Mitochondrial mass governs the extent of human T cell senescence
Bibliographic record
Abstract
Abstract The susceptibility of human CD4 + and CD8 + T cells to senesce differs, with CD8 + T cells acquiring an immunosenescent phenotype faster than the CD4 + T cell compartment. We show here that it is the inherent difference in mitochondrial content that drives this phenotype, with senescent human CD4 + T cells displaying a higher mitochondrial mass. The loss of mitochondria in the senescent human CD8 + T cells has knock‐on consequences for nutrient usage, metabolism and function. Senescent CD4 + T cells uptake more lipid and glucose than their CD8 + counterparts, leading to a greater metabolic versatility engaging either an oxidative or a glycolytic metabolism. The enhanced metabolic advantage of senescent CD4 + T cells allows for more proliferation and migration than observed in the senescent CD8 + subset. Mitochondrial dysfunction has been linked to both cellular senescence and aging; however, it is still unclear whether mitochondria play a causal role in senescence. Our data show that reducing mitochondrial function in human CD4 + T cells, through the addition of low‐dose rotenone, causes the generation of a CD4 + T cell with a CD8 + ‐like phenotype. Therefore, we wish to propose that it is the inherent metabolic stability that governs the susceptibility to an immunosenescent phenotype.
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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 | 0.000 |
| 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".