Leveraging Increased Nucleoside Kinase Activity to Selectively Deplete Mitochondrial DNA (mtDNA), Impair Oxidative Phosphorylation, and Target AML Cells
Bibliographic record
Abstract
Abstract Mitochondrial DNA (mtDNA) replication requires adequate nucleotide pools from the mitochondria and cytoplasm to support DNA biosynthesis. Gene expression profiling of 542 AML patient samples (GSE13159) demonstrated that 55% of AML patients had upregulated mtDNA biosynthesis pathway expression compared to 73 normal hematopoietic cells (mononuclear cells isolated from peripheral blood and bone marrow). We also identified upregulation of pathways which support mitochondrial nucleotide pools, which include mitochondrial nucleotide transporters and a subset of cytoplasmic nucleotide salvage enzymes, which phosphorylate nucleosides to nucleotides. Upregulation of nucleoside kinases in a subset of primary AML samples compared to normal hematopoietic progenitor cells (normal G-CSF (granulocyte-colony stimulating factor) mobilized peripheral blood stem cells (PBSC's)) was confirmed by immunoblotting. These results suggest that AML cells import cytoplasmic nucleotides to support mitochondrial DNA biogenesis. To determine if cytoplasmic nucleoside kinases regulate mtDNA content, we knocked down nucleoside kinases in AML cells. Partial target knockdown of DCK (deoxycytidine kinase) and CMPK1 (cytidine/uridine monophosphate kinase 1) reduced mtDNA content (60+8%, and 62+13%, respectively compared to controls, 5 and 7 days post-shRNA transduction), indicating a role in mtDNA biogenesis. As expected, knockdown of mtDNA replication factors POLG and TFAM reduced mtDNA content in AML cells. The cytidine nucleoside analog, 2'3'-dideoxycytidine (ddC) is activated by DCK and CMPK1 to produce its triphosphate form, ddC-triphosphate (ddC-TP). To assess nucleoside kinase activity, primary AML and normal hematopoietic cells were treated with ddC and total levels of ddC and ddC-TP were measured by mass spectrometry. Levels of ddC did not differ between AML and normal, but ddC-TP levels was increased in AML samples > 7-fold compared to normal (p< 0.05, one-way ANOVA). Previously we and others demonstrated that AML cells and stem cells have increased mitochondrial biogenesis and reliance on oxidative phosphorylation due to decreased spare reserve capacity and an inability to upregulate glycolysis. ddC-TP inhibits the sole mtDNA polymerase POLG, but not nuclear DNA polymerases. Given the increased activity of nucleoside kinases in AML cells over normal, we examined the effects of ddC treatment on mtDNA content and cellular bioenergetics. AML and normal cells were treated with increasing concentrations of ddC. At increasing times after treatment, ddC depleted mtDNA levels > 85% at 0.5 uM, 3 day treatment in OCI-AML2 and TEX cells as assessed by qPCR. ddC decreased protein expression of mtDNA encoded electron transport chain (ETC) subunits COXI and COX II, but not nuclear encoded subunit COXIV) and reduced basal oxygen consumption. ddC also decreased proliferation of AML cell lines (OCI-AML2, TEX, HL-60, K562) (> 95% reduction at 0.5uM, 10 days). Knockdown of DCK abrogated the effects of ddC on AML cell proliferation. We next examined the effects of ddC in primary human leukemia cells (AML = 7, CML blast crisis = 1, CMML-2 = 1) and normal hematopoietic progenitor cells (n=8). ddC preferentially inhibited mtDNA biosynthesis and reduced viability in a subset of primary cells (6 of 9 AML) compared to normal PBSC's (n=8). Sensitivity to ddC positively correlated with mtDNA depletion. Finally, we evaluated the efficacy and toxicity of ddC in mouse models of human AML. ddC (35 mg/kg daily i.p. x 11 days) caused tumor regression in an OCI-AML2 xenograft model without toxicity (changes body weight, behavior, serum chemistries). In OCI-AML2 cells isolated from treated mice, ddC reduced mtDNA by 95% and mtDNA-encoded ETC proteins by 90%. In addition, ddC (75 mg/kg i.p x 6 weeks) significantly reduced human AML bone marrow engraftment in primary AML (n=2, P<0.0001, n=1,P<0.05, t-test) and secondary AML (n=1, 14 vs 3%, P<0.01, t-test) without toxicity. Thus, AML cells have increased nucleoside kinase activity that is functionally important for mtDNA biogenesis. We leveraged this unique biological vulnerability to preferentially activate ddC, deplete mtDNA and selectively target oxidative phosphorylation in AML. Disclosures Schimmer: Novartis: Honoraria.
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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".