List of participants and affiliations:
- (Team Leader) Marcus Nguyen, ANL (Argonne National Lab)
- (Writer) Nicole Bowers, ANL (Argonne National Lab)
- (Tech Lead) Clark Cucinell, ANL (Argonne National Lab)
- (
) Curtis Hendrickson, UAB (University of Alabama at Birmingham) - ( ) Don Dempsey, UAB (University of Alabama at Birmingham)
- ( ) Andrew Warren, BII (University of Virginia Biocomplexity Institute and Initiative)
The primary goal of this project is to establish robust correlations between the results of Antimicrobial Susceptibility Testing (AST) and the presence of Antimicrobial Resistance (AMR) genes, both plasmid-borne and chromosomal. By systematically analyzing bacterial isolates, we aim to identify specific patterns in resistance profiles that correspond to the presence of particular AMR genes and their location on plasmids or the chromosome. This will provide valuable insights into the mechanisms by which resistance is conferred and transmitted within microbial populations, potentially informing future therapeutic strategies and public health interventions aimed at combating the spread of antimicrobial resistance.
This project will employ a 3-pronged approach to systematically identify AMR genes on both plasmids and chromosomal DNA, and correlate them with antimicrobial resistance phenotypes. Using open-source data, including sequences from NCBI, we will first predict whether genomic sequences or contigs originate from plasmids or chromosomal regions. Next, we will apply AMR gene identification algorithms to detect the presence of resistance genes. Additionally, we augment the AST data using phenotype prediction models, thereby increasing our set of antimicrobial resistance profiles. By integrating plasmid predictions with AMR gene data, we will categorize AMR genes based on their genomic location (plasmid vs. chromosomal). These results will then be correlated with the phenotypic resistance data to assess the relationship between the genetic context of AMR genes and observed antimicrobial susceptibility. This approach will enable a detailed analysis of the genomic architecture of resistance and its phenotypic expression.
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