Decoding Shared Versus Divergent Transcriptomic Signatures Across Cortico-Amygdala Circuitry in PTSD and Depressive Disorders

Posttraumatic stress disorder (PTSD) is a debilitating disorder that develops in a subset of individuals following trauma exposure. PTSD is highly comorbid with other mental health disorders (1–3); for example >50% of individuals with PTSD also have a diagnosis of major depressive disorder (MDD) (4–6), and the prevalence of PTSD among individuals with a bipolar disorder diagnosis is two to three times that of the general population (7, 8). PTSD is characterized by a unique set of clinical phenotypes but shares some diagnostic symptoms with depressive disorders. Comorbidity may arise from shared mechanistic underpinnings, including overlapping genetic heritability and common environmental risk factors, such as chronic stress and trauma exposure (4). However, the cellular and molecular mechanisms unique to PTSD versus those shared with MDD are not well understood. Here, we examined transcriptional patterns within and across PTSD and MDD in the human cortex and amygdala.

Aberrant activity in neural circuits that link amygdala and prefrontal cortical regions has been identified in individuals with PTSD as well as in animal models relevant for PTSD (9–11). The amygdala and prefrontal cortex are critical for emotional regulation, including the expression and extinction of fear, behavioral functions that are dysregulated in PTSD. Coordinated patterns of neural activity in cortico-amygdala circuits underlie functional connectivity between these regions, which controls fear and anxiety (12–15). In accordance with human neuroimaging studies showing aberrant cortico-amygdala activity in PTSD (12, 15–17), animal studies demonstrate that function in these circuits is strongly impacted by exposure to trauma, and experimentally manipulating neuronal activity or key cell signaling pathways in cortico-amygdala circuits impacts fear processing and anxiety (18–21).

At the cellular level, deficits in inhibitory neurotransmission in cortico-amygdala circuits are associated with PTSD and depressive disorders (22, 23). Chronic stress and trauma exposure are hypothesized to impair inhibitory neuron function, impacting excitation-inhibition balance in cortico-amygdala circuits (24, 25). This is important because GABAergic inhibitory neurons in these circuits control neural activity and synaptic plasticity to regulate fear-related behaviors in animal models (26). However, how the molecular sequelae following chronic stress and trauma exposure impact inhibitory neuron function is not well understood. In addition to GABAergic inhibition, inflammation and immune signaling have emerged as potential contributors to PTSD and depressive disorders (27–30). While inflammatory markers and genes related to immune signaling are altered in PTSD (31–33), whether observed changes result from central versus peripheral immune signaling pathways, and whether they reflect increased risk or epiphenomena related to the pathophysiological sequelae of PTSD, is not clear.

Conducting the largest RNA sequencing study of PTSD in the human brain to date, we identified downregulation of microglia-related transcripts and immune-related coexpression modules in both the cortex and amygdala. We identified notable reductions in specific transcripts encoding neuromodulators that are associated with GABAergic neuron function, but there was also evidence for increased expression of transcripts associated with both excitatory and inhibitory neurons. Collectively, the findings contribute evidence supporting the involvement of immune signaling, neuroinflammation, and inhibitory neuron function in MDD and PTSD.

Methods

Detailed methods are available in the online supplement.

Postmortem human brains were donated through U.S. medical examiners’ offices at the time of autopsy, and a retrospective clinical diagnostic review was conducted on every brain to diagnose each donor into one of the three diagnosis groups (control, PTSD, MDD). Tissue was dissected from two subregions of the frontal cortex (the dorsolateral prefrontal cortex and the dorsal anterior cingulate cortex), and two subregions of the amygdala (the basolateral amygdala and the medial amygdala) under visual guidance. RNA was extracted and sequenced using Ribo-Zero Gold ribosomal RNA depletion on an Illumina HiSeq 3000. Raw sequencing reads were processed as previously described (34) to obtain gene counts relative to GENCODE release 25 (GRCh38.p7). Quality control, including sequencing quality and sample identity checks, resulted in 1,285 samples across 325 unique donors and four brain regions. We performed differential expression analyses within and across brain subregions using the voom function in the limma package for R (35), adjusting for clinical and technical covariates, as well as quality surrogate variables (qSVs) (36). These models account for donors from all three diagnosis groups to jointly estimate the effects of PTSD versus control, MDD versus control, and PTSD versus MDD. We performed RNAscope to validate cell type specificity of candidate DEGs. We defined sets of marginally significant (at p<0.005) genes, with and without enforcing directionality of effects (i.e., higher vs. lower expression in PTSD vs. control), and performed gene set and cell type enrichment analyses using the hypergeometric test. Lastly, we performed weighted gene coexpression network analysis (37) to assign genes to modules and assess the role of diagnosis on coexpressed gene sets.

Results

We generated deep bulk/homogenate RNA sequencing (RNA-seq) data from postmortem human tissue in two subregions of the frontal cortex (dorsolateral prefrontal cortex [dlPFC] and dorsal anterior cingulate cortex [dACC]) and two subregions of the amygdala (basolateral amygdala [BLA] and medial amygdala [MeA]) (see Tables S1–S3 in the online supplement) from neurotypical donors as well as donors with a singular diagnosis of PTSD or MDD, or PTSD comorbid with MDD or bipolar disorder (see section 1 in Supplementary Results and Table S2 in the online supplement). After extensive and rigorous quality control of RNA-seq data (see Supplementary Methods, Figure S1, and Table S4 in the online supplement), we performed differential expression and network analyses using 1,285 samples from 325 unique donors (Table 1) and across 26,020 jointly expressed genes (see section 2 in Supplementary Results in the online supplement).

Expression Differences Related to PTSD Diagnosis

We first explored the gene expression effects of PTSD diagnosis versus neurotypical control donors. We identified 41 PTSD differentially expressed genes (DEGs) in cortex (Figure 1A) and one PTSD DEG in amygdala (Figure 1B) at genome-wide significance (FDR<0.05), while a more liberal threshold of FDR<0.1 identified an additional 78 genes in cortex (and no additional genes in amygdala). We highlight several representative DEGs in PTSD versus neurotypical control donors in cortex, including decreased expression of CORT, which is expressed in a subpopulation of GABAergic inhibitory neurons (38) (Figure 1C); increased expression of the histone deacetylase HDAC4 (Figure 1D); and increased expression of SPRED1, which encodes a protein involved in the Ras/MAPK signaling pathway (Figure 1E). In amygdala, a single gene was consistently downregulated in PTSD versus neurotypical control donors across both subregions—CRHBP (Figure 1F), the gene encoding corticotropin-releasing hormone binding protein, which is an antagonist of the stress hormone corticotropin-releasing hormone (39). Overall, cortical regions showed more association with PTSD than amygdala subregions, and the observed expression differences were largely consistent across subregions of the cortex, with only five genes showing marginal interaction (at p<0.01) between PTSD diagnosis and cortical subregion (NRSN1, PHF20L1, RP11-505E24.2, OXLD1, and CARD8-AS1), and CRHBP only showing modest interaction between PTSD and amygdala subregions (p=0.037).

We next performed secondary analyses within each of the four subregions (dlPFC, dACC, BLA, and MeA) to identify additional DEGs associated with PTSD diagnosis. Differential expression statistics were highly correlated with the combined subregion analyses, with the cortical associations driven predominantly by dACC, and the amygdala associations driven primarily by BLA (see Figure S2 in the online supplement). The cortical subregions again showed more PTSD DEGs, with 16 genes in the dACC (see Figure S3A in the online supplement) and one gene in the dlPFC (see Figure S3B in the online supplement) (and no genes in amygdala subregions) at genome-wide significance (FDR<0.05). Using a more liberal cutoff of FDR<0.1, we identified 74 unique genes across the cortical subregions (72 genes in dACC and three genes in dlPFC; one gene was shared: AC124804.1, a novel transcript, antisense to SDK2) and 18 unique genes across amygdala subregions (three genes in BLA and 16 in MeA; one gene was shared: CORT). Joint analysis of all data identified 117 genes with consistent PTSD versus control effects across all four subregions at FDR<0.05 (and with 276 genes at FDR<0.1) (see Figure S4 in the online supplement), further highlighting the similar effects of PTSD across multiple brain regions. Interestingly, these cross-region results were best represented by the amygdala (predominantly BLA), and not the cortex, even though the cortex had more DEGs when considered alone (see Figure S2 in the online supplement). A comprehensive list of all differential expression statistics for all expressed genes and all statistical models is presented in Data S1 in the online supplement.

We next used a series of sensitivity analyses to determine the robustness of our differential expression model by specifically interrogating the role of potential confounders and risk factors. Specifically, we tested a series of additional potential variables (including antidepressant treatment and presence of opioids via toxicology) for attenuating the DEGs identified above in each brain region. Overall, subsequently adjusting our models for these variables had minimal effects on differential expression signals across all expressed genes, including those identified as DEGs (see Figure S5 in the online supplement). We further examined the role of sex on our identified DEGs using sex-specific analyses and found that subsets of DEGs were more strongly explained by effects within a single sex (see Figures S6 and S7 and Table S5 in the online supplement). The identified DEGs mostly confirmed results from a recent study that used a cohort of partially overlapping subjects, with upwards of 80% of expressed genes being directionally consistent (see section 2 of Supplemental Results and Figure S7A in the online supplement) (40). Lastly, we assessed the effects of combat, comparing the 25 combat-exposed donors with PTSD to the 82 PTSD donors without combat exposure within each brain region, and found DEGs exclusively in the MeA (three genes at FDR<0.05, 29 at FDR<0.1, and 116 at FDR<0.2) (see Table S6 in the online supplement).

Taken together, these analyses identified robust sets of differentially expressed genes associated with PTSD that are not a result of association with substance abuse or mood disorder diagnoses.

Gene Sets and Cell Types Associated With PTSD

We next performed gene set and pathway enrichment analyses to identify biological and molecular functions associated with PTSD within and across brain regions. To facilitate these analyses, we used more liberal significance thresholds to define PTSD DEGs (marginal p<0.005 rather than FDR control) and directionality, and tested for enrichment among DEGs more highly and more lowly expressed in PTSD subjects compared with neurotypical control subjects. Overall, genes associated with PTSD showed the strongest enrichment for immune-related gene sets and pathways in both the cortex and the amygdala (Figure 2A; see also Table S7 in the online supplement), largely driven by decreased expression of genes in donors with PTSD compared with control subjects. Interrogating PTSD differences within subregions further identified unique molecular associations. For example, the MeA and dlPFC each showed decreased expression of genes associated with receptor ligand activity (that were further marginally significant in other regions). Interestingly, dlPFC associations were driven by eight genes (CORT, CSF1, SST, OSTN, CXCL10, CXCL11, GDF9, and CCL3) and MeA associations by 10 genes (CORT, TNFSF10, CXCL11, SFRP2, OSGIN2, OGN, IGF2, CTF1, CCL5, and TTR), with only two genes in common (CORT and CXCL11), highlighting the convergence of molecular functions across brain regions.

We next used cell type-specific enrichment analysis (CSEA) (41) to identify cell types that preferentially express these sets of differentially expressed genes. We found consistent enrichment of cortistatin-expressing GABAergic inhibitory neurons (“Ctx.cort”) and immune cells (“Ctx.etv1_ts88”) among genes where expression was decreased in donors with PTSD compared with neurotypical control subjects. Stronger enrichments were observed in the amygdala, particularly the BLA, compared with the cortex (Figure 2B; see also Table S8 in the online supplement). For example, using a specificity threshold of 0.01 and the BLA, immune cell enrichments were driven by decreased expression of FERMT3, CRHBP, FOLR2, PTGS1, SLCO1C1, P2RY13, and GLT8D2 (odds ratio=8.9, p=2.94e−5), and cortistatin-positive inhibitory neuron enrichments were driven by decreased expression of NPY, CORT, CRHBP, DLL3, NXPH2, and SST (odds ratio=23.7, p=5.9e−7).

We further confirmed enrichment of PTSD DEGs related to immune signaling and inhibitory neurons using snRNA-seq data generated in the human brain from amygdala and dlPFC (42) (see Table S9 in the online supplement). For amygdala DEGs where expression was lower in individuals with PTSD compared with neurotypical control subjects, we found strong enrichment within microglial populations identified in human amygdala (42). These enrichments for DEGs with lower expression in PTSD were strongest in a combined subregion analysis (odds ratio=7.1, p=1.8e−23), but results were driven by the BLA (odds ratio=3.0, p=9.7e−7), with no significant enrichment in the MeA (p=0.17). These more lowly expressed DEGs were also enriched in T-cells at the subregion level (p=2.7e−5), with these results driven by the MeA (p=8.6e−3). Unlike with CSEA, we found evidence for inhibitory neuron enrichments among DEGs with higher expression in PTSD, particularly in the MeA (Inhib_C: odds ratio=2.6, p=2e−5; Inhib_F: odds ratio=3.6, p=1.8e−9). However, this discrepancy could arise as a result of low expression levels in the snRNA-seq data of some genes that drove enrichments in the CSEA analysis. Analogous enrichment analyses using snRNA-seq data on cortical cell types in human brain similarly showed strong enrichment with PTSD DEGs. Using our snRNA-seq data from the dlPFC (42), we found similar strong microglial cell enrichments among DEGs with decreased expression in PTSD (microglia: p=7.7e−22; macrophage: p=1.8e−15), whereas DEGs with increased expression in PTSD were enriched in neuronal populations (Excit_A: p=7.1e−6; Excit_E: p=3.1e−7; Inhib_B: p=1.1e−6; Inhib_D: p=1.1e−6) (42). Using snRNA-seq data from a second study of human prefrontal and cingulate cortices (43), we found that DEGs with increased expression in PTSD were most enriched in a somatostatin (SST)–expressing inhibitory neuron population (IN-SST: p=5.3e−5), whereas DEGs decreased in PTSD were enriched for microglial (p=4.5e−24) and endothelial (p=7.1e−6) populations. Finally, other snRNA-seq data from human prefrontal cortex (44) showed that DEGs with increased expression in PTSD were associated with Ast0, Ex12, In0, In1, In6, In7, and In9 populations, while DEGs with decreased expression in PTSD were associated with Ast2, End1, Mic0, Mic1, Mic2, and Mic3 populations (see Table S9 in the online supplement). Given the limitations of snRNA-seq for detecting relatively rare cell populations, we used an RNAscope single-molecule fluorescence in situ hybridization approach (see Supplementary Methods in the online supplement) in BLA and dlPFC tissue derived from independent neurotypical donors to better understand coexpression of PTSD DEGs associated with inhibitory neurons. For RNAscope analysis, we targeted expression of PTSD DEGs: CORT, SST, and CRHBP (see Data S1 in the online supplement), as well as GAD2 as a cell marker of inhibitory GABAergic cells (Figure 3B). We compared expression levels of these genes across nuclei and found high correlations (Figure 3B), with the highest between CORT and SST (ρ=0.72, p=8.7e−84) and the lowest between GAD2 and CRHBP (ρ=0.166, p=2.1e−13). Almost all SST-positive neurons coexpressed CORT, whereas less than half of CORT-positive neurons coexpressed SST. The top amygdala DEG—CRHBP—showed coexpression with both CORT and GAD2 across many regions of interest in both brain regions (45, 46).

Gene Expression Comparisons Between PTSD and MDD

We next incorporated existing bulk RNA-seq data from MDD donors to better understand the gene expression differences unique to PTSD. We first compared donors with MDD to neurotypical control subjects among the broader cortical and amygdala brain regions, and again identified a larger number of differentially expressed genes in the cortex (182 genes at FDR<0.05, 352 genes at FDR<0.1) (Table 2) compared with the amygdala (zero genes at FDR<0.05, one gene at FDR<0.1). These differences were driven by the dACC (249 genes at FDR<0.1) compared with the dlPFC (two genes at FDR<0.1), similar to PTSD effects. There were similarly increased MDD differences in the MeA (16 genes at FDR<0.05, 32 genes at FDR<0.1) and no differences in BLA when stratifying the amygdala into subregions. Genes with decreased expression in MDD donors compared with neurotypical control subjects showed analogous enrichment of immune-related processes in the cortex using both gene set enrichment analysis (see Table S10 in the online supplement) and CSEA (“Ctx.etv1_ts88” cell type; see Table S11 in the online supplement). CSEA results related to cortistatin-positive neurons were attenuated in MDD compared with PTSD, particularly in the amygdala (best p value, 0.01).

Globally, there was high concordance between PTSD and MDD effects on gene expression (Figure 4A; see also Figure S10 in the online supplement; ρ range, 0.647–0.695), with highly overlapping DEGs at marginal significance in each brain region or subregion (all Fisher p values <1.72e−46). While global effects were correlated and significant genes were overlapping, there was nevertheless variation among significantly differentially expressed genes across the two disorders. For example, among the genes marginally associated with MDD in each subregion, only one-quarter were significantly differentially expressed when comparing PTSD to control subjects (each at p<0.005), and among the genes that were marginally associated with PTSD, only one-third of genes in cortical regions and one-quarter of genes in amygdala regions showed similar marginal association in MDD.

We therefore directly compared expression between PTSD and MDD donors to better partition these differences across diagnoses (see Supplementary Methods in the online supplement), and identified only a limited number of differentially expressed genes (at FDR<0.1) (Figure 4B). Specifically, we saw increased expression of KCNC1, FAM234B, and RASD2 and decreased expression of CH507-513H4.4 in PTSD versus MDD in the cortex, decreased expression of LMCD1 in PTSD in the MeA, and decreased expression of DNAH11 in PTSD in the dlPFC. In the cortex, marginally significant genes that were more highly expressed in PTSD compared with MDD (at p<0.005) were associated with neuronal processes and synapses (both inhibitory and excitatory), whereas marginally significant genes with decreased expression in PTSD compared with MDD in the amygdala were associated with neuronal migration and PI3K signaling (Figure 4C; see also Table S12 in the online supplement). There were no enrichments for the immune-related gene sets for these disorder-specific contrasts, suggesting that decreased expression of immune processes and/or microglia involvement were shared across both disorders relative to neurotypical individuals (see Table S13 in the online supplement). These results together suggest largely similar transcriptomic changes in PTSD and MDD compared with neurotypical donors.

We then used RNA deconvolution to better determine whether microglia or neurons were more or less prevalent in PTSD and MDD donors (see Supplementary Methods in the online supplement) (47). While the proportion of microglia and neuron RNAs differed by brain region (see Figures S11A,B in the online supplement), there were no differences between diagnoses for either cell type (microglia: PTSD, p=0.84; MDD, p=0.59; neurons: PTSD, p=0.632; MDD, p=0.649). The RNA fractions across all evaluated cell types also were strongly associated with the qSVs used to control for latent heterogeneity—in line with our previous work (34)—suggesting that our DEGs were not confounded by tissue composition (see Figure S11C in the online supplement).

Lastly, we performed weighted gene coexpression analyses (WGCNA) to better understand network-level gene expression differences between PTSD and MDD (see section 4 of Supplementary Results in the online supplement). This analysis identified a total of 156 modules across six WGCNA runs (regions: cortex, amygdala; subregions: dACC, dlPFC, MeA, BLA; see Table S14 in the online supplement), of which 35 were enriched for PTSD DEGs (N=22) or MDD DEGs (N=22; nine overlapping) (Table 3; see also Table S15 in the online supplement). In the cortex and its subregions, the strongest disorder-related module (Cortex.ME7) related to regulation of cell activation, a broad category encompassing many immune processes, associated with both PTSD (p=1.6e−25) and MDD (p=3.3e−126) DEGs, with its eigengene further associated with these diagnoses at the subject level (PTSD, p=2.9e−4; MDD, p=8.4e−6). The strongest disorder-related module in the amygdala (Amygdala.ME2) was specifically enriched with PTSD DEGs (p=2.97e−23), with its eigengene further associated with PTSD compared with control subjects (p=0.005). Sensitivity analyses for other potential confounders, including combat, childhood maltreatment, and toxicology-determined smoking, SSRI antidepressant use, and opioid use, showed minor effects on the WGCNA eigengene associations with PTSD or MDD diagnosis. These variables themselves had weak associations with only a few eigengenes (see section 4 of Supplementary Results in the online supplement). These analyses further highlight biological processes associated with PTSD and MDD using convergent approaches to traditional gene set enrichments of DEGs.

Discussion

The goal of this study was to identify shared versus divergent in transcriptional patterns within and across PTSD and MDD in the prefrontal cortex and amygdala. We identified a limited number of DEGs specific to PTSD, with nearly all mapping to cortex versus amygdala. PTSD-specific DEGs were enriched in gene sets associated with immune-related pathways and microglia and with subpopulations of GABAergic inhibitory neurons. While we identified a greater number of DEGs associated with MDD, most overlapped with PTSD, and only a few were MDD specific. These findings provide supporting evidence for involvement of immune signaling and neuroinflammation in MDD and PTSD pathophysiology and extend evidence that GABAergic neurons have functional significance in PTSD.

Decreased expression of genes included in immune-related Gene Ontology sets were associated with PTSD diagnosis in both cortical and amygdala brain regions (Figure 3A). CSEA using mouse cell-specific markers and snRNA-seq data from human brain demonstrated enrichment of these DEGs, with decreased expression in PTSD among microglia profiles (41, 42). Genes with decreased expression in MDD donors compared with neurotypical control subjects showed analogous enrichment of immune-related processes using both gene set enrichment analysis and CSEA, and there were no enrichments for the immune and microglia-related genes when contrasting PTSD and MDD, suggesting that decreased expression of immune processes and microglia involvement are not specific to PTSD. The downward direction of dysregulation was somewhat surprising, considering that higher pre-trauma levels of C-reactive protein (a marker of blood inflammation) have been reported to predict elevated PTSD symptoms after trauma (48). Furthermore, elevated levels of selected markers of low-grade blood inflammation have been reported in a meta-analysis of PTSD studies (49). However, over time, and with repeated exposure to chronic stress and trauma, immune function may become dysregulated in a myriad of ways, with neuronal, glial, and peripheral systems attempting to compensate for immune activation and increased inflammation (33, 50–52). While decreased expression of the microglial immune transcriptome and/or reductions in microglial cell ratios due to chronic immune dysregulation are possible explanations for the present data, we noted that many of the genes included in the associated immune Gene Ontology sets encode proteins with known immunosuppressive activity. This could also explain the somewhat paradoxical finding of decreased expression of immune-related genes. For example, in the immune-related regulation of cell activity category, we identified 13 member PTSD DEGs (IL1RL2, DPP4, IGFBP2, TGFBR2, TAC1, MDK, CD4, PTPN6, TESPA1, IGF1, ITGAM, TYROBP, and ITGB2), of which seven have potential immunosuppressive activity (DPP4, TGFBR2, CD4, IGF1, ITGAM, TYROBP, and ITGB2) (53). These observations do not support a high level of microglial immune activation in chronic PTSD or MDD in cortex or amygdala, but they do suggest dysregulation or possibly a compensatory response to stress.

We observed downregulation of CORT mRNA across all four subregions in individuals with PTSD. CORT encodes the secreted neuropeptide cortistatin, which is expressed in the cerebral cortex, hippocampus, and amygdala in a subset of GABAergic neurons (38, 54). Loss of cortistatin cells in mice causes spontaneous seizures, demonstrating that these cells provide strong inhibitory control (55, 56). In the rodent, cells expressing cortistatin constitute a subset of SST-expressing neurons (55), and in human brain we confirmed coexpression of CORT with GABAergic inhibitory neuron markers (GAD2, SST, and CRHBP). Decreased CORT and SST expression were previously reported in amygdala of female postmortem human brain donors with MDD (57), and our CSEA analyses showed enrichment of genes differentially expressed in PTSD in cortistatin-expressing cells. We also identified enrichment of PTSD DEGs with specific inhibitory neuron clusters from snRNA-seq data in human amygdala that have been associated with anxiety and HPA axis function (42, 58). WGCNA further implicated inhibitory neuron function, in line with the gene set enrichment results applied directly to PTSD DEGs. Decreased expression of CORT, SST, and CRHBP mRNA provides additional support for the hypothesis that GABAergic neuron dysfunction is mechanistically associated with PTSD (22). Strong evidence implicates GABAergic neurons in controlling fear-related behaviors in preclinical animal models relevant for PTSD and other trauma-related disorders by controlling neural activity and synaptic plasticity in cortico-amygdala circuits (23). For example, firing of excitatory cells that project from the BLA to the frontal cortex is under tight regulation by local GABAergic inhibitory neurons (25), which provides negative feedback regulation of the BLA to control both the expression and extinction of fear (26, 59). Strong evidence links somatostatin signaling and SST-positive cells in cortico-amygdala circuits with threat perception and fear memory processing (60–64). CRHBP, which we showed to be coexpressed with CORT and SST in GABAergic neurons in the human brain, was the only gene consistently downregulated in PTSD versus neurotypical control subjects across both subregions of the amygdala. CRHBP encodes corticotropin-releasing hormone binding protein, which sequesters and antagonizes CRH signaling (39). The robust DEG signal for CRHBP is interesting given many studies implicating the stress hormones corticotropin-releasing hormone (CRH) and cortisol in PTSD (65–68). CRH activates the release of adrenocorticotropic hormone (ACTH), which stimulates production of cortisol to control the body’s response to stress and trauma, which is important given that stress is a leading risk factor for the development of PTSD (69). Additional genetic support for a critical role of the stress axis in PTSD comes from recent genome-wide association study associations of CRHR1 (70), which encodes the primary CRH receptor—CRF₁—and CRH binds to CRF₁ to mediate the behavioral and endocrine responses to stress exposure.

We further ran a number of analyses to better identify gene expression differences that were selectively associated with PTSD but not MDD. In general, we identified more DEGs for MDD than PTSD, particularly in the cortex (which were primarily driven by the dACC). However, gene expression differences were highly concordant between the two diagnoses, with most highly significant DEGs showing the same directionality of effects (i.e., log₂ fold changes) in both diagnoses. Marginally selective between-diagnosis DEG gene sets included more highly expressed glutamatergic synapse-related DEGs in PTSD cortex (driven by the dACC) and more highly expressed neuronal activity-related DEGs in MDD amygdala (driven by the BLA). Differences between the two diagnoses were more prominent in WGCNA analyses, where seven potentially overlapping modules showed PTSD-specific enrichment (Cortex_ME31, dlPFC_ME20, dACC_ME9, Amygdala_ME2, MeA_ME3, BLA_ME2, BLA_ME13) and five modules showed MDD-specific enrichment (dlPFC_ME11, dACC_ME3, dACC_ME7, MeA_ME9, BLA_ME20).

DEGs from a recent RNA-seq study of human postmortem PTSD tissue by Girgenti et al. (40), which used a partially overlapping set of donors (see below), provide support for top DEGs identified here. For example, within our combined cortical PTSD analyses (see Figure 1), six of the seven most robustly affected transcripts comparing PTSD and control subjects (CORT, HDAC4, CRHBP, ADAMTS2, FBXO9, and APOC1) were directionally consistent and at least marginally significant in this previous data set. These genes further showed decreased expression in MDD versus control subjects in the present study, with at least marginal significance, suggesting that these particular findings may be related to shared pathophysiological changes accompanying PTSD and MDD. A key upregulated gene identified in the Girgenti et al. study, ELK1, was significantly upregulated in both cortical regions in the present study, and SST, identified as robustly downregulated in several regions of the cortex in the Girgenti et al. study, was in the top 10 of all downregulated transcripts in both dlPFC and dACC here. ADAMTS2, the second highest upregulated DEG in the combined cortical sample, was the top upregulated gene in the dACC and the third most upregulated in the dlPFC in the Girgenti et al. study. HDAC4, a top DEG, has been associated previously with both PTSD and rodent models of PTSD (71, 72). The Girgenti et al. study also identified enrichment of downregulated PTSD-associated DEGs using CSEA that were related to GABAergic neurons and their molecular functions.

In addition to these common elements, the present results extend previous findings from Girgenti et al. (40) in several key areas. First, this study extended the search for differential gene expression beyond the cortex and into the amygdala, a relatively understudied brain area in postmortem human brain research with high relevance to PTSD. Second, we provide compelling evidence implicating decreased expression of immune-related genes and associated processes in PTSD and MDD compared with neurotypical control subjects. This is an important observation because it runs counter to most expectations for immune response directionality. We further refined these cellular enrichments in GABAergic neurons more specifically to CORT-positive interneurons, which we subsequently validated with RNAscope. The cell type analyses in the present study provide direct evidence of these enrichments by interrogating DEGs directly against cell type-specific genes from both human and mouse studies, complementing the indirect strategy taken by Girgenti et al. of first identifying genes in discrete coexpressed modules and then associating those genes with both PTSD DEGs and cell type-specific genes separately (such that different genes captured the cell type vs. PTSD signal in the same module). Third, we believe our larger sample size (more than twice as large in all diagnostic groups), obtained from a single postmortem brain collection under identical sample ascertainment and inclusion criteria, refined several of the clinical associations identified by Girgenti et al. We identified more similarities than differences between PTSD and MDD and replicated this finding across both amygdala and cortical subregions, with far less sex-specific diagnosis-associated signal than discussed in the earlier study. Contributing to both the similarities and differences between the two studies was the fact that 77 donors were shared across the studies, although the two studies used different hemispheres, independent dissections and RNA extractions, and different data analysis pipelines. Over half (53.8%) of the donors in the Girgenti et al. study overlapped with one-quarter (23.7%) of the donors in the present study.

Therefore, it might seem counterintuitive that we identified many fewer DEGs in this much larger study, particularly with overlapping donors. We believe these differences can be accounted for by our more conservative statistical analyses, including the modeling of both diagnostic groups in a single statistical model against the neurotypical group, which further accounted for robust observed and latent confounders. The differential expression models in the Girgenti et al. study (40) only adjusted for age, RNA integrity number, postmortem interval, and race, and did not account for sequencing-derived RNA quality metrics and other latent confounders, which can greatly increase false positive rates in human postmortem brain gene expression studies (36). For example, this less comprehensive statistical model applied to our larger data set resulted in 1,243 DEGs in the dlPFC, 1,719 DEGs in the dACC, 1,813 DEGs in the BLA, and 10,283 DEGs in the MeA for PTSD at FDR<0.05, which is many more genes than obtained with our more conservative approach. There has been some debate regarding the optimal methods of latent variable correction in these types of postmortem studies, including the potential for “overcorrection” (73). A major analytic element of the present investigation was the use of quality surrogate variable (qSV) analysis to identify and correct for expressed sequences that are particularly prone to degradation in human postmortem brain (36). The qSVs utilized here were defined from the top 1,000 degradation-susceptible expressed regions generated from independent time course experiments. Dropping the qSVs from our main analyses resulted in 209 DEGs in the dlPFC, 43 DEGs in the dACC, 62 DEGs in the BLA, and 1,054 DEGs in the MeA (at FDR<0.05) for PTSD in the present data set. It is, however, possible that if sex or disease-associated interactions related to gene transcript degradation exist, use of qSVs may have limited the emergence of these genes as DEGs and contributed to the differences between the present findings and those discussed in the Girgenti et al. study (40). Similarly, in MDD, the most prominent previous report of differential gene expression (74) actually identified no DEGs when correcting for multiple testing via the FDR (from the supplementary tables included with that study), making it difficult to assess replication of our DEGs using previously published data sets. While these issues may seem rather nuanced, they nevertheless have important consequences for identifying DEGs in human postmortem RNA-seq data sets and require careful consideration in past and future work.

Limitations of this work include potential underrepresentation of female donors in the neurotypical control group (21.1% female) compared with the case group (∼50% female). While we adjusted for sex in differential expression analyses, secondary analyses did suggest some potential differences in diagnosis effects across sex. Our cohort included donors with only a PTSD diagnosis or only an MDD diagnosis, as well as PTSD donors with a comorbid MDD or bipolar disorder diagnosis. However, we did not have subjects with only a bipolar disorder diagnosis. Furthermore, as is common in most psychiatric postmortem human studies, psychotropic medications, substance use, smoking, and suicide were more common in the MDD and PTSD groups, and further work will be needed to investigate their potential influence on brain gene expression patterns.

In summary, these analyses of the largest postmortem brain cohort of patients with PTSD and MDD to date highlight microglia and other immune cell types as having potential functional significance in PTSD, and provide additional evidence for dysregulated neuroinflammation and neuroimmune signaling in MDD and PTSD pathophysiology.