Phenotypic plasticity in the hepatic transcriptome of the <scp>E</scp>uropean common frog (<i><scp>R</scp>ana temporaria</i>): the interplay between environmental induction and geographical lineage on developmental response
Bibliographic record
Abstract
Phenotypic plasticity might facilitate adaptation to new environmental conditions through the enhancement of initial survival of organisms. Once a population is established, further adaptation and diversification may occur through adaptive trait evolution. While several studies have found evidence for this mechanism using phenotypic traits, much less is known at the level of gene expression. Here, we use an islands system of frog populations that show local adaptation and phenotypic plasticity to pool drying conditions in development time until metamorphoses. We examined gene expression differences in Rana temporaria tadpole livers with respect to pool drying at the source population and in response to simulated pool drying in the laboratory. Using a MAGEX cDNA microarray and quantitative real-time polymerase chain reaction (qPCR), we identified an increase in several gene transcripts in response to artificial pool drying including thyroid hormone receptor alpha and beta, carbamoyl phosphate synthetase 1, ornithine transcarbamylase and catalase. In addition, these gene transcripts also showed greater abundance in island populations that developed faster. Hence, the gene transcripts were related to both constitutive response (higher levels in island populations that developed faster) and plastic response (increased abundance under decreasing water levels). This pattern is in accordance with genetic accommodation, which predicts similarities between plastic gene expression and constitutive expression in locally adapted populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".