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The MIC of an antibiotic against\n            <jats:named-content content-type=\"genus-species\">S. aureus<\/jats:named-content>\n            , 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\n            <jats:named-content content-type=\"genus-species\">S. aureus<\/jats:named-content>\n            using\n            <jats:underline>t<\/jats:underline>\n            ransposo\n            <jats:underline>n seq<\/jats:underline>\n            uencing (Tn-Seq) to detect transposon mutants with reduced fitness in the presence of six clinically important antibiotics\u2014ciprofloxacin, 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\n            <jats:italic>graRS<\/jats:italic>\n            and\n            <jats:italic>vraFG<\/jats:italic>\n            (\n            <jats:italic>graRS\/vraFG<\/jats:italic>\n            ),\n            <jats:italic>mprF<\/jats:italic>\n            , and\n            <jats:italic>fmtA<\/jats:italic>\n            , validating the approach, and found several to be important across multiple classes of antibiotics. We also identified two new, previously uncharacterized genes,\n            <jats:italic>SAOUHSC_01025<\/jats:italic>\n            and\n            <jats:italic>SAOUHSC_01050<\/jats:italic>\n            , encoding polytopic membrane proteins, as important in limiting the effectiveness of multiple antibiotics. Machine learning identified similarities in the fitness profiles of\n            <jats:italic>graXRS\/vraFG<\/jats:italic>\n            ,\n            <jats:italic>SAOUHSC_01025<\/jats:italic>\n            , and\n            <jats:italic>SAOUHSC_01050<\/jats:italic>\n            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\n            <jats:named-content content-type=\"genus-species\">S. aureus<\/jats:named-content>\n            strains\n            <jats:italic>.<\/jats:italic>\n          <\/jats:p>\n          <jats:p>\n            <jats:bold>IMPORTANCE<\/jats:bold>\n            Bacterial resistance to every major class of antibiotics has emerged, and we are entering a \u201cpost-antibiotic era\u201d where relatively minor infections can lead to serious complications or even death. The utility of an antibiotic for a specific pathogen is limited by both intrinsic and acquired factors. Identifying the repertoire of intrinsic resistance factors of an antibiotic for\n            <jats:named-content content-type=\"genus-species\">Staphylococcus aureus<\/jats:named-content>\n            , a leading cause of community- and hospital-acquired infections, would inform the design of new drugs as well as the identification of compounds that enhance the activity of existing drugs. To identify factors that limit the activity of antibiotics against\n            <jats:named-content content-type=\"genus-species\">S. aureus<\/jats:named-content>\n            , we used Tn-Seq to simultaneously assess fitness of transposon mutants in every nonessential gene in the presence of six clinically important antibiotics. This work provides an efficient approach for identifying promising targets for drugs that can enhance susceptibility or restore sensitivity to existing antibiotics.\n          <\/jats:p>","DOI":"10.1128\/mbio.00950-16","type":"journal-article","created":{"date-parts":[[2016,8,17]],"date-time":"2016-08-17T00:48:54Z","timestamp":1471394934000},"update-policy":"https:\/\/doi.org\/10.1128\/asmj-crossmark-policy-page","source":"Crossref","is-referenced-by-count":58,"title":["Multidrug Intrinsic Resistance Factors in Staphylococcus aureus Identified by Profiling Fitness within High-Diversity Transposon Libraries"],"prefix":"10.1128","volume":"7","author":[{"given":"Mithila","family":"Rajagopal","sequence":"first","affiliation":[{"name":"Department of Microbiology and Immunobiology, Harvard Medical School, Boston, Massachusetts, USA"},{"name":"Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, 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