Defining hierarchical protein interaction networks from spectral analysis of bacterial proteomes
- Washington University in St. Louis, United States
- University of Chicago, United States
Abstract
Cellular behaviors emerge from layers of molecular interactions: proteins interact to form complexes, pathways, and phenotypes. We show that hierarchical networks of protein interactions can be defined from the statistical pattern of proteome variation measured across thousands of diverse bacteria and that these networks reflect the emergence of complex bacterial phenotypes. Our results are validated through gene-set enrichment analysis and comparison to existing experimentally-derived databases. We demonstrate the biological utility of our approach by creating a model of motility in Pseudomonas aeruginosa and using it to identify a protein that affects pilus-mediated motility. Our method, SCALES (Spectral Correlation Analysis of Layered Evolutionary Signals), may be useful for interrogating genotype-phenotype relationships in bacteria.
Data availability
All data relevant to this manuscript can be downloaded, in Table format, at www.github.com/arjunsraman/Zaydman_et_al. All tables are available for download in .zip format. All code used for analyses contained within the manuscript can also be found within the same github repository; please refer to Readme.m and Supplemental_Code_9_23_2020.m for relevant Matlab scripts and to reproduce results.
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Funding
No external funding was received for this work.
Copyright
© 2022, Zaydman et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Defining hierarchical protein interaction networks from spectral analysis of bacterial proteomes
eLife 11:e74104.
https://doi.org/10.7554/eLife.74104
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