Clinical diagnosis of metabolic disorders using untargeted metabolomic profiling and disease-specific networks learned from profiling data.

Autor: Thistlethwaite LR; Quantitative and Computational Biosciences Program, Baylor College of Medicine, One Baylor Plaza, 400D, Houston, TX, 77030, USA.; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA., Li X; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA., Burrage LC; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.; Texas Children's Hospital, Houston, TX, USA., Riehle K; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA., Hacia JG; Department of Biochemistry and Molecular Medicine, Keck School of Medicine of the University of Southern California, Los Angeles, CA, USA., Braverman N; Department of Pediatrics and Human Genetics, McGill University, Montreal, QC, Canada., Wangler MF; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.; Texas Children's Hospital, Houston, TX, USA.; Jan and Dan Duncan Texas Children's Hospital Neurological Research Institute, Houston, TX, USA., Miller MJ; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA., Elsea SH; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA., Milosavljevic A; Quantitative and Computational Biosciences Program, Baylor College of Medicine, One Baylor Plaza, 400D, Houston, TX, 77030, USA. amilosav@bcm.edu.; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA. amilosav@bcm.edu.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2022 Apr 21; Vol. 12 (1), pp. 6556. Date of Electronic Publication: 2022 Apr 21.
DOI: 10.1038/s41598-022-10415-5
Abstrakt: Untargeted metabolomics is a global molecular profiling technology that can be used to screen for inborn errors of metabolism (IEMs). Metabolite perturbations are evaluated based on current knowledge of specific metabolic pathway deficiencies, a manual diagnostic process that is qualitative, has limited scalability, and is not equipped to learn from accumulating clinical data. Our purpose was to improve upon manual diagnosis of IEMs in the clinic by developing novel computational methods for analyzing untargeted metabolomics data. We employed CTD, an automated computational diagnostic method that "connects the dots" between metabolite perturbations observed in individual metabolomics profiling data and modules identified in disease-specific metabolite co-perturbation networks learned from prior profiling data. We also extended CTD to calculate distances between any two individuals (CTDncd) and between an individual and a disease state (CTDdm), to provide additional network-quantified predictors for use in diagnosis. We show that across 539 plasma samples, CTD-based network-quantified measures can reproduce accurate diagnosis of 16 different IEMs, including adenylosuccinase deficiency, argininemia, argininosuccinic aciduria, aromatic L-amino acid decarboxylase deficiency, cerebral creatine deficiency syndrome type 2, citrullinemia, cobalamin biosynthesis defect, GABA-transaminase deficiency, glutaric acidemia type 1, maple syrup urine disease, methylmalonic aciduria, ornithine transcarbamylase deficiency, phenylketonuria, propionic acidemia, rhizomelic chondrodysplasia punctata, and the Zellweger spectrum disorders. Our approach can be used to supplement information from biochemical pathways and has the potential to significantly enhance the interpretation of variants of uncertain significance uncovered by exome sequencing. CTD, CTDdm, and CTDncd can serve as an essential toolset for biological interpretation of untargeted metabolomics data that overcomes limitations associated with manual diagnosis to assist diagnosticians in clinical decision-making. By automating and quantifying the interpretation of perturbation patterns, CTD can improve the speed and confidence by which clinical laboratory directors make diagnostic and treatment decisions, while automatically improving performance with new case data.
(© 2022. The Author(s).)
Databáze: MEDLINE
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