Extending Investigations of <scp>miR</scp>‐126‐5p on the Regulation of <scp>CYP2A6</scp>, the Major Nicotine‐Inactivating Enzyme
Bibliographic record
Abstract
ABSTRACT CYP2A6 is the hepatic enzyme responsible for the metabolic inactivation of nicotine. Variation in CYP2A6 alters nicotine clearance, affecting numerous smoking behaviors and tobacco‐related diseases, making investigating sources of variation important. A published molecular study of microRNA‐126‐5p, the microRNA‐126 functional arm, showed it decreased CYP2A6 expression post‐transcriptionally; it also showed that higher CYP2A7 mRNA competed for microRNA‐126‐5p binding, mitigating the CYP2A6 reduction. To extend these observations, we investigated relationships between microRNA‐126‐5p and CYP2A6 protein and activity using a large human liver bank (n = 282). MicroRNA‐126‐5p was not inversely correlated with CYP2A6 protein (rs = 0.04, p > 0.05), nor was it significant in an unadjusted regression model (p > 0.05) or in an adjusted model (with genotype, age, and sex) (p > 0.05). Although CYP2A7 mRNA was positively correlated with CYP2A6 protein (rs = 0.48, p < 0.001), adding CYP2A7 mRNA to the adjusted model did not alter the relationship between microRNA‐126‐5p and CYP2A6 protein (p > 0.05), nor did CYP2A7 mRNA interact with microRNA‐126‐5p on CYP2A6 (p > 0.05). Similar results were found in modeling CYP2A6 activity. MicroRNA‐21 was used as a positive control (inversely correlated with CYP2A6 protein, rs = −0.33, p < 0.001) and microRNA‐152 as a negative control (not correlated with CYP2A6 protein, rs = −0.06, p > 0.05). These data do not support a role for microRNA‐126‐5p in downregulating CYP2A6 protein or activity, or for CYP2A7 mRNA in playing a decoy role, even when other predictors (genotype, age, and sex) were included in the model.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".