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. 2020 Jul;41(7):1250-1262.
doi: 10.1002/humu.24010. Epub 2020 Mar 18.

Large-scale in vitro functional testing and novel variant scoring via protein modeling provide insights into alkaline phosphatase activity in hypophosphatasia

Guillermo Del Angel  1 , John Reynders  1 , Christopher Negron  2 , Thomas Steinbrecher  2 , Etienne Mornet  3
Affiliations

Large-scale in vitro functional testing and novel variant scoring via protein modeling provide insights into alkaline phosphatase activity in hypophosphatasia

Guillermo Del Angel et al. Hum Mutat. 2020 Jul.

Abstract

Hypophosphatasia (HPP) is a rare metabolic disorder characterized by low tissue-nonspecific alkaline phosphatase (TNSALP) typically caused by ALPL gene mutations. HPP is heterogeneous, with clinical presentation correlating with residual TNSALP activity and/or dominant-negative effects (DNE). We measured residual activity and DNE for 155 ALPL variants by transient transfection and TNSALP enzymatic activity measurement. Ninety variants showed low residual activity and 24 showed DNE. These results encompass all missense variants with carrier frequencies above 1/25,000 from the Genome Aggregation Database. We used resulting data as a reference to develop a new computational algorithm that scores ALPL missense variants and predicts high/low TNSALP enzymatic activity. Our approach measures the effects of amino acid changes on TNSALP dimer stability with a physics-based implicit solvent energy model. We predict mutation deleteriousness with high specificity, achieving a true-positive rate of 0.63 with false-positive rate of 0, with an area under receiver operating curve (AUC) of 0.9, better than all in silico predictors tested. Combining this algorithm with other in silico approaches can further increase performance, reaching an AUC of 0.94. This study expands our understanding of HPP heterogeneity and genotype/phenotype relationships with the aim of improving clinical ALPL variant interpretation.

Keywords: algorithms; genetic data bases; hypophosphatasia; rare disease; tissue-nonspecific alkaline phosphatase; variant effect prediction.

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Conflict of interest statement

G. d. A. and J. R. are employees of Alexion Pharmaceuticals, Inc., the study sponsor, and may own stock and/or stock options in the company. E. M. has received honoraria from Alexion Pharmaceuticals, Inc. C. N. is a former employee and T. S. is a current employee of Schrödinger and they may own stock and/or stock options in the company.

Figures

Figure 1
Figure 1
Residual activity of ALPL variants relative to wild‐type by variant type
Figure 2
Figure 2
Residual activity of ALPL variants expressed as a fraction of wild‐type activity by the source of the variant. p Values from pairwise Kruskal–Wallis nonparametric tests are added on top of each pair of groups if they are below the significance threshold. gnomAD, Genome Aggregation Database; HPP, hypophosphatasia
Figure 3
Figure 3
Residual activity of ALPL variants (mutant plasmids and 50:50 WT:mutant plasmid cotransfections) relative to WT (a) and fraction of residual activity of ALPL variants relative to WT (b) categorized by protein domain location. Each mutant's cDNA is listed relative to canonical RefSeq transcript NM_000478.6, and is colored according to the protein domain in which it appears, based on the descriptions and positions referenced in Silvent et al. (2014). p Values from pairwise Kruskal–Wallis nonparametric tests are added on top of each pair of groups if they are below significance threshold. cDNA, complementary deoxyribonucleic acid; TNSALP, tissue‐nonspecific alkaline phosphatase; WT, wild‐type
Figure 4
Figure 4
Residual activity of ALPL variants relative to wild‐type by HPP subtype. p Values from pairwise Kruskal–Wallis nonparametric tests are added on top of each pair of groups if they are below significance threshold. ALPL, tissue‐nonspecific alkaline phosphatase gene; HPP, hypophosphatasia
Figure 5
Figure 5
Fraction of residual activity relative to wild‐type based on ClinVar significance (left) and gnomAD global allele frequency (right). ALP, alkaline phosphatase; B, benign; gnomAD, Genome Aggregation Database; LB, likely benign; LP, likely pathogenic; P, pathogenic; TNSALP, tissue‐nonspecific alkaline phosphatase; VUS, variant of uncertain significance
Figure 6
Figure 6
Location of variants with DNE within the ribbon structure of the TNSALP dimer homology model. Positions where a dominant‐negative variant was found, are shown as red atoms. Mirror residues on the opposite chain are not marked with arrows/circles. DNE, dominant‐negative effect; TNSALP, tissue‐nonspecific alkaline phosphatase
Figure 7
Figure 7
ROC curves showing TPR versus FPR for variant prediction algorithms (a) and bootstrap AUCs for in silico algorithms by original AUC values and AUC and ROC values obtained after combining in silico scores with Δs (b). AUC, area under the ROC curve; CADD, Combined Annotation Dependent Depletion; DANN, Deleterious Annotation of Genetic Variants Using Neural Networks; FPR, false‐positive rate; PROVEAN, Protein Variation Effect Analyzer; ROC, receiver operating characteristics; SIFT, Sorting Intolerant From Tolerant; TPR, true‐positive rate; Δs, computationally predicted change in protein stability
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