Concerted roles of PTEN and ATM in controlling hematopoietic stem cell fitness and dormancy
Bibliographic record
Abstract
In order to sustain proficient life-long hematopoiesis, hematopoietic stem cells (HSCs) must possess robust mechanisms to preserve their quiescence and genome integrity. DNA-damaging stress can perturb HSC homeostasis by affecting their survival, self-renewal, and differentiation. Ablation of the kinase ataxia telangiectasia mutated (ATM), a master regulator of the DNA damage response, impairs HSC fitness. Paradoxically, we show here that loss of a single allele of Atm enhances HSC functionality in mice. To explain this observation, we explored a possible link between ATM and the tumor suppressor phosphatase and tensin homolog (PTEN), which also regulates HSC function. We generated and analyzed a knockin mouse line (PtenS398A/S398A), in which PTEN cannot be phosphorylated by ATM. Similar to Atm+/-, PtenS398A/S398A HSCs have enhanced hematopoietic reconstitution ability, accompanied by resistance to apoptosis induced by genotoxic stress. Single-cell transcriptomic analyses and functional assays revealed that dormant PtenS398A/S398A HSCs aberrantly tolerate elevated mitochondrial activity and the accumulation of reactive oxygen species, which are normally associated with HSC priming for self-renewal or differentiation. Our results unveil a molecular connection between ATM and PTEN, which couples the response to genotoxic stress and dormancy in HSCs.
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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".