GBA variants in Parkinson’s disease: clinical, metabolomic and multimodal neuroimaging phenotypes

in Movement Disorders (2020), 35(12), 2201-2210

Background: Alterations in the GBA gene (NM_000157.3) are the most important genetic risk factor for Parkinson’s disease. Biallelic GBA mutations cause the lysosomal storage disorder Gaucher´s disease ... [more ▼]

Background: Alterations in the GBA gene (NM_000157.3) are the most important genetic risk factor for Parkinson’s disease. Biallelic GBA mutations cause the lysosomal storage disorder Gaucher´s disease. The GBA variants p.E365K and p.T408M are associated with Parkinson’s but not with Gaucher´s disease. The pathophysiological role of these variants needs to be further explored. Objective: This study analyzed the clinical, neuropsychological, metabolic and neuroimaging phenotypes of Parkinson’s disease patients carrying the GBA variants p.E365K and p.T408M. Methods: GBA was sequenced in 56 mid-stage Parkinson’s disease patients. Carriers of GBA variants were compared to non-carriers regarding clinical history and symptoms, neuropsychological features, metabolomics and multimodal neuroimaging. Blood plasma gas chromatography coupled to mass spectrometry, [18F]FDopa PET, [18F]FDG PET, and resting-state fMRI were performed. Results: Sequence analysis detected 13 heterozygous GBA variant carriers (seven with p.E365K, six with p.T408M). One patient carried a GBA mutation (p.N409S) and was excluded. Clinical history and symptoms were not significantly different between groups. Global cognitive performance was lower in variant carriers. Metabolomic group differences were suggestive of more severe Parkinson’s disease-related alterations in carriers versus non-carriers. [18F]FDopa and [18F]FDG PET showed signs of a more advanced disease; [18F]FDG PET and fMRI showed similarities with Lewy body dementia and Parkinson’s disease dementia in carriers. Conclusions: This is the first study to comprehensively assess (neuro-)biological phenotypes of GBA variants in Parkinson’s disease. Metabolomics and neuroimaging detected more significant group differences than clinical and behavioral evaluation. These alterations could be promising to monitor effects of disease-modifying treatments targeting glucocerebrosidase metabolism. [less ▲]

Integrative analysis of blood metabolomics and PET brain neuroimaging data for Parkinson's disease

in Neurobiology of Disease (2019), 124(1), 555-562

The diagnosis of Parkinson's disease (PD) often remains a clinical challenge. Molecular neuroimaging can facilitate the diagnostic process. The diagnostic potential of metabolomic signatures has recently ... [more ▼]

The diagnosis of Parkinson's disease (PD) often remains a clinical challenge. Molecular neuroimaging can facilitate the diagnostic process. The diagnostic potential of metabolomic signatures has recently been recognized. Methods: We investigated whether the joint data analysis of blood metabolomics and PET imaging by machine learning provides enhanced diagnostic discrimination and gives further pathophysiological insights. Blood plasma samples were collected from 60 PD patients and 15 age- and gender-matched healthy controls. We determined metabolomic profiles by gas chromatography coupled to mass spectrometry (GC-MS). In the same cohort and at the same time we performed FDOPA PET in 44 patients and 14 controls and FDG PET in 51 patients and 16 controls. 18 PD patients were available for a follow-up exam after one year. Both data sets were analysed by two machine learning approaches, applying either linear support vector machines or random forests within a leave-one-out cross-validation and computing receiver operating characteristic (ROC) curves. Results: In the metabolomics data, the baseline comparison between cases and controls as well as the followup assessment of patients pointed to metabolite changes associated with oxidative stress and inflammation. For the FDOPA and FDG PET data, the diagnostic predictive performance (DPP) in the ROC analyses was highest when combining imaging features with metabolomics data (ROC AUC for best FDOPA + metabolomics model: 0.98; AUC for best FDG + metabolomics model: 0.91). DPP was lower when using only PET attributes or only metabolomics signatures. Conclusion: Integrating blood metabolomics data combined with PET data considerably enhances the diagnostic discrimination power. Metabolomic signatures also indicate interesting disease-inherent changes in cellular processes, including oxidative stress response and inflammation. [less ▲]

Microbiome-derived Multi-omicBiomarkers for Early-stage Detection and Stratification of Parkinson's Disease

Poster (2018, October)

Quantification of Stable Isotope Traces Close to Natural Enrichment in Human Plasma Metabolites Using Gas Chromatography-Mass Spectrometry

in Metabolites (2018), 8(1), 15

Currently, changes in metabolic fluxes following consumption of stable isotope-enriched foods are usually limited to the analysis of postprandial kinetics of glucose. Kinetic information on a larger ... [more ▼]

Currently, changes in metabolic fluxes following consumption of stable isotope-enriched foods are usually limited to the analysis of postprandial kinetics of glucose. Kinetic information on a larger diversity of metabolites is often lacking, mainly due to the marginal percentage of fully isotopically enriched plant material in the administered food product, and hence, an even weaker 13C enrichment in downstream plasma metabolites. Therefore, we developed an analytical workflow to determine weak 13C enrichments of diverse plasma metabolites with conventional gas chromatography-mass spectrometry (GC-MS). The limit of quantification was increased by optimizing (1) the metabolite extraction from plasma, (2) the GC-MS measurement, and (3) most importantly, the computational data processing. We applied our workflow to study the catabolic dynamics of 13C-enriched wheat bread in three human subjects. For that purpose, we collected time-resolved human plasma samples at 16 timepoints after the consumption of 13C-labeled bread and quantified 13C enrichment of 12 metabolites (glucose, lactate, alanine, glycine, serine, citrate, glutamate, glutamine, valine, isoleucine, tyrosine, and threonine). Based on isotopomer specific analysis, we were able to distinguish catabolic profiles of starch and protein hydrolysis. More generally, our study highlights that conventional GC-MS equipment is sufficient to detect isotope traces below 1% if an appropriate data processing is integrated. [less ▲]

Distinct metabolomic signature in cerebrospinal fluid in early parkinson's disease: Early Parkinson'S CSF Metabolic Signature

in Movement Disorders (2017)

Objective: The purpose of this study was to profile cerebrospinal fluid (CSF) from early-stage PD patients for disease-related metabolic changes and to determine a robust biomarker signature for early ... [more ▼]

Objective: The purpose of this study was to profile cerebrospinal fluid (CSF) from early-stage PD patients for disease-related metabolic changes and to determine a robust biomarker signature for early-stage PD diagnosis. Methods: By applying a non-targeted and mass spectrometry-driven approach, we investigated the CSF metabolome of 44 early-stage sporadic PD patients yet without treatment (DeNoPa cohort). We compared all detected metabolite levels with those measured in CSF of 43 age- and gender-matched healthy controls. After this analysis, we validated the results in an independent PD study cohort (T€ubingen cohort). Results: We identified that dehydroascorbic acid levels were significantly lower and fructose, mannose, and threonic acid levels were significantly higher (P <.05) in PD patients when compared with healthy controls. These changes reflect pathological oxidative stress responses, as well as protein glycation/glycosylation reactions in PD. Using a machine learning approach based on logistic regression, we successfully predicted the origin (PD patients vs healthy controls) in a second (n518) as well as in a third and completely independent validation set (n536). The biomarker signature is composed of the three markers—mannose, threonic acid, and fructose—and allows for sample classification with a sensitivity of 0.790 and a specificity of 0.800. Conclusion: We identified PD-specific metabolic changes in CSF that were associated with antioxidative stress response, glycation, and inflammation. Our results disentangle the complexity of the CSF metabolome to unravel metabolome changes related to earlystage PD. The detected biomarkers help understanding PD pathogenesis and can be applied as biomarkers to increase clinical diagnosis accuracy and patient care in early-stage PD. [less ▲]

Metabolic profiling of body fluids and multivariate data analysis

in MethodsX (2017), 4(1), 95-103

Metabolome analyses of body fluids are challenging due pre-analytical variations, such as pre-processing delay and temperature, and constant dynamical changes of biochemical processes within the samples ... [more ▼]

Metabolome analyses of body fluids are challenging due pre-analytical variations, such as pre-processing delay and temperature, and constant dynamical changes of biochemical processes within the samples. Therefore, proper sample handling starting from the time of collection up to the analysis is crucial to obtain high quality samples and reproducible results. A metabolomics analysis is divided into 4 main steps: 1) Sample collection, 2) Metabolite extraction, 3) Data acquisition and 4) Data analysis. Here, we describe a protocol for gas chromatography coupled to mass spectrometry (GC–MS) based metabolic analysis for biological matrices, especially body fluids. This protocol can be applied on blood serum/plasma, saliva and cerebrospinal fluid (CSF) samples of humans and other vertebrates. It covers sample collection, sample pre-processing, metabolite extraction, GC–MS measurement and guidelines for the subsequent data analysis. [less ▲]

The Mouse Brain Metabolome: Region-Specific Signatures and Response to Excitotoxic Neuronal Injury

in American Journal of Pathology (2015), 185(6), 1699-1712

Neurodegeneration is a multistep process characterized by a multitude of molecular entities and their interactions. Systems' analyses, or omics approaches, have become an important tool in characterizing ... [more ▼]

Neurodegeneration is a multistep process characterized by a multitude of molecular entities and their interactions. Systems' analyses, or omics approaches, have become an important tool in characterizing this process. Although RNA and protein profiling made their entry into this field a couple of decades ago, metabolite profiling is a more recent addition. The metabolome represents a large part or all metabolites in a tissue, and gives a snapshot of its physiology. By using gas chromatography coupled to mass spectrometry, we analyzed the metabolic profile of brain regions of the mouse, and found that each region is characterized by its own metabolic signature. We then analyzed the metabolic profile of the mouse brain after excitotoxic injury, a mechanism of neurodegeneration implicated in numerous neurological diseases. More important, we validated our findings by measuring, histologically and molecularly, actual neurodegeneration and glial response. We found that a specific global metabolic signature, best revealed by machine learning algorithms, rather than individual metabolites, was the most robust correlate of neuronal injury and the accompanying gliosis, and this signature could serve as a global biomarker for neurodegeneration. We also observed that brain lesioning induced several metabolites with neuroprotective properties. Our results deepen the understanding of metabolic changes accompanying neurodegeneration in disease models, and could help rapidly evaluate these changes in preclinical drug studies. [less ▲]

Method validation for preparing urine samples for downstream proteomic and metabolomic applications.

in Biopreservation and biobanking (2014), 12(5), 351-7

BACKGROUND: Formal validation of methods for biospecimen processing in the context of accreditation in laboratories and biobanks is lacking. A protocol for processing of a biospecimen (urine) was ... [more ▼]

BACKGROUND: Formal validation of methods for biospecimen processing in the context of accreditation in laboratories and biobanks is lacking. A protocol for processing of a biospecimen (urine) was validated for fitness-for-purpose in terms of key downstream endpoints. METHODS: Urine processing was optimized for centrifugation conditions on the basis of microparticle counts at room temperature (RT) and at 4 degrees C. The optimal protocol was validated for performance (microparticle counts), and for reproducibility and robustness for centrifugation temperature (4 degrees C vs. RT) and brake speed (soft, medium, hard). Acceptance criteria were based on microparticle counts, cystatin C and creatinine concentrations, and the metabolomic profile. RESULTS: The optimal protocol was a 20-min, 12,000 g centrifugation at 4 degrees C, and was validated for urine collection in terms of microparticle counts. All reproducibility acceptance criteria were met. The protocol was robust for centrifugation at 4 degrees C versus RT for all parameters. The protocol was considered robust overall in terms of brake speeds, although a hard brake gave significantly fewer microparticles than a soft brake. CONCLUSIONS: We validated a urine processing method suitable for downstream proteomic and metabolomic applications. Temperature and brake speed can influence analytic results, with 4 degrees C and high brake speed considered optimal. Laboratories and biobanks should ensure these conditions are systematically recorded in the scope of accreditation. [less ▲]