Butyrate and propionate are microbial danger signals that activate the NLRP3-inflammasome in human macrophages in the presence of TLR stimulation
Bibliographic record
Abstract
Abstract Short chain fatty acids (SCFAs) are immunomodulatory compounds produced by the microbiome through fermentation of dietary fibre. Although they are generally considered beneficial for gut health, patients suffering from inflammatory bowel disease (IBD) have shown poor tolerance to fibre-rich diets, suggesting that SCFAs may have contrary effects under inflammatory conditions. To investigate this, we examined the effect of SCFAs on human macrophages in the presence of toll-like receptor agonists. In contrast to their anti-inflammatory effects under steady state conditions, we observed that the SCFAs butyrate and propionate triggered the activation of the NLRP3 inflammasome when added in conjunction with TLR agonists. Mechanistically, butyrate and propionate activated NLRP3 by inhibiting HDACs 1-3 and 10, leading to an uneven distribution of histone hyperacetylation that resulted in alterations in the transcriptome. Specifically, there was a lack of hyperacetylation at the loci of the CFLAR and IL10 genes, two important inhibitors of NLRP3 inflammasome activation. The concurrent loss of transcription and protein expression of cFLIP and IL-10 enabled caspase-8-dependent NLRP3-inflammasome activation. SCFA-driven NLRP3 activation did not require potassium efflux and did not result in cell death but rather triggered hyperactivation and IL-1β release. Our findings demonstrate that butyrate and propionate are bacterially-derived, viability-dependent danger signals (vita-PAMPs) that regulate NLRP3 inflammasome activation through epigenetic modulation of the inflammatory response. Summary Under inflammatory conditions, SCFAs are bacterially-derived, viability-dependent danger signals that, through HDAC inhibition and epigenetic modification, prevent expression of the anti-cell death gene cFLIP to trigger activation of the NLRP3 inflammasome.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".