Transcriptomic analysis of the trade-off between endurance and burst-performance in the frog Xenopus allofraseri
Bibliographic record
Abstract
BACKGROUND: Variation in locomotor capacity among animals often reflects adaptations to different environments. Despite evidence that physical performance is heritable, the molecular basis of locomotor performance and performance trade-offs remains poorly understood. In this study we identify the genes, signaling pathways, and regulatory processes possibly responsible for the trade-off between burst performance and endurance observed in Xenopus allofraseri, using a transcriptomic approach. RESULTS: We obtained a total of about 121 million paired-end reads from Illumina RNA sequencing and analyzed 218,541 transcripts obtained from a de novo assembly. We identified 109 transcripts with a significant differential expression between endurant and burst performant individuals (FDR ≤ 0.05 and logFC ≥2), and blast searches resulted in 103 protein-coding genes. We found major differences between endurant and burst-performant individuals in the expression of genes involved in the polymerization and ATPase activity of actin filaments, cellular trafficking, proteoglycans and extracellular proteins secreted, lipid metabolism, mitochondrial activity and regulators of signaling cascades. Remarkably, we revealed transcript isoforms of key genes with functions in metabolism, apoptosis, nuclear export and as a transcriptional corepressor, expressed in either burst-performant or endurant individuals. Lastly, we find two up-regulated transcripts in burst-performant individuals that correspond to the expression of myosin-binding protein C fast-type (mybpc2). This suggests the presence of mybpc2 homoeologs and may have been favored by selection to permit fast and powerful locomotion. CONCLUSION: These results suggest that the differential expression of genes belonging to the pathways of calcium signaling, endoplasmic reticulum stress responses and striated muscle contraction, in addition to the use of alternative splicing and effectors of cellular activity underlie locomotor performance trade-offs. Ultimately, our transcriptomic analysis offers new perspectives for future analyses of the role of single nucleotide variants, homoeology and alternative splicing in the evolution of locomotor performance trade-offs.
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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".