Computational models identifying within-host evolution of pathogens using high-throughput DNA sequencing

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Antimicrobial resistance (AMR) is a global threat to animals. However, its occurrence and transmission are poorly understood for pathogens infecting important animals, e.g. beef and dairy cattle. Each infection represents a population of multiple strains of a pathogen with different sensitivities to antimicrobials. Often AMR occurs when this within-host population evolves under the selective pressure of drug treatments in conjunction with immune responses. Huge leaps in next-generation DNA sequencing (NGS) technology allow us to sequence within-host populations of a pathogen without the need to grow them in the laboratory (i.e., uncultured), offering materials to speed up the characterization of within-host evolution and transmission of AMR. However, the lack of suitable computational models blocks this promising research. First, in the uncultured sample, pathogen DNA of multiple strains is collected and sequenced together in pools. To access genomic information at individual-level, novel computational methods are needed to estimate the identity and frequency of strains or haplotypes. This is a fundamental technical roadblock to many critical analyses in pooled sequencing. Second, assuming the above technical barriers are conquered, to scientifically examine genes and strains under selection, standard population genetic models developed for multicellular organisms need to be extended in the context of within-host pathogen evolution. For instance, what should we use as a neutral (without selection) expectation of strain or haplotype diversity, and how do we best estimate alleles of ancestors, the founding strains of the pathogen acquired by a host? My long-term goal is to understand the mechanism of AMR by genomic analysis of within- and between-host evolution and ultimately contribute to its prevention and control in agriculture. Supported by this NSERC Discovery Grant, my first short-term goal is to develop novel computational models to resolve the methodological challenges described. This paves the way for my second short-term goal to benefit the research of AMR of Treponema spp. and E. coli that infect cattle in Alberta, by identifying genetic underpinnings of within-host selection.Fundamentally, the proposed work augments the standard theoretical models of pathogen evolution to better account for within-host environments. Practically, the ability to identify genes that confer AMR will be relevant to agricultural practice in Canada.