Structural and biophysical characterization of transcription factor HNF-1A as a tool to study MODY3 diabetes variants

Abstract Hepatocyte nuclear factor 1A (HNF-1A) is a transcription factor expressed in several embryonic and adult tissues, modulating the expression of numerous target genes. Pathogenic variants in the HNF1A gene are known to cause maturity-onset diabetes of the young 3 (MODY3 or HNF1A MODY), a disease characterized by dominant inheritance, age of onset before 25 to 35 years of age, and pancreatic β-cell dysfunction. A precise diagnosis can alter management of this disease, as insulin can be exchanged with sulfonylurea tablets and genetic counseling differs from polygenic forms of diabetes. Therefore, more knowledge on the mechanisms of HNF-1A function and the level of pathogenicity of the numerous HNF1A variants is required for precise diagnostics. Here, we structurally and biophysically characterized an HNF-1A protein containing both the DNA-binding domain and the dimerization domain, and determined the folding and DNA-binding capacity of two established MODY3 HNF-1A variant proteins (P112L, R263C) and one variant of unknown significance (N266S). All three variants showed reduced functionality compared to the WT protein. Furthermore, while the R263C and N266S variants displayed reduced binding to an HNF-1A target promoter, we found the P112L variant was unstable in vitro and in cells. Our results support and mechanistically explain disease causality for these investigated variants and present a novel approach for the dissection of structurally unstable and DNA-binding defective variants. This study indicates that structural and biochemical investigation of HNF-1A is a valuable tool in reliable variant classification needed for precision diabetes diagnostics and management

STUB1 mutations in autosomal recessive ataxias – evidence for mutation-specific clinical heterogeneity

Background A subset of hereditary cerebellar ataxias is inherited as autosomal recessive traits (ARCAs). Classification of recessive ataxias due to phenotypic differences in the cerebellum and cerebellar structures is constantly evolving due to new identified disease genes. Recently, reports have linked mutations in genes involved in ubiquitination (RNF216, OTUD4, STUB1) to ARCA with hypogonadism. Methods and results With a combination of homozygozity mapping and exome sequencing, we identified three mutations in STUB1 in two families with ARCA and cognitive impairment; a homozygous missense variant (c.194A > G, p.Asn65Ser) that segregated in three affected siblings, and a missense change (c.82G > A, p.Glu28Lys) which was inherited in trans with a nonsense mutation (c.430A > T, p.Lys144Ter) in another patient. STUB1 encodes CHIP (C-terminus of Heat shock protein 70 – Interacting Protein), a dual function protein with a role in ubiquitination as a co-chaperone with heat shock proteins, and as an E3 ligase. We show that the p.Asn65Ser substitution impairs CHIP’s ability to ubiquitinate HSC70 in vitro, despite being able to self-ubiquitinate. These results are consistent with previous studies highlighting this as a critical residue for the interaction between CHIP and its co-chaperones. Furthermore, we show that the levels of CHIP are strongly reduced in vivo in patients’ fibroblasts compared to controls. Conclusions These results suggest that STUB1 mutations might cause disease by impacting not only the E3 ligase function, but also its protein interaction properties and protein amount. Whether the clinical heterogeneity seen in STUB1 ARCA can be related to the location of the mutations remains to be understood, but interestingly, all siblings with the p.Asn65Ser substitution showed a marked appearance of accelerated aging not previously described in STUB1 related ARCA, none display hormonal aberrations/clinical hypogonadism while some affected family members had diabetes, alopecia, uveitis and ulcerative colitis, further refining the spectrum of STUB1 related disease

The use of precision diagnostics for monogenic diabetes: a systematic review and expert opinion.

BACKGROUND: Monogenic diabetes presents opportunities for precision medicine but is underdiagnosed. This review systematically assessed the evidence for (1) clinical criteria and (2) methods for genetic testing for monogenic diabetes, summarized resources for (3) considering a gene or (4) variant as causal for monogenic diabetes, provided expert recommendations for (5) reporting of results; and reviewed (6) next steps after monogenic diabetes diagnosis and (7) challenges in precision medicine field. METHODS: Pubmed and Embase databases were searched (1990-2022) using inclusion/exclusion criteria for studies that sequenced one or more monogenic diabetes genes in at least 100 probands (Question 1), evaluated a non-obsolete genetic testing method to diagnose monogenic diabetes (Question 2). The risk of bias was assessed using the revised QUADAS-2 tool. Existing guidelines were summarized for questions 3-5, and review of studies for questions 6-7, supplemented by expert recommendations. Results were summarized in tables and informed recommendations for clinical practice. RESULTS: There are 100, 32, 36, and 14 studies included for questions 1, 2, 6, and 7 respectively. On this basis, four recommendations for who to test and five on how to test for monogenic diabetes are provided. Existing guidelines for variant curation and gene-disease validity curation are summarized. Reporting by gene names is recommended as an alternative to the term MODY. Key steps after making a genetic diagnosis and major gaps in our current knowledge are highlighted. CONCLUSIONS: We provide a synthesis of current evidence and expert opinion on how to use precision diagnostics to identify individuals with monogenic diabetes

Unsupervised clustering of missense variants in HNF1A using multidimensional functional data aids clinical interpretation

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

Unsupervised clustering of missense variants in HNF1A using multidimensional functional data aids clinical interpretation

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

Primary immunodeficiency diseases: Genomic approaches delineate heterogeneous Mendelian disorders

BACKGROUND: Primary immunodeficiency diseases (PIDDs) are clinically and genetically heterogeneous disorders thus far associated with mutations in more than 300 genes. The clinical phenotypes derived from distinct genotypes may overlap. Genetic etiology can be a prognostic indicator of disease severity and can influence treatment decisions. OBJECTIVE: To investigate the ability of whole-exome screening methods to detect disease-causing variants in individuals with PIDDs. METHODS: Individuals with PIDDs from 278 families from 22 countries were investigated using whole-exome sequencing (WES). Computational CNV prediction pipelines and an exome-tiling chromosomal microarray were also applied to identify intragenic copy number variants (CNVs). Analytic approaches initially focused on 475 known or candidate PIDD genes, but were non-exclusive and were further tailored based upon clinical data, family history and immunophenotyping. RESULTS: A likely molecular diagnosis was achieved in 110 (40%) unrelated probands. Clinical diagnosis was revised in about half (60/110) and management was directly altered in nearly a quarter (26/110) of families based on the molecular findings. Twelve PIDD-causing CNVs were detected, including seven smaller than 30 Kb that would not have been detected with conventional diagnostic CNV arrays. CONCLUSION: This high-throughput genomic approach enabled detection of disease-related variants in unexpected genes, permitted detection of low-grade constitutional, somatic and revertant mosaicism, and provided evidence of a mutational burden in mixed PIDD immunophenotypes