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000009872 02470 $$ahttps://doi.org/10.7554/eLife.74104$$2doi
000009872 037__ $$aTEXTUAL
000009872 041__ $$aeng
000009872 245__ $$aDefining hierarchical protein interaction networks from spectral analysis of bacterial proteomes
000009872 269__ $$a2022-08-17
000009872 336__ $$aArticle
000009872 520__ $$aCellular 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 phenotypesOur results are validated through gene-set enrichment analysis and comparison to existing exper-imentally 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.
000009872 540__ $$a<p> © 2022, Zaydman et al.</p> <p>This article is distributed under the terms of the <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">Creative Commons Attribution License</a>, which permits unrestricted use and redistribution provided that the original author and source are credited.</p>
000009872 542__ $$fCC BY
000009872 594__ $$a<p class="paragraph">All data relevant to this manuscript can be downloaded, in Table format, at <a href="https://www.github.com/arjunsraman/Zaydman_et_al" data-behaviour-initialised="true">https://www.github.com/arjunsraman/Zaydman_et_al</a> copy archived at <a href="https://archive.softwareheritage.org/swh:1:dir:5f544dd668a2ecb87cfcd875bb93348ef3c4a4ac;origin=https://www.github.com/arjunsraman/Zaydman_et_al;visit=swh:1:snp:59aa1342ed544d879235d8da0052e3323b5e9600;anchor=swh:1:rev:b2c1091aafb726d88a925ad16e07f617a44c8cdc" data-behaviour-initialised="true">swh:1:rev:b2c1091aafb726d88a925ad16e07f617a44c8cdc</a>. All tables are available for download in .zip format. All code used for analyses contained within the manuscript can also be found within the same github repository; please refer to Readme.m and Supplemental_Code_9_23_2020.m for relevant Matlab scripts and to reproduce results.</p>
000009872 6531_ $$acomplexity
000009872 6531_ $$aemergence
000009872 6531_ $$aPseudomonas aeruginosa
000009872 6531_ $$aproteome
000009872 6531_ $$aprotein interaction networks
000009872 6531_ $$ahierarchy
000009872 690__ $$aBiological Sciences Division
000009872 691__ $$aPathology
000009872 692__ $$aCenter for the Physics of Evolving Systems
000009872 692__ $$aDuchossois Family Institute
000009872 7001_ $$1https://orcid.org/0000-0002-4236-1459$$2ORCID$$aZaydman, Mark A.$$uWashington University in St. Louis
000009872 7001_ $$aLittle, Alexander S.$$uUniversity of Chicago
000009872 7001_ $$aHaro, Fidel$$uUniversity of Chicago
000009872 7001_ $$aAksianiuk, Valeryia$$uUniversity of Chicago
000009872 7001_ $$aBuchser, William J.$$uUniversity of Chicago
000009872 7001_ $$1https://orcid.org/0000-0002-7262-0968$$2ORCID$$aDiantonio, Aaron$$uWashington University in St. Louis
000009872 7001_ $$1https://orcid.org/0000-0001-8304-3548$$2ORCID$$aGordon, Jeffrey I.$$uWashington University in St. Louis
000009872 7001_ $$aMilbrandt, Jeffrey$$uWashington University in St. Louis
000009872 7001_ $$1https://orcid.org/0000-0002-0070-1953$$2ORCID$$aRaman, Arjun S.$$uUniversity of Chicago
000009872 773__ $$teLife
000009872 789__ $$eIs Version Of$$whttps://doi.org/10.1101/2021.09.28.462107
000009872 8564_ $$yArticle$$935a3f3e6-06ff-4004-ad1d-cdd047156d09$$s9487300$$uhttps://knowledge.uchicago.edu/record/9872/files/elife-74104-v2.pdf$$ePublic
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