Identification of a possible proteomic biomarker in Parkinson's disease: discovery and replication in blood, brain and cerebrospinal fluid

Complement C4 exacerbates astrocyte-mediated neuroinflammation and promotes α-synuclein pathology in Parkinson's disease

Complement C4, implicated in neuroinflammation and synaptic dysfunction, plays a poorly defined role in Parkinson's disease (PD). Here, we demonstrate elevated C4 levels in PD patient plasma and the substantia nigra of α-synuclein preformed fibril (α-syn PFF)-injected mice, correlating with disease severity. α-syn PFF treatment induces complement C4 expression, particularly in neurons, with astrocytes further enhancing this response. Complement C4 was found to amplify astrocytic inflammatory responses, leading to increased neuronal apoptosis and synaptic damage. Additionally, conditioned media from astrocytes treated with α-syn PFF and complement C4 accelerated α-syn aggregation and synaptic loss in cultured neurons. In vivo, complement C4 exacerbated motor dysfunction, dopaminergic neuronal loss, and α-syn pathology in α-syn PFF-injected mice. These findings reveal that complement C4 significantly contributes to the neuroinflammatory environment and α-syn pathology in PD, highlighting its potential as a therapeutic target for mitigating neurodegeneration in this disorder.

Inhibition of tRF- 02514 in Extracellular Vesicles Preserves Microglia Pyroptosis and Protects Against Parkinson's Disease

Extracellular vesicles (EVs), ubiquitous in peripheral blood and bodily fluids, are important regulators of neuronal communication, facilitating the intercellular transfer of bioactive molecules crucial for maintaining homeostasis. Uncovering EV-mediated mechanisms is pivotal for Parkinson's disease (PD) therapy. tRNA-derived fragments (tRFs) are a novel class of small non-coding RNAs found in EVs. They are essential for gene regulation, directly binding to target mRNAs to inhibit their translation, and hold promise as innovative therapeutic targets. We isolated EVs from the serum of patients with PD (PD-EVs) and co-cultured them with microglial cells to systematically investigate the modulation of inflammatory mediators and autophagy-related proteins. Small-RNA sequencing was performed to identify significantly differentially expressed target genes in PD-EVs. This analysis led to the identification of tRF-02514, whose associated molecular pathways were found to be involved in pyroptosis. Subsequently, the target genes of tRF-02514 were identified. To validate the findings in a physiological context, in vivo experiments were performed using mice with PD. Behavioral changes in mice were observed before and after the targeted inhibition of tRF-02514. Additionally, the whole brain tissue, substantia nigra, and peripheral blood samples of mice were collected to evaluate the expression of inflammatory factors, autophagy markers, pyroptosis-related proteins, and neuroprotective genes, including brain-derived neurotrophic factor (BDNF) and nerve growth factor (NGF), which are necessary for defense against neuronal damage. tRF-02514 promoted the release of inflammatory factors, induced pyroptosis in microglia, and accelerated neuronal loss in PD by targeting ATG5 and inhibiting autophagy. Inhibition of tRF-02514 effectively mitigated these detrimental effects, protecting neurons, promoting autophagy, and delaying the progression of PD. These findings offer valuable insights into the role of tRF-02514 in the pathogenesis of PD and highlight its potential as a therapeutic target for PD.

Recent Advances in Human Cerebrospinal Fluid Proteomics

Cerebrospinal fluid (CSF) proteomics has become an alternative that allows zooming-in where pathophysiological alterations are taking place, detecting protein mediators that might eventually be considered as potential biomarkers in neurological or psychiatric diseases. From a technological point of view, mass-spectrometry-based-proteomics as well as antibody/aptamer-based platforms allow the simultaneous monitoring of secreted and circulating proteins in a broad concentration range in a robust manner, generating innovative facets in biomarker validation. This chapter highlights recent discoveries in the field of CSF protein analysis, covering sample preparation methods for proteomic workflows, shotgun proteomics, subproteomics, and other omics applied to CSF.

Machine learning analysis of population-wide plasma proteins identifies hormonal biomarkers of Parkinson's Disease

As the number of Parkinson's patients is expected to increase with the growth of the aging population there is a growing need to identify new diagnostic markers that can be used cheaply and routinely to monitor the population, stratify patients towards treatment paths and provide new therapeutic leads. Genetic predisposition and familial forms account for only around 10% of PD cases [1] leaving a large fraction of the population with minimal effective markers for identifying high risk individuals. The establishment of population-wide omics and longitudinal health monitoring studies provides an opportunity to apply machine learning approaches on these unbiased cohorts to identify novel PD markers. Here we present the application of three machine learning models to identify protein plasma biomarkers of PD using plasma proteomics measurements from 43,408 UK Biobank subjects as the training and test set and an additional 103 samples from Parkinson's Progression Markers Initiative (PPMI) as external validation. We identified a group of highly predictive plasma protein markers including known markers such as DDC and CALB2 as well as new markers involved in the JAK-STAT, PI3K-AKT pathways and hormonal signaling. We further demonstrate that these features are well correlated with UPDRS severity scores and stratify these to protective and adversarial features that potentially contribute to the pathogenesis of PD.

Biological Function Analysis of MicroRNAs and Proteins in the Cerebrospinal Fluid of Patients with Parkinson's Disease

Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by alpha-synuclein aggregation into Lewy bodies in the neurons. Cerebrospinal fluid (CSF) is considered the most suited source for investigating PD pathogenesis and identifying biomarkers. While microRNA (miRNA) profiling can aid in the investigation of post-transcriptional regulation in neurodegenerative diseases, information on miRNAs in the CSF of patients with PD remains limited. This review combines miRNA analysis with proteomic profiling to explore the collective impact of CSF miRNAs on the neurodegenerative mechanisms in PD. We constructed separate networks for altered miRNAs and proteomes using a bioinformatics method. Altered miRNAs were poorly linked to biological functions owing to limited information; however, changes in protein expression were strongly associated with biological functions. Subsequently, the networks were integrated for further analysis. In silico prediction from the integrated network revealed relationships between miRNAs and proteins, highlighting increased reactive oxygen species generation, neuronal loss, and neurodegeneration and suppressed ATP synthesis, mitochondrial function, and neurotransmitter release in PD. The approach suggests the potential of miRNAs as biomarkers for critical mechanisms underlying PD. The combined strategy could enhance our understanding of the complex biochemical networks of miRNAs in PD and support the development of diagnostic and therapeutic strategies for precision medicine.

Integrative Metabolome and Proteome Analysis of Cerebrospinal Fluid in Parkinson's Disease

Parkinson's disease (PD) is a common neurodegenerative disorder characterized by the loss of dopaminergic neurons in the substantia nigra. Recent studies have highlighted the significant role of cerebrospinal fluid (CSF) in reflecting pathophysiological PD brain conditions by analyzing the components of CSF. Based on the published literature, we created a single network with altered metabolites in the CSF of patients with PD. We analyzed biological functions related to the transmembrane of mitochondria, respiration of mitochondria, neurodegeneration, and PD using a bioinformatics tool. As the proteome reflects phenotypes, we collected proteome data based on published papers, and the biological function of the single network showed similarities with that of the metabolomic network. Then, we analyzed the single network of integrated metabolome and proteome. In silico predictions based on the single network with integrated metabolomics and proteomics showed that neurodegeneration and PD were predicted to be activated. In contrast, mitochondrial transmembrane activity and respiration were predicted to be suppressed in the CSF of patients with PD. This review underscores the importance of integrated omics analyses in deciphering PD's complex biochemical networks underlying neurodegeneration.

The humoral immune landscape in Parkinson's disease: Unraveling antibody and B cell changes

Parkinson's disease (PD) is a complex neurodegenerative disorder characterized by the accumulation of α-synuclein (α-syn) in the brain and progressive loss of dopaminergic neurons in the substantia nigra (SN) region of the brain. Although the role of neuroinflammation and cellular immunity in PD has been extensively studied, the involvement of humoral immunity mediated by antibodies and B cells has received less attention. This article provides a comprehensive review of the current understanding of humoral immunity in PD. Here, we discuss alterations in B cells in PD, including changes in their number and phenotype. Evidence mostly indicates a decrease in the quantity of B cells in PD, accompanied by a shift in the population from naïve to memory cells. Furthermore, the existence of autoantibodies that target several antigens in PD has been investigated (i.e., anti-α-syn autoantibodies, anti-glial-derived antigen antibodies, anti-Tau antibodies, antineuromelanin antibodies, and antibodies against the renin-angiotensin system). Several autoantibodies are generated in PD, which may either provide protection or have harmful effects on disease progression. Furthermore, we have reviewed studies focusing on the utilization of antibodies as a potential treatment for PD, both in animal and clinical trials. This review sheds light on the intricate interplay between antibodies and the pathological processes in PD, including complement system activation.

Prediction of Future Parkinson Disease Using Plasma Proteins Combined With Clinical-Demographic Measures

BACKGROUND AND OBJECTIVES

Identification of individuals at high risk of developing Parkinson disease (PD) several years before diagnosis is crucial for developing treatments to prevent or delay neurodegeneration. This study aimed to develop predictive models for PD risk that combine plasma proteins and easily accessible clinical-demographic variables.

METHODS

Using data from the UK Biobank (UKB), which recruited participants across the United Kingdom, we conducted a longitudinal study to identify predictors for incident PD. Participants with baseline plasma proteins and no PD were included. Through machine learning, we narrowed down predictors from a pool of 1,463 plasma proteins and 93 clinical-demographic. These predictors were then externally validated using the Parkinson's Progression Marker Initiative (PPMI) cohort. To further investigate the temporal trends of predictors, a nested case-control study was conducted within the UKB.

RESULTS

A total of 52,503 participants without PD (median age 58, 54% female) were included. Over a median follow-up duration of 14.0 years, 751 individuals were diagnosed with PD (median age 65, 37% female). Using a forward selection approach, we selected a panel of 22 plasma proteins for optimal prediction. Using an ensemble tree-based Light Gradient Boosting Machine (LightGBM) algorithm, the model achieved an area under the receiver operating characteristic curve (AUC) of 0.800 (95% CI 0.785-0.815). The LightGBM prediction model integrating both plasma proteins and clinical-demographic variables demonstrated enhanced predictive accuracy, with an AUC of 0.832 (95% CI 0.815-0.849). Key predictors identified included age, years of education, history of traumatic brain injury, and serum creatinine. The incorporation of 11 plasma proteins (neurofilament light, integrin subunit alpha V, hematopoietic PGD synthase, histamine N-methyltransferase, tubulin polymerization promoting protein family member 3, ectodysplasin A2 receptor, Latexin, interleukin-13 receptor subunit alpha-1, BAG family molecular chaperone regulator 3, tryptophanyl-TRNA synthetase, and secretogranin-2) augmented the model's predictive accuracy. External validation in the PPMI cohort confirmed the model's reliability, producing an AUC of 0.810 (95% CI 0.740-0.873). Notably, alterations in these predictors were detectable several years before the diagnosis of PD.

DISCUSSION

Our findings support the potential utility of a machine learning-based model integrating clinical-demographic variables with plasma proteins to identify individuals at high risk for PD within the general population. Although these predictors have been validated by PPMI, additional validation in a more diverse population reflective of the general community is essential.

Proteogenomic network analysis reveals dysregulated mechanisms and potential mediators in Parkinson's disease

Parkinson's disease is highly heterogeneous across disease symptoms, clinical manifestations and progression trajectories, hampering the identification of therapeutic targets. Despite knowledge gleaned from genetics analysis, dysregulated proteome mechanisms stemming from genetic aberrations remain underexplored. In this study, we develop a three-phase system-level proteogenomic analytical framework to characterize disease-associated proteins and dysregulated mechanisms. Proteogenomic analysis identified 577 proteins that enrich for Parkinson's disease-related pathways, such as cytokine receptor interactions and lysosomal function. Converging lines of evidence identified nine proteins, including LGALS3, CSNK2A1, SMPD3, STX4, APOA2, PAFAH1B3, LDLR, HSPB1, BRK1, with potential roles in disease pathogenesis. This study leverages the largest population-scale proteomics dataset, the UK Biobank Pharma Proteomics Project, to characterize genetically-driven protein disturbances associated with Parkinson's disease. Taken together, our work contributes to better understanding of genome-proteome dynamics in Parkinson's disease and sets a paradigm to identify potential indirect mediators connected to GWAS signals for complex neurodegenerative disorders.

Neuroimaging and fluid biomarkers in Parkinson's disease in an era of targeted interventions

A major challenge in Parkinson's disease is the variability in symptoms and rates of progression, underpinned by heterogeneity of pathological processes. Biomarkers are urgently needed for accurate diagnosis, patient stratification, monitoring disease progression and precise treatment. These were previously lacking, but recently, novel imaging and fluid biomarkers have been developed. Here, we consider new imaging approaches showing sensitivity to brain tissue composition, and examine novel fluid biomarkers showing specificity for pathological processes, including seed amplification assays and extracellular vesicles. We reflect on these biomarkers in the context of new biological staging systems, and on emerging techniques currently in development.

Synergizing drug repurposing and target identification for neurodegenerative diseases

Despite dedicated research efforts, the absence of disease-curing remedies for neurodegenerative diseases (NDDs) continues to jeopardize human society and stands as a challenge. Drug repurposing is an attempt to find new functionality of existing drugs and take it as an opportunity to discourse the clinically unmet need to treat neurodegeneration. However, despite applying this approach to rediscover a drug, it can also be used to identify the target on which a drug could work. The primary objective of target identification is to unravel all the possibilities of detecting a new drug or repurposing an existing drug. Lately, scientists and researchers have been focusing on specific genes, a particular site in DNA, a protein, or a molecule that might be involved in the pathogenesis of the disease. However, the new era discusses directing the signaling mechanism involved in the disease progression, where receptors, ion channels, enzymes, and other carrier molecules play a huge role. This review aims to highlight how target identification can expedite the whole process of drug repurposing. Here, we first spot various target-identification methods and drug-repositioning studies, including drug-target and structure-based identification studies. Moreover, we emphasize various drug repurposing approaches in NDDs, namely, experimental-based, mechanism-based, and in silico approaches. Later, we draw attention to validation techniques and stress on drugs that are currently undergoing clinical trials in NDDs. Lastly, we underscore the future perspective of synergizing drug repurposing and target identification in NDDs and present an unresolved question to address the issue.

The complement system in neurodegenerative diseases

Complement is an important component of innate immune defence against pathogens and crucial for efficient immune complex disposal. These core protective activities are dependent in large part on properly regulated complement-mediated inflammation. Dysregulated complement activation, often driven by persistence of activating triggers, is a cause of pathological inflammation in numerous diseases, including neurological diseases. Increasingly, this has become apparent not only in well-recognized neuroinflammatory diseases like multiple sclerosis but also in neurodegenerative and neuropsychiatric diseases where inflammation was previously either ignored or dismissed as a secondary event. There is now a large and rapidly growing body of evidence implicating complement in neurological diseases that cannot be comprehensively addressed in a brief review. Here, we will focus on neurodegenerative diseases, including not only the 'classical' neurodegenerative diseases such as Alzheimer's disease and Parkinson's disease, but also two other neurological diseases where neurodegeneration is a neglected feature and complement is implicated, namely, schizophrenia, a neurodevelopmental disorder with many mechanistic features of neurodegeneration, and multiple sclerosis, a demyelinating disorder where neurodegeneration is a major cause of progressive decline. We will discuss the evidence implicating complement as a driver of pathology in these diverse diseases and address briefly the potential and pitfalls of anti-complement drug therapy for neurodegenerative diseases.

Cerebrospinal fluid protein biomarkers in Parkinson's disease

Proteomic profiling is an effective way to identify biomarkers for Parkinson's disease (PD). Cerebrospinal fluid (CSF) has direct connectivity with the brain and could be a source of finding biomarkers and their clinical implications. Comparative proteomic profiling has shown that a group of differentially displayed proteins exist. The studies performed using conventional and classical tools also supported the occurrence of these proteins. Many studies have highlighted the potential of CSF proteomic profiling for biomarker identification and their clinical applications. Some of these proteins are useful for disease diagnosis and prediction. Proteomic profiling of CSF also has immense potential to distinguish PD from similar neurodegenerative disorders. A few protein biomarkers help in fundamental knowledge generation and clinical interpretation. However, the specific biomarker of PD is not yet known. The use of proteomic approaches in clinical settings is also rare. A large-scale, multi-centric, multi-population and multi-continental study using multiple proteomic tools is warranted. Such a study can provide valuable, comprehensive and reliable information for a better understanding of PD and the development of specific biomarkers. The current article sheds light on the role of CSF proteomic profiling in identifying biomarkers of PD and their clinical implications. The article also explains the achievements, obstacles and hopes for future directions of this approach.

High-resolution omics of vascular ageing and inflammatory pathways in neurodegeneration

High-resolution omics, particularly single-cell and spatial transcriptomic profiling, are rapidly enhancing our comprehension of the normal molecular diversity of gliovascular cells, as well as their age-related changes that contribute to neurodegeneration. With more omic profiling studies being conducted, it is becoming increasingly essential to synthesise valuable information from the rapidly accumulating findings. In this review, we present an overview of the molecular features of neurovascular and glial cells that have been recently discovered through omic profiling, with a focus on those that have potentially significant functional implications and/or show cross-species differences between human and mouse, and that are linked to vascular deficits and inflammatory pathways in ageing and neurodegenerative disorders. Additionally, we highlight the translational applications of omic profiling, and discuss omic-based strategies to accelerate biomarker discovery and facilitate disease course-modifying therapeutics development for neurodegenerative conditions.

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.

Mapping the human brain proteome: opportunities, challenges, and clinical potential

INTRODUCTION

Due to the segmented functions and complexity of the human brain, the characterization of molecular profiles within specific areas such as brain structures and biofluids is essential to unveil the molecular basis for structure specialization as well as the molecular imbalance associated with neurodegenerative and psychiatric diseases.

AREAS COVERED

Much of our knowledge about brain functionality derives from neurophysiological, anatomical, and transcriptomic approaches. More recently, laser capture and imaging proteomics, technological and computational developments in LC-MS/MS, as well as antibody/aptamer-based platforms have allowed the generation of novel cellular, spatial, and posttranslational dimensions as well as innovative facets in biomarker validation and druggable target identification.

EXPERT OPINION

Proteomics is a powerful toolbox to functionally characterize, quantify, and localize the extensive protein catalog of the human brain across physiological and pathological states. Brain function depends on multi-dimensional protein homeostasis, and its elucidation will help us to characterize biological pathways that are essential to properly maintain cognitive functions. In addition, comprehensive human brain pathological proteomes may be the basis in computational drug-repositioning methods as a strategy for unveiling potential new therapies in neurodegenerative and psychiatric disorders.

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

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

Inflammatory Blood Biomarkers Are Associated with Long-Term Clinical Disease Severity in Parkinson's Disease

An altered immune response has been identified as a pathophysiological factor in Parkinson's disease (PD). We aimed to identify blood immunity-associated proteins that discriminate PD from controls and that are associated with long-term disease severity in PD patients. Immune response-derived proteins in blood plasma were measured using Proximity Extension Technology by OLINK in a cohort of PD patients (N = 66) and age-matched healthy controls (N = 52). In a selection of 30 PD patients, we evaluated changes in protein levels 7-10 years after the baseline and assessed correlations with motor and cognitive assessments. Data from the Parkinson's Disease Biomarkers Program (PDBP) cohort and the Parkinson's Progression Markers Initiative (PPMI) cohort were used for independent validation. PD patients showed an altered immune response compared to controls based on a panel of four proteins (IL-12B, OPG, CXCL11, and CSF-1). The expression levels of five inflammation-associated proteins (CCL23, CCL25, TNFRSF9, TGF-alpha, and VEGFA) increased over time in PD and were partially associated with more severe motor and cognitive symptoms at follow-up. Increased CCL23 levels were associated with cognitive decline and the APOE4 genotype. Our findings provide further evidence for an altered immune response in PD that is associated with disease severity in PD over a long period of time.