A tissue-based approach to understanding TB-associated lung fibrosis

About This Grant

Post-TB lung disease (PTLD) contributes substantially to the overall morbidity and mortality associated with TB infection. On a histopathological level, fibrosis is a significant feature of PTLD and likely underpins many associated symptoms and clinical findings. Adjunctive therapies that could be used with traditional anti-TB antibiotics to modulate tissue destruction and pathologic remodeling could improve post-treatment lung health, quality of life, and longevity for TB survivors. To date, our insight into the pathological processes driving TB- associated fibrosis is limited, consequently limiting the development of such adjunctive treatments. In the proposed work, we will begin to address that knowledge gap by creating a baseline understanding of the molecules and cells associated with fibrogenesis during TB infection. Our work will make use of unique resources in the Steyn group, including a Human Tissue Biobank of lung specimens that represent a range of TB- associated fibrotic pathologies, and the unique resources of the Barczak laboratory, who are applying a mouse model to study pathogenic mechanisms of TB-associated fibrosis. In Aim 1, we will use our mouse model and a combination of histopathology, immunohistochemistry, and immunofluorescence to identify spatial correlates of fibrogenesis. We will first test our hypothesis that defined macrophage subsets are spatially associated with fibrogenesis (1A). The idiopathic pulmonary fibrosis (IPF) mouse model is the canonical model for fibrogenesis in lung; we will next test whether molecular and cellular factors identified in the IPF model are associated with TB-associated fibrogenesis (1B). Complementing our hypothesis-driven work, we will use spatial transcriptomics to identify novel candidate molecules, cells, and pathways (1C). In Aim 2, we will use the Steyn lab Human Tissue Bank to benchmark findings from the mouse model and identify clinical drivers of TB-associated fibrosis. We will use metadata to identify clinical correlates of four intermediate-stage fibrosis morphologies (2A). We will then test associations between fibrosis and macrophage subsets (2B), additional molecular and cellular factors relevant in the IPF model (2C), and novel cellular and molecular factors identified in Aim 1C (2D). We will explicitly compare results between mouse and human specimens. We will then test associations between clinical factors and cellular and pathway correlates of fibrosis (2E). In Aim 3, we will use µCT and artificial intelligence approaches to develop algorithms that characterize and comprehensively quantify fibrosis in human lung sections and whole mouse lungs to identify candidate microanatomic contributors to fibrogenesis during TB infection. In addition to enabling a full comparison between mouse and human fibrosis, results of Aim 3 will enable the development of new hypotheses around microenvironmental and anatomical cues for fibrogenesis in TB infection. Successfully completing our three aims will create the foundational knowledge and novel tools necessary to ultimately build a mechanistic model for the path to TB-associated fibrosis and for preclinical testing of candidate interventions. We anticipate this work will directly contribute to new strategies for treating TB.