Multi-omics characterization of chronic social defeat stress recall-activated engram nuclei in Arc-GFP mice

doi:10.21203/rs.3.rs-4643912/v1

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Multi-omics characterization of chronic social defeat stress recall-activated engram nuclei in Arc-GFP mice

https://doi.org/10.21203/rs.3.rs-4643912/v1

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Susceptibility to chronic social stressors often results in the development of mental health disorders including major depressive and anxiety disorders. In contrast, some individuals remain resilient even after repeated stress exposure. Understanding the molecular drivers behind these divergent phenotypic outcomes is crucial. However, previous studies using the chronic social defeat (CSD) stress model have been limited by the use of bulk tissues investigating single omics domains. To overcome these limitations, here, we applied the CSD mouse model to Arc-GFP mice for investigating the mechanistic divergence between susceptibility and resilience, specifically in stress recall-activated engram nuclei. By conducting an in-depth analysis of the less-known differential methylome landscape in the ventral hippocampal engrams, we noted unique phenotype-specific alterations in multiple biological processes with an overrepresentation of GTPase-related mechanisms. Interestingly, the differentially methylated regions were enriched in ETS transcription factor binding sites (TFBSs), important targets of the Ras-ETS signaling pathway. This differential methylation in the ETS TFBSs could form the basis of persisting stress effects long after stressor exposure. Furthermore, by integrating the methylome modifications with transcriptomic alterations, we resolved the GTPase-related mechanisms differentially activated in the resilient and susceptible phenotypes with alterations in endocytosis overrepresented in the susceptible phenotype. Overall, our findings implicate critical avenues for future therapeutic applications.

Biological sciences/Neuroscience

Biological sciences/Molecular biology

Health sciences/Diseases/Psychiatric disorders

Biological sciences/Cell biology

Chronic social stressor forms one of the most common stressors in humans and have been widely studied in rodents using chronic social defeat (CSD) stress models (1–10). Physiologically, stress is the body’s natural response to a stressor, facilitating the escape from a situation or initiating behavioral adaptations for survival (11, 12). However, chronic stress can be detrimental. Chronic stress-triggered hyperactivation of the hypothalamic-pituitary-adrenal (HPA) axis results in dendritic atrophy (13, 14) and loss of synapses (15–17) in multiple brain regions via different signaling cascades. Such disruptions increase the risk for various disorders, including sleep disturbances (18, 19), psychiatric disorders (20–22), and neurodegenerative diseases (23, 24). Apart from this, constant exposure to stress can also lead to debilitating conditions, including cardiovascular disorders (25) and cancer (26, 27). Interestingly, despite exposure to chronic stress, a fraction of the population develops adaptive stress-coping strategies (resilience) in contrast to a maladaptive coping strategy (susceptibility).

Molecular studies using transcriptomic approaches in both bulk tissues and specific cell types have contributed immensely to the understanding of resilience and susceptibility (10, 28–31) mechanisms. However, transcriptome alterations are transient and, therefore, cannot fully explain the persistence of stress effects long after the stressor’s termination. On the other hand, DNA methylation represents a rather stable epigenetic mark in the biological embedding of experience (32, 33), with the potential to restructure transcriptional capacity and, ultimately, behavior (34–36). As such, studies on DNA methylation/demethylation are gaining interest in childhood trauma/adult post-traumatic stress disorder (PTSD) (37–41), and chronic stress research (9, 42–46). However, despite this growing interest, a detailed characterization of the methylation landscape for resilient and susceptible phenotypes, following CSD stress, is still missing. Apart from this, a combined study exploring both the methylome and the transcriptome, in resilient- or susceptible-specific engrams, is not available. Therefore, in this study, we investigated the methylome landscape, specifically, in distinct phenotype-specific engrams (3, 31) from the ventral hippocampus of the Arc-GFP mice and integrated our recent transcriptome findings, generated within the same experiments (31). We argue that an integrated approach of coupling engram nuclei with multi-omics analyses could facilitate the identification of the ‘built-in truth’ (47) and the flow of information (48) within the cellular environment after stressor exposure. This approach would offer novel insights into the complex etiology of molecular alterations following stressor exposure, increasing the probability of determining potent therapeutically targetable systems.

Animals and behavior experiments:

Animals and genotyping:

7–8 weeks old ArcCreER^T2(TG/WT) × R26-CAG-Sun1-sfGFP-Myc (M/WT) (Arc-GFP) adult male mice were used for all our experiments. All animals were bred in-house. Acclimatization to the experimental environment was done at least one week before the start of any behavioral experiment. All behavioral experiments were performed in accordance with the institutional animal welfare guidelines approved by the ethical committee of the state government of Rhineland-Palatinate, Germany (G-17-1-021).

Basal character identification for test animals:

For a controlled, non-biased separation of test animals into stressed and non-stressed group, we used open field/eagle exploration test along with a social interaction test using conspecifics on consecutive days. Briefly, in the OF/E test, näive mice were initially allowed to explore an open field for 5 mins followed by an exploration of a toy eagle in the centre of the open arena for 2.5 mins. See more details in(49) and(31) .

Chronic social defeat (CSD) experiments:

Older, larger and more aggressive retired male breeder of the CD-1 strain were preselected for this paradigm. Following the segregation of the Arc-GFP mice to control groups and stress groups, the mice from the stress group were subjected to CSD stress similar to that in(50) with slight modifications. In our particular paradigm, the Arc-GFP mouse was placed in the cage of the resident CD1 mouse. Following a physical attack for 15 s each, the CD1 mouse was separated from the Arc-GFP mouse with a perforated wall, introduced in the middle of the cage, for 30 mins interval time. This allows for sensory stimulation but no physical contact. After a separation interval of 30 mins, the Arc-GFP mouse was introduced into the cage of a new CD1 mouse for another 15 s physical attack. The procedure was repeated for one more time, amounting to physical attacks and social subordination of 3×15 s per day. At the end of the last defeat session for the day, the test mouse was left with the recent CD1 mouse, physically separated by the perforated wall, undisturbed for 24 hours till the next day of the stress experiment. The whole procedure was repeated for a total of 10 days. After the last day of the CSD defeat session, stressed mice were placed into a new cage, where they remained undisturbed for 7 days. Meanwhile, the non-stressed control group was handled daily throughout 10 days. During the daily handling, control mice were allowed to explore the in-house conspecific for about 15 s, repeated for a total of 3 times. During this period, no fighting/dominant/submissive behaviors were visible as mice with similar SI/CO scores were paired up. Control mice were also housed in similar conditions to the stressed mice but in a different room with another control conspecific-mouse on the other side of the perforated wall. After 10 days, all control animals were separated and also housed individually as in the case of the stressed group. All cages were maintained in environmentally controlled cabinets (Uniprotect NG, Zoonlab GmbH), distinct for stressed and non-stressed groups.

Tamoxifen injection and SI test with CD1 mice (SI/CD1):

As previously described (31); to acclimatize animals to the injection stress, poking was performed with a syringe similar for that to be used for TAM injection. Tamoxifen (TAM − 150 mg/Kg, Sigma Aldrich; solvent- 1:9 of 100% ethanol: corn oil, Sigma Aldrich) was injected 5 hours before SI test to both stressed and non-stressed controls. SI test was assessed by calculating the time spent by a test mouse near the cylindrical cage harbouring the CD1 mouse (test phase) compared to the time spent by the mouse near the cylindrical cage without the CD1 mouse (habituation phase). Selected mice for downstream molecular analyses, were carefully screened by an experienced person apart from the SI scores (SI > 100, resilient and SI < 100, susceptible) to ascertain stringency.

Tissue extraction, nuclei isolation and Fluorescence Activated Nuclei Sorting (FANS):

Mice were sacrificed by cervical dislocation, roughly 72 hours after TAM injection. Ventral hippocampus (vHipp) was dissected according to the regions specified in Allen brain atlas. Tissues from two-three mice were pooled in reaction tubes containing the homogenisation buffer for nuclei isolation. Nuclei were isolated as described in(51) and 30,000 activated (sfGFP+) nuclei were sorted in 100 µL of buffer ATL (Qiagen), using FANS(52). After collection, all nuclei were rapidly frozen in a mixture of dry ice and 100% ethanol and subsequently stored at -80˚C to maintain the integrity of the samples until the extraction step.

Molecular biology experiments and sequencing:

Reduced Representation Bisulfite sequencing (RRBS-Seq):

DNA extraction:

DNA was extracted using QiAamp DNA Micro Kit (#56304, Qiagen, protocol for small volumes of blood). Additional changes to the protocol included warming the AE buffer at 37˚C before applying to the MinElute column in Step 13 of the protocol. DNA quantity was measured using Qubit fluorometer.

Library preparation:

To obtain a total starting input DNA of 50 ng, one to two samples were pooled. 50 ng (instead of 100 ng) was used as starting concentration for bisulfite treatment following the RRBS protocol (53) from Premium RRBS kit, Diagenode, as we had low starting concentrations. Additional modifications were made in the following steps:

Step 3.2,3.3 and 3.5 – Volumes reduced by half. Step 3.7–40 µL of Ampure beads (instead of 60 µL). Step 4.9 – Sample pooling was performed for 5 libraries instead of 6 libraries in the original protocol. Step 5.6–97.5 µL instead of 117µL of BS conversion reagent. Step 5.9 – Inversions were done for a total of 30 times. Steps 5.14 and 5.15–5 minutes of standing time for elution buffer containing column and 2 minutes for the next step.

NB: The modifications were necessary to adapt the protocol to 50 ng of DNA (100 ng in the Diagenode protocol). The volume of the BS conversion agent was reduced due to the specific conditions of the experiment.

Sequencing:

Following the library preparation, the DNA concentration was measured by both Qubit dsDNA High sensitivity kit and qualitatively by Agilent High Sensitivity 2100 Bioanalyzer. Single end sequencing was performed using a NextSeq 500/550 High Output Cartridge (150 cycles). Two DNA pools (10 libraries) were used for each sequencing run. Using Illumina recommendations, a final concentration of 1.2 pM of the DNA library is loaded per sequencing run (78 µL of 20 pM DNA library, 520 µL of 1.5 pM of 20% PhiX-freshly prepared and 702 µL of HTT).

Computational analyses:

Behavior data statistical analyses:

All statistical calculations were performed using GraphPad Prism (8.4.2). See more details in (31).

RRBS-seq data analysis:

After the sequencing bcl2fastq v2.20 conversion software (Illumina, San Diego, CA, USA) was used to demultiplex sequence data and covert base call (BCL) files into Fastq files. Quality check and adapter trimming were performed using Trim Galore! v0.5.0 (http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/) and –rrbs as option for RRBS sequence reads. The mapping of the trimmed data to mouse (mm10) reference genome and the methylation calling were conducted using Bismark v0.20.1(54). The obtained methylation calls for the CpG context were further analyzed using the R package methylKit v1.10(55). For differentially methylated cytosines (DMC) and differentially methylated regions (DMR) bases with less than 10×coverage and more than 99.9% percentile of coverage in each sample, were filtered out. A window of 1kb length and 1kb step-size for the DMR analysis and q-value cutoff < 0.01 for both DMC and DMR analysis were applied. The genomic annotation of the DMC/DMR were performed using the R package annotatr v1.14 (56).

GO overrepresentation tests of differentially methylated genes were performed with the clusterProfiler R package v4.10.0. As different enrichment pipelines use different statistical approaches, Enrichr (57–59) and STRING were also used for enrichment analyses (p < 0.05 and adjusted p < 0.05) to provide additional comparisons or validations of results. Identification of transcription factors (TFs) were performed by parallel analysis of the 1676 TFs in http://genome.gsc.riken.jp/TFdb/ and may not cover new TFs. Identification of synaptic genes (SGs) were performed by screening genes from SynGO (60).

Manhattan plots were produced using a custom R script. Heatmaps were visualized using the pheatmap R package v1.0.12. Log-odds rations for enrichment of hyper- and hypo-methylated cytosines and regions in distinct genomic locations were obtained by performing a series of logistic regression analysis separately for each genomic feature.

Regulatory region overlap and motif enrichment analysis

Regulatory regions were obtained from the cENCODE data requested from(61) and cATLAS (62). Enhancer annotation for mouse brain were downloaded from the EnhancerAtlas 2.0 website (63). Motif enrichment analysis was performed using HOMER(64) .

STRING analysis and CYTOSCAPE

Network analyses were performed on the STRING (65–67) platform with the confidence setting at 0.400. For integrated DMG-DEG data, a confidence of 0.700 was used. Hubs were generated using the CytoHubba App (68) in Cytoscape (69) based on ‘Degree’, with the ‘top 10 network’ feature. Network enrichment maps were generated from the STRING analyses in Cytoscape version 3.8.2, using the in- built settings.

Experimental design to access phenotype-specific engram nuclei activated in the social interaction (SI) test following chronic social defeat stressor exposure

Though bulk studies have provided valuable insights into the intricate molecular alterations following stressor exposure, the inherent heterogeneity of molecular signatures in different cell types (70, 71) could potentially mask critical signals from active cells. Therefore, to increase the precision of identifying specific molecular alterations associated with resilience and susceptibility following stressor exposure, we used the ArcCreER^T2 mouse line (72) coupled with the reporter Sun1sfGFP (73) mouse line (Arc-GFP). In the presence of TAM, this experimental system allows for the expression of sfGFP in the nuclear membrane of the stimulus-activated cells. This facilitates the meticulous tracing of spatiotemporally active nuclei or phenotype-specific engrams in response to our predefined stimulus, i.e., SI in our experiment (Fig. 1a).

A detailed description of the behavior paradigm and corresponding result are available as previously reported (31). Briefly, 7–8-week-old male mice (Arc-GFP) were examined for their baseline behavior using an open field/eagle test and social interaction test with conspecifics (SI/CO, Fig. 1b, see (31) and Methods for more details) for counter-balance and segregation into stress and control groups. Following the segregation, the stressed groups were subjected to CSD stress for 3×15s attack daily, with a new bully CD1 mouse for each attack session, followed by 24 hours of sensory stimulation without physical attacks (scheme in Fig. 1b). This procedure was repeated for ten days. The non-stressed control groups were also handled daily in a separate room. After the last day of CSD, mice (both stressed and non-stressed controls) were allowed to rest for seven days before performing the SI test with a novel CD1 male mouse (SI/CD1). TAM (150 mg/kg) was delivered intraperitoneally 5 hours before the SI test for spatiotemporal tracing of SI-activated nuclei. Interestingly, the SI/CD1 test (n = 43 controls, n = 73 stressed) showed a significant group stress effect after the CSD (Fig. 1b, p < 0.05, Kolmogorov-Smirnov test), along with a significant increase in weight of the stressed group (31). Following the confirmation of effective stress application, mice that showed a strong interaction with the CD1 mice similar to the control non-stress group (SI-index > 100) were termed as resilient (Res) mice. In contrast, those individuals that exhibited a deficit in social interaction (SI-index < 100) were labelled as susceptible (Sus) mice (Fig. 1b). For stringency, the behaviors of these selected mice were also studied carefully by an experienced researcher and substantiated in the recorded videos. After the validations, representatives of the control (n = 6), resilient (n = 6), and susceptible (n = 9) mice (see Supplementary Fig. 1) were sacrificed 72 hours after TAM injection/SI/CD1. We focused on the activated nuclei (51, 52) from the ventral hippocampus (vHipp), given its pivotal role in social and fear memory (74–77), goal-directed behavior (8) and decision-making (78, 79), which are consistently altered in stress disorders. SI-activated sfGFP positive (sfGFP+) nuclei from the vHipp were collected using FANS (52). Thereafter, the methylome was accessed using reduced representation bisulfite sequencing (RRBS-seq (53)), and RNA-seq data (31) was integrated to generate an increased resolution of the molecular mechanisms underlying susceptibility and resilience. For capturing the distinct engram populations, we employed a tamoxifen (TAM)-inducible ArcCreER^T2 mouse line (72) coupled with a reporter line expressing sfGFP on the nuclear membrane (73) (Arc-GFP mice) in stimulus-activated cells.

Accessing the DNA methylation landscapes of the control, resilient and susceptible groups using activated engram nuclei

To investigate the methylome landscape using RRBS-seq, sfGFP + nuclei were collected from a pool of 2–3 mice each from the selected control, resilient and susceptible groups (Supplementary Fig. 1) to yield 3, 3 and 4 biological replicates, respectively. We obtained approximately 30 million reads per sample, detecting 3.4×10⁸ cytosines in resilient, 3.8×10⁸ cytosines in susceptible and 3.0×10⁸ cytosines in control groups with > 10× coverage (average detection per sample: 3.5×10⁸). From the detected cytosines, considering only the methylated cytosines, we noted that all three groups showed similar global CpG methylation levels (Supplementary Fig. 2a). The CHG and CHH global methylation levels were also similar between the groups but were less than 5%. As the CHG and CHH methylation levels were comparatively lower, we focused our further analyses for this manuscript on the methylation at CpG dinucleotide sites (> 10× coverage).

Landscape and functional characterization of the differentially methylated cytosines (dmCs) and genes (dmGs) harbouring the dmCs

For a detailed investigation of the methylation landscape, we first looked at the DNA methylation differences at single CpG sites. This approach is essential because individual CpG modifications at genomic locations of importance, such as transcription factor binding sites (80) (TFBSs), can alter gene expression. Using a 25% difference in methylation levels as a threshold in all pairwise group comparisons, we identified 1421 unique dmCs in resilient compared to control (Res_c), 911 unique dmCs in susceptible compared to control (Sus_c) and 1316 dmCs in the direct comparison between the stressed phenotypes, susceptible compared to resilient (Sus_r) (Supplementary Table 1). These stress-induced modifications were prevalent in genic as well as intergenic regions (Supplementary Fig. 2b) in both the susceptible (Sus) and resilient (Res) groups. Tracing the genomic locations of these modifications using log odds ratio, we noted significant enrichments (either hypo-/hypermethylated or both) in CpG shelves, introns and 3’UTRs in all pairwise comparisons (Supplementary Fig. 2c). The findings are similar to studies investigating methylome in the non-CSD context (46, 61, 81, 82), where enrichment of methylation alterations in intronic regions indicates induced neuronal modifications (81), following stressor exposure. Next, focusing on the genic regions, we traced the dmC alterations to 884 genes in Res_c, 598 genes in Sus_c and 920 genes in Sus_r (Supplementary Fig. 2d). Of note, most genes (> 70% dmGs) harbored only one dmC (Supplementary Fig. 2e). Interestingly, some alterations detected in Sus_r were specific to only one of the two stress phenotypes (124 genes specific to Res_c and 72 genes specific to Sus_c (Supplementary Fig. 2f, Supplementary Table 1). However, we did not detect phenotype-specific genomic loci, as we observed an equivalent distribution of dmCs over the chromosomes (Supplementary Fig. 2g).

Next, to obtain a comprehensive understanding of the functionalities of the altering molecular landscape following stressor exposure, we performed gene ontology (GO) analyses of the dmGs from each dual comparison (Supplementary Table 1) as well as a combination of the genes from all dual comparisons. From the combined analysis, we identified an enrichment of biological processes (BP), including ‘pattern specification’, ‘neuron cell fate commitment’, and ‘axonogenesis’. Enriched molecular functions (MF) included activities related to ‘transcription activator/repressor and ‘ion channels’. On the other hand, enriched GO cellular components (CC) included `transcription regulator complex’, `synaptic membrane’ and ‘actin-based cell projection’ (Supplementary Fig. 3a, Supplementary Table 1). Additionally, we noted a higher fraction of differentially methylated transcription factors (TFs, orange) than synaptic genes (SGs (60), blue, Supplementary Fig. 3b).

Landscape and functional characterization of the differentially methylated regions (DMRs) and genes (DMGs) harbouring the DMRs

Since regional methylation modifications have been considered better indicators of gene expression alterations and more robust to statistical power (83, 84), we, next,determined the differential methylation averaged over blocks of genomic DNA (1000 bps). Similar to the dmCs, the DMRs were equivalently distributed in the genic and intergenic regions and showed alterations in both the susceptible and resilient phenotypes (Supplementary Fig. 4a, Supplementary Table 2) as opposed to the controls. Additionally, the DMRs were also enriched in the genomic locations (Fig. 2a) of CpG shelves, 3’ UTRs and intronic regions (mostly first introns, Supplementary Fig. 4b). While DMRs were similarly distributed along the genome in all dual comparisons (Supplementary Fig. 4c), noteworthy is that chromosome 2 possessed the highest number of DMRs in both Res_c (n = 152) and Sus_c (n = 116). Next, focusing on the genic regions, the DMRs were assigned to 790 DMGs in Res_c (Fig. 2b), 578 DMGs in Sus_c (Fig. 2b) and 728 DMGs in Sus_r (Fig. 2b). Amongst the DMGs in Sus_r, 130 were specific to Res_c, 69 to Sus_c, 41 genes appeared in both Res_c and Sus_c (Supplementary Fig. 4d), while 488 genes were not detected in the respective comparisons with the controls. Nevertheless, the majority (74%) of the DMGs belonged to protein-coding genes (Supplementary Fig. 4d), with most genes harbouring only one DMR (Supplementary Fig. 4d, Supplementary Table 2).

To estimate the probable functional roles of the DMGs in the dual comparisons, we performed gene ontology (GO) enrichment analysis. Across all pairwise comparisons, we observed a significant enrichment of the BP ‘synapse organization’ (Fig. 2c), indicating extensive neuronal remodelling after stressor exposure. This observation was supported by an increased prevalence of synaptic genes (SynGo genes (60), Supplementary Fig. 4e) in the DMR analysis as compared to the dmCs. Indeed, there was little overlap between the dmGs and DMGs (Supplementary Fig. 4f). Other top GO BPs included ‘small-GTPase mediated signal transduction’ in Res_c, ‘regulation of Wnt signaling pathway’ and ‘regulation of endocytosis’ in Sus_c, whereas ‘regulation of neurogenesis’ and ‘regulation of neuron differentiation’ were significantly enriched in Sus_r (Fig. 2c, Supplementary Table 2). A deeper analysis of the remaining BPs revealed that the terms ‘neuronal differentiation’, ‘gliogenesis’ and ‘axonogenesis’ were specific to Res_c. At the same time, ‘endocytosis’, ‘cell-matrix adhesion’ and ‘Wnt signaling’ were unique to Sus_c (Supplementary Fig. 4g, Supplementary Table 2). Next, a UMAP dimensional reduction (Fig. 2d, Enrichr (57–59) ) was employed to visualize the collective stress-induced alterations in MFs using the DMGs from all pairwise comparisons. We noted significant enrichments of gene clusters related to ‘guanine nucleotide exchange factors (GEF)/GTPase activator activity’ (cluster 6), ‘Wnt signaling’ (cluster 9), ‘tyrosine kinase activity’ (clusters 2,5,6,9), ‘ion channel/receptor binding activity’ (cluster 3) and ‘actin/tropomyosin binding’ (cluster 12) (Supplementary Table 2, ‘GO terms_combined modifications’). Thereafter, clustering of the common methylated regions based on absolute methylation values (Fig. 2e) identified that MF ‘tyrosine kinase activity’ was the most representative for the gene sets that lost methylation in both phenotypes upon stressor exposure (p < 0.05, Enrichr (57–59)). Meanwhile, alterations in the ‘guanine nucleotide exchange factors (GEFs)’ and ‘cell-cell adhesion’ activities were the most representative for the gene sets that gained methylation in the resilient group. Interestingly, GO MFs of ‘actin/microtubule binding’ were the most representative of gene sets that gained methylation in the susceptible group. On the other hand, the GO CCs showed that both stressed phenotypes lost methylation for genes associated with GO terms of ‘synaptic membrane’ and ‘extracellular matrix’ (ECM) (Fig. 2e, Supplementary Table 2). CCs `glutamate receptor complex’ and `cell-cell contact zone’ (Fig. 2e, Supplementary Table 2) were enriched in gene sets that gained methylation in the resilient group. In contrast, the susceptible phenotype gained methylation in the domains of ‘actin filament/membrane rafts’ (Fig. 2e, Supplementary Table 2). Cumulatively, these findings revealed that despite some common stress-invoked pathways in the methylome of the stressed group, there were clear divergent molecular processes active in the resilient and susceptible engrams.

Regulatory functions of the DMRs suggest a major involvement of GTPase signaling in stress-related alterations

Having determined the probable functional roles (GO terms) of the genes (DMGs) that contain DMRs, we investigated whether these DMRs could possess potential regulatory roles. For this purpose, we matched the genomic coordinates of the DMRs with publicly available data of cis-regulatory elements such as enhancers, insulators and inhibitors (brain-specific data from cATLAS (62), and non-brain-specific data from cENCODE (61). Interestingly, we noted a substantial overlap of the DMRs (68.59%, cATLAS and 50.69%, cENCODE) with the regulatory regions (hence termed ‘rrDMRs’, Fig. 3a). This finding indicates a potential regulatory role of the methylation alterations following stressor exposure. Further, enrichment analyses revealed that the genes harbouring rrDMRs were top enriched (p-adjusted < 0.05, Enrichr (57–59)) for the GO BPs of ‘regulation of intracellular/small GTPase mediated signal transduction’, ‘enzyme-linked receptor protein signaling pathway’ and ‘peptidyl-tyrosine modification/phosphorylation’ (Fig. 3a). The top MFs of these rrDMR genes included ‘protein tyrosine kinase’, ‘GTPase regulator’, ‘GEF factor’ and ‘Wnt signaling co-receptor’ activities (Supplementary Table 3). These findings provide evidence that the alterations in the methylome landscape could potentially influence key signaling pathways. To increase stringency, we overlapped our data with age-matched brain enhancer data available from EnhancerAtlas2.0(63) (Mus musculus, brain). Supporting the previous findings, the majority of the genes with DMRs in enhancer regions (termed ‘eDMGs’) were associated with GTPase signaling (Arhgap9, Cdk14, Rasgef1a, Gabbr2, Arhgap35, Gnas, Tbc19db), tyrosine kinase signaling (Tex14, Aatk), synaptic vesicle functions (Ap1m1, Sorl1, Coro1c) and synaptic plasticity (Map7, Camk2b, Fmnl1, Npas4, Arpc3, Prph, Cdh22) (Fig. 3b, Supplementary Table 3). Noteworthy is that apart from the genic regions, a considerable number of intergenic regions also overlapped with the regulatory regions (Supplementary Table 1), indicating potential regulatory roles for non-coding regions in stress-related alterations.

Motifs for E26 Transformation Specific (ETS) family of TFs enriched within the DMRs

Following the substantial overlap of the DMRs with the regulatory regions (Fig. 3a, b), we hypothesized that there could be multiple TFBSs within the DMRs. Methylation of TFBSs plays an important role in TF binding(80, 85, 86) and chromatin accessibility, thereby affecting gene expression (36, 87–90). Therefore, we performed TF binding DNA sequence motif enrichment analyses using HOMER (64), similar to Zhang and colleagues (91). Interestingly, we identified members of the ETS family of TFs(92) to be substantially enriched, with the top position occupied by ETS-like 4 (Elk4), p = e^− 126 (Fig. 3c), followed by ETS-like 1 (Elk1), p = e^− 65, with very similar seed motifs (TTCCGG) (92, 93). Further, separate HOMER analyses of the pairwise comparisons of Res_c, Sus_c and Sus_r identified a similar top enrichment of the ETS family of TFs (Fig. 3d, Supplementary Table 3). Interestingly, the TFBS enrichment analyses using only rrDMRs yielded a similar result (Supplementary Table 3). Notably, the ETS family of TFs are nuclear targets of the Ras (GTPase)-extracellular signal-regulated kinase (ERK) signaling (94, 95), crucial for activity-dependent transcription (95), synaptic plasticity, learning and memory, among other important neuronal functions (96–98). These findings implicate an important role for the ETS family of TFs and GTPase signaling in regulating the effects of stress via the methylome. Though one could argue that the observed enrichment of the ETS TF might be because of the enrichment of the CG-rich genomic regions in the RRBS-seq, this is unlikely because RRBS-seq data for non-stress research in the hippocampus showed enrichment of other TFs (61).

Regulatory influence of the differential methylation in gene expression at the sample collection timepoint

Having determined the potential regulatory role of the DMRs, we next aimed at identifying the impact of differential methylation on gene expression following stressor exposure. For a preliminary screen, we compared the DMGs with the differentially expressed genes (DEGs (31) obtained within the same experiment). Interestingly, we identified only about 10% (190) of the total DEGs overlapping with DMGs (Fig. 3e). This is consistent with existing literature (99), where epigenetic modifications do not alter transcript levels shortly after an environmental challenge but instead act as a priming module for future alterations. Surprisingly, despite the low overlap, the genes shared between the two omics datasets showed GO MF enrichment of ‘GTPase signaling’ activities (Fig. 3e, GO terms) and ‘GEFs’ (highlighted in the rectangle in Fig. 3e). Of note, this enrichment in MFs was accompanied by enrichment of BPs related to ‘cell adhesion’ and ‘ion channel’ activities (Supplementary Table 4). However, these genes revealed a complex, non-conventional relationship between DNA methylation and gene expression (Supplementary Fig. 5a). Indeed, such features have been observed wherein, methylation increases gene expression (100) or facilitates permissive environments for alternative splicing mechanisms (82, 101) instead of gene downregulation. In fact, only eight of the eDMGs (black arrows, Fig. 3b) showed alterations at the transcript levels (Fig. 3e, right panel) following stressor exposure.

Divergent patterns of GTPase signaling and related mechanisms in the susceptible and resilient phenotypes at multiple omics levels

As the previous sections revealed an important contribution of GTPase signaling in regulating stress effects, we next sought to clarify the existence of a phenotypic-specific divergence in this signaling pathway via a multi-omics molecular profiling. For this, we adopted a networks approach by integrating the genes that exhibited altered methylation or expression patterns into plausible protein-protein interactions (PPI) in the STRING platform (see Methods)(65–67).

Following a preliminary analysis, we identified an enrichment of the domains of the GTPase regulators (GEFs) in both the methylome (combined DMGs) and transcriptome (combined DEGs) datasets (Pfam(102) and SMART (103), STRING) (Supplementary Fig. 5b, Supplementary Table 4). Therefore, this family was investigated in-depth. Intriguingly, we noted a divergent pattern of the RhoGEFs between the resilient and susceptible phenotypes. While a higher fraction of hypermethylated GEFs (intronic) was prevalent in the resilient animals (Fig. 4a, Res_c_s, resilient compared to either control or susceptible), a higher fraction of hypomethylation was prevalent in the susceptible group (Fig. 4a, Sus_c_r, susceptible compared to either control or resilient). Interestingly, this trend was accompanied by a higher fraction of upregulated GEFs in the resilient (Fig. 4a, Res_c_r) as opposed to downregulated GEFs in the susceptible phenotype (Sus_c_r). This phenotype-specific divergence in the RhoGEF family, surpassing different omics layers, suggests a central and novel role of this GTPase regulator in directing the stress phenotypes. Next, analyzing hub genes (CytoHubba (68)) within the PPI networks, we ascertained that GTPases functioned as hubs in our network, albeit with a unique and distinct pattern in the Res_c compared to Sus_c in both omics’ levels. Specifically, in the methylome Res_c, GTPases (eg., Rhoa, Gnas, Fras1) and GTPase regulators (Kalrn, Syngap1) formed the majority of the top 10 hub genes (Fig. 4b), while the hubs in Sus_c showed a low representation of the GTPases (Gnas). In contrast, in the transcriptome, the GTPases formed the top 3 hubs in Sus_c (e.g., Rhoa, Rac1) while the spliceosome family of heterogeneous nuclear ribonucleoprotein particles (Hnrnps) and serine/arginine-rich splicing factors (Srsfs) dominated the top hub ranks in Res_c (Fig. 4b). The hub genes for Sus_r for the two omics levels are illustrated in Supplementary Fig. 5c.

While the crucial involvement of GTPase signaling at the different omics layers is clear, given the presence of hub genes involved in other BPs, we wanted to determine if we could obtain other phenotype-specific signaling networks through an integrated multi-omics readout.

For this integrated approach, DMGs and DEGs of each phenotype comparisons were combined, and plausible PPI networks were generated in STRING (65–67). We identified network clusters specific to the integrated resilient comparisons (RES_C, example, spliceosome assembly, cadherin clusters) and integrated susceptible comparisons (SUS_C, example, actin nucleation, oligodendrocyte development). Interestingly, GTPase families remained as critical hubs (Supplementary Fig. 5d, Supplementary Table 4) in the integrated data sets. On the other hand, BPs related to ‘exocytosis/endocytosis’, ‘Wnt signaling’, and ‘myelination’ were specific to SUS_C, whereas, amongst the limited BPs specific to RES_C, we noted ‘mRNA processing’, and ‘Rho protein signal transduction’ to be of importance. We validated these findings using Enrichr (Supplementary Table 4). Of note, specificity of BP ‘endocytosis’ to the susceptible comparisons persisted both across individual omics comparisons and upon data integration (Supplementary Fig. 5e, Supplementary Table 4), as opposed to other BPs that changed phenotype-specificity at different omics levels (Supplementary Fig. 5e, Supplementary Table 4). With this gain of information on the complex yet convergent molecular alterations, the next step was to screen for potential targetable pathways/genes within networks.

Screening of potential targetable systems in the integrated networks of the stress-altered pathways

We reasoned that an ideal targetable system should be within a group of molecular functions prevalent at the multi-omics levels in all comparisons. For a smooth visualization, we gathered the collective stress-related alterations in molecular functions for each omics level and generated integrated enrichment maps using Cytoscape(69) (Fig. 5a,). Here, we identified alterations in targetable molecular functions of ion channels and receptors (Cluster 3) prevalent at all omics levels. Additionally, the alterations in the DMGs and DEGs overlapped at the targetable MFs of GTPase activities (Cluster 5) and actin/cytoskeletal binding (Cluster 6). As these molecular functions are critical for synaptic functions and stress-related disorders, we focused our search for potential therapeutic targets within the synaptic genes.

As networks provide more information on the importance of a signaling molecule, we generated a PPI network of the synaptic genes detected as significantly different in any pairwise group comparisons for any omics level. Following the generation of the network, we screened for novel genes, which have not been reported as prominently implicated in depression or stress research and assessed the degree of connections with every other gene. Our screening list (Supplementary Table 5) consisted of data from the ‘major depression’ gene list from Harmonizome 3.0 (104) (links in Supplementary Table 5), Bagot and colleagues (105) (vHipp only), Fan and colleagues (106) Scarpa and colleagues (107), Gammie and colleagues (108) (Top 1000 gene list); and Kuehner and colleagues (109) (using only the shared genes between three and six months old animals) (Supplementary Table 5).

Supporting previous findings, we noted several genes previously identified as relevant to stress-related alterations in our network (blue nodes, green nodes, Fig. 5b), including GTPases, Rho GTPase activating proteins (GAPs), and RhoGEFs, On the other hand, we identified a few genes that have not yet been implicated as significantly relevant in stress pathways in previous studies (red nodes, Fig. 5b, Supplementary Table 5). Amongst these novel genes, we noted five genes (black, Fig. 5b) belonging to the GTPase family and Rho GTPase regulators. Interestingly, four of these genes (Rab5a, Rab6b, Ophn1and Arf6) are tightly linked to endocytic/exocytic processes (110–113). Amongst these genes, ADP-ribosylation factor 6 (Arf6) stands prominent because of its distinct nature (114) and critical roles in synaptic vesicle functions, including endocytosis (110, 115) and actin polymerization (114), which are consistently enriched in susceptible group comparisons. Since Arf6 expression is upregulated in the susceptible (log2 fold change ~ 0.4) compared to the resilient group, given its connection with hub genes, decreasing its expression could be beneficial. Of note, decreasing the activity of Arf6 has been shown to be essential for the regenerative capacity of the axons in the central nervous system (CNS) (116) as well as increasing the readily releasable pools at the synapses (115). Last but not the least, it is interesting to note that gm9887, partially overlapping with Arf6 (117) showed 3 CpG modifications at the 1–5 kb positions in the Res_c comparison (Supplementary Table 1).

Using CSD on TAM-inducible Arc-GFP mice, our data traced the shifting molecular landscape in specific engrams of control, susceptible and resilient mice, employing both methylome and transcriptome data (31) at a critical phase post-stressor exposure (seven days later). To this end, our study provides a massive repository of methylation data for a CSD resilience and susceptibility model from specific engram-activated vHipp nuclei, for the first time. Interestingly, both resilient and susceptible phenotypes reflected a higher methylation loss than controls, indicating a possible difference in the DNA methyl transferases (DNMTs) or ten-11 translocation (TETs) enzymes. Indeed, Wei and colleagues (46) reported decreased DNMT3a after stress. Consistently, reinvestigation of our transcriptome data revealed downregulation of DNMT3a and DNMT1 mRNA levels in the susceptible group. In addition to gaining a multi-omics overview, the integrated approach, gave us the unique opportunity to uncover a fascinating biological phenomenon revolving around GTPase signaling with a plausible mechanism underlying the divergence and persistence of stressor effects.

The significant enrichment of functionalities directly related to GTPase signaling in all phenotypic comparisons (Supplementary Table 2), as well as the members of the GTPase family forming major hubs (Fig. 4b) highlight a critical role of this signaling cascade following stressor exposure. Indeed, Lutz and colleagues(118) showed that small GTPases and regulators were critical in the development of risk for depression following childhood abuse. Additionally, dysregulations in the GTPases are well known to be associated with autism spectrum disorder (119, 120), depression and early life environmental/social stress (121). and psychiatric disorders (122, 123). However, research regarding the role of the regulators for GTPases, specifically, in stress resilience/susceptibility have not been well-researched. Bondar and colleagues(124) suggested the involvement of the GTPase regulators (GEFs) in their CSD study, as two RhoGEFs were downregulated in the stressed population, however, they did not specify the differential pattern between resilience and susceptibility. Therefore, the findings in our study, i.e., a gain of intronic methylation in the GEF genes for the resilient group (Fig. 4a), accompanied by an upregulated expression in the transcriptome (Fig. 4a), provide novel mechanistic insights in the stress-responsive GTPase signaling. Given that we did not observe a divergent pattern of alteration between the resilient and susceptible mice in the GTPase activating proteins, which are equally important in regulating GTPases, the findings on the GEFs are crucial.

In addition to the variance in the GEF regulators, the differential trend visible in the upstream and downstream signals of GTPase signaling is noteworthy. In the resilient group, the cell adhesion family of protocadherins (cell-cell adhesion) that lie upstream of the GTPase signaling are hypomethylated (Supplementary Table 2) and upregulated (31) in the resilient groups. This disparity in the cell adhesion mechanisms i.e., more pronounced cell-cell adhesion alterations in the resilient with more prominent cell-matrix adhesion alterations in the susceptible can influence divergent RhoA signaling processes (125, 126). These differential trends in the upstream signals, coupled with the alterations in the downstream processes including a. endocytosis and b. actin/microtubule processes, more enriched in the susceptible, can form the basis of divergent synaptic GTPase-nuclei signaling in the phenotypes.

This divergent GTPase signaling can influence the epigenome via their nuclear targets - the ETS family of activity-dependent TFBSs (127–130), which are significantly enriched within the DMRs. More elaborately, the ETS family of TFs integrates synaptic GTPase signaling in the nuclear epigenome(130) via the ETS-SRF ternary complex factor at TFBSs(127, 131) and regulates activity-dependent IEGs, actin cytoskeletal or synaptic plasticity genes (126, 132). Repeated waves of signal integration of neuronal activities, i.e., differential synaptic-nuclei encoding via the Ras-SRF-ETS pathway, over time, could prime the epigenome (126). Such differential priming entails altered cytoskeletal dynamics(131) and synaptic architecture, manifesting as differential behavioral patterns(133) between resilience and susceptibility. Thus, the GTPase-ETS coupling could function as one mechanism by which the effects of stressors can be sustained long-term in the epigenomic landscape (134), after the eventual removal of the stressor apart from regulating immediate reactions to acute stress (135). In fact, the Ras-ERK-ETS (Elk) is one path by which post-traumatic stress disorder (PTSD) states emerge(136) in stress-susceptible groups.In addition, recent studies demonstrated that the modulation of Elk1 is necessary for antidepressant functions(128, 137, 138) and Ras-ERK-ETS signaling for longevity (129), usually affected in stress disorders. Apart from the critical role of GTPases in synaptic-nuclei signaling via the ETS family of TFs, RhoGTPase signaling can directly modulate epigenome/chromatin plasticity and gene transcription (139–141), via actin remodelling in the nucleus itself (142). Alterations in the GTPase signaling pathways could also contribute to the difference in neurogenesis(143) via ERK signaling(144) or RhoGEFs (DOCK1) (145). Artemin, a neuroprotective factor inhibited by Rac GTPase(146) was upregulated in a study of resilient mice(147) in females, which is interesting because Rac1 expression decreased in the resilient groups in our transcriptome data (31). Thus, our findings suggest a crucial link in intercepting and integrating alterations in the extracellular/synaptic environment into the long-term epigenome of an organism via the GTPase signaling, generatingdistinct homeostatic equilibrium in the divergent stress phenotypes for future responses. A summary of the differential trend of alterations in the resilient and susceptible groups, providing a plausible mechanism for long-term alterations, is shown in Fig. 6a.

Given the critical role of GTPases in regulating stress outcomes, it is surprising that GTPase pathways were not significantly enriched in terminal neurological disorders (148). However, cell adhesion pathways were altered (148). This could suggest that GTPases play critical roles during a crucial window period of signal integration for long-term epigenetic modifications following an environmental insult. Thus, modulation of the GTPase signaling or related mechanisms in synaptic functions could be important for rescuing susceptibility phenotypes. Here, we hypothesize that Arf6, a GTPase that plays a central role in endocytosis(114, 149–151) could be an important therapeutic target as ‘vesicle dynamics or endocytic’ processes were preferentially enriched in the susceptible comparisons across both the methylome and transcriptome levels. Remarkably, Arf6’s potential of being a therapeutic target in other non-stress studies has recently been recognized (152). Apart from its critical role in regulating readily releasable pools of vesicles, it exhibits strong molecular interactions with stress-relevant pathways (Fig. 6b) such as Wnt signaling(153) and integrin signaling (149). More elaborately, Arf6 upregulates integrins and downregulates E-cadherin (154), which goes well with our data, where cell-matrix adhesion (integrin) signaling processes are more prevalent in Sus_c and cell-cell (cadherin) signaling more in Res_c. It lies upstream of the Wnt signaling (153), a BP more prevalent in the susceptible comparisons. Additionally, Arf6 is linked to the major hubs of Hras, Rac1(150) and plays crucial roles in synaptic depression(155) and neuronal differentiation (97). Apart from this, it is also closely associated with the mTOR pathway (156), relevant in stress disorders Thus, the crucial position of Arf6 in regulating various pathways lends support to the hypothesis of it being a potentially effective target to rescue susceptible phenotypes. Given the recent finding of Ophn1, which is a RhoGAP and an intellectual disability gene, as a potent therapeutic target for depressive-like behavior (157), the therapeutic potential of Arf6, a specific target of the intellectual disability gene IQSEC2 (158), in rescuing susceptible phenotypes, is substantial, within a suitable window period.

Last but not least, besides the identification of the divergent GTPase signaling patterns and potential targets, our engram-specific multi-omics approach facilitated an integrative view of the ongoing molecular changes in the different phenotypes. This facilitated the observation of certain phenomena that would otherwise not have been possible by studying only individual techniques. Indeed, the low overlap of epigenetic mechanisms with transcriptome is consistent with literature (46, 99), where the sample collection point is close to the period of environmental insult. This can be due to the temporal difference in the activation of the interconnected but different omics layers, i.e., DNA methylation may occur before or remain long after the duration for transcriptional modification (159, 160). Although only 10% of the DMGs showed alterations in expression, the high overlap with putative cis-regulatory regions and enrichment of ETS TFBSs strengthen the likelihood of a primed epigenome (82, 161). A primed epigenome could alter future transcriptional states by facilitating a faster or stronger transcription of certain genes, thereby modulating behavior upon reintroduction of a similar stimulus (161). For instance, Chastain and colleagues(162) demonstrated that the epigenetic remodelling following alcohol exposure resulted in transcriptome changes only at a later stage in life. Additionally, Robison & Nestler (163) and Provencal and colleagues (164) also reported delays in the manifestation of the gene alterations following exposure-induced epigenome remodelling. Apart from this, it is interesting to note that a few biological processes specifically enriched for a phenotype at one omics level were detected as specific for the opposing phenotype in the other omics level (DMGs vs DEGs) (Supplementary Fig. 5e, Supplementary Table 4). Given the temporal dissonance between the transcriptome and methylome (81, 159), such observations allow for the speculation that molecular modifications in the resilient and susceptible groups could themselves possess a different sequence/speed of recruiting a particular molecular pathway. In fact, Bagot and colleagues (105) highlighted the possibility that resilient phenotypes might exhibit a faster rate of adaptive modifications compared to susceptible ones in their bulk transcriptomic studies.

Indeed, accessing and interpreting the different omics layers is a challenge. However, the integrated use of methylome and transcriptomics data coupled with a network analyses approach facilitated a better understanding of the ongoing cellular alterations following stress exposure. For example, the temporal dissonance between the transcriptome and methylome alterations highlights plausible window periods for therapeutic interventions before transient alterations become stabilized. Nevertheless, we acknowledge that investigating the methylome alterations from a 3D perspective, fully exploiting the intergenic alterations, could have provided a deeper insight. Additionally, as the CSD paradigm is mostly limited to males; to understand sex-specific differences (165, 166) in stress response, it would be interesting to apply the engram-multiomics approach in females using other stress paradigms like chronic variable stressors (167, 168) in the future.

Conflict of interest:

We declare no conflict of interest

Acknowledgements:

We would like to thank Junhao Li and the Diagenode company for giving us directions during data analysis. We also acknowledge the immense amount of help from Marija Milic in establishing the behavior experiments and the Flow Cytometry Core Facility of the Institute of Molecular Biology for their help with FANS performed on the BD FACSAria III SORP (DFG P#210144599). We would also like to thank Tamer Butto and Kanak Mungikar for their help with the generation of the transcriptome data, which was recently published in Molecular Psychiatry. ChatGPT (Open AI) was used for checking grammatical efficiency. The project is funded by the DFG through subproject A05 of the Collaborative Research Center (CRC) 1193 (“Neurobiology of Resilience”) and the Landesinitiative Rheinland-Pfalz and the ReALity initiative of the Johannes Gutenberg University Mainz.

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Multi-omics characterization of chronic social defeat stress recall-activated engram nuclei in Arc-GFP mice

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Abstract

Figures

Introduction

Materials and Methods

Animals and genotyping:

Basal character identification for test animals:

Chronic social defeat (CSD) experiments:

Tamoxifen injection and SI test with CD1 mice (SI/CD1):

Reduced Representation Bisulfite sequencing (RRBS-Seq):

DNA extraction:

Library preparation:

Sequencing:

Behavior data statistical analyses:

RRBS-seq data analysis:

Regulatory region overlap and motif enrichment analysis

Results

Motifs for E26 Transformation Specific (ETS) family of TFs enriched within the DMRs

Regulatory influence of the differential methylation in gene expression at the sample collection timepoint

Discussion

Declarations

Conflict of interest:

Acknowledgements:

References

Additional Declarations

Supplementary Files

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Version 1