Systems-level analyses of protein-protein interaction network dysfunctions via epichaperomics identify cancer-specific mechanisms of stress adaptation
Systems-level assessments of protein-protein interaction (PPI) network dysfunctions are currently out-of-reach because approaches enabling proteome-wide identification, analysis, and modulation of context-specific PPI changes in native (unengineered) cells and tissues are lacking. Herein, we take ad...
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Veröffentlicht in: | Nature communications 2023-06, Vol.14 (1), p.3742-26, Article 3742 |
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Sprache: | eng |
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Zusammenfassung: | Systems-level assessments of protein-protein interaction (PPI) network dysfunctions are currently out-of-reach because approaches enabling proteome-wide identification, analysis, and modulation of context-specific PPI changes in native (unengineered) cells and tissues are lacking. Herein, we take advantage of chemical binders of maladaptive scaffolding structures termed epichaperomes and develop an epichaperome-based ‘omics platform, epichaperomics, to identify PPI alterations in disease. We provide multiple lines of evidence, at both biochemical and functional levels, demonstrating the importance of these probes to identify and study PPI network dysfunctions and provide mechanistically and therapeutically relevant proteome-wide insights. As proof-of-principle, we derive systems-level insight into PPI dysfunctions of cancer cells which enabled the discovery of a context-dependent mechanism by which cancer cells enhance the fitness of mitotic protein networks. Importantly, our systems levels analyses support the use of epichaperome chemical binders as therapeutic strategies aimed at normalizing PPI networks.
Epichaperomics allow the study of protein-protein interactions and their alterations, but probes have been limited to capturing HSP90 epichaperomes. Here, the authors introduce and validate a toolset of HSP70 epichaperome ligands, and use them in epichaperomics to identify a mechanism with which cancer cells can enhance the fitness of mitotic protein networks. |
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ISSN: | 2041-1723 2041-1723 |
DOI: | 10.1038/s41467-023-39241-7 |