Investigators reported lower microbial richness in sputum from adults with pulmonary tuberculosis compared with contacts. Short statement: Chao1 and ACE estimators were reduced in TB cases. The team observed that overall community composition in sputum did not remain statistically significant after correction for multiple testing, which tempers claims about broad community restructuring. At the taxonomic level, differential-abundance analysis identified associations involving Streptococcus, Parvimonas, and Actinobacillus in sputum; the authors noted those specific taxa differed between cases and controls while the global community signal was weak. These paired findings—the drop in richness but the absence of a robust corrected community-level difference—suggest localized shifts in dominant or subdominant taxa rather than wholesale microbiome replacement.
Stool profiles diverged from sputum results. The study team observed multiple alpha-diversity measures that differed between TB cases and contacts, and beta-diversity testing detected compositional differences in stool. Nevertheless, TB cases showed greater within-group dispersion than controls, indicating more heterogeneity among cases. A striking observation: the sequencing platform used contributed substantially to variation in stool microbial profiles. In multivariable models, sequencing platform was the only factor that remained independently associated with stool community composition after correction for multiple testing. That finding complicates attribution of observed stool differences directly to disease status, because technical variation explained a large fraction of the signal.
The investigators enrolled 200 adults at Mulago National Referral Hospital—150 with pulmonary TB and 50 contacts—then processed sputum and stool using 16S rRNA gene sequencing. After quality-control steps and removal of contaminants, 135 sputum and 151 stool samples passed filters and were retained for downstream analyses. Within-sample diversity was assessed with multiple alpha-diversity indices; between-sample differences were tested using distance-based beta-diversity metrics followed by permutational multivariate analysis of variance (PERMANOVA) and homogeneity of dispersion tests. Differential-abundance testing employed ANCOM-BC2. The authors reported that some comparisons lost statistical significance after multiple-testing correction, and they explicitly accounted for dispersion differences when interpreting beta-diversity results.
Taxa-level tests returned different signatures in each body compartment. In sputum, the team identified differential abundance involving Streptococcus, Parvimonas, and Actinobacillus. In stool, taxa that differed included Dorea, Lachnoclostridium, and Turicibacter. The source did not report mechanistic experiments linking these taxa to disease pathogenesis, and it did not report functional metagenomic profiling. Investigators therefore presented these taxonomic associations as observational signals—body site-specific changes that merit further study rather than causal claims.
Data indicated two technical and analytical challenges. First, greater within-group dispersion among TB cases affected beta-diversity interpretation; higher dispersion can inflate apparent group differences and complicate PERMANOVA results. Second, the sequencing platform exerted a major influence on stool profiles and, in adjusted analyses, was the lone independent predictor of stool community composition after correction for multiple testing. The authors reported these factors explicitly and used them to temper conclusions about disease-driven community shifts. They emphasized that platform effects must be controlled in future work to isolate biologic signals from technical artifacts.
The investigators acknowledged key limitations. The study design was cross-sectional, so the source did not report temporal dynamics or causal inference between pulmonary tuberculosis and microbiome changes. The authors noted that sequencing-platform variation and within-group dispersion complicated interpretation, and they recommended longitudinal follow-up with standardized sequencing methods to clarify how microbiome patterns evolve during treatment and whether observed taxa have diagnostic or clinical relevance. The source did not report details about treatment status, antibiotic exposures outside of TB therapy, dietary controls, or host clinical covariates beyond those included in multivariable analyses; those items were not specified in the abstract. No regulatory milestones or trial endpoints were reported. The final statement in the preprint called for longitudinal studies using standardized sequencing to resolve remaining questions about temporal change and clinical applicability.