Shotgun-based proteomics of extracellular vesicles in Alzheimer's disease reveals biomarkers involved in immunological and coagulation pathways

Proteomics profiling of extracellular vesicle for identification of potential biomarkers in Alzheimer's disease: A comprehensive review

The intricate origins and diverse symptoms of Alzheimer's disease (AD) pose significant challenges for both diagnosis and treatment. Exosomes and microvesicles, which carry disease-specific cargo from a variety of central nervous system cell types, have emerged as promising reservoirs of biomarkers for AD. Research on the screening of possible biomarkers in Alzheimer's disease using proteomic profiling of EVs is systematically reviewed in this comprehensive review. We highlight key methodologies employed in EV isolation, characterization, and proteomic analysis, elucidating their advantages and limitations. Furthermore, we summarize the evolving landscape of EV-associated biomarkers implicated in AD pathogenesis, including proteins involved in amyloid-beta metabolism, tau phosphorylation, neuroinflammation, synaptic dysfunction, and neuronal injury. The literature review highlights the necessity for robust validation strategies and standardized protocols to effectively transition EV-based biomarkers into clinical use. In the concluding section, this review delves into potential future avenues and technological advancements pivotal in crafting EV-derived biomarkers applicable to AD diagnostics and prognostics. This review contributes to our comprehension of AD pathology and the advancement of precision medicine in neurodegenerative diseases, hinting at a promising era in AD precision medicine.

Extracellular vesicles-based point-of-care testing for the diagnosis and monitoring of Alzheimer's disease

Alzheimer's disease (AD) is a debilitating condition that affects millions of people worldwide. One promising strategy for detecting and monitoring AD early on is using extracellular vesicles (EVs)-based point-of-care testing; however, diagnosing AD using EVs poses a challenge due to the low abundance of EV-biomarkers. Here, we present a fully integrated organic electrochemical transistor (OECT) that enables high accuracy, speed, and convenience in the detection of EVs from AD patients. We incorporated self-aligned acoustoelectric enhancement of EVs on a chip that rapidly propels, enriches, and specifically binds EVs to the OECT detection area. With our enhancement of pre-concentration, we increased the sensitivity to a limit of detection of 500 EV particles/μL and reduced the required detection time to just two minutes. We also tested the sensor on an AD mouse model to monitor AD progression, examined mouse Aβ EVs at different time courses, and compared them with intraneuronal Aβ cumulation using MRI. This innovative technology has the potential to diagnose Alzheimer's and other neurodegenerative diseases accurately and quickly, enabling monitoring of disease progression and treatment response.

Identification of differentially expressed exosome proteins in serum as potential biomarkers for cognitive impairments in cerebral small vessel disease

BACKGROUND

Cognitive impairment arising from cerebral small vessel disease (CSVD) represents a critical subtype of vascular cognitive impairments (VCI) and is the primary cause of vascular dementia. However, identifying reliable clinical and laboratory indicators for this disease remain elusive. We hypothesize that plasma exosome proteins hold the potential to serve as biomarkers for the onset of cognitive dysfunction associated with cerebrovascular diseases.

METHODS

We employed TMT-based proteomics to discern variations in serum exosome proteomes between individuals with cognitive impairments due to CSVD and healthy volunteers.

RESULTS

Each group comprised 18 subjects, and through differential expression analysis, we identified 22 down-regulated and 8 up-regulated proteins between the two groups. Our research revealed 30 differentially expressed plasma exosome proteins, including histone, proteasome, clusterin and coagulation factor XIII, in individuals with cognitive impairments caused by CSVD.

CONCLUSION

The 30 differentially expressed plasma exosome proteins identified in our study are promising as biomarkers for diagnosing cognitive impairments resulting from CSVD. These findings may help us better understand the underlying pathological mechanisms involved in the diseases.

Phosphoproteome Microarray Analysis of Extracellular Particles as a Tool to Explore Novel Biomarker Candidates for Alzheimer's Disease

Phosphorylation plays a key role in Alzheimer's disease (AD) pathogenesis, impacting distinct processes such as amyloid-beta (Aβ) peptide production and tau phosphorylation. Impaired phosphorylation events contribute to senile plaques and neurofibrillary tangles' formation, two major histopathological hallmarks of AD. Blood-derived extracellular particles (bdEP) can represent a disease-related source of phosphobiomarker candidates, and hence, in this pilot study, bdEP of Control and AD cases were analyzed by a targeted phosphoproteomics approach using a high-density microarray that featured at least 1145 pan-specific and 913 phosphosite-specific antibodies. This approach, innovatively applied to bdEP, allowed the identification of 150 proteins whose expression levels and/or phosphorylation patterns were significantly altered across AD cases. Gene Ontology enrichment and Reactome pathway analysis unraveled potentially relevant molecular targets and disease-associated pathways, and protein-protein interaction networks were constructed to highlight key targets. The discriminatory value of both the total proteome and the phosphoproteome was evaluated by univariate and multivariate approaches. This pilot experiment supports that bdEP are enriched in phosphotargets relevant in an AD context, holding value as peripheral biomarker candidates for disease diagnosis.

Music compensates for altered gene expression in age-related cognitive disorders

Extensive literature has explored the beneficial effects of music in age-related cognitive disorders (ACD), but limited knowledge exists regarding its impact on gene expression. We analyzed transcriptomes of ACD patients and healthy controls, pre-post a music session (n = 60), and main genes/pathways were compared to those dysregulated in mild cognitive impairment (MCI) and Alzheimer's disease (AD) as revealed by a multi-cohort study (n = 1269 MCI/AD and controls). Music was associated with 2.3 times more whole-genome gene expression, particularly on neurodegeneration-related genes, in ACD than in controls. Co-expressed gene-modules and pathways analysis demonstrated that music impacted autophagy, vesicle and endosome organization, biological processes commonly dysregulated in MCI/AD. Notably, the data indicated a strong negative correlation between musically-modified genes/pathways in ACD and those dysregulated in MCI/AD. These findings highlight the compensatory effect of music on genes/biological processes affected in MCI/AD, providing insights into the molecular mechanisms underlying the benefits of music on these disorders.

Identification of gene-level methylation for disease prediction

DNA methylation is an epigenetic alteration that plays a fundamental part in governing gene regulatory processes. The DNA methylation mechanism affixes methyl groups to distinct cytosine residues, influencing chromatin architectures. Multiple studies have demonstrated that DNA methylation's regulatory effect on genes is linked to the beginning and progression of several disorders. Researchers have recently uncovered thousands of phenotype-related methylation sites through the epigenome-wide association study (EWAS). However, combining the methylation levels of several sites within a gene and determining the gene-level DNA methylation remains challenging. In this study, we proposed the supervised UMAP Assisted Gene-level Methylation method (sUAGM) for disease prediction based on supervised UMAP (Uniform Manifold Approximation and Projection), a manifold learning-based method for reducing dimensionality. The methylation values at the gene level generated using the proposed method are evaluated by employing various feature selection and classification algorithms on three distinct DNA methylation datasets derived from blood samples. The performance has been assessed employing classification accuracy, F-1 score, Mathews Correlation Coefficient (MCC), Kappa, Classification Success Index (CSI) and Jaccard Index. The Support Vector Machine with the linear kernel (SVML) classifier with Recursive Feature Elimination (RFE) performs best across all three datasets. From comparative analysis, our method outperformed existing gene-level and site-level approaches by achieving 100% accuracy and F1-score with fewer genes. The functional analysis of the top 28 genes selected from the Parkinson's disease dataset revealed a significant association with the disease.

One-Pot Analytical Pipeline for Efficient and Sensitive Proteomic Analysis of Extracellular Vesicles

Extracellular vesicle (EV) proteomics emerges as an effective tool for discovering potential biomarkers for disease diagnosis, monitoring, and therapeutics. However, the current workflow of mass spectrometry-based EV proteome analysis is not fully compatible in a clinical setting due to inefficient EV isolation methods and a tedious sample preparation process. To streamline and improve the efficiency of EV proteome analysis, here we introduce a one-pot analytical pipeline integrating a robust EV isolation approach, EV total recovery and purification (EVtrap), with in situ protein sample preparation, to detect urinary EV proteome. By incorporating solvent-driven protein capture and fast on-bead digestion, the one-pot pipeline enabled the whole EV proteome analysis to be completed within one day. In comparison with the existing workflow, the one-pot pipeline was able to obtain better peptide yield and identify the equivalent number of unique EV proteins from 1 mL of urine. Finally, we applied the one-pot pipeline to profile proteomes in urinary EVs of bladder cancer patients. A total of 2774 unique proteins were identified in 53 urine samples using a 15 min gradient library-free data-independent acquisition method. Taken altogether, our novel one-pot analytical pipeline demonstrated its potential for routine and robust EV proteomics in biomedical applications.

Serum retinol-binding protein 4 in stroke patients: correlation with T helper 17/regulatory T cell imbalance and 3-year cognitive function decline

Objective

Retinol-binding protein 4 (RBP4) promotes atherosclerotic progression and neuronal loss, whereas its association with cognitive impairment in stroke is unclear. Hence, this prospective study aimed to explore the association of serum RBP4 with the T helper (Th)17/regulatory T (Treg) cell ratio and its correlation with cognitive impairment in stroke patients.

Methods

Peripheral blood samples from 265 stroke patients and 50 healthy controls (HCs) were collected at enrollment for serum RBP4 (by enzyme-linked immunosorbent assay) and Th17 and Treg cells (by flow cytometry) determination. Additionally, stroke patients underwent routine follow-ups, and their Mini-Mental State Examination (MMSE) scores were assessed at baseline and in years 1, 2, and 3 after enrollment.

Results

Serum RBP4 was elevated in stroke patients compared to HCs (p < 0.001), with a good ability to differentiate stroke patients from HCs (area under the curve: 0.815). Serum RBP4 was positively associated with Th17 cells (p < 0.001) and the Th17/Treg cell ratio (p < 0.001) and negatively associated with Treg cells (p = 0.003) in stroke patients, whereas it was only positively associated with the Th17/Treg cell ratio (p = 0.027) but not with Th17 (p = 0.075) or Treg (p = 0.130) cells in HCs. Furthermore, increased serum RBP4 was associated with a lower MMSE score (p < 0.001) and a lower incidence of cognition impairment (p = 0.005) at enrollment in stroke patients, as were Th17 cells and the Th17/Treg cell ratio (all p < 0.050). The 1-, 2-, and 3-year MMSE scores in stroke patients were 25.9 ± 2.0, 25.3 ± 2.3, and 24.9 ± 2.3, respectively. More importantly, serum RBP4 was negatively correlated with 1-, 2-, and 3-year MMSE scores (all p < 0.001) and positively associated with 1-year (p = 0.013), 2-year (p = 0.007), and 3-year (p = 0.001) MMSE score declines in stroke patients.

Conclusion

Serum RBP4 is positively associated with a Th17/Treg cell imbalance and, more importantly, it is indicative of cognitive function decline within 3 years in stroke patients. Thus, early and timely interventions and physical rehabilitation are more necessary in stroke patients with high serum RBP4.

Cerebrospinal Fluid Protein Biomarker Discovery in CLN3

Syndromic CLN3-Batten is a fatal, pediatric, neurodegenerative disease caused by variants in CLN3, which encodes the endolysosomal transmembrane CLN3 protein. No approved treatment for CLN3 is currently available. The protracted and asynchronous disease presentation complicates the evaluation of potential therapies using clinical disease progression parameters. Biomarkers as surrogates to measure the progression and effect of potential therapeutics are needed. We performed proteomic discovery studies using cerebrospinal fluid (CSF) samples from 28 CLN3-affected and 32 age-similar non-CLN3 individuals. Proximal extension assay (PEA) of 1467 proteins and untargeted data-dependent mass spectrometry [MS; MassIVE FTP server (ftp://MSV000090147@massive.ucsd.edu)] were used to generate orthogonal lists of protein marker candidates. At an adjusted p-value of <0.1 and threshold CLN3/non-CLN3 fold-change ratio of 1.5, PEA identified 54 and MS identified 233 candidate biomarkers. Some of these (NEFL, CHIT1) have been previously linked with other neurologic conditions. Others (CLPS, FAM217B, QRICH2, KRT16, ZNF333) appear to be novel. Both methods identified 25 candidate biomarkers, including CHIT1, NELL1, and ISLR2 which had absolute fold-change ratios >2. NELL1 and ISLR2 regulate axonal development in neurons and are intriguing new candidates for further investigation in CLN3. In addition to identifying candidate proteins for CLN3 research, this study provides a comparison of two large-scale proteomic discovery methods in CSF.

Role of Exosomes in the Pathogenesis and Theranostic of Alzheimer's Disease and Parkinson's Disease

Alzheimer's disease (AD) and Parkinson's disease (PD) are the most common neurodegenerative diseases (NDDs) threatening the lives of millions of people worldwide, including especially elderly people. Currently, due to the lack of a timely diagnosis and proper intervention strategy, AD and PD largely remain incurable. Innovative diagnosis and therapy are highly desired. Exosomes are small vesicles that are present in various bodily fluids, which contain proteins, nucleic acids, and active biomolecules, and which play a crucial role especially in intercellular communication. In recent years, the role of exosomes in the pathogenesis, early diagnosis, and treatment of diseases has attracted ascending attention. However, the exact role of exosomes in the pathogenesis and theragnostic of AD and PD has not been fully illustrated. In the present review, we first introduce the biogenesis, components, uptake, and function of exosomes. Then we elaborate on the involvement of exosomes in the pathogenesis of AD and PD. Moreover, the application of exosomes in the diagnosis and therapeutics of AD and PD is also summarized and discussed. Additionally, exosomes serving as drug carriers to deliver medications to the central nervous system are specifically addressed. The potential role of exosomes in AD and PD is explored, discussing their applications in diagnosis and treatment, as well as their current limitations. Given the limitation in the application of exosomes, we also propose future perspectives for better utilizing exosomes in NDDs. Hopefully, it would pave ways for expanding the biological applications of exosomes in fundamental research as well as theranostics of NDDs.

Proteomic Research of Extracellular Vesicles in Clinical Biofluid

Extracellular vesicles (EVs), the lipid bilayer membranous structures of particles, are produced and released from almost all cells, including eukaryotes and prokaryotes. The versatility of EVs has been investigated in various pathologies, including development, coagulation, inflammation, immune response modulation, and cell-cell communication. Proteomics technologies have revolutionized EV studies by enabling high-throughput analysis of their biomolecules to deliver comprehensive identification and quantification with rich structural information (PTMs, proteoforms). Extensive research has highlighted variations in EV cargo depending on vesicle size, origin, disease, and other features. This fact has sparked activities to use EVs for diagnosis and treatment to ultimately achieve clinical translation with recent endeavors summarized and critically reviewed in this publication. Notably, successful application and translation require a constant improvement of methods for sample preparation and analysis and their standardization, both of which are areas of active research. This review summarizes the characteristics, isolation, and identification approaches for EVs and the recent advances in EVs for clinical biofluid analysis to gain novel knowledge by employing proteomics. In addition, the current and predicted future challenges and technical barriers are also reviewed and discussed.

Emerging Links between Nonalcoholic Fatty Liver Disease and Neurodegeneration

The association between liver and brain health has gained attention as biomarkers of liver function have been revealed to predict neurodegeneration. The liver is a central regulator in metabolic homeostasis. However, in nonalcoholic fatty liver disease (NAFLD), homeostasis is disrupted which can result in extrahepatic organ pathologies. Emerging literature provides insight into the mechanisms behind the liver-brain health axis. These include the increased production of liver-derived factors that promote insulin resistance and loss of neuroprotective factors under conditions of NAFLD that increase insulin resistance in the central nervous system. In addition, elevated proinflammatory cytokines linked to NAFLD negatively impact the blood-brain barrier and increase neuroinflammation. Furthermore, exacerbated dyslipidemia associated with NAFLD and hepatic dysfunction can promote altered brain bioenergetics and oxidative stress. In this review, we summarize the current knowledge of the crosstalk between liver and brain as it relates to the pathophysiology between NAFLD and neurodegeneration, with an emphasis on Alzheimer's disease. We also highlight knowledge gaps and future areas for investigation to strengthen the potential link between NAFLD and neurodegeneration.

Cytoscape stringApp 2.0: Analysis and Visualization of Heterogeneous Biological Networks

Biological networks are often used to represent complex biological systems, which can contain several types of entities. Analysis and visualization of such networks is supported by the Cytoscape software tool and its many apps. While earlier versions of stringApp focused on providing intraspecies protein-protein interactions from the STRING database, the new stringApp 2.0 greatly improves the support for heterogeneous networks. Here, we highlight new functionality that makes it possible to create networks that contain proteins and interactions from STRING as well as other biological entities and associations from other sources. We exemplify this by complementing a published SARS-CoV-2 interactome with interactions from STRING. We have also extended stringApp with new data and query functionality for protein-protein interactions between eukaryotic parasites and their hosts. We show how this can be used to retrieve and visualize a cross-species network for a malaria parasite, its host, and its vector. Finally, the latest stringApp version has an improved user interface, allows retrieval of both functional associations and physical interactions, and supports group-wise enrichment analysis of different parts of a network to aid biological interpretation. stringApp is freely available at https://apps.cytoscape.org/apps/stringapp.

Multi-Omics Integrative Approach of Extracellular Vesicles: A Future Challenging Milestone

In the era of multi-omic sciences, dogma on singular cause-effect in physio-pathological processes is overcome and system biology approaches have been providing new perspectives to see through. In this context, extracellular vesicles (EVs) are offering a new level of complexity, given their role in cellular communication and their activity as mediators of specific signals to target cells or tissues. Indeed, their heterogeneity in terms of content, function, origin and potentiality contribute to the cross-interaction of almost every molecular process occurring in a complex system. Such features make EVs proper biological systems being, therefore, optimal targets of omic sciences. Currently, most studies focus on dissecting EVs content in order to either characterize it or to explore its role in various pathogenic processes at transcriptomic, proteomic, metabolomic, lipidomic and genomic levels. Despite valuable results being provided by individual omic studies, the categorization of EVs biological data might represent a limit to be overcome. For this reason, a multi-omic integrative approach might contribute to explore EVs function, their tissue-specific origin and their potentiality. This review summarizes the state-of-the-art of EVs omic studies, addressing recent research on the integration of EVs multi-level biological data and challenging developments in EVs origin.

Characterising Alzheimer's disease through integrative NMR- and LC-MS-based metabolomics

Background

Alzheimer's Disease (AD) is a complex and multifactorial disease and novel approaches are needed to illuminate the underlying pathology. Metabolites comprise the end-product of genes, transcripts, and protein regulations and might reflect disease pathogenesis. Blood is a common biofluid used in metabolomics; however, since extracellular vesicles (EVs) hold cell-specific biological material and can cross the blood-brain barrier, their utilization as biological material warrants further investigation. We aimed to investigate blood- and EV-derived metabolites to add insigts to the pathological mechanisms of AD.

Methods

Blood samples were collected from 10 AD and 10 Mild Cognitive Impairment (MCI) patients, and 10 healthy controls. EVs were enriched from plasma using 100,000×g, 1 h, 4 °C with a wash. Metabolites from serum and EVs were measured using liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) spectroscopy. Multivariate and univariate analyses were employed to identify altered metabolites in cognitively impaired individuals.

Results

While no significant EV-derived metabolites were found differentiating patients from healthy individuals, six serum metabolites were found important; valine (p = 0.001, fold change, FC = 0.8), histidine (p = 0.001, FC = 0.9), allopurinol riboside (p = 0.002, FC = 0.2), inosine (p = 0.002, FC = 0.3), 4-pyridoxic acid (p = 0.006, FC = 1.6), and guanosine (p = 0.004, FC = 0.3). Pathway analysis revealed branched-chain amino acids, purine and histidine metabolisms to be downregulated, and vitamin B6 metabolism upregulated in patients compared to controls.

Conclusion

Using a combination of LC-MS and NMR methodologies we identified several altered mechanisms possibly related to AD pathology. EVs require additional optimization prior to their possible utilization as a biological material for AD-related metabolomics studies.