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Unsupervised Clustering of Missense Variants in HNF1A Using Multidimensional Functional Data Aids Clinical Interpretation.

ABSTRACT: Exome sequencing in diabetes presents a diagnostic challenge because depending on frequency, functional impact, and genomic and environmental contexts, HNF1A variants can cause maturity-onset diabetes of the young (MODY), increase type 2 diabetes risk, or be benign. A correct diagnosis matters as it informs on treatment, progression, and family risk. We describe a multi-dimensional functional dataset of 73 HNF1A missense variants identified in exomes of 12,940 individuals. Our aim was to develop an analytical framework for stratifying variants along the HNF1A phenotypic continuum to facilitate diagnostic interpretation. HNF1A variant function was determined by four different molecular assays. Structure of the multi-dimensional dataset was explored using principal component analysis, k-means, and hierarchical clustering. Weights for tissue-specific isoform expression and functional domain were integrated. Functionally annotated variant subgroups were used to re-evaluate genetic diagnoses in national MODY diagnostic registries. HNF1A variants demonstrated a range of behaviors across the assays. The structure of the multi-parametric data was shaped primarily by transactivation. Using unsupervised learning methods, we obtained high-resolution functional clusters of the variants that separated known causal MODY variants from benign and type 2 diabetes risk variants and led to reclassification of 4% and 9% of HNF1A variants identified in the UK and Norway MODY diagnostic registries, respectively. Our proof-of-principle analyses facilitated informative stratification of HNF1A variants along the continuum, allowing improved evaluation of clinical significance, management, and precision medicine in diabetes clinics. Transcriptional activity appears a superior readout supporting pursuit of transactivation-centric experimental designs for high-throughput functional screens.

SUBMITTER: Althari S

PROVIDER: S-EPMC7536579 | biostudies-literature |

REPOSITORIES: biostudies-literature

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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.

Project description:Genetic variants in low-density lipoprotein receptor (LDLR) are known to cause familial hypercholesterolemia (FH), occurring in up to 1 in 200 people (Youngblom E. et al. 1993 and Nordestgaard BG et al. 34:3478-3490a, 2013) and leading to significant risk for heart disease. Clinical genomics testing using high-throughput sequencing is identifying novel genomic variants of uncertain significance (VUS) in individuals suspected of having FH, but for whom the causal link to the disease remains to be established (Nordestgaard BG et al. 34:3478-3490a, 2013). Unfortunately, experimental data about the atomic structure of the LDL binding domains of LDLR at extracellular pH does not exist. This leads to an inability to apply protein structure-based methods for assessing novel variants identified through genetic testing. Thus, the ambiguities in interpretation of LDLR variants are a barrier to achieving the expected clinical value for personalized genomics assays for management of FH. In this study, we integrated data from the literature and related cellular receptors to develop high-resolution models of full-length LDLR at extracellular conditions and use them to predict which VUS alter LDL binding. We believe that the functional effects of LDLR variants can be resolved using a combination of structural bioinformatics and functional assays, leading to a better correlation with clinical presentation. We have completed modeling of LDLR in two major physiologic conditions, generating detailed hypotheses for how each of the 1007 reported protein variants may affect function. KEY MESSAGES: • Hundreds of variants are observed in the LDLR, but most lack interpretation. • Molecular modeling is aided by biochemical knowledge. • We generated context-specific 3D protein models of LDLR. • Our models allowed mechanistic interpretation of many variants. • We interpreted both rare and common genomic variants in their physiologic context. • Effects of genomic variants are often context-specific.

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Unsupervised Clustering of Missense Variants in HNF1A Using Multidimensional Functional Data Aids Clinical Interpretation.

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