Genetic Determinants of the hVISA Mechanism

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DESCRIPTION (provided by applicant): Genetic alterations leading to hetero-vancomycin intermediate (hVISA) expression presently defies characterization. A major problem in hVISA mechanistic studies has been the lack of isogenic vancomycin-susceptible and hVISA, especially related or isogenic real-world clinical strains. We have now characterized a clonal hVISA and vancomycin-susceptible S. aureus clinical strain set. The specific aims of this proposal are to determine: (1) genomic alterations that occur in a clinical hVISA and clones of this strain expressing elevated susceptible vancomycin MICs; (2) transcriptome alterations that occur as a result of hVISA mechanism acquisition and genetic alterations leading to elevated vancomycin susceptible MICs; and (3) transcriptome alterations due to vancomycin induction that occur in hVISA and S. aureus strains expressing elevated vancomycin susceptible MICs. The first aim will be accomplished by either 454-based genomic sequencing of a temporally isolated clonal strain set that includes hVISA and non-hVISA expressing varied low-level and elevated susceptible vancomycin MICs, followed by genome annotation and comparison, or Nimblegene comparative genomic sequencing. During this process the PI will be trained on 454-sequencing as well as the genome annotation pipelines XGI, Alpheus, and the experimental GenVar. The second and third aims will be accomplished by comparing and contrasting the transcriptomes of a clonal set of hVISA, non-hVISA strains expressing elevated susceptible vancomycin MICs and non-hVISA strains expressing low-level susceptible vancomycin MICs, isolated following growth with and without vancomycin. These aims will also be enhanced by applying an investigative transcriptome tool referred to as the Staphylococcus microarray meta-database (SAMMD). During this process the PI will be trained on the current NIAID-PFGRC-TIGR-supported S. aureus microarrays version 4 and microarray analysis software. These research objectives are designed to produce enormous datasets that will make this SCORE-funded PI competitive for traditional NIH funding.