Beyond bacilli: integrating the microbiome into the TB research agenda

Abstract

Tuberculosis (TB) remains a leading infectious killer, with growing evidence that the human microbiome-particularly in the gut and lungs-shapes susceptibility, progression, and treatment outcomes. Over the past decade, studies have reported that TB-associated dysbiosis, which is more common in the gut than in the lung, is often marked by the loss of short-chain fatty acid-producing taxa and the expansion of opportunistic microbes. However, findings are frequently confounded by diet, antibiotic exposure, comorbidities, geography, and methodological variability. Most research has relied on compositional profiling, offering limited insight into functional mechanisms. This narrative review synthesizes recent evidence, emphasizing the need to integrate multiomics approaches-metagenomics, metatranscriptomics, and metabolomics-and experimental validation to uncover causal links between microbiome alterations and TB pathogenesis or therapy response. We discuss potential clinical applications, including microbiome-based diagnostics (such as stool-based microbial or metabolite signatures for TB risk stratification), prognostic indicators (such as gut microbiome recovery predicting immune normalization during therapy), and adjunctive interventions (including microbiome-derived products to reduce drug-induced liver injury or fecal microbiota transplantation, which has been shown to be safe in people with HIV on stable ART) to mitigate drug toxicity or enhance immune recovery. Key priorities include methodological standardization, confounder control, mechanistic studies, and the inclusion of high-burden settings. By moving beyond descriptive surveys toward functional, translational research, integrating insights from different microbiome methods into TB prevention, diagnosis, and treatment could redefine the clinical research agenda and open new avenues for precision medicine in this global disease.

Keywords: Microbiome; confounder control; mechanistic studies; methodological standardization; tuberculosis.

Figure 1.

Overview of Confounders in Microbiome Research for TB. These include: (1) Patient characteristics, such as coinfections (e.g., HIV, helminths), comorbidities (e.g., diabetes), antibiotic use, nutrition, and lifestyle; (2) Environmental and contextual factors, including geography, sanitation, climate, and socioeconomic status; and (3) Methodological variables, encompassing sample type, laboratory protocols, sequencing platforms, and study design. Some confounders (e.g., HIV, diabetes, malnutrition, antibiotic use, and socioeconomic status) are particularly important because they can influence both the microbiome and TB susceptibility or progression directly. These dual-impact factors must be carefully accounted for to avoid spurious associations and ensure accurate interpretation and reproducibility of results.

Figure 2.

Directed Acyclic Graphs (DAGs) illustrating sources of confounding in microbiome–tuberculosis (TB) association studies. DAG 1–3 depict distinct confounding domains: patient-level (DAG 1), environmental/contextual (DAG 2), and methodological (DAG 3). Each includes variables that may influence both the microbiome and TB outcomes, potentially biasing the observed associations. DAG 4 integrates these domains into a unified causal framework. Node colors denote variable categories. Arrows indicate hypothesized causal relationships.

Figure 3.

Integrated multiomics and experimental validation pipeline for TB–microbiome research. Microbiome studies in tuberculosis face methodological challenges, including sampling variability, low biomass, and batch effects (left panel). To move beyond descriptive composition, a multiomics approach combines metagenomics, metatranscriptomics, metabolomics, and host omics to uncover functional pathways linking microbiota to TB outcomes (center). These insights must be tested through experimental validation, including mechanistic studies in animal models and host immune assays (right). Together, this framework enables robust causal inference and supports the discovery of translational applications, such as biomarkers or microbiome-based interventions. Arrows do not imply causality unless supported by experimental validation.

Figure 4.

Microbiome-driven modulation of host immunity in tuberculosis: pathways to latency or active disease. This figure illustrates two contrasting immunological outcomes in tuberculosis—immune containment vs. disease progression—driven by gut microbiome composition and function. Left panel (latent TB): a balanced and diverse gut microbiota supports homeostatic immunity through the production of short-chain fatty acids (SCFAs), including butyrate and propionate. These metabolites enhance regulatory T cell (Treg) function and IL-10 secretion via G protein-coupled receptor (GPCR) signaling, promote gut epithelial integrity, and prevent microbial translocation. Commensal-driven modulation of Toll-like receptor (TLR) signaling (TLR2/TLR4) ensures appropriate activation of IFN-γ and TNF-α responses, sustaining granuloma formation and stability. These mechanisms collectively support the containment of Mycobacterium tuberculosis within granulomas, maintaining a latent infection state. Right panel (Active TB): microbiome dysbiosis—resulting from antibiotic exposure, HIV infection, or metabolic comorbidities—alters SCFA-producing species (e.g., Clostridia, Roseburia, Bacteroides fragilis) and increases the abundance of potentially detrimental taxa (e.g., Enterococcus, Escherichia, Shigella, and Enterobacteriaceae). SCFA depletion weakens epithelial barrier integrity, facilitating the translocation of microbial products such as lipopolysaccharide (LPS) and elevating the levels of LPS-binding protein (LBP). This systemic microbial translocation contributes to dysregulated TLR signaling and cytokine production toward an inflammatory profile (↑ IL-6, ↓ IL-10, and ↓ IFN-γ), impairing macrophage activation and granuloma integrity. The resulting immune dysregulation promotes breakdown of granuloma structures and progression to active TB disease.

Figure 5.

Knowledge gaps and research priorities in TB–microbiome research. Unanswered questions include: (1) identifying keystone microbial species or consortia that influence TB susceptibility or recovery; (2) defining microbial metabolites that mediate host–microbiome crosstalk, such as short-chain fatty acids or tryptophan catabolites; (3) clarifying the timing and persistence of microbiome perturbations, from early-life influences on TB risk to restoration after treatment; (4) developing practical microbiome-based tools for clinical use, such as simplified metabolite indices or dried blood spot assays; and (5) characterizing how multidrug-resistant TB regimens reshape microbial communities and impact outcomes. Addressing these gaps will require adaptive clinical trials, systems biology and multiomics integration, and experimental validation to move from observational associations to causal and translational insights.