A shared functional organisation underlies vascular disease remodelling
Bradford, A.; Bidula, S.; Fabian, L.; Warren, D.
Show abstract
Cardiovascular disease involves coordinated remodelling across multiple biological processes. Gene-level signatures vary substantially between studies because different combinations of genes can support similar biological processes. Pathway-level analyses can provide more stable representations of these processes but typically consider pathways independently. We hypothesised that grouping related pathways into conserved biological functions and quantifying their relative weighting would reveal a higher-order, transferable property of vascular tissue that we term functional organisation. We quantified the relative weighting of six conserved biological functions across independent transcriptomic datasets spanning human vascular disease, experimental models and therapeutic interventions. Vascular tissues exhibited a reproducible functional organisation defined by the balance of these functions. A dominant remodelling trajectory captured coordinated, nonlinear rebalancing of structural, immune, signalling and metabolic programmes, while a second dimension distinguished contractile/ECM organisation from immune activity. This organisation was preserved across independently reconstructed reference cohorts and remained robust to analytical sensitivity testing. When independent datasets were projected into this fixed framework, biological and clinical phenotypes occupied coherent positions along the remodelling landscape. Plaque-derived vascular, stromal and immune cell populations also occupied ordered functional positions, linking cellular heterogeneity to tissue-level organisation. The same organisational structure generalised across atherosclerosis, peripheral vascular disease and abdominal aortic aneurysm and revealed continuous biological heterogeneity within conventional clinical classifications. Genetic, pharmacological and dietary perturbations reproducibly shifted functional organisation, demonstrating that organisational state is dynamic and biologically responsive. Together, these findings identify functional organisation as a reproducible tissue-level property of vascular remodelling and provide a framework for understanding cardiovascular disease as coordinated rebalancing of biological functions rather than alteration of individual pathways in isolation.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Vascular smooth muscle cell atherosclerosis trajectories characterized at single cell resolution identify causal transcriptomic and epigenomic mechanisms of disease risk 96%
- TWIST1 drives endothelial-to-mesenchymal-transition to stabilize atherosclerotic plaques 94%
- IL-27 receptor signaling regulated stress myelopoiesis drives Abdominal Aortic Aneurysm development 94%
Similar papers in this journal
- PRDM16 controls smooth muscle cell fate in atherosclerosis 94%
- Network-based prioritisation and validation of novel regulators of vascular smooth muscle cell proliferation in disease 93%
- Smooth muscle expression of RNA editing enzyme ADAR1 controls activation of RNA sensor MDA5 in atherosclerosis 93%
Similar papers in this journal
- Intercellular Conduction Optimizes Arterial Network Function and Conserves Blood Flow Homeostasis during Cerebrovascular Challenges 94%
- High-Dimensional Single-Cell Multimodal Landscape of Human Carotid Atherosclerosis 94%
- The 9p21.3 coronary artery disease risk locus drives vascular smooth muscle cells to an osteochondrogenic state 93%
Similar papers in this journal
- Network Modeling Predicts Personalized Gene Expression and Drug Responses in Valve Myofibroblasts Cultured with Patient Sera 92%
- Matrix mechanotransduction mediated by thrombospondin-1/integrin/YAP signaling pathway in the remodeling of blood vessels 91%
- Capillary regression leads to sustained local hypoperfusion by inducing constriction of upstream transitional vessels 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.