Untargeted metabolomic profiling reveals multiple pathway perturbations and new clinical biomarkers in urea cycle disorders.
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ABSTRACT:
SUBMITTER: Burrage LC
PROVIDER: S-EPMC6650380 | biostudies-literature | 2019 Sep
REPOSITORIES: biostudies-literature
Publications
Untargeted metabolomic profiling reveals multiple pathway perturbations and new clinical biomarkers in urea cycle disorders.
Burrage Lindsay C LC Thistlethwaite Lillian L Stroup Bridget M BM Sun Qin Q Miller Marcus J MJ Nagamani Sandesh C S SCS Craigen William W Scaglia Fernando F Sutton V Reid VR Graham Brett B Kennedy Adam D AD Milosavljevic Aleksandar A Lee Brendan H BH Elsea Sarah H SH
Genetics in medicine : official journal of the American College of Medical Genetics 20190123 9
<h4>Purpose</h4>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).<h4>Methods</h4>Untargeted mass spectrometry-based metabolomic analysis was used to generate z-scores for more than 900 metabolites in plasma from 48 individuals with various ...[more]
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Project description:The urea cycle is a liver-based pathway enabling disposal of nitrogen waste. Urea cycle disorders (UCDs) are inherited metabolic diseases caused by deficiency of enzymes or transporters involved in the urea cycle and have a prevalence of 1:35,000 live births. Patients present recurrent acute hyperammonaemia, which causes high rate of death and neurological sequelae. Long-term therapy relies on a protein-restricted diet and ammonia scavenger drugs. Currently, liver transplantation is the only cure. Hence, high unmet needs require the identification of effective methods to model these diseases to generate innovative therapeutics. Advances in both induced pluripotent stem cells (iPSCs) and genome editing technologies have provided an invaluable opportunity to model patient-specific phenotypes in vitro by creating patients' avatar models, to investigate the pathophysiology, uncover novel therapeutic targets and provide a platform for drug discovery. This review summarises the progress made thus far in generating 2- and 3-dimensional iPSCs models for UCDs, the challenges encountered and how iPSCs offer future avenues for innovation in developing the next-generation of therapies for UCDs.
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Project description:ObjectiveSepsis is a life-threatening condition secondary to infection that evolves into a dysregulated host response and is associated with acute organ dysfunction. Sepsis-induced cardiac dysfunction is one of the most complex organ failures to characterize. This study performed comprehensive metabolomic profiling that distinguished between septic patients with and without cardiac dysfunction.MethodPlasma samples collected from 80 septic patients were analysed by untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics. Principal component analysis (PCA), partial least squares discrimination analysis (PLS-DA), and orthogonal partial least square discriminant analysis (OPLS-DA) were applied to analyse the metabolic model between septic patients with and without cardiac dysfunction. The screening criteria for potential candidate metabolites were as follows: variable importance in the projection (VIP) >1, P < 0.05, and fold change (FC) > 1.5 or < 0.7. Pathway enrichment analysis further revealed associated metabolic pathways. In addition, we constructed a subgroup metabolic analysis between the survivors and non-survivors according to 28-day mortality in the cardiac dysfunction group.ResultsTwo metabolite markers, kynurenic acid and gluconolactone, could distinguish the cardiac dysfunction group from the normal cardiac function group. Two metabolites, kynurenic acid and galactitol, could distinguish survivors and non-survivors in the subgroup analysis. Kynurenic acid is a common differential metabolite that could be used as a candidate for both diagnosis and prognosis for septic patients with cardiac dysfunction. The main associated pathways were amino acid metabolism, glucose metabolism and bile acid metabolism.ConclusionMetabolomic technology could be a promising approach for identifying diagnostic and prognostic biomarkers of sepsis-induced cardiac dysfunction.
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Project description:IntroductionThe chromosome 22q11.2 deletion syndrome (22q11.2DS) is characterized by a well-defined microdeletion and is associated with a wide range of brain-related phenotypes including schizophrenia spectrum disorders (SCZ), autism spectrum disorders (ASD), anxiety disorders and attention deficit disorders (ADHD). The typically deleted region in 22q11.2DS contains multiple genes which haploinsufficiency has the potential of altering the protein and the metabolic profiles.ObjectivesAlteration in metabolic processes and downstream protein pathways during the early brain development may help to explain the increased prevalence of the observed neurodevelopmental phenotypes in 22q11.2DS. However, relatively little is known about the correlation of dysregulated protein/metabolite expression and neurobehavioral impairments in individuals who developed them over time.MethodsIn this study, we performed untargeted metabolic and proteomic analysis in plasma samples derived from 30 subjects including 16 participants with 22q11.2DS and 14 healthy controls (TD) enrolled in a longitudinal study, aiming to identify a metabolic and protein signature informing about the underlying mechanisms involved in disease development and progression. The metabolic and proteomic profiles were also compared between the participants with 22q11.2DS with and without various comorbidities, such as medical involvement, psychiatric conditions, and autism spectrum disorder (ASD) to detect potential changes among multiple specimens, collected overtime, with the aim to understand the basic underlying mechanisms involved in disease development and progression.ResultsWe observed a large number of statistically significant differences in metabolites between the two groups. Among them, the levels of taurine and arachidonic acid were significantly lower in 22q11.2DS compared to the TD group. In addition, we identified 16 proteins that showed significant changes in expression levels (adjusted P < 0.05) in 22q11.2DS as compared to TD, including those involved in 70 pathways such as gene expression, the PI3K-Akt signaling pathway and the complement system. Within participants with 22q11.2DS, no significant changes in those with and without medical or psychiatric conditions were observed.ConclusionTo our knowledge, this is the first report on plasma metabolic and proteomic profiling and on the identification of unique biomarkers in 22q11.2DS. These findings may suggest the potential role of the identified metabolites and proteins as biomarkers for the onset of comorbid conditions in 22q11.2DS. Ultimately, the altered protein pathways in 22q11.2DS may provide insights of the biological mechanisms underlying the neurodevelopmental phenotype and may provide missing molecular outcome measures in future clinical trials to assess early-diagnosis treatment and the efficacy of response to targeted treatment.
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