Predicting disease severity in metachromatic leukodystrophy using protein activity and a patient phenotype matrix

Abstract

Background Metachromatic leukodystrophy (MLD) is a lysosomal storage disorder caused by mutations in the arylsulfatase A gene (ARSA) and categorized into three subtypes according to age of onset. The functional effect of most ARSA mutants remains unknown; better understanding of the genotype–phenotype relationship is required to support newborn screening (NBS) and guide treatment. Results We collected a patient data set from the literature that relates disease severity to ARSA genotype in 489 individuals with MLD. Patient-based data were used to develop a phenotype matrix that predicts MLD phenotype given ARSA alleles in a patient’s genotype with 76% accuracy. We then employed a high-throughput enzyme activity assay using mass spectrometry to explore the function of ARSA variants from the curated patient data set and the Genome Aggregation Database (gnomAD). We observed evidence that 36% of variants of unknown significance (VUS) in ARSA may be pathogenic. By classifying functional effects for 251 VUS from gnomAD, we reduced the incidence of genotypes of unknown significance (GUS) by over 98.5% in the overall population. Conclusions These results provide an additional tool for clinicians to anticipate the disease course in MLD patients, identifying individuals at high risk of severe disease to support treatment access. Our results suggest that more than 1 in 3 VUS in ARSA may be pathogenic. We show that combining genetic and biochemical information increases diagnostic yield. Our strategy may apply to other recessive diseases, providing a tool to address the challenge of interpreting VUS within genotype–phenotype relationships and NBS.

QAFI: A Novel Method for Quantitative Estimation of Missense Variant Impact Using Protein-Specific Predictors and Ensemble Learning

“…As we await the final evaluation results by the CAGI6 assessors, it is noteworthy that the QAFI versions show Pearson correlations with the experimental results. (Trinidad et al 2023) that are within the high range observed for our method (Supplementary Table S5).…”

“…1B) designed to improve the quantitative prediction of the functional impact of missense variants, an important goal in the evolution of in silico pathogenicity predictors (Masica and Karchin 2016;Diaz et al 2023) that clears the way for novel applications. For example, continuous scores can serve to predict disease severity in some diseases, like in the case of ARSA (Trinidad et al 2023) where it has fueled the corresponding CAGI6 challenge. They can also contribute to build more realistic fitness models in evolutionary studies, a field where in many cases only free energy estimates are employed (Echave and Wilke 2017).…”

“…B. Comparison of experimental values of mean percentage wild-type activity for ARSA challenge variants (Trinidad et al 2023) with predictions from QAFIMeta…”

Newborn screening in metachromatic leukodystrophy – European consensus-based recommendations on clinical management

“…The sensitivity and specificity of such studies are still under investigation. However, due to the genetic heterogeneity inherent in MLD and the presence of numerous rare and/or private variants especially in non-Caucasian groups, genetic screening has the risk of being less sensitive than biochemical screening methods [60].…”

Inventory of current practices regarding hematopoietic stem cell transplantation in metachromatic leukodystrophy in Europe and neighboring countries

“…Almost all MLD cases are caused by biallelic variants in ARSA ; cases caused by variants in PSAP are extremely rare [ 6 , 7 ]. The MLD field is rapidly evolving due to new innovations, such as the recently authorized ex vivo gene therapy in the European Union for LI and EJ MLD [ 8 ] and development of emerging guidelines and newborn screening programs [ 9 , 10 ]. Gene therapy is not yet universally accessible nor approved in late-onset MLD.…”