A Novel Mutation of VPS33B Gene Associated with Incomplete Arthrogryposis-Renal Dysfunction-Cholestasis Phenotype

Arthrogryposis, renal dysfunction, cholestasis syndrome with a novel mutation in two siblings

This current case series adds to the spectrum of Arthrogryposis renal dysfunction cholestasis (ARC)-associated variants. Increased awareness and early genetic testing for ARC are suggested in cases with failure to thrive, renal tubular dysfunction, and rickets, even when the degree of cholestasis is mild. Prompt identification and intervention may improve the quality of life.

Two novel mutations in the VPS33B gene in a Chinese patient with arthrogryposis, renal dysfunction and cholestasis syndrome 1: A case report

BACKGROUND

The VPS33B (OMIM: 608552) gene is located on chromosome 15q26.1. We found a female infant with autosomal recessive arthrogryposis, renal dysfunction and cholestasis syndrome 1 (ARCS1) caused by mutation in VPS33B. The child was diagnosed with ARCS1 (OMIM: 208085) after the whole exome sequencing revealed two heterozygous mutations (c.96+1G>C, c.242delT) in the VPS33B gene.

CASE SUMMARY

We report a Chinese female infant with neonatal cholestasis disorder, who was eventually diagnosed with ARCS1 by genetic analysis. Genetic testing revealed two new mutations (c.96+1G>C and c.242delT) in VPS33B, which is the causal gene. The patient was compound heterozygous, and her parents were both heterozygous.

CONCLUSION

This study extends the mutational spectrum of the VPS33B gene to provide a molecular basis for the etiological diagnosis of ARCS1 and for genetic counseling of the family.

Mild Phenotype of Arthrogryposis, Renal Dysfunction, and Cholestasis Syndrome 1 Caused by a Novel VPS33B Variant

The arthrogryposis, renal dysfunction, and cholestasis syndrome (ARCS) is an autosomal recessive multisystem disease caused by variants in VPS33B or VIPAS39. The classical presentation includes congenital joint contractures, renal tubular dysfunction, cholestasis, and early death. Additional features include ichthyosis, central nervous system malformations, platelet dysfunction, and severe failure to thrive. We studied three patients with cholestasis, increased aminotransferases, normal gamma-glutamyl transferase, and developmental and language delay. Whole exome sequencing analysis identified VPS33B variants in all patients: patients 1 and 2 presented a novel homozygous variant at position c.1148T>A. p.(Ile383Asn), and patient 3 was compound heterozygous for the same c.1148T>A. variant, in addition to the c.940-2A>G. variant. ARCS is compatible with the symptomatology presented by the studied patients. However, most patients that have been described in the literature with ARCS had severe failure to thrive and died in the first 6 months of life. The three patients studied here have a mild ARCS phenotype with prolonged survival. Consequently, we believe that the molecular analysis of the VPS33B and VIPAS39 should be considered in patients with normal gamma-glutamyl transferase cholestasis.

A machine learning approach based on ACMG/AMP guidelines for genomic variant classification and prioritization

Genomic variant interpretation is a critical step of the diagnostic procedure, often supported by the application of tools that may predict the damaging impact of each variant or provide a guidelines-based classification. We propose the application of Machine Learning methodologies, in particular Penalized Logistic Regression, to support variant classification and prioritization. Our approach combines ACMG/AMP guidelines for germline variant interpretation as well as variant annotation features and provides a probabilistic score of pathogenicity, thus supporting the prioritization and classification of variants that would be interpreted as uncertain by the ACMG/AMP guidelines. We compared different approaches in terms of variant prioritization and classification on different datasets, showing that our data-driven approach is able to solve more variant of uncertain significance (VUS) cases in comparison with guidelines-based approaches and in silico prediction tools.