GL20

The gastrointestinal microbiome influences local and systemic immune responses. However how the microbiome impacts infectious disease pathogenesis and antibiotic therapy outcome for Mycobacterium Tubercolosis is still poorly understood. Here we present recent work from several longitudinal studies of tuberculosis (TB) therapy in which we collected fecal samples (for microbiome analysis), sputum (for determination of Mycobacterium tuberculosis (Mtb) bacterial load), and peripheral blood (for transcriptomic analysis). We first decouple microbiome effects from pathogen sterilization by comparing standard TB therapy (HRZE) with an experimental TB treatment that did not reduce Mtb bacterial load. Specifically, machine learning modeling applied to the microbiome-transcriptome-sputum data from these two longitudinal datasets reveals that renormalization of the TB inflammatory state is associated with Mtb pathogen clearance, increased abundance of Clusters IV and XIVa Clostridia, and decreased abundance of Bacilli and Proteobacteria. We then demonstrate the generality of our results by performing a similar analysis on an independent cohorts of individuals undergoing multidrug resistant TB treatment. Our findings indicate that antibiotic-induced reduction in pathogen burden and changes in the microbiome are independently associated with treatment-induced changes of the inflammatory response of active TB, and the response to antibiotic therapy may be a combined effect of pathogen killing and microbiome driven immunomodulation.