Serum metabolomics study in a group of Parkinson's disease patients from northern India

SIRT1 improves lactate homeostasis in the brain to alleviate parkinsonism via deacetylation and inhibition of PKM2

Sirtuin 1 (SIRT1) is a histone deacetylase and plays diverse functions in various physiological events, from development to lifespan regulation. Here, in Parkinson's disease (PD) model mice, we demonstrated that SIRT1 ameliorates parkinsonism, while SIRT1 knockdown further aggravates PD phenotypes. Mechanistically, SIRT1 interacts with and deacetylates pyruvate kinase M2 (PKM2) at K135 and K206, thus leading to reduced PKM2 enzyme activity and lactate production, which eventually results in decreased glial activation in the brain. Administration of lactate in the brain recapitulates PD-like phenotypes. Furthermore, increased expression of PKM2 worsens PD symptoms, and, on the contrary, inhibition of PKM2 by shikonin or PKM2-IN-1 alleviates parkinsonism in mice. Collectively, our data indicate that excessive lactate in the brain might be involved in the progression of PD. By improving lactate homeostasis, SIRT1, together with PKM2, are likely drug targets for developing agents for the treatment of neurodegeneration in PD.

Gut microbiota from patients with Parkinson's disease causes motor deficits in honeybees

Objective

Parkinson's disease (PD) is possibly caused by genetic factors, environmental factors, and gut microbiota dysbiosis. This study aims to explore whether the microbiota contributes to the behavior abnormalities of PD.

Methods

We transplanted gut microbiota from patients with PD or healthy controls (HC) into microbiota-free honeybees. We also established two more groups, namely the rotenone (ROT) group, in which PD-like symptoms of honeybees were induced by rotenone, and the conventional (CV) group, in which honeybees were colonized with conventional gut microbiota. The climbing assay was performed to assess the motor capabilities of honeybees. Histopathological examination was conducted to evaluate the integrity of gut mucosa. Tyrosine hydroxylase (TH) gene expression levels and dopamine (DA) concentrations in the brain were also examined. Additionally, metagenomics and full-length 16S rRNA analyses were performed to identify alterations in gut microbiota profiles, both in PD patients and honeybees.

Results

Honeybees in the PD and ROT groups exhibited slower climbing speeds, downregulated TH gene expression, and impaired gut barriers. Both the HC and PD groups of honeybees successfully harbored a portion of gut microbiota from corresponding human donors, and differences in microbial composition were identified. Morganella morganii and Erysipelatoclostridium ramosum exhibited significantly increased relative abundance in the HC group, while Dorea longicatena, Collinsella aerofaciens, Lactococcus garvieae, Holdemanella biformis, Gemmiger formicilis, and Blautia obeum showed significantly increased relative abundance in the PD group. Functional predictions of microbial communities in the PD group indicated an increased synthesis of hydrogen sulfide and methane.

Conclusion

A novel PD model was induced in honeybees with rotenone and gut microbiota from PD patients. This study linked PD-related behaviors to altered gut microbiota, highlighting a potential gut microbiota-brain axis involvement in PD pathogenesis. We identify previously unrecognized associations of Dorea longicatena, Collinsella aerofaciens, Lactococcus garvieae, Holdemanella biformis, Gemmiger formicilis, and Blautia obeum with PD. Additionally, pathways related to hydrogen sulfide and methane synthesis have been previously suggested as potential contributors to the development of PD, and our research further supports this hypothesis.

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.

A crazy trio in Parkinson's disease: metabolism alteration, α-synuclein aggregation, and oxidative stress

Parkinson's disease (PD) is an aging-associated neurodegenerative disorder, characterized by the progressive loss of dopaminergic neurons in the pars compacta of the substantia nigra and the presence of Lewy bodies containing α-synuclein within these neurons. Oligomeric α-synuclein exerts neurotoxic effects through mitochondrial dysfunction, glial cell inflammatory response, lysosomal dysfunction and so on. α-synuclein aggregation, often accompanied by oxidative stress, is generally considered to be a key factor in PD pathology. At present, emerging evidences suggest that metabolism alteration is closely associated with α-synuclein aggregation and PD progression, and improvement of key molecules in metabolism might be potentially beneficial in PD treatment. In this review, we highlight the tripartite relationship among metabolic changes, α-synuclein aggregation, and oxidative stress in PD, and offer updated insights into the treatments of PD, aiming to deepen our understanding of PD pathogenesis and explore new therapeutic strategies for the disease.

A fast and non-invasive artificial intelligence olfactory-like system that aids diagnosis of Parkinson's disease

BACKGROUND AND PURPOSE

Several previous studies have shown that skin sebum analysis can be used to diagnose Parkinson's disease (PD). The aim of this study was to develop a portable artificial intelligence olfactory-like (AIO) system based on gas chromatographic analysis of the volatile organic compounds (VOCs) in patient sebum and explore its application value in the diagnosis of PD.

METHODS

The skin VOCs from 121 PD patients and 129 healthy controls were analyzed using the AIO system and three classic machine learning models were established, including the gradient boosting decision tree (GBDT), random forest and extreme gradient boosting, to assist the diagnosis of PD and predict its severity.

RESULTS

A 20-s time series of AIO system data were collected from each participant. The VOC peaks at a large number of time points roughly concentrated around 5-12 s were significantly higher in PD subjects. The gradient boosting decision tree model showed the best ability to differentiate PD from healthy controls, yielding a sensitivity of 83.33% and a specificity of 84.00%. However, the system failed to predict PD progression scored by Hoehn-Yahr stage.

CONCLUSIONS

This study provides a fast, low-cost and non-invasive method to distinguish PD patients from healthy controls. Furthermore, our study also indicates abnormal sebaceous gland secretion in PD patients, providing new evidence for exploring the pathogenesis of PD.

Transcriptome-based biomarker prediction for Parkinson's disease using genome-scale metabolic modeling

Parkinson's disease (PD) is the second most common neurodegenerative disease in the world. Identification of PD biomarkers is crucial for early diagnosis and to develop target-based therapeutic agents. Integrative analysis of genome-scale metabolic models (GEMs) and omics data provides a computational approach for the prediction of metabolite biomarkers. Here, we applied the TIMBR (Transcriptionally Inferred Metabolic Biomarker Response) algorithm and two modified versions of TIMBR to investigate potential metabolite biomarkers for PD. To this end, we mapped thirteen post-mortem PD transcriptome datasets from the substantia nigra region onto Human-GEM. We considered a metabolite as a candidate biomarker if its production was predicted to be more efficient by a TIMBR-family algorithm in control or PD case for the majority of the datasets. Different metrics based on well-known PD-related metabolite alterations, PD-associated pathways, and a list of 25 high-confidence PD metabolite biomarkers compiled from the literature were used to compare the prediction performance of the three algorithms tested. The modified algorithm with the highest prediction power based on the metrics was called TAMBOOR, TrAnscriptome-based Metabolite Biomarkers by On-Off Reactions, which was introduced for the first time in this study. TAMBOOR performed better in terms of capturing well-known pathway alterations and metabolite secretion changes in PD. Therefore, our tool has a strong potential to be used for the prediction of novel diagnostic biomarkers for human diseases.

The Associations Among Gut Microbiota, Branched Chain Amino Acids, and Parkinson's Disease: Mendelian Randomization Study

Background

In experimental and observational studies, the characteristics of gut microbiota have been associated with Parkinson's disease (PD), among which metabolic pathways played an important role. However, the causality remained unclear.

Objective

Herein, we aimed to determine the potential impact of gut microbiota and gut microbiota-derived metabolites on PD risk using a Mendelian randomization (MR) approach.

Methods

We included as exposures gut microbial taxa abundance and gut-derived metabolites (branched chain amino acids [BCAAs]), with PD as the outcome. In addition, we explored whether BCAAs act as a mediating factor in the pathway from gut microbiota to PD.

Results

We found evidence of a causality of 15 microbial taxa and PD before and after sensitivity analyses, but not after multiple testing correction. There was significant association between BCAAs levels and the risk of PD, especially isoleucine (OR = 0.995, 95% CI 0.992-0.999, p = 0.004, pFDR = 0.012). In addition, the causality of gut microbiota and BCAAs was also explored that the increased g_Coprococcus abundance can result in the decrease in isoleucine level (OR = 1.046; 95% CI, 1.009-1.085; p = 0.016).

Conclusions

Our findings indicated suggestive association between gut microbiota and its metabolites and PD. Furthermore, higher BCAAs levels were associated with the decreased PD risk. This study may provide new targets for PD treatment, such as dietary BCAAs supplementation.

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.

Parkinson's disease and gut microbiota: from clinical to mechanistic and therapeutic studies

Parkinson's disease (PD) is one of the most prevalent neurodegenerative diseases. The typical symptomatology of PD includes motor symptoms; however, a range of nonmotor symptoms, such as intestinal issues, usually occur before the motor symptoms. Various microorganisms inhabiting the gastrointestinal tract can profoundly influence the physiopathology of the central nervous system through neurological, endocrine, and immune system pathways involved in the microbiota-gut-brain axis. In addition, extensive evidence suggests that the gut microbiota is strongly associated with PD. This review summarizes the latest findings on microbial changes in PD and their clinical relevance, describes the underlying mechanisms through which intestinal bacteria may mediate PD, and discusses the correlations between gut microbes and anti-PD drugs. In addition, this review outlines the status of research on microbial therapies for PD and the future directions of PD-gut microbiota research.

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.

Omics data integration suggests a potential idiopathic Parkinson's disease signature

The vast majority of Parkinson's disease cases are idiopathic. Unclear etiology and multifactorial nature complicate the comprehension of disease pathogenesis. Identification of early transcriptomic and metabolic alterations consistent across different idiopathic Parkinson's disease (IPD) patients might reveal the potential basis of increased dopaminergic neuron vulnerability and primary disease mechanisms. In this study, we combine systems biology and data integration approaches to identify differences in transcriptomic and metabolic signatures between IPD patient and healthy individual-derived midbrain neural precursor cells. Characterization of gene expression and metabolic modeling reveal pyruvate, several amino acid and lipid metabolism as the most dysregulated metabolic pathways in IPD neural precursors. Furthermore, we show that IPD neural precursors endure mitochondrial metabolism impairment and a reduced total NAD pool. Accordingly, we show that treatment with NAD precursors increases ATP yield hence demonstrating a potential to rescue early IPD-associated metabolic changes.

1H nuclear magnetic resonance-based metabolomics study of serum and pectoralis major for different commercial chicken breeds

This study aimed to characterize the metabolic composition of four types of commercially available chicken breeds [village chicken, colored broiler (Hubbard), broiler (Cobb), and spent layers (Dekalb)] by 1H NMR coupling and discriminate them using multivariate analysis. Five chickens were collected for each chicken breed based on the marketing age from the respective commercial farms. The orthogonal partial least squares discriminant analysis (OPLS-DA) results showed an obvious separation of local village chickens from the other breeds based on the metabolites present in their serum and meat (pectoralis major). The cumulative values of Q 2, R 2 X, and R 2 Y of the OPLS-DA model for chicken serum were 0.722, 0.877, and 0.841. For the pectoralis major muscle, the cumulative values of Q 2, R 2 X, and R 2 Y of the OPLS-DA model were reported as 0.684, 0.781, and 0.786, respectively. The quality of both OPLS-DA models was accepted by the cumulative values of Q 2 ≥ 0.5 and R 2 ≥ 0.65. The 1H NMR result with multivariate analysis has successfully distinguished local village chicken from the other three commercial chicken breeds based on serum and pectoralis major muscle. Nonetheless, colored broiler (Hubbard) was not distinguished from broiler (Cobb) and spent layers (Dekalb) in serum and pectoralis major, respectively. The OPLS-DA assessment in this study identified 19 and 15 potential metabolites for discriminating different chicken breeds in serum and pectoralis major muscle, respectively. Some of the prominent metabolites identified include amino acids (betaine, glycine, glutamine, guanidoacetate, phenylalanine, and valine), nucleotides (IMP and NAD+), organic acids (lactate, malate, and succinate), peptide (anserine), and sugar alcohol (myo-inositol).

Effect of DHA-Enriched Phospholipids from Fish Roe on Rat Fecal Metabolites: Untargeted Metabolomic Analysis

Lipid metabolism disorder has become an important hidden danger threatening human health, and various supplements to treat lipid metabolism disorder have been studied. Our previous studies have shown that DHA-enriched phospholipids from large yellow croaker (Larimichthys Crocea) roe (LYCRPLs) have lipid-regulating effects. To better explain the effect of LYCRPLs on lipid regulation in rats, the fecal metabolites of rats were analyzed from the level of metabolomics in this study, and GC/MS metabolomics measurements were performed to figure out the effect of LYCRPLs on fecal metabolites in rats. Compared with the control (K) group, 101 metabolites were identified in the model (M) group. There were 54, 47, and 57 metabolites in the low-dose (GA), medium-dose (GB), and high-dose (GC) groups that were significantly different from that of group M, respectively. Eighteen potential biomarkers closely related to lipid metabolism were screened after intervention with different doses of LYCRPLs on rats, which were classified into several metabolic pathways in rats, including pyrimidine metabolism, the citric acid cycle (TCA cycle), the metabolism of L-cysteine, carnitine synthesis, pantothenate and CoA biosynthesis, glycolysis, and bile secretion. L-cysteine was speculated to be a useful biomarker of LYCRPLs acting on rat fecal metabolites. Our findings indicated that LYCRPLs may regulate lipid metabolism disorders in SD rats by activating these metabolic pathways.

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.

Biochemical Discrimination of the Down Syndrome-Related Metabolic and Oxidative/Nitrosative Stress Alterations from the Physiologic Age-Related Changes through the Targeted Metabolomic Analysis of Serum

Down Syndrome (DS) is a neurodevelopmental disorder that is characterized by an accelerated aging process, frequently associated with the development of Alzheimer’s disease (AD). Previous studies evidenced that DS patients have various metabolic anomalies, easily measurable in their serum samples, although values that were found in DS patients were compared with those of age-matched non-DS patients, thus hampering to discriminate the physiologic age-related changes of serum metabolites from those that are truly caused by the pathologic processes associated with DS. In the present study we performed a targeted metabolomic evaluation of serum samples from DS patients without dementia of two age classes (Younger DS Patients, YDSP, aging 20–40 years; Aged DS Patients, ADSP, aging 41–60 years), comparing the results with those that were obtained in two age classes of non-DS patients (Younger non-DS Patients, YnonDSP, aging 30–60 years; Aged-nonDS Patients, AnonDSP, aging 75–90 years). Of the 36 compounds assayed, 30 had significantly different concentrations in Pooled non-DS Patients (PnonDSP), compared to Pooled DS Patients (PDSP). Age categorization revealed that 11/30 compounds were significantly different in AnonDSP, compared to YnonDSP, indicating physiologic, age-related changes of their circulating concentrations. A comparison between YDSP and ADSP showed that 19/30 metabolites had significantly different values from those found in the corresponding classes of non-DS patients, strongly suggesting pathologic, DS-associated alterations of their serum levels. Twelve compounds selectively and specifically discriminated PnonDSP from PDSP, whilst only three discriminated YDSP from ADSP. The results allowed to determine, for the first time and to the best of our knowledge, the true, age-independent alterations of metabolism that are measurable in serum and attributable only to DS. These findings may be of high relevance for better strategies (pharmacological, nutritional) aiming to specifically target the dysmetabolism and decreased antioxidant defenses that are associated with DS.

Plasma branched-chain and aromatic amino acids correlate with the gut microbiota and severity of Parkinson's disease

Disturbances of circulating amino acids have been demonstrated in patients with Parkinson’s disease (PD). However, there have been no consistent results for branched-chain amino acids (BCAAs) and aromatic amino acids (AAAs), and related factors have not been explored. We aimed to explore plasma BCAA and AAA profiles in PD patients, and identify their correlations with clinical characteristics and the gut microbiota. Plasma BCAA (leucine, isoleucine, and valine) and AAA (tyrosine and phenylalanine) levels were measured in 106 PD patients and 114 controls. Fecal samples were collected from PD patients for microbiota sequencing and functional analysis. We found that plasma BCAAs and tyrosine were decreased in PD patients. BCAAs and AAAs were correlated with clinical characteristics and microbial taxa, and, in particular, they were negatively correlated with the Hoehn and Yahr stage. Compared with early PD patients, BCAA and AAA levels were even lower, and microbial composition was altered in advanced PD patients. Predictive functional analysis indicated that predicted genes numbers involved in BCAA biosynthesis were lower in advanced PD patients. What’s more, the fecal abundances of critical genes (ilvB, ilvC, ilvD, and ilvN) involved in BCAA biosynthesis were reduced and fecal BCAA concentrations were lower in advanced PD patients. In conclusion, the disturbances of plasma BCAAs and AAAs in PD patients may be related to the gut microbiota and exacerbated with PD severity. The microbial amino acid metabolism may serve as a potential mechanistic link.

A metabolic biomarker predicts Parkinson's disease at the early stages in patients and animal models

BackgroundCare management of Parkinson's disease (PD) patients currently remains symptomatic, mainly because diagnosis relying on the expression of the cardinal motor symptoms is made too late. Earlier detection of PD therefore represents a key step for developing therapies able to delay or slow down its progression.MethodsWe investigated metabolic markers in 3 different animal models of PD, mimicking different phases of the disease assessed by behavioral and histological evaluation, and in 3 cohorts of de novo PD patients and matched controls (n = 129). Serum and brain tissue samples were analyzed by nuclear magnetic resonance spectroscopy and data submitted to advanced multivariate statistics.ResultsOur translational strategy reveals common metabolic dysregulations in serum of the different animal models and PD patients. Some of them were mirrored in the tissue samples, possibly reflecting pathophysiological mechanisms associated with PD development. Interestingly, some metabolic dysregulations appeared before motor symptom emergence and could represent early biomarkers of PD. Finally, we built a composite biomarker with a combination of 6 metabolites. This biomarker discriminated animals mimicking PD from controls, even from the first, nonmotor signs and, very interestingly, also discriminated PD patients from healthy subjects.ConclusionFrom our translational study, which included 3 animal models and 3 de novo PD patient cohorts, we propose a promising biomarker exhibiting a high accuracy for de novo PD diagnosis that may possibly predict early PD development, before motor symptoms appear.FundingFrench National Research Agency (ANR), DOPALCOMP, Institut National de la Santé et de la Recherche Médicale, Université Grenoble Alpes, Association France Parkinson.

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 Features of Brain Function with Relevance to Clinical Features of Alzheimer and Parkinson Diseases

Brain metabolism is comprised in Alzheimer’s disease (AD) and Parkinson’s disease (PD). Since the brain primarily relies on metabolism of glucose, ketone bodies, and amino acids, aspects of these metabolic processes in these disorders—and particularly how these altered metabolic processes are related to oxidative and/or nitrosative stress and the resulting damaged targets—are reviewed in this paper. Greater understanding of the decreased functions in brain metabolism in AD and PD is posited to lead to potentially important therapeutic strategies to address both of these disorders, which cause relatively long-lasting decreased quality of life in patients.

Neurodegenerative Disorders of Alzheimer, Parkinsonism, Amyotrophic Lateral Sclerosis and Multiple Sclerosis: An Early Diagnostic Approach for Precision Treatment

Neurodegenerative diseases (NDs) are characterised by progressive dysfunction of synapses, neurons, glial cells and their networks. Neurodegenerative diseases can be classified according to primary clinical features (e.g., dementia, parkinsonism, or motor neuron disease), anatomic distribution of neurodegeneration (e.g., frontotemporal degenerations, extrapyramidal disorders, or spinocerebellar degenerations), or principal molecular abnormalities. The most common neurodegenerative disorders are amyloidosis, tauopathies, a-synucleinopathy, and TAR DNA-binding protein 43 (TDP-43) proteopathy. The protein abnormalities in these disorders have abnormal conformational properties along with altered cellular mechanisms, and they exhibit motor deficit, mitochondrial malfunction, dysfunctions in autophagic-lysosomal pathways, synaptic toxicity, and more emerging mechanisms such as the roles of stress granule pathways and liquid-phase transitions. Finally, for each ND, microglial cells have been reported to be implicated in neurodegeneration, in particular, because the microglial responses can shift from neuroprotective to a deleterious role. Growing experimental evidence suggests that abnormal protein conformers act as seed material for oligomerization, spreading from cell to cell through anatomically connected neuronal pathways, which may in part explain the specific anatomical patterns observed in brain autopsy sample. In this review, we mention the human pathology of select neurodegenerative disorders, focusing on how neurodegenerative disorders (i.e., Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and multiple sclerosis) represent a great healthcare problem worldwide and are becoming prevalent because of the increasing aged population. Despite many studies have focused on their etiopathology, the exact cause of these diseases is still largely unknown and until now with the only available option of symptomatic treatments. In this review, we aim to report the systematic and clinically correlated potential biomarker candidates. Although future studies are necessary for their use in early detection and progression in humans affected by NDs, the promising results obtained by several groups leads us to this idea that biomarkers could be used to design a potential therapeutic approach and preclinical clinical trials for the treatments of NDs.

Serum fractalkine and 3-nitrotyrosine levels correlate with disease severity in Parkinson's disease: a pilot study

Parkinson's disease (PD) and Parkinsonian syndromes; Progressive supranuclear palsy (PSP), and Multiple system atrophy (MSA) are debilitating neurodegenerative disorders. Fractalkine is a chemokine involved in neuroinflammation, whereas, 3-nitrotyrosine (3-NT) is a marker of early neurodegenerative cellular-damage. We measured Fractalkine and 3-NT levels in the serum of these patients to examine the neuroinflammation hypothesis and also to decipher the propensity of these biologics to be used as early (5 years from onset) biochemical markers in neurodegenerative Parkinsonism. The diagnoses of PD, PSP and MSA were performed as per the respective clinical criteria. 21 PD, 9 PSP and 8 MSA patients along with controls participated in this study. Serum concentrations of Fractalkine and 3-NT were measured by ELISA. Fractalkine levels were increased in PD, PSP and MSA cohorts in comparison with controls with p < 0.001, p < 0.05 and p < 0.05 respectively. Levels of 3-NT also showed elevation in PD (p < 0.01) vs. controls. However, Pearson plot showed that Fractalkine levels were high in the patients with unified Parkinson's disease rating scale (UPDRS) part III motor score of 1, meaning slight disability, but gradually dropped in patients with motor score of 4, which is a measure of severe motor disability. This negative correlation (- .565, p < .0.01) also accentuates the neuroprotectant/anti-inflammatory nature of Fractalkine in PD. Continuous rise of 3-NT in PD, positively correlating (.512, p < 0.05) with worsening motor symptoms points to deleterious consequences of nitrosative stress. To our knowledge, this is the first report providing evidence that serum Fractalkine and 3-NT have early diagnostic/prognostic significance as PD biomarkers.

Metabolomics in degenerative brain diseases

Among the most studied diseases that affect the central nervous system are Parkinson's, Alzheimer's, and Huntington's diseases, but the lack of effective biomarkers, accurate diagnosis, and precise treatment for each of them is currently an issue. Due to the contribution of biomarkers in supporting diagnosis, many recent efforts have focused on their identification and validation at the beginning or during the progression of the mental illness. Metabolome reveals the metabolic processes that result from protein activities under the guided gene expression and environmental factors, either in healthy or pathological conditions. In this context, metabolomics has proven to be a valuable approach. Currently, magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) are the most commonly used bioanalytical techniques for metabolomics. MS-assisted profiling is considered the most versatile technique, and the NMR is the most reproductive. However, each one of them has its drawbacks. In this review, we summarized several alterations in metabolites that have been reported for these three classic brain diseases using MS and NMR-based research, which might suggest some possible biomarkers to support the diagnosis and/or new targets for their treatment.

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.

Metabolomics of neurological disorders in India

Metabolomics is the comprehensive study of the metabolome and its alterations within biological fluids and tissues. Over the years, applications of metabolomics have been explored in several areas, including personalised medicine in diseases, metabolome-wide association studies (MWAS), pharmacometabolomics and in combination with other branches of omics such as proteomics, transcriptomics and genomics. Mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy are the major analytical techniques widely employed in metabolomics. In addition, MS is coupled with chromatography techniques like gas chromatography (GC) and liquid chromatography (LC) to separate metabolites before analysis. These analytical techniques have made possible identification and quantification of large numbers of metabolites, encompassing characterization of diseases and facilitating a systematic and rational therapeutic strategy based on metabolic patterns. In recent years, the metabolomics approach has been used to obtain a deeper insight into the underlying biochemistry of neurodegenerative disorders and the discovery of biomarkers of clinical implications. The current review mainly focuses on an Indian perspective of metabolomics for the identification of metabolites and metabolic alterations serving as potential diagnostic biomarkers for neurological diseases including acute spinal cord injury, amyotrophic lateral sclerosis, tethered cord syndrome, spina bifida, stroke, Parkinson's disease, glioblastoma and neurological disorders with inborn errors of metabolism.

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.

Current advances in metabolomic studies on non-motor psychiatric manifestations of Parkinson's disease (Review)

Life expectancy has increased worldwide and, along with it, a greater prevalence of age-dependent disorders, chronic illnesses and comorbidities can be observed. In 2019, in both Europe and the Americas, dementias ranked 3rd among the top 10 causes of death. Parkinson's disease (PD) is the second most frequent type of neurodegenerative disease. In the last decades, globally, the number of people suffering from PD has more than doubled to over 6 million. Of all the neurological disorders, PD increased with the fastest rate. This troubling trend highlights the stringent need for accurate diagnostic biomarkers, especially in the early stages of the disease and to evaluate treatment response. To gain a broad and complex understanding of the recent advances in the '-omics' research fields, electronic databases such as PubMed, Google Academic, and Science Direct were searched for publications regarding metabolomic studies on PD to identify specific biomarkers for PD, and especially PD with associated psychiatric symptomatology. Discoveries in the fields of metagenomics, transcriptomics and proteomics, may lead to an improved comprehension of the metabolic pathways involved in disease etiology and progression and contribute to the discovery of novel therapeutic targets for effective treatment options.

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.

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.

Brain Branched-Chain Amino Acids in Maple Syrup Urine Disease: Implications for Neurological Disorders

Maple syrup urine disease (MSUD) is an autosomal recessive disorder caused by decreased activity of the branched-chain α-ketoacid dehydrogenase complex (BCKDC), which catalyzes the irreversible catabolism of branched-chain amino acids (BCAAs). Current management of this BCAA dyshomeostasis consists of dietary restriction of BCAAs and liver transplantation, which aims to partially restore functional BCKDC activity in the periphery. These treatments improve the circulating levels of BCAAs and significantly increase survival rates in MSUD patients. However, significant cognitive and psychiatric morbidities remain. Specifically, patients are at a higher lifetime risk for cognitive impairments, mood and anxiety disorders (depression, anxiety, and panic disorder), and attention deficit disorder. Recent literature suggests that the neurological sequelae may be due to the brain-specific roles of BCAAs. This review will focus on the derangements of BCAAs observed in the brain of MSUD patients and will explore the potential mechanisms driving neurologic dysfunction. Finally, we will discuss recent evidence that implicates the relevance of BCAA metabolism in other neurological disorders. An understanding of the role of BCAAs in the central nervous system may facilitate future identification of novel therapeutic approaches in MSUD and a broad range of neurological disorders.

Cerebrospinal and blood levels of amino acids as potential biomarkers for Parkinson's disease: review and meta-analysis

BACKGROUND AND PURPOSE

The present systematic review and meta-analysis aims to establish the possible value of cerebrospinal fluid (CSF) and serum/plasma levels of amino acids as markers of Parkinson's disease (PD).

METHODS

This is a review of four databases (PubMed, Embase, MEDLINE and Web of Science - Core Collection) from 1966 to 14 March 2020, with identification of references of interest for the topic. The meta-analysis of eligible studies was done using R software package meta, following the PRISMA and MOOSE guidelines.

RESULTS

Compared with age- and sex-matched controls, PD patients showed decreased CSF levels of glutamate and taurine and increased CSF levels of tyrosine; decreased serum/plasma levels of aspartate, serine, tryptophan and lysine, and increased serum/plasma proline and homocysteine levels.

CONCLUSION

Despite the limitations of this study due to the important variability of results between different series, our findings suggest the value of CSF or serum/plasma levels of several amino acids in the discrimination of PD patients from healthy subjects, related to the levels of some amino acids.

Identification of potential urine biomarkers in idiopathic parkinson's disease using NMR

INTRODUCTION

Parkinson's disease (PD) is the most common neurodegenerative disease caused by the loss of dopamine chemicals resulting in urinary incontinence, gastrointestinal dysfunction, gait impairment and mitochondrial dysfunction. Study investigated urinary metabolic profiles of patients with idiopathic PD as compared to healthy controls (HC) to identify the potential biomarkers.

METHODS

Urine samples were collected from 100 PD subjects and 50 HC using standard protocol. Metabolomic analyses were performed using high resolution nuclear magnetic resonance (NMR) spectroscopy. The integral values of 17 significant metabolites were estimated and concentration values were calculated, which were subjected to univariate and multivariate statistical analysis.

RESULTS

We found significantly increased levels of ornithine, phenylalanine, isoleucine, β-hydroxybutyrate, tyrosine and succinate in the urine of patients with PD in comparison with HC. These metabolites exhibited area under the curve greater than 0.60 on ROC curve analysis. We also observed a significant association between succinate concentration and UPDRS motor scores of PD.

DISCUSSION

Metabolic pathway alterations were observed in aromatic amino acid metabolism, ketone bodies synthesis, branched chain amino acid metabolism and ornithine metabolism. Comprehensive metabolomic profiling revealed variations in urinary signatures associated with severity of idiopathic PD. This profiling relies on non-invasive sampling and is complementary to existing clinical modalities.

An update on blood-based biomarkers for non-Alzheimer neurodegenerative disorders

Cerebrospinal fluid analyses and neuroimaging can identify the underlying pathophysiology at the earliest stage of some neurodegenerative disorders, but do not have the scalability needed for population screening. Therefore, a blood-based marker for such pathophysiology would have greater utility in a primary care setting and in eligibility screening for clinical trials. Rapid advances in ultra-sensitive assays have enabled the levels of pathological proteins to be measured in blood samples, but research has been predominantly focused on Alzheimer disease (AD). Nonetheless, proteins that were identified as potential blood-based biomarkers for AD, for example, amyloid-β, tau, phosphorylated tau and neurofilament light chain, are likely to be relevant to other neurodegenerative disorders that involve similar pathological processes and could also be useful for the differential diagnosis of clinical symptoms. This Review outlines the neuropathological, clinical, molecular imaging and cerebrospinal fluid features of the most common neurodegenerative disorders outside the AD continuum and gives an overview of the current status of blood-based biomarkers for these disorders.

DJ-1 suppresses ferroptosis through preserving the activity of S-adenosyl homocysteine hydrolase

Ferroptosis is a newly characterized form of regulated cell death mediated by iron-dependent accumulation of lipid reactive oxygen species and holds great potential for cancer therapy. However, the molecular mechanisms underlying ferroptosis remain largely elusive. In this study, we define an integrative role of DJ-1 in ferroptosis. Inhibition of DJ-1 potently enhances the sensitivity of tumor cells to ferroptosis inducers both in vitro and in vivo. Metabolic analysis and metabolite rescue assay reveal that DJ-1 depletion inhibits the transsulfuration pathway by disrupting the formation of the S-adenosyl homocysteine hydrolase tetramer and impairing its activity. Consequently, more ferroptosis is induced when homocysteine generation is decreased, which might be the only source of glutathione biosynthesis when cystine uptake is blocked. Thus, our findings show that DJ-1 determines the response of cancer cells to ferroptosis, and highlight a candidate therapeutic target to potentially improve the effect of ferroptosis-based antitumor therapy.

Genes to treat excitotoxicity ameliorate the symptoms of the disease in mice models of multiple system atrophy

Multiple system atrophy (MSA) is a sporadic neurodegenerative disorder characterized by striatonigral degeneration and olivopontocerebellar atrophy. The main hallmark of MSA is the aggregation of alpha-synuclein in oligodendrocytes, which contributes to the dysfunction and death of the oligodendrocytes, followed by neurodegeneration. Studies suggested that oxidative-excitatory pathway is associated with the progression of the disease. The aim of the current study was to test this concept by overexpression of excitatory amino acid transporter 2, glutamate dehydrogenase and nuclear factor (erythroid-derived 2)-related factor 2 genes in the striatum of two established mouse models of MSA. To induce the first model, we injected the mitochondrial neurotoxin, 3-nitropropionic acid (3-NP), unilaterally into the right striatum in 2-month-old C57BL/6 male mice. We demonstrate a significant improvement in two drug-induced rotational behavior tests, following unilateral injection the three genes. For the second model, we used transgenic mice expressing the alpha-synuclein gene under the proteolipid protein, in the age of 7 months, boosted with 3-NP to enhance the motor deficits and neurodegeneration. We show that the overexpression of the three genes attenuated the motor-related deficit in the elevated bridge and pole tests. Thus, our study indicates that glutamate excito-oxidative toxicity plays a major role in this MSA model and our gene therapy approach might suggest a novel strategy for MSA treatment.

Quantitative metabolomics of saliva using proton NMR spectroscopy in patients with Parkinson's disease and healthy controls

INTRODUCTION

Parkinson's disease (PD) is a multisystem disorder of unknown etiology, highlights a broad array of symptoms and pathological features influencing organs throughout the body. The metabolic profile of saliva in patients with PD may be influenced by malabsorption in the gastroenteric tract, neurodegeneration, and mitochondrial dysfunction. In the present study, we apply a powerful NMR metabolomics approach for biomarker identification in PD using saliva, a non-invasive bio-fluid.

METHODS

Metabolic profiling of saliva were studied in patients with PD (n = 76) and healthy controls (HC, n = 37) were analyzed and differentiated PD from HC. A total of 40 metabolites including aromatic amino acids, short-chain fatty acids, branched chain amino acids, taurine, and N-acetylglutamate were identified. Spectral binned data and concentration of metabolites were estimated for analysis.

RESULTS

Increased concentration of phenylalanine, tyrosine, histidine, glycine, acetoacetate, taurine, TMAO, GABA, N-acetylglutamate, acetoin, acetate, alanine, fucose, propionate, isoleucine, and valine were observed in PD as compared to HC. Further, subgroup analysis among early PD, advanced PD, and HC groups, revealed increased metabolite concentration in early PD group as compared to advanced PD and HC group.

DISCUSSION

Analysis revealed potential biomarkers and their involvement in amino acid metabolism, energy metabolism, neurotransmitters metabolism, and microflora system. Patients with early PD exhibited higher metabolite concentration as compared to advanced PD group which might be associated with dopaminergic treatment.

CONCLUSION

The results of our data indicate that patients with PD might be characterized by metabolic imbalances like gut microflora system, energy metabolites, and neurotransmitters which may contribute to the PD pathogenesis.

Application of l-methionine γ-lyase in chiral amino acid analysis

Here, a conventional chiral amino acid analysis method using high-performance liquid chromatography was coupled with a sample pretreatment using l-methionine γ-lyase from Pseudomonas putida ICR 3460 for the selective analysis of l-methionine and l-tryptophan. The sample was analyzed after the degradation of l-methionine with l-methionine γ-lyase, as l-methionine coelutes with l-tryptophan under the standard chiral amino acid analytical conditions used for precolumn derivatization with o-phthalaldehyde and N-acetyl-l-cysteine. The l-tryptophan in the sample was then eluted as a clearly separated peak and analyzed further. Since the l-methionine γ-lyase did not act on l-tryptophan, we were able to calculate the l-methionine or l-tryptophan concentration based on the data obtained from 2 individual runs: the sample with and without l-methionine γ-lyase pretreatment. The concentration of l-tryptophan was calculated from the data obtained from the sample with l-methionine γ-lyase pretreatment, while the concentration of l-methionine was calculated using the following equation: l-methionine concentration = {the data from the sample without l-methionine γ-lyase pretreatment}-{the data from the sample with l-methionine γ-lyase pretreatment}. Model samples containing authentic amino acids and a fermented food sample were analyzed by our method, and the calculated concentrations of l-methionine and l-tryptophan were consistently in agreement with the theoretical values.

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

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

Stratification of cancer and diabetes based on circulating levels of formate and glucose

BACKGROUND

Serum and urine metabolites have been investigated for their use as cancer biomarkers. The specificity of candidate metabolites can be limited by the impact of other disorders on metabolite levels. In particular, the increasing incidence of obesity could become a significant confounding factor.

METHODS

Here we developed a multinomial classifier for the stratification of cancer, obesity and healthy phenotypes based on circulating glucose and formate levels. We quantified the classifier performance from the retrospective analysis of samples from breast cancer, lung cancer, obese individuals and healthy controls.

RESULTS

We discovered that circulating formate levels are significantly lower in breast and lung cancer patients than in healthy controls. However, the performance of a cancer classifier based on formate levels alone is limited because obese patients also have low serum formate levels. By introducing a multinomial classifier based on circulating glucose and formate levels, we were able to improve the classifier performance, reaching a true positive rate of 79% with a false positive rate of 8%.

CONCLUSIONS

Circulating formate is reduced in HER2+ breast cancer, non-small cell lung cancer and highly obese patients relative to healthy controls. Further studies are required to determine the relevance of these observations in other cancer types and diseases.

Analysis of Mucopolysaccharidosis Type VI through Integrative Functional Metabolomics

Metabolic phenotyping is poised as a powerful and promising tool for biomarker discovery in inherited metabolic diseases. However, few studies applied this approach to mcopolysaccharidoses (MPS). Thus, this innovative functional approach may unveil comprehensive impairments in MPS biology. This study explores mcopolysaccharidosis VI (MPS VI) or Maroteaux–Lamy syndrome (OMIM #253200) which is an autosomal recessive lysosomal storage disease caused by the deficiency of arylsulfatase B enzyme. Urine samples were collected from 16 MPS VI patients and 66 healthy control individuals. Untargeted metabolomics analysis was applied using ultra-high-performance liquid chromatography combined with ion mobility and high-resolution mass spectrometry. Furthermore, dermatan sulfate, amino acids, carnitine, and acylcarnitine profiles were quantified using liquid chromatography coupled to tandem mass spectrometry. Univariate analysis and multivariate data modeling were used for integrative analysis and discriminant metabolites selection. Pathway analysis was done to unveil impaired metabolism. The study revealed significant differential biochemical patterns using multivariate data modeling. Pathway analysis revealed that several major amino acid pathways were dysregulated in MPS VI. Integrative analysis of targeted and untargeted metabolomics data with in silico results yielded arginine-proline, histidine, and glutathione metabolism being the most affected. This study is one of the first metabolic phenotyping studies of MPS VI. The findings might shed light on molecular understanding of MPS pathophysiology to develop further MPS studies to enhance diagnosis and treatments of this rare condition.

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

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

The effects of electrical stimulation of the peripheral vestibular system on neurochemical release in the rat striatum

For over a century, it has been speculated that the vestibular system transmits information about self-motion to the striatum. There have been inconsistent reports of such a connection, and interest in the subject has been increased by the experimental use of galvanic vestibular stimulation in the treatment of Parkinson's Disease patients. Nonetheless, there are few data available on the effects of vestibular stimulation on neurochemical changes in the striatum. We used in vivo microdialysis to analyse changes in the extracellular levels of amino acids and monoamines in the rat striatum, following electrical vestibular stimulation. Stimulation caused a significant decrease in serine and threonine, compared to the no-stimulation controls (P ≤ 0.005 and P ≤ 0.01, respectively). The ratio of DOPAC:dopamine, decreased on the ipsilateral side following stimulation (P ≤ 0.005). There was a significant treatment x side x intensity interaction for taurine levels (P ≤ 0.002), due to a decrease on the contralateral side in stimulated animals, which varied as a function of current. These results show that peripheral vestibular stimulation causes some neurochemical changes in the striatum and support the view that activaton of the vestibular system exerts effects on the function of the striatum.

Biophysical, Biochemical, and Cell Based Approaches Used to Decipher the Role of Carbonic Anhydrases in Cancer and to Evaluate the Potency of Targeted Inhibitors

Carbonic anhydrases (CAs) are thought to be important for regulating pH in the tumor microenvironment. A few of the CA isoforms are upregulated in cancer cells, with only limited expression in normal cells. For these reasons, there is interest in developing inhibitors that target these tumor-associated CA isoforms, with increased efficacy but limited nonspecific cytotoxicity. Here we present some of the biophysical, biochemical, and cell based techniques and approaches that can be used to evaluate the potency of CA targeted inhibitors and decipher the role of CAs in tumorigenesis, cancer progression, and metastatic processes. These techniques include esterase activity assays, stop flow kinetics, and mass inlet mass spectroscopy (MIMS), all of which measure enzymatic activity of purified protein, in the presence or absence of inhibitors. Also discussed is the application of X-ray crystallography and Cryo-EM as well as other structure-based techniques and thermal shift assays to the studies of CA structure and function. Further, large-scale genomic and proteomic analytical methods, as well as cell based techniques like those that measure cell growth, apoptosis, clonogenicity, and cell migration and invasion, are discussed. We conclude by reviewing approaches that test the metastatic potential of CAs and how the aforementioned techniques have contributed to the field of CA cancer research.