Cover Story Current Issue

White adipose tissue (WAT) is a complex organ that plays a central role in systemic energy balance through its interrelated metabolic, endocrine, and immune functions. Adipocytes, the parenchymal cells of adipose tissue, have diverse functions that include storage and mobilization of lipids. They also release endocrine signals that report energy status to the brain, regulating metabolic functions in peripheral organs. Importantly, the metabolic character of white adipocytes is flexible, with cells capable of assuming distinct anabolic and catabolic/thermogenic phenotypes, often within the same adipose tissue depot

Elizabeth A. Rondini, Vanesa D. Ramseyer, Rayanne B. Burl, Roger Pique-Regi, James G. Granneman

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Current Issue

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

Valentina S. Klaus, Sonja C. Schriever, José Manuel Monroy Kuhn, Andreas Peter, ... Dominik Lutter

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.

 

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

Valentina S. Klaus, Sonja C. Schriever, José Manuel Monroy Kuhn, Andreas Peter, ... Dominik Lutter

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.

 

2020 impact factor: 7.4

The 60 Second Metabolist

In this section authors briefly report on their work recently published in Molecular Metabolism.

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