SLC6A1 variant pathogenicity, molecular function and phenotype: a genetic and clinical analysis.

Genetic variants in the SLC6A1 gene can cause a broad phenotypic disease spectrum by altering the protein function. Thus, systematically curated clinically relevant genotype-phenotype associations are needed to understand the disease mechanism and improve therapeutic decision-making. We aggregated genetic and clinical data from 172 individuals with likely pathogenic/pathogenic (lp/p) SLC6A1 variants and functional data for 184 variants (14.1% lp/p). Clinical and functional data were available for a subset of 126 individuals. We explored the potential associations of variant positions on the GAT1 3D structure with variant pathogenicity, altered molecular function and phenotype severity using bioinformatic approaches. The GAT1 transmembrane domains 1, 6 and extracellular loop 4 (EL4) were enriched for patient over population variants. Across functionally tested missense variants (n = 156), the spatial proximity from the ligand was associated with loss-of-function in the GAT1 transporter activity. For variants with complete loss of in vitro GABA uptake, we found a 4.6-fold enrichment in patients having severe disease versus non-severe disease (P = 2.9 × 10-3, 95% confidence interval: 1.5-15.3). In summary, we delineated associations between the 3D structure and variant pathogenicity, variant function and phenotype in SLC6A1-related disorders. This knowledge supports biology-informed variant interpretation and research on GAT1 function. All our data can be interactively explored in the SLC6A1 portal (https://slc6a1-portal.broadinstitute.org/).

Image Classification of Degraded Polysorbate, Protein and Silicone Oil Sub-Visible Particles Detected by Flow-Imaging Microscopy in Biopharmaceuticals Using a Convolutional Neural Network Model.

Degradation of polysorbates in biopharmaceutical formulations can induce the formation of sub-visible particles (SvPs) in the form of free-fatty acids (FFAs) and potentially protein aggregates. Flow-imaging microscopy (FIM) is one of the most common techniques for enumerating and characterizing the SvPs, allowing for collection of image data of the SvPs in the size ranges of two to several hundred micrometers. The vast amounts of data obtained with FIM do not allow for rapid manual characterization by an experienced analyst and can be ambiguous. In this work, we present the application of a custom convolutional neural network (CNN) for classification of SvP images of FFAs, proteinaceous particles and silicon oil droplets, by FIM. The network was then used to predict the composition of artificially pooled test samples of unknown and labeled data with varying compositions. Minor misclassifications were observed between the FFAs and proteinaceous particles, considered tolerable for application to pharmaceutical development. The network is considered to be suitable for fast and robust classification of the most common SvPs found during FIM analysis.

Bi-allelic loss-of-function OBSCN variants predispose individuals to severe recurrent rhabdomyolysis.

Rhabdomyolysis is the acute breakdown of skeletal myofibres in response to an initiating factor, most commonly toxins and over exertion. A variety of genetic disorders predispose to rhabdomyolysis through different pathogenic mechanisms, particularly in patients with recurrent episodes. However, most cases remain without a genetic diagnosis. Here we present six patients who presented with severe and recurrent rhabdomyolysis, usually with onset in the teenage years; other features included a history of myalgia and muscle cramps. We identified 10 bi-allelic loss-of-function variants in the gene encoding obscurin (OBSCN) predisposing individuals to recurrent rhabdomyolysis. We show reduced expression of OBSCN and loss of obscurin protein in patient muscle. Obscurin is proposed to be involved in sarcoplasmic reticulum function and Ca2+ handling. Patient cultured myoblasts appear more susceptible to starvation as evidenced by a greater decreased in sarcoplasmic reticulum Ca2+ content compared to control myoblasts. This likely reflects a lower efficiency when pumping Ca2+ back into the sarcoplasmic reticulum and/or a decrease in Ca2+ sarcoplasmic reticulum storage ability when metabolism is diminished. OSBCN variants have previously been associated with cardiomyopathies. None of the patients presented with a cardiomyopathy and cardiac examinations were normal in all cases in which cardiac function was assessed. There was also no history of cardiomyopathy in first degree relatives, in particular in any of the carrier parents. This cohort is relatively young, thus follow-up studies and the identification of additional cases with bi-allelic null OBSCN variants will further delineate OBSCN-related disease and the clinical course of disease.

Reproductive outcomes in individuals with chromosomal reciprocal translocations.

PURPOSE: Patients with reciprocal balanced translocations (RBT) have a risk for recurrent pregnancy losses (RPL), affected child, and infertility. Currently, genetic counseling is based on karyotypes found among the products of conception (POC), although factors influencing the success of assisted reproductive technologies (ART) in RBT couples are not established. METHODS: Cytogenetic results from 261 POC and offspring of the parents (113 women and 90 men) with RBT were evaluated. Chromosome segregation modes and number of euploid embryos were assessed in couples undergoing in vitro fertilization. RESULTS: Patients with translocations involving an acrocentric chromosome have a higher risk of unbalanced gametes caused by a 3:1 segregation. Female RBT patients have a statistically higher risk of aneuploidy due to an interchromosomal effect. The rate of euploid embryos is low due to meiosis I malsegregation of RBT, meiosis II nondisjunction, additional whole chromosome or segmental aneusomies. RBT patients with RPL have a higher rate of miscarriage of euploid fetuses with RBT. CONCLUSION: Chromosome-specific factors, female gender, age, and history of RPL are the risk elements influencing pregnancy and in vitro fertilization success in RBT patients. Chromosomal microarray analysis of POC is necessary to provide an accurate and timely diagnosis for patients with adverse reproductive outcomes.

A familial case of CAMK2B mutation with variable expressivity.

Variants in CAMK2-associated genes have recently been implicated in neurodevelopmental disorders and intellectual disability. The clinical manifestations reported in patients with mutations in these genes include intellectual disability (ranging from mild to severe), global developmental delay, seizures, delayed speech, behavioral abnormalities, hypotonia, episodic ataxia, progressive cerebellar atrophy, visual impairments, and gastrointestinal issues. Phenotypic heterogeneity has been postulated. We present a child with neurodevelopmental disorder caused by a pathogenic CAMK2B variant inherited from a healthy mother. A more mildly affected sib was determined to have the same variant. Monoallelic mutations in CAMK2B in patients have previously only been reported as de novo mutations. This report adds to the clinical phenotypic spectrum of the disease and demonstrates intrafamilial variability of expression of a CAMK2B mutation.

Learning joint intensity-depth sparse representations.

This paper presents a method for learning overcomplete dictionaries of atoms composed of two modalities that describe a 3D scene: 1) image intensity and 2) scene depth. We propose a novel joint basis pursuit (JBP) algorithm that finds related sparse features in two modalities using conic programming and we integrate it into a two-step dictionary learning algorithm. The JBP differs from related convex algorithms because it finds joint sparsity models with different atoms and different coefficient values for intensity and depth. This is crucial for recovering generative models where the same sparse underlying causes (3D features) give rise to different signals (intensity and depth). We give a bound for recovery error of sparse coefficients obtained by JBP, and show numerically that JBP is superior to the group lasso algorithm. When applied to the Middlebury depth-intensity database, our learning algorithm converges to a set of related features, such as pairs of depth and intensity edges or image textures and depth slants. Finally, we show that JBP outperforms state of the art methods on depth inpainting for time-of-flight and Microsoft Kinect 3D data.