Gut microbial-mediated polyphenol metabolism is restrained by parasitic whipworm infection and associated with altered immune function in mice
Bibliographic record
Abstract
Abstract Polyphenols are phytochemicals commonly found in plant-based diets which have demonstrated immunomodulatory and anti-inflammatory properties. However, the interplay between polyphenols and pathogens at mucosal barrier surfaces has not yet been elucidated in detail. Here, we show that proanthocyanidin (PAC) polyphenols interact with gut parasites to influence immune function and gut microbial-derived metabolites in mice. PAC intake inhibited mastocytosis during infection with the small intestinal roundworm Heligmosomoides polygyrus , and induced a type-1, interferon-driven mucosal immune response during infection with the large intestinal whipworm Trichuris muris. PAC also induced alterations in mesenteric lymph node T-cell populations that were dependent on infection model, with a Th2/Treg bias during H. polygyrus infection, and a Th1 bias during T. muris infection. In the absence of infection, PAC intake promoted the expansion of Turicibacter sp. within the gut microbiota, increased faecal short chain fatty acids, and enriched phenolic metabolites such as phenyl-γ-valerolactones in the caecum. However, these putatively beneficial effects were reduced in PAC-fed mice infected with T. muris, suggesting concomitant parasite infection can attenuate gut microbial-mediated PAC catabolism. Collectively, our results suggest an inter-relationship between a phytonutrient and infection, whereby PAC may augment parasite-induced inflammation (most prominently with the caecum dwelling T. muris ), and infection may abrogate the beneficial effects of health-promoting phytochemicals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".