{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T23:33:24Z","timestamp":1788478404281,"version":"build-2803163510"},"reference-count":63,"publisher":"Public Library of Science (PLoS)","issue":"5","license":[{"start":{"date-parts":[[2023,5,15]],"date-time":"2023-05-15T00:00:00Z","timestamp":1684108800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000024","name":"CIHR","doi-asserted-by":"crossref","award":["FRN\/MOP#111260"],"award-info":[{"award-number":["FRN\/MOP#111260"]}],"id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100000038","name":"NSERC","doi-asserted-by":"crossref","award":["RGPIN-2019-0621"],"award-info":[{"award-number":["RGPIN-2019-0621"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003151","name":"FRQNT","doi-asserted-by":"crossref","award":["NC-268592"],"award-info":[{"award-number":["NC-268592"]}],"id":[{"id":"10.13039\/501100003151","id-type":"DOI","asserted-by":"crossref"}]},{"name":"HBHL","award":["G249591"],"award-info":[{"award-number":["G249591"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:sec id=\"sec001\">\n<jats:title>Background<\/jats:title>\n<jats:p>Major depressive disorder (MDD) is a leading cause of disability worldwide, and is commonly treated with antidepressant drugs (AD). Although effective, many patients fail to respond to AD treatment, and accordingly identifying factors that can predict AD response would greatly improve treatment outcomes. In this study, we developed a machine learning tool to integrate multi-omic datasets (gene expression, DNA methylation, and genotyping) to identify biomarker profiles associated with AD response in a cohort of individuals with MDD.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec002\">\n<jats:title>Materials and methods<\/jats:title>\n<jats:p>Individuals with MDD (N = 111) were treated for 8 weeks with antidepressants and were separated into responders and non-responders based on the Montgomery\u2013\u00c5sberg Depression Rating Scale (MADRS). Using peripheral blood samples, we performed RNA-sequencing, assessed DNA methylation using the Illumina EPIC array, and performed genotyping using the Illumina PsychArray. To address this rich multi-omic dataset with high dimensional features, we developed integrative Geneset-Embedded non-negative Matrix factorization (iGEM), a non-negative matrix factorization (NMF) based model, supplemented with auxiliary information regarding gene sets and gene-methylation relationships. In particular, we factorize the subjects by features (i.e., gene expression or DNA methylation) into subjects-by-factors and factors-by-features. We define the factors as the meta-phenotypes as they represent integrated composite scores of the molecular measurements for each subject.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec003\">\n<jats:title>Results<\/jats:title>\n<jats:p>Using our model, we identified a number of meta-phenotypes which were related to AD response. By integrating geneset information into the model, we were able to relate these meta-phenotypes to biological processes, including a meta-phenotype related to immune and inflammatory functions as well as other genes related to depression or AD response. The meta-phenotype identified several genes including immune interleukin 1 receptor like 1 (IL1RL1) and interleukin 5 receptor (IL5) subunit alpha (IL5RA), AKT\/PIK3 pathway related phosphoinositide-3-kinase regulatory subunit 6 (PIK3R6), and sphingomyelin phosphodiesterase 3 (SMPD3), which has been identified as a target of AD treatment.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec004\">\n<jats:title>Conclusions<\/jats:title>\n<jats:p>The derived meta-phenotypes and associated biological functions represent both biomarkers to predict response, as well as potential new treatment targets. Our method is applicable to other diseases with multi-omic data, and the software is open source and available on Github (<jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/li-lab-mcgill\/iGEM\" xlink:type=\"simple\">https:\/\/github.com\/li-lab-mcgill\/iGEM<\/jats:ext-link>).<\/jats:p>\n<\/jats:sec>","DOI":"10.1371\/journal.pone.0285123","type":"journal-article","created":{"date-parts":[[2023,5,15]],"date-time":"2023-05-15T17:24:54Z","timestamp":1684171494000},"page":"e0285123","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":15,"title":["Multi-omic modeling of antidepressant response implicates dynamic immune and inflammatory changes in individuals who respond to treatment"],"prefix":"10.1371","volume":"18","author":[{"given":"Shih-Chieh","family":"Fuh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laura M.","family":"Fiori","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gustavo","family":"Turecki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Corina","family":"Nagy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3844-4865","authenticated-orcid":true,"given":"Yue","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2023,5,15]]},"reference":[{"issue":"10028","key":"pone.0285123.ref001","doi-asserted-by":"crossref","first-page":"1672","DOI":"10.1016\/S0140-6736(15)00390-6","article-title":"Addressing the burden of mental, neurological, and substance use disorders: key messages from Disease Control Priorities","volume":"387","author":"V Patel","year":"2016","journal-title":"The Lancet"},{"key":"pone.0285123.ref002","unstructured":"Association AP. 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