A macroevolution-inspired approach to reveal novel antibiotic resistance mechanisms

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The preprint presents a macroevolution-inspired study that builds a library of representative Mycobacterium species and determines their antibiotic resistance profiles to enable systematic multispecies comparisons. By analyzing resistance patterns in the context of other closely related organisms, the authors identify species with exceptional traits and use this genus-level strategy to discover new resistance determinants, including a previously unrecognized rifamycin-inactivating enzyme found across a wide range of bacterial genera. A key caveat is that the work is a Research Square preprint and is explicitly stated as not peer reviewed. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract With the continuous rise in antibiotic resistance, novel methods that can reveal currently unknown antibiotic resistance mechanisms are essential to prepare and inform health responses. Here we built a library of species representative of the genus Mycobacterium and determined their antibiotic resistance profiles, allowing systematic multispecies comparisons. Analyzing antibiotic resistance in the context of other closely related organisms revealed species with truly exceptional traits, thus providing a solid starting point for the exploration of novel determinants of antibiotic resistance. We illustrate the utility of this genus-level approach to discovery of novel traits by characterizing a previously unrecognized rifamycin-inactivating enzyme that is present in a wide range of bacterial genera.
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A macroevolution-inspired approach to reveal novel antibiotic resistance mechanisms | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A macroevolution-inspired approach to reveal novel antibiotic resistance mechanisms Luiz Pedro de Carvalho, Fernanda Subtil, Teresa Machado, Holly Douglas, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3838489/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract With the continuous rise in antibiotic resistance, novel methods that can reveal currently unknown antibiotic resistance mechanisms are essential to prepare and inform health responses. Here we built a library of species representative of the genus Mycobacterium and determined their antibiotic resistance profiles, allowing systematic multispecies comparisons. Analyzing antibiotic resistance in the context of other closely related organisms revealed species with truly exceptional traits, thus providing a solid starting point for the exploration of novel determinants of antibiotic resistance. We illustrate the utility of this genus-level approach to discovery of novel traits by characterizing a previously unrecognized rifamycin-inactivating enzyme that is present in a wide range of bacterial genera. Biological sciences/Microbiology/Antimicrobials/Antimicrobial resistance Biological sciences/Microbiology/Bacteria/Bacterial systems biology Full Text Additional Declarations The authors declare no competing interests. Supplementary Files Supplementarytable1.xlsx Supplementary Data Set 1 Supplementarytable2.csv Supplementary Data Set 2 Supplementarytable3draft.xlsx Supplementary Data Set 3 Supplementarytable4.xlsx Supplementary Data Set 4 SupplementaryInformation.docx Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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