Systematic screening of tryptophan metabolism identifies site- and microbial-specific signatures of tryptophan utilization in experimental colitis
Bibliographic record
Abstract
Abstract Background Altered tryptophan (Trp) metabolism and disrupted nicotinamide adenine dinucleotide (NAD⁺) synthesis are hallmarks of IBD, yet how intestinal microbiota contribute to these metabolic shifts during intestinal inflammation remains poorly understood. Methods We used targeted metabolomics to systematically profile Trp- and NAD⁺-related metabolites across multiple biological compartments – including tissues, luminal contents, stool, and serum – in mice treated with dextran sulfate sodium (DSS) alone or in combination with a broad-spectrum antibiotic (ABX) cocktail. Results Microbial depletion significantly attenuated colitis and increased host Trp bioavailability, implicating the gut microbiota as a competitive Trp consumer. In DSS colitis, Trp degradation along the kynurenine pathway (KP) was exaggerated but blocked at the key KP enzyme quinolinate phosphoribosyltransferase (QPRT), resulting in mucosal NAD(H) depletion. ABX co-treatment normalized metabolite conversion along the KP and restored mucosal NAD(H) levels, revealing a dual role of the gut microbiota during colitis: while they compete with the host for Trp utilization, they simultaneously shape host KP regulation and NAD⁺ de novo synthesis, supporting host energy homeostasis. Conclusion Our findings demonstrate that mucosal NAD⁺ de novo synthesis is a microbially regulated metabolic process that alleviates intestinal inflammation and may represent a novel therapeutic target in IBD through modulation of the gut microbiota or their metabolites.
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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 | 0.001 | 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) | 0.001 | 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".