RNA-Seq Analysis of Pubertal Mammary Epithelial Cells Reveals Novel n-3 Polyunsaturated Fatty Acid Transcriptomic Changes in the fat-1 Mouse Model
Bibliographic record
Abstract
Background: The early exposure of nutrients during pubertal mammary gland development may reduce the risk of developing breast cancer later in life. Anticancer n-3 polyunsaturated fatty acids (n-3 PUFA) are shown to modulate pubertal mammary gland development; however, the mechanisms of action remain unclear. Prior work focused on effects at the whole tissue level, and little is known at the cellular level, such as at the level of mammary epithelial cells (MECs), which are implicated in cancer development. Methods: This pilot study examined the effects of lifelong n-3 PUFA exposure on the transcriptome by RNA-Seq in the isolated MECs of pubertal (6–8-week-old) female fat-1 transgenic mice capable of de novo n-3 PUFA synthesis. edgeR and DESeq2 were used separately for the differential expression analysis of RNA sequencing data followed by the Benjamani–Hochberg procedure for multiple testing correction. Results: Nine genes were found concordant and significantly different (p ≤ 0.05) by both the DESeq2 and edgeR methods. These genes were associated with multiple pathways, suggesting that n-3 PUFA stimulates estrogen-related signaling (Mlltl0, Galr3, and Nrip1) and a glycolytic profile (Soga1, Pdpr, and Uso1) while offering protective effects for immune and DNA damage responses (Glpd1, Garre1, and Rpa1) in MECs during puberty. Conclusions: This pilot study highlights the utility of RNA-Seq to better understanding the mechanistic effects of specific nutrients such as n-3 PUFA in a cell-specific manner. Thus, further studies are warranted to investigate the cell-specific mechanisms by which n-3 PUFA influences pubertal mammary gland development and breast cancer risk later in life.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".