Characterization of novel <i>lncRNA</i> muscle expression profiles associated with meat quality in beef cattle
Bibliographic record
Abstract
Abstract The aim of this study was to identify novel lncRNA differentially expressed (DE) between divergent animals for beef tenderness and marbling traits in Nellore cattle. Longissimus thoracis muscle samples from the 20 most extreme bulls (of 80 bulls set) for tenderness, tender (n = 10) and tough (n = 10) groups, and marbling trait, high (n = 10) and low (n = 10) groups were used to perform transcriptomic analysis using RNA‐Sequencing. For tenderness, 29 lncRNA were DE (p‐value ≤ 0.01) in tough beef animals in relation to tender beef animals. We observed that genic lncRNAs, for example, lncRNA_595.1, were overlapping exonic part of the PICK gene, while lncRNA_3097.2 and lncRNA_3129.5 overlapped intronic part of the genes GADL1 and PSMD6. The lncRNA associated with PICK1, GADL1, and PMD6 genes were enriched in the pathways associated with the ionotropic glutamate receptor, gamma‐aminobutyric acid synthesis, and the ubiquitin–proteasome pathway. For marbling, 50 lncRNA were DE (p‐value ≤ 0.01) in high marbling group compared with low marbling animals. The genic lncRNAs, such as lncRNA_3191.1, were overlapped exonic part of the ITGAL gene, and the lncRNA_512.1, lncRNA_3721.1, and lncRNA_41.4 overlapped intronic parts of the KRAS and MASP1 genes. The KRAS and ITGAL genes were enriched in pathways associated with integrin signaling, which is involved in intracellular signals in response to the extracellular matrix, including cell form, mobility, and mediates progression through the cell cycle. In addition, the lincRNAs identified to marbling trait were associated with several genes related to calcium binding, muscle hypertrophy, skeletal muscle, lipase, and oxidative stress response pathways that seem to play a role important in the physiological processes related to meat quality. These findings bring new insights to better understand the biology mechanisms involved in the gene regulation of these traits, which will be valuable for a further investigation of the interactions between lncRNA and mRNAs, and of how these interactions may affect meat quality traits.
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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".