A27 THE IMPACT OF MICROBIAL-DERIVED METABOLITES ON TYPE III INTERFERON SIGNALING IN INTESTINAL EPITHELIAL CELLS
Bibliographic record
Abstract
Abstract Background Interferons (IFNs) are key cytokines that protect mucosal barriers. There are three types – types I, II and III. Unlike types I and II, which are pro-inflammatory, type III interferons (IFN-λs) are highly expressed in the gut and exert beneficial effects such as mucosal healing and dampening inflammation in mouse colitis models. Gut microbes can directly induce IFN-λ expression and prior studies have shown that microbial-derived metabolites can upregulate IFN-λ activity in the lungs. However, much less is known about the role of gut microbial-derived metabolites in the regulation of IFN-λ activity in the gut. Aims We hypothesized that specific gut microbial-derived metabolites upregulate IFN-λ activity in human gut epithelial cells. Methods Caco-2 cells were pre-treated for 2 hours with anaerobic whole microbe secretions (1-10% v/v) and microbial-derived metabolites, including short-chain fatty acids (SCFAs; acetate, butyrate, and propionate), a metabolic intermediate (succinate), and a tryptophan metabolite (kynurenine). Next, cells were treated with or without IFN-λ3 (20-50ng/ml) for 22 hours (n=3 biological repeats). IFN-stimulated genes (ISGs; MX1 and IFIT1) were quantified by RT-qPCR. IFN-λR1 levels were quantified by flow cytometry. Results Out of microbe secretions added from four gut microbes thus far, secretions from one pathobiont inhibited IFN-λ activity. All SCFAs tested had a concentration-dependent inhibitory effect on IFN-λ3 induction of ISGs (MX1 and IFIT1; p<0.05). Succinate and kynurenine did not affect ISG induction by IFN-λ3 at any concentration tested. Only specific metabolites affected surface IFN-λR1 levels on Caco-2 cells. Conclusions Our findings demonstrate that specific microbial-derived metabolites can regulate intestinal IFN-λ immune responses. Contrary to our hypothesis, some metabolites can downregulate IFN-λ activity. Knowledge from this study provides fundamental knowledge about IFN-λ regulation in the human gut with implications for how to promote optimal IFN-λ activity to promote gut health, such as in inflammatory bowel diseases. Funding Agencies Children’s Hospital Research Institute of Manitoba, University of Manitoba
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".