Systems genetics of tuberculosis. Overall program abstract: mycobacterium tuberculosis (MTB) infection outcomes are highly variable

Most individuals contain the infection and remain asymptomatic for a lifetime. A fraction of those infected develop disease; and even among these patients, the timing, location, and presentation of the pathology is remarkably diverse. This variability is also evident in the efficacy of both chemotherapy and vaccination. While this heterogeneity represents a great challenge for TB control efforts, the biological determinants of MTB infection outcome have been difficult to define due to the complexity of contributing factors. Both human and bacterial populations are genetically and phenotypically diverse, and interactions between this genetic complexity and a variety of environmental factors ultimately determines clinical course. To overcome this complexity, we leveraged new mammalian and bacterial genetic resources to create a model system that can be used to study the effect of each of these variables in isolation and in combination. Host diversity is incorporated using mice from the collaborative cross (CC) and diversity outbred (do) resources, newly generated reference panels that reflect the diversity of an outbred population. Bacterial variation is incorporated using large panels of MTB strains that reflect both naturally- and experimentally-generated diversity. Controlled interventions, such as vaccination, can be overlaid on this host- pathogen diversity. Using this highly-tractable system, our program discovered that the combinatorial complexity of these interactions converge on a discrete number of biological pathways that influence outcome. Supported by the cutting-edge mouse and human genetic, genomic, and analytical resources provided by the cores, our sgtb program will now focus on parallel studies in this model system and human clinical samples to identify and dissect the pathways that influence outcome. This structure will ensure that mechanistic mouse studies are linked to relevant human phenotypes. Ultimately, these insights will be leveraged to develop more precise correlates of risk, more specific diagnostics based on clinical phenotypes, and new strategies for the optimization and preclinical development of vaccines.