eIF4E-Dependent Translational Control: A Central Mechanism for Regulation of Pain Plasticity
Why this work is in the frame
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Bibliographic record
Abstract
Translational control of gene expression has emerged as a key mechanism in regulating different forms of long-lasting neuronal plasticity. Maladaptive plastic reorganization of peripheral and spinal nociceptive circuits underlies many chronic pain states and relies on new gene expression. Accordingly, downregulation of mRNA translation in primary afferents and spinal dorsal horn neurons inhibits tissue injury-induced sensitization of nociceptive pathways, supporting a central role for translation dysregulation in the development of persistent pain. Translation is primarily regulated at the initiation stage via the coordinated activity of translation initiation factors. The mRNA cap-binding protein, eukaryotic translation initiation factor 4E (eIF4E), is involved in the recruitment of the ribosome to the mRNA cap structure, playing a central role in the regulation of translation initiation. eIF4E integrates inputs from the mTOR and ERK signaling pathways, both of which are activated in numerous painful conditions to regulate the translation of a subset of mRNAs. Many of these mRNAs are involved in the control of cell growth, proliferation, and neuroplasticity. However, the full repertoire of eIF4E-dependent mRNAs in the nervous system and their translation regulatory mechanisms remain largely unknown. In this review, we summarize the current evidence for the role of eIF4E-dependent translational control in the sensitization of pain circuits and present pharmacological approaches to target these mechanisms. Understanding eIF4E-dependent translational control mechanisms and their roles in aberrant plasticity of nociceptive circuits might reveal novel therapeutic targets to treat persistent pain states.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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