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Klaus, V. ; Schriever, S.C. ; Monroy Kuhn, J.M. ; Peter, A. ; Irmler, M. ; Tokarz, J. ; Prehn, C. ; Kastenmüller, G. ; Beckers, J. ; Adamski, J. ; Königsrainer, A.* ; Müller, T.D. ; Heni, M. ; Tschöp, M.H. ; Pfluger, P.T. ; Lutter, D.

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

Mol. Metab. 53, 101295 (2021)
Publ. Version/Full Text DOI PMC
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
Language english
Publication Year 2021
HGF-reported in Year 2021
ISSN (print) / ISBN 2212-8778
e-ISSN 2212-8778
Journal Molecular Metabolism
Quellenangaben Volume: 53, Issue: , Pages: 101295 Article Number: , Supplement: ,
Publisher Elsevier
Publishing Place Amsterdam
Reviewing status Peer reviewed
Institute(s) Institute of Diabetes and Obesity (IDO)
Institute of Diabetes Research and Metabolic Diseases (IDM)
Institute of Experimental Genetics (IEG)
Molekulare Endokrinologie und Metabolismus (MEM)
Institute of Computational Biology (ICB)
CF Metabolomics & Proteomics (CF-MPC)
POF-Topic(s) 30201 - Metabolic Health
90000 - German Center for Diabetes Research
30205 - Bioengineering and Digital Health
30505 - New Technologies for Biomedical Discoveries
Research field(s) Helmholtz Diabetes Center
Genetics and Epidemiology
Enabling and Novel Technologies
PSP Element(s) G-502297-001
G-502200-001
G-502294-001
G-502400-001
G-500600-004
G-505600-003
G-503891-001
G-501900-221
A-630710-001
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)
DOI 10.1016/j.molmet.2021.101295
WOS ID WOS:000704226900005
Scopus ID 85112639179
PubMed ID 34271221
Erfassungsdatum 2021-07-21
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