Transcriptome Profile in Dairy Cows Resistant or Sensitive to Milk Fat Depression
Bibliographic record
Abstract
Feeding linseed to dairy cows results in milk fat depression (MFD), but there is a wide range of sensitivity among cows. The objectives of this study were to identify target genes containing SNP that may play a key role in the regulation of milk fat synthesis in cows resistant or sensitive to MFD. Four cows were selected from a dairy farm after a switch from a control diet to a linseed-rich diet; two were resistant to MFD with a high milk fat content in the control (4.06%) and linseed-rich (3.90%) diets; and two were sensitive to MFD with the milk fat content decreasing after the change from the control (3.87%) to linseed-rich (2.52%) diets. Transcriptome and SNP discovery analyses were performed using RNA-sequencing technology. There was a large number of differentially expressed genes in the control (n = 1316) and linseed-rich (n = 1888) diets. Of these, 15 genes were detected as key gene regulators and harboring SNP in the linseed-rich diet. The selected genes MTOR, PDPK1, EREG, NOTCH1, ZNF217 and TGFB3 may form a network with a principal axis PI3K/Akt/MTOR/SREBP1 involved in milk fat synthesis and in the response to diets that induced MFD. These 15 genes are novel candidate genes to be involved in the resistance or sensitivity of dairy cows to milk fat depression.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".