A yeast-based complementation assay elucidates the functional impact of 200 missense variants in human PSAT1.
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ABSTRACT:
SUBMITTER: Sirr A
PROVIDER: S-EPMC7444316 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Deficiency of argininosuccinate lyase (ASL) causes argininosuccinic aciduria, an urea cycle defect that may present with a severe neonatal onset form or with a late onset phenotype. To date phenotype-genotype correlations are still not clear because biochemical assays of ASL activity correlate poorly with clinical severity in patients. We employed a yeast-based functional complementation assay to assess the pathogenicity of 12 missense ASL mutations, to establish genotype-phenotype correlations, and to screen for intragenic complementation. Rather than determining ASL enzyme activity directly, we have measured the growth rate in arginine-free medium of a yeast ASL(null) strain transformed with individual mutant ASL alleles. Individual haploid strains were also mated to obtain diploid, "compound heterozygous" yeast. We show that the late onset phenotypes arise in patients because they harbor individual alleles retaining high residual enzymatic activity or because of intragenic complementation among different mutated alleles. In these cases complementation occurs because in the hybrid tetrameric enzyme at least one active site without mutations can be formed or because the differently mutated alleles can stabilize each other, resulting in partial recovery of enzymatic activity. Functional complementation in yeast is simple and reproducible and allows the analysis of large numbers of mutant alleles. Moreover, it can be easily adapted for the analysis of mutations in other genes involved in urea cycle disorders.
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Project description:MUTYH-associated polyposis (MAP) is an adenomatous polyposis transmitted in an autosomal-recessive pattern, involving biallelic inactivation of the MUTYH gene. Loss of a functional MUTYH protein will result in the accumulation of G:T mismatched DNA caused by oxidative damage. Although p.Y179C and p.G396D are the two most prevalent MUTYH variants, more than 200 missense variants have been detected. It is difficult to determine whether these variants are disease-causing mutations or single-nucleotide polymorphisms. To understand the functional consequences of these variants, we generated 47 MUTYH gene variants via site-directed mutagenesis, expressed the encoded proteins in MutY-disrupted Escherichia coli, and assessed their abilities to complement the functional deficiency in the E. coli by monitoring spontaneous mutation rates. Although the majority of variants exhibited intermediate complementation relative to the wild type, some variants severely interfered with this complementation. However, some variants retained functioning similar to the wild type. In silico predictions of functional effects demonstrated a good correlation. Structural prediction of MUTYH based on the MutY protein structure allowed us to interpret effects on the protein stability or catalytic activity. These data will be useful for evaluating the functional consequences of missense MUTYH variants detected in patients with suspected MAP.
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Project description:AimThe study aimed to develop a scalable dual-fluorescence assay in cells to enable the functional interpretation of HNF-4α missense variants identified in exome sequencing, which can be used to guide clinical diagnosis.MethodsUsing mOrange2 and GFP fluorescence proteins to track the expression of HNF-4α (HNF-4α-mOrange2) and reporter activity under the control of the HNF-1α promoter (pHNF1A-GFP), respectively, we designed a dual-fluorescence assay to evaluate the expression level, cellular localization, and transcriptional function of HNF-4α simultaneously in live cells. To assess the scalable characteristic of the assay, a small library containing five previously reported mutations and wild-type HNF-4α was constructed. Cells infected with this library were sorted into different populations through fluorescence-activated cell sorting (FACS) according to the transcription activity and expression abundance. Cloning and Sanger sequencing were used to detect the mutations of the different groups. High content screening (HCS) assay was used for the validation of individual mutants in the function and expression point of view.ResultsHNF-4α-mOrange2 exhibited nuclear localization and transactivation capability on the HNF-1α promoter as physical HNF-4α does. The expression of HNF-4α-mOrange2 shows a 6-fold induction of GFP expression compared to the control without HNF-4α-mOrange2, which was significantly abolished by the known loss-of-function mutant M373R. The different performances of wild-type and mutant M373R made them distinguishable in the FACS system, empowering the scalable capability of this assay for classifying large numbers of variants combining functional stratification and sequencing. Further application of the assay in the small library showed that three cell populations were seen grouped as Normal (same transactivation as wild type), Reducedexp_nor (reduced transactivation with normal or higher expression), and Reducedexp_low (reduced transactivation with lower expression). Subsequently, Sanger sequencing showed that wild-type HNF-4α was in the Normal group, two mutations (M373R and G79C) were enriched in the Reducedexp_nor group, and three mutations (C115S, L272P, and F83C) belonged to the Reducedexp_low group. These results were validated by further imaging data using HCS assay for individual mutation.ConclusionsOur study proposes a scalable and informative approach for the characterization of the variants in HNF-4α genes in a quantitative and high-throughput manner.
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Project description:Many individuals tested for inherited cancer susceptibility at the BRCA1 gene locus are discovered to have variants of unknown clinical significance (UCVs). Most UCVs cause a single amino acid residue (missense) change in the BRCA1 protein. They can be biochemically assayed, but such evaluations are time-consuming and labor-intensive. Computational methods that classify and suggest explanations for UCV impact on protein function can complement functional tests. Here we describe a supervised learning approach to classification of BRCA1 UCVs. Using a novel combination of 16 predictive features, the algorithms were applied to retrospectively classify the impact of 36 BRCA1 C-terminal (BRCT) domain UCVs biochemically assayed to measure transactivation function and to blindly classify 54 documented UCVs. Majority vote of three supervised learning algorithms is in agreement with the assay for more than 94% of the UCVs. Two UCVs found deleterious by both the assay and the classifiers reveal a previously uncharacterized putative binding site. Clinicians may soon be able to use computational classifiers such as those described here to better inform patients. These classifiers can be adapted to other cancer susceptibility genes and systematically applied to prioritize the growing number of potential causative loci and variants found by large-scale disease association studies.
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