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Multidrug Intrinsic Resistance Factors in Staphylococcus aureus Identified by Profiling Fitness within High-Diversity Transposon Libraries
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 nont...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Society for Microbiology
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4992970/ https://www.ncbi.nlm.nih.gov/pubmed/27531908 http://dx.doi.org/10.1128/mBio.00950-16 |
Sumario: | 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. |
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