Transcriptomic analysis reveals key regulators of mammogenesis and the pregnancy-lactation cycle
Bibliographic record
Abstract
An organ unique to mammals, the mammary gland develops 90% of its mass after birth and experiences the pregnancylactation-involution cycle (PL cycle) during reproduction. To understand mammogenesis at the transcriptomic level and using a ribo-minus RNA-seq protocol, we acquired greater than 50 million reads each for the mouse mammary gland during pregnancy (day 12 of pregnancy), lactation (day 14 of lactation), and involution (day 7 of involution). The pregnancy-, lactation- and involution-related sequencing reads were assembled into 17344, 10160, and 13739 protein-coding transcripts and 1803, 828, and 1288 non-coding RNAs (ncRNAs), respectively. Differentially expressed genes (DEGs) were defined in the three samples, which comprised 4843 DEGs (749 up-regulated and 4094 down-regulated) from pregnancy to lactation and 4926 DEGs (4706 up-regulated and 220 down-regulated) from lactation to involution. Besides the obvious and substantive up- and down-regulation of the DEGs, we observe that lysosomal enzymes were highly expressed and that their expression coincided with milk secretion. Further analysis of transcription factors such as Trps1, Gtf2i, Tcf7l2, Nupr1, Vdr, Rb1, and Aebp1, and ncRNAs such as mir-125b, Let7, mir-146a, and mir-15 has enabled us to identify key regulators in mammary gland development and the PL cycle.
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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".