Session 3, Abstract 16


Samantha Hall*, Victoria Guzman*, Jacob Henderson, Lucas Ustick, Sarah Pyle, and Claudia Castilleja (Celeste Brown, Eva Top, Ryan Botts, David Cummings), Point Loma Nazarene University, Departments of Biology and Mathematics, 3900 Lomaland Dr., San Diego, CA 92106

The emergence of antibiotic-resistant bacteria is of growing prominence within clinical, community, and environmental settings. Components from these different settings combine in urban watersheds, which become reservoirs for diverse microbial communities. This problem is significantly exacerbated by inadequate human wastewater treatment. Of particular interest are clinically relevant antibiotic resistance genes on non-chromosomal DNA molecules known as plasmids. Plasmids are mobile genetic elements often capable of horizontal gene transfer, contributing to the generation of so-called ‘superbugs’, multidrug-resistant pathogenic bacteria that are nearly impossible to treat. In this study, we analyzed the genomic and phenotypic characteristics of unique plasmids extracted from sediments of the Tijuana River Estuary, a local urban watershed impacted by all of the sources described above. A total of 52 plasmids were initially isolated via enrichment methods, using cefotaxime as selective pressure. Of the original 52 plasmids, four plasmids conferring cefotaxime resistance were chosen for further analysis. Genomic analysis showed that these four plasmids harbored ß-lactamase genes, CTX-M-15 or CTX-M-55, which contributed to their ability to phenotypically express clinical resistance to many ß-lactams antibiotics in addition to other antibiotic drug classes including aminoglycosides, tetracyclines, and chloramphenicol. The original hosts of the four select plasmids were strains of E. coli and all were able to transfer between E. coli, Aeromonas, and Pseudomonas (with varying results for different plasmids). Overall, the spread of drug resistance plasmids in urban wetlands is a threat that must be addressed through local and global genotypic and phenotypic data analysis

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