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

Molecular Metabolism

José Manuel Monroy Kuhn¹  Johannes Beckers¹  Martin Heni²  Timo D. Müller³  Janina Tokarz⁴  Andreas Peter⁴  Jerzy Adamski⁴  Cornelia Prehn⁵  Sonja C. Schriever⁶  Valentina S. Klaus⁷  Gabi Kastenmüller⁷  Martin Irmler⁸  Alfred Königsrainer⁸  Matthias H. Tschöp⁹  Dominik Lutter^1,10  Paul T. Pfluger^1,10 

[1] German Center for Diabetes Research (DZD), Neuherberg, Germany;Institute for Clinical Chemistry and Pathobiochemistry, University Hospital Tübingen, Germany;Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Center Munich at the University of Tübingen, Tübingen, Germany;Institute for Diabetes and Obesity, Helmholtz Diabetes Center at Helmholtz Zentrum München, Germany;Research Unit Neurobiology of Diabetes, Helmholtz Zentrum München, Neuherberg, Germany;TUM School of Medicine, Neurobiology of Diabetes, Technical University Munich, Germany;Computational Discovery Research Unit, Institute for Diabetes and Obesity, Helmholtz Zentrum München, Neuherberg, Germany;German Center for Diabetes Research (DZD), Neuherberg, Germany;Institute of Experimental Genetics, Helmholtz Zentrum München, Neuherberg, Germany;Research Unit Molecular Endocrinology and Metabolism, Helmholtz Zentrum München, Neuherberg, Germany;

关键词: Data integration;    Hepatic steatosis;    Multi-omics;    Systems biology;   

DOI  :  

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown