Exploratory Untargeted Metabolomics of Dried Blood Spot Samples from Newborns with Maple Syrup Urine Disease.

Currently, tandem mass spectrometry-based newborn screening (NBS), which examines targeted biomarkers, is the first approach used for the early detection of maple syrup urine disease (MSUD) in newborns, followed by confirmatory genetic mutation tests. However, these diagnostic approaches have limitations, demanding the development of additional tools for the diagnosis/screening of MUSD. Recently, untargeted metabolomics has been used to explore metabolic profiling and discover the potential biomarkers/pathways of inherited metabolic diseases. Thus, we aimed to discover a distinctive metabolic profile and biomarkers/pathways for MSUD newborns using untargeted metabolomics. Herein, untargeted metabolomics was used to analyze dried blood spot (DBS) samples from 22 MSUD and 22 healthy control newborns. Our data identified 210 altered endogenous metabolites in MSUD newborns and new potential MSUD biomarkers, particularly L-alloisoleucine, methionine, and lysoPI. In addition, the most impacted pathways in MSUD newborns were the ascorbate and aldarate pathways and pentose and glucuronate interconversions, suggesting that oxidative and detoxification events may occur in early life. Our approach leads to the identification of new potential biomarkers/pathways that could be used for the early diagnosis/screening of MSUD newborns but require further validation studies. Our untargeted metabolomics findings have undoubtedly added new insights to our understanding of the pathogenicity of MSUD, which helps us select the appropriate early treatments for better health outcomes.

DeepSVP: integration of genotype and phenotype for structural variant prioritization using deep learning.

MOTIVATION: Structural genomic variants account for much of human variability and are involved in several diseases. Structural variants are complex and may affect coding regions of multiple genes, or affect the functions of genomic regions in different ways from single nucleotide variants. Interpreting the phenotypic consequences of structural variants relies on information about gene functions, haploinsufficiency or triplosensitivity and other genomic features. Phenotype-based methods to identifying variants that are involved in genetic diseases combine molecular features with prior knowledge about the phenotypic consequences of altering gene functions. While phenotype-based methods have been applied successfully to single nucleotide variants as well as short insertions and deletions, the complexity of structural variants makes it more challenging to link them to phenotypes. Furthermore, structural variants can affect a large number of coding regions, and phenotype information may not be available for all of them. RESULTS: We developed DeepSVP, a computational method to prioritize structural variants involved in genetic diseases by combining genomic and gene functions information. We incorporate phenotypes linked to genes, functions of gene products, gene expression in individual cell types and anatomical sites of expression, and systematically relate them to their phenotypic consequences through ontologies and machine learning. DeepSVP significantly improves the success rate of finding causative variants in several benchmarks and can identify novel pathogenic structural variants in consanguineous families. AVAILABILITY AND IMPLEMENTATION: https://github.com/bio-ontology-research-group/DeepSVP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Proteomic and Molecular Assessment of the Common Saudi Variant in ACADVL Gene Through Mesenchymal Stem Cells.

Very-long-chain acyl-coenzyme A dehydrogenase (VLCAD) is a coenzyme encoded by ACADVL that converts very-long-chain fatty acids into energy. This process is disrupted by c.65C > A; p.Ser22∗ mutation. To clarify mechanisms by which this mutation leads to VLCAD deficiency, we evaluated differences in molecular and cellular functions between mesenchymal stem cells with normal and mutant VLCAD. Saudi Arabia have a high incidence of this form of mutation. Stem cells with mutant VLCAD were isolated from skin of two patients. Metabolic activity and proliferation were evaluated. The Same evaluation was repeated on normal stem cells introduced with same mutation by CRISPR. Mitochondrial depiction was done by electron microscope and proteomic analysis was done on patients' cells. Metabolic activity and proliferation were significantly lower in patients' cells. Introducing the same mutation into normal stem cells resulted in the same defects. We detected mitochondrial abnormalities by electron microscopy in addition to poor wound healing and migration processes in mutant cells. Furthermore, in a proteomic analysis, we identified several upregulated or downregulated proteins related to hypoglycemia, liver disorder, and cardiac and muscle involvement. We concluded experimental assays of mutant ACADVL (c.65C > A; p.Ser22∗) contribute to severe neonatal disorders with hypoglycemia, liver disorder, and cardiac and muscle involvement.