Network Analysis of Protein Adaptation: Modeling the Functional Impact of Multiple Mutations.

The evolution of new biochemical activities frequently involves complex dependencies between mutations and rapid evolutionary radiation. Mutation co-occurrence and covariation have previously been used to identify compensating mutations that are the result of physical contacts and preserve protein function and fold. Here, we model pairwise functional dependencies and higher order interactions that enable evolution of new protein functions. We use a network model to find complex dependencies between mutations resulting from evolutionary trade-offs and pleiotropic effects. We present a method to construct these networks and to identify functionally interacting mutations in both extant and reconstructed ancestral sequences (Network Analysis of Protein Adaptation). The time ordering of mutations can be incorporated into the networks through phylogenetic reconstruction. We apply NAPA to three distantly homologous ß-lactamase protein clusters (TEM, CTX-M-3, and OXA-51), each of which has experienced recent evolutionary radiation under substantially different selective pressures. By analyzing the network properties of each protein cluster, we identify key adaptive mutations, positive pairwise interactions, different adaptive solutions to the same selective pressure, and complex evolutionary trajectories likely to increase protein fitness. We also present evidence that incorporating information from phylogenetic reconstruction and ancestral sequence inference can reduce the number of spurious links in the network, whereas preserving overall network community structure. The analysis does not require structural or biochemical data. In contrast to function-preserving mutation dependencies, which are frequently from structural contacts, gain-of-function mutation dependencies are most commonly between residues distal in protein structure.

Three-Dimensional Vascular Network Assembly From Diabetic Patient-Derived Induced Pluripotent Stem Cells.

OBJECTIVE: In diabetics, hyperglycemia results in deficient endothelial progenitors and cells, leading to cardiovascular complications. We aim to engineer 3-dimensional (3D) vascular networks in synthetic hydrogels from type 1 diabetes mellitus (T1D) patient-derived human-induced pluripotent stem cells (hiPSCs), to serve as a transformative autologous vascular therapy for diabetic patients. APPROACH AND RESULTS: We validated and optimized an adherent, feeder-free differentiation procedure to derive early vascular cells (EVCs) with high portions of vascular endothelial cadherin-positive cells from hiPSCs. We demonstrate similar differentiation efficiency from hiPSCs derived from healthy donor and patients with T1D. T1D-hiPSC-derived vascular endothelial cadherin-positive cells can mature to functional endothelial cells-expressing mature markers: von Willebrand factor and endothelial nitric oxide synthase are capable of lectin binding and acetylated low-density lipoprotein uptake, form cords in Matrigel and respond to tumor necrosis factor-α. When embedded in engineered hyaluronic acid hydrogels, T1D-EVCs undergo morphogenesis and assemble into 3D networks. When encapsulated in a novel hypoxia-inducible hydrogel, T1D-EVCs respond to low oxygen and form 3D networks. As xenografts, T1D-EVCs incorporate into developing zebrafish vasculature. CONCLUSIONS: Using our robust protocol, we can direct efficient differentiation of T1D-hiPSC to EVCs. Early endothelial cells derived from T1D-hiPSC are functional when mature. T1D-EVCs self-assembled into 3D networks when embedded in hyaluronic acid and hypoxia-inducible hydrogels. The capability of T1D-EVCs to assemble into 3D networks in engineered matrices and to respond to a hypoxic microenvironment is a significant advancement for autologous vascular therapy in diabetic patients and has broad importance for tissue engineering.