Hypertrophic cardiomyopathy (HCM) is a genetic disorder caused by mutations in sarcomeric proteins (excluding phenocopy). The causal genes in approximately one-third of the cases remain unknown. We identified a family comprised of 6 clinically affected members. The phenotype was characterized by early onset of symptoms, pronounced cardiac hypertrophy, and cardiac arrhythmias. We excluded MYH7, MYBPC3, TNNT2, and ACTC1 as the causal gene either by direct sequencing or by haplotype analysis. To map the putative candidate sarcomeric gene, we perforbold locusspecific haplotyping to detect cosegregation of the locus haplotype with the phenotype, followed by mutation screening. We genotyped 5 short-tandem-repeat markers that spanned a 4.4-centimorgan region on 4q26-q27 locus and encompassed myozenin 2 (MYOZ2), a Z-disk protein. The maximum logarithm of odds score was 2.03 (P.)500.0؍ All affected members shared a common haplotype, implicating MYOZ2 as the causal gene. To detect the causal mutation, we sequenced all exons and exon-intron boundaries of MYOZ2 in 10 family members and identified a T3 C missense mutation corresponding to S48P substitution, which cosegregated with inheritance of HCM (N.)6؍ It was absent in 4 clinically normal family members and in 658 additional normal individuals. To determine frequency of the MYOZ2 mutations in HCM, we sequenced MYOZ2 in 516 HCM probands and detected another missense mutation (I246M). It was absent in 2 normal family members and 517 controls. Both mutations affect highly conserved amino acids. We conclude MYOZ2 is a novel causal gene for human HCM.
BackgroundFermentation of xylose, the major component in hemicellulose, is essential for economic conversion of lignocellulosic biomass to fuels and chemicals. The yeast Scheffersomyces stipitis (formerly known as Pichia stipitis) has the highest known native capacity for xylose fermentation and possesses several genes for lignocellulose bioconversion in its genome. Understanding the metabolism of this yeast at a global scale, by reconstructing the genome scale metabolic model, is essential for manipulating its metabolic capabilities and for successful transfer of its capabilities to other industrial microbes.ResultsWe present a genome-scale metabolic model for Scheffersomyces stipitis, a native xylose utilizing yeast. The model was reconstructed based on genome sequence annotation, detailed experimental investigation and known yeast physiology. Macromolecular composition of Scheffersomyces stipitis biomass was estimated experimentally and its ability to grow on different carbon, nitrogen, sulphur and phosphorus sources was determined by phenotype microarrays. The compartmentalized model, developed based on an iterative procedure, accounted for 814 genes, 1371 reactions, and 971 metabolites. In silico computed growth rates were compared with high-throughput phenotyping data and the model could predict the qualitative outcomes in 74% of substrates investigated. Model simulations were used to identify the biosynthetic requirements for anaerobic growth of Scheffersomyces stipitis on glucose and the results were validated with published literature. The bottlenecks in Scheffersomyces stipitis metabolic network for xylose uptake and nucleotide cofactor recycling were identified by in silico flux variability analysis. The scope of the model in enhancing the mechanistic understanding of microbial metabolism is demonstrated by identifying a mechanism for mitochondrial respiration and oxidative phosphorylation.ConclusionThe genome-scale metabolic model developed for Scheffersomyces stipitis successfully predicted substrate utilization and anaerobic growth requirements. Useful insights were drawn on xylose metabolism, cofactor recycling and mechanism of mitochondrial respiration from model simulations. These insights can be applied for efficient xylose utilization and cofactor recycling in other industrial microorganisms. The developed model forms a basis for rational analysis and design of Scheffersomyces stipitis metabolic network for the production of fuels and chemicals from lignocellulosic biomass.
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.