Differential expression of genes in adipose tissue of first-lactation dairy cattle
Bibliographic record
Abstract
Adipose tissue metabolism is an essential factor in establishment of a successful lactation, and we have a good understanding of changes in metabolic flux in relation to lactation, parity, and diet. However, the mechanisms of control of flux are less well understood. To continue our investigations into the control of adipose tissue metabolism, we conducted a transcriptomic analysis of adipose tissue of dairy cattle in late pregnancy and early lactation. Our objective was to determine the changes in gene expression in adipose tissue between 30 d prepartum and 14 d in milk in first-lactation animals, and to determine if changes in expression were related to practical production variables. Animals were Holstein heifers fed the same diet to National Research Council requirements, and adipose tissue was biopsied at 30 d prepartum and 14 DIM. Total RNA was extracted and used to determine gene expression on a bovine gene array. Genes that code for proteins controlling fatty acid transport were highly expressed including fatty acid binding proteins (FABP4 and FABP5) and lipoprotein lipase. Among those genes increasing in expression were those controlling lipolysis, including ADRB2 (52%) and LIPE (23%). Many genes coding for enzymes controlling lipogenesis decreased, including SREBP (-25%), TSHSP14 (-30.8%), LPL (-48.4%), and ACACA (-63.9%). This gene expression array analysis in adipose tissue of lactating dairy cattle identifies several key genes that are components of the adaptation to lactation that can be incorporated into models of nutritional efficiency and may be amenable to genetic or dietary manipulation.
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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 | 0.001 | 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".