Identification of potential changes in protein abundance associated with post-traumatic stress disorder through brain proteome-wide association study

Identification of potential changes in protein abundance associated with post-traumatic stress disorder through brain proteome-wide association study | medRxiv /* */ /* */ <!-- <!-- /*! * 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-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Identification of potential changes in protein abundance associated with post-traumatic stress disorder through brain proteome-wide association study View ORCID Profile Jiewei Liu , View ORCID Profile Zhongchun Liu doi: https://doi.org/10.1101/2025.08.28.25334625 Jiewei Liu 1 Department of Psychiatry, Wuhan Mental Health Center , Wuhan 430012, Hubei, China 2 Wuhan Hospital for Psychotherapy , Wuhan 430012, Hubei, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jiewei Liu For correspondence: liujiewei{at}hust.edu.cn zcliu6{at}whu.edu.cn Zhongchun Liu 3 Department of Psychiatry, Renmin Hospital of Wuhan University , 238 Jiefang Road, Wuhan, 430060, Hubei, China 4 Taikang Center for Life and Medical Sciences, Wuhan University , 115 Donghu Road, Wuhan, 430071, Hubei, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Zhongchun Liu For correspondence: liujiewei{at}hust.edu.cn zcliu6{at}whu.edu.cn Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Aim Post-traumatic stress disorder (PTSD) is a common mental disorder with a substantial genetic background. Recent genome-wide association analysis (GWAS) has identified 95 genome-wide significant (GWS) loci in a large cross-ancestry cohort comprising over 1 million samples. However, bridging the GWS findings to potential therapeutic candidates is lagged. Method In this study, we integrated the most up-to-date PTSD GWAS data with two human brain proteome datasets using FUSION software to identify potential changes in protein abundance that confer risk for PTSD through proteome-wide association analysis (PWAS). Results We identified 20 and 24 proteome-wide significant (PWS) genes from the Banner (n=152) and ROSMAP (n=376) PWAS results, yielding a total of 33 non-overlapping PWS genes. Notably, CTNND1 was the top PWS gene in both PWAS analysis panels. In addition to CTNND1 , the genes TMEM106B , ICA1L , KHK , GPX1 , CCBL2 , MKRN1 , INPP4A , SIRPA , CACNA2D2 , and PDLIM2 also reached PWS levels in both Banner and ROSMAP proteome datasets. Furthermore, we performed colocalization, drug-gene interaction analysis, and synapse function annotation analysis. Conclusion Our study uncovered the changes in protein abundance that underlie PTSD etiology, and these genes represent promising candidates for further exploration of the mechanisms underlying PTSD pathogenesis. Introduction Posttraumatic stress disorder (PTSD) is a common psychiatric disorder that develops in individuals who have experienced traumatic events. The clinical symptoms of PTSD include intrusive thoughts, avoidance behaviors, negative cognition and mood symptoms, as well as changes in arousal and reactivity [ 1 ]. It has been reported that the prevalence of PTSD is approximately 3.9% in the general population, increasing to 5.6% among those who have experienced trauma [ 2 ]. The high prevalence of PTSD results in a significant economic burden on society [ 3 ]. Therefore, effective and efficient methods are needed for the treatment of PTSD. While environmental factors play an important role in PTSD, genetic background should not be overlooked. Recent studies estimate that the heritability of PTSD is about 46% [ 4 ]. Genome-wide association studies (GWASs) are commonly used strategies for dissecting the genetic basis of complex diseases/traits, including many neuropsychiatric disorders. Several GWASs have been conducted to explore genomic risk loci associated with PTSD [ 5 – 7 ]. Recently, the Psychiatric Genomic Consortium for PTSD (PGC-PTSD) performed the largest PTSD GWAS to date, which included 1,307,247 samples from 88 studies and reported 95 genome-wide significant (GWS) loci. Most GWS variants are located in non-coding regions, it is implied that these variants may contribute to disease risk by regulating gene expression rather than directly affecting protein function [ 8 ]. However, bridging the summary statistics from PTSD GWAS with disease risk genes is a key issue for interpreting the loci identified in GWAS, due to the complex linkage disequilibrium structure of the human genome [ 9 ]. Multi-omics integration methods, such as transcriptome-wide association analysis (TWAS) and Summary-data-based Mendelian randomization (SMR) [ 10 , 11 ], have been developed to identify candidate risk genes for diseases/traits by integrating GWAS summary statistics with transcriptomic or proteomic data. In this study, we conducted a brain proteome-wide association study (PWAS) for PTSD by combining the new PGC released PTSD GWAS summary statistics and two brain proteome datasets. Our goal was to identify changes in protein abundance related to PTSD, which will be helpful for further studies on disease mechanisms and therapeutic target evaluation. Methods PTSD GWAS summary statistics Nievergel et al. (2024) conducted a multi-ancestry PTSD GWAS with the largest sample size to date (n=1,307,247) [ 12 ], including three ancestries: European, African, and Latin American. In this study, we downloaded the European ancestry GWAS summary for the subsequent PWAS analysis (n=1,222,882). For detailed information about the PTSD GWAS data, please refer to the original GWAS publication. Brain proteome datasets We retrieved two brain proteome datasets, both containing protein abundance data and genotype information from a previous study ( https://www.synapse.org/Synapse:syn23627957 ) [ 13 ]. The first dataset is from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP) (n=376), and the second dataset is from Banner Sun Health Research Institute (Banner, n=152). Both ROSMAP and Banner brain proteomes were generated from postmortem samples of the brain dorsolateral prefrontal cortex (dlPFC) region, with protein abundance measured using isobaric tandem mass tag (TMT) peptide labeling. Genotypes were generated using various methods, including whole genome sequencing (WGS) and whole genome genotyping arrays (e.g., Illumina and Affymetrix platforms). All samples are of European descent. PWAS analysis by FUSION software We performed PWAS analysis using FUSION software ( https://gusevlab.org/projects/fusion/ ) [ 10 ]. The brain proteome weigh files for both Banner and ROSMAP were downloaded from https://www.synapse.org/#!Synapse:syn23627957 [ 13 ]. We used the default parameters and 1000 Genome project European samples as the reference for FUSION analysis. Additionally, we employed the –coloc_P tag to trigger colocalization analysis. Drug-gene interaction analysis Drug-gene interactions were analyzed using DGIdb databases ( https://www.dgidb.org/ ) [ 14 ]. The DGIdb database curates the drug-gene interactions derived from well-established databases such as DrugBank and ChEMBL, as well as through text mining from literature. DGIdb is an ongoing project, with the latest version was DGIdb 5.0. It contains approximately 70,000 drug-gene interactions, including over 10,000 genes and 20,000 drugs. Gene set enrichment analysis in synapse To explore the potential functions of the PWS genes, we utilized SynGO online web tool ( https://www.syngoportal.org/ ) for annotation [ 15 ]. The newly released SynGO 1.2 includes 4,218 annotations of a total of 1,620 genes. The annotations and genes are curated by experts from the SynGO consortium. For a more detailed description, please refer to the SynGO paper Results 33 genes reached proteome-wide significant (PWS) level in PWAS analysis We integrated the PTSD GWAS with two brain proteomes to conduct a PWAS. In the ROSMAP PWAS analysis, we identified 24 PWS genes, of which 15 replicated their association with PTSD in the Banner PWAS analysis ( Figure 1a , Table 1 ). Additionally, 20 PWS genes were identified in the Banner PWAS analysis, with 14 replicating in the ROSMAP PWAS analysis ( Figure 1b , Table 1 ). 11 genes, including CTNND1 , TMEM106B , ICA1L , KHK , GPX1 , CCBL2 , MKRN1 , INPP4A , SIRPA , CACNA2D2 , and PDLIM2 , exhibited PWS in both PWAS analysis panels ( Figure 1 ). Notably, CTNND1 was the most significant gene in both the ROSMAP ( P value = 1.68×10 -17 ) and Banner (P value = 8.05×10 -13 ) PWAS results. In total, we identified 33 non-overlapping PWS genes in our PWAS analysis. Download figure Open in new tab Figure 1. The Manhattan plot of the brain proteome-wide association study (PWAS) analysis a) Results of the PWAS analysis integrating ROSMAP (n=376) proteome data with PTSD GWAS summary statistics. The red dashed line indicates the Bonferroni-corrected significance level ( P = 2.89×10 -05 ); b) Results of the PWAS analysis integrating Banner (n=152) proteome data with PTSD GWAS summary statistics. The red dashed line indicates the Bonferroni-corrected significance level ( P = 4.43×10 -05 ). View this table: View inline View popup Table 1. Proteome-wide association studies result of PTSD GWAS and two brain proteome data. Colocalization analysis further confirmed the potential causal role of 23 PWS genes To confirm the potential causal relationship between the PWS genes and PTSD, we conducted a colocalization analysis. Among the 33 PWS genes, 23 exhibited a PP4 value greater than 0.5 in at least one PWAS analysis panel ( Table 1 ). TMEM106B , ICA1L , KHK , GPX1 , CCBL2 and SIRPA demonstrated high PP4 values (>0.8) in both PWAS panels. CTNND1 showed a high PP4 in the ROSMAP PWAS analysis ( PP4 =0.931) but a relatively low value in the Banner PWAS result ( PP4 =0.533). Drug target gene analysis identified multiple drug-gene interactions of PWS genes To investigate potential therapeutic drugs targeting the PWS proteins identified in this study, we utilized DGIdb to explore the drug-gene interactions. We found 12 genes with documented interactions ( Table 2 ), including PDE1A (2 interactions) , C4A (4 interactions) , C4B (4 interactions) , PRKCA (24 interactions) , GPX1 (2 interactions) , TAOK3 (1 interactions) , CACNA2D2 (14 interactions) , STAT6 (15 interactions) , KHK (1 interactions) , SIRPA (1 interactions) , CTNND1 (11 interactions) , and GLG1 (5 interactions) in the DGIdb databases. View this table: View inline View popup Download powerpoint Table 2. The drug-gene interactions of PWS genes from the DGIdb database. Gene expression and function analysis of the PWS genes in synapse We queried the 33 PWS genes on SynGO website and found 5 genes, RAB27B , PRKCA , CTNND1 , CACNA2D2 , and INPP4A were annotated as function in synapse ( Figure 2 ). RAB27B, CACNA2D2, CTNND1, and PRKCA were annotated as components of the presynapse. While CTNND1 and INPP4A were annotated as components of the postsynapse. Noteblely, CTNND1 was annotated in both the pre and postsynapse categories. The 33 PWS genes were found to be overrepresented in presynapse Cellular Component ontology ( P = 0.04) and enriched in biological processes, including ‘regulation of postsynaptic membrane neurotransmitter receptor levels’ ( P = 3.63×10 -03 ) and ‘process in the synapse’ ( P =0.05). Download figure Open in new tab Figure 2. The Proteome-wide significant (PWS) genes that has annotation that related synapse by using SynGO web tool. Discussion In this study, we performed PWAS analysis of PTSD GWAS summary statistics to identify potential protein abundance change that related to PTSD, ultimately identified 33 PWS proteins. Our findings expand our understanding of risk genes associated with PTSD. We compared our findings in this study with previous publications. Thomas et al. (2022), which conducted a PWAS analysis by integrating brain proteome and eight psychiatric traits [ 16 ]. They reported 16 PWS proteins that associated with PTSD. Of which 7 genes including CTNND1 , CCBL2 , GPX1 , INPP4A , KHK , PDLIM2 , and RAB27B were also identified in our study. Thomas et al. used a relatively small sample size PTSD GWAS summary statistics compared to this study, and they used a relaxed FDR adjustment for multiple comparison, while we employed a more stringent Bonferroni correction. Additionally, the newly released PGC-PTSD GWAS conducted a transcriptome-wide association study (TWAS) using GTEx brain tissue data, reporting 62 significant genes [ 12 ]. 6 genes overlapped with the PWS gene identified in our study, including TMEM106B , GMPPB , KHK , GPX1 , BTN3A3 and C4A . In summary, we identified 33 PWS genes in this study, providing several novel candidates that confer risk for PTSD and offering new molecular insights into the disease. CTNND1 (Catenin Delta 1) is our top finding in this study. It has been reported to function in cell-cell adhesion and play a role in neuronal systems pathways [ 17 – 19 ]. CTNND1 has also been associated with depression, which is known to have a high comorbidity with PTSD [ 20 – 22 ]. Another gene C4A, was identified as a top association signal in schizophrenia and involved in synaptic function [ 23 ]. Our gene set enrichment analysis highlighted the 33 PWS genes are enriched in biological process including ‘regulation of postsynaptic membrane neurotransmitter receptor levels’ and ‘process in the synapse’. These evidences suggest that dysregulation of synapse function may contribute to the etiology of PTSD [ 24 ]. Our study has several limitations. Firstly, the proteome data we used was based entirely on human prefrontal cortex sample with a relatively small sample size. Further studies with larger sample size and data from multiple brain regions are warranted to identify additional risk genes for PTSD. Secondly, the PTSD GWAS cohort and brain proteome dataset utilized in this paper were predominantly from individuals of European ancestry. Future studies should include diverse ancestries to confirm our findings. Lastly, while our study aimed to identify risk genes associated with PTSD, the mechanisms through which these genes contribute to PTSD etiology require further investigation. In summary, we performed PWAS analyses of PTSD by integrating the latest PTSD GWAS data with human brain proteome information. We identified a total of 33 PWS genes, which may serve as promising candidates for further studies on disease mechanisms and for the development of novel therapeutic drugs targeting these proteins. Data Availability All data produced in the present study are available upon reasonable request to the authors Funding Statement This study was funded by Hubei provincial Natural Science Foundation of China (2024AFB1022 to J.L) and the National Natural Science Foundation of China (U21A20364 to Z.L) Author contributions JL and ZL convinced and supervised this study. JL performed the data analysis, visualization, and wrote the original draft. JL and ZL edited the manuscript. 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Share Identification of potential changes in protein abundance associated with post-traumatic stress disorder through brain proteome-wide association study Jiewei Liu , Zhongchun Liu medRxiv 2025.08.28.25334625; doi: https://doi.org/10.1101/2025.08.28.25334625 Share This Article: Copy Citation Tools Identification of potential changes in protein abundance associated with post-traumatic stress disorder through brain proteome-wide association study Jiewei Liu , Zhongchun Liu medRxiv 2025.08.28.25334625; doi: https://doi.org/10.1101/2025.08.28.25334625 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 Psychiatry and Clinical Psychology Subject Areas All Articles Addiction Medicine (567) Allergy and Immunology (863) Anesthesia (297) Cardiovascular Medicine (4411) Dentistry and Oral Medicine (443) Dermatology (380) Emergency Medicine (606) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1505) Epidemiology (15205) Forensic Medicine (30) Gastroenterology (1119) Genetic and Genomic Medicine (6574) Geriatric Medicine (666) Health Economics (994) Health Informatics (4511) Health Policy (1365) Health Systems and Quality Improvement (1608) Hematology (537) HIV/AIDS (1263) Infectious Diseases (except HIV/AIDS) (15903) Intensive Care and Critical Care Medicine (1103) Medical Education (620) Medical Ethics (144) Nephrology (665) Neurology (6573) Nursing (345) Nutrition (998) Obstetrics and Gynecology (1139) Occupational and Environmental Health (954) Oncology (3319) Ophthalmology (967) Orthopedics (369) Otolaryngology (420) Pain Medicine (435) Palliative Medicine (129) Pathology (662) Pediatrics (1689) Pharmacology and Therapeutics (691) Primary Care Research (710) Psychiatry and Clinical Psychology (5421) Public and Global Health (9205) Radiology and Imaging (2191) Rehabilitation Medicine and Physical Therapy (1367) Respiratory Medicine (1191) Rheumatology (593) Sexual and Reproductive Health (709) Sports Medicine (529) Surgery (709) Toxicology (99) Transplantation (288) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9fe8baf89b154193',t:'MTc3OTI1MjcyOQ=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();