Mammary epithelial cell transcriptome reveals potential roles of lncRNAs in regulating milk synthesis pathways in Jersey and Kashmiri cattle
Bibliographic record
Abstract
BACKGROUND: Long noncoding RNAs (lncRNAs) are now proven as essential regulatory elements, playing diverse roles in many biological processes including mammary gland development. However, little is known about their roles in the bovine lactation process. RESULTS: To identify and characterize the roles of lncRNAs in bovine lactation, high throughput RNA sequencing data from Jersey (high milk yield producer), and Kashmiri cattle (low milk yield producer) were utilized. Transcriptome data from three Kashmiri and three Jersey cattle throughout their lactation stages were utilized for differential expression analysis. At each stage (early, mid and late) three samples were taken from each breed. A total of 45 differentially expressed lncRNAs were identified between the three stages of lactation. The differentially expressed lncRNAs were found co-expressed with genes involved in the milk synthesis processes such as GPAM, LPL, and ABCG2 indicating their potential regulatory effects on milk quality genes. KEGG pathways analysis of potential cis and trans target genes of differentially expressed lncRNAs indicated that 27 and 48 pathways were significantly enriched between the three stages of lactation in Kashmiri and Jersey respectively, including mTOR signaling, PI3K-Akt signaling, and RAP1 signaling pathways. These pathways are known to play key roles in lactation biology and mammary gland development. CONCLUSIONS: Expression profiles of lncRNAs across different lactation stages in Jersey and Kashmiri cattle provide a valuable resource for the study of the regulatory mechanisms involved in the lactation process as well as facilitate understanding of the role of lncRNAs in bovine lactation biology.
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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 | 0.001 | 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".