Isolated sulfite oxidase deficiency (ISOD) is a rare hereditary metabolic disease caused by absence of functional sulfite oxidase (SO) due to mutations of the SUOX gene. SO oxidizes toxic sulfite and sulfite accumulation is associated with neurological disorders, progressive brain atrophy and early death. Similarities of these neurological symptoms to abundant diseases like neonatal encephalopathy underlines the raising need to increase the awareness for ISOD. Here we report an interdisciplinary approach utilizing exome/genome data derived from gnomAD database as well as published variants to predict the pathogenic outcome of 303 naturally occurring SO missense variants and combining these with activity determination. We identified 15 novel ISOD-causing SO variants and generated a databank of pathogenic SO missense variants to support future diagnosis of ISOD patients. We found six inactive variants (W101G, H118Y, E197K, R217W, S427W, D512Y, Q518R) and seven (D110H, P119S, G121E, G130R, Y140C, R269H, Q396P, R459Q) with severe reduction in activity. Based on the Hardy-Weinberg-equilibrium and the combination of our results with published SO missense and protein truncating variants, we calculated the first comprehensive incidence rate for ISOD of 1 in 1,377,341 births and provide a pathogenicity score to 303 naturally occurring SO missense variants.
Keywords: Isolated sulfite oxidase deficiency; Machine learning; Molybdenum cofactor; Random forest classification; SUOX; Sulfite oxidase.
Copyright © 2021. Published by Elsevier Inc.