Flagellin in the human gut microbiome is a diet-adjustable adjuvant for vaccination
Bibliographic record
Abstract
ABSTRACT The intestinal microbiota is thought to modulate immune responsiveness to vaccines. Human studies on this topic, however, have yielded inconsistent results 1,2 . We hypothesized that the microbiome would influence innate immune responses, and thus vaccine reactogenicity, more directly than vaccine immunogenicity. To test this, we established the µHEAT (Microbial-Human Ecology And Temperature) study, which longitudinally profiled the fecal microbiota, oral body temperature and serum antibody responses of 171 healthy adults (18-40 years old) before and after vaccination for SARS-CoV-2. Increased temperature after vaccination (ΔT) was associated with habitual diet and with baseline metabolic and immune markers. The microbiomes of ΔT-high (ΔT hi ) participants were characterized by high expression of flagellin and an overabundance of the flagellated bacterium Waltera . Fecal samples from ΔT hi participants induced more inflammation in human cells and stronger post-vaccine temperature responses in mice compared to ΔT lo samples, suggesting a causal role for the microbiome. Moreover, Waltera flagellin replicated the inflammatory phenotypes in vitro and was modulable via a dietary additive. Overall, these data suggest that flagellin from the gut microbiome stimulates innate immunity and vaccine reactogenicity, and that this axis can be manipulated via diet. These findings have implications for improving human vaccine tolerance and immunogenicity.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".