Untargeted metabolomic profiling reveals multiple pathway perturbations and new clinical biomarkers in urea cycle disorders

Abstract

Purpose: Untargeted metabolomic analysis is increasingly being used in the screening and management of individuals with inborn errors of metabolism (IEM). We aimed to test whether untargeted metabolomic analysis in plasma might be useful for monitoring the disease course and management of urea cycle disorders (UCDs). Methods: Untargeted mass spectrometry-based metabolomic analysis was used to generate z-scores for more than 900 metabolites in plasma from 48 individuals with various UCDs. Pathway analysis was used to identify common pathways that were perturbed in each UCD. Results: Our metabolomic analysis in plasma identified multiple potentially neurotoxic metabolites of arginine in arginase deficiency and, thus, may have utility in monitoring the efficacy of treatment in arginase deficiency. In addition, we were also able to detect multiple biochemical perturbations in all UCDs that likely reflect clinical management, including metabolite alterations secondary to dietary and medication management. Conclusions: In addition to utility in screening for IEM, our results suggest that untargeted metabolomic analysis in plasma may be beneficial for monitoring efficacy of clinical management and off-target effects of medications in UCDs and potentially other IEM.

Metabolomics to Improve the Diagnostic Efficiency of Inborn Errors of Metabolism

“…Urine, plasma and CSF metabolomic assays have been employed, revealing significant findings on clinically analysed samples [58,[85][86][87][88]. These insights include new diagnostic biomarkers [87][88][89], a broader range of phenotype in adenylosuccinate lyase deficiency [90], insights around pre-analytical factors [91], primary metabolic variations from drug-related changes [92][93][94] and a better understanding of the underlying pathobiological mechanism of disease [95][96][97].…”

Untargeted Metabolomics for Metabolic Diagnostic Screening with Automated Data Interpretation Using a Knowledge-Based Algorithm

“…Computational phenotype analysis has been proven successful in exome prioritization [6,7] and is likely to also have great potential for the prioritization of IEM, provided that a patient's phenotype is described as comprehensively as possible. Fourth, we support the (4) addition of biomarkers that are identified by untargeted metabolomics studies to the library [3,[8][9][10][11], (5) the inclusion of additional [12] or newly discovered IEM, and (6) the testing of the algorithm in other body fluids, like cerebrospinal fluid [4] or urine [13].…”

“…Moreover, the development of the R Shiny application ensures that we can present the algorithm as an easily accessible diagnostic tool for NGMS. In addition, since the number of known IEM is, in conjunction the number of potential biomarkers for IEM [3,[8][9][10][11], expanding at an unprecedented pace [12], it gets increasingly hard for laboratory specialists to keep knowledge up-to-date. The expected library, which serves as the input for the diagnostic algorithm, can be easily expanded with newly discovered IEM, as well as new biomarkers, provided that the markers included in the library are validated and that the algorithm can correctly preselect the IEM in a patient sample.…”

Metabolomics for Animal Models of Rare Human Diseases: An Expert Review and Lessons Learned

“…Intoxication and/or hypoglycemia phenotype and changes in metabolites can be directly associated with the genotype. Accumulated metabolites serve well as diagnostic biomarkers (e.g., Abela et al, 2017;Burrage et al, 2019;Sanchez-Roman et al, 2018). However, not all rare diseases cause a characteristic spike in a certain metabolite level.…”