Cellular Responses to Anthracyclines Identify Ku70, a DNA Repair Factor that Changes Compartment and Remains Stable in Leukemic Cells
Bibliographic record
Abstract
Anthracyclines such as doxorubicin and daunorubicin are anticancer drugs that act by damaging the DNA and used for treating a variety of cancers including adult acute myeloid leukemia. To date, nearly 50 % of acute myeloid leukemia patients show resistance to anthracyclines although the cause is not known. We first investigate if there is a relationship between the expression level of 23 DNA repair genes in three leukemic cell lines (KG-1, HL-60 and Mono-Mac1) and cellular responses to anthracyclines. We observed that the DNA repair genes were all downregulated in these cell lines following exposure to doxorubicin. Further analysis revealed that the general downregulation of the genes was linked to a substantial decrease in the recovery of total RNA raising the possibility that assessment of total RNA, and not specific gene or set of genes, can be used as a simple indicator of cellular responses to anthracyclines. Furthermore, examination of total protein extracts derived from these cell lines revealed for the first time that Ku70 is a key protein that remained stable, while the majority of proteins were loss, upon anthracycline treatment. Importantly, Ku70 redistributes from the cytoplasm to the nucleoli in a time-dependent manner in response to anthracycline exposure. We propose that Ku70 redistribution might play a vital role in predicting cellular response to anthracycline and promoting cell death.
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 | 0.000 |
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".