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Correlation guided Network Integration (CoNI) reveals novel genes affecting hepatic metabolism.

Mol. Metab. 53, 101295 (2021)
Publ. Version/Full Text DOI
Open Access Gold
Creative Commons Lizenzvertrag
Technological advances have brought a steady increase in the availability of various types of omics data, from genomics to metabolomics. Integrating these multi-omics data is a chance and challenge for systems biology, yet tools to fully tap their potential remain scarce. We here present a fully unsupervised and versatile correlation-based method, termed Correlation guided Network Integration (CoNI), to integrate multi-omics data into a hypergraph structure that allows for the identification of effective modulators of metabolism. Our approach yields single transcripts of potential relevance that map to specific, densely connected metabolic sub-graphs or pathways. By applying our method on transcriptomics and metabolomics data from murine livers under standard Chow or high-fat diet, we identified eleven genes with potential regulatory effects on hepatic metabolism. Five candidates, including the hepatokine INHBE, were validated in human liver biopsies to correlate with diabetes-related traits such as overweight, hepatic fat content, and insulin resistance (HOMA-IR). Our method's successful application to an independent omics dataset confirmed that the novel CoNI framework is a transferable, entirely data-driven, flexible, and versatile tool for multiple omics data integration and interpretation.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Data Integration ; Hepatic Steatosis ; Multi Omics ; Systems Biology; Insulin-resistance; Transgenic Mice; Myc; Aquaporin-7; Homeostasis; Obesity; Atlas
ISSN (print) / ISBN 2212-8778
e-ISSN 2212-8778
Quellenangaben Volume: 53, Issue: , Pages: 101295 Article Number: , Supplement: ,
Publisher Elsevier
Publishing Place Amsterdam
Reviewing status Peer reviewed
Grants Initiative and Networking Fund of the Helmholtz Association
Helmholtz Alliance Aging and Metabolic Programming (AMPro)
Helmholtz Initiative for Personalized Medicine (iMed)
DZD tandem grant funds (SCS, PP, MH)
Alexander von Humboldt Foundation
Helmholtz Portfolio Program "Metabolic Dysfunction"
German Research Foundation (DFG)
German Federal Ministry of Education and Research (BMBF)