Chrna4 A529 knock-in mice exhibit altered nicotine sensitivity
Bibliographic record
Abstract
The reasons why people smoke are varied, but research has shown that genetic influences on various aspects of nicotine addiction are a major factor. There also is a strong genetic influence on measures of nicotine sensitivity in mice. Despite the established contribution of genetics to nicotine sensitivity in mice and humans, no naturally occurring genetic variation has been identified that demonstrably alters sensitivity to nicotine in either species. However, one genetic variant has been implicated in altering nicotine sensitivity in mice is a T529A polymorphism in Chrna4, the gene that encodes the nicotinic receptor (nAChR) alpha4 subunit. The Chrna4 T529A polymorphism leads to a threonine to alanine substitution at position 529 of the alpha4 subunit. To more definitively address whether the Chrna4 T529A polymorphism does, in fact, influence sensitivity to nicotine, knock-in mice were generated in which the threonine codon at position 529 was mutated to an alanine codon. Compared with Chrna4 T529 littermate controls, the Chrna4 A529 knock-in mice exhibited greater sensitivity to the hypothermic effects of nicotine, reduced oral nicotine consumption and did not develop conditioned place preference to nicotine. The Chrna4 A529 knock-in mice also differed from T529 littermates for two parameters of acetylcholine-stimulated Rb+ efflux in midbrain: maximal efflux and the percentage of alpha4beta2* receptors with high sensitivity to activation by agonists. Results indicate that the polymorphism affects the function of midbrain alpha4beta2* nAChRs and contributes to individual differences in several behavioral and physiological responses to nicotine thought to be modulated by midbrain alpha4beta2* nAChRs.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".