Nutrient allocation fuels T cell-mediated immunity
Bibliographic record
Abstract
T cell activation and function are intricately linked to metabolic reprogramming. The classic view of T cell metabolic reprogramming centers on glucose as the dominant bioenergetic fuel, where T cell receptor (TCR) stimulation promotes a metabolic switch from relying primarily on oxidative phosphorylation (OXPHOS) for energy production to aerobic glycolysis (i.e., the Warburg effect). More recently, studies have revealed this classic model to be overly simplistic. Activated T cells run both glycolysis and OXPHOS programs concurrently, allocating diverse nutrient sources toward distinct biosynthetic and bioenergetic fates. Moreover, studies of T cell metabolism in vivo and ex vivo highlight that physiologic nutrient availability influences how glucose is allocated by T cells to fuel both optimal proliferation and effector function. Here, we summarize recent advancements that support a revised model of effector T cell metabolism, where strategic nutrient allocation fuels optimal T cell-mediated immunity. Glucose has traditionally been considered the primary fuel for T cells. Longo et al. present evidence for a revised, “nutrient allocation” model, in which effector T cells channel diverse nutrient sources into specific metabolic pathways to fuel T cell-mediated immunity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".