microRNAs in Adult Rodent Liver Are Refractory to Dioxin Treatment
Bibliographic record
Abstract
Dioxin-like chemicals are well known for their ability to upregulate expression of numerous genes via the AH receptor (AHR). However, recent transcriptomic analyses in several laboratories indicate that dioxin-like chemicals or AHR genotype itself also can downregulate levels of mRNAs encoded by numerous genes. The mechanism responsible for such downregulation is unknown. We hypothesized that microRNAs (miRNAs), which have emerged as powerful negative regulators of mRNA levels in several systems, might be responsible for mRNA downregulation in dioxin/AHR pathways. We used two miRNA array platforms as well as quantitative reverse transcriptase-polymerase chain reaction to measure miRNA levels in wild-type (WT) versus Ahr-null mice, in dioxin-sensitive Long-Evans (L-E; Turku/AB) rats versus dioxin-resistant Han/Wistar (H/W; Kuopio) rats and in rat 5L and mouse Hepa-1 hepatoma cells in culture. Treatment with 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) in vivo caused few changes in miRNA levels in mouse or rat livers, and those changes that were statistically significant were of modest magnitude. Hepatoma cells in culture also exhibited few changes in miRNA levels in response to TCDD. AHR genotype had little effect on hepatic miRNA levels, either in constitutive expression or in response to TCDD-only a few miRNAs differed in expression between Ahr-null mice compared to mice with WT AHR or between L-E rats (that have WT AHR) compared to H/W rats (whose AHR has a large deletion in the transactivation domain). It is unlikely that mRNA downregulation by dioxins is mediated by miRNAs, nor are miRNAs likely to play a significant role in dioxin toxicity in adult rodent liver.
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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".