The transcriptomics of life‐history trade‐offs in whitefish species pairs ( <i>Coregonus</i> sp.)
Bibliographic record
Abstract
Despite the progress achieved in elucidating the ecological mechanisms of adaptive radiation, there has been little focus on documenting the extent of adaptive differentiation in physiological functions during this process. Moreover, a thorough understanding of the genomic basis underlying phenotypic adaptive divergence is still in its infancy. One important evolutionary process for which causal genetic mechanisms are largely unknown pertains to life-history trade-offs. We analysed patterns of gene transcription in liver tissue of sympatric dwarf and normal whitefish from two natural lakes, as well as from populations reared in controlled environments, using a 16,006-gene cDNA microarray in order to: (i) document the extent of physiological adaptive divergence between sympatric dwarf and normal species pairs, and (ii) explore the molecular mechanisms of differential life history trade-offs between growth and survival potentially involved in their adaptive divergence. In the two natural lakes, 6.45% of significantly transcribed genes showed regulation either in parallel fashion (2.39%) or in different directions (4.06%). Among genes showing parallelism in regulation patterns, we observed a higher proportion of over-expressed genes in dwarf relative to normal whitefish (70.6%). Patterns observed in controlled conditions were also generally congruent with those observed in natural populations. Dwarf whitefish consistently showed significant over-expression of genes potentially associated with survival through enhanced activity (energy metabolism, iron homeostasis, lipid metabolism, detoxification), whereas more genes associated with growth (protein synthesis, cell cycle, cell growth) were generally down-regulated in dwarf relative to normal whitefish. Overall, parallelism in patterns of gene transcription, as well as patterns of interindividual variation across controlled and natural environments, provide strong indirect evidence for the role of selection in the evolution of differential regulation of genes involving a vast array of potentially adaptive physiological processes between dwarf and normal whitefish. Our results also provide a first mechanistic, genomic basis for the observed trade-off in life-history traits distinguishing dwarf and normal whitefish species pairs, wherein enhanced survival via more active swimming, necessary for increased foraging and predator avoidance, engages energetic costs that translate into slower growth rate and reduced fecundity in dwarf relative to normal whitefish.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".