Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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A weighted gene co-expression network analysis reveals lncRNA abnormalities in the peripheral blood associated with ultra-high-risk for psychosis

ABSTRACT: The primary study aim was to identify long non-coding RNA (lncRNA) abnormalities associated with ultra-high-risk (UHR) for psychosis based on a weighted gene co-expression network analysis.UHR patients were screened by the structured interview for prodromal syndromes (SIPS). We performed a WGCNA analysis on lncRNA and mRNA microarray profiles generated from the peripheral blood samples in fourteen treatment-seeking patients with UHR who never received psychiatric medication and eighteen demographically matched typically developing controls. Gene Ontology (GO) analysis and canonical correlation analysis were then applied to reveal functions and correlation between lncRNAs and mRNAs.The lncRNAs were organized into co-expressed modules by WGCNA, two modules of which were strongly associated with UHR. The mRNA networks were constructed and two disease-associated mRNA modules were identified. A functional enrichment analysis showed that mRNAs were highly enriched for immune regulation and inflammation. Moreover, a significant correlation between lncRNAs and mRNAs were verified by a canonical correlation analysis.We identified novel lncRNA modules related to UHR. These results contribute to our understanding of the molecular basis of UHR from the perspective of systems biology and provide a theoretical basis for early intervention in the assumed development of schizophrenia.

ORGANISM(S): Homo sapiens

SUBMITTER: Xu Yong

PROVIDER: E-MTAB-9804 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:We aimed at systematically inferring the regulatory functions of host lncRNAs in response to influenza A virus (IAV) and severe acute respiratory syndrome coronavirus (SARS-CoV) in the mouse model, using a ‘guilt-by-association’ approach which relies on finding which lncRNAs have similar expression profiles to protein-coding genes of known function. To build a large panel of diverse host responses to viral infection, we took advantage of the genetic diversity present in the 8 founder strains of the Collaborative Cross (CC) mouse resource. Extensive pulmonary host-response profiling was performed on mock and viral-infected lungs at 2 and 4 days post-infection using total RNA-Seq. Overall lncRNAs accounted for about 40% of total genes differentially expressed upon infections (5,329 DE lncRNAs). To predict the functions of these lncRNAs, we constructed a co-expression network using the weighted correlation network analysis (WGCNA) and identified modules of co-expressed genes. Several lncRNAs were identified as belonging to gene modules associated with viral replication or weight loss, and enriched in various infection-related biological processes such as immune response. In addition, each lncRNA was individually annotated using ranked list of DE genes based on signed correlation with each LncRNA. We predicted that a few lncRNAs may be implicated in more than 50 biological processes or pathways. Finally, we identified the lncRNAs that were positively or negatively correlated with all their neighbors and may have cis enhancer or inhibitor effects. We validated our prediction by examining additional RNASeq dataset of mice treated with IFN-alpha. Altogether, these results provide a broad categorization of lncRNA functions and identify subsets of lncRNAs with potential key roles for respiratory virus pathogenesis. These data are fully accessible through The MOuse NOn-Code Lung database (MONOCLdb) http://monocldb.viromics.washington.edu/ At 2 or 4 days post infection, Collaborative Cross founders mice (n=2-3 for infected conditions, n=2 for mocks) were euthanized and lungs were used for total RNA-Seq.

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A weighted gene co-expression network analysis reveals lncRNA abnormalities in the peripheral blood associated with ultra-high-risk for psychosis

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