Inflammatory signals from fatty bone marrow supports the early stages of <i>DNMT3a</i> driven clonal hematopoiesis
Bibliographic record
Abstract
Abstract Age related cancer is not only due to the random accumulation of mutations, but also how phenotypes are selected by the aging environment. While fatty bone marrow (FBM), is one of the hallmarks of bone marrow ageing, it is unknown whether FBM can modify the evolution of the early stages of leukemia and clonal hematopoiesis (CH). To address this question, we established FBM mice models and transplanted both human and mice preleukemic hematopoietic stem cells (PreL-HSCs) carrying DNMT3A mutations. We demonstrate that castration which models age related andropenia result in FBM. A significant increase in self-renewal was found when DNMT3A Mut - preL-HSPCs were exposed to FBM. To better understand the mechanisms of the FBM-preL-HSPCs interaction, we performed single cell RNA-sequencing on HSPCs three days after FBM exposure. A 20-50 fold increase in DNMT3A Mut -preL-HSCs was observed under FBM conditions in comparison to other conditions. PreL-HSPCs exposed to FBM exhibited an activated inflammatory signaling (IL-6 and INFγ). Cytokine analysis of BM fluid demonstrated increased IL-6 levels under FBM conditions. Anti-IL-6 neutralizing antibodies significantly reduced the selective advantage of DNMT3A Mut -preL-HSPCs exposed to FBM. Overall, age related paracrine FBM inflammatory signals promote DNMT3A -driven clonal hematopoiesis, which can be inhibited by blocking the IL-6 receptor.
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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".