Maternal Environment Alters DNA Methylation Inheritance in Chinook Salmon (Oncorhynchus tshawytscha): Maternal Effects on Gene-Specific DNA Methylation
Bibliographic record
Abstract
Maternal effects are a prevalent source of early life phenotypic variation in offspring across diverse taxa and have been shown to provide offspring an adaptive advantage in response to maternal environmental stimuli. There are several well studied examples of adaptive maternally induced intergenerational effects in response to the parental environment mediated by mechanisms such as stress hormones and nutrients. DNA methylation is a fundamental cellular process that affects gene transcription and can respond rapidly to changing environments yet remains a largely unexplored mechanism for maternal effect signaling. We manipulated maternal environment in sexually maturing female Chinook salmon (Oncorhynchus tshawytscha) by reducing food availability or increasing day length. We used a factorial breeding design as well as gene-specific sequencing assays to analyze how maternal effects influenced DNA methylation in offspring at the eyed egg and alevin developmental stages We found significant maternal effects on gene-specific DNA methylation as well as heightened levels of maternal effects in response to an increase in maternal photoperiod. We report higher maternal effects in early life stages that decline through development in four gene functional categories: growth, immune response, metabolic function, and histone protein regulation. Despite a potential resetting of the methylome following fertilization, we provide evidence that maternal effects can modulate gene-specific DNA methylation, and that effect is sensitive to the environment the mothers experience. This pattern of epigenetically-mediated maternal effects responding to maternal environment is consistent with a fundamental process driving intergenerational phenotypic variation.
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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".