The renal outer medullary potassium (ROMK) channel is essential for potassium transport in the kidney, and its dysfunction is associated with a salt-wasting disorder known as Bartter syndrome. Despite its physiological significance, we lack a mechanistic understanding of the molecular defects in ROMK underlying most Bartter syndrome-associated mutations. To this end, we employed a ROMK-dependent yeast growth assay and tested single amino acid variants selected by a series of computational tools representative of different approaches to predict each variants' pathogenicity. In one approach, we used in silico saturation mutagenesis, i.e. the scanning of all possible single amino acid substitutions at all sequence positions to estimate their impact on function, and then employed a new machine learning classifier known as Rhapsody. We also used two additional tools, EVmutation and Polyphen-2, which permitted us to make consensus predictions on the pathogenicity of single amino acid variants in ROMK. Experimental tests performed for selected mutants in different classes validated the vast majority of our predictions and provided insights into variants implicated in ROMK dysfunction. On a broader scope, our analysis suggests that consolidation of data from complementary computational approaches provides an improved and facile method to predict the severity of an amino acid substitution and may help accelerate the identification of disease-causing mutations in any protein. Author summaryAs the number of sequenced human genomes rises, a major challenge is to identify which single amino acid variations in a protein affect function and predispose individuals to disease. While predictive algorithms are available for this purpose, a comparative analysis of recently developed algorithms has not been adequately performed, nor is it clear whether combining algorithms would improve predictive power. To this end, we compared the efficacy of three publicly available algorithms and applied the results to Bartter syndrome, PLOS COMPUTATIONAL BIOLOGY PLOS Computational Biology | https://doi.Citation: Ponzoni L, Nguyen NH, Bahar I, Brodsky JL (2020) Complementary computational and experimental evaluation of missense variants in the ROMK potassium channel. PLoS Comput Biol 16 (4): e1007749. https://doi.org/10.a human disease for which numerous poorly-characterized single amino acid variants have been identified and for which there is no cure. In silico saturation mutagenesis, i.e., the computational prediction of pathogenesis for every possible amino acid substitution, allowed us to experimentally test predictions by measuring the activity of an ion channel linked to Bartter syndrome. Based on data from blinded experiments, we discovered that Rhapsody and EVmutation successfully predicted deleterious mutations. Moreover, Rhapsody-which takes into account evolutionary as well as structural and dynamic considerations-predicted that >90% of known Bartter syndrome mutations are deleterious. Overall, our data will aid investigators who wish...
Current methods for the evaluation of retention of endothelial cells seeded on vascular grafts are limited by the inability to specifically identify and quantitate seeded cells on a long-term basis. To address this problem we developed a method of quantification of graft surface coverage using genetic labeling of endothelial cells combined with computer-assisted image analysis. Rabbit aortic endothelial cells were transduced with a marker gene (lac-Z) and seeded on polytetrafluoroethylene grafts. After histochemical staining in which the genetically labeled cells turn blue, computer-assisted image analysis was used to measure the percentage of graft surface covered by the seeded cells. The utility of the method was evaluated by using it to assess the effect on graft coverage of seeded cell density and by precoating with fibronectin. Quantification of surface area coverage was automated and reproducible both between scans and between observers. Use of this method allowed the determination of a linear correlation between cell density in the seeding suspension and graft coverage (r2 = 0.93, p less than 0.0001). The method also permitted confirmation of the positive contribution of fibronectin coating to graft coverage by seeded cells: 73% coverage coated versus 8% coverage uncoated (p less than 0.0001). The ability of this method to specifically identify genetically marked endothelial cells and their progeny makes it attractive for use in studies targeted at optimization of graft coverage in vivo.
Retroviral vector-mediated expression of plasminogen activators (PAs) from endothelial cells (ECs) has been proposed as a potential therapeutic approach for intravascular thrombosis. To define the potential for gene transfer to increase fibrinolytic activity in a primate system, baboon ECs were transduced with retroviral vectors expressing wild-type and glycosylphosphatidylinositol-anchored urokinase, as well as wild-type and serpin-resistant tissue PA (t-PA). Expression of either t-PA or urokinase was increased by one log over baseline levels. There was no specific effect of either t-PA or urokinase overexpression on endogenous t-PA, urokinase, or PA inhibitor 1 (PAI-1) expression. Recombinant urokinase could be anchored to the cell surface at a level eight-fold above that of receptor-bound urokinase. The majority of secreted urokinase accumulated in conditioned medium as a free proenzyme, whereas both wild-type and serpin-resistant t-PA accumulated almost exclusively in complexes with PAI-1. In most but not all of the assays, the urokinase vectors conferred PA activity above that of the t-PA vectors. These data show that PA synthesis and activity are specifically increased subsequent to retroviral vector-mediated gene transfer in primate ECs. However, definition of an optimal PA vector will require in vivo experimentation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.