Advances of Mechanisms-Related Metabolomics in Parkinson's Disease

Invasive laser acupuncture targeting muscle: a novel approach to protect dopaminergic neurons and reduce neuroinflammation in a brain of Parkinson's disease model

Parkinson's disease (PD) affects 1-2% of the global population and presents significant therapeutic challenges. Due to the limitations of existing treatments, there is a pressing need for alternative approaches. This study investigated the effects of invasive laser acupuncture (ILA), which combines acupuncture and photobiomodulation. In this method, optical fibers are inserted into the muscle layers of the acupoint to enhance therapeutic outcomes. Mice with MPTP-induced PD were treated with ILA at 830 nm or 650 nm. Protective effects of nigrostriatal dopaminergic neurons and fibers were assessed by examining TH immunoreactivity in the brain. Neuroinflammation markers in the brain and muscle metabolomic profiles were also analyzed. Comparisons between invasive and non-invasive laser application, as well as the impact of nerve blocking with lidocaine, were also evaluated. ILA at 830 nm (ILA830) significantly improved motor performance and increased the nigrostriatal TH-positive immunoreactivities. It reduced the levels of α-synuclein, apoptotic proteins, and inflammatory cytokines, while increasing anti-inflammatory in the brain. ILA830 also decreased nigrostriatal astrocyte and microglia activation. Muscle metabolomic analysis showed distinct group clustering and significant changes in metabolites like glucose and galactose, correlating with improved motor functions. Invasive laser treatment was more effective than non-invasive, and lidocaine pre-treatment did not block its effects. ILA at 830 nm effectively ameliorates PD symptoms by protecting dopaminergic neurons, and reducing neuroinflammation in the brain. Muscle metabolomic changes by ILA830, such as increased glucose and galactose, correlate with motor improvement. This approach offers a promising strategy for PD treatment, warranting further research to optimize its use in clinical settings.

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.

Metabolomics in Preclinical Drug Safety Assessment: Current Status and Future Trends

Metabolomics is emerging as a powerful systems biology approach for improving preclinical drug safety assessment. This review discusses current applications and future trends of metabolomics in toxicology and drug development. Metabolomics can elucidate adverse outcome pathways by detecting endogenous biochemical alterations underlying toxicity mechanisms. Furthermore, metabolomics enables better characterization of human environmental exposures and their influence on disease pathogenesis. Metabolomics approaches are being increasingly incorporated into toxicology studies and safety pharmacology evaluations to gain mechanistic insights and identify early biomarkers of toxicity. However, realizing the full potential of metabolomics in regulatory decision making requires a robust demonstration of reliability through quality assurance practices, reference materials, and interlaboratory studies. Overall, metabolomics shows great promise in strengthening the mechanistic understanding of toxicity, enhancing routine safety screening, and transforming exposure and risk assessment paradigms. Integration of metabolomics with computational, in vitro, and personalized medicine innovations will shape future applications in predictive toxicology.

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.

LargeMetabo: an out-of-the-box tool for processing and analyzing large-scale metabolomic data

Large-scale metabolomics is a powerful technique that has attracted widespread attention in biomedical studies focused on identifying biomarkers and interpreting the mechanisms of complex diseases. Despite a rapid increase in the number of large-scale metabolomic studies, the analysis of metabolomic data remains a key challenge. Specifically, diverse unwanted variations and batch effects in processing many samples have a substantial impact on identifying true biological markers, and it is a daunting challenge to annotate a plethora of peaks as metabolites in untargeted mass spectrometry-based metabolomics. Therefore, the development of an out-of-the-box tool is urgently needed to realize data integration and to accurately annotate metabolites with enhanced functions. In this study, the LargeMetabo package based on R code was developed for processing and analyzing large-scale metabolomic data. This package is unique because it is capable of (1) integrating multiple analytical experiments to effectively boost the power of statistical analysis; (2) selecting the appropriate biomarker identification method by intelligent assessment for large-scale metabolic data and (3) providing metabolite annotation and enrichment analysis based on an enhanced metabolite database. The LargeMetabo package can facilitate flexibility and reproducibility in large-scale metabolomics. The package is freely available from https://github.com/LargeMetabo/LargeMetabo.

Metabolomics of ischemic stroke: insights into risk prediction and mechanisms

Ischemic stroke (IS) is the most prevalent type of stroke. The early diagnosis and prognosis of IS are crucial for successful therapy and early intervention. Metabolomics, a tool in systems biology based on several innovative technologies, can be used to identify disease biomarkers and unveil underlying pathophysiological processes. Accordingly, in recent years, an increasing number of studies have identified metabolites from cerebral ischemia patients and animal models that could improve the diagnosis of IS and prediction of its outcome. In this paper, metabolomic research is comprehensively reviewed with a focus on describing the metabolic changes and related pathways associated with IS. Most clinical studies use biofluids (e.g., blood or plasma) because their collection is minimally invasive and they are ideal for analyzing changes in metabolites in patients of IS. We review the application of animal models in metabolomic analyses aimed at investigating potential mechanisms of IS and developing novel therapeutic approaches. In addition, this review presents the strengths and limitations of current metabolomic studies on IS, providing a reference for future related studies.

G2019S LRRK2 Mutation Enhances MPP+-Induced Inflammation of Human Induced Pluripotent Stem Cells-Differentiated Dopaminergic Neurons

Induced pluripotent stem cells (iPSCs) offer an unprecedented opportunity to mimic human diseases of related cell types, but it is unclear whether they can successfully mimic age-related diseases such as Parkinson’s disease (PD). We generated iPSCs lines from three patients with familial PD associated with the G2019S mutation in the LRRK2 gene and one age-matched healthy individual (control). During long-term culture, dopaminergic (DA) neurons differentiated from iPSCs of G2019S LRRK2 PD patients exhibited morphological changes, including a reduced number of neurites and neurite arborization, which were not evident in DA neurons differentiated from control iPSCs. To mimic PD pathology in vitro, we used 1-methyl-4-phenylpyridium (MPP⁺) to damage DA neurons and found that DA neurons differentiated from patients with G2019S LRRK2 mutation significantly reduced the survival rate and increased apoptosis compared with the controls. We also found that the mRNA level of inflammatory factors [interleukin (IL)-1β, tumor necrosis factor-α, cyclooxygenase-2, IL-6, and inducible NO synthase] with G2019S LRRK2 mutation were higher than control group after exposure to MPP⁺. Our study provides an in vitro model based on iPSCs that captures the patients’ genetic complexity and investigates the pathogenesis of familial PD cases in a disease-associated cell type.