Helminth-mediated disease tolerance in TB: A role for microbiota?
Bibliographic record
Abstract
Intestinal helminth infections are most prevalent in peri-equatorial regions of the world and have an overlapping geographical distribution with Mycobacterium tuberculosis (Mtb) infection—the causative agent of tuberculosis (TB). Importantly, approximately 40% of TB patients are asymptomatically infected with helminth parasites. While experimental and epidemiological evidence suggest that helminth infections alter the course of TB, other studies do not support this link [1]. Although the direct immunomodulatory effects of helminth infections on adaptive host immunity have been studied extensively, these can only partially explain the complex nature of helminth–TB interactions. Indeed, the potent immunomodulatory abilities of helminths may even reduce TB-associated tissue pathology [1] and contribute to disease tolerance [2]. However, helminth infections also induce changes to the gut microbiota that can have a systemic impact on heterologous infectious diseases [3]. Given previous studies demonstrating that the gut microbiota can shape disease tolerance to pulmonary infections [4], here, we discuss the current understanding of how the gut microbiota impacts TB and posit that helminth-mediated changes to this vast microbial community may contribute to the clinical course of TB in co-endemic regions (Fig 1). Open in a separate window Fig 1 Helminth infections, eliciting robust type 2 immune responses, might contribute to Mtb disease tolerance by inhibiting type 1 and type 3 immune responses, thus reducing inflammation and pathology while maintaining bacterial burden. An alternative, but not mutually exclusive, possibility is that helminth-mediated changes to the gut microbiota shape TB outcomes. The robust regulatory capacity of the gut microbiota (via immune suppression, metabolite processing, and niche competition) is an appealing mechanism to explain the contradicting data regarding the exact role of helminth infections in TB disease progression and disease tolerance in asymptomatic infected patients. This figure was created with BioRender.com. Mtb, Mycobacterium tuberculosis; TB, tuberculosis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".