Exploring gut microbiota and its predicted functions in pulmonary tuberculosis: A multi-regional study using public 16S datasets

Abstract

Background: Pulmonary tuberculosis, caused by the bacillus Mycobacterium tuberculosis, remains a major global health challenge, particularly in developing countries. In this study, we analyzed publicly available 16S amplicon sequencing datasets from four geographical locations using a single workflow.

Methods: We employed Quantitative Insights Into Microbial Ecology v.2 for microbial diversity analysis and Phylogenetic Investigation of Communities by Reconstruction of Unobserved States v.2 for functional pathway predictions of the gut microbiota in patients with PTB and antitubercular therapy.

Results: Our analysis revealed statistically significant alpha diversity differences in West Africa with decreased microbial diversity in pulmonary tuberculosis patients after two months of antitubercular therapy. Additionally, there were no statistically significant differences observed in pairwise comparisons within the same location or in the aggregate beta diversity of the datasets. The predicted microbial metabolic pathways related to vitamin biosynthesis, amino acid synthesis, and energy production were depleted in pulmonary tuberculosis patients following antitubercular therapy.

Conclusions: The observed alterations of gut microbial diversity and predicted functional profile underscores the influence of antitubercular therapy on gut health, suggesting that longer treatment durations may aggravate these alterations in gut microbial function. Moreover, geographical location exerts a more significant impact on microbial diversity than the disease state in a specific location, highlighting the potential for precision medicine to tailor interventions based on individual or regional microbiome characteristics.

Fig 1. Alpha diversity.

Alpha diversity illustrates the Observed taxa and Shannon index among the groups.

Fig 2. Beta Diversity.

Beta diversity illustrates the weighted UniFrac distances among the groups.

Fig 3. LEfSe Analysis.

LEfSe Analysis illustrates a LDA score greater than 2 (p < 0.05) of genera among the groups in the followings: a. Indian Population; b. South African Population; c. Taiwan Population; d. West African population.

Fig 4. Relative abundance.

Relative abundance of bacteria illustrated among the groups in the following taxa levels: a. At Phylum level; b. At genus level.

Fig 5. Heatmap of topmost bacteria genera.

The heatmap of topmost genera among the groups in followings: a. Indian Population; b. South African Population; c. Taiwan Population; d. West African population.