Leveraging increased cytoplasmic nucleoside kinase activity to target mtDNA and oxidative phosphorylation in AML
Bibliographic record
Abstract
Mitochondrial DNA (mtDNA) biosynthesis requires replication factors and adequate nucleotide pools from the mitochondria and cytoplasm. We performed gene expression profiling analysis of 542 human acute myeloid leukemia (AML) samples and identified 55% with upregulated mtDNA biosynthesis pathway expression compared with normal hematopoietic cells. Genes that support mitochondrial nucleotide pools, including mitochondrial nucleotide transporters and a subset of cytoplasmic nucleoside kinases, were also increased in AML compared with normal hematopoietic samples. Knockdown of cytoplasmic nucleoside kinases reduced mtDNA levels in AML cells, demonstrating their contribution in maintaining mtDNA. To assess cytoplasmic nucleoside kinase pathway activity, we used a nucleoside analog 2'3'-dideoxycytidine (ddC), which is phosphorylated to the activated antimetabolite, 2'3'-dideoxycytidine triphosphate by cytoplasmic nucleoside kinases. ddC is a selective inhibitor of the mitochondrial DNA polymerase γ. ddC was preferentially activated in AML cells compared with normal hematopoietic progenitor cells. ddC treatment inhibited mtDNA replication, oxidative phosphorylation, and induced cytotoxicity in a panel of AML cell lines. Furthermore, ddC preferentially inhibited mtDNA replication in a subset of primary human leukemia cells and selectively targeted leukemia cells while sparing normal progenitor cells. In animal models of human AML, treatment with ddC decreased mtDNA, electron transport chain proteins, and induced tumor regression without toxicity. ddC also targeted leukemic stem cells in secondary AML xenotransplantation assays. Thus, AML cells have increased cytidine nucleoside kinase activity that regulates mtDNA biogenesis and can be leveraged to selectively target oxidative phosphorylation in AML.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".