Multidrug Intrinsic Resistance Factors inStaphylococcus aureusIdentified by Profiling Fitness within High-Diversity Transposon Libraries

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Multidrug Intrinsic Resistance Factors inStaphylococcus aureusIdentified by Profiling Fitness within High-Diversity Transposon Libraries

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Title: Multidrug Intrinsic Resistance Factors inStaphylococcus aureusIdentified by Profiling Fitness within High-Diversity Transposon Libraries
Author: Rajagopal, Mithila; Martin, Melissa Janet; Santiago, Marina Joy ORCID  0000-0002-5446-3452 ; Lee, Wonsik; Kos, Veronica N.; Meredith, Timothy Charles; Gilmore, Michael S.; Kahne, Suzanne Walker

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Citation: Rajagopal, Mithila, Melissa J. Martin, Marina Santiago, Wonsik Lee, Veronica N. Kos, Tim Meredith, Michael S. Gilmore, and Suzanne Walker. 2016. Multidrug Intrinsic Resistance Factors inStaphylococcus aureusIdentified by Profiling Fitness Within High-Diversity Transposon Libraries. mBio 7, no. 4: e00950–16. doi:10.1128/mbio.00950-16.
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Abstract: Staphylococcus aureus is a leading cause of life-threatening infections worldwide. The MIC of an antibiotic against S. aureus, as well as other microbes, is determined by the affinity of the antibiotic for its target in addition to a complex interplay of many other cellular factors. Identifying nontarget factors impacting resistance to multiple antibiotics could inform the design of new compounds and lead to more-effective antimicrobial strategies. We examined large collections of transposon insertion mutants in S. aureus using transposon sequencing (Tn-Seq) to detect transposon mutants with reduced fitness in the presence of six clinically important antibiotics-ciprofloxacin, daptomycin, gentamicin, linezolid, oxacillin, and vancomycin. This approach allowed us to assess the relative fitness of many mutants simultaneously within these libraries. We identified pathways/genes previously known to be involved in resistance to individual antibiotics, including graRS and vraFG (graRS/vraFG), mprF, and fmtA, validating the approach, and found several to be important across multiple classes of antibiotics. We also identified two new, previously uncharacterized genes, SAOUHSC_01025 and SAOUHSC_01050, encoding polytopic membrane proteins, as important in limiting the effectiveness of multiple antibiotics. Machine learning identified similarities in the fitness profiles of graXRS/vraFG, SAOUHSC_01025, and SAOUHSC_01050 mutants upon antibiotic treatment, connecting these genes of unknown function to modulation of crucial cell envelope properties. Therapeutic strategies that combine a known antibiotic with a compound that targets these or other intrinsic resistance factors may be of value for enhancing the activity of existing antibiotics for treating otherwise-resistant S. aureus strains.
Published Version: doi:10.1128/mBio.00950-16
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:33867379
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