Using Bioinformatics and Data-mining Techniques to Transform Whole Genome Sequencing Data and Survey Data of Mycobacterial Pathogens to Epidemiological Inferences

Abstract

Like scanning barcodes on shipping labels, Whole Genome Sequencing (WGS) of pathogen genomes isolated from infected hosts provides the ultimate resolution to track the spread of disease. This is possible because transmission events are recorded in the mutations of pathogen genomes: the mutations are passed down through the chain of transmission between infected hosts. In other words, hosts sharing similar nucleotide sequences can be linked to investigate their contact history. Understanding how diseases spread at a local scale is important because disease control and surveillance strategies need to adapt to the heterogeneity in disease risk at different locations. In the local epidemiological investigations, however, a key challenge is to differentiate individuals with genetically similar WGS patterns. In this thesis, we discuss using both genomic and behavioral epidemiology to understand the local spread of two Mycobacterial pathogens, Mycobacterium bovis (M. bovis) and Mycobacterium avium subsp. paratuberculosis (Map). Both pathogens cause chronic non-treatable infections in cattle but lead to different disease scenarios: a regional outbreak of M. bovis in Minnesota, and persistent infections of Map among four U.S cattle herds in Minnesota, New York, Pennsylvania, and Vermont. First, we present two novel methods to computationally identify samples infected with multiple strains. The first method is a highly scalable tool for fast screening mixtures samples in outbreak datasets. The method generates artificial admixtures by averaging the principal component coordinates of each sample to detect true admixtures in close proximity to the artificial ones. The second method is developed for endemic diseases with persistent strains circulating and evolving in a local environment. The method utilizes temporal Non-negative Matrix Factorization to derive an evolving panel of template strains from time-series WGS data. Then, we discuss the use of dimension reduction techniques to improve visualizations of the population structure from genomic data at a local scale. Our results show that t-distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) can effectively reduce the noise from outliers in WGS data. Finally, we showcase the importance of incorporating behavioral data to the policymaking of infectious diseases. Specifically, we designed a factorial survey to examine how slaughtering policy and government indemnity of bovine tuberculosis can impact the purchasing behavior of cattle producers. Collectively, our interdisciplinary research integrated WGS bioinformatics, data-mining techniques and behavior epidemiology to understand the local spread of mycobacterial diseases. Our admixture detection methods highlighted the role of rare but informative heterozygous variants in recovering genealogical relationships between infected hosts. In addition, our qualitative comparison analysis demonstrated the use of dimension reduction techniques to improve resolution of visualizing herd-level population structures. Last but not least, our factorial survey revealed the complexity of human risk perception in the context of infectious diseases.