Elevated mitochondrial membrane potential is a therapeutic vulnerability in Dnmt3a-mutant clonal hematopoiesis
Bibliographic record
Abstract
Abstract The competitive advantage of mutant hematopoietic stem and progenitor cells (HSPCs) underlies clonal hematopoiesis (CH). Drivers of CH include aging and inflammation; however, how CH-mutant cells gain a selective advantage in these contexts is an unresolved question. Using a murine model of CH (Dnmt3a R878H/+), we discover that mutant HSPCs sustain elevated mitochondrial respiration which is associated with their resistance to aging-related changes in the bone marrow microenvironment. Mutant HSPCs have DNA hypomethylation and increased expression of oxidative phosphorylation gene signatures, increased functional oxidative phosphorylation capacity, high mitochondrial membrane potential (Δψm), and greater dependence on mitochondrial respiration compared to wild-type HSPCs. Exploiting the elevated Δψm of mutant HSPCs, long-chain alkyl-TPP molecules (MitoQ, d-TPP) selectively accumulate in the mitochondria and cause reduced mitochondrial respiration, mitochondrial-driven apoptosis and ablate the competitive advantage of HSPCs ex vivo and in vivo in aged recipient mice. Further, MitoQ targets elevated mitochondrial respiration and the selective advantage of human DNMT3A-knockdown HSPCs, supporting species conservation. These data suggest that mitochondrial activity is a targetable mechanism by which CH-mutant HSPCs gain a selective advantage over wild-type HSPCs.
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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".