Susceptibility to chronic pain following nerve injury is genetically affected by <i>CACNG2</i>
Bibliographic record
Abstract
Chronic neuropathic pain is affected by specifics of the precipitating neural pathology, psychosocial factors, and by genetic predisposition. Little is known about the identity of predisposing genes. Using an integrative approach, we discovered that CACNG2 significantly affects susceptibility to chronic pain following nerve injury. CACNG2 encodes for stargazin, a protein intimately involved in the trafficking of glutamatergic AMPA receptors. The protein might also be a Ca(2+) channel subunit. CACNG2 has previously been implicated in epilepsy. Initially, using two fine-mapping strategies in a mouse model (recombinant progeny testing [RPT] and recombinant inbred segregation test [RIST]), we mapped a pain-related quantitative trait locus (QTL) (Pain1) into a 4.2-Mb interval on chromosome 15. This interval includes 155 genes. Subsequently, bioinformatics and whole-genome microarray expression analysis were used to narrow the list of candidates and ultimately to pinpoint Cacng2 as a likely candidate. Analysis of stargazer mice, a Cacng2 hypomorphic mutant, provided electrophysiological and behavioral evidence for the gene's functional role in pain processing. Finally, we showed that human CACNG2 polymorphisms are associated with chronic pain in a cohort of cancer patients who underwent breast surgery. Our findings provide novel information on the genetic basis of neuropathic pain and new insights into pain physiology that may ultimately enable better treatments.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".