Tuberculosis

Karen Pendergrass

Did you know?
In active TB, low blood iron is usually the body deliberately starving the bacillus (nutritional immunity), not a deficiency. Reflexively giving iron can feed the pathogen.

Tuberculosis

The microbiome and metallomic signature of tuberculosis: how iron withholding, copper/zinc intoxication, granuloma oxygen ecology, and airway–gut dysbiosis shape susceptibility, progression, and treatment, with triangle-tested interventions and STOPs.

Interventions

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Researched by:

Karen PendergrassID

Last Updated: 2026-06-27

Page Snapshot

41 References
17 Identified MMAs

Overview

Tuberculosis (TB) is a chronic infectious disease caused by Mycobacterium tuberculosis (Mtb), an obligate-aerobic, acid-fast bacillus spread by respiratory aerosols. It remains one of the world's deadliest infections, and most people who are infected contain the bacillus in a clinically silent latent state while roughly 5–10% progress to active disease.^[1] Whether an exposed host clears, contains, or progresses is a property of the host–pathogen interface, not of the organism alone.

Mtb is the established and undisputed cause of TB. What the single-cause model does not explain is the disease's heterogeneity, and that is what this page addresses through the host metallome, nutritional immunity, and the airway/gut microbiome. The master variable is iron: Mtb is an intracellular iron pirate that scavenges host iron with the siderophores mycobactin and carboxymycobactin,^[2] while the host withholds it through hepcidin, lactoferrin, transferrin, and siderocalin.^[3] Because iron loading worsens outcomes,^[4]^[5] the metallomic lens reframes care, low serum iron in active TB is usually host defense, not a deficiency to correct, so reflexive iron repletion can feed the pathogen, and because Mtb is an obligate aerobe held in check by granuloma hypoxia, oxygenation strategies such as HBOT are the wrong lever.

Associated Conditions

TB risk and severity rise sharply with conditions that weaken cell-mediated immunity or alter the host metallome. The strongest associations are HIV co-infection, diabetes mellitus (a bidirectional relationship in which hyperglycaemia impairs immunity and TB worsens glycaemic control),^[6] silicosis, undernutrition, smoking and chronic lung disease, and harmful alcohol use. Iron overload is itself a recognised risk factor,^[4] and active TB commonly produces an anemia of inflammation that independently predicts worse outcomes.^[7] Survivors frequently carry long-term post-TB lung disease.

Causes

The cause of TB is infection with M. tuberculosis; the open question is what determines progression from latent infection to active, tissue-destroying disease. A systems view holds that the outcome is set by interacting constraints, host iron and redox status, macrophage genetics, the phagosomal metal battle, granuloma oxygen tension, and the surrounding microbiome, rather than by exposure alone.

What determines progression from latent to active tuberculosis?

Determinant	Role in progression
Iron availability	Iron fuels Mtb growth and biofilm; macrophage/dietary iron loading and high ferritin track with progression and death.^[5]^[4]
NRAMP1/SLC11A1 genotype	The phagosomal metal-efflux transporter; polymorphisms are reproducibly associated with susceptibility.^[8]
Phagosomal Cu/Zn intoxication	Macrophages poison the bacillus with copper and zinc; the capacity to resist (or to overwhelm) this defense shapes survival.^[9]^[10]
Immune status (HIV, diabetes)	Loss of effective Th1/macrophage immunity is the dominant driver of reactivation.^[6]
Granuloma oxygen tension	Hypoxia forces non-replicating dormancy (latency); reoxygenation permits regrowth.^[11]^[12]
Airway/gut microbiome	Dysbiosis and lost SCFA producers may tilt mucosal immunity toward tolerance (associative, antibiotic-confounded).^[13]

Diagnosis

WHO-recommended rapid molecular tests (NAATs such as Xpert MTB/RIF / Ultra) are the initial diagnostic, detecting Mtb DNA and rifampicin resistance within hours; mycobacterial culture remains the reference standard, and sputum-smear microscopy, chest imaging (upper-lobe infiltrates and cavitation), and tests of infection (IGRA, tuberculin skin test, which cannot distinguish latent from active disease) complete the work-up.^[14] Within the metallomic frame, the anemia of active TB is typically an anemia of inflammation, low serum iron with normal/high ferritin and elevated hepcidin, a host-defense pattern, not a cue to replete iron.^[15]^[16]

Primer

The interplay between metal homeostasis, host nutritional immunity, granuloma oxygen ecology, and the airway/gut microbiome explains how a single pathogen produces such varied disease, and where microbiome- and metal-targeted strategies could intervene.

Nutritional Immunity

In active TB, IL-6 drives hepcidin, which degrades ferroportin to produce hypoferremia and the functional anemia of inflammation;^[17]^[16] lactoferrin and transferrin chelate iron, and lactoferrin specifically lowers mycobacterial burden in iron-overloaded hosts.^[18] Siderocalin (lipocalin-2) intercepts Mtb's carboxymycobactin directly,^[3] while calprotectin sequesters manganese and zinc.^[19] The decisive interpretive rule: low serum iron and zinc here are usually the host starving the bacillus, defenses to support, not deficiencies to override.

Metallomic Signature

The metallomic signature of tuberculosis is read in the patient, not the pathogen: serum iron falls as the host sequesters it (an anemia of inflammation), yet tissue iron loading and high ferritin track with treatment failure and death, so iron repletion is counterproductive;^[5]^[4] the serum copper/zinc ratio rises as an acute-phase marker of disease activity;^[20] and serum zinc falls, impairing cell-mediated immunity.^[21] These host-level findings argue for monitoring the copper/zinc ratio, withholding routine iron, and correcting only documented zinc deficiency.

What is the metallomic signature of tuberculosis?

Metal	Findings
Iron	In active TB serum iron is low, an hepcidin-driven anemia of inflammation that withholds iron from the bacillus; paradoxically, macrophage and tissue iron loading with high ferritin track with treatment failure and mortality, and iron supplementation worsens outcomes, so it should be withheld.^[5]^[4]^[16]
Copper	The serum copper/zinc ratio is elevated in active TB as an acute-phase response, with 87% of patients above 2.0 versus none of controls; it is a more sensitive marker of disease than either metal alone and falls with effective treatment.^[20]
Zinc	Serum zinc is significantly lower in pulmonary TB than in controls, reflecting cytokine-driven redistribution compounded by malnutrition; the deficit impairs cell-mediated immunity, though supplementation trials are mixed and routine repletion is unproven.^[21]^[22]

Explore the interactive Microbiome Signature chart above for the per-taxon metallome panels. The airway signature is dominated by enrichment of oral-origin anaerobes (Prevotella, Streptococcus, Megasphaera, Campylobacter, Kingella) and a collapse of diversity, with active disease often Mtb-dominated;^[23]^[24] in non-TB lungs the same oral-taxa "pneumotype" provokes a Th17 inflammatory tone.^[25] Through the gut–lung axis, depletion of SCFA producers and butyrate may blunt protective Th1/Th17 immunity.^[6]^[13] These are associations around a known pathogen and are heavily antibiotic-confounded, interpret each taxon in that light.

Oxygen Ecology

TB's disease-specific mechanism is oxygen ecology. The granuloma's caseous core becomes hypoxic, and because Mtb is an obligate aerobe, falling O₂ does not aid it, it forces non-replicating dormancy through the DosR regulon, the basis of latency and decades-long persistence.^[11]^[12] This is why raising tissue oxygen (HBOT) is the wrong lever for TB, the exact opposite of anaerobe-dominated conditions. The bacillus is also vulnerable to host redox: glutathione (and its precursor NAC) has direct anti-mycobacterial and immune-enhancing effects in the granuloma.^[26]

Interventions

Our validation method evaluates each candidate with the Triangle Test, does the intervention move a host feature in the predicted direction, does it improve TB clinically, and is that feature causally linked to TB, then classifies it as Validated, Promising Candidate, or Validation In Progress. All of the below are host-directed adjuncts to standard multidrug chemotherapy (isoniazid, rifampicin, pyrazinamide, ethambutol, or the appropriate drug-resistant regimen); none replaces it.

Intervention	Classification	Status
N-acetylcysteine (NAC)	Supplement	Promising Candidate
Vitamin D	Supplement	Promising Candidate
L-arginine / nitric oxide	Supplement	Promising Candidate
Lactoferrin	Supplement	Validation In Progress
Iron restriction / chelation	Diet / Pharmaceutical	Validation In Progress

What interventions are still in the process of validation?

Intervention	Classification	Notes
N-acetylcysteine (NAC)	Supplement	Restores glutathione and has direct anti-Mtb activity;^[27] a 140-patient RCT confirmed glutathione repletion and improved lung-function recovery but no faster culture conversion, promising for tissue protection, not bacterial clearance.^[28]
Vitamin D	Supplement	Induces cathelicidin (LL-37) and autophagy;^[29] an IPD meta-analysis (n=1,850) found no overall culture-conversion benefit but a marked acceleration in multidrug-resistant TB.^[30]
L-arginine	Supplement	Substrate for macrophage nitric oxide; one RCT positive in HIV-negative patients,^[31] a larger trial null with no rise in exhaled NO.^[32]
Lactoferrin	Supplement	Reinforces iron-withholding and shifts immunity toward Th1; reduces Mtb burden in iron-overloaded models, preclinical, no human treatment trials yet.^[33]^[18]
Metformin	Drug Repurposing	AMPK-driven autophagy; observational mortality benefit in diabetics with TB,^[34] but an adjunctive RCT did not shorten sputum conversion.^[35]
Statins	Drug Repurposing	Mtb consumes host cholesterol;^[36] statins shorten treatment in mice^[37] and are linked to lower TB incidence observationally^[38], no efficacy RCT yet.
Zinc + vitamin A	Supplement	Adjunct micronutrients; trials are mixed and a malnourished-patient RCT showed no benefit on sputum conversion, reasonable for documented deficiency only.^[22]
Phenylbutyrate + vitamin D	Supplement	Both induce cathelicidin; a Bangladesh RCT reported faster clinical recovery and intracellular Mtb killing, a single positive trial awaiting confirmation.^[39]

STOPs

A STOP (Suggested Termination Of Practice) flags a conventional practice that is counterproductive because it feeds the pathogen, overrides host defense, or relieves a host-imposed constraint. In TB the clearest example is routine iron supplementation: the low serum iron of active disease is hepcidin-driven sequestration, and exogenous iron can feed Mtb's siderophore machinery while iron loading is independently associated with worse outcomes.^[16]^[40]

What STOPs should be considered?

STOP	Rationale and SWAP
Routine iron supplementation in active TB	Low serum iron is anemia of inflammation (host defense), not deficiency; iron feeds Mtb and loading worsens outcomes; iron repletion of iron-deficient hosts increased infections and TB reactivation in a randomized study.^[16]^[40]^[41] Treat the infection (the anemia resolves with effective therapy); support sequestration with lactoferrin; reserve iron for documented true iron-deficiency anemia, cautiously.
HBOT as a strategy	Mtb is an obligate aerobe restrained by granuloma hypoxia; raising O₂ relieves the constraint that holds it dormant, the opposite of anaerobe-dominated disease. (Mechanistic caution; direct TB-HBOT trials are essentially absent.)^[12]^[11] Prioritize effective multidrug chemotherapy; do not pursue hyperoxygenation as anti-TB therapy.
High-dose zinc supplementation	Serum zinc is low by redistribution, not dietary lack; routine repletion is unproven and trials are mixed.^[22] Correct documented deficiency only; otherwise support host sequestration.

Frequently Asked Questions

What is Tuberculosis?

Quick answer: Tuberculosis (TB) is a chronic infectious disease caused by Mycobacterium tuberculosis (Mtb), an obligate-aerobic, acid-fast bacillus spread by respiratory aerosols. It remains one of the world's deadliest infections, and most people who are infected contain the bacillus in a clinically silent latent state while roughly 5–10% progress to active disease. [1] Whether an exposed host clears, contains, or progresses is a property of the host–pathogen interface—not of the organism alone.

What conditions are associated with Tuberculosis?

Quick answer: TB risk and severity rise sharply with conditions that weaken cell-mediated immunity or alter the host metallome. The strongest associations are HIV co-infection, diabetes mellitus (a bidirectional relationship in which hyperglycaemia impairs immunity and TB worsens glycaemic control), [6] silicosis, undernutrition, smoking and chronic lung disease, and harmful alcohol use. Iron overload is itself a recognised risk factor, [4] and active TB commonly produces an anemia of inflammation that independently predicts worse outcomes. [7] Survivors frequently carry long-term post-TB lung disease.

What causes Tuberculosis?

Quick answer: The cause of TB is infection with M. tuberculosis ; the open question is what determines progression from latent infection to active, tissue-destroying disease. A systems view holds that the outcome is set by interacting constraints—host iron and redox status, macrophage genetics, the phagosomal metal battle, granuloma oxygen tension, and the surrounding microbiome—rather than by exposure alone.

Research Feed

2023

Infectious Disease

In poorly controlled diabetes, a six-genus gut microbiota signature distinguished latent tuberculosis infection and tracked with Th1/Th17 cytokine responses, predicting LTBI status with 87% accuracy.

Location

Taiwan

Sample Site

Feces

Species

Homo sapiens

What Was Studied?

This prospective observational study examined whether gut microbiota composition reflects host immune responses and latent tuberculosis infection (LTBI) status in patients with poorly controlled diabetes. Fecal microbiota were profiled by 16S rRNA gene sequencing of the V3-V4 hypervariable regions on the Illumina MiSeq platform, and LTBI was defined using the QuantiFERON-TB Gold In-Tube interferon-gamma release assay. Investigators compared taxonomic diversity and differential genera between LTBI and non-LTBI groups and built a random forest classifier from the most discriminating taxa.

Who Was Studied?

The cohort comprised 130 patients with poorly controlled diabetes (HbA1c greater than 9.0% within the prior year) recruited at Kaohsiung Medical University Hospital and National Taiwan University Hospital in Taiwan, of whom 43 had LTBI and 87 did not. Stool and blood samples were collected at enrollment. Patients who had used systemic antibiotics within three months prior to enrollment were excluded.

What Were the Key Findings?

Alpha-diversity did not differ between groups, but beta-diversity was significantly distinct (p=0.007 unweighted and p=0.002 weighted UniFrac). The LTBI group showed enrichment of Bacteroides (37.79% vs 29.72%, p=0.001) along with Alistipes and Blautia, and depletion of Prevotella_9 (2.53% vs 8.95%, p<0.001) together with Streptococcus and Actinomyces; LTBI patients also had lower serum IL-17F (p=0.025) and TNF-alpha (p=0.038). These taxa correlated with Th1/Th17 cytokine production, with Bacteroides inversely associated with IFN-gamma, IL-17A, IL-10, IL-22, and TNF-alpha, while Blautia showed positive correlations. A model using the six differential genera (Bacteroides, Streptococcus, Alistipes, Blautia, Prevotella_9, and Actinomyces) predicted LTBI status with an accuracy of 0.872 and an AUROC of 0.834.

What Are the Implications?

These findings suggest that the gut microbiota may modulate Th1/Th17-mediated immunity relevant to tuberculosis susceptibility in patients with poorly controlled diabetes, and that a microbiota-based signature could potentially aid LTBI detection in this high-risk population. Because the data are cross-sectional and associative, they cannot establish whether microbial shifts drive altered immune responses or merely accompany LTBI, and the predictive model requires external validation. The three-month antibiotic exclusion reduces but does not eliminate confounding from diet, glycemic control, and other host factors.

2023

Infectious Disease

A Chinese fecal 16S rDNA study found that the gut microbiota becomes progressively less diverse and more pro-inflammatory along the latent-to-active tuberculosis spectrum, with depletion of beneficial taxa such as Romboutsia and enrichment of organisms like Ruminococcus gnavus and Streptococcus.

Location

China

Sample Site

Feces

Species

Homo sapiens

What Was Studied?

This cross-sectional case-control study investigated whether intestinal microbiota composition differs across the tuberculosis (TB) disease spectrum and how those differences might relate to the transition from latent infection to active disease. Fecal samples from individuals with active TB, latent TB infection (LTBI), and healthy controls were profiled by 16S rDNA amplicon sequencing, with comparisons of alpha and beta diversity, differential taxa (LEfSe), predicted metabolic pathways, and the diagnostic performance of candidate microbial markers. The authors framed the analysis around identifying microbiota-driven mechanisms that could plausibly favor reactivation and progression.

Who Was Studied?

The cohort was recruited in China and divided into three groups: patients with active tuberculosis, individuals with latent tuberculosis infection, and healthy controls without M. tuberculosis exposure. All microbiome characterization was performed on stool (fecal) samples. As with most gut microbiota studies in TB, recent or concurrent antibiotic and anti-tuberculosis exposure in the active disease group is an important consideration when interpreting between-group differences.

What Were the Key Findings?

Alpha diversity was lowest in active TB and highest in healthy controls, with the latent infection group falling in between, and beta diversity differed significantly across the three groups. Differential-abundance analysis showed that latent infection was enriched for beneficial, largely commensal taxa including Romboutsia, Bifidobacterium, and Lactobacillus, whereas active disease was enriched for pro-inflammatory organisms including Ruminococcus gnavus, Streptococcus, and Erysipelatoclostridium. Functional prediction indicated greater activity of the pentose phosphate pathway in active disease, and Romboutsia showed modest standalone diagnostic value for distinguishing TB (reported AUC of approximately 0.65, with low sensitivity and higher specificity).

What Are the Implications?

The data are consistent with a model in which progressive loss of diversity and depletion of beneficial taxa, alongside expansion of pro-inflammatory bacteria, accompany the shift from latent infection to active tuberculosis, potentially by dampening protective IFN-γ and IL-17 responses and thereby favoring M. tuberculosis survival and spread. These findings are associative and cannot establish causation or direction, and antibiotic and anti-tuberculosis drug exposure may confound the active-disease signature. Nonetheless, the results support further investigation of gut microbial markers such as Romboutsia as adjuncts for risk stratification and of microbiota modulation as a potential avenue to limit progression.

2024

Infectious Disease

A cross-sectional study of 90 Chinese adults linked progressive gut microbiome depletion to falling CD4/CD8 ratios and shifted cytokines across tuberculosis stages, yielding a 20-genus classifier that distinguished patients from healthy controls.

Location

China

Sample Site

Feces

Species

Homo sapiens

What Was Studied?

This cross-sectional comparative study examined the interplay between the gut microbiome, peripheral T cell subsets, and serum cytokines across stages of tuberculosis (TB). Fecal communities were profiled by 16S rRNA gene sequencing (V3–V4 region, Illumina MiSeq, QIIME2), with predicted metabolic pathway analysis; T cell subsets (CD3+, CD4+, CD8+) were measured by flow cytometry, and a panel of cytokines (including IL-2, IL-4, IL-6, IL-10, IL-17A, IL-12p70, IFN-γ, and TNF-α) by ELISA. The authors integrated these layers and applied logistic regression, random forest modeling, and ROC analysis to identify candidate microbiome-based biomarkers.

Who Was Studied?

The cohort comprised 90 adults enrolled at the 8th Medical Center of the PLA General Hospital in Beijing, China, divided into three equal groups: 30 healthy controls, 30 patients with initial TB (treatment-naive or less than one month of anti-tuberculosis therapy), and 30 patients with recurrent TB (prior treatment exceeding one month or treatment failure). Fecal, serum, and whole-blood specimens were collected. Individuals who had taken probiotics, prebiotics, or antibiotics within the preceding month were excluded.

What Were the Key Findings?

Twenty-six differential taxa and 44 metabolic pathways distinguished the groups, and alpha diversity (Chao1, Shannon, Pielou, observed species) was higher in healthy controls than in recurrent TB patients, while beta diversity showed limited separation. Immunologically, CD4+ cells and the CD4/CD8 ratio were significantly reduced in TB (lowest in recurrent disease), CD8+ and NKT cells were elevated, IL-4 was decreased and IL-6 increased in both TB groups, and IL-10 was elevated in initial TB. Several genera correlated with immune markers, Bacteroides, Bifidobacterium, Faecalibacterium, Collinsella, and Clostridium with the CD4/CD8 ratio, and Faecalibacterium, Ruminococcus, and Dorea with cytokines such as IL-4, and a 20-genus classifier separated patients from controls with AUCs of approximately 0.81 (initial TB) and 0.72 (recurrent TB).

What Are the Implications?

These associations suggest that progressive gut dysbiosis in TB tracks with declining cellular immunity and altered cytokine balance, and that fecal microbiome signatures may have potential as adjunctive, non-invasive markers to help distinguish disease stages. Because the design is cross-sectional, causality cannot be inferred, and although recent antibiotic and probiotic use was excluded, residual confounding from disease severity and treatment cannot be ruled out. The findings are hypothesis-generating and require longitudinal and mechanistic validation, including in independent cohorts, before any diagnostic or microbiome-modulating therapeutic application in TB.

2025

Infectious Disease

A 16S rRNA study of rifampicin-resistant tuberculosis found depleted sputum microbiota diversity and a distinct pathobiont-shifted community that persisted unchanged through six months of intensive-phase second-line therapy.

Location

China

Sample Site

Sputum

Species

Homo sapiens

What Was Studied?

This was a longitudinal observational study examining how the sputum (respiratory) microbiota of patients with rifampicin-resistant tuberculosis (RR-TB) compares to healthy individuals and whether it changes over the course of the six-month intensive phase of second-line anti-TB treatment. Sputum samples were profiled by 16S rRNA gene sequencing targeting the V1–V3 region on the Illumina HiSeq 2500 platform. Investigators compared community composition, alpha and beta diversity, and LEfSe-defined differential taxa across three groups, and used PICRUSt2 to predict shifts in microbial metabolic pathways. A candidate genus-based classifier was also evaluated for diagnostic discrimination.

Who Was Studied?

The cohort comprised 14 RR-TB patients and 14 healthy controls recruited at Guangzhou Chest Hospital, Guangzhou, China, between May 2019 and July 2020. RR-TB patients were sampled at two timepoints, at baseline before treatment (DR0) and again after six months of intensive-phase second-line therapy (DR6), while healthy controls (H) provided a single comparison group. All specimens were sputum, sampling the lower respiratory niche relevant to pulmonary TB.

What Were the Key Findings?

RR-TB patients had significantly lower microbial diversity than healthy controls across Chao1, observed richness, Shannon, Simpson, and Faith's phylogenetic diversity, and beta-diversity analysis (PERMANOVA R² = 0.20, p = 0.001) showed RR-TB communities were compositionally distinct and more dispersed than the tightly clustered healthy group. RR-TB sputum was enriched for pathobionts including Streptococcus, Granulicatella, and Lautropia, whereas healthy controls were enriched for the commensals Fusobacterium and Prevotella; after treatment, Haemophilus and the phylum Bacteroidetes became more prominent. Predicted metabolic capacity was broadly reduced in RR-TB (including UDP-glucose biosynthesis, pyruvate fermentation, and amino acid metabolism), and a five-genus classifier distinguished RR-TB from healthy controls with an AUC of 0.94. Notably, no significant diversity or compositional recovery occurred between baseline and six months (DR0 vs DR6, p_adj = 0.577).

What Are the Implications?

The findings indicate that RR-TB is associated with a depleted, dysbiotic respiratory microbiota that was not reversed by six months of intensive-phase second-line treatment, suggesting that the disease state, rather than the antibiotics alone, primarily shapes the observed community structure. The persistence of dysbiosis through therapy, together with a discriminating genus signature, points to potential roles for respiratory microbiota in RR-TB pathogenesis and as a candidate diagnostic adjunct. These associations are drawn from a small single-center cohort and cannot establish causality, and antibiotic exposure remains a likely confounder of specific taxonomic shifts (such as the post-treatment rise in Haemophilus); larger, controlled studies are needed before clinical application.

2025

Tuberculosis and Respiratory Infection

A four-group sputum microbiome study in India found that tuberculosis and COVID-19 co-infection reshaped airway bacterial composition and upregulated surfactant-lipid metabolic pathways relative to TB alone, with shifts that tracked toward adverse TB outcomes.

Location

India

China

Sample Site

Sputum

Throat

Species

Homo sapiens

What Was Studied?

This observational cross-sectional study characterized the sputum microbiome across single infection and co-infection with the pathogens of tuberculosis (TB) and COVID-19. Investigators compared four groups: TB only, COVID-19 only, TB and COVID-19 co-infection (TBCOVID), and uninfected controls. The airway bacterial community was profiled by 16S rRNA (V3-V4) amplicon sequencing with metagenomic analysis, complemented by whole-genome sequencing of Mycobacterium tuberculosis in a subset to assess strain-level differences and by functional pathway inference.

Who Was Studied?

Participants were recruited in Chennai, India, through IIT Madras and the ICMR National Institute for Research in Tuberculosis, with 1,132 sputum samples initially screened and a stratified subset of 82 samples analyzed (24 TB, 10 COVID-19, 24 TBCOVID, and 24 controls). TB and TBCOVID cases were newly diagnosed, none were HIV co-infected, and the cohort was predominantly male (about 85%), with roughly 30% tobacco users and 48% alcohol consumers. Previously treated TB patients and those with unknown HIV status or inconsistent treatment regimens were excluded.

What Were the Key Findings?

Alpha diversity (richness, Shannon, Simpson) did not differ significantly among groups, but beta diversity by PERMANOVA distinguished TBCOVID from both TB (P=0.006) and controls (P=0.02), while COVID-only versus control did not separate (P=0.665). Relative to TB alone, the TBCOVID community was enriched for Capnocytophaga gingivalis, Prevotella melaninogenica, Veillonella parvula, and Escherichia coli and depleted of Rothia mucilaginosa and Saccharibacteria (TM7), and 30 functional pathways were upregulated, notably pulmonary surfactant lipid metabolism (fold change 7.46) and CDP-diacylglycerol biosynthesis, with several pathways attributable to Stenotrophomonas maltophilia. Clustering by these pathways largely separated TB from TBCOVID samples, and the co-infection-associated cluster carried roughly twice as many recognized respiratory pathogens and more adverse outcomes.

What Are the Implications?

The findings suggest that COVID-19 co-infection is associated with a distinct sputum microbiome and metabolic profile in TB patients, including enrichment of opportunistic respiratory organisms such as S. maltophilia that has been linked to multidrug-resistant TB, raising the possibility that dysbiosis accompanies more complicated disease courses. Because the analysis is cross-sectional with modest group sizes and no diagnostic classifier or AUC was derived, the authors emphasize that the associations are exploratory and do not establish causation or prediction. Strain-level whole-genome sequencing showed no clear M. tuberculosis genetic differences between TB and TBCOVID groups, pointing to host-microbiome rather than pathogen-genotype drivers, though residual confounding from treatment and antibiotic exposure cannot be excluded.

Update History

2026-06-26

Tuberculosis major

Page published: microbiome + metallomic signature, primer, triangle-tested interventions, and STOPs.

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