Smooth muscle-specific deletion of cellular communication network factor 2 causes severe aorta malformation and atherosclerosis
Bibliographic record
Abstract
AIMS: Cellular communication network factor 2 (CCN2) is a matricellular protein implicated in fibrotic diseases, with ongoing clinical trials evaluating anti-CCN2-based therapies. By uncovering CCN2 as abundantly expressed in non-diseased artery tissue, this study aimed to investigate the hypothesis that CCN2 plays a pivotal role in maintaining smooth muscle cell (SMC) phenotype and protection against atherosclerosis. METHODS AND RESULTS: Global- and SMC-specific Ccn2 knockout mouse models were employed to demonstrate that Ccn2 deficiency leads to SMC de-differentiation, medial thickening, and aorta elongation under normolipidaemic conditions. Inducing hyperlipidaemia in both models resulted in severe aorta malformation and a 17-fold increase in atherosclerosis formation. Lipid-rich lesions developed at sites of the vasculature typically protected from atherosclerosis development by laminar blood flow, covering 90% of aortas and extending to other vessels, including coronary arteries. Evaluation at earlier time points revealed medial lipid accumulation as a lesion-initiating event. Fluorescently labelled LDL injection followed by confocal microscopy showed increased LDL retention in the medial layer of Ccn2 knockout aortas, likely attributed to marked proteoglycan enrichment of the medial extracellular matrix. Analyses leveraging data from the Athero-Express study cohort indicated the relevance of CCN2 in established human lesions, as CCN2 correlated with SMC marker transcripts across 654 transcriptomically profiled carotid plaques. These findings were substantiated through in situ hybridization showing CCN2 expression predominantly in the fibrous cap. CONCLUSION: This study identifies CCN2 as a major constituent of the normal artery wall, critical in regulating SMC differentiation and aorta integrity and possessing a protective role against atherosclerosis development. These findings underscore the need for further investigation into the potential effects of anti-CCN2-based therapies on the vasculature.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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) | 0.000 | 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 teacher head, 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".