Phenotypic prediction in glutaric aciduria type 1 combining in silico and in vitro modeling with real-world data.
Yuheng YuanBianca DimitrovNikolas BoyFlorian GleichMatthias ZielonkaStefan KölkerPublished in: Journal of inherited metabolic disease (2023)
Glutaric aciduria type 1 (GA1, OMIM, #231670) is caused by inherited deficiency of glutaryl-CoA dehydrogenase (GCDH). To further understand the unclear genotype-phenotype correlation, we transfected mutated GCDH into COS-7 cells resembling known biallelic GCDH variants of 47 individuals with GA1. In total, we modeled 36 genotypes with 32 missense variants. Spectrophotometry demonstrated an inverse correlation between residual enzyme activity and the urinary concentration of glutaric acid and 3-hydroxyglutaric acid, confirming previous studies (Pearson correlation, r = -0.34 and r = -0.49, p = 0.045 and p = 0.002, respectively). In silico modeling predicted high pathogenicity for all genotypes which caused a low enzyme activity. Western blotting revealed a 2.6-times higher GCDH protein amount in patients with an acute encephalopathic crisis (t-test, p = 0.015), and high protein expression correlated with high in silico protein stability (Pearson correlation, r = -0.42, p = 0.011). The protein amount was not correlated with the enzyme activity (Pearson correlation, r = 0.09, p = 0.59). To further assess protein stability, proteolysis was performed, showing that p.Arg88Cys stabilized a heterozygous less stable variant. We conclude that an integration of different data sources helps to predict the complex clinical phenotype in individuals with GA1. This article is protected by copyright. All rights reserved.
Keyphrases
- pet ct
- protein protein
- amino acid
- molecular docking
- binding protein
- public health
- big data
- copy number
- intellectual disability
- electronic health record
- escherichia coli
- fatty acid
- machine learning
- cell death
- dna methylation
- liver failure
- pseudomonas aeruginosa
- artificial intelligence
- hepatitis b virus
- early onset
- data analysis
- cell cycle arrest
- deep learning
- staphylococcus aureus
- cystic fibrosis
- case control
- cell proliferation