Common Variants Coregulate Expression of GBA and Modifier Genes to Delay Parkinson's Disease Onset

Identification of 27 allele-specific regulatory variants in Parkinson's disease using a massively parallel reporter assay

Genome wide association studies (GWAS) have identified a number of genomic loci that are associated with Parkinson's disease (PD) risk. However, the majority of these variants lie in non-coding regions, and thus the mechanisms by which they influence disease development, and/or potential subtypes, remain largely elusive. To address this, we used a massively parallel reporter assay (MPRA) to screen the regulatory function of 5254 variants that have a known or putative connection to PD. We identified 138 loci with enhancer activity, of which 27 exhibited allele-specific regulatory activity in HEK293 cells. The identified regulatory variant(s) typically did not match the original tag variant within the PD associated locus, supporting the need for deeper exploration of these loci. The existence of allele specific transcriptional impacts within HEK293 cells, confirms that at least a subset of the PD associated regions mark functional gene regulatory elements. Future functional studies that confirm the putative targets of the empirically verified regulatory variants will be crucial for gaining a greater understanding of how gene regulatory network(s) modulate PD risk.

GBA1 Variants and Parkinson's Disease: Paving the Way for Targeted Therapy

Glucosylceramidase beta 1 (GBA1) variants have attracted enormous attention as the most promising and important genetic candidates for precision medicine in Parkinson's disease (PD). A substantial correlation between GBA1 genotypes and PD phenotypes could inform the prediction of disease progression and promote the development of a preventive intervention for individuals at a higher risk of a worse disease prognosis. Moreover, the GBA1-regulated pathway provides new perspectives on the pathogenesis of PD, such as dysregulated sphingolipid metabolism, impaired protein quality control, and disrupted endoplasmic reticulum-Golgi trafficking. These perspectives have led to the development of novel disease-modifying therapies for PD targeting the GBA1-regulated pathway by repositioning treatment strategies for Gaucher's disease. This review summarizes the current hypotheses on a mechanistic link between GBA1 variants and PD and possible therapeutic options for modulating GBA1-regulated pathways in PD patients.

Genetic meta-analysis of levodopa induced dyskinesia in Parkinson's disease

The genetic basis of levodopa-induced-dyskinesia (LiD) is poorly understood, and there have been few well-powered genome-wide studies. We performed a genome-wide survival meta-analyses to study the effect of genetic variation on the development of LiD in five separate longitudinal cohorts, and meta-analysed the results. We included 2784 PD patients, of whom 14.6% developed LiD. We found female sex (HR = 1.35, SE = 0.11, P = 0.007) and younger age at onset (HR = 1.8, SE = 0.14, P = 2 × 10-5) increased the probability of developing LiD. We identified three genetic loci significantly associated with time-to-LiD onset. rs72673189 on chromosome 1 (HR = 2.77, SE = 0.18, P = 1.53 × 10-8) located at the LRP8 locus, rs189093213 on chromosome 4 (HR = 3.06, SE = 0.19, P = 2.81 × 10-9) in the non-coding RNA LINC02353 locus, and rs180924818 on chromosome 16 (HR = 3.13, SE = 0.20, P = 6.27 × 10-9) in the XYLT1 locus. Based on a functional annotation analysis on chromosome 1, we determined that changes in DNAJB4 gene expression, close to LRP8, are an additional potential cause of increased susceptibility to LiD. Baseline anxiety status was significantly associated with LiD (OR = 1.14, SE = 0.03, P = 7.4 × 10-5). Finally, we performed a candidate variant analysis of previously reported loci, and found that genetic variability in ANKK1 (rs1800497, HR = 1.27, SE = 0.09, P = 8.89 × 10-3) and BDNF (rs6265, HR = 1.21, SE = 0.10, P = 4.95 × 10-2) loci were significantly associated with time to LiD in our large meta-analysis.

De novo identification of complex traits associated with asthma

Introduction

Asthma is a heterogeneous inflammatory disease often associated with other complex phenotypes. Identifying asthma-associated diseases and uncovering the molecular mechanisms mediating their interaction can help detangle the heterogeneity of asthma. Network analysis is a powerful approach for untangling such inter-disease relationships.

Methods

Here, we integrated information on physical contacts between common single nucleotide polymorphisms (SNPs) and gene expression with expression quantitative trait loci (eQTL) data from the lung and whole blood to construct two tissue-specific spatial gene regulatory networks (GRN). We then located the asthma GRN (level 0) within each tissue-specific GRN by identifying the genes that are functionally affected by asthma-associated spatial eQTLs. Curated protein interaction partners were subsequently identified up to four edges or levels away from the asthma GRN. The eQTLs spatially regulating genes on levels 0-4 were queried against the GWAS Catalog to identify the traits enriched (hypergeometric test; FDR ≤ 0.05) in each level.

Results

We identified 80 and 82 traits significantly enriched in the lung and blood GRNs, respectively. All identified traits were previously reported to be comorbid or associated (positively or negatively) with asthma (e.g., depressive symptoms and lung cancer), except 8 traits whose association with asthma is yet to be confirmed (e.g., reticulocyte count). Our analysis additionally pinpoints the variants and genes that link asthma to the identified asthma-associated traits, a subset of which was replicated in a comorbidity analysis using health records of 26,781 asthma patients in New Zealand.

Discussion

Our discovery approach identifies enriched traits in the regulatory space proximal to asthma, in the tissue of interest, without a priori selection of the interacting traits. The predictions it makes expand our understanding of possible shared molecular interactions and therapeutic targets for asthma, where no cure is currently available.

Mechanisms of Glucocerebrosidase Dysfunction in Parkinson's Disease

Beta-glucocerebrosidase is a lysosomal hydrolase, encoded by GBA1 that represents the most common risk gene associated with Parkinson's disease (PD) and Lewy Body Dementia. Glucocerebrosidase dysfunction has been also observed in the absence of GBA1 mutations across different genetic and sporadic forms of PD and related disorders, suggesting a broader role of glucocerebrosidase in neurodegeneration. In this review, we highlight recent advances in mechanistic characterization of glucocerebrosidase function as the foundation for development of novel therapeutics targeting glucocerebrosidase in PD and related disorders.

Genetic meta-analysis of levodopa induced dyskinesia in Parkinson's disease

Importance

Forty percent of Parkinson's disease patients develop levodopa-induced-dyskinesia (LiD) within 4 years of starting levodopa. The genetic basis of LiD remains poorly understood, and there have been few well powered studies.

Objective

To discover common genetic variants in the PD population that increase the probability of developing LiD.

Design setting and Participants

We performed survival analyses to study the development of LiD in 5 separate longitudinal cohorts. We performed a meta-analysis to combine the results of genetic association from each study based on a fixed effects model weighting the effect sizes by the inverse of their standard error. The selection criteria was specific to each cohort. We studied individuals that were genotyped from each cohort and that passed our analysis specific inclusion criteria.

Main Outcomes and Measures

We measured the time for PD patients on levodopa treatment to develop LiD as defined by reaching a score higher or equal than 2 from the MDS-UPDRS part IV, item 1, which is equivalent to a range of 26%-50% of the waking time with dyskinesia. We carried out a genome-wide analysis of the hazard ratio and the association of genome-wide SNPs with the probability of developing LiD using cox proportional hazard models (CPH).

Results

This study included 2,784 PD patients of European ancestry, of whom 14.6% developed LiD. Consistent with previous studies, we found female gender (HR = 1.35, SE = 0.11, P = 0.007) and younger age at onset (HR = 1.8, SE = 0.14, P = 2 × 10 -5 ) to increase the probability of developing LiD. We identified three loci significantly associated with time-to-LiD onset. rs72673189 on chromosome 1 (HR = 2.77, SE = 0.18, P = 1.53 × 10 -8 ) located in the LRP8 locus, rs189093213 on chromosome 4 (HR = 3.06,, SE = 0.19, P = 2.81 × 10 -9 ) in the non-coding RNA LINC02353 locus, and rs180924818 on chromosome 16 (HR = 3.13, SE = 0.20, P = 6.27 × 10 -9 ) in the XYLT1 locus. Subsequent colocalization analyses on chromosome 1 identified DNAJB4 as a candidate gene associated with LiD through a change in gene expression. We computed a PRS based on our GWAS meta-analysis and found high accuracy to stratify between PD-LID and PD (AUC 83.9). We also performed a stepwise regression analysis for baseline features selection associated with LiD status. We found baseline anxiety status to be significantly associated with LiD (OR = 1.14, SE = 0.03, P = 7.4 × 10 -5 ). Finally, we performed a candidate variant analysis and found that genetic variability in ANKK1 ( rs1800497 , Beta = 0.24, SE = 0.09, P = 8.89 × 10 -3 ) and BDNF ( rs6265 , Beta = 0.19, SE = 0.10, P = 4.95 × 10 -2 ) loci were significantly associated with time to LiD in our large meta-analysis.

Conclusion

In this association study, we have found three novel genetic variants associated with LiD, as well as confirming reports that variability in ANKK1 and BDNF loci were significantly associated with LiD probability. A PRS nominated from our time-to-LiD meta-analysis significantly differentiated between PD-LiD and PD. In addition, we have found female gender, young PD onset and anxiety to be significantly associated with LiD.

A severe neurodegenerative disease with Lewy bodies and a mutation in the glucocerebrosidase gene

Several heterozygous variants of the glucocerebrosidase gene (GBA1) have been reported to increase the risk of Parkinson's disease (PD) and dementia with Lewy bodies (DLB). GBA1-associated PD has been reported to be more severe than idiopathic PD, and more deleterious variants are associated with more severe clinical phenotypes. We report a family with a heterozygous p.Pro454Leu variant in GBA1. The variant was associated with a severe and rapidly progressive neurodegenerative disease with Lewy bodies that were clinically and pathologically diverse. Pathogenicity prediction algorithms and evolutionary analyses suggested that p.Pro454Leu is deleterious.

Transcriptome deregulation of peripheral monocytes and whole blood in GBA-related Parkinson's disease

BACKGROUND

Genetic mutations in beta-glucocerebrosidase (GBA) represent the major genetic risk factor for Parkinson's disease (PD). GBA participates in both the endo-lysosomal pathway and the immune response, two important mechanisms involved in the pathogenesis of PD. However, modifiers of GBA penetrance have not yet been fully elucidated.

METHODS

We characterized the transcriptomic profiles of circulating monocytes in a population of patients with PD and healthy controls (CTRL) with and without GBA variants (n = 23 PD/GBA, 13 CTRL/GBA, 56 PD, 66 CTRL) and whole blood (n = 616 PD, 362 CTRL, 127 PD/GBA, 165 CTRL/GBA). Differential expression analysis, pathway enrichment analysis, and outlier detection were performed. Ultrastructural characterization of isolated CD14+ monocytes in the four groups was also performed through electron microscopy.

RESULTS

We observed hundreds of differentially expressed genes and dysregulated pathways when comparing manifesting and non-manifesting GBA mutation carriers. Specifically, when compared to idiopathic PD, PD/GBA showed dysregulation in genes involved in alpha-synuclein degradation, aging and amyloid processing. Gene-based outlier analysis confirmed the involvement of lysosomal, membrane trafficking, and mitochondrial processing in manifesting compared to non-manifesting GBA-carriers, as also observed at the ultrastructural levels. Transcriptomic results were only partially replicated in an independent cohort of whole blood samples, suggesting cell-type specific changes.

CONCLUSIONS

Overall, our transcriptomic analysis of primary monocytes identified gene targets and biological processes that can help in understanding the pathogenic mechanisms associated with GBA mutations in the context of PD.

Establishing gene regulatory networks from Parkinson's disease risk loci

The latest meta-analysis of genome-wide association studies identified 90 independent variants across 78 genomic regions associated with Parkinson's disease, yet the mechanisms by which these variants influence the development of the disease remains largely elusive. To establish the functional gene regulatory networks associated with Parkinson's disease risk variants, we utilized an approach combining spatial (chromosomal conformation capture) and functional (expression quantitative trait loci) data. We identified 518 genes subject to regulation by 76 Parkinson's variants across 49 tissues, whicih encompass 36 peripheral and 13 CNS tissues. Notably, one-third of these genes were regulated via trans-acting mechanisms (distal; risk locus-gene separated by >1 Mb, or on different chromosomes). Of particular interest is the identification of a novel trans-expression quantitative trait loci-gene connection between rs10847864 and SYNJ1 in the adult brain cortex, highlighting a convergence between familial studies and Parkinson's disease genome-wide association studies loci for SYNJ1 (PARK20) for the first time. Furthermore, we identified 16 neurodevelopment-specific expression quantitative trait loci-gene regulatory connections within the foetal cortex, consistent with hypotheses suggesting a neurodevelopmental involvement in the pathogenesis of Parkinson's disease. Through utilizing Louvain clustering we extracted nine significant and highly intraconnected clusters within the entire gene regulatory network. The nine clusters are enriched for specific biological processes and pathways, some of which have not previously been associated with Parkinson's disease. Together, our results not only contribute to an overall understanding of the mechanisms and impact of specific combinations of Parkinson's disease variants, but also highlight the potential impact gene regulatory networks may have when elucidating aetiological subtypes of Parkinson's disease.

Comprehensive short and long read sequencing analysis for the Gaucher and Parkinson's disease-associated GBA gene

GBA variants carriers are at increased risk of Parkinson’s disease (PD) and Lewy body dementia (LBD). The presence of pseudogene GBAP1 predisposes to structural variants, complicating genetic analysis. We present two methods to resolve recombinant alleles and other variants in GBA: Gauchian, a tool for short-read, whole-genome sequencing data analysis, and Oxford Nanopore sequencing after PCR enrichment. Both methods were concordant for 42 samples carrying a range of recombinants and GBAP1-related mutations, and Gauchian outperformed the GATK Best Practices pipeline. Applying Gauchian to sequencing of over 10,000 individuals shows that copy number variants (CNVs) spanning GBAP1 are relatively common in Africans. CNV frequencies in PD and LBD are similar to controls. Gains may coexist with other mutations in patients, and a modifying effect cannot be excluded. Gauchian detects more GBA variants in LBD than PD, especially severe ones. These findings highlight the importance of accurate GBA analysis in these patients.

Two methods fully resolve the GBA gene: Gauchian, a tool for short-read, whole-genome sequencing data analysis, and Oxford Nanopore sequencing after PCR enrichment. The approach improves our understanding of the relationship between GBA, Gaucher disease and Parkinson disease.

Redefining the hypotheses driving Parkinson's diseases research

Parkinson’s disease (PD) research has largely focused on the disease as a single entity centred on the development of neuronal pathology within the central nervous system. However, there is growing recognition that PD is not a single entity but instead reflects multiple diseases, in which different combinations of environmental, genetic and potential comorbid factors interact to direct individual disease trajectories. Moreover, an increasing body of recent research implicates peripheral tissues and non-neuronal cell types in the development of PD. These observations are consistent with the hypothesis that the initial causative changes for PD development need not occur in the central nervous system. Here, we discuss how the use of neuronal pathology as a shared, qualitative phenotype minimises insights into the possibility of multiple origins and aetiologies of PD. Furthermore, we discuss how considering PD as a single entity potentially impairs our understanding of the causative molecular mechanisms, approaches for patient stratification, identification of biomarkers, and the development of therapeutic approaches to PD. The clear consequence of there being distinct diseases that collectively form PD, is that there is no single biomarker or treatment for PD development or progression. We propose that diagnosis should shift away from the clinical definitions, towards biologically defined diseases that collectively form PD, to enable informative patient stratification. N-of-one type, clinical designs offer an unbiased, and agnostic approach to re-defining PD in terms of a group of many individual diseases.

Machine Learning Identifies Six Genetic Variants and Alterations in the Heart Atrial Appendage as Key Contributors to PD Risk Predictivity

Parkinson's disease (PD) is a complex neurodegenerative disease with a range of causes and clinical presentations. Over 76 genetic loci (comprising 90 SNPs) have been associated with PD by the most recent GWAS meta-analysis. Most of these PD-associated variants are located in non-coding regions of the genome and it is difficult to understand what they are doing and how they contribute to the aetiology of PD. We hypothesised that PD-associated genetic variants modulate disease risk through tissue-specific expression quantitative trait loci (eQTL) effects. We developed and validated a machine learning approach that integrated tissue-specific eQTL data on known PD-associated genetic variants with PD case and control genotypes from the Wellcome Trust Case Control Consortium. In so doing, our analysis ranked the tissue-specific transcription effects for PD-associated genetic variants and estimated their relative contributions to PD risk. We identified roles for SNPs that are connected with INPP5P, CNTN1, GBA and SNCA in PD. Ranking the variants and tissue-specific eQTL effects contributing most to the machine learning model suggested a key role in the risk of developing PD for two variants (rs7617877 and rs6808178) and eQTL associated transcriptional changes of EAF1-AS1 within the heart atrial appendage. Similarly, effects associated with eQTLs located within the Brain Cerebellum were also recognized to confer major PD risk. These findings were replicated in two additional, independent cohorts (the UK Biobank, and NeuroX) and thus warrant further mechanistic investigations to determine if these transcriptional changes could act as early contributors to PD risk and disease development.

Glycosphingolipid metabolism and its role in ageing and Parkinson's disease

It is well established that lysosomal glucocerebrosidase gene (GBA) variants are a risk factor for Parkinson’s disease (PD), with increasing evidence suggesting a loss of function mechanism. One question raised by this genetic association is whether variants of genes involved in other aspects of sphingolipid metabolism are also associated with PD. Recent studies in sporadic PD have identified variants in multiple genes linked to diseases of glycosphingolipid (GSL) metabolism to be associated with PD. GSL biosynthesis is a complex pathway involving the coordinated action of multiple enzymes in the Golgi apparatus. GSL catabolism takes place in the lysosome and is dependent on the action of multiple acid hydrolases specific for certain substrates and glycan linkages. The finding that variants in multiple GSL catabolic genes are over-represented in PD in a heterozygous state highlights the importance of GSLs in the healthy brain and how lipid imbalances and lysosomal dysfunction are associated with normal ageing and neurodegenerative diseases. In this article we will explore the link between lysosomal storage disorders and PD, the GSL changes seen in both normal ageing, lysosomal storage disorders (LSDs) and PD and the mechanisms by which these changes can affect neurodegeneration.

Glucocerebrosidase Activity is not Associated with Parkinson's Disease Risk or Severity

Background

Mutations in the GBA gene, which encodes the lysosomal enzyme glucocerebrosidase (GCase), are risk factors for Parkinson's disease (PD).

Objective

To explore the association between GCase activity, PD phenotype, and probability for prodromal PD among carriers of mutations in the GBA and LRRK2 genes.

Methods

Participants were genotyped for the G2019S‐LRRK2 and nine GBA mutations common in Ashkenazi Jews. Performance‐based measures enabling the calculation of the Movement Disorder Society (MDS) prodromal probability score were collected.

Results

One hundred and seventy PD patients (102 GBA‐PD, 38 LRRK2‐PD, and 30 idiopathic PD) and 221 non‐manifesting carriers (NMC) (129 GBA‐NMC, 45 LRRK2‐NMC, 15 GBA‐LRRK2‐NMC, and 32 healthy controls) participated in this study. GCase activity was lower among GBA‐PD (3.15 ± 0.85 μmol/L/h), GBA‐NMC (3.23 ± 0.91 μmol/L/h), and GBA‐LRRK2‐NMC (3.20 ± 0.93 μmol/L/h) compared to the other groups of participants, with no correlation to clinical phenotype.

Conclusions

Low GCase activity does not explain the clinical phenotype or risk for prodromal PD in this cohort. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society

From DNA human sequence to the chromatin higher order organisation and its biological meaning: Using biomolecular interaction networks to understand the influence of structural variation on spatial genome organisation and its functional effect

The three-dimensional structure of the human genome has been proven to have a significant functional impact on gene expression. The high-order spatial chromatin is organised first by looping mediated by multiple protein factors, and then it is further formed into larger structures of topologically associated domains (TADs) or chromatin contact domains (CCDs), followed by A/B compartments and finally the chromosomal territories (CTs). The genetic variation observed in human population influences the multi-scale structures, posing a question regarding the functional impact of structural variants reflected by the variability of the genes expression patterns. The current methods of evaluating the functional effect include eQTLs analysis which uses statistical testing of influence of variants on spatially close genes. Rarely, non-coding DNA sequence changes are evaluated by their impact on the biomolecular interaction network (BIN) reflecting the cellular interactome that can be analysed by the classical graph-theoretic algorithms. Therefore, in the second part of the review, we introduce the concept of BIN, i.e. a meta-network model of the complete molecular interactome developed by integrating various biological networks. The BIN meta-network model includes DNA-protein binding by the plethora of protein factors as well as chromatin interactions, therefore allowing connection of genomics with the downstream biomolecular processes present in a cell. As an illustration, we scrutinise the chromatin interactions mediated by the CTCF protein detected in a ChIA-PET experiment in the human lymphoblastoid cell line GM12878. In the corresponding BIN meta-network the DNA spatial proximity is represented as a graph model, combined with the Proteins-Interaction Network (PIN) of human proteome using the Gene Association Network (GAN). Furthermore, we enriched the BIN with the signalling and metabolic pathways and Gene Ontology (GO) terms to assert its functional context. Finally, we mapped the Single Nucleotide Polymorphisms (SNPs) from the GWAS studies and identified the chromatin mutational hot-spots associated with a significant enrichment of SNPs related to autoimmune diseases. Afterwards, we mapped Structural Variants (SVs) from healthy individuals of 1000 Genomes Project and identified an interesting example of the missing protein complex associated with protein Q6GYQ0 due to a deletion on chromosome 14. Such an analysis using the meta-network BIN model is therefore helpful in evaluating the influence of genetic variation on spatial organisation of the genome and its functional effect in a cell.

Exploring the Genotype-Phenotype Correlation in GBA-Parkinson Disease: Clinical Aspects, Biomarkers, and Potential Modifiers

Variants in the glucocerebrosidase (GBA) gene are the most common genetic risk factor for Parkinson disease (PD). These include pathogenic variants causing Gaucher disease (GD) (divided into “severe,” “mild,” or “complex”—resulting from recombinant alleles—based on the phenotypic effects in GD) and “risk” variants, which are not associated with GD but nevertheless confer increased risk of PD. As a group, GBA-PD patients have more severe motor and nonmotor symptoms, faster disease progression, and reduced survival compared with noncarriers. However, different GBA variants impact variably on clinical phenotype. In the heterozygous state, “complex” and “severe” variants are associated with a more aggressive and rapidly progressive disease. Conversely, “mild” and “risk” variants portend a more benign course. Homozygous or compound heterozygous carriers usually display severe phenotypes, akin to heterozygous “complex” or “severe” variants carriers. This article reviews genotype–phenotype correlations in GBA-PD, focusing on clinical and nonclinical aspects (neuroimaging and biochemical markers), and explores other disease modifiers that deserve consideration in the characterization of these patients.

Intronic Haplotypes in the GBA Gene Do Not Predict Age at Diagnosis of Parkinson's Disease

BACKGROUND

GBA mutations are a common risk factor for Parkinson's disease (PD). A recent study has suggested that GBA haplotypes, identified by intronic variants, can affect age at diagnosis of PD.

OBJECTIVES

In this study, we assess this hypothesis using long reads across a large cohort and the publicly available Accelerating Medicines Partnership-Parkinson's Disease (AMP-PD) cohort.

METHODS

We recruited a PD cohort through the Remote Assessment of Parkinsonism Supporting Ongoing Development of Interventions in Gaucher Disease study (RAPSODI) and sequenced GBA using Oxford Nanopore technology. Genetic and clinical data on the full AMP-PD cohort were obtained from the online portal of the consortium.

RESULTS

A total of 1417 participants were analyzed. There was no significant difference in age at PD diagnosis between the two main haplotypes of the GBA gene.

CONCLUSIONS

GBA haplotypes do not affect age at diagnosis of PD in the two independent cohorts studied. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

The GBA-370Rec Parkinson's disease risk haplotype harbors a potentially pathogenic variant in the mitochondrial gene SLC25A44

GBA variations are common risk factors for Parkinson's disease (PD), and are found in 21.7% of Ashkenazi PD patients (AJ-PD), 4.23% of them carry an allele, 370Rec, which is different from the common GBA-N370S allele. Using whole-genome-sequencing of 370Rec carriers, N370S carriers, and non-carriers, we characterize the unique 370Rec haplotype in AJ-PDs, and show that it harbors a missense variant replacing the highly conserved methionine-27 with valine in the transmembrane domain of the mitochondrial SLC25A44.

Aerts J, Moratalla R. The Role of Cholesterol in α-Synuclein and Lewy Body Pathology in GBA1 Parkinson's Disease

Parkinson's disease (PD) is a progressive neurodegenerative disease where dopaminergic neurons in the substantia nigra are lost, resulting in a decrease in striatal dopamine and, consequently, motor control. Dopaminergic degeneration is associated with the appearance of Lewy bodies, which contain membrane structures and proteins, including α-synuclein (α-Syn), in surviving neurons. PD displays a multifactorial pathology and develops from interactions between multiple elements, such as age, environmental conditions, and genetics. Mutations in the GBA1 gene represent one of the major genetic risk factors for PD. This gene encodes an essential lysosomal enzyme called β-glucocerebrosidase (GCase), which is responsible for degrading the glycolipid glucocerebroside into glucose and ceramide. GCase can generate glucosylated cholesterol via transglucosylation and can also degrade the sterol glucoside. Although the molecular mechanisms that predispose an individual to neurodegeneration remain unknown, the role of cholesterol in PD pathology deserves consideration. Disturbed cellular cholesterol metabolism, as reflected by accumulation of lysosomal cholesterol in GBA1-associated PD cellular models, could contribute to changes in lipid rafts, which are necessary for synaptic localization and vesicle cycling and modulation of synaptic integrity. α-Syn has been implicated in the regulation of neuronal cholesterol, and cholesterol facilitates interactions between α-Syn oligomers. In this review, we integrate the results of previous studies and describe the cholesterol landscape in cellular homeostasis and neuronal function. We discuss its implication in α-Syn and Lewy body pathophysiological mechanisms underlying PD, focusing on the role of GCase and cholesterol. © 2020 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

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

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