Parallel transcriptome evolution in stream threespine sticklebacks
Bibliographic record
Abstract
Natural selection can cause similar phenotypic evolution in phylogenetically independent lineages inhabiting similar environments. Compared to morphological, behavioral, and physiological traits, little is known about the parallel evolution of transcriptome. Furthermore, the relative contribution of cis- and trans-regulatory changes to parallel transcriptome evolution largely remains unclear. The threespine stickleback fish (Gasterosteus aculeatus) is a great model for studying parallel evolution because its ancestral marine populations independently colonized freshwater habitats in multiple geographical regions, resulting in independent pairs of marine and freshwater ecotypes in each region. Here, we investigated transcriptomic parallelism among the marine and stream ecotypes of Japanese and Canadian threespine sticklebacks by conducting common garden experiments and microarray analysis of the brain, which controls several physiological and behavioral traits differing between these ecotypes. We found parallel expression differences in 103 genes, including those encoding the enzymes involved in taurine synthesis and glycoprotein hydrolysis. The number of genes differentially expressed in parallel was significantly larger than the number of genes showing an antiparallel pattern (71 genes). To investigate the genetic architecture underlying transcriptome divergence, we re-analyzed the previous expression quantitative trait locus (eQTL) data and found that most eQTLs were located on the same chromosome as the transcripts, possibly in cis-regulatory regions. Furthermore, the effect sizes of the eQTLs on the same chromosomes were larger than those on different chromosomes. Thus, we found that divergence in the brain transcriptome between the ecotypes shows parallelism and is mainly caused by genetic changes occurring on the same chromosome as the target genes.
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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".