Computational Methods for Large Scale Analysis of Microbial Genome Evolution and Application to Antibiotic Resistant Pathogens.

다국어 초록 (Multilingual Abstract)

Horizontal Gene Transfer is a powerful force shaping microbial evolution. This constant process by which genes are acquired into and excised from bacterial genomes enables an enormous capacity for rapid phenotypic evolution. HGT enables the dissemination of clinically important genes, including antibiotic resistance genes, genes mediating virulence, environmental persistence genes, and metabolic genes. Acquisition of these genes potentiates phenotypic evolution in several important contexts: increasing the capacity for transmission, enhancing the ability for infection, limiting the efficacy of antibiotic therapies, and facilitating the metabolism of new substrates. Methods to characterize the pathways by which these genes spread through bacterial populations are critical for understanding the evolution of these phenotypes and their implications for public health. In this dissertation, I develop a novel computational approach to generate core gene alignments for large numbers of bacterial genomes and implement two methods to characterize HGT events from bacterial whole-genome sequences. I then apply these methods to understand the dissemination of antimicrobial resistance genes and the evolution of carbohydrate utilization phenotypes in the microbiome. First, we developed cognac (Core Gene Alignment Concatenation), an open-source R package for generating concatenated, core gene alignments for microbial genomes. cognac rapidly identifies shared phylogenetic marker genes, creates gene alignments, and concatenates them into a single alignment for downstream phylogenetic analysis. We demonstrate that this method can efficiently handle extremely large whole-genome sequencing datasets of diverse bacterial lineages. Second, we sought to trace the spread of the KPC gene, a carbapenemase conferring broad-spectrum resistance to commonly used antibiotics for treating infections caused by Enterobacterales. Using comprehensive collections of clinical isolates from regional healthcare networks in three US states, we quantify the role of importation, clonal dissemination, and HGT on the total burden of KPC in these regions. To identify HGT events, we implemented a novel marker gene-based approach that enabled us to track KPC plasmid transfer using short-read data and identify HGT events occurring between circulating strains in the same region. Using this approach, we show that while the horizontal transfer of KPC frequently occurs in all three states, the strains and species involved and the overall contribution to the regional burden of KPC-carrying organisms differ substantially across the three states. Third, we investigated the role of HGT in common members of the human gut microbiome. We developed a novel method to identify ancestral HGT loci by identifying core genes with significantly greater than expected divergence from the assigned species and greater similarity to the putative donor species. We then characterized HGT loci with conserved synteny and collinearity between donor and recipient species that have enabled pan-genome expansion and evolution of new phenotypes. This approach illustrates that HGT is common between two closely related species of Bacteroides, with many loci exhibiting evidence of HGT. These data, in conjunction with molecular data, provide insight into the breadth and complexity of metabolism in the microbiome and the underlying genomic events that enable the evolution of complex phenotypes. In summary, this body of work establishes computational tools with broad application in computational genomics and genomic epidemiology: enabling phylogenetic analysis of large genomic datasets, identifying recent plasmid-mediated transfer occurring within and across regional healthcare networks, and identification of ancestral HGT loci carried on the chromosome mediating the development of complex phenotypes in the microbiome.