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.

AKR1D1 is a novel regulator of metabolic phenotype in human hepatocytes and is dysregulated in non-alcoholic fatty liver disease.

OBJECTIVE: Non-alcoholic fatty liver disease (NAFLD) is the hepatic manifestation of metabolic syndrome. Steroid hormones and bile acids are potent regulators of hepatic carbohydrate and lipid metabolism. Steroid 5ß-reductase (AKR1D1) is highly expressed in human liver where it inactivates steroid hormones and catalyzes a fundamental step in bile acid synthesis. METHODS: Human liver biopsies were obtained from 34 obese patients and AKR1D1 mRNA expression levels were measured using qPCR. Genetic manipulation of AKR1D1 was performed in human HepG2 and Huh7 liver cell lines. Metabolic assessments were made using transcriptome analysis, western blotting, mass spectrometry, clinical biochemistry, and enzyme immunoassays. RESULTS: In human liver biopsies, AKR1D1 expression decreased with advancing steatosis, fibrosis and inflammation. Expression was decreased in patients with type 2 diabetes. In human liver cell lines, AKR1D1 knockdown decreased primary bile acid biosynthesis and steroid hormone clearance. RNA-sequencing identified disruption of key metabolic pathways, including insulin action and fatty acid metabolism. AKR1D1 knockdown increased hepatocyte triglyceride accumulation, insulin sensitivity, and glycogen synthesis, through increased de novo lipogenesis and decreased ß-oxidation, fueling hepatocyte inflammation. Pharmacological manipulation of bile acid receptor activation prevented the induction of lipogenic and carbohydrate genes, suggesting that the observed metabolic phenotype is driven through bile acid rather than steroid hormone availability. CONCLUSIONS: Genetic manipulation of AKR1D1 regulates the metabolic phenotype of human hepatoma cell lines, driving steatosis and inflammation. Taken together, the observation that AKR1D1 mRNA is down-regulated with advancing NAFLD suggests that it may have a crucial role in the pathogenesis and progression of the disease.

When is it MODY? Challenges in the Interpretation of Sequence Variants in MODY Genes.

The genomics revolution has raised more questions than it has provided answers. Big data from large population-scale resequencing studies are increasingly deconstructing classic notions of Mendelian disease genetics, which support a simplistic correlation between mutational severity and phenotypic outcome. The boundaries are being blurred as the body of evidence showing monogenic disease-causing alleles in healthy genomes, and in the genomes of individu-als with increased common complex disease risk, continues to grow. In this review, we focus on the newly emerging challenges which pertain to the interpretation of sequence variants in genes implicated in the pathogenesis of maturity-onset diabetes of the young (MODY), a presumed mono-genic form of diabetes characterized by Mendelian inheritance. These challenges highlight the complexities surrounding the assignments of pathogenicity, in particular to rare protein-alerting variants, and bring to the forefront some profound clinical diagnostic implications. As MODY is both genetically and clinically heterogeneous, an accurate molecular diagnosis and cautious extrapolation of sequence data are critical to effective disease management and treatment. The biological and translational value of sequence information can only be attained by adopting a multitude of confirmatory analyses, which interrogate variant implication in disease from every possible angle. Indeed, studies which have effectively detected rare damaging variants in known MODY genes in normoglycemic individuals question the existence of a sin-gle gene mutation scenario: does monogenic diabetes exist when the genetic culprits of MODY have been systematical-ly identified in individuals without MODY?

Describing sequencing results of structural chromosome rearrangements with a suggested next-generation cytogenetic nomenclature.

With recent rapid advances in genomic technologies, precise delineation of structural chromosome rearrangements at the nucleotide level is becoming increasingly feasible. In this era of "next-generation cytogenetics" (i.e., an integration of traditional cytogenetic techniques and next-generation sequencing), a consensus nomenclature is essential for accurate communication and data sharing. Currently, nomenclature for describing the sequencing data of these aberrations is lacking. Herein, we present a system called Next-Gen Cytogenetic Nomenclature, which is concordant with the International System for Human Cytogenetic Nomenclature (2013). This system starts with the alignment of rearrangement sequences by BLAT or BLAST (alignment tools) and arrives at a concise and detailed description of chromosomal changes. To facilitate usage and implementation of this nomenclature, we are developing a program designated BLA(S)T Output Sequence Tool of Nomenclature (BOSToN), a demonstrative version of which is accessible online. A standardized characterization of structural chromosomal rearrangements is essential both for research analyses and for application in the clinical setting.