CD8 memory T cells have a bioenergetic advantage that underlies their rapid recall ability
Bibliographic record
Abstract
A characteristic of memory T (TM) cells is their ability to mount faster and stronger responses to reinfection than naïve T (TN) cells do in response to an initial infection. However, the mechanisms that allow this rapid recall are not completely understood. We found that CD8 TM cells have more mitochondrial mass than CD8 TN cells and, that upon activation, the resulting secondary effector T (TE) cells proliferate more quickly, produce more cytokines, and maintain greater ATP levels than primary effector T cells. We also found that after activation, TM cells increase oxidative phosphorylation and aerobic glycolysis and sustain this increase to a greater extent than TN cells, suggesting that greater mitochondrial mass in TM cells not only promotes oxidative capacity, but also glycolytic capacity. We show that mitochondrial ATP is essential for the rapid induction of glycolysis in response to activation and the initiation of proliferation of both TN and TM cells. We also found that fatty acid oxidation is needed for TM cells to rapidly respond upon restimulation. Finally, we show that dissociation of the glycolysis enzyme hexokinase from mitochondria impairs proliferation and blocks the rapid induction of glycolysis upon T-cell receptor stimulation in TM cells. Our results demonstrate that greater mitochondrial mass endows TM cells with a bioenergetic advantage that underlies their ability to rapidly recall in response to reinfection.
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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) | 0.002 | 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".