Characterization of the Role of eIF4G in Stimulating Cap- and IRES-Dependent Translation in Aplysia Neurons
Bibliographic record
Abstract
The rate-limiting step(s) of translation in the nervous system have not been clearly identified. We have been examining this question in the cell body of the Aplysia sensory neuron, where translational regulation is important for the regulation of synaptic strength. In the present study, we examined the role of the adaptor protein eIF4G. We cloned Aplysia eIF4G (Ap4G) and Ap4G contains all the standard metazoan eIF4G protein-protein interaction domains. Overexpressing Ap4G in Aplysia sensory neurons caused an increase in both cap-dependent and internal ribosome entry site (IRES)-dependent translation using a previously characterized bicistronic fluorescent reporter. Unexpectedly, measurement of overall translation using the methionine analog, L-azidohomoalanine, revealed that overexpression of Ap4G did not lead to an increase in overall translation rates. Indeed, the effect of Ap4G on the bicistronic reporter depended on the presence of an upstream open reading frame (uORF) in the 5' UTR encoded by the vector. We have previously shown that Mnk strongly decreased cap-dependent translation and this depended on a putative 4G binding domain. Here we extend these results showing that even in the absence of the uORF, overexpression of Mnk strongly decreases cap-dependent translation and this depends on the Mnk binding site in eIF4G. Similarly, an increase in cap-dependent translation seen with overexpression of elongation factor 2 kinase did not depend on the uORF. Overall, we show that eIF4G is rate limiting for translation of an mRNA encoding an uORF, but is not generally a rate-limiting step for translation.
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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 | 0.001 |
| 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 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".