Browsing by Subject "Mycobacterium bovis"

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    Population genetic frameworks and functional genomics of Mycobacterium bovis
    (2012-09) Joshi, Deepti J.
    Bovine tuberculosis is a zoonotic infection of cattle caused by Mycobacterium bovis. Approximately one-third of the world's population is infected with M. tuberculosis or M. bovis, "the world's most successful pathogen", the majority in developing countries. The global spread and increasing severity of tuberculosis are due in part to the high number of individuals infected with HIV, and in part to the increasing intensity of human-animal interactions as land use patterns around the globe rapidly change. The resistance of M. bovis to two frontline drugs used to treat tuberculosis--isoniazid and pyrazinamide--threatens to return tuberculosis-associated mortality rates to those of the pre-antibiotic era. The severe and growing threat of M. bovis necessitates rapid, thorough national and international surveillance of strain distribution dynamics in the population. To date, piecemeal analysis of Mycobacterium bovis genomes and conventional genotyping methods have not themselves lent to a comprehensive resolution of its genetic diversity to explain the wide range of disease phenotypes caused by this zoonotic pathogen. Conventional genotyping methods target small hypervariable regions on the genome of M. bovis and provide anonymous allelic information insufficient to develop M. bovis phylogeny. Genome-wide single nucleotide polymorphisms (SNPs) studies in M. tuberculosis have shown sufficient resolution to develop trait-allele associations. We hypothesized that genetic and phenotypic diversity in M. bovis is enciphered in their genomes. To study genetic variations we first interrogated the M. bovis genome for 350 loci including geneic (n =306) and intergeneic (n =44) regions for SNPs. A collection of 75 M. bovis isolates associated with bovine bovine tuberculosis outbreaks in the US between 1990-2009 and isolated from a variety of mammalian hosts - cattle (n=25), deer (n=6), elk (n=10), elephant (n=2), swine (n=7), and humans (n=24) were used for the study. Sixty-one M. tuberculosis isolates from human, primates, birds, and elephants were also included in the analysis. Based on 206 variant SNPs among the M. bovis strains, five major clusters consistent with epidemiologic and other strain-typing information were identified. Forty-nine of the 51 human M. tuberculosis isolates were identical at the 350 loci. This SNP based phylogeny provides new insights into the evolution of M. bovis and a gateway to study strain genotype-disease phenotype correlations that we next undertook in an in vitro infection model of the disease with 4 virulent M. bovis strains isolated from human (n=1), cattle (n=2) and deer (n=1). We investigated their virulence based on entry and survival in macrophages and relative gene expression profile of previously identified virulence genes. The results revealed that the 4 strains had differential survival patterns in the macrophage mode coupled with a variation in relative gene expression profile for 6 six virulence-associated genes mce4C, PE6, speE, mmpL12. These studies led me to conclude that M. bovis isolates from diverse geographic origins and host species represent an array of genetic profiles that may potentially relate to their phenotypic variation. Next, to improve resolution of genomic variability among M. bovis strains circulating in the United States, we undertook genome sequencing of 2 strains based on phylogeny developed in the SNP study. The genome of M. bovis Corsentino comprises a circular chromosome of 4307383 bp with average G+C content of 65.4% and with 4008 predicted protein-coding regions. The genome of M. bovis NE elk comprises a circular chromosome of 4302584 bp with an average G+C content of 65.4% and with 4009 predicted protein coding sequences. Genome comparisons against the UK origin reference strain AF2122/97 did not reveal any unique genes or large sequence polymorphisms. A total of 1139 and 1184 SNPs were identified in Corsentino and NE elk genomes when compared to AF2122/97 genome, respectively. Comparison of M. bovis Corsentino and M. bovis NE elk genomes identified ~900 SNPs between them. Comparative genomics with other members of the Mycobacterium Tuberculosis Complex revealed a high percentage of sequence similarity between the strains. Thus, this study provides new evidence in favor of low genetic variability in this organism, suggesting variations in gene expression and post-transcriptional or post-translational regulation events as the likely sources of host specificity and phenotypic variation. Alternately, we reasoned that host genetics may contribute significantly to the range of pathology and transmission cycles seen in bovine tuberculosis. The restricted allelic variation among M. bovis strains also supports the contention that long-term host-pathogen co-evolution has likely selected a few successful organisms. We next set out to explore the biology of granuloma by transcriptional profiling of M. bovis during its infection cycle within the host. This study aimed to decipher mechanisms of pathogenecity and to identify virulence markers of M. bovis and to associate host responses within a granuloma. Mediastinal lymph nodes from two experimentally infected cattle and two age matched control cattle were obtained for the study. The infected animals displayed characteristic granulomatous pathology consistent with bovine tuberculosis. Total RNA was extracted and enriched for bacterial mRNA. The enriched samples were submitted for next-gen sequencing employing the Illumina RNA-Seq Platform for transcriptomics profiling. The contigs obtained from the sequencing were assembled against the bacterial reference genome of M. bovis strain AF2122/97 and the bovine genome (Bos taurus) to build the gene expression profiles of the bacteria as well as the host. However the enrichment protocol used failed, leading to poor quality of bacterial sequences and no significant gene expression profile could be obtained for the host sequences. We would recommend a re-evaluation and standardization of RNA extraction techniques for future studies. In conclusion, our studies identified that SNP based genotyping was successful in building a phylogeny among isolates of M. bovis from a variety of hosts and geographic locations. We further demonstrated that SNP genotypic variations correlated with intra-macrophage survival. Future studies should use these genotypically well-characterized strains to evaluate pathogenesis of bovine tuberculosis at the cellular and molecular levels. We demonstrated by complete genome sequencing of 2 isolates that this organism has undergone severe evolutionary bottleneck resulting in host specialization to the bovine host as indexed by the restricted allelic variation. Future analyses should study genomewide SNPs, their location on genomes, and whether they result in amino acid changes or not, to decipher the extent of selective evolution M. bovis has undergone in the bovine host. Finally, while our transcriptional analysis of the granuloma failed to provide information, these studies should be repeated with further refinements in techniques to enable the elucidation of host-pathogen interaction as it occurs inside a granuloma.
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    Using Bioinformatics and Data-mining Techniques to Transform Whole Genome Sequencing Data and Survey Data of Mycobacterial Pathogens to Epidemiological Inferences
    (2020-05) Wang, Yuanyuan
    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.