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Spatial Network Mapping of Pulmonary Multidrug-Resistant Tuberculosis Cavities Using RNA Sequencing.

1Centre for Lung Infection and Immunity, Division of Pulmonology, Department of Medicine and UCT Lung Institute and South African MRC/UCT Centre for the Study of Antimicrobial Resistance, University of Cape Town, Cape Town, South Africa. 2Faculty of Infectious and Tropical Diseases, Department of Immunology and Infection, London School of Hygiene and Tropical Medicine, London, United Kingdom. 3Center for Infectious Diseases Research and Experimental Therapeutics, Baylor Research Institute, Baylor University Medical Center, Dallas, Texas. 4Department of Immunology, University of Texas Southwestern Medical Center, Dallas, Texas. 5Department of Pathology. 6Division of Infection and Immunity, University College London, London, United Kingdom; and. 7Department of Nuclear Medicine, and. 8Chris Barnard Division of Cardiothoracic Surgery, Department of Surgery, Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa. 9South African Medical Research Council Centre for Tuberculosis Research/Department of Science and Technology/National Research Foundation Centre of Excellence for Biomedical Tuberculosis Research, Division of Molecular Biology and Human Genetics, Stellenbosch University, Cape Town, South Africa.

American journal of respiratory and critical care medicine. 2019;(3):370-380
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Abstract

Rationale: There is poor understanding about protective immunity and the pathogenesis of cavitation in patients with tuberculosis.Objectives: To map pathophysiological pathways at anatomically distinct positions within the human tuberculosis cavity.Methods: Biopsies were obtained from eight predetermined locations within lung cavities of patients with multidrug-resistant tuberculosis undergoing therapeutic surgical resection (n = 14) and healthy lung tissue from control subjects without tuberculosis (n = 10). RNA sequencing, immunohistochemistry, and bacterial load determination were performed at each cavity position. Differentially expressed genes were normalized to control subjects without tuberculosis, and ontologically mapped to identify a spatially compartmentalized pathophysiological map of the cavity. In silico perturbation using a novel distance-dependent dynamical sink model was used to investigate interactions between immune networks and bacterial burden, and to integrate these identified pathways.Measurements and Main Results: The median (range) lung cavity volume on positron emission tomography/computed tomography scans was 50 cm3 (15-389 cm3). RNA sequence reads (31% splice variants) mapped to 19,049 annotated human genes. Multiple proinflammatory pathways were upregulated in the cavity wall, whereas a downregulation "sink" in the central caseum-fluid interface characterized 53% of pathways including neuroendocrine signaling, calcium signaling, triggering receptor expressed on myeloid cells-1, reactive oxygen and nitrogen species production, retinoic acid-mediated apoptosis, and RIG-I-like receptor signaling. The mathematical model demonstrated that neuroendocrine, protein kinase C-θ, and triggering receptor expressed on myeloid cells-1 pathways, and macrophage and neutrophil numbers, had the highest correlation with bacterial burden (r > 0.6), whereas T-helper effector systems did not.Conclusions: These data provide novel insights into host immunity to Mycobacterium tuberculosis-related cavitation. The pathways defined may serve as useful targets for the design of host-directed therapies, and transmission prevention interventions.