Parallelism in gene transcription among sympatric lake whitefish (<i>Coregonus clupeaformis</i>Mitchill) ecotypes
Bibliographic record
Abstract
Abstract We tested the hypothesis that phenotypic parallelism between dwarf and normal whitefish ecotypes (Coregonus clupeaformis, Salmonidae) is accompanied by parallelism in gene transcription. The most striking phenotypic differences between these forms implied energetic metabolism and swimming activity. Therefore, we predicted that genes showing parallel expression should mainly belong to functional groups associated with these phenotypes. Transcriptome profiles were obtained from white muscle by using a 3557 cDNA gene microarray developed for the Atlantic salmon (Salmo salar). A total of 1181 genes expressed in both lake populations hybridized on the array. Significant differential expression between ecotypes was detected for 134 (11.3%) and 195 (16.5%) gene clones in Cliff Lake and Indian Pond, respectively. Fifty-one genes (4.3%) showed parallel differential expression between lakes, among which 35 were expressed in opposite directions. Sixteen genes (1.35%) showed true parallelism of transcription, which mainly belonged to energetic metabolism and regulation of muscle contraction functional groups. Variance in expression was significantly reduced for these genes compared to those not showing directionality in parallelism of expression. Candidate genes associated with parallelism in swimming activity and energetic metabolism based on their level and variance in expression were identified. These results add to the growing evidence that parallel phenotypic evolution also involves parallelism at both the genotypic and regulatory level, which may at least partly be associated with genetic constraints. It also provides further evidence for the determinant role of divergent natural selection in driving phenotypic divergence, and perhaps reproductive isolation, in the adaptive radiation of lake whitefish. This study adds to a nascent field employing microarrays as powerful tools for investigating the evolutionary processes of adaptive divergence among natural populations.
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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 | 0.001 | 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".