Expression of IL‐7Rα and KLRG1 defines functionally distinct CD8 <sup>+</sup> T‐cell populations in humans
Bibliographic record
Abstract
Abstract During acute viral infections in mice, IL‐7Rα and KLRG1 together are used to distinguish the short‐lived effector cells (SLEC; IL‐7Rα lo KLRG hi ) from the precursors of persisting memory cells (MPEC; IL‐7Rα hi KLRG1 lo ). We here show that these markers can be used to define distinct subsets in the circulation and lymph nodes during the acute phase and in “steady state” in humans. In contrast to the T cells in the circulation, T cells derived from lymph nodes hardly contain any KLRG1‐expressing cells. The four populations defined by IL‐7Rα and KLRG1 differ markedly in transcription factor, granzyme and chemokine receptor expression. When studying renal transplant recipients experiencing a primary hCMV and EBV infection, we also found that after viral control, during latency, Ki‐67‐negative SLEC can be found in the peripheral blood in considerable numbers. Thus, combined analyses of IL‐7Rα and KLRG1 expression on human herpes virus‐specific CD8 + T cells can be used to separate functionally distinct subsets in humans. As a noncycling IL‐7Rα lo KLRG1 hi population is abundant in healthy humans, we conclude that this combination of markers not only defines short‐lived effector cells during the acute response but also stable effector cells that are formed and remain present during latent herpes infections.
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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".