Fatty acid depletion is a reversible cause of kynurenine induced T cell apoptosis
Bibliographic record
Abstract
Abstract Metabolic conditions in the tumor microenvironment (TME) are a barrier for anti-tumor immunotherapy. The TME metabolite kynurenine binds aryl hydrocarbon receptor which has been linked to immunosuppressive effects. We questioned if kynurenines could be utilized for therapeutic immunosuppression, and examined the effect of kynurenines on human and murine effector T cell (Teff) metabolism and function. We co-stimulated C57BL/6 Teff for three days and measured bioenergetic function with Seahorse. Injection of 1 mM L- or D-kynurenine reduced extracellular acidification by 4.2 ±0.2 and 7.1 ±0.4% over 1 hr, respectively (p<0.05). Next, we labeled activated Teff with 60 mg/dL [13C] glucose for 3 hr. We observed a 44.2% and 49.3% reduction of [13C]-glucose derived M+3 pyruvate production with D- and L-kynurenine, respectively (p<0.05, 3/group). We also noted a reduction in lipid pools, with a 56.5/33.8%, 40.1/43.9%, 52.5/43.6%, and 70.7/43.2% decline in the palmitate, oleate, lineolate, and myristate with L-/D-kynurenine, respectively. These data pointed to an important role for lipid catabolism in kynurenine rich environments. We observed, that 3-day co-stimulated Teffs ±1 mM L- or D-kynurenine had 98.9 ±0.55 or 97.3 ±0.55% cell death (vs. 69.7 ±18.84% in untreated Teff, 4/grp). Addition of 0.2–0.6 mM oleate/palmitate rescued the apoptosis phenotype, restoring Teff viability and proliferation. Our results show lipid consumption as a reversible cause of kynurenine-induced Teff apoptosis, indicating Teff apoptosis as a potential in vitro artifact of low lipid content in traditional cell culture media, the immune modulatory potential of TME fatty acids, or the possible use of kynurenines to induce lipid catabolism therapeutically.
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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".