Timing-dependent effects of elevated maternal glucocorticoids on offspring brain gene expression in a wild small mammal
Bibliographic record
Abstract
Abstract An increase in maternal stress during offspring development can have cascading, life-long impacts on offspring behavior and physiology, which can vary depending on the timing of exposure to the stressor. By responding to stressors through increasing production of glucocorticoids (GCs), the hypothalamic-pituitary-adrenal (HPA) axis is a key mediator of maternal effects – both on the side of the mother and the offspring. At a molecular level, maternal effects are thought to be mediated through modifying transcription of genes, particularly in the brain. To better understand the evolutionary implications of maternal effects, more studies are needed on mechanisms of maternal effects in wild populations. To test how the timing of maternal stress impacts gene expression in the brains of offspring, we treated free-ranging North American red squirrels ( Tamiasciurus hudsonicus ) with GCs during late pregnancy or early lactation and collected brains from offspring around weaning. We used RNA-sequencing to measure gene expression in the hypothalamus and hippocampus. We found small differences in gene expression between GC-treated and control individuals suggesting long-term effects of the GC treatment on neural gene transcription. The general patterns of gene regulation across the transcriptome were consistent between the pregnancy and lactation-treated individuals. However, the number of significantly differentially expressed genes was higher in the lactation treatment group. These results support the idea that maternal stress affects neural gene expression in offspring, and these effects are dependent on timing. Our findings add valuable insight into the impact of maternal hormones on neural transcriptomics in a wild population.
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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 | 0.000 |
| 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".