Role of STAT5 and epigenetics in lactation-associated upregulation of multidrug transporter ABCG2 in the mammary gland
Bibliographic record
Abstract
The multidrug resistance efflux transporter ATP-binding cassette subfamily G member 2 (ABCG2) is not only overexpressed in certain drug-resistant cancers but is also highly expressed in the mammary gland during lactation, carrying xenobiotics and nutrients into milk. We sought to investigate the molecular mechanisms involved in the upregulation of ABCG2 during lactation. Expression profiling of different mouse Abcg2 mRNA isoforms (E1a, E1b, and E1c) revealed that E1b is predominantly expressed and induced in the lactating mouse mammary gland. Despite this induction, analyses of CpG methylation status and published ChIP-seq datasets reveal that E1b promoter sequences in the virgin gland are already hypomethylated and marked with the open chromatin histone mark H3K4me2. Using a forced-weaning model to shut down lactation, we found that within 24 h there was a significant reduction in Abcg2 mRNA expression and a loss of signal transducer and activator of transcription-5 (STAT5) occupancy at the mouse Abcg2 gene. Luciferase reporter assays further showed that some of these STAT5-binding regions that contained interferon-γ-activated sequence (GAS) motifs function as an enhancer after prolactin treatment. We conclude that Abcg2 is already poised for expression in the virgin mammary gland and that STAT5 plays an important role in Abcg2 expression during lactation.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".