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. 2019 Mar 12;15(3):43.
doi: 10.1007/s11306-019-1505-6.

Metabolomics profiles of patients with Wilson disease reveal a distinct metabolic signature

Gaurav V Sarode  1 , Kyoungmi Kim  2 , Dorothy A Kieffer  1 , Noreene M Shibata  1 , Tomas Litwin  3 , Anna Czlonkowska  3 , Valentina Medici  4
Affiliations

Metabolomics profiles of patients with Wilson disease reveal a distinct metabolic signature

Gaurav V Sarode et al. Metabolomics. .

Erratum in

Abstract

Introduction: Wilson disease (WD) is characterized by excessive intracellular copper accumulation in liver and brain due to defective copper biliary excretion. With highly varied phenotypes and a lack of biomarkers for the different clinical manifestations, diagnosis and treatment can be difficult.

Objective: The aim of the present study was to analyze serum metabolomics profiles of patients with Wilson disease compared to healthy subjects, with the goal of identifying differentially abundant metabolites as potential biomarkers for this condition.

Methods: Hydrophilic interaction liquid chromatography-quadrupole time of flight mass spectrometry was used to evaluate the untargeted serum metabolome of 61 patients with WD (26 hepatic and 25 neurologic subtypes, 10 preclinical) compared to 15 healthy subjects. We conducted analysis of covariance with potential confounders (body mass index, age, sex) as covariates and partial least-squares analysis.

Results: After adjusting for clinical covariates and multiple testing, we identified 99 significantly different metabolites (FDR < 0.05) between WD and healthy subjects. Subtype comparisons also revealed significantly different metabolites compared to healthy subjects: WD hepatic subtype (67), WD neurologic subtype (57), WD hepatic-neurologic combined (77), and preclinical (36). Pathway analysis revealed these metabolites are involved in amino acid metabolism, the tricarboxylic acid cycle, choline metabolism, and oxidative stress.

Conclusions: Patients with WD are characterized by a distinct metabolomics profile providing new insights into WD pathogenesis and identifying new potential diagnostic biomarkers.

Keywords: Biomarkers; Copper; Metabolomics; Phenotype.

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Conflict of interest statement

Conflict of Interest: The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The authors who participated in this study declared they do not have anything to disclose regarding funding or conflict of interest with respect to this manuscript.

Figures

Fig. 1
Fig. 1
Metabolomics profiling analyses for healthy subjects vs. WD patients 1a – Volcano plot of metabolite abundance changes (covariate-adjusted). The x-axis specifies the log2 fold changes and the y-axis specifies the negative logarithm to the base 10 of the FDR values. Dotted vertical and horizontal lines reflect the filtering criteria (log2 FC=±2.0 and FDR=0.05). 1b – Score plots of the PLS-LDA analysis distinguishing WD patients (n=61) from healthy subjects (n=15) based on their metabolomics pattern. WD, Wilson disease; FC, fold change; FDR, false discovery rate, PLS-LDA, partial least-squares regression with linear discriminant analysis
Fig. 2
Fig. 2
Comparison of select serum metabolites between healthy subjects and WD patients Results are presented as mean ± SD; healthy subjects n=15, WD patients n=61. **p< 0.01, ****p< 0.0001 compared to healthy subjects. WD, Wilson disease
Fig. 3
Fig. 3
Metabolomics profiling analyses based on clinical manifestations 3a – Number of unique and concordant metabolites in healthy vs. hepatic and healthy vs. neurologic. 3b – Score plots of the PLS-LDA analysis for healthy subjects, hepatic subtype, and neurologic subtype. 3c, 3d – Volcano plot of metabolite abundance changes (covariate-adjusted), comparing comparing healthy vs. HN (3c) and healthy vs. preclinical (3d). The x-axis specifies the log2 fold changes and the y-axis specifies the negative logarithm to the base 10 of the FDR values. Dotted vertical and horizontal lines reflect the filtering criteria (log2 FC=± 2.0 and FDR=0.05). FC, fold change; FDR, false discovery rate; PLS-LDA, partial least-squares regression with linear discriminant analysis
Fig. 4
Fig. 4
Pathway analysis by MetaboAnalyst: metabolome view X-axis – pathway impact values from pathway topology analysis; y-axis – matched pathways from pathway enrichment analysis arranged by -log(p-value). Red indicates the most significant effects according to p-value and the node size is determined by pathway impact value
Fig. 5
Fig. 5
Network of key metabolites and their associated pathways relevant to copper and oxidative stress in WD Differential metabolites are indicated in bold text. Red arrows represent decreased or increased levels in the serum of WD patients. 2-HBA, 2-hydroxybutanoic acid; αKGDH, α-ketoglutarate dehydrogenase; Cu, copper; GSH, glutathione; PDH, pyruvate dehydrogenase; ROS, reactive oxygen species; SAH, S-adenosylhomocysteine; SAHH, S-adenosylhomocysteine hydrolase; SAM, S-adenosylmethionine
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