Integrated Metabolomics and Proteomics Analysis Reveals Plasma Lipid Metabolic Disturbance in Patients With Parkinson's Disease

Causal relationship between gut microbiota, lipids, and neuropsychiatric disorders: A Mendelian randomization mediation study

BACKGROUND

Numerous studies have shown an interconnection between the gut microbiota and the brain via the "gut-brain" axis. However, the causal relationships between gut microbiota, lipids, and neuropsychiatric disorders remain unclear. This study aimed to analyze potential associations among gut microbiota, lipids, and neuropsychiatric disorders-including AD, PD, ALS, MS, SCZ, MDD, and BD-using summary data from large-scale GWAS.

METHODS

Bidirectional Mendelian randomization (MR) with inverse variance weighting (IVW) was the primary method. Supplementary analyses included sensitivity analyses, Steiger tests, and Bayesian weighted MR (BWMR). Mediation analyses used two-step MR (TSMR) and multivariable MR (MVMR).

RESULTS

The analyses revealed 51 positive correlations (risk factors) (β > 0, P < 0.05) and 47 negative correlations (protective factors) (β < 0, P < 0.05) between gut microbiota and neuropsychiatric disorders. In addition, 35 positive correlations (β > 0, P < 0.05) and 22 negative correlations (β < 0, P < 0.05) between lipids and neuropsychiatric disorders were observed. Assessment of reverse causality with the seven neuropsychiatric disorders as exposures and the identified gut microbiota and lipids as outcomes revealed no evidence of reverse causality (P > 0.05). Mediation analysis indicated that the effect of the species Bacteroides plebeius on MDD is partially mediated through the regulation of phosphatidylcholine (16:0_20:4) levels (mediation proportion = 10.9 % [95 % CI = 0.0110-0.2073]).

CONCLUSION

This study provides evidence of a causal relationship between gut microbiota and neuropsychiatric disorders, suggesting lipids as mediators. These findings offer new insights into the mechanisms by which gut microbiota may influence neuropsychiatric disorders.

Large-scale proteomic analyses of incident Parkinson's disease reveal new pathophysiological insights and potential biomarkers

The early pathophysiology of Parkinson's disease (PD) is poorly understood. We analyzed 2,920 Olink-measured plasma proteins in 51,804 UK Biobank participants, identifying 859 incident PD cases after 14.45 years. We found 38 PD-related proteins, with six of the top ten validated in the Parkinson's Progression Markers Initiative (PPMI) cohort. ITGAV, HNMT and ITGAM showed consistent significant association (hazard ratio: 0.11-0.57, P = 6.90 × 10-24 to 2.10 × 10-11). Lipid metabolism dysfunction was evident 15 years before PD onset, and levels of BAG3, HPGDS, ITGAV and PEPD continuously decreased before diagnosis. These proteins were linked to prodromal symptoms and brain measures. Mendelian randomization suggested ITGAM and EGFR as potential causes of PD. A predictive model using machine learning combined the top 16 proteins and demographics, achieving high accuracy for 5-year (area under the curve (AUC) = 0.887) and over-5-year PD prediction (AUC = 0.816), outperforming demographic-only models. It was externally validated in PPMI (AUC = 0.802). Our findings reveal early peripheral pathophysiological changes in PD crucial for developing early biomarkers and precision therapies.

A computational framework for detecting inter-tissue gene-expression coordination changes with aging

Aging is a complex and systematic biological process that involves multiple genes and biological pathways across different tissues. While existing studies focus on tissue-specific aging factors, the inter-tissue interplay between molecular pathways during aging remains insufficiently explored. To bridge this gap, we propose a novel computational framework to identify the effect of aging on the coordinated patterns of gene-expression across multiple tissues. Our framework includes (1) an adjusted multi-tissue weighted gene co-expression network analysis, (2) differential network connectivity analysis between age groups and (3) machine learning models, XGBoost and Random Forest (RF) fed by gene expression levels and lower-dimensional pathway score space, to identify unique key inter-tissue genes and biological pathways for classifying aging. We applied our approach to three representative tissues: Adipose-Subcutaneous, Muscle-Skeletal and Brain-Cortex. The RF model demonstrated the best performance in predicting age group (AUC < 88%) highlighting key genes involved in inter-tissue coordination processes in aging. We also identified the inter-tissue involvement of lipid metabolism, immune system, and cell communication pathways during aging and detected distinct aging pathways manifested between tissues. The proposed framework highlights the importance of inter-tissue coordination processes underlying aging and provides valuable insights into aging mechanisms which can further assist in the development of therapeutic strategies promoting healthy aging.

Metabolomics in Parkinson's Disease and Correlation with Disease State

Changes in the level of metabolites, small molecules that are intermediates produced by metabolism or catabolism, are associated with developing diseases. Metabolite signatures in body fluids such as plasma, cerebrospinal fluid, urine, and saliva are associated with Parkinson's disease. Here, we discuss alteration of metabolites in the TCA cycle, pentose phosphate pathway, kynurenic network, and redox system. We also summarize the efforts of many research groups to differentiate between metabolite profiles that characterize PD motor progression and dyskinesia, gait and balance, and non-motor symptoms such as depression and cognitive decline. Understanding how changes in metabolites lead to progression in PD may allow for the identification of individuals at the earliest stage of the disease and the development of new therapeutic strategies.

Proteomic insights into molecular alterations associated with Kawasaki disease in children

BACKGROUND

Kawasaki disease (KD) is a pediatric vasculitis that can lead to coronary artery complications if not promptly diagnosed. Its nonspecific early symptoms, primarily fever, often result in misdiagnosis. This study aimed to identify potential biomarkers for early KD diagnosis using proteomic analysis of blood samples.

METHODS

Serum samples were collected from three groups: children with acute KD (n = 20, CQB group), age-matched febrile children with bacterial infections (n = 20, C group), and children recovered from KD (n = 8, CQBC group). Proteomic analysis was performed to identify differentially expressed proteins in serum specimens, followed by functional and pathway enrichment analysis.

RESULTS

Compared to controls, 92 proteins were upregulated and 101 were downregulated in acute KD, with significant enrichment in the AMPK pathway. In recovered KD, 537 proteins were upregulated and 231 downregulated, predominantly affecting the PI3K-Akt pathway. A total of 56 proteins showed contrasting expression patterns between acute and recovery phases, implicating the complement and coagulation cascades. Notably, complement component 6 (C6), complement component 3 (C3), and α1-antitrypsin (A1AT) emerged as potential biomarkers involved in KD progression and recovery.

CONCLUSIONS

C6, C3, and A1AT may serve as novel biomarkers for early KD diagnosis and monitoring. These findings provide new insights into KD pathogenesis and potential targets for clinical application.

Cardio-metabolic and cytoskeletal proteomic signatures differentiate stress hypersensitivity in dystrophin-deficient mdx mice

Extreme heterogeneity exists in the hypersensitive stress response exhibited by the dystrophin-deficient mdx mouse model of Duchenne muscular dystrophy. Because stress hypersensitivity can impact dystrophic phenotypes, this research aimed to understand the peripheral pathways driving this inter-individual variability. Male and female mdx mice were phenotypically stratified into "stress-resistant" or "stress-sensitive" groups based on their response to two laboratory stressors. Quantitative proteomics of striated muscle revealed that stress-resistant females were most dissimilar from all other groups, with over 250 proteins differentially regulated with stress hypersensitivity. Males showed less proteomic variation with stress hypersensitivity; however, these changes were associated with pathway enrichment. In the heart, stress-sensitive males had significant enrichment of pathways related to mitochondrial ATP synthesis, suggesting that increased cardio-metabolic capacity is associated with stress hypersensitivity in male mdx mice. In both sexes, stress hypersensitivity was associated with greater expression of beta-actin-like protein 2, indicative of altered cytoskeletal organisation. Despite identifying proteomic signatures associated with stress hypersensitivity, these did not correlate with differences in the serum metabolome acutely after a stressor. These data suggest that the heterogeneity in stress hypersensitivity in mdx mice is partially driven by cytoskeletal organisation, but that sex-specific cardio-metabolic reprogramming may also underpin this phenotype. SIGNIFICANCE: Duchenne muscular dystrophy (DMD) is a fatal muscle wasting disease which is associated with a premature loss of ambulation and neurocognitive dysfunction. The hypersensitive stress response in DMD is a heterogeneous phenotype which is poorly understood. This study provided the first investigation of the peripheral mechanisms regulating the hypersensitive stress response by undertaking multi-omics analysis of phenotypically stratified mdx mice. Variations in behaviour and the striated muscle proteomic profiles suggest that cardio-metabolic remodelling and cytoskeletal organisation may contribute to this phenotype. This research offers significant insights into understanding how peripheral dystrophin deficiency relates to the cognitive abnormalities seen in patients with DMD.

Integrated analysis of proteomics and metabolomics in infantile epileptic spasms syndrome

Infantile Epileptic Spasms Syndrome (IESS) is a severe developmental epileptic encephalopathy that manifests in infancy, significantly impacting the health and quality of life of affected children. The treatment of IESS poses a significant challenge, primarily due to the incomplete understanding of its etiology and pathogenesis. Objective: This study aims to investigate the pathogenic mechanisms of IESS, utilizing metabolomics and proteomics analyses to uncover potential biomarkers for the disease, thereby providing new insights for diagnostic and therapeutic strategies. Cerebrospinal fluid samples from 6 IESS patients and 6 control subjects with benign intracranial hypertension were collected and analyzed using metabolomics and proteomics techniques. Significant differential metabolites and proteins were identified and correlated to determine key proteins associated with specific metabolites. The study then expanded the sample size to 10 per group and validated the identified proteins through ELISA analysis. A total of 24 differential metabolites (12 upregulated and 12 downregulated) and 79 differential proteins (18 upregulated and 61 downregulated) were identified. Metabolomic analysis suggests that linoleic acid is a highly noteworthy differential metabolite in the cerebrospinal fluid of IESS patients. The associated differential protein HLA-A and SEZ6L2 proteins were notably downregulated (p < 0.05). Linoleic acid and its metabolism-related proteins HLA-A and SEZ6L2 could serve as potential biomarkers for IESS, providing new insights into the complex pathogenic mechanisms of the disease. Additionally, these findings also assist in identifying new therapeutic targets and developing more effective treatment strategies.

Metabolism and metabolomics in senescence, aging, and age-related diseases: a multiscale perspective

The pursuit of healthy aging has long rendered aging and senescence captivating. Age-related ailments, such as cardiovascular diseases, diabetes, and neurodegenerative disorders, pose significant threats to individuals. Recent studies have shed light on the intricate mechanisms encompassing genetics, epigenetics, transcriptomics, and metabolomics in the processes of senescence and aging, as well as the establishment of age-related pathologies. Amidst these underlying mechanisms governing aging and related pathology metabolism assumes a pivotal role that holds promise for intervention and therapeutics. The advancements in metabolomics techniques and analysis methods have significantly propelled the study of senescence and aging, particularly with the aid of multiscale metabolomics which has facilitated the discovery of metabolic markers and therapeutic potentials. This review provides an overview of senescence and aging, emphasizing the crucial role metabolism plays in the aging process as well as age-related diseases.

Cerebrospinal Fluid Metabolomics and Proteomics Integration in Neurological Syndromes

The integration of multi-omics data has increasingly been recognized as an effective approach to addressing complex problems and advancing precision medicine. Metabolomics and proteomics are closely related, and their integration provides complementary insights and enables cross-validation of experimental results. The integration of proteomics and metabolomics in cerebrospinal fluid is expected to reconstruct the complex biological networks underlying nervous system diseases, enhance understanding of molecular mechanisms, and aid in disease classification and prognosis prediction. However, integrating multi-omics data still faces numerous challenges, limiting the application of combined proteomic and metabolomic analyses in neurological diseases. Based on the advantages of integrated proteomics and metabolomics, this chapter introduces, for the first time, common strategies for the integrated analyses of omics data. Furthermore, we review advances in cerebrospinal fluid proteomics and metabolomics for neurological syndromes, highlighting current challenges and future directions.

Role of dietary and nutritional interventions in ceramide-associated diseases

Ceramides are important intermediates in sphingolipid metabolism and serve as signaling molecules with independent biological significance. Elevated cellular and circulating ceramide levels are consistently associated with pathological conditions including cardiometabolic diseases, neurological diseases, autoimmune diseases, and cancers. Although pharmacological inhibition of ceramide formation often protects against these diseases in animal models, pharmacological modulation of ceramides in humans remains impractical. Dietary interventions including the Mediterranean diet, lacto-ovo-vegetarian diet, calorie-restricted diet, restriction of dairy product consumption, and dietary supplementation with polyunsaturated fatty acids, dietary fibers, and polyphenols, all have beneficial effects on modulating ceramide levels. Mechanistic insights into these interventions are discussed. This article reviews the relationships between ceramides and disease pathogenesis, with a focus on dietary intervention as a viable strategy for lowering the concentration of circulating ceramides.

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.

Plasma metabolomics profiles indicate sex differences of lipid metabolism in patients with Parkinson's disease

The effect of sexual dimorphism on the metabolism of patients with Parkinson's disease has not been clarified. A group of patients with Parkinson's disease and healthy controls were recruited, and their clinical characteristics and plasma were collected. Untargeted liquid chromatography-mass spectrometry-based plasma metabolomics profiling was performed. Differentially expressed metabolites between patients and healthy controls were respectively identified in the male and female participants and metabolite set enrichment analyses were further employed. A total of 75 patients with Parkinson's disease (37 males and 38 females) and 31 healthy controls (16 males and 15 females) were enrolled while no significant differences can be discovered in clinical characteristics. The constructed male-specific metabolic model from orthogonal partial least squares-discriminant analysis can't well recognize female patients and the female-specific model also can't accurately identify male patients. There were 55 differentially expressed metabolites in the male participants, and fatty acids and conjugates and eicosanoids were the significantly enriched metabolite sets. Meanwhile, 86 metabolites were differentially expressed in the female participants while fatty acids and conjugates and glycerophosphocholines were enriched. Only 17 metabolites were simultaneously changed in both male and female patients. Significant sex differences of lipid metabolism were found in patients with Parkinson's disease.

The Plasma Metabolome and Risk of Incident Kidney Stones

Key Points

Information on metabolomic profiles in kidney stone formers is limited. This article describes investigations of associations between plasma metabolomic profiles and the risk of incident, symptomatic kidney stones. Three novel metabolites had negative associations with kidney stones: β -cryptoxanthin and two forms of sphingomyelin.

Background

Information on metabolomic profiles in kidney stone formers is limited. To examine independent associations between plasma metabolomic profiles and the risk of incident, symptomatic kidney stones in adults, we conducted prospective nested case-control studies in two large cohorts.

Methods

We performed plasma metabolomics on 1758 participants, including 879 stone formers (346 from the Health Professionals Follow-Up Study [HPFS] cohort, 533 from the Nurses' Health Study [NHS] II cohort) and 879 non–stone formers (346 from HPFS, 533 from NHS II) matched for age, race, time of blood collection, fasting status, and (for NHS II) menopausal status and luteal day of menstrual cycle for premenopausal participants. Conditional logistic regression models were used to estimate the odds ratio (OR) of kidney stones adjusted for body mass index; hypertension; diabetes; thiazide use; and intake of potassium, animal protein, oxalate, dietary and supplemental calcium, caffeine, and alcohol. A plasma metabolite–based score was developed in each cohort in a conditional logistic regression model with a lasso penalty. The scores derived in the HPFS (“kidney stones metabolite score [KMS]_HPFS”) and the NHS II (“KMS_NHS”) were tested for their association with kidney stone risk in the other cohort.

Results

A variety of individual metabolites were associated with incident kidney stone formation at prespecified levels of metabolome-wide statistical significance. We identified three metabolites associated with kidney stones in both HPFS and NHS II cohorts: β -cryptoxanthin, sphingomyelin (d18:2/24:1, d18:1/24:2), and sphingomyelin (d18:2/24:2). The standardized KMS_HPFS yielded an OR of 1.23 (95% confidence interval, 1.05 to 1.44) for stones in the NHS II cohort. The standardized KMS_NHS was in the expected direction but did not reach statistical significance in HPFS (OR, 1.16; 95% confidence interval, 0.97 to 1.39).

Conclusions

The findings of specific metabolites associated with kidney stone status in two cohorts and a plasma metabolomic signature offer a novel approach to characterize stone formers.

Biomarker discovery in progressive supranuclear palsy from human cerebrospinal fluid

BACKGROUND

Progressive supranuclear palsy (PSP) is a neurodegenerative disorder often misdiagnosed as Parkinson's Disease (PD) due to shared symptoms. PSP is characterized by the accumulation of tau protein in specific brain regions, leading to loss of balance, gaze impairment, and dementia. Diagnosing PSP is challenging, and there is a significant demand for reliable biomarkers. Existing biomarkers, including tau protein and neurofilament light chain (NfL) levels in cerebrospinal fluid (CSF), show inconsistencies in distinguishing PSP from other neurodegenerative disorders. Therefore, the development of new biomarkers for PSP is imperative.

METHODS

We conducted an extensive proteome analysis of CSF samples from 40 PSP patients, 40 PD patients, and 40 healthy controls (HC) using tandem mass tag-based quantification. Mass spectrometry analysis of 120 CSF samples was performed across 13 batches of 11-plex TMT experiments, with data normalization to reduce batch effects. Pathway, interactome, cell-type-specific enrichment, and bootstrap receiver operating characteristic analyses were performed to identify key candidate biomarkers.

RESULTS

We identified a total of 3,653 unique proteins. Our analysis revealed 190, 152, and 247 differentially expressed proteins in comparisons of PSP vs. HC, PSP vs. PD, and PSP vs. both PD and HC, respectively. Gene set enrichment and interactome analysis of the differentially expressed proteins in PSP CSF showed their involvement in cell adhesion, cholesterol metabolism, and glycan biosynthesis. Cell-type enrichment analysis indicated a predominance of neuronally-derived proteins among the differentially expressed proteins. The potential biomarker classification performance demonstrated that ATP6AP2 (reduced in PSP) had the highest AUC (0.922), followed by NEFM, EFEMP2, LAMP2, CHST12, FAT2, B4GALT1, LCAT, CBLN3, FSTL5, ATP6AP1, and GGH.

CONCLUSION

Biomarker candidate proteins ATP6AP2, NEFM, and CHI3L1 were identified as key differentiators of PSP from the other groups. This study represents the first large-scale use of mass spectrometry-based proteome analysis to identify cerebrospinal fluid (CSF) biomarkers specific to progressive supranuclear palsy (PSP) that can differentiate it from Parkinson's disease (PD) and healthy controls. Our findings lay a crucial foundation for the development and validation of reliable biomarkers, which will enhance diagnostic accuracy and facilitate early detection of PSP.

Plasma lipidome, circulating inflammatory proteins, and Parkinson's disease: a Mendelian randomization study

Background

Observational studies have suggested that plasma lipidome play a pivotal role in the occurrence of Parkinson's disease (PD). However, it remains unknown which lipids among plasma lipidome affect PD and how they exert their influence. Clarity is lacking regarding the causal relationship between plasma lipidome and PD, as well as whether circulating inflammatory proteins serve as mediators.

Methods

Single nucleotide polymorphisms (SNPs) significantly associated with 179 plasma lipidome were selected as instrumental variables to assess their causal impact on PD. PD data, serving as the outcome, were sourced from the International Parkinson's Disease Genomics Consortium, which boasts the largest sample size to date. The inverse variance weighted (IVW), Weighted median method, MR-Egger method, Simple mode method, Weighted mode method and MR-PRESSO were employed to evaluate the influence of the 179 plasma lipidome on PD. Heterogeneity, pleiotropy tests, and reverse causality analyses were conducted accordingly. Additionally, we analyzed the causal relationship between 91 circulating inflammatory proteins and PD, exploring whether these proteins serve as mediators in the pathway from plasma lipidome to PD.

Results

Among the 179 plasma lipidome, three were found to be associated with a reduced risk of PD: Phosphatidylcholine (14:0_18:2) (IVW, OR = 0.877; 95%CI, 0.787-0.978; p = 0.018), Phosphatidylcholine (16:0_16:1) levels (IVW, OR = 0.835; 95%CI, 0.717-0.973; p = 0.021), and Phosphatidylcholine (O-17:0_17:1) levels (IVW, OR = 0.854; 95%CI, 0.779-0.936; p = 0.001). Meanwhile, Sphingomyelin (d38:1) was linked to an increased risk of PD (IVW, OR = 1.095; 95%CI, 1.027-1.166; p = 0.005). Among the 91 circulating inflammatory proteins, three were associated with a lower PD risk: Fibroblast growth factor 21 levels (IVW, OR = 0.817; 95%CI, 0.674-0.990; p = 0.039), Transforming growth factor-alpha levels (IVW, OR = 0.825; 95%CI, 0.683-0.998; p = 0.048), and Tumor necrosis factor receptor superfamily member 9 levels (IVW, OR = 0.846; 95%CI, 0.744-0.963; p = 0.011). Two were associated with a higher risk of PD: Interleukin-17A levels (IVW, OR = 1.285; 95%CI, 1.051-1.571; p = 0.014) and TNF-beta levels (IVW, OR = 1.088; 95%CI, 1.010-1.171; p = 0.026). Additionally, a positive correlation was observed between Phosphatidylcholine (14:0_18:2) levels and Fibroblast growth factor 21 levels (IVW, OR = 1.125; 95%CI, 1.006-1.257; p = 0.038), suggesting that Fibroblast growth factor 21 levels may serve as a mediating factor in the pathway between Phosphatidylcholine (14.0_18.2) levels and PD. The mediation effect was estimated to be -0.024, accounting for approximately 18% of the total effect.

Conclusion

Both plasma lipidome and circulating inflammatory proteins demonstrate a causal relationship with PD. Additionally, circulating inflammatory proteins may serve as mediators in the pathway from plasma lipidome to PD. These findings may contribute to the prediction and diagnosis of PD and potentially pave the way for targeted therapies in the future.

Biomarkers of Parkinson's Disease: From Basic Research to Clinical Practice

Parkinson's disease (PD) is a common neurodegenerative disease characterized pathologically by dopaminergic neuron loss and the formation of Lewy bodies, which are enriched with aggregated α-synuclein (α-syn). PD currently has no cure, but therapeutic strategies are available to alleviate symptoms. Early diagnosis can greatly improve therapeutic interventions, but the clinical diagnosis of PD remains challenging and depends mainly on clinical features and imaging tests. Efficient and specific biomarkers are crucial for the diagnosis, monitoring, and evaluation of PD. Here, we reviewed the biomarkers of PD in different tissues and biofluids, along with the current clinical biochemical detection methods. We found that the sensitivity and specificity of single biomarkers are limited, and selecting appropriate indicators for combined detection can improve the diagnostic accuracy of PD.

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.

Lipid and Transcriptional Regulation in a Parkinson's Disease Mouse Model by Intranasal Vesicular and Hexosomal Plasmalogen-Based Nanomedicines

Plasmalogens (vinyl-ether phospholipids) are an emergent class of lipid drugs against various diseases involving neuro-inflammation, oxidative stress, mitochondrial dysfunction, and altered lipid metabolism. They can activate neurotrophic and neuroprotective signaling pathways but low bioavailabilities limit their efficiency in curing neurodegeneration. Here, liquid crystalline lipid nanoparticles (LNPs) are created for the protection and non-invasive intranasal delivery of purified scallop-derived plasmalogens. The in vivo results with a transgenic mouse Parkinson's disease (PD) model (characterized by motor impairments and α-synuclein deposition) demonstrate the crucial importance of LNP composition, which determines the self-assembled nanostructure type. Vesicle and hexosome nanostructures (characterized by small-angle X-ray scattering) display different efficacy of the nanomedicine-mediated recovery of motor function, lipid balance, and transcriptional regulation (e.g., reduced neuro-inflammation and PD pathogenic gene expression). Intranasal vesicular and hexosomal plasmalogen-based LNP treatment leads to improvement of the behavioral PD symptoms and downregulation of the Il6, Il33, and Tnfa genes. Moreover, RNA-sequencing and lipidomic analyses establish a dramatic effect of hexosomal nanomedicines on PD amelioration, lipid metabolism, and the type and number of responsive transcripts that may be implicated in neuroregeneration.

A Systematic Review of Extracellular Matrix-Related Alterations in Parkinson's Disease

The role of the extracellular matrix (ECM) in Parkinson's disease (PD) is not well understood, even though it is critical for neuronal structure and signaling. This systematic review identified the top deregulated ECM-related pathways in studies that used gene set enrichment analyses (GSEA) to document transcriptomic, proteomic, or genomic alterations in PD. PubMed and Google scholar were searched for transcriptomics, proteomics, or genomics studies that employed GSEA on data from PD tissues or cells and reported ECM-related pathways among the top-10 most enriched versus controls. Twenty-seven studies were included, two of which used multiple omics analyses. Transcriptomics and proteomics studies were conducted on a variety of tissue and cell types. Of the 17 transcriptomics studies (16 data sets), 13 identified one or more adhesion pathways in the top-10 deregulated gene sets or pathways, primarily related to cell adhesion and focal adhesion. Among the 8 proteomics studies, 5 identified altered overarching ECM gene sets or pathways among the top 10. Among the 4 genomics studies, 3 identified focal adhesion pathways among the top 10. The findings summarized here suggest that ECM organization/structure and cell adhesion (particularly focal adhesion) are altered in PD and should be the focus of future studies.

Unveiling the role of iPLA2β in neurodegeneration: From molecular mechanisms to advanced therapies

Calcium-independent phospholipase A2β (iPLA2β), a member of the phospholipase A2 (PLA2s) superfamily, is encoded by the PLA2G6 gene. Mutations in the PLA2G6 gene have been identified as the primary cause of infantile neuroaxonal dystrophy (INAD) and, less commonly, as a contributor to Parkinson's disease (PD). Recent studies have revealed that iPLA2β deficiency leads to neuroinflammation, iron accumulation, mitochondrial dysfunction, lipid dysregulation, and other pathological changes, forming a complex pathogenic network. These discoveries shed light on potential mechanisms underlying PLA2G6-associated neurodegeneration (PLAN) and offer valuable insights for therapeutic development. This review provides a comprehensive analysis of the fundamental characteristics of iPLA2β, its association with neurodegeneration, the pathogenic mechanisms involved in PLAN, and potential targets for therapeutic intervention. It offers an overview of the latest advancements in this field, aiming to contribute to ongoing research endeavors and facilitate the development of effective therapies for PLAN.

A top-down proteomic approach reveals a salivary protein profile able to classify Parkinson's disease with respect to Alzheimer's disease patients and to healthy controls

Parkinson's disease (PD) is a complex neurodegenerative disease with motor and non-motor symptoms. Diagnosis is complicated by lack of reliable biomarkers. To individuate peptides and/or proteins with diagnostic potential for early diagnosis, severity and discrimination from similar pathologies, the salivary proteome in 36 PD patients was investigated in comparison with 36 healthy controls (HC) and 35 Alzheimer's disease (AD) patients. A top-down platform based on HPLC-ESI-IT-MS allowed characterizing and quantifying intact peptides, small proteins and their PTMs (overall 51). The three groups showed significantly different protein profiles, PD showed the highest levels of cystatin SA and antileukoproteinase and the lowest of cystatin SN and some statherin proteoforms. HC exhibited the lowest abundance of thymosin β4, short S100A9, cystatin A, and dimeric cystatin B. AD patients showed the highest abundance of α-defensins and short oxidized S100A9. Moreover, different proteoforms of the same protein, as S-cysteinylated and S-glutathionylated cystatin B, showed opposite trends in the two pathological groups. Statherin, cystatins SA and SN classified accurately PD from HC and AD subjects. α-defensins, histatin 1, oxidized S100A9, and P-B fragments were the best classifying factors between PD and AD patients. Interestingly statherin and thymosin β4 correlated with defective olfactory functions in PD patients. All these outcomes highlighted implications of specific proteoforms involved in the innate-immune response and inflammation regulation at oral and systemic level, suggesting a possible panel of molecular and clinical markers suitable to recognize subjects affected by PD.

The potential role of glucose metabolism, lipid metabolism, and amino acid metabolism in the treatment of Parkinson's disease

PURPOSE OF REVIEW

Parkinson's disease (PD) is a common neurodegenerative disease, which can cause progressive deterioration of motor function causing muscle stiffness, tremor, and bradykinesia. In this review, we hope to describe approaches that can improve the life of PD patients through modifications of energy metabolism.

RECENT FINDINGS

The main pathological features of PD are the progressive loss of nigrostriatal dopaminergic neurons and the production of Lewy bodies. Abnormal aggregation of α-synuclein (α-Syn) leading to the formation of Lewy bodies is closely associated with neuronal dysfunction and degeneration. The main causes of PD are said to be mitochondrial damage, oxidative stress, inflammation, and abnormal protein aggregation. Presence of abnormal energy metabolism is another cause of PD. Many studies have found significant differences between neurodegenerative diseases and metabolic decompensation, which has become a biological hallmark of neurodegenerative diseases.

SUMMARY

In this review, we highlight the relationship between abnormal energy metabolism (Glucose metabolism, lipid metabolism, and amino acid metabolism) and PD. Improvement of key molecules in glucose metabolism, fat metabolism, and amino acid metabolism (e.g., glucose-6-phosphate dehydrogenase, triglycerides, and levodopa) might be potentially beneficial in PD. Some of these metabolic indicators may serve well during the diagnosis of PD. In addition, modulation of these metabolic pathways may be a potential target for the treatment and prevention of PD.

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.

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

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

Advances in mass spectrometry-based multi-scale metabolomic methodologies and their applications in biological and clinical investigations

Metabolomics is a nascent field of inquiry that emerged in the late 20th century. It encompasses the comprehensive profiling of metabolites across a spectrum of organisms, ranging from bacteria and cells to tissues. The rapid evolution of analytical methods and data analysis has greatly accelerated progress in this dynamic discipline over recent decades. Sophisticated techniques such as liquid chromatograph mass spectrometry (MS), gas chromatograph MS, capillary electrophoresis MS, and nuclear magnetic resonance serve as the cornerstone of metabolomic analysis. Building upon these methods, a plethora of modifications and combinations have emerged to propel the advancement of metabolomics. Despite this progress, scrutinizing metabolism at the single-cell or single-organelle level remains an arduous task over the decades. Some of the most thrilling advancements, such as single-cell and single-organelle metabolic profiling techniques, offer profound insights into the intricate mechanisms within cells and organelles. This allows for a comprehensive study of metabolic heterogeneity and its pivotal role in multiple biological processes. The progress made in MS imaging has enabled high-resolution in situ metabolic profiling of tissue sections and even individual cells. Spatial reconstruction techniques enable the direct representation of metabolic distribution and alteration in three-dimensional space. The application of novel metabolomic techniques has led to significant breakthroughs in biological and clinical studies, including the discovery of novel metabolic pathways, determination of cell fate in differentiation, anti-aging intervention through modulating metabolism, metabolomics-based clinicopathologic analysis, and surgical decision-making based on on-site intraoperative metabolic analysis. This review presents a comprehensive overview of both conventional and innovative metabolomic techniques, highlighting their applications in groundbreaking biological and clinical studies.

Sphingolipid metabolism in brain insulin resistance and neurological diseases

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

The regulatory role of lipophagy in central nervous system diseases

Lipid droplets (LDs) are the organelles for storing neutral lipids, which are broken down when energy is insufficient. It has been suggested that excessive accumulation of LDs can affect cellular function, which is important to coordinate homeostasis of lipids in vivo. Lysosomes play an important role in the degradation of lipids, and the process of selective autophagy of LDs through lysosomes is known as lipophagy. Dysregulation of lipid metabolism has recently been associated with a variety of central nervous system (CNS) diseases, but the specific regulatory mechanisms of lipophagy in these diseases remain to be elucidated. This review summarizes various forms of lipophagy and discusses the role that lipophagy plays in the development of CNS diseases in order to reveal the related mechanisms and potential therapeutic targets for these diseases.

Serum metabolomics analysis revealed metabolic disorders in Parkinson's disease

BACKGROUND

Parkinson's disease (PD) is by now the second of the most prevalent neurodegenerative diseases in the world, and its incidence is increasing rapidly as the global population ages, with 14.2 million PD patients expected worldwide by 2040.

METHODS

We gathered a completion of 45 serum samples, including 15 of healthy controls and 30 from the PD group. We used non-targeted metabolomics analysis based on liquid chromatography-mass spectrometry to identify the molecular changes in PD patients, and conducted bioinformatics analysis on this basis to explore the possible pathogenesis of PD.

RESULTS

We found significant metabolomics changes in the levels of 30 metabolites in PD patients compared with healthy controls.

CONCLUSION

Lipids and lipid-like molecules accounted for the majority of the 30 differentially expressed metabolites. Also, pathway enrichment analysis showed significant enrichment in sphingolipid metabolic pathway. These assessments can improve our perception on the underlying mechanism of PD as well as facilitate a better targeting on therapeutic interventions.

Liquid chromatography-mass spectrometry-based metabolomic profiling reveals sex differences of lipid metabolism among the elderly from Southwest China

BACKGROUND

The sexual dimorphism represents one of the triggers of the metabolic disparities while the identification of sex-specific metabolites in the elderly has not been achieved.

METHODS

A group of aged healthy population from Southwest China were recruited and clinical characteristics were collected. Fasting plasma samples were obtained and untargeted liquid chromatography-mass spectrometry-based metabolomic analyses were performed. Differentially expressed metabolites between males and females were identified from the metabolomic analysis and metabolite sets enrichment analysis was employed.

RESULTS

Sixteen males and fifteen females were finally enrolled. According to clinical characteristics, no significant differences can be found except for smoking history. There were thirty-six differentially expressed metabolites between different sexes, most of which were lipids and lipid-like molecules. Twenty-three metabolites of males were increased while thirteen were decreased compared with females. The top four classes of metabolites were fatty acids and conjugates (30.6%), glycerophosphocholines (22.2%), sphingomyelins (11.1%), and flavonoids (8.3%). Fatty acids and conjugates, glycerophosphocholines, and sphingomyelins were significantly enriched in metabolite sets enrichment analysis.

CONCLUSIONS

Significant lipid metabolic differences were found between males and females among the elderly. Fatty acids and conjugates, glycerophosphocholines, and sphingomyelins may partly account for sex differences and can be potential treatment targets for sex-specific diseases.

Cross-sectional proteomic expression in Parkinson's disease-related proteins in drug-naïve patients vs healthy controls with longitudinal clinical follow-up

There is an urgent need to find reliable and accessible blood-based biomarkers for early diagnosis of Parkinson's disease (PD) correlating with clinical symptoms and displaying predictive potential to improve future clinical trials. This led us to a conduct large-scale proteomics approach using an advanced high-throughput proteomics technology to create a proteomic profile for PD. Over 1300 proteins were measured in serum samples from a de novo Parkinson's (DeNoPa) cohort made up of 85 deep clinically phenotyped drug-naïve de novo PD patients and 93 matched healthy controls (HC) with longitudinal clinical follow-up available of up to 8 years. The analysis identified 73 differentially expressed proteins (DEPs) of which 14 proteins were confirmed as stable potential diagnostic markers using machine learning tools. Among the DEPs identified, eight proteins-ALCAM, contactin 1, CD36, DUS3, NEGR1, Notch1, TrkB, and BTK- significantly correlated with longitudinal clinical scores including motor and non-motor symptom scores, cognitive function and depression scales, indicating potential predictive values for progression in PD among various phenotypes. Known functions of these proteins and their possible relation to the pathophysiology or symptomatology of PD were discussed and presented with a particular emphasis on the potential biological mechanisms involved, such as cell adhesion, axonal guidance and neuroinflammation, and T-cell activation. In conclusion, with the use of advance multiplex proteomic technology, a blood-based protein signature profile was identified from serum samples of a well-characterized PD cohort capable of potentially differentiating PD from HC and predicting clinical disease progression of related motor and non-motor PD symptoms. We thereby highlight the need to validate and further investigate these markers in future prospective cohorts and assess their possible PD-related mechanisms.

Association between serum lipid levels over time and risk of Parkinson's disease

The role of serum lipids in Parkinson's disease (PD) remains controversial. We aimed to evaluate the association between time-varying serum lipid levels and the risk of PD. This study included an assessment of the complete lipid profiles of 200,454 individuals from the 2002-2019 Korean National Health Insurance Health Screening Cohort. Time-dependent Cox proportional hazard regression models were used to evaluate the association between serum lipid levels over time and the risk of PD. Individuals in the lowest tertile of total cholesterol and low-density lipoprotein cholesterol had a 1.17 times [hazard ratio (HR) 1.17; 95% confidence interval (CI) 1.04-1.31] and 1.19 times (HR 1.19; 95% CI 1.06-1.34) higher risk of PD than those in middle tertile, respectively. Individuals in the highest high-density lipoprotein cholesterol tertile had a 0.89 times (HR 0.89; 95% CI 0.79-1.00) lower risk of PD than those in middle tertile, but the association was less robust in sensitivity analyses. Serum triglyceride levels were not related to the risk of PD. Our results suggest that the serum total and low-density lipoprotein cholesterol levels over time are inversely associated with the risk of PD. Further research is warranted to confirm these findings and reveal the underlying mechanisms.

Integrating nutriepigenomics in Parkinson's disease management: New promising strategy in the omics era

Parkinson's disease (PD) is the most prevalent brain motor disorder and is frequently regarded as an idiopathic and sporadic disease due to its unclear etiology. Although the pathological mechanisms of PD have already been investigated at various omics levels, no disease-modifying drugs are currently available. At the moment, treatments can only provide symptomatic relief to control or improve motor symptoms. Parkinson's disease is a multifactorial disease, the development and progression of which are influenced by multiple factors, including the genetic markups and the environment. As an indispensable component of our daily life, nutrition is considered one of the most robust environmental factors affecting our health. Consequently, depending on our dietary habits, nutrition can either induce or reduce our susceptibility to PD. Epigenetic mechanisms regulate gene expression through DNA methylation, histone modifications, and non-coding RNAs (ncRNAs) activity. Accumulating evidence from nutriepigenomics studies has reported altered epigenetic mechanisms in clinical and pre-clinical PD models, and the potential role of nutrition in modifying the changes. In addition, through nutrigenetics and nutrigenomics studies, the diet-gene, and gene-diet interactions concerning PD development and progression have been investigated. Herein, current findings on the roles of nutrition in epigenetic mechanisms underpinning PD development and progression are discussed. Recent advancements in the multi-omics approach in PD nutrition research are also underlined. The ability of nutrients to influence epigenetic mechanisms and the availability of multi-omics applications compel the immediate use of personalized nutrition as adjuvant therapy for PD.

Biomarkers in Neurodegenerative Diseases: Proteomics Spotlight on ALS and Parkinson’s Disease

Neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS) and Parkinson’s disease (PD) are both characterized by pathogenic protein aggregates that correlate with the progressive degeneration of neurons and the loss of behavioral functions. Both diseases lack biomarkers for diagnosis and treatment efficacy. Proteomics is an unbiased quantitative tool capable of the high throughput quantitation of thousands of proteins from minimal sample volumes. We review recent proteomic studies in human tissues, plasma, cerebrospinal fluid (CSF), and exosomes in ALS and PD that identify proteins with potential utility as biomarkers. Further, we review disease-related post-translational modifications in key proteins TDP43 in ALS and α-synuclein in PD studies, which may serve as biomarkers. We compare relative and absolute quantitative proteomic approaches in key biomarker studies in ALS and PD and discuss recent technological advancements which may identify suitable biomarkers for the early-diagnosis treatment efficacy of these diseases.

Genomic, transcriptomic, and metabolomic profiles of hiPSC-derived dopamine neurons from clinically discordant brothers with identical PRKN deletions

We previously reported on two brothers who carry identical compound heterozygous PRKN mutations yet present with significantly different Parkinson's Disease (PD) clinical phenotypes. Juvenile cases demonstrate that PD is not necessarily an aging-associated disease. Indeed, evidence for a developmental component to PD pathogenesis is accumulating. Thus, we hypothesized that the presence of additional genetic modifiers, including genetic loci relevant to mesencephalic dopamine neuron development, could potentially contribute to the different clinical manifestations of the two brothers. We differentiated human-induced pluripotent stem cells (hiPSCs) derived from the two brothers into mesencephalic neural precursor cells and early postmitotic dopaminergic neurons and performed wholeexome sequencing and transcriptomic and metabolomic analyses. No significant differences in the expression of canonical dopamine neuron differentiation markers were observed. Yet our transcriptomic analysis revealed a significant downregulation of the expression of three neurodevelopmentally relevant cell adhesion molecules, CNTN6, CNTN4 and CHL1, in the cultures of the more severely affected brother. In addition, several HLA genes, known to play a role in neurodevelopment, were differentially regulated. The expression of EN2, a transcription factor crucial for mesencephalic dopamine neuron development, was also differentially regulated. We further identified differences in cellular processes relevant to dopamine metabolism. Lastly, wholeexome sequencing, transcriptomics and metabolomics data all revealed differences in glutathione (GSH) homeostasis, the dysregulation of which has been previously associated with PD. In summary, we identified genetic differences which could potentially, at least partially, contribute to the discordant clinical PD presentation of the two brothers.

Dysregulated Gene Expression in Lymphoblasts from Parkinson’s Disease

Parkinson’s disease is the second largest neurodegenerative disease worldwide and is caused by a combination of genetics and environment. It is characterized by the death of neurons in the substantia nigra of the brain but is not solely a disease of the brain, as it affects multiple tissues and organs. Studying Parkinson’s disease in accessible tissues such as skin and blood has increased our understanding of the disease’s pathogenesis. Here, we used lymphoblast cell lines generated from Parkinson’s disease patient and healthy age- and sex-matched control groups and obtained their whole-cell transcriptomes and proteomes. Our analysis revealed, in both the transcriptomes and the proteomes of PD cells, a global downregulation of genes involved in protein synthesis, as well as the upregulation of immune processes and sphingolipid metabolism. In contrast, we discovered an uncoupling of mRNA and protein expression in processes associated with mitochondrial respiration in the form of a general downregulation in associated transcripts and an upregulation in proteins. Complex V was different to the other oxidative phosphorylation complexes in that the levels of its associated transcripts were also lower, but the levels of their encoded polypeptides were not elevated. This may suggest that further layers of regulation specific to Complex V are in play.

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

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

Multiomics implicate gut microbiota in altered lipid and energy metabolism in Parkinson's disease

We aimed to investigate the link between serum metabolites, gut bacterial community composition, and clinical variables in Parkinson’s disease (PD) and healthy control subjects (HC). A total of 124 subjects were part of the study (63 PD patients and 61 HC subjects). 139 metabolite features were found to be predictive between the PD and Control groups. No associations were found between metabolite features and within-PD clinical variables. The results suggest alterations in serum metabolite profiles in PD, and the results of correlation analysis between metabolite features and microbiota suggest that several bacterial taxa are associated with altered lipid and energy metabolism in PD.

HDL Accessory Proteins in Parkinson’s Disease—Focusing on Clusterin (Apolipoprotein J) in Regard to Its Involvement in Pathology and Diagnostics—A Review

Parkinson’s disease (PD)—a neurodegenerative disorder (NDD) characterized by progressive destruction of dopaminergic neurons within the substantia nigra of the brain—is associated with the formation of Lewy bodies containing mainly α-synuclein. HDL-related proteins such as paraoxonase 1 and apolipoproteins A1, E, D, and J are implicated in NDDs, including PD. Apolipoprotein J (ApoJ, clusterin) is a ubiquitous, multifunctional protein; besides its engagement in lipid transport, it modulates a variety of other processes such as immune system functionality and cellular death signaling. Furthermore, being an extracellular chaperone, ApoJ interacts with proteins associated with NDD pathogenesis (amyloid β, tau, and α-synuclein), thus modulating their properties. In this review, the association of clusterin with PD is delineated, with respect to its putative involvement in the pathological mechanism and its application in PD prognosis/diagnosis.

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

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

Advances in Proteomic and Metabolomic Profiling of Neurodegenerative Diseases

Proteomics and metabolomics are two emerging fields that hold promise to shine light on the molecular mechanisms causing neurodegenerative diseases. Research in this area may reveal and quantify specific metabolites and proteins that can be targeted by therapeutic interventions intended at halting or reversing the neurodegenerative process. This review aims at providing a general overview on the current status of proteomic and metabolomic profiling in neurodegenerative diseases. We focus on the most common neurodegenerative disorders, including Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis. We discuss the relevance of state-of-the-art metabolomics and proteomics approaches and their potential for biomarker discovery. We critically review advancements made so far, highlighting how metabolomics and proteomics may have a significant impact in future therapeutic and biomarker development. Finally, we further outline technologies used so far as well as challenges and limitations, placing the current information in a future-facing context.

Systems Biology to Address Unmet Medical Needs in Neurological Disorders

Neurological diseases are highly prevalent and constitute a significant cause of mortality and disability. Neurological disorders encompass a heterogeneous group of neurodegenerative conditions, broadly characterized by injury to the peripheral and/or central nervous system. Although the etiology of neurological diseases varies greatly, they share several characteristics, such as heterogeneity of clinical presentation, non-cell autonomous nature, and diversity of cellular, subcellular, and molecular pathways. Systems biology has emerged as a valuable platform for addressing the challenges of studying heterogeneous neurological diseases. Systems biology has manifold applications to address unmet medical needs for neurological illness, including integrating and correlating different large datasets covering the transcriptome, epigenome, proteome, and metabolome associated with a specific condition. This is particularly useful for disentangling the heterogeneity and complexity of neurological conditions. Hence, systems biology can help in uncovering pathophysiology to develop novel therapeutic targets and assessing the impact of known treatments on disease progression. Additionally, systems biology can identify early diagnostic biomarkers, to help diagnose neurological disease preceded by a long subclinical phase, as well as define the exposome, the collection of environmental toxicants that increase risk of certain neurological diseases. In addition to these current applications, there are numerous potential emergent uses, such as precision medicine.

Identification of blood-based biomarkers for diagnosis and prognosis of Parkinson's disease: A systematic review of proteomics studies

Parkinson's Disease (PD), a neurodegenerative disorder, is characterised by the loss of motor function and dopamine neurons. Therapeutic avenues remain a challenge due to lack of accuracy in early diagnosis, monitoring of disease progression and limited therapeutic options. Proteomic platforms have been utilised to discover biomarkers for numerous diseases, a tool that may benefit the diagnosis and monitoring of disease progression in PD patients. Therefore, this systematic review focuses on analysing blood-based candidate biomarkers (CB) identified via proteomics platforms for PD. This study systematically reviewed articles across six databases (EMBASE, Cochrane, Ovid Medline, Scopus, Science Direct and PubMed) published between 2010 and 2020. Of the 504 articles identified, 12 controlled-PD studies were selected for further analysis. A total of 115 candidate biomarkers (CB) were identified across selected 12-controlled studies, of which 23 CB were found to be replicable in more than two cohorts. Using the PANTHER Go-Slim classification system and STRING network, the gene function and protein interactions between biomarkers were analysed. Our analysis highlights Apolipoprotein A-I (ApoA-I), which is essential in lipid metabolism, oxidative stress, and neuroprotection demonstrates high replicability across five cohorts with consistent downregulation across four cohorts. Since ApoA-I was highly replicable across blood fractions, proteomic platforms and continents, its relationship with cholesterol, statin and oxidative stress as PD biomarker, its role in the pathogenesis of PD is discussed in this paper. The present study identified ApoA-I as a potential biomarker via proteomics analysis of PD for the early diagnosis and prediction of disease progression.

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

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

Transcriptome analysis in LRRK2 and idiopathic Parkinson's disease at different glucose levels

Type-2 diabetes (T2D) and glucose metabolic imbalances have been linked to neurodegenerative diseases, including Parkinson's disease (PD). To detect potential effects of different glucose levels on gene expression, by RNA-seq we analyzed the transcriptome of dermal fibroblasts from idiopathic PD (iPD) patients, LRRK2-associated PD (L2PD) patients, and healthy controls (total n = 21 cell lines), which were cultured at two different glucose concentrations (25 and 5 mM glucose). In PD patients we identified differentially expressed genes (DEGs) that were related to biological processes mainly involving the plasmatic cell membrane, the extracellular matrix, and also neuronal functions. Such pathway deregulation was largely similar in iPD or L2PD fibroblasts. Overall, the gene expression changes detected in this study were associated with PD independently of glucose concentration.

The disturbance of lipid metabolism is correlated with neuropsychiatric symptoms in patients with Parkinson's disease

OBJECTIVE

We aimed to identify the detailed relationships between serum lipid levels and neuropsychiatric symptoms in patients with Parkinson's disease (PD).

METHODS

Consecutive PD patients and healthy controls were recruited and demographic data were collected. The disease stages of PD patients were assessed using Hoehn-Yahr scale while neuropsychiatric symptoms were determined using Hamilton depression rating scale (HAMD), Hamilton anxiety rating scale (HAMA), and mini-mental state examination scale. Fast serum samples were obtained and the serum levels of lipids were identified. Linear regression analyses and correlation analyses were performed to explore the relationships between serum lipid levels and neuropsychiatric symptoms.

RESULTS

The serum levels of triglyceride had significantly decreased while the levels of HDL-c and lipoprotein a had increased in PD patients. Linear regression analyses confirmed that the levels of triglyceride were mainly correlated with age and HAMA score, the levels of HDL-c were correlated with disease duration and gender, and the levels of lipoprotein a were correlated with HAMD score. Correlation analyses further confirmed that the levels of triglyceride were negatively correlated with HAMA score when the levels of lipoprotein a were negatively correlated with HAMD score.

CONCLUSIONS

Lipid metabolism is significantly correlated with neuropsychiatric disorders in PD patients.

Sphingomyelin and Medullary Sponge Kidney Disease: A Biological Link Identified by Omics Approach

Background: Molecular biology has recently added new insights into the comprehension of the physiopathology of the medullary sponge kidney disease (MSK), a rare kidney malformation featuring nephrocalcinosis and recurrent renal stones. Pathogenesis and metabolic alterations associated to this disorder have been only partially elucidated.

Methods: Plasma and urine samples were collected from 15 MSK patients and 15 controls affected by idiopathic calcium nephrolithiasis (ICN). Plasma metabolomic profile of 7 MSK and 8 ICN patients was performed by liquid chromatography combined with electrospray ionization tandem mass spectrometry (UHPLC–ESI-MS/MS). Subsequently, we reinterrogated proteomic raw data previously obtained from urinary microvesicles of MSK and ICN focusing on proteins associated with sphingomyelin metabolism. Omics results were validated by ELISA in the entire patients' cohort.

Results: Thirteen metabolites were able to discriminate MSK from ICN (7 increased and 6 decreased in MSK vs. ICN). Sphingomyelin reached the top level of discrimination between the two study groups (FC: −1.8, p < 0.001). Ectonucleotide pyrophophatase phosphodiesterase 6 (ENPP6) and osteopontin (SPP1) resulted the most significant deregulated urinary proteins in MSK vs. ICN (p < 0.001). ENPP6 resulted up-regulated also in plasma of MSK by ELISA.

Conclusion: Our data revealed a specific high-throughput metabolomics signature of MSK and indicated a pivotal biological role of sphingomyelin in this disease.

Advancing Personalized Medicine in Common Forms of Parkinson's Disease through Genetics: Current Therapeutics and the Future of Individualized Management

Parkinson’s disease (PD) is a condition with heterogeneous clinical manifestations that vary in age at onset, rate of progression, disease course, severity, motor and non-motor symptoms, and a variable response to antiparkinsonian drugs. It is considered that there are multiple PD etiological subtypes, some of which could be predicted by genetics. The characterization and prediction of these distinct molecular entities provides a growing opportunity to use individualized management and personalized therapies. Dissecting the genetic architecture of PD is a critical step in identifying therapeutic targets, and genetics represents a step forward to sub-categorize and predict PD risk and progression. A better understanding and separation of genetic subtypes has immediate implications in clinical trial design by unraveling the different flavors of clinical presentation and development. Personalized medicine is a nascent area of research and represents a paramount challenge in the treatment and cure of PD. This manuscript summarizes the current state of precision medicine in the PD field and discusses how genetics has become the engine to gain insights into disease during our constant effort to develop potential etiological based interventions.

Lipid metabolic dysregulation is involved in Parkinson's disease dementia

Dementia is very common in the late stage of patient with Parkinson's disease (PD). We aim to explore its underlying pathogenesis and identify candidate biomarkers using untargeted metabolomics analysis. Consecutive PD patients and healthy controls were recruited. Clinical data were assessed and patients were categorized into Parkinson's disease without dementia (PDND) and Parkinson's disease dementia (PDD). Fast plasma samples were obtained and untargeted liquid chromatography-mass spectrometry-based metabolomics analysis was performed. Based on the identified differentially-expressed metabolites from the metabolomics analysis, multivariate linear regression analyses and receiver operating characteristic (ROC) curves were further employed. According to the clinical data, the mean ages of PDND and PDD patients were significantly higher than those of healthy controls. The incidence of hypercholesterolemia was decreased in PDD patients. PDD patients also had lower levels of triglyceride, low-density lipoprotein cholesterol, and apolipoprotein B. There were 24 and 57 differentially expressed metabolites in PDD patients when compared with the healthy controls and PDND patients from the metabolomics analysis. Eleven lipid metabolites were simultaneously decreased between these two groups, and can be further subcategorized into fatty acyls, glycerolipids, glycerophospholipids, sphingolipids, and prenol lipids. The plasma levels of the eleven metabolites were positively correlated with MMSE score and can be candidate biomarkers for PDD patients with areas under the curve ranging from 0.724 to 0.806 based on the ROC curves. Plasma lipoproteins are significantly lower in PDD patients. A panel of eleven lipid metabolites were also decreased and can be candidate biomarkers for the diagnosis of PDD patients. Lipid metabolic dysregulation is involved in the pathogenesis of Parkinson's disease dementia.

The Association Between the Gut Microbiota and Parkinson's Disease, a Meta-Analysis

Patients with Parkinson's disease (PD) were often observed with gastrointestinal symptoms, which preceded the onset of motor symptoms. Neuropathology of PD has also been found in the enteric nervous system (ENS). Many studies have reported significant PD-related alterations of gut microbiota. This meta-analysis was performed to evaluate the differences of gut microbiota between patients with PD and healthy controls (HCs) across different geographical regions. We conducted a systematic online search for case-control studies detecting gut microbiota in patients with PD and HCs. Mean difference (MD) and 95% confidence interval (CI) were calculated to access alterations in the abundance of certain microbiota families in PD. Fifteen case-control studies were included in this meta-analysis study. Our results showed significant lower abundance levels of Prevotellaceae (MD = -0.37, 95% CI = -0.62 to -0.11), Faecalibacterium (MD = -0.41, 95% CI: -0.57 to -0.24), and Lachnospiraceae (MD = -0.34, 95% CI = -0.59 to -0.09) in patients with PD compared to HCs. Significant higher abundance level of Bifidobacteriaceae (MD = 0.38, 95%; CI = 0.12 to 0.63), Ruminococcaceae (MD = 0.58, 95% CI = 0.07 to 1.10), Verrucomicrobiaceae (MD = 0.45, 95% CI = 0.21 to 0.69), and Christensenellaceae (MD = 0.20, 95% CI = 0.07 to 0.34) was also found in patients with PD. Thus, shared alterations of certain gut microbiota were detected in patients with PD across different geographical regions. These PD-related gut microbiota dysbiosis might lead to the impairment of short-chain fatty acids (SCFAs) producing process, lipid metabolism, immunoregulatory function, and intestinal permeability, which contribute to the pathogenesis of PD.

Metabolomics profiling reveals altered lipid metabolism and identifies a panel of lipid metabolites as biomarkers for Parkinson's disease related anxiety disorder

OBJECTIVES

Anxiety disorder is a common non-motor symptom in patient with Parkinson's disease (PD). We aimed to explore its pathogenesis and identify plasma biomarkers using untargeted metabolomics analysis.

METHODS

Consecutive PD patients and healthy controls were recruited. Clinical data were assessed and patients with Parkinson's disease related anxiety disorder (PDA) were recognized. Fast plasma samples were obtained and untargeted liquid chromatography-mass spectrometry-based metabolomics analysis was performed. Based on the differentially expressed metabolites from the above metabolomics analysis, correlation analyses and receiver operating characteristic curves (ROC) were further employed.

RESULTS

According to the clinical data, PDA patients had lower plasma levels of total cholesterol, triglyceride, low-density lipoprotein cholesterol, and apolipoprotein B. There were thirty-nine differentially expressed metabolites in PDA patients when compared with the other two groups from the metabolomics analysis, respectively. Fourteen lipid metabolites were simultaneously altered between these two groups, and all of them were significantly decreased. They can be further subcategorized into fatty acyls, glycerolipids, sterol lipids, sphingolipids, and prenol lipids. The plasma levels of thirteen metabolites were negatively correlated with HAMA scores except 10-oxo-nonadecanoic acid. Based on the ROC curves, the fourteen lipid metabolites can be diagnostic biomarkers for PDA patients separately and the areas under the curve of the fourteen lipid metabolites ranged from 0.681 to 0.798.

CONCLUSIONS

Significantly lower plasma lipoproteins can be found in PDA patients. A panel of fourteen lipid metabolites were also significantly decreased and can be clinical biomarkers for the diagnosis of PDA patients.