Genome-wide methylomics identifies pre-existing DNA methylation signatures in the prefrontal cortex of alcohol-naïve rhesus monkeys defining neural vulnerability for future risky ethanol consumption.

Genome-wide methylomics identifies pre-existing DNA methylation signatures in the prefrontal cortex of alcohol-naïve rhesus monkeys defining neural vulnerability for future risky ethanol consumption. | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Genome-wide methylomics identifies pre-existing DNA methylation signatures in the prefrontal cortex of alcohol-naïve rhesus monkeys defining neural vulnerability for future risky ethanol consumption. Rita Cervera-Juanes, Kip D. Zimmerman, Larry Wilhelm, Clara Christine Lowe, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5406434/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Alcohol use disorder (AUD) is a highly prevalent, complex, multifactorial, and heterogeneous disorder. Currently, 11% and 30% of adults meet the criteria for past-year and lifetime AUD, respectively. Identification of the molecular mechanisms underlying risk for AUD would facilitate effective deployment of personalized interventions. Previous studies using rhesus monkeys and rats, have demonstrated that individuals with low cognitive flexibility and a predisposition towards habitual behaviors show an increased risk for future heavy drinking. Further, low cognitive flexibility is associated with reduced dorsolateral prefrontal cortex (dlPFC) function in rhesus monkeys. To explore the underlying unique molecular signatures that increase risk for chronic heavy drinking, a genome-wide DNA methylation (DNAm) analysis of the alcohol-naïve dlPFC-A46 biopsy prior to chronic alcohol self-administration was conducted in 11 male macaques. The DNAm profile provides a molecular snapshot of the alcohol-naïve dlPFC, with mapped genes and associated signaling pathways that vary across individuals. The analysis identified 1,463 differentially methylated regions related to unique genes that were strongly associated with a range of daily voluntary ethanol intakes consumed over 6 months. These findings translate behavioral phenotypes into neural markers of risk for AUD, and therefore hold promise for parallel discoveries in risk for other disorders involving impaired cognitive flexibility. rhesus macaque alcohol use disorder DNA methylation vulnerability prefrontal cortex Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Alcohol use disorder (AUD) is a highly prevalent, complex, multifactorial, and heterogeneous disorder; with 11% and 30% of adults meeting the criteria for past-year and lifetime AUD, respectively[ 1 – 3 ]. This amount of chronic heavy alcohol use results in significant social, economic, and public health costs[ 4 – 6 ]. Despite the availability of a few FDA approved and psychotherapy treatments, only ~ 5% of AUD individuals received treatment in 2021, and among those, 60% relapsed within 6 months following treatment[ 7 – 11 ]. Prevention and treatment strategies, based on risk factors such as early and accurate identification of individuals with a high risk for developing an AUD, are needed to reduce the incidence, prevalence, severity, duration, and consequences of future AUD and comorbid psychiatric disorders. Neural-based risk factors obtained in alcohol-naïve humans have largely been studied using longitudinal studies of the brain using evoked potentials or MRI imaging[ 12 , 13 ]. For example, the Collaborative Study of the Genetics of Alcoholism (COGA) recently reported on longitudinal data collected from subjects as early as age 12 (before developing AUD), and years later when they were diagnosed with AUD or remained unaffected (at ~ 30 years old) to explore risk biomarkers. Based on multidimensional data (e.g. clinical, electrophysiological, genetic and family history) acquired prior to AUD development, the study found that a combination of brain functional MRI connectivity within the salience network and single nucleotide polymorphisms (SNPs) data were predictive of future AUD outcomes[ 14 ]. This study highlighted the relevance of using neural measures taken prior to developing AUD, and the significance of underlying molecular signatures in association with functional brain activity predicting vulnerability for AUD. It is very well-known that an interaction of genetics and environmental factors contribute to the risk for AUD[ 15 – 21 ]. Environmental factors can lead to very specific alterations in epigenetic mechanisms, which, ultimately, regulate gene expression and can lead to altered phenotypes and behaviors[ 16 ]. Among the different epigenetic modifications, DNA methylation (DNAm) has been applied to assess risk, exposure, or disease progression to a wide range of biomedical conditions (i.e. cancer, cardiovascular, drug use and neurological diseases[ 17 – 20 ]; see review[ 21 ]). This is because, unlike static genetic risk estimates, DNAm varies dynamically in relation to diverse exogenous and endogenous factors, including environmental risk factors and complex disease pathology[ 22 – 30 ]. DNAm measures are likely to capture tissue- and time-specific information and have the potential to refine or improve genotype-based mechanisms, such as GWAS), propagating disease processes beyond the limit of phenotype heritability[ 18 , 31 – 33 ]. Due to limitations in sample availability, epigenetic studies of brain mechanisms of disease processes have been limited to neural samples collected postmortem or are inferred from peripheral tissues. For example, early detection and treatment in Alzheimer and Parkinson’s disease [ 34 , 35 ]. Cross-sectional approaches have addressed risk for severity in behavioral-based, for example smoking, where peripheral blood DNAm predictor of smoking explained greater proportions of variance in cognitive function, structural brain integrity, inflammatory markers and other smoking-related health measures than smoking status[ 36 ]. However, the question of how reliably peripheral DNAm signatures can inform the molecular function of specific brain mechanisms, rather than as a biomarker, remains unanswered and limit prevention and therapeutic strategies. The macaque model of alcohol self-administration recapitulates many aspects of the human AUD phenotype, with a continuum in drinking levels from low to binge to very heavy alcohol consumption. In this protocol, rhesus monkeys are first induced to drink water and then ethanol (4% w/v) under a schedule-induced polydipsia (SIP) procedure. After induction, the monkeys are allowed to drink up to 7.0 g/kg/day (a 28 drink-equivalent based on 17g ethanol/drink) with water concurrently available, nominally termed “open-access” to ethanol (22 hrs/day). Given the voluntary nature of this access, the monkeys show a wide spectrum of individual daily intake ranging from an average of 0.3 g/kg/day up to 4 g/kg/day[ 37 ]. Furthermore, when the drinking protocol was extended to include intermittent abstinence from alcohol, alcohol intakes increased upon reintroduction of alcohol availability (i.e., modeling relapse to heavy drinking)[ 38 ]. Thus, this model offers a unique opportunity for identifying neural risk for chronic ethanol drinking, as well as, relapse. Using this model, a biopsy of the dorsolateral prefrontal cortex area 46 (dlPFC-A46) was obtained prior to ethanol exposure and then compared to a biopsy of contralateral area following the chronic drinking protocol. The dlPFC was chosen because it is known to be involved in mediating cognitive flexibility using an attentional set-shifting task (ASST) in rhesus monkeys[ 39 ] and performance on the ASST can predict future status as a heavy drinker[ 39 , 40 ]. Transcriptomic analyses of dlPFC-A46 pre- and post-ethanol biopsies from cynomolgus monkeys found changes in miRNA target sites and transcription factors that can serve as epigenetic regulators of alcohol consumption[ 41 ]. However, as discussed above, DNAm is a stable marker of long-term environmental conditions compared to mRNA expression. Here, we report on a novel opportunity to use nonhuman primate frontal cortical epigenomic signatures to understand AUD vulnerability. In parallel, and given the range of alcohol intakes in the rhesus population, this work also allows identification of resilience factors that may confer protective mechanisms against development of chronic heavy drinking. Results DNAm profiling in alcohol-naïve individuals identifies pre-existing differences in neural networks. We identified 14,012 differentially methylated cytosines (DMCs) at false discovery rate (FDR) < 0.001 and 3,539 differentially methylated regions (DMRs) at p Sidak < 0.05 (Supp. Table 2). All samples showed a similar distribution of DNAm rates (Fig. 1 A), with all samples having a larger proportion of hypermethylated sites. Among these significant DMRs, 2,276 had an average DNAm over 10% (Fig. 1 B) and 1,531 DMRs demonstrated agreement in the directionality of their respective DMCs (see Methods). Most of the DMRs mapped to the gene body (59%), preferentially to introns, and to intergenic locations, while only 12% of the DMRs mapped to promoter regions (Fig. 1 C). Since there is very limited information of CpG island context annotation in the macaque genome, we used the homologous human coordinates (hg38) and identified approximately 20% of DMRs mapping to CpG islands, while the majority mapped to open sea regions (Fig. 1 D). The genes mapped by the topmost DMRs significantly associated with the ethanol drinking levels at 6 months of open alcohol-access are shown in Fig. 1 . Among the most significantly associated DMRs, there was a DMR mapping to ankyrin repeat and SOCS box containing 1 protein ( ASB1 ), a protein predicted to play a role in protein ubiquitination and cytokine signaling, that was negatively associated with ethanol intake levels (Fig. 1 E). Three additional DMRs that had DNAm levels positively correlated with future 6-month ethanol intake included DMRs mapping to the protease activated receptor 1 gene ( F2R or PAR1 ); to the transmembrane transporter SVOPL , and to the first exon-intron of the glutamate ionotropic receptor, GRIN3A (Fig. 1 F-G; 2 C). According to these results, and only using these four genes as an example, individuals with the hypermethylated DMRs mapping to F2R , SVOPL and GRIN3A , but hypomethylated at ASB1 at baseline, are expected to consume higher levels of ethanol. Using the genes mapped by the whole list of significantly ethanol-associated DMRs (p Sidak < 0.05; annotated to genes), we next performed network and pathway analyses to identify the biological functions involving pre-existing differentially methylated genes (DMGs). There was an enrichment in cytoskeletal and cell-cell junction (purple clusters), metabolism (blue clusters), regulation of transcription and translation (green clusters), mitochondria organization (pink clusters) and in numerous neural-relevant pathways (red clusters; Fig. 2 A). Among these pathways, the following three, neurogenesis, forebrain development and central nervous system (CNS) neuron differentiation, point to developmental processes potentially setting up different risk for future problematic drinking. Other neural relevant pathways included mechanisms involved in synaptic transmission and the glutamate receptor signaling pathway. Within this pathway, DMGs encoding for glutamate receptor subunits ( GRIN1 , GRIN2A , GRIN3A , GRM2 , GRIK3 ), dopamine and GABA receptors ( DRD1 , GABRA6 ), the GABA synthesizing enzyme ( GAD2 ), calcium channels ( CACNA2D2 , CACNA2D3 , CACNG6 , CACNB2 , CACNB4 , CACNA1C , CACNA1H and CACNA1I ) and important modulators of intracellular calcium dynamics in neurons ( PVALB, RYR1 ), as well as in synaptic cell adhesion and scaffolding proteins ( SHANK2 , NRXN2 , NRXN3 , HOMER , DLGAP2 ) were identified (Fig. 2 B-J). While all these DMRs show a positive association with ethanol levels, those mapping to GRIN2A , DRD1 , GABRA6, CACNB2, PVALB, NRXN2 and NRXN3 were negatively correlated with ethanol intake levels. Among these genes within the glutamate receptor signaling pathway, DLGAP2 which encodes a critical component of the postsynaptic density, regulating synaptic function and dendritic spine morphology and organization, contained three different DMRs (Fig. 3 A-D). This gene is highly expressed in the brain and encodes four different transcripts (201–204; Ensembl ENSMMUG00000057265; Mmul_10:CM014343.1) resulting in different protein isoforms which specific function is currently unknown. These three DMRs showed a positive significant correlation with the ethanol drinking levels, indicating that higher DNAm levels at baseline are associated with a higher risk for future ethanol intake. Further analysis of the whole network identified genes encoding for calcium voltage-gates channel subunits: CACNA2D2 , CACNA2D3 , CACNB2 , CACNB4 , CACNA1C , CACNA1H , CACNG6 and CACNA1I and for two genes involved in ncRNA processing, IMP4 and RRP1B (Fig. 3 E-F) as hub genes, with CACNA1C being the strongest hub gene (with the most connections with other genes within the network). These findings highlight the relevance of these two pathways in potentially setting the foundational neural regulatory pathway for vulnerability. The pre-existing differentially methylated signatures are associated with gene expression levels in the alcohol-naïve dlPFC-A46 cortex. Although we did not identify differentially expressed genes (DEGs) associated with future ethanol drinking levels at FDR < 0.05, when we relaxed the significance threshold (p < 0.05), we identified 317 DEGs (Supp. Table 3). We next used two different approaches to investigate the potential role of DNAm in regulating gene expression. Our first approach was narrower in scope as we looked at how many of the DEGs were also DMGs. From the 317 DEGs, 100 of them are not annotated to known genes in the rhesus macaque transcriptome. From the remaining 217, 19 were also differentially methylated (Fig. 4 A; Supp. Table 4). Among this smaller subset, 10 DMGs were hypermethylated and downregulated, and 2 DMGs were hypomethylated and upregulated (Fig. 4 B). For example, the DMRs mapping to the canonical or alternative promoters of the acyl-coA oxidase 2 gene ( ACOX2 ), leiomodin 1 ( LMOD1 ), LDL receptor related protein 5 ( LRP5 ), solute carrier family 46 member 1 ( SLC46A1 ), and tri Rho guanine nucleotide exchange factor ( TRIO ) were hypermethylated and downregulated with increasing doses of alcohol (see example for ACOX2 in Fig. 4 C-D). Furthermore, the DNAm and the expression levels of a subset of these DMGs/DEGs pairs was associated. For instance, for ACOX2 and LMOD1 , hypermethylation was negatively associated with expression levels (Fig. 4 E, H). Conversely, hypermethylation was associated with upregulation for NPRU2 and LRP5 (please note that for LMOD1 and LRP5 this association trends towards significance). Given the small number of DEGs, we did not conduct pathway or network analyses. However, these DEG/DMGs are within the glycerophospholipid metabolism ( TRIO ), lipid metabolism ( ACOX2 , THBS2 and NUPR2 ), cell-cell junction ( CENATAC and COL6A2 ), regulation of cytoskeleton organization ( COBL , LMOD1 , SLC22A8 and SLC46A1 ), and neurogenesis ( LRP5 ) pathways that were identified by the pre-existing DMGs (Fig. 2 A). We also used a more unbiased approach to identify DMRs which DNAm levels could be associated with other genes, beyond the one mapping to the DMR. As described in the methods, we used eQTM analysis to find associations between the DNAm levels of the significant DMRs, and the expression levels of DEGs (unadjusted p ≤ 0.05). We identified 2 DMR/DEG pairs with an FDR ≤ 0.05 (Supp. Table 5). One of this eQTMs is a cis -eQTM in chromosome 14. Specifically, the DMR maps within the BRSK2 gene, but its DNAm levels are associated with the EPS8L2 gene, located ~ 723kb downstream (Fig. 5 A-B). There was no association between this DMR and the BRSK2 levels of expression (r2 = 0.1; p(reg) = 1.81e − 01 ). We also identified a potential trans -eQTM, with the DMR mapping to chromosome 16 and showing a significant association with RRP7A expression levels, a gene located in chromosome 10 (Fig. 5 E). In both cases, these DMRs were hypermethylated and downregulated with future higher amounts of alcohol use (Fig. 5 C-D, F-G). Alcohol-naïve DNAm signatures map to genes within the glutamatergic signaling pathway. Our results suggest that the glutamatergic receptor signaling pathway plays a critical role in establishing a vulnerable brain for future risky ethanol behavior. Among all the genes within this network (Fig. 6 A), the DMR associated with SHANK2 showed the strongest association with future ethanol intake (Fig. 6 B). Furthermore, its DNAm levels were positively associated with DNAm levels of 11 DMRs within this network (i.e. GRIN1 , GRIN3A , SLC12A5 , CACNA1C and DLGAP2 , red genes in Fig. 6 A, 6 D-E, 6 G-I), and negatively associated with other 8 DMRs (i.e. DRD1 , NRXN2 and NRXN3 , blue genes in Fig. 6 A, 6 C, 6 F, 6 J). Accordingly, in those individuals with lower risk for heavy alcohol intake, hypomethylation in SHANK2 and in genes encoding different glutamate receptors, a GABA receptor or KCC2 transporter; but hypermethylation in the dopamine receptor ( DRD1 ) and in two of the presynaptic master regulators, neurexins, are observed. Five of these correlated DMRs are directly connected to SHANK2 ( DLGAP2, GRIN2A, GRIN1, SHANK1, NRXN3 and NRXN2) , while the others connect to SHANK2 through CACNA1C and GRIN2A . Interestingly, the DNAm levels of the DMRs mapping to SHANK2 and CACNA1C are strongly correlated (Fig. 6 H, p-value = 5.57e − 17 ). The pre-existing DMR mapping to NRXN3 functions as an alternative promoter. Our studies indicate that the pre-existing DMR mapping to an intron of NRXN3 (Fig. 7 A) functions as a weak promoter (p = 4.50x10 − 2 ; Fig. 7 B-C). Furthermore, we showed that it can regulate the expression of the shorter NRXN3β (p = 2.32x10 − 2 ) transcript but not NRXN3α (p = 5.017x10 − 1 ) (Fig. 7 D). Alcohol-naïve DNAm signatures and mapped genes are associated with drinking at 6m, 12m and following protracted abstinence (PA). Using the same alcohol-naïve DNAm dataset described above, we were interested in identifying DNAm signals at baseline that were associated with drinking levels following a longer chronic ethanol intake period (12 months) and following repeated cycles of abstinence and relapse (PA). These analyses resulted in 2,025 DMRs at 12 months and 4,890 DMRs after PA. Given the high correlation between the drinking levels at 6m and 12m (p = 4.23x10 − 5 , Fig. 8 A-B), it was not surprising to find an overlap of 919 DMRs (Fig. 8 C). As expected, the comparisons of either of the chronic drinking periods (6m or 12m) with PA rendered less overlap (6m&PA = 626 DMRs, 12m&PA = 780 DMRs; Fig. 8 C), which agrees with the weaker correlation between the drinking levels at these timepoints, potentially due to the higher within-subject variability of post-abstinence intakes (Fig. 8 A-B). Across the three timepoints, a subset of 363 DMRs remained significantly associated with future drinking. Among these, hypermethylation of the Bx-DMRs mapping to GRIN3A and CACNA1I , are associated with heavier amounts of alcohol intake at the three time points (Fig. 8 E). Network and pathway analysis revealed that these 363 shared DMGs were part of the neural development, metabolism, cytoskeleton regulation, nucleic acid regulation and synaptic regulation (Fig. 8 D). These findings may suggest that these genes and associated pathways may play critical functions in establishing and maintaining vulnerability to risky alcohol intake over time. Discussion Our genome-wide DNAm analysis of the alcohol-naïve dlPFC-A46 is the first study to be conducted using a biopsy of the brain in living individuals prior to their enrollment in a chronic alcohol self-administration protocol. This unique study provides a molecular snapshot of the alcohol-naïve dlPFC, with the DNAm profile, mapped genes, and associated signaling pathways that distinguish individuals with different future drinking behavior, and that may contribute to risky drinking. Pre-existing DMRs might regulate alternative promoter use and splicing . While it is known that DNAm regulates gene expression[ 42 ], increasing evidence indicates a critical role of epigenetic signals in regulating splicing mechanisms in a cell- and tissue-type manner. A study found that on average each gene encodes for ~ 7 transcripts. Furthermore, over 30% of tissue-specific transcripts are due to splicing events[ 43 ], and DNAm plays a critical role in regulating these mechanisms[ 44 ]. In agreement with these results, and as previously observed in the nucleus accumbens of rhesus macaques[ 45 , 46 ], the majority of alcohol-associated DMRs identified in this study mapped within the gene body, with minor overlap of promoter regions. We have also shown in the macaque that intragenic DMRs might regulate splicing or alternative promoter use[ 45 ]. In the present study, we analyzed overall gene expression, without distinguishing among splicing transcripts, and found no significant differential gene expression associated with future ethanol intake. These results agree with a group comparison analysis conducted using the same dataset as used here[ 47 ] where no differences between heavy-very heavy drinkers (HVHD) and non-heavy drinkers were identified in the alcohol-naïve state. Although the tissue heterogeneity could contribute to the lack of differences, it is possible that pre-existing differences in gene expression associated with future drinking reside in splicing to produce alternative transcripts instead of overall gene expression. To address this question, future studies need to be conducted to identify differential expression of transcripts, either by deeper sequencing or using long-read RNA sequencing. When the significance threshold was relaxed (p < 0.05), 64 genes were differentially expressed in our analysis and in the published study using the same dataset[ 47 ]. Furthermore, in 11 of the 19 DEGs and DMGs, the DMRs mapped to promoter (2) or alternative promoter (9) locations. This could indicate that these DMRs might be contributing to the regulation of specific transcripts. Supporting this hypothesis, we show that the DMR mapping to NRXN3 functions as a promoter, and that it regulates the expression of the shorter transcript NRXN3β . Alcohol-naïve individuals display a vulnerable neural signaling pathway network in the dlPFC . Chronic alcohol use gradually reduces cognitive control, deteriorating the individual’s ability to inhibit perseverative responses and adapt to changes in environmental contingencies[ 48 ]. The dlPFC is a component of the central executive network[ 49 ] and alcohol use compromises its proper function[ 50 , 51 ]. Studies have shown that alcohol impairs neuroplasticity in the dlPFC[ 52 ]. Furthermore, modulation of neuroplasticity in the dlPFC through repetitive transcranial magnetic stimulation shows promise as a treatment for AUD[ 53 , 54 ]. In addition, low cognitive flexibility assessed with ASST predicted future classification as a heavy alcohol drinker[ 40 ], findings that have been replicated[ 39 ]. The present study used samples from the same macaques included in these studies[ 39 , 40 ], and support the hypothesis that individual differences in dlPFC function in an alcohol-naïve state and risk for future chronic heavy drinking is due, at least in part, to common underlying epigenetic signatures of a vulnerable brain state. In all, these results suggest that pre-existing individual variability in dlPFC epigenetic footprint and function, implicating neural-specific signaling pathways, reduces cognitive flexibility and establishes a predisposition towards inflexible cognitive behavior that contributes to future risk for heavy drinking. Similar findings have been reported in the few available human longitudinal studies, where decreased academic performance was associated with future alcohol use[ 55 – 57 ]. Among the neural-specific signaling pathways, three developmental functions, such as neurogenesis, CNS neuron differentiation, and forebrain development, emphasize the late adolescent to early adulthood developmental stage of the monkeys upon enrolment in the study. The developmental signatures may indicate individual differences in cortical maturation processes at this stage of primate brain development [ 58 ]. Accordingly, and within individuals of similar chronological age (~ 4–6 years old, approximately the equivalent to 16–24 human years), such differences could be engrained in an inherited epigenetic code that preconditions individuals to a life-long battle with AUD upon a first encounter with alcohol intoxication. We cannot exclude the possibility that we are capturing different stages of the cortical maturation process, as it is expected individual variability in reaching a matured brain. Notably, the rhesus monkeys were all born and raised at ONPRC under the same environmental conditions (i.e. diet, enrichment in corrals, same experimental conditions), and experimentally naïve before enrollment in the study. Nonetheless, their early life up to weaning was in large social groups, followed by smaller social peer groups post-weaning. This diversity of social interactions could have contributed to differential early-life social stressors shaping their epigenome prior to their enrolment in this study, in addition to epigenetic inheritance[ 59 ]. Even with the possible differential social history, the voluntary alcohol consumption in late adolescence/early adulthood reduces brain volume compared to age-matched controls and indicates an increased risk for future development of phenotypic drinking similar to risk in humans with AUD (see review[ 60 ]). In addition to exclusive developmental signaling pathways, we identified enrichment in modulation of synaptic transmission, regulation of membrane potential, neurotransmitter and vesicle-transport, and glutamate receptor signaling as relevant molecular signatures in the dlPFC of vulnerable individuals. Overall, it is not surprising that additional enrichment is detected for signaling pathways with global functions that are required for the neural processes described above given ethanol’s pharmacology. These include regulation of the cytoskeleton and its components, metabolic modulation and transcriptional and translational regulation. Alterations in glutamate receptor signaling contribute to vulnerability for future risky drinking . Analysis of the hub genes for the alcohol-naïve network pointed to a critical and central role of the glutamate receptor signaling pathway. These include multiple members of glutamate receptors (metabotropic and ionotropic; GRIN1 , GRIN2A , GRIN3A , GRM2 , GRIK3 ), the dopamine receptor ( DRD1 ), multiple members of the voltage gated calcium channels that control neurotransmitter release ( CACNA2D2 , CACNA2D3 , CACNG6 , CACNB2 , CACNB4 , CACNA1C , CACNA1H and CACNA1I ), important modulators of intracellular calcium dynamics in neurons ( PVALB, RYR1 ), and numerous synaptic proteins (i.e. SHANK2 , NRXN2 , NRXN3 , HOMER , DLGAP2 ). As expected, a combination of hypo and hypermethylated DMRs were associated with increasing amounts of alcohol. We further provide evidence of the functional implications of these DMRs, indicating that DMRs mapping to promoters and intragenic introns/exons function as enhancers of alternative promoters regulating the expression of alternative transcripts. In this study, we show that the intragenic DMR mapping to NRXN3 functions as an alternative promoter regulating the expression of NRXN3β , and without impacting NRXN3α . Further strengthening the relevance of the glutamate receptor pathway in establishing a vulnerable brain, one of the central genes in this pathway, SHANK2 , is also one of the top genes identified in this study. Importantly, SHANK2 -DMR DNAm levels were strongly correlated with numerous members of this signaling pathway (i.e. CACNA1C , GRIN1 ). Underscoring the broader significance of the SHANK2 -DMR, its DNAm levels were also correlated with genes implicated in neuron differentiation and development, axonal transport, regulation of action potential, ECM organization and synaptic transmission. SHANK2 (also known as ProSAP1) is an abundant postsynaptic scaffolding protein involved in establishing postsynaptic density of glutamate synapses through a dense network of molecular interactions[ 61 – 68 ]. SHANK2 loss of function and missense mutations have been extensively implicated in neurodevelopmental and neuropsychiatric disorders, including autism spectrum disorder (ASD), intellectual disability, developmental delay, schizophrenia and alcohol addiction[ 65 , 68 – 81 ]. Different mutations in SHANK2 can lead to increased or decreased expression of SHANK2 , dysfunctional SHANK2 domains or truncated proteins lacking interaction domains. This can lead to alterations in protein-protein interactions and the organization of the postsynaptic protein network, and ultimately to neuropsychiatric disorders[ 70 ]. In addition to the role of the SHANK2 variants, epigenetics may have a strong influence on the expression of the SHANK2 variant-mediated phenotypes[ 82 – 84 ] by affecting the penetrance of SHANK2 variants, as well as the expression of the different SHANK2 isoforms[ 84 ]. This has been observed for Shank3 , where tissue-specific promoter and intragenic DNAm regions with differential DNAm patterns were associated with alternative transcript expression in a brain region- and cell-type specific manner[ 85 – 87 ]. Furthermore, treatment with the DNAm inhibitor 5-azacytidine in cultured cells resulted in demethylation of SHANK3 CpG islands and altered isoform-specific expression of SHANK3 . Although it is unknown if a similar regulatory mechanism exists for SHANK2 , this will be particularly important to discern. Specifically, the different SHANK2 isoforms determine the organization of the post-synaptic density zone at different developmental stages and/or different brain regions[ 75 , 88 ], have different pre and postsynaptic functions, and the endogenous balanced expression of SHANK2 isoforms is essential for establishing and maintaining higher brain functions such as social and cognitive behaviors[ 89 ]. The different isoforms lack domains involved in protein-protein interactions that bridge glutamate receptors, scaffolding proteins and intracellular effectors to the actin cytoskeleton[ 62 , 68 ]. Importantly, an analysis of human tissues showed that expression of SHANK2A (equivalent to the macaque SHANK2-204 ) is only detected in the brain[ 90 ]. In addition, SHANK2E (equivalent to the macaque longest transcripts, 201–203) was detected in the human brain[ 90 ], contrary to previous findings stating SHANK2E was not expressed in the brain[ 69 ]. Three SHANK2 exons (19, 20, and 23) that code for a region between the PDZ and the proline rich domains, were only detected in brain samples[ 90 ]. Another important finding from this study, was the interindividual variability in the proportions of these three transcripts expressed in the brain[ 90 ]; a feature that has been observed for other synaptic proteins[ 91 – 93 ]. The macaque DMR is located within SHANK2 , and ongoing studies are underway to determine if the differential DNAm in this region contributes to the regulation of alternative transcripts in the dlPFC of macaques. Alcohol-naïve DNAm signals identify several signaling pathways modulating synaptic transmission that define vulnerability for future risky drinking. We described above the relevance of the glutamatergic system in potentially establishing vulnerability for future heavy ethanol use, with a central role for SHANK2 . Additional biological functions that could contribute to vulnerability included GTPase binding activity ( TBC1D30 [ 94 – 97 ] , [ 98 , 99 ]; MEX3B [ 100 ] , [ 101 – 103 ] , [ 104 ]), MAPK signaling ( DUSP2 [ 105 ] [ 106 ]), ECM organization, axonogenesis, axonal growth and guidance ( ROBO3 )[ 107 ] , [ 108 – 110 ] , [ 110 – 115 ] , [ 116 ], synapse formation, and synaptic plasticity ( SMAD3 and SLC9A3 )[ 117 – 119 ] , [ 120 ] [ 121 ]. All the genes listed above and the functions they link to are central to mediating neuronal communication and synaptic plasticity in AUD. Our results suggest that the DMRs mapping to these genes influence the regulation of alternative expression of transcripts and consequently, the regulated neuronal processes in some individuals, rendering them more vulnerable for heavy alcohol use. These results, together with the lack of differential gene expression, strengthen the hypothesis that DNAm signals that are inherited or acquired early-in-life could predetermine an individual’s risk for alcohol misuse if alcohol is encountered during their lifetime. Conclusions To the best of our knowledge, this is the first study to characterize the methylomic profile of the dlPFC-A46 in the alcohol-naïve primate brain of males. The main findings of our study show that DNAm signals that are inherited or acquired early in life contribute to defining the “epigenetic signature” of a resilient or vulnerable brain. The most abundant DNAm signals point to circuitry and mechanisms associated with prefrontal cortical development, synaptic functions, glutamatergic signaling and other coordinated cell signaling pathways. While several of the genes uncovered by our study have been independently linked to alcohol use, our study points to a role of DNAm in regulating these critical neural pathways. In addition, the use of alcohol-naïve primates that subsequently exhibited disparate, long-term drinking behaviors, enabled the first identification of pre-existing neural molecular signatures of future risk. The mechanism to propagate excessive alcohol consumption throughout adulthood could very well be linked to these established pre-alcohol DNAm patterns in a key decision-making area of the brain (dlPFC-A46) on the first encounter of alcohol intoxication. Some of the genes identified in this study have also been previously implicated in psychiatric disorders, i.e. SHANK2 has been linked to autism, neurodevelopmental disorder, schizophrenia, or bipolar disorders (see[ 80 ]). It is important to note that excessive alcohol use is common among individuals suffering from these disorders[ 122 – 124 ]. Our study may be revealing the molecular circuitry that places individuals at risk for AUD. With a complex disorder as AUD, having the ability to identify the molecular mechanisms underlying AUD risk is critical for better development of personalized effective treatments. Methods Animals. Two cohorts, named cohorts 10 and 14 in the Monkey Alcohol and Tissue Research Resource (MATRR database)[ 125 ], of young adult (~ 5 years old; Supp. Table 1) male rhesus macaques were housed in quadrant cages (0.8 x 0.8 x 0.9 m) with constant temperature (20–22 ̊C), humidity (65%), and an 11/13-hour light/dark cycle. Animals had visual, auditory, and olfactory contact with other animals in the protocol. All animals were maintained on positive caloric (i.e., weight gain) and fluid balance throughout the experiment, and body weights were recorded weekly. All procedures were conducted in accordance with the Guide for the Care and Use of Laboratory Animals and the NIH PHS Policy on Humane Care and Use of Laboratory Animals for the care and use of laboratory animal resources. In addition, all procedures were approved by the Oregon National Primate Research Center (ONPRC) Institutional Animal Care and Use Committee. Animals in these cohorts were chosen from the ONPRC breeding colony to avoid common parents or grandparents. From these cohorts, only monkeys that had a biopsy of the dlPFC-A46 taken prior to enrollment in an alcohol self-administration protocol were included in this study. Detailed drinking and physiological data are available for these cohorts online (MATRR.com; Supp. Table 1). In addition, and using data from mGAP (mgap.ohsu.edu), we checked the genetic variation of these animals in genes directly involved in the ethanol metabolic pathway including all the alcohol dehydrogenase and aldehyde dehydrogenase genes. We identified 69 variants in these genes with a minor allele frequency > 0.1 and a genotyping rate of at least 95%. To further filter these variants down, we associated them with ethanol consumption levels. No variants were significantly associated (FDR < 0.05) with ethanol consumption levels. Ethanol subjects. Monkeys were trained to use operant drinking panels and underwent scheduled induced polydipsia (SIP) to induce ethanol self-administration in daily 16-hour (h) sessions, as previously described[ 126 ]. Following induction, open-access (or ethanol self-administration) began, and water and 4% (w/v) ethanol were concurrently available in daily 22-h sessions for twelve to fourteen months (cohorts 10 and 14). The details of the open-access protocol are discussed in Grant et al. (2008)[ 126 ] and the data modeling for drinking categories is presented in Baker et al. (2014)[ 37 ]. Ethanol intake was recorded daily with a 0.5 second resolution, 22-h/day, 365 days/year. Monkeys in cohorts 10 and 14 experienced three repetitive cycles of relapse/abstinence (protracted abstinence) following the chronic ethanol open-access period. However, for the present study, only the biopsy samples and the ethanol drinking levels following 12 months and protracted abstinence of self-administration were used. Biopsy Samples. MRI was used to obtain the stereotaxic coordinates for the craniotomy and surface dlPFC A46 biopsies along the lateral sulcus were obtained from the contralateral dominant hemisphere (determined by handedness). Biopsy samples (120–150 mg wet weight) were taken under anesthesia (Ketamine HCl 15 mg/kg; intramuscular) and prior to ethanol exposure. Samples were immediately flash frozen in liquid nitrogen in a 2ml cryovial and stored at -80 ̊C for future processing. The samples were processed using a standard Qiagen AllPrep DNA/RNA/miRNA Universal Kit protocol as described[ 45 , 46]. Genome-wide DNA methylation profiling . Genomic DNA was checked for quality by electrophoresis on a 0.7% agarose gel, using a NanoDrop 8000 spectrophotometer (Thermo Scientific, Wilmington, DE, USA) and quantified using a Qubit (Thermo Scientific, Wilmington, DE, USA). One microgram of genomic DNA was sheared using a Bioruptor UCD200 (Diagenode, Denville, NJ, USA), generating fragments ~ 180 bp. The SureSelect XT Human Methyl-Seq library preparation (Agilent Technologies, Santa Clara, CA, USA) was used following the manufacturer’s instructions. The SureSelect MethylSeq probes interrogate 3.7 million individual CpG sites per sample at single-nucleotide resolution and it covers RefSeq genes (including, 5’-UTR, exon, intron, 3’-UTR, TSS), Gencode promoters, CpG islands, shores, shelves and open sea, DNase I hypersensitive sites (intergenic regions), Ensembl regulatory features, known DMRs[ 127 ]. The libraries were then bisulfite-treated using EZ DNA Methylation-Gold (Zymo Research, Irvine, CA, USA), and quantified using a 2100 Bioanalyzer (Agilent Technologies). DNA libraries were sequenced on an Illumina NovaSeq6000 at the University of Oregon Genomics & Cell Characterization Core Facility (GC3F). The quality of the bisulfite-converted sequencing reads was assessed with FastQC[ 128 ]. Reads were trimmed and aligned to the macaque reference genome (Mmul10)[ 129 ], and then the bisulfite conversion rates were evaluated, all libraries were > 98% converted, and CpG methylation counts were obtained using Bismark[ 130 ]. The DNAm rates were calculated as the ratio of methylated reads over the total number of reads. Methylation rates for CpGs with fewer than 10 reads were excluded from further analysis. We next removed sites on sex chromosomes, and over 3 million CpGs per sample were used for downstream analyses. The differential DNAm analysis was carried out by applying a generalized linear mixed effects model (GLMM) implemented in R package PQLseq (version 1.2.1)[ 131 , 132 ] separately for each CpG site. PQLseq models the technical sampling variation in bisulfite sequencing data with a binomial distribution, effects of biological and technical covariates with the linear model, and the random effects with a correlated multivariate normal distribution. The most common methylation proportion values are 0 and 1, which are problematic in the context of generalized linear models with the logit link function (infinite in the logit-transformed space). We used a common pseudo-count transformation to avoid both extremes, as recommended[ 133 ]. This was done after adding + 1 to the numbers of methylated reads and + 2 to the total numbers of reads to avoid modeling methylation proportions that are exactly 0 or 1, as recommended [ 133 ]. This pseudo-count transformation was only applied to non-missing values (coverage > 10x). We modeled the average consumption of ethanol at 6 months, 12 months and protracted abstinence as a predictor of DNAm rate and included age as covariate in the binomial model. Relatedness of the animals was accounted as random effects in the model. Each nominal p-value was corrected for multiple comparisons by the False Discovery Rate (FDR). In parallel, the nominal p-value was used as input for Comb-p[ 134 ] analysis to identify differentially methylated regions (DMRs) in AUD subjects as previously described[ 45 ]. Agreement analysis Prior to network and other downstream analysis, we computed beta regression with the average DNAm rates of each DMR to assess whether there was agreement in the direction of effects across the DMR. This process was completed to filter out DMRs where there may have been differentially methylated cytosines (DMCs) that were in opposing directions (i.e., some hypermethylated and some hypomethylated) across the region and are more difficult to interpret and are more likely to be false positives. Prior to beta regression, average DNAm rates equal to 0 or 1 were changed to 1x10 − 10 and 1–1x10 − 10 . If the average DNAm rates across the DMR were not associated with ethanol usage (“agreement” p-value > 0.05), then they were no longer considered for downstream analysis. In general, we found this process effective at identifying DMRs with either inconsistent DNAm patterns or highly correlated effects across the region. Differential gene expression analysis The RNAseq library preparation and data analysis has been previously described[ 47 ]. Using the already processed RNA-Seq read counts we reanalyzed only the baseline biopsy samples for this manuscript. Genes with an average read count below 5 were filtered out and we computed negative binomial regression with gene expression as the outcome and later stage (6 months) drinking amounts as a continuous predictor. The negative binomial regression was computed in DESeq2[ 135 ]. Expression quantitative trait methylation (eQTM) analysis We used the significant DMRs which DNAm was concordant among the DMCs within each DMR (1,531) and DEGs with significance p < 0.05 (316) as input for eQTM analysis (MatrixEQTL R package [ 136 ]). We used a linear model, a threshold p < 1e-02 to compute FDR correction for cis (up to 1e 6 nucleotides) and trans associations. Network analysis Significant DMRs that had gene annotations were analyzed in KEGG, STRING, and MCODE to find biological pathways enriched with future ethanol drinking[ 137 , 138 ]. STRING was used to obtain protein-protein interactions for all genes that met our filtering criteria. STRING was applied to find only “High confidence” protein-protein interactions with options for “textmining” and “neighborhood” disabled[ 137 ]. MCODE was applied to the remaining interactions to obtain a set of highly interconnected gene clusters[ 138 ] and the biological functions of each clusters with MCODE scores greater than 4.0 were identified through the KEGG pathways[ 139 ]. Reporter assay To determine the promoter or enhancer activity of the rhesus macaque DMR located upstream of the NRXN3β first exon, we cloned the human homologous DMR region (GRCh38: chr14:79279949–79280157) in two different Firefly luciferase reporter pGL3 vector (Promega, Madison, WI, USA), the pGL3-Enh and the pGL3-Prom to test the promoter or enhancer activity of the DMR; respectively. Three vectors were used as controls: pGL3-basic (E1751, Promega, Madison, WI, USA) lacks eukaryotic enhancer and promoter; pGL3-control (E1741, Promega, Madison, WI, USA) has both an SV40 promoter and an SV40 enhancer; pGL3-enhancer (E1771, Promega, Madison, WI, USA) has an SV40 enhancer, but no promoter. HEK293 cells were seeded in 96-well plates at a density of 10,000 cells per well and cultured in DMEM containing high glucose (4.5 g/L) supplemented with 10% fetal bovine serum (FBS) and maintained at 37°C and 5% CO 2 . Twenty-four hours later, cells were transfected using 100ng (10:1 ratio of 90ng of the corresponding pGL3 vector to 10ng of pGL3-control expressing renilla luciferase, which is used to normalize the luciferase signal) in Opti-MEM reduced serum (51985091, Thermo Scientific, Wilmington, DE, USA) media and 0.03% Xtreme Gene HD DNA Transfection Reagent (06366236001, Sigma Aldrich, Darmstadt, Germany). Forty-eight hours following transfection, cells were harvested, and luciferase activity was measured using the Dual-Glo® Luciferase Assay System (E2920, Promega, Madison, WI, USA) according to the manufacturer’s instructions using a SpectraMax iD3 Microplate Reader (Molecular Devices). After luminescence was measured, the firefly luciferase signal was normalized against the renilla signal to account for variability across plates and experiments, the resulting relative luminescence units (RLUs) were calculated for each well. All experiments were performed in triplicate, and data are presented as mean ± standard deviation (Pierce). Transcriptional activation using the VP64-p65-Rta (VPR) system gRNAs targeting the NRXN3 -DMR were designed using the sgRNA Scorer by Frederick National Laboratory for Cancer Research and as recommended in [ 140 ]. The final gRNA sequence used was CACCGAGATCCGGAGGAAGCCGCGC. The gRNAs were cloned in pSB700 (#64046, Addgene). The SP-dCas9-VPR vector (#63798, Addgene), which contains the VP64-p65-Rta transcriptional activators, was used to activate NRXN3β transcription. One gRNA was designed based on human sequences homologous to the rhesus macaque DMR to recruit the dCas9-VPR complex to target transcriptional start sites. HEK293 cells were seeded into 24-well plates at a density of 50,000 cells/well and transfected in Opti-MEM (51985091, Thermo Scientific, Wilmington, DE, USA) with 450 ng of SP-dCas9-VPR plasmid (#63798, Addgene) and 50 ng of gRNA-pSB700 using 1.5 µL XtremeGENE (06366244001, Roche, Mannheim, Germany) per well. Controls included transfections with the pSB700 lacking the gRNA and dCas9-VPR. After 48 hours, transfection efficiency was assessed by GFP fluorescence. After transfection, cells were harvested, and RNA was isolated for quantitative PCR (qPCR). RNA isolation and reverse transcription . RNA was isolated from the dlPFC-A46 using the Qiagen AllPrep DNA/RNA/miRNA Universal Kit as described[ 45 , 46 , 141 ]. cDNA was synthesized from total RNA isolated from the dlPFC-A46 or PL cortex using the SuperScript IV Reverse Transcriptase (LT-02241, Invitrogen, Vilnius, Lithuania) system following the manufacturer’s instructions. RNA (250ng) was combined with random hexamers (2.5ng) (18091050, Invitrogen, Vilnius, Lithuania) and dNTPs mix (0.5mM) (18091050 Invitrogen, Vilnius, Lithuania). For control reactions, HeLa RNA (10ng) was used. After an initial incubation at 65°C for 5 minutes, SSIV buffer (1x) (LT-02241, Invitrogen, Vilnius, Lithuania), DTT (5mM) (LT-02241, Invitrogen, Vilnius, Lithuania), ribonuclease inhibitor (40U) (100000840, Invitrogen, Carlsbad, CA), and SuperScript IV Reverse Transcriptase (100U) (LT-02241, Invitrogen, Vilnius, Lithuania) were added and incubated at 50–55°C for 10 minutes, followed by heat inactivation at 80°C for 10 minutes. Remaining RNA was removed by E. coli RNase H treatment (1U) (18021-014, Invitrogen, Carlsbad, CA) at 37°C for 20 minutes. The resulting cDNA was either used immediately or stored at -20°C. Quantitative PCR analysis (qPCR) was performed using GoTaq® qPCR Master Mix (1x, A6001, Promega, Madison, WI, USA), forward and reverse primer (400nM each, PGK1 -F: GCTCTGTGAGCAGTGCCAAAA; PGK1 -R: GGAAAAGATGCTTCTGGGAACA; NRXN3α -F: ACCCAGTACCACCTGCCAGGAA; NRXN3α -R: TCATTGCACTGGTTTCCAGAA; NRXN3β -F: CAAGATGCCATCCTTCACAG; NRXN3β -R: GCATCACTCAGTGCCTATTTC), and template DNA (7.5ng) or nuclease-free water as negative control. The following conditions were used: 95°C for 2 minutes (1 cycle); 95°C for 15 seconds; 59°C for 30 seconds (40 cycles). Post-amplification melting curves were analyzed to assess primer specificity. Data were normalized to the expression levels of the housekeeping gene PGK1 . Relative quantification was performed using the ΔΔCt method. Declarations Author Contribution KAG, BMF and RCJ designed the experiments. KAG and SWG oversaw the alcohol self-administration protocol and provided the rhesus macaque samples. TC and RCJ isolated all the DNA samples and prepared all the omics libraries. DNA methylation bioinformatic analyses were performed by KDZ and LJW, and KDZ advised and conducted the appropriate statistical analyses in all the experiments. CCL generated the reporter and transcriptional activation assays. RCJ supervised all the analytical aspects of these experiments and prepared the first draft of the manuscript. RH generated and supervised the transcriptomics datasets and analyses. KAG, BMF, KDZ, BH and RCJ all provided edits to the manuscript and helped to write various sections. Data Availability The data that support the findings of this study are available on GEO under the following accession number: TBD References SAMHSA CfBHSaQ. 2022 National Survey on Drug Use and Health. 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Nucleic Acids Res. 2016;44:D457–462. Nageshwaran S, Chavez A, Cher Yeo N, Guo X, Lance-Byrne A, Tung A, Collins JJ, Church GM. CRISPR Guide RNA Cloning for Mammalian Systems. J Vis Exp 2018. Cuzon Carlson VC, Ford MM, Carlson TL, Lomniczi A, Grant KA, Ferguson B, Cervera-Juanes RP. Modulation of Gpr39, a G-protein coupled receptor associated with alcohol use in non-human primates, curbs ethanol intake in mice. Neuropsychopharmacology 2019. Additional Declarations No competing interests reported. Supplementary Files Supp.Table1.xlsx Supplementary Table 1. Phenotypic summary of the rhesus monkeys used in this study. BD: binge drinker; HVHD: heavy-very heavy drinker; ABS: abstinence. Supp.Table2.xlsx Supplementary Table 2. List of significant DMRs identified in biopsies of the dlPFC-A46 from male rhesus monkeys. Supp.Table3.xlsx Supplementary Table 3. List of the differentially expressed genes identified in biopsies of the dlPFC-A46 from male rhesus monkeys. Supp.Table4.xlsx Supplementary Table 4. List of differentially methylated and expressed genes (DMGs, DEGs). Supp.Table5.xlsx Supplementary Table 5. List of eQTMs identified in biopsies of the dlPFC-A46 from male rhesus monkeys. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5406434","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":379560037,"identity":"a940b641-de32-432f-89a9-e9a37bc5a9a3","order_by":0,"name":"Rita Cervera-Juanes","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYDADfgYGZgbGBgYGPgYeIJeNCC2SDVAtbGzEajE4QKwW8/bDxz7zVNyTN76R/Njg4w4beTb53gMMH8oO49QicyYteTbPmWLDbTfSjBNnnkkzbGPjS2CccQ63FgkJHmPGmW0JjNtuJxgf5m07zNjGxmPADGTg0cL/mXHmvwT7zbPTPx/+2/bfHqzlL14tPMwMHxsSEjdI5xgnM7YdSARrYcSnhSfNmOHDsYTkGfffFBv2tiUnt7HlGBzsOZeOWwv74ccMCTUJtv09xzdL/Gyzs+1nPmP44EeZNU4t2MEBEtWPglEwCkbBKEADACO6UTmVh9FlAAAAAElFTkSuQmCC","orcid":"","institution":"Wake Forest University School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Rita","middleName":"","lastName":"Cervera-Juanes","suffix":""},{"id":379560042,"identity":"4fbd9fd0-9683-46f2-b0c2-175a56e8c678","order_by":1,"name":"Kip D. Zimmerman","email":"","orcid":"","institution":"Wake Forest University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Kip","middleName":"D.","lastName":"Zimmerman","suffix":""},{"id":379560044,"identity":"0b8ed2cb-bf43-4fdb-97fa-793e047d3d5a","order_by":2,"name":"Larry Wilhelm","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Larry","middleName":"","lastName":"Wilhelm","suffix":""},{"id":379560047,"identity":"0bd9259e-2b52-4255-9a29-070be6298f6b","order_by":3,"name":"Clara Christine Lowe","email":"","orcid":"","institution":"Wake Forest University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Clara","middleName":"Christine","lastName":"Lowe","suffix":""},{"id":379560048,"identity":"5f1c946a-1786-4162-bf81-7b9c289d8313","order_by":4,"name":"Steven W. 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Ferguson","email":"","orcid":"","institution":"Oregon Health \u0026 Science University","correspondingAuthor":false,"prefix":"","firstName":"Betsy","middleName":"M.","lastName":"Ferguson","suffix":""},{"id":379560052,"identity":"9dbe7dbe-dc82-4aea-a5f0-5eb2cdfe0720","order_by":8,"name":"Kathy A. Grant","email":"","orcid":"","institution":"Oregon Health \u0026 Science University","correspondingAuthor":false,"prefix":"","firstName":"Kathy","middleName":"A.","lastName":"Grant","suffix":""}],"badges":[],"createdAt":"2024-11-07 03:38:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5406434/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5406434/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69918768,"identity":"30fe046d-832c-4308-8838-a40149ce0e0c","added_by":"auto","created_at":"2024-11-26 14:59:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":232226,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e) Distribution of the alcohol-naïve DNA methylation (DNAm) rates across rhesus macaques. \u003cstrong\u003eB\u003c/strong\u003e) Volcano plot of the DNAm rates of the identified DMRs. Those with a DNAm rate below 10% are represented in blue, and those with higher DNAm rate in red. \u003cstrong\u003eC-D) \u003c/strong\u003eDoughnut diagrams depicting the distribution of the DMRs in genomic (\u003cstrong\u003eC\u003c/strong\u003e) and CpG island context\u003cstrong\u003e \u003c/strong\u003e(\u003cstrong\u003eD\u003c/strong\u003e)\u003cstrong\u003e. E-G\u003c/strong\u003e) Linear regression plots of the DNAm and average ethanol intake (at 6 months of open-access) of three of the top most significant DMRs identified in alcohol-naïve individuals.\u003c/p\u003e","description":"","filename":"Figure1811.png","url":"https://assets-eu.researchsquare.com/files/rs-5406434/v1/e6d8ab9f3d451c50eaad70fa.png"},{"id":69918775,"identity":"2da6659f-57a2-4b94-a11e-edb3ac7ad9f0","added_by":"auto","created_at":"2024-11-26 14:59:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":618139,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork and pathway analyses of the pre-existing differentially methylated genes (DMGs). \u003cstrong\u003eA\u003c/strong\u003e) Network and pathway analyses identify clusters of DMGs that are enriched in cytoskeletal and cell-cell junction (purple clusters), metabolism (blue clusters), regulation of transcription and translation (green clusters), mitochondria organization (pink clusters) and in neural-relevant pathways (red clusters). \u003cstrong\u003eB\u003c/strong\u003e) Detail of the glutamate receptor signaling network structure and gene composition. \u003cstrong\u003eC-J\u003c/strong\u003e) Linear regression of the average DNAm of the DMRs and average ethanol intake (at 6 months) of the genes in the glutamate receptor signaling pathway.\u003c/p\u003e","description":"","filename":"Figure1812.png","url":"https://assets-eu.researchsquare.com/files/rs-5406434/v1/c35926151019e479b8cc54c4.png"},{"id":69918772,"identity":"f3439c2d-1b06-4e57-b33c-cd9cffcd4839","added_by":"auto","created_at":"2024-11-26 14:59:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":220939,"visible":true,"origin":"","legend":"\u003cp\u003eGene structure and DNAm data for the gene \u003cem\u003eDLGAP2\u003c/em\u003e. \u003cstrong\u003eA\u003c/strong\u003e) Description of the structure of the \u003cem\u003eDLGAP2\u003c/em\u003e gene. Common exons across the different transcripts are depicted in gray, while those in orange are alternative first exons that are unique to different transcripts. The green exon is an alternative exon for two of the transcripts (Ensembl ENSMMUG00000057265 transcripts 202 and 203). The DMRs are shown in light blue. \u003cstrong\u003eB-C\u003c/strong\u003e) Regression plots of the DNAm levels and the average level of ethanol intake at 6m for the 3 DMRs mapping to \u003cem\u003eDLGAP2\u003c/em\u003e. \u003cstrong\u003eE-F\u003c/strong\u003e) Network clusters of the hub genes (colored boxes) and their closest connected genes.\u003c/p\u003e","description":"","filename":"Figure1813.png","url":"https://assets-eu.researchsquare.com/files/rs-5406434/v1/b19492dfbcf58ff1a6566321.png"},{"id":69919123,"identity":"3d39d420-16b9-4f25-b989-91e02b88fa69","added_by":"auto","created_at":"2024-11-26 15:07:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":153061,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential expression associated with future ethanol drinking. \u003cstrong\u003eA\u003c/strong\u003e) Venn diagram of the overlap of differentially methylated genes (DMGs) and differentially expressed genes (DEGs). \u003cstrong\u003eB\u003c/strong\u003e) Histogram of the number of DMGs and DEGs that were hypermethylated (HyperM), hypomethylated (HypoM), upregulated (UpR) and downregulated (DownR). \u003cstrong\u003eL\u003c/strong\u003e) Linear regression of the normalized counts of the DEGs and average ethanol intake (at 6 months).\u003c/p\u003e","description":"","filename":"Figure1814.png","url":"https://assets-eu.researchsquare.com/files/rs-5406434/v1/086dab23949788280332be84.png"},{"id":69918769,"identity":"dd299152-9c4d-4ab3-8643-93506e6fa35d","added_by":"auto","created_at":"2024-11-26 14:59:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":128895,"visible":true,"origin":"","legend":"\u003cp\u003eSummary of the results of the eQTM analysis. \u003cstrong\u003eA)\u003c/strong\u003e Genomic location of the DMR (orange) associated in cis (\u003cem\u003ecis\u003c/em\u003e-eQTM) to the expression levels of \u003cem\u003eEPS8L2\u003c/em\u003e, located 723kb upstream. \u003cstrong\u003eB)\u003c/strong\u003e Linear regression of the normalized counts of \u003cem\u003eEPS8L2 \u003c/em\u003eand the DNAm levels of a DMR located 723kb downstream, mapping to \u003cem\u003eBRSK2\u003c/em\u003e. \u003cstrong\u003eC)\u003c/strong\u003e Linear regression of the normalized counts of \u003cem\u003eEPS8L2\u003c/em\u003eand average ethanol intake (at 6 months). \u003cstrong\u003eD) \u003c/strong\u003eLinear regression of the DNAm levels of the DMR mapping to \u003cem\u003eBRSK2\u003c/em\u003e and average ethanol intake (at 6 months).\u003cstrong\u003e E)\u003c/strong\u003e Linear regression of the normalized counts of \u003cem\u003eRRP7A7 \u003c/em\u003e(chromosome 10) and the DNAm levels of a DMR located in chromosome 16. \u003cstrong\u003eC)\u003c/strong\u003e Linear regression of the normalized counts of \u003cem\u003eRRP7A7 \u003c/em\u003eand average ethanol intake (at 6 months). \u003cstrong\u003eD) \u003c/strong\u003eLinear regression of the DNAm levels of the DMR mapping to chromosome 16 and average ethanol intake (at 6 months).\u003c/p\u003e","description":"","filename":"Figure1815.png","url":"https://assets-eu.researchsquare.com/files/rs-5406434/v1/5683d65165244e880f9458f3.png"},{"id":69920040,"identity":"1eb2f8fa-11fd-4cf9-9233-c50d493206f9","added_by":"auto","created_at":"2024-11-26 15:15:43","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":321408,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between the DNAm of the \u003cem\u003eSHANK2\u003c/em\u003e-DMR and that of the rest of genes in the glutamatergic signaling pathway. \u003cstrong\u003eA\u003c/strong\u003e) Network analysis showing eleven DMRs that had DNAm levels positively (red genes)\u003cem\u003e \u003c/em\u003eand negatively (blue genes) correlated with the DNAm in the \u003cem\u003eSHANK2\u003c/em\u003e-DMR (green). \u003cstrong\u003eB\u003c/strong\u003e) Linear regression of a subset of the correlated DMRs with the DNAm levels of \u003cem\u003eSHANK2\u003c/em\u003e-DMR.\u003c/p\u003e","description":"","filename":"Figure1816.png","url":"https://assets-eu.researchsquare.com/files/rs-5406434/v1/fc93f4d9d56c8a989bbfff6e.png"},{"id":69918777,"identity":"42536029-614e-4e45-9408-7cd0559f7adb","added_by":"auto","created_at":"2024-11-26 14:59:43","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":41264,"visible":true,"origin":"","legend":"\u003cp\u003eThe DMR mapping to an alternative exon functions as promoter and regulates the expression of an alternative transcript. \u003cstrong\u003eA)\u003c/strong\u003e Genomic structure of the \u003cem\u003eNRXN3\u003c/em\u003egene. Common exons across the different transcripts are depicted in gray, while those in orange are alternative first exons that are unique to different transcripts. The green exons are alternative exons. The DMR is shown in light blue. \u003cstrong\u003eB-C)\u003c/strong\u003eReporter assay shows that the DMR functions as a weak promoter, with no enhancer activity. Con_Enh: control plasmid with SV40 enhancer but lacking the SV40 promoter; Con_Prom+Enh: control plasmid with the SV40 promoter and enhancer; DMR_Enh: the DMR was cloned in the control plasmid lacking the SV40 promoter but with the SV40 enhancer; Con_Prom: control plasmid with SV40 promoter but lacking the SV40 enhancer; Con_Prom+Enh: control plasmid with the SV40 promoter and enhancer; DMR_Prom: the DMR was cloned in the control plasmid lacking the SV40 enhancer but with the SV40 promoter. \u003cstrong\u003eD)\u003c/strong\u003e Quantitative PCR results following the transcriptional activation assay shows that recruiting transcriptional machinery to the DMR upregulates the expression of \u003cem\u003eNRXN3b\u003c/em\u003e without altering the expression of \u003cem\u003eNRXN3a\u003c/em\u003e. Con_VPR: cells were transfected with the VPR plasmid, no gRNA was co-transfected; \u003cem\u003eNRXN3a\u003c/em\u003e_and \u003cem\u003eNRXN3b_\u003c/em\u003eVPR+gRNA: cells were co-transfected with the VPR plasmid, and the gRNA targeting the DMR.\u003c/p\u003e","description":"","filename":"Figure1817.png","url":"https://assets-eu.researchsquare.com/files/rs-5406434/v1/d8e640dc1ec82923a509fa45.png"},{"id":69919120,"identity":"6be12117-ddbd-43fd-9e20-c72ac8fdfd22","added_by":"auto","created_at":"2024-11-26 15:07:43","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":796607,"visible":true,"origin":"","legend":"\u003cp\u003eAlcohol-naïve DNAm signatures and mapped genes are associated with drinking at 6m, 12m and following PA.\u003cstrong\u003e A) \u003c/strong\u003eAverage ethanol intake at different timepoints per each individual included in the exploratory analysis; 6m, 12m and following two cycles of abstinence and relapse (ABS2). \u003cstrong\u003eB\u003c/strong\u003e) Linear regression of the ethanol intake levels between the three time points. \u003cstrong\u003eC\u003c/strong\u003e) Venn diagrams of the DMRs associated with the average ethanol intake after 6m (red), 12m (orange) and ABS2 (blue). \u003cstrong\u003eD\u003c/strong\u003e) Network and pathway analysis of the 363 DMR-mapped genes that are in common across the three timepoints. \u003cstrong\u003eE\u003c/strong\u003e) Linear regression of the DNAm and average ethanol intake of the DMRs mapping to \u003cem\u003eGRIN3A\u003c/em\u003e and \u003cem\u003eCACNA1I\u003c/em\u003e across the three timepoints.\u003c/p\u003e","description":"","filename":"Figure1818.png","url":"https://assets-eu.researchsquare.com/files/rs-5406434/v1/24d552df7cd68d215930f04f.png"},{"id":70422765,"identity":"d459e10b-116a-449b-aa42-4a36cfc80bac","added_by":"auto","created_at":"2024-12-03 04:39:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3338358,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5406434/v1/06d20ca7-4655-464f-a2b1-ea0c72370357.pdf"},{"id":69918770,"identity":"a344dd59-8441-469f-b6be-ae3d3a4a2959","added_by":"auto","created_at":"2024-11-26 14:59:43","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":9772,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 1\u003c/strong\u003e. Phenotypic summary of the rhesus monkeys used in this study. BD: binge drinker; HVHD: heavy-very heavy drinker; ABS: abstinence.\u003c/p\u003e","description":"","filename":"Supp.Table1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5406434/v1/dfdc7bab868d8af1614bc5a4.xlsx"},{"id":69918780,"identity":"e6fbe46a-9887-4b1a-9248-41430eb41785","added_by":"auto","created_at":"2024-11-26 14:59:43","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":733208,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 2\u003c/strong\u003e. List of significant DMRs identified in biopsies of the dlPFC-A46 from male rhesus monkeys.\u003c/p\u003e","description":"","filename":"Supp.Table2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5406434/v1/6e4ac04745248648142896f3.xlsx"},{"id":69918771,"identity":"e55a4f61-5196-4d0e-b498-662ee8aff0c8","added_by":"auto","created_at":"2024-11-26 14:59:43","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":26576,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 3\u003c/strong\u003e. List of the differentially expressed genes identified in biopsies of the dlPFC-A46 from male rhesus monkeys.\u003c/p\u003e","description":"","filename":"Supp.Table3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5406434/v1/8e28cb0cdb54559e78468f9f.xlsx"},{"id":69918774,"identity":"81db76c3-0b97-4fa7-8ac7-af54e8ab53c0","added_by":"auto","created_at":"2024-11-26 14:59:43","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":10918,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 4\u003c/strong\u003e. List of differentially methylated and expressed genes (DMGs, DEGs).\u003c/p\u003e","description":"","filename":"Supp.Table4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5406434/v1/51c2ef83c6bcb3716bcd35a1.xlsx"},{"id":69919121,"identity":"448462bc-bb3f-405d-a641-19f50303ea64","added_by":"auto","created_at":"2024-11-26 15:07:43","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":548996,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 5\u003c/strong\u003e. List of eQTMs identified in biopsies of the dlPFC-A46 from male rhesus monkeys.\u003c/p\u003e","description":"","filename":"Supp.Table5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5406434/v1/795fae742b0bf9222643f52f.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genome-wide methylomics identifies pre-existing DNA methylation signatures in the prefrontal cortex of alcohol-naïve rhesus monkeys defining neural vulnerability for future risky ethanol consumption.","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAlcohol use disorder (AUD) is a highly prevalent, complex, multifactorial, and heterogeneous disorder; with 11% and 30% of adults meeting the criteria for past-year and lifetime AUD, respectively[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This amount of chronic heavy alcohol use results in significant social, economic, and public health costs[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Despite the availability of a few FDA approved and psychotherapy treatments, only\u0026thinsp;~\u0026thinsp;5% of AUD individuals received treatment in 2021, and among those, 60% relapsed within 6 months following treatment[\u003cspan additionalcitationids=\"CR8 CR9 CR10\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Prevention and treatment strategies, based on risk factors such as early and accurate identification of individuals with a high risk for developing an AUD, are needed to reduce the incidence, prevalence, severity, duration, and consequences of future AUD and comorbid psychiatric disorders.\u003c/p\u003e \u003cp\u003eNeural-based risk factors obtained in alcohol-na\u0026iuml;ve humans have largely been studied using longitudinal studies of the brain using evoked potentials or MRI imaging[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. For example, the Collaborative Study of the Genetics of Alcoholism (COGA) recently reported on longitudinal data collected from subjects as early as age 12 (before developing AUD), and years later when they were diagnosed with AUD or remained unaffected (at ~\u0026thinsp;30 years old) to explore risk biomarkers. Based on multidimensional data (e.g. clinical, electrophysiological, genetic and family history) acquired prior to AUD development, the study found that a combination of brain functional MRI connectivity within the salience network and single nucleotide polymorphisms (SNPs) data were predictive of future AUD outcomes[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This study highlighted the relevance of using neural measures taken prior to developing AUD, and the significance of underlying molecular signatures in association with functional brain activity predicting vulnerability for AUD.\u003c/p\u003e \u003cp\u003eIt is very well-known that an interaction of genetics and environmental factors contribute to the risk for AUD[\u003cspan additionalcitationids=\"CR16 CR17 CR18 CR19 CR20\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Environmental factors can lead to very specific alterations in epigenetic mechanisms, which, ultimately, regulate gene expression and can lead to altered phenotypes and behaviors[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Among the different epigenetic modifications, DNA methylation (DNAm) has been applied to assess risk, exposure, or disease progression to a wide range of biomedical conditions (i.e. cancer, cardiovascular, drug use and neurological diseases[\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]; see review[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]). This is because, unlike static genetic risk estimates, DNAm varies dynamically in relation to diverse exogenous and endogenous factors, including environmental risk factors and complex disease pathology[\u003cspan additionalcitationids=\"CR23 CR24 CR25 CR26 CR27 CR28 CR29\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. DNAm measures are likely to capture tissue- and time-specific information and have the potential to refine or improve genotype-based mechanisms, such as GWAS), propagating disease processes beyond the limit of phenotype heritability[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDue to limitations in sample availability, epigenetic studies of brain mechanisms of disease processes have been limited to neural samples collected postmortem or are inferred from peripheral tissues. For example, early detection and treatment in Alzheimer and Parkinson\u0026rsquo;s disease [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Cross-sectional approaches have addressed risk for severity in behavioral-based, for example smoking, where peripheral blood DNAm predictor of smoking explained greater proportions of variance in cognitive function, structural brain integrity, inflammatory markers and other smoking-related health measures than smoking status[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. However, the question of how reliably peripheral DNAm signatures can inform the molecular function of specific brain mechanisms, rather than as a biomarker, remains unanswered and limit prevention and therapeutic strategies.\u003c/p\u003e \u003cp\u003eThe macaque model of alcohol self-administration recapitulates many aspects of the human AUD phenotype, with a continuum in drinking levels from low to binge to very heavy alcohol consumption. In this protocol, rhesus monkeys are first induced to drink water and then ethanol (4% w/v) under a schedule-induced polydipsia (SIP) procedure. After induction, the monkeys are allowed to drink up to 7.0 g/kg/day (a 28 drink-equivalent based on 17g ethanol/drink) with water concurrently available, nominally termed \u0026ldquo;open-access\u0026rdquo; to ethanol (22 hrs/day). Given the voluntary nature of this access, the monkeys show a wide spectrum of individual daily intake ranging from an average of 0.3 g/kg/day up to 4 g/kg/day[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Furthermore, when the drinking protocol was extended to include intermittent abstinence from alcohol, alcohol intakes increased upon reintroduction of alcohol availability (i.e., modeling relapse to heavy drinking)[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Thus, this model offers a unique opportunity for identifying neural risk for chronic ethanol drinking, as well as, relapse. Using this model, a biopsy of the dorsolateral prefrontal cortex area 46 (dlPFC-A46) was obtained prior to ethanol exposure and then compared to a biopsy of contralateral area following the chronic drinking protocol. The dlPFC was chosen because it is known to be involved in mediating cognitive flexibility using an attentional set-shifting task (ASST) in rhesus monkeys[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] and performance on the ASST can predict future status as a heavy drinker[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Transcriptomic analyses of dlPFC-A46 pre- and post-ethanol biopsies from cynomolgus monkeys found changes in miRNA target sites and transcription factors that can serve as epigenetic regulators of alcohol consumption[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. However, as discussed above, DNAm is a stable marker of long-term environmental conditions compared to mRNA expression. Here, we report on a novel opportunity to use nonhuman primate frontal cortical epigenomic signatures to understand AUD vulnerability. In parallel, and given the range of alcohol intakes in the rhesus population, this work also allows identification of resilience factors that may confer protective mechanisms against development of chronic heavy drinking.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eDNAm profiling in alcohol-na\u0026iuml;ve individuals identifies pre-existing differences in neural networks.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe identified 14,012 differentially methylated cytosines (DMCs) at false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and 3,539 differentially methylated regions (DMRs) at p\u003csub\u003eSidak\u003c/sub\u003e \u0026lt; 0.05 (Supp. Table\u0026nbsp;2). All samples showed a similar distribution of DNAm rates (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), with all samples having a larger proportion of hypermethylated sites. Among these significant DMRs, 2,276 had an average DNAm over 10% (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB) and 1,531 DMRs demonstrated agreement in the directionality of their respective DMCs (see Methods). Most of the DMRs mapped to the gene body (59%), preferentially to introns, and to intergenic locations, while only 12% of the DMRs mapped to promoter regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Since there is very limited information of CpG island context annotation in the macaque genome, we used the homologous human coordinates (hg38) and identified approximately 20% of DMRs mapping to CpG islands, while the majority mapped to open sea regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003eThe genes mapped by the topmost DMRs significantly associated with the ethanol drinking levels at 6 months of open alcohol-access are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Among the most significantly associated DMRs, there was a DMR mapping to ankyrin repeat and SOCS box containing 1 protein (\u003cem\u003eASB1\u003c/em\u003e), a protein predicted to play a role in protein ubiquitination and cytokine signaling, that was negatively associated with ethanol intake levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Three additional DMRs that had DNAm levels positively correlated with future 6-month ethanol intake included DMRs mapping to the protease activated receptor 1 gene (\u003cem\u003eF2R\u003c/em\u003e or \u003cem\u003ePAR1\u003c/em\u003e); to the transmembrane transporter \u003cem\u003eSVOPL\u003c/em\u003e, and to the first exon-intron of the glutamate ionotropic receptor, \u003cem\u003eGRIN3A\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF-G; \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). According to these results, and only using these four genes as an example, individuals with the hypermethylated DMRs mapping to \u003cem\u003eF2R\u003c/em\u003e, \u003cem\u003eSVOPL\u003c/em\u003e and \u003cem\u003eGRIN3A\u003c/em\u003e, but hypomethylated at \u003cem\u003eASB1\u003c/em\u003e at baseline, are expected to consume higher levels of ethanol.\u003c/p\u003e \u003cp\u003eUsing the genes mapped by the whole list of significantly ethanol-associated DMRs (p\u003csub\u003eSidak\u003c/sub\u003e \u0026lt; 0.05; annotated to genes), we next performed network and pathway analyses to identify the biological functions involving pre-existing differentially methylated genes (DMGs). There was an enrichment in cytoskeletal and cell-cell junction (purple clusters), metabolism (blue clusters), regulation of transcription and translation (green clusters), mitochondria organization (pink clusters) and in numerous neural-relevant pathways (red clusters; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Among these pathways, the following three, neurogenesis, forebrain development and central nervous system (CNS) neuron differentiation, point to developmental processes potentially setting up different risk for future problematic drinking. Other neural relevant pathways included mechanisms involved in synaptic transmission and the glutamate receptor signaling pathway. Within this pathway, DMGs encoding for glutamate receptor subunits (\u003cem\u003eGRIN1\u003c/em\u003e, \u003cem\u003eGRIN2A\u003c/em\u003e, \u003cem\u003eGRIN3A\u003c/em\u003e, \u003cem\u003eGRM2\u003c/em\u003e, \u003cem\u003eGRIK3\u003c/em\u003e), dopamine and GABA receptors (\u003cem\u003eDRD1\u003c/em\u003e, \u003cem\u003eGABRA6\u003c/em\u003e), the GABA synthesizing enzyme (\u003cem\u003eGAD2\u003c/em\u003e), calcium channels (\u003cem\u003eCACNA2D2\u003c/em\u003e, \u003cem\u003eCACNA2D3\u003c/em\u003e, \u003cem\u003eCACNG6\u003c/em\u003e, \u003cem\u003eCACNB2\u003c/em\u003e, \u003cem\u003eCACNB4\u003c/em\u003e, \u003cem\u003eCACNA1C\u003c/em\u003e, \u003cem\u003eCACNA1H\u003c/em\u003e and \u003cem\u003eCACNA1I\u003c/em\u003e) and important modulators of intracellular calcium dynamics in neurons (\u003cem\u003ePVALB, RYR1\u003c/em\u003e), as well as in synaptic cell adhesion and scaffolding proteins (\u003cem\u003eSHANK2\u003c/em\u003e, \u003cem\u003eNRXN2\u003c/em\u003e, \u003cem\u003eNRXN3\u003c/em\u003e, \u003cem\u003eHOMER\u003c/em\u003e, \u003cem\u003eDLGAP2\u003c/em\u003e) were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB-J). While all these DMRs show a positive association with ethanol levels, those mapping to \u003cem\u003eGRIN2A\u003c/em\u003e, \u003cem\u003eDRD1\u003c/em\u003e, \u003cem\u003eGABRA6, CACNB2, PVALB, NRXN2\u003c/em\u003e and \u003cem\u003eNRXN3\u003c/em\u003e were negatively correlated with ethanol intake levels.\u003c/p\u003e \u003cp\u003eAmong these genes within the glutamate receptor signaling pathway, \u003cem\u003eDLGAP2\u003c/em\u003e which encodes a critical component of the postsynaptic density, regulating synaptic function and dendritic spine morphology and organization, contained three different DMRs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-D). This gene is highly expressed in the brain and encodes four different transcripts (201\u0026ndash;204; Ensembl ENSMMUG00000057265; Mmul_10:CM014343.1) resulting in different protein isoforms which specific function is currently unknown. These three DMRs showed a positive significant correlation with the ethanol drinking levels, indicating that higher DNAm levels at baseline are associated with a higher risk for future ethanol intake.\u003c/p\u003e\u003cp\u003eFurther analysis of the whole network identified genes encoding for calcium voltage-gates channel subunits: \u003cem\u003eCACNA2D2\u003c/em\u003e, \u003cem\u003eCACNA2D3\u003c/em\u003e, \u003cem\u003eCACNB2\u003c/em\u003e, \u003cem\u003eCACNB4\u003c/em\u003e, \u003cem\u003eCACNA1C\u003c/em\u003e, \u003cem\u003eCACNA1H\u003c/em\u003e, \u003cem\u003eCACNG6\u003c/em\u003e and \u003cem\u003eCACNA1I\u003c/em\u003e and for two genes involved in ncRNA processing, \u003cem\u003eIMP4\u003c/em\u003e and \u003cem\u003eRRP1B\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE-F) as hub genes, with \u003cem\u003eCACNA1C\u003c/em\u003e being the strongest hub gene (with the most connections with other genes within the network). These findings highlight the relevance of these two pathways in potentially setting the foundational neural regulatory pathway for vulnerability.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe pre-existing differentially methylated signatures are associated with gene expression levels in the alcohol-na\u0026iuml;ve dlPFC-A46 cortex.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAlthough we did not identify differentially expressed genes (DEGs) associated with future ethanol drinking levels at FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05, when we relaxed the significance threshold (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), we identified 317 DEGs (Supp. Table\u0026nbsp;3). We next used two different approaches to investigate the potential role of DNAm in regulating gene expression. Our first approach was narrower in scope as we looked at how many of the DEGs were also DMGs. From the 317 DEGs, 100 of them are not annotated to known genes in the rhesus macaque transcriptome. From the remaining 217, 19 were also differentially methylated (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA; Supp. Table\u0026nbsp;4). Among this smaller subset, 10 DMGs were hypermethylated and downregulated, and 2 DMGs were hypomethylated and upregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). For example, the DMRs mapping to the canonical or alternative promoters of the acyl-coA oxidase 2 gene (\u003cem\u003eACOX2\u003c/em\u003e), leiomodin 1 (\u003cem\u003eLMOD1\u003c/em\u003e), LDL receptor related protein 5 (\u003cem\u003eLRP5\u003c/em\u003e), solute carrier family 46 member 1 (\u003cem\u003eSLC46A1\u003c/em\u003e), and tri Rho guanine nucleotide exchange factor (\u003cem\u003eTRIO\u003c/em\u003e) were hypermethylated and downregulated with increasing doses of alcohol (see example for \u003cem\u003eACOX2\u003c/em\u003e in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC-D). Furthermore, the DNAm and the expression levels of a subset of these DMGs/DEGs pairs was associated. For instance, for \u003cem\u003eACOX2\u003c/em\u003e and \u003cem\u003eLMOD1\u003c/em\u003e, hypermethylation was negatively associated with expression levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE, H). Conversely, hypermethylation was associated with upregulation for \u003cem\u003eNPRU2\u003c/em\u003e and \u003cem\u003eLRP5\u003c/em\u003e (please note that for \u003cem\u003eLMOD1\u003c/em\u003e and \u003cem\u003eLRP5\u003c/em\u003e this association trends towards significance).\u003c/p\u003e \u003cp\u003eGiven the small number of DEGs, we did not conduct pathway or network analyses. However, these DEG/DMGs are within the glycerophospholipid metabolism (\u003cem\u003eTRIO\u003c/em\u003e), lipid metabolism (\u003cem\u003eACOX2\u003c/em\u003e, \u003cem\u003eTHBS2\u003c/em\u003e and \u003cem\u003eNUPR2\u003c/em\u003e), cell-cell junction (\u003cem\u003eCENATAC\u003c/em\u003e and \u003cem\u003eCOL6A2\u003c/em\u003e), regulation of cytoskeleton organization (\u003cem\u003eCOBL\u003c/em\u003e, \u003cem\u003eLMOD1\u003c/em\u003e, \u003cem\u003eSLC22A8\u003c/em\u003e and \u003cem\u003eSLC46A1\u003c/em\u003e), and neurogenesis (\u003cem\u003eLRP5\u003c/em\u003e) pathways that were identified by the pre-existing DMGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eWe also used a more unbiased approach to identify DMRs which DNAm levels could be associated with other genes, beyond the one mapping to the DMR. As described in the methods, we used eQTM analysis to find associations between the DNAm levels of the significant DMRs, and the expression levels of DEGs (unadjusted p \u0026le; 0.05). We identified 2 DMR/DEG pairs with an FDR \u0026le; 0.05 (Supp. Table\u0026nbsp;5). One of this eQTMs is a \u003cem\u003ecis\u003c/em\u003e-eQTM in chromosome 14. Specifically, the DMR maps within the \u003cem\u003eBRSK2\u003c/em\u003e gene, but its DNAm levels are associated with the \u003cem\u003eEPS8L2\u003c/em\u003e gene, located\u0026thinsp;~\u0026thinsp;723kb downstream (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-B). There was no association between this DMR and the \u003cem\u003eBRSK2\u003c/em\u003e levels of expression (r2\u0026thinsp;=\u0026thinsp;0.1; p(reg)\u0026thinsp;=\u0026thinsp;1.81e\u003csup\u003e\u0026minus;\u0026thinsp;01\u003c/sup\u003e). We also identified a potential \u003cem\u003etrans\u003c/em\u003e-eQTM, with the DMR mapping to chromosome 16 and showing a significant association with \u003cem\u003eRRP7A\u003c/em\u003e expression levels, a gene located in chromosome 10 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). In both cases, these DMRs were hypermethylated and downregulated with future higher amounts of alcohol use (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC-D, F-G).\u003c/p\u003e \u003cp\u003e \u003cb\u003eAlcohol-na\u0026iuml;ve DNAm signatures map to genes within the glutamatergic signaling pathway.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eOur results suggest that the glutamatergic receptor signaling pathway plays a critical role in establishing a vulnerable brain for future risky ethanol behavior. Among all the genes within this network (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA), the DMR associated with \u003cem\u003eSHANK2\u003c/em\u003e showed the strongest association with future ethanol intake (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Furthermore, its DNAm levels were positively associated with DNAm levels of 11 DMRs within this network (i.e. \u003cem\u003eGRIN1\u003c/em\u003e, \u003cem\u003eGRIN3A\u003c/em\u003e, \u003cem\u003eSLC12A5\u003c/em\u003e, \u003cem\u003eCACNA1C\u003c/em\u003e and \u003cem\u003eDLGAP2\u003c/em\u003e, red genes in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD-E, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG-I), and negatively associated with other 8 DMRs (i.e. \u003cem\u003eDRD1\u003c/em\u003e, \u003cem\u003eNRXN2\u003c/em\u003e and \u003cem\u003eNRXN3\u003c/em\u003e, blue genes in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eJ). Accordingly, in those individuals with lower risk for heavy alcohol intake, hypomethylation in \u003cem\u003eSHANK2\u003c/em\u003e and in genes encoding different glutamate receptors, a GABA receptor or KCC2 transporter; but hypermethylation in the dopamine receptor (\u003cem\u003eDRD1\u003c/em\u003e) and in two of the presynaptic master regulators, neurexins, are observed. Five of these correlated DMRs are directly connected to \u003cem\u003eSHANK2\u003c/em\u003e (\u003cem\u003eDLGAP2, GRIN2A, GRIN1, SHANK1, NRXN3\u003c/em\u003e and \u003cem\u003eNRXN2)\u003c/em\u003e, while the others connect to \u003cem\u003eSHANK2\u003c/em\u003e through \u003cem\u003eCACNA1C\u003c/em\u003e and \u003cem\u003eGRIN2A\u003c/em\u003e. Interestingly, the DNAm levels of the DMRs mapping to \u003cem\u003eSHANK2\u003c/em\u003e and \u003cem\u003eCACNA1C\u003c/em\u003e are strongly correlated (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH, p-value\u0026thinsp;=\u0026thinsp;5.57e\u003csup\u003e\u0026minus;\u0026thinsp;17\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe pre-existing DMR mapping to\u003c/b\u003e \u003cb\u003eNRXN3\u003c/b\u003e \u003cb\u003efunctions as an alternative promoter.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eOur studies indicate that the pre-existing DMR mapping to an intron of NRXN3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA) functions as a weak promoter (p\u0026thinsp;=\u0026thinsp;4.50x10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB-C). Furthermore, we showed that it can regulate the expression of the shorter \u003cem\u003eNRXN3β\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;2.32x10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) transcript but not \u003cem\u003eNRXN3α\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;5.017x10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003cb\u003eAlcohol-na\u0026iuml;ve DNAm signatures and mapped genes are associated with drinking at 6m, 12m and following protracted abstinence (PA).\u003c/b\u003e \u003c/p\u003e \u003cp\u003eUsing the same alcohol-na\u0026iuml;ve DNAm dataset described above, we were interested in identifying DNAm signals at baseline that were associated with drinking levels following a longer chronic ethanol intake period (12 months) and following repeated cycles of abstinence and relapse (PA). These analyses resulted in 2,025 DMRs at 12 months and 4,890 DMRs after PA. Given the high correlation between the drinking levels at 6m and 12m (p\u0026thinsp;=\u0026thinsp;4.23x10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA-B), it was not surprising to find an overlap of 919 DMRs (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC). As expected, the comparisons of either of the chronic drinking periods (6m or 12m) with PA rendered less overlap (6m\u0026amp;PA\u0026thinsp;=\u0026thinsp;626 DMRs, 12m\u0026amp;PA\u0026thinsp;=\u0026thinsp;780 DMRs; Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC), which agrees with the weaker correlation between the drinking levels at these timepoints, potentially due to the higher within-subject variability of post-abstinence intakes (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA-B).\u003c/p\u003e \u003cp\u003eAcross the three timepoints, a subset of 363 DMRs remained significantly associated with future drinking. Among these, hypermethylation of the Bx-DMRs mapping to \u003cem\u003eGRIN3A\u003c/em\u003e and \u003cem\u003eCACNA1I\u003c/em\u003e, are associated with heavier amounts of alcohol intake at the three time points (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE). Network and pathway analysis revealed that these 363 shared DMGs were part of the neural development, metabolism, cytoskeleton regulation, nucleic acid regulation and synaptic regulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD). These findings may suggest that these genes and associated pathways may play critical functions in establishing and maintaining vulnerability to risky alcohol intake over time.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur genome-wide DNAm analysis of the alcohol-na\u0026iuml;ve dlPFC-A46 is the first study to be conducted using a biopsy of the brain in living individuals prior to their enrollment in a chronic alcohol self-administration protocol. This unique study provides a molecular snapshot of the alcohol-na\u0026iuml;ve dlPFC, with the DNAm profile, mapped genes, and associated signaling pathways that distinguish individuals with different future drinking behavior, and that may contribute to risky drinking.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003ePre-existing DMRs might regulate alternative promoter use and splicing\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eWhile it is known that DNAm regulates gene expression[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], increasing evidence indicates a critical role of epigenetic signals in regulating splicing mechanisms in a cell- and tissue-type manner. A study found that on average each gene encodes for ~\u0026thinsp;7 transcripts. Furthermore, over 30% of tissue-specific transcripts are due to splicing events[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], and DNAm plays a critical role in regulating these mechanisms[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. In agreement with these results, and as previously observed in the nucleus accumbens of rhesus macaques[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], the majority of alcohol-associated DMRs identified in this study mapped within the gene body, with minor overlap of promoter regions. We have also shown in the macaque that intragenic DMRs might regulate splicing or alternative promoter use[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. In the present study, we analyzed overall gene expression, without distinguishing among splicing transcripts, and found no significant differential gene expression associated with future ethanol intake. These results agree with a group comparison analysis conducted using the same dataset as used here[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] where no differences between heavy-very heavy drinkers (HVHD) and non-heavy drinkers were identified in the alcohol-na\u0026iuml;ve state. Although the tissue heterogeneity could contribute to the lack of differences, it is possible that pre-existing differences in gene expression associated with future drinking reside in splicing to produce alternative transcripts instead of overall gene expression. To address this question, future studies need to be conducted to identify differential expression of transcripts, either by deeper sequencing or using long-read RNA sequencing.\u003c/p\u003e \u003cp\u003eWhen the significance threshold was relaxed (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), 64 genes were differentially expressed in our analysis and in the published study using the same dataset[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Furthermore, in 11 of the 19 DEGs and DMGs, the DMRs mapped to promoter (2) or alternative promoter (9) locations. This could indicate that these DMRs might be contributing to the regulation of specific transcripts. Supporting this hypothesis, we show that the DMR mapping to NRXN3 functions as a promoter, and that it regulates the expression of the shorter transcript \u003cem\u003eNRXN3β\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eAlcohol-na\u0026iuml;ve individuals display a vulnerable neural signaling pathway network in the dlPFC\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eChronic alcohol use gradually reduces cognitive control, deteriorating the individual\u0026rsquo;s ability to inhibit perseverative responses and adapt to changes in environmental contingencies[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. The dlPFC is a component of the central executive network[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] and alcohol use compromises its proper function[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Studies have shown that alcohol impairs neuroplasticity in the dlPFC[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Furthermore, modulation of neuroplasticity in the dlPFC through repetitive transcranial magnetic stimulation shows promise as a treatment for AUD[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. In addition, low cognitive flexibility assessed with ASST predicted future classification as a heavy alcohol drinker[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], findings that have been replicated[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The present study used samples from the same macaques included in these studies[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], and support the hypothesis that individual differences in dlPFC function in an alcohol-na\u0026iuml;ve state and risk for future chronic heavy drinking is due, at least in part, to common underlying epigenetic signatures of a vulnerable brain state. In all, these results suggest that pre-existing individual variability in dlPFC epigenetic footprint and function, implicating neural-specific signaling pathways, reduces cognitive flexibility and establishes a predisposition towards inflexible cognitive behavior that contributes to future risk for heavy drinking. Similar findings have been reported in the few available human longitudinal studies, where decreased academic performance was associated with future alcohol use[\u003cspan additionalcitationids=\"CR56\" citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAmong the neural-specific signaling pathways, three developmental functions, such as neurogenesis, CNS neuron differentiation, and forebrain development, emphasize the late adolescent to early adulthood developmental stage of the monkeys upon enrolment in the study. The developmental signatures may indicate individual differences in cortical maturation processes at this stage of primate brain development [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Accordingly, and within individuals of similar chronological age (~\u0026thinsp;4\u0026ndash;6 years old, approximately the equivalent to 16\u0026ndash;24 human years), such differences could be engrained in an inherited epigenetic code that preconditions individuals to a life-long battle with AUD upon a first encounter with alcohol intoxication. We cannot exclude the possibility that we are capturing different stages of the cortical maturation process, as it is expected individual variability in reaching a matured brain. Notably, the rhesus monkeys were all born and raised at ONPRC under the same environmental conditions (i.e. diet, enrichment in corrals, same experimental conditions), and experimentally na\u0026iuml;ve before enrollment in the study. Nonetheless, their early life up to weaning was in large social groups, followed by smaller social peer groups post-weaning. This diversity of social interactions could have contributed to differential early-life social stressors shaping their epigenome prior to their enrolment in this study, in addition to epigenetic inheritance[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEven with the possible differential social history, the voluntary alcohol consumption in late adolescence/early adulthood reduces brain volume compared to age-matched controls and indicates an increased risk for future development of phenotypic drinking similar to risk in humans with AUD (see review[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]). In addition to exclusive developmental signaling pathways, we identified enrichment in modulation of synaptic transmission, regulation of membrane potential, neurotransmitter and vesicle-transport, and glutamate receptor signaling as relevant molecular signatures in the dlPFC of vulnerable individuals. Overall, it is not surprising that additional enrichment is detected for signaling pathways with global functions that are required for the neural processes described above given ethanol\u0026rsquo;s pharmacology. These include regulation of the cytoskeleton and its components, metabolic modulation and transcriptional and translational regulation.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eAlterations in glutamate receptor signaling contribute to vulnerability for future risky drinking\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eAnalysis of the hub genes for the alcohol-na\u0026iuml;ve network pointed to a critical and central role of the glutamate receptor signaling pathway. These include multiple members of glutamate receptors (metabotropic and ionotropic; \u003cem\u003eGRIN1\u003c/em\u003e, \u003cem\u003eGRIN2A\u003c/em\u003e, \u003cem\u003eGRIN3A\u003c/em\u003e, \u003cem\u003eGRM2\u003c/em\u003e, \u003cem\u003eGRIK3\u003c/em\u003e), the dopamine receptor (\u003cem\u003eDRD1\u003c/em\u003e), multiple members of the voltage gated calcium channels that control neurotransmitter release (\u003cem\u003eCACNA2D2\u003c/em\u003e, \u003cem\u003eCACNA2D3\u003c/em\u003e, \u003cem\u003eCACNG6\u003c/em\u003e, \u003cem\u003eCACNB2\u003c/em\u003e, \u003cem\u003eCACNB4\u003c/em\u003e, \u003cem\u003eCACNA1C\u003c/em\u003e, \u003cem\u003eCACNA1H\u003c/em\u003e and \u003cem\u003eCACNA1I\u003c/em\u003e), important modulators of intracellular calcium dynamics in neurons (\u003cem\u003ePVALB, RYR1\u003c/em\u003e), and numerous synaptic proteins (i.e. \u003cem\u003eSHANK2\u003c/em\u003e, \u003cem\u003eNRXN2\u003c/em\u003e, \u003cem\u003eNRXN3\u003c/em\u003e, \u003cem\u003eHOMER\u003c/em\u003e, \u003cem\u003eDLGAP2\u003c/em\u003e). As expected, a combination of hypo and hypermethylated DMRs were associated with increasing amounts of alcohol. We further provide evidence of the functional implications of these DMRs, indicating that DMRs mapping to promoters and intragenic introns/exons function as enhancers of alternative promoters regulating the expression of alternative transcripts. In this study, we show that the intragenic DMR mapping to NRXN3 functions as an alternative promoter regulating the expression of \u003cem\u003eNRXN3β\u003c/em\u003e, and without impacting \u003cem\u003eNRXN3α\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eFurther strengthening the relevance of the glutamate receptor pathway in establishing a vulnerable brain, one of the central genes in this pathway, \u003cem\u003eSHANK2\u003c/em\u003e, is also one of the top genes identified in this study. Importantly, \u003cem\u003eSHANK2\u003c/em\u003e-DMR DNAm levels were strongly correlated with numerous members of this signaling pathway (i.e. \u003cem\u003eCACNA1C\u003c/em\u003e, \u003cem\u003eGRIN1\u003c/em\u003e). Underscoring the broader significance of the \u003cem\u003eSHANK2\u003c/em\u003e-DMR, its DNAm levels were also correlated with genes implicated in neuron differentiation and development, axonal transport, regulation of action potential, ECM organization and synaptic transmission.\u003c/p\u003e \u003cp\u003eSHANK2 (also known as ProSAP1) is an abundant postsynaptic scaffolding protein involved in establishing postsynaptic density of glutamate synapses through a dense network of molecular interactions[\u003cspan additionalcitationids=\"CR62 CR63 CR64 CR65 CR66 CR67\" citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. \u003cem\u003eSHANK2\u003c/em\u003e loss of function and missense mutations have been extensively implicated in neurodevelopmental and neuropsychiatric disorders, including autism spectrum disorder (ASD), intellectual disability, developmental delay, schizophrenia and alcohol addiction[\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan additionalcitationids=\"CR69 CR70 CR71 CR72 CR73 CR74 CR75 CR76 CR77 CR78 CR79 CR80\" citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. Different mutations in \u003cem\u003eSHANK2\u003c/em\u003e can lead to increased or decreased expression of \u003cem\u003eSHANK2\u003c/em\u003e, dysfunctional SHANK2 domains or truncated proteins lacking interaction domains. This can lead to alterations in protein-protein interactions and the organization of the postsynaptic protein network, and ultimately to neuropsychiatric disorders[\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition to the role of the \u003cem\u003eSHANK2\u003c/em\u003e variants, epigenetics may have a strong influence on the expression of the \u003cem\u003eSHANK2\u003c/em\u003e variant-mediated phenotypes[\u003cspan additionalcitationids=\"CR83\" citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e] by affecting the penetrance of \u003cem\u003eSHANK2\u003c/em\u003e variants, as well as the expression of the different \u003cem\u003eSHANK2\u003c/em\u003e isoforms[\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e]. This has been observed for \u003cem\u003eShank3\u003c/em\u003e, where tissue-specific promoter and intragenic DNAm regions with differential DNAm patterns were associated with alternative transcript expression in a brain region- and cell-type specific manner[\u003cspan additionalcitationids=\"CR86\" citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e]. Furthermore, treatment with the DNAm inhibitor 5-azacytidine in cultured cells resulted in demethylation of \u003cem\u003eSHANK3\u003c/em\u003e CpG islands and altered isoform-specific expression of \u003cem\u003eSHANK3\u003c/em\u003e. Although it is unknown if a similar regulatory mechanism exists for \u003cem\u003eSHANK2\u003c/em\u003e, this will be particularly important to discern. Specifically, the different SHANK2 isoforms determine the organization of the post-synaptic density zone at different developmental stages and/or different brain regions[\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e], have different pre and postsynaptic functions, and the endogenous balanced expression of SHANK2 isoforms is essential for establishing and maintaining higher brain functions such as social and cognitive behaviors[\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e]. The different isoforms lack domains involved in protein-protein interactions that bridge glutamate receptors, scaffolding proteins and intracellular effectors to the actin cytoskeleton[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Importantly, an analysis of human tissues showed that expression of \u003cem\u003eSHANK2A\u003c/em\u003e (equivalent to the macaque \u003cem\u003eSHANK2-204\u003c/em\u003e) is only detected in the brain[\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e]. In addition, \u003cem\u003eSHANK2E\u003c/em\u003e (equivalent to the macaque longest transcripts, 201\u0026ndash;203) was detected in the human brain[\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e], contrary to previous findings stating \u003cem\u003eSHANK2E\u003c/em\u003e was not expressed in the brain[\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Three SHANK2 exons (19, 20, and 23) that code for a region between the PDZ and the proline rich domains, were only detected in brain samples[\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e]. Another important finding from this study, was the interindividual variability in the proportions of these three transcripts expressed in the brain[\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e]; a feature that has been observed for other synaptic proteins[\u003cspan additionalcitationids=\"CR92\" citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e]. The macaque DMR is located within \u003cem\u003eSHANK2\u003c/em\u003e, and ongoing studies are underway to determine if the differential DNAm in this region contributes to the regulation of alternative transcripts in the dlPFC of macaques.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eAlcohol-na\u0026iuml;ve DNAm signals identify several signaling pathways modulating synaptic transmission that define vulnerability for future risky drinking.\u003c/span\u003e \u003c/p\u003e \u003cp\u003eWe described above the relevance of the glutamatergic system in potentially establishing vulnerability for future heavy ethanol use, with a central role for \u003cem\u003eSHANK2\u003c/em\u003e. Additional biological functions that could contribute to vulnerability included GTPase binding activity (\u003cem\u003eTBC1D30\u003c/em\u003e[\u003cspan additionalcitationids=\"CR95 CR96\" citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e]\u003csup\u003e,\u003c/sup\u003e[\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e, \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e]; \u003cem\u003eMEX3B\u003c/em\u003e[\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e]\u003csup\u003e,\u003c/sup\u003e[\u003cspan additionalcitationids=\"CR102\" citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e]\u003csup\u003e,\u003c/sup\u003e[\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e]), MAPK signaling (\u003cem\u003eDUSP2\u003c/em\u003e[\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e] [\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e]), ECM organization, axonogenesis, axonal growth and guidance (\u003cem\u003eROBO3\u003c/em\u003e)[\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e]\u003csup\u003e,\u003c/sup\u003e[\u003cspan additionalcitationids=\"CR109\" citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e]\u003csup\u003e,\u003c/sup\u003e[\u003cspan additionalcitationids=\"CR111 CR112 CR113 CR114\" citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e]\u003csup\u003e,\u003c/sup\u003e[\u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e], synapse formation, and synaptic plasticity (\u003cem\u003eSMAD3\u003c/em\u003e and \u003cem\u003eSLC9A3\u003c/em\u003e)[\u003cspan additionalcitationids=\"CR118\" citationid=\"CR117\" class=\"CitationRef\"\u003e117\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e119\u003c/span\u003e]\u003csup\u003e,\u003c/sup\u003e[\u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e120\u003c/span\u003e] [\u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e121\u003c/span\u003e]. All the genes listed above and the functions they link to are central to mediating neuronal communication and synaptic plasticity in AUD. Our results suggest that the DMRs mapping to these genes influence the regulation of alternative expression of transcripts and consequently, the regulated neuronal processes in some individuals, rendering them more vulnerable for heavy alcohol use. These results, together with the lack of differential gene expression, strengthen the hypothesis that DNAm signals that are inherited or acquired early-in-life could predetermine an individual\u0026rsquo;s risk for alcohol misuse if alcohol is encountered during their lifetime.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eTo the best of our knowledge, this is the first study to characterize the methylomic profile of the dlPFC-A46 in the alcohol-na\u0026iuml;ve primate brain of males. The main findings of our study show that DNAm signals that are inherited or acquired early in life contribute to defining the \u0026ldquo;epigenetic signature\u0026rdquo; of a resilient or vulnerable brain. The most abundant DNAm signals point to circuitry and mechanisms associated with prefrontal cortical development, synaptic functions, glutamatergic signaling and other coordinated cell signaling pathways. While several of the genes uncovered by our study have been independently linked to alcohol use, our study points to a role of DNAm in regulating these critical neural pathways. In addition, the use of alcohol-na\u0026iuml;ve primates that subsequently exhibited disparate, long-term drinking behaviors, enabled the first identification of pre-existing neural molecular signatures of future risk. The mechanism to propagate excessive alcohol consumption throughout adulthood could very well be linked to these established pre-alcohol DNAm patterns in a key decision-making area of the brain (dlPFC-A46) on the first encounter of alcohol intoxication. Some of the genes identified in this study have also been previously implicated in psychiatric disorders, i.e. \u003cem\u003eSHANK2\u003c/em\u003e has been linked to autism, neurodevelopmental disorder, schizophrenia, or bipolar disorders (see[\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]). It is important to note that excessive alcohol use is common among individuals suffering from these disorders[\u003cspan additionalcitationids=\"CR123\" citationid=\"CR122\" class=\"CitationRef\"\u003e122\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e124\u003c/span\u003e]. Our study may be revealing the molecular circuitry that places individuals at risk for AUD. With a complex disorder as AUD, having the ability to identify the molecular mechanisms underlying AUD risk is critical for better development of personalized effective treatments.\u003c/p\u003e"},{"header":"Methods","content":" \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAnimals.\u003c/span\u003e Two cohorts, named cohorts 10 and 14 in the Monkey Alcohol and Tissue Research Resource (MATRR database)[\u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e125\u003c/span\u003e], of young adult (~\u0026thinsp;5 years old; Supp. Table\u0026nbsp;1) male rhesus macaques were housed in quadrant cages (0.8 x 0.8 x 0.9 m) with constant temperature (20\u0026ndash;22 ̊C), humidity (65%), and an 11/13-hour light/dark cycle. Animals had visual, auditory, and olfactory contact with other animals in the protocol. All animals were maintained on positive caloric (i.e., weight gain) and fluid balance throughout the experiment, and body weights were recorded weekly. All procedures were conducted in accordance with the Guide for the Care and Use of Laboratory Animals and the NIH PHS Policy on Humane Care and Use of Laboratory Animals for the care and use of laboratory animal resources. In addition, all procedures were approved by the Oregon National Primate Research Center (ONPRC) Institutional Animal Care and Use Committee. Animals in these cohorts were chosen from the ONPRC breeding colony to avoid common parents or grandparents. From these cohorts, only monkeys that had a biopsy of the dlPFC-A46 taken prior to enrollment in an alcohol self-administration protocol were included in this study. Detailed drinking and physiological data are available for these cohorts online (MATRR.com; Supp. Table\u0026nbsp;1). In addition, and using data from mGAP (mgap.ohsu.edu), we checked the genetic variation of these animals in genes directly involved in the ethanol metabolic pathway including all the alcohol dehydrogenase and aldehyde dehydrogenase genes. We identified 69 variants in these genes with a minor allele frequency\u0026thinsp;\u0026gt;\u0026thinsp;0.1 and a genotyping rate of at least 95%. To further filter these variants down, we associated them with ethanol consumption levels. No variants were significantly associated (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) with ethanol consumption levels.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eEthanol subjects.\u003c/span\u003e Monkeys were trained to use operant drinking panels and underwent scheduled induced polydipsia (SIP) to induce ethanol self-administration in daily 16-hour (h) sessions, as previously described[\u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e126\u003c/span\u003e]. Following induction, open-access (or ethanol self-administration) began, and water and 4% (w/v) ethanol were concurrently available in daily 22-h sessions for twelve to fourteen months (cohorts 10 and 14). The details of the open-access protocol are discussed in Grant et al. (2008)[\u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e126\u003c/span\u003e] and the data modeling for drinking categories is presented in Baker et al. (2014)[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Ethanol intake was recorded daily with a 0.5 second resolution, 22-h/day, 365 days/year. Monkeys in cohorts 10 and 14 experienced three repetitive cycles of relapse/abstinence (protracted abstinence) following the chronic ethanol open-access period. However, for the present study, only the biopsy samples and the ethanol drinking levels following 12 months and protracted abstinence of self-administration were used.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eBiopsy Samples.\u003c/span\u003e MRI was used to obtain the stereotaxic coordinates for the craniotomy and surface dlPFC A46 biopsies along the lateral sulcus were obtained from the contralateral dominant hemisphere (determined by handedness). Biopsy samples (120\u0026ndash;150 mg wet weight) were taken under anesthesia (Ketamine HCl 15 mg/kg; intramuscular) and prior to ethanol exposure. Samples were immediately flash frozen in liquid nitrogen in a 2ml cryovial and stored at -80 ̊C for future processing. The samples were processed using a standard Qiagen AllPrep DNA/RNA/miRNA Universal Kit protocol as described[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, 46].\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eGenome-wide DNA methylation profiling\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eGenomic DNA was checked for quality by electrophoresis on a 0.7% agarose gel, using a NanoDrop 8000 spectrophotometer (Thermo Scientific, Wilmington, DE, USA) and quantified using a Qubit (Thermo Scientific, Wilmington, DE, USA). One microgram of genomic DNA was sheared using a Bioruptor UCD200 (Diagenode, Denville, NJ, USA), generating fragments\u0026thinsp;~\u0026thinsp;180 bp. The SureSelect XT Human Methyl-Seq library preparation (Agilent Technologies, Santa Clara, CA, USA) was used following the manufacturer\u0026rsquo;s instructions. The SureSelect MethylSeq probes interrogate 3.7\u0026nbsp;million individual CpG sites per sample at single-nucleotide resolution and it covers RefSeq genes (including, 5\u0026rsquo;-UTR, exon, intron, 3\u0026rsquo;-UTR, TSS), Gencode promoters, CpG islands, shores, shelves and open sea, DNase I hypersensitive sites (intergenic regions), Ensembl regulatory features, known DMRs[\u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e127\u003c/span\u003e]. The libraries were then bisulfite-treated using EZ DNA Methylation-Gold (Zymo Research, Irvine, CA, USA), and quantified using a 2100 Bioanalyzer (Agilent Technologies). DNA libraries were sequenced on an Illumina NovaSeq6000 at the University of Oregon Genomics \u0026amp; Cell Characterization Core Facility (GC3F). The quality of the bisulfite-converted sequencing reads was assessed with FastQC[\u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e128\u003c/span\u003e]. Reads were trimmed and aligned to the macaque reference genome (Mmul10)[\u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e129\u003c/span\u003e], and then the bisulfite conversion rates were evaluated, all libraries were \u0026gt;\u0026thinsp;98% converted, and CpG methylation counts were obtained using Bismark[\u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e130\u003c/span\u003e]. The DNAm rates were calculated as the ratio of methylated reads over the total number of reads. Methylation rates for CpGs with fewer than 10 reads were excluded from further analysis. We next removed sites on sex chromosomes, and over 3\u0026nbsp;million CpGs per sample were used for downstream analyses.\u003c/p\u003e \u003cp\u003eThe differential DNAm analysis was carried out by applying a generalized linear mixed effects model (GLMM) implemented in R package PQLseq (version 1.2.1)[\u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e131\u003c/span\u003e, \u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e132\u003c/span\u003e] separately for each CpG site. PQLseq models the technical sampling variation in bisulfite sequencing data with a binomial distribution, effects of biological and technical covariates with the linear model, and the random effects with a correlated multivariate normal distribution. The most common methylation proportion values are 0 and 1, which are problematic in the context of generalized linear models with the logit link function (infinite in the logit-transformed space). We used a common pseudo-count transformation to avoid both extremes, as recommended[\u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e133\u003c/span\u003e]. This was done after adding\u0026thinsp;+\u0026thinsp;1 to the numbers of methylated reads and +\u0026thinsp;2 to the total numbers of reads to avoid modeling methylation proportions that are exactly 0 or 1, as recommended [\u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e133\u003c/span\u003e]. This pseudo-count transformation was only applied to non-missing values (coverage\u0026thinsp;\u0026gt;\u0026thinsp;10x). We modeled the average consumption of ethanol at 6 months, 12 months and protracted abstinence as a predictor of DNAm rate and included age as covariate in the binomial model. Relatedness of the animals was accounted as random effects in the model.\u003c/p\u003e \u003cp\u003eEach nominal p-value was corrected for multiple comparisons by the False Discovery Rate (FDR). In parallel, the nominal p-value was used as input for Comb-p[\u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e134\u003c/span\u003e] analysis to identify differentially methylated regions (DMRs) in AUD subjects as previously described[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eAgreement analysis\u003c/h3\u003e\n\u003cp\u003ePrior to network and other downstream analysis, we computed beta regression with the average DNAm rates of each DMR to assess whether there was agreement in the direction of effects across the DMR. This process was completed to filter out DMRs where there may have been differentially methylated cytosines (DMCs) that were in opposing directions (i.e., some hypermethylated and some hypomethylated) across the region and are more difficult to interpret and are more likely to be false positives. Prior to beta regression, average DNAm rates equal to 0 or 1 were changed to 1x10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e and 1\u0026ndash;1x10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e. If the average DNAm rates across the DMR were not associated with ethanol usage (\u0026ldquo;agreement\u0026rdquo; p-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05), then they were no longer considered for downstream analysis. In general, we found this process effective at identifying DMRs with either inconsistent DNAm patterns or highly correlated effects across the region.\u003c/p\u003e\n\u003ch3\u003eDifferential gene expression analysis\u003c/h3\u003e\n\u003cp\u003eThe RNAseq library preparation and data analysis has been previously described[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Using the already processed RNA-Seq read counts we reanalyzed only the baseline biopsy samples for this manuscript. Genes with an average read count below 5 were filtered out and we computed negative binomial regression with gene expression as the outcome and later stage (6 months) drinking amounts as a continuous predictor. The negative binomial regression was computed in DESeq2[\u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e135\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eExpression quantitative trait methylation (eQTM) analysis\u003c/h2\u003e \u003cp\u003eWe used the significant DMRs which DNAm was concordant among the DMCs within each DMR (1,531) and DEGs with significance p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (316) as input for eQTM analysis (MatrixEQTL R package [\u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e136\u003c/span\u003e]). We used a linear model, a threshold p\u0026thinsp;\u0026lt;\u0026thinsp;1e-02 to compute FDR correction for \u003cem\u003ecis\u003c/em\u003e (up to 1e\u003csup\u003e6\u003c/sup\u003e nucleotides) and \u003cem\u003etrans\u003c/em\u003e associations.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eNetwork analysis\u003c/h3\u003e\n\u003cp\u003eSignificant DMRs that had gene annotations were analyzed in KEGG, STRING, and MCODE to find biological pathways enriched with future ethanol drinking[\u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e137\u003c/span\u003e, \u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e138\u003c/span\u003e]. STRING was used to obtain protein-protein interactions for all genes that met our filtering criteria. STRING was applied to find only \u0026ldquo;High confidence\u0026rdquo; protein-protein interactions with options for \u0026ldquo;textmining\u0026rdquo; and \u0026ldquo;neighborhood\u0026rdquo; disabled[\u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e137\u003c/span\u003e]. MCODE was applied to the remaining interactions to obtain a set of highly interconnected gene clusters[\u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e138\u003c/span\u003e] and the biological functions of each clusters with MCODE scores greater than 4.0 were identified through the KEGG pathways[\u003cspan citationid=\"CR139\" class=\"CitationRef\"\u003e139\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eReporter assay\u003c/h3\u003e\n\u003cp\u003eTo determine the promoter or enhancer activity of the rhesus macaque DMR located upstream of the \u003cem\u003eNRXN3β\u003c/em\u003e first exon, we cloned the human homologous DMR region (GRCh38: chr14:79279949\u0026ndash;79280157) in two different Firefly luciferase reporter pGL3 vector (Promega, Madison, WI, USA), the pGL3-Enh and the pGL3-Prom to test the promoter or enhancer activity of the DMR; respectively. Three vectors were used as controls: pGL3-basic (E1751, Promega, Madison, WI, USA) lacks eukaryotic enhancer and promoter; pGL3-control (E1741, Promega, Madison, WI, USA) has both an SV40 promoter and an SV40 enhancer; pGL3-enhancer (E1771, Promega, Madison, WI, USA) has an SV40 enhancer, but no promoter. HEK293 cells were seeded in 96-well plates at a density of 10,000 cells per well and cultured in DMEM containing high glucose (4.5 g/L) supplemented with 10% fetal bovine serum (FBS) and maintained at 37\u0026deg;C and 5% CO\u003csub\u003e2\u003c/sub\u003e. Twenty-four hours later, cells were transfected using 100ng (10:1 ratio of 90ng of the corresponding pGL3 vector to 10ng of pGL3-control expressing renilla luciferase, which is used to normalize the luciferase signal) in Opti-MEM reduced serum (51985091, Thermo Scientific, Wilmington, DE, USA) media and 0.03% Xtreme Gene HD DNA Transfection Reagent (06366236001, Sigma Aldrich, Darmstadt, Germany). Forty-eight hours following transfection, cells were harvested, and luciferase activity was measured using the Dual-Glo\u0026reg; Luciferase Assay System (E2920, Promega, Madison, WI, USA) according to the manufacturer\u0026rsquo;s instructions using a SpectraMax iD3 Microplate Reader (Molecular Devices). After luminescence was measured, the firefly luciferase signal was normalized against the renilla signal to account for variability across plates and experiments, the resulting relative luminescence units (RLUs) were calculated for each well. All experiments were performed in triplicate, and data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (Pierce).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eTranscriptional activation using the VP64-p65-Rta (VPR) system\u003c/h2\u003e \u003cp\u003egRNAs targeting the \u003cem\u003eNRXN3\u003c/em\u003e-DMR were designed using the sgRNA Scorer by Frederick National Laboratory for Cancer Research and as recommended in [\u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e140\u003c/span\u003e]. The final gRNA sequence used was CACCGAGATCCGGAGGAAGCCGCGC. The gRNAs were cloned in pSB700 (#64046, Addgene). The SP-dCas9-VPR vector (#63798, Addgene), which contains the VP64-p65-Rta transcriptional activators, was used to activate \u003cem\u003eNRXN3β\u003c/em\u003e transcription. One gRNA was designed based on human sequences homologous to the rhesus macaque DMR to recruit the dCas9-VPR complex to target transcriptional start sites.\u003c/p\u003e \u003cp\u003eHEK293 cells were seeded into 24-well plates at a density of 50,000 cells/well and transfected in Opti-MEM (51985091, Thermo Scientific, Wilmington, DE, USA) with 450 ng of SP-dCas9-VPR plasmid (#63798, Addgene) and 50 ng of gRNA-pSB700 using 1.5 \u0026micro;L XtremeGENE (06366244001, Roche, Mannheim, Germany) per well. Controls included transfections with the pSB700 lacking the gRNA and dCas9-VPR. After 48 hours, transfection efficiency was assessed by GFP fluorescence. After transfection, cells were harvested, and RNA was isolated for quantitative PCR (qPCR).\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eRNA isolation and reverse transcription\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eRNA was isolated from the dlPFC-A46 using the Qiagen AllPrep DNA/RNA/miRNA Universal Kit as described[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e141\u003c/span\u003e]. cDNA was synthesized from total RNA isolated from the dlPFC-A46 or PL cortex using the SuperScript IV Reverse Transcriptase (LT-02241, Invitrogen, Vilnius, Lithuania) system following the manufacturer\u0026rsquo;s instructions. RNA (250ng) was combined with random hexamers (2.5ng) (18091050, Invitrogen, Vilnius, Lithuania) and dNTPs mix (0.5mM) (18091050 Invitrogen, Vilnius, Lithuania). For control reactions, HeLa RNA (10ng) was used. After an initial incubation at 65\u0026deg;C for 5 minutes, SSIV buffer (1x) (LT-02241, Invitrogen, Vilnius, Lithuania), DTT (5mM) (LT-02241, Invitrogen, Vilnius, Lithuania), ribonuclease inhibitor (40U) (100000840, Invitrogen, Carlsbad, CA), and SuperScript IV Reverse Transcriptase (100U) (LT-02241, Invitrogen, Vilnius, Lithuania) were added and incubated at 50\u0026ndash;55\u0026deg;C for 10 minutes, followed by heat inactivation at 80\u0026deg;C for 10 minutes. Remaining RNA was removed by E. coli RNase H treatment (1U) (18021-014, Invitrogen, Carlsbad, CA) at 37\u0026deg;C for 20 minutes. The resulting cDNA was either used immediately or stored at -20\u0026deg;C.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eQuantitative PCR analysis\u003c/span\u003e (qPCR) was performed using GoTaq\u0026reg; qPCR Master Mix (1x, A6001, Promega, Madison, WI, USA), forward and reverse primer (400nM each, \u003cem\u003ePGK1\u003c/em\u003e-F: GCTCTGTGAGCAGTGCCAAAA; \u003cem\u003ePGK1\u003c/em\u003e-R: GGAAAAGATGCTTCTGGGAACA; \u003cem\u003eNRXN3α\u003c/em\u003e-F: ACCCAGTACCACCTGCCAGGAA; \u003cem\u003eNRXN3α\u003c/em\u003e-R: TCATTGCACTGGTTTCCAGAA; \u003cem\u003eNRXN3β\u003c/em\u003e-F: CAAGATGCCATCCTTCACAG; \u003cem\u003eNRXN3β\u003c/em\u003e-R: GCATCACTCAGTGCCTATTTC), and template DNA (7.5ng) or nuclease-free water as negative control. The following conditions were used: 95\u0026deg;C for 2 minutes (1 cycle); 95\u0026deg;C for 15 seconds; 59\u0026deg;C for 30 seconds (40 cycles). Post-amplification melting curves were analyzed to assess primer specificity. Data were normalized to the expression levels of the housekeeping gene \u003cem\u003ePGK1\u003c/em\u003e. Relative quantification was performed using the ΔΔCt method.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eKAG, BMF and RCJ designed the experiments. KAG and SWG oversaw the alcohol self-administration protocol and provided the rhesus macaque samples. TC and RCJ isolated all the DNA samples and prepared all the omics libraries. DNA methylation bioinformatic analyses were performed by KDZ and LJW, and KDZ advised and conducted the appropriate statistical analyses in all the experiments. CCL generated the reporter and transcriptional activation assays. RCJ supervised all the analytical aspects of these experiments and prepared the first draft of the manuscript. RH generated and supervised the transcriptomics datasets and analyses. KAG, BMF, KDZ, BH and RCJ all provided edits to the manuscript and helped to write various sections.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available on GEO under the following accession number: TBD\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSAMHSA CfBHSaQ. 2022 National Survey on Drug Use and Health. Table 5.9A\u0026mdash;Alcohol use disorder in past year: among people aged 12 or older; by age group and demographic characteristics, numbers in thousands, 2021 and 2022. 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSAMHSA CfBHSaQ. 2022 National Survey on Drug Use and Health. 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Modulation of Gpr39, a G-protein coupled receptor associated with alcohol use in non-human primates, curbs ethanol intake in mice. \u003cem\u003eNeuropsychopharmacology\u003c/em\u003e 2019.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"rhesus macaque, alcohol use disorder, DNA methylation, vulnerability, prefrontal cortex","lastPublishedDoi":"10.21203/rs.3.rs-5406434/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5406434/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAlcohol use disorder (AUD) is a highly prevalent, complex, multifactorial, and heterogeneous disorder. Currently, 11% and 30% of adults meet the criteria for past-year and lifetime AUD, respectively. Identification of the molecular mechanisms underlying risk for AUD would facilitate effective deployment of personalized interventions. Previous studies using rhesus monkeys and rats, have demonstrated that individuals with low cognitive flexibility and a predisposition towards habitual behaviors show an increased risk for future heavy drinking. Further, low cognitive flexibility is associated with reduced dorsolateral prefrontal cortex (dlPFC) function in rhesus monkeys. To explore the underlying unique molecular signatures that increase risk for chronic heavy drinking, a genome-wide DNA methylation (DNAm) analysis of the alcohol-na\u0026iuml;ve dlPFC-A46 biopsy prior to chronic alcohol self-administration was conducted in 11 male macaques. The DNAm profile provides a molecular snapshot of the alcohol-na\u0026iuml;ve dlPFC, with mapped genes and associated signaling pathways that vary across individuals. The analysis identified 1,463 differentially methylated regions related to unique genes that were strongly associated with a range of daily voluntary ethanol intakes consumed over 6 months. These findings translate behavioral phenotypes into neural markers of risk for AUD, and therefore hold promise for parallel discoveries in risk for other disorders involving impaired cognitive flexibility.\u003c/p\u003e","manuscriptTitle":"Genome-wide methylomics identifies pre-existing DNA methylation signatures in the prefrontal cortex of alcohol-naïve rhesus monkeys defining neural vulnerability for future risky ethanol consumption.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-26 14:59:38","doi":"10.21203/rs.3.rs-5406434/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ce672bd9-76c5-4c41-a6d2-4d8b48846c86","owner":[],"postedDate":"November 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-03T04:38:30+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-26 14:59:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5406434","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5406434","identity":"rs-5406434","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}