Reversible Senescence in Human CD4+CD45RA+CD27− Memory T Cells
Bibliographic record
Abstract
Persistent viral infections and inflammatory syndromes induce the accumulation of T cells with characteristics of terminal differentiation or senescence. However, the mechanism that regulates the end-stage differentiation of these cells is unclear. Human CD4(+) effector memory (EM) T cells (CD27(-)CD45RA(-)) and also EM T cells that re-express CD45RA (CD27(-)CD45RA(+); EMRA) have many characteristics of end-stage differentiation. These include the expression of surface KLRG1 and CD57, reduced replicative capacity, decreased survival, and high expression of nuclear γH2AX after TCR activation. A paradoxical observation was that although CD4(+) EMRA T cells exhibit defective telomerase activity after activation, they have significantly longer telomeres than central memory (CM)-like (CD27(+)CD45RA(-)) and EM (CD27(-)CD45RA(-)) CD4(+) T cells. This suggested that telomerase activity was actively inhibited in this population. Because proinflammatory cytokines such as TNF-α inhibited telomerase activity in T cells via a p38 MAPK pathway, we investigated the involvement of p38 signaling in CD4(+) EMRA T cells. We found that the expression of both total and phosphorylated p38 was highest in the EM and EMRA compared with that of other CD4(+) T cell subsets. Furthermore, the inhibition of p38 signaling, especially in CD4(+) EMRA T cells, significantly enhanced their telomerase activity and survival after TCR activation. Thus, activation of the p38 MAPK pathway is directly involved in certain senescence characteristics of highly differentiated CD4(+) T cells. In particular, CD4(+) EMRA T cells have features of telomere-independent senescence that are regulated by active cell signaling pathways that are reversible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".