Temperature-dependent gene regulatory divergence underlies local adaptation with gene flow in the Atlantic silverside
Bibliographic record
Abstract
Abstract Gene regulatory divergence is thought to play an important role in adaptation, yet its extent and underlying mechanisms remain largely elusive under scenarios of local adaptation with gene flow. Local adaptation is widespread in marine species despite generally high connectivity and often associated with tightly-linked genomic architectures, such as chromosomal inversions. To investigate gene regulatory evolution under gene flow and the role of discrete genomic regions associated with local adaptation to a steep thermal gradient, we generated RNA-seq data from Atlantic silversides ( Menidia menidia ) from two locally adapted populations and their F1 hybrids, reared under two different temperatures. We found substantial divergence in gene expression and thermal plasticity, with up to 31% of genes being differentially expressed, and primarily trans -rather than cis -regulatory divergence between populations, despite ongoing gene flow. Substantially reduced thermal plasticity, temperature-dependent gene misexpression and the disruption of co-expression networks in hybrids point toward a role of regulatory incompatibilities in maintaining local adaptation, particularly under colder temperatures, which appear more challenging for this species. Adaptive chromosomal inversions seem to play an important role in gene regulatory divergence through the accumulation of regulatory incompatibilities but are not consistently enriched for divergently regulated genes. Together, these results highlight that gene regulation can diverge substantially among populations connected by strong gene flow in marine environments, partly due to the accumulation of temperature-dependent regulatory incompatibilities within inversions.
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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".