The transcription factor AFF3 maintains naïve CD8 T cell quiescence restraining premature cell activation allowing for optimal T cell response to viral infection 3057
Bibliographic record
Abstract
Abstract Description Quiescent naïve CD8 T cells are poised to rapidly proliferate upon activation to eliminate infected and cancerous cells. The mechanisms that control naïve CD8 T cell quiescence is incompletely understood. We identified the transcription factor AF4/FMR2 Family Member 3 (Aff3) as a novel regulator of CD8 T cell quiescence to prevent premature activation and differentiation. To determine the role of AFF3 in CD8 T cells we designed a T cell specific conditional AFF3 knockout (KO) mouse model. Total RNA-sequencing data show that the largest differences in gene expression occur between naïve and recently activated CD8 T cells and involve pathways linked to TCR signaling and metabolism. Although at baseline naïve CD8 AFF3 KO T cells do not develop an antigen-experienced phenotype, they have higher ERK phosphorylation. In co-adoptive transfer experiments, KO cells are present at a higher frequency at 5 days post infection (dpi) but at a lower frequency at 8 dpi compared to the WT cells following acute LCMV infection, indicating an accumulation defect. This deficit is partially restored by rapamycin treatment suggesting that AFF3 may regulate mTORc1 activation. When naïve CD8 T cells are stimulated ex vivo, KO cells activate and enter the cell cycle more rapidly but fail to sustain proliferation compared to WT. Together, our data suggest that AFF3 is important to maintain naïve CD8 T cell quiescence leading to optimal accumulation and function of CD8 T cells in viral infection. Funding Sources CTSI BRG, IUSCC HHM, IUSM Topic Categories Lymphocyte Differentiation and Peripheral Maintenance (LYM)
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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 | 0.001 |
| 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".