Genomic Analysis Identifies New Loci Associated With Motor Complications in Parkinson's Disease

The A53T Mutation in α-Synuclein Enhances Proinflammatory Activation in Human Microglia Upon Inflammatory Stimulus

BACKGROUND

Parkinson's disease (PD) is the second most common neurodegenerative disease, following Alzheimer's. It is characterized by the aggregation of α-synuclein into Lewy bodies and Lewy neurites in the brain. Microglia-driven neuroinflammation may contribute to neuronal death in PD; however, the exact role of microglia remains unclear and has been understudied. The A53T mutation in the gene coding for α-synuclein has been linked to early-onset PD, and exposure to A53T mutant human α-synuclein increases the potential for inflammation of murine microglia. To date, its effect has not been studied in human microglia.

METHODS

Here, we used 2-dimensional cultures of human pluripotent stem cell-derived microglia and transplantation of these cells into the mouse brain to assess the cell autonomous effects of the A53T mutation on human microglia.

RESULTS

We found that A53T mutant human microglia had an intrinsically increased propensity toward proinflammatory activation upon inflammatory stimulus. Additionally, transplanted A53T mutant microglia showed a strong decrease in catalase expression in noninflammatory conditions and increased oxidative stress.

CONCLUSIONS

Our results indicate that A53T mutant human microglia display cell autonomous phenotypes that may worsen neuronal damage in early-onset PD.

The A53T mutation in α-synuclein enhances pro-inflammatory activation in human microglia

Parkinson's disease (PD) is characterized by the aggregation of α-synuclein into Lewy bodies and Lewy neurites in the brain. Microglia-driven neuroinflammation may contribute to neuronal death in PD, however the exact role of microglia remains unclear and has been understudied. The A53T mutation in the gene coding for α-synuclein has been linked to early-onset PD, and exposure to A53T-mutant human α-synuclein increases the potential for inflammation of murine microglia. To date, its effect has not been studied in human microglia. Here, we used 2-dimensional cultures of human iPSC-derived microglia and transplantation of these cells into the mouse brain to assess the effects of the A53T mutation on human microglia. We found that A53T-mutant human microglia had an intrinsically increased propensity towards pro-inflammatory activation upon inflammatory stimulus. Additionally, A53T mutant microglia showed a strong decrease in catalase expression in non-inflammatory conditions, and increased oxidative stress. Our results indicate that A53T mutant human microglia display cell-autonomous phenotypes that may worsen neuronal damage in early-onset PD.

SNPs in cytochrome P450 genes decide on the fate of individuals with genetic predisposition to Parkinson's disease

Parkinson's disease (PD) is one of the most frequent neurological diseases affecting millions of people worldwide. While the majority of PD cases are of unknown origin (idiopathic), about 5%-10% are familial and linked to mutations in different known genes. However, there are also people with a genetic predisposition to PD who do not develop the disease. To elucidate factors leading to the manifestation of PD we compared the occurrence of single nucleotide polymorphisms (SNPs) in various cytochrome P450 (P450) genes in people with a genetic predisposition and suffering from PD (GPD) to that of people, who are genetically predisposed, but show no symptoms of the disease (GUN). We used the PPMI (Parkinson's Progression Markers Initiative) database and the gene sequences of all 57 P450s as well as their three redox partners. Corresponding odds ratios (OR) and confidence intervals (CI) were calculated to assess the incidence of the various SNPs in the two groups of individuals and consequently their relation to PD. We identified for the first time SNPs that are significantly (up to 10fold!) over- or under-represented in GPD patients compared to GUN. SNPs with OR > 5 were found in 10 P450s being involved in eicosanoid, vitamin A and D metabolism as well as cholesterol degradation pointing to an important role of endogenous factors for the manifestation of PD clinical symptoms. Moreover, 12 P450s belonging to all P450 substrate classes as well as POR have SNPs that are significantly under-represented (OR < 0.2) in GPD compared to GUN, indicating a protective role of those SNPs and the corresponding P450s regarding disease advancement. To the best of our knowledge our data for the first time demonstrate an association between known PD predisposition genes and SNPs in other genes, shown here for different P450 genes and for their redox partner POR, which promote the manifestation of the disease in familial PD. Our results thus shed light onto the pathogenesis of PD, especially the switch from GUN to GPD and might further help to advance novel strategies for preventing the development or progression of the disease.

Deep multimodality-disentangled association analysis network for imaging genetics in neurodegenerative diseases

Imaging genetics is a crucial tool that is applied to explore potentially disease-related biomarkers, particularly for neurodegenerative diseases (NDs). With the development of imaging technology, the association analysis between multimodal imaging data and genetic data is gradually being concerned by a wide range of imaging genetics studies. However, multimodal data are fused first and then correlated with genetic data in traditional methods, which leads to an incomplete exploration of their common and complementary information. In addition, the inaccurate formulation in the complex relationships between imaging and genetic data and information loss caused by missing multimodal data are still open problems in imaging genetics studies. Therefore, in this study, a deep multimodality-disentangled association analysis network (DMAAN) is proposed to solve the aforementioned issues and detect the disease-related biomarkers of NDs simultaneously. First, the imaging data are nonlinearly projected into a latent space and imaging representations can be achieved. The imaging representations are further disentangled into common and specific parts by using a multimodal-disentangled module. Second, the genetic data are encoded to achieve genetic representations, and then, the achieved genetic representations are nonlinearly mapped to the common and specific imaging representations to build nonlinear associations between imaging and genetic data through an association analysis module. Moreover, modality mask vectors are synchronously synthesized to integrate the genetic and imaging data, which helps the following disease diagnosis. Finally, the proposed method achieves reasonable diagnosis performance via a disease diagnosis module and utilizes the label information to detect the disease-related modality-shared and modality-specific biomarkers. Furthermore, the genetic representation can be used to impute the missing multimodal data with our learning strategy. Two publicly available datasets with different NDs are used to demonstrate the effectiveness of the proposed DMAAN. The experimental results show that the proposed DMAAN can identify the disease-related biomarkers, which suggests the proposed DMAAN may provide new insights into the pathological mechanism and early diagnosis of NDs. The codes are publicly available at https://github.com/Meiyan88/DMAAN.

Genetics in parkinson's disease: From better disease understanding to machine learning based precision medicine

Parkinson's Disease (PD) is a neurodegenerative disorder with highly heterogeneous phenotypes. Accordingly, it has been challenging to robustly identify genetic factors associated with disease risk, prognosis and therapy response via genome-wide association studies (GWAS). In this review we first provide an overview of existing statistical methods to detect associations between genetic variants and the disease phenotypes in existing PD GWAS. Secondly, we discuss the potential of machine learning approaches to better quantify disease phenotypes and to move beyond disease understanding towards a better-personalized treatment of the disease.

Discovery of levodopa-induced dyskinesia-associated genes using genomic studies in patients and Drosophila behavioral analyses

Although levodopa is the most effective medication for Parkinson’s disease, long-term levodopa treatment is largely compromised due to late motor complications, including levodopa-induced dyskinesia (LID). However, the genetic basis of LID pathogenesis has not been fully understood. Here, we discover genes pathogenic for LID using Drosophila genetics and behavioral analyses combined with genome-wide association studies on 578 patients clinically diagnosed with LID. Similar to the therapeutic effect of levodopa in patients, acute levodopa treatments restore the motor defect of Parkinson’s disease model flies, while prolonged treatments cause LID-related symptoms, such as increased yawing, freezing and abrupt acceleration of locomotion. These symptoms require dopamine 1-like receptor 1 and are induced by neuronal overexpression of the receptor. Among genes selected from our analyses in the patient genome, neuronal knockdown of adenylyl cyclase 2 suppresses the levodopa-induced phenotypes and the receptor overexpression-induced symptoms in Drosophila. Together, our study provides genetic insights for LID pathogenesis through the D1-like receptor-adenylyl cyclase 2 signaling axis.

A combined research approach using GWAS on Parkinson's disease patients and a Drosophila model of L-DOPA-induced dyskinesia (LID) reveals that LID is linked to ADCY2 signaling.

The Big Picture of Neurodegeneration: A Meta Study to Extract the Essential Evidence on Neurodegenerative Diseases in a Network-Based Approach

The common features of all neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease, Amyotrophic Lateral Sclerosis (ALS), and Huntington's disease, are the accumulation of aggregated and misfolded proteins and the progressive loss of neurons, leading to cognitive decline and locomotive dysfunction. Still, they differ in their ultimate manifestation, the affected brain region, and the kind of proteinopathy. In the last decades, a vast number of processes have been described as associated with neurodegenerative diseases, making it increasingly harder to keep an overview of the big picture forming from all those data. In this meta-study, we analyzed genomic, transcriptomic, proteomic, and epigenomic data of the aforementioned diseases using the data of 234 studies in a network-based approach to study significant general coherences but also specific processes in individual diseases or omics levels. In the analysis part, we focus on only some of the emerging findings, but trust that the meta-study provided here will be a valuable resource for various other researchers focusing on specific processes or genes contributing to the development of neurodegeneration.

Methylated Cytochrome P450 and the Solute Carrier Family of Genes Correlate With Perturbations in Bile Acid Metabolism in Parkinson’s Disease

Parkinson’s disease (PD) is second most prevalent neurodegenerative disorder following Alzheimer’s disease. Parkinson’s disease is hypothesized to be caused by a multifaceted interplay between genetic and environmental factors. Herein, and for the first time, we describe the integration of metabolomics and epigenetics (genome-wide DNA methylation; epimetabolomics) to profile the frontal lobe from people who died from PD and compared them with age-, and sex-matched controls. We identified 48 metabolites to be at significantly different concentrations (FDR q < 0.05), 4,313 differentially methylated sites [5’-C-phosphate-G-3’ (CpGs)] (FDR q < 0.05) and increased DNA methylation age in the primary motor cortex of people who died from PD. We identified Primary bile acid biosynthesis as the major biochemical pathway to be perturbed in the frontal lobe of PD sufferers, and the metabolite taurine (p-value = 5.91E-06) as being positively correlated with CpG cg14286187 (SLC25A27; CYP39A1) (FDR q = 0.002), highlighting previously unreported biochemical changes associated with PD pathogenesis. In this novel multi-omics study, we identify regulatory mechanisms which we believe warrant future translational investigation and central biomarkers of PD which require further validation in more accessible biomatrices.

Microarray Genotyping Identifies New Loci Associated with Dementia in Parkinson's Disease

Dementia is one of the most disabling nonmotor symptoms of Parkinson’s disease (PD). However, the risk factors contributing to its development remain unclear. To investigate genetic variants associated with dementia in PD, we performed microarray genotyping based on a customized platform utilizing variants identified in previous genetic studies. Microarray genotyping was performed in 313 PD patients with dementia, 321 PD patients without dementia, and 635 healthy controls. The primary analysis was performed using a multiple logistic regression model adjusted for age and sex. SNCA single nucleotide polymorphism (SNP) rs11931074 was determined to be most significantly associated with PD (odds ratio = 0.66, 95% confidence interval = 0.56–0.78, p = 7.75 × 10⁻⁷). In the analysis performed for patients with PD only, MUL1 SNP rs3738128 (odds ratio = 2.52, 95% confidence interval = 1.68–3.79, p = 8.75 × 10⁻⁶) was found to be most significantly associated with dementia in PD. SNPs in ZHX2 and ERP29 were also associated with dementia in PD. This microarray genomic study identified new loci of MUL1 associated with dementia in PD, suggesting an essential role of mitochondrial dysfunction in the development of dementia in patients with PD.

Current Knowledge on the Background, Pathophysiology and Treatment of Levodopa-Induced Dyskinesia-Literature Review

Levodopa remains the primary drug for controlling motor symptoms in Parkinson’s disease through the whole course, but over time, complications develop in the form of dyskinesias, which gradually become more frequent and severe. These abnormal, involuntary, hyperkinetic movements are mainly characteristic of the ON phase and are triggered by excess exogenous levodopa. They may also occur during the OFF phase, or in both phases. Over the past 10 years, the issue of levodopa-induced dyskinesia has been the subject of research into both the substrate of this pathology and potential remedial strategies. The purpose of the present study was to review the results of recent research on the background and treatment of dyskinesia. To this end, databases were reviewed using a search strategy that included both relevant keywords related to the topic and appropriate filters to limit results to English language literature published since 2010. Based on the selected papers, the current state of knowledge on the morphological, functional, genetic and clinical features of levodopa-induced dyskinesia, as well as pharmacological, genetic treatment and other therapies such as deep brain stimulation, are described.

The Genetics of Parkinson's Disease and Implications for Clinical Practice

The genetic landscape of Parkinson’s disease (PD) is characterised by rare high penetrance pathogenic variants causing familial disease, genetic risk factor variants driving PD risk in a significant minority in PD cases and high frequency, low penetrance variants, which contribute a small increase of the risk of developing sporadic PD. This knowledge has the potential to have a major impact in the clinical care of people with PD. We summarise these genetic influences and discuss the implications for therapeutics and clinical trial design.

The role of APOE in cognitive trajectories and motor decline in Parkinson's disease

We aimed to investigate the role of the APOE genotype in cognitive and motor trajectories in Parkinson's disease (PD). Using PD registry data, we retrospectively investigated a total of 253 patients with PD who underwent the Mini-Mental State Exam (MMSE) two or more times at least 5 years apart, were aged over 40 years, and free of dementia at the time of enrollment. We performed group-based trajectory modeling to identify patterns of cognitive change using the MMSE. Kaplan-Meier survival analysis was used to investigate the role of the APOE genotype in cognitive and motor progression. Trajectory analysis divided patients into four groups: early fast decline, fast decline, gradual decline, and stable groups with annual MMSE scores decline of - 2.8, - 1.8, - 0.6, and - 0.1 points per year, respectively. The frequency of APOE ε4 was higher in patients in the early fast decline and fast decline groups (50.0%) than those in the stable group (20.1%) (p = 0.007). APOE ε4, in addition to older age at onset, depressive mood, and higher H&Y stage, was associated with the cognitive decline rate, but no APOE genotype was associated with motor progression. APOE genotype could be used to predict the cognitive trajectory in PD.

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.