eIF4E Phosphorylation Influences Bdnf mRNA Translation in Mouse Dorsal Root Ganglion Neurons
Bibliographic record
Abstract
Plasticity in dorsal root ganglion (DRG) neurons that promotes pain requires activity-dependent mRNA translation. Protein synthesis inhibitors block the ability of many pain-promoting molecules to enhance excitability in DRG neurons and attenuate behavioral signs of pain plasticity. In line with this, we have recently shown that phosphorylation of the 5’ cap-binding protein, eIF4E, plays a pivotal role in plasticity of DRG nociceptors in models of hyperalgesic priming. However, mRNA targets of eIF4E phosphorylation have not been elucidated in the DRG. Brain-derived neurotrophic factor (BDNF) signaling from nociceptors in the DRG to spinal dorsal horn neurons is an important mediator of hyperalgesic priming. Regulatory mechanisms that promote pain plasticity via controlling BDNF expression that are involved in promoting pain plasticity have not been identified. We show that phosphorylation of eIF4E is paramount for Bdnf mRNA translation in the DRG. Bdnf mRNA translation is reduced in mice lacking eIF4E phosphorylation (eIF4ES209A) and pro-nociceptive factors fail to increase BDNF protein levels in the DRGs of these mice despite robust upregulation of Bdnf-201 mRNA levels. Importantly, bypassing the DRG by giving intrathecal injection of BDNF in eIF4ES209A mice creates a strong hyperalgesic priming response that is normally absent or reduced in these mice. We conclude that eIF4E phosphorylation-mediated translational control of BDNF expression is a key mechanism for nociceptor plasticity leading to hyperalgesic priming.
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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) | 0.001 | 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".