A lysosome independent role for TFEB in activating DNA repair and inhibiting apoptosis in breast cancer cells
Bibliographic record
Abstract
Transcription factor EB (TFEB) is a master regulator of lysosomal biogenesis and autophagy with critical roles in several cancers. Lysosomal autophagy promotes cancer survival through the degradation of toxic molecules and the maintenance of adequate nutrient supply. Doxorubicin (DOX) is the standard of care treatment for triple-negative breast cancer (TNBC); however, chemoresistance at lower doses and toxicity at higher doses limit its usefulness. By targeting pathways of survival, DOX can become an effective antitumor agent. In this study, we examined the role of TFEB in TNBC and its relationship with autophagy and DNA damage induced by DOX. In TNBC cells, TFEB was hypo-phosphorylated and localized to the nucleus upon DOX treatment. TFEB knockdown decreased the viability of TNBC cells while increasing caspase-3 dependent apoptosis. Additionally, inhibition of the TFEB-phosphatase calcineurin sensitized cells to DOX-induced apoptosis in a TFEB dependent fashion. Regulation of apoptosis by TFEB was not a consequence of altered lysosomal function, as TFEB continued to protect against apoptosis in the presence of lysosomal inhibitors. RNA-Seq analysis of MDA-MB-231 cells with TFEB silencing identified a down-regulation in cell cycle and homologous recombination genes while interferon-γ and death receptor signaling genes were up-regulated. In consequence, TFEB knockdown disrupted DNA repair following DOX, as evidenced by persistent γH2A.X detection. Together, these findings describe in TNBC a novel lysosomal independent function for TFEB in responding to DNA damage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
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".