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

Author

Listed:
  • Karla Misselbeck

    (Centre for Computational and Systems Biology (COSBI)
    University of Trento)

  • Silvia Parolo

    (Centre for Computational and Systems Biology (COSBI))

  • Francesca Lorenzini

    (University of Trento)

  • Valeria Savoca

    (University of Trento)

  • Lorena Leonardelli

    (Centre for Computational and Systems Biology (COSBI))

  • Pranami Bora

    (Centre for Computational and Systems Biology (COSBI))

  • Melissa J. Morine

    (Centre for Computational and Systems Biology (COSBI))

  • Maria Caterina Mione

    (University of Trento)

  • Enrico Domenici

    (Centre for Computational and Systems Biology (COSBI)
    University of Trento)

  • Corrado Priami

    (Centre for Computational and Systems Biology (COSBI)
    University of Pisa)

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.

Suggested Citation

  • Karla Misselbeck & Silvia Parolo & Francesca Lorenzini & Valeria Savoca & Lorena Leonardelli & Pranami Bora & Melissa J. Morine & Maria Caterina Mione & Enrico Domenici & Corrado Priami, 2019. "A network-based approach to identify deregulated pathways and drug effects in metabolic syndrome," Nature Communications, Nature, vol. 10(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-13208-z
    DOI: 10.1038/s41467-019-13208-z
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