RNA-seq analysis reveals extensive transcriptional plasticity to temperature stress in a freshwater fish species
Bibliographic record
Abstract
BACKGROUND: Identifying genes of adaptive significance in a changing environment is a major focus of ecological genomics. Such efforts were restricted, until recently, to researchers studying a small group of model organisms or closely related taxa. With the advent of next generation sequencing (NGS), genomes and transcriptomes of virtually any species are now available for studies of adaptive evolution. We experimentally manipulated temperature conditions for two groups of crimson spotted rainbowfish (Melanotaenia duboulayi) and measured differences in RNA transcription between them. This non-migratory species is found across a latitudinal thermal gradient in eastern Australia and is predicted to be negatively impacted by ongoing environmental and climatic change. RESULTS: Using next generation RNA-seq technologies on an Illumina HiSeq2000 platform, we assembled a de novo transcriptome and tested for differential expression across the treatment groups. Quality of the assembly was high with a N50 length of 1856 bases. Of the 107,749 assembled contigs, we identified 4251 that were differentially expressed according to a consensus of four different mapping and significance testing approaches. Once duplicate isoforms were removed, we were able to annotate 614 up-regulated transfrags and 349 that showed reduced expression in the higher temperature group. CONCLUSIONS: Annotated blast matches reveal that differentially expressed genes correspond to critical metabolic pathways previously shown to be important for temperature tolerance in other fish species. Our results indicate that rainbowfish exhibit predictable plastic regulatory responses to temperature stress and the genes we identified provide excellent candidates for further investigations of population adaptation to increasing temperatures.
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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".