Mechanical strain modulates extracellular matrix degradation and byproducts in an isoform-specific manner.

Many studies have shown that mechanical forces can alter collagen degradation by proteases, and this mechanochemical effect may potentially serve an important role in determining extracellular matrix content and organization in load-bearing tissues. However, it is not yet known whether mechano-sensitive degradation depends on particular protease isoforms, nor is it yet known whether particular degradation byproducts can be altered by mechanical loading. In this study, we tested the hypothesis that different types of proteases exhibit different sensitivities to mechanical loading both in degradation rates and byproducts. Decellularized porcine pericardium samples were treated with human recombinant matrix metalloproteinases-1, -8, -9, cathepsin K, or a protease-free control while subjected to different levels of strain in a planar, biaxial mechanical tester. Tissue degradation was monitored by tracking the decay in mechanical stresses during displacement control tests, and byproducts were assessed by mass spectrometry analysis of the sample supernatant after degradation. Our key finding shows that cathepsin K-mediated degradation of collagenous tissue was enhanced with increasing strain, while MMP1-, MMP8-, and MMP9-mediated degradation were first decreased and then increased by strain. Degradation induced changes in tissue mechanical properties, and proteomic analysis revealed strain-sensitive degradome signatures with different ECM byproducts released at low vs. high strains. This evidence suggests a potentially new type of mechanobiology wherein mechanical forces alter the degradation products that can provide important signaling feedback functions during tissue remodeling.

Ensemble machine learning model identifies patients with HFpEF from matrix-related plasma biomarkers.

Arterial hypertension can lead to structural changes within the heart including left ventricular hypertrophy (LVH) and eventually heart failure with preserved ejection fraction (HFpEF). The initial diagnosis of HFpEF is costly and generally based on later stage remodeling; thus, improved predictive diagnostic tools offer potential clinical benefit. Recent work has shown predictive value of multibiomarker plasma panels for the classification of patients with LVH and HFpEF. We hypothesized that machine learning algorithms could substantially improve the predictive value of circulating plasma biomarkers by leveraging more sophisticated statistical approaches. In this work, we developed an ensemble classification algorithm for the diagnosis of HFpEF within a population of 480 individuals including patients with HFpEF, patients with LVH, and referent control patients. Algorithms showed strong diagnostic performance with receiver-operating-characteristic curve (ROC) areas of 0.92 for identifying patients with LVH and 0.90 for identifying patients with HFpEF using demographic information, plasma biomarkers related to extracellular matrix remodeling, and echocardiogram data. More impressively, the ensemble algorithm produced an ROC area of 0.88 for HFpEF diagnosis using only demographic and plasma panel data. Our findings demonstrate that machine learning-based classification algorithms show promise as a noninvasive diagnostic tool for HFpEF, while also suggesting priority biomarkers for future mechanistic studies to elucidate more specific regulatory roles.NEW & NOTEWORTHY Machine learning algorithms correctly classified patients with heart failure with preserved ejection fraction with over 90% area under receiver-operating-characteristic curves. Classifications using multidomain features (demographics and circulating biomarkers and echo-based ventricle metrics) proved more accurate than previous studies using single-domain features alone. Excitingly, HFpEF diagnoses were generally accurate even without echo-based measurements, demonstrating that such algorithms could provide an early screening tool using blood-based measurements before sophisticated imaging.

A Simple Method to Test Mechanical Strain on Epithelial Cell Monolayers Using a 3D-Printed Stretcher.

With the realization that mechanical forces mediate many biological processes and contribute to disease progression, researchers are focusing on developing new methods to understand the role of mechanotransduction in biological systems. Despite recent advances in stretching devices that analyze the effects of mechanical strain in vitro, there are still possibilities to develop new equipment. For example, many of these devices tend be expensive, whereas few have been designed to assess the effects of mechanical strain driven by the extracellular matrix (ECM) to epithelial cell monolayers and to cell-cell adhesion. In this chapter, we introduce a cost-efficient, user-friendly, 3D-printed stretching device that can be used to test the effects of mechanical strain on cultured epithelial cells. Evaluation of the device using speckle-tracking shows homogeneous strain distribution along the horizontal plane of membranes at 2.5% and 5% strains, supporting the reliability of the device. Since cell-cell junctions are mechanosensitive protein complexes, we hereby used this device to examine effects on cell-cell adhesion. For this, we used colon epithelial Caco2 cell monolayers that well-differentiate in culture and form mature adherens junctions. Subjecting Caco2 cells to 2.5% and 5% strain using our device resulted in significant reduction in the localization of the core adherens junction component E-cadherin at areas of cell-cell contact and its increased translocation to the cytoplasm, which in agreement with other methodologies showing that increased ECM-driven strain negatively affects cell-cell adhesion. In summary, we here present a new, cost-effective, homemade device that can be reliably used to examine effects of mechanical strain on epithelial cell monolayers and cell-cell adhesion, in vitro.