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Identification of disease treatment mechanisms through the multiscale interactome

Author

Listed:
  • Camilo Ruiz

    (Stanford University
    Stanford University)

  • Marinka Zitnik

    (Harvard University)

  • Jure Leskovec

    (Stanford University
    Chan Zuckerberg Biohub)

Abstract

Most diseases disrupt multiple proteins, and drugs treat such diseases by restoring the functions of the disrupted proteins. How drugs restore these functions, however, is often unknown as a drug’s therapeutic effects are not limited to the proteins that the drug directly targets. Here, we develop the multiscale interactome, a powerful approach to explain disease treatment. We integrate disease-perturbed proteins, drug targets, and biological functions into a multiscale interactome network. We then develop a random walk-based method that captures how drug effects propagate through a hierarchy of biological functions and physical protein-protein interactions. On three key pharmacological tasks, the multiscale interactome predicts drug-disease treatment, identifies proteins and biological functions related to treatment, and predicts genes that alter a treatment’s efficacy and adverse reactions. Our results indicate that physical interactions between proteins alone cannot explain treatment since many drugs treat diseases by affecting the biological functions disrupted by the disease rather than directly targeting disease proteins or their regulators. We provide a general framework for explaining treatment, even when drugs seem unrelated to the diseases they are recommended for.

Suggested Citation

  • Camilo Ruiz & Marinka Zitnik & Jure Leskovec, 2021. "Identification of disease treatment mechanisms through the multiscale interactome," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-21770-8
    DOI: 10.1038/s41467-021-21770-8
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    Cited by:

    1. Pisanu Buphamalai & Tomislav Kokotovic & Vanja Nagy & Jörg Menche, 2021. "Network analysis reveals rare disease signatures across multiple levels of biological organization," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
    2. Yiqi Huang & Valter Bergant & Vincent Grass & Quirin Emslander & M. Sabri Hamad & Philipp Hubel & Julia Mergner & Antonio Piras & Karsten Krey & Alexander Henrici & Rupert Öllinger & Yonas M. Tesfamar, 2024. "Multi-omics characterization of the monkeypox virus infection," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    3. Adrià Fernández-Torras & Miquel Duran-Frigola & Martino Bertoni & Martina Locatelli & Patrick Aloy, 2022. "Integrating and formatting biomedical data as pre-calculated knowledge graph embeddings in the Bioteque," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    4. Dongmin Bang & Sangsoo Lim & Sangseon Lee & Sun Kim, 2023. "Biomedical knowledge graph learning for drug repurposing by extending guilt-by-association to multiple layers," Nature Communications, Nature, vol. 14(1), pages 1-17, December.

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