Translational control of memory CD8 T cell differentiation (LYM4P.752)
Bibliographic record
Abstract
Abstract CD8 T cells play an essential role in controlling viral as well as intracellular bacterial and parasitic infections. Memory CD8 T cells provide protective immunity upon re-encounter with the same pathogens. A better understanding of memory CD8 T cell development is critical for the rational design of vaccines. Transcriptome analyses in antigen-specific CD8 T cells have made a significant contribution to characterize global re-programming of gene expression during memory CD8 T cell differentiation. However, there has been minimal emphasis on understanding translational control of gene expression in antigen specific CD8 T cells. To examine mRNA translation during memory CD8 T cell differentiation, we performed longitudinal analyses of polysome profiles in antigen-specific CD8 T cells after acute LCMV infection. We found that mRNA translation was dynamically regulated during CD8 T cell responses; translation activity was significantly enhanced during clonal expansion phase, and it immediately returned to basal level at the peak of CD8 T cell responses. Genome-wide analyses of mRNA translation showed that a significant number of mRNAs were translationally regulated during CD8 T cell responses. Further experiments revealed that translational control of gene expression plays an important role in memory CD8 T cell formation. Thus, our studies provide a framework for understanding translational regulation that occurs during memory T cell differentiation.
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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) | 0.002 | 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".