Nontuberculous mycobacterial lung diseases, primarily due to M. avium complex (MAC), is an increasing clinical problem nationwide and now overtakes domestic TB in terms of morbidity and mortality. It is also harder to treat and results in poorer outcomes despite longer drug regimens. In this context NIH issued a notice AI-17-016 to request applications for NTM diseases. Here we present a proposal from the University of Virginia, with collaborators at the Virginia Department of Health and Virginia Tech, that addresses many of the needs that were cited. First, we will perform whole genome sequencing of MAC isolates from all of our state's MAC lung disease patients to discern relapse versus reinfection and the environmental sources of acquisition. Second we will utilize a state-wide cohort of new MAC lung disease patients to correlate clinical outcomes with MAC species, drug susceptibility, serum drug levels, and biofilm bioassay. Innovation includes the use of a state-wide epidemiological cohort, exquisite resolution with extensive preliminary data of whole genome sequencing to discern species and mixed infections, a comprehensive state-wide serum drug monitoring program (as we have done already for TB), and expertise in molecular diagnostics and genotypic-phenotypic correlations. In sum, for this most vexing clinical problem of MAC lung disease we bring to bear extensive preliminary data, unique expertise, innovative technology, cohesive collaborations, and synergistic aims.
M. avium complex (MAC) lung disease is a growing clinical problem that surpasses that of TB, particularly in certain states including Virginia. We will utilize a state-wide clinical cohort of all MAC lung disease patients to understand what features of the organism (such as the species or subtype or drug resistance profile), the host (such as antibiotic drug absorption), and the environment (such as household exposure to MAC) are most important in predicting clinical outcome. The sum of this research will direct future therapies and management algorithms for this difficult disease.
