Ketolysis is a metabolic driver of CD8 <sup>+</sup> T cell effector function through histone acetylation
Bibliographic record
Abstract
Abstract Environmental nutrient availability influences T cell metabolism, impacting T cell function and shaping immune outcomes. However, the metabolic pathways critical for optimal T cell responses remain poorly understood. Here, we identify ketone bodies (KBs) – including β-hydroxybutyrate (βOHB) and acetoacetate (AcAc) – as essential fuels supporting CD8 + T cell metabolism and effector function. Ketolysis is an intrinsic feature of highly functional CD8 + T effector (Teff) cells and βOHB directly increases CD8 + Teff cell IFN-γ production and cytolytic activity. Using metabolic tracers, we establish that CD8 + Teff cells preferentially use KBs over glucose to fuel the tricarboxylic acid (TCA) cycle in vitro and in vivo . KBs directly boost the respiratory capacity of CD8 + T cells and TCA cycle-dependent metabolic pathways that fuel T cell growth. Mechanistically, we find that βOHB is a major substrate for acetyl-CoA production in CD8 + T cells and regulates effector responses through effects on histone acetylation. Together, our results identify cell-intrinsic ketolysis as a metabolic and epigenetic driver of optimal CD8 + T cell effector responses. One Sentence summary Ketone bodies promote CD8 + T cell metabolism and effector function through regulation of epigenetic programming
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".