Loss of transcription factor EB dysregulates the G1/S transition and DNA replication in mammary epithelial cells
Bibliographic record
Abstract
Triple-negative breast cancer (TNBC) poses significant challenges for treatment given the lack of targeted therapies and increased probability of relapse. It is pertinent to identify vulnerabilities in TNBC and develop newer treatments. Our prior research demonstrated that transcription factor EB (TFEB) is necessary for TNBC survival by regulating DNA repair, apoptosis signaling, and the cell cycle. However, specific mechanisms by which TFEB targets DNA repair and cell cycle pathways are unclear, and whether these effects dictate TNBC survival is yet to be determined. Here, we show that TFEB knockdown decreased the expression of genes and proteins involved in DNA replication and cell cycle progression in MDA-MB-231 TNBC cells. DNA replication was decreased in cells lacking TFEB, as measured by EdU incorporation. TFEB silencing in MDA-MB-231 and noncancerous MCF10A cells impaired progression through the S-phase following G1/S synchronization; however, this proliferation defect could not be rescued by co-knockdown of suppressor RB1. Instead, TFEB knockdown reduced origin licensing in G1 and early S-phase MDA-MB-231 cells. TFEB silencing was associated with replication stress in MCF10A but not in TNBC cells. Lastly, we identified that TFEB knockdown renders TNBC cells more sensitive to inhibitors of Aurora Kinase A, a protein facilitating mitosis. Thus, inhibition of TFEB impairs cell cycle progress by decreasing origin licensing, leading to delayed entry into the S-phase, while rendering TNBC cells sensitive to Aurora kinase A inhibitors and decreasing cell viability. In contrast, TFEB silencing in noncancerous cells is associated with replication stress and leads to G1/S arrest.
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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) | 0.001 | 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".