Atomic Force Microscopy Stiffness Mapping in Human Aortic Smooth Muscle Cells.

Aortic smooth muscle cells (SMCs) play a vital role in maintaining mechanical homeostasis in the aorta. We recently found that SMCs of aneurysmal aortas apply larger traction forces than SMCs of healthy aortas. This result was explained by the significant increase of hypertrophic SMCs abundance in aneurysms. In this study, we investigate whether the cytoskeleton stiffness of SMCs may also be altered in aneurysmal aortas. For that, we use atomic force microscopy (AFM) nano-indentation with a specific mode that allows subcellular-resolution mapping of the local stiffness across a specified region of interest of the cell. Aortic SMCs from a commercial human lineage (AoSMCs, Lonza) and primary aneurysmal SMCs (AnevSMCs) are cultured in conditions promoting the development of their contractile apparatus, and seeded on hydrogels with stiffness properties of 12 kPa and 25 kPa. Results show that all SMCs exhibit globally a lognormal stiffness distribution, with medians in the range 10-30 kPa. The mean of stiffness distributions is 16 kPa in aneurysmal SMCs and 12 kPa in healthy cells, but the differences are not statistically significant due to the large dispersion of AFM nano-indentation stiffness. We conclude that the possible alterations previously found in aneurysmal SMCs do not affect significantly the AFM nano-indentation stiffness of their cytoskeleton.

AntAngioCOOL: computational detection of anti-angiogenic peptides.

BACKGROUND: Angiogenesis inhibition research is a cutting edge area in angiogenesis-dependent disease therapy, especially in cancer therapy. Recently, studies on anti-angiogenic peptides have provided promising results in the field of cancer treatment. METHODS: A non-redundant dataset of 135 anti-angiogenic peptides (positive instances) and 135 non anti-angiogenic peptides (negative instances) was used in this study. Also, 20% of each class were selected to construct an independent test dataset (see Additional files 1, 2). We proposed an effective machine learning based R package (AntAngioCOOL) to predict anti-angiogenic peptides. We have examined more than 200 different classifiers to build an efficient predictor. Also, more than 17,000 features were extracted to encode the peptides. RESULTS: Finally, more than 2000 informative features were selected to train the classifiers for detecting anti-angiogenic peptides. AntAngioCOOL includes three different models that can be selected by the user for different purposes; it is the most sensitive, most specific and most accurate. According to the obtained results AntAngioCOOL can effectively suggest anti-angiogenic peptides; this tool achieved sensitivity of 88%, specificity of 77% and accuracy of 75% on the independent test set. AntAngioCOOL can be accessed at https://cran.r-project.org/ . CONCLUSIONS: Only 2% of the extracted descriptors were used to build the predictor models. The results revealed that physico-chemical profile is the most important feature type in predicting anti-angiogenic peptides. Also, atomic profile and PseAAC are the other important features.

Patient-specific computational simulations of wound healing following midline laparotomy closure.

In the current study, we developed a new computational methodology to simulate wound healing in soft tissues. We assumed that the injured tissue recovers partially its mechanical strength and stiffness by gradually increasing the volume fraction of collagen fibers. Following the principles of the constrained mixture theory, we assumed that new collagen fibers are deposited at homeostatic tension while the already existing tissue undergoes a permanent deformation due to the effects of remodeling. The model was implemented in the finite-element software Abaqus® through a VUMAT subroutine and applied to a complex and realistic case: simulating wound healing following midline laparotomy closure. The incidence of incisional hernia is still quite significant clinically, and our goal was to investigate different conditions hampering the success of these procedures. We simulated wound healing over periods of 6 months on a patient-specific geometry. One of the outcomes of the finite-element simulations was the width of the wound tissue, which was found to be clinically correlated with the development of incisional hernia after midline laparotomy closure. We studied the impact of different suturing modalities and the effects of situations inducing increased intra-abdominal pressure or its intermittent variations such as coughing. Eventually, the results showed that the main risks of developing an incisional hernia mostly depend on the elastic strains reached in the wound tissue after degradation of the suturing wires. Despite the need for clinical validation, these results are promising for establishing a digital twin of wound healing in midline laparotomy incision.

Stiffness sensing by smooth muscle cells: Continuum mechanics modeling of the acto-myosin role.

Aortic smooth muscle cells (SMCs) play a vital role in maintaining homeostasis in the aorta by sensing and responding to mechanical stimuli. However, the mechanisms that underlie the ability of SMCs to sense and respond to stiffness change in their environment are still partially unclear. In this study, we focus on the role of acto-myosin contractility in stiffness sensing and introduce a novel continuum mechanics approach based on the principles of thermal strains. Each stress fiber satisfies a universal stress-strain relationship driven by a Young's modulus, a contraction coefficient scaling the fictitious thermal strain, a maximum contraction stress and a softening parameter describing the sliding effects between actin and myosin filaments. To account for the inherent variability of cellular responses, large populations of SMCs are modeled with the finite-element method, each cell having a random number and a random arrangement of stress fibers. Moreover, the level of myosin activation in each stress fiber satisfies a Weibull probability density function. Model predictions are compared to traction force measurements on different SMC lineages. It is demonstrated that the model not only predicts well the effects of substrate stiffness on cellular traction, but it can also successfully approximate the statistical variations of cellular tractions induced by intercellular variability. Finally, stresses in the nuclear envelope and in the nucleus are computed with the model, showing that the variations of cytoskeletal forces induced by substrate stiffness directly induce deformations of the nucleus which can potentially alter gene expression. The predictability of the model combined to its relative simplicity are promising assets for further investigation of stiffness sensing in 3D environments. Eventually, this could contribute to decipher the effects of mechanosensitivity impairment, which are known to be at the root of aortic aneurysms.

Regulation of SMC traction forces in human aortic thoracic aneurysms.

Smooth muscle cells (SMCs) usually express a contractile phenotype in the healthy aorta. However, aortic SMCs have the ability to undergo profound changes in phenotype in response to changes in their extracellular environment, as occurs in ascending thoracic aortic aneurysms (ATAA). Accordingly, there is a pressing need to quantify the mechanobiological effects of these changes at single cell level. To address this need, we applied Traction Force Microscopy (TFM) on 759 cells coming from three primary healthy (AoPrim) human SMC lineages and three primary aneurysmal (AnevPrim) human SMC lineages, from age and gender matched donors. We measured the basal traction forces applied by each of these cells onto compliant hydrogels of different stiffness (4, 8, 12, 25 kPa). Although the range of force generation by SMCs suggested some heterogeneity, we observed that: 1. the traction forces were significantly larger on substrates of larger stiffness; 2. traction forces in AnevPrim were significantly higher than in AoPrim cells. We modelled computationally the dynamic force generation process in SMCs using the motor-clutch model and found that it accounts well for the stiffness-dependent traction forces. The existence of larger traction forces in the AnevPrim SMCs were related to the larger size of cells in these lineages. We conclude that phenotype changes occurring in ATAA, which were previously known to reduce the expression of elongated and contractile SMCs (rendering SMCs less responsive to vasoactive agents), tend also to induce stronger SMCs. Future work aims at understanding the causes of this alteration process in aortic aneurysms.

Laser Ablation-Assisted Synthesis of Poly (Vinylidene Fluoride)/Au Nanocomposites: Crystalline Phase and Micromechanical Finite Element Analysis.

In this research, piezoelectric polymer nanocomposite films were produced through solution mixing of laser-synthesized Au nanoparticles in poly (vinylidene fluoride) (PVDF) matrix. Synthetization of Au nanoparticles was carried out by laser ablation in N-methyle-2-pyrrolidene (NMP), and then it was added to PVDF: NMP solution with three different concentrations. Fourier transformed infrared spectroscopy (FTIR) and X-ray diffraction (XRD) were carried out in order to study the crystalline structure of the nanocomposite films. Results revealed that a remakable change in crystalline polymorph of PVDF has occurred by embedding Au nanoparticles into the polymer matrix. The polar phase fraction was greatly improved by increasing the loading content of Au nanoparticle. Thermogravimetric analysis (TGA) showed that the nanocomposite films are more resistant to high temperature and thermal degradation. An increment in dielectric constant was noticed by increasing the concentration of Au nanoparticles through capacitance, inductance, and resistance (LCR) measurement. Moreover, the mechanical properties of nanocomposites were numerically anticipated by a finite element based micromechanical model. The results reveal an enhancement in both tensile and shear moduli.

A visco-hyperelastic constitutive model and its application in bovine tongue tissue.

Material properties of the human tongue tissue have a significant role in understanding its function in speech, respiration, suckling, and swallowing. Tongue as a combination of various muscles is surrounded by the mucous membrane and is a complicated architecture to study. As a first step before the quantitative mechanical characterization of human tongue tissues, the passive biomechanical properties in the superior longitudinal muscle (SLM) and the mucous tissues of a bovine tongue have been measured. Since the rate of loading has a sizeable contribution to the resultant stress of soft tissues, the rate dependent behavior of tongue tissues has been investigated via uniaxial tension tests (UTTs). A method to determine the mechanical properties of transversely isotropic tissues using UTTs and inverse finite element (FE) method has been proposed. Assuming the strain energy as a general nonlinear relationship with respect to the stretch and the rate of stretch, two visco-hyperelastic constitutive laws (CLs) have been proposed for isotropic and transversely isotropic soft tissues to model their stress-stretch behavior. Both of them have been implemented in ABAQUS explicit through coding a user-defined material subroutine called VUMAT and the experimental stress-stretch points have been well tracked by the results of FE analyses. It has been demonstrated that the proposed laws make a good description of the viscous nature of tongue tissues. Reliability of the proposed models has been compared with similar nonlinear visco-hyperelastic CLs.

A new model of passive muscle tissue integrating Collagen Fibers: Consequences for muscle behavior analysis.

Mechanical properties of muscle tissue are crucial in biomechanical modeling of the human body. Muscle tissue is a combination of Muscle Fibers (MFs) and connective tissue including collagen and elastin fibers. There are a lot of passive muscle models in the literature but most of them do not consider any distinction between Collagen Fibers (CFs) and MFs, or at least do not consider the mechanical effects of the CFs on the Three-Dimensional (3-D) behavior of tissue. As a consequence, unfortunately, they cannot describe the observed stress-stretch behavior in tissue in which the reinforced direction is not parallel to the MF direction. In this research, a new passive muscle model is presented, in which the CFs are separately considered in the formulation: they are distributed along the MFs in a cross-shaped arrangement. Thanks to this new architecture, a mechanical reinforced direction can be proposed, in addition to the muscle main fiber direction. The passive biomechanical properties of the genioglossus muscle of a bovine tongue have been measured under uniaxial tensile tests. To characterize the 3-D response of the tissue, tests have been performed in different directions with respect to the MF direction. Moreover, a Constitutive Law (CL) has been proposed for modeling this behavior. In addition to our measurements on the bovine genioglossus muscle, results published in the literature on experimental data from the longissimus dorsi of pigs and the chicken pectoralis muscle were used to appraise the applicability of the proposed model. It is demonstrated that the proposed passive muscle model provides an accurate description of the fiber-oriented nature of muscle tissue. Also, it has been shown that using Finite Element Analysis (FEA) it might be possible to predict the angle θ between CFs and MF.