Gene expression profiling analysis reveals fur development in rex rabbits (<i>Oryctolagus cuniculus</i>)
Bibliographic record
Abstract
Fur is an important economic trait in rabbits. The identification of genes that influence fur development and knowledge regarding the actions of these genes provides useful tools for improving fur quality. However, the mechanism of fur development is unclear. To obtain candidate genes related to fur development, the transcriptomes of tissues from backs and bellies of Chinchilla rex rabbits were compared. Of the genes analyzed, 336 showed altered expression in the two groups (285 upregulated and 51 downregulated, P ≤ 0.05, fold-change ≥2 or ≤0.5). Using GO and KEGG to obtain gene classes that were differentially enriched, we found several genes to be involved in many important biological processes. In addition, we identified several signaling pathways involved in fur development, including the Wnt and MAPK signaling pathways, revealing mechanisms of skin and hair follicle development, and epidermal cell and keratinocytes differentiation. The obtained rabbit transcriptome and differentially expressed gene profiling data provided comprehensive gene expression information for SFRP2, FRZB, CACNG1, SLC25A4, and SLC16A3. To validate the RNA-seq data, the expression levels of eight differentially expressed genes involved in fur development were confirmed by qRT-PCR. The results of rabbit transcriptomic profiling provide a basis for understanding the molecular mechanisms of fur development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".