Targeting stressor-induced dysfunctions in protein–protein interaction networks via epichaperomes Editorial


Authors: Ginsberg, S. D.; Sharma, S.; Norton, L.; Chiosis, G.
Title: Targeting stressor-induced dysfunctions in protein–protein interaction networks via epichaperomes
Abstract: Diseases are manifestations of complex changes in protein–protein interaction (PPI) networks whereby stressors, genetic, environmental, and combinations thereof, alter molecular interactions and perturb the individual from the level of cells and tissues to the entire organism. Targeting stressor-induced dysfunctions in PPI networks has therefore become a promising but technically challenging frontier in therapeutics discovery. This opinion provides a new framework based upon disrupting epichaperomes – pathological entities that enable dysfunctional rewiring of PPI networks – as a mechanism to revert context-specific PPI network dysfunction to a normative state. We speculate on the implications of recent research in this area for a precision medicine approach to detecting and treating complex diseases, including cancer and neurodegenerative disorders. © 2022 Elsevier Ltd
Keywords: genetics; neoplasm; neoplasms; protein analysis; protein interaction mapping; personalized medicine; alzheimer's disease; protein interaction maps; cancer; humans; human; precision medicine; epichaperome; neurodegenerative disorders; network medicine; protein network dysfunction; protein–protein interaction networks
Journal Title: Trends in Pharmacological Sciences
Volume: 44
Issue: 1
ISSN: 0165-6147
Publisher: Cell Press  
Date Published: 2023-01-01
Start Page: 20
End Page: 33
Language: English
DOI: 10.1016/j.tips.2022.10.006
PUBMED: 36414432
PROVIDER: scopus
PMCID: PMC9789192
DOI/URL:
Notes: The MSK Cancer Center Support Grant (P30 CA008748) is acknowledged in the PDF. Corresponding author is MSK author Gabriela Chiosis -- Export Date: 3 January 2023 -- Source: Scopus
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MSK Authors
  1. Larry Norton
    758 Norton
  2. Gabriela Chiosis
    279 Chiosis
  3. Sahil Sharma
    31 Sharma