CCL5-mediated T-cell chemotaxis involves the initiation of mRNA translation through mTOR/4E-BP1
Bibliographic record
Abstract
The multistep, coordinated process of T-cell chemotaxis requires chemokines, and their chemokine receptors, to invoke signaling events to direct cell migration. Here, we examined the role for CCL5-mediated initiation of mRNA translation in CD4(+) T-cell chemotaxis. Using rapamycin, an inhibitor of mTOR, our data show the importance of mTOR in CCL5-mediated T-cell migration. Cycloheximide, but not actinomycin D, significantly reduced chemotaxis, suggesting a possible role for mRNA translation in T-cell migration. CCL5 induced phosphorylation/activation of mTOR, p70 S6K1, and ribosomal protein S6. In addition, CCL5 induced PI-3'K-, phospholipase D (PLD)-, and mTOR-dependent phosphorylation and deactivation of the transcriptional repressor 4E-BP1, which resulted in its dissociation from the eukaryotic initiation factor-4E (eIF4E). Subsequently, eIF4E associated with scaffold protein eIF4G, forming the eIF4F translation initiation complex. Indeed, CCL5 initiated active translation of mRNA, shown by the increased presence of high-molecular-weight polysomes that were significantly reduced by rapamycin treatment. Notably, CCL5 induced protein translation of cyclin D1 and MMP-9, known mediators of migration. Taken together, we describe a novel mechanism by which CCL5 influences translation of rapamycin-sensitive mRNAs and "primes" CD4(+) T cells for efficient chemotaxis.
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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) | 0.001 | 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 | 0.000 |
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".