(235) Antitubercular Therapy Associated Gut Microbiota Dysbiosis in Pulmonary Tuberculosis: A Computational Analysis from Open Datasets

Background: Dysbiosis of the gut microbiota (GM) in pulmonary tuberculosis (PTB) is an emerging research area. Understanding the use of computational tools on publicly available raw 16S amplicon-sequenced data may provide substantial evidence to fill the research gap in microbial data analysis. This computational analysis was preceded by an evidence synthesis to ensure comprehensive data inclusion, and the insights gained from the review findings served as motivation and provided direction for the current work.

Objectives: To study the gut dysbiosis pattern in PTB patients using a computational approach and the latest reference database.

Methods: A total of three datasets were selected for our analysis. The corresponding raw 16S amplicon sequenced reads were retrieved from the NCBI Sequence Read Archive upon finalizing the list of targeted SRA IDs from selected datasets. Amplicon-sequenced raw reads were processed on the T-BioInfo platform using the QIIME2 pipeline with the DADA2 plugin. The downstream analysis of obtained data from the server was done by Phyloseq for the microbial abundancy and taxonomy. We calculated the enriched and depleted taxa based on the obtained amplicon sequence variant (ASV) counts.

Results: In the first dataset, phylum Bacteroidota was abundant in healthy household contacts compared to the PTB group without anti-tuberculosis therapy (ATT). In the PTB group, the abundance of Bacteroidota further declined after one week of ATT and continued to decline after one month of ATT. In the second dataset, there was an abundance of phylum Firmicutes in the PTB group with two months of ATT, followed by the PTB group without ATT. In the third dataset, there was a depletion of phylum Firmicutes, Bacteroidota, Proteobacteria, Cyanobacteriota, and Verrucomicrobiota in the PTB group without ATT compared to the close contact group.

Conclusions: Our findings diverged from published data, which we found from our evidence synthesis. The analysis reflects the impact of updated pipelines and novel clustering approaches on ASV. These methodological advancements highlight the potential for continual enhancement in microbiome research.