Inhibition of IRAK4 by microbial trimethylamine blunts metabolic inflammation and ameliorates glycemic control
Bibliographic record
Abstract
Abstract The global type 2 diabetes epidemic is a major health crisis and there is a critical need for innovative strategies to fight it. Although the microbiome plays important roles in the onset of insulin resistance (IR) and low-grade inflammation, the microbial compounds regulating these phenomena remain to be discovered. Here, we reveal that the microbiome inhibits a central kinase, eliciting immune and metabolic benefits. Through a series of in vivo experiments based on choline supplementation, blocking trimethylamine (TMA) production then administering TMA, we demonstrate that TMA decouples inflammation and IR from obesity in the context of high-fat diet (HFD) feeding. Through in vitro kinome screens, we reveal TMA specifically inhibits Interleukin-1 Receptor-associated Kinase 4 (IRAK4), a central kinase integrating signals from various toll-like receptors and cytokine receptors. TMA blunts TLR4 signalling in primary human hepatocytes and peripheral blood monocytic cells, and improves mouse survival after a lipopolysaccharide-induced septic shock. Consistent with this, genetic deletion and chemical inhibition of IRAK4 result in similar metabolic and immune improvements in HFD. In summary, TMA appears to be a key microbial compound inhibiting IRAK4 and mediating metabolic and immune effects with benefits upon HFD. Thereby we highlight the critical contribution of the microbial signalling metabolome in homeostatic regulation of host disease and the emerging role of the kinome in microbial–mammalian chemical crosstalk.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".