Modelling Medial Degenerative Features by Enzymatic Digestion to Evaluate Disease-Relevant Structure-Function Relationships in the Thoracic Aorta
In brief
Enzyme tests show three aortic matrix components alter tissue mechanics differently
In treated porcine and human aortic tissue, degrading elastin, collagen, or proteoglycans changed stiffness, stretch behavior, and energy loss in distinct ways. The controlled model suggests several routes by which matrix degeneration can make the aortic wall more vulnerable to failure, but whether these mechanical changes predict dissection risk in patients remains unknown.
- Journal
- Acta biomaterialia (Q1)
- Published
- 5 November 2026
- Study design
- Unclassified
- Evidence level
- Level 5, Expert Opinion (CEBM 5)
- Authors
- Daniella Eliathamby, Lucas Ung, Hayley Yap, Malak Elbatarny, Maral Ouzounian, Michelle Bendeck, et al.
- PMID
- 42988031
- DOI
- 10.1016/j.actbio.2026.11.003
Why clinicians should know about it
- Picked for Histology (paper of the day, 7 November 2026): Direct tissue structure-function study using staining and advanced microscopy
Abstract
BACKGROUND: To clarify aortic microstructure-function relationships, we used improved, disease-motivated enzymatic digestion models of medial degeneration to evaluate individual extracellular matrix (ECM) components (i.e., elastin, collagen, and proteoglycans) and their influence on biomechanical properties of ascending aortic tissue. METHODS: Thin, 200 µm medial porcine ascending aortic sections were progressively digested with elastase or collagenase for selective degradation of these ECM components. Full thickness human aortic tissues were treated with chondroitinase, hyaluronidase, and heparinase to completely remove proteoglycans. Low- and high-strain modulus, transition-zone behaviour, strain-energy density, and energy loss were derived from planar biaxial tensile testing. Degradation of elastin was captured with two photon excitation fluorescence imaging. Degradation of collagen was captured with picrosirius red staining and polarized light microscopy. Proteoglycan content and organization was confirmed by Alcian blue staining. RESULTS: Degradation of elastin fibers reduced low-strain modulus, strain-energy density, transition zone stress, and energy loss. Collagen degradation similarly reduced strain-energy density and decreased strain at the onset of transition, with reductions in high strain modulus and energy loss with increased degradation. Proteoglycan removal decreased energy loss and reduced strain-energy density and strain at onset of transition, and increased high strain modulus. CONCLUSIONS: Through targeted modelling of ECM degenerative features, we have delineated the overlapping effects of major matrix constituents, underscoring the complex, multifactorial nature of structure-function relationships in aortic disease. We also identified multiple microstructural pathways to increasing energy loss and the vulnerability of aortic tissue to failure. STATEMENT OF SIGNIFICANCE: Thoracic aortic dissection results from the progressive degeneration of the aortic wall extracellular matrix, yet the contributions of individual matrix constituents to this mechanical failure remain understudied. To address this gap, we developed a disease-motivated enzymatic model that enables controlled, graded modification of key extracellular matrix constituents in aortic tissue, better replicating the partial degeneration observed in vivo than existing approaches. Using this framework, we systematically evaluated the biomechanical consequences of elastin, collagen, and proteoglycan degeneration. Here, we revealed multiple microstructural pathways to altered viscoelastic behaviour, which is associated with increased susceptibility to aortic failure. These findings provide new mechanistic insight into structure-function relationships in aortic disease and help explain how matrix degeneration contributes to tissue vulnerability and dissection risk.
Abstract as published, via PubMed.
For healthcare professionals. The summary is generated by AI from the published abstract, and the evidence level is assigned automatically from the study design on the Oxford CEBM hierarchy. Neither is medical advice. Read the full paper before changing practice.