Gene Regulation at the RNA Layer: RNA Binding Proteins in Intercellular Signaling Networks
Bibliographic record
Abstract
Transcriptional regulators are sometimes believed to be the only targets through which signal transduction pathways regulate gene expression. Although it is certainly true that many well-characterized intercellular signaling pathways operate by modifying the activity of specific transcription factors, an increasing body of evidence indicates that external signals can modulate gene expression by posttranscriptional mechanisms. RNA binding motifs are combined with other conserved domains, such as protein-interaction domains and consensus phosphorylation motifs, to allow gene expression to be regulated at the level of the RNA in response to extracellular signals. In this review, I discuss evidence that reveals how a particular family of RNA binding proteins, called signal transduction and activation of RNA (STAR) proteins, function in signaling and in the development of multicellular organisms. Furthermore, insulin and related growth factors regulate cell growth, at least in part, by moderating the activity of eukaryotic initiation factor 4E (eIF4E)-binding protein (4EBP), a protein that does not bind RNA directly but inhibits the activity of eIF4E, which is an mRNA cap-binding protein. I discuss the evidence linking insulin signaling to 4EBP phosphorylation. Finally, several other genes have been identified from invertebrate model organisms that encode RNA binding proteins and whose mutant phenotypes implicate them in intercellular signaling, but for which the mechanisms of function currently are unclear. The study of these and other similar genes is likely to uncover a diversity of roles for RNA binding proteins in signal transduction.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".