Introduction
The brain’s remarkable ability to learn and adapt to new experiences, known as Change In Response Learning, is underpinned by dynamic molecular processes within neurons and glial cells. Neuronal activity, crucial for processing information and forming memories, has been shown to induce DNA double-strand breaks (DSBs) at specific genomic locations in laboratory settings. Furthermore, studies in living organisms have revealed that physiological neuronal activity, such as that experienced during learning behaviors, also leads to DSB formation. However, the precise distribution of these DNA breaks across the genome and their functional relevance to brain processes, particularly in the context of change in response learning, has remained largely unexplored.
Initially observed in cultured neurons following glutamate receptor activation, DSBs, marked by γH2AX, were found to increase rapidly with neuronal stimulation. Subsequent research confirmed DSB generation in rodent brains after seizures or behavioral manipulations. Interestingly, periods of wakefulness, associated with heightened neuronal activity and exploration, were linked to increased DSBs in neurons, which were reduced during sleep, suggesting a dynamic regulation of DNA integrity in response to neural activity states.
One potential source of genomic stress in the brain is its high transcriptional demand. Neurons are constantly adapting to environmental cues, necessitating continuous adjustments in gene expression. Our previous work made a significant discovery: stimulating primary cortical neurons in vitro resulted in DSB formation specifically at early response genes (ERGs), genes known for their rapid transcriptional activation. This DSB formation, in turn, facilitated the expression of these ERGs. Increased γH2AX at certain ERGs was later observed in the brain during fear learning and memory recall, suggesting a role for DSBs in change in response learning and memory processes. Similar links between DSBs and gene induction have been found in other contexts, such as transcriptional activation by nuclear receptors, heat shock, and serum stimulation.
Given the complexity of the brain, it’s plausible that multiple pathways contribute to DSB generation in vivo. However, the exact locations of these DSBs in the brain and their relationship to brain function and change in response learning are still open questions. Because DSBs pose a potential threat to genomic stability, understanding the brain’s genome-wide DSB landscape is crucial. This knowledge would shed light on how the brain balances rapid transcriptional responses with the inherent risk of DNA damage, and identify areas of genomic vulnerability that could contribute to neuronal dysfunction, brain aging, and neurodegenerative diseases.
To address these critical questions, we aimed to map the in vivo landscape of DSBs in the brain during learning and investigate their correlation with gene expression changes in both neurons and glial cells. We hypothesized that fear learning, a robust model for change in response learning, would induce DSBs at specific genes involved in memory formation. Our findings reveal that genes induced by fear learning are indeed overrepresented among genes with high DSB levels in the medial prefrontal cortex and hippocampus. These genes are associated with pathways shared or unique to neurons and non-neurons. Intriguingly, we discovered potential DSB hotspots in glia-enriched genes that exhibit a strong transcriptional response to glucocorticoid receptor signaling. These results indicate that change in response learning behaviors trigger widespread DSB formation in the brain, associated with experience-driven transcriptional changes across both neuronal and glial cell populations, highlighting a novel dimension of brain plasticity in response to learning.
Results
Fear Learning Induces DNA Double-Strand Breaks in the Brain
Previous research has demonstrated that increased neuronal activity leads to DSB formation both in vitro and in vivo. However, it was unclear whether these DSBs occur at specific genomic locations within the brain and in which cell types during a normal physiological event like learning, a fundamental aspect of change in response learning. To investigate this, we used contextual fear conditioning (CFC) in mice. CFC is a well-established paradigm that creates a strong associative memory between a novel environment and an aversive stimulus, such as a foot shock. This model is ideal for studying change in response learning at a molecular level. We focused on the hippocampus (HIP) and the medial prefrontal cortex (mPFC), two brain regions known to be critical for CFC and subsequent memory formation. It’s known that neuronal activation rapidly induces the expression of ERGs, and we confirmed this induction for genes like Npas4 and Arc in both HIP and mPFC 30 minutes after CFC, with a more pronounced induction in the mPFC (S1A Fig).
To map DSB formation genome-wide, we employed γH2AX ChIP-Seq, a sensitive technique that identifies DSBs by targeting γH2AX, a known chromatin marker for DSBs. We performed this assay 30 minutes after CFC to capture the early wave of DSB formation. In the naive hippocampus, we detected 136 γH2AX peaks, which increased to 280 peaks after CFC, with 125 peaks shared between both conditions (S2 Table). Similarly, in the naive mPFC, we found 120 γH2AX peaks, rising to 255 after CFC, with 102 shared peaks (S2 Table). Considering all peaks identified in both naive and CFC conditions, we found 291 γH2AX peaks associated with 323 genes in the hippocampus and 273 peaks associated with 306 genes in the mPFC (Fig 1A; S1B Fig; S2 Table). Consistent with previous studies, we observed that γH2AX peaks were primarily located within gene bodies and their size was proportional to gene length, often extending beyond the 3’-UTR (S1C Fig).
There was a significant overlap in γH2AX peaks between the hippocampus and mPFC, reflecting their coordinated activity during change in response learning (Fig 1A). Gene ontology (GO) analysis of the 206 genes sharing γH2AX peaks in both regions, using clusterProfiler, revealed four main categories of biological processes (S1D Fig). The largest category was related to synaptic function, including genes involved in ‘modulation of chemical synaptic transmission’ such as glutamate receptors Gria2 and Grin2b, synaptic plasticity regulators like Camk2a, and ERGs like Arc and Plk2 (Fig 1B). Many of these genes are lineage-specific, such as the transcription factor Neurod2. Two other clusters were related to RNA binding (‘Regulation of mRNA splicing, via spliceosome’) and cytoskeleton-related genes (‘protein depolymerization’) (Fig 1B). The fourth cluster was associated with hormone or biological rhythms (‘response to hormone’) (Fig 1B). To validate γH2AX peaks at ERGs, we performed γH2AX ChIP-qPCR on hippocampi 30 minutes post-CFC. Compared to naive mice, CFC-treated mice showed significant increases in γH2AX at the gene bodies of ERGs Npas4 and Nr4a1, but not at the housekeeping gene B2m (S1E Fig). These results suggest that many genes critical for neuronal function and memory formation, far more than previously seen in cultured neurons after NMDA stimulation, are potential hotspots for DSB formation during change in response learning. Given the severe threat DSBs pose to genomic integrity, including transcriptional dysregulation and genomic rearrangements, this indicates that genes essential for neuronal function might be particularly vulnerable to DNA damage during change in response learning.
We previously observed a correlation between DSB formation and rapid gene induction in neurons, especially ERGs, which we now find are also DSB sites in the brain. To understand the relationship between these DSBs and CFC-induced gene expression changes, we performed nuclear RNA-Seq. Nuclear RNA levels provide a more direct measure of transcriptional activity compared to whole-cell mRNA levels, which are influenced by both synthesis and degradation. We sorted neuronal (NeuN+) and non-neuronal (NeuN-) nuclei from HIP and mPFC at 10 and 30 minutes after CFC using fluorescence-activated cell sorting (FACS) (S2A Fig). Using an intronic primer, we confirmed higher transcriptional induction of the neuron-specific ERG Npas4 in FACS-isolated neuronal nuclear RNA compared to whole mPFC lysate, with minimal expression in the non-neuronal fraction, confirming effective cell type separation (S2B Fig). mRNA analysis of the canonical ERG Arc showed induction in both neuronal and non-neuronal nuclei after CFC (S2B Fig). Since ERG induction peaked at either 10 or 30 minutes post-CFC, we included both time points in our sequencing analyses (S2B Fig).
Nuclear RNA-seq of sorted neurons and non-neurons at 10 and 30 minutes post-CFC was then conducted. We validated successful neuronal nuclei isolation by examining the aggregate expression of known cell type-enriched genes. Pyramidal and interneuron-enriched genes were strongly correlated with NeuN+ RNA-Seq, while genes enriched in glia (astrocytes, microglia, oligodendrocytes) and other non-neuronal cells were highly enriched in NeuN- RNA-Seq (S3A Fig). We identified hundreds of upregulated genes, demonstrating that fear learning activates the transcriptomes of both neurons and non-neurons in these brain regions within minutes (S3B–S3E Fig; S3 Table). The mPFC exhibited the highest number of upregulated genes, suggesting a stronger transcriptional response to change in response learning in this area (S3F Fig). Consistent with our γH2AX ChIP-seq data, there was significant overlap between HIP and mPFC upregulated genes in neurons (202 at 10 minutes, 448 at 30 minutes) (S3F Fig). Non-neuronal nuclei also showed substantial transcriptional changes in response to CFC, with comparable numbers of upregulated genes across brain regions and a large overlap at 30 minutes (34 at 10 minutes, 242 at 30 minutes) (S3G Fig). Furthermore, biological processes related to synaptic structure and function were among the most enriched GO categories in upregulated neuronal genes, mirroring our γH2AX ChIP-Seq findings (Fig 1C). In contrast, downregulated genes in neurons showed minimal biological process enrichment (only “cell-cell adhesion via plasma-membrane adhesion molecules”).
To assess the link between activity-induced DSBs and gene expression in the brain during change in response learning, we compared ChIP-seq and RNA-seq data. Examining the ERG Arc, we observed increased γH2AX signal alongside upregulation in both neurons and non-neurons (Fig 1D). Globally, we identified four categories of γH2AX-associated genes with altered expression after CFC: upregulated exclusively in neurons (56 HIP, 114 mPFC), upregulated in both neurons and non-neurons (12 HIP, 28 mPFC), upregulated specifically in non-neurons (19 HIP, 12 mPFC), and a small group of downregulated genes (16 HIP, 15 mPFC) (Fig 1E and 1F). Overall, transcriptional changes were more strongly linked to γH2AX in the brain than previously expected. While we previously identified only twenty gene-associated γH2AX loci after stimulating cultured neurons, we now see over 100-150 gene-associated γH2AX loci in HIP and mPFC that are transcriptionally induced during change in response learning (Fig 1E and 1F).
Activity-Dependent Genes Are a Source of DNA Breaks in the Brain
Next, we investigated the overlap between CFC-upregulated genes and γH2AX peaks to understand the mechanisms behind DSB formation in the context of change in response learning. We found that γH2AX peaks were significantly overrepresented in genes upregulated by fear learning, especially in the mPFC, which showed higher gene expression induction (Fig 2A and S4A Fig). However, it’s known that baseline transcription level can correlate with DSBs in both human and mouse cells. Examining all expressed genes in mPFC at 30 minutes post-CFC, binned by expression level, we observed that genes with higher RNA expression also had higher γH2AX levels in their gene bodies (S4B Fig). This could explain why approximately 55% of γH2AX-associated genes in mPFC and 80% in HIP were not responsive to CFC (Fig 2A).
To determine if upregulated genes had more γH2AX than expected based solely on their transcription levels, we used permutation testing. We binned neuronal upregulated genes by RNA expression level and then randomly sampled from all expressed genes within these bins. We found that upregulated genes indeed had higher γH2AX intensity than predicted by their transcriptional level alone (Fig 2B and 2C and S4C Fig). Furthermore, the faster the induction (CFC 10 minutes), the greater the difference between observed and expected γH2AX levels (Fig 2B and 2C). Thus, while some DSB sites may reflect high baseline expression (e.g., housekeeping genes like histones or neuronal lineage genes), gene induction during change in response learning also appears to contribute to increased γH2AX.
To identify pathways mediating rapid gene induction post-CFC in neurons, we searched for overrepresented transcription factor motifs in the promoters of differentially expressed genes, using the Molecular Signatures Database (MSigDB). Motifs for CREB/ATF family members, EGR family members, and SRF were all enriched (Fig 2D and S5A–S5C Fig). These transcription factors are known downstream effectors of cellular activation and calcium influx, including via MAPK signaling. Examining all upregulated genes associated with CREB/ATF, EGR, and SRF motifs for γH2AX enrichment yielded 48 genes in mPFC and 20 in HIP (Fig 2F and S5D Fig). Importantly, many of these activity-regulated genes, including Npas4, Fos, Nr4a1, Actb, Ntrk2, and Egr1, are known targets of these transcription factors and are crucial for efficient memory formation after CFC, highlighting their role in change in response learning. Other genes in this category, like Arc and 1700016P03Rik (mir212/mir132), were excluded due to regulatory motifs located further from the transcription start site (TSS).
Having established a link between rapid gene induction and γH2AX foci in the brain during change in response learning, we investigated whether any DSBs corresponded to late response genes, the second wave of genes induced after stimulation. Comparing our mPFC findings to a published single-cell RNA-Seq dataset that measured cell-type-specific induction of early and late response genes after visual cortex light stimulation, we found that rapidly induced early response genes were enriched among our mPFC DSB-labeled genes (S6 Fig). Tuba1a was the only γH2AX site exclusively upregulated at the late response time point (S6 Fig). This suggests we are not missing DSBs at late response genes and supports our nuclear RNA-Seq findings. Collectively, these results indicate that DSB formation is more widespread in the brain than previously reported and is associated with a significant subset of transcriptionally upregulated genes following CFC, contributing to the molecular mechanisms of change in response learning.
Fear Learning Induces a Proteostasis Response in Neurons and Non-Neurons
We observed several γH2AX-associated genes with altered expression after CFC in both neurons and non-neurons (Fig 1E). These included early genes (Arc, Egr1) and chaperones (Hsp90ab1, Hspa8). We also found the heat shock transcription factor HSF1, which induces genes in response to protein folding stress, enriched in the promoters of neuronal upregulated genes in both HIP and mPFC (Fig 2D and S5A and S5C Fig). Motif analysis of promoters of genes upregulated in non-neuronal nuclei 30 minutes post-CFC also revealed HSF1 and the activity-regulated transcription factor CREB, similar to neurons (Fig 3A). Many CFC-induced genes in non-neuronal nuclei appear to be activity-regulated (S7A Fig). Given the known importance of astrocyte activation during learning for memory formation, these rapid transcriptional responses mediated by activity-regulated transcription factors may reflect a crucial role for glia in change in response learning. GO term clustering of these non-neuronal genes revealed biological processes related to protein folding, hormone response, metabolism, and signaling (Fig 3B and S7B and S7C Fig). This indicates that CFC elicits a protein folding and cellular activity-regulated response shared across cell types. Signal tracks for the HSP70 family member Hspa8 illustrated this relationship, showing an inducible γH2AX peak (S1B Fig) and increased RNA expression after CFC in both neurons and non-neurons (Fig 3C). Confirming increased HSF1 activity post-CFC, we found increased nuclear HSF1 in neurons and non-neurons and enhanced HSF1 binding to the promoters of Hsp90ab1 and Hspa8 (S8A and S8B Fig).
In HIP and mPFC, we identified multiple genes with γH2AX peaks induced after CFC that are potential HSF1 targets based on promoter HSF1 binding following heat shock in mouse embryonic fibroblasts (HIP: Hspa8, Baiap2, Sh3gl1, Dnaja1, Hsp90ab1, Dynll1, Mbp, Ywhah, Dnajb5, Ddit4, Prkag2, Gse1, Ptk2b, Arpc2, Ywhag; mPFC: Tcf4, Hspa8, Baiap2, Hsp90ab1, Hnrnpa2b1, Gfod1, Lncpint, Ywhah, Dnajb5, Ddit4, Ywhag). We also identified ATF6, part of the unfolded protein response (UPR) and crucial for endoplasmic reticulum protein quality control, as a potential regulator of additional genes during change in response learning. Known ATF6 targets such as Hspa5 (Grp78), Calr, Xbp1, and others (Ywhaz, Atp2b1) were enriched with γH2AX peaks and upregulated in neurons, and to a lesser extent in non-neurons. These findings suggest that change in response learning via CFC triggers a rapid proteostasis response in both neurons and non-neurons, with induced genes becoming sites of DNA breaks, highlighting a potential link between stress response and genomic dynamics during learning.
Glucocorticoid-Regulated Genes Are Sites of DNA Double-Strand Breaks
‘Response to hormone’ emerged as a top enriched biological process among CFC-induced genes in non-neuronal nuclei (Fig 3B) and γH2AX peaks (Fig 1B). Further examination revealed genes like Sgk1 and Ddit4, known to be regulated by the glucocorticoid receptor (GR), which, while not upregulated in neuronal nuclei, were upregulated at the mRNA level in whole HIP and mPFC lysates (Fig 4A and S9A Fig). Unlike immediate neuronal activity responses, hormonal responses to stress are delayed, involving the hypothalamic-pituitary-adrenal axis and subsequent glucocorticoid release. Glucocorticoids increase in blood within 30 minutes of stress exposure, correlating with increased intrahippocampal corticosterone and GR nuclear localization in the brain. We observed that non-neuronal CFC-upregulated genes were most likely associated with γH2AX peaks at the 30-minute time point (Fig 4A). Moreover, mPFC and HIP have some of the highest GR expression levels in the brain, suggesting they are key targets of the stress response during change in response learning. To identify potential GR-regulated genes, we used two rat cortex GR binding ChIP-Seq datasets to map GR binding sites containing the glucocorticoid-responsive element (GRE) in the mouse genome and linked them to the nearest gene. Intriguingly, many γH2AX-containing genes responsive to CFC only in non-neuronal nuclei coincided with genes annotated to a GR-binding site (Fig 4A).
We tested whether a subset of these genes could be induced by the GR-specific agonist dexamethasone in cultured primary glia. Dexamethasone induced expression of Ddit4, Sgk1, and Glul, genes specifically upregulated in non-neuronal nuclei during CFC and annotated to GR-binding sites, while Actb, a non-GR target, was not induced (Fig 4B). This implicated GR in mediating gene induction in glia post-fear learning, a key component of change in response learning. To assess if GR activity could increase DSBs at these genes, we treated cultured primary glia with dexamethasone and measured γH2AX enrichment by ChIP-qPCR. Ddit4, Glul, and Sgk1, along with the canonical GR-inducible gene Mt1, showed significant increases in γH2AX enrichment (Fig 4C). Arc, with high γH2AX after CFC, and the housekeeping gene B2m, showed no γH2AX enrichment in response to dexamethasone (S9B Fig).
Our nuclear RNA-seq data showed Ddit4 upregulation only in non-neuronal nuclei after CFC, similar to other putative GR-regulated genes (Fig 4D). To investigate whether non-neurons had more active GR-bound enhancers, we used H3K27Ac ChIP-Seq data, a marker of enhancer/promoter activity, from purified neuronal and non-neuronal nuclei. Glia showed higher H3K27Ac signal at GR-bound enhancers around Ddit4, indicating greater GR-regulated enhancer activity in non-neuronal nuclei (Fig 4D). Genome-wide analysis of aggregate H3K27Ac signal at all GR-binding sites revealed higher baseline acetylation around GR peaks in non-neurons versus neurons in both anterior cingulate cortex (ACC) and hippocampal area CA1 (Fig 4E and S9C Fig). Examining H3K27Ac signal in CA1 under context and shock conditions, neuronal H3K27Ac signal at GR peaks increased similarly after context or shock exposure, suggesting generalized enhancer activation during exploratory behavior, possibly stress-independent. Conversely, non-neuronal H3K27Ac increased after shock, indicating enhancer responsiveness to stress in non-neurons but not neurons (Fig 4E; S9D Fig, intergenic peaks).
These findings identified a group of CFC-responsive non-neuronal genes likely regulated by GR signaling (Fig 4A–4E). GR gene expression (Nr3c1) was about half as high in neurons as non-neurons (S9E Fig). This difference in GR expression could contribute to the lack of induction or enhancer activity in neurons. We verified GR nuclear translocation in response to receptor agonism in both cell types. Corticosterone treatment increased nuclear GR in both neurons and non-neurons (non-significant trend in NeuN- fraction) (S9F Fig). The absence of neuronal stress-mediated enhancer activity change is likely due to decreased chromatin accessibility at the enhancer level, suggesting glia play a significant role in the homeostatic stress response during change in response learning. However, it remains unclear if neurons can mount a transcriptional stress hormone response and if hormone-responsive gene induction in neurons is accompanied by DSBs.
Glia but Not Neurons Have a Robust Transcriptional Response to Corticosterone
To test if an endogenous GR agonist could upregulate glial genes showing elevated γH2AX and transcription after CFC, we injected mice with corticosterone at a stress-mimicking dose and collected hippocampi after 30 minutes. We FACS-sorted nuclei into neuronal (NeuN+), astrocytic (GFAP+), microglial (PU.1+), and oligodendrocyte-enriched (NeuN-, GFAP-, PU.1-; 3X-) populations and performed RNA extraction (S10A Fig). RT-qPCR confirmed cell type marker enrichment, validating successful isolation (S10B Fig). We assessed gene expression changes in Sgk1 and Glul, which have CFC-inducible γH2AX peaks, and found that all three glial subtypes, but not neurons, responded to the endogenous GR agonist (S10C Fig). While GR agonists induce putative glucocorticoid-regulated genes seen after CFC both in vitro (dexamethasone) and in vivo (corticosterone), we aimed to determine if these genes depend on GR for CFC-induced expression changes, linking GR signaling to change in response learning. Pretreatment with the GR antagonist RU-486 (mifepristone) blocked CFC-induced transcription of Sgk1, Ddit4, and Glul in whole hippocampal lysates, but not Gapdh or ERG Arc (Fig 5A).
We then performed RNA-Seq from hippocampal cell types after corticosterone treatment to better understand how transcriptomes of major brain cell types respond to GR-mediated transcriptional regulation during stress, a factor that can significantly impact change in response learning. Brain cell type isolation was validated by examining aggregate expression of known cell type-enriched genes (S11A Fig). Neurons showed a modest transcriptional response to corticosterone (112 genes; Fig 5B and S11B Fig; S3 Table). In contrast, astrocytes, oligodendrocyte-enriched cells, and microglia had hundreds of upregulated genes (276, 453, and 551 respectively; Fig 5B and S11C–S11E Fig; S3 Table). These results align with in vitro findings reporting extensive dexamethasone response in astrocytes but minimal response in neurons. Glia’s robust transcriptional response to glucocorticoids suggests they play a larger role in stress response and its impact on the brain during change in response learning than previously recognized.
GO term clustering of genes upregulated post-corticosterone revealed major biological process categories: proliferation, cell death, cellular motility, homeostasis, signaling, inflammation, cellular functions, and glucocorticoid response (Fig 5C; S12A–S12C Fig). No enriched terms were found in neuronal upregulated genes. Downregulated genes were enriched for cell motility, inflammation, differentiation, and proliferation (S13A–S13D Fig). Glial function is known to be affected by cellular activity and motility, with morphological changes reflecting functional changes. These significant transcriptome changes in glial cell types likely impact their functions and could affect memory formation and change in response learning.
We then investigated how GR-mediated gene induction explains glia-specific DSBs in vivo and whether GR-regulated genes in neurons incur DSBs. Examining all γH2AX-containing genes upregulated in corticosterone-treated cell types, we found that most (32/43; 74%) were regulated only in glia (Fig 5D). This identifies a glia-enriched pathway possibly causing DSBs during CFC. Collectively, these results show that stress hormone-responsive genes are predominantly glial, some with high γH2AX levels, likely modulating important glial functions and influencing the brain’s capacity for change in response learning under stress.
Discussion
The association between neuronal activity and DSB generation is increasingly evident, but their in vivo location and relevance to brain function, particularly change in response learning, were unclear. Using γH2AX as a DSB proxy, we identified hundreds of gene-associated DSBs in the medial prefrontal cortex and hippocampus, regions critical for learning and memory. The unexpectedly high number of genes with DSBs significantly expands upon previous observations in cultured neurons after NMDA stimulation, providing a broader understanding of genomic dynamics during change in response learning.
We observed that gene induction exhibits higher γH2AX levels than expected based on expression level alone. Diverse γH2AX peak classes, including lincRNAs, housekeeping genes, and lineage-specific genes (especially neuronal function-related genes), are regulated by CFC, highlighting a complex interplay between gene expression and DNA integrity during change in response learning. Although there is a clear link between γH2AX peaks and CFC-induced genes, γH2AX enrichment changes at many genes are not highly significant, with most peaks present in naive conditions. Our prior study showed significant γH2AX peaks only after inducing neuronal activity in vitro, suggesting that DSBs in naive conditions at known activity-induced genes may reflect basal neuronal activation in the brain.
γH2AX is a sensitive DSB marker, successfully used to map genome-wide DSB sites via ChIP-Seq, including at known cleavage sites after restriction enzyme induction. However, γH2AX may not always reflect underlying DSBs, as seen in neurons with pan-nuclear γH2AX staining without DSB evidence during non-physiological stimulation. Closely spaced single-strand DNA breaks (SSBs) could also be recognized as DSBs by cells. γH2AX increases after SSB induction in postmitotic neurons were transcription-dependent, suggesting SSBs may need conversion to DSBs to trigger a DNA damage response (DDR) and γH2AX formation. While γH2AX ChIP-Seq measures DDR and DSBs recognized by cells as requiring repair, methods directly measuring DNA breaks are needed to confirm and extend our findings with cell-type-specific analysis, further refining our understanding of change in response learning and genomic stability.
Postmitotic brain cells rely on non-homologous end joining (NHEJ) for DSB repair. While NHEJ can be error-free, blocked DNA ends can promote end resection, leading to sequence loss, rearrangements, or translocations. Accumulated irreversible sequence damage over time can disrupt brain function in aging and disease. Efficient DNA repair pathways are crucial for preventing functional decline during brain aging and neurodegeneration. ERGs and heat shock genes, DSB hotspots induced by CFC in neurons and non-neurons, were found to be sites of transcriptional noise and somatic mutations in the aged pancreas. It’s intriguing to consider whether a similar process occurs in the aging brain and if it impairs the brain’s response to cellular stresses during aging or neurodegeneration, where protein folding factors are upregulated. Whether their overexpression contributes to DNA break accumulation in neurodegenerative diseases is unclear. Overall, we identified DSB sites at genes important for neuronal and glial functions, suggesting that impaired repair of recurrent DNA breaks generated during brain activity could lead to genomic instability, contributing to brain aging and disease and impacting change in response learning capacity over time.
Convergent transcription, causing polymerase collision, is known to generate DSBs. We observed a few instances of small γH2AX peaks near antisense transcription sites. For example, a small γH2AX peak in Polr3e intron 1 overlaps with transcriptional interference between RNA polymerase II and antisense transcription by RNA polymerase III (S14 Fig). Other examples include a small peak at 29000060B14Rik antisense to Clasp1, the Pcif1 promoter, or peaks overlapping 3’ UTRs of closely spaced genes.
We found that glia likely play an underappreciated role in nervous system stress response, correlating with DSBs, a link also seen in mouse fibroblasts. Our results resemble observations in the nucleus accumbens after morphine treatment, where oligodendrocytes particularly induced GR-targeted genes. Why neurons show limited corticosterone responses is uncertain. However, given more active GR-bound enhancers in glia and increased neuronal GR nuclear intensity post-corticosterone, chromatin accessibility likely plays a key role in determining GR response, as previously reported. This suggests a predominantly epigenetic mechanism underlying neurons’ modest corticosterone transcriptional response.
Our findings indicate stress significantly impacts glial physiology via transcriptome modulation, affecting numerous cellular processes. These changes may explain stress-induced glial morphology and function changes, including after CFC. Glucocorticoids’ role in brain stress response may be partly divided into a predominantly non-transcriptional role in neurons, where GR has a transcription-independent synaptic function aiding memory formation, and a homeostatic stress response primarily mediated by glia, consistent with their general role in brain homeostasis. Beyond homeostasis, astrocytic GR expression is needed for CFC-induced memory formation, and future research is needed to understand how glia facilitate or hinder learning through their GR response, and how these glial responses shape change in response learning.
Our observations suggest a stronger glial contribution to stress hormone deleterious effects, potentially including steroid dementia, anxiety, and depression. Interestingly, the microglial gene expression signature after corticosterone was enriched for disease associations like inflammation and depression (S15 Fig). This aligns with stress potentiating microglial inflammatory responses and their role in depression etiology. Because glial GR-bound enhancers are more stress-responsive than neuronal enhancers, stress susceptibility may include an underappreciated genetic component involving glia-specific variants. This also implicates glia, particularly microglia, in the genetics of psychiatric and neurodegenerative disorders where stress is a risk factor, including Alzheimer’s disease and schizophrenia, highlighting the importance of glia in understanding change in response learning and its dysregulation in disease.
Materials and methods
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Supporting information
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Acknowledgments
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References
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