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

Precision neurology

Over the past several decades, high-resolution brain imaging, blood and cerebrospinal fluid analyses, and other advanced technologies have changed diagnosis from an exercise depending primarily on the history and physical examination to a computer- and online resource-aided process that relies on larger and larger quantities of data. In addition, randomized controlled trials (RCT) at a population level have led to many new drugs and devices to treat neurological disease, including disease-modifying therapies. We are now at a crossroads. Combinatorially profound increases in data about individuals has led to an alternative to population-based RCTs. Genotyping and comprehensive "deep" phenotyping can sort individuals into smaller groups, enabling precise medical decisions at a personal level. In neurology, precision medicine that includes prediction, prevention and personalization requires that genomic and phenomic information further incorporate imaging and behavioral data. In this article, we review the genomic, phenomic, and computational aspects of precision medicine for neurology. After defining biological markers, we discuss some applications of these "-omic" and neuroimaging measures, and then outline the role of computation and ultimately brain simulation. We conclude the article with a discussion of the relation between precision medicine and value-based care.

Metabolomics-driven approaches for identifying therapeutic targets in drug discovery

Identification of therapeutic targets can directly elucidate the mechanism and effect of drug therapy, which is a central step in drug development. The disconnect between protein targets and phenotypes under complex mechanisms hampers comprehensive target understanding. Metabolomics, as a systems biology tool that captures phenotypic changes induced by exogenous compounds, has emerged as a valuable approach for target identification. A comprehensive overview was provided in this review to illustrate the principles and advantages of metabolomics, delving into the application of metabolomics in target identification. This review outlines various metabolomics-based methods, such as dose-response metabolomics, stable isotope-resolved metabolomics, and multiomics, which identify key enzymes and metabolic pathways affected by exogenous substances through dose-dependent metabolite-drug interactions. Emerging techniques, including single-cell metabolomics, artificial intelligence, and mass spectrometry imaging, are also explored for their potential to enhance target discovery. The review emphasizes metabolomics' critical role in advancing our understanding of disease mechanisms and accelerating targeted drug development, while acknowledging current challenges in the field.

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.

Metabolite transport across central nervous system barriers

Metabolomic analysis of cerebrospinal fluid (CSF) is used to improve diagnostics and pathophysiological understanding of neurological diseases. Alterations in CSF metabolite levels can partly be attributed to changes in brain metabolism, but relevant transport processes influencing CSF metabolite concentrations should be considered. The entry of molecules including metabolites into the central nervous system (CNS), is tightly controlled by the blood-brain, blood-CSF, and blood-spinal cord barriers, where aquaporins and membrane-bound carrier proteins regulate influx and efflux via passive and active transport processes. This report therefore provides reference for future CSF metabolomic work, by providing a detailed summary of the current knowledge on the location and function of the involved transporters and routing of metabolites from blood to CSF and from CSF to blood.

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.

LC-MS based metabonomics study on protective mechanism of ESWW in cerebral ischemia via CYTC/Apaf-1/NDRG4 pathway

BACKGROUND

Ershiwuwei Zhenzhu pills was originally recorded in the Tibetan medical book Si Bu Yi Dian in the 8th century AD and is now included in the Pharmacopoeia of the People's Republic of China (2020). The pills can calm the nerves and open the mind as well as treat cerebral ischemia reperfusion injury, stroke, hemiplegia. However, its quality standards have not yet been established, and the therapeutic effect on cerebral ischemia by regulating the mitochondrial apoptosis pathway has not been elucidated.

STUDY DESIGN AND METHODS

LC-MS was used to establish quality standards for Ershiwuwei Zhenzhu pills. Metabonomics, molecular docking, neuroethology, cerebral infarction ratio, pathological detection of diencephalon, cortex, and hippocampus, and molecular biology techniques were used to reveal the mechanism of the pills in regulating the mitochondrial apoptosis pathway to treat cerebral ischemia.

RESULTS

The contents of 20 chemical components in Ershiwuwei Zhenzhu pills from 12 batches and 8 manufacturers was determined for the first time. Eleven differential metabolites and three metabolic pathways, namely, fructose and mannose metabolism, glycerophospholipid metabolism, and purine metabolism, were identified by metabonomics. The pills improved the neuroethology abnormalities of MCAO rats and the pathological damage in the diencephalon and decreased the ratio of cerebral infarction. It also significantly reduced the mRNA expression of AIF, Apaf-1, cleared caspase8, CytC, and P53 mRNA in the brain tissue and the protein expression of Apaf-1 and CYTC and increased the protein expression of NDRG4.

CONCLUSION

In vitro quantitative analysis of the in vitro chemical components of Ershiwuwei Zhenzhu pills has laid the foundation for improving its quality control. The potential mechanism of the pills in treating cerebral ischemia may be related to the Apaf-1/CYTC/NDRG4 apoptosis pathway. This work provides guidance for clinical drug use for patients.

An exploratory investigation of the CSF metabolic profile of HIV in a South African paediatric cohort using GCxGC-TOF/MS

INTRODUCTION

 Because cerebrospinal fluid (CSF) samples are difficult to obtain for paediatric HIV, few studies have attempted to profile neurometabolic dysregulation.

AIM AND OBJECTIVE

The aim of this exploratory study was to profile the neurometabolic state of CSF from a South African paediatric cohort using GCxGC-TOF/MS. The study included 54 paediatric cases (< 12 years), 42 HIV-negative controls and 12 HIV-positive individuals.

RESULTS

The results revealed distinct metabolic alterations in the HIV-infected cohort. In the PLS-DA model, 18 metabolites significantly discriminated between HIV-infected and control groups. In addition, fold-change analysis, Mann-Whitney U tests, and effect size measurements verified these findings. Notably, lactose, myo-inositol, and glycerol, although not significant by p-value alone, demonstrated practical significance based on the effect size.

CONCLUSIONS

This study provided valuable insights on the impact of HIV on metabolic pathways, including damage to the gut and blood-brain barrier, disruption of bioenergetics processes, gliosis, and a potential marker for antiretroviral therapy. Nevertheless, the study recognized certain constraints, notably a limited sample size and the absence of a validation cohort. Despite these limitations, the rarity of the study's focus on paediatric HIV research underscores the significance and unique contributions of its findings.

Novel metabolic biomarker for early detection and diagnosis to the patients with gastric cardia adenocarcinoma

BACKGROUND

Gastric cardia adenocarcinoma (GCA) is classified as Siewert type II adenocarcinoma at the esophagogastric junction in Western countries. The majority of GCA patients do not exhibit early warning symptoms, leading to over 90% of diagnoses at an advanced stage, resulting in a grim prognosis, with less than a 20% 5-year survival rate.

METHOD

Metabolic features of 276 GCA and 588 healthy controls were characterized through a widely-targeted metabolomics by UPLC-MS/MS analysis. This study encompasses a joint pathway analysis utilizing identified metabolites, survival analysis in both early and advanced stages, as well as high and negative and low expression of HER2 immunohistochemistry staining. Machine learning techniques and Cox regression models were employed to construct a diagnostic panel.

RESULTS

A total of 25 differential metabolites were consistently identified in both discovery and validation sets based on criteria of p < 0.05, (VIP) ≥ 1, and FC ≥ 2 or FC ≤ 0.5. Early-stage GCA patients exhibited a more favorable prognosis compared to those in advanced stages. HER2 overexpression was associated with a more positive outcome compared to the negative and low expression groups. Metabolite panel demonstrated a robust diagnostic performance with AUC of 0.869 in discovery set and 0.900 in validation set.

CONCLUSIONS

A total of 25 common and stable differential metabolites may hold promise as liquid non-invasive indicators for GCA diagnosis. HER2 may function as a tumor suppressor gene in GCA, as its overexpression is associated with improved survival. The downregulation of bile acid metabolism in GCA may offer valuable theoretical insights and innovative approaches for precision-targeted treatments in GCA patients.

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.

Rapid Metabolic Profiling of 1 μL Crude Cerebrospinal Fluid by Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging Can Differentiate De Novo Parkinson's Disease

Parkinson's disease (PD) is a highly prevalent neurodegenerative disorder affecting the motor system. However, the correct diagnosis of PD and atypical parkinsonism may be difficult with high clinical uncertainty. There is an urgent need to identify reliable biomarkers using high-throughput, molecular-specific methods to improve current diagnostics. Here, we present a matrix-assisted laser desorption/ionization mass spectrometry imaging method that requires minimal sample preparation and only 1 μL of crude cerebrospinal fluid (CSF). The method enables analysis of hundreds of samples in a single experiment while simultaneously detecting numerous metabolites with subppm mass accuracy. To test the method, we analyzed CSF samples from 12 de novo PD patients (that is, newly diagnosed and previously untreated) and 12 age-matched controls. Within the identified molecules, we found neurotransmitters and their metabolites such as γ-aminobutyric acid, 3-methoxytyramine, homovanillic acid, serotonin, histamine, amino acids, and metabolic intermediates. Limits of detection were estimated for multiple neurotransmitters with high linearity (R2 > 0.99) and sensitivity (as low as 16 pg/μL). Application of multivariate classification led to a highly significant (P < 0.001) model of PD prediction with a 100% classification rate, which was further thoroughly validated with a permutation test and univariate analysis. Molecules related to the neuromelanin pathway were found to be significantly increased in the PD group, indicated by their elevated relative intensities compared to the control group. Our method enables rapid detection of PD-related biomarkers in low sample volumes and could serve as a valuable tool in the development of robust PD diagnostics.

Prediction of Parkinson's Disease Using Machine Learning Methods

The detection of Parkinson's disease (PD) in its early stages is of great importance for its treatment and management, but consensus is lacking on what information is necessary and what models should be used to best predict PD risk. In our study, we first grouped PD-associated factors based on their cost and accessibility, and then gradually incorporated them into risk predictions, which were built using eight commonly used machine learning models to allow for comprehensive assessment. Finally, the Shapley Additive Explanations (SHAP) method was used to investigate the contributions of each factor. We found that models built with demographic variables, hospital admission examinations, clinical assessment, and polygenic risk score achieved the best prediction performance, and the inclusion of invasive biomarkers could not further enhance its accuracy. Among the eight machine learning models considered, penalized logistic regression and XGBoost were the most accurate algorithms for assessing PD risk, with penalized logistic regression achieving an area under the curve of 0.94 and a Brier score of 0.08. Olfactory function and polygenic risk scores were the most important predictors for PD risk. Our research has offered a practical framework for PD risk assessment, where necessary information and efficient machine learning tools were highlighted.

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.

HPLC for simultaneous quantification of free mannose and glucose concentrations in serum: use in detection of ovarian cancer

Background: Abnormal levels of monosaccharides in blood have been linked to tumorigenesis. In this study, a novel high-performance liquid chromatography (HPLC) method was established for the simultaneous determination of free mannose and glucose in the serum. Methods: The serum was directly derivatized by 1-phenyl-3-methyl-5-pyrazolone under alkaline conditions using L-rhamnose as an internal standard. The chromatographic separation was then performed on a Poroshell EC-C18 chromatographic column (4.6 × 100 mm, particle size 2.7 μm, Agilent) with gradient elution using NH4Ac-HAc and acetonitrile as the mobile phases. The method was thereafter validated according to international guidelines. The serum samples obtained from 200 healthy individuals and 200 ovarian cancer (OC) patients were analyzed for free mannose and glucose. Results: The method was found to be reproducible for quantification within 20 min and included online sample purification. The method displayed excellent linearity in the concentration range (for mannose: 0.5-500 μg/mL; glucose: 0.5-1500 μg/mL). The precision, recovery, and stability met the FDA bioanalytical method validation acceptance criteria. Overall, the measurement of glucose content by HPLC correlated well with the different enzymatic methods. Ovarian cancer mannose levels in the serum were significantly higher in the advanced stage (61.22 μmol/L, p < 0.0001) than those in healthy volunteers and early-stage patients (44.51 μmol/L versus 50.09 μmol/L, p < 0.0001). The AUC for the ratio of serum free glucose to mannose (G/M) was 0.98 (p < 0.0001), with a sensitivity of 91.46% and a specificity of 98.50%, which served as a biomarker for OC diagnosis. Conclusion: We report a simple, repeatable, and attractive analytical method by HPLC, which can be used for quantitative estimation of free mannose and glucose simultaneously in human serum. Our results indicate that the serum level of mannose could be used as a potential biomarker of ovarian cancer.

Anti-inflammatory effects of myristic acid mediated by the NF-κB pathway in lipopolysaccharide-induced BV-2 microglial cells

Parkinson's disease (PD) is a serious neurodegenerative disorder wherein changes in metabolites related to lipids, glutathione, and energy metabolism occur. Currently, metabolite changes in PD have been reported, yet their role in the prognosis of disease remains poorly understood. Functional metabolites can be used to diagnose diseases, especially PD, and can exert neuroprotective effects. This study used a PD animal model and a lipopolysaccharide (LPS)-mediated inflammatory response model (using the BV-2 mouse microglial cell line) to identify functional metabolites that can identify important metabolic disorders during PD, and comprehensively evaluated their profiles using a metabolomics-based approach. Our results showed that co-treatment with myristic acid and heptadecanoic acid downregulated the expression of interleukin (IL)-1β, IL-6, and tumor necrosis factor-α in BV-2 cells. Additionally, myristic acid and 10 μM heptadecanoic acid significantly inhibited the LPS-induced inflammatory response through the nuclear factor-κB pathway in BV-2 microglial cells, which provides a potential approach for PD treatment. Myristic acid and heptadecanoic acid were the active metabolites found by active metabolomics technology, but at present, there is no research report about their function for PD treatment, and our findings offer a novel research strategy for PD diagnosis and treatment.

Mass spectrometry methods for analysis of extracellular matrix components in neurological diseases

The brain extracellular matrix (ECM) is a highly glycosylated environment and plays important roles in many processes including cell communication, growth factor binding, and scaffolding. The formation of structures such as perineuronal nets (PNNs) is critical in neuroprotection and neural plasticity, and the formation of molecular networks is dependent in part on glycans. The ECM is also implicated in the neuropathophysiology of disorders such as Alzheimer's disease (AD), Parkinson's disease (PD), and Schizophrenia (SZ). As such, it is of interest to understand both the proteomic and glycomic makeup of healthy and diseased brain ECM. Further, there is a growing need for site-specific glycoproteomic information. Over the past decade, sample preparation, mass spectrometry, and bioinformatic methods have been developed and refined to provide comprehensive information about the glycoproteome. Core ECM molecules including versican, hyaluronan and proteoglycan link proteins, and tenascin are dysregulated in AD, PD, and SZ. Glycomic changes such as differential sialylation, sulfation, and branching are also associated with neurodegeneration. A more thorough understanding of the ECM and its proteomic, glycomic, and glycoproteomic changes in brain diseases may provide pathways to new therapeutic options.

Peripheral metabolic alterations associated with pathological manifestations of Parkinson's disease in gut-brain axis-based mouse model

Introduction

Parkinson's disease (PD) is a representative neurodegenerative disease, and its diagnosis relies on the evaluation of clinical manifestations or brain neuroimaging in the absence of a crucial noninvasive biomarker. Here, we used non-targeted metabolomics profiling to identify metabolic alterations in the colon and plasma samples of Proteus mirabilis (P. mirabilis)-treated mice, which is a possible animal model for investigating the microbiota-gut-brain axis.

Methods

We performed gas chromatography-mass spectrometry to analyze the samples and detected metabolites that could reflect P. mirabilis-induced disease progression and pathology.

Results and discussion

Pattern, correlation and pathway enrichment analyses showed significant alterations in sugar metabolism such as galactose metabolism and fructose and mannose metabolism, which are closely associated with energy metabolism and lipid metabolism. This study indicates possible metabolic factors for P. mirabilis-induced pathological progression and provides evidence of metabolic alterations associated with P. mirabilis-mediated pathology of brain neurodegeneration.

Alpha synuclein post translational modifications: potential targets for Parkinson's disease therapy?

Parkinson's disease (PD) is the most common neurodegenerative disorder with motor symptoms. The neuropathological alterations characterizing the brain of patients with PD include the loss of dopaminergic neurons of the nigrostriatal system and the presence of Lewy bodies (LB), intraneuronal inclusions that are mainly composed of alpha-synuclein (α-Syn) fibrils. The accumulation of α-Syn in insoluble aggregates is a main neuropathological feature in PD and in other neurodegenerative diseases, including LB dementia (LBD) and multiple system atrophy (MSA), which are therefore defined as synucleinopathies. Compelling evidence supports that α-Syn post translational modifications (PTMs) such as phosphorylation, nitration, acetylation, O-GlcNAcylation, glycation, SUMOylation, ubiquitination and C-terminal cleavage, play important roles in the modulation α-Syn aggregation, solubility, turnover and membrane binding. In particular, PTMs can impact on α-Syn conformational state, thus supporting that their modulation can in turn affect α-Syn aggregation and its ability to seed further soluble α-Syn fibrillation. This review focuses on the importance of α-Syn PTMs in PD pathophysiology but also aims at highlighting their general relevance as possible biomarkers and, more importantly, as innovative therapeutic targets for synucleinopathies. In addition, we call attention to the multiple challenges that we still need to face to enable the development of novel therapeutic approaches modulating α-Syn PTMs.

Instrumental Drift in Untargeted Metabolomics: Optimizing Data Quality with Intrastudy QC Samples

Untargeted metabolomics is an important tool in studying health and disease and is employed in fields such as biomarker discovery and drug development, as well as precision medicine. Although significant technical advances were made in the field of mass-spectrometry driven metabolomics, instrumental drifts, such as fluctuations in retention time and signal intensity, remain a challenge, particularly in large untargeted metabolomics studies. Therefore, it is crucial to consider these variations during data processing to ensure high-quality data. Here, we will provide recommendations for an optimal data processing workflow using intrastudy quality control (QC) samples that identifies errors resulting from instrumental drifts, such as shifts in retention time and metabolite intensities. Furthermore, we provide an in-depth comparison of the performance of three popular batch-effect correction methods of different complexity. By using different evaluation metrics based on QC samples and a machine learning approach based on biological samples, the performance of the batch-effect correction methods were evaluated. Here, the method TIGER demonstrated the overall best performance by reducing the relative standard deviation of the QCs and dispersion-ratio the most, as well as demonstrating the highest area under the receiver operating characteristic with three different probabilistic classifiers (Logistic regression, Random Forest, and Support Vector Machine). In summary, our recommendations will help to generate high-quality data that are suitable for further downstream processing, leading to more accurate and meaningful insights into the underlying biological processes.

Quantitative proteomics and phosphoproteomics of urinary extracellular vesicles define putative diagnostic biosignatures for Parkinson's disease

BACKGROUND

Mutations in the leucine-rich repeat kinase 2 (LRRK2) gene have been recognized as genetic risk factors for Parkinson's disease (PD). However, compared to cancer, fewer genetic mutations contribute to the cause of PD, propelling the search for protein biomarkers for early detection of the disease.

METHODS

Utilizing 138 urine samples from four groups, healthy individuals (control), healthy individuals with G2019S mutation in the LRRK2 gene (non-manifesting carrier/NMC), PD individuals without G2019S mutation (idiopathic PD/iPD), and PD individuals with G2019S mutation (LRRK2 PD), we applied a proteomics strategy to determine potential diagnostic biomarkers for PD from urinary extracellular vesicles (EVs).

RESULTS

After efficient isolation of urinary EVs through chemical affinity followed by mass spectrometric analyses of EV peptides and enriched phosphopeptides, we identify and quantify 4476 unique proteins and 2680 unique phosphoproteins. We detect multiple proteins and phosphoproteins elevated in PD EVs that are known to be involved in important PD pathways, in particular the autophagy pathway, as well as neuronal cell death, neuroinflammation, and formation of amyloid fibrils. We establish a panel of proteins and phosphoproteins as novel candidates for disease biomarkers and substantiate the biomarkers using machine learning, ROC, clinical correlation, and in-depth network analysis. Several putative disease biomarkers are further partially validated in patients with PD using parallel reaction monitoring (PRM) and immunoassay for targeted quantitation.

CONCLUSIONS

These findings demonstrate a general strategy of utilizing biofluid EV proteome/phosphoproteome as an outstanding and non-invasive source for a wide range of disease exploration.

Quality Control—A Stepchild in Quantitative Proteomics: A Case Study for the Human CSF Proteome

Proteomic studies using mass spectrometry (MS)-based quantification are a main approach to the discovery of new biomarkers. However, a number of analytical conditions in front and during MS data acquisition can affect the accuracy of the obtained outcome. Therefore, comprehensive quality assessment of the acquired data plays a central role in quantitative proteomics, though, due to the immense complexity of MS data, it is often neglected. Here, we address practically the quality assessment of quantitative MS data, describing key steps for the evaluation, including the levels of raw data, identification and quantification. With this, four independent datasets from cerebrospinal fluid, an important biofluid for neurodegenerative disease biomarker studies, were assessed, demonstrating that sample processing-based differences are already reflected at all three levels but with varying impacts on the quality of the quantitative data. Specifically, we provide guidance to critically interpret the quality of MS data for quantitative proteomics. Moreover, we provide the free and open source quality control tool MaCProQC, enabling systematic, rapid and uncomplicated data comparison of raw data, identification and feature detection levels through defined quality metrics and a step-by-step quality control workflow.

Diagnostic metabolomic profiling of Parkinson's disease biospecimens

BACKGROUND

Reliable and sensitive biomarkers are needed for enhancing and predicting Parkinson's disease (PD) diagnosis.

OBJECTIVE

To investigate comprehensive metabolomic profiling of biochemicals in CSF and serum for determining diagnostic biomarkers of PD.

METHODS

Fifty subjects, symptomatic with PD for ≥5 years, were matched to 50 healthy controls (HCs). We used ultrahigh-performance liquid chromatography linked to tandem mass spectrometry (UHPLC-MS/MS) for measuring relative concentrations of ≤1.5 kDalton biochemicals. A reference library created from authentic standards facilitated chemical identifications. Analytes underwent univariate analysis for PD association, with false discovery rate-adjusted p-value (≤0.05) determinations. Multivariate analysis (for identifying a panel of biochemicals discriminating PD from HCs) used several biostatistical methods, including logistic LASSO regression.

RESULTS

Comparing PD and HCs, strong differentiation was achieved from CSF but not serum specimens. With univariate analysis, 21 CSF compounds exhibited significant differential concentrations. Logistic LASSO regression led to selection of 23 biochemicals (11 shared with those determined by the univariate analysis). The selected compounds, as a group, distinguished PD from HCs, with Area-Under-the-Receiver-Operating-Characteristic (ROC) curve of 0.897. With optimal cutoff, logistic LASSO achieved 100% sensitivity and 96% specificity (and positive and negative predictive values of 96% and 100%). Ten-fold cross-validation gave 84% sensitivity and 82% specificity (and 82% positive and 84% negative predictive values). From the logistic LASSO-chosen regression model, 2 polyamine metabolites (N-acetylcadaverine and N-acetylputrescine) were chosen and had the highest fold-changes in comparing PD to HCs. Another chosen biochemical, acisoga (N-(3-acetamidopropyl)pyrrolidine-2-one), also is a polyamine metabolism derivative.

CONCLUSIONS

UHPLC-MS/MS assays provided a metabolomic signature highly predictive of PD. These findings provide further evidence for involvement of polyamine pathways in the neurodegeneration of PD.

Microbiome and Metabolome Insights into the Role of the Gastrointestinal-Brain Axis in Parkinson's and Alzheimer's Disease: Unveiling Potential Therapeutic Targets

Neurodegenerative diseases such as Parkinson's (PD) and Alzheimer's disease (AD), the prevalence of which is rapidly rising due to an aging world population and westernization of lifestyles, are expected to put a strong socioeconomic burden on health systems worldwide. Clinical trials of therapies against PD and AD have only shown limited success so far. Therefore, research has extended its scope to a systems medicine point of view, with a particular focus on the gastrointestinal-brain axis as a potential main actor in disease development and progression. Microbiome and metabolome studies have already revealed important insights into disease mechanisms. Both the microbiome and metabolome can be easily manipulated by dietary and lifestyle interventions, and might thus offer novel, readily available therapeutic options to prevent the onset as well as the progression of PD and AD. This review summarizes our current knowledge on the interplay between microbiota, metabolites, and neurodegeneration along the gastrointestinal-brain axis. We further illustrate state-of-the art methods of microbiome and metabolome research as well as metabolic modeling that facilitate the identification of disease pathomechanisms. We conclude with therapeutic options to modulate microbiome composition to prevent or delay neurodegeneration and illustrate potential future research directions to fight PD and AD.

Metabolomics as a Crucial Tool to Develop New Therapeutic Strategies for Neurodegenerative Diseases

Neurodegenerative diseases (NDs), such as Alzheimer's (AD), Parkinson's (PD), and amyotrophic lateral sclerosis (ALS), share common pathological mechanisms, including metabolism alterations. However, their specific neuronal cell types affected and molecular biomarkers suggest that there are both common and specific alterations regarding metabolite levels. In this review, we were interested in identifying metabolite alterations that have been reported in preclinical models of NDs and that have also been documented as altered in NDs patients. Such alterations could represent interesting targets for the development of targeted therapy. Importantly, the translation of such findings from preclinical to clinical studies is primordial for the study of possible therapeutic agents. We found that N-acetyl-aspartate (NAA), myo-inositol, and glutamate are commonly altered in the three NDs investigated here. We also found other metabolites commonly altered in both AD and PD. In this review, we discuss the studies reporting such alterations and the possible pathological mechanism underlying them. Finally, we discuss clinical trials that have attempted to develop treatments targeting such alterations. We conclude that the treatment combination of both common and differential alterations would increase the chances of patients having access to efficient treatments for each ND.

Imperative Role of Machine Learning Algorithm for Detection of Parkinson’s Disease: Review, Challenges and Recommendations

Parkinson’s disease (PD) is a neurodegenerative disease that affects the neural, behavioral, and physiological systems of the brain. This disease is also known as tremor. The common symptoms of this disease are a slowness of movement known as ‘bradykinesia’, loss of automatic movements, speech/writing changes, and difficulty with walking at early stages. To solve these issues and to enhance the diagnostic process of PD, machine learning (ML) algorithms have been implemented for the categorization of subjective disease and healthy controls (HC) with comparable medical appearances. To provide a far-reaching outline of data modalities and artificial intelligence techniques that have been utilized in the analysis and diagnosis of PD, we conducted a literature analysis of research papers published up until 2022. A total of 112 research papers were included in this study, with an examination of their targets, data sources and different types of datasets, ML algorithms, and associated outcomes. The results showed that ML approaches and new biomarkers have a lot of promise for being used in clinical decision-making, resulting in a more systematic and informed diagnosis of PD. In this study, some major challenges were addressed along with a future recommendation.

Metabolomic Analysis of Human Astrocytes in Lipotoxic Condition: Potential Biomarker Identification by Machine Learning Modeling

The association between neurodegenerative diseases (NDs) and obesity has been well studied in recent years. Obesity is a syndrome of multifactorial etiology characterized by an excessive accumulation and release of fatty acids (FA) in adipose and non-adipose tissue. An excess of FA generates a metabolic condition known as lipotoxicity, which triggers pathological cellular and molecular responses, causing dysregulation of homeostasis and a decrease in cell viability. This condition is a hallmark of NDs, and astrocytes are particularly sensitive to it, given their crucial role in energy production and oxidative stress management in the brain. However, analyzing cellular mechanisms associated with these conditions represents a challenge. In this regard, metabolomics is an approach that allows biochemical analysis from the comprehensive perspective of cell physiology. This technique allows cellular metabolic profiles to be determined in different biological contexts, such as those of NDs and specific metabolic insults, including lipotoxicity. Since data provided by metabolomics can be complex and difficult to interpret, alternative data analysis techniques such as machine learning (ML) have grown exponentially in areas related to omics data. Here, we developed an ML model yielding a 93% area under the receiving operating characteristic (ROC) curve, with sensibility and specificity values of 80% and 93%, respectively. This study aimed to analyze the metabolomic profiles of human astrocytes under lipotoxic conditions to provide powerful insights, such as potential biomarkers for scenarios of lipotoxicity induced by palmitic acid (PA). In this work, we propose that dysregulation in seleno-amino acid metabolism, urea cycle, and glutamate metabolism pathways are major triggers in astrocyte lipotoxic scenarios, while increased metabolites such as alanine, adenosine, and glutamate are suggested as potential biomarkers, which, to our knowledge, have not been identified in human astrocytes and are proposed as candidates for further research and validation.

Pesticides Exposure-Induced Changes in Brain Metabolome: Implications in the Pathogenesis of Neurodegenerative Disorders

Pesticides have been used in agriculture, public health programs, and pharmaceuticals for many decades. Though pesticides primarily target pests by affecting their nervous system and causing other lethal effects, these chemical entities also exert toxic effects in inadvertently exposed humans through inhalation or ingestion. Mounting pieces of evidence from cellular, animal, and clinical studies indicate that pesticide-exposed models display metabolite alterations of pathways involved in neurodegenerative diseases. Hence, identifying common key metabolites/metabolic pathways between pesticide-induced metabolic reprogramming and neurodegenerative diseases is necessary to understand the etiology of pesticides in the rise of neurodegenerative disorders. The present review provides an overview of specific metabolic pathways, including tryptophan metabolism, glutathione metabolism, dopamine metabolism, energy metabolism, mitochondrial dysfunction, fatty acids, and lipid metabolism that are specifically altered in response to pesticides. Furthermore, we discuss how these metabolite alterations are linked to the pathogenesis of neurodegenerative diseases and to identify novel biomarkers for targeted therapeutic approaches.

Metabolomic profiling of cerebrospinal fluid reveals an early diagnostic model for central nervous system involvement in acute lymphoblastic leukaemia

The pathogenesis of central nervous system involvement (CNSI) in patients with acute lymphoblastic leukaemia (ALL) remains unclear and a robust biomarker of early diagnosis is missing. An untargeted cerebrospinal fluid (CSF) metabolomics analysis was performed to identify independent risk biomarkers that could diagnose CNSI at the early stage. Thirty-three significantly altered metabolites between ALL patients with and without CNSI were identified, and a CNSI evaluation score (CES) was constructed to predict the risk of CNSI based on three independent risk factors (8-hydroxyguanosine, l-phenylalanine and hypoxanthine). This predictive model could diagnose CNSI with positive prediction values of 95.9% and 85.6% in the training and validation sets respectively. Moreover, CES score increased with the elevated level of central nervous system (CNSI) involvement. In addition, we validated this model by tracking the changes in CES at different stages of CNSI, including before CNSI and during CNSI, and in remission after CNSI. The CES showed good ability to predict the progress of CNSI. Finally, we constructed a nomogram to predict the risk of CNSI in clinical practice, which performed well compared with observed probability. This unique CSF metabolomics study may help us understand the pathogenesis of CNSI, diagnose CNSI at the early stage, and sequentially achieve personalized precision treatment.

A Set of Reliable Samples for the Study of Biomarkers for the Early Diagnosis of Parkinson's Disease

Background

Parkinson's disease (PD) is a progressive neurodegenerative disorder, diagnosed according to the clinical criteria that occur in already advanced stages of PD. The definition of biomarkers for the early diagnosis of PD represents a challenge that might improve treatment and avoid complications in this disease. Therefore, we propose a set of reliable samples for the identification of altered metabolites to find potential prognostic biomarkers for early PD.

Methods

This case–control study included plasma samples of 12 patients with PD and 21 control subjects, from the Spanish European Prospective Investigation into Cancer and Nutrition (EPIC)-Navarra cohort, part of the EPIC-Spain study. All the case samples were provided by healthy volunteers who were followed-up for 15.9 (±4.1) years and developed PD disease later on, after the sample collection. Liquid chromatography coupled to tandem mass spectrometry was used for the analysis of samples.

Results

Out of 40 that were selected and studied due to their involvement in established cases of PD, seven significantly different metabolites between PD cases and healthy control subjects were obtained in this study (benzoic acid, palmitic acid, oleic acid, stearic acid, myo-inositol, sorbitol, and quinolinic acid). These metabolites are related to mitochondrial dysfunction, the oxidative stress, and the mechanisms of energy production.

Conclusion

We propose the samples from the EPIC study as reliable and invaluable samples for the search of early biomarkers of PD. Likewise, this study might also be a starting point in the establishment of a well-founded panel of metabolites that can be used for the early detection of this disease.

Metabolites and Biomarker Compounds of Neurodegenerative Diseases in Cerebrospinal Fluid

Despite recent advances in diagnostic procedures for neurological disorders, it is still difficult to definitively diagnose some neurodegenerative diseases without neuropathological examination of autopsied brain tissue. As pathological processes in the brain are frequently reflected in the components of cerebrospinal fluid (CSF), CSF samples are sometimes useful for diagnosis. After CSF is secreted from the choroid plexus epithelial cells in the ventricles, some flows in the brain, some is mixed with intracerebral interstitial fluid, and some is excreted through two major drainage pathways, i.e., the intravascular periarterial drainage pathway and the glymphatic system. Accordingly, substances produced by metabolic and pathological processes in the brain may be detectable in CSF. Many papers have reported changes in the concentration of substances in the CSF of patients with metabolic and neurological disorders, some of which can be useful biomarkers of the disorders. In this paper, we show the significance of glucose- and neurotransmitter-related CSF metabolites, considering their transporters in the choroid plexus; summarize the reported candidates of CSF biomarkers for neurodegenerative diseases, including amyloid-β, tau, α-synuclein, microRNAs, and mitochondrial DNA; and evaluate their potential as efficient diagnostic tools.

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.

Metabolite and lipoprotein profiles reveal sex-related oxidative stress imbalance in de novo drug-naive Parkinson's disease patients

Parkinson’s disease (PD) is the neurological disorder showing the greatest rise in prevalence from 1990 to 2016. Despite clinical definition criteria and a tremendous effort to develop objective biomarkers, precise diagnosis of PD is still unavailable at early stage. In recent years, an increasing number of studies have used omic methods to unveil the molecular basis of PD, providing a detailed characterization of potentially pathological alterations in various biological specimens. Metabolomics could provide useful insights to deepen our knowledge of PD aetiopathogenesis, to identify signatures that distinguish groups of patients and uncover responsive biomarkers of PD that may be significant in early detection and in tracking the disease progression and drug treatment efficacy. The present work is the first large metabolomic study based on nuclear magnetic resonance (NMR) with an independent validation cohort aiming at the serum characterization of de novo drug-naive PD patients. Here, NMR is applied to sera from large training and independent validation cohorts of German subjects. Multivariate and univariate approaches are used to infer metabolic differences that characterize the metabolite and the lipoprotein profiles of newly diagnosed de novo drug-naive PD patients also in relation to the biological sex of the subjects in the study, evidencing a more pronounced fingerprint of the pathology in male patients. The presence of a validation cohort allowed us to confirm altered levels of acetone and cholesterol in male PD patients. By comparing the metabolites and lipoproteins levels among de novo drug-naive PD patients, age- and sex-matched healthy controls, and a group of advanced PD patients, we detected several descriptors of stronger oxidative stress.

Metabolic Alterations in a Drosophila Model of Parkinson's Disease Based on DJ-1 Deficiency

Parkinson’s disease (PD) is the second-most common neurodegenerative disorder, whose physiopathology is still unclear. Moreover, there is an urgent need to discover new biomarkers and therapeutic targets to facilitate its diagnosis and treatment. Previous studies performed in PD models and samples from PD patients already demonstrated that metabolic alterations are associated with this disease. In this context, the aim of this study is to provide a better understanding of metabolic disturbances underlying PD pathogenesis. To achieve this goal, we used a Drosophila PD model based on inactivation of the DJ-1β gene (ortholog of human DJ-1). Metabolomic analyses were performed in 1-day-old and 15-day-old DJ-1β mutants and control flies using ¹H nuclear magnetic resonance spectroscopy, combined with expression and enzymatic activity assays of proteins implicated in altered pathways. Our results showed that the PD model flies exhibited protein metabolism alterations, a shift fromthe tricarboxylic acid cycle to glycolytic pathway to obtain ATP, together with an increase in the expression of some urea cycle enzymes. Thus, these metabolic changes could contribute to PD pathogenesis and might constitute possible therapeutic targets and/or biomarkers for this disease.

Quantification of carbohydrates in human serum using gas chromatography–mass spectrometry with the stable isotope-labeled internal standard method

Comparison of two derivatization approaches (silylation and acylation) for carbohydrate separation based on optimizing reaction conditions by artificial neural networks.

Metabolomics as an Important Tool for Determining the Mechanisms of Human Skeletal Muscle Deconditioning

Muscle deconditioning impairs both locomotor function and metabolic health, and is associated with reduced quality life and increased mortality rates. Despite an appreciation of the existence of phenomena such as muscle anabolic resistance, mitophagy, and insulin resistance with age and disease in humans, little is known about the mechanisms responsible for these negative traits. With the complexities surrounding these unknowns and the lack of progress to date in development of effective interventions, there is a need for alternative approaches. Metabolomics is the study of the full array of metabolites within cells or tissues, which collectively constitute the metabolome. As metabolomics allows for the assessment of the cellular metabolic state in response to physiological stimuli, any chronic change in the metabolome is likely to reflect adaptation in the physiological phenotype of an organism. This, therefore, provides a holistic and unbiased approach that could be applied to potentially uncover important novel facets in the pathophysiology of muscle decline in ageing and disease, as well as identifying prognostic markers of those at risk of decline. This review will aim to highlight the current knowledge and potential impact of metabolomics in the study of muscle mass loss and deconditioning in humans and will highlight key areas for future research.

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.

Severe and Regionally Widespread Increases in Tissue Urea in the Human Brain Represent a Novel Finding of Pathogenic Potential in Parkinson's Disease Dementia

Widespread elevations in brain urea have, in recent years, been reported in certain types of age-related dementia, notably Alzheimer’s disease (AD) and Huntington’s disease (HD). Urea increases in these diseases are substantive, and approximate in magnitude to levels present in uraemic encephalopathy. In AD and HD, elevated urea levels are widespread, and not only in regions heavily affected by neurodegeneration. However, measurements of brain urea have not hitherto been reported in Parkinson’s disease dementia (PDD), a condition which shares neuropathological and symptomatic overlap with both AD and HD. Here we report measurements of tissue urea from nine neuropathologically confirmed regions of the brain in PDD and post-mortem delay (PMD)-matched controls, in regions including the cerebellum, motor cortex (MCX), sensory cortex, hippocampus (HP), substantia nigra (SN), middle temporal gyrus (MTG), medulla oblongata (MED), cingulate gyrus, and pons, by applying ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS). Urea concentrations were found to be substantively elevated in all nine regions, with average increases of 3–4-fold. Urea concentrations were remarkably consistent across regions in both cases and controls, with no clear distinction between regions heavily affected or less severely affected by neuronal loss in PDD. These urea elevations mirror those found in uraemic encephalopathy, where equivalent levels are generally considered to be pathogenic, and those previously reported in AD and HD. Increased urea is a widespread metabolic perturbation in brain metabolism common to PDD, AD, and HD, at levels equal to those seen in uremic encephalopathy. This presents a novel pathogenic mechanism in PDD, which is shared with two other neurodegenerative diseases.

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.

Prognostic biomarkers of Parkinson's disease in the Spanish EPIC cohort: a multiplatform metabolomics approach

The lack of knowledge about the onset and progression of Parkinson's disease (PD) hampers its early diagnosis and treatment. Metabolomics might shed light on the PD imprint seeking a broader view of the biochemical remodeling induced by this disease in an early and pre-symptomatic stage and unveiling potential biomarkers. To achieve this goal, we took advantage of the great potential of the European Prospective Study on Nutrition and Cancer (EPIC) cohort to apply metabolomics searching for early diagnostic PD markers. This cohort consisted of healthy volunteers that were followed for around 15 years until June 2011 to ascertain incident PD. For this untargeted metabolomics-based study, baseline preclinical plasma samples of 39 randomly selected individuals that developed PD (Pre-PD group) and the corresponding control group were analyzed using a multiplatform approach. Data were statistically analyzed and exposed alterations in 33 metabolites levels, including significantly lower levels of free fatty acids (FFAs) in the preclinical samples from PD subjects. These results were then validated by adopting a targeted HPLC-QqQ-MS approach. After integrating all the metabolites affected, our finding revealed alterations in FFAs metabolism, mitochondrial dysfunction, oxidative stress, and gut-brain axis dysregulation long before the development of PD hallmarks. Although the biological purpose of these events is still unknown, the remodeled metabolic pathways highlighted in this work might be considered worthy prognostic biomarkers of early prodromal PD. The findings revealed by this work are of inestimable value since this is the first study conducted with samples collected many years before the disease development.

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.

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.

Cerebrospinal α-Synuclein Oligomers Reflect Disease Motor Severity in DeNoPa Longitudinal Cohort

BACKGROUND

Tangible efforts have been made to identify biomarkers for Parkinson's disease (PD) diagnosis and progression, with α-synuclein (α-syn) related biomarkers being at the forefront.

OBJECTIVES

The objectives of this study were to explore whether cerebrospinal fluid (CSF) levels of total, oligomeric, phosphorylated Ser 129 α-synuclein, along with total tau, phosphorylated tau 181, and β-amyloid 1-42 are (1) informative as diagnostic markers for PD, (2) changed over disease progression, and/or (3) correlated with motor and cognitive indices of disease progression in the longitudinal De Novo Parkinson cohort.

METHODS

A total of 94 de novo PD patients and 52 controls at baseline and 24- and 48-month follow-up were included, all of whom had longitudinal lumbar punctures and clinical assessments for both cognitive and motor functions. Using our in-house enzymelinked immunosorbent assays and commercially available assays, different forms of α-synuclein, tau, and β-amyloid 1-42 were quantified in CSF samples from the De Novo Parkinson cohort.

RESULTS

Baseline CSF total α-synuclein was significantly lower in early de novo PD compared with healthy controls, whereas the ratio of oligomeric/total and phosphorylated/total were significantly higher in the PD group. CSF oligomeric-α-synuclein longitudinally increased over the 4-year follow-up in the PD group and correlated with PD motor progression. Patients at advanced stages of PD presented with elevated CSF oligomeric-α-synuclein levels compared with healthy controls.

CONCLUSIONS

Longitudinal transitions of CSF biomarkers over disease progression might not occur linearly and are susceptible to disease state. CSF oligomeric-α-synuclein levels appear to increase with diseases severity and reflect PD motor rather than cognitive trajectories. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

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.

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.

Comprehensive serum metabolic and proteomic characterization on cognitive dysfunction in Parkinson's disease

Background

Given the increased incidence of Parkinson's disease (PD) and the lack of accurate early diagnosis of PD with cognitive impairment (PD-CI), we compared the serum metabolomes and proteomes of 26 patients with PD without cognitive impairment (PD-N) and 31 patients with PD-CI by combining grade-dependent proteomics and metabolomics analyses.

Methods

Logistic and linear regression analyses were performed for differential metabolic indicators, cognition, and clinical diagnosis. Ingenuity pathway analysis (IPA) was used to identify metabolites linked to different pathways. Bioinformatics revealed 16 differentially expressed proteins and 32 metabolites.

Results

The positive metabolic indicators related to the differential proteins were one sphingolipid, five phosphatidylcholines, and five long-chain fatty acids. The obtained metabolic and proteomics IPA network highlighted the central term of this network was inflammation and abnormal lipid metabolism which are prominent in PD-CI. There was a strong negative correlation between the Mini-Mental State Examination (MMSE)score and LPC (18:1). The receiver operating characteristic (ROC) of LPC (18:1) for PD-N and PD-CI showed that the area under the curve (AUC) value was 0.660 (P=0.039).

Conclusions

In conclusion, serum LPC (18:1) is inversely linked to cognition in PD and presented its potential clinical value in distinguishing the presence or absence of cognitive impairment in PD. The deeper implication of our discovery indicates abnormal lipid metabolism is associated with changes of cognitive status and suggests the potential for possibility of immune system- inflammatory involvement.

Metabolomics of Cerebrospinal Fluid from Healthy Subjects Reveal Metabolites Associated with Ageing

To increase our understanding of age-related diseases affecting the central nervous system (CNS) it is important to understand the molecular processes of biological ageing. Metabolomics of cerebrospinal fluid (CSF) is a promising methodology to increase our understanding of naturally occurring processes of ageing of the brain and CNS that could be reflected in CSF. In the present study the CSF metabolomes of healthy subjects aged 30-74 years (n = 23) were studied using liquid chromatography high-resolution mass spectrometry (LC-HRMS), and investigated in relation to age. Ten metabolites were identified with high confidence as significantly associated with ageing, eight with increasing levels with ageing: isoleucine, acetylcarnitine, pipecolate, methionine, glutarylcarnitine, 5-hydroxytryptophan, ketoleucine, and hippurate; and two decreasing with ageing: methylthioadenosine and 3-methyladenine. To our knowledge, this is the first time the CSF metabolomes of healthy subjects are assessed in relation to ageing. The present study contributes to the field of ageing metabolomics by presenting a number of metabolites present in CSF with potential relevance for ageing and the results motivate further studies.

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.

Simultaneous analysis of monosaccharides using ultra high performance liquid chromatography-high resolution mass spectrometry without derivatization for validation of certified reference materials

Monosaccharide composition of biological samples can reflect an individual's health status. Monitoring the concentration of individual monosaccharides in human serum requires a technique for the simultaneous analysis of multiple monosaccharide molecules. Furthermore, certified reference materials (CRMs) for overall monosaccharide composition of human serum are required in order to validate the performance of clinical laboratory instruments. In the present study, we present a novel method for the simultaneous analysis of numerous monosaccharide molecules without the need for derivatization or post-column treatment. We utilized ultra-high-performance liquid chromatography (UHPLC)-quadrupole/orbitrap mass spectrometry incorporating a hydrophilic interaction chromatography (HILIC) column. We optimized the precursor ions, product ions, mobile phase composition and gradient program, flow rate, and column temperature. Seven monosaccharides (D-Ribose, L-Arabinose, D-Xylose, D-Fructose, D-Mannose, D-Galactose and D-Glucose) were able to be separated and quantified. We validated the method and the seven molecules showed favorable limits of detection and quantification, recovery rates, carry-over effects, intra- and inter-day accuracy and precision, resolution, and measurement uncertainty. We analyzed human serum samples using the method. To avoid ion suppression and D-d₂-Glucose peak interference, compounds present at concentrations outside of the calibration range were analyzed from diluted samples. Quantification of serum samples corroborated some previous clinical research, in that increased D-Glucose concentration was associated with increased concentrations of D-Mannose and D-Ribose. We also validated the CRMs, and expect these to have utility as standards for serum monosaccharide profiling, thus contributing to public health.

Shared perturbations in the metallome and metabolome of Alzheimer's, Parkinson's, Huntington's, and dementia with Lewy bodies: A systematic review

Despite differences in presentation, age-related dementing diseases such as Alzheimer's (AD), Parkinson's (PD), and Huntington's diseases (HD), and dementia with Lewy bodies (DLB) may share pathogenic processes. This review aims to systematically assemble and compare findings in various biochemical pathways across these four dementias. PubMed and Google Scholar were screened for articles reporting on brain and biofluid measurements of metals and/or metabolites in AD, PD, HD, or DLB. Articles were assessed using specific a priori-defined inclusion and exclusion criteria. Of 284 papers identified, 198 met criteria for inclusion. Although varying coverage levels of metals and metabolites across diseases and tissues made comparison of many analytes impossible, several common findings were identified: elevated glucose in both brain tissue and biofluids of AD, PD, and HD cases; increased iron and decreased copper in AD, PD and HD brain tissue; and decreased uric acid in biofluids of AD and PD cases. Other analytes were found to differ between diseases or were otherwise not covered across all conditions. These findings indicate that disturbances in glucose and purine pathways may be common to AD, PD, and HD. However, standardisation of methodologies and better coverage in some areas - notably of DLB - are necessary to validate and extend these findings.

Metabolic Profiling of CSF from People Suffering from Sporadic and LRRK2 Parkinson's Disease: A Pilot Study

CSF from unique groups of Parkinson’s disease (PD) patients was biochemically profiled to identify previously unreported metabolic pathways linked to PD pathogenesis, and novel biochemical biomarkers of the disease were characterized. Utilizing both ¹H NMR and DI-LC-MS/MS we quantitatively profiled CSF from patients with sporadic PD (n = 20) and those who are genetically predisposed (LRRK2) to the disease (n = 20), and compared those results with age and gender-matched controls (n = 20). Further, we systematically evaluated the utility of several machine learning techniques for the diagnosis of PD. ¹H NMR and mass spectrometry-based metabolomics, in combination with bioinformatic analyses, provided useful information highlighting previously unreported biochemical pathways and CSF-based biomarkers associated with both sporadic PD (sPD) and LRRK2 PD. Results of this metabolomics study further support our group’s previous findings identifying bile acid metabolism as one of the major aberrant biochemical pathways in PD patients. This study demonstrates that a combination of two complimentary techniques can provide a much more holistic view of the CSF metabolome, and by association, the brain metabolome. Future studies for the prediction of those at risk of developing PD should investigate the clinical utility of these CSF-based biomarkers in more accessible biomatrices. Further, it is essential that we determine whether the biochemical pathways highlighted here are recapitulated in the brains of PD patients with the aim of identifying potential therapeutic targets.