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Nat Commun. 2019 Nov 18;10(1):5215. doi: 10.1038/s41467-019-13208-z.

A network-based approach to identify deregulated pathways and drug effects in metabolic syndrome.

Author information

1
Fondazione The Microsoft Research University of Trento, Centre for Computational and Systems Biology (COSBI), Rovereto, Italy.
2
Department of Mathematics, University of Trento, Trento, Italy.
3
Fondazione The Microsoft Research University of Trento, Centre for Computational and Systems Biology (COSBI), Rovereto, Italy. parolo@cosbi.eu.
4
Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy.
5
Fondazione The Microsoft Research University of Trento, Centre for Computational and Systems Biology (COSBI), Rovereto, Italy. enrico.domenici@unitn.it.
6
Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy. enrico.domenici@unitn.it.
7
Fondazione The Microsoft Research University of Trento, Centre for Computational and Systems Biology (COSBI), Rovereto, Italy. priami@cosbi.eu.
8
Department of Computer Science, University of Pisa, Pisa, Italy. priami@cosbi.eu.

Abstract

Metabolic syndrome is a pathological condition characterized by obesity, hyperglycemia, hypertension, elevated levels of triglycerides and low levels of high-density lipoprotein cholesterol that increase cardiovascular disease risk and type 2 diabetes. Although numerous predisposing genetic risk factors have been identified, the biological mechanisms underlying this complex phenotype are not fully elucidated. Here we introduce a systems biology approach based on network analysis to investigate deregulated biological processes and subsequently identify drug repurposing candidates. A proximity score describing the interaction between drugs and pathways is defined by combining topological and functional similarities. The results of this computational framework highlight a prominent role of the immune system in metabolic syndrome and suggest a potential use of the BTK inhibitor ibrutinib as a novel pharmacological treatment. An experimental validation using a high fat diet-induced obesity model in zebrafish larvae shows the effectiveness of ibrutinib in lowering the inflammatory load due to macrophage accumulation.

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