The Combined Effects of Tryptophan Starvation and Tryptophan Catabolites Down-Regulate T Cell Receptor ζ-Chain and Induce a Regulatory Phenotype in Naive T Cells
Bibliographic record
Abstract
Tryptophan catabolism is a tolerogenic effector system in regulatory T cell function, yet the general mechanisms whereby tryptophan catabolism affects T cell responses remain unclear. We provide evidence that the short-term, combined effects of tryptophan deprivation and tryptophan catabolites result in GCN2 kinase-dependent down-regulation of the TCR zeta-chain in murine CD8+ T cells. TCR zeta down-regulation can be demonstrated in vivo and is associated with an impaired cytotoxic effector function in vitro. The longer-term effects of tryptophan catabolism include the emergence of a regulatory phenotype in naive CD4+CD25- T cells via TGF-beta induction of the forkhead transcription factor Foxp3. Such converted cells appear to be CD25+, CD69-, CD45RBlow, CD62L+, CTLA-4+, BTLAlow and GITR+, and are capable of effective control of diabetogenic T cells when transferred in vivo. Thus, both tryptophan starvation and tryptophan catabolites contribute to establishing a regulatory environment affecting CD8+ as well as CD4+ T cell function, and not only is tryptophan catabolism an effector mechanism of tolerance, but it also results in GCN2-dependent generation of autoimmune-preventive regulatory T cells.
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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) | 0.001 | 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".