Evolution of resilience in protein interactomes across the tree of life
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Abstract
Significance: The interactome network of protein–protein interactions captures the structure of molecular machinery that underlies organismal complexity. The resilience to network failures is a critical property of the interactome as the breakdown of interactions may lead to cell death or disease. By studying interactomes from 1,840 species across the tree of life, we find that evolution leads to more resilient interactomes, providing evidence for a longstanding hypothesis that interactomes evolve favoring robustness against network failures. We find that a highly resilient interactome has a beneficial impact on the organism’s survival in complex, variable, and competitive habitats. Our findings reveal how interactomes change through evolution and how these changes affect their response to environmental unpredictability.
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CNP and ATP5PO may be functionally related or operate together to mediate phenotypic features associated with UDN-P9's disease and hypomyelinating leukodystropy-20.
“suggesting that they may be functionally related69–71 or operate together72,73 to mediate phenotypic features associated with UDN-P9’s disease and hypomyelinating leukodystropy-20”
From Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases (2025) · cites this paper as [71]
The Results section, “Modeling Resilience of the Interactome” (page unavailable), says protein interactions contribute to biological function and that disrupting them can increase disease risk. This supports the general possibility that proteins operate together, but the paper does not identify CNP and ATP5PO as partners or link them to UDN-P9’s disease or hypomyelinating leukodystrophy-20. (LLM, conf=0.82)
Experimental — accuracy not yet measuredThese strategies identify therapeutic candidates based on their impact on cell signaling, gene expression and disease phenotypes.
“These strategies identify therapeutic candidates based on their impact on cell signaling, gene expression and disease phenotypes5,10–12.”
From A foundation model for clinician-centered drug repurposing (2024) · cites this paper as [11]
Network organizing principles connect network structure to molecular phenotypes, biological roles, disease, and health.
“The organizing principles governing networks link network structure to molecular phenotypes, biological roles, disease, and health, thus providing the conceptual grounding that, we argue, can explain the successes (and limitations) of graph representation learning and inform future development of the field.”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [15]
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Self-citation
- Authors
- Zitnik, M., Sosič, R., Feldman, M. W., & Leskovec, J.
- Affiliations
- Department of Computer Science, Stanford University, Stanford, CA 94305;
- Stanford University (US)
- Chan Zuckerberg Biohub San Francisco (US)
- Corresponding author
- Feldman, Marcus W.
- Leskovec, Jure
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2019-02-14
- Volume
- 116
- Issue
- 10
- Pages
- 4426-4433
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC6410798
- OpenAlex
- W2950411297
- Cited by
- 130
- References
- 50
- Citations by year
- 2026: 8 · 2025: 18 · 2024: 26 · 2023: 17 · 2022: 20 · 2021: 21 · 2020: 13 · 2019: 7
- FWCI
- 5.04
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- National Science Foundation
- HHS | National Institutes of Health
- DOD | Defense Advanced Research Projects Agency
- Boeing
- Stanford Data Science Inititative
- Chan Zuckerberg BioHub
- SU | Center for Computational, Evolutionary and Human Genomics, Stanford University
- John Templeton Foundation (13337)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
- Keywords
- protein-protein interaction network, network robustness, network failures, tree of life, bacteria, environmental variability, protein family evolution, network topology, interactome rewiring
- MeSH terms
- Biological Evolution, Species Specificity, Proteome, Protein Interaction Mapping
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