EBV-Specific CD8+ T Cell Memory: Relationships Between Epitope Specificity, Cell Phenotype, and Immediate Effector Function
Bibliographic record
Abstract
EBV infection in humans induces CD8+ T cell memory to viral epitopes derived from both lytic and latent cycle Ags. We have analyzed the relationship between the phenotype and function of the memory pool of T cells specific for these Ags. Lytic epitope-specific populations were heterogeneous in terms of CD45RO/RA and CD28 expression, whereas latent epitope-specific populations were uniformly CD45RO+ and CD28+, consistent with the higher antigenic challenge from lytic epitopes driving some memory cells toward a CD45RA+, CD28- phenotype. However, both types of memory population showed immediate epitope-specific cytotoxicity and type 1 cytokine production in ex vivo assays. Cytotoxic function was not associated with preactivated T cells, as EBV-specific populations were negative for activation markers such as CD69 or CD38, nor could cytotoxic function be ascribed to CD27- or CD56+ subsets, as such cells were not detected in EBV-specific memory. Furthermore, cytotoxicity was not limited to CD45RA+ and/or CD28- fractions, but also was observed in CD45RO+, CD28+ populations in lytic and latent epitope-specific memory. Cytokine (IFN-gamma, TNF-alpha) responses, measured by intracytoplasmic staining after peptide stimulation, also were detectable in CD45RO+ and RA+ subsets as well as CD28+ and CD28- subsets. Of other markers that were heterogeneous in both lytic and latent epitope populations, CCR7 gave the best discrimination of functionality; thus, CCR7+ cells consistently failed to give an IFN-gamma or TNF-alpha response, whereas many CCR7- cells were responsive. Our data are consistent with effector functions having a broad distribution among phenotypically distinct subsets of "effector memory" cells that have lost the CCR7 marker.
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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".