Peripheral pain is enhanced by insulin-like growth factor 1 through a G protein–mediated stimulation of T-type calcium channels
Bibliographic record
Abstract
Insulin-like growth factor 1 (IGF-1) is implicated in the nociceptive (pain) sensitivity of primary afferent neurons. We found that the IGF-1 receptor (IGF-1R) functionally stimulated voltage-gated T-type Ca(2+) (CaV3) channels in mouse dorsal root ganglia (DRG) neurons through a mechanism dependent on heterotrimeric G protein (heterotrimeric guanine nucleotide-binding protein) signaling. IGF-1 increased T-type channel currents in small-diameter DRG neurons in a manner dependent on IGF-1 concentration and IGF-1R but independent of phosphatidylinositol 3-kinase (PI3K). The intracellular subunit of IGF-1R coimmunoprecipitated with Gαo. Blocking G protein signaling by the intracellular application of guanosine diphosphate (GDP)-β-S or with pertussis toxin abolished the stimulatory effects of IGF-1. Antagonists of protein kinase Cα (PKCα), but not of PKCβ, abolished the IGF-1-induced T-type channel current increase. Application of IGF-1 increased membrane abundance of PKCα, and PKCα inhibition (either pharmacologically or genetically) abolished the increase in T-type channel currents stimulated by IGF-1. IGF-1 increased action potential firing in DRG neurons and increased the sensitivity of mice to both thermal and mechanical stimuli applied to the hindpaw, both of which were attenuated by intraplantar injection of a T-type channel inhibitor. Furthermore, inhibiting IGF-1R signaling or knocking down CaV3.2 or PKCα in DRG neurons abolished the increased mechanical and thermal sensitivity that mice exhibited under conditions modeling chronic hindpaw inflammation. Together, our results showed that IGF-1 enhances T-type channel currents through the activation of IGF-1R that is coupled to a G protein-dependent PKCα pathway, thereby increasing the excitability of DRG neurons and the sensitivity to pain.
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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".