Transcriptional dynamics following freezing stress reveal selection for mechanisms of freeze tolerance at the poleward range margin in the cold water intertidal barnacle <i>Semibalanus balanoides</i>
Bibliographic record
Abstract
Abstract The ability to survive freezing has repeatedly evolved across multiple phyla. This suggests that the mechanisms of freeze tolerance must be readily evolvable from basal physiological traits. While several biochemical correlates to freeze tolerance have been described, the mechanism that confers freeze tolerance is still not well understood. To understand both the basic biochemical mechanisms of freeze tolerance as well as their role in local adaptation at the poleward range edge, we conducted a transcriptomic study on two populations (one from the poleward range margin in the White Sea, Russia, and one from the central coast of British Columbia, Canada) of the cold water acorn barnacle Semibalanus balanoides on a time series following a freezing event. We found that the British Columbia population (at the equatorward range margin) was significantly less freeze tolerant than the White Sea population (at the poleward range margin). After assembling and annotating a de novo transcriptome for S. balanoides , we found that the patterns of differential transcript expression following freezing were almost entirely non-overlapping between the two populations, with the White Sea population expressing a series of heat shock proteins in response to freezing stress as well as several aquaporins, while the British Columbia population expressed a series of proteases instead, indicating severe protein damage. We found strong evidence of purifying selection on the significantly upregulated transcripts in the White Sea population, suggesting local adaptation to freezing threat. Taken together, this shows the importance of freeze tolerance to population survival at the poleward range margin, and highlights the central roles of aquaporins and heat shock proteins to the trait of freeze tolerance across taxa.
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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".