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

Plasma and urinary metabolomic signatures differentiate genetic and idiopathic Parkinson's disease

Parkinson's disease (PD) is marked by alpha-synuclein accumulation and progressive dopaminergic neuron loss. Using Nuclear Magnetic Resonance (NMR)-based metabolomics, we uncovered metabolic disturbances in idiopathic PD (iPD) and PD linked to LRRK2, GBA1, and PRKN variants in a Brazilian ethnically diverse cohort, free of comorbidities, in comparison to healthy, age-matched controls. In plasma, significant PD-associated metabolites included histidine, acetate, acetoacetate, glutamine, glucose, lipids and lipoproteins, N-acetyl-glycoproteins, and sarcosine. Urine samples revealed alterations in creatine, creatinine, L-asparagine, trimethylamine, 3-beta-hydroxybutyrate, isovaleric acid, glutamine, urea, glycine, choline, arginine, and cysteine in association with PD. Notably, creatine, creatinine, acetate, glucose, and histidine showed pathway influences from LRRK2, GBA1, and PRKN variants. Enrichment analyses highlighted disruptions in glyoxylate and dicarboxylate metabolism (plasma) as well as serine, threonine, and glycine metabolism (urine). Additionally, a metabolite-gene-disease interaction network identified 15 genes associated with PD that interact with key metabolites, highlighting MAPT, SNCA, RERE, and KCNN3 as key players in both plasmaandurine. NMR in saliva samples did not show significant differences between PD groups and controls. Our findings underscore PD-associated metabolites, particularly related to arginine metabolism, the urea cycle, glutamate metabolism, glucose metabolism, and gut microbiota. These pathways and gene interactions may serve as potential biomarkers for PD diagnosis and precision medicine strategies.

Multiomics approach identifies dysregulated lipidomic and proteomic networks in Parkinson's disease patients mutated in TMEM175

Parkinson's disease (PD) represents one of the most frequent neurodegenerative disorders for which clinically useful biomarkers remain to be identified and validated. Here, we adopted an untargeted omics approach to disclose lipidomic, metabolomic and proteomic alterations in plasma and in dermal fibroblasts of PD patients carrying mutations in TMEM175 gene. We revealed a wide dysregulation of lysosome, autophagy, and mitochondrial pathways in these patients, supporting a role of this channel in regulating these cellular processes. The most significant altered lipid classes were Fatty acyls, Glycerophospholipids and Phosphosphingolipids. The plasma level of Phosphatidylcholines (PC) and Phosphatidylinositol (PI) 34:1 significantly correlated with an earlier age at onset of the disease in TMEM175 patients (p = 0.008; p = 0.006). In plasma we also observed altered amino acids metabolic pathways in PD patients. We highlighted that increased level of L-glutamate strongly correlated (p < 0.001) with the severity of motor and non-motor symptoms in PD_TMEM175 patients. In dermal fibroblasts, we disclosed alterations of proteins involved in lipids biosynthesis (PAG15, PP4P1, GALC, FYV1, PIGO, PGPS1, PLPP1), in the insulin pathway (IGF2R), in mitochondrial metabolism (ACD10, ACD11, ACADS) and autophagy (RAB7L). Interestingly, we quantified 43 lysosomal or lysosomal-related proteins, which were differentially modulated between TMEM175 patients and controls. Integrative correlation analysis of proteome and lipidome of PD_TMEM175 cellular models identified a strong positive correlation of 13 proteins involved in biosynthetic processes with PC and Ceramides. Altogether, these data provide novel insights into the molecular and metabolic alterations underlying TMEM175 mutations and may be relevant for PD prediction, diagnosis and treatment.

Decoding aging clocks: New insights from metabolomics

Chronological age is a crucial risk factor for diseases and disabilities among older adults. However, individuals of the same chronological age often exhibit divergent biological aging states, resulting in distinct individual risk profiles. Chronological age estimators based on omics data and machine learning techniques, known as aging clocks, provide a valuable framework for interpreting molecular-level biological aging. Metabolomics is an intriguing and rapidly growing field of study, involving the comprehensive profiling of small molecules within the body and providing the ultimate genome-environment interaction readout. Consequently, leveraging metabolomics to characterize biological aging holds immense potential. The aim of this review was to provide an overview of metabolomics approaches, highlighting the establishment and interpretation of metabolomic aging clocks while emphasizing their strengths, limitations, and applications, and to discuss their underlying biological significance, which has the potential to drive innovation in longevity research and development.

Causal Relationship Between Intestinal Microbiota, Inflammatory Cytokines, Peripheral Immune Cells, Plasma Metabolome and Parkinson's Disease: A Mediation Mendelian Randomization Study

Parkinson's disease (PD) is a neurodegenerative disease involving multiple factors. We explored the connection between intestinal microbiome levels and PD by examining inflammatory cytokines, peripheral immune cell counts and plasma metabolomics as potential factors. By obtaining the Genome-Wide Association Study (GWAS) data needed for this study from GWAS Catalog, including summary data for 473 intestinal microbiota traits (N = 5959), 91 inflammatory cytokine traits (N = 14,824), 118 peripheral immune cell count traits (N = 3757), 1400 plasma metabolite traits (N = 8299) and PD traits (N = 482,730). We used two-step Mendelian randomization (MR) mediated analysis to investigate possible pathways from intestinal microbiota to PD mediated by inflammatory cytokines, peripheral immune cells and plasma metabolites. MR has revealed the causal effects of 19 intestinal microbiota, 1 inflammatory cytokine and 12 plasma metabolites on PD, whereas there is no significant causal relationship between immune cell count characteristics and the occurrence of PD. Mediation analysis showed that the associations between the genus Demequina and PD were mediated by tryptophan with mediated proportions of 17.51% (p = 0.0393). Our study demonstrates that genus Demequina may promote the occurrence of PD by reducing the levels of tryptophan.

The Balance of Ketoacids α-Ketoglutarate and α-Ketoglutaramate Reflects the Degree of the Development of Hepatoencephalopathy in Rats

Hepatoencephalopathy (HE) is a liver disease that can lead to brain pathology and the impairment of human cognitive abilities. The objective assessment of HE disease severity is difficult due to the lack of reliable diagnostic markers. This paper examines the background to the emergence of HE markers and provides a brief overview of research results indicating the diagnostic value of potential markers isolated from a wide range of metabolites analyzed. It has been suggested that metabolites of the glutamate-glutamine (Glu-Gln) cycle, α-ketoglutarate (αKG), and α-ketoglutaramate (αKGM) can act as such markers of HE. The informative value of these markers was revealed during a comparative analysis of the distribution of αKG and αKGM in samples of the blood plasma and tissues (liver, kidneys, and brain) of rats exposed to the strong hepatotoxin thioacetamide (TAA). A comparative analysis of the balance of αKG and αKGM, as well as their ratio (αKG/αKGM) in the examined samples of blood plasma and animal tissues in these models, revealed their diagnostic value for assessing the severity of HE and/or monitoring the recovery process.

Monitoring central nervous system tumour metabolism using cerebrospinal fluid

Central nervous system (CNS) tumours are the most common cancer cause of death in under 40s in the UK, largely because they persist and recur and sometimes metastasise during treatment. Therefore, longitudinal monitoring of patients during and following treatment must be undertaken to understand the course of the disease and alter treatment plans reactively. This monitoring must be specific, sensitive, rapid, low cost, simple, and accepted by the patient. Cerebrospinal fluid (CSF) examination obtained following lumbar puncture, already a routine part of treatment in paediatric cases, could be better utilised with improved biomarkers. In this review, we discuss the potential for metabolites in the CSF to be used as biomarkers of CNS tumour remission, progression, response to drugs, recurrence and metastasis. We confer the clinical benefits and risks of this approach and conclude that there are many potential advantages over other tests and the required instrumentation is already present in UK hospitals. On the other hand, the approach needs more research investment to find more metabolite biomarkers, better understand their relation to the tumour, and validate those biomarkers in a standardised assay in order for the assay to become a clinical reality.

Myelin Lipid Alterations in Neurodegenerative Diseases: Landscape and Pathogenic Implications

Significance: Lipids, which constitute the highest portion (over 50%) of brain dry mass, are crucial for brain integrity, energy homeostasis, and signaling regulation. Emerging evidence revealed that lipid profile alterations and abnormal lipid metabolism occur during normal aging and in different forms of neurodegenerative diseases. Moreover, increasing genome-wide association studies have validated new targets on lipid-associated pathways involved in disease development. Myelin, the protective sheath surrounding axons, is crucial for efficient neural signaling transduction. As the primary site enriched with lipids, impairments of myelin are increasingly recognized as playing significant and complex roles in various neurodegenerative diseases, beyond simply being secondary effects of neuronal loss. Recent Advances: With advances in the lipidomics field, myelin lipid alterations and their roles in contributing to or reflecting the progression of diseases, including Alzheimer's disease, Parkinson's disease, Huntington's disease, amyotrophic lateral sclerosis, multiple sclerosis, and others, have recently caught great attention. Critical Issues: This review summarizes recent findings of myelin lipid alterations in the five most common neurodegenerative diseases and discusses their implications in disease pathogenesis. Future Directions: By highlighting myelin lipid abnormalities in neurodegenerative diseases, this review aims to encourage further research focused on lipids and the development of new lipid-oriented therapeutic approaches in this area. Antioxid. Redox Signal. 00, 000-000.

Integrative Metabolome and Proteome Analysis of Cerebrospinal Fluid in Parkinson's Disease

Parkinson's disease (PD) is a common neurodegenerative disorder characterized by the loss of dopaminergic neurons in the substantia nigra. Recent studies have highlighted the significant role of cerebrospinal fluid (CSF) in reflecting pathophysiological PD brain conditions by analyzing the components of CSF. Based on the published literature, we created a single network with altered metabolites in the CSF of patients with PD. We analyzed biological functions related to the transmembrane of mitochondria, respiration of mitochondria, neurodegeneration, and PD using a bioinformatics tool. As the proteome reflects phenotypes, we collected proteome data based on published papers, and the biological function of the single network showed similarities with that of the metabolomic network. Then, we analyzed the single network of integrated metabolome and proteome. In silico predictions based on the single network with integrated metabolomics and proteomics showed that neurodegeneration and PD were predicted to be activated. In contrast, mitochondrial transmembrane activity and respiration were predicted to be suppressed in the CSF of patients with PD. This review underscores the importance of integrated omics analyses in deciphering PD's complex biochemical networks underlying neurodegeneration.

Plasma lipidomic signatures of dementia with Lewy bodies revealed by machine learning, and compared to alzheimer's disease

BACKGROUND

Dementia with Lewy Bodies (DLB) is a complex neurodegenerative disorder that often overlaps clinically with Alzheimer's disease (AD), presenting challenges in accurate diagnosis and underscoring the need for novel biomarkers. Lipidomic emerges as a promising avenue for uncovering disease-specific metabolic alterations and potential biomarkers, particularly as the lipidomics landscape of DLB has not been previously explored. We aim to identify potential diagnostic biomarkers and elucidate the disease's pathophysiological mechanisms.

METHODS

This study conducted a lipidomic analysis of plasma samples from patients with DLB, AD, and healthy controls (HCs) at Xuanwu Hospital. Untargeted plasma lipidomic profiling was conducted via liquid chromatography coupled with mass spectrometry. Machine learning methods were employed to discern lipidomic signatures specific to DLB and to differentiate it from AD.

RESULTS

The study enrolled 159 participants, including 57 with AD, 48 with DLB, and 54 HCs. Significant differences in lipid profiles were observed between the DLB and HC groups, particularly in the classes of sphingolipids and phospholipids. A total of 55 differentially expressed lipid species were identified between DLB and HCs, and 17 between DLB and AD. Correlations were observed linking these lipidomic profiles to clinical parameters like Unified Parkinson's Disease Rating Scale III (UPDRS III) and cognitive scores. Machine learning models demonstrated to be highly effective in distinguishing DLB from both HCs and AD, achieving substantial accuracy through the utilization of specific lipidomic signatures. These include PC(15:0_18:2), PC(15:0_20:5), and SPH(d16:0) for differentiation between DLB and HCs; and a panel includes 13 lipid molecules: four PCs, two PEs, three SPHs, two Cers, and two Hex1Cers for distinguishing DLB from AD.

CONCLUSIONS

This study presents a novel and comprehensive lipidomic profile of DLB, distinguishing it from AD and HCs. Predominantly, sphingolipids (e.g., ceramides and SPHs) and phospholipids (e.g., PE and PC) were the most dysregulated lipids in relation to DLB patients. The lipidomics panels identified through machine learning may serve as effective plasma biomarkers for diagnosing DLB and differentiating it from AD dementia.

The Characteristics of the Metabolomic Profile in Patients with Parkinson's Disease and Vascular Parkinsonism

The gradually increasing age of the world population implies that the prevalence of neurodegenerative diseases also continues to rise. These diseases are characterized by a progressive loss of cognitive and motor functions. Parkinson's disease, which involves the gradual death of specialized neural tissue, is a striking example of a neurodegenerative process. The pathomorphological analysis shows that chronic cerebral ischemia is accompanied by extensive complex neurodegeneration; parkinsonism is its clinical manifestation in 20-30% of cases. Although Parkinson's disease and vascular parkinsonism are similar, these two pathologies have fundamentally different etiopathogeneses. But their set of differential diagnosis traits is confined to some features of the neurological status. There currently exist no diagnostic markers for individual neurodegenerative pathologies or the neurodegeneration phenomenon in general. Metabolomic profiling can be a promising means for finding a unique "fingerprint" of the disease. Identifying the biomarkers of various neurodegenerative diseases will help shorten the time to the diagnosis, forecast the course of the disease, and personalize the therapeutic approach. This review summarizes and compares the current concepts of metabolomics research into Parkinson's disease and vascular parkinsonism, as well as the respective animal models.

Comprehensive analysis of the cerebrospinal fluid and serum metabolome in neurological diseases

BACKGROUND

Comprehensive characterization of the metabolome in cerebrospinal fluid (CSF) and serum by Nuclear Magnetic Resonance (NMR) spectroscopy may identify biomarkers and contribute to the understanding of the pathophysiology of neurological diseases.

METHODS

Metabolites were determined by NMR spectroscopy in stored CSF/serum samples of 20 patients with Parkinson's disease, 25 patients with other neuro-degenerative diseases, 22 patients with cerebral ischemia, 48 patients with multiple sclerosis, and 58 control patients with normal CSF findings. The data set was analysed using descriptive and multivariate statistics, as well as machine learning models.

RESULTS

CSF glucose and lactic acid measured by NMR spectroscopy and routine clinical chemistry showed a strong correlation between both methods (glucose, R2 = 0.87, n = 173; lactic acid, R2 = 0.74, n = 173). NMR spectroscopy detected a total of 99 metabolites; 51 in both, CSF and serum, 16 in CSF only, and 32 in serum only. CSF concentrations of some metabolites increased with age and/or decreasing blood-brain-barrier function. Metabolite detection rates were overall similar among the different disease groups. However, in two-group comparisons, absolute metabolite levels in CSF and serum discriminated between multiple sclerosis and neurodegenerative diseases (area under the curve (AUC) = 0.96), multiple sclerosis and Parkinson's disease (AUC = 0.89), and Parkinson's disease and control patients (AUC = 0.91), as demonstrated by random forest statistical models. Orthogonal partial least square discriminant analysis using absolute metabolite levels in CSF and serum furthermore permitted separation of Parkinson's disease and neurodegenerative diseases. CSF propionic acid levels were about fourfold lower in Parkinson's disease as compared to neurodegenerative diseases.

CONCLUSIONS

These findings outline the landscape of the CSF and serum metabolome in different categories of neurological diseases and identify age and blood-brain-barrier function as relevant co-factors for CSF levels of certain metabolites. Metabolome profiles as determined by NMR spectroscopy may potentially aid in differentiating groups of patients with different neurological diseases, including clinically meaningful differentiations, such as Parkinson's disease from other neurodegenerative diseases.

Increased burden of rare risk variants across gene expression networks predisposes to sporadic Parkinson's disease

Alpha-synuclein (αSyn) is an intrinsically disordered protein that accumulates in the brains of patients with Parkinson's disease and forms intraneuronal inclusions called Lewy Bodies. While the mechanism underlying the dysregulation of αSyn in Parkinson's disease is unclear, it is thought that prionoid cell-to-cell propagation of αSyn has an important role. Through a high throughput screen, we recently identified 38 genes whose knock down modulates αSyn propagation. Follow up experiments were undertaken for two of those genes, TAX1BP1 and ADAMTS19, to study the mechanism with which they regulate αSyn homeostasis. We used a recently developed M17D neuroblastoma cell line expressing triple mutant (E35K+E46K+E61K) "3K" αSyn under doxycycline induction. 3K αSyn spontaneously forms inclusions that show ultrastructural similarities to Lewy Bodies. Experiments using that cell line showed that TAX1BP1 and ADAMTS19 regulate how αSyn interacts with lipids and phase separates into inclusions, respectively, adding to the growing body of evidence implicating those processes in Parkinson's disease. Through RNA sequencing, we identified several genes that are differentially expressed after knock-down of TAX1BP1 or ADAMTS19. Burden analysis revealed that those differentially expressed genes (DEGs) carry an increased frequency of rare risk variants in Parkinson's disease patients versus healthy controls, an effect that was independently replicated across two separate cohorts (GP2 and AMP-PD). Weighted gene co-expression network analysis (WGCNA) showed that the DEGs cluster within modules in regions of the brain that develop high degrees of αSyn pathology (basal ganglia, cortex). We propose a novel model for the genetic architecture of sporadic Parkinson's disease: increased burden of risk variants across genetic networks dysregulates pathways underlying αSyn homeostasis, thereby leading to pathology and neurodegeneration.

Identification of metabolites reproducibly associated with Parkinson's Disease via meta-analysis and computational modelling

Many studies have reported metabolomic analysis of different bio-specimens from Parkinson's disease (PD) patients. However, inconsistencies in reported metabolite concentration changes make it difficult to draw conclusions as to the role of metabolism in the occurrence or development of Parkinson's disease. We reviewed the literature on metabolomic analysis of PD patients. From 74 studies that passed quality control metrics, 928 metabolites were identified with significant changes in PD patients, but only 190 were replicated with the same changes in more than one study. Of these metabolites, 60 exclusively increased, such as 3-methoxytyrosine and glycine, 54 exclusively decreased, such as pantothenic acid and caffeine, and 76 inconsistently changed in concentration in PD versus control subjects, such as ornithine and tyrosine. A genome-scale metabolic model of PD and corresponding metabolic map linking most of the replicated metabolites enabled a better understanding of the dysfunctional pathways of PD and the prediction of additional potential metabolic markers from pathways with consistent metabolite changes to target in future studies.

Diagnosing Parkinson's disease and monitoring its progression: Biomarkers from combined GC-TOF MS and LC-MS/MS untargeted metabolomics

Parkinson's disease (PD) is a prevalent neurodegenerative disorder with a poorly understood etiology. An accurate diagnosis of idiopathic PD remains challenging as misdiagnosis is common in routine clinical practice. Moreover, current therapeutics focus on symptomatic management rather than curing or slowing down disease progression. Therefore, identification of potential PD biomarkers and providing a better understanding of the underlying disease pathophysiology are urgent. Herein, hydrophilic interaction liquid chromatography-mass spectrometry (LC-MS/MS) and gas chromatography-mass spectrometry (GC-TOF MS) based metabolomics approaches were used to profile the serum metabolome of 50 patients with different stages of idiopathic PD (early, mid and advanced) and 45 age-matched controls. Levels of 57 metabolites including cysteine-S-sulfate and N-acetyl tryptophan were significantly higher in patients with PD compared to controls, with lower amounts of additional 51 metabolites including vanillic acid, and N-acetylaspartic acid. Xanthines, including caffeine and its downstream metabolites, were lowered in patients with PD relative to controls indicating a potential role caffeine and its metabolites against neuronal damage. Seven metabolites, namely cysteine-S-sulfate, 1-methylxanthine, vanillic acid, N-acetylaspartic acid, 3-N-acetyl tryptophan, 5-methoxytryptophol, and 13-HODE yielded a ROC curve with a high classification accuracy (AUC 0.977). Comparison between different PD stages showed that cysteine-S-sulfate levels were significantly increasing with the advancement of PD stages while LPI 20:4 was significantly decreasing with disease progression. Our findings provide new biomarker candidates to assist in the diagnosis of PD and monitor its progression. Unusual metabolites like cysteine-S-sulfate might point to therapeutic targets that could enhance the development of novel PD treatments, such as NMDA antagonists.

Integrated metabolomics and proteomics analysis of plasma lipid metabolism in Parkinson's disease

INTRODUCTION

Metabolomics and proteomics are two growing fields of science which may shed light on the molecular mechanisms that contribute to neurodegenerative diseases. Studies focusing on these aspects can reveal specific metabolites and proteins that can halt or reverse the progressive neurodegenerative process leading to dopaminergic cell death in the brain.

AREAS COVERED

In this article, an overview of the current status of metabolomic and proteomic profiling in the neurodegenerative disease such as Parkinson's disease (PD) is presented. We discuss the importance of state-of-the-art metabolomics and proteomics using advanced analytical methodologies and their potential for discovering new biomarkers in PD. We critically review the research to date, highlighting how metabolomics and proteomics can have an important impact on early disease diagnosis, future therapy development and the identification of new biomarkers. Finally, we will discuss interactions between lipids and α-synuclein (SNCA) and also consider the role of SNCA in lipid metabolism.

EXPERT OPINION

Metabolomic and proteomic studies contribute to understanding the biological basis of PD pathogenesis, identifying potential biomarkers and introducing new therapeutic strategies. The complexity and multifactorial nature of this disease requires a comprehensive approach, which can be achieved by integrating just these two omic studies.

Untargeted serum metabolomics reveals novel metabolite associations and disruptions in amino acid and lipid metabolism in Parkinson's disease

BACKGROUND

Untargeted high-resolution metabolomic profiling provides simultaneous measurement of thousands of metabolites. Metabolic networks based on these data can help uncover disease-related perturbations across interconnected pathways.

OBJECTIVE

Identify metabolic disturbances associated with Parkinson's disease (PD) in two population-based studies using untargeted metabolomics.

METHODS

We performed a metabolome-wide association study (MWAS) of PD using serum-based untargeted metabolomics data derived from liquid chromatography with high-resolution mass spectrometry (LC-HRMS) using two distinct population-based case-control populations. We also combined our results with a previous publication of 34 metabolites linked to PD in a large-scale, untargeted MWAS to assess external validation.

RESULTS

LC-HRMS detected 4,762 metabolites for analysis (HILIC: 2716 metabolites; C18: 2046 metabolites). We identified 296 features associated with PD at FDR<0.05, 134 having a log2 fold change (FC) beyond ±0.5 (228 beyond ±0.25). Of these, 104 were independently associated with PD in both discovery and replication studies at p<0.05 (170 at p<0.10), while 27 were associated with levodopa-equivalent dose among the PD patients. Intriguingly, among the externally validated features were the microbial-related metabolites, p-cresol glucuronide (FC=2.52, 95% CI=1.67, 3.81, FDR=7.8e-04) and p-cresol sulfate. P-cresol glucuronide was also associated with motor symptoms among patients. Additional externally validated metabolites associated with PD include phenylacetyl-L-glutamine, trigonelline, kynurenine, biliverdin, and pantothenic acid. Novel associations include the anti-inflammatory metabolite itaconate (FC=0.79, 95% CI=0.73, 0.86; FDR=2.17E-06) and cysteine-S-sulfate (FC=1.56, 95% CI=1.39, 1.75; FDR=3.43E-11). Seventeen pathways were enriched, including several related to amino acid and lipid metabolism.

CONCLUSIONS

Our results revealed PD-associated metabolites, confirming several previous observations, including for p-cresol glucuronide, and newly implicating interesting metabolites, such as itaconate. Our data also suggests metabolic disturbances in amino acid and lipid metabolism and inflammatory processes in PD.

Exploring the interplay between metabolomics and genetics in Parkinson's disease: Insights from ongoing research and future avenues

Parkinson's disease (PD) is a widespread neurodegenerative disorder, whose complex aetiology remains under construction. While rare variants have been associated with the monogenic PD form, most PD cases are influenced by multiple genetic and environmental aspects. Nonetheless, the pathophysiological pathways and molecular networks involved in monogenic/idiopathic PD overlap, and genetic variants are decisive in elucidating the convergent underlying mechanisms of PD. In this scenario, metabolomics has furnished a dynamic and systematic picture of the synergy between the genetic background and environmental influences that impact PD, making it a valuable tool for investigating PD-related metabolic dysfunctions. In this review, we performed a brief overview of metabolomics current research in PD, focusing on significant metabolic alterations observed in idiopathic PD from different biofluids and strata and exploring how they relate to genetic factors associated with monogenic PD. Dysregulated amino acid metabolism, lipid metabolism, and oxidative stress are the critical metabolic pathways implicated in both genetic and idiopathic PD. By merging metabolomics and genetics data, it is possible to distinguish metabolic signatures of specific genetic backgrounds and to pinpoint subgroups of PD patients who could derive personalized therapeutic benefits. This approach holds great promise for advancing PD research and developing innovative, cost-effective treatments.

Chemical catalysis by biological amyloids

Toxic aggregation of proteins and peptides into amyloid fibers is the basis of several human diseases. In each disease, a particular peptide noncovalently assembles into long thin structures with an overall cross-β fold. Amyloids are not only related to disease: functional amyloids are found in many biological systems and artificial peptide amyloids are developed into novel nanomaterials. Amyloid fibers can act as template for the generation of more amyloids but are considered nonreactive in chemical catalysis. The perception of amyloids as chemically inert species was recently challenged by in vitro work on three human amyloid systems. With the use of model substrates, amyloid-β, α-synuclein and glucagon amyloids were found to catalyze biologically relevant chemical reactions. The detected catalytic activity was much less than that of 'real' enzymes, but like that of designed (synthetic) catalytic amyloids. I here describe the current knowledge around this new activity of natural amyloids and the putative connection to metabolic changes in amyloid diseases. These pioneering studies imply that catalytic activity is an unexplored gain-of-function activity of disease amyloids. In fact, all biological amyloids may harbor intrinsic catalytic activity, tuned by each amyloid's particular fold, that await discovery.

Sphingolipid metabolism in brain insulin resistance and neurological diseases

Sphingolipids, as members of the large lipid family, are important components of plasma membrane. Sphingolipids participate in biological signal transduction to regulate various important physiological processes such as cell growth, apoptosis, senescence, and differentiation. Numerous studies have demonstrated that sphingolipids are strongly associated with glucose metabolism and insulin resistance. Insulin resistance, including peripheral insulin resistance and brain insulin resistance, is closely related to the occurrence and development of many metabolic diseases. In addition to metabolic diseases, like type 2 diabetes, brain insulin resistance is also involved in the progression of neurodegenerative diseases including Alzheimer's disease and Parkinson's disease. However, the specific mechanism of sphingolipids in brain insulin resistance has not been systematically summarized. This article reviews the involvement of sphingolipids in brain insulin resistance, highlighting the role and molecular biological mechanism of sphingolipid metabolism in cognitive dysfunctions and neuropathological abnormalities of the brain.

More than meets the eye in Parkinson's disease and other synucleinopathies: from proteinopathy to lipidopathy

The accumulation of proteinaceous inclusions in the brain is a common feature among neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease (PD), and dementia with Lewy bodies (DLB). The main neuropathological hallmark of PD and DLB are inclusions, known as Lewy bodies (LBs), enriched not only in α-synuclein (aSyn), but also in lipid species, organelles, membranes, and even nucleic acids. Furthermore, several genetic risk factors for PD are mutations in genes involved in lipid metabolism, such as GBA1, VSP35, or PINK1. Thus, it is not surprising that mechanisms that have been implicated in PD, such as inflammation, altered intracellular and vesicular trafficking, mitochondrial dysfunction, and alterations in the protein degradation systems, may be also directly or indirectly connected through lipid homeostasis. In this review, we highlight and discuss the recent evidence that suggests lipid biology as important drivers of PD, and which require renovated attention by neuropathologists. Particularly, we address the implication of lipids in aSyn accumulation and in the spreading of aSyn pathology, in mitochondrial dysfunction, and in ER stress. Together, this suggests we should broaden the view of PD not only as a proteinopathy but also as a lipidopathy.

Amyloids of α-Synuclein Promote Chemical Transformations of Neuronal Cell Metabolites

The assembly of α-synuclein into cross-β structured amyloid fibers results in Lewy body deposits and neuronal degeneration in Parkinson's disease patients. As the cell environment is highly crowded, interactions between the formed amyloid fibers and a range of biomolecules can occur in cells. Although amyloid fibers are considered chemically inert species, recent in vitro work using model substrates has shown α-synuclein amyloids, but not monomers, to catalyze the hydrolysis of ester and phosphoester bonds. To search for putative catalytic activity of α-synuclein amyloids on biologically relevant metabolites, we here incubated α-synuclein amyloids with neuronal SH-SY5Y cell lysates devoid of proteins. LC-MS-based metabolomic (principal component and univariate) analysis unraveled distinct changes in several metabolite levels upon amyloid (but not monomer) incubation. Of 63 metabolites identified, the amounts of four increased (3-hydroxycapric acid, 2-pyrocatechuic acid, adenosine, and NAD), and the amounts of seventeen decreased (including aromatic and apolar amino acids, metabolites in the TCA cycle, keto acids) in the presence of α-synuclein amyloids. Many of these metabolite changes match what has been reported previously in Parkinson's disease patients and animal-model metabolomics studies. Chemical reactivity of α-synuclein amyloids may be a new gain-of-function that alters the metabolite composition in cells and, thereby, modulates disease progression.

Metabolomic Footprint of Disrupted Energetics and Amino Acid Metabolism in Neurodegenerative Diseases: Perspectives for Early Diagnosis and Monitoring of Therapy

The prevalence of neurodegenerative diseases (NDs) is increasing due to the aging population and improved longevity. They are characterized by a range of pathological hallmarks, including protein aggregation, mitochondrial dysfunction, and oxidative stress. The aim of this review is to summarize the alterations in brain energy and amino acid metabolism in Alzheimer’s disease (AD), Parkinson’s disease (PD), and Huntington’s disease (HD). Based on our findings, we proposed a group of selected metabolites related to disturbed energy or mitochondrial metabolism as potential indicators or predictors of disease. We also discussed the hidden challenges of metabolomics studies in NDs and proposed future directions in this field. We concluded that biochemical parameters of brain energy metabolism disruption (obtained with metabolomics) may have potential application as a diagnostic tool for the diagnosis, prediction, and monitoring of the effectiveness of therapies for NDs. However, more studies are needed to determine the sensitivity of the proposed candidates. We suggested that the most valuable biomarkers for NDs studies could be groups of metabolites combined with other neuroimaging or molecular techniques. To attain clinically applicable results, the integration of metabolomics with other “omic” techniques might be required.

The lipidomes of C. elegans with mutations in asm-3/acid sphingomyelinase and hyl-2/ceramide synthase show distinct lipid profiles during aging

Lipid metabolism affects cell and physiological functions that mediate animal healthspan and lifespan. Lipidomics approaches in model organisms have allowed us to better understand changes in lipid composition related to age and lifespan. Here, using the model C. elegans, we examine the lipidomes of mutants lacking enzymes critical for sphingolipid metabolism; specifically, we examine acid sphingomyelinase (asm-3), which breaks down sphingomyelin to ceramide, and ceramide synthase (hyl-2), which synthesizes ceramide from sphingosine. Worm asm-3 and hyl-2 mutants have been previously found to be long- and short-lived, respectively. We analyzed longitudinal lipid changes in wild type animals compared to mutants at 1-, 5-, and 10-days of age. We detected over 700 different lipids in several lipid classes. Results indicate that wildtype animals exhibit increased triacylglycerols (TAG) at 10-days compared to 1-day, and decreased lysophoshatidylcholines (LPC). We find that 10-day hyl-2 mutants have elevated total polyunsaturated fatty acids (PUFA) and increased LPCs compared to 10-day wildtype animals. These changes mirror another short-lived model, the daf-16/FOXO transcription factor that is downstream of the insulin-like signaling pathway. In addition, we find that hyl-2 mutants have poor oxidative stress response, supporting a model where mutants with elevated PUFAs may accumulate more oxidative damage. On the other hand, 10-day asm-3 mutants have fewer TAGs. Intriguingly, asm-3 mutants have a similar lipid composition as the long-lived, caloric restriction model eat-2/mAChR mutant. Together, these analyses highlight the utility of lipidomic analyses to characterize metabolic changes during aging in C. elegans.

Future Prospects in Parkinson's Disease Diagnosis and Treatment

Parkinson's disease (PD) is a neurodegenerative disease with a rapidly increasing incidence and prevalence. Although it affects more than 6 million people worldwide, it is predicted to be doubled by 2040. Current criteria used in the diagnosis of PD include the presence of bradykinesia as well as the presence of rest tremor and/or rigidity, but the clinic is multifaceted and includes many non-motor symptoms. Non-motor symptoms may occur in the prodromal period, years before clinically evident Parkinson's disease. During this period, diagnosing the disease will likely be even more important when disease-modifying treatments are available. Currently, there is no single biomarker that can be used in the diagnosis of PD and no disease-modifying treatment is available. Identification of biomarkers in early diagnosis will enable the most effective use of disease-modifying therapies and will shed light on possible underlying pathologies, studies in this area have gained momentum in recent years. Molecular imaging methods, genetic studies, salivary gland and skin biopsies, metabolomics, lysosomal pathway are some of them. In this article, besides the current diagnosis and treatment methods of the disease, biomarkers and treatments that are expected to be better understood in the near future will be mentioned.

Global metabolic profiles in a non-human primate model of maternal immune activation: implications for neurodevelopmental disorders

Epidemiological evidence implicates severe maternal infections as risk factors for neurodevelopmental disorders, such as ASD and schizophrenia. Accordingly, animal models mimicking infection during pregnancy, including the maternal immune activation (MIA) model, result in offspring with neurobiological, behavioral, and metabolic phenotypes relevant to human neurodevelopmental disorders. Most of these studies have been performed in rodents. We sought to better understand the molecular signatures characterizing the MIA model in an organism more closely related to humans, rhesus monkeys (Macaca mulatta), by evaluating changes in global metabolic profiles in MIA-exposed offspring. Herein, we present the global metabolome in six peripheral tissues (plasma, cerebrospinal fluid, three regions of intestinal mucosa scrapings, and feces) from 13 MIA and 10 control offspring that were confirmed to display atypical neurodevelopment, elevated immune profiles, and neuropathology. Differences in lipid, amino acid, and nucleotide metabolism discriminated these MIA and control samples, with correlations of specific metabolites to behavior scores as well as to cytokine levels in plasma, intestinal, and brain tissues. We also observed modest changes in fecal and intestinal microbial profiles, and identify differential metabolomic profiles within males and females. These findings support a connection between maternal immune activation and the metabolism, microbiota, and behavioral traits of offspring, and may further the translational applications of the MIA model and the advancement of biomarkers for neurodevelopmental disorders such as ASD or schizophrenia.

Serum Phospholipid Profile Changes in Gaucher Disease and Parkinson's Disease

Alterations in the levels of serum sphingolipids and phospholipids have been reported in Gaucher disease and in Parkinson's disease, suggesting a potential role of these lipids as biomarkers. This project's objective is to detect novel associations and novel candidate biomarkers in the largest Spanish Gaucher and Parkinson diseases of the Iberian Peninsula. For that, 278 participants were included: 100 sporadic Parkinson's patients, 70 Gaucher patients, 15 GBA1-mutation-carrier Parkinson's patients and 93 controls. A serum lipidomics array including 10 phospholipid groups, 368 species, was performed using high-performance liquid chromatography-mass spectrometry. Lipid levels were compared between groups via multiple-regression analyses controlling for clinical and demographic parameters. Additionally, lipid levels were compared within the Gaucher and Parkinson's groups controlling for medication and/or disease severity. Results were controlled for robustness by filtering of non-detectable lipid values. There was an increase in the levels of phosphatidylcholine, with a simultaneous decrease in lyso-phosphatidylcholine, in the Gaucher, Parkinson's and GBA1-mutation-carrier Parkinson's patients vs. controls. Phosphatidylethanolamine, lyso- and plasmalogen-phosphatidylethanolamine were also increased in Gaucher and Parkinson's. Gaucher patients also showed an increase in lyso-phosphatidylserine and phosphatidylglycerol. While in the Gaucher and Parkinson's groups, velaglucerase alpha and dopamine agonists, respectively, showed positive associations with the lipid changes, miglustat treatment in Gaucher patients normalized the altered phosphatidylcholine/lyso-phosphatidylcholine ratio. In conclusion, Gaucher and Parkinson's patients showed changes in various serum phospholipid levels when compared with healthy controls, further supporting the role of such lipids in disease development and, possibly, as putative biomarkers. This hypothesis was reinforced by the normalizing effect of miglustat, and by controlling for data robustness, even though the limited number of participants, especially in the sub-distribution by treatment groups in GD requires validation in a larger number of patients.

Lipid pathway dysfunction is prevalent in patients with Parkinson's disease

Many genetic risk factors for Parkinson's disease have lipid-related functions and lipid-modulating drugs such as statins may be protective against Parkinson's disease. Moreover, the hallmark Parkinson's disease pathological protein, α-synuclein, has lipid membrane function and pathways dysregulated in Parkinson's disease such as the endosome-lysosome system and synaptic signaling rely heavily on lipid dynamics. Despite the potential role for lipids in Parkinson's disease, most research to date has been protein-centric, with large-scale, untargeted serum and CSF lipidomic comparisons between genetic and idiopathic Parkinson's disease and neurotypical controls limited. In particular, the extent to which lipid dysregulation occurs in mutation carriers of one of the most common Parkinson's disease risk genes, LRRK2, is unclear. Further, the functional lipid pathways potentially dysregulated in idiopathic and LRRK2 mutation Parkinson's disease is underexplored. To better determine the extent of lipid dysregulation in Parkinson's disease, untargeted high performance liquid chromatography-tandem mass spectrometry was performed on serum (N = 221) and CSF (N = 88) obtained from a multiethnic population from the Michael J Fox Foundation LRRK2 Clinical Cohort Consortium. The cohort consisted of controls, asymptomatic LRRK2 G2019S carriers, LRRK2 G2019S carriers with Parkinson's disease and Parkinson's disease patients without a LRRK2 mutation. Age and sex were adjusted for in analyses where appropriate. Approximately one thousand serum lipid species per participant were analyzed. The main serum lipids that distinguished both Parkinson's disease patients and LRRK2 mutation carriers from controls included species of ceramide, triacylglycerol, sphingomyelin, acylcarnitine, phosphatidylcholine and lysophosphatidylethanolamine. Significant alterations in sphingolipids and glycerolipids were also reflected in Parkinson's disease and LRRK2 mutation carrier CSF, although no correlations were observed between lipids identified in both serum and CSF. Pathway analysis of altered lipid species indicated that sphingolipid metabolism, insulin signaling and mitochondrial function were the major metabolic pathways dysregulated in Parkinson's disease. Importantly, these pathways were also found to be dysregulated in serum samples from a second Parkinson's disease cohort (N = 315). Results from this study demonstrate that dysregulated lipids in Parkinson's disease generally, and in LRRK2 mutation carriers, are from functionally and metabolically related pathways. These findings provide new insight into the extent of lipid dysfunction in Parkinson's disease and therapeutics manipulating these pathways may potentially be beneficial for Parkinson's disease patients. Moreover, serum lipid profiles may be novel biomarkers for both genetic and idiopathic Parkinson's disease.

Update on CSF Biomarkers in Parkinson's Disease

Progress in developing disease-modifying therapies in Parkinson’s disease (PD) can only be achieved through reliable objective markers that help to identify subjects at risk. This includes an early and accurate diagnosis as well as continuous monitoring of disease progression and therapy response. Although PD diagnosis still relies mainly on clinical features, encouragingly, advances in biomarker discovery have been made. The cerebrospinal fluid (CSF) is a biofluid of particular interest to study biomarkers since it is closest to the brain structures and therefore could serve as an ideal source to reflect ongoing pathologic processes. According to the key pathophysiological mechanisms, the CSF status of α-synuclein species, markers of amyloid and tau pathology, neurofilament light chain, lysosomal enzymes and markers of neuroinflammation provide promising preliminary results as candidate biomarkers. Untargeted approaches in the field of metabolomics provide insights into novel and interconnected biological pathways. Markers based on genetic forms of PD can contribute to identifying subgroups suitable for gene-targeted treatment strategies that might also be transferable to sporadic PD. Further validation analyses in large PD cohort studies will identify the CSF biomarker or biomarker combinations with the best value for clinical and research purposes.

Cerebrospinal Fluid Metabolome in Parkinson's Disease and Multiple System Atrophy

Parkinson’s disease (PD) and multiple system atrophy (MSA) belong to the neurodegenerative group of synucleinopathies; differential diagnosis between PD and MSA is difficult, especially at early stages, owing to their clinical and biological similarities. Thus, there is a pressing need to identify metabolic biomarkers for these diseases. The metabolic profile of the cerebrospinal fluid (CSF) is reported to be altered in PD and MSA; however, the altered metabolites remain unclear. We created a single network with altered metabolites in PD and MSA based on the literature and assessed biological functions, including metabolic disorders of the nervous system, inflammation, concentration of ATP, and neurological disorder, through bioinformatics methods. Our in-silico prediction-based metabolic networks are consistent with Parkinsonism events. Although metabolomics approaches provide a more quantitative understanding of biochemical events underlying the symptoms of PD and MSA, limitations persist in covering molecules related to neurodegenerative disease pathways. Thus, omics data, such as proteomics and microRNA, help understand the altered metabolomes mechanism. In particular, integrated omics and machine learning approaches will be helpful to elucidate the pathological mechanisms of PD and MSA. This review discusses the altered metabolites between PD and MSA in the CSF and omics approaches to discover diagnostic biomarkers.

Alterations of Sphingolipid and Phospholipid Pathways and Ornithine Level in the Plasma as Biomarkers of Parkinson's Disease

The biomarkers of Parkinson’s disease (PD) remain to be investigated. This work aimed to identify blood biomarkers for PD using targeted metabolomics analysis. We quantified the plasma levels of 255 metabolites in 92 PD patients and 60 healthy controls (HC). PD patients were sub-grouped into early (Hoehn–Yahr stage ≤ 2, n = 72) and advanced (Hoehn–Yahr stage > 2, n = 20) stages. Fifty-nine phospholipids, 3 fatty acids, 3 amino acids, and 7 biogenic amines, demonstrated significant alterations in PD patients. Six of them, dihydro sphingomyelin (SM) 24:0, 22:0, 20:0, phosphatidylethanolamine-plasmalogen (PEp) 38:6, and phosphatidylcholine 38:5 and 36:6, demonstrated lowest levels in PD patients in the advanced stage, followed by those in the early stage and HC. By contrast, the level of ornithine was highest in PD patients at the advanced stage, followed by those at the early stage and HC. These biomarker candidates demonstrated significant correlations with scores of motor disability, cognitive dysfunction, depression, and quality of daily life. The support vector machine algorithm using α-synuclein, dihydro SM 24:0, and PEp 38:6 demonstrated good ability to separate PD from HC (AUC: 0.820). This metabolomic analysis demonstrates new plasma biomarker candidates for PD and supports their role in participating PD pathogenesis and monitoring disease progression.

Small phenolic and indolic gut-dependent molecules in the primate central nervous system: levels vs. bioactivity

INTRODUCTION

A rapidly growing body of data documents associations between disease of the brain and small molecules generated by gut-microbiota (GMB). While such metabolites can affect brain function through a variety of mechanisms, the most direct action would be on the central nervous system (CNS) itself.

OBJECTIVE

Identify indolic and phenolic GMB-dependent small molecules that reach bioactive concentrations in primate CNS.

METHODS

We conducted a PubMed search for metabolomic studies of the primate CNS [brain tissue or cerebrospinal fluid (CSF)] and then selected for phenolic or indolic metabolites that (i) had been quantified, (ii) were GMB-dependent. For each chemical we then conducted a search for studies of bioactivity conducted in vitro in human cells of any kind or in CNS cells from the mouse or rat.

RESULTS

36 metabolites of interests were identified in primate CNS through targeted metabolomics. Quantification was available for 31/36 and in vitro bioactivity for 23/36. The reported CNS range for 8 metabolites 2-(3-hydroxyphenyl)acetic acid, 2-(4-hydroxyphenyl)acetic acid, 3-(3-hydroxyphenyl)propanoic acid, (E)-3-(3,4-dihydroxyphenyl)prop-2-enoic acid [caffeic acid], 3-hydroxybenzoic acid, 4-hydroxybenzoic acid, 2-acetamido-3-(1H-indol-3-yl)propanoic acid [N-acetyltryptophan], 1H-indol-3-yl hydrogen sulfate [indoxyl-3-sulfate] overlapped with a bioactive concentration. However, the number and quality of relevant studies of CNS neurochemistry as well as of bioactivity were highly limited. Structural isomers, multiple metabolites and potential confounders were inadequately considered.

CONCLUSION

The potential direct bioactivity of GMB-derived indolic and phenolic molecules on primate CNS remains largely unknown. The field requires additional strategies to identify and prioritize screening of the most promising small molecules that enter the CNS.

The Sphingolipid Asset Is Altered in the Nigrostriatal System of Mice Models of Parkinson’s Disease

Parkinson’s disease (PD) is a neurodegenerative disease incurable due to late diagnosis and treatment. Therefore, one of the priorities of neurology is to study the mechanisms of PD pathogenesis at the preclinical and early clinical stages. Given the important role of sphingolipids in the pathogenesis of neurodegenerative diseases, we aimed to analyze the gene expression of key sphingolipid metabolism enzymes (ASAH1, ASAH2, CERS1, CERS3, CERS5, GBA1, SMPD1, SMPD2, UGCG) and the content of 32 sphingolipids (subspecies of ceramides, sphingomyelins, monohexosylceramides and sphinganine, sphingosine, and sphingosine-1-phosphate) in the nigrostriatal system in 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) mouse models of the preclinical and clinical stages of PD. It has been shown that in PD models, the expression of five of the nine studied genes (CERS1, CERS5, ASAH1, ASAH2, and GBA1) increases but only in the substantia nigra (SN) containing dopaminergic cell bodies. Changes in the expression of enzyme genes were accompanied by an increase in the content of 7 of the 32 studied sphingolipids. Such findings suggest these genes as attractive candidates for diagnostic purposes for preclinical and clinical stages of PD.

Lipotoxicity Downstream of α-Synuclein Imbalance: A Relevant Pathomechanism in Synucleinopathies?

Neuronal loss in Parkinson's disease and related brain diseases has been firmly linked to the abundant neuronal protein α-synuclein (αS). However, we have gained surprisingly little insight into how exactly αS exerts toxicity in these diseases. Hypotheses of proteotoxicity, disturbed vesicle trafficking, mitochondrial dysfunction and other toxicity mechanisms have been proposed, and it seems possible that a combination of different mechanisms may drive pathology. A toxicity mechanism that has caught increased attention in the recent years is αS-related lipotoxicity. Lipotoxicity typically occurs in a cell when fatty acids exceed the metabolic needs, triggering a flux into harmful pathways of non-oxidative metabolism. Genetic and experimental approaches have revealed a significant overlap between lipid storage disorders, most notably Gaucher's disease, and synucleinopathies. There is accumulating evidence for lipid aberrations causing synuclein misfolding as well as for αS excess and misfolding causing lipid aberration. Does that mean the key problem in synucleinopathies is lipotoxicity, the accumulation of harmful lipid species or alteration in lipid equilibrium? Here, we review the existing literature in an attempt to get closer to an answer.

Plasma Metabolite Markers of Parkinson's Disease and Atypical Parkinsonism

Differentiating between Parkinson’s disease (PD) and the atypical Parkinsonian disorders of multiple system atrophy (MSA) and progressive supranuclear palsy (PSP) is difficult clinically due to overlapping symptomatology, especially at early disease stages. Consequently, there is a need to identify metabolic markers for these diseases and to develop them into viable biomarkers. In the present investigation, solution nuclear magnetic resonance and mass spectrometry metabolomics were used to quantitatively characterize the plasma metabolomes (a total of 167 metabolites) of a cohort of 94 individuals comprising 34 PD, 12 MSA, and 17 PSP patients, as well as 31 control subjects. The distinct and statistically significant differences observed in the metabolite concentrations of the different disease and control groups enabled the identification of potential plasma metabolite markers of each disorder and enabled the differentiation between the disorders. These group-specific differences further implicate disturbances in specific metabolic pathways. The two metabolites, formic acid and succinate, were altered similarly in all three disease groups when compared to the control group, where a reduced level of formic acid suggested an effect on pyruvate metabolism, methane metabolism, and/or the kynurenine pathway, and an increased succinate level suggested an effect on the citric acid cycle and mitochondrial dysfunction.

Metabolic Profile in Plasma AND CSF of LEVODOPA-induced Dyskinesia in Parkinson's Disease: Focus on Neuroinflammation

The existence of few biomarkers and the lack of a better understanding of the pathophysiology of levodopa-induced dyskinesia (LID) in Parkinson's disease (PD) require new approaches, as the metabolomic analysis, for discoveries. We aimed to identify a metabolic profile associated with LID in patients with PD in an original cohort and to confirm the results in an external cohort (BioFIND). In the original cohort, plasma and CSF were collected from 20 healthy controls, 23 patients with PD without LID, and 24 patients with PD with LID. LC-MS/MS and metabolomics data analysis were used to perform untargeted metabolomics. Untargeted metabolomics data from the BioFIND cohort were analyzed. We identified a metabolic profile associated with LID in PD, composed of multiple metabolic pathways. In particular, the dysregulation of the glycosphingolipid metabolic pathway was more related to LID and was strongly associated with the severity of dyskinetic movements. Furthermore, bile acid biosynthesis metabolites simultaneously found in plasma and CSF have distinguished patients with LID from other participants. Data from the BioFIND cohort confirmed dysregulation in plasma metabolites from the bile acid biosynthesis pathway. There is a distinct metabolic profile associated with LID in PD, both in plasma and CSF, which may be associated with the dysregulation of lipid metabolism and neuroinflammation.

Review of Metabolomics-Based Biomarker Research for Parkinson's Disease

Parkinson's disease (PD), as the second most common neurodegenerative disease, is seriously affecting the life quality of the elderly. However, there is still a lack of efficient medical methods to diagnosis PD before apparent symptoms occur. In recent years, clinical biomarkers including genetic, imaging, and tissue markers have exhibited remarkable benefits in assisting PD diagnoses. Due to the advantages of high-throughput detection of metabolites and almost non-invasive sample collection, metabolomics research of PD is widely used for diagnostic biomarker discovery. However, there are also a few shortages for those identified biomarkers, such as the scarcity of verifications regarding the sensitivity and specificity. Thus, reviewing the research progress of PD biomarkers based on metabolomics techniques is of great significance for developing PD diagnosis. To comprehensively clarify the progress of current metabolic biomarker studies in PD, we reviewed 20 research articles regarding the discovery and validation of biomarkers for PD diagnosis from three mainstream academic databases (NIH PubMed, ISI Web of Science, and Elsevier ScienceDirect). By analyzing those materials, we summarized the metabolic biomarkers identified by those metabolomics studies and discussed the potential approaches used for biomarker verifications. In conclusion, this review provides a comprehensive and updated overview of PD metabolomics research in the past two decades and particularly discusses the validation of disease biomarkers. We hope those discussions might provide inspiration for PD biomarker discovery and verification in the future.

A six-metabolite panel as potential blood-based biomarkers for Parkinson's disease

Characterisation and diagnosis of idiopathic Parkinson's disease (iPD) is a current challenge that hampers both clinical assessment and clinical trial development with the potential inclusion of non-PD cases. Here, we used a targeted mass spectrometry approach to quantify 38 metabolites extracted from the serum of 231 individuals. This cohort is currently one of the largest metabolomic studies including iPD patients, drug-naïve iPD, healthy controls and patients with Alzheimer's disease as a disease-specific control group. We identified six metabolites (3-hydroxykynurenine, aspartate, beta-alanine, homoserine, ornithine (Orn) and tyrosine) that are significantly altered between iPD patients and control participants. A multivariate model to predict iPD from controls had an area under the curve (AUC) of 0.905, with an accuracy of 86.2%. This panel of metabolites may serve as a potential prognostic or diagnostic assay for clinical trial prescreening, or for aiding in diagnosing pathological disease in the clinic.

The Metabolomic Approach Reveals the Alteration in Human Serum and Cerebrospinal Fluid Composition in Parkinson's Disease Patients

Parkinson’s disease (PD) is a major public health problem. Since currently there are no reliable diagnostic tools to reveal the early steps of PD, new methods should be developed, including those searching the variations in human metabolome. Alterations in human metabolites could help to establish an earlier and more accurate diagnosis. The presented research shows a targeted metabolomics study of both of the serum and CSF from PD patients, atypical parkinsonian disorders (APDs) patients, and the control. The use of the LC-MS/MS system enabled to quantitate 144 analytes in the serum and 51 in the CSF. This information about the concentration enabled for selection of the metabolites useful for differentiation between the studied group of patients, which should be further evaluated as candidates for markers of screening and differential diagnosis of PD and APDs. Among them, the four compounds observed to be altered in both the serum and CSF seem to be the most important: tyrosine, putrescine, trans-4-hydroxyproline, and total dimethylarginine. Furthermore, we indicated the metabolic pathways potentially related to neurodegeneration processes. Our studies present evidence that the proline metabolism might be related to neurodegeneration processes underlying PD and APDs. Further studies on the proposed metabolites and founded metabolic pathways may significantly contribute to understanding the molecular background of PD and improving the diagnostics and treatment in the future.

Metabolomics in Parkinson's disease

Parkinson's disease (PD) is a multifactorial neurodegenerative disorder in which environmental (lifestyle, dietary, infectious disease) factors as well as genetic make-up play a role. Metabolomics, an evolving research field combining biomarker discovery and pathogenetics, is particularly useful in studying complex pathophysiology in general and Parkinson's disease (PD) specifically. PD, the second most frequent neurodegenerative disorder, is characterized by the loss of dopaminergic neurons in the substantia nigra and the presence of intraneural inclusions of α-synuclein aggregates. Although considered a predominantly movement disorder, PD is also associated with number of non-motor features. Metabolomics has provided useful information regarding this neurodegenerative process with the aim of identifying a disease-specific fingerprint. Unfortunately, many disease variables such as clinical presentation, motor system involvement, disease stage and duration substantially affect biomarker relevance. As such, metabolomics provides a unique approach to studying this multifactorial neurodegenerative disorder.

Comprehensive Serum Lipidomics for Detecting Incipient Dementia in Parkinson's Disease

While a number of methods are available for analyzing lipids, unbiased untargeted lipidomics with high coverage remains a challenge. In this work, we report a study of isotope-standard-assisted liquid chromatography mass spectrometry lipidomics of serum for biomarker discovery. We focus on Parkinson's disease (PD), a neurodegenerative disorder that often progresses to dementia. Currently, the diagnosis of PD is purely clinical and there is limited ability to predict which PD patients will transition to dementia, hampering early interventions. We studied serum samples from healthy controls and PD patients with no clinical signs of dementia. A follow-up 3 years later revealed that a subset of PD patients had transitioned to dementia. Using the baseline samples, we constructed two biomarker panels to differentiate (1) PD patients from healthy controls and (2) PD patients that remained cognitively stable from PD patients with incipient dementia (diagnosed 3 years after sample collection). The proposed biomarker panels displayed excellent performance and may be useful for detecting prodromal PD dementia, allowing early interventions and prevention efforts. The biochemistry of significantly changed lipids is also discussed within the current knowledge of neurological pathologies. Our results are promising and future work using a larger cohort of samples is warranted.

Alpha-Synuclein as a Biomarker of Parkinson's Disease: Good, but Not Good Enough

Parkinson's disease (PD) is the second most common neurodegenerative disorder of the elderly, presenting primarily with symptoms of motor impairment. The disease is diagnosed most commonly by clinical examination with a great degree of accuracy in specialized centers. However, in some cases, non-classical presentations occur when it may be difficult to distinguish the disease from other types of degenerative or non-degenerative movement disorders with overlapping symptoms. The diagnostic difficulty may also arise in patients at the early stage of PD. Thus, a biomarker could help clinicians circumvent such problems and help them monitor the improvement in disease pathology during anti-parkinsonian drug trials. This review first provides a brief overview of PD, emphasizing, in the process, the important role of α-synuclein in the pathogenesis of the disease. Various attempts made by the researchers to develop imaging, genetic, and various biochemical biomarkers for PD are then briefly reviewed to point out the absence of a definitive biomarker for this disorder. In view of the overwhelming importance of α-synuclein in the pathogenesis, a detailed analysis is then made of various studies to establish the biomarker potential of this protein in PD; these studies measured total α-synuclein, oligomeric, and post-translationally modified forms of α-synuclein in cerebrospinal fluid, blood (plasma, serum, erythrocytes, and circulating neuron-specific extracellular vesicles) and saliva in combination with certain other proteins. Multiple studies also examined the accumulation of α-synuclein in various forms in PD in the neural elements in the gut, submandibular glands, skin, and the retina. The measurements of the levels of certain forms of α-synuclein in some of these body fluids or their components or peripheral tissues hold a significant promise in establishing α-synuclein as a definitive biomarker for PD. However, many methodological issues related to detection and quantification of α-synuclein have to be resolved, and larger cross-sectional and follow-up studies with controls and patients of PD, parkinsonian disorders, and non-parkinsonian movement disorders are to be undertaken.

Emerging role of metabolomics in protein conformational disorders

Introduction: Metabolomics focuses on interactions among different metabolites associated with various cellular functions in cells, tissues, and organs. In recent years, metabolomics has emerged as a powerful tool to identify perturbed metabolites, pathways influenced by the environment, for protein conformational diseases (PCDs) and also offers wide clinical application.Area Covered: This review provides a brief overview of recent advances in metabolomics as applied to identify metabolic variations in PCDs, such as Alzheimer's disease, Parkinson's disease, Huntington's disease, prion disease, and cardiac amyloidosis. The 'PubMed' and 'Google Scholar' database search methods have been used to screen the published reports with key search terms: metabolomics, biomarkers, and protein conformational disorders.Expert opinion: Metabolomics is the large-scale study of metabolites and is deemed to overwhelm other omics. It plays a crucial role in finding variations in diseases due to protein conformational changes. However, many PCDs are yet to be identified. Metabolomics is still an emerging field; there is a need for new high-resolution analytical techniques and more studies need to be carried out to generate new information.

Ceramide Metabolism and Parkinson's Disease-Therapeutic Targets

Ceramide is a bioactive sphingolipid involved in numerous cellular processes. In addition to being the precursor of complex sphingolipids, ceramides can act as second messengers, especially when they are generated at the plasma membrane of cells. Its metabolic dysfunction may lead to or be a consequence of an underlying disease. Recent reports on transcriptomics and electrospray ionization mass spectrometry analysis have demonstrated the variation of specific levels of sphingolipids and enzymes involved in their metabolism in different neurodegenerative diseases. In the present review, we highlight the most relevant discoveries related to ceramide and neurodegeneration, with a special focus on Parkinson's disease.

Identification of bioactive metabolites in human iPSC-derived dopaminergic neurons with PARK2 mutation: Altered mitochondrial and energy metabolism

PARK2 (parkin) mutations cause early-onset Parkinson's disease (PD). Parkin is an ubiquitin E3 ligase that participates in several cellular functions, including mitochondrial homeostasis. However, the specific metabolomic changes caused by parkin depletion remain unknown. Here, we used isogenic human induced pluripotent stem cells (iPSCs) with and without PARK2 knockout (KO) to investigate the effect of parkin loss of function by comparative metabolomics supplemented with ultrastructural and functional analyses. PARK2 KO neurons displayed increased tricarboxylic acid (TCA) cycle activity, perturbed mitochondrial ultrastructure, ATP depletion, and dysregulation of glycolysis and carnitine metabolism. These perturbations were combined with increased oxidative stress and a decreased anti-oxidative response. Key findings for PARK2 KO cells were confirmed using patient-specific iPSC-derived neurons. Overall, our data describe a unique metabolomic profile associated with parkin dysfunction and show that combining metabolomics with an iPSC-derived dopaminergic neuronal model of PD is a valuable approach to obtain novel insight into the disease pathogenesis.

Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature

Diagnosis of Parkinson's disease (PD) is commonly based on medical observations and assessment of clinical signs, including the characterization of a variety of motor symptoms. However, traditional diagnostic approaches may suffer from subjectivity as they rely on the evaluation of movements that are sometimes subtle to human eyes and therefore difficult to classify, leading to possible misclassification. In the meantime, early non-motor symptoms of PD may be mild and can be caused by many other conditions. Therefore, these symptoms are often overlooked, making diagnosis of PD at an early stage challenging. To address these difficulties and to refine the diagnosis and assessment procedures of PD, machine learning methods have been implemented for the classification of PD and healthy controls or patients with similar clinical presentations (e.g., movement disorders or other Parkinsonian syndromes). To provide a comprehensive overview of data modalities and machine learning methods that have been used in the diagnosis and differential diagnosis of PD, in this study, we conducted a literature review of studies published until February 14, 2020, using the PubMed and IEEE Xplore databases. A total of 209 studies were included, extracted for relevant information and presented in this review, with an investigation of their aims, sources of data, types of data, machine learning methods and associated outcomes. These studies demonstrate a high potential for adaptation of machine learning methods and novel biomarkers in clinical decision making, leading to increasingly systematic, informed diagnosis of PD.

Alteration in the Cerebrospinal Fluid Lipidome in Parkinson's Disease: A Post-Mortem Pilot Study

Lipid metabolism is clearly associated to Parkinson's disease (PD). Although lipid homeostasis has been widely studied in multiple animal and cellular models, as well as in blood derived from PD individuals, the cerebrospinal fluid (CSF) lipidomic profile in PD remains largely unexplored. In this study, we characterized the post-mortem CSF lipidomic imbalance between neurologically intact controls (n = 10) and PD subjects (n = 20). The combination of dual extraction with ultra-performance liquid chromatography-electrospray ionization quadrupole-time-of-flight mass spectrometry (UPLC-ESI-qToF-MS/MS) allowed for the monitoring of 257 lipid species across all samples. Complementary multivariate and univariate data analysis identified that glycerolipids (mono-, di-, and triacylglycerides), saturated and mono/polyunsaturated fatty acids, primary fatty amides, glycerophospholipids (phosphatidylcholines, phosphatidylethanolamines), sphingolipids (ceramides, sphingomyelins), N-acylethanolamines and sterol lipids (cholesteryl esters, steroids) were significantly increased in the CSF of PD compared to the control group. Interestingly, CSF lipid dyshomeostasis differed depending on neuropathological staging and disease duration. These results, despite the limitation of being obtained in a small population, suggest extensive CSF lipid remodeling in PD, shedding new light on the deployment of CSF lipidomics as a promising tool to identify potential lipid markers as well as discriminatory lipid species between PD and other atypical parkinsonisms.

Lipids: biomarkers of healthy aging

Life expectancy, and longevity have been increasing in recent years. However, this is, in most cases, accompanied by age-related diseases. Thus, it became essential to better understand the mechanisms inherent to aging, and to establish biomarkers that characterize this physiological process. Among all biomolecules, lipids appear to be a good target for the study of these biomarkers. In fact, some lipids have already been associated with age-related diseases. With the development of analytical techniques such as Mass Spectrometry, and Nuclear Magnetic Resonance, Lipidomics has been increasingly used to study pathological, and physiological states of an organism. Thus, the study of serum, and plasma lipidome in centenarians, and elderly individuals without age-related diseases can be a useful tool for the identification of aging biomarkers, and to understand physiological aging, and longevity. This review focus on the importance of lipids as biomarkers of aging, and summarize the changes in the lipidome that have been associated with aging, and longevity.

Clinical Insights into Mitochondrial Neurodevelopmental and Neurodegenerative Disorders: Their Biosignatures from Mass Spectrometry-Based Metabolomics

Mitochondria are dynamic multitask organelles that function as hubs for many metabolic pathways. They produce most ATP via the oxidative phosphorylation pathway, a critical pathway that the brain relies on its energy need associated with its numerous functions, such as synaptic homeostasis and plasticity. Therefore, mitochondrial dysfunction is a prevalent pathological hallmark of many neurodevelopmental and neurodegenerative disorders resulting in altered neurometabolic coupling. With the advent of mass spectrometry (MS) technology, MS-based metabolomics provides an emerging mechanistic understanding of their global and dynamic metabolic signatures. In this review, we discuss the pathogenetic causes of mitochondrial metabolic disorders and the recent MS-based metabolomic advances on their metabolomic remodeling. We conclude by exploring the MS-based metabolomic functional insights into their biosignatures to improve diagnostic platforms, stratify patients, and design novel targeted therapeutic strategies.

Vesicle trafficking and lipid metabolism in synucleinopathy

The neuronal protein α-synuclein (αS) is central to the pathogenesis of Parkinson's disease and other progressive brain diseases such as Lewy body dementia and multiple system atrophy. These diseases, collectively referred to as 'synucleinopathies', have long been considered purely proteinopathies: diseases characterized by the misfolding of a protein into small and large aggregates mainly consisting of that protein (in this case: α-synuclein). However, recent morphological insights into Lewy bodies, the hallmark neuropathology of human synucleinopathies, suggests these lesions are also rich in vesicles and other membranous organelles. Moreover, αS physiology and pathology are both strongly associated with various aspects of intracellular vesicle trafficking and lipid biology. αS physiologically binds to synaptic and other small vesicles, and several functions of αS in regulating vesicle biology have been proposed. Familial PD-linked αS excess and missense mutations have been shown to impair vesicle trafficking and alter lipid homeostasis. On the other hand, vesicle trafficking and lipid-related genes have emerged as Parkinson's risk factors, suggesting a bidirectional relationship. The answer to the question "Does abnormal αS accumulation cause impaired vesicle trafficking and lipid dyshomeostasis or is αS aggregation the consequence of such impairments?" may be "Both". Here, we review current knowledge of the αS-lipid and αS-vesicle trafficking interplay, with a special focus on Parkinson's disease and Lewy body dementia.

Advances of Mechanisms-Related Metabolomics in Parkinson's Disease

Parkinson's disease (PD) is a multifactorial disorder characterized by progressively debilitating dopaminergic neurodegeneration in the substantia nigra and the striatum, along with various metabolic dysfunctions and molecular abnormalities. Metabolomics is an emerging study and has been demonstrated to play important roles in describing complex human diseases by integrating endogenous and exogenous sources of alterations. Recently, an increasing amount of research has shown that metabolomics profiling holds great promise in providing unique insights into molecular pathogenesis and could be helpful in identifying candidate biomarkers for clinical detection and therapies of PD. In this review, we briefly summarize recent findings and analyze the application of molecular metabolomics in familial and sporadic PD from genetic mutations, mitochondrial dysfunction, and dysbacteriosis. We also review metabolic biomarkers to assess the functional stage and improve therapeutic strategies to postpone or hinder the disease progression.