Rac2-MRC-cIII–generated ROS cause genomic instability in chronic myeloid leukemia stem cells and primitive progenitors
Bibliographic record
Abstract
Chronic myeloid leukemia in chronic phase (CML-CP) is induced by BCR-ABL1 oncogenic tyrosine kinase. Tyrosine kinase inhibitors eliminate the bulk of CML-CP cells, but fail to eradicate leukemia stem cells (LSCs) and leukemia progenitor cells (LPCs) displaying innate and acquired resistance, respectively. These cells may accumulate genomic instability, leading to disease relapse and/or malignant progression to a fatal blast phase. In the present study, we show that Rac2 GTPase alters mitochondrial membrane potential and electron flow through the mitochondrial respiratory chain complex III (MRC-cIII), thereby generating high levels of reactive oxygen species (ROS) in CML-CP LSCs and primitive LPCs. MRC-cIII-generated ROS promote oxidative DNA damage to trigger genomic instability, resulting in an accumulation of chromosomal aberrations and tyrosine kinase inhibitor-resistant BCR-ABL1 mutants. JAK2(V617F) and FLT3(ITD)-positive polycythemia vera cells and acute myeloid leukemia cells also produce ROS via MRC-cIII. In the present study, inhibition of Rac2 by genetic deletion or a small-molecule inhibitor and down-regulation of mitochondrial ROS by disruption of MRC-cIII, expression of mitochondria-targeted catalase, or addition of ROS-scavenging mitochondria-targeted peptide aptamer reduced genomic instability. We postulate that the Rac2-MRC-cIII pathway triggers ROS-mediated genomic instability in LSCs and primitive LPCs, which could be targeted to prevent the relapse and malignant progression of CML.
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".