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

Advances in proteomic and metabolomic profiling of neurodegenerative diseases

Proteomics and metabolomics are two emerging fields that hold promise to shine light on the molecular mechanisms causing neurodegenerative diseases. Research in this area may reveal and quantify specific metabolites and proteins that can be targeted by therapeutic interventions intended at halting or reversing the neurodegenerative process. This review aims at providing a general overview on the current status of proteomic and metabolomic profiling in neurodegenerative diseases. We focus on the most common neurodegenerative disorders, including Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis. We discuss the relevance of state-of-the-art metabolomics and proteomics approaches and their potential for biomarker discovery. We critically review advancements made so far, highlighting how metabolomics and proteomics may have a significant impact in future therapeutic and biomarker development. Finally, we further outline technologies used so far as well as challenges and limitations, placing the current information in a future-facing context

Evaluating Lipid-Lowering Drug Targets for Parkinson's Disease Prevention with Mendelian Randomization

Long-term exposure to lipid-lowering drugs might affect Parkinson's disease (PD) risk. We conducted Mendelian randomization analyses where genetic variants indexed expected effects of modulating lipid-lowering drug targets on PD. Statin exposure was not predicted to increase PD risk, although results were not precise enough to support benefits for prevention clearly (odds ratio [OR] = 0.83; 95% confidence interval [CI] = 0.65, 1.07). Other target results were null, except for variants indicating Apolipoprotein-A5 or Apolipoprotein-C3 inhibition might confer protection. These findings suggest peripheral lipid variation may not have a prominent role in PD etiology, but some related drug targets could influence PD via alternate pathways. ANN NEUROL 2020;88:1043–104

Heritability Enrichment Implicates Microglia in Parkinson's Disease Pathogenesis

Objective Understanding how different parts of the immune system contribute to pathogenesis in Parkinson's disease is a burning challenge with important therapeutic implications. We studied enrichment of common variant heritability for Parkinson's disease stratified by immune and brain cell types. Methods We used summary statistics from the most recent meta-analysis of genomewide association studies in Parkinson's disease and partitioned heritability using linkage disequilibrium score regression, stratified for specific cell types, as defined by open chromatin regions. We also validated enrichment results using a polygenic risk score approach and intersected disease-associated variants with epigenetic data and expression quantitative loci to nominate and explore a putative microglial locus. Results We found significant enrichment of Parkinson's disease risk heritability in open chromatin regions of microglia and monocytes. Genomic annotations overlapped substantially between these 2 cell types, and only the enrichment signal for microglia remained significant in a joint model. We present evidence suggesting P2RY12, a key microglial gene and target for the antithrombotic agent clopidogrel, as the likely driver of a significant Parkinson's disease association signal on chromosome 3. Interpretation Our results provide further support for the importance of immune mechanisms in Parkinson's disease pathogenesis, highlight microglial dysregulation as a contributing etiological factor, and nominate a targetable microglial gene candidate as a pathogenic player. Immune processes can be modulated by therapy, with potentially important clinical implications for future treatment in Parkinson's disease. ANN NEUROL 202

Finding genetically-supported drug targets for Parkinson's disease using Mendelian randomization of the druggable genome

There is currently no disease-modifying treatment for Parkinson's disease, a common neurodegenerative disorder. Here, the authors use genetic variation associated with gene and protein expression to find putative drug targets for Parkinson's disease using Mendelian randomization of the druggable genome. Parkinson's disease is a neurodegenerative movement disorder that currently has no disease-modifying treatment, partly owing to inefficiencies in drug target identification and validation. We use Mendelian randomization to investigate over 3,000 genes that encode druggable proteins and predict their efficacy as drug targets for Parkinson's disease. We use expression and protein quantitative trait loci to mimic exposure to medications, and we examine the causal effect on Parkinson's disease risk (in two large cohorts), age at onset and progression. We propose 23 drug-targeting mechanisms for Parkinson's disease, including four possible drug repurposing opportunities and two drugs which may increase Parkinson's disease risk. Of these, we put forward six drug targets with the strongest Mendelian randomization evidence. There is remarkably little overlap between our drug targets to reduce Parkinson's disease risk versus progression, suggesting different molecular mechanisms. Drugs with genetic support are considerably more likely to succeed in clinical trials, and we provide compelling genetic evidence and an analysis pipeline to prioritise Parkinson's disease drug development.Peer reviewe

Finding genetically-supported drug targets for Parkinson's disease using Mendelian randomization of the druggable genome

There is currently no disease-modifying treatment for Parkinson's disease, a common neurodegenerative disorder. Here, the authors use genetic variation associated with gene and protein expression to find putative drug targets for Parkinson's disease using Mendelian randomization of the druggable genome. Parkinson's disease is a neurodegenerative movement disorder that currently has no disease-modifying treatment, partly owing to inefficiencies in drug target identification and validation. We use Mendelian randomization to investigate over 3,000 genes that encode druggable proteins and predict their efficacy as drug targets for Parkinson's disease. We use expression and protein quantitative trait loci to mimic exposure to medications, and we examine the causal effect on Parkinson's disease risk (in two large cohorts), age at onset and progression. We propose 23 drug-targeting mechanisms for Parkinson's disease, including four possible drug repurposing opportunities and two drugs which may increase Parkinson's disease risk. Of these, we put forward six drug targets with the strongest Mendelian randomization evidence. There is remarkably little overlap between our drug targets to reduce Parkinson's disease risk versus progression, suggesting different molecular mechanisms. Drugs with genetic support are considerably more likely to succeed in clinical trials, and we provide compelling genetic evidence and an analysis pipeline to prioritise Parkinson's disease drug development.Peer reviewe

The CACNA1B R1389H variant is not associated with myoclonus-dystonia in a large European multicentric cohort.

Myoclonus-dystonia (M-D) is a very rare movement disorder, caused in ∼30-50% of cases by mutations in SGCE. The CACNA1B variant c.4166G>A; (p.R1389H) was recently reported as the likely causative mutation in a single 3-generation Dutch pedigree with five subjects affected by a unique dominant M-D syndrome and cardiac arrhythmias. In an attempt to replicate this finding, we assessed by direct sequencing the frequency of CACNA1B c.4166G>A; (p.R1389H) in a cohort of 520 M-D cases, in which SGCE mutations had been previously excluded. A total of 146 cases (28%) had a positive family history of M-D. The frequency of the variant was also assessed in 489 neurologically healthy controls and in publicly available data sets of genetic variation (1000 Genomes, Exome Variant Server and Exome Aggregation Consortium). The variant was detected in a single sporadic case with M-D, but in none of the 146 probands with familial M-D. Overall, the variant was present at comparable frequencies in M-D cases (1 out of 520; 0.19%) and healthy controls (1 out of 489; 0.2%). A similar frequency of the variant was also reported in all publicly available databases. These results do not support a causal association between the CACNA1B c.4166G>A; (p.R1389H) variant and M-D

Polygenic Resilience Modulates the Penetrance of Parkinson Disease Genetic Risk Factors

peer reviewedObjective: The aim of the current study is to understand why some individuals avoid developing Parkinson disease (PD) despite being at relatively high genetic risk, using the largest datasets of individual-level genetic data available. Methods: We calculated polygenic risk score to identify controls and matched PD cases with the highest burden of genetic risk for PD in the discovery cohort (International Parkinson's Disease Genomics Consortium, 7,204 PD cases and 9,412 controls) and validation cohorts (Comprehensive Unbiased Risk Factor Assessment for Genetics and Environment in Parkinson's Disease, 8,968 cases and 7,598 controls; UK Biobank, 2,639 PD cases and 14,301 controls; Accelerating Medicines Partnership–Parkinson's Disease Initiative, 2,248 cases and 2,817 controls). A genome-wide association study meta-analysis was performed on these individuals to understand genetic variation associated with resistance to disease. We further constructed a polygenic resilience score, and performed multimarker analysis of genomic annotation (MAGMA) gene-based analyses and functional enrichment analyses. Results: A higher polygenic resilience score was associated with a lower risk for PD (β = −0.054, standard error [SE] = 0.022, p = 0.013). Although no single locus reached genome-wide significance, MAGMA gene-based analyses nominated TBCA as a putative gene. Furthermore, we estimated the narrow-sense heritability associated with resilience to PD (h2 = 0.081, SE = 0.035, p = 0.0003). Subsequent functional enrichment analysis highlighted histone methylation as a potential pathway harboring resilience alleles that could mitigate the effects of PD risk loci. Interpretation: The present study represents a novel and comprehensive assessment of heritable genetic variation contributing to PD resistance. We show that a genetic resilience score can modify the penetrance of PD genetic risk factors and therefore protect individuals carrying a high-risk genetic burden from developing PD. ANN NEUROL 202

Artificial intelligence for dementia genetics and omics

Genetics and omics studies of Alzheimer's disease and other dementia subtypes enhance our understanding of underlying mechanisms and pathways that can be targeted. We identified key remaining challenges: First, can we enhance genetic studies to address missing heritability? Can we identify reproducible omics signatures that differentiate between dementia subtypes? Can high-dimensional omics data identify improved biomarkers? How can genetics inform our understanding of causal status of dementia risk factors? And which biological processes are altered by dementia-related genetic variation? Artificial intelligence (AI) and machine learning approaches give us powerful new tools in helping us to tackle these challenges, and we review possible solutions and examples of best practice. However, their limitations also need to be considered, as well as the need for coordinated multidisciplinary research and diverse deeply phenotyped cohorts. Ultimately AI approaches improve our ability to interrogate genetics and omics data for precision dementia medicine