Deep sequencing shows microRNA involvement in bovine mammary gland adaptation to diets supplemented with linseed oil or safflower oil
Bibliographic record
Abstract
BACKGROUND: Bovine milk fat composition is responsive to dietary manipulation providing an avenue to modify the content of fatty acids and especially some specific unsaturated fatty acid (USFA) isomers of benefit to human health. MicroRNAs (miRNAs) regulate gene expression but their specific roles in bovine mammary gland lipogenesis are unclear. The objective of this study was to determine the expression pattern of miRNAs following mammary gland adaptation to dietary supplementation with 5 % linseed or safflower oil using next generation RNA-sequencing. METHODS: Twenty-four Canadian Holstein dairy cows (twelve per treatment) in mid lactation were fed a control diet (total mixed ration of corn:grass silages) for 28 days followed by a treatment period (control diet supplemented with 5 % linseed or safflower oil) of 28 days. Milk samples were collected weekly for fat and individual fatty acid determination. RNA from mammary gland biopsies harvested on day-14 (control period) and on days +7 and +28 (treatment period) from six randomly selected cows per treatment was subjected to small RNA sequencing. RESULTS: Milk fat percentage decreased significantly (P < 0.001) during treatment with the two diets as compared to the control period. The individual saturated fatty acids C4:0, C6:0, C8:0, C14:0 and C16:0 decreased significantly (P < 0.05) while five USFAs (C14:1, C18:1n11t, C20:3n3, C20:5n3 and CLA:t10c12) increased remarkably (P < 0.05) in response to both treatments. Analysis of 361 million sequence reads generated 321 known bovine miRNAs and 176 novel miRNAs. The expression of fourteen and twenty-two miRNAs was affected (P < 0.05) by linseed and safflower oil treatments, respectively. Seven miRNAs including six up-regulated (bta-miR-199c, miR-199a-3p, miR-98, miR-378, miR-148b and miR-21-5p) and one down-regulated (bta-miR-200a) were found to be regulated (P < 0.05) by both treatments, and thus considered core differentially expressed (DE) miRNAs. The gene targets of core DE miRNAs have functions related to gene expression and general cellular metabolism (P < 0.05) and are enriched in four pathways of lipid metabolism (3-phosphoinositide biosynthesis, 3-phosphoinositide degradation, D-myo-inisitol-5-phosphate metabolism and the superpathway of inositol phosphate compounds). CONCLUSION: Our results suggest that DE miRNAs in this study might be important regulators of bovine mammary lipogenesis and metabolism. The novel miRNAs identified in this study will further enrich the bovine miRNome repertoire and contribute to understanding mammary gland biology.
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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 | 0.001 | 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".