Monocyte-endothelial interactions as a targetable node in clonal hematopoiesis-mediated cardiovascular disease
Bibliographic record
Abstract
Abstract Background Clonal hematopoiesis of indeterminate potential (CHIP) increases risk of cardiovascular disease yet the molecular mechanisms driving this association remain incompletely understood. We hypothesized that aberrant monocyte-endothelial interactions contribute to CHIP-mediated cardiovascular disease. Methods We performed single-cell RNA sequencing on blood and peripheral vascular tissue from 4 individuals with TET2 CHIP, 6 individuals with DNMT3A CHIP, and 25 controls. We predicted interactions between monocytes and endothelial cells based on expression of ligand-receptor pairs then modeled interactions between monocytes carrying CHIP mutations with endothelial cells in vitro . We performed an in silico genomewide perturbation screen to identify genetic targets capable of mediating these interactions and then experimentally evaluated the effect of inhibiting predicted targets on monocyte-endothelial interactions in vitro . Results Expression of ligand-receptor pairs on monocytes and endothelial cells from patients with and without CHIP highlighted differences in signaling likelihood for 6 key ligand-receptor pairs related to transendothelial migration. Co-culture of monocytes with human aortic endothelial cells demonstrated that monocytes carrying CHIP mutations have decreased velocity compared to monocytes without CHIP mutations. The perturbation screen suggested 11 druggable genetic targets capable of rescuing TET2 CHIP monocytes. Experimental inhibition of ICAM1 in endothelial cells and inhibition of CXCR2 in monocytes significantly increased the velocity of TET2 -mutated monocytes over endothelial cells. Conclusions CHIP mutations alter interactions between monocytes and endothelial cells. Therapeutics targeting CXCR2 and ICAM1 may restore normal interactions between monocytes and endothelial cells among patients with TET2 CHIP.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".