Gene Regulation at the RNA Layer: RNA Binding Proteins in Intercellular Signaling Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it