A translational rheostat for RFLAT-1 regulates RANTES expression in T lymphocytes
Bibliographic record
Abstract
Activation of T lymphocytes by specific antigen triggers a 3- to 7-day maturation process. Terminal differentiation begins late after T cell activation and involves expression of effector genes, including the chemokine RANTES and its major transcriptional regulator, RANTES factor of late-activated T lymphocytes-1 (RFLAT-1). In this article we demonstrate that RFLAT-1 expression is translationally regulated through its 5'-UTR and in a cell type-specific manner. Overexpression of the translation initiation factor eIF4E increases RFLAT-1 protein, while inhibition of Mnk1, which phosphorylates eIF4E, reduces RFLAT-1 production, indicating cap-dependent translational regulation. These events are regulated by ERK-1/2 and p38 MAP kinases and allow T cells to rapidly adjust RANTES expression in response to changes in the cellular environment, such as stress and/or growth factors. These findings provide a molecular mechanism for a rheostat effect of increasing or decreasing RANTES expression at sites of inflammation. Memory T cells, already poised to make RANTES, are finely regulated by translational control of the major transcription factor regulating RANTES expression. This is the first example of such a mechanism regulating a chemokine, but it seems likely that this will prove to be a general way for cells to rapidly respond to stress, cytokines, and other proinflammatory factors in their local environment.
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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".