TET2 deficiency increases the competitive advantage of hematopoietic stem and progenitor cells through upregulation of thrombopoietin receptor signaling
Bibliographic record
Abstract
Ten-Eleven Translocation-2 (TET2) mutations drive the expansion of mutant hematopoietic stem cells (HSCs) in clonal hematopoiesis (CH). However, the precise mechanisms by which TET2 mutations confer a competitive advantage to HSCs remain unclear. Here, through an epigenetic drug screen, we discover that inhibition of disruptor of telomeric silencing 1-like (DOT1L), a H3K79 methyltransferase, selectively reduces the fitness of Tet2 knockout (Tet2KO) hematopoietic stem and progenitor cells (HSPCs). Mechanistically, we find that TET2 deficiency increases H3K79 dimethylation and expression of Mpl, which encodes the thrombopoietin receptor (TPO-R). Correspondingly, TET2 deficiency is associated with a higher proportion of primitive Mpl-expressing (Mpl+) cells in the HSC compartment. Importantly, inhibition of Mpl expression or the signaling downstream of TPO-R is sufficient to reduce the competitive advantage of murine and human TET2-deficient HSPCs. Our findings demonstrate a critical role for aberrant TPO-R signaling in TET2 mutation-driven CH and uncover potential therapeutic strategies against this condition. Clonal hematopoiesis is associated with an increased risk of hematologic and a range of inflammation-related diseases. Here, Yang et al. demonstrate a critical role for aberrant thrombopoietin receptor signaling in TET2-mutation driven clonal hematopoiesis.
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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) | 0.002 | 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".