scholarly journals Integration of eQTL and Parkinson’s disease GWAS data implicates 11 disease genes

2019 ◽  
Author(s):  
Demis A. Kia ◽  
David Zhang ◽  
Sebastian Guelfi ◽  
Claudia Manzoni ◽  
Leon Hubbard ◽  
...  
Keyword(s):  
Gene Expression ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Candidate Genes ◽  
Gwas Data ◽  
Cell Type ◽  
Cell Type Specificity ◽  
Protein Ubiquitination ◽  
Ppi Networks ◽  
Core Proteins

AbstractSubstantial genome-wide association study (GWAS) work in Parkinson’s disease (PD) has led to an increasing number of loci shown reliably and robustly to be associated with the increased risk of the disease. Prioritising causative genes and pathways from these studies has proven problematic. Here, we present a comprehensive analysis of PD GWAS data with expression and methylation quantitative trait loci (eQTL/mQTL) using Colocalisation analysis (Coloc) and transcriptome-wide association analysis (TWAS) to uncover putative gene expression and splicing mechanisms driving PD GWAS signals. Candidate genes were further characterised by determining cell-type specificity, weighted gene co-expression (WGNCA) and protein-protein interaction (PPI) networks.Gene-level analysis of expression revealed 5 genes (WDR6, CD38, GPNMB, RAB29, TMEM163) that replicated using both Coloc and TWAS analyses in both GTEx and Braineac expression datasets. A further 6 genes (ZRANB3, PCGF3, NEK1, NUPL2, GALC, CTSB) showed evidence of disease-associated splicing effects. Cell-type specificity analysis revealed that gene expression was overall more prevalent in glial cell-types compared to neurons. The WGNCA analysis showed that NUPL2 is a key gene in 3 modules implicated in catabolic processes related with protein ubiquitination (protein ubiquitination (p=7.47e-10) and ubiquitin-dependent protein catabolic process (p = 2.57e-17) in nucleus accumbens, caudate and putamen, while TMEM163 and ZRANB3 were both important in modules indicating regulation of signalling (p=1.33e-65] and cell communication (p=7.55e-35) in the frontal cortex and caudate respectively. PPI analysis and simulations using random networks demonstrated that the candidate genes interact significantly more with known Mendelian PD and parkinsonism proteins than would be expected by chance. The proteins core proteins this network were enriched for regulation of the ERBB receptor tyrosine protein kinase signalling pathways.Together, these results point to a number of candidate genes and pathways that are driving the associations observed in PD GWAS studies.

scholarly journals LRRK2 at Striatal Synapses: Cell-Type Specificity and Mechanistic Insights

Cells ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 169
Author(s):  
Patrick D. Skelton ◽  
Valerie Tokars ◽  
Loukia Parisiadou
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Dopaminergic Neurons ◽  
Disease Risk ◽  
Lessons Learned ◽  
Striatal Neurons ◽  
Cell Type ◽  
Cell Type Specificity ◽  
A Cell ◽  
Cell Type Specific

Mutations in leucine-rich repeat kinase 2 (LRRK2) cause Parkinson’s disease with a similar clinical presentation and progression to idiopathic Parkinson’s disease, and common variation is linked to disease risk. Recapitulation of the genotype in rodent models causes abnormal dopamine release and increases the susceptibility of dopaminergic neurons to insults, making LRRK2 a valuable model for understanding the pathobiology of Parkinson’s disease. It is also a promising druggable target with targeted therapies currently in development. LRRK2 mRNA and protein expression in the brain is highly variable across regions and cellular identities. A growing body of work has demonstrated that pathogenic LRRK2 mutations disrupt striatal synapses before the onset of overt neurodegeneration. Several substrates and interactors of LRRK2 have been identified to potentially mediate these pre-neurodegenerative changes in a cell-type-specific manner. This review discusses the effects of pathogenic LRRK2 mutations in striatal neurons, including cell-type-specific and pathway-specific alterations. It also highlights several LRRK2 effectors that could mediate the alterations to striatal function, including Rabs and protein kinase A. The lessons learned from improving our understanding of the pathogenic effects of LRRK2 mutations in striatal neurons will be applicable to both dissecting the cell-type specificity of LRRK2 function in the transcriptionally diverse subtypes of dopaminergic neurons and also increasing our understanding of basal ganglia development and biology. Finally, it will inform the development of therapeutics for Parkinson’s disease.

scholarly journals Multimodal Analysis Reveals New Genetic Effectors in Parkinson'S Disease-Related Gene LRRK2 Gly2019Ser Mutation

2021 ◽  
Author(s):  
Longping Yao ◽  
Shizhong Zhang
Keyword(s):  
Gene Expression ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Interaction Analysis ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
In The Wild ◽  
Potential Factors ◽  
Wild Group

Abstract BackgroundMutations in the LRRK2 gene, which encodes leucine-rich repeat kinase 2 (LRRK2), generate one of the most prevalent monogenic forms of Parkinson's disease (PD). Patients with autosomal dominant PD and apparent sporadic PD, who are clinically indistinguishable from those with idiopathic PD, are found to have LRRK2 mutations, particularly the most prevalent variant Gly2019Ser. Nonetheless, potential effectors of Gly2019Ser remain unknown.MethodsWe used the GEO database to undertake and evaluate a multitiered bioinformatic investigation to look into the gene expression implicated in the development of Parkinson's disease. Individual differences in gene expression were then confirmed in whole blood samples collected in the clinic. These genetic factors were also subjected to an interaction analysis and prediction. ResultsIn total, 607 genes in the LRRK2 Gly2019Ser mutation group expressed differently from those in the wild group. The following 10 top hub genes were discovered in protein-protein interaction (PPI) networks: CD44, CTGF, THBS1, VEGFA, SPP1, EGF, VCAM1, MMP3, CXCR4, and LOX. The gene expression of CD44, CTGF, THBS1, SPP1, EGF, and LOX was considerably higher in the LRRK2 Gly2019Ser mutant group than in the LRRK2 wild group. Meanwhile, CXCR4 gene expression in the LRRK2 Gly2019Ser mutant group was significantly lower than in the LRRK2 wild group. We then confirmed the expression of the hub genes in LRRK2 Gly2019Ser mutated iPSC-induced DA cells. As a result, the levels of CD44, CTGF, THBS1, VEGFA, SPP1 were positively correlated to the mutation of LRRK2, displayed promising effectors for discriminating the pathogenesis of PD. ConclusionsWe identified CD44, CTGF, THBS1, VEGFA, and SPP1 as the potential genetic effectors responding to the mutation of LRRK2. They could be a promising mechanism for discriminating the PD and potential factors contributing to the disease's development.

scholarly journals ERASE: Extended Randomization for assessment of annotation enrichment in ASE datasets

2019 ◽  
Author(s):  
Karishma D’Sa ◽  
Regina H. Reynolds ◽  
Sebastian Guelfi ◽  
David Zhang ◽  
Sonia Garcia Ruiz ◽  
...  
Keyword(s):  
Gene Expression ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Association Studies ◽  
Genetic Regulation ◽  
Regulation Of Gene Expression ◽  
Gwas Data ◽  
Genome Wide Association Studies ◽  
Specific Expression ◽  
Individual Snps

AbstractGenome-wide association studies (GWAS) have identified thousands of genetic variants associated with various human phenotypes and many of these loci are thought to act at a molecular level by regulating gene expression. Detection of allele specific expression (ASE), namely preferential usage of an allele at a transcribed locus, is an increasingly important means of studying the genetic regulation of gene expression. However, there are currently a paucity of tools available to link ASE sites with GWAS risk loci. Existing integration methods first use ASE sites to infer cis-acting expression quantitative trait loci (eQTL) and then apply eQTL-based approaches. ERASE is a method that assesses the enrichment of risk loci amongst ASE sites directly. Furthermore, ERASE enables additional biological insights to be made through the addition of other SNP level annotations. ERASE is based on a randomization approach and controls for read depth, a significant confounder in ASE analyses. In this paper, we demonstrate that ERASE can efficiently detect the enrichment of eQTLs and risk loci within ASE data and that it remains sensitive even when used with underpowered GWAS datasets. Finally, using ERASE in combination with GWAS data for Parkinson’s disease and data on the splicing potential of individual SNPs, we provide evidence to suggest that risk loci for Parkinson’s disease are enriched amongst ASEs likely to affect splicing. Thus, we show that ERASE is an important new tool for the integration of ASE and GWAS data, capable of providing novel insights into the pathophysiology of complex diseases.

scholarly journals A human single-cell atlas of the Substantia nigra reveals novel cell-specific pathways associated with the genetic risk of Parkinson’s disease and neuropsychiatric disorders

2020 ◽  
Author(s):  
Devika Agarwal ◽  
Cynthia Sandor ◽  
Viola Volpato ◽  
Tara Caffrey ◽  
Jimena Monzon-Sandoval ◽  
...  
Keyword(s):  
Gene Expression ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Substantia Nigra ◽  
Genetic Risk ◽  
Expression Profiles ◽  
Neuropsychiatric Disorders ◽  
Specific Gene ◽  
Cell Type ◽  
Specific Gene Expression

AbstractWe describe a human single-nuclei transcriptomic atlas for the Substantia nigra (SN), generated by sequencing ~ 17,000 nuclei from matched cortical and SN samples. We show that the common genetic risk for Parkinson’s disease (PD) is associated with dopaminergic neuron (DaN)-specific gene expression, including mitochondrial functioning, protein folding and ubiquitination pathways. We identify a distinct cell type association between PD risk and oligodendrocyte-specific gene expression. Unlike Alzheimer’s disease (AD), we find no association between PD risk and microglia or astrocytes, suggesting that neuroinflammation plays a less causal role in PD than AD. Beyond PD, we find associations between SN DaNs and GABAergic neuron gene expression patterns with multiple neuropsychiatric disorders. Nevertheless, we find that each neuropsychiatric disorder is associated with a distinct set of genes within that neuron type. This atlas guides our aetiological understanding by associating SN cell type expression profiles with specific disease risk.

Multimodal analysis of gene expression from postmortem brains and blood identifies synaptic vesicle trafficking genes to be associated with Parkinson’s disease

2020 ◽  
Author(s):  
Xiaoya Gao ◽  
Zifeng Huang ◽  
Cailing Feng ◽  
Chaohao Guan ◽  
Ruidong Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Synaptic Vesicle ◽  
Multinomial Logistic Regression ◽  
Vesicle Trafficking ◽  
Susceptibility Gene ◽  
Blood Levels ◽  
Ppi Networks ◽  
Postmortem Brains

Abstract Objective We aimed to identify key susceptibility gene targets in multiple datasets generated from postmortem brains and blood of Parkinson’s disease (PD) patients and healthy controls (HC). Methods We performed a multitiered analysis to integrate the gene expression data using multiple-gene chips from 244 human postmortem tissues. We identified hub node genes in the highly PD-related consensus module by constructing protein–protein interaction (PPI) networks. Next, we validated the top four interacting genes in 238 subjects (90 sporadic PD, 125 HC and 23 Parkinson’s Plus Syndrome (PPS)). Utilizing multinomial logistic regression analysis (MLRA) and receiver operating characteristic (ROC), we analyzed the risk factors and diagnostic power for discriminating PD from HC and PPS. Results We identified 1333 genes that were significantly different between PD and HCs based on seven microarray datasets. The identified MEturquoise module is related to synaptic vesicle trafficking (SVT) dysfunction in PD (P < 0.05), and PPI analysis revealed that SVT genes PPP2CA, SYNJ1, NSF and PPP3CB were the top four hub node genes in MEturquoise (P < 0.001). The levels of these four genes in PD postmortem brains were lower than those in HC brains. We found lower blood levels of PPP2CA, SYNJ1 and NSF in PD compared with HC, and lower SYNJ1 in PD compared with PPS (P < 0.05). SYNJ1, negatively correlated to PD severity, displayed an excellent power to discriminating PD from HC and PPS. Conclusions This study highlights that SVT genes, especially SYNJ1, may be promising markers in discriminating PD from HCs and PPS.

scholarly journals Single-Cell RNA Sequencing in Parkinson’s Disease

Biomedicines ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 368
Author(s):  
Shi-Xun Ma ◽  
Su Bin Lim
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Single Cell ◽  
Rna Sequencing ◽  
Rna Degradation ◽  
Postmortem Brain ◽  
Cell Type ◽  
New Findings ◽  
Postmortem Brain Tissue ◽  
Technical Challenges

Single-cell and single-nucleus RNA sequencing (sc/snRNA-seq) technologies have enhanced the understanding of the molecular pathogenesis of neurodegenerative disorders, including Parkinson’s disease (PD). Nonetheless, their application in PD has been limited due mainly to the technical challenges resulting from the scarcity of postmortem brain tissue and low quality associated with RNA degradation. Despite such challenges, recent advances in animals and human in vitro models that recapitulate features of PD along with sequencing assays have fueled studies aiming to obtain an unbiased and global view of cellular composition and phenotype of PD at the single-cell resolution. Here, we reviewed recent sc/snRNA-seq efforts that have successfully characterized diverse cell-type populations and identified cell type-specific disease associations in PD. We also examined how these studies have employed computational and analytical tools to analyze and interpret the rich information derived from sc/snRNA-seq. Finally, we highlighted important limitations and emerging technologies for addressing key technical challenges currently limiting the integration of new findings into clinical practice.

scholarly journals Identification of sixteen novel candidate genes for late onset Parkinson’s disease

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Alessandro Gialluisi ◽  
Mafalda Giovanna Reccia ◽  
Nicola Modugno ◽  
Teresa Nutile ◽  
Alessia Lombardi ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Candidate Genes ◽  
Rare Variants ◽  
Late Onset ◽  
High Specificity ◽  
Diagnostic Tools ◽  
Multi Stage ◽  
Whole Exome ◽  
Family Based

Abstract Background Parkinson’s disease (PD) is a neurodegenerative movement disorder affecting 1–5% of the general population for which neither effective cure nor early diagnostic tools are available that could tackle the pathology in the early phase. Here we report a multi-stage procedure to identify candidate genes likely involved in the etiopathogenesis of PD. Methods The study includes a discovery stage based on the analysis of whole exome data from 26 dominant late onset PD families, a validation analysis performed on 1542 independent PD patients and 706 controls from different cohorts and the assessment of polygenic variants load in the Italian cohort (394 unrelated patients and 203 controls). Results Family-based approach identified 28 disrupting variants in 26 candidate genes for PD including PARK2, PINK1, DJ-1(PARK7), LRRK2, HTRA2, FBXO7, EIF4G1, DNAJC6, DNAJC13, SNCAIP, AIMP2, CHMP1A, GIPC1, HMOX2, HSPA8, IMMT, KIF21B, KIF24, MAN2C1, RHOT2, SLC25A39, SPTBN1, TMEM175, TOMM22, TVP23A and ZSCAN21. Sixteen of them have not been associated to PD before, were expressed in mesencephalon and were involved in pathways potentially deregulated in PD. Mutation analysis in independent cohorts disclosed a significant excess of highly deleterious variants in cases (p = 0.0001), supporting their role in PD. Moreover, we demonstrated that the co-inheritance of multiple rare variants (≥ 2) in the 26 genes may predict PD occurrence in about 20% of patients, both familial and sporadic cases, with high specificity (> 93%; p = 4.4 × 10− 5). Moreover, our data highlight the fact that the genetic landmarks of late onset PD does not systematically differ between sporadic and familial forms, especially in the case of small nuclear families and underline the importance of rare variants in the genetics of sporadic PD. Furthermore, patients carrying multiple rare variants showed higher risk of manifesting dyskinesia induced by levodopa treatment. Conclusions Besides confirming the extreme genetic heterogeneity of PD, these data provide novel insights into the genetic of the disease and may be relevant for its prediction, diagnosis and treatment.

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