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

Melatonin alleviates brain injury in copper-laden rats: Underlying benefits for Wilson's disease

Copper serves as an indispensable cofactor for all living organisms, and its excessive accumulation has been associated with a variety of diseases. Wilson's disease (WD) serves as an illustrative example of copper toxicity in humans, frequently presenting with liver and/or neuropsychiatric symptoms. The current therapeutic drugs, penicillamine (PA) and zinc gluconate (ZnG), have constraints, and research on their combination efficacy remains insufficient. It has been reported that melatonin (MLT) plays a vital role in binding to transition metals and exhibits strong antioxidant capacity. To investigate the therapeutic efficacy of MLT and combined treatment, rats were randomly divided into the following seven groups: the control (Con) group, copper-laden model rat (Mod) group, PA-treated group, ZnG-treated group, MLT- treated group, PA-ZnG-treated group, and PA-MLT-treated group. Then potential mechanisms and targets were investigated using a combination of metabolomics and network pharmacology and verified by molecular docking and qPCR. The findings revealed that MLT and the combination significantly improved behavior, pathology and copper levels in copper-laden rats. The results of the metabolomics study showed that profoundly altered metabolites were identified, and alanine, aspartate and glutamate metabolism, pyruvate metabolism, citrate cycle (TCA cycle), and glycolysis/gluconeogenesis were explored. In addition, molecular docking showed that MLT had high binding affinity with key targets, and qPCR results revealed that MLT could reverse the mRNA expression of targets GOT2 and PKM2. It was concluded that MLT effectively improves brain injury in copper-laden rats, and this effect was linked with the altered features of the metabolite profiles.

Mechanistic insights into the neurotoxicity of F53B: Effects on metabolic dysregulation and apoptosis of dopaminergic neurons

F53B (6:2 chlorinated polyfluorinated ether sulfonate), a substitute for perfluorooctane sulfonate (PFOS), is widely used as a chromium mist inhibitor in the electroplating industry. However, significant concern has arisen owing to its biological toxicity. Several studies on F53B toxicity in mammals have focused on hepatotoxicity, immunotoxicity, developmental toxicity, and reproductive toxicity, while its neurotoxic effects, especially in relation to neurodegenerative diseases such as Parkinson's disease (PD), remain unclear. In this study, we investigated the neurotoxic effects of F53B on dopaminergic neurons and explored its potential risk associated with PD in a cellular model. Potential target prediction and validation experiments demonstrated that F53B induced apoptosis in dopaminergic neurons. We also discovered that F53B triggered oxidative stress and inflammatory responses, and stimulated nitric oxide (NO) generation in the PD cellular model. Subsequently, untargeted metabolomics and lipidomics approaches were integrated to explore the molecular mechanisms underlying the response of dopaminergic neurons to F53B exposure. The results suggested that F53B disrupted arginine and proline metabolism, energy metabolism, and caused lipid dysregulation, particularly promoting the hydrolysis of sphingomyelin (SM) into ceramide (Cer). Overall, this study provides evidence that F53B exposure could increase the potential risk of PD and offers novel insights into its neurotoxicity mechanisms.

Dopaminergic neuron metabolism: relevance for understanding Parkinson's disease

BACKGROUND

Dopaminergic neurons from the substantia nigra pars compacta (SNc) have a higher susceptibility to aging-related degeneration, compared to midbrain dopaminergic cells present in the ventral tegmental area (VTA); the death of dopamine neurons in the SNc results in Parkinson´s disease (PD). In addition to increased loss by aging, dopaminergic neurons from the SNc are more prone to cell death when exposed to genetic or environmental factors, that either interfere with mitochondrial function, or cause an increase of oxidative stress. The oxidation of dopamine is a contributing source of reactive oxygen species (ROS), but this production is not enough to explain the differences in susceptibility to degeneration between SNc and VTA neurons.

AIM OF REVIEW

In this review we aim to highlight the intrinsic differences between SNc and VTA dopamine neurons, in terms of gene expression, calcium oscillations, bioenergetics, and ROS responses. Also, to describe the changes in the pentose phosphate pathway and the induction of apoptosis in SNc neurons during aging, as related to the development of PD.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Recent work showed that neurons from the SNc possess intrinsic characteristics that result in metabolic differences, related to their intricate morphology, that render them more susceptible to degeneration. In particular, these neurons have an elevated basal energy metabolism, that is required to fulfill the demands of the constant firing of action potentials, but at the same time, is associated to higher ROS production, compared to VTA cells. Finally, we discuss how mutations related to PD affect metabolic pathways, and the related mechanisms, as revealed by metabolomics.

Machine learning model base on metabolomics and proteomics to predict cognitive impairment in Parkinson's disease

There is an urgent need to identify predictive biomarkers of Parkinson's disease (PD) with cognitive impairment (PDCI) in order to individualize patient management, ensure timely intervention, and improve prognosis. The aim of this study was to screen for these biomarkers by comparing the plasma proteome and metabolome of PD patients with or without cognitive impairment. Proteomics and metabolomics analyses were performed on a discover cohort. A machine learning model was used to identify candidate protein and metabolite biomarkers of PDCI, which were validated in an independent cohort. The predictive ability of these biomarkers for PDCI was evaluated by plotting receiver operating characteristic curves and calculating the area under the curve (AUC). Moreover, we assessed the predictive ability of these proteins in combination with neuroimaging. In the discover cohort (n = 100), we identified 25 protein features with best results in the machine learning model, including top-ranked PSAP and H3C15. The two-proteins were used for model construction, achieving an Area under the curve (AUC) of 0.951 in the train set and AUC of 0.981 in the test set. Similarly, the model gives a rank list of endogenous metabolite features, Glycocholic Acid and 6-Methylnicotinamide were two top features. Combining these two markers further got the AUC of 0.969 in train set and 0.867 in the test set. To validate the performance of the protein biomarkers, we performed targeted analysis of selected proteins (H3C15 and PSAP) and proteins likely associated with PDCI (NCAM2 and LAMB2) using parallel reaction monitoring in validation cohort (n = 116). The AUC of the classifier built with H3C15 and PSAP is 0.813. Moreover, when combining H3C15, PSAP, NCAM2, and LAMB2, the model achieved AUC of 0.983 in the train set, AUC of 0.981 in the test set, and AUC of 0.839 in the validation set. Furthermore, we verified that these protein markers we discovered can improve the predictive effect of neuroimaging on PDCI: the classifier built with neuroimaging features had AUC of 0.833, which improved to 0.905 when combined with H3C15. Taken together, our integrated proteomics and metabolomics analysis successfully identified potential biomarkers for PDCI. Additionally, H3C15 showed promise in enhancing the predictive performance of neuroimaging for cognitive impairment.

Increased serum phenylalanine/tyrosine ratio associated with the psychiatric symptom of anti-NMDAR encephalitis

Background

Encephalitis associated with antibodies against the N-methyl-D-aspartate receptor (NMDAR) results in a distinctive neuro-psychiatric syndrome. It has been reported that the serum phenylalanine-tyrosine (Phe/Tyr) ratio increases during infection. However, the connection between phenylalanine-tyrosine metabolism and psychiatric symptoms remains unclear.

Methods

We enrolled 24 individuals with anti-NMDAR encephalitis and 18 individuals with non-inflammatory neurological diseases (OND). Chromatography was used to measure serum levels of phenylalanine and tyrosine. Serum and cerebrospinal fluid (CSF) TNF-α levels were obtained from the clinical database. The modified Rankin Scale (mRS) score and Glasgow Coma Scale (GCS) score were recorded during the acute phase. The area under the curve (AUC) of the receiver operating characteristic curve was used to assess prediction efficacy.

Results

In NMDAR patients, levels of serum Phe and the ratio of serum Phe/Tyr were higher compared to OND patients. The serum Phe/Tyr ratio was also elevated in NMDAR patients with psychiatric syndrome. Furthermore, serum Phe and Tyr levels were correlated with inflammatory indexes.

Conclusion

The serum Phe/Tyr ratio is elevated in NMDAR patients with psychiatric syndrome and is associated with severity. Therefore, the serum Phe/Tyr ratio may serve as a potential prognostic biomarker.

The Role of Microbial Metabolites in the Progression of Neurodegenerative Diseases-Therapeutic Approaches: A Comprehensive Review

The objective of this review is to provide a comprehensive examination of the role of microbial metabolites in the progression of neurodegenerative diseases, as well as to investigate potential therapeutic interventions targeting the microbiota. A comprehensive literature search was conducted across the following databases: PubMed, Scopus, Web of Science, ScienceDirect, and Wiley. Key terms related to the gut microbiota, microbial metabolites, neurodegenerative diseases, and specific metabolic products were used. The review included both preclinical and clinical research articles published between 2000 and 2024. Short-chain fatty acids have been demonstrated to play a crucial role in modulating neuroinflammation, preserving the integrity of the blood-brain barrier, and influencing neuronal plasticity and protection. Furthermore, amino acids and their derivatives have been demonstrated to exert a significant influence on CNS function. These microbial metabolites impact CNS health by regulating intestinal permeability, modulating immune responses, and directly influencing neuroinflammation and oxidative stress, which are integral to neurodegenerative diseases. Therapeutic strategies, including prebiotics, probiotics, dietary modifications, and fecal microbiota transplantation have confirmed the potential to restore microbial balance and enhance the production of neuroprotective metabolites. Furthermore, novel drug developments based on microbial metabolites present promising therapeutic avenues. The gut microbiota and its metabolites represent a promising field of research with the potential to advance our understanding of and develop treatments for neurodegenerative diseases.

A new chemical derivatization reagent sulfonyl piperazinyl for the quantification of fatty acids using LC-MS/MS

In our previous study, a chemical derivatization reagent named 5-(dimethylamino) naphthalene-1-sulfonyl piperazine (Dns-PP) was developed to enhance the chromatographic retention and the mass spectrometric response of free fatty acids (FFAs) in reversed-phase liquid chromatography coupled with electrospray ionization-mass spectrometry (RPLC-ESI-MS). However, Dns-PP exhibited strong preferences for long-chain FFAs, with limited improvement for short- or medium-chain FFAs. In this study, a new series of labeling reagents targeting FFAs were designed, synthesized, and evaluated. Among these reagents, Tmt-PP (N2, N2, N4, N4-tetramethyl-6-(4-(piperazin-1-ylsulfonyl) phenyl)-1,3,5-triazine-2,4-diamine) exhibited the best MS response and was selected for further evaluations. We compared Tmt-PP with Dns-PP and four commonly used carboxyl labeling reagents from existing studies, demonstrating the advantages of Tmt-PP. Further comparisons between Tmt-PP and Dns-PP in measuring FFAs from biological samples revealed that Tmt-PP labeling enhanced the MS response for about 80 % (30/38) of the measured FFAs, particularly for short- and medium-chain FFAs. Moreover, Tmt-PP labeling significantly improved the chromatographic retention of short-chain FFAs. To ensure accurate quantification, we developed a stable isotope-labeled Tmt-PP (i.e., d12-Tmt-PP) to react with chemical standards and serve as one-to-one internal standards (IS). The method was validated for accuracy, precision, sensitivity, linearity, stability, extraction efficiency, as well as matrix effect. Overall, this study introduced a new chemical derivatization reagent Tmt-PP (d12-Tmt-PP), providing a sensitive and accurate option for quantifying FFAs in biological samples.

Multiscale metabolomics techniques: Insights into neuroscience research

The field of metabolomics examines the overall composition and dynamic patterns of metabolites in living organisms. The primary methods used in metabolomics include liquid chromatography (LC), nuclear magnetic resonance (NMR), and mass spectrometry (MS) analysis. These methods enable the identification and examination of metabolite types and contents within organisms, as well as modifications to metabolic pathways and their connection to the emergence of diseases. Research in metabolomics has extensive value in basic and applied sciences. The field of metabolomics is growing quickly, with the majority of studies concentrating on biomedicine, particularly early disease diagnosis, therapeutic management of human diseases, and mechanistic knowledge of biochemical processes. Multiscale metabolomics is an approach that integrates metabolomics techniques at various scales, including the holistic, tissue, cellular, and organelle scales, to enable more thorough and in-depth studies of metabolic processes in organisms. Multiscale metabolomics can be combined with methods from systems biology and bioinformatics. In recent years, multiscale metabolomics approaches have become increasingly important in neuroscience research due to the nervous system's high metabolic demands. Multiscale metabolomics can offer novel concepts and approaches for the diagnosis, treatment, and development of medication for neurological illnesses in addition to a more thorough understanding of brain metabolism and nervous system function. In this review, we summarize the use of multiscale metabolomics techniques in neuroscience, address the promise and constraints of these techniques, and provide an overview of the metabolome and its applications in neuroscience.

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.

Dopamine‑iron homeostasis interaction rescues mitochondrial fitness in Parkinson's disease

Imbalances of iron and dopamine metabolism along with mitochondrial dysfunction have been linked to the pathogenesis of Parkinson's disease (PD). We have previously suggested a direct link between iron homeostasis and dopamine metabolism, as dopamine can increase cellular uptake of iron into macrophages thereby promoting oxidative stress responses. In this study, we investigated the interplay between iron, dopamine, and mitochondrial activity in neuroblastoma SH-SY5Y cells and human induced pluripotent stem cell (hiPSC)-derived dopaminergic neurons differentiated from a healthy control and a PD patient with a mutation in the α-synuclein (SNCA) gene. In SH-SY5Y cells, dopamine treatment resulted in increased expression of the transmembrane iron transporters transferrin receptor 1 (TFR1), ferroportin (FPN), and mitoferrin2 (MFRN2) and intracellular iron accumulation, suggesting that dopamine may promote iron uptake. Furthermore, dopamine supplementation led to reduced mitochondrial fitness including decreased mitochondrial respiration, increased cytochrome c control efficiency, reduced mtDNA copy number and citrate synthase activity, increased oxidative stress and impaired aconitase activity. In dopaminergic neurons derived from a healthy control individual, dopamine showed comparable effects as observed in SH-SY5Y cells. The hiPSC-derived PD neurons harboring an endogenous SNCA mutation demonstrated altered mitochondrial iron homeostasis, reduced mitochondrial capacity along with increased oxidative stress and alterations of tricarboxylic acid cycle linked metabolic pathways compared with control neurons. Importantly, dopamine treatment of PD neurons promoted a rescue effect by increasing mitochondrial respiration, activating antioxidant stress response, and normalizing altered metabolite levels linked to mitochondrial function. These observations provide evidence that dopamine affects iron homeostasis, intracellular stress responses and mitochondrial function in healthy cells, while dopamine supplementation can restore the disturbed regulatory network in PD cells.

Amyloid detection in neurodegenerative diseases using MOFs

Neurodegenerative diseases (amyloid diseases such as Alzheimer's and Parkinson's), stemming from protein misfolding and aggregation, encompass a spectrum of disorders with severe systemic implications. Timely detection is pivotal in managing these diseases owing to their significant impact on organ function and high mortality rates. The diverse array of amyloid disorders, spanning localized and systemic manifestations, underscores the complexity of these conditions and highlights the need for advanced detection methods. Traditional approaches have focused on identifying biomarkers using imaging techniques (PET and MRI) or invasive procedures. However, recent efforts have focused on the use of metal-organic frameworks (MOFs), a versatile class of materials known for their unique properties, in revolutionizing amyloid disease detection. The high porosity, customizable structures, and biocompatibility of MOFs enable their integration with biomolecules, laying the groundwork for highly sensitive and specific biosensors. These sensors have been employed using electrochemical and photophysical techniques that target amyloid species under neurodegenerative conditions. The adaptability of MOFs allows for the precise detection and quantification of amyloid proteins, offering potential advancements in early diagnosis and disease management. This review article delves into how MOFs contribute to detecting amyloid diseases by categorizing their uses based on different sensing methods, such as electrochemical (EC), electrochemiluminescence (ECL), fluorescence, Förster resonance energy transfer (FRET), up-conversion luminescence resonance energy transfer (ULRET), and photoelectrochemical (PEC) sensing. The drawbacks of MOF biosensors and the challenges encountered in the field are also briefly explored from our perspective.

Untargeted serum metabolic profiling of diabetes mellitus among Parkinson's disease patients

Type 2 diabetes mellitus (T2DM) is a common comorbidity among Parkinson's disease (PD) patients. Yet, little is known about dysregulated pathways that are unique in PD patients with T2DM. We applied high-resolution metabolomic profiling in serum samples of 636 PD and 253 non-PD participants recruited from Central California. We conducted an initial discovery metabolome-wide association and pathway enrichment analysis. After adjusting for multiple testing, in positive (or negative) ion mode, 30 (25) metabolic features were associated with T2DM in both PD and non-PD participants, 162 (108) only in PD participants, and 32 (7) only in non-PD participants. Pathway enrichment analysis identified 17 enriched pathways associated with T2DM in both the PD and non-PD participants, 26 pathways only in PD participants, and 5 pathways only in non-PD participants. Several amino acid, nucleic acids, and fatty acid metabolisms were associated with T2DM only in the PD patient group suggesting a possible link between PD and T2DM.

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.

Metabolomics comparison of metabolites and functional pathways in the SH-SY5Y cell model of Parkinson's disease under PEMF exposure

Objective

PEMF is an emerging technique in the treatment of Parkinson's disease (PD) due to its potential improvement of movement speed. The aim of this study was to investigate the metabolic profiles of pulsed electromagnetic fields (PEMFs) in an SH-SY5Y cell model of PD.

Methods

The SH-SY5Y cell model of PD was induced by 1-methyl-4-phenylpyridinium (MPP+). Liquid chromatography mass spectrometry (LC‒MS)-based untargeted metabolomics was performed to examine changes in the PD cell model with or without PEMF exposure. We conducted KEGG pathway enrichment analysis to explore the potentially related pathways of the differentially expressed metabolites.

Results

A total of 275 metabolites were annotated, and 27 significantly different metabolites were found between the PEMF treatment and control groups (VIP >1, P < 0.05), mainly including 4 amino acids and peptides, 4 fatty acid esters, 2 glycerophosphoethanolamines, 2 ceramides and 2 monoradylglycerols; among them, 12 metabolites were upregulated, and 15 were downregulated. The increased expression levels of glutamine, adenosine monophosphate and taurine were highly associated with PEMF stimulation in the PD model. The enrichment results of differentially abundant metabolite functional pathways showed that biological processes such as the mTOR signaling pathway, PI3K-Akt signaling pathway, and cAMP signaling pathway were significantly affected.

Conclusion

PEMFs affected glutamine, adenosine monophosphate and taurine as well as their functional pathways in an in vitro model of PD. Further functional studies regarding the biological effect of these changes are required to evaluate the clinical efficacy and safety of PEMF treatment in PD.

Nanoparticles and neurodegeneration: Insights on multiple pathways of programmed cell death regulated by nanoparticles

Currently, nanoparticles (NPs) are extensively applied in the diagnosis and treatment of neurodegenerative diseases (NDs). With the rapid development and increasing exposure to the public, the potential neurotoxicity associated with NDs caused by NPs has attracted the researchers' attentions but their biosafety assessments are still far behind relevant application studies. Based on recent research, this review aims to conduct a comprehensive and systematic analysis of neurotoxicity induced by NPs. The 191 studies selected according to inclusion and exclusion criteria were imported into the software, and the co-citations and keywords of the included literatures were analyzed to find the breakthrough point of previous studies. According to the available studies, the routes of NPs entering into the normal and injured brain were various, and then to be distributed and accumulated in living bodies. When analyzing the adverse effects induced by NPs, we focused on multiple programmed cell deaths (PCDs), especially ferroptosis triggered by NPs and their tight connection and crosstalk that have been found playing critical roles in the pathogenesis of NDs and their underlying toxic mechanisms. The activation of multiple PCD pathways by NPs provides a scientific basis for the occurrence and development of NDs. Furthermore, the adoption of new methodologies for evaluating the biosafety of NPs would benefit the next generation risk assessment (NGRA) of NPs and their toxic interventions. This would help ensure their safe application and sustainable development in the field of medical neurobiology.

The use of biological fluids in microfluidic paper-based analytical devices (μPADs): Recent advances, challenges and future perspectives

The use of microfluidic paper-based analytical devices (μPADs) for aiding medical diagnosis is a growing trend in the literature mainly due to their low cost, easy use, simple manufacturing, and great potential for application in low-resource settings. Many important biomarkers (proteins, ions, lipids, hormones, DNA, RNA, drugs, whole cells, and more) and biofluids are available for precise detection and diagnosis. We have reviewed the advances μPADs in medical diagnostics have achieved in the last few years, focusing on the most common human biofluids (whole blood/plasma, sweat, urine, tears, and saliva). The challenges of detecting specific biomarkers in each sample are discussed, along with innovative techniques that overcome such limitations. Finally, the difficulties of commercializing μPADs are considered, and future trends are presented, including wearable devices and integrating multiple steps in a single platform.

Liquid chromatography coupled to mass spectrometry metabolomic analysis of cerebrospinal fluid revealed the metabolic characteristics of moyamoya disease

Objective

Metabolomics has found extensive applications in the field of neurological diseases, significantly contributing to their diagnosis and treatment. However, there has been limited research applying metabolomics to moyamoya disease (MMD). This study aims to investigate and identify differential metabolites associated with MMD.

Methods

We employed a liquid chromatography coupled with mass spectrometry (LC-MS) approach, complemented by univariate and multivariate analyses, to discern metabolic biomarkers in cerebrospinal fluid samples. We then compared these biomarkers between MMD patients and healthy controls (Ctl).

Results

Sixteen patients diagnosed with MMD via cerebral angiography and eight healthy controls were enrolled in this study. Comparative analyses, including univariate and multivariate analyses, correlation studies, heatmaps, Volcano Plots, and KEGG pathway enrichment, were performed between MMD patients and controls. As a result, we identified 129 significant differential metabolites in the cerebrospinal fluid between MMD patients and controls. These metabolic biomarkers are associated with various pathways, with notable involvement in purine and pyrimidine metabolism.

Conclusion

Utilizing an LC-MS-based metabolomics approach holds promise for enhancing the clinical diagnosis of MMD. The identified biomarkers offer potential avenues for the development of novel diagnostic methods for MMD and offer fresh insights into the pathogenesis of the disease.

Plasma metabolites distinguish dementia with Lewy bodies from Alzheimer's disease: a cross-sectional metabolomic analysis

Background

In multifactorial diseases, alterations in the concentration of metabolites can identify novel pathological mechanisms at the intersection between genetic and environmental influences. This study aimed to profile the plasma metabolome of patients with dementia with Lewy bodies (DLB) and Alzheimer's disease (AD), two neurodegenerative disorders for which our understanding of the pathophysiology is incomplete. In the clinical setting, DLB is often mistaken for AD, highlighting a need for accurate diagnostic biomarkers. We therefore also aimed to determine the overlapping and differentiating metabolite patterns associated with each and establish whether identification of these patterns could be leveraged as biomarkers to support clinical diagnosis.

Methods

A panel of 630 metabolites (Biocrates MxP Quant 500) and a further 232 metabolism indicators (biologically informative sums and ratios calculated from measured metabolites, each indicative for a specific pathway or synthesis; MetaboINDICATOR) were analyzed in plasma from patients with probable DLB (n = 15; age 77.6 ± 8.2 years), probable AD (n = 15; 76.1 ± 6.4 years), and age-matched cognitively healthy controls (HC; n = 15; 75.2 ± 6.9 years). Metabolites were quantified using a reversed-phase ultra-performance liquid chromatography column and triple-quadrupole mass spectrometer in multiple reaction monitoring (MRM) mode, or by using flow injection analysis in MRM mode. Data underwent multivariate (PCA analysis), univariate and receiving operator characteristic (ROC) analysis. Metabolite data were also correlated (Spearman r) with the collected clinical neuroimaging and protein biomarker data.

Results

The PCA plot separated DLB, AD and HC groups (R2 = 0.518, Q2 = 0.348). Significant alterations in 17 detected metabolite parameters were identified (q ≤ 0.05), including neurotransmitters, amino acids and glycerophospholipids. Glutamine (Glu; q = 0.045) concentrations and indicators of sphingomyelin hydroxylation (q = 0.039) distinguished AD and DLB, and these significantly correlated with semi-quantitative measurement of cardiac sympathetic denervation. The most promising biomarker differentiating AD from DLB was Glu:lysophosphatidylcholine (lysoPC a 24:0) ratio (AUC = 0.92; 95%CI 0.809-0.996; sensitivity = 0.90; specificity = 0.90).

Discussion

Several plasma metabolomic aberrations are shared by both DLB and AD, but a rise in plasma glutamine was specific to DLB. When measured against plasma lysoPC a C24:0, glutamine could differentiate DLB from AD, and the reproducibility of this biomarker should be investigated in larger cohorts.

Integrated non-targeted metabolomics and network pharmacology to reveal the mechanisms of berberine in the long-term treatment of PTZ-induced epilepsy

AIMS

The increasing resistance to anti-seizure medications (ASMs) and the ambiguous mechanisms of epilepsy highlight the pressing demand for the discovery of pioneering lead compounds. Berberine (BBR) has received significant attention in recent years within the field of chronic metabolic disorders. However, the reports on the treatment of epilepsy with BBR are not systematic and the mechanism remains unclear.

MAIN METHODS

In this study, the seizure behaviors of mice were recorded following subcutaneous injection of pentetrazol (PTZ). Non-targeted metabolomics was used to analyze the serum metabolites based on ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS). Meanwhile, multivariate statistical methods were used for metabolite identification and pathway analysis. Furthermore, network pharmacology, molecular docking, and quantitative real-time PCR assay were used for the target identification.

KEY FINDINGS

BBR had anti-seizure effects on PTZ-induced seizure mice after long-term treatment. Tryptophan metabolism and phenylalanine metabolism were involved in regulating the therapeutic effects of BBR.

SIGNIFICANCE

This study reveals the potential mechanism of BBR for epilepsy treatment based on non-targeted metabolomics and network pharmacology, which provides evidence for uncovering the pathogenesis of epilepsy, suggesting that BBR is a potential lead compound for anti-epileptic treatment.

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

INTRODUCTION

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

AREAS COVERED

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

EXPERT OPINION

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

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

BACKGROUND

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Volatile organic compounds analysis as promising biomarkers for Parkinson's disease diagnosis: A systematic review and meta-analysis

OBJECTIVE

Researchers are investigating the potential of volatile organic compounds (VOCs) obtained from exhaled breath and sebum as non-invasive tools for early Parkinson's disease (PD) diagnosis. The present study aims to assess the feasibility of using VOC analysis for PD diagnosis and determine the overall diagnostic accuracy of the proposed tests.

METHODS

We performed systematic searches based on the PRISMA guidelines to identify relevant studies on VOCs in PD diagnosis using exhaled breath or sebum samples. The selected articles were described, and meta-analysis was conducted on those that provided the sensitivity and specificity data.

RESULTS

Out of 1268 articles initially identified, 8 met the inclusion criteria and provided specific sensitivity and specificity data for PD, which were included in the current meta-analysis. The pooled analysis of these findings showed a mean area under the receiver operating characteristic curve of 0.85, a sensitivity of 0.81 (95% confidence interval (CI): 0.72, 0.88), and a specificity of 0.76 (95% CI: 0.66, 0.84).

CONCLUSION

The analysis of VOCs in exhaled breath and sebum has shown promise as a new avenue for non-invasive diagnosis of PD. VOCs' ability to distinguish PD from healthy controls suggests their potential clinical application in screening for the disease. Consequently, VOCs hold significant potential as biomarkers for PD diagnosis and offer a promising novel approach to identifying and diagnosing the condition.

A non-invasive method for concurrent detection of multiple early-stage cancers in women

Untargeted serum metabolomics was combined with machine learning-powered data analytics to develop a test for the concurrent detection of multiple cancers in women. A total of fifteen cancers were tested where the resulting metabolome data was sequentially analysed using two separate algorithms. The first algorithm successfully identified all the cancer-positive samples with an overall accuracy of > 99%. This result was particularly significant given that the samples tested were predominantly from early-stage cancers. Samples identified as cancer-positive were next analysed using a multi-class algorithm, which then enabled accurate discernment of the tissue of origin for the individual samples. Integration of serum metabolomics with appropriate data analytical tools, therefore, provides a powerful screening platform for early-stage cancers.

Cerebral venous congestion alters brain metabolite profiles, impairing cognitive function

Vascular cognitive impairment (VCI) represents the second most common cause of dementia after Alzheimer's disease, and pathological changes in cerebral vascular structure and function are pivotal causes of VCI. Cognitive impairment caused by arterial ischemia has been extensively studied the whole time; the influence of cerebral venous congestion on cognitive impairment draws doctors' attention in recent clinical practice, but the underlying neuropathophysiological alterations are not completely understood. This study elucidated the specific pathogenetic role of cerebral venous congestion in cognitive-behavioral deterioration and possible electrophysiological mechanisms. Using cerebral venous congestion rat models, we found these rats exhibited decreased long-term potentiation (LTP) in the hippocampal dentate gyrus and impaired spatial learning and memory. Based on untargeted metabolomics, N-acetyl-L-cysteine (NAC) deficiency was detected in cerebral venous congestion rats; supplementation with NAC appeared to ameliorate synaptic deficits, rescue impaired LTP, and mitigate cognitive impairment. In a cohort of cerebral venous congestion patients, NAC levels were decreased; NAC concentration was negatively correlated with subjective cognitive decline (SCD) score but positively correlated with mini-mental state examination (MMSE) score. These findings provide a new perspective on cognitive impairment and support further exploration of NAC as a therapeutic target for the prevention and treatment of VCI.

Chemical catalysis by biological amyloids

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

Profiling Analysis of Tryptophan Metabolites in the Urine of Patients with Parkinson's Disease Using LC-MS/MS

Although Parkinson's disease (PD) is a representative neurodegenerative disorder and shows characteristic motor impediments, the pathophysiological mechanisms and treatment targets for PD have not yet been clearly identified. Since several tryptophan metabolites produced by gut microbiota could pass the blood-brain barrier and, furthermore, might influence the central nervous system, tryptophan metabolites within the indole, kynurenine, and serotonin metabolic pathways might be the most potent targets for PD development. Furthermore, most metabolites are circulated via the blood, play roles in and/or are metabolized via the host organs, and finally are excreted into the urine. Therefore, profiling the overall tryptophan metabolic pathways in urine samples of patients with PD is important to understanding the pathological mechanisms, finding biomarkers, and discovering therapeutic targets for PD. However, the development of profiling analysis based on tryptophan metabolism pathways in human urine samples is still challenging due to the wide physiological ranges, the varied signal response, and the structural diversity of tryptophan metabolites in complicated urine matrices. In this study, an LC-MS/MS method was developed to profile 21 tryptophan metabolites within the indole, kynurenine, and serotonin metabolic pathways in human urine samples using ion-pairing chromatography and multiple reaction monitoring determination. The developed method was successfully applied to urine samples of PD patients (n = 41) and controls (n = 20). Further, we investigated aberrant metabolites to find biomarkers for PD development and therapeutic targets based on the quantitative results. Unfortunately, most tryptophan metabolites in the urine samples did not present significant differences between control and PD patients, except for indole-3-acetic acid. Nonetheless, indole-3-acetic acid was reported for the first time for its aberrant urinary levels in PD patients and tentatively selected as a potential biomarker for PD. This study provides accurate quantitative results for 21 tryptophan metabolites in biological samples and will be helpful in revealing the pathological mechanisms of PD development, discovering biomarkers for PD, and further providing therapeutic targets for various PD symptoms. In the near future, to further investigate the relationship between gut microbial metabolites and PD, we will employ studies on microbial metabolites using plasma and stool samples from control and PD patients.

Recent Advances and Perspectives in Relation to the Metabolomics-Based Study of Diabetic Retinopathy

Diabetic retinopathy (DR), a prevalent microvascular complication of diabetes, is a major cause of acquired blindness in adults. Currently, a clinical diagnosis of DR primarily relies on fundus fluorescein angiography, with a limited availability of effective biomarkers. Metabolomics, a discipline dedicated to scrutinizing the response of various metabolites within living organisms, has shown noteworthy advancements in uncovering metabolic disorders and identifying key metabolites associated with DR in recent years. Consequently, this review aims to present the latest advancements in metabolomics techniques and comprehensively discuss the principal metabolic outcomes derived from analyzing blood, vitreous humor, aqueous humor, urine, and fecal samples.

Metabolomic profiling of a neurodegenerative retina following optic nerve transection

The degeneration of retinal ganglion cells (RGCs) often causes irreversible vision impairment. Prevention of RGC degeneration can prevent or delay the deterioration of visual function. The present study aimed to investigate retinal metabolic profiles following optic nerve transection (ONT) injury and identify the potential metabolic targets for the prevention of RGC degeneration. Retinal samples were dissected from ONT group and non‑ONT group. The untargeted metabolomics were carried out using liquid chromatography‑tandem mass spectrometry. The involved pathways and biomarkers were analyzed using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and MetaboAnalyst 5.0. In the ONT group, 689 disparate metabolites were detected, including lipids and lipid‑like molecules. A total of 122 metabolites were successfully annotated and enriched in 50 KEGG pathways. Among them, 'sphingolipid metabolism' and 'primary bile acid biosynthesis' were identified involved in RGC degeneration. A total of five metabolites were selected as the candidate biomarkers for detecting RGC degeneration with an AUC value of 1. The present study revealed that lipid‑related metabolism was involved in the pathogenesis of retinal neurodegeneration. Taurine, taurochenodesoxycholic acid, taurocholic acid (TCA), sphingosine, and galabiosylceramide are shown as the promising biomarkers for the diagnosis of RGC degeneration.

Metabolomics: A useful tool for ischemic stroke research

Ischemic stroke (IS) is a multifactorial and heterogeneous disease. Despite years of studies, effective strategies for the diagnosis, management and treatment of stroke are still lacking in clinical practice. Metabolomics is a growing field in systems biology. It is starting to show promise in the identification of biomarkers and in the use of pharmacometabolomics to help patients with certain disorders choose their course of treatment. The development of metabolomics has enabled further and more biological applications. Particularly, metabolomics is increasingly being used to diagnose diseases, discover new drug targets, elucidate mechanisms, and monitor therapeutic outcomes and its potential effect on precision medicine. In this review, we reviewed some recent advances in the study of metabolomics as well as how metabolomics might be used to identify novel biomarkers and understand the mechanisms of IS. Then, the use of metabolomics approaches to investigate the molecular processes and active ingredients of Chinese herbal formulations with anti-IS capabilities is summarized. We finally summarized recent developments in single cell metabolomics for exploring the metabolic profiles of single cells. Although the field is relatively young, the development of single cell metabolomics promises to provide a powerful tool for unraveling the pathogenesis of IS.

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

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

Circulating Neurofilament Light Chain Levels Increase with Age and Are Associated with Worse Physical Function and Body Composition in Men but Not in Women

This study aimed to assess the relationship between age-related changes in Neurofilament Light Chain (NFL), a marker of neuronal function, and various factors including muscle function, body composition, and metabolomic markers. The study included 40 participants, aged 20 to 85 years. NFL levels were measured, and muscle function, body composition, and metabolomic markers were assessed. NFL levels increased significantly with age, particularly in men. Negative correlations were found between NFL levels and measures of muscle function, such as grip strength, walking speed, and chair test performance, indicating a decline in muscle performance with increasing NFL. These associations were more pronounced in men. NFL levels also negatively correlated with muscle quality in men, as measured by 50 kHz phase angle. In terms of body composition, NFL was positively correlated with markers of fat mass and negatively correlated with markers of muscle mass, predominantly in men. Metabolomic analysis revealed significant associations between NFL levels and specific metabolites, with gender-dependent relationships observed. This study provides insights into the relationship between circulating serum NFL, muscle function, and aging. Our findings hint at circulating NFL as a potential early marker of age-associated neurodegenerative processes, especially in men.

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.

Integrative multi-omics and systems bioinformatics in translational neuroscience: A data mining perspective

Bioinformatic analysis of large and complex omics datasets has become increasingly useful in modern day biology by providing a great depth of information, with its application to neuroscience termed neuroinformatics. Data mining of omics datasets has enabled the generation of new hypotheses based on differentially regulated biological molecules associated with disease mechanisms, which can be tested experimentally for improved diagnostic and therapeutic targeting of neurodegenerative diseases. Importantly, integrating multi-omics data using a systems bioinformatics approach will advance the understanding of the layered and interactive network of biological regulation that exchanges systemic knowledge to facilitate the development of a comprehensive human brain profile. In this review, we first summarize data mining studies utilizing datasets from the individual type of omics analysis, including epigenetics/epigenomics, transcriptomics, proteomics, metabolomics, lipidomics, and spatial omics, pertaining to Alzheimer's disease, Parkinson's disease, and multiple sclerosis. We then discuss multi-omics integration approaches, including independent biological integration and unsupervised integration methods, for more intuitive and informative interpretation of the biological data obtained across different omics layers. We further assess studies that integrate multi-omics in data mining which provide convoluted biological insights and offer proof-of-concept proposition towards systems bioinformatics in the reconstruction of brain networks. Finally, we recommend a combination of high dimensional bioinformatics analysis with experimental validation to achieve translational neuroscience applications including biomarker discovery, therapeutic development, and elucidation of disease mechanisms. We conclude by providing future perspectives and opportunities in applying integrative multi-omics and systems bioinformatics to achieve precision phenotyping of neurodegenerative diseases and towards personalized medicine.

Parkinson's Disease and the Heart: Studying Cardiac Metabolism in the 6-Hydroxydopamine Model

Parkinson's-disease (PD) is an incurable, age-related neurodegenerative disease, and its global prevalence of disability and death has increased exponentially. Although motor symptoms are the characteristic manifestations of PD, the clinical spectrum also contains a wide variety of non-motor symptoms, which are the main cause of disability and determinants of the decrease in a patient's quality of life. Noteworthy in this regard is the stress on the cardiac system that is often observed in the course of PD; however, its effects have not yet been adequately researched. Here, an untargeted metabolomics approach was used to assess changes in cardiac metabolism in the 6-hydroxydopamine model of PD. Beta-sitosterol, campesterol, cholesterol, monoacylglycerol, α-tocopherol, stearic acid, beta-glycerophosphoric acid, o-phosphoethanolamine, myo-inositol-1-phosphate, alanine, valine and allothreonine are the metabolites that significantly discriminate parkinsonian rats from sham counterparts. Upon analysis of the metabolic pathways with the aim of uncovering the main biological pathways involved in concentration patterns of cardiac metabolites, the biosynthesis of both phosphatidylethanolamine and phosphatidylcholine, the glucose-alanine cycle, glutathione metabolism and plasmalogen synthesis most adequately differentiated sham and parkinsonian rats. Our results reveal that both lipid and energy metabolism are particularly involved in changes in cardiac metabolism in PD. These results provide insight into cardiac metabolic signatures in PD and indicate potential targets for further investigation.

Metabolomic Profiling in Children with Celiac Disease: Beyond the Gluten-Free Diet

Celiac disease (CD) is included in the group of complex or multifactorial diseases, i.e., those caused by the interaction of genetic and environmental factors. Despite a growing understanding of the pathophysiological mechanisms of the disease, diagnosis is still often delayed and there are no effective biomarkers for early diagnosis. The only current treatment, a gluten-free diet (GFD), can alleviate symptoms and restore intestinal villi, but its cellular effects remain poorly understood. To gain a comprehensive understanding of CD's progression, it is crucial to advance knowledge across various scientific disciplines and explore what transpires after disease onset. Metabolomics studies hold particular significance in unravelling the complexities of multifactorial and multisystemic disorders, where environmental factors play a significant role in disease manifestation and progression. By analyzing metabolites, we can gain insights into the reasons behind CD's occurrence, as well as better comprehend the impact of treatment initiation on patients. In this review, we present a collection of articles that showcase the latest breakthroughs in the field of metabolomics in pediatric CD, with the aim of trying to identify CD biomarkers for both early diagnosis and treatment monitoring. These advancements shed light on the potential of metabolomic analysis in enhancing our understanding of the disease and improving diagnostic and therapeutic strategies. More studies need to be designed to cover metabolic profiles in subjects at risk of developing the disease, as well as those analyzing biomarkers for follow-up treatment with a GFD.

Metabolomics in aging research: aging markers from organs

Metabolism plays an important role in regulating aging at several levels, and metabolic reprogramming is the main driving force of aging. Due to the different metabolic needs of different tissues, the change trend of metabolites during aging in different organs and the influence of different levels of metabolites on organ function are also different, which makes the relationship between the change of metabolite level and aging more complex. However, not all of these changes lead to aging. The development of metabonomics research has opened a door for people to understand the overall changes in the metabolic level in the aging process of organisms. The omics-based "aging clock" of organisms has been established at the level of gene, protein and epigenetic modifications, but there is still no systematic summary at the level of metabolism. Here, we reviewed the relevant research published in the last decade on aging and organ metabolomic changes, discussed several metabolites with high repetition rate, and explained their role in vivo, hoping to find a group of metabolites that can be used as metabolic markers of aging. This information should provide valuable information for future diagnosis or clinical intervention of aging and age-related diseases.

Application of Benchtop NMR for Metabolomics Study Using Feces of Mice with DSS-Induced Colitis

Nuclear magnetic resonance (NMR)-based metabolomics, which comprehensively measures metabolites in biological systems and investigates their response to various perturbations, is widely used in research to identify biomarkers and investigate the pathogenesis of underlying diseases. However, further applications of high-field superconducting NMR for medical purposes and field research are restricted by its high cost and low accessibility. In this study, we applied a low-field, benchtop NMR spectrometer (60 MHz) employing a permanent magnet to characterize the alterations in the metabolic profile of fecal extracts obtained from dextran sodium sulfate (DSS)-induced ulcerative colitis model mice and compared them with the data acquired from high-field NMR (800 MHz). Nineteen metabolites were assigned to the 60 MHz ¹H NMR spectra. Non-targeted multivariate analysis successfully discriminated the DSS-induced group from the healthy control group and showed high comparability with high-field NMR. In addition, the concentration of acetate, identified as a metabolite with characteristic behavior, could be accurately quantified using a generalized Lorentzian curve fitting method based on the 60 MHz NMR spectra.

CSF neopterin, quinolinic acid and kynurenine/tryptophan ratio are biomarkers of active neuroinflammation

BACKGROUND

Defining the presence of acute and chronic brain inflammation remains a challenge to clinicians due to the heterogeneity of clinical presentations and aetiologies. However, defining the presence of neuroinflammation, and monitoring the effects of therapy is important given its reversible and potentially damaging nature. We investigated the utility of CSF metabolites in the diagnosis of primary neuroinflammatory disorders such as encephalitis and explored the potential pathogenic role of inflammation in epilepsy.

METHODS

Cerebrospinal fluid (CSF) collected from 341 paediatric patients (169 males, median age 5.8 years, range 0.1-17.1) were examined. The patients were separated into a primary inflammatory disorder group (n = 90) and epilepsy group (n = 80), who were compared with three control groups including neurogenetic and structural (n = 76), neurodevelopmental disorders, psychiatric and functional neurological disorders (n = 63), and headache (n = 32).

FINDINGS

There were statistically significant increases of CSF neopterin, kynurenine, quinolinic acid and kynurenine/tryptophan ratio (KYN/TRP) in the inflammation group compared to all control groups (all p < 0.0003). As biomarkers, at thresholds with 95% specificity, CSF neopterin had the best sensitivity for defining neuroinflammation (82%, CI 73-89), then quinolinic acid (57%, CI 47-67), KYN/TRP ratio (47%, CI 36-56) and kynurenine (37%, CI 28-48). CSF pleocytosis had sensitivity of 53%, CI 42-64). The area under the receiver operating characteristic curve (ROC AUC) of CSF neopterin (94.4% CI 91.0-97.7%) was superior to that of CSF pleocytosis (84.9% CI 79.5-90.4%) (p = 0.005). CSF kynurenic acid/kynurenine ratio (KYNA/KYN) was statistically decreased in the epilepsy group compared to all control groups (all p ≤ 0.0003), which was evident in most epilepsy subgroups.

INTERPRETATION

Here we show that CSF neopterin, kynurenine, quinolinic acid and KYN/TRP are useful diagnostic and monitoring biomarkers of neuroinflammation. These findings provide biological insights into the role of inflammatory metabolism in neurological disorders and provide diagnostic and therapeutic opportunities for improved management of neurological diseases.

FUNDING

Financial support for the study was granted by Dale NHMRC Investigator grant APP1193648, University of Sydney, Petre Foundation, Cerebral Palsy Alliance and Department of Biochemistry at the Children's Hospital at Westmead. Prof Guillemin is funded by NHMRC Investigator grant APP 1176660 and Macquarie University.

Genipin prevents alpha-synuclein aggregation and toxicity by affecting endocytosis, metabolism and lipid storage

Parkinson's Disease (PD) is a common neurodegenerative disorder affecting millions of people worldwide for which there are only symptomatic therapies. Small molecules able to target key pathological processes in PD have emerged as interesting options for modifying disease progression. We have previously shown that a (poly)phenol-enriched fraction (PEF) of Corema album L. leaf extract modulates central events in PD pathogenesis, namely α-synuclein (αSyn) toxicity, aggregation and clearance. PEF was now subjected to a bio-guided fractionation with the aim of identifying the critical bioactive compound. We identified genipin, an iridoid, which relieves αSyn toxicity and aggregation. Furthermore, genipin promotes metabolic alterations and modulates lipid storage and endocytosis. Importantly, genipin was able to prevent the motor deficits caused by the overexpression of αSyn in a Drosophila melanogaster model of PD. These findings widens the possibility for the exploitation of genipin for PD therapeutics.

A journey across dopamine Metabolism: A rotational study of DOPAC

DOPAC, a relevant scaffold in dopamine metabolism, was probed in the gas phase and interrogated by high-resolution rotational spectroscopy. Herein, three distinct conformers were isolated in a supersonic jet and identified for the first time through an examination of the trend of the rotational constants and the dipole moment selection rules. Additionally, we examined the plausible relaxation pathways of the low-energy conformers of DOPAC, which helped us to claim the indirect detection of two additional conformers, providing conclusive experimental evidence of the flexible nature of this biomolecule. The current investigation sheds some light on the differences between jet-cooled rotational experiments and matrix-isolation infrared spectroscopy.

Molecular insights into quality and authentication of sheep meat from proteomics and metabolomics

Sheep meat (encompassing lamb, hogget and mutton) is an important source of animal protein in many countries, with a unique flavour and sensory profile compared to other red meats. Flavour, colour and texture are the key quality attributes contributing to consumer liking of sheep meat. Over the last decades, various factors from 'farm to fork', including production system (e.g., age, breed, feeding regimes, sex, pre-slaughter stress, and carcass suspension), post-mortem manipulation and processing (e.g., electrical stimulation, ageing, packaging types, and chilled and frozen storage) have been identified as influencing different aspects of sheep meat quality. However conventional meat-quality assessment tools are not able to elucidate the underlying mechanisms and pathways for quality variations. Advances in broad-based analytical techniques have offered opportunities to obtain deeper insights into the molecular changes of sheep meat which may become biomarkers for specific variations in quality traits and meat authenticity. This review provides an overview on how omics techniques, especially proteomics (including peptidomics) and metabolomics (including lipidomics and volatilomics) are applied to elucidate the variations in sheep meat quality, mainly in loin muscles, focusing on colour, texture and flavour, and as tools for authentication. SIGNIFICANCE: From this review, we observed that attempts have been made to utilise proteomics and metabolomics techniques on sheep meat products for elucidating pathways of quality variations due to various factors. For instance, the improvement of colour stability and tenderness could be associated with the changes to glycolysis, energy metabolism and endogenous antioxidant capacity. Several studies identify proteolysis as being important, but potentially conflicting for quality as the enhanced proteolysis improves tenderness and flavour, while reducing colour stability. The use of multiple analytical methods e.g., lipidomics, metabolomics, and volatilomics, detects a wider range of flavour precursors (including both water and lipid soluble compounds) that underlie the possible pathways for sheep meat flavour evolution. The technological advancement in omics (e.g., direct analysis-mass spectrometry) could make analysis of the proteins, lipids and metabolites in sheep meat routine, as well as enhance the confidence in quality determination and molecular-based assurance of meat authenticity.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

Rhynchophylline alleviates neuroinflammation and regulates metabolic disorders in a mouse model of Parkinson's disease

Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder with limited therapeutic agents. Rhynchophylline (RIN), a tetracyclic oxindole alkaloid isolated from Uncaria rhynchophylla, has multiple neuropharmacological activities, including anti-inflammatory, anti-depression, anti-neurodegenerative disease, and anti-drug addiction. Though it is reported that RIN exerts a neuroprotective effect against PD, the underlying protective mechanism remains obscure. In this study, a mass spectrometry-based metabolomic strategy combined with neurobehavioral tests, serum biochemical assays, and immunohistochemistry were employed to decipher the protective mechanism of RIN against 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine hydrochloride (MPTP)-induced subacute PD in mice. Our results indicated that RIN significantly improved the MPTP-induced behavioral abnormalities, reduced the loss of dopaminergic neurons, and reversed the secretion of inflammatory cytokines and oxidative stress indicators. Further studies showed that RIN significantly suppressed the expression of toll-like receptor 4, NOD-like receptor protein 3, and cyclooxygenase 2 in the mouse striatum. The results of serum metabolomics showed that RIN could ameliorate metabolic disorders in PD mainly through the regulation of retinol metabolism, arachidonic acid metabolism, glycerophospholipid metabolism, and purine metabolism. These pieces of evidence revealed that RIN is a promising drug candidate for PD by alleviating neuroinflammation and maintaining metabolic homeostasis.

Changes in Liver Lipidomic Profile in G2019S-LRRK2 Mouse Model of Parkinson's Disease

The identification of Parkinson's disease (PD) biomarkers has become a main goal for the diagnosis of this neurodegenerative disorder. PD has not only been intrinsically related to neurological problems, but also to a series of alterations in peripheral metabolism. The purpose of this study was to identify metabolic changes in the liver in mouse models of PD with the scope of finding new peripheral biomarkers for PD diagnosis. To achieve this goal, we used mass spectrometry technology to determine the complete metabolomic profile of liver and striatal tissue samples from WT mice, 6-hydroxydopamine-treated mice (idiopathic model) and mice affected by the G2019S-LRRK2 mutation in LRRK2/PARK8 gene (genetic model). This analysis revealed that the metabolism of carbohydrates, nucleotides and nucleosides was similarly altered in the liver from the two PD mouse models. However, long-chain fatty acids, phosphatidylcholine and other related lipid metabolites were only altered in hepatocytes from G2019S-LRRK2 mice. In summary, these results reveal specific differences, mainly in lipid metabolism, between idiopathic and genetic PD models in peripheral tissues and open up new possibilities to better understand the etiology of this neurological disorder.

The foundation and architecture of precision medicine in neurology and psychiatry

Neurological and psychiatric diseases have high degrees of genetic and pathophysiological heterogeneity, irrespective of clinical manifestations. Traditional medical paradigms have focused on late-stage syndromic aspects of these diseases, with little consideration of the underlying biology. Advances in disease modeling and methodological design have paved the way for the development of precision medicine (PM), an established concept in oncology with growing attention from other medical specialties. We propose a PM architecture for central nervous system diseases built on four converging pillars: multimodal biomarkers, systems medicine, digital health technologies, and data science. We discuss Alzheimer's disease (AD), an area of significant unmet medical need, as a case-in-point for the proposed framework. AD can be seen as one of the most advanced PM-oriented disease models and as a compelling catalyzer towards PM-oriented neuroscience drug development and advanced healthcare practice.

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

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

Reconstruction of Glutathione Metabolism in the Neuronal Model of Rotenone-Induced Neurodegeneration Using Mass Isotopologue Analysis with Hydrophilic Interaction Liquid Chromatography-Zeno High-Resolution Multiple Reaction Monitoring

Accurate reconstruction of metabolic pathways is an important prerequisite for interpreting metabolomics changes and understanding the diverse biological processes in disease models. A tracer-based metabolomics strategy utilizes stable isotope-labeled precursors to resolve complex pathways by tracing the labeled atom(s) to downstream metabolites through enzymatic reactions. Isotope enrichment analysis is informative and achieved by counting total labeled atoms and acquiring the mass isotopologue distribution (MID) of the intact metabolite. However, quantitative analysis of labeled metabolite substructures/moieties (MS² fragments) can offer more valuable insights into the reaction connections through measuring metabolite transformation. In order to acquire the isotopic labeling information at the intact metabolite and moiety level simultaneously, we developed a method that couples hydrophilic interaction liquid chromatography (HILIC) with Zeno trap-enabled high-resolution multiple reaction monitoring (MRM^HR). The method enabled accurate and reproducible MID quantification for intact metabolites as well as their fragmented moieties, with notably high sensitivity in the MS² fragmentation mode based on the measurement of ¹³C- or ¹⁵N-labeled cellular samples. The method was applied to human-induced pluripotent stem cell-derived neurons to trace the fate of ¹³C/¹⁵N atoms from D-¹³C₆-glucose/L-¹⁵N₂-glutamine added to the media. With the MID analysis of both intact metabolites and fragmented moieties, we validated the pathway reconstruction of de novo glutathione synthesis in mid-brain neurons. We discovered increased glutathione oxidization from both basal and newly synthesized glutathione pools under neuronal oxidative stress. Furthermore, the significantly decreased de novo glutathione synthesis was investigated and associated with altered activities of several key enzymes, as evidenced by suppressed glutamate supply via glucose metabolism and a diminished flux of glutathione synthetic reaction in the neuronal model of rotenone-induced neurodegeneration.

Efficacy of Tai Chi on lower limb function of Parkinson's disease patients: A systematic review and meta-analysis

Background

At present, the effect of Tai Chi (TC) on lower limb function in patients with Parkinson's disease (PD) is controversial. Therefore, we conducted a meta-analysis on the influence of TC on lower limb function in PD patients.

Methods

According to the PRISMA guidelines, seven databases were searched. Randomized controlled trials (RCTS) were selected and screened according to inclusion and exclusion criteria. We assessed the quality of the studies using the Cochrane Risk of Bias tool and then extracted the characteristics of the included studies. The random effect model was adopted, and heterogeneity was measured by I ² statistic.

Results

A total of 441 articles were screened, and 10 high-quality RCTs were with a total of 532 patients with PD met Our inclusion criteria. Meta-analysis showed that compared To control groups TC improved several outcomes. TC significantly improved motor function (SMD = -0.70; 95% CI = -0.95, -0.45; p < 0.001; I ² = 35%), although The results were not statistically significant for The subgroup analysis of TC duration (SMD = -0.70; 95% CI = -0.95, -0.45; p = 0.88; I ² = 0%;). TC significantly improved balance function (SMD = 0.89; 95% CI = 0.51, 1.27; p < 0.001; I ² = 54%), functional walking capacity (SMD = -1.24; 95% CI = -2.40, -0.09; p = 0.04; I ² = 95%), and gait velocity (SMD = 0.48; 95% CI = -0.02, 0.94; p = 0.04; I ² = 78%), But Did Not improve endurance (SMD = 0.31; 95% CI = -0.12, 0.75; p = 0.16; I ² = 0%), step length (SMD = 0.01; 95% CI = -0.34, 0.37; p = 0.94; I ² = 29%), and cadence (SMD = 0.06; 95% CI = -0.25, 0.36; p = 0.70; I ² = 0%).

Conclusion

TC has beneficial effects on motor function, balance function, functional walking ability, and gait velocity, but does not improve walking endurance, stride length, and cadence.

Metabolites as extracellular vesicle cargo in health, cancer, pleural effusion, and cardiovascular diseases: An emerging field of study to diagnostic and therapeutic purposes

Extracellular vesicles (EVs) are highly diverse nanoscale membrane-bound structures released from different cell types into the extracellular environment. They play essential functions in cell signaling by transporting their cargo, such as proteins, RNA, DNA, lipids, metabolites, and small molecules, to recipient cells. It has recently been shown that EVs might modulate carcinogenesis by delivering cargo to recipient cells. Furthermore, recent discoveries revealed that changes in plasma-derived EV levels and cargo in subjects with metabolic diseases were documented by many researchers, suggesting that EVs might be a promising source of disease biomarkers. One of the cargos of EVs that has recently attracted the most attention is metabolites. The metabolome of these vesicles introduces a plethora of disease indicators; hence, examining the metabolomics of EVs detected in human biofluids would be an effective approach. On the other hand, metabolites have various roles in biological systems, including the production of energies, synthesizing macromolecules, and serving as signaling molecules and hormones. Metabolome rewiring in cancer and stromal cells is a characteristic of malignancy, but the current understanding of how this affects the metabolite composition and activity of tumor-derived EVs remains in its infancy. Since new findings and studies in the field of exosome biology and metabolism are constantly being published, it is likely that diagnostic and treatment techniques, including the use of exosome metabolites, will be launched in the coming years. Recent years have seen increased interest in the EV metabolome as a possible source for biomarker development. However, our understanding of the role of these molecules in health and disease is still immature. In this work, we have provided the latest findings regarding the role of metabolites as EV cargoes in the pathophysiology of diseases, including cancer, pleural effusion (PE), and cardiovascular disease (CVD). We also discussed the significance of metabolites as EV cargoes of microbiota and their role in host-microbe interaction. In addition, the latest findings on metabolites in the form of EV cargoes as biomarkers for disease diagnosis and treatment are presented in this study.