Arginyl-asparagine

Ischemic stroke is the leading cause of disability and death worldwide. An effective therapeutic approach is urgently needed. Stroke-induced angiogenesis and neurogenesis are essential mechanisms in the long-term repair. Extracellular matrix proteins are also involved in tissue self-repair. Recently, a PHSRN (Pro-His-Ser-Arg-Asn) peptide from the fibronectin synergistic motif that can promote wound healing in epithelia and induce endothelial proliferation and cancer cell migration was identified. The therapeutic potential of this peptide in stroke is unknown. Here, we examined the potential of PHSRN in stroke therapy using an ischemic rat model of middle cerebral artery occlusion (MCAO). PHSRN reduced the infarct volume in MCAO rats, improved neurological function, and alleviated motor function impairment. PHSRN targeted the damaged brain region and distributed to endothelial cells after intraperitoneal injection. PHSRN significantly promoted angiogenesis and vascular endothelial growth factor secretion through activation of integrin α5β1 and its downstream intracellular signals, e.g., focal adhesion kinase, Ras, cRaf, and extracellular-signal-regulated kinase. PHSRN treatment also stimulated neurogenesis in MCAO rats, and maintained neuronal survival and neuronal morphologic complexity via induction of VEGF secretion. Together, these results provide insights into the role of integrin α5β1 following ischemia and support the feasibility of using PHSRN peptide in stroke therapy.

Engineering of biomimetic motives have emerged as promising approaches to improving cells' binding properties of biomaterials for tissue engineering and regenerative medicine. In this study, a bio-adhesive ligand including cell-binding domains of human fibronectin (FN) was engineered using recombinant protein technology, a major extracellular matrix (ECM) protein that interacts with a variety of integrins cell-surface's receptors and other ECM proteins through specific binding domains. 9th and 10th fibronectin type III repeat containing Arginine-Glycine-Aspartic acid (RGD) and Pro-His-Ser-Arg-Asn (PHSRN) synergic site (FNIII9-10) were expressed in fusion with a Colored Multi Affinity Tag (CMAT) to develop a simplified production and characterization process. A recombinant fragment was produced in the bacterial system using E. coli with high yield purified protein by double affinity chromatography. Bio-adhesive surfaces were developed by passive coating of produced fragment onto non adhesive surfaces model. The recombinant fusion protein (CMAT-FNIII9/10) demonstrated an accurate monitoring capability during expression purification and adsorption assay. Finally, biological activity of recombinant FNIII9/10 was validated by cellular adhesion assay. Binding to α5β1 integrins were successfully validated using a produced fragment as a ligand. These results are robust supports to the rational development of bioactivation strategies for biomedical and biotechnological applications.

Surrogate methods for rapid calculation of femoral strain are limited by the scope of the training data. We compared a newly developed training-free method based on the superposition principle (Superposition Principle Method, SPM) and popular surrogate methods for calculating femoral strain during activity. Finite-element calculations of femoral strain, muscle, and joint forces for five different activity types were obtained previously. Multi-linear regression, multivariate adaptive regression splines, and Gaussian process were trained for 50, 100, 200, and 300 random samples generated using Latin Hypercube (LH) and Design of Experiment (DOE) sampling. The SPM method used weighted linear combinations of 173 activity-independent finite-element analyses accounting for each muscle and hip contact force. Across the surrogate methods, we found that 200 DOE samples consistently provided low error (RMSE < 100 µε), with model construction time ranging from 3.8 to 63.3 h and prediction time ranging from 6 to 1236 s per activity. The SPM method provided the lowest error (RMSE = 40 µε), the fastest model construction time (3.2 h) and the second fastest prediction time per activity (36 s) after Multi-linear Regression (6 s). The SPM method will enable large numerical studies of femoral strain and will narrow the gap between bone strain prediction and real-time clinical applications.