mRNA Levels in Control Rat Liver Display Strain-Specific, Hereditary, and AHR-Dependent Components
Bibliographic record
Abstract
Rat is a major model organism in toxicogenomics and pharmacogenomics. Hepatic mRNA profiles after treatment with xenobiotic chemicals are used to predict and understand drug toxicity and mechanisms. Surprisingly, neither inter- and intra-strain variability of mRNA abundances in control rats nor the heritability of rat mRNA abundances yet been established. We address these issues by studying five populations: the popular Sprague-Dawley strain, sub-strains of Long-Evans and Wistar rats, and two lines derived from crosses between the Long-Evans and Wistar sub-strains. Using three independent techniques--variance analysis, linear modelling, and unsupervised pattern recognition--we characterize extensive intra- and inter-strain variability in mRNA levels. We find that both sources of variability are non-random and are enriched for specific functional groups. Specific transcription-factor binding-sites are enriched in their promoter regions and these genes occur in "islands" scattered throughout the rat genome. Using the two lines generated by crossbreeding we tested heritability of hepatic mRNA levels: the majority of rat genes appear to exhibit directional genetics, with only a few interacting loci. Finally, a comparison of inter-strain heterogeneity between mouse and rat orthologs shows more heterogeneity in rats than mice; thus rat and mouse heterogeneity are uncorrelated. Our results establish that control hepatic mRNA levels are relatively homogeneous within rat strains but highly variable between strains. This variability may be related to increased activity of specific transcription-factors and has clear functional consequences. Future studies may take advantage of this phenomenon by surveying panels of rat strains.
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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 | 0.000 |
| 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".