Time-dependent regulation of cytokine production by RNA binding proteins defines T cell effector function
Bibliographic record
Abstract
Potent T cell responses against infections and malignancies require a rapid yet tightly regulated production of toxic effector molecules. Their production level is defined by post-transcriptional events at 3' untranslated regions (3' UTRs). RNA binding proteins (RBPs) are key regulators in this process. With an RNA aptamer-based capture assay, we identify >130 RBPs interacting with IFNG, TNF, and IL2 3' UTRs in human T cells. RBP-RNA interactions show plasticity upon T cell activation. Furthermore, we uncover the intricate and time-dependent regulation of cytokine production by RBPs: whereas HuR supports early cytokine production, ZFP36L1, ATXN2L, and ZC3HAV1 dampen and shorten the production duration, each at different time points. Strikingly, even though ZFP36L1 deletion does not rescue the dysfunctional phenotype, tumor-infiltrating T cells produce more cytokines and cytotoxic molecules, resulting in superior anti-tumoral T cell responses. Our findings thus show that identifying RBP-RNA interactions reveals key modulators of T cell responses in health and disease.
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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".