Genes and proteins associated with ribeye area and meat tenderness in a commercial Nellore cattle population
Bibliographic record
Abstract
Despite several studies on genetic markers and differentially expressed genes related to ribeye area (REA) and tenderness traits in beef cattle, there is divergence in the results regarding the genes associated with these traits. Thirteen genes associated with or exhibiting biological functions that might influence such phenotypes were included in this study. A total of five genes for REA (IGF-1, IGF-2, MSTN, NEDD4, and UBE4A) and eight genes for meat tenderness (CAPN1, CAPN2, CAST, HSPB1, DNAJA1, FABP4, SCD, and PRKAG3) were selected from previous studies on beef cattle. Genes and their respective proteins expression were validated in a commercial population of Nellore cattle using quantitative real-time PCR (RT-qPCR) and advanced mass spectrometry (LC/MS-MS) techniques, respectively. The MSTN gene was upregulated in animals with low REA. The CAPN1, CAPN2, CAST, HSPB1, and DNAJA1 genes were upregulated in animals with tough meat. The proteins translated by these genes were not differentially expressed. Our results confirm the potential of some of the studied genes as biomarkers for carcass and meat quality traits in Nellore cattle.
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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 | 0.001 |
| 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".