Brain Region-Specific Gene Co-expression Networks Reveal Neuroinflammation and ER Stress Signatures in Major Depressive Disorder

Brain Region-Specific Gene Co-expression Networks Reveal Neuroinflammation and ER Stress Signatures in Major Depressive Disorder | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Brain Region-Specific Gene Co-expression Networks Reveal Neuroinflammation and ER Stress Signatures in Major Depressive Disorder Lejun Li , Meiqi Wang , Yanbing Jie doi: https://doi.org/10.1101/2025.06.18.660280 Lejun Li 1 Beijing ETown Academy Find this author on Google Scholar Find this author on PubMed Search for this author on this site Meiqi Wang 2 Beijing Mingcheng academy Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yanbing Jie 3 University of Toronto Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: yanbing.jie{at}utoronto.ca Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract This study aims to investigate gene expression changes in different brain regions of Major Depressive Disorder (MDD) patients using transcriptomics and bioinformatics approaches. Through differential expression gene (DEG) analysis, functional enrichment analysis, and Weighted Gene Co-expression Network Analysis (WGCNA), we aimed to identify key genes, pathways, and co-expression modules associated with MDD, with particular emphasis on the role of neuroinflammation and endoplasmic reticulum (ER) stress pathways in MDD pathology and their region-specific characteristics. The findings of this study will provide novel insights into the complex molecular mechanisms of MDD and lay the foundation for the future development of diagnostic biomarkers and therapeutic targets. Introduction Major Depressive Disorder (MDD) is a debilitating psychiatric illness characterized by severe mood disturbances, anhedonia, and cognitive dysfunction, affecting millions worldwide. Despite its significant public health burden, the precise molecular mechanisms underlying MDD remain largely elusive, hindering the development of effective and personalized treatments.[ 1 ] While traditional theories have focused on monoamine neurotransmitter imbalances, emerging evidence increasingly implicates a broader array of biological processes, including neuroinflammation and endoplasmic reticulum (ER) stress, in the pathophysiology of various central nervous system (CNS) disorders, including neuropsychiatric conditions. Neuroinflammation, characterized by the activation of glial cells (e.g., microglia and astrocytes) and the release of pro-inflammatory cytokines, can lead to neuronal damage, synaptic dysfunction, and altered neurocircuitry, all of which are pertinent to MDD. Simultaneously, the endoplasmic reticulum, a crucial organelle for protein folding, modification, and transport, can experience stress when its capacity is overwhelmed, triggering the Unfolded Protein Response (UPR). Chronic activation of ER stress and UPR has been consistently linked to neurodegenerative diseases and is gaining recognition as a contributor to psychiatric disorders. The complex interplay between neuroinflammation and ER stress, forming a vicious cycle of cellular dysfunction, is increasingly recognized as a critical factor in brain pathology. However, the specific gene expression signatures of these interconnected biological processes, particularly their regional specificity across different brain areas, have not been systematically investigated in MDD patients. To address these gaps, this study leveraged advanced transcriptomics and bioinformatics approaches on publicly available gene expression datasets (GSE80655 and GSE102556) from the Gene Expression Omnibus (GEO) database. Our primary objective was to comprehensively explore gene expression alterations in distinct brain regions of MDD patients. By employing differential expression gene (DEG) analysis, Gene Ontology (GO) functional enrichment analysis, Gene Set Enrichment Analysis (GSEA), and Weighted Gene Co-expression Network Analysis (WGCNA), we aimed to identify key genes, pathways, and co-expression modules associated with MDD. A particular focus was placed on elucidating the role of neuroinflammation and ER stress pathways in MDD pathology, as well as their brain region-specific characteristics. The findings from this research are expected to provide novel insights into the complex molecular mechanisms of MDD, identify potential diagnostic biomarkers, and suggest new avenues for therapeutic intervention. Methods This study aimed to deeply investigate gene expression changes in various brain regions of Major Depressive Disorder (MDD) patients and their associations with neuroinflammation and endoplasmic reticulum (ER) stress pathways. The research workflow encompassed differential expression gene analysis, functional enrichment analysis, and Weighted Gene Co-expression Network Analysis. Sample and Data Processing This study utilized publicly available gene expression data from the Gene Expression Omnibus (GEO) database, specifically datasets GSE80655 and GSE102556. These datasets contain transcriptomic information from MDD patients and healthy control subjects across different brain regions. Raw count data underwent quality control prior to subsequent bioinformatics analyses.[ 2 ][ 3 ][ 4 ][ 5 ] Differential Expression Gene Analysis Differential Expressed Genes (DEGs) analysis was performed using the DESeq2 R package. Raw count data were first subjected to normalization to mitigate batch effects. Subsequently, a generalized linear model was employed to identify genes with significantly differential expression between MDD patients and healthy controls. To control for false positives arising from multiple hypothesis testing, the Benjamini-Hochberg method was applied for multiple testing correction. Finally, genes with an absolute log2FoldChange (|log2FoldChange|) > 1 and a Benjamini-Hochberg adjusted P-value < 0.05 were considered as differentially expressed.[ 6 ] Functional Enrichment Analysis Functional enrichment analysis was conducted using the clusterProfiler R package to perform Gene Ontology (GO) functional enrichment analysis on differentially expressed genes, with a primary focus on Biological Process terms. This analysis aimed to identify the enrichment of DEGs in specific biological functions, thereby elucidating biological pathways potentially affected in MDD patients. The significance of enrichment was assessed using a hypergeometric test, with an adjusted P-value < 0.05 as the criterion for significant enrichment. Enrichment results were visualized using dot plots and bar plots, offering an intuitive representation of the number of enriched pathways, their significance, and associated genes.[ 7 ] Weighted Gene Co-expression Network Analysis Weighted Gene Co-expression Network Analysis (WGCNA)[ 8 ] was performed using the WGCNA R package. The aim was to construct gene co-expression networks and identify gene modules exhibiting similar expression patterns, further exploring the association of these modules with MDD phenotypes. Initially, gene expression data underwent quality control: genes with a standard deviation less than 0.5 were removed to exclude those with minimal expression variation and limited information content. Subsequently, sample clustering was examined to identify and remove potential outlier samples, ensuring data quality. To construct a scale-free network, the optimal soft thresholding parameter (soft power, β) was selected by analyzing the scale-free topology of the network across different soft thresholds. In this study, a soft threshold β = 14 was chosen, as this parameter ensured the network met the scale-free topology criterion (scale-free topology R 2 > 0.8), thereby ensuring the biological plausibility of the network. Gene co-expression modules were identified using the dynamic tree cut method, with a minimum module size of 60 genes to ensure each module possessed sufficient statistical power and biological interpretability. To merge highly correlated and similar modules, the correlation between module eigengenes was calculated, and modules with high correlation were merged, with a merging threshold set at 0.25. Functional enrichment analysis was performed on the identified gene modules using GO databases[ 9 ][ 10 ] to annotate their biological functions, thereby elucidating the biological processes represented by each gene module. Module eigengenes were utilized to assess the overall expression pattern of each module. Results This study conducted an in-depth analysis of gene expression profiles in different brain regions of MDD patients, revealing molecular characteristics associated with neuroinflammation, ER stress, and related cellular damage and neurological dysfunction in MDD. Differential Expression Genes and Regional Functional Enrichment Analysis Differential expression gene analysis using DESeq2 identified multiple sets of significantly differentially expressed genes between MDD patients and healthy control subjects. We performed Gene Ontology (GO) Biological Process enrichment analysis for differentially expressed genes identified in the Cg25, nAcc, and Sub brain regions. The results showed that DEGs in these brain regions were significantly enriched in various stress responses (e.g., response to corticosterone, response to mineralocorticoid) and immune-related processes (e.g., humoral immune response, antimicrobial humoral immune response mediated by antimicrobial peptide), suggesting a pervasive stress and inflammatory response in the brains of MDD patients ( Figure 1 ). Furthermore, the Cg25 region also showed enrichment in the regulation of nerve impulse transmission, the nAcc region in pathways related to musculoskeletal development, while the Sub region uniquely enriched for cilium assembly and neurotransmitter uptake (e.g., dopamine uptake, catecholamine uptake), indicating certain functional specific alterations across brain regions. Download figure Open in new tab FIGURE 1. WGCNA Identifies Core MDD-Related Gene Co-expression Modules Weighted Gene Co-expression Network Analysis (WGCNA) identified multiple gene co-expression modules. Among them, the module eigengene score of the turquoise module showed a certain difference between MDD patient samples and the normal control group (MDD group average value was 0.21, control group average value was -0.21), suggesting that the overall expression pattern of genes in this module changed in MDD. GO enrichment analysis of genes within the turquoise module showed that this module was significantly enriched in terms related to cell adhesion, extracellular matrix organization, gliogenesis and glial cell differentiation, suggesting abnormal activity of glial cells and remodeling of the brain microenvironment. At the cellular component level, structures related to intracellular homeostasis and autophagy, such as vacuolar membrane and lysosomal membrane, were significantly enriched. At the molecular function level, ion channel activity (including voltage-gated and metal ion transmembrane transport), extracellular matrix structural constituent, and growth factor binding were also significantly enriched ( Figure 2 ). These findings collectively depict that the turquoise module in MDD may participate in neuroinflammation, stress response, and neural circuit dysfunction by regulating glial cell function, intracellular membrane system homeostasis, and neuronal electrophysiological activity. Download figure Open in new tab FIGURE 2. Regional GSEA Reveals Widespread Activation of Neuroinflammation and ER Stress Pathways To further delve into the regional specificity of MDD-related pathways, we performed GSEA enrichment analysis on the Cg25, nAcc, and Sub brain regions. GSEA enrichment score curves showed that multiple pathway gene sets exhibited positive enrichment in these brain regions in MDD patients. GSEA dot plots provided more detailed pathway enrichment information ( Figure 3 ): Download figure Open in new tab FIGURE 3. Cg25 brain region: Significantly enriched for inflammatory pathways such as “inflammatory cytokines” and “complement system.” More importantly, the enrichment of “ER stress apoptosis,” “IRE1alpha pathway,” and various cell death pathways (e.g., ferroptosis, necroptosis) provided direct evidence of ER stress activation and widespread cell death in the Cg25 brain region. Additionally, the enrichment of “neurotrophic factors” and “synaptic plasticity core” also suggested potential impairment in neural function and plasticity. nAcc brain region: Showed the most significant and comprehensive ER stress activation features, with all three core branches of UPR:”ATF6 pathway,” “IRE1alpha pathway,” and “PERK pathway”, being significantly enriched. Simultaneously, the enrichment of “microglia activation markers” and “inflammatory cytokines” directly confirmed neuroinflammatory activation in the nAcc brain region. Furthermore, neuro-functional related pathways such as “synaptic vesicle proteins” and “synaptic plasticity core” were also significantly upregulated, indicating alterations in synaptic integrity and function. Sub brain region: GSEA results showed the enrichment of “astrocyte activation markers,” “microglia activation markers,” “inflammatory cytokines,” and “complement system,” clearly indicating neuroglia-mediated neuroinflammation in the Sub brain region. Concurrently, the enrichment of “ER stress apoptosis,” “IRE1alpha pathway,” and various cell death pathways (e.g., ferroptosis, necroptosis) again emphasized the active ER stress and cellular damage in this region. Integration of Key Modules and Regional Findings The WGCNA and regional GSEA results of this study are highly consistent and mutually corroborative. The turquoise module identified by WGCNA not only showed overall differences in MDD, but its genes were also significantly enriched in inflammation, UPR, and depression-related pathways. This module had a higher module eigengene score in the nucleus accumbens (nAcc) brain region (0.13). The GSEA results for the nAcc brain region also provided strong molecular evidence, indicating the simultaneous activation of the three core branches of UPR, neuroglia-mediated inflammation, and pathways related to neural function in this region. These findings collectively depict a complex molecular landscape of neuroinflammation, ER stress, and related cellular damage and neurological dysfunction in MDD, and suggest that nAcc may be a core brain region connecting these pathological processes. 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Share Brain Region-Specific Gene Co-expression Networks Reveal Neuroinflammation and ER Stress Signatures in Major Depressive Disorder Lejun Li , Meiqi Wang , Yanbing Jie bioRxiv 2025.06.18.660280; doi: https://doi.org/10.1101/2025.06.18.660280 Share This Article: Copy Citation Tools Brain Region-Specific Gene Co-expression Networks Reveal Neuroinflammation and ER Stress Signatures in Major Depressive Disorder Lejun Li , Meiqi Wang , Yanbing Jie bioRxiv 2025.06.18.660280; doi: https://doi.org/10.1101/2025.06.18.660280 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Neuroscience Subject Areas All Articles Animal Behavior and Cognition (7635) Biochemistry (17690) Bioengineering (13892) Bioinformatics (41936) Biophysics (21451) Cancer Biology (18588) Cell Biology (25499) Clinical Trials (138) Developmental Biology (13378) Ecology (19899) Epidemiology (2067) Evolutionary Biology (24320) Genetics (15609) Genomics (22506) Immunology (17736) Microbiology (40394) Molecular Biology (17181) Neuroscience (88603) Paleontology (666) Pathology (2832) Pharmacology and Toxicology (4824) Physiology (7641) Plant Biology (15152) Scientific Communication and Education (2045) Synthetic Biology (4294) Systems Biology (9825) Zoology (2271)