Comprehensive blood metabolomics profiling of Parkinson’s disease reveals coordinated alterations in xanthine metabolism

Parkinson’s disease (PD) is a highly heterogeneous disorder influenced by several environmental and genetic factors. Effective disease-modifying therapies and robust early-stage biomarkers are still lacking, and an improved understanding of the molecular changes in PD could help to reveal new diagnostic markers and pharmaceutical targets. Here, we report results from a cohort-wide blood plasma metabolic profiling of PD patients and controls in the Luxembourg Parkinson’s Study to detect disease-associated alterations at the level of systemic cellular process and network alterations. We identified statistically significant changes in both individual metabolite levels and global pathway activities in PD vs. controls and significant correlations with motor impairment scores. As a primary observation when investigating shared molecular sub-network alterations, we detect pronounced and coordinated increased metabolite abundances in xanthine metabolism in de novo patients, which are consistent with previous PD case/control transcriptomics data from an independent cohort in terms of known enzyme-metabolite network relationships. From the integrated metabolomics and transcriptomics network analysis, the enzyme hypoxanthine phosphoribosyltransferase 1 (HPRT1) is determined as a potential key regulator controlling the shared changes in xanthine metabolism and linking them to a mechanism that may contribute to pathological loss of cellular adenosine triphosphate (ATP) in PD. Overall, the investigations revealed significant PD-associated metabolome alterations, including pronounced changes in xanthine metabolism that are mechanistically congruent with alterations observed in independent transcriptomics data. The enzyme HPRT1 may merit further investigation as a main regulator of these network alterations and as a potential therapeutic target to address downstream molecular pathology in PD.


Supplementary Materials
Assure that all aspects of the Metabolon process are operating within specifications.

CMTRX
Pool created by taking a small aliquot from every customer sample.
Assess the effect of a non-plasma matrix on the Metabolon process and distinguish biological variability from process variability.PRCS Aliquot of ultra-pure water Process Blank used to assess the contribution to compound signals from the process.

STUDY DESIGN
• Case definition and inclusion/exclusion criteria: Inclusion of diagnosed PD patients according to United Kingdom Parkinson's Disease Society Brain Bank (UKPDSBB) criteria.Exclusion criteria also follow UKPDSBB specifications.
• Control definition and inclusion/exclusion criteria: (i) age over 18 years, (ii) no evidence of neurodegenerative disorder; (iii) no active cancer, (iv) no pregnancy.
• Total number of cases and controls: 546 PD patients, 590 controls • Case matching criteria: Age and sex adjusted during differential abundance analysis for de novo PD vs. control; case-control analyses involving patients who had received dopaminergic medication additionally include adjustments for the L-DOPA metabolite 3-O-Methyldopa (3-OMD) • Randomization procedures: To adjust for potential confounding variables, we conducted a multivariate analysis using linear modeling techniques provided by the 'limma' package in R (see Methods section in the main manuscript).

STUDY POPULATION CHARACTERISTICS
• Geographical location of recruitment: Luxembourg and surrounding area defined as Greater Region.

SAMPLING PROTOCOL
• Protocol Title: Blood plasma sampling • Organism: Homo sapiens sapiens • Organism part: Blood plasma • Factor description: PD patients vs. controls.Subsets -de novo PD patients and treated PD patients.
• Location of collection: Nationwide in Luxembourg • Time of collection: Not restricted, samples collected at various times throughout the day during routine clinical visits • Volume collected: 10 ml (in EDTA tubes) • Tissue / body fluid harvesting method: Venipuncture into Ethylenediaminetetraacetic acid (EDTA) tubes, centrifugation, transfer of plasma to cryovials • Time from separation to freezing: Not specified • Storage conditions and aliquoting: Stored at -80°C in aliquots until analysis • Relocation or shipping info: Shipped on dry ice to the metabolomics service provider (Metabolon) for analysis

EXTRACTION PROTOCOL
• Protocol Title: Metabolon standard metabolite extraction • Instrument: Hamilton ML STAR® MicroLab system • Extraction solvent(s): Methanol • Extract storage: Kept frozen at -80°C until analysis • Other details: Raw data archived, extracted, and processed using Metabolon proprietary workflows (for further details, see section 'Metabolomics sample processing' in Suppl.Materials)

METABOLITE ANNOTATION PROTOCOL
• Protocol Title: Metabolon library annotation • Database(s): Proprietary Metabolon reference library • Identifiers: HMDB, PubChem, InChI, SMILES, CAS, ChemSpider, KEGG • Annotation procedure: Automated comparison of ions, retention times and fragmentation signatures to library entries followed by manual curation • Confidence scoring: Four confidence levels based on match to library entry • Other details: for further details, see section 'Metabolomics sample processing' in Suppl.Materials

QUALITY CONTROL
Quality control (QC) samples and procedures: Pooled matrix samples, process blanks, solvent blanks, and internal standards tracked throughout experiments.Median RSD for standards and all endogenous metabolites monitored.Experimental samples randomized with QC samples spaced evenly (for further details, see section 'Metabolomics sample processing' in the Suppl.Materials) of client-specific technical replicates.A small aliquot of each client sample (colored cylinders) is pooled to create a CMTRX technical replicate sample (multi-colored cylinder), which is then injected periodically throughout the platform run.Variability among consistently detected biochemicals can be used to calculate an estimate of overall process and platform variability.

Suppl. Fig. 2 :
Histogram of relative standard deviation (RSD) values.A histogram has been generated which bins the RSD values for all molecules detected in MTRX7, including those not present in 100% of the MTXR7 technical replicates.The horizontal axis shows the bins of RSD values, the vertical axis the number of molecules within these bins).The calculated median RSD for this data is 14.Suppl.Fig. 3: Average density estimation plots.a) Average density estimation plot of the peak area data prior to log transformation; b) Average density estimation plot of the peak area data after transformation using the natural log.Suppl.Tab.4: Complete list of studied metabolites.The complete list of studied metabolites, including public database IDs, chemical properties, and associated biochemical pathways (provided as a separate dataset file to enable further editing and processing by the reader).
Overall process variability was determined by calculating the median RSD for all endogenous metabolites (i.e., non-instrument standards) present in 100% of the MTXR7 technical replicates.Values for instrument and process variability meet Metabolon's acceptance criteria as shown in the table above.
Suppl.Tab.3: Instrument and Process Variability.Instrument variability was determined by calculating the median relative standard deviation (RSD) for the internal standards that were added to each sample prior to injection into the mass spectrometers.

Suppl. Tab. 5: Complete ranking table of metabolites in terms of differential abundance between de novo Parkinson's disease patients and controls. Statistics are adjusted for age and sex; logFC = log fold change between de novo Parkinson
's disease and controls; P.Value = nominal p-value score; adj.P.Val = p-value adjusted for multiple hypothesis testing using the Benjamini-Hochberg approach (provided as a separate dataset file to enable further editing and processing by the reader).Suppl.Tab.

6: Complete ranking table of metabolites in terms of differential abundance between all Parkinson's disease patients and controls in the cohort
. Statistics are adjusted for age, sex and dopaminergic treatment effects; logFC = log fold change between Parkinson's disease and controls; P.Value = nominal p-value score; adj.P.Val = p-value adjusted for multiple hypothesis testing using the Benjamini-Hochberg approach(provided as a separate dataset file to enable further editing and processing by the reader).Suppl.Tab.7:Completeranking table of metabolites in terms of differential abundance between Parkinson's disease patients who received dopaminergic medication and controls in the cohort.Statistics are adjusted for age, sex and dopaminergic treatment effects; logFC = log fold change between Parkinson's disease and controls; P.Value = nominal p-value score; adj.P.Val = p-value adjusted for multiple hypothesis testing using the Benjamini-Hochberg approach (provided as a separate dataset file to enable further editing and processing by the reader).Suppl.

Table 8 : Reference metabolome for the metabolite set enrichment analysis using the MetaboAnalyst software
(Query = HMDB metabolite ID used for the query); Match = matched metabolite name recognized by MetaboAnalyst, NA otherwise; PubChem = PubChem identifier for mapped compounds; KEGG = KEGG identifier for mapped compounds, SMILES = SMILES code for mapped compounds; all mappings are derived from the MetaboAnalyst software).The table is provided as a separate dataset file to enable further editing and processing by the reader.Suppl.Tab.9:Metabolitesetenrichment analysis results for chemical structure classes (main set) in MetaboAnalyst (showing all metabolites with a nominal p-value <= 0.05) Suppl.Tab.10:Pathwayenrichmentanalysis results for the KEGG database (showing all metabolites with a nominal p-value <= 0.05) Suppl.Table11: Ranking table of metabolite features in terms of their estimated predictive value for supervised sample classification of de novo PD and control samples, showing each metabolite feature (column Metabolite), the Area Under the ROC Curve for the training set (columns Train_Linear_AUC, Train_Radial_AUC) and for the test set (columns Test_Linear_AUC, Test_Radial_AUC) for both linear and radial Support Vector Machine (SVM) classifiers.The table is provided as a separate dataset file to enable further editing and processing by the reader.Suppl.

Table 12 :
Ranking table of metabolite features in terms of their estimated predictive value for regression analysis of UPDRS III total motor scores, displaying each metabolite feature (column Metabolite), the Mean Absolute Error (columns Linear_MAE, Radial_MAE), Rows are sorted by the sum of these ranks (column Sum_of_Ranks).The table is provided as a separate dataset file to enable further editing and processing by the reader.
the coefficient of determination (columns Linear_R², Radial_R²) for both linear and radial SVM regression models, their respective ranks (Linear_RMSE_Rank, Linear_R²_Rank, Radial_MAE_Rank, Radial_R²_Rank), and the total rank score (Sum_of_Ranks) indicating overall performance.The ranks for the Root Mean Square Error (RMSE) are sorted by increasing RMSE values, the ranks for the coefficient of determination (R²) are sorted by decreasing R² values.

Table 13 :
Metadata for the metabolomics samples from the Luxembourg Parkinson's Study, including samples from patients receiving dopaminergic treatment (TREATED_PD), de novo patients (DENOVO_PD), controls (CONTROLS), and samples from a second study on longitudinal PD and atypical forms of parkinsonism, that were measured in the same batches, and are being prepared for separate publication (SECOND_STUDY).

•
Source: Participants in the Luxembourg Parkinson's Study(Hipp et al., Front.Aging Neurosci., 2018) Quality control: Median Relative Standard Deviation (RSD) of standards and pooled samples monitored.Experimental samples randomized with quality control (QC) samples interspersed.Multiple replicates, internal standards, and total ion current normalization used.
• Resulting data: Log-transformed, batch normalized and imputed peak area data•