| Mycobacterium tuberculosis (MTB) infection and subsequent tuberculosis (TB) is the second-leading cause of mortality from a single infectious agent worldwide, after the human immunodeficiency virus (HIV)1,2. In 2013, 9 million new cases of clinical tuberculosis were diagnosed and 1.5 million deaths were attributed to the disease2. An estimated 1.1 million new cases and 360,000 of the deaths occurred in people co-infected with HIV2. The immunosuppression resulting from HIV infection increases the risk of progression to active disease following new exposure to MTB, or reactivation of latent MTB in patients with prior infection 3,4. Sub-Saharan Africa is the location where most HIV/TB co-infection occurs, with 75% of all cases reporting coinfection1,2. The influence of host genetics on tuberculosis disease has been extensively studied, mostly in HIV-negative patients, and revealed that variation in pathways pertinent to macrophage and Type 1 helper T cell (TH1) signaling, among others, modulate disease risk5-11. Generally, HIV seropositive status has been viewed as a confounder in such studies and it is either adjusted for or used as an exclusion criterion.;In the current project we present a novel hypothesis for studying resistance to either TB disease or MTB infection, using the immunosuppression of HIV-positive patients to identify an extreme phenotype. Namely, we posit that HIV-positive patients living in areas endemic for MTB who do not develop TB are resistant, and that this protection has a genetic component with an effect size large enough to permit using a smaller sample size than those seen in prior genome-wide association studies (GWAS) on TB10,11. The goal of the following chapters is to evaluate this hypothesis as it pertains to resistance to TB disease and MTB infection, in single variant and epistatic models.;Background information pertinent to this project is described in part A of Chapter II, including clinical presentation, diagnostics, treatments, a summary of the worldwide burden of tuberculosis as well as a description of prior genetic variants associating with TB.;A genome-wide association study of common genetic variants and with TB resistance in HIV-positive patients is described in Chapter III. Of note, I recruited patients on-site and isolated DNA in one of the cohorts, the DarDar vaccine trial extended follow up, in Dar es Salaam, Tanzania. I also isolated DNA from samples collected in two other studies, the DarDar Women's Nutrition Study from Tanzania and the Household Contacts Study from Kampala, Uganda. The samples for the Nutrition and Household Contacts Studies were made available to us by fellow investigators. In a study combining the cohorts, we found a common variant associating at the genome-wide significance threshold in the IL12B region. Linkage disequilibrium (LD) patterns in the region suggested that the region is conserved and integrated haplotype score analyses using sub-Saharan populations demonstrated that the LD block containing rs4921437 has undergone selection. The single nucleotide polymorphism (SNP) of interest is located in an area enriched for a histone acetylation mark often found in active regulatory elements, suggesting possible functionality and a genetic-epigenetic interaction at the site. Further studies of this interaction are warranted.;Chapter IV describes two approaches used to evaluate the genetics of MTB infection. In Part A, we used a genome-wide approach to identify variants associating with MTB infection. In Part B, we validated and fine-mapped regions previously associated through genome-wide linkage analyses, SLC6A3 +/- 0.5 mb12, 2q1413, 2q21-2q24 133, 5p13-5q2213 and chromosome 11 p14.112.;The immune response to MTB infection and resultant disease is complex 8,14-17. The molecular signaling profile and extent tissue involvement change over the course of disease. Therefore, it is likely that no single factor alone can adequately explain risk of TB disease or MTB infection risk. In Chapter V we present a study in which we examined multi-locus relationships between previously associated candidate genes and TB disease in Part A, and MTB infection in Part B, using Multifactor Dimensionality Reduction (MDR) software18,19.;Chapter VII summarizes the conclusions of this project and proposes future directions. *Please refer to dissertation for footnotes. (Abstract shortened by UMI.). |
