Distinct metabolomic signature in cerebrospinal fluid in early parkinson's disease

Gut Microbiota and Metabolome Alterations Associated with Parkinson's Disease

To our knowledge, this is one of the few studies thus far that correlates the composition of the gut microbiota with the direct analysis of fecal metabolites in patients with Parkinson’s disease. Overall, our data highlight microbiota modifications correlated with numerous fecal metabolites. This suggests that Parkinson’s disease is associated with gut dysregulation that involves a synergistic relationship between gut microbes and several bacterial metabolites favoring altered homeostasis. Interestingly, a reduction of short-chain fatty acid (SCFA)-producing bacteria influenced the shape of the metabolomics profile, affecting several metabolites with potential protective effects in the Parkinson group. On the other hand, the extensive impact that intestinal dysbiosis has at the level of numerous metabolic pathways could encourage the identification of specific biomarkers for the diagnosis and treatment of Parkinson’s disease, also in light of the effect that specific drugs have on the composition of the intestinal microbiota.

Parkinson’s disease is a neurodegenerative disorder characterized by the accumulation of intracellular aggregates of misfolded alpha-synuclein along the cerebral axis. Several studies report the association between intestinal dysbiosis and Parkinson’s disease, although a cause-effect relationship remains to be established. Herein, the gut microbiota composition of 64 Italian patients with Parkinson’s disease and 51 controls was determined using a next-generation sequencing approach. A real metagenomics shape based on gas chromatography-mass spectrometry was also investigated. The most significant changes within the Parkinson’s disease group highlighted a reduction in bacterial taxa, which are linked to anti-inflammatory/neuroprotective effects, particularly in the Lachnospiraceae family and key members, such as Butyrivibrio, Pseudobutyrivibrio, Coprococcus, and Blautia. The direct evaluation of fecal metabolites revealed changes in several classes of metabolites. Changes were seen in lipids (linoleic acid, oleic acid, succinic acid, and sebacic acid), vitamins (pantothenic acid and nicotinic acid), amino acids (isoleucine, leucine, phenylalanine, glutamic acid, and pyroglutamic acid) and other organic compounds (cadaverine, ethanolamine, and hydroxy propionic acid). Most modified metabolites strongly correlated with the abundance of members belonging to the Lachnospiraceae family, suggesting that these gut bacteria correlate with altered metabolism rates in Parkinson’s disease.

IMPORTANCE To our knowledge, this is one of the few studies thus far that correlates the composition of the gut microbiota with the direct analysis of fecal metabolites in patients with Parkinson’s disease. Overall, our data highlight microbiota modifications correlated with numerous fecal metabolites. This suggests that Parkinson’s disease is associated with gut dysregulation that involves a synergistic relationship between gut microbes and several bacterial metabolites favoring altered homeostasis. Interestingly, a reduction of short-chain fatty acid (SCFA)-producing bacteria influenced the shape of the metabolomics profile, affecting several metabolites with potential protective effects in the Parkinson group. On the other hand, the extensive impact that intestinal dysbiosis has at the level of numerous metabolic pathways could encourage the identification of specific biomarkers for the diagnosis and treatment of Parkinson’s disease, also in light of the effect that specific drugs have on the composition of the intestinal microbiota.

Metabolomics Fingerprint Induced by the Intranigral Inoculation of Exogenous Human Alpha-Synuclein Oligomers in a Rat Model of Parkinson's Disease

Parkinson’s disease (PD) is considered a synucleinopathy because of the intraneuronal accumulation of aggregated α-synuclein (αSyn). Recent evidence points to soluble αSyn-oligomers (αSynO) as the main cytotoxic species responsible for cell death. Given the pivotal role of αSyn in PD, αSyn-based models are crucial for the investigation of toxic mechanisms and the identification of new therapeutic targets in PD. By using a metabolomics approach, we evaluated the metabolic profile of brain and serum samples of rats infused unilaterally with preformed human αSynOs (HαSynOs), or vehicle, into the substantia nigra pars compacta (SNpc). Three months postinfusion, the striatum was dissected for striatal dopamine (DA) measurements via High Pressure Liquid Chromatography (HPLC) analysis and mesencephalon and serum samples were collected for the evaluation of metabolite content via gas chromatography mass spectrometry analysis. Multivariate, univariate and correlation statistics were applied. A 40% decrease of DA content was measured in the HαSynO-infused striatum as compared to the contralateral and the vehicle-infused striata. Decreased levels of dehydroascorbic acid, myo-inositol, and glycine, and increased levels of threonine, were found in the mesencephalon, while increased contents of fructose and mannose, and a decrease in glycine and urea, were found in the serum of HαSynO-infused rats. The significant correlation between DA and metabolite content indicated that metabolic variations reflected the nigrostriatal degeneration. Collectively, the metabolomic fingerprint of HαSynO-infused rats points to an increase of oxidative stress markers, in line with PD neuropathology, and provides hints for potential biomarkers of PD.

Simple and Complex Sugars in Parkinson's Disease: a Bittersweet Taste

Neuronal homeostasis depends on both simple and complex sugars (the glycoconjugates), and derangement of their metabolism is liable to impair neural function and lead to neurodegeneration. Glucose levels boost glycation phenomena, a wide series of non-enzymatic reactions that give rise to various intermediates and end-products that are potentially dangerous in neurons. Glycoconjugates, including glycoproteins, glycolipids, and glycosaminoglycans, contribute to the constitution of the unique features of neuron membranes and extracellular matrix in the nervous system. Glycosylation defects are indeed frequently associated with nervous system disturbances and neurodegeneration. Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor and non-motor symptoms associated with the loss of dopaminergic neurons in the pars compacta of the substantia nigra. Neurons present intracytoplasmic inclusions of α-synuclein aggregates involved in the disease pathogenesis together with the impairment of the autophagy-lysosome function, oxidative stress, and defective traffic and turnover of membrane components. In the present review, we selected relevant recent contributions concerning the direct involvement of glycation and glycosylation in α-synuclein stability, impaired autophagy and lysosomal function in PD, focusing on potential models of PD pathogenesis provided by genetic variants of glycosphingolipid processing enzymes, especially glucocerebrosidase (GBA). Moreover, we collected data aimed at defining the glycomic profile of PD patients as a tool to help in diagnosis and patient subtyping, as well as those pointing to sugar-related compounds with potential therapeutic applications in PD.

Plasma metabolomics study of Vogt-Koyanagi-Harada disease identifies potential diagnostic biomarkers

Vogt-Koyanagi-Harada (VKH) disease is a common type of uveitis in China, but the diagnosis criteria of VKH disease is controversial. The aim of this study was to investigate potential diagnostic plasma biomarkers for VKH disease. A case-control study including 55 VKH patients (28 active patients and 27 inactive VKH patients) and 30 healthy controls in a tertiary referral center was performed. The metabolic phenotype of VKH patients showed a significant difference compared to healthy controls. Fifteen differentially expressed metabolites (DEMs) were identified between active VKH patients and healthy controls and nine DEMs were found between inactive VKH patients and healthy controls after controlling variable importance in the projection (VIP) value > 1 and false discovery rate (FDR) < 0.05. D-mannose, stearic acid and L-lysine were shown to be potential diagnostic biomarkers which can discriminate active VKH patients from healthy controls with a diagnostic performance with AUC = 0.965, 0.936 and 0.910 respectively in independent diagnosis and an AUC = 0.999 when combined. Sarcosine was recognized as an independent potential biomarker which could distinguish inactive VKH patients from healthy controls. This study reveals a significant difference of plasma metabolic phenotype and identifies diagnostic biomarkers for VKH disease. Changes in the metabolic profile may provide clues towards the pathophysiology of VKH disease.

Parkinson's disease prognostic scores for progression of cognitive decline

Clinical and biochemical diversity of Parkinson’s disease (PD) presents a major challenge for accurate diagnosis and prediction of its progression. We propose, develop and optimize PD clinical scores as efficient integrated progression biomarkers for prediction of the likely rate of cognitive decline in PD patients. We considered 269 drug-naïve participants from the Parkinson’s Progression Marker Initiative database, diagnosed with idiopathic PD and observed between 4 and 6 years. Nineteen baseline clinical and pathological measures were systematically considered. Relative variable importance and logistic regressions were used to optimize combinations of significant baseline measures as integrated biomarkers. Parkinson’s disease cognitive decline scores were designed as new clinical biomarkers using optimally categorized baseline measures. Specificities and sensitivities of the biomarkers reached ~93% for prediction of severe rate of cognitive decline (with more than 5 points decline in 4 years on the Montreal Cognitive Assessment scale), and up to ~73% for mild-to-moderate decline (between 1 and 5 points decline). The developed biomarkers and clinical scores could resolve the long-standing clinical problem about reliable prediction of PD progression into cognitive deterioration. The outcomes also provide insights into the contributions of individual clinical and pathological measures to PD progression, and will assist with better-targeted treatment regiments, stratification of clinical trial and their evaluation.

Cecal Gut Microbiota and Metabolites Might Contribute to the Severity of Acute Myocardial Ischemia by Impacting the Intestinal Permeability, Oxidative Stress, and Energy Metabolism

Emerging evidence highlights the role of gut microbiota in regulating the pathogenesis of coronary heart disease. Here, we performed 16S rRNA gene sequencing and UPLC-Q-TOF/MS-based metabolomics to investigate the gut microbiome and metabolomes of cecal contents in the isoproterenol (ISO)-induced acute myocardial ischemia (AMI) rats. As expected, considerable gut microbiota alterations were observed in the AMI rats compared with the control rats, paralleling with intestinal inflammation and apoptosis. At phylum level, the abundance of Firmicutes was significantly decreased, whereas the abundance of Bacteroidetes and Spirochaetae was strikingly enriched in the AMI group. At genus level, the significant alteration of genera Treponema 2, Rikenellaceae RC9 gut group, Prevotellaceae UCG-003, and Bacteroides may contribute to the pathogenesis of AMI. These altered microbiota might influence the intestinal permeability and subsequently impair intestinal barrier and stimulate gut inflammation. Consistently, significantly metabolic differences of cecal contents between the AMI and control groups were revealed, and threonic acid, L-urobilin and L-urobilinogen were considered the most associated cecal metabolites with AMI. These strikingly altered metabolites were mainly related to energy metabolism and oxidative stress which could lead to apoptosis and further affect gut barrier. Ultimately, we revealed the potential link of these altered gut microbiota/metabolomes and intestinal inflammatory factors and apoptotic proteins and further confirmed their intimate connections with intestinal inflammation and gut barrier. Our findings depict uncovered potential relationship among the gut microbiome, cecal metabolomes and AMI.

LC-MS/MS method with chemical derivatization for quantitation of L-threonate in human plasma

An LC-MS/MS-based bioanalytical method has been developed to measure the concentration of L-threonate at its endogenous level in human plasma. Following isotope dilution and protein precipitation, the samples were acetylated and chromatographed under reversed-phase conditions for baseline separation of the derivatized L-threonate and its stereoisomer D-erythronate. The method was assessed by a fit-for-purpose validation with a calibration range from 100 to 10,000 ng/mL. The intra-run coefficients of variation (CVs) were <3.6% and the inter-run CV was 3.2% for the QC samples at endogenous level. At the lower limit of quantitation, the intra-run CV was 6.1% and the average inaccuracy was -1.4%. This method provides an efficient and reliable quantitation of L-threonate and could be useful to certain biomarker investigators.

Metabolomics-driven identification of adenosine deaminase as therapeutic target in a mouse model of Parkinson's disease

Neuroinflammation is one of the driving forces of progressive neurodegeneration in Parkinson's disease (PD). The metabolomics approach has been proved highly useful in identifying potential therapeutic targets. Here, to identify inflammation-relevant treatment targets for PD, mass spectrometry-based untargeted metabolomics was applied to characterize metabolic changes in the striatum of mice with double-hit PD induced by lipopolysaccharide plus 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP). Seven days after the final MPTP administration, metabolites from the purine metabolism pathway, including adenosine, 1-methyladenosine, adenine, inosine, hypoxanthine, xanthine, xanthosine, and guanosine, were found to be significantly dysregulated. The metabolite-protein interaction network and changes in the concentration ratio of these metabolites indicated that adenosine and adenosine deaminase (ADA; EC 3.5.4.4) were the most promising therapeutic targets and adenosine augmentation might be a rational approach to slow PD progression. These findings were then verified in a subacute MPTP-induced PD mouse model treated with ADA inhibition alone or in conjunction with antagonism of adenosine A_2A receptors (A_2A R). Behavioral, biochemical, and immunohistochemical analysis demonstrated that ADA inhibition significantly ameliorated the MPTP-mediated motor disabilities, dopamine depletion, and dopaminergic cell death. Significantly enhanced neuroprotective effects were further observed when the ADA inhibitor was utilized in conjunction with an A_2A R antagonist. Together, our study indicated for the first time that ADA inhibitors protected against neurodegeneration induced by the neurotoxin MPTP, and ADA inhibitors in combination with A_2A R antagonists showed additive antiparkinsonian effects.

A metabolomic signature of treated and drug-naïve patients with Parkinson's disease: a pilot study

INTRODUCTION

About 90% of cases of Parkinson's disease (PD) are idiopathic and attempts to understand pathogenesis typically assume a multifactorial origin. Multifactorial diseases can be studied using metabolomics, since the cellular metabolome reflects the interplay between genes and environment.

OBJECTIVE

The aim of our case-control study is to compare metabolomic profiles of whole blood obtained from treated PD patients, de-novo PD patients and controls, and to study the perturbations correlated with disease duration, disease stage and motor impairment.

METHODS

We collected blood samples from 16 drug naïve parkinsonian patients, 84 treated parkinsonian patients, and 42 age matched healthy controls. Metabolomic profiles have been obtained using gas chromatography coupled to mass spectrometry. Multivariate statistical analysis has been performed using supervised models; partial least square discriminant analysis and partial least square regression.

RESULTS

This approach allowed separation between discrete classes and stratification of treated patients according to continuous variables (disease duration, disease stage, motor score). Analysis of single metabolites and their related metabolic pathways revealed unexpected possible perturbations related to PD and underscored existing mechanisms that correlated with disease onset, stage, duration, motor score and pharmacological treatment.

CONCLUSION

Metabolomics can be useful in pathogenetic studies and biomarker discovery. The latter needs large-scale validation and comparison with other neurodegenerative conditions.

An NMR-based lipidomic approach to identify Parkinson's disease-stage specific lipoprotein-lipid signatures in plasma

Disturbances in lipid composition and lipoproteins metabolism can play a crucial role in the pathogenesis of Parkinson's disease (PD) and other neurodegenerative diseases. The lipidomic strategy proposed here involves lipoprotein profiling using NMR spectroscopy and multivariate data pre-processing and analysis tools on 94 plasma samples (belonging to 38 early-stage PD patients, 10 PD-related dementia patients, 23 persons with Alzheimer's dementia, and 23 healthy control subjects) to firstly differentiate PD patients (irrespective of the stage of the disease) from persons with Alzheimer's disease (AD) as well as from controls, and then to discriminate among PD patients according to disease severity. The whole data set was subdivided into 86 training and 8 external test samples for validation purposes. A two-step classification scheme, based on linear discriminant analysis with variable selection accomplished by a stepwise orthogonalisation procedure, was proposed to optimise classification performance. Careful pre-processing of NMR signals was crucial to ensure data set quality. A total of 30 chemical shift buckets enabled differentiation between PD patients (regardless of disease severity), AD and control subjects, providing classification, cross-validation and external prediction rates of 100% in all cases. Only 15 variables were required to further discriminate between early-stage PD and PD-related dementia, again with 100% correct classifications, and internal/external predictions. The simplicity and effectiveness of the classification methodology proposed support the use of NMR spectroscopy, in combination with chemometrics, as a viable alternative diagnostic tool to conventional PD clinical diagnosis.

Postprandial Metabolic Effects of Fiber Mixes Revealed by in vivo Stable Isotope Labeling in Humans

Food supplementation with a fiber mix of guar gum and chickpea flour represents a promising approach to reduce the risk of type 2 diabetes mellitus (T2DM) by attenuating postprandial glycemia. To investigate the effects on postprandial metabolic fluxes of glucose-derived metabolites in response to this fiber mix, a randomized, cross-over study was designed. Twelve healthy, male subjects consumed three different flatbreads either supplemented with 2% guar gum or 4% guar gum and 15% chickpea flour or without supplementation (control). The flatbreads were enriched with ~2% of ¹³C-labeled wheat flour. Blood was collected at 16 intervals over a period of 360 min after bread intake and plasma samples were analyzed by GC-MS based metabolite profiling combined with stable isotope-assisted metabolomics. Although metabolite levels of the downstream metabolites of glucose, specifically lactate and alanine, were not altered in response to the fiber mix, supplementation of 4% guar gum was shown to significantly delay and reduce the exogenous formation of these metabolites. Metabolic modeling and computation of appearance rates revealed that the effects induced by the fiber mix were strongest for glucose and attenuated downstream of glucose. Further investigations to explore the potential of fiber mix supplementation to counteract the development of metabolic diseases are warranted.

Recent advances and perspectives of metabolomics-based investigations in Parkinson's disease

Parkinson's disease (PD) is the second most prevalent neurodegenerative disease of the central nervous system (CNS), which affects mostly older adults. In recent years, the incidence of PD has been dramatically increasing with the aging population expanding. Due to the lack of effective biomarkers, the accurate diagnosis and precise treatment of PD are currently compromised. Notably, metabolites have been considered as the most direct reflection of the physiological and pathological conditions in individuals and represent attractive candidates to provide deep insights into disease phenotypes. By profiling the metabolites in biofluids (cerebrospinal fluid, blood, urine), feces and brain tissues, metabolomics has become a powerful and promising tool to identify novel biomarkers and provide valuable insights into the etiopathogenesis of neurological diseases. In this review, we will summarize the recent advancements of major analytical platforms implemented in metabolomics studies, dedicated to the improvement and extension of metabolome coverage for in-depth biological research. Based on the current metabolomics studies in both clinical populations and experimental PD models, this review will present new findings in metabolomics biomarkers research and abnormal metabolic pathways in PD, and will discuss the correlation between metabolomic changes and clinical conditions of PD. A better understanding of the biological underpinning of PD pathogenesis might offer novel diagnostic, prognostic, and therapeutic approaches to this devastating disease.

Co-extraction for Metabolomics and Proteomics from a Single CSF Sample

In the field of neurodegeneration, it is important to identify biomarkers that enable early disease prediction, since these disorders start decades before clinical symptoms manifest. Cerebrospinal fluid (CSF) is considered an excellent source for biomarker discovery since it is in direct contact with the extracellular space of the brain and directly reflects disease-specific changes.While the liquor drainage is no major risk factor for patients, it is still not as easy and popular as simple blood sampling and less liquid can be collected. Especially when a variety of experiments for one cohort is planned, the volume of CSF can be a limiting factor. Therefore, it is essential that extraction and analytical methods are adapted to low amounts of liquor. If in follow-up studies, additional replicates to increase statistical significance or different extraction approaches are planned, the required amounts have to be minimized.With this extraction method, a combined proteomics and metabolomics approach is possible. This opportunity implies a variety of advantages. First, a classification matrix based on the comprehensive data set has a potentially higher accuracy even without a deeper understanding of the biological meaning of the different omics changes. If the proteome and metabolome differences can be linked to each other, this approach can conceivably open so far unknown doors regarding the cause or progression of different diseases like Alzheimer's or Parkinson's disease.

Bacterial Metabolites Mirror Altered Gut Microbiota Composition in Patients with Parkinson's Disease

Increasing evidence is supporting the hypothesis of α-synuclein pathology spreading from the gut to the brain although the exact etiology of Parkinson's disease (PD) is unknown. Furthermore, it has been proposed that inflammation, via the gastrointestinal tract, potentially through infections, may contribute to α-synuclein pathogenesis, and thus to the risk of developing PD. Recently, many studies have shown that PD patients have an altered microbiota composition compared to healthy controls. Inflammation in the gut might drive microbiota alterations or vice versa. Many studies focused on the detection of biomarkers of the etiology, onset, or progression of PD however also report metabolites from bacterial origin. These metabolites might reflect the bacterial composition and as well play an important role in immune homeostasis, ultimately affecting the progression of PD. Besides the bacterial metabolites, pharmacological treatment of PD might play a crucial role during the progression and thus treatment of the disease on the immune system. This review aims to establish a link between the microbial composition with the observed alterations of bacterial metabolites and their impact on the immune system, which could have influential effect in onset, progression and etiology of PD.

Cerebrospinal fluid biomarker for Parkinson's disease: An overview

In Parkinson's disease (PD), there is a wide field of recent and ongoing search for useful biomarkers for early and differential diagnosis, disease monitoring or subtype characterization. Up to now, no biofluid biomarker has entered the daily clinical routine. Cerebrospinal fluid (CSF) is often used as a source for biomarker development in different neurological disorders because it reflects changes in central-nervous system homeostasis. This review article gives an overview about different biomarker approaches in PD, mainly focusing on CSF analyses. Current state and future perspectives regarding classical protein markers like alpha‑synuclein, but also different "omics" techniques are described. In conclusion, technical advancements in the field already yielded promising results, but further multicenter trials with well-defined cohorts, standardized protocols and integrated data analysis of different modalities are needed before successful translation into routine clinical application.

Metabolomic investigations in cerebrospinal fluid of Parkinson's disease

The underlying mechanisms of Parkinson´s disease are not completely revealed. Especially, early diagnostic biomarkers are lacking. To characterize early pathophysiological events, research is focusing on metabolomics. In this case-control study we investigated the metabolic profile of 31 Parkinson´s disease-patients in comparison to 95 neurologically healthy controls. The investigation of metabolites in CSF was performed by a 12 Tesla SolariX Fourier transform-ion cyclotron resonance-mass spectrometer (FT-ICR-MS). Multivariate statistical analysis sorted the most important biomarkers in relation to their ability to differentiate Parkinson versus control. The affected metabolites, their connection and their conversion pathways are described by means of network analysis. The metabolic profiling by FT-ICR-MS in CSF yielded in a good group separation, giving insights into the disease mechanisms. A total number of 243 metabolites showed an affected intensity in Parkinson´s disease, whereas 15 of these metabolites seem to be the main biological contributors. The network analysis showed a connection to the tricarboxylic cycle (TCA cycle) and therefore to mitochondrial dysfunction and increased oxidative stress within mitochondria. The metabolomic analysis of CSF in Parkinson´s disease showed an association to pathways which are involved in lipid/ fatty acid metabolism, energy metabolism, glutathione metabolism and mitochondrial dysfunction.

Integrated Analysis Reveals Altered Lipid and Glucose Metabolism and Identifies NOTCH2 as a Biomarker for Parkinson's Disease Related Depression

Depression is a common comorbidity in Parkinson's disease (PD) but is underdiagnosed. We aim to investigate the altered metabolic pathways of Parkinson's disease-related depression (PDD) in plasma and to identify potential biomarkers for clinical diagnosis. Consecutive patients with PD were recruited, clinically assessed, and patients with PDD identified. Fasting plasma samples were collected from 99 patients and differentially expressed metabolites and proteins between patients with PDD and PD were identified using non-targeted liquid chromatography-mass spectrometry (LC-MS)-based metabolomics and tandem mass tag (TMT)-based proteomics analysis, followed by an integrated analysis. Based on the above results, enzyme-linked immune sorbent assay (ELISA) tests were then performed to identify potential biomarkers for PDD. In clinics, patients with PDD suffered less hypertension and had lower serum low-density lipoprotein cholesterol and apolipoprotein B levels when compared to the other patients with PD. A total of 85 differentially expressed metabolites were identified in metabolomics analysis. These metabolites were mainly lipids and lipid-like molecules, involved in lipid and glucose metabolic pathways. According to proteomics analysis, 17 differentially expressed proteins were identified, and 12 metabolic pathways were enriched, which were predominantly related to glucose metabolism. Integrated analysis indicated that altered lipid and glucose metabolism in PDD may induce cellular injury through oxidative stress. Additionally, plasma levels of several proteins were confirmed to be significantly altered and correlated with depressive severity. NOTCH2 may be a potential blood biomarker for PDD, with an optimal cut-off point of 0.91 ng/ml, a sensitivity value of 95.65%, and a specificity value of 81.58%. Depressive symptoms are associated with lipid and glucose metabolism in patients with PD and NOTCH2 may be a potential blood biomarker for the clinical diagnosis of PDD.

Computational systems biology approaches for Parkinson's disease

Parkinson's disease (PD) is a prime example of a complex and heterogeneous disorder, characterized by multifaceted and varied motor- and non-motor symptoms and different possible interplays of genetic and environmental risk factors. While investigations of individual PD-causing mutations and risk factors in isolation are providing important insights to improve our understanding of the molecular mechanisms behind PD, there is a growing consensus that a more complete understanding of these mechanisms will require an integrative modeling of multifactorial disease-associated perturbations in molecular networks. Identifying and interpreting the combinatorial effects of multiple PD-associated molecular changes may pave the way towards an earlier and reliable diagnosis and more effective therapeutic interventions. This review provides an overview of computational systems biology approaches developed in recent years to study multifactorial molecular alterations in complex disorders, with a focus on PD research applications. Strengths and weaknesses of different cellular pathway and network analyses, and multivariate machine learning techniques for investigating PD-related omics data are discussed, and strategies proposed to exploit the synergies of multiple biological knowledge and data sources. A final outlook provides an overview of specific challenges and possible next steps for translating systems biology findings in PD to new omics-based diagnostic tools and targeted, drug-based therapeutic approaches.

Linking Glycation and Glycosylation With Inflammation and Mitochondrial Dysfunction in Parkinson's Disease

Parkinson’s disease (PD) is the second most common neurodegenerative disorder, affecting about 6.3 million people worldwide. PD is characterized by the progressive degeneration of dopaminergic neurons in the Substantia nigra pars compacta, resulting into severe motor symptoms. The cellular mechanisms underlying dopaminergic cell death in PD are still not fully understood, but mitochondrial dysfunction, oxidative stress and inflammation are strongly implicated in the pathogenesis of both familial and sporadic PD cases. Aberrant post-translational modifications, namely glycation and glycosylation, together with age-dependent insufficient endogenous scavengers and quality control systems, lead to cellular overload of dysfunctional proteins. Such injuries accumulate with time and may lead to mitochondrial dysfunction and exacerbated inflammatory responses, culminating in neuronal cell death. Here, we will discuss how PD-linked protein mutations, aging, impaired quality control mechanisms and sugar metabolism lead to up-regulated abnormal post-translational modifications in proteins. Abnormal glycation and glycosylation seem to be more common than previously thought in PD and may underlie mitochondria-induced oxidative stress and inflammation in a feed-forward mechanism. Moreover, the stress-induced post-translational modifications that directly affect parkin and/or its substrates, deeply impairing its ability to regulate mitochondrial dynamics or to suppress inflammation will also be discussed. Together, these represent still unexplored deleterious mechanisms implicated in neurodegeneration in PD, which may be used for a more in-depth knowledge of the pathogenic mechanisms, or as biomarkers of the disease.

A review of validated biomarkers obtained through metabolomics

INTRODUCTION

Studying changes in the whole set of small molecules, final products of biochemical reactions in living systems or metabolites, is extremely appealing because they represent the best approach to identifying what occurs in an organism when samples are collected. However, their usefulness as potential biomarkers is limited by discoveries obtained in small groups without proper validation or even confirmation of the chemical structure. Areas covered: During the past 5 years, more than 900 papers have been published on metabolomics for biomarker discovery, but the numbers are much lower when some criteria of validation are applied. In total, 102 papers have been included in this review. The most frequent disease areas in which these markers have been discovered include the following: cancer, diabetes, and related diseases and neurodegenerative, cardiovascular, autoimmune, liver, and kidney diseases. Expert commentary: Metabolomics has been demonstrated as rapidly growing due to the improvements in instrumentation, mainly mass spectrometry, and data mining software. For application in the clinic, the results should be validated in different stages, from analytical validation to validation in independent sets of samples, using thousands of samples from different sources.

Promising Metabolite Profiles in the Plasma and CSF of Early Clinical Parkinson's Disease

Parkinson's disease (PD) shows high heterogeneity with regard to the underlying molecular pathogenesis involving multiple pathways and mechanisms. Diagnosis is still challenging and rests entirely on clinical features. Thus, there is an urgent need for robust diagnostic biofluid markers. Untargeted metabolomics allows establishing low-molecular compound biomarkers in a wide range of complex diseases by the measurement of various molecular classes in biofluids such as blood plasma, serum, and cerebrospinal fluid (CSF). Here, we applied untargeted high-resolution mass spectrometry to determine plasma and CSF metabolite profiles. We semiquantitatively determined small-molecule levels (≤1.5 kDa) in the plasma and CSF from early PD patients (disease duration 0-4 years; n = 80 and 40, respectively), and sex- and age-matched controls (n = 76 and 38, respectively). We performed statistical analyses utilizing partial least square and random forest analysis with a 70/30 training and testing split approach, leading to the identification of 20 promising plasma and 14 CSF metabolites. These metabolites differentiated the test set with an AUC of 0.8 (plasma) and 0.9 (CSF). Characteristics of the metabolites indicate perturbations in the glycerophospholipid, sphingolipid, and amino acid metabolism in PD, which underscores the high power of metabolomic approaches. Further studies will enable to develop a potential metabolite-based biomarker panel specific for PD.

Alpha-Synuclein Glycation and the Action of Anti-Diabetic Agents in Parkinson's Disease

Parkinson's disease (PD) is a neurodegenerative disorder with complex etiology and variable pathology. While a subset of cases is associated with single-gene mutations, the majority originates from a combination of factors we do not fully understand. Thus, understanding the underlying causes of PD is indispensable for the development of novel therapeutics. Glycation, the non-enzymatic reaction between reactive dicarbonyls and amino groups, gives rise to a variety of different reaction products known as advanced glycation end products (AGEs). AGEs accumulate over a proteins life-time, and increased levels of glycation reaction products play a role in diabetic complications. It is now also becoming evident that PD patients also display perturbed sugar metabolism and protein glycation, including that of alpha-synuclein, a key player in PD. Here, we hypothesize that anti-diabetic drugs targeting the levels of glycation precursors, or promoting the clearance of glycated proteins may also prove beneficial for PD patients.

Robust Regression Analysis of GCMS Data Reveals Differential Rewiring of Metabolic Networks in Hepatitis B and C Patients

About one in 15 of the world’s population is chronically infected with either hepatitis virus B (HBV) or C (HCV), with enormous public health consequences. The metabolic alterations caused by these infections have never been directly compared and contrasted. We investigated groups of HBV-positive, HCV-positive, and uninfected healthy controls using gas chromatography-mass spectrometry analyses of their plasma and urine. A robust regression analysis of the metabolite data was conducted to reveal correlations between metabolite pairs. Ten metabolite correlations appeared for HBV plasma and urine, with 18 for HCV plasma and urine, none of which were present in the controls. Metabolic perturbation networks were constructed, which permitted a differential view of the HBV- and HCV-infected liver. HBV hepatitis was consistent with enhanced glucose uptake, glycolysis, and pentose phosphate pathway metabolism, the latter using xylitol and producing threonic acid, which may also be imported by glucose transporters. HCV hepatitis was consistent with impaired glucose uptake, glycolysis, and pentose phosphate pathway metabolism, with the tricarboxylic acid pathway fueled by branched-chain amino acids feeding gluconeogenesis and the hepatocellular loss of glucose, which most probably contributed to hyperglycemia. It is concluded that robust regression analyses can uncover metabolic rewiring in disease states.