Identification of cerebrospinal fluid biomarkers for parkinsonism using a proteomics approach

Mass Spectrometry-Based Proteomics of Poly(methylmethacrylate)-Embedded Bone

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is a widely employed technique in proteomics research for studying the proteome biology of various clinical samples. Hard tissues, such as bone and teeth, are routinely preserved using synthetic poly(methyl methacrylate) (PMMA) embedding resins that enable histological, immunohistochemical, and morphological examination. However, the suitability of PMMA-embedded hard tissues for large-scale proteomic analysis remained unexplored. This study is the first to report on the feasibility of PMMA-embedded bone samples for LC-MS/MS analysis. Conventional workflows yielded merely limited coverage of the bone proteome. Using advanced strategies of prefractionation by high-pH reversed-phase liquid chromatography in combination with isobaric tandem mass tag labeling resulted in proteome coverage exceeding 1000 protein identifications. The quantitative comparison with cryopreserved samples revealed that each sample preparation workflow had a distinct impact on the proteomic profile. However, workflow replicates exhibited a high reproducibility for PMMA-embedded samples. Our findings further demonstrate that decalcification prior to protein extraction, along with the analysis of solubilization fractions, is not preferred for PMMA-embedded bone. The biological applicability of the proposed workflow was demonstrated using samples of human PMMA-embedded alveolar bone and the iliac crest, which revealed anatomical site-specific proteomic profiles. Overall, these results establish a crucial foundation for large-scale proteomics studies contributing to our knowledge of bone biology.

The potential of phosphorylated α-synuclein as a biomarker for the diagnosis and monitoring of multiple system atrophy

INTRODUCTION

Multiple system atrophy (MSA) is a rapidly progressive neurodegenerative disorder characterized by the presence of glial cytoplasmic inclusions (GCIs) containing aggregated α-synuclein (α-Syn). Accurate diagnosis and monitoring of MSA present significant challenges, which can lead to potential misdiagnosis and inappropriate treatment. Biomarkers play a crucial role in improving the accuracy of MSA diagnosis, and phosphorylated α-synuclein (p-syn) has emerged as a promising biomarker for aiding in diagnosis and disease monitoring.

METHODS

A literature search was conducted on PubMed, Scopus, and Google Scholar using specific keywords and MeSH terms without imposing a time limit. Inclusion criteria comprised various study designs including experimental studies, case-control studies, and cohort studies published only in English, while conference abstracts and unpublished sources were excluded.

RESULTS

Increased levels of p-syn have been observed in various samples from MSA patients, such as red blood cells, cerebrospinal fluid, oral mucosal cells, skin, and colon biopsies, highlighting their diagnostic potential. The α-Syn RT-QuIC assay has shown sensitivity in diagnosing MSA and tracking its progression. Meta-analyses and multicenter investigations have confirmed the diagnostic value of p-syn in cerebrospinal fluid, demonstrating high specificity and sensitivity in distinguishing MSA from other neurodegenerative diseases. Moreover, combining p-syn with other biomarkers has further improved the diagnostic accuracy of MSA.

CONCLUSION

The p-syn stands out as a promising biomarker for MSA. It is found in oligodendrocytes and shows a correlation with disease severity and progression. However, further research and validation studies are necessary to establish p-syn as a reliable biomarker for MSA. If proven, p-syn could significantly contribute to early diagnosis, disease monitoring, and assessing treatment response.

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.

Proteomic profiling of cerebrospinal fluid in pediatric myelin oligodendrocyte glycoprotein antibody-associated disease

BACKGROUND

Myelin oligodendrocyte glycoprotein (MOG) antibody-associated disease (MOGAD) is an autoimmune demyelinating disorder of the central nervous system.

METHODS

Extracted proteins from 34 cerebrospinal fluid (CSF) samples [patients with MOGAD (MOG group, n = 12); healthy controls (HC group, n = 12); patients with MOG seronegative and metagenomics next-generation sequencing-negative inflammatory neurological diseases (IND group, n = 10)] were processed and subjected to label-free quantitative proteomics. Supervised partial least squares-discriminant analysis (PLS-DA) and orthogonal PLS-DA (O-PLS-DA) models were also performed based on proteomics data. Functional analysis of differentially expressed proteins (DEPs) was performed using Gene Ontology, InterPro, and Kyoto Encyclopedia Genes and Genomes. An enzyme-linked immunosorbent assay was used to determine the complement levels in serum from patients with MOGAD.

RESULTS

Four hundred and twenty-nine DEPs (149 upregulated and 280 downregulated proteins) were identified in the MOG group compared to the HC group according to the P value and fold change (FC). Using the O-PLS-DA model, 872 differentially abundant proteins were identified with variable importance projection (VIP) scores > 1. Five proteins (gamma-glutamyl hydrolase, cathepsin F, interalpha-trypsin inhibitor heavy chain 5, latent transforming growth factor beta-binding protein 4 and leukocyte-associated immunoglobulin-like receptor 1) overlapping between the top 30 DEPs with top-ranked P value and FC and top 30 proteins in PLS-DA VIP lists were acquired. Functional analysis revealed that the dysregulated proteins in the MOG group were primarily involved in complement and coagulation cascades, cell adhesion, axon guidance, and glycosphingolipid biosynthesis compared to the HC group.

CONCLUSION

The proteomic alterations in CSF samples from children with MOGAD identified in the current study might provide opportunities for developing novel biomarker candidates.

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.

High-Throughput CSF Proteomics and Machine Learning to Identify Proteomic Signatures for Parkinson Disease Development and Progression

BACKGROUND AND OBJECTIVES

This study aimed to identify CSF proteomic signatures characteristic of Parkinson disease (PD) and evaluate their clinical utility.

METHODS

This observational study used data from the Parkinson's Progression Markers Initiative (PPMI), which enrolled patients with PD, healthy controls (HCs), and non-PD participants carrying GBA1, LRRK2, and/or SNCA pathogenic variants (genetic prodromals) at international sites. Study participants were chosen from PPMI enrollees based on the availability of aptamer-based CSF proteomic data, quantifying 4,071 proteins, and classified as patients with PD without GBA1, LRRK2, and/or SNCA pathogenic variants (nongenetic PD), HCs, patients with PD carrying the aforementioned pathogenic variants (genetic PD), or genetic prodromals. Differentially expressed protein (DEP) analysis and the least absolute shrinkage and selection operator (LASSO) were applied to the data from nongenetic PD and HCs. Signatures characteristics of nongenetic PD were quantified as a PD proteomic score (PD-ProS), validated internally and then externally using data of 1,556 CSF proteins from the LRRK2 Cohort Consortium (LCC). We further tested the PD-ProS in genetic PD and genetic prodromals and examined associations with clinical progression.

RESULTS

Data from 279 patients with nongenetic PD (mean ± SD, age 62.0 ± 9.6 years; male 67.7%) and 141 HCs (age 60.5 ± 11.9 years; male 64.5%) were used for PD-ProS derivation. From 23 DEPs, LASSO determined weights of 14 DEPs for the PD-ProS (area under the curve [AUC] 0.83, 95% CI 0.78-0.87), validated in an independent internal validation cohort of 71 patients with nongenetic PD and 35 HCs (AUC 0.81, 95% CI 0.73-0.90). In the LCC, only 5 of the 14 DEPs were also measured. Notably, these 5 DEPs still distinguished 34 patients with nongenetic PD from 31 HCs with the same weights (AUC 0.75, 95% CI 0.63-0.87). Furthermore, the PD-ProS distinguished 258 patients with genetic PD from 365 genetic prodromals. Finally, regardless of genetic status, the PD-ProS independently predicted both cognitive and motor decline in PD (dementia, adjusted hazard ratio in the highest quintile [aHR-Q5] 2.8 [95% CI 1.6-5.0]; Hoehn and Yahr stage IV, aHR-Q5 2.1 [95% CI 1.1-4.0]).

DISCUSSION

By integrating high-throughput proteomics with machine learning, we identified PD-associated CSF proteomic signatures crucial for PD development and progression.

TRIAL REGISTRATION INFORMATION

ClinicalTrials.gov (NCT01176565). A link to the trial registry page is clinicaltrials.gov/ct2/show/NCT01141023.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that the CSF proteome contains clinically important information regarding the development and progression of Parkinson disease that can be deciphered by a combination of high-throughput proteomics and machine learning.

Plasma Metabolic Analysis Reveals the Dysregulation of Short-Chain Fatty Acid Metabolism in Parkinson's Disease

Parkinson's disease (PD) is a progressive neurodegenerative disorder, characterized by high morbidity, high disability rate, and slow course of disease. The clinical diagnostic method of PD is complex and time-consuming, and there is no clear biomarker for clinical use. We aimed to investigate the plasma metabolites in PD and find out potential biomarkers with diagnostic ability. In the analysis of more than 40 metabolites including short-chain fatty acids, long-chain fatty acids, amino acids, and carbohydrates, the difference of short-chain fatty acids was observed. Acetic acid concentration was higher in PD than in healthy controls, and propanoic acid and 2,3,4-trihydroxybutyric acid were lower in PD. Compared with the early stage of PD, acetic acid increased significantly in the advanced stage of PD. Propanoic acid increased significantly in medicated PD compared with drug naïve PD. ROC analysis revealed acetic acid discriminated PD from healthy controls with 100% sensitivity, 88.9% specificity, and an area under the curve (AUC) of 0.981, and propanoic acid discriminated PD from healthy controls with an AUC of 0.981, 100% sensitivity, and 94.4% specificity. Acetic acid and propanoic acid may be a potential biomarker for differentiating PD from health, and the propanoic acid was evaluated as the most potential diagnostic marker because of its extremely high sensitivity and specificity.

A proteogenomic view of Parkinson's disease causality and heterogeneity

The pathogenesis and clinical heterogeneity of Parkinson's disease (PD) have been evaluated from molecular, pathophysiological, and clinical perspectives. High-throughput proteomic analysis of cerebrospinal fluid (CSF) opened new opportunities for scrutinizing this heterogeneity. To date, this is the most comprehensive CSF-based proteomics profiling study in PD with 569 patients (350 idiopathic patients, 65 GBA + mutation carriers and 154 LRRK2 + mutation carriers), 534 controls, and 4135 proteins analyzed. Combining CSF aptamer-based proteomics with genetics we determined protein quantitative trait loci (pQTLs). Analyses of pQTLs together with summary statistics from the largest PD genome wide association study (GWAS) identified 68 potential causal proteins by Mendelian randomization. The top causal protein, GPNMB, was previously reported to be upregulated in the substantia nigra of PD patients. We also compared the CSF proteomes of patients and controls. Proteome differences between GBA + patients and unaffected GBA + controls suggest degeneration of dopaminergic neurons, altered dopamine metabolism and increased brain inflammation. In the LRRK2 + subcohort we found dysregulated lysosomal degradation, altered alpha-synuclein processing, and neurotransmission. Proteome differences between idiopathic patients and controls suggest increased neuroinflammation, mitochondrial dysfunction/oxidative stress, altered iron metabolism and potential neuroprotection mediated by vasoactive substances. Finally, we used proteomic data to stratify idiopathic patients into "endotypes". The identified endotypes show differences in cognitive and motor disease progression based on previously reported protein-based risk scores.Our findings not only contribute to the identification of new therapeutic targets but also to shape personalized medicine in CNS neurodegeneration.

Temporal proteomics of human cerebrospinal fluid after severe traumatic brain injury

The pathophysiology of traumatic brain injury (TBI) requires further characterization to fully elucidate changes in molecular pathways. Cerebrospinal fluid (CSF) provides a rich repository of brain-associated proteins. In this retrospective observational study, we implemented high-resolution mass spectrometry to evaluate changes to the CSF proteome after severe TBI. 91 CSF samples were analyzed with mass spectrometry, collected from 16 patients with severe TBI (mean 32 yrs; 81% male) on day 0, 1, 2, 4, 7 and/or 10 post-injury (8-16 samples/timepoint) and compared to CSF obtained from 11 non-injured controls. We quantified 1152 proteins with mass spectrometry, of which approximately 80% were associated with CSF. 1083 proteins were differentially regulated after TBI compared to control samples. The most highly-upregulated proteins at each timepoint included neutrophil elastase, myeloperoxidase, cathepsin G, matrix metalloproteinase-8, and S100 calcium-binding proteins A8, A9 and A12-all proteins involved in neutrophil activation, recruitment, and degranulation. Pathway enrichment analysis confirmed the robust upregulation of proteins associated with innate immune responses. Conversely, downregulated pathways included those involved in nervous system development, and several proteins not previously identified after TBI such as testican-1 and latrophilin-1. We also identified 7 proteins (GM2A, Calsyntenin 1, FAT2, GANAB, Lumican, NPTX1, SFRP2) positively associated with an unfavorable outcome at 6 months post-injury. Together, these findings highlight the robust innate immune response that occurs after severe TBI, supporting future studies to target neutrophil-related processes. In addition, the novel proteins we identified to be differentially regulated by severe TBI warrant further investigation as potential biomarkers of brain damage or therapeutic targets.

Opportunities and challenges of alpha-synuclein as a potential biomarker for Parkinson's disease and other synucleinopathies

Parkinson’s disease (PD), the second most common progressive neurodegenerative disease, develops and progresses for 10–15 years before the clinical diagnostic symptoms of the disease are manifested. Furthermore, several aspects of PD pathology overlap with other neurodegenerative diseases (NDDs) linked to alpha-synuclein (aSyn) aggregation, also called synucleinopathies. Therefore, there is an urgent need to discover and validate early diagnostic and prognostic markers that reflect disease pathophysiology, progression, severity, and potential differences in disease mechanisms between PD and other NDDs. The close association between aSyn and the development of pathology in synucleinopathies, along with the identification of aSyn species in biological fluids, has led to increasing interest in aSyn species as potential biomarkers for early diagnosis of PD and differentiate it from other synucleinopathies. In this review, we (1) provide an overview of the progress toward mapping the distribution of aSyn species in the brain, peripheral tissues, and biological fluids; (2) present comparative and critical analysis of previous studies that measured total aSyn as well as other species such as modified and aggregated forms of aSyn in different biological fluids; and (3) highlight conceptual and technical gaps and challenges that could hinder the development and validation of reliable aSyn biomarkers; and (4) outline a series of recommendations to address these challenges. Finally, we propose a combined biomarker approach based on integrating biochemical, aggregation and structure features of aSyn, in addition to other biomarkers of neurodegeneration. We believe that capturing the diversity of aSyn species is essential to develop robust assays and diagnostics for early detection, patient stratification, monitoring of disease progression, and differentiation between synucleinopathies. This could transform clinical trial design and implementation, accelerate the development of new therapies, and improve clinical decisions and treatment strategies.

A Species-Specific Strategy for the Identification of Hemocoagulase Agkistrodon halys pallas Based on LC-MS/MS-MRM

Hemocoagulase Agkistrodon halys pallas is a complex mixture composed of snake venom thrombin-like enzymes (svTLEs) and small amounts of thrombokinase-like enzymes. It has been widely used as a hemostatic with rapidly growing marketing due to its advantage of localized clotting fibrinogen other than systemic coagulation. However, svTLEs from different species have various structures, functions, and hemostatic mechanisms. To ensure the efficacy and safety of Hemocoagulase Agkistrodon halys pallas, an exclusive and sensitive method has been developed to identify specific marker peptides based on liquid chromatography-tandem mass spectrometry with multiple reaction monitoring (LC-MS/MS-MRM) mode. By combining transcriptomics and proteomics, a series of species-specific peptides of Agkistrodon halys pallas were predicted and examined by LC-MS/MS. After reduction, alkylation, and tryptic digestion were performed on Hemocoagulase Agkistrodon halys pallas, a target peptide TLCAGVMEGGIDTCNR was analyzed by LC-MS/MS-MRM. It offers a new and effective approach for the quality control of Hemocoagulase Agkistrodon halys pallas products. This method is superior to the current assays in terms of sensitivity, specificity, precision, accuracy, and throughput. The strategy can also be applied in studying other important protein-based medicines.

Cerebrospinal Fluid Metabolome in Parkinson's Disease and Multiple System Atrophy

Parkinson’s disease (PD) and multiple system atrophy (MSA) belong to the neurodegenerative group of synucleinopathies; differential diagnosis between PD and MSA is difficult, especially at early stages, owing to their clinical and biological similarities. Thus, there is a pressing need to identify metabolic biomarkers for these diseases. The metabolic profile of the cerebrospinal fluid (CSF) is reported to be altered in PD and MSA; however, the altered metabolites remain unclear. We created a single network with altered metabolites in PD and MSA based on the literature and assessed biological functions, including metabolic disorders of the nervous system, inflammation, concentration of ATP, and neurological disorder, through bioinformatics methods. Our in-silico prediction-based metabolic networks are consistent with Parkinsonism events. Although metabolomics approaches provide a more quantitative understanding of biochemical events underlying the symptoms of PD and MSA, limitations persist in covering molecules related to neurodegenerative disease pathways. Thus, omics data, such as proteomics and microRNA, help understand the altered metabolomes mechanism. In particular, integrated omics and machine learning approaches will be helpful to elucidate the pathological mechanisms of PD and MSA. This review discusses the altered metabolites between PD and MSA in the CSF and omics approaches to discover diagnostic biomarkers.