea that epigenetic regulation, including DNA methylation, contributes to age- related changes in transcription in the prefrontal cortex.

An important finding of the current study was the association of DNA methylation in the mPFC with age-related changes in cognitive function that depend on the mPFC.

Similar to changes observed during aging, CpG sites are much more likely to exhibit differential methylation, relative to CHG and CHH sites, when considering differences linked to cognitive function of aged animals. However, differential methylation of CpG and CHG sites, across the cognitively impaired and unimpaired groups point to genes linked to neuronal structure and function.

In the dentate gyrus, hypermethylation of CpG and CHG sites is associated with decreased gene expression (Guo et al., 2014). While hypermethylation for aging and cognitive impairment was observed for synaptic genes, and a decline in expression of synaptic genes is reported for aging and in relation to cognitive decline, correlations between methylation and gene expression was observed for only a subset of synaptic genes. Thus, an examination of genes that regulate synaptic function and were correlated with methylation may be enlightening. For CpG methylation that correlated with RNA expression during aging, hypermethylation was correlated with decreased expression of three glutamate receptor genes (Grik2, Grm5, Grm1), and Ppp1r9a, which localizes protein phosphatase 1 to the synapse. Similarly, hypermethylation in cognitively impaired animals was associated with decreased expression of the NMDA receptor subunit, Grin2b, Nlgn1, which is involved in localizing proteins to the synapse and Stau2, an RNA-binding protein required for transport of neuronal RNA from the cell

114

body to the dendrite. The results point to impaired glutamate synapses over the course of aging and in cognitively impaired animals. It is likely that gene expression depends on a dynamic interaction of transcriptional signals and epigenetic regulation, such that decreased expression does not readily correlated with increased methylation.

In summary, the current study profiled the genome-wide DNA methylation changes associated with aging and cognitive decline in the mPFC at single base pair resolution. The results suggest that while the alterations in the number of hypomethylated and hypermethylated CpG sites are similar across aging and cognitive performance, hypermethylation is more likely to affect functional terms linked to synaptic plasticity, ion channel activity and GTPase activity.

Acknowledgements to Chapter 4

Financial support by National Institutes of Aging Grants R01AG037984,

R37AG036800, R01AG49711 and the Evelyn F. McKnight Brain Research Foundation is highly appreciated. We would also like to thank Dr. Leonid Moroz and his lab members for sharing the Illumina platform for multiplex sequencing of WGBS and RNA- seq libraries.

115

Figure 4-1. Genomic distribution of CpG sites in the aging mPFC. A) Total CpG sites detected across young and aged rats were annotated according to genomic location in exons, introns, exon/intron boundaries and promoters. B) Differentially methylated sites (p < 0.05) whose methylation increased (filled) or decreased (open) with aging. A large number CpG sites were located in the gene body regions relative to the promoter.

116

Figure 4-2. Biotypes of differentially methylated CpGs in the gene body and promoter regions from the aging mPFC. A large number of CpG sites (p < 0.05) were annotated to protein coding genes followed by short noncoding RNA, pseudogenes and long noncoding RNA.

117

Figure 4-3. DNA methylome profile of the aging mPFC. Circos plot displaying the genome-wide distribution of CpG methylation in gene body and promoters. The outer track displays the rat genome ideogram by chromosome number. The second track indicates the distribution of all detected CpGs in the aged rats (blue background, n=20) followed by the distribution in young in the third track (n=10). Green dots are CpGs with rates >50%; Orange dots are CpGs with rates <50%. The fourth track is a heatmap of differentially methylated CpGs (p<0.05) relative to aged rats (hypermethylated – red; hypomethylated – blue). The links represent significant CpGs associated with genes which contain functional annotation GO clusters. The ‘GO Links’ table lists the GO terms shown as links in the Circos plot followed by the FDR corrected p- value.

118

Figure 4-4. Diagram summarizing CpG methylation in aging mPFC. The blue arrows indicate the analysis steps where a statistical cutoff was applied to determine differentially methylated sites (p<0.05) in aged rats relative to young, and enrichment of genes containing differentially methylated CpG sites (FDR p<0.05).

119

Figure 4-5. Diagram summarizing CpG correlation to RNA in the aging mPFC. The blue arrows indicate the analysis steps where a statistical cutoff was applied to determine DNA to RNA correlation (Pearson’s correlation p<0.05), differential methylation in aged rats relative to young (p<0.05) and enrichment analysis (FDR p<0.05).

120

Figure 4-6. DNA methylation of repetitive elements in the aging mPFC. A) Total number of detected CpG sites within repetitive elements across the genome. B) Genome-wide DNA methylation ratios across the repetitive elements in young and aged rats. The grey dots indicate the outliers from the boxplots. A significant difference for age was observed across all CpG sites within low complexity and RNA repeats (p<0.05).

121

Figure 4-7. DNA methylation from the LINE and SINE families. The x-axis indicates the class, family and number of CpG sites within each family respectively. The grey dots indicate outliers within each family. The genome-wide methylation levels of the indicated families were not different between young and aged rats.

122

Figure 4-8. Genomic distribution of CpG sites relative to cognitive flexibility performance in aged rats. A) Total CpG sites detected across all aged rats were annotated according to genomic location in exons, introns, exon/intron boundaries and promoters. B) Differentially methylated sites (Pearson’s correlation, p<0.05) whose methylation increased (filled) or decreased (open) in aged-impaired rats. A large number of CpG sites were located in the gene body regions relative to the promoter.

123

Figure 4-9. Biotypes of CpG sites from gene body and promoter regions correlated to cognitive flexibility of aged rats (Pearson’s correlation, p<0.05).

124

Figure 4-10. DNA methylation patterns in aged rats with delayed set-shift behavior. The outer circle of the Circos plot highlights a subset of genes from CpGs which were correlated to impairment (p<0.05, n=9 (aged-impaired), n= 11 (aged- unimpaired); hypermethylated – red, hypomethylated – blue), followed by a heatmap of all significant CpGs. The links represent CpG sites associated with genes containing GOs shown at the ‘GO Links’ table.

125

Figure 4-11. Pearson’s correlation between TTC scores of each aged animal (represented by each circle) and the average DNA methylation rates of all CpG sites from the gene clusters related to the synapse (A – 40 genes; 60 CpG sites) and ion channel activity (B – 25 genes; 35 CpG sites).

126

Figure 4-12. Diagram summarizing CpG methylation in aged-impaired and aged- unimpaired rats. The blue arrows indicate the analysis steps where a statistical cutoff was applied to determine differentially methylated sites (Pearson’s correlation, p<0.05) in aged-impaired relative to unimpaired, and enrichment of genes containing differentially methylated CpG sites (FDR p<0.05).

127

Figure 4-13. Diagram summarizing CpG correlation to RNA in aged-impaired and aged- unimpaired rats. The blue arrows indicate the analysis steps where a statistical cutoff was applied to determine DNA to RNA correlation (Pearson’s correlation p<0.05), differential methylation in aged-impaired relative to aged- unimpaired (Pearson’s correlation p<0.05) and enrichment analysis (FDR p<0.05).

128

Figure 4-14. Boxplots of DNA methylation in young and aged rats from the gene body and promoters in CpG, CHG and CHH sites. The grey dots indicate the outliers from each boxplot representing that many non-CG sites detected contained low DNA methylation levels.

129

Figure 4-15. Genomic distribution of CHG and CHH sites in the aging mPFC. Differentially methylated sites (p < 0.05) whose methylation increased (filled) or decreased (open) in aged rats relative to young at CHG sites (A) and CHH sites (B).

130

Figure 4-16. Boxplots of DNA methylation in aged-unimpaired (AU) and aged-impaired (AI) rats from the gene body and promoters in CpG, CHG and CHH sites. The grey dots indicate the outliers from each boxplot representing that many non- CG sites detected among aged rats contained low DNA methylation levels.

131

Figure 4-17. Genomic distribution of CHG and CHH sites relative to cognitive flexibility performance in aged rats. Differentially methylated sites (Pearson’s correlation, p<0.05) whose methylation increased (filled) or decreased (open) in aged-impaired rats relative to aged-unimpaired at CHG sites (A) and CHH sites (B).

132

Table 4-1. Hypermethylation of the mPFC during aging Ion Gene GTPase Gene Name Synapse transmembrane Symbol activity transport ArfGAP with GTPase domain, Agap2 ankyrin repeat and PH domain X 2 ArfGAP with SH3 domain, Asap1 ankyrin repeat and PH domain X X 1 Cdc42 guanine nucleotide Arhgef9 X exchange factor 9 Dennd1b DENN domain containing 1B X Epha3 Eph receptor A3 X FERM, ARH/RhoGEF and Farp1 X X pleckstrin domain protein 1 Rasal2 RAS protein activator like 2 X Ral GTPase activating protein Ralgapa1 X catalytic alpha subunit 1 Rho GTPase activating protein Arhgap10 X 10 Rho GTPase activating protein Arhgap15 X 15 Rho GTPase activating protein Arhgap26 X 26 Sbf2 SET binding factor 2 X SLIT-ROBO Rho GTPase Srgap3 X activating protein 3 TBC1 domain family, member Tbc1d10b X 10b TBC1 domain family, member Tbc1d15 X 15 Tbc1d5 TBC1 domain family, member 5 X Amph amphiphysin X X Dock3 dedicator of cyto-kinesis 3 X Dock9 dedicator of cytokinesis 9 X Elmo1 engulfment and cell motility 1 X protein kinase, cGMP- Prkg1 X X dependent, type 1 ral guanine nucleotide Rgl1 X dissociation stimulator,-like 1 LOC69103 similar to GTPase activating X 3 protein testicular GAP1 Ank3 ankyrin 3 X X calcium voltage-gated channel Cacnb4 X X auxiliary subunit beta 4

133

Table 4-1. Continued Ion Gene GTPase Gene Name Synapse transmembrane Symbol activity transport Clstn2 calsyntenin 2 X Ctnna2 catenin alpha 2 X Cep112 centrosomal protein 112 X erb-b2 receptor tyrosine kinase Erbb4 X 4 Exoc4 exocyst complex component 4 X gamma-aminobutyric acid type Gabra3 X X A receptor alpha3 subunit glutamate ionotropic receptor Grik2 X X kainate type subunit 2 glutamate metabotropic Grm5 X receptor 5 glutamate receptor, Grm1 X metabotropic 1 muscle associated receptor Musk X tyrosine kinase Nlgn1 neuroligin 1 X X Nos1 nitric oxide synthase 1 X X parkin RBR E3 ubiquitin protein Park2 X ligase potassium voltage-gated Kcnd2 X X channel subfamily D member 2 protein phosphatase 1, Ppp1r9a X regulatory subunit 9A Pcdh15 protocadherin 15 X sortilin-related VPS10 domain Sorcs3 X containing receptor 3 synaptosomal-associated Snap91 X protein 91 Akap6 A-kinase anchoring protein 6 X Atp13a5 ATPase 13A5 X Gpr39 G protein-coupled receptor 39 X Cav1 caveolin 1 X Fgf14 fibroblast growth factor 14 X glucagon-like peptide 1 Glp1r X receptor Hk1 hexokinase 1 X interleukin 1 receptor Il1rapl1 X accessory protein-like 1 piezo-type mechanosensitive Piezo1 X ion channel component 1

134

Table 4-1. Continued Ion Gene GTPase Gene Name Synapse transmembrane Symbol activity transport potassium voltage-gated Kcnc1 X channel subfamily C member 1 sodium voltage-gated channel Scn5a X alpha subunit 5 solute carrier family 4 member Slc4a10 X 10 Stim1 stromal interaction molecule 1 X transient receptor potential Trpc3 cation channel, subfamily C, X member 3 transient receptor potential Trpc4 cation channel, subfamily C, X member 4 transient receptor potential Trpm3 cation channel, subfamily M, X member 3

135

Table 4-2. Hypermethylation of the mPFC during delayed shifting Postsynaptic Ion channel Gene Symbol Gene Name Synapse density activity DnaJ heat shock protein Dnajc6 X X family ELKS/RAB6- Erc2 interacting/CAST family X X member 2 FCH and double SH3 Fchsd2 X domains 2 Gsg1l GSG1-like X Amph amphiphysin X Ank3 ankyrin 3 X ankyrin repeat and sterile Anks1b alpha motif domain X X containing 1B calcium voltage-gated Cacnb4 channel auxiliary subunit X X beta 4 Clstn2 calsyntenin 2 X X Ctnna2 catenin alpha 2 X X Cep112 centrosomal protein 112 X Dpysl2 dihydropyrimidinase-like 2 X discs large MAGUK Dlg2 X X scaffold protein 2 DMD dystrophin X epidermal growth factor Egfr X receptor erb-b2 receptor tyrosine Erbb4 X X kinase 4 Esr1 estrogen receptor 1 X exocyst complex Exoc4 X X component 4 gamma-aminobutyric acid Gabra3 type A receptor alpha3 X X subunit glutamate ionotropic Gria4 receptor AMPA type X X X subunit 4 glutamate ionotropic Grin2b receptor NMDA type X X X subunit 2B glutamate ionotropic Grik1 receptor kainate type X X X subunit 1

136

Table 4-2. Continued Postsynaptic Ion channel Gene Symbol Gene Name Synapse density activity glutamate ionotropic Grik2 receptor kainate type X X X subunit 2 glutamate receptor Grip1 X X interacting protein 1 Insr insulin receptor X leucine rich repeat and Lrfn1 fibronectin type III domain X X containing 1 muscle associated Musk X receptor tyrosine kinase Nlgn1 neuroligin 1 X X Nos1 nitric oxide synthase 1 X X Ophn1 oligophrenin 1 X parkin RBR E3 ubiquitin Park2 X X protein ligase Pclo piccolo X X protein phosphatase 1, Ppp1r9a X X regulatory subunit 9A regulating synaptic Rims2 X membrane exocytosis 2 sortilin-related VPS10 Sorcs3 domain containing X X receptor 3 Synj1 synaptojanin 1 X synaptosomal-associated Snap91 X X protein 91 teneurin transmembrane Tenm2 X protein 2 vesicle-associated Vamp7 X membrane protein 7 zinc finger, MYND-type Zmynd8 X containing 8 Ano5 anoctamin 5 X Ano6 anoctamin 6 X calcium voltage-gated Cacnb2 channel auxiliary subunit X beta 2 calcium voltage-gated Cacng3 channel auxiliary subunit X gamma 3 calcium voltage-gated Cacna1e X channel subunit alpha1 E

137

Table 4-2. Continued Postsynaptic Ion channel Gene Symbol Gene Name Synapse density activity cystic fibrosis Cftr transmembrane X conductance regulator interleukin 1 receptor Il1rapl1 X accessory protein-like 1 Mcoln1 mucolipin 1 X piezo-type Piezo1 mechanosensitive ion X channel component 1 potassium voltage-gated Kcnip3 channel interacting protein X 3 potassium voltage-gated Kcnip4 channel interacting protein X 4 potassium voltage-gated channel subfamily A Kcnab1 X member regulatory beta subunit 1 potassium voltage-gated Kcnh8 channel subfamily H X member 8 sodium leak channel, non- Nalcn X selective solute carrier family 24 Slc24a3 X member 3 transient receptor potential Trpc1 cation channel, subfamily X C, member 1 transient receptor potential Trpc4 cation channel, subfamily X C, member 4 transient receptor potential Trpc5 cation channel, subfamily X C, member 5 two pore segment channel Tpcn1 X 1

138

Table 4-3. Hypomethylation of the mPFC during delayed shifting Gene Cytoskeleton Gene Name Neuron part Symbol organization Htr2a 5-hydroxytryptamine receptor 2A X Brinp3 BMP/retinoic acid inducible neural specific 3 X Dcc DCC netrin 1 receptor X Dlgap1 DLG associated protein 1 X Polg DNA polymerase gamma, catalytic subunit X Dnajc6 DnaJ heat shock protein family X Elmod3 ELMO domain containing 3 X Ephb1 Eph receptor B1 X Gpr149 G protein-coupled receptor 149 X Rab3c RAB3C, member RAS oncogene family X Ric3 RIC3 acetylcholine receptor chaperone X Rapgef2 Rap guanine nucleotide exchange factor 2 X Tbc1d24 TBC1 domain family, member 24 X acyl-CoA dehydrogenase, C-4 to C-12 Acadm X straight chain Ahcyl2 adenosylhomocysteinase-like 2 X Adcy2 adenylate cyclase 2 X Agtr1a angiotensin II receptor, type 1a X Ank3 ankyrin 3 X X ankyrin repeat and sterile alpha motif domain Anks1b X containing 1B Atg5 autophagy related 5 X Begain brain-enriched guanylate kinase-associated X Cdh13 cadherin 13 X calcium voltage-gated channel subunit alpha1 Cacna1d X D Ctnna2 catenin alpha 2 X Cep290 centrosomal protein 290 X Cobl cordon-bleu WH2 repeat protein X X Cngb3 cyclic nucleotide gated channel beta 3 X Cyfip1 cytoplasmic FMR1 interacting protein 1 X X Dmd dystrophin X X Erbb4 erb-b2 receptor tyrosine kinase 4 X Esr1 estrogen receptor 1 X Exoc4 exocyst complex component 4 X gamma-aminobutyric acid type A receptor Gabrb1 X beta 1 subunit glutamate ionotropic receptor NMDA type Grin2b X subunit 2B Grm8 glutamate metabotropic receptor 8 X glutaredoxin and cysteine rich domain Grxcr1 X containing 1 Glrb glycine receptor, beta X

139

Table 4-3. Continued Gene Cytoskeleton Gene Name Neuron part Symbol organization Ghr growth hormone receptor X Inha inhibin alpha subunit X Il1rapl1 interleukin 1 receptor accessory protein-like 1 X Katna1 katanin catalytic subunit A1 X X Kirrel kin of IRRE like X X Lama2 laminin subunit alpha 2 X Mapk10 mitogen activated protein kinase 10 X Mbp myelin basic protein X Myo3a myosin IIIA X Nfasc neurofascin X Nlgn1 neuroligin 1 X X Ncoa1 nuclear receptor coactivator 1 X Ophn1 oligophrenin 1 X X Pard3 par-3 family cell polarity regulator X X Pde1a phosphodiesterase 1A X Pde1c phosphodiesterase 1C X Pde4b phosphodiesterase 4B X Plcb4 phospholipase C, beta 4 X Pclo piccolo X X potassium voltage-gated channel interacting Kcnip3 X protein 3 potassium voltage-gated channel interacting Kcnip4 X protein 4 Pcdh15 protocadherin 15 X X Reln reelin X Rims2 regulating synaptic membrane exocytosis 2 X Rgs7 regulator of G-protein signaling 7 X secretagogin, EF-hand calcium binding Scgn X protein Slc4a10 solute carrier family 4 member 10 X Spta1 spectrin, alpha, erythrocytic 1 X X Ston2 stonin 2 X Sv2b synaptic vesicle glycoprotein 2b X Syt17 synaptotagmin 17 X Wls wntless Wnt ligand secretion mediator X Zmynd8 zinc finger, MYND-type containing 8 X Cdc42bpa CDC42 binding protein kinase alpha X Epha3 Eph receptor A3 X Fer FER tyrosine kinase X Frmd3 FERM domain containing 3 X Frmd5 FERM domain containing 5 X Fry FRY microtubule binding protein X

140

Table 4-3. Continued Gene Cytoskeleton Gene Name Neuron part Symbol organization Rho-associated coiled-coil containing protein Rock1 X kinase 1 Slain2 SLAIN motif family, member 2 X Tnik TRAF2 and NCK interacting kinase X WD repeat containing planar cell polarity Wdpcp X effector Chmp3 charged multivesicular body protein 3 X Cluap1 clusterin associated protein 1 X Ccdc13 coiled-coil domain containing 13 X Ccdc151 coiled-coil domain containing 151 X Diaph2 diaphanous-related formin 2 X Dnah5 dynein, axonemal, heavy chain 5 X Dnah7 dynein, axonemal, heavy chain 7 X Elmo1 engulfment and cell motility 1 X Efna5 ephrin A5 X Fign fidgetin, microtubule severing factor X Kif4a kinesin family member 4A X progesterone immunomodulatory binding Pibf1 X factor 1 Prkce protein kinase C, epsilon X Ptk2 protein tyrosine kinase 2 X protein tyrosine phosphatase, non-receptor Ptpn1 X type 1 protein tyrosine phosphatase, non-receptor Ptpn21 X type 21 Sdccag8 serologically defined colon cancer antigen 8 X LOC688970 similar to serine/threonine kinase X Spag16 sperm associated antigen 16 X Synpo2 synaptopodin 2 X Sdcbp syndecan binding protein X Tnks tankyrase X Ttc17 tetratricopeptide repeat domain 17 X Trdn triadin X Tuba1a tubulin, alpha 1A X Ulk4 unc-51 like kinase 4 X Vasp vasodilator-stimulated phosphoprotein X Xirp2 xin actin-binding repeat containing 2 X Zmym4 zinc finger MYM-type containing 4 X

141

Table 4-4. Promoter and Gene Body index features in the mPFC in young and aged Young Aged Young Aged Young Aged

CG CG CHG CHG CHH CHH Average depth 63.8 130.7 58.4 127.0 55.5 122.4 Number of sites 16,090 16,090 51,099 51,099 128,268 128,268 Mean ratio 0.731 0.730 0.023 0.023 0.018 0.018 557 573 48,877 49,219 123,719 124,185 Sites ≤ 0.1 (%) (3.5%) (3.6%) (95.7%) (96.3%) (96.5%) (96.8%)

Table 4-5. Promoter and Gene Body index features in the mPFC in aged-impaired (AI) and aged-unimpaired (AU) AU AI AU AI AU AI

CG CG CHG CHG CHH CHH Average depth 77.6 67.3 74.3 62.4 71.2 59.2 Number of sites 14,696 14,696 48,524 48,524 123,892 123,892 Mean ratio 0.722 0.723 0.023 0.023 0.018 0.018 583 553 465,43 463,49 119,699 119,465 Sites ≤ 0.1 (%) (4.0%) (3.8%) (95.9%) (95.5%) (96.6%) (96.4%)

142

Table 4-6. Hypermethylation in CHG sites in rats with delayed shifting Kinase GTPase Gene Symbol Gene Name Synapse activity activity Dennd1a DENN domain containing 1A X X ELKS/RAB6-interacting/CAST Erc2 X family member 2 Fchsd2 FCH and double SH3 domains 2 X FYN proto-oncogene, Src family Fyn X X tyrosine kinase Amph amphiphysin X X Ank3 ankyrin 3 X Ctnna2 catenin alpha 2 X Dgki diacylglycerol kinase, iota X X X diazepam binding inhibitor, acyl- Dbi X CoA binding protein Erbb4 erb-b2 receptor tyrosine kinase 4 X X Exoc4 exocyst complex component 4 X glutamate ionotropic receptor Gria4 X AMPA type subunit 4 insulin-like growth factor 2 mRNA Igf2bp1 X binding protein 1 Ica1 islet cell autoantigen 1 X mitogen activated protein kinase Mapk10 X X 10 muscle associated receptor Musk X X tyrosine kinase potassium voltage-gated channel Kcnd2 X subfamily D member 2 protein tyrosine phosphatase, Ptprn2 X X receptor type N2 Pcdh15 protocadherin 15 X regulating synaptic membrane Rims2 X exocytosis 2 sortilin-related VPS10 domain Sorcs3 X containing receptor 3 Sv2b synaptic vesicle glycoprotein 2b X Unc5c unc-5 netrin receptor C X Akap6 A-kinase anchoring protein 6 X Epha10 EPH receptor A10 X Epha3 Eph receptor A3 X X FGGY carbohydrate kinase Fggy X domain containing Tnni3k TNNI3 interacting kinase X

143

Table 4-6. Continued Kinase GTPase Gene Symbol Gene Name Synapse activity activity TRAF2 and NCK interacting Tnik X kinase microtubule associated Mast4 serine/threonine kinase family X member 4 mitogen activated protein kinase Map3k4 X kinase kinase 4 Myo3a myosin IIIA X obscurin, cytoskeletal calmodulin Obscn X and titin-interacting RhoGEF phosphatidylinositol 4-kinase Pi4ka X alpha protein kinase, cGMP-dependent, Prkg1 X X type 1 Ptk2 protein tyrosine kinase 2 X Rps6ka6 ribosomal protein S6 kinase A6 X Stk39 serine threonine kinase 39 X similar to hypothetical protein LOC300308 X 4930509O22 LOC688970 similar to serine/threonine kinase X GTPase activating Rap/RanGAP Garnl3 X domain-like 3 RAB3 GTPase activating non- Rab3gap2 X catalytic protein subunit 2 RAS protein-specific guanine Rasgrf1 X nucleotide-releasing factor 1 Ral GEF with PH domain and SH3 Ralgps2 X binding motif 2 Sbf2 SET binding factor 2 X SLIT-ROBO Rho GTPase Srgap3 X activating protein 3 Tbc1d4 TBC1 domain family, member 4 X Tbc1d5 TBC1 domain family, member 5 X Dock9 dedicator of cytokinesis 9 X Elmo1 engulfment and cell motility 1 X obscurin, cytoskeletal calmodulin Obscn X and titin-interacting RhoGEF similar to GTPase activating LOC691033 X protein testicular GAP1 LOC304239 similar to RalA binding protein 1 X

144

CHAPTER 5 CONCLUSIONS AND FUTURE DIRECTIONS

General Conclusions

Transcriptional and epigenetic alterations are well known to influence several processes which affect brain function, however, the specificity of these changes to aging and age-related cognitive function are not well known. The goal of the current dissertation was to characterize gene-specific and genome/transcriptome wide modifications which may contribute to these processes in aging and cognition. The current work demonstrated that specificity in transcriptional alterations across brain regions, including the mPFC and the CA1 region, may contribute to differences in cognitive function in the brain. Additionally, age-related modifications in the mPFC were linked to the up regulation of inflammatory genes and decreased synaptic genes.

Importantly, the pattern of transcription associated with aging did not predict cognitive decline. Rather, increased mPFC expression of genes involved in regulation of transcription, including transcription factors that regulate the strength of excitatory and inhibitory inputs, and neural activity-related immediate-early genes was observed in aged animals that exhibit delayed set shift behavior. Furthermore, the enrichment of differential methylation of several genes functionally linked to the synaptic function, suggests that epigenetic modifications within the identified genes may alter cognitive function in the mPFC.

In particular, Chapter 2 explored the transcriptional and DNA methylation changes of the ERα gene, Esr1, in aging, E2 deprivation time and hippocampal regions

CA1 and CA3 of OVX F344 rats. The results indicated higher expression of Esr1 in region CA3 relative to CA1 which is associated with hypermethylation of the first CpG

145

site of the promoter exon 1b region in CA1 relative to CA3. In addition, the same site contained the highest level of methylation and has been implicated to be an important regulatory CpG for Esr1 expression in the rat brain (Kurian et al., 2010). Therefore, it is likely that hypermethylation of the first site in the promoter region is epigenetically associated with the repression of Esr1 transcription across hippocampal regions.

Furthermore, the expression of ERα mRNA was up regulated in aging and long-term E2 deprivation. Additionally, up regulation of Esr1 in aged animals in region CA1 was associated with decreased DNA methylation of CpG site 15 and up regulation of E2 long-term deprivation in young and aged rats were linked to decreased DNA methylation of sites 11 and 14. Indeed, differential methylation in the exonic region was not anti-directional to expression in all cases, highlighting the dynamic mechanism that

DNA methylation contains according to genomic location. However, the current study suggests that, while methylation of site 1 may be established earlier in life to contribute to hippocampal sub-regional differences, methylation of the downstream sites may be modifiable across the lifespan for the regulation ERα transcription for aging and hormone exposure time.

To expand our knowledge to another region of the rodent brain which is not well studied at the transcriptional and epigenetic level, the current work investigated the transcriptome and DNA methylome of the mPFC according to aging and executive function. Specifically, the current dissertation focused on the function of cognitive flexibility which has been shown to decline with age (Beas et al., 2013; Moore et al.,

2006; Rhodes, 2004; Robbins et al., 1998). Chapter 3 discussed the transcriptomic findings which include the up regulation of genes related to inflammation and immune

146

function and the down regulation of genes related to synaptic function. Furthermore, the

RNA profile of aged animals which delayed shifting, was correlated to the up regulation of genes functionally related to transcriptional regulation and synaptic activity.

Interestingly, a comparison of the mPFC transcriptome to the behavioral scores of the set shift task and the spatial episodic memory task revealed that there is specificity in the mPFC transcriptional profile to cognitive function. In addition, the transcriptome of the mPFC is also distinct from the hippocampal CA1 region and the white matter, indicating differences in vulnerability to aging across regions. This suggests that specificity in the regulation of transcription across brain regions is likely to be associated with distinct cognitive function observed within subregions.

Finally, Chapter 4 investigated the alterations in DNA methylation across the entire genome from the mPFC using a single base pair resolution approach. An interesting finding of this study was the specificity of biological function associated with differential methylation where age-related and cognitive function changes in the mPFC contained hypermethylation of several genes linked to synaptic function and plasticity.

Surprisingly, the majority of DNA methylation was found to be in gene body regions, with higher levels in introns relative to exons. While promoter methylation is known to repress transcription by the direct blocking of the binding sites from transcriptional factors and by the recruitment of proteins (e.g.: methyl-CpG-binding proteins) which promote heterochromatin with the further recruitment of chromatin remodeling factors

(e.g.: histones deacetylases); the mechanism of gene body methylation is not well known (Irvine et al., 2002; Moore et al., 2013; Nan et al., 1998; Ng et al., 1999). Several hypothesis have been proposed to the mechanism of gene body methylation, including

147

the idea that it may be an important element for alternative splicing, for the repression of aberrant transcription, and that it is likely to be dependent on cell population

(Chodavarapu et al., 2010; Jjingo et al., 2012; Lister et al., 2013; Neri et al., 2017; Wen et al., 2014). Altogether, further studies are necessary to elucidate the mechanism, however the results provide several target genes which may be important for epigenetic regulation in learning and memory.

Additional contributions from Chapter 4 include the analysis of the abundance of repetitive elements where CpG methylation is located, and the quantification of the levels of DNA methylation in non-CG sites in the mPFC. An interesting finding from the analysis of repetitive elements, was the clustering of hypermethylated postsynaptic genes found within LINE in aged rats, which suggest that the identified genes may contain additional regulatory mechanisms which may affect synaptic function in the mPFC (Su et al., 2012). Further, in agreement with previous reports, the global levels of non-CG methylation are much lower than methylation occurring in CpG sites (Guo et al.,

2014; Lister et al., 2013; Xie et al., 2012). However, among the non-CG sites detected in the mPFC, differential methylation at CHG sites during aging and cognitive function was observed for genes linked to GTPase activity suggesting that methylation at CHG sites may have a role in proper cognitive flexibility function.

Therefore, the results from the current dissertation suggest that transcriptional and epigenetic alterations play a critical role into aging and cognitive function in the hippocampus and the mPFC. The current work also suggests the presence of the dynamic interaction of transcriptional signals and epigenetic regulation of several synaptic genes. The differentially expressed genes and differentially methylated CpG

148

sites, provide excellent targets for future studies in the field of aging and cognitive function. With the rapid advancement of next-generation sequencing technologies, including targeted gene sequencing and single cell genomics, future studies are likely to uncover more specific roles that each individual gene target may have on cognitive decline and aging in the brain regions investigated.

Future Directions

While the current work focused in the profiling of transcriptional and DNA methylation patterns in aging and cognitive decline, many questions still remain regarding several areas of the epigenomic code which have not been well studied in aging and cognitive decline of specific brain regions. These areas include miRNA and transcriptional factor regulation, DNA 5-hydroxymethylation, nucleosome positioning and chromosomal conformation. The necessity of further studies in these specific areas are arising, since many modification in the epigenome are likely to ‘crosstalk’ among one or more modifications for the regulation of gene expression (Du et al., 2015; Moore et al., 2013). One of the best examples of this idea is the interaction of DNA methylation and histone modifications where several studies have reported the co-localization of the two epigenetic marks and their respective association to gene expression, however, additional profiling of this interaction in the adult brain of rodents and humans is needed

(Cedar and Bergman, 2009; Du et al., 2015; Meissner et al., 2008; Severson et al.,

2013; Wen et al., 2014). The finding from the current work would be greatly complemented with additional genomic sequencing using techniques which allow the distinction of 5-methylcytosine (5mC) from 5-hydroxymethylcytosine (5hmC), an oxidized form of 5mC catalyzed by the ten-eleven-translocation enzymes (Tahiliani et al., 2009). Several reports have shown that 5hmC is abundant in the adult brain, alters

149

with aging, and it may have a different regulatory role on transcription from 5mC, including regulatory differences in the gene body region which may contribute to splicing

(Chen et al., 2012; Globisch et al., 2010; Khare et al., 2012; Lister et al., 2013;

Szulwach et al., 2011; Wen et al., 2014). Interestingly, a study in the mouse prefrontal cortex investigated the role of Tet3 and the genome-wide alterations in the levels of

5hmC following fear extinction (Li et al., 2014). The results indicated a redistribution of

5hmC patterns across the genome following fear extinction training, indicating that

5hmc may have an important role in behavioral adaptation in the prefrontal cortex of rodents (Li et al., 2014). Thus, future studies implementing techniques which will bioinformatically complement bisulfite sequencing data, such as Tet-assisted bisulfite sequencing or oxidative bisulfite sequencing, will provide additional information of the active mechanism of the genes identified in the current work (Booth et al., 2013; Yu et al., 2012).

Furthermore, the findings of the current work may also be complimented with future studies investigating the chromatin accessibility states with techniques such as

ATAC-seq (Assay for Transposase-Accessible Chromatin) which provides the direct mapping of open chromatin along with transcriptional factor and nucleosome occupancy

(Buenrostro et al., 2013; Buenrostro et al., 2015; Tsompana and Buck, 2014). In addition, a topic of the epigenome which is very elusive in the neuroscience field is the role of spatial organization of chromatin on gene regulation (e.g.: trans-acting factor effects on transcriptional patterns) (Cao et al., 2015). While DNA methylation or transcriptional factor binding activity are epigenetic mechanisms contributing to chromatin structure, higher order mechanisms at the nuclear level involved with three-

150

dimensional spatial organization of chromatin and its interaction to specific regions of the genome have not been investigated in models of aging and cognitive decline (Cao et al., 2015; Zhao et al., 2006). The advancement of chromatin conformation capture

(3C) techniques from the original 3C method to the Hi-C technique provide the opportunity to investigate such mechanisms at a genome-wide level in an unbiased method where all pairs of interactions across the genome may be analyzed (Cao et al.,

2015; Lieberman-Aiden et al., 2009; Miele and Dekker, 2008). It is likely that chromatin conformation may also crosstalk to more well studied areas of epigenetics, including the findings from the current dissertation, which provides an opportunity for future studies to analyze multiple datasets to identify overlapping mechanisms which may contribute to transcriptomic changes.

Finally, the findings from the current dissertation may be extended to a mechanistic study where the relationship between cognitive function and the alterations of methylation in specific CpG sites may be investigated. In particular, the use of

CRISPR (clustered regularly interspaced palindromic repeats) Cas9 is a promising technique to edit gene methylation (Liu et al., 2016). Recently, Liu et al. constructed a catalytically inactive Cas9 (dCas9) with the Tet1 enzymatic domain or Dnmt3a to demethylate or methylate specific genes respectively (Liu et al., 2016). Among the findings from this study, the constructed dCas9-Tet1 system demethylated the Bdnf promoter IV, which resulted in the activation of Bdnf in mouse cortical neurons. Further, the dCas9-Dnmt3a system was used to induce de novo methylation in the binding sites of CTCF, a zinc finger protein implicated in several epigenetic functions including chromatin organization (Phillips and Corces, 2009). The results showed successful

151

blocking of CTCF which lead to modifications in CTCF-mediated gene loops (Liu et al.,

2016). Therefore, the use of this technique in the area of behavioral neuroscience will elucidate the effect of DNA methylation of specific genes known to be differentially expressed or methylated in the brain. In particular, the targeting of genes identified in the current study may be used to investigate whether induction or removal of DNA methylation alters set shifting performance in aged rats.

Thus, the current dissertation has uncovered information from an fundamental section of the aging and cognitive epigenome, however many layers of the epigenetics of memory and cognition remain abstract. The results of the current work will provide multiple targets for future studies, which are likely to continue to discover the interactions of the epigenome to functions affecting aging and cognition decline.

152

APPENDIX A SUPPLEMENTARY FIGURES TO RNA-SEQ PROFILING

Figure A-1. RNA-seq spike-in controls. The figure shows the correlations in a subset of young and aged rats from the mPFC region to the ERCC spike-in controls. A positive correlation is observed between the number of sequences and the concentration of the controls in the biological replicates. The ERCC number indicates the spike-in’s transcripts detected in the replicate, where 92 is the total number of transcripts and a minimum of 60 is recommended. The average R value across all replicates was 0.84 and the mean number of transcripts was 86.

153

Figure A-2. Heatmap of gene expression correlated to set shift performance. Each row represents a gene correlated (Pearson’s correlation p<0.025) to the set shift TTC scores. Each column represents an aged rat and the number represents the TTC score, where numbers followed by an underscore indicate an aged animal with the same score as the animal in the previous column. Gene level counts were standardized to z-scores and the color represents the standard deviation increasing (red) or decreasing (blue) relative to the mean (gray). Up regulated genes in animals with higher TTC scores were functionally related to the regulation of transcription, regulation of apoptosis and response to organic substance.

154

APPENDIX B SUPPLEMENTARY FIGURE AND TABLES TO GENOME-WIDE PROFILING OF DNA METHYLATION

Figure B-1. Pearson’s correlation of CpGs detected genome-wide across all biological replicates in the aged and young groups. The range of the correlations in young animals was 0.75 – 0.78. Aged animals contained r values from 0.75 – 0.80

155

Table B-1. Age-related differentially methylated CpGs correlated to RNA levels Direction of DNA and Gene Genomic DNA Aged Aged Young Young Chrom & Pos RNA R name Feature methylation N depth N depth value in aging Chr5- Erp44 Intron Decreased -0.69 4 23 5 31 68479557 Chr5- Crocc Exon Increased -0.67 3 16 3 19 163257661 Chr3- Sh3glb2 Intron Decreased -0.67 8 53 6 37 14271337 Chr7- Fer1l6 Intron Increased -0.65 9 63 4 30 99074742 AABR06 Chr19- 096719. Intron Increased -0.65 6 43 3 21 314425 1 ChrX- Il1rapl2 Intron Decreased -0.64 8 58 4 23 109119203 Chr13- Thsd7b Intron Increased -0.64 8 53 6 37 51463293 Chr1- LOC100 Intron Increased -0.63 6 39 3 19 26351829 361380 AABR06 Chr19- 098126. Intron Decreased -0.62 3 19 5 35 39861109 1 Chr17- Gpr158 Intron Decreased -0.61 4 21 3 20 90214440 Chr2- Ralyl Intron Increased -0.60 9 81 4 28 108908799 Chr7- Asap1 Intron Increased -0.59 7 40 3 19 91777384 Chr9- LOC100 Intron Decreased -0.58 6 37 3 17 7459929 360856 Chr20- Grik2 Intron Increased -0.58 8 68 3 21 55666422 AABR06 Chr1- 000596. Intron Decreased -0.56 12 96 3 25 13505401 1 Chr6- Akap6 Intron Increased -0.56 11 75 3 20 83492681 Chr12- Fry Intron Increased -0.55 9 51 5 33 7650654 Chr13- Dennd1 Intron Increased -0.55 13 88 5 43 61223431 b Chr6- Npas3 Intron Increased -0.55 3 19 3 19 84427101 Chr10- Cep112 Intron Increased -0.55 8 48 3 18 96478278 Chr13- Thsd7b Intron Increased -0.53 15 104 9 69 51462477 AABR06 Chr19- 098126. Intron Decreased -0.51 8 45 3 18 39885578 1 Chr5- Zswim5 Intron Increased -0.50 6 35 4 31 139550170

156

Table B-1. Continued Direction of DNA and Gene Genomic DNA Aged Aged Young Young Chrom & Pos RNA R name Feature methylation N depth N depth value in aging AABR06 Chr1- 007800. Exon Decreased -0.50 9 65 3 26 181825821 1 Chr5- Necab1 Intron Increased -0.49 10 79 7 54 33238917 Chr2- Ralyl Intron Increased -0.49 7 43 5 42 109001537 Chr2- Prlr Intron Increased -0.49 12 94 3 25 84491900 AABR06 Chr19- 098126. Intron Increased -0.49 5 29 4 32 39901210 1 Chr12- Vom2r6 Intron Decreased -0.48 7 42 5 34 6464425 0 Chr4- Ppp1r9a ExonIntron Increased -0.48 5 30 3 17 29957195 Chr15- Cd99l2 Intron Increased -0.48 9 65 4 34 9864570 Chr15- LOC680 Intron Increased -0.48 3 24 3 21 9698049 653 Chr1- Grm1 Intron Increased -0.48 13 86 6 40 6693136 Chr2- Sclt1 Intron Increased -0.47 10 70 4 25 148258660 Chr13- Thsd7b Intron Increased -0.47 14 103 8 69 51462457 Chr7- Csmd3 Intron Decreased -0.47 13 91 4 30 87037198 ChrX- Lancl3 Intron Increased -0.47 5 34 4 26 15177103 Chr3- Jph2 Intron Decreased -0.47 5 38 3 19 165917589 AABR06 Chr1- 000596. Intron Decreased -0.46 10 77 4 31 13506631 1 Chr4- Mgam Intron Increased -0.46 16 126 5 33 133773929 Chr12- Tctn1 Intron Decreased -0.46 9 65 5 33 41683448 Chr8- Itga9 Intron Decreased -0.45 13 82 6 37 126512442 Chr15- LOC100 Intron Increased -0.44 12 117 6 66 9549577 364581 Chr17- Sugct Intron Decreased -0.44 9 75 7 43 48613491 Chr4- RGD13 Exon Increased -0.44 6 47 7 48 233285565 06151 Chr9- LOC100 Intron Decreased -0.43 11 94 8 51 7420092 360856 Chr16- Asb14 Intron Decreased -0.42 6 42 5 30 2514289

157

Table B-1. Continued Direction of DNA and Gene Genomic DNA Aged Aged Young Young Chrom & Pos RNA R name Feature methylation N depth N depth value in aging Chr2- Hnf4g Intron Increased -0.42 5 30 4 21 120074973 Chr9- RGD13 Intron Decreased -0.41 10 84 4 26 50199264 05645 Chr10- Map2k4 Intron Increased -0.41 20 246 9 110 51961598 Chr1- Grm5 ExonIntron Increased -0.41 9 54 4 26 157808013 Chr18- Arhgap2 Intron Increased -0.41 8 51 4 27 31813821 6 AABR06 Chr19- 096719. Exon Increased -0.41 11 79 4 25 249387 1 Chr1- Grm5 Intron Increased -0.41 5 39 3 21 158069854 ChrX- Med14 Intron Decreased -0.41 8 48 5 35 11783099 Chr6- Lrfn5 Intron Increased -0.40 5 28 4 20 92639171 Chr1- Tcerg1l Intron Decreased -0.40 4 27 4 22 217501178 Chr3- Frmd5 Intron Decreased -0.40 10 75 4 24 120127582 Chr19- 5S_rRN Promoter Decreased -0.40 9 58 9 96 67048745 A AABR06 Chr1- 000596. Intron Decreased -0.40 11 103 3 33 13506494 1 Chr1- Trpm6 Intron Decreased -0.40 14 101 3 16 241608497 Chr8- Ube3d Intron Decreased -0.39 7 55 3 17 93628221 Chr14- Prorsd1 Intron Increased -0.38 14 93 7 49 113548042 Chr4- Sumf1 Intron Increased -0.38 8 62 3 29 204617354 Chr19- 5S_rRN Exon Decreased -0.38 20 786 10 802 67048450 A Chr2- Wdr41 Intron Increased -0.38 5 34 3 18 43873732 Chr19- 5S_rRN Promoter Decreased -0.38 9 60 10 108 67048749 A Chr19- 5S_rRN Promoter Decreased -0.38 20 1053 10 979 67048512 A Chr17- Gpr158 Intron Increased -0.38 9 58 6 39 90012547 Chr19- 5S_rRN Promoter Decreased -0.38 20 847 10 742 67048539 A AABR06 Chr1- 000596. Intron Decreased -0.37 14 112 6 49 13506663 1

158

Table B-1. Continued Direction of DNA and Gene Genomic DNA Aged Aged Young Young Chrom & Pos RNA R name Feature methylation N depth N depth value in aging Chr19- 5S_rRN Exon Decreased -0.37 20 979 10 905 67048432 A AABR06 Chr1- 000596. Intron Decreased -0.37 13 108 5 38 13506654 1 Chr19- 5S_rRN Exon Decreased -0.37 20 432 10 459 67048465 A Chr19- 5S_rRN Promoter Decreased -0.37 14 151 10 192 67048585 A Chr19- 5S_rRN Exon Decreased -0.37 20 1160 10 1092 67048466 A Chr11- Atp13a5 Intron Increased -0.37 11 100 4 51 78104332 Chr19- LOC100 Intron Increased -0.36 15 200 6 72 342492 912892 Chr19- 5S_rRN Promoter Decreased -0.36 20 862 10 760 67048535 A Chr19- LOC100 Exon Increased 0.36 15 215 6 60 38816887 359783 Chr19- LOC100 Intron Increased 0.36 20 385 10 166 37461544 363633 AABR06 Chr1- 001371. Intron Decreased 0.37 14 110 6 57 28955567 1 AABR06 Chr19- 098126. Intron Decreased 0.37 13 155 5 38 39876764 1 ChrX- Eda Intron Increased 0.37 13 102 4 28 70710311 Chr14- LOC257 Intron Decreased 0.38 20 237 10 169 46702753 642 Chr1- Prkg1 Intron Increased 0.38 12 86 7 63 256542175 Chr16- Erich1 Intron Increased 0.38 7 56 5 49 80186935 Chr6- Ncoa1 Intron Increased 0.38 4 23 3 15 38695100 Chr10- Emp2 ExonIntron Increased 0.39 11 72 4 30 4265259 Chr16- Sh2d4b ExonIntron Increased 0.39 6 43 4 26 17573194 Chr1- Sox6 Intron Increased 0.39 7 52 5 35 192732090 Chr7- Eif4b Intron Increased 0.40 10 66 3 30 141489639 Chr20- RGD15 Intron Increased 0.40 7 47 4 30 46768496 61777 Chr14- LOC257 Intron Decreased 0.40 15 116 8 78 46702776 642 Chr14- LOC257 Intron Decreased 0.40 19 208 10 143 46702762 642

159

Table B-1. Continued Direction of DNA and Gene Genomic DNA Aged Aged Young Young Chrom & Pos RNA R name Feature methylation N depth N depth value in aging AABR06 Chr14- 078903. Intron Decreased 0.41 14 202 6 87 46834543 1 Chr5- Nphp4 Intron Decreased 0.42 7 44 3 19 173266603 Chr13- Rgl1 Intron Increased 0.42 7 46 5 36 74903736 ChrX- Med14 Intron Decreased 0.43 9 66 7 48 11871589 Chr5- LOC100 Intron Increased 0.43 7 49 5 37 176291236 362748 Chr10- Pemt Intron Decreased 0.44 5 35 3 18 46153386 Chr4- Fam13a ExonIntron Increased 0.44 5 36 3 19 154070676 Chr4- Exoc4 Intron Decreased 0.45 7 47 3 17 60547314 Chr2- Cdh10 Intron Decreased 0.45 14 107 6 49 89196027 AABR06 Chr9- 058265. Exon Decreased 0.45 7 53 3 19 7647305 1 Chr9- LOC100 Intron Increased 0.45 3 19 4 24 7412833 360856 Chr14- Ccdc18 Intron Increased 0.46 14 102 7 57 2532285 Chr1- Sbf2 Intron Increased 0.46 18 163 9 103 182276873 Chr7- Tbc1d1 Intron Decreased 0.47 10 63 8 57 58219846 5 Chr4- Tpk1 Intron Decreased 0.47 6 57 3 26 137695539 Chr7- LOC300 Intron Increased 0.47 5 28 3 21 20567186 308 Chr4- Ccdc17 Intron Increased 0.47 7 50 4 33 189491314 4 Chr6- Vipr2 Intron Increased 0.48 3 21 3 17 152930491 Chr4- Mgll Intron Decreased 0.48 5 31 5 34 185923444 Chr6- LOC257 Intron Decreased 0.48 5 35 5 49 39261366 642 AABR06 Chr9- 058610. Intron Increased 0.50 6 41 3 16 11422318 1 Chr15- Itgbl1 Intron Decreased 0.50 8 49 4 25 113593705 Chr15- Cd99l2 Intron Decreased 0.50 3 22 3 21 9890830 Chr3- Ttc17 Intron Decreased 0.52 4 26 5 30 89914740

160

Table B-1. Continued Direction of DNA and Gene Genomic DNA Aged Aged Young Young Chrom & Pos RNA R name Feature methylation N depth N depth value in aging AABR06 Chr14- 078903. Intron Decreased 0.52 9 78 6 54 46834644 1 Chr17- Pou6f2 Intron Decreased 0.52 3 23 3 17 47451361 Chr14- LOC257 Intron Decreased 0.53 17 165 9 98 46702785 642 Chr15- Abcc4 Intron Increased 0.53 5 31 3 22 107345479 Chr7- Angpt1 Intron Decreased 0.54 9 110 4 33 81379608 Chr13- Pld5 Intron Decreased 0.58 3 21 5 31 98770861 Chr11- Xxylt1 Intron Decreased 0.60 5 32 3 16 76389519 Chr13- Cntnap5 Intron Increased 0.60 7 39 4 25 27448648 b Chr9- LOC100 Intron Decreased 0.62 8 65 5 36 7414403 360856 Chr14- Grb10 Intron Increased 0.62 7 45 3 16 91851642 Chr1- LOC100 Intron Decreased 0.63 13 168 4 41 26345148 361380 Chr5- Tdrd7 Intron Decreased 0.63 6 53 3 19 66090466 Chr4- LOC100 Intron Decreased 0.64 20 369 9 155 141196987 364190 Chr4- Cav1 Intron Increased 0.69 4 25 4 23 45229758 Chr18- Jakmip2 Intron Decreased 0.71 8 57 4 21 37648862 Chr19- RGD15 Intron Increased 0.79 16 135 3 46 39208258 62877 Chr6- Ralgapa Intron Increased 0.79 4 26 4 30 85973536 1

161

Table B-2. Genes of CpGs correlated to set shifting and RNA levels (AI: Aged- impaired; AU: Aged-unimpaired; N = number of rats) Chrom & Gene Genomic DNA-Set DNA-RNA AI AI AU AU Pos name feature Shift R value R value N depth N depth chr14- LOC6851 Intron -0.62 -0.98 3 22 3 19 12698040 58 chr1- Tle4 Intron 0.47 -0.95 3 18 3 20 238604286 chr13- Cntnap5b Intron -0.68 -0.90 3 19 3 18 27285285 chr13- Cfh Intron 0.52 -0.89 3 21 4 32 62056259 chr10- Adamts2 Exon -0.56 -0.88 3 21 3 16 36072106 chr9- Mfsd6 Intron 0.50 -0.87 3 22 3 17 53368402 chr19- LOC1009 Intron -0.54 -0.86 3 18 3 19 416459 12892 chr11- Mylk Intron -0.63 -0.85 3 17 4 27 72222723 chr2- Spata5 Intron -0.58 -0.85 3 22 4 34 143805843 chr6- Tc2n Intron -0.82 -0.84 3 19 3 25 134713974 chr2- Tnik Exon -0.62 -0.83 3 17 3 17 134027247 chrX- Ophn1 Intron 0.61 -0.83 3 17 4 23 69086319 chr1- Ighmbp2 Intron 0.45 -0.83 3 20 3 19 225377700 chr2- Lrba Intron -0.53 -0.83 3 19 3 20 205109270 chrX- Il1rapl2 Intron 0.86 -0.83 5 27 3 31 109119203 chr5- AABR060 Intron -0.47 -0.82 3 24 3 18 134666161 39340.1 chr14- Wdpcp Intron -0.57 -0.81 3 45 4 35 106613535 chr20- Unc5b Intron 0.66 -0.81 3 19 3 15 32195549 chr1- Fbxw4 Intron -0.52 -0.80 3 17 3 18 272811877 chr8- Wdr72 Exon 0.55 -0.79 3 20 4 24 80606729 chr19- AABR060 Intron 0.80 -0.79 3 20 3 24 39003252 97983.1 chr1- Eif3c Promoter 0.56 -0.79 3 20 3 26 204962126 chr1- Zfp428 Exon 0.80 -0.79 3 23 3 20 82642117 chr2- Schip1 Intron -0.64 -0.78 4 30 4 25 191651490 chr9- LOC1003 Intron 0.50 -0.77 5 43 7 59 7455347 60856

162

Table B-2. Continued Chrom & Gene Genomic DNA-Set DNA-RNA AI AI AU AU Pos name feature Shift R value R value N depth N depth chr19- Pard3 Intron -0.64 -0.77 5 37 3 15 70702501 chr4- Vom1r90 Intron 0.77 -0.77 3 17 3 17 184301437 chr7- Rims2 Intron -0.71 -0.76 6 35 5 34 78011941 chr1- Adgb Intron 0.72 -0.76 3 20 4 35 6193611 chr18- Jakmip2 Intron 0.56 -0.75 4 20 6 40 37540297 chr1- BC02497 Intron -0.80 -0.75 3 20 4 23 85221649 8 chr7- AABR060 Promoter -0.83 -0.75 3 27 5 35 569405 46926.1 chr9- Phf3 Intron 0.76 -0.75 5 29 6 36 35899643 chr9- AABR060 Intron 0.66 -0.74 3 21 5 34 11439292 58610.1 chr6- Greb1 Exon 0.57 -0.74 3 15 3 16 51670725 chr2- Ptgfrn Intron 0.63 -0.74 4 22 3 18 222894626 chr6- LOC6805 Promoter -0.62 -0.74 3 32 6 43 112109242 19 chr2- Fam81b Intron 0.74 -0.73 4 22 6 37 3074559 chr1- AABR060 Intron 0.49 -0.73 6 43 6 56 13506637 00596.1 chr5- Stau2 Intron 0.64 -0.73 4 22 3 16 2349760 chr4- Cftr Intron 0.67 -0.73 5 28 3 19 42377148 chr10- Zfp39 Intron -0.80 -0.73 4 27 3 21 44989356 chr1- Sbf2 Intron 0.65 -0.73 4 22 4 21 182277037 chr6- Dgkb Intron -0.45 -0.72 3 18 3 19 67156639 chr9- Tbc1d5 Intron 0.82 -0.72 4 29 4 23 1364876 chr9- Spag16 Intron -0.57 -0.72 4 21 4 26 77464701 chr18- Zfp236 Intron 0.51 -0.72 3 18 3 18 78537598 chr11- Zbtb20 Intron 0.68 -0.72 4 22 4 26 61533588 chr3- Xirp2 Intron 0.68 -0.72 6 43 8 70 60264614 chr1- Esr1 Intron -0.72 -0.72 5 50 8 59 42776221 chr4- Ccdc174 Intron 0.81 -0.71 3 23 3 27 189491324

163

Table B-2. Continued Chrom & Gene Genomic DNA-Set DNA-RNA AI AI AU AU Pos name feature Shift R value R value N depth N depth chr9- Coq10b Intron -0.48 -0.71 4 25 4 32 61351094 chr6- Prkce Intron -0.95 -0.70 3 17 3 18 9572613 chr1- LOC1003 Intron -0.54 -0.70 3 21 6 51 26397237 61380 chr1- Muc6 ExonIntron -0.62 -0.70 4 35 3 21 221531475 chr1- RGD156 Exon 0.85 -0.70 3 17 3 20 177221868 1034 chr10- Mfsd11 Intron -0.50 -0.70 5 32 3 17 105470140 chr5- Zmym4 Exon 0.52 -0.69 4 31 3 22 148654743 chr14- Kcnip4 Intron 0.47 -0.69 4 22 6 43 66769485 chr15- 4930452 Intron 0.68 -0.69 4 22 4 26 21660645 B06Rik chr14- Cobl Intron -0.78 -0.69 4 26 3 18 92199200 chr9- Kcnh8 Intron 0.55 -0.69 3 18 4 22 37079745 chr4- Thsd7a Intron 0.81 -0.68 3 21 3 20 38731402 chr1- Tmem13 Intron 0.72 -0.68 5 35 5 32 159237599 5 chr5- Col27a1 Intron -0.57 -0.68 4 32 3 19 83218442 chr15- LOC6806 Intron 0.51 -0.68 3 18 4 24 9694055 53 chr8- AABR060 Exon -0.49 -0.68 3 23 4 25 41138225 54289.1 chr19- Wwox Intron 0.53 -0.67 3 19 4 20 57695168 chr20- RT1-CE2 Intron -0.60 -0.67 3 24 3 24 6853736 chr20- Cdk19 Intron 0.45 -0.67 4 30 6 34 47223904 chr15- Fndc3a Intron 0.53 -0.67 3 21 3 19 58230065 chr4- Clec2dl1 Intron -0.55 -0.66 9 68 7 68 227688157 chr15- Dock9 Intron 0.59 -0.66 4 28 5 33 111354048 chr8- Ccdc151 Exon -0.81 -0.66 3 19 3 16 23057843 chr19- AABR060 Intron 0.78 -0.66 5 43 3 28 40076095 98126.1 chr14- Ccdc18 ExonIntron -0.56 -0.66 6 43 6 55 2532505 chr8- Bckdhb Intron 0.49 -0.66 6 36 4 28 91134224

164

Table B-2. Continued Chrom & Gene Genomic DNA-Set DNA-RNA AI AI AU AU Pos name feature Shift R value R value N depth N depth ENSRNO chr7- G000000 Intron 0.92 -0.65 4 29 4 40 9946636 51152 chr9- RGD130 Intron -0.94 -0.65 3 16 3 18 50203898 5645 chr4- Fam19a4 Intron -0.53 -0.65 6 42 6 44 193856673 chr2- Spata5 Intron -0.62 -0.65 4 26 4 32 143772832 chr19- LOC1009 Intron -0.50 -0.64 4 30 5 44 400438 12892 chr1- Vom2r16 Intron 0.50 -0.64 4 29 7 46 65331862 chr9- AABR060 Intron 0.64 -0.64 4 41 5 42 11435578 58610.1 chr11- Lztr1 Exon -0.72 -0.64 3 28 4 29 90446012 chr5- Mmel1 Intron 0.72 -0.64 3 17 3 17 175752102 chr9- Fer Intron -0.46 -0.63 3 22 3 16 111286875 chr9- Wash Intron 0.51 -0.63 3 21 3 17 113456209 chr15- LOC1003 Intron 0.47 -0.62 3 25 3 19 9558539 64581 chr17- LOC6889 Intron 0.50 -0.62 3 19 6 45 91120997 70 chr20- RT1-T24- Exon -0.59 -0.62 5 36 5 41 5294288 4 chr9- AABR060 Exon 0.48 -0.62 3 18 4 38 11299031 58583.1 chr4- Sumf1 Intron -0.56 -0.62 5 31 3 26 204617272 chr5- Tp73 Intron -0.64 -0.62 3 18 4 21 174858842 chr7- LOC1003 Intron -0.65 -0.62 3 25 4 33 23169547 61411 chr6- LOC2576 Intron 0.45 -0.62 3 29 3 16 39261354 42 chr1- Eif3c Promoter 0.96 -0.62 3 21 3 28 204962080 chr14- Kctd8 Intron -0.57 -0.61 3 17 5 30 62189838 chr10- Cep112 Intron 0.51 -0.61 4 21 4 29 96478246 chr5- Fam219a Intron -0.88 -0.61 3 25 3 23 62469277 chr2- Spata5 Intron -0.61 -0.61 4 37 4 41 143772872 chr9- LOC1003 Intron 0.80 -0.61 3 23 3 16 7457196 60856 chr5- Melk Intron -0.57 -0.61 3 22 3 17 64362501

165

Table B-2. Continued Chrom & Gene Genomic DNA-Set DNA-RNA AI AI AU AU Pos name feature Shift R value R value N depth N depth chr12- RGD155 Intron -0.48 -0.60 8 88 6 56 22079224 9588 chr19- AABR060 Intron 0.52 -0.60 3 16 3 19 322776 96719.1 chr8- Dock3 Intron -0.85 -0.60 3 25 3 19 115073916 chr4- Fam19a4 Intron -0.49 -0.60 5 34 6 43 193856428 chr2- Ghr Intron 0.58 -0.59 5 38 7 47 72261455 chr7- LOC1003 Exon -0.63 -0.59 4 49 8 76 23168943 61411 chr5- Aco1 Intron 0.52 -0.59 3 19 3 20 60994046 chr6- Sptlc2 Intron 0.58 -0.59 5 31 4 24 120691171 chr4- Pde1c Intron 0.51 -0.58 4 26 5 37 151266095 chr16- Fut10 Intron -0.62 -0.58 3 16 3 17 64294110 chr2- Zfp704 Intron -0.62 -0.58 4 23 4 32 114139394 chr18- Dcc Intron -0.46 -0.58 3 17 4 26 66017457 chr6- LOC2576 Intron 0.49 -0.58 6 62 9 90 39260727 42 chr7- LOC3003 Intron -0.56 -0.58 3 30 5 33 20629427 08 chr3- Fam83d Intron -0.54 -0.57 3 27 3 28 160334236 chr1- Fchsd2 Intron 0.74 -0.57 5 41 5 36 172434656 chr17- Gpr137b Intron -0.84 -0.57 3 17 3 17 92354592 chr14- Abca13 Intron -0.50 -0.57 4 39 5 36 89518512 chr14- LOC6851 Intron -0.82 -0.57 3 24 4 24 12698019 58 chr9- Tbc1d5 Intron 0.59 -0.57 8 149 9 167 1367778 chr4- Exoc4 Intron 0.45 -0.57 3 23 6 52 60559066 chr9- Gulp1 Intron 0.81 -0.57 3 27 3 24 51104228 chr9- AABR060 Intron 0.67 -0.57 4 35 4 25 11425000 58610.1 chr19- AABR060 ExonIntron 0.55 -0.56 3 35 5 51 300594 96719.1 chr19- Cdh13 Intron 0.49 -0.56 3 19 3 17 61656117 chr15- Cd99l2 Intron -0.54 -0.56 6 65 7 62 9908549

166

Table B-2. Continued Chrom & Gene Genomic DNA-Set DNA-RNA AI AI AU AU Pos name feature Shift R value R value N depth N depth chr12- Ttc28 Intron -0.64 -0.56 4 27 3 17 53240421 chr14- Poln Intron -0.56 -0.56 3 16 4 22 82551167 chr10- Cep112 Intron -0.50 -0.56 4 24 4 28 96478345 chr1- AABR060 Intron 0.68 -0.56 4 25 3 16 28913233 01371.1 chr2- Nbea Intron 0.65 -0.56 4 23 3 19 164676974 chr1- Sox6 Intron 0.45 -0.56 3 19 4 26 192727382 chr9- AABR060 Intron 0.50 -0.56 5 33 3 20 11436097 58610.1 chr9- LOC1003 Intron 0.59 -0.56 3 21 6 63 7441491 60856 chr8- Tle3 Exon -0.46 -0.56 5 38 3 19 66075932 chr2- Fam81b Intron 0.46 -0.55 3 15 6 34 3074596 chr19- AABR060 Intron 0.52 -0.55 4 34 8 49 40056671 98126.1 chr3- Gpsm1 Exon -0.85 -0.55 4 28 3 18 9136369 chr14- AABR060 Intron 0.61 -0.55 7 66 6 43 46836573 78903.1 chr2- Chia Intron -0.64 -0.55 3 17 3 16 228216029 chr1- Fam168a ExonIntron 0.91 -0.55 3 20 3 19 172040987 chr19- LOC1003 Intron 0.46 -0.55 8 123 8 129 37384608 63633 chr1- Eif3c Promoter 0.57 -0.55 3 17 3 25 204962138 chr2- Schip1 Intron -0.50 -0.55 4 25 5 38 191619277 chr7- LOC1003 Intron -0.67 -0.54 3 27 4 34 23169500 61411 chr11- Synj1 Intron 0.75 -0.54 3 19 3 17 34735104 chr14- Epha5 Intron 0.61 -0.54 7 50 8 68 25465028 chr5- Ubap2 Intron -0.49 -0.53 5 38 3 19 62122538 chr19- AABR060 Intron 0.55 -0.53 3 20 3 19 308416 96719.1 chr13- AABR060 Intron -0.70 -0.53 4 23 5 33 100462039 76326.1 chr1- Pde3b Intron 0.48 -0.53 3 22 3 19 191002687 chr16- Galntl6 Intron -0.45 -0.53 4 24 5 38 35498216

167

Table B-2. Continued Chrom & Gene Genomic DNA-Set DNA-RNA AI AI AU AU Pos name feature Shift R value R value N depth N depth chr1- Aff2 Intron 0.47 -0.53 3 26 5 35 149529967 chr19- AABR060 Intron -0.52 -0.52 4 27 4 22 26041120 97301.1 chr9- Erbb4 Intron -0.86 -0.52 3 19 3 19 75324411 chr5- Stau2 Intron 0.67 -0.52 4 20 3 15 2349773 chr19- AABR060 Intron 0.47 -0.52 7 68 6 56 303837 96719.1 chr2- Gpr98 Intron 0.52 -0.52 6 48 7 44 8910250 chr20- RGD130 ExonIntron 0.73 -0.52 3 18 4 29 23487161 6739 chr17- Agtr1a Intron -0.65 -0.52 3 20 3 18 37253537 chr2- Chmp4c Intron 0.46 -0.52 4 25 5 38 113371933 chr7- Arhgap39 Exon 0.68 -0.52 3 25 3 17 117789948 chr19- Piezo1 Exon 0.82 -0.52 3 18 3 18 66029743 chr20- RGD130 ExonIntron -0.59 -0.52 4 26 5 31 23487345 6739 chr1- Clvs2 Intron 0.59 -0.52 9 80 7 51 26693596 chr19- LOC1009 ExonIntron 0.54 -0.52 5 41 6 57 343643 12892 chr10- Mfsd11 Intron -0.84 -0.51 5 30 3 16 105470099 chr1- Gabra3 Intron -0.94 -0.51 4 30 3 17 148288574 chr20- RT1-CE7 Intron -0.76 -0.51 3 19 4 40 6743568 chr15- LOC1003 Intron 0.64 -0.51 3 25 4 31 9491943 64581 chr15- Nalcn Intron -0.65 -0.51 3 18 3 17 113313498 chr7- Atxn10 Intron 0.88 -0.51 3 19 3 22 126019176 chrX- Trpc5 Intron 0.46 -0.51 5 32 4 26 114187746 chr2- Nbea ExonIntron -0.77 -0.51 3 24 3 22 164514876 chr3- Ptprt Intron -0.45 -0.51 4 25 3 18 164064269 chr20- RT1-CE7 Intron -0.46 -0.50 5 33 6 38 6728308 chr5- Snx30 Intron 0.75 -0.50 4 23 3 17 81257420 chr6- LOC6805 Exon 0.56 -0.50 3 26 4 23 112106815 19

168

Table B-2. Continued Chrom & Gene Genomic DNA-Set DNA-RNA AI AI AU AU Pos name feature Shift R value R value N depth N depth chr9- LOC1003 Intron 0.51 -0.50 3 17 5 29 7483787 60856 chr6- Begain Exon -0.53 -0.50 3 18 3 18 142110120 chr15- LOC1003 Intron 0.52 -0.50 4 29 4 34 9490216 64581 chr15- Cd99l2 Intron -0.75 -0.50 5 46 6 56 9865540 chr14- Wdpcp Intron -0.68 -0.50 3 21 3 16 106807252 chr1- Tert Exon -0.69 -0.50 4 30 3 20 33696028 chr19- LOC1003 Intron -0.50 -0.50 3 17 3 36 37394441 63633 chr5- Sync Intron -0.60 -0.49 3 16 4 21 151211650 chr20- Ank3 Intron 0.56 -0.49 4 25 6 40 22590203 chr20- Ank3 Intron -0.45 -0.49 3 22 5 31 22374967 chr9- Efna5 Intron -0.48 -0.49 3 21 4 24 109632650 chr15- Cd99l2 Intron 0.67 -0.49 3 19 3 28 9911325 chr1- Herc2 Intron 0.61 -0.49 4 22 6 35 115530929 chr1- LOC1009 Intron 0.55 -0.49 4 32 8 45 13555978 09555 chr1- LOC1003 Intron 0.49 -0.49 9 103 9 94 26362921 61380 chr9- LOC1003 Intron 0.74 -0.49 3 19 3 18 7410305 60856 chr2- Cdh12 Intron 0.92 -0.49 3 16 3 19 92137358 chr4- Tpk1 Intron 0.54 -0.49 5 45 6 64 137674392 chr9- LOC1003 Intron 0.58 -0.49 6 55 6 55 7468697 60856 chr4- RGD130 Intron -0.48 -0.49 4 24 5 32 27526657 6626 chr19- LOC2967 Exon 0.57 -0.49 3 18 5 46 37275194 78 chr15- Cdadc1 Intron 0.69 -0.49 4 29 5 31 43696151 chr11- Lmln Intron -0.74 -0.48 6 32 7 45 74005944 chr4- Cftr Intron 0.53 -0.48 3 26 5 36 42439697 chr10- Cep112 Intron -0.89 -0.48 3 23 4 33 96759181 chrX- Ormdl1 Exon -0.78 -0.48 5 40 3 17 3195101

169

Table B-2. Continued Chrom & Gene Genomic DNA-Set DNA-RNA AI AI AU AU Pos name feature Shift R value R value N depth N depth chr9- LOC6882 Intron 0.50 -0.48 3 17 5 33 10679131 41 chr19- AABR060 Intron -0.70 -0.48 5 46 4 24 314425 96719.1 chr15- Scel Intron -0.79 -0.48 5 33 8 58 91253619 chr19- LOC1003 Intron -0.75 -0.48 4 39 4 26 37452841 63633 chr17- LOC6889 Exon 0.55 -0.47 5 65 7 77 91122611 70 chr1- LOC1003 Intron 0.49 -0.47 4 27 5 34 26377811 61380 chr4- Grin2b Intron 0.77 -0.47 3 19 3 22 234054440 chr19- LOC1003 Exon 0.55 -0.47 3 26 6 64 38816659 59783 chr9- Col19a1 Intron 0.49 -0.47 3 18 4 25 29404405 chr9- Col19a1 Intron -0.54 -0.47 4 23 3 15 29549956 chrX- Il1rapl1 Intron 0.60 -0.47 3 19 3 21 56449113 chr10- Cep112 Intron 0.55 -0.47 4 25 4 25 96478351 chr2- Nlgn1 Intron 0.45 -0.47 5 34 4 25 130811318 chr17- Sugct Intron -0.65 -0.46 3 17 3 18 48612554 chr15- Fgf14 Intron 0.47 -0.46 3 18 5 45 113815530 chr9- Rn50_9_ Promoter 0.48 -0.46 3 22 4 26 11353671 0114.1 chr1- Igf2r Intron 0.51 -0.46 4 29 4 22 51497089 chrX- Pola1 Intron 0.49 -0.46 4 26 4 26 63234373 chr1- LOC1003 Intron -0.74 -0.46 3 21 4 26 26415949 61380 chr3- Rbms1 Intron -0.91 -0.46 3 16 3 15 51927740 chr15- LOC1003 Intron -0.55 -0.46 5 55 6 55 9488922 64581 chr4- Hk2 Exon 0.66 -0.46 4 33 4 25 178248923 chr1- Ptov1 Exon 0.50 -0.46 4 30 4 28 101928303 chr2- Ralyl Intron 0.49 -0.46 5 75 5 57 108908853 chr15- LOC1003 ExonIntron -0.51 -0.46 8 102 9 148 9570365 64581 chr17- Cntnap3 Intron 0.63 -0.46 8 54 4 31 2208839

170

Table B-2. Continued Chrom & Gene Genomic DNA-Set DNA-RNA AI AI AU AU Pos name feature Shift R value R value N depth N depth chr3- Zfp64 Intron -0.73 -0.46 4 34 4 31 171859442 chr13- Rgs7 Intron -0.75 -0.45 3 20 5 40 97706396 chr18- AABR060 Exon -0.59 -0.45 4 27 4 26 30522175 94669.1 chr9- Tbc1d5 Intron 0.65 -0.45 3 32 6 45 1394216 chr13- Cntnap5c Intron 0.45 -0.45 5 37 7 49 12343070 chr7- LOC1003 Intron -0.53 -0.45 3 16 5 35 23170936 61411 chr17- Hecw1 Intron -0.45 -0.45 9 114 11 139 51311506 chr15- LOC6806 ExonIntron 0.83 -0.45 3 18 3 21 9622580 53 chr19- Fa2h Intron 0.69 -0.45 3 23 3 25 54377552 chr19- LOC1003 Intron 0.64 -0.45 3 26 4 28 37397144 63633 chr12- AABR060 Intron -0.65 -0.45 4 23 4 27 22507965 70908.1 chr1- Adam12 Intron 0.51 0.44 7 56 8 90 213183097 chr7- Pkhd1l1 Intron -0.46 0.45 3 19 4 22 83419246 chr19- LOC1009 Intron -0.49 0.45 7 105 9 121 374881 12892 chr19- Trim67 Exon 0.67 0.45 3 23 3 18 68301797 chr1- Pip5k1b Intron -0.55 0.45 3 18 3 15 249447689 chr2- Dpyd Intron -0.50 0.45 3 24 5 31 240460188 chr3- Tox2 Exon 0.67 0.45 3 18 3 17 165838500 chr15- Nid2 Intron -0.69 0.45 3 19 3 19 9018030 chr19- AABR060 Intron -0.57 0.45 5 47 7 55 39863500 98126.1 chr9- LOC1003 Intron -0.65 0.45 5 42 5 32 7412846 60856 chr19- Slc10a7 Intron 0.51 0.45 3 19 3 21 43922075 chr18- Rock1 Intron -0.45 0.45 4 37 3 29 1371889 chr9- LOC1003 Intron -0.79 0.45 3 20 5 38 7409793 60856 chr2- Synpo2 Intron -0.65 0.45 4 32 7 79 246634374 chr3- Trim44 Intron -0.57 0.45 6 41 5 36 98879436

171

Table B-2. Continued Chrom & Gene Genomic DNA-Set DNA-RNA AI AI AU AU Pos name feature Shift R value R value N depth N depth chr16- March1 Intron 0.59 0.45 8 66 8 78 25083259 chr15- Fndc3a Intron -0.54 0.45 5 33 6 36 58230324 chr1- Gda Intron 0.52 0.45 4 28 4 26 246285726 chr9- Ccdc150 Intron 0.62 0.46 7 48 7 45 60631179 chr4- Ctnna2 Intron -0.81 0.46 3 16 3 19 173123195 chr14- LOC3641 Intron -0.62 0.46 7 58 7 57 46260556 57 chr14- Fras1 Intron 0.80 0.46 3 21 3 19 14534651 chr19- AABR060 Intron 0.46 0.46 4 27 5 27 26041154 97301.1 chr9- LOC1003 Intron -0.54 0.46 4 35 6 51 7413323 60856 chr14- Eml6 Intron -0.50 0.46 5 33 5 27 113925030 chr15- LOC6806 Intron 0.54 0.47 5 31 6 45 9595063 53 chr19- LOC1003 Intron 0.78 0.47 4 27 6 55 37464399 63633 chr2- Schip1 Intron 0.59 0.47 4 31 4 23 191651311 chr13- Cdc42bp Intron -0.78 0.47 5 42 5 42 103277272 a chr4- Tpk1 Intron -0.62 0.47 4 25 5 31 137696628 chr3- AABR060 Intron -0.74 0.47 3 16 3 20 102530406 25251.1 chr2- Lrba Intron -0.53 0.47 3 22 3 15 205211651 chr2- March3 Intron 0.78 0.47 3 15 3 19 104229510 chr19- LOC1009 Exon -0.70 0.47 6 90 9 98 38795109 09409 chr7- LOC1003 Exon 0.47 0.47 3 36 5 53 23168998 61411 chr1- LOC1009 Intron 0.52 0.47 5 57 5 41 13556131 09555 chr6- Klhdc1 Intron -0.57 0.47 5 32 5 29 101036460 chr17- Plxdc2 Intron -0.46 0.47 3 19 5 32 85222369 chr6- Supt7l Exon 0.63 0.48 4 24 3 16 36071331 chr17- LOC6889 Intron -0.61 0.48 3 31 7 60 91075231 70 chr19- RGD156 Intron -0.54 0.48 9 83 10 103 39208357 2877

172

Table B-2. Continued Chrom & Gene Genomic DNA-Set DNA-RNA AI AI AU AU Pos name feature Shift R value R value N depth N depth chr3- Xirp2 Intron -0.69 0.48 4 34 9 90 60264722 chr4- Pclo Intron 0.47 0.48 4 32 6 47 16634476 chr10- Unkl ExonIntron -0.70 0.48 5 35 4 21 14405641 chr13- Hmcn1 Intron -0.50 0.48 9 129 11 114 73074635 chr3- Cacnb4 Intron -0.55 0.49 4 29 6 44 43116743 chrX- AABR061 Intron 0.64 0.49 4 37 4 29 53314599 04465.1 chr2- Ghr Intron -0.77 0.49 6 43 6 39 72205202 chr15- LOC1003 Intron 0.45 0.49 8 100 8 100 9520337 64581 chr13- Ptprc Intron 0.73 0.49 3 19 4 24 60201086 chr2- Nlgn1 Intron -0.86 0.49 4 34 3 22 131072360 chr2- Kirrel Intron -0.46 0.49 3 17 4 22 205865955 chr6- Dgkb Intron 0.85 0.50 3 16 3 17 67859868 chr10- Cluap1 Intron -0.45 0.50 9 536 11 581 10616731 chr19- LOC1009 Exon -0.58 0.50 3 36 8 76 40231714 12892 chr18- Psma8 Intron 0.86 0.50 3 17 3 22 6119975 chr3- Cacnb4 Intron -0.54 0.50 4 30 5 35 43116097 chr15- LOC1003 Intron -0.53 0.50 5 30 3 20 9512206 64581 chr8- Slc9a9 Intron 0.54 0.50 7 48 9 78 102307459 chr19- LOC1003 Intron -0.53 0.50 8 107 8 129 37508930 63633 chr2- Mctp1 Intron 0.53 0.50 4 39 7 56 3714556 chr1- Tle4 Intron 0.53 0.50 3 19 3 18 238604289 chr16- Mtmr7 Intron -0.57 0.50 5 37 7 50 54513208 chr1- Rsph6a Exon -0.56 0.51 3 17 3 15 81219682 chr13- Nckap5 Intron 0.46 0.51 6 50 6 47 47728835 chr1- Abcc2 Intron 0.57 0.51 3 22 3 16 271007236 chr5- Faf1 Intron 0.84 0.51 3 19 4 22 133489115

173

Table B-2. Continued Chrom & Gene Genomic DNA-Set DNA-RNA AI AI AU AU Pos name feature Shift R value R value N depth N depth chr19- LOC1009 Exon -0.82 0.51 3 24 4 37 38798042 09409 chr2- Tbck Intron 0.58 0.51 5 30 6 42 256399039 chr2- Arl15 Intron -0.79 0.51 3 20 3 16 65080670 chr7- Fer1l6 Intron -0.80 0.51 3 25 5 28 99074755 chr9- LOC1003 Exon 0.50 0.51 3 16 3 17 25974711 61830 chr3- LOC5026 Intron 0.59 0.51 3 30 8 55 26748580 22 chr6- AABR060 Exon 0.49 0.51 3 25 3 24 111983770 45117.2 chr1- LOC1003 Intron -0.79 0.52 3 24 3 22 26371514 61380 chr19- LOC2967 Exon 0.52 0.52 3 23 3 19 37277869 78 chr12- Insr Intron 0.50 0.52 8 63 9 78 3865856 chr1- LOC1003 Intron 0.50 0.52 3 20 5 30 26359639 61380 chr4- Dysf Intron 0.52 0.52 4 26 6 33 180432762 chr5- Necab1 Intron -0.53 0.52 4 25 5 37 33239170 chr18- AABR060 Exon 0.59 0.52 5 29 4 26 30522131 94669.1 chr13- Cntnap5b Intron -0.71 0.52 4 29 3 19 27008261 chr9- Pard3b Intron -0.71 0.52 3 17 4 28 68365175 chr16- Ddx60 Intron 0.82 0.53 3 19 3 17 29790925 chr1- Ptprh Intron 0.82 0.53 3 16 3 19 75711570 chr5- Necab1 Intron -0.76 0.53 4 36 5 43 33359119 chr14- LOC3641 Intron 0.45 0.53 5 30 3 20 46196015 57 chr10- Cep112 Intron 0.73 0.54 4 25 4 29 96478349 chr6- Rtn1 Intron 0.71 0.54 3 15 4 24 104485557 chr4- Chmp3 Intron -0.58 0.54 4 21 5 27 164245325 chr1- Sox6 Intron -0.51 0.54 4 30 7 36 192727466 chr5- Tdrd7 Intron -0.57 0.54 3 33 3 20 66090466 chr2- Rab3c Intron -0.63 0.54 3 20 4 25 60694620

174

Table B-2. Continued Chrom & Gene Genomic DNA-Set DNA-RNA AI AI AU AU Pos name feature Shift R value R value N depth N depth chr13- Hmcn1 Intron -0.46 0.54 4 22 4 32 73019089 chr5- Cyp2j10 Intron 0.48 0.55 3 15 4 23 119108282 chr1- Aff2 Intron -0.68 0.55 4 37 6 41 149529807 chr10- Cct6b Intron 0.57 0.55 3 18 3 22 69724940 chr20- Dnah8 Exon 0.48 0.55 5 32 4 26 11615350 chr1- Thumpd1 Intron 0.55 0.55 7 59 6 43 197188202 chr2- Atp6ap1l Intron -0.59 0.55 5 41 3 23 19664251 chr14- AABR060 Intron 0.64 0.56 7 94 10 137 46826974 78903.1 chr15- Lmo7 Intron -0.76 0.56 3 19 3 16 87845778 chr16- Adam32 Intron 0.50 0.56 8 99 9 143 71635177 chr16- Cars2 Intron 0.46 0.56 3 26 3 23 82786462 chr6- Pygl Exon -0.53 0.56 3 21 6 47 102053463 chrX- Il1rapl2 Intron -0.57 0.56 3 22 3 20 109570343 chr3- Slc28a2 Exon 0.66 0.57 3 25 3 19 120905161 chr2- Ttc37 Intron -0.53 0.57 4 30 4 26 2982624 chr4- Clec2dl1 Intron -0.46 0.57 3 18 4 22 227505829 chr8- Snx14 Intron 0.54 0.57 4 30 5 34 95520468 chr5- Dnajc6 Intron 0.74 0.57 3 21 5 36 124354955 chr15- Lmo7 Intron 0.72 0.57 3 19 4 23 87845769 chr13- Cntnap5b Intron 0.67 0.57 4 21 5 32 26973829 chrX- Thoc2 Intron -0.50 0.57 3 17 3 16 128270884 chr19- LOC1009 Intron 0.48 0.57 3 25 5 47 38796055 09409 chr17- LOC6889 Intron -0.77 0.57 3 24 4 23 91096563 70 chr7- Ptk2 Exon -0.62 0.58 3 19 3 18 114498195 chr3- Zfp64 Intron 0.61 0.58 4 33 4 32 171859478 chr1- Galnt18 Intron -0.49 0.58 3 24 3 18 183410818

175

Table B-2. Continued Chrom & Gene Genomic DNA-Set DNA-RNA AI AI AU AU Pos name feature Shift R value R value N depth N depth chr8- Ccdc13 Intron -0.68 0.59 3 19 3 20 129643656 chr18- RGD156 Intron 0.74 0.59 5 36 6 63 17222153 2608 chr14- Slain2 Intron 0.52 0.59 3 21 6 43 37654754 chr1- Fam189a Intron 0.80 0.59 4 21 4 23 126954895 1 chr19- Nlrc5 Intron 0.70 0.59 3 19 3 18 10912214 chr8- Ulk4 Intron -0.53 0.59 3 26 6 37 129068049 chr20- Ctnna3 Intron 0.46 0.59 6 50 8 49 28596316 chr9- AABR060 Exon 0.74 0.59 4 31 3 15 7677772 58265.2 chr19- AABR060 Intron 0.57 0.60 4 35 6 65 319769 96719.1 chr4- Cftr Intron -0.66 0.60 5 34 3 22 42377107 chr9- AABR060 Intron -0.90 0.60 3 19 3 16 11429089 58610.1 chr10- Synrg Intron -0.84 0.61 3 16 4 23 71254068 chr19- LOC1009 Intron -0.45 0.61 3 21 6 47 337695 12892 chr7- Tbc1d15 Intron -0.47 0.61 6 36 4 27 58219846 chr19- LOC1003 Intron 0.51 0.61 3 23 4 28 37497022 63633 chr20- Ros1 Intron 0.59 0.61 4 24 3 22 34929288 chr4- LOC1003 Intron 0.46 0.61 3 19 3 18 141196975 64190 chr4- Mitf Intron -0.45 0.61 4 24 3 24 194906178 chr3- Gpsm1 Exon 0.71 0.62 5 32 3 18 9136394 chr1- Rdh13 Intron 0.56 0.62 6 42 5 33 75575066 chr9- Wash Intron 0.85 0.62 3 26 5 26 113449818 chr4- Cntn4 Intron -0.66 0.62 3 19 3 19 203144438 chr2- Ndst4 Intron 0.57 0.62 4 26 5 33 248719916 chr9- Pard3b Intron -0.47 0.63 5 42 6 56 68365125 chr12- Zfp394 Intron -0.72 0.63 4 24 4 25 13251697 chr8- Dscaml1 Intron 0.54 0.63 3 19 3 21 48574628

176

Table B-2. Continued Chrom & Gene Genomic DNA-Set DNA-RNA AI AI AU AU Pos name feature Shift R value R value N depth N depth chr19- LOC1003 ExonIntron 0.63 0.63 3 15 5 39 37410939 63633 chr7- Nsmce2 Intron -0.71 0.63 3 19 3 19 100398611 chr14- LOC2576 Intron -0.60 0.63 3 16 4 21 46703935 42 chr17- Sugct Intron 0.77 0.63 3 19 5 47 48687945 chr19- LOC1003 Intron 0.62 0.64 4 28 6 55 38818544 59783 chr8- Mthfs Intron 0.75 0.64 3 23 3 17 96092255 chr7- LOC3003 Intron 0.86 0.64 3 25 3 18 20634311 08 chr20- RGD130 Exon 0.86 0.64 4 23 3 19 46739691 4770 chr1- Naa40 Intron 0.62 0.64 3 16 4 25 229478633 chr16- F2rl3 Exon -0.79 0.64 3 23 3 18 18689028 chr3- Tsc1 Intron 0.48 0.64 3 22 3 21 12609876 chr12- RGD155 Intron 0.47 0.64 9 114 10 98 22079253 9588 chr11- Phldb2 Exon 0.74 0.64 4 21 3 22 60567404 chr19- LOC1003 Intron 0.62 0.64 3 26 3 20 37441330 63633 chr20- RT1-CE4 Exon 0.86 0.65 4 24 3 16 6997061 chr7- AABR060 Promoter -0.45 0.65 4 31 5 38 2202388 47141.1 chr13- Hmcn1 Intron -0.56 0.65 3 17 3 25 73019090 chr4- Mitf Intron -0.58 0.65 3 20 3 16 194826564 chr10- Snx29 Intron -0.69 0.65 3 22 3 23 3068574 chr13- Rabgap1l Intron -0.66 0.65 5 38 4 23 83322498 chr19- LOC1009 Intron -0.56 0.66 3 20 4 32 394712 12892 chrX- Tuba1a Exon -0.73 0.66 4 29 3 21 115144181 chrX- Acot9 Intron -0.51 0.66 6 48 6 60 43866977 chr3- Ext2 Intron 0.54 0.66 3 17 3 18 89423452 chr9- Kansl3 Intron -0.61 0.66 3 26 3 20 42560551 chr2- Cdh12 Intron -0.50 0.66 4 24 3 24 92257304

177

Table B-2. Continued Chrom & Gene Genomic DNA-Set DNA-RNA AI AI AU AU Pos name feature Shift R value R value N depth N depth chr1- Polg Exon -0.46 0.66 3 19 4 25 142146520 chr9- Efna5 Intron -0.48 0.67 4 23 3 19 109781645 chr1- Capn5 Intron -0.45 0.67 3 21 3 17 169323994 chrX- Uxt Intron 0.52 0.67 3 16 4 28 2099486 chr16- Myo9b Exon 0.91 0.67 3 15 4 22 19547128 chr1- Galnt18 Intron -0.63 0.67 4 27 4 24 183410821 chr2- Spata5 Intron 0.54 0.67 4 26 5 36 143772791 chr6- Tc2n Intron -0.50 0.67 3 21 5 34 134724578 chr19- AABR060 Intron 0.61 0.67 4 30 3 21 302178 96719.1 chr15- AABR060 Exon 0.96 0.68 3 22 3 19 22933819 82338.1 chr12- Vom2r60 Intron -0.52 0.68 6 33 5 33 6464453 chrX- Col4a6 Intron -0.62 0.68 7 50 6 46 111196279 chr3- Pde1a Intron -0.45 0.69 7 66 8 79 73785883 chr3- Zmynd8 Intron 0.58 0.69 5 32 4 26 168661253 chr13- Cntnap5c Intron -0.49 0.69 4 24 5 32 12719025 chr7- LOC3003 Intron 0.50 0.69 4 32 7 54 20624714 08 chr2- LOC6798 Intron 0.90 0.69 3 19 3 23 202424566 11 chr3- Sdcbp2 Intron 0.50 0.70 3 21 4 28 153443231 chr16- Sfmbt1 Intron 0.69 0.70 5 37 4 27 6799238 chr2- Nbea Intron -0.47 0.70 4 38 3 26 164724527 chr4- Mitf Intron -0.48 0.70 4 36 4 28 194906373 chr1- Nell1 Intron -0.56 0.70 4 27 4 21 106784302 chr4- Acn9 Intron -0.62 0.70 3 17 3 17 32401721 chr20- Prdm1 Intron 0.61 0.70 3 17 3 20 51108194 chr15- Fndc3a Intron -0.65 0.70 4 30 4 27 58235235 chr1- Rnls Intron 0.52 0.71 3 31 4 35 259283629

178

Table B-2. Continued Chrom & Gene Genomic DNA-Set DNA-RNA AI AI AU AU Pos name feature Shift R value R value N depth N depth chr13- Cntnap5c Intron -0.88 0.71 4 31 3 23 12618733 chr7- LOC3003 Intron 0.71 0.71 4 38 4 32 20632305 08 chr15- LOC1003 ExonIntron 0.53 0.71 3 34 4 32 9579569 64581 chr19- Trim67 Exon 0.76 0.72 3 23 3 16 68301773 chr2- Gstcd Intron 0.60 0.72 4 26 3 15 256745184 chr1- LOC1003 Intron 0.45 0.72 5 56 8 112 26345148 61380 chrX- Il1rapl1 Intron -0.59 0.73 4 29 4 27 57116745 chr9- LOC5011 Intron 0.52 0.73 5 36 5 43 26325981 10 chr2- Mnd1 Exon 0.72 0.74 4 29 4 23 202533419 chr5- Snx30 Intron -0.70 0.75 3 17 3 22 81257339 chr19- LOC1003 Intron 0.57 0.75 4 31 3 27 37516212 63633 chr13- Rn50_13 Intron 0.68 0.76 3 21 3 18 82297368 _0822.1 chr14- AABR060 Intron -0.49 0.76 3 20 3 22 46822996 78903.1 chr7- Cpq Intron 0.66 0.77 3 22 3 18 71967610 chr11- Arvcf Intron 0.50 0.77 3 18 3 18 89876930 chr17- Mpp7 Intron -0.55 0.77 5 33 4 31 62024620 chr1- LOC1003 Intron 0.53 0.77 4 27 3 24 26352163 61380 chr7- Stk3 Intron -0.59 0.77 3 19 3 17 74022390 chr2- Schip1 Intron 0.57 0.77 6 47 7 43 191651454 chr15- Pibf1 Intron 0.66 0.78 3 18 3 22 87160695 chr12- 5S_rRNA Exon 0.45 0.78 3 18 3 27 1491156 chr14- Egfr Intron 0.51 0.78 3 18 4 28 100519428 chr19- AABR060 Intron 0.62 0.79 4 29 4 24 39874540 98126.1 chr15- Itgbl1 Intron 0.69 0.79 4 25 4 24 113593705 chr7- Tbc1d15 Intron -0.82 0.79 3 19 4 25 58219870 chr2- AABR060 Exon -0.56 0.79 3 27 5 28 71024181 13141.2

179

Table B-2. Continued Chrom & Gene Genomic DNA-Set DNA-RNA AI AI AU AU Pos name feature Shift R value R value N depth N depth chr4- Pde1c Intron -0.48 0.80 5 34 4 30 151173202 chr17- Bmp6 Intron 0.84 0.80 3 27 3 18 28946208 chr13- Clasp1 Intron 0.56 0.80 3 17 6 33 39644388 chr19- LOC1003 Intron 0.51 0.82 3 21 4 22 37448800 63633 chr1- Eif3c Promoter -0.54 0.83 4 36 5 36 204961612 chr2- St7l Intron -0.46 0.83 3 21 4 20 226808985 chr14- LOC3641 Intron 0.66 0.83 3 18 3 19 46200048 57 chr13- Nfasc ExonIntron -0.45 0.83 5 38 3 20 54434209 chr9- Tbc1d5 Intron 0.53 0.83 3 18 3 17 1380927 chr3- Zmynd8 Intron 0.68 0.83 5 31 4 27 168661232 chr14- Eml6 Intron 0.61 0.84 3 29 3 24 113859908 chr16- AABR060 Intron 0.65 0.85 3 16 3 19 77032746 89318.1 chr2- LOC1009 Exon -0.78 0.86 3 18 3 17 216013971 11180 chr7- AABR060 Exon 0.58 0.86 4 35 6 35 2197740 47140.1 chr6- LOC1009 Promoter 0.48 0.86 3 20 3 22 111985569 09961 chr8- Bbs9 Intron 0.77 0.86 3 20 3 15 24100540 chr7- Dcaf13 Intron -0.74 0.88 3 18 3 18 78223365 chr1- Rps6ka2 Intron 0.64 0.88 3 22 4 22 54699127 chr14- LOC2576 Intron 0.51 0.89 4 29 4 42 46701845 42 chr4- Fam13a Intron -0.46 0.90 3 29 3 29 154067628 chr1- LOC1003 Intron 0.70 0.92 3 16 3 21 26351817 61380 chr14- LOC2576 Intron 0.56 0.92 4 33 4 44 46701852 42 chr17- RGD130 Intron 0.70 0.92 3 19 3 16 44044765 7443 chr17- Sugct Intron -0.61 0.93 3 16 3 17 48688336 chr4- Ccdc174 Intron -0.64 0.94 4 28 3 22 189491314 chr19- LOC1003 Exon 0.55 0.94 3 24 4 32 38815594 59783

180

APPENDIX C SEQUENCING DEPTH CONSIDERATIONS FOR RNA-SEQ AND WHOLE GENOME BISULFITE SEQUENCING

Differential Expression with RNA-seq

Next-generation sequencing depth across different application is a variable factor where careful consideration should be taken according to the goal of the study. First, it is important to note that the number of reads or depth will vary according to the technique being used such as RNA-seq, miRNA-seq, WGBS and others (Genohub,

2017). The number will also depend on the targeted molecule being sequenced. For instance, RNA-seq of poly-A selected mRNA may be sequenced at lower depths than whole transcriptome sequencing where the abundance and variety of RNA molecules will be higher. An additional factor which has been an oversight in some RNA-seq studies, is the number of replicates. An interesting study by Liu et al. investigated the statistical power of differential expression of samples sequenced at higher depths with lower replication, relative to samples sequenced at lower depths with higher biological replication (Liu et al., 2014). The study concludes that sequencing depths above 10-20 million reads per biological replicate do not contribute significantly for differential expression studies. Rather, an increase in the number of biological replicates increased the power of the analysis (Liu et al., 2014). Thus, the current study from Chapter 3 followed these guidelines; sequencing samples with a minimum of 10 million reads with biological replication of young and aged rats. The number of reads for each biological replicate is shown in Figure C-1A and the sequencing coverage from aligned reads across the transcriptome from the Ensembl database (rn5 genome) are shown in Figure

C-1B.

181

Whole Genome Bisulfite Sequencing

Several approaches exits to the quantification of DNA methylation across the genome, however the current study in Chapter 4 utilized the WGBS technique to quantify DNA methylation at single base pair resolution in an unbiased approach such that the origin of the sequences would be independent of CpG content across the genome (Harris et al., 2010). Similar to the findings described in the previous section, a study by Ziller et al. highlighted the power of biological replication over high sequencing depths of few replicates in WGBS (Ziller et al., 2015). The study investigated this relationship by performing a downsampling analysis, where the same biological samples are analyzed at varying sequencing depths. The study found lower false discovery rates from the analyzes which contained more replication. It also suggests, that the sensitivity in the recovery of true positives were sufficient with 5-10X coverage per replicate, such that total coverages above 60x per group (e.g.: age groups for our current study) resulted in minimal recovery of true positives (Ziller et al., 2015). Thus, the sequencing depths presented in Chapter 4 are sufficient for the determination of differentially methylated sites in young and aged rats, where the minimum is set to 15x.

However, in many cases, the sites identified in Chapter 4 contained more than 15x. The genome-wide depths are presented in the methods of Chapter 4, and the sequencing depths from promoter and gene body sites is shown in Table 4-4 and Table 4-5.

Further, Table B-1 and Table B-2 indicates site specific depth for several genes. Figure

C-2 shows the total reads sequenced per biological replicate, where the mean across all replicates was 133 million paired-end reads.

182

Figure C-1. Number of reads and coverage of RNA-seq across the mPFC biological replicates. A) Indicates the number of reads sequenced for each aged and young rat. B) Indicates the transcriptomic coverage of Ensembl transcripts from the aligned reads from each biological replicate.

183

Figure C-2. Number of paired-end reads of WGBS across the biological replicates from the mPFC.

184

APPENDIX D THE UNITS OF NORMALIZATION FOR RNA-SEQ

Much debate is present in bioinformatics regarding the choice of a unit for normalization, including normalized counts, RPKM/FPKM (Reads/Fragments Per

Kilobase of transcript per Million mapped reads) and TPM (Transcripts Per Million)

(Anders and Huber, 2010; Bullard et al., 2010; Dillies et al., 2013; Wagner et al., 2012).

The idea across the methods is to normalize the biological replicates for the number of reads sequenced, to avoid errors in the quantification of expression.

The DESeq/DESeq2 method normalizes gene level counts by defining the

“effective library size” (the sequencing depth) of each replicate and dividing the raw read count of each gene by the size factor of the library (Anders and Huber, 2010;

Dillies et al., 2013; Love et al., 2014). Thus if replicate A was sequenced twice as deeply as replicate B, the size factor for A would be twice as larger than B (Anders and

Huber, 2010). The normalization effect from DESeq from the mPFC replicates presented in Chapter 3 is illustrated in Figure D-1A and Figure D-1B, where it is shown that samples with more sequencing depths (Figure C-1A) are normalized to their respective size factor.

In contrast, the RPKM method attempts to normalize for sequencing depth and gene length (Dillies et al., 2013). However, this method has been shown have a bias in differential expression and enrichment analysis of longer genes due to the higher number of reads sequenced from longer genes relative to shorter genes (Oshlack and

Wakefield, 2009; Wagner et al., 2012). Thus, recently, the unit of count has become more commonly used in differential expression studies. However, it is important to note, that the TPM unit was created in an attempt to remove the gene length bias from

185

RPKM, making the TPM unit also powerful for RNA-seq analysis (Wagner et al., 2012).

Further, while the current study utilized DESeq normalization, additional methods for count normalization exist and have been reviewed and cross-compared by previous work (Dillies et al., 2013; Zhang et al., 2014).

186

Figure D-1. DESeq normalization of RNA-seq across the mPFC biological replicates. A) Raw counts for each replicate where each dot represents a gene and the lines are the median and upper quartile of lower abundance genes. B) Normalized counts demonstrating the size factor normalization per replicate. Thus, differences in counts represent biological variability, rather than technical variability from sequencing runs.

187

LIST OF REFERENCES

Adams, M.M., Fink, S.E., Shah, R.A., Janssen, W.G., Hayashi, S., Milner, T.A., McEwen, B.S., Morrison, J.H., 2002. Estrogen and aging affect the subcellular distribution of estrogen receptor-alpha in the hippocampus of female rats. J Neurosci 22, 3608-3614.

Aenlle, K.K., Foster, T.C., 2010. Aging alters the expression of genes for neuroprotection and synaptic function following acute estradiol treatment. Hippocampus 20, 1047-1060.

Aenlle, K.K., Kumar, A., Cui, L., Jackson, T.C., Foster, T.C., 2009. Estrogen effects on cognition and hippocampal transcription in middle-aged mice. Neurobiol Aging 30, 932-945.

Al-Mahdawi, S., Virmouni, S.A., Pook, M.A., 2014. The emerging role of 5- hydroxymethylcytosine in neurodegenerative diseases. Front Neurosci 8, 397.

Anders, S., Huber, W., 2010. Differential expression analysis for sequence count data. Genome biology 11, R106.

Andrews-Hanna, J.R., Snyder, A.Z., Vincent, J.L., Lustig, C., Head, D., Raichle, M.E., Buckner, R.L., 2007. Disruption of large-scale brain systems in advanced aging. Neuron 56, 924-935.

Andrews, S., 2010. FastQC. http://www.bioinformatics.babraham.ac.uk/projects/fastqc/.

Angermueller, C., Clark, S.J., Lee, H.J., Macaulay, I.C., Teng, M.J., Hu, T.X., Krueger, F., Smallwood, S.A., Ponting, C.P., Voet, T., Kelsey, G., Stegle, O., Reik, W., 2016. Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity. Nat Methods 13, 229-232.

Ashendorf, L., McCaffrey, R.J., 2008. Exploring age-related decline on the Wisconsin Card Sorting Test. Clin Neuropsychol 22, 262-272.

Barense, M.D., Fox, M.T., Baxter, M.G., 2002. Aged rats are impaired on an attentional set-shifting task sensitive to medial frontal cortex damage in young rats. Learning & memory 9, 191-201.

Bean, L.A., Ianov, L., Foster, T.C., 2014. Estrogen receptors, the hippocampus, and memory. The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry 20, 534-545.

Bean, L.A., Kumar, A., Rani, A., Guidi, M., Rosario, A.M., Cruz, P.E., Golde, T.E., Foster, T.C., 2015. Re-Opening the Critical Window for Estrogen Therapy. J Neurosci 35, 16077-16093.

188

Beas, B.S., Setlow, B., Bizon, J.L., 2013. Distinct manifestations of executive dysfunction in aged rats. Neurobiol Aging 34, 2164-2174.

Benedusi, V., Meda, C., Della Torre, S., Monteleone, G., Vegeto, E., Maggi, A., 2012. A lack of ovarian function increases neuroinflammation in aged mice. Endocrinology 153, 2777-2788.

Benloucif, S., Masana, M.I., Dubocovich, M.L., 1997. Light-induced phase shifts of circadian activity rhythms and immediate early gene expression in the suprachiasmatic nucleus are attenuated in old C3H/HeN mice. Brain Res 747, 34-42.

Bennett, I.J., Madden, D.J., Vaidya, C.J., Howard, J.H., Jr., Howard, D.V., 2011. White matter integrity correlates of implicit sequence learning in healthy aging. Neurobiol Aging 32, 2317 e2311-2312.

Berchtold, N.C., Coleman, P.D., Cribbs, D.H., Rogers, J., Gillen, D.L., Cotman, C.W., 2013. Synaptic genes are extensively downregulated across multiple brain regions in normal human aging and Alzheimer's disease. Neurobiol Aging 34, 1653-1661.

Bicaku, E., Marchion, D.C., Schmitt, M.L., Munster, P.N., 2008. Selective inhibition of histone deacetylase 2 silences progesterone receptor-mediated signaling. Cancer Res 68, 1513-1519.

Bird, A.P., Wolffe, A.P., 1999. Methylation-induced repression--belts, braces, and chromatin. Cell 99, 451-454.

Birrell, J.M., Brown, V.J., 2000. Medial frontal cortex mediates perceptual attentional set shifting in the rat. J Neurosci 20, 4320-4324.

Bishop, N.A., Lu, T., Yankner, B.A., 2010. Neural mechanisms of ageing and cognitive decline. Nature 464, 529-535.

Bizon, J.L., Foster, T.C., Alexander, G.E., Glisky, E.L., 2012. Characterizing cognitive aging of working memory and executive function in animal models. Frontiers in aging neuroscience 4, 19.

Blalock, E.M., Chen, K.C., Sharrow, K., Herman, J.P., Porter, N.M., Foster, T.C., Landfield, P.W., 2003. Gene microarrays in hippocampal aging: statistical profiling identifies novel processes correlated with cognitive impairment. J Neurosci 23, 3807-3819.

Bock, C., Reither, S., Mikeska, T., Paulsen, M., Walter, J., Lengauer, T., 2005. BiQ Analyzer: visualization and quality control for DNA methylation data from bisulfite sequencing. Bioinformatics 21, 4067-4068.

189

Bohacek, J., Daniel, J.M., 2009. The ability of oestradiol administration to regulate protein levels of oestrogen receptor alpha in the hippocampus and prefrontal cortex of middle-aged rats is altered following long-term ovarian hormone deprivation. J Neuroendocrinol 21, 640-647.

Bolger, A.M., Lohse, M., Usadel, B., 2014. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114-2120.

Booth, M.J., Ost, T.W., Beraldi, D., Bell, N.M., Branco, M.R., Reik, W., Balasubramanian, S., 2013. Oxidative bisulfite sequencing of 5-methylcytosine and 5-hydroxymethylcytosine. Nat Protoc 8, 1841-1851.

Bordner, K.A., Kitchen, R.R., Carlyle, B., George, E.D., Mahajan, M.C., Mane, S.M., Taylor, J.R., Simen, A.A., 2011. Parallel declines in cognition, motivation, and locomotion in aging mice: association with immune gene upregulation in the medial prefrontal cortex. Experimental gerontology 46, 643-659.

Borghesani, P.R., Madhyastha, T.M., Aylward, E.H., Reiter, M.A., Swarny, B.R., Schaie, K.W., Willis, S.L., 2013. The association between higher order abilities, processing speed, and age are variably mediated by white matter integrity during typical aging. Neuropsychologia 51, 1435-1444.

Bossers, K., Wirz, K.T., Meerhoff, G.F., Essing, A.H., van Dongen, J.W., Houba, P., Kruse, C.G., Verhaagen, J., Swaab, D.F., 2010. Concerted changes in transcripts in the prefrontal cortex precede neuropathology in Alzheimer's disease. Brain 133, 3699-3723.

Brant, J.O., Riva, A., Resnick, J.L., Yang, T.P., 2014. Influence of the Prader-Willi syndrome imprinting center on the DNA methylation landscape in the mouse brain. Epigenetics 9, 1540-1556.

Brown, V.J., Bowman, E.M., 2002. Rodent models of prefrontal cortical function. Trends in neurosciences 25, 340-343.

Brunner, A.L., Johnson, D.S., Kim, S.W., Valouev, A., Reddy, T.E., Neff, N.F., Anton, E., Medina, C., Nguyen, L., Chiao, E., Oyolu, C.B., Schroth, G.P., Absher, D.M., Baker, J.C., Myers, R.M., 2009. Distinct DNA methylation patterns characterize differentiated human embryonic stem cells and developing human fetal liver. Genome Res 19, 1044-1056.

Buenrostro, J.D., Giresi, P.G., Zaba, L.C., Chang, H.Y., Greenleaf, W.J., 2013. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat Methods 10, 1213-1218.

Buenrostro, J.D., Wu, B., Chang, H.Y., Greenleaf, W.J., 2015. ATAC-seq: A Method for Assaying Chromatin Accessibility Genome-Wide. Curr Protoc Mol Biol 109, 21 29 21-29.

190

Bullard, J.H., Purdom, E., Hansen, K.D., Dudoit, S., 2010. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC Bioinformatics 11, 94.

Burger, C., 2010. Region-specific genetic alterations in the aging hippocampus: implications for cognitive aging. Frontiers in aging neuroscience 2, 140.

Burger, C., Lopez, M.C., Feller, J.A., Baker, H.V., Muzyczka, N., Mandel, R.J., 2007. Changes in transcription within the CA1 field of the hippocampus are associated with age-related spatial learning impairments. Neurobiology of learning and memory 87, 21-41.

Cao, J., Luo, Z., Cheng, Q., Xu, Q., Zhang, Y., Wang, F., Wu, Y., Song, X., 2015. Three-dimensional regulation of transcription. Protein Cell 6, 241-253.

Castles, C.G., Oesterreich, S., Hansen, R., Fuqua, S.A., 1997. Auto-regulation of the estrogen receptor promoter. J Steroid Biochem Mol Biol 62, 155-163.

CDC, February 2011. Cognitive Impairment: A Call for Action, Now! https://www.cdc.gov/aging/pdf/cognitive_impairment/cogimp_poilicy_final.pdf.

Cedar, H., Bergman, Y., 2009. Linking DNA methylation and histone modification: patterns and paradigms. Nat Rev Genet 10, 295-304.

Census, 2016. FFF: Older Americans Month: May 2016. http://www.census.gov/newsroom/facts-for-features/2016/cb16-ff08.html.

Champagne, F.A., Weaver, I.C., Diorio, J., Dymov, S., Szyf, M., Meaney, M.J., 2006. Maternal care associated with methylation of the estrogen receptor-alpha1b promoter and estrogen receptor-alpha expression in the medial preoptic area of female offspring. Endocrinology 147, 2909-2915.

Chen, C.Y., Logan, R.W., Ma, T., Lewis, D.A., Tseng, G.C., Sibille, E., McClung, C.A., 2016. Effects of aging on circadian patterns of gene expression in the human prefrontal cortex. Proc Natl Acad Sci U S A 113, 206-211.

Chen, H., Dzitoyeva, S., Manev, H., 2012. Effect of aging on 5-hydroxymethylcytosine in the mouse hippocampus. Restor Neurol Neurosci 30, 237-245.

Cho, K., Ennaceur, A., Cole, J.C., Suh, C.K., 2000. Chronic jet lag produces cognitive deficits. J Neurosci 20, RC66.

Chodavarapu, R.K., Feng, S., Bernatavichute, Y.V., Chen, P.Y., Stroud, H., Yu, Y., Hetzel, J.A., Kuo, F., Kim, J., Cokus, S.J., Casero, D., Bernal, M., Huijser, P., Clark, A.T., Kramer, U., Merchant, S.S., Zhang, X., Jacobsen, S.E., Pellegrini, M., 2010. Relationship between nucleosome positioning and DNA methylation. Nature 466, 388-392.

191

Churchwell, J.C., Morris, A.M., Musso, N.D., Kesner, R.P., 2010. Prefrontal and hippocampal contributions to encoding and retrieval of spatial memory. Neurobiology of learning and memory 93, 415-421.

Cordaux, R., Batzer, M.A., 2009. The impact of retrotransposons on human genome evolution. Nat Rev Genet 10, 691-703.

Cribbs, D.H., Berchtold, N.C., Perreau, V., Coleman, P.D., Rogers, J., Tenner, A.J., Cotman, C.W., 2012. Extensive innate immune gene activation accompanies brain aging, increasing vulnerability to cognitive decline and neurodegeneration: a microarray study. Journal of neuroinflammation 9, 179.

Dalton, G.L., Ma, L.M., Phillips, A.G., Floresco, S.B., 2011. Blockade of NMDA GluN2B receptors selectively impairs behavioral flexibility but not initial discrimination learning. Psychopharmacology 216, 525-535.

Darby, M.M., Leek, J.T., Langmead, B., Yolken, R.H., Sabunciyan, S., 2016. Widespread splicing of repetitive element loci into coding regions of gene transcripts. Human molecular genetics 25, 4962-4982.

Day, J.J., Sweatt, J.D., 2010. DNA methylation and memory formation. Nat Neurosci 13, 1319-1323.

Day, J.J., Sweatt, J.D., 2011. Epigenetic mechanisms in cognition. Neuron 70, 813-829.

De Bruin, N.M., Kiliaan, A.J., De Wilde, M.C., Broersen, L.M., 2003. Combined uridine and choline administration improves cognitive deficits in spontaneously hypertensive rats. Neurobiology of learning and memory 80, 63-79. de Magalhaes, J.P., Curado, J., Church, G.M., 2009. Meta-analysis of age-related gene expression profiles identifies common signatures of aging. Bioinformatics 25, 875-881.

Debski, K.J., Pitkanen, A., Puhakka, N., Bot, A.M., Khurana, I., Harikrishnan, K.N., Ziemann, M., Kaspi, A., El-Osta, A., Lukasiuk, K., Kobow, K., 2016. Etiology matters - Genomic DNA Methylation Patterns in Three Rat Models of Acquired Epilepsy. Sci Rep 6, 25668.

Dennis, N.A., Hayes, S.M., Prince, S.E., Madden, D.J., Huettel, S.A., Cabeza, R., 2008. Effects of aging on the neural correlates of successful item and source memory encoding. Journal of experimental psychology. Learning, memory, and cognition 34, 791-808.

Dias, N.S., Ferreira, D., Reis, J., Jacinto, L.R., Fernandes, L., Pinho, F., Festa, J., Pereira, M., Afonso, N., Santos, N.C., Cerqueira, J.J., Sousa, N., 2015. Age effects on EEG correlates of the Wisconsin Card Sorting Test. Physiol Rep 3.

192

Dias, R., Robbins, T.W., Roberts, A.C., 1997. Dissociable forms of inhibitory control within prefrontal cortex with an analog of the Wisconsin Card Sort Test: restriction to novel situations and independence from "on-line" processing. J Neurosci 17, 9285-9297.

Dillies, M.A., Rau, A., Aubert, J., Hennequet-Antier, C., Jeanmougin, M., Servant, N., Keime, C., Marot, G., Castel, D., Estelle, J., Guernec, G., Jagla, B., Jouneau, L., Laloe, D., Le Gall, C., Schaeffer, B., Le Crom, S., Guedj, M., Jaffrezic, F., French StatOmique, C., 2013. A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Brief Bioinform 14, 671- 683.

Donaghue, C., Westley, B.R., May, F.E., 1999. Selective promoter usage of the human estrogen receptor-alpha gene and its regulation by estrogen. Mol Endocrinol 13, 1934-1950.

Du, J., Johnson, L.M., Jacobsen, S.E., Patel, D.J., 2015. DNA methylation pathways and their crosstalk with histone methylation. Nat Rev Mol Cell Biol 16, 519-532.

Edelmann, M.N., Auger, A.P., 2011. Epigenetic impact of simulated maternal grooming on estrogen receptor alpha within the developing amygdala. Brain Behav Immun 25, 1299-1304.

Ellison-Zelski, S.J., Solodin, N.M., Alarid, E.T., 2009. Repression of ESR1 through actions of estrogen receptor alpha and Sin3A at the proximal promoter. Mol Cell Biol 29, 4949-4958.

Erraji-Benchekroun, L., Underwood, M.D., Arango, V., Galfalvy, H., Pavlidis, P., Smyrniotopoulos, P., Mann, J.J., Sibille, E., 2005. Molecular aging in human prefrontal cortex is selective and continuous throughout adult life. Biological psychiatry 57, 549-558.

Feng, J., Zhou, Y., Campbell, S.L., Le, T., Li, E., Sweatt, J.D., Silva, A.J., Fan, G., 2010. Dnmt1 and Dnmt3a maintain DNA methylation and regulate synaptic function in adult forebrain neurons. Nat Neurosci 13, 423-430.

Floresco, S.B., Block, A.E., Tse, M.T., 2008. Inactivation of the medial prefrontal cortex of the rat impairs strategy set-shifting, but not reversal learning, using a novel, automated procedure. Behavioural brain research 190, 85-96.

Foster, T.C., 2005. Interaction of rapid signal transduction cascades and gene expression in mediating estrogen effects on memory over the life span. Front Neuroendocrinol 26, 51-64.

Foster, T.C., 2012a. Dissecting the age-related decline on spatial learning and memory tasks in rodent models: N-methyl-D-aspartate receptors and voltage-dependent Ca2+ channels in senescent synaptic plasticity. Progress in neurobiology 96, 283-303.

193

Foster, T.C., 2012b. Role of estrogen receptor alpha and beta expression and signaling on cognitive function during aging. Hippocampus 22, 656-669.

Foster, T.C., Barnes, C.A., Rao, G., McNaughton, B.L., 1991. Increase in perforant path quantal size in aged F-344 rats. Neurobiol Aging 12, 441-448.

Foster, T.C., Defazio, R.A., Bizon, J.L., 2012. Characterizing cognitive aging of spatial and contextual memory in animal models. Frontiers in aging neuroscience 4, 12.

Foster, T.C., Kumar, A., 2007. Susceptibility to induction of long-term depression is associated with impaired memory in aged Fischer 344 rats. Neurobiology of learning and memory 87, 522-535.

Foster, T.C., Rani, A., Kumar, A., Cui, L., Semple-Rowland, S.L., 2008. Viral vector- mediated delivery of estrogen receptor-alpha to the hippocampus improves spatial learning in estrogen receptor-alpha knockout mice. Mol Ther 16, 1587- 1593.

Fraser, H.B., Khaitovich, P., Plotkin, J.B., Paabo, S., Eisen, M.B., 2005. Aging and gene expression in the primate brain. PLoS biology 3, e274.

Freedman, M., Oscar-Berman, M., 1986. Bilateral frontal lobe disease and selective delayed response deficits in humans. Behavioral neuroscience 100, 337-342.

Freyschuss, B., Grandien, K., 1996. The 5' flank of the rat estrogen receptor gene: structural characterization and evidence for tissue- and species-specific promoter utilization. J Mol Endocrinol 17, 197-206.

Frye, C.A., Duffy, C.K., Walf, A.A., 2007. Estrogens and progestins enhance spatial learning of intact and ovariectomized rats in the object placement task. Neurobiology of learning and memory 88, 208-216.

Fugger, H.N., Foster, T.C., Gustafsson, J., Rissman, E.F., 2000. Novel effects of estradiol and estrogen receptor alpha and beta on cognitive function. Brain Res 883, 258-264.

Gallagher, M., Stocker, A.M., Koh, M.T., 2011. Mindspan: lessons from rat models of neurocognitive aging. ILAR J 52, 32-40.

Genohub, 2017. Recommended Coverage and Read Depth for NGS Applications. https://genohub.com/recommended-sequencing-coverage-by-application/.

Ghosh, A., Ginty, D.D., Bading, H., Greenberg, M.E., 1994. Calcium regulation of gene expression in neuronal cells. Journal of neurobiology 25, 294-303.

Gibbs, R.B., 2000. Long-term treatment with estrogen and progesterone enhances acquisition of a spatial memory task by ovariectomized aged rats. Neurobiol Aging 21, 107-116.

194

Globisch, D., Munzel, M., Muller, M., Michalakis, S., Wagner, M., Koch, S., Bruckl, T., Biel, M., Carell, T., 2010. Tissue distribution of 5-hydroxymethylcytosine and search for active demethylation intermediates. PLoS One 5, e15367.

Gore, A.C., Walker, D.M., Zama, A.M., Armenti, A.E., Uzumcu, M., 2011. Early life exposure to endocrine-disrupting chemicals causes lifelong molecular reprogramming of the hypothalamus and premature reproductive aging. Mol Endocrinol 25, 2157-2168.

Grady, C.L., McIntosh, A.R., Craik, F.I., 2005. Task-related activity in prefrontal cortex and its relation to recognition memory performance in young and old adults. Neuropsychologia 43, 1466-1481.

Gray, D.T., Barnes, C.A., 2015. Distinguishing adaptive plasticity from vulnerability in the aging hippocampus. Neuroscience 309, 17-28.

Guan, J.S., Haggarty, S.J., Giacometti, E., Dannenberg, J.H., Joseph, N., Gao, J., Nieland, T.J., Zhou, Y., Wang, X., Mazitschek, R., Bradner, J.E., DePinho, R.A., Jaenisch, R., Tsai, L.H., 2009. HDAC2 negatively regulates memory formation and synaptic plasticity. Nature 459, 55-60.

Guidi, M., Kumar, A., Foster, T.C., 2015. Impaired attention and synaptic senescence of the prefrontal cortex involves redox regulation of NMDA receptors. J Neurosci 35, 3966-3977.

Guidi, M., Kumar, A., Rani, A., Foster, T.C., 2014. Assessing the emergence and reliability of cognitive decline over the life span in Fisher 344 rats using the spatial water maze. Frontiers in aging neuroscience 6, 2.

Guo, J.U., Ma, D.K., Mo, H., Ball, M.P., Jang, M.H., Bonaguidi, M.A., Balazer, J.A., Eaves, H.L., Xie, B., Ford, E., Zhang, K., Ming, G.L., Gao, Y., Song, H., 2011. Neuronal activity modifies the DNA methylation landscape in the adult brain. Nat Neurosci 14, 1345-1351.

Guo, J.U., Su, Y., Shin, J.H., Shin, J., Li, H., Xie, B., Zhong, C., Hu, S., Le, T., Fan, G., Zhu, H., Chang, Q., Gao, Y., Ming, G.L., Song, H., 2014. Distribution, recognition and regulation of non-CpG methylation in the adult mammalian brain. Nat Neurosci 17, 215-222.

Haberman, R.P., Colantuoni, C., Stocker, A.M., Schmidt, A.C., Pedersen, J.T., Gallagher, M., 2011. Prominent hippocampal CA3 gene expression profile in neurocognitive aging. Neurobiol Aging 32, 1678-1692.

Hall, J.M., McDonnell, D.P., 1999. The estrogen receptor beta-isoform (ERbeta) of the human estrogen receptor modulates ERalpha transcriptional activity and is a key regulator of the cellular response to estrogens and antiestrogens. Endocrinology 140, 5566-5578.

195

Han, X., Aenlle, K.K., Bean, L.A., Rani, A., Semple-Rowland, S.L., Kumar, A., Foster, T.C., 2013. Role of estrogen receptor alpha and beta in preserving hippocampal function during aging. The Journal of neuroscience : the official journal of the Society for Neuroscience 33, 2671-2683.

Harony-Nicolas, H., Mamrut, S., Brodsky, L., Shahar-Gold, H., Barki-Harrington, L., Wagner, S., 2014. Brain region-specific methylation in the promoter of the murine oxytocin receptor gene is involved in its expression regulation. Psychoneuroendocrinology 39, 121-131.

Harris, R.A., Wang, T., Coarfa, C., Nagarajan, R.P., Hong, C., Downey, S.L., Johnson, B.E., Fouse, S.D., Delaney, A., Zhao, Y., Olshen, A., Ballinger, T., Zhou, X., Forsberg, K.J., Gu, J., Echipare, L., O'Geen, H., Lister, R., Pelizzola, M., Xi, Y., Epstein, C.B., Bernstein, B.E., Hawkins, R.D., Ren, B., Chung, W.Y., Gu, H., Bock, C., Gnirke, A., Zhang, M.Q., Haussler, D., Ecker, J.R., Li, W., Farnham, P.J., Waterland, R.A., Meissner, A., Marra, M.A., Hirst, M., Milosavljevic, A., Costello, J.F., 2010. Comparison of sequencing-based methods to profile DNA methylation and identification of monoallelic epigenetic modifications. Nat Biotechnol 28, 1097-1105.

Hellman, A., Chess, A., 2007. Gene body-specific methylation on the active X chromosome. Science 315, 1141-1143.

Hernandez, D.G., Nalls, M.A., Gibbs, J.R., Arepalli, S., van der Brug, M., Chong, S., Moore, M., Longo, D.L., Cookson, M.R., Traynor, B.J., Singleton, A.B., 2011. Distinct DNA methylation changes highly correlated with chronological age in the human brain. Human molecular genetics 20, 1164-1172.

Homayoun, H., Moghaddam, B., 2007. NMDA receptor hypofunction produces opposite effects on prefrontal cortex interneurons and pyramidal neurons. J Neurosci 27, 11496-11500.

Hoover, W.B., Vertes, R.P., 2007. Anatomical analysis of afferent projections to the medial prefrontal cortex in the rat. Brain structure & function 212, 149-179.

Huang, D.W., Sherman, B.T., Tan, Q., Collins, J.R., Alvord, W.G., Roayaei, J., Stephens, R., Baseler, M.W., Lane, H.C., Lempicki, R.A., 2007a. The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists. Genome biology 8, R183.

Huang, D.W., Sherman, B.T., Tan, Q., Kir, J., Liu, D., Bryant, D., Guo, Y., Stephens, R., Baseler, M.W., Lane, H.C., Lempicki, R.A., 2007b. DAVID Bioinformatics Resources: expanded annotation database and novel algorithms to better extract biology from large gene lists. Nucleic Acids Res 35, W169-175.

Ianov, L., Kumar, A., Foster, T.C., 2016a. Epigenetic regulation of estrogen receptor alpha contributes to age-related differences in transcription across the hippocampal regions CA1 and CA3. Neurobiol Aging 49, 79-85.

196

Ianov, L., Rani, A., Beas, B.S., Kumar, A., Foster, T.C., 2016b. Transcription Profile of Aging and Cognition-Related Genes in the Medial Prefrontal Cortex. Frontiers in aging neuroscience 8, 113.

Igarashi, S., Suzuki, H., Niinuma, T., Shimizu, H., Nojima, M., Iwaki, H., Nobuoka, T., Nishida, T., Miyazaki, Y., Takamaru, H., Yamamoto, E., Yamamoto, H., Tokino, T., Hasegawa, T., Hirata, K., Imai, K., Toyota, M., Shinomura, Y., 2010. A novel correlation between LINE-1 hypomethylation and the malignancy of gastrointestinal stromal tumors. Clin Cancer Res 16, 5114-5123.

Irvine, R.A., Lin, I.G., Hsieh, C.L., 2002. DNA methylation has a local effect on transcription and histone acetylation. Mol Cell Biol 22, 6689-6696.

Ishunina, T.A., Swaab, D.F., 2007. Alterations in the human brain in menopause. Maturitas 57, 20-22.

Issa, J.P., Ottaviano, Y.L., Celano, P., Hamilton, S.R., Davidson, N.E., Baylin, S.B., 1994. Methylation of the oestrogen receptor CpG island links ageing and neoplasia in human colon. Nature genetics 7, 536-540.

Jackson, T.C., Rani, A., Kumar, A., Foster, T.C., 2009. Regional hippocampal differences in AKT survival signaling across the lifespan: implications for CA1 vulnerability with aging. Cell death and differentiation 16, 439-448.

Jjingo, D., Conley, A.B., Yi, S.V., Lunyak, V.V., Jordan, I.K., 2012. On the presence and role of human gene-body DNA methylation. Oncotarget 3, 462-474.

Jones, P.A., 2012. Functions of DNA methylation: islands, start sites, gene bodies and beyond. Nat Rev Genet 13, 484-492.

Kadish, I., Thibault, O., Blalock, E.M., Chen, K.C., Gant, J.C., Porter, N.M., Landfield, P.W., 2009. Hippocampal and cognitive aging across the lifespan: a bioenergetic shift precedes and increased cholesterol trafficking parallels memory impairment. J Neurosci 29, 1805-1816.

Karolchik, D., Hinrichs, A.S., Furey, T.S., Roskin, K.M., Sugnet, C.W., Haussler, D., Kent, W.J., 2004. The UCSC Table Browser data retrieval tool. Nucleic Acids Res 32, D493-496.

Keleshian, V.L., Modi, H.R., Rapoport, S.I., Rao, J.S., 2013. Aging is associated with altered inflammatory, arachidonic acid cascade, and synaptic markers, influenced by epigenetic modifications, in the human frontal cortex. J Neurochem 125, 63-73.

Kesner, R.P., Churchwell, J.C., 2011. An analysis of rat prefrontal cortex in mediating executive function. Neurobiology of learning and memory 96, 417-431.

197

Khare, T., Pai, S., Koncevicius, K., Pal, M., Kriukiene, E., Liutkeviciute, Z., Irimia, M., Jia, P., Ptak, C., Xia, M., Tice, R., Tochigi, M., Morera, S., Nazarians, A., Belsham, D., Wong, A.H., Blencowe, B.J., Wang, S.C., Kapranov, P., Kustra, R., Labrie, V., Klimasauskas, S., Petronis, A., 2012. 5-hmC in the brain is abundant in synaptic genes and shows differences at the exon-intron boundary. Nat Struct Mol Biol 19, 1037-1043.

Klose, R.J., Bird, A.P., 2006. Genomic DNA methylation: the mark and its mediators. Trends Biochem Sci 31, 89-97.

Kozlenkov, A., Roussos, P., Timashpolsky, A., Barbu, M., Rudchenko, S., Bibikova, M., Klotzle, B., Byne, W., Lyddon, R., Di Narzo, A.F., Hurd, Y.L., Koonin, E.V., Dracheva, S., 2014. Differences in DNA methylation between human neuronal and glial cells are concentrated in enhancers and non-CpG sites. Nucleic Acids Res 42, 109-127.

Kozlenkov, A., Wang, M., Roussos, P., Rudchenko, S., Barbu, M., Bibikova, M., Klotzle, B., Dwork, A.J., Zhang, B., Hurd, Y.L., Koonin, E.V., Wegner, M., Dracheva, S., 2016. Substantial DNA methylation differences between two major neuronal subtypes in human brain. Nucleic Acids Res 44, 2593-2612.

Krzywinski, M., Schein, J., Birol, I., Connors, J., Gascoyne, R., Horsman, D., Jones, S.J., Marra, M.A., 2009. Circos: an information aesthetic for comparative genomics. Genome Res 19, 1639-1645.

Kuiper, G.G., Carlsson, B., Grandien, K., Enmark, E., Haggblad, J., Nilsson, S., Gustafsson, J.A., 1997. Comparison of the ligand binding specificity and transcript tissue distribution of estrogen receptors alpha and beta. Endocrinology 138, 863-870.

Kumar, A., Bodhinathan, K., Foster, T.C., 2009. Susceptibility to Calcium Dysregulation during Brain Aging. Frontiers in aging neuroscience 1, 2.

Kumar, A., Foster, T.C., 2013. Linking redox regulation of NMDAR synaptic function to cognitive decline during aging. J Neurosci 33, 15710-15715.

Kurian, J.R., Olesen, K.M., Auger, A.P., 2010. Sex differences in epigenetic regulation of the estrogen receptor-alpha promoter within the developing preoptic area. Endocrinology 151, 2297-2305.

Lardenoije, R., Iatrou, A., Kenis, G., Kompotis, K., Steinbusch, H.W., Mastroeni, D., Coleman, P., Lemere, C.A., Hof, P.R., van den Hove, D.L., Rutten, B.P., 2015. The epigenetics of aging and neurodegeneration. Progress in neurobiology 131, 21-64.

LaSarge, C.L., Montgomery, K.S., Tucker, C., Slaton, G.S., Griffith, W.H., Setlow, B., Bizon, J.L., 2007. Deficits across multiple cognitive domains in a subset of aged Fischer 344 rats. Neurobiol Aging 28, 928-936.

198

Lee, C.K., Weindruch, R., Prolla, T.A., 2000. Gene-expression profile of the ageing brain in mice. Nature genetics 25, 294-297.

Levenson, J.M., Roth, T.L., Lubin, F.D., Miller, C.A., Huang, I.C., Desai, P., Malone, L.M., Sweatt, J.D., 2006. Evidence that DNA (cytosine-5) methyltransferase regulates synaptic plasticity in the hippocampus. J Biol Chem 281, 15763-15773.

Li, L.C., Shiina, H., Deguchi, M., Zhao, H., Okino, S.T., Kane, C.J., Carroll, P.R., Igawa, M., Dahiya, R., 2004. Age-dependent methylation of ESR1 gene in prostate cancer. Biochem Biophys Res Commun 321, 455-461.

Li, X., Wei, W., Zhao, Q.Y., Widagdo, J., Baker-Andresen, D., Flavell, C.R., D'Alessio, A., Zhang, Y., Bredy, T.W., 2014. Neocortical Tet3-mediated accumulation of 5- hydroxymethylcytosine promotes rapid behavioral adaptation. Proc Natl Acad Sci U S A 111, 7120-7125.

Liao, Y., Smyth, G.K., Shi, W., 2014. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923-930.

Lieberman-Aiden, E., van Berkum, N.L., Williams, L., Imakaev, M., Ragoczy, T., Telling, A., Amit, I., Lajoie, B.R., Sabo, P.J., Dorschner, M.O., Sandstrom, R., Bernstein, B., Bender, M.A., Groudine, M., Gnirke, A., Stamatoyannopoulos, J., Mirny, L.A., Lander, E.S., Dekker, J., 2009. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326, 289- 293.

Lipinski, M.M., Zheng, B., Lu, T., Yan, Z., Py, B.F., Ng, A., Xavier, R.J., Li, C., Yankner, B.A., Scherzer, C.R., Yuan, J., 2010. Genome-wide analysis reveals mechanisms modulating autophagy in normal brain aging and in Alzheimer's disease. Proc Natl Acad Sci U S A 107, 14164-14169.

Lister, R., Mukamel, E.A., Nery, J.R., Urich, M., Puddifoot, C.A., Johnson, N.D., Lucero, J., Huang, Y., Dwork, A.J., Schultz, M.D., Yu, M., Tonti-Filippini, J., Heyn, H., Hu, S., Wu, J.C., Rao, A., Esteller, M., He, C., Haghighi, F.G., Sejnowski, T.J., Behrens, M.M., Ecker, J.R., 2013. Global epigenomic reconfiguration during mammalian brain development. Science 341, 1237905.

Liu, X.S., Wu, H., Ji, X., Stelzer, Y., Wu, X., Czauderna, S., Shu, J., Dadon, D., Young, R.A., Jaenisch, R., 2016. Editing DNA Methylation in the Mammalian Genome. Cell 167, 233-247 e217.

Liu, Y., Zhou, J., White, K.P., 2014. RNA-seq differential expression studies: more sequence or more replication? Bioinformatics 30, 301-304.

Livak, K.J., Schmittgen, T.D., 2001. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods 25, 402-408.

199

Loerch, P.M., Lu, T., Dakin, K.A., Vann, J.M., Isaacs, A., Geula, C., Wang, J., Pan, Y., Gabuzda, D.H., Li, C., Prolla, T.A., Yankner, B.A., 2008. Evolution of the aging brain transcriptome and synaptic regulation. PLoS One 3, e3329.

Love, M.I., Huber, W., Anders, S., 2014. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome biology 15, 550.

Lu, P.H., Lee, G.J., Raven, E.P., Tingus, K., Khoo, T., Thompson, P.M., Bartzokis, G., 2011. Age-related slowing in cognitive processing speed is associated with myelin integrity in a very healthy elderly sample. Journal of clinical and experimental neuropsychology 33, 1059-1068.

Lu, T., Pan, Y., Kao, S.Y., Li, C., Kohane, I., Chan, J., Yankner, B.A., 2004. Gene regulation and DNA damage in the ageing human brain. Nature 429, 883-891.

MacPherson, S.E., Phillips, L.H., Della Sala, S., 2002. Age, executive function, and social decision making: a dorsolateral prefrontal theory of cognitive aging. Psychol Aging 17, 598-609.

Maillet, D., Rajah, M.N., 2014. Age-related differences in brain activity in the subsequent memory paradigm: a meta-analysis. Neuroscience and biobehavioral reviews 45, 246-257.

Mamrut, S., Harony, H., Sood, R., Shahar-Gold, H., Gainer, H., Shi, Y.J., Barki- Harrington, L., Wagner, S., 2013. DNA methylation of specific CpG sites in the promoter region regulates the transcription of the mouse oxytocin receptor. PLoS One 8, e56869.

Marguerat, S., Bahler, J., 2010. RNA-seq: from technology to biology. Cell Mol Life Sci 67, 569-579.

Markowska, A.L., Savonenko, A.V., 2002. Effectiveness of estrogen replacement in restoration of cognitive function after long-term estrogen withdrawal in aging rats. J Neurosci 22, 10985-10995.

McEwen, B.S., Morrison, J.H., 2013. The brain on stress: vulnerability and plasticity of the prefrontal cortex over the life course. Neuron 79, 16-29.

Mehra, R.D., Sharma, K., Nyakas, C., Vij, U., 2005. Estrogen receptor alpha and beta immunoreactive neurons in normal adult and aged female rat hippocampus: a qualitative and quantitative study. Brain Res 1056, 22-35.

Meissner, A., Mikkelsen, T.S., Gu, H., Wernig, M., Hanna, J., Sivachenko, A., Zhang, X., Bernstein, B.E., Nusbaum, C., Jaffe, D.B., Gnirke, A., Jaenisch, R., Lander, E.S., 2008. Genome-scale DNA methylation maps of pluripotent and differentiated cells. Nature 454, 766-770.

200

Merchenthaler, I., Dellovade, T.L., Shughrue, P.J., 2003. Neuroprotection by estrogen in animal models of global and focal ischemia. Ann N Y Acad Sci 1007, 89-100.

Miele, A., Dekker, J., 2008. Long-range chromosomal interactions and gene regulation. Mol Biosyst 4, 1046-1057.

Migo, E.M., O'Daly, O., Mitterschiffthaler, M., Antonova, E., Dawson, G.R., Dourish, C.T., Craig, K.J., Simmons, A., Wilcock, G.K., McCulloch, E., Jackson, S.H., Kopelman, M.D., Williams, S.C., Morris, R.G., 2015. Investigating virtual reality navigation in amnestic mild cognitive impairment using fMRI. Neuropsychology, development, and cognition. Section B, Aging, neuropsychology and cognition, 1- 22.

Miller, C.A., Campbell, S.L., Sweatt, J.D., 2008. DNA methylation and histone acetylation work in concert to regulate memory formation and synaptic plasticity. Neurobiology of learning and memory 89, 599-603.

Miller, C.A., Sweatt, J.D., 2007. Covalent modification of DNA regulates memory formation. Neuron 53, 857-869.

Milner, T.A., McEwen, B.S., Hayashi, S., Li, C.J., Reagan, L.P., Alves, S.E., 2001. Ultrastructural evidence that hippocampal alpha estrogen receptors are located at extranuclear sites. J Comp Neurol 429, 355-371.

Mitra, S.W., Hoskin, E., Yudkovitz, J., Pear, L., Wilkinson, H.A., Hayashi, S., Pfaff, D.W., Ogawa, S., Rohrer, S.P., Schaeffer, J.M., McEwen, B.S., Alves, S.E., 2003. Immunolocalization of estrogen receptor beta in the mouse brain: comparison with estrogen receptor alpha. Endocrinology 144, 2055-2067.

Mitterling, K.L., Spencer, J.L., Dziedzic, N., Shenoy, S., McCarthy, K., Waters, E.M., McEwen, B.S., Milner, T.A., 2010. Cellular and subcellular localization of estrogen and progestin receptor immunoreactivities in the mouse hippocampus. J Comp Neurol 518, 2729-2743.

Moore, L.D., Le, T., Fan, G., 2013. DNA methylation and its basic function. Neuropsychopharmacology 38, 23-38.

Moore, T.L., Killiany, R.J., Herndon, J.G., Rosene, D.L., Moss, M.B., 2006. Executive system dysfunction occurs as early as middle-age in the rhesus monkey. Neurobiol Aging 27, 1484-1493.

Muir, J.L., Everitt, B.J., Robbins, T.W., 1996. The cerebral cortex of the rat and visual attentional function: dissociable effects of mediofrontal, cingulate, anterior dorsolateral, and parietal cortex lesions on a five-choice serial reaction time task. Cerebral cortex 6, 470-481.

201

Nan, X., Ng, H.H., Johnson, C.A., Laherty, C.D., Turner, B.M., Eisenman, R.N., Bird, A., 1998. Transcriptional repression by the methyl-CpG-binding protein MeCP2 involves a histone deacetylase complex. Nature 393, 386-389.

Neri, F., Rapelli, S., Krepelova, A., Incarnato, D., Parlato, C., Basile, G., Maldotti, M., Anselmi, F., Oliviero, S., 2017. Intragenic DNA methylation prevents spurious transcription initiation. Nature 543, 72-77.

Newell-Price, J., Clark, A.J., King, P., 2000. DNA methylation and silencing of gene expression. Trends Endocrinol Metab 11, 142-148.

Ng, H.H., Zhang, Y., Hendrich, B., Johnson, C.A., Turner, B.M., Erdjument-Bromage, H., Tempst, P., Reinberg, D., Bird, A., 1999. MBD2 is a transcriptional repressor belonging to the MeCP1 histone deacetylase complex. Nature genetics 23, 58- 61.

Nicolle, M.M., Gonzalez, J., Sugaya, K., Baskerville, K.A., Bryan, D., Lund, K., Gallagher, M., McKinney, M., 2001. Signatures of hippocampal oxidative stress in aged spatial learning-impaired rodents. Neuroscience 107, 415-431.

Nile, C.J., Read, R.C., Akil, M., Duff, G.W., Wilson, A.G., 2008. Methylation status of a single CpG site in the IL6 promoter is related to IL6 messenger RNA levels and rheumatoid arthritis. Arthritis Rheum 58, 2686-2693.

Numata, S., Ye, T., Hyde, T.M., Guitart-Navarro, X., Tao, R., Wininger, M., Colantuoni, C., Weinberger, D.R., Kleinman, J.E., Lipska, B.K., 2012. DNA methylation signatures in development and aging of the human prefrontal cortex. Am J Hum Genet 90, 260-272.

O'Sullivan, M., Jones, D.K., Summers, P.E., Morris, R.G., Williams, S.C., Markus, H.S., 2001. Evidence for cortical "disconnection" as a mechanism of age-related cognitive decline. Neurology 57, 632-638.

Oshlack, A., Wakefield, M.J., 2009. Transcript length bias in RNA-seq data confounds systems biology. Biol Direct 4, 14.

Paban, V., Billard, J.M., Bouet, V., Freret, T., Boulouard, M., Chambon, C., Loriod, B., Alescio-Lautier, B., 2013. Genomic transcriptional profiling in LOU/C/Jall rats identifies genes for successful aging. Brain structure & function 218, 1501-1512.

Park, D.C., Reuter-Lorenz, P., 2009. The adaptive brain: aging and neurocognitive scaffolding. Annual review of psychology 60, 173-196.

Patil, V., Ward, R.L., Hesson, L.B., 2014. The evidence for functional non-CpG methylation in mammalian cells. Epigenetics 9, 823-828.

202

Pavlidis, P., Qin, J., Arango, V., Mann, J.J., Sibille, E., 2004. Using the gene ontology for microarray data mining: a comparison of methods and application to age effects in human prefrontal cortex. Neurochem Res 29, 1213-1222.

Paxinos, G., Watson, C., 1986. The rat brain in stereotaxic coordinates. Academic, San Diego.

Peleg, S., Sananbenesi, F., Zovoilis, A., Burkhardt, S., Bahari-Javan, S., Agis-Balboa, R.C., Cota, P., Wittnam, J.L., Gogol-Doering, A., Opitz, L., Salinas-Riester, G., Dettenhofer, M., Kang, H., Farinelli, L., Chen, W., Fischer, A., 2010. Altered histone acetylation is associated with age-dependent memory impairment in mice. Science 328, 753-756.

Persson, J., Nyberg, L., Lind, J., Larsson, A., Nilsson, L.G., Ingvar, M., Buckner, R.L., 2006. Structure-function correlates of cognitive decline in aging. Cerebral cortex 16, 907-915.

Pettersson, K., Delaunay, F., Gustafsson, J.A., 2000. Estrogen receptor beta acts as a dominant regulator of estrogen signaling. Oncogene 19, 4970-4978.

Pfefferbaum, A., Adalsteinsson, E., Sullivan, E.V., 2005. Frontal circuitry degradation marks healthy adult aging: Evidence from diffusion tensor imaging. NeuroImage 26, 891-899.

Phillips, J.E., Corces, V.G., 2009. CTCF: master weaver of the genome. Cell 137, 1194- 1211.

Pinzone, J.J., Stevenson, H., Strobl, J.S., Berg, P.E., 2004. Molecular and cellular determinants of estrogen receptor alpha expression. Mol Cell Biol 24, 4605-4612.

Post, W.S., Goldschmidt-Clermont, P.J., Wilhide, C.C., Heldman, A.W., Sussman, M.S., Ouyang, P., Milliken, E.E., Issa, J.P., 1999. Methylation of the estrogen receptor gene is associated with aging and atherosclerosis in the cardiovascular system. Cardiovasc Res 43, 985-991.

Preuss, T.M., 1995. Do rats have prefrontal cortex? The rose-woolsey-akert program reconsidered. Journal of cognitive neuroscience 7, 1-24.

Primiani, C.T., Ryan, V.H., Rao, J.S., Cam, M.C., Ahn, K., Modi, H.R., Rapoport, S.I., 2014. Coordinated gene expression of neuroinflammatory and cell signaling markers in dorsolateral prefrontal cortex during human brain development and aging. PLoS One 9, e110972.

Prolla, T.A., 2002. DNA microarray analysis of the aging brain. Chemical senses 27, 299-306.

203

Qiu, J., Hong, Q., Chen, R.H., Tong, M.L., Zhang, M., Fei, L., Pan, X.Q., Guo, M., Guo, X.R., Chi, X., 2010. Gene expression profiles in the prefrontal cortex of SHR rats by cDNA microarrays. Molecular biology reports 37, 1733-1740.

Qu, N., Wang, L., Liu, Z.C., Tian, Q., Zhang, Q., 2013. Oestrogen receptor alpha agonist improved long-term ovariectomy-induced spatial cognition deficit in young rats. Int J Neuropsychopharmacol 16, 1071-1082.

Rhodes, M.G., 2004. Age-related differences in performance on the Wisconsin card sorting test: a meta-analytic review. Psychol Aging 19, 482-494.

Richardson, S.R., Doucet, A.J., Kopera, H.C., Moldovan, J.B., Garcia-Perez, J.L., Moran, J.V., 2015. The Influence of LINE-1 and SINE Retrotransposons on Mammalian Genomes. Microbiol Spectr 3, MDNA3-0061-2014.

Riva, A., 2016a. cscall. http://compbio.ufl.edu/software/cscall/.

Riva, A., 2016b. DMAP2. http://compbio.ufl.edu/software/dmap2/. (Accessed November 12 2016).

Robbins, T.W., 1996. Dissociating executive functions of the prefrontal cortex. Philosophical transactions of the Royal Society of London. Series B, Biological sciences 351, 1463-1470; discussion 1470-1461.

Robbins, T.W., James, M., Owen, A.M., Sahakian, B.J., Lawrence, A.D., McInnes, L., Rabbitt, P.M., 1998. A study of performance on tests from the CANTAB battery sensitive to frontal lobe dysfunction in a large sample of normal volunteers: implications for theories of executive functioning and cognitive aging. Cambridge Neuropsychological Test Automated Battery. J Int Neuropsychol Soc 4, 474-490.

Robertson, K.D., 2002. DNA methylation and chromatin - unraveling the tangled web. Oncogene 21, 5361-5379.

Rosano, C., Aizenstein, H.J., Cochran, J.L., Saxton, J.A., De Kosky, S.T., Newman, A.B., Kuller, L.H., Lopez, O.L., Carter, C.S., 2005. Event-related functional magnetic resonance imaging investigation of executive control in very old individuals with mild cognitive impairment. Biological psychiatry 57, 761-767.

Rowe, W.B., Blalock, E.M., Chen, K.C., Kadish, I., Wang, D., Barrett, J.E., Thibault, O., Porter, N.M., Rose, G.M., Landfield, P.W., 2007. Hippocampal expression analyses reveal selective association of immediate-early, neuroenergetic, and myelinogenic pathways with cognitive impairment in aged rats. J Neurosci 27, 3098-3110.

Rudenko, A., Dawlaty, M.M., Seo, J., Cheng, A.W., Meng, J., Le, T., Faull, K.F., Jaenisch, R., Tsai, L.H., 2013. Tet1 is critical for neuronal activity-regulated gene expression and memory extinction. Neuron 79, 1109-1122.

204

Rune, G.M., Wehrenberg, U., Prange-Kiel, J., Zhou, L., Adelmann, G., Frotscher, M., 2002. Estrogen up-regulates estrogen receptor alpha and synaptophysin in slice cultures of rat hippocampus. Neuroscience 113, 167-175.

Sagvolden, T., Russell, V.A., Aase, H., Johansen, E.B., Farshbaf, M., 2005. Rodent models of attention-deficit/hyperactivity disorder. Biological psychiatry 57, 1239- 1247.

Salat, D.H., Tuch, D.S., Hevelone, N.D., Fischl, B., Corkin, S., Rosas, H.D., Dale, A.M., 2005. Age-related changes in prefrontal white matter measured by diffusion tensor imaging. Ann N Y Acad Sci 1064, 37-49.

Sarvari, M., Kallo, I., Hrabovszky, E., Solymosi, N., Liposits, Z., 2014. Ovariectomy and subsequent treatment with estrogen receptor agonists tune the innate immune system of the hippocampus in middle-aged female rats. PLoS One 9, e88540.

Schwarz, J.M., Nugent, B.M., McCarthy, M.M., 2010. Developmental and hormone- induced epigenetic changes to estrogen and progesterone receptor genes in brain are dynamic across the life span. Endocrinology 151, 4871-4881.

Severson, P.L., Tokar, E.J., Vrba, L., Waalkes, M.P., Futscher, B.W., 2013. Coordinate H3K9 and DNA methylation silencing of ZNFs in toxicant-induced malignant transformation. Epigenetics 8, 1080-1088.

Sharrow, K.M., Kumar, A., Foster, T.C., 2002. Calcineurin as a potential contributor in estradiol regulation of hippocampal synaptic function. Neuroscience 113, 89-97.

Sherwin, B.B., 2009. Estrogen therapy: is time of initiation critical for neuroprotection? Nat Rev Endocrinol 5, 620-627.

Shughrue, P.J., Lane, M.V., Merchenthaler, I., 1997. Comparative distribution of estrogen receptor-alpha and -beta mRNA in the rat central nervous system. J Comp Neurol 388, 507-525.

Stefani, M.R., Groth, K., Moghaddam, B., 2003. Glutamate receptors in the rat medial prefrontal cortex regulate set-shifting ability. Behavioral neuroscience 117, 728- 737.

Stefani, M.R., Moghaddam, B., 2005. Systemic and prefrontal cortical NMDA receptor blockade differentially affect discrimination learning and set-shift ability in rats. Behavioral neuroscience 119, 420-428.

Su, J., Shao, X., Liu, H., Liu, S., Wu, Q., Zhang, Y., 2012. Genome-wide dynamic changes of DNA methylation of repetitive elements in human embryonic stem cells and fetal fibroblasts. Genomics 99, 10-17.

205

Sun, D., Xi, Y., Rodriguez, B., Park, H.J., Tong, P., Meong, M., Goodell, M.A., Li, W., 2014. MOABS: model based analysis of bisulfite sequencing data. Genome biology 15, R38.

Szulwach, K.E., Li, X., Li, Y., Song, C.X., Wu, H., Dai, Q., Irier, H., Upadhyay, A.K., Gearing, M., Levey, A.I., Vasanthakumar, A., Godley, L.A., Chang, Q., Cheng, X., He, C., Jin, P., 2011. 5-hmC-mediated epigenetic dynamics during postnatal neurodevelopment and aging. Nat Neurosci 14, 1607-1616.

Tahiliani, M., Koh, K.P., Shen, Y., Pastor, W.A., Bandukwala, H., Brudno, Y., Agarwal, S., Iyer, L.M., Liu, D.R., Aravind, L., Rao, A., 2009. Conversion of 5- methylcytosine to 5-hydroxymethylcytosine in mammalian DNA by MLL partner TET1. Science 324, 930-935.

Tohgi, H., Utsugisawa, K., Yamagata, M., Yoshimura, M., 1995. Effects of age on messenger RNA expression of glucocorticoid, thyroid hormone, androgen, and estrogen receptors in postmortem human hippocampus. Brain Res 700, 245-253.

Tsompana, M., Buck, M.J., 2014. Chromatin accessibility: a window into the genome. Epigenetics Chromatin 7, 33.

Turner, G.R., Spreng, R.N., 2012. Executive functions and neurocognitive aging: dissociable patterns of brain activity. Neurobiol Aging 33, 826 e821-813.

Twine, N.A., Janitz, K., Wilkins, M.R., Janitz, M., 2011. Whole transcriptome sequencing reveals gene expression and splicing differences in brain regions affected by Alzheimer's disease. PLoS One 6, e16266.

Uylings, H.B., van Eden, C.G., 1990. Qualitative and quantitative comparison of the prefrontal cortex in rat and in primates, including humans. Progress in brain research 85, 31-62.

VanGuilder, H.D., Bixler, G.V., Brucklacher, R.M., Farley, J.A., Yan, H., Warrington, J.P., Sonntag, W.E., Freeman, W.M., 2011. Concurrent hippocampal induction of MHC II pathway components and glial activation with advanced aging is not correlated with cognitive impairment. Journal of neuroinflammation 8, 138.

Varley, K.E., Gertz, J., Bowling, K.M., Parker, S.L., Reddy, T.E., Pauli-Behn, F., Cross, M.K., Williams, B.A., Stamatoyannopoulos, J.A., Crawford, G.E., Absher, D.M., Wold, B.J., Myers, R.M., 2013. Dynamic DNA methylation across diverse human cell lines and tissues. Genome Res 23, 555-567.

Verbitsky, M., Yonan, A.L., Malleret, G., Kandel, E.R., Gilliam, T.C., Pavlidis, P., 2004. Altered hippocampal transcript profile accompanies an age-related spatial memory deficit in mice. Learning & memory 11, 253-260.

Vertes, R.P., 2004. Differential projections of the infralimbic and prelimbic cortex in the rat. Synapse 51, 32-58.

206

Wagner, G.P., Kin, K., Lynch, V.J., 2012. Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples. Theory Biosci 131, 281-285.

Wang, Y.S., Chou, W.W., Chen, K.C., Cheng, H.Y., Lin, R.T., Juo, S.H., 2012. MicroRNA-152 mediates DNMT1-regulated DNA methylation in the estrogen receptor alpha gene. PLoS One 7, e30635.

Waters, E.M., Yildirim, M., Janssen, W.G., Lou, W.Y., McEwen, B.S., Morrison, J.H., Milner, T.A., 2011. Estrogen and aging affect the synaptic distribution of estrogen receptor beta-immunoreactivity in the CA1 region of female rat hippocampus. Brain Res 1379, 86-97.

Weidner, C.I., Lin, Q., Koch, C.M., Eisele, L., Beier, F., Ziegler, P., Bauerschlag, D.O., Jockel, K.H., Erbel, R., Muhleisen, T.W., Zenke, M., Brummendorf, T.H., Wagner, W., 2014. Aging of blood can be tracked by DNA methylation changes at just three CpG sites. Genome biology 15, R24.

Wen, L., Li, X., Yan, L., Tan, Y., Li, R., Zhao, Y., Wang, Y., Xie, J., Zhang, Y., Song, C., Yu, M., Liu, X., Zhu, P., Li, X., Hou, Y., Guo, H., Wu, X., He, C., Li, R., Tang, F., Qiao, J., 2014. Whole-genome analysis of 5-hydroxymethylcytosine and 5- methylcytosine at base resolution in the human brain. Genome biology 15, R49.

Westberry, J.M., Trout, A.L., Wilson, M.E., 2010. Epigenetic regulation of estrogen receptor alpha gene expression in the mouse cortex during early postnatal development. Endocrinology 151, 731-740.

Wilson, M.E., Westberry, J.M., Prewitt, A.K., 2008. Dynamic regulation of estrogen receptor-alpha gene expression in the brain: a role for promoter methylation? Front Neuroendocrinol 29, 375-385.

Woods, D.L., Knight, R.T., 1986. Electrophysiologic evidence of increased distractibility after dorsolateral prefrontal lesions. Neurology 36, 212-216.

Wu, H., Coskun, V., Tao, J., Xie, W., Ge, W., Yoshikawa, K., Li, E., Zhang, Y., Sun, Y.E., 2010. Dnmt3a-dependent nonpromoter DNA methylation facilitates transcription of neurogenic genes. Science 329, 444-448.

Xi, Y., Li, W., 2009. BSMAP: whole genome bisulfite sequence MAPping program. BMC Bioinformatics 10, 232.

Xie, W., Barr, C.L., Kim, A., Yue, F., Lee, A.Y., Eubanks, J., Dempster, E.L., Ren, B., 2012. Base-resolution analyses of sequence and parent-of-origin dependent DNA methylation in the mouse genome. Cell 148, 816-831.

Xu, X., 2015. DNA methylation and cognitive aging. Oncotarget 6, 13922-13932.

207

Yang, X., Han, H., De Carvalho, D.D., Lay, F.D., Jones, P.A., Liang, G., 2014. Gene body methylation can alter gene expression and is a therapeutic target in cancer. Cancer Cell 26, 577-590.

Yang, X., Phillips, D.L., Ferguson, A.T., Nelson, W.G., Herman, J.G., Davidson, N.E., 2001. Synergistic activation of functional estrogen receptor (ER)-alpha by DNA methyltransferase and histone deacetylase inhibition in human ER-alpha- negative breast cancer cells. Cancer Res 61, 7025-7029.

Yu, M., Hon, G.C., Szulwach, K.E., Song, C.X., Jin, P., Ren, B., He, C., 2012. Tet- assisted bisulfite sequencing of 5-hydroxymethylcytosine. Nat Protoc 7, 2159- 2170.

Yuan, Y., Chen, Y.P., Boyd-Kirkup, J., Khaitovich, P., Somel, M., 2012. Accelerated aging-related transcriptome changes in the female prefrontal cortex. Aging cell 11, 894-901.

Zeier, Z., Madorsky, I., Xu, Y., Ogle, W.O., Notterpek, L., Foster, T.C., 2011. Gene expression in the hippocampus: regionally specific effects of aging and caloric restriction. Mech Ageing Dev 132, 8-19.

Zhang, Q.G., Han, D., Wang, R.M., Dong, Y., Yang, F., Vadlamudi, R.K., Brann, D.W., 2011. C terminus of Hsc70-interacting protein (CHIP)-mediated degradation of hippocampal estrogen receptor-alpha and the critical period hypothesis of estrogen neuroprotection. Proc Natl Acad Sci U S A 108, E617-624.

Zhang, Q.G., Raz, L., Wang, R., Han, D., De Sevilla, L., Yang, F., Vadlamudi, R.K., Brann, D.W., 2009. Estrogen attenuates ischemic oxidative damage via an estrogen receptor alpha-mediated inhibition of NADPH oxidase activation. J Neurosci 29, 13823-13836.

Zhang, Z.H., Jhaveri, D.J., Marshall, V.M., Bauer, D.C., Edson, J., Narayanan, R.K., Robinson, G.J., Lundberg, A.E., Bartlett, P.F., Wray, N.R., Zhao, Q.Y., 2014. A comparative study of techniques for differential expression analysis on RNA-Seq data. PLoS One 9, e103207.

Zhao, Z., Tavoosidana, G., Sjolinder, M., Gondor, A., Mariano, P., Wang, S., Kanduri, C., Lezcano, M., Sandhu, K.S., Singh, U., Pant, V., Tiwari, V., Kurukuti, S., Ohlsson, R., 2006. Circular chromosome conformation capture (4C) uncovers extensive networks of epigenetically regulated intra- and interchromosomal interactions. Nature genetics 38, 1341-1347.

Ziller, M.J., Hansen, K.D., Meissner, A., Aryee, M.J., 2015. Coverage recommendations for methylation analysis by whole-genome bisulfite sequencing. Nat Methods 12, 230-232, 231 p following 232.

208

BIOGRAPHICAL SKETCH

Lara Ianov was born in Brazil and moved to the United States in 2006 at the age of 16 with her family. In 2012, Lara graduated with a Bachelor of Science in biology from the University of Arkansas at Little Rock (UALR) with honors and as a Donaghey

Scholar. As an undergraduate, Lara performed research at the Center for Integrative

Nanotechnology Sciences at UALR where she investigated the effects of thermal ablation of biocompatible nanoparticles in a breast cancer cells. In the fall of 2012, Lara started her Ph.D. studies in the Genetics and Genomics program at the University of

Florida. After finishing her first year rotations, Lara joined Dr. Thomas C Foster’s lab in the spring of 2013 with the goal of investigating transcriptomic and epigenomic changes associated with aging and cognitive function. In the fall of 2016, Lara earned the Berns

Award for Excellence in Genetics in recognition of her graduate school research. Lara

Ianov completed her graduate degree in the spring of 2017.

209