NR4A3 controls CD8+ T cell metabolism and differentiation
Bibliographic record
Abstract
Abstract Following an infection, naive CD8+ T cells expand and differentiate into two major populations of effectors: short-lived effector cells meant to die by apoptosis and memory precursor effector cells (MPECs) destined to survive as memory cells that will confer long-term protection. We postulated that the transcription factor NR4A3, a member of the orphan nuclear receptor family, whose expression is induced by TCR signalling, will regulate in vivo CD8+ T cell response. To elucidate the role of NR4A3 during CD8+ T cell response, we have adoptively transferred wild-type and Nr4a3−/− OT-I T cells (specific for the ovalbumin (OVA) peptide in the context of Kb) into naive recipients and analyzed their response following infection with a recombinant strain of Listeria monocytogenes encoding OVA. Although Nr4a3+/+ and Nr4a3−/− OT-I T cells expanded similarly, we observed an increased generation of MPECs at the peak of the T cell response, which led to an enhanced (2–3X) generation of memory T cells. Furthermore, Nr4a3−/− effector and memory T cells produce more cytokines than their wild-type counterpart. The analysis of the early T cell response demonstrated that NR4A3 controls the metabolic activity of activated T cells, as Nr4a3−/− OT-I T cells express less nutrient receptors and have reduced phosphorylation of S6. Altogether, these results suggest that NR4A3 controls effector and memory T cell differentiation by modulating their metabolic activity during the immune response.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".