Author/Editor		Klaus, Valentina S.; Schriever, Sonja C.; Monroy Kuhn, José Manuel; Peter, Andreas; Irmler, Martin; Tokarz, Janina; Prehn, Cornelia; Kastenmüller, Gabi; Beckers, Johannes; Adamski, Jerzy
Title		Correlation guided Network Integration (CoNI) reveals novel genes affecting hepatic metabolism
Type		članek
Vol. and No.		, št. Vol. 53
Publication year		2021
Volume		str. 1-15
ISSN		2212-8778
Language		eng
Abstract		Objective 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. Methods We present here 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 subgraphs or pathways. Results 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). Conclusion 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.
Keywords		integracija podatkov steatoza jeter sistemska biologija data integration hepatic steatosis systems biology