The sputum microbiome composition in pulmonary TB patients and its changes through TB treatment.
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Background: Commensal microbiota residing in or on the human body play key roles in maintaining health and immunity. The abundance and dynamics of these microbial communities when disrupted can result in dysbiosis and the proliferation of pathobionts. Several studies have highlighted the association between dysbiosis of lung microbiota with lung diseases. In relation to TB, lung microbiota influence treatment outcome and may serve as biomarkers for predicting treatment outcomes. Objectives: This study aimed to determine the variations in the sputum microbiome and variations through TB treatment in pulmonary TB patients in Uganda and also to characterize the bacterial communities that may be associated with poor TB treatment outcome Methods: This was a longitudinal study that used induced sputum samples from 120 Mtb positive patients at Mulago National Referral Hospital. Samples were collected at baseline, and follow-up visits after 2 and 5 months of anti-TB therapy initiation by the parent study. Total microbial DNA was extracted and the V4-V5 region of the 16S rRNA gene was PCR amplified and the amplicons were verified using high through put gel-electrophoresis. This was followed by high throughput DNA sequencing and microbial analysis was done using the QIIME 2 pipeline. The scripts and codes for generating tables, graphs were done in R studio. Results: In this study we obtained a total count of 93116821 sequence reads equivalent to 3.6 GB in a zipped format, these belonged in kingdom bacteria and archaea and a small proportion that was unclassified. At the kingdom level, bacteria were the most dominant with 26 phyla that were observed and the first six highly abundant phyla in this kingdom were: Firmicutes, Bacteroidetes, Proteobacteria, Fusobacteria, Actinobacteria and Cyanobacteria. Based on the Unweighted unifrac beta diversity Metrix, TB treatment showed an impact on these bacterial communities by affecting their abundance and diversity hence implying that this treatment may lead to a state of dysbiosis. We observed high abundance of phylum Fusobacteria, Spirochetes, Bacteroidetes, Tenericutes and Cyanobacteria in patients with poor treatment outcome. Conclusion: Understanding the community where the organism (Mtb) resides and where the drug operates, it would be right approach to solve challenges caused by TB infection. Also, based on our results we conclude that microbial communities can be potential biomarkers for predicting TB treatment outcome.