Expression levels of STAT5A and STAT5B in mammary parenchymal tissue from Upton-Meishan and Large White gilts
Bibliographic record
Abstract
The implication of STAT5A and STAT5B in mammary gland development and maintenance of lactation is well documented in rodents and humans. However, little is known regarding their roles in mammary gland development during gestation in pigs. We identified and analyzed the complete coding sequences of swine STAT5A and STAT5B and evaluated their mRNA levels in mammary glands of gestating gilts (day 110) in two different breeds, Upton-Meishan and Large White. Sequence analysis revealed a new APASA insertion in the STAT5A amino acid sequence that is in close proximity to residue Tyr 699 and whose phosporylation leads to the activation of target genes’ transcription. STAT5A mRNA levels were higher in Upton-Meishan than in Large White. In both breeds, STAT5B mRNA levels were higher than those of STAT5A , which is contrary to what was found in other mammals. A correlation between circulating IGF-I levels and STAT5B mRNA levels in the mammary gland was noticed in the Upton-Meishan breed only. STAT5B mRNA levels in mammary tissue of Large White gilts were highly correlated with extra-parenchymal tissue weight, parenchymal tissue weight, total parenchymal DNA, RNA and RNA/DNA ratio. In Upton-Meishan gilts, correlations were observed only between extra-parenchymal weight and STAT5A and STAT5B mRNA levels. These results indicate that there are significant differences in mRNA levels of STAT5A and STAT5B in the mammary glands of pregnant gilts when compared to other mammals, and between swine breeds. Key words: Mammary glands, signal transducers, pregnancy, kinases, pig, expression
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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 | 0.000 |
| 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 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".