Exploring differentially expressed genes associated with coat color in goat skin using RNA-seq
Bibliographic record
Abstract
Fur color in domestic goats is an important, genetically determined characteristic that is associated with economic value. This study was designed to perform a comprehensive expression profiling of genes expressed in the skin tissues from Laiwu Black goat and Lubei White goat. Comparisons of black and white goat skin transcriptomes revealed 102 differentially expressed genes (DEGs), of which 38 were upregulated and 64 downregulated in black skin compared with white skin. Among the DEGs, we identified six genes involved in pigmentation, including agouti signaling protein (ASIP), CAMP responsive element binding protein 3-like 1 (CREB3L1), dopachrome tautomerase (DCT), premelanosome protein (PMEL), transient receptor potential cation channel subfamily M member 1 (TRPM1), and tyrosinase-related protein 1 (TYRP1). Notably, there were no significant differences in the expression of melanocortin 1 receptor, microphthalmia-associated transcription factor, tyrosinase, and KIT proto-oncogene receptor tyrosine kinase between the black and white skin samples, whereas ASIP expression was detected only in white skin. PMEL, TRPM1, TYRP1, and DCT showed higher expression in black goat skin, but ASIP and CREB3L1 had higher expression in white goat skin. Quantitative polymerase chain reaction results for PMEL, TRPM1, DCT, TYRP1, and CREB3L1 expression were consistent with those for RNA-seq. These results will expand our understanding of the complex molecular mechanisms of skin physiology and melanogenesis in goats, and provide a foundation for future studies.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".