Gut microbiota: a promising new target in immune tolerance
Bibliographic record
Abstract
Gut microbiota research has highlighted its pivotal role in human health and disease. Its composition is shaped by diet, genetics, age, and environmental factors. When the balance of these microbes is disrupted (dysbiosis), it can contribute to health problems like metabolic, inflammatory, and mental disorders. The microbiota supports digestion, fermentation, and vitamin production, which are essential for overall health. The gut microbiota has emerged as a critical modulator of immune function, with increasing evidence highlighting its role in establishing and maintaining immune tolerance. Despite significant advances in understanding the interactions between the gut microbiome and immune system, gaps remain in the literature regarding the specific mechanisms through which microbiota influences immune tolerance. This review aims to address these knowledge gaps by synthesizing current research on the microbiota impact on immune tolerance, emphasizing key factors such as microbial diversity, metabolic byproducts, and the microbiota interaction with immune cells, specifically focusing on the role of microbial tryptophan metabolites in PD-1/PD-L1 tolerance. We also highlight critical areas for future research, including the identification of microbial species or strains that can modulate immune tolerance, the influence of diet and environmental factors on microbiota composition, and the development of microbiota-based therapies. By bridging these gaps, this review seeks to provide a comprehensive understanding of the mechanistic role of microbiota immune tolerance and its potential as a novel therapeutic target for autoimmune and inflammatory diseases.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".