Effects of cross-fostering and developmental exposure to mixtures of environmental contaminants on hepatic gene expression in prepubertal 21 days old and adult male Sprague-Dawley rats
Bibliographic record
Abstract
The notion that adverse health effects produced by exposure to environmental contaminants (EC) may be modulated by the presence of non-chemical stressors is gaining attention. Previously, our lab demonstrated that cross-fostering (adoption of a litter at birth) acted as a non-chemical stressor that amplified the influence of developmental exposure to EC on the glucocorticoid stress-response in adult rats. Using liver from the same rats, the aim of the current study was to investigate whether cross-fostering might also modulate EC-induced alterations in hepatic gene expression profiles. During pregnancy and nursing, Sprague-Dawley dams were fed cookies laced with corn oil (control, C) or a chemical mixture (M) composed of polychlorinated biphenyls (PCB), organochlorine pesticides (OCP), and methylmercury (MeHg), at 1 mg/kg/day. This mixture simulated the contaminant profile reported in maternal human blood. At birth, some control and M treated litters were cross-fostered to form two additional groups with different biological/nursing mothers (CC and MM). The hepatic transcriptome was analyzed by DNA microarray in male offspring at postnatal days 21 and 78-86. Mixture exposure altered the expression of detoxification and energy metabolism genes in both age groups, but with different sets of genes affected at day 21 and 78-86. Cross-fostering modulated the effects of M on gene expression pattern (MM vs M), as well as expression of energy metabolism genes between control groups (CC vs C). In conclusion, while describing short and long-term effects of developmental exposure to EC on hepatic transcriptomes, these cross-fostering results further support the consideration of non-chemical stressors in EC risk assessments.
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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 | 0.000 | 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 | 0.001 |
| 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".