Determining the Essentiality of Spy-1 as a Novel Regulator of Mammary Cell Development Using CRISPR/Cas9
Bibliographic record
Abstract
Breast cancer is the second most common type of cancer in women comprising one in eight new cases of cancer in Canadian women. Mammary development is a tightly regulated process that involves pubertal growth, pregnancy, lactation, and involution. Each of these stages is characterized through unique changes in morphology of mammary glands due to cellular hormonal signaling and regulation of cell cycle progression. The cell cycle is regulated by interactions of cyclins and cyclin-dependent kinases that mediate the passage of the cell through checkpoints that allow for division. Spy1 is an atypical cyclin-like protein that can override cellular damage checkpoints to promote cellular proliferation without requiring cyclin-dependent kinase phosphorylation for activation. Spy1 levels are tightly regulated during mammary gland development with high levels during puberty, early pregnancy, and involution and reduced levels during lactation. This study seeks to understand the essentiality of Spy1 for normal cell proliferation and differentiation during mammary development. Using CRISPR-Cas9, Spy1 was knocked out in the mouse mammary epithelial cell line, HC11. The cell cycle profile for the knockout cell lines was determined to understand the effect of the loss of Spy1 on proliferation. Furthermore, differentiation assays and organoids were used to study the effects of the loss of Spy1 on differentiation. Our results show that the loss of Spy1 results in a delay in the cell cycle as well as increased differentiation. Understanding the role of Spy1 in mammary cell proliferation and differentiation will shed light on the mechanisms regulating normal mammary development, and ultimately allow for better diagnosis and treatment of breast cancer.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".