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Distinct Persistence Fate of Mycobacterium tuberculosis in Various Types of Cells.

ABSTRACT: Mycobacterium tuberculosis can invade different cells with distinct persistence fates because cells are equipped with different host restriction factors. However, the underlying mechanisms remain elusive. Here, we infected THP1 and Raw264.7 macrophages cell lines, A549 epithelial cell line, and hBMEC and bEnd.3 endothelial cell lines with M. tuberculosis and demonstrated that M. tuberculosis significantly inhibited lysosome acidification in THP1, hBMEC, A549, and Raw264.7 cells, while, in bEnd.3 cells, M. tuberculosis was mainly delivered into acidified phagolysosomes and auto-lysosomes. The systematic gene profile analysis of different cells and intracellular M. tuberculosis showed that the phagosome autophagy-pathway-related genes itgb3 and atg3 were highly expressed in bEnd.3 cells. Knockdown of these genes significantly increased the number of viable intracellular M. tuberculosis bacilli by altering phagosomal trafficking in bEnd.3 cells. Treatment with itgb3 agonist significantly decreased M. tuberculosis survival in vivo. These findings could facilitate the identification of anti-M. tuberculosis host genes and guide M. tuberculosis-resistant livestock breeding. IMPORTANCE As an intracellular pathogen, Mycobacterium tuberculosis could avoid host cell immune clearance using multiple strategies for its long-term survival. Understanding these processes could facilitate the development of new approaches to restrict intracellular M. tuberculosis survival. Here, we characterized the detailed molecular events occurring during intracellular trafficking of M. tuberculosis in macrophage, epithelial, and endothelial cell lines and found that ITGB3 facilitates M. tuberculosis clearance in endothelial cells through altering phagosomal trafficking. Meanwhile, the treatment with ITGB3 agonist could reduce bacterial load in vivo. Our results identified new anti-M. tuberculosis restriction factors and illuminated a new anti-M. tuberculosis defense mechanism.

SUBMITTER: Chen X

PROVIDER: S-EPMC8409741 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Identifying Mycobacterium tuberculosis persistence genes is important for developing novel drugs to shorten the duration of tuberculosis (TB) treatment. We developed computational algorithms that predict M. tuberculosis genes required for long-term survival in mouse lungs. As the input, we used high-throughput M. tuberculosis mutant library screen data, mycobacterial global transcriptional profiles in mice and macrophages, and functional interaction networks. We selected 57 unique, genetically defined mutants (18 previously tested and 39 untested) to assess the predictive power of this approach in the murine model of TB infection. We observed a 6-fold enrichment in the predicted set of M. tuberculosis genes required for persistence in mouse lungs relative to randomly selected mutant pools. Our results also allowed us to reclassify several genes as required for M. tuberculosis persistence in vivo. Finally, the new results implicated additional high-priority candidate genes for testing. Experimental validation of computational predictions demonstrates the power of this systems biology approach for elucidating M. tuberculosis persistence genes.Mycobacterium tuberculosis, the causative agent of tuberculosis (TB), has a genetic repertoire that permits it to persist in the face of host immune responses. Identification of such persistence genes could reveal novel drug targets and elucidate mechanisms by which the organism eludes the immune system and resists drugs. Genetic screens have identified a total of 31 persistence genes, but to date only 15% of the ~4,000 M. tuberculosis genes have been tested experimentally. In this paper, as an alternative to brute force experimental screens, we describe computational methods that predict new persistence genes by combining known examples with growing databases of biological networks. Experimental testing demonstrated that these predictions are highly accurate, validating the computational approach and providing new information about M. tuberculosis persistence in host tissues. Using the new experimental results as additional input highlights additional genes for testing. Our approach can be extended to other data types and target organisms to characterize host-pathogen interactions relevant to this and other infectious diseases.

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Distinct Persistence Fate of Mycobacterium tuberculosis in Various Types of Cells.

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