A macaque clonal hematopoiesis model demonstrates expansion of TET2-disrupted clones and utility for testing interventions
Bibliographic record
Abstract
Individuals with age-related clonal hematopoiesis (CH) are at greater risk for hematologic malignancies and cardiovascular diseases. However, predictive preclinical animal models to recapitulate the spectrum of human CH are lacking. Through error-corrected sequencing of 56 human CH/myeloid malignancy genes, we identified natural CH driver mutations in aged rhesus macaques matching genes somatically mutated in human CH, with DNMT3A mutations being the most frequent. A CH model in young adult macaques was generated via autologous transplantation of clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9-mediated gene-edited hematopoietic stem and progenitor cells (HSPCs), targeting the top human CH genes with loss-of-function (LOF) mutations. Long-term follow-up revealed reproducible and significant expansion of multiple HSPC clones with heterozygous TET2 LOF mutations, compared with minimal expansion of clones bearing other mutations. Although the blood counts of these CH macaques were normal, their bone marrows were hypercellular and myeloid-predominant. TET2-disrupted myeloid colony-forming units isolated from these animals showed a distinct hyperinflammatory gene expression profile compared with wild type. In addition, mature macrophages purified from the CH macaques showed elevated NLRP3 inflammasome activity and increased interleukin-1β (IL-1β) and IL-6 production. The model was used to test the impact of IL-6 blockage by tocilizumab, documenting a slowing of TET2-mutated expansion, suggesting that interruption of the IL-6 axis may remove the selective advantage of mutant HSPCs. These findings provide a model for examining the pathophysiology of CH and give insights into potential therapeutic interventions.
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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 | 0.001 |
| 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".