Short-term and persistent changes to the muscle transcriptome of Lake sturgeon (Acipenser fulvescens) following early-life exposures to elevated temperatures
Bibliographic record
Abstract
In fishes, environmental change during early development may impact performance later in life. Exposure to elevated temperatures during these periods may result in a variety of changes to organismal physiology including both short-term and persistent impacts to growth. However, the underlying mechanisms which promote these physiological changes are unclear. In this study, we used mRNA-seq of white muscle tissue to investigate mechanisms underlying short-term (3 vs. 6 months post-hatch) and persistent (3 vs. 13 months post-hatch) impacts of temperature (16 °C, 18 °C, and 20 °C) during early development, which underlie enhanced growth performance in young of year lake sturgeon. Functional analysis of differentially expressed transcripts revealed that in the short-term, elevated temperatures of 20 °C had impacts on transcriptional regulation of epigenetic mechanisms, neuron development, muscle structure and function, and energy supply. Further, persistent effects led to the establishment of increased growth, altered muscle phenotypes, and transcriptional changes to alternative splicing, neuron signaling, as well as muscle type and function. Taken together, these results suggest that at 20 °C, epigenetic modifications may lead to a molecular switch inducing neuromuscular junction proliferation, which in turn alters developmental trajectories by increasing lake sturgeon muscle development and growth. These findings are pertinent to hatchery management processes and the impacts of increasing temperatures in natural environments such as from heat waves during early development, which both may have persistent impacts on the developmental trajectory of fishes.
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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".