Unraveling a history of overlap: A phenotypic comparison of RBCK1-related disease and glycogen storage disease type IV

RBCK1-related disease is a rare, multisystemic disorder for which our current understanding of the natural history is limited. A number of individuals initially carried clinical diagnoses of glycogen storage disease IV (GSD IV), but were later found to harbor RBCK1 pathogenic variants, demonstrating challenges of correctly diagnosing RBCK1-related disease. This study carried out a phenotypic comparison between RBCK1-related disease and GSD IV to identify features that clinically differentiate these diagnoses. Literature review and retrospective chart review identified 25 individuals with RBCK1-related disease and 36 with the neuromuscular subtype of GSD IV. Clinical features were evaluated to assess for statistically significant differences between the conditions. At a system level, any cardiac, autoinflammation, immunodeficiency, growth, or dermatologic involvement were suggestive of RBCK1, whereas any respiratory involvement suggested GSD IV. Several features warrant further exploration as predictors of RBCK1, such as generalized weakness, heart transplant, and recurrent infections, among others. Distinguishing RBCK1-related disease will facilitate correct diagnoses and pave the way for accurately identifying affected individuals, as well as for developing management recommendations, treatment, and an enhanced understanding of the natural history. This knowledge may also inform which individuals thought to have GSD IV should undergo reevaluation for RBCK1.

SVInterpreter: A Comprehensive Topologically Associated Domain-Based Clinical Outcome Prediction Tool for Balanced and Unbalanced Structural Variants

With the advent of genomic sequencing, a number of balanced and unbalanced structural variants (SVs) can be detected per individual. Mainly due to incompleteness and the scattered nature of the available annotation data of the human genome, manual interpretation of the SV's clinical significance is laborious and cumbersome. Since bioinformatic tools developed for this task are limited, a comprehensive tool to assist clinical outcome prediction of SVs is warranted. Herein, we present SVInterpreter, a free Web application, which analyzes both balanced and unbalanced SVs using topologically associated domains (TADs) as genome units. Among others, gene-associated data (as function and dosage sensitivity), phenotype similarity scores, and copy number variants (CNVs) scoring metrics are retrieved for an informed SV interpretation. For evaluation, we retrospectively applied SVInterpreter to 97 balanced (translocations and inversions) and 125 unbalanced (deletions, duplications, and insertions) previously published SVs, and 145 SVs identified from 20 clinical samples. Our results showed the ability of SVInterpreter to support the evaluation of SVs by (1) confirming more than half of the predictions of the original studies, (2) decreasing 40% of the variants of uncertain significance, and (3) indicating several potential position effect events. To our knowledge, SVInterpreter is the most comprehensive TAD-based tool to identify the possible disease-causing candidate genes and to assist prediction of the clinical outcome of SVs. SVInterpreter is available at http://dgrctools-insa.min-saude.pt/cgi-bin/SVInterpreter.py.