Plasticity across levels: relating epigenomic, transcriptomic, and phenotypic responses to osmotic stress in a halotolerant microalga
Bibliographic record
Abstract
Abstract Phenotypic plasticity, the ability of a given genotype to produce alternative phenotypes in response to its environment of development, is an important mechanism for coping with variable environments. While the mechanisms underlying phenotypic plasticity are diverse, their relative contributions need to be investigated quantitatively to better understand the evolvability of plasticity across biological levels. This requires relating plastic responses of the epigenome, transcriptome, and organismal phenotype, and how they vary with the genotype. Here we carried out this approach for responses to osmotic stress in Dunaliella salina , a green microalga that is a model organism for salinity tolerance. We compared two strains that show markedly different demographic responses to osmotic stress, and showed that these phenotypic responses involve strain- and environment-specific variation in gene expression levels, but a relative low - but significant - effect of strain × environment interaction. We also found an important genotype effect on the genome-wide methylation pattern, but little contribution from environmental conditions to the latter. However, we did detect a significant marginal effect of epigenetic variation on gene expression, beyond the influence of genetic differences on epigenetic state, and we showed that hypomethylated regions are correlated with higher gene expression. Our results indicate that epigenetic mechanisms are either not involved in the rapid plastic response to environmental change in this species, or involve only few changes in trans that are sufficient to trigger concerted changes in the expression of many genes, and phenotypic responses by multiple traits.
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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) | 0.000 | 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 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".