The antimicrobial resistome of peromyscus and their environment:
Thomas, Jesse C
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Contamination of the environment by heavy metals is a widespread and rapidly increasing global issue. Common sources of heavy metal pollution include coal-fired power plants, mining and industrial wastes, vehicle emissions, and agriculture. Elevated metal levels have been observed in urban topsoils, runoff, rivers, and coastal zones, despite national regulatory standards limiting the maximum contaminant levels. Furthermore, because metals and metal-containing compounds can persist in the environment and bioaccumulate in living tissues, they can provide a long-term selective pressure on bacteria. In addition, current evidence suggests that heavy metals can indirectly co-select antibiotic resistance in bacteria, an issue that presents serious public health concerns. Resistance determinants are frequently located on mobile genetic elements (e.g. integrons, transposons, and plasmids), which can rapidly be disseminated among phylogenetically diverse microorganisms via horizontal gene transfer. Therefore, the biological processes that drive the co-selection of antibiotic resistance following exposure to heavy metals may also facilitate the spread of multi-resistant human pathogens. In this context, the microbiome of wild animals, particularly those from areas near sources of contamination, provide interesting models for examining the prevalence of antimicrobial resistance. In this dissertation, I investigate the influence of heavy metals on the microbiome of wild Peromyscus and their environment. In the first study, I explore how heavy metals in the environment influence the structure, diversity, and co-abundance of soil microbial communities (archaea, bacteria, fungi) using a combination of Illumina based 16S rRNA gene and ITS amplicon sequencing and network-based analysis. I also examine the co-occurrence of heavy metal and antibiotic resistance, and investigate the potential co-selection of antimicrobial resistance using predictive functional profiling. In the second study, I explore how exposures to heavy metals may modulate the co-occurrence of heavy metal and antibiotic resistance, co-selection of antimicrobial resistance, and host dysbiosis in the gut microbiota of wild Peromyscus using a combination of Illumina based 16S rRNA gene sequencing, predictive functional profiling, and network-based analysis. In the third study, I examine how exposures to heavy metals and a ciprofloxacin treatment differentially affect the abundance and diversity of the gut microbiota of captive Peromyscus leucopus using Illumina-based 16S rRNA gene sequencing. I also examine the co-selection of antibiotic resistance in gut microbiota following heavy metal exposures using predictive functional profiling and network-based analysis.