Identification and replication of RNA-Seq gene network modules associated with depression severity

Abstract Genomic variation underlying major depressive disorder (MDD) likely involves the interaction and regulation of multiple genes in a network. Data-driven co-expression network module inference has the potential to account for variation within regulatory networks, reduce the dimensionality of...

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Main Authors:	Trang T. Le, Jonathan Savitz, Hideo Suzuki, Masaya Misaki, T. Kent Teague, Bill C. White, Julie H. Marino, Graham Wiley, Patrick M. Gaffney, Wayne C. Drevets, Brett A. McKinney, Jerzy Bodurka
Format:	Article
Language:	English
Published:	Nature Publishing Group 2018-09-01
Series:	Translational Psychiatry
Online Access:	http://link.springer.com/article/10.1038/s41398-018-0234-3

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spelling	doaj-art-f57a805af6f348ada21e8fc313403bc82018-09-09T12:30:41ZengNature Publishing GroupTranslational Psychiatry2158-31882018-09-018111210.1038/s41398-018-0234-3Identification and replication of RNA-Seq gene network modules associated with depression severityTrang T. Le0Jonathan Savitz1Hideo Suzuki2Masaya Misaki3T. Kent Teague4Bill C. White5Julie H. Marino6Graham Wiley7Patrick M. Gaffney8Wayne C. Drevets9Brett A. McKinney10Jerzy Bodurka11Department of Mathematics, The University of TulsaLaureate Institute for Brain ResearchLaureate Institute for Brain ResearchLaureate Institute for Brain ResearchDepartments of Surgery and Psychiatry, University of Oklahoma School of Community MedicineTandy School of Computer Sciences, The University of TulsaDepartment of Surgery, Integrative Immunology Center, University of Oklahoma School of Community MedicineArthritis and Clinical Immunology Research Program, Division of Genomics and Data Sciences, Oklahoma Medical Research FoundationArthritis and Clinical Immunology Research Program, Division of Genomics and Data Sciences, Oklahoma Medical Research FoundationJanssen Research & Development, LLC, Johnson & Johnson, IncDepartment of Mathematics, The University of TulsaLaureate Institute for Brain ResearchAbstract Genomic variation underlying major depressive disorder (MDD) likely involves the interaction and regulation of multiple genes in a network. Data-driven co-expression network module inference has the potential to account for variation within regulatory networks, reduce the dimensionality of RNA-Seq data, and detect significant gene-expression modules associated with depression severity. We performed an RNA-Seq gene co-expression network analysis of mRNA data obtained from the peripheral blood mononuclear cells of unmedicated MDD (n = 78) and healthy control (n = 79) subjects. Across the combined MDD and HC groups, we assigned genes into modules using hierarchical clustering with a dynamic tree cut method and projected the expression data onto a lower-dimensional module space by computing the single-sample gene set enrichment score of each module. We tested the single-sample scores of each module for association with levels of depression severity measured by the Montgomery-Åsberg Depression Scale (MADRS). Independent of MDD status, we identified 23 gene modules from the co-expression network. Two modules were significantly associated with the MADRS score after multiple comparison adjustment (adjusted p = 0.009, 0.028 at 0.05 FDR threshold), and one of these modules replicated in a previous RNA-Seq study of MDD (p = 0.03). The two MADRS-associated modules contain genes previously implicated in mood disorders and show enrichment of apoptosis and B cell receptor signaling. The genes in these modules show a correlation between network centrality and univariate association with depression, suggesting that intramodular hub genes are more likely to be related to MDD compared to other genes in a module.http://link.springer.com/article/10.1038/s41398-018-0234-3
institution	Open Data Bank
collection	Open Access Journals
building	Directory of Open Access Journals
language	English
format	Article
author	Trang T. Le Jonathan Savitz Hideo Suzuki Masaya Misaki T. Kent Teague Bill C. White Julie H. Marino Graham Wiley Patrick M. Gaffney Wayne C. Drevets Brett A. McKinney Jerzy Bodurka
spellingShingle	Trang T. Le Jonathan Savitz Hideo Suzuki Masaya Misaki T. Kent Teague Bill C. White Julie H. Marino Graham Wiley Patrick M. Gaffney Wayne C. Drevets Brett A. McKinney Jerzy Bodurka Identification and replication of RNA-Seq gene network modules associated with depression severity Translational Psychiatry
author_facet	Trang T. Le Jonathan Savitz Hideo Suzuki Masaya Misaki T. Kent Teague Bill C. White Julie H. Marino Graham Wiley Patrick M. Gaffney Wayne C. Drevets Brett A. McKinney Jerzy Bodurka
author_sort	Trang T. Le
title	Identification and replication of RNA-Seq gene network modules associated with depression severity
title_short	Identification and replication of RNA-Seq gene network modules associated with depression severity
title_full	Identification and replication of RNA-Seq gene network modules associated with depression severity
title_fullStr	Identification and replication of RNA-Seq gene network modules associated with depression severity
title_full_unstemmed	Identification and replication of RNA-Seq gene network modules associated with depression severity
title_sort	identification and replication of rna-seq gene network modules associated with depression severity
publisher	Nature Publishing Group
series	Translational Psychiatry
issn	2158-3188
publishDate	2018-09-01
description	Abstract Genomic variation underlying major depressive disorder (MDD) likely involves the interaction and regulation of multiple genes in a network. Data-driven co-expression network module inference has the potential to account for variation within regulatory networks, reduce the dimensionality of RNA-Seq data, and detect significant gene-expression modules associated with depression severity. We performed an RNA-Seq gene co-expression network analysis of mRNA data obtained from the peripheral blood mononuclear cells of unmedicated MDD (n = 78) and healthy control (n = 79) subjects. Across the combined MDD and HC groups, we assigned genes into modules using hierarchical clustering with a dynamic tree cut method and projected the expression data onto a lower-dimensional module space by computing the single-sample gene set enrichment score of each module. We tested the single-sample scores of each module for association with levels of depression severity measured by the Montgomery-Åsberg Depression Scale (MADRS). Independent of MDD status, we identified 23 gene modules from the co-expression network. Two modules were significantly associated with the MADRS score after multiple comparison adjustment (adjusted p = 0.009, 0.028 at 0.05 FDR threshold), and one of these modules replicated in a previous RNA-Seq study of MDD (p = 0.03). The two MADRS-associated modules contain genes previously implicated in mood disorders and show enrichment of apoptosis and B cell receptor signaling. The genes in these modules show a correlation between network centrality and univariate association with depression, suggesting that intramodular hub genes are more likely to be related to MDD compared to other genes in a module.
url	http://link.springer.com/article/10.1038/s41398-018-0234-3
_version_	1612607869439442944

Identification and replication of RNA-Seq gene network modules associated with depression severity

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