Abstract P-43: Effect of Glucocerebrosidase Dysfunction on the Pool of Plasma Exosomes of Patients with Gaucher Disease

Background: Extracellular vesicles (EVs) are small membrane vesicles released from different types of cells. EVs are found in many human biological fluids. Exosomes are a subtype of EVs that are released by the fusion of multivesicular bodies with the plasma membrane. This type of vesicles is characterized by specific exosomal markers. Exosomes extracted from peripheral body liquids could have specific properties associated with different physiological conditions as well as human disorders, including neurodegenerative diseases. Gaucher disease (GD) – is the most common form of lysosomal storage disorders caused by mutations in the glucocerebrosidase (GBA) gene. Lysosome functionality is critical for the regulation of extracellular vesicle secretion and content. In model animals, the inhibition of glucocerebrosidase has been shown to increase the secretion of extracellular vesicles in brain tissues. Amount evaluation of EVs and their size in the biological fluids of patients with GD has not been early performed; therefore, it is unknown whether lysosomal dysfunction found in GD patients influences the plasma pool of EVs. The aim of this study was to evaluate the amount of blood plasma EVs in patients with GD and their characterization for morphology and size. Methods: EVs were isolated from the blood plasma of 8 GD patients and 8 controls by ultracentrifugation, and were characterized using cryo-electron microscopy (cryo-EM), nanoparticle tracking analysis (NTA), and dynamic light scattering (DLS). Also, the presence of exosomal markers CD9, CD63, CD81, and HSP70 was analyzed by flow cytometry and western blot. Results: Here, it was first shown an increased proportion of exosome fraction in EVs from plasma of GD patients compared to controls by DLS and cryo-EM (p<0.001) that was confirmed by mode size detected by NTA (p<0.02). Moreover, an increased number of double and multilayer vesicles in plasma EVs from GD patients was demonstrated by cryo-EM. We also detected an increase in the expression of exosomal markers on the surface of vesicles from the blood plasma of patients with GD compared to controls. Conclusion: Here, we firstly report that the exosomes obtained from the blood plasma of GD patients have a larger size and altered morphology. Thus, we have shown that lysosomal dysfunction in GD patients leads to a striking alteration of blood plasma extracellular vesicle pool

Neurofilament light levels predict clinical progression and death in multiple system atrophy

Disease-modifying treatments are currently being trialed in multiple system atrophy (MSA). Approaches based solely on clinical measures are challenged by heterogeneity of phenotype and pathogenic complexity. Neurofilament light chain protein has been explored as a reliable biomarker in several neurodegenerative disorders but data in multiple system atrophy have been limited. Therefore, neurofilament light chain is not yet routinely used as an outcome measure in MSA. We aimed to comprehensively investigate the role and dynamics of neurofilament light chain in multiple system atrophy combined with cross-sectional and longitudinal clinical and imaging scales and for subject trial selection. In this cohort study we recruited cross-sectional and longitudinal cases in multicentre European set-up. Plasma and cerebrospinal fluid neurofilament light chain concentrations were measured at baseline from 212 multiple system atrophy cases, annually for a mean period of 2 years in 44 multiple system atrophy patients in conjunction with clinical, neuropsychological and MRI brain assessments. Baseline neurofilament light chain characteristics were compared between groups. Cox regression was used to assess survival; ROC analysis to assess the ability of neurofilament light chain to distinguish between multiple system atrophy patients and healthy controls. Multivariate linear mixed effects models were used to analyse longitudinal neurofilament light chain changes and correlated with clinical and imaging parameters. Polynomial models were used to determine the differential trajectories of neurofilament light chain in multiple system atrophy. We estimated sample sizes for trials aiming to decrease NfL levels. We show that in multiple system atrophy, baseline plasma neurofilament light chain levels were better predictors of clinical progression, survival, and degree of brain atrophy than the NfL rate of change. Comparative analysis of multiple system atrophy progression over the course of disease, using plasma neurofilament light chain and clinical rating scales, indicated that neurofilament light chain levels rise as the motor symptoms progress, followed by deceleration in advanced stages. Sample size prediction suggested that significantly lower trial participant numbers would be needed to demonstrate treatment effects when incorporating plasma neurofilament light chain values into multiple system atrophy clinical trials in comparison to clinical measures alone. In conclusion, neurofilament light chain correlates with clinical disease severity, progression, and prognosis in multiple system atrophy. Combined with clinical and imaging analysis, neurofilament light chain can inform patient stratification and serve as a reliable biomarker of treatment response in future multiple system atrophy trials of putative disease-modifying agents.European Union’s Horizon 2020 research and innovation programm

Whole-Exome Sequencing in Searching for New Variants Associated With the Development of Parkinson’s Disease

Background: Parkinson’s disease (PD) is a complex disease with its monogenic forms accounting for less than 10% of all cases. Whole-exome sequencing (WES) technology has been used successfully to find mutations in large families. However, because of the late onset of the disease, only small families and unrelated patients are usually available. WES conducted in such cases yields in a large number of candidate variants. There are currently a number of imperfect software tools that allow the pathogenicity of variants to be evaluated.Objectives: We analyzed 48 unrelated patients with an alleged autosomal dominant familial form of PD using WES and developed a strategy for selecting potential pathogenetically significant variants using almost all available bioinformatics resources for the analysis of exonic areas.Methods: DNA sequencing of 48 patients with excluded frequent mutations was performed using an Illumina HiSeq 2500 platform. The possible pathogenetic significance of identified variants and their involvement in the pathogenesis of PD was assessed using SNP and Variation Suite (SVS), Combined Annotation Dependent Depletion (CADD) and Rare Exome Variant Ensemble Learner (REVEL) software. Functional evaluation was performed using the Pathway Studio database.Results: A significant reduction in the search range from 7082 to 25 variants in 23 genes associated with PD or neuronal function was achieved. Eight (FXN, MFN2, MYOC, NPC1, PSEN1, RET, SCN3A and SPG7) were the most significant.Conclusions: The multistep approach developed made it possible to conduct an effective search for potential pathogenetically significant variants, presumably involved in the pathogenesis of PD. The data obtained need to be further verified experimentally

Cryo-electron microscopy of extracellular vesicles from cerebrospinal fluid.

Extracellular vesicles (EVs) are membrane-enclosed vesicles which play important role for cell communication and physiology. EVs are found in many human biological fluids, including blood, breast milk, urine, cerebrospinal fluid (CSF), ejaculate, saliva etc. These nano-sized vesicles contain proteins, mRNAs, microRNAs, non-coding RNAs and lipids that are derived from producing cells. EVs deliver complex sets of biological information to recipient cells thereby modulating their behaviors by their molecular cargo. In this way EVs are involved in the pathological development and progression of many human disorders, including neurodegenerative diseases. In this study EVs purified by ultracentrifugation from CSF of patients with Parkinson's disease (PD) and individuals of the comparison group were characterized using nanoparticle tracking analysis, flow cytometry and cryo-electron microscopy. Vesicular size and the presence of exosomal marker CD9 on the surface provided evidence that most of the EVs were exosome-like vesicles. Cryo-electron microscopy allowed us to visualize a large spectrum of extracellular vesicles of various size and morphology with lipid bilayers and vesicular internal structures. Thus, we described the diversity and new characteristics of the vesicles from CSF suggesting that subpopulations of EVs with different and specific functions may exist

Transcriptomic Profiles Reveal Downregulation of Low-Density Lipoprotein Particle Receptor Pathway Activity in Patients Surviving Severe COVID-19

To assess the biology of the lethal endpoint in patients with SARS-CoV-2 infection, we compared the transcriptional response to the virus in patients who survived or died during severe COVID-19. We applied gene expression profiling to generate transcriptional signatures for peripheral blood mononuclear cells (PBMCs) from patients with SARS-CoV-2 infection at the time when they were placed in the Intensive Care Unit of the Pavlov First State Medical University of St. Petersburg (Russia). Three different bioinformatics approaches to RNA-seq analysis identified a downregulation of three common pathways in survivors compared with nonsurvivors among patients with severe COVID-19, namely, low-density lipoprotein (LDL) particle receptor activity (GO:0005041), important for maintaining cholesterol homeostasis, leukocyte differentiation (GO:0002521), and cargo receptor activity (GO:0038024). Specifically, PBMCs from surviving patients were characterized by reduced expression of PPARG, CD36, STAB1, ITGAV, and ANXA2. Taken together, our findings suggest that LDL particle receptor pathway activity in patients with COVID-19 infection is associated with poor disease prognosis

Data_Sheet_1_Whole-Exome Sequencing in Searching for New Variants Associated With the Development of Parkinson’s Disease.DOCX

<p>Background: Parkinson’s disease (PD) is a complex disease with its monogenic forms accounting for less than 10% of all cases. Whole-exome sequencing (WES) technology has been used successfully to find mutations in large families. However, because of the late onset of the disease, only small families and unrelated patients are usually available. WES conducted in such cases yields in a large number of candidate variants. There are currently a number of imperfect software tools that allow the pathogenicity of variants to be evaluated.</p><p>Objectives: We analyzed 48 unrelated patients with an alleged autosomal dominant familial form of PD using WES and developed a strategy for selecting potential pathogenetically significant variants using almost all available bioinformatics resources for the analysis of exonic areas.</p><p>Methods: DNA sequencing of 48 patients with excluded frequent mutations was performed using an Illumina HiSeq 2500 platform. The possible pathogenetic significance of identified variants and their involvement in the pathogenesis of PD was assessed using SNP and Variation Suite (SVS), Combined Annotation Dependent Depletion (CADD) and Rare Exome Variant Ensemble Learner (REVEL) software. Functional evaluation was performed using the Pathway Studio database.</p><p>Results: A significant reduction in the search range from 7082 to 25 variants in 23 genes associated with PD or neuronal function was achieved. Eight (FXN, MFN2, MYOC, NPC1, PSEN1, RET, SCN3A and SPG7) were the most significant.</p><p>Conclusions: The multistep approach developed made it possible to conduct an effective search for potential pathogenetically significant variants, presumably involved in the pathogenesis of PD. The data obtained need to be further verified experimentally.</p

Data_Sheet_2_Whole-Exome Sequencing in Searching for New Variants Associated With the Development of Parkinson’s Disease.docx

<p>Background: Parkinson’s disease (PD) is a complex disease with its monogenic forms accounting for less than 10% of all cases. Whole-exome sequencing (WES) technology has been used successfully to find mutations in large families. However, because of the late onset of the disease, only small families and unrelated patients are usually available. WES conducted in such cases yields in a large number of candidate variants. There are currently a number of imperfect software tools that allow the pathogenicity of variants to be evaluated.</p><p>Objectives: We analyzed 48 unrelated patients with an alleged autosomal dominant familial form of PD using WES and developed a strategy for selecting potential pathogenetically significant variants using almost all available bioinformatics resources for the analysis of exonic areas.</p><p>Methods: DNA sequencing of 48 patients with excluded frequent mutations was performed using an Illumina HiSeq 2500 platform. The possible pathogenetic significance of identified variants and their involvement in the pathogenesis of PD was assessed using SNP and Variation Suite (SVS), Combined Annotation Dependent Depletion (CADD) and Rare Exome Variant Ensemble Learner (REVEL) software. Functional evaluation was performed using the Pathway Studio database.</p><p>Results: A significant reduction in the search range from 7082 to 25 variants in 23 genes associated with PD or neuronal function was achieved. Eight (FXN, MFN2, MYOC, NPC1, PSEN1, RET, SCN3A and SPG7) were the most significant.</p><p>Conclusions: The multistep approach developed made it possible to conduct an effective search for potential pathogenetically significant variants, presumably involved in the pathogenesis of PD. The data obtained need to be further verified experimentally.</p

Neurofilament light levels predict clinical progression and death in multiple system atrophy

Disease-modifying treatments are currently being trialed in multiple system atrophy (MSA). Approaches based solely on clinical measures are challenged by heterogeneity of phenotype and pathogenic complexity. Neurofilament light chain protein has been explored as a reliable biomarker in several neurodegenerative disorders but data in multiple system atrophy have been limited. Therefore, neurofilament light chain is not yet routinely used as an outcome measure in MSA. We aimed to comprehensively investigate the role and dynamics of neurofilament light chain in multiple system atrophy combined with cross-sectional and longitudinal clinical and imaging scales and for subject trial selection. In this cohort study we recruited cross-sectional and longitudinal cases in multicentre European set-up. Plasma and cerebrospinal fluid neurofilament light chain concentrations were measured at baseline from 212 multiple system atrophy cases, annually for a mean period of 2 years in 44 multiple system atrophy patients in conjunction with clinical, neuropsychological and MRI brain assessments. Baseline neurofilament light chain characteristics were compared between groups. Cox regression was used to assess survival; ROC analysis to assess the ability of neurofilament light chain to distinguish between multiple system atrophy patients and healthy controls. Multivariate linear mixed effects models were used to analyse longitudinal neurofilament light chain changes and correlated with clinical and imaging parameters. Polynomial models were used to determine the differential trajectories of neurofilament light chain in multiple system atrophy. We estimated sample sizes for trials aiming to decrease NfL levels. We show that in multiple system atrophy, baseline plasma neurofilament light chain levels were better predictors of clinical progression, survival, and degree of brain atrophy than the NfL rate of change. Comparative analysis of multiple system atrophy progression over the course of disease, using plasma neurofilament light chain and clinical rating scales, indicated that neurofilament light chain levels rise as the motor symptoms progress, followed by deceleration in advanced stages. Sample size prediction suggested that significantly lower trial participant numbers would be needed to demonstrate treatment effects when incorporating plasma neurofilament light chain values into multiple system atrophy clinical trials in comparison to clinical measures alone. In conclusion, neurofilament light chain correlates with clinical disease severity, progression, and prognosis in multiple system atrophy. Combined with clinical and imaging analysis, neurofilament light chain can inform patient stratification and serve as a reliable biomarker of treatment response in future multiple system atrophy trials of putative disease-modifying agents