Prioritizing Parkinson's disease genes using population-scale transcriptomic data

Parkinson's disease risk genes act in glia to control neuronal α-synuclein toxicity

Idiopathic Parkinson's disease is the second most common neurodegenerative disease and is estimated to be approximately 30% heritable. Genome wide association studies have revealed numerous loci associated with risk of development of Parkinson's disease. The majority of genes identified in these studies are expressed in glia at either similar or greater levels than their expression in neurons, suggesting that glia may play a role in Parkinson's disease pathogenesis. The role of individual glial risk genes in Parkinson's disease development or progression is unknown, however. We hypothesized that some Parkinson's disease risk genes exert their effects through glia. We developed a Drosophila model of α-synucleinopathy in which we can independently manipulate gene expression in neurons and glia. Human wild type α-synuclein is expressed in all neurons, and these flies develop the hallmarks of Parkinson's disease, including motor impairment, death of dopaminergic and other neurons, and α-synuclein aggregation. In these flies, we performed a candidate genetic screen, using RNAi to knockdown 14 well-validated Parkinson's disease risk genes in glia and measuring the effect on locomotion in order to identify glial modifiers of the α-synuclein phenotype. We identified 4 modifiers: aux, Lrrk, Ric, and Vps13, orthologs of the human genes GAK, LRRK2, RIT2, and VPS13C, respectively. Knockdown of each gene exacerbated neurodegeneration as measured by total and dopaminergic neuron loss. Knockdown of each modifier also increased α-synuclein oligomerization. These results suggest that some Parkinson's disease risk genes exert their effects in glia and that glia can influence neuronal α-synuclein proteostasis in a non-cell-autonomous fashion. Further, this study provides proof of concept that our novel Drosophila α-synucleinopathy model can be used to study glial modifier genes, paving the way for future large unbiased screens to identify novel glial risk factors that contribute to PD risk and progression.

Transcriptome-Wide Association Study Provides Insights Into the Genetic Component of Gene Expression in Anxiety

Anxiety disorders are common mental disorders that often result in disability. Recently, large-scale genome-wide association studies (GWASs) have identified several novel risk variants and loci for anxiety disorders (or anxiety traits). Nevertheless, how the reported risk variants confer risk of anxiety remains unknown. To identify genes whose cis-regulated expression levels are associated with risk of anxiety traits, we conducted a transcriptome-wide association study (TWAS) by integrating genome-wide associations from a large-scale GWAS (N = 175,163) (which evaluated anxiety traits based on Generalized Anxiety Disorder 2-item scale (GAD-2) score) and brain expression quantitative trait loci (eQTL) data (from the PsychENCODE and GTEx). We identified 19 and 17 transcriptome-wide significant (TWS) genes in the PsychENCODE and GTEx, respectively. Intriguingly, 10 genes showed significant associations with anxiety in both datasets, strongly suggesting that genetic risk variants may confer risk of anxiety traits by regulating the expression of these genes. Top TWS genes included RNF123, KANSL1-AS1, GLYCTK, CRHR1, DND1P1, MAPT and ARHGAP27. Of note, 25 TWS genes were not implicated in the original GWAS. Our TWAS identified 26 risk genes whose cis-regulated expression were significantly associated with anxiety, providing important insights into the genetic component of gene expression in anxiety disorders/traits and new clues for future drug development.

Fine-mapping of Parkinson's disease susceptibility loci identifies putative causal variants

Recent genome-wide association studies have identified 78 loci associated with Parkinson’s disease susceptibility but the underlying mechanisms remain largely unclear. To identify likely causal variants for disease risk, we fine-mapped these Parkinson’s-associated loci using four different fine-mapping methods. We then integrated multi-assay cell type–specific epigenomic profiles to pinpoint the likely mechanism of action of each variant, allowing us to identify Consensus single nucleotide polymorphism (SNPs) that disrupt LRRK2 and FCGR2A regulatory elements in microglia, an MBNL2 enhancer in oligodendrocytes, and a DYRK1A enhancer in neurons. This genome-wide functional fine-mapping investigation of Parkinson’s disease substantially advances our understanding of the causal mechanisms underlying this complex disease while avoiding focus on spurious, non-causal mechanisms. Together, these results provide a robust, comprehensive list of the likely causal variants, genes and cell-types underlying Parkinson’s disease risk as demonstrated by consistently greater enrichment of our fine-mapped SNPs relative to lead GWAS SNPs across independent functional impact annotations. In addition, our approach prioritized an average of 3/85 variants per locus as putatively causal, making downstream experimental studies both more tractable and more likely to yield disease-relevant, actionable results. Large-scale studies comparing individuals with Parkinson’s disease to age-matched controls have identified many regions of the genome associated with the disease. However, there is widespread correlation between different parts of the genome, making it difficult to tell which genetic variants cause Parkinson’s and which are simply co-inherited with causal variants. We therefore applied a suite of statistical models to identify the most likely causal genetic variants (i.e. fine-mapping). We then linked these genetic variants with epigenomic and gene expression signatures across a wide variety of tissues and cell types to identify how these variants cause disease. Therefore, this study provides a comprehensive and robust list of cellular and molecular mechanisms that may serve as targets in the development of more effective Parkinson’s therapeutics.

A transcriptome-wide association study identifies susceptibility genes for Parkinson's disease

Genome-wide association study (GWAS) has seen great strides in revealing initial insights into the genetic architecture of Parkinson's disease (PD). Since GWAS signals often reside in non-coding regions, relatively few of the associations have implicated specific biological mechanisms. Here, we aimed to integrate the GWAS results with large-scale expression quantitative trait loci (eQTL) in 13 brain tissues to identify candidate causal genes for PD. We conducted a transcriptome-wide association study (TWAS) for PD using the summary statistics of over 480,000 individuals from the most recent PD GWAS. We identified 18 genes significantly associated with PD after Bonferroni corrections. The most significant gene, LRRC37A2, was associated with PD in all 13 brain tissues, such as in the hypothalamus (P = 6.12 × 10-22) and nucleus accumbens basal ganglia (P = 5.62 × 10-21). We also identified eight conditionally independent genes, including four new genes at known PD loci: CD38, LRRC37A2, RNF40, and ZSWIM7. Through conditional analyses, we demonstrated that several of the GWAS significant signals on PD could be driven by genetically regulated gene expression. The most significant TWAS gene LRRC37A2 accounts for 0.855 of the GWAS signal at its loci, and ZSWIM7 accounts for all the GWAS signals at its loci. We further identified several phenotypes previously associated with PD by querying the single nucleotide polymorphisms (SNPs) in the final model of the identified genes in phenome databases. In conclusion, we prioritized genes that are likely to affect PD by using a TWAS approach and identified phenotypes associated with PD.

Mitochondrial dysfunction-induced H3K27 hyperacetylation perturbs enhancers in Parkinson's disease

Mitochondrial dysfunction is a major pathophysiological contributor to the progression of Parkinson’s disease (PD); however, whether it contributes to epigenetic dysregulation remains unknown. Here, we show that both chemically and genetically driven mitochondrial dysfunctions share a common mechanism of epigenetic dysregulation. Under both scenarios, lysine 27 acetylation of likely variant H3.3 (H3.3K27ac) increased in dopaminergic neuronal models of PD, thereby opening that region to active enhancer activity via H3K27ac. These vulnerable epigenomic loci represent potential transcription factor motifs for PD pathogenesis. We further confirmed that mitochondrial dysfunction induces H3K27ac in ex vivo and in vivo (MitoPark) neurodegenerative models of PD. Notably, the significantly increased H3K27ac in postmortem PD brains highlights the clinical relevance to the human PD population. Our results reveal an exciting mitochondrial dysfunction-metabolism-H3K27ac-transcriptome axis for PD pathogenesis. Collectively, the mechanistic insights link mitochondrial dysfunction to epigenetic dysregulation in dopaminergic degeneration and offer potential new epigenetic intervention strategies for PD.

Cross-platform transcriptional profiling identifies common and distinct molecular pathologies in Lewy body diseases

Parkinson's disease (PD), Parkinson's disease with dementia (PDD) and dementia with Lewy bodies (DLB) are three clinically, genetically and neuropathologically overlapping neurodegenerative diseases collectively known as the Lewy body diseases (LBDs). A variety of molecular mechanisms have been implicated in PD pathogenesis, but the mechanisms underlying PDD and DLB remain largely unknown, a knowledge gap that presents an impediment to the discovery of disease-modifying therapies. Transcriptomic profiling can contribute to addressing this gap, but remains limited in the LBDs. Here, we applied paired bulk-tissue and single-nucleus RNA-sequencing to anterior cingulate cortex samples derived from 28 individuals, including healthy controls, PD, PDD and DLB cases (n = 7 per group), to transcriptomically profile the LBDs. Using this approach, we (i) found transcriptional alterations in multiple cell types across the LBDs; (ii) discovered evidence for widespread dysregulation of RNA splicing, particularly in PDD and DLB; (iii) identified potential splicing factors, with links to other dementia-related neurodegenerative diseases, coordinating this dysregulation; and (iv) identified transcriptomic commonalities and distinctions between the LBDs that inform understanding of the relationships between these three clinical disorders. Together, these findings have important implications for the design of RNA-targeted therapies for these diseases and highlight a potential molecular "window" of therapeutic opportunity between the initial onset of PD and subsequent development of Lewy body dementia.

Dysregulation of mitochondrial and proteolysosomal genes in Parkinson's disease myeloid cells

An increasing number of identified Parkinson's disease (PD) risk loci contain genes highly expressed in innate immune cells, yet their role in pathology is not understood. We hypothesize that PD susceptibility genes modulate disease risk by influencing gene expression within immune cells. To address this, we have generated transcriptomic profiles of monocytes from 230 individuals with sporadic PD and healthy subjects. We observed a dysregulation of mitochondrial and proteasomal pathways. We also generated transcriptomic profiles of primary microglia from brains of 55 subjects and observed discordant transcriptomic signatures of mitochondrial genes in PD monocytes and microglia. We further identified 17 PD susceptibility genes whose expression, relative to each risk allele, is altered in monocytes. These findings reveal widespread transcriptomic alterations in PD monocytes, with some being distinct from microglia, and facilitate efforts to understand the roles of myeloid cells in PD as well as the development of biomarkers.

Epigenetic inactivation of the autophagy-lysosomal system in appendix in Parkinson's disease

The gastrointestinal tract may be a site of origin for α-synuclein pathology in idiopathic Parkinson's disease (PD). Disruption of the autophagy-lysosome pathway (ALP) may contribute to α-synuclein aggregation. Here we examined epigenetic alterations in the ALP in the appendix by deep sequencing DNA methylation at 521 ALP genes. We identified aberrant methylation at 928 cytosines affecting 326 ALP genes in the appendix of individuals with PD and widespread hypermethylation that is also seen in the brain of individuals with PD. In mice, we find that DNA methylation changes at ALP genes induced by chronic gut inflammation are greatly exacerbated by α-synuclein pathology. DNA methylation changes at ALP genes induced by synucleinopathy are associated with the ALP abnormalities observed in the appendix of individuals with PD specifically involving lysosomal genes. Our work identifies epigenetic dysregulation of the ALP which may suggest a potential mechanism for accumulation of α-synuclein pathology in idiopathic PD.

Low molecular weight polysialic acid prevents lipopolysaccharide-induced inflammatory dopaminergic neurodegeneration in humanized SIGLEC11 transgenic mice

Parkinson's disease is one of the most common neurodegenerative diseases in the elderly population, with a pathophysiology linked to neuroinflammation, complement activation, and oxidative damage. Soluble polysialic acid with an average degree of polymerization 20 (polySia avDP20) prevents inflammation and oxidative burst in human macrophages via sialic acid-binding immunoglobulin like lectin-11 (SIGLEC11) receptor and interferes with alternative complement activation. Here, we confirmed the anti-inflammatory capacity of polySia avDP20 on cultured murine embryonic stem cell-derived microglia and analyzed the effect of polySia avDP20 in a lipopolysaccharide-triggered animal model of Parkinson's disease. We demonstrated a neuroprotective effect of intraperitoneally applied polySia avDP20 in humanized SIGLEC11 transgenic mice after repeated systemic challenge with lipopolysaccharide. Pathway enrichment analysis of the brain transcriptome on day 19 after disease initiation showed that intraperitoneal application of 10 μg/g body weight polySia avDP20 prevented excessive inflammation. In line with these data, polySia avDP20 attenuated the lipopolysaccharide-triggered increase in mRNA levels of immune-related genes (Il1b, Cd14, Myd88, Fcer1g, Itgam, C4, Cybb, Iba1 and Cd68) and cell death-related genes (Casp8, Ripk1 and Ripk3) in the brains of SIGLEC11 transgenic mice on day 19, but not on day 5. Moreover, immunohistochemistry demonstrated that polySia avDP20 reduced the lipopolysaccharide-induced increase in immunoreactivity of IBA1 and CD68 in the substantia nigra pars reticulata in SIGLEC11 transgenic and wild type mice on day 19. Furthermore, treatment with polySia avDP20 prevented the loss of dopaminergic neurons in the substantia nigra pars compacta induced by lipopolysaccharide challenge in both SIGLEC11 transgenic and wild type mice on day 19. Thus, our data demonstrate that polySia avDP20 ameliorates inflammatory dopaminergic neurodegeneration and therefore is a promising drug candidate to prevent Parkinson's disease-related inflammation and neurodegeneration.

Monocyte markers correlate with immune and neuronal brain changes in REM sleep behavior disorder

Synucleinopathies are neurodegenerative diseases with both central and peripheral immune responses. However, whether the peripheral immune changes occur early in disease and their relation to brain events is yet unclear. Isolated rapid-eye-movement (REM) sleep behavior disorder (iRBD) can precede synucleinopathy-related parkinsonism and provides a prodromal phenotype to study early Parkinson's disease events. In this prospective case-control study, we describe monocytic markers in a cohort of iRBD patients that were associated with the brain-imaging markers of inflammation and neuronal dysfunction. Using ¹¹C-PK11195 positron emission tomography (PET), we previously showed increased immune activation in the substantia nigra of iRBD patients, while ¹⁸F-DOPA PET detected reduced putaminal dopaminergic function. Here we describe that patients' blood monocytic cells showed increased expression of CD11b, while HLA-DR expression was decreased compared to healthy controls. The iRBD patients had increased classical monocytes and mature natural killer cells. Remarkably, the levels of expression of Toll-like receptor 4 (TLR4) on blood monocytes in iRBD patients were positively correlated with nigral immune activation measured by ¹¹C-PK11195 PET and negatively correlated with putaminal ¹⁸F-DOPA uptake; the opposite was seen for the percentage of CD163⁺ myeloid cells. This suggesting a deleterious role for TLR4 and, conversely, a protective one for the CD163 expression. We show an association between peripheral blood monocytes and brain immune and dopaminergic changes in a synucleinopathy-related disorder, thus suggesting a cross-talk among periphery and brain during the disease.

A Disease Progression Model to Quantify the Nonmotor Symptoms of Parkinson's Disease in Participants With Leucine-Rich Repeat Kinase 2 Mutation

Leucine-rich repeat kinase 2 (LRRK2) inhibitors are currently in clinical development as interventions to slow progression of Parkinson's disease (PD). Understanding the rate of progression in PD as measured by both motor and nonmotor features is particularly important in assessing the potential therapeutic effect of LRRK2 inhibitors in clinical development. Using standardized data from the Critical Path for Parkinson's Unified Clinical Database, we quantified the rate of progression of the Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) Part I (nonmotor aspects of experiences of daily living) in 158 participants with PD who were carriers and 598 participants with PD who were noncarriers of at least one of three different LRRK2 gene mutations (G2019S, R1441C/G, or R1628P). Age and disease duration were found to predict baseline disease severity, while presence of at least one of these three LRRK2 mutations was a predictor of the rate of MDS-UPDRS Part I progression. The estimated progression rate in MDS-UPDRS Part I was 0.648 (95% confidence interval: 0.544, 0.739) points per year in noncarriers of a LRRK2 mutation and 0.259 (95% confidence interval: 0.217, 0.295) points per year in carriers of a LRRK2 mutation. This analysis demonstrates that the rate of progression based on MDS-UPDRS Part I is ~ 60% lower in carriers as compared with noncarriers of LRRK2 gene mutations.

Cascading from SARS-CoV-2 to Parkinson's Disease through Protein-Protein Interactions

Extensive extrapulmonary damages in a dozen of organs/systems, including the central nervous system (CNS), are reported in patients of the coronavirus disease 2019 (COVID-19). Three cases of Parkinson's disease (PD) have been reported as a direct consequence of COVID-19. In spite of the scarce data for establishing a definitive link between COVID-19 and PD, some hypotheses have been proposed to explain the cases reported. They, however, do not fit well with the clinical findings reported for COVID-19 patients, in general, and for the PD cases reported, in particular. Given the importance of this potential connection, we present here a molecular-level mechanistic hypothesis that explains well these findings and will serve to explore the potential CNS damage in COVID-19 patients. The model explaining the cascade effects from COVID-19 to CNS is developed by using bioinformatic tools. It includes the post-translational modification of host proteins in the lungs by viral proteins, the transport of modified host proteins via exosomes out the lungs, and the disruption of protein-protein interaction in the CNS by these modified host proteins. Our hypothesis is supported by finding 44 proteins significantly expressed in the CNS which are associated with PD and whose interactions can be perturbed by 24 host proteins significantly expressed in the lungs. These 24 perturbators are found to interact with viral proteins and to form part of the cargoes of exosomes in human tissues. The joint set of perturbators and PD-vulnerable proteins form a tightly connected network with significantly more connections than expected by selecting a random cluster of proteins of similar size from the human proteome. The molecular-level mechanistic hypothesis presented here provides several routes for the cascading of effects from the lungs of COVID-19 patients to PD. In particular, the disruption of autophagy/ubiquitination processes appears as an important mechanism that triggers the generation of large amounts of exosomes containing perturbators in their cargo, which would insult several PD-vulnerable proteins, potentially triggering Parkinsonism in COVID-19 patients.

Genetic variation and function: revealing potential factors associated with microbial phenotypes

Innovations in sequencing technology have generated voluminous microbial and host genomic data, making it possible to detect these genetic variations and analyze the function influenced by them. Recently, many studies have linked such genetic variations to phenotypes through association or comparative analysis, which have further advanced our understanding of multiple microbial functions. In this review, we summarized the application of association analysis in microbes like Mycobacterium tuberculosis, focusing on screening of microbial genetic variants potentially associated with phenotypes such as drug resistance, pathogenesis and novel drug targets etc.; reviewed the application of additional comparative genomic or transcriptomic methods to identify genetic factors associated with functions in microbes; expanded the scope of our study to focus on host genetic factors associated with certain microbes or microbiome and summarized the recent host genetic variations associated with microbial phenotypes, including susceptibility and load after infection of HIV, presence/absence of different taxa, and quantitative traits of microbiome, and lastly, discussed the challenges that may be encountered and the apparent or potential viable solutions. Gene-function analysis of microbe and microbiome is still in its infancy, and in order to unleash its full potential, it is necessary to understand its history, current status, and the challenges hindering its development.

Parkinson's disease and mitophagy: an emerging role for LRRK2

Parkinson's disease (PD) is a progressive neurodegenerative disorder that affects around 2% of individuals over 60 years old. It is characterised by the loss of dopaminergic neurons in the substantia nigra pars compacta of the midbrain, which is thought to account for the major clinical symptoms such as tremor, slowness of movement and muscle stiffness. Its aetiology is poorly understood as the physiological and molecular mechanisms leading to this neuronal loss are currently unclear. However, mitochondrial and lysosomal dysfunction seem to play a central role in this disease. In recent years, defective mitochondrial elimination through autophagy, termed mitophagy, has emerged as a potential contributing factor to disease pathology. PINK1 and Parkin, two proteins mutated in familial PD, were found to eliminate mitochondria under distinct mitochondrial depolarisation-induced stress. However, PINK1 and Parkin are not essential for all types of mitophagy and such pathways occur in most cell types and tissues in vivo, even in the absence of overt mitochondrial stress - so-called basal mitophagy. The most common mutation in PD, that of glycine at position 2019 to serine in the protein kinase LRRK2, results in increased activity and this was recently shown to disrupt basal mitophagy in vivo. Thus, different modalities of mitophagy are affected by distinct proteins implicated in PD, suggesting impaired mitophagy may be a common denominator for the disease. In this short review, we discuss the current knowledge about the link between PD pathogenic mutations and mitophagy, with a particular focus on LRRK2.

Towards physiologically relevant human pluripotent stem cell (hPSC) models of Parkinson's disease

The derivation of human embryonic stem cells followed by the discovery of induced pluripotent stem cells and leaps in genome editing approaches have continuously fueled enthusiasm for the development of new models of neurodegenerative diseases such as Parkinson's disease (PD). PD is characterized by the relative selective loss of dopaminergic neurons (DNs) in specific areas of substantia nigra pars compacta (SNpc). While degeneration in late stages can be widespread, there is stereotypic early degeneration of these uniquely vulnerable neurons. Various causes of selective vulnerability have been investigated but much remains unclear. Most studies have sought to identify cell autonomous properties of the most vulnerable neurons. However, recent findings from genetic studies and model systems have added to our understanding of non-cell autonomous contributions including regional-specific neuro-immune interactions with astrocytes, resident or damage-activated microglia, neuro-glia cell metabolic interactions, involvement of endothelial cells, and damage to the vascular system. All of these contribute to specific vulnerability and, along with aging and environmental factors, might be integrated in a complex stressor-threshold model of neurodegeneration. In this forward-looking review, we synthesize recent advances in the field of PD modeling using human pluripotent stem cells, with an emphasis on organoid and complex co-culture models of the nigrostriatal niche, with emerging CRISPR applications to edit or perturb expression of causal PD genes and associated risk factors, such as GBA, to understand the impact of these genes on relevant phenotypes.

NIDM: network impulsive dynamics on multiplex biological network for disease-gene prediction

The prediction of genes related to diseases is important to the study of the diseases due to high cost and time consumption of biological experiments. Network propagation is a popular strategy for disease-gene prediction. However, existing methods focus on the stable solution of dynamics while ignoring the useful information hidden in the dynamical process, and it is still a challenge to make use of multiple types of physical/functional relationships between proteins/genes to effectively predict disease-related genes. Therefore, we proposed a framework of network impulsive dynamics on multiplex biological network (NIDM) to predict disease-related genes, along with four variants of NIDM models and four kinds of impulsive dynamical signatures (IDSs). NIDM is to identify disease-related genes by mining the dynamical responses of nodes to impulsive signals being exerted at specific nodes. By a series of experimental evaluations in various types of biological networks, we confirmed the advantage of multiplex network and the important roles of functional associations in disease-gene prediction, demonstrated superior performance of NIDM compared with four types of network-based algorithms and then gave the effective recommendations of NIDM models and IDS signatures. To facilitate the prioritization and analysis of (candidate) genes associated to specific diseases, we developed a user-friendly web server, which provides three kinds of filtering patterns for genes, network visualization, enrichment analysis and a wealth of external links (http://bioinformatics.csu.edu.cn/DGP/NID.jsp). NIDM is a protocol for disease-gene prediction integrating different types of biological networks, which may become a very useful computational tool for the study of disease-related genes.

Single-Cell RNA Sequencing in Parkinson's Disease

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.

Novel Variance-Component TWAS method for studying complex human diseases with applications to Alzheimer's dementia

Transcriptome-wide association studies (TWAS) have been widely used to integrate transcriptomic and genetic data to study complex human diseases. Within a test dataset lacking transcriptomic data, traditional two-stage TWAS methods first impute gene expression by creating a weighted sum that aggregates SNPs with their corresponding cis-eQTL effects on reference transcriptome. Traditional TWAS methods then employ a linear regression model to assess the association between imputed gene expression and test phenotype, thereby assuming the effect of a cis-eQTL SNP on test phenotype is a linear function of the eQTL's estimated effect on reference transcriptome. To increase TWAS robustness to this assumption, we propose a novel Variance-Component TWAS procedure (VC-TWAS) that assumes the effects of cis-eQTL SNPs on phenotype are random (with variance proportional to corresponding reference cis-eQTL effects) rather than fixed. VC-TWAS is applicable to both continuous and dichotomous phenotypes, as well as individual-level and summary-level GWAS data. Using simulated data, we show VC-TWAS is more powerful than traditional TWAS methods based on a two-stage Burden test, especially when eQTL genetic effects on test phenotype are no longer a linear function of their eQTL genetic effects on reference transcriptome. We further applied VC-TWAS to both individual-level (N = ~3.4K) and summary-level (N = ~54K) GWAS data to study Alzheimer's dementia (AD). With the individual-level data, we detected 13 significant risk genes including 6 known GWAS risk genes such as TOMM40 that were missed by traditional TWAS methods. With the summary-level data, we detected 57 significant risk genes considering only cis-SNPs and 71 significant genes considering both cis- and trans- SNPs, which also validated our findings with the individual-level GWAS data. Our VC-TWAS method is implemented in the TIGAR tool for public use.

Deep sequencing of sncRNAs reveals hallmarks and regulatory modules of the transcriptome during Parkinson’s disease progression

Noncoding RNAs have diagnostic and prognostic importance in Parkinson's disease (PD). We studied circulating small noncoding RNAs (sncRNAs) in two large-scale longitudinal PD cohorts (Parkinson's Progression Markers Initiative (PPMI) and Luxembourg Parkinson's Study (NCER-PD)) and modeled their impact on the transcriptome. Sequencing of sncRNAs in 5,450 blood samples of 1,614 individuals in PPMI yielded 323 billion reads, most of which mapped to microRNAs but covered also other RNA classes such as piwi-interacting RNAs, ribosomal RNAs and small nucleolar RNAs. Dysregulated microRNAs associated with disease and disease progression occur in two distinct waves in the third and seventh decade of life. Originating predominantly from immune cells, they resemble a systemic inflammation response and mitochondrial dysfunction, two hallmarks of PD. Profiling 1,553 samples from 1,024 individuals in the NCER-PD cohort validated biomarkers and main findings by an independent technology. Finally, network analysis of sncRNA and transcriptome sequencing from PPMI identified regulatory modules emerging in patients with progressing PD.

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.

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.

Inferring latent temporal progression and regulatory networks from cross-sectional transcriptomic data of cancer samples

Unraveling molecular regulatory networks underlying disease progression is critically important for understanding disease mechanisms and identifying drug targets. The existing methods for inferring gene regulatory networks (GRNs) rely mainly on time-course gene expression data. However, most available omics data from cross-sectional studies of cancer patients often lack sufficient temporal information, leading to a key challenge for GRN inference. Through quantifying the latent progression using random walks-based manifold distance, we propose a latent-temporal progression-based Bayesian method, PROB, for inferring GRNs from the cross-sectional transcriptomic data of tumor samples. The robustness of PROB to the measurement variabilities in the data is mathematically proved and numerically verified. Performance evaluation on real data indicates that PROB outperforms other methods in both pseudotime inference and GRN inference. Applications to bladder cancer and breast cancer demonstrate that our method is effective to identify key regulators of cancer progression or drug targets. The identified ACSS1 is experimentally validated to promote epithelial-to-mesenchymal transition of bladder cancer cells, and the predicted FOXM1-targets interactions are verified and are predictive of relapse in breast cancer. Our study suggests new effective ways to clinical transcriptomic data modeling for characterizing cancer progression and facilitates the translation of regulatory network-based approaches into precision medicine.

Disentangling the Molecular Pathways of Parkinson's Disease using Multiscale Network Modeling

Parkinson's disease (PD) is a complex neurodegenerative disorder. The identification of genetic variants has shed light on the molecular pathways for inherited PD, while the disease mechanism for idiopathic PD remains elusive, partly due to a lack of robust tools. The complexity of PD arises from the heterogeneity of clinical symptoms, pathologies, environmental insults contributing to the disease, and disease comorbidities. Molecular networks have been increasingly used to identify molecular pathways and drug targets in complex human diseases. Here, we review recent advances in molecular network approaches and their application to PD. We discuss how network modeling can predict functions of PD genetic risk factors through network context and assist in the discovery of network-based therapeutics for neurodegenerative diseases.

Data-driven biomarker analysis using computational omics approaches to assess neurodegenerative disease progression

The complexity of biological systems suggests that current definitions of molecular dysfunctions are essential distinctions of a complex phenotype. This is well seen in neurodegenerative diseases (ND), such as Alzheimer's disease (AD) and Parkinson's disease (PD), multi-factorial pathologies characterized by high heterogeneity. These challenges make it necessary to understand the effectiveness of candidate biomarkers for early diagnosis, as well as to obtain a comprehensive mapping of how selective treatment alters the progression of the disorder. A large number of computational methods have been developed to explain network-based approaches by integrating individual components for modeling a complex system. In this review, high-throughput omics methodologies are presented for the identification of potent biomarkers associated with AD and PD pathogenesis as well as for monitoring the response of dysfunctional molecular pathways incorporating multilevel clinical information. In addition, principles for efficient data analysis pipelines are being discussed that can help address current limitations during the experimental process by increasing the reproducibility of benchmarking studies.

Genetic Effects on Transcriptome Profiles in Colon Epithelium Provide Functional Insights for Genetic Risk Loci

BACKGROUND & AIMS

The association of genetic variation with tissue-specific gene expression and alternative splicing guides functional characterization of complex trait-associated loci and may suggest novel genes implicated in disease. Here, our aims were as follows: (1) to generate reference profiles of colon mucosa gene expression and alternative splicing and compare them across colon subsites (ascending, transverse, and descending), (2) to identify expression and splicing quantitative trait loci (QTLs), (3) to find traits for which identified QTLs contribute to single-nucleotide polymorphism (SNP)-based heritability, (4) to propose candidate effector genes, and (5) to provide a web-based visualization resource.

METHODS

We collected colonic mucosal biopsy specimens from 485 healthy adults and performed bulk RNA sequencing. We performed genome-wide SNP genotyping from blood leukocytes. Statistical approaches and bioinformatics software were used for QTL identification and downstream analyses.

RESULTS

We provided a complete quantification of gene expression and alternative splicing across colon subsites and described their differences. We identified thousands of expression and splicing QTLs and defined their enrichment at genome-wide regulatory regions. We found that part of the SNP-based heritability of diseases affecting colon tissue, such as colorectal cancer and inflammatory bowel disease, but also of diseases affecting other tissues, such as psychiatric conditions, can be explained by the identified QTLs. We provided candidate effector genes for multiple phenotypes. Finally, we provided the Colon Transcriptome Explorer web application.

CONCLUSIONS

We provide a large characterization of gene expression and splicing across colon subsites. Our findings provide greater etiologic insight into complex traits and diseases influenced by transcriptomic changes in colon tissue.

Parkinson's Disease Subtypes: Critical Appraisal and Recommendations

BACKGROUND

In Parkinson's disease (PD), there is heterogeneity in the clinical presentation and underlying biology. Research on PD subtypes aims to understand this heterogeneity with potential contribution for the knowledge of disease pathophysiology, natural history and therapeutic development. There have been many studies of PD subtypes but their impact remains unclear with limited application in research or clinical practice.

OBJECTIVE

To critically evaluate PD subtyping systems.

METHODS

We conducted a systematic review of PD subtypes, assessing the characteristics of the studies reporting a subtyping system for the first time. We completed a critical appraisal of their methodologic quality and clinical applicability using standardized checklists.

RESULTS

We included 38 studies. The majority were cross-sectional (n = 26, 68.4%), used a data-driven approach (n = 25, 65.8%), and non-clinical biomarkers were rarely used (n = 5, 13.1%). Motor characteristics were the domain most commonly reported to differentiate PD subtypes. Most of the studies did not achieve the top rating across items of a Methodologic Quality checklist. In a Clinical Applicability Checklist, the clinical importance of differences between subtypes, potential treatment implications and applicability to the general population were rated poorly, and subtype stability over time and prognostic value were largely unknown.

CONCLUSION

Subtyping studies undertaken to date have significant methodologic shortcomings and most have questionable clinical applicability and unknown biological relevance. The clinical and biological signature of PD may be unique to the individual, rendering PD resistant to meaningful cluster solutions. New approaches that acknowledge the individual-level heterogeneity and that are more aligned with personalized medicine are needed.

Identifying drug targets for neurological and psychiatric disease via genetics and the brain transcriptome

Discovering drugs that efficiently treat brain diseases has been challenging. Genetic variants that modulate the expression of potential drug targets can be utilized to assess the efficacy of therapeutic interventions. We therefore employed Mendelian Randomization (MR) on gene expression measured in brain tissue to identify drug targets involved in neurological and psychiatric diseases. We conducted a two-sample MR using cis-acting brain-derived expression quantitative trait loci (eQTLs) from the Accelerating Medicines Partnership for Alzheimer's Disease consortium (AMP-AD) and the CommonMind Consortium (CMC) meta-analysis study (n = 1,286) as genetic instruments to predict the effects of 7,137 genes on 12 neurological and psychiatric disorders. We conducted Bayesian colocalization analysis on the top MR findings (using P<6x10-7 as evidence threshold, Bonferroni-corrected for 80,557 MR tests) to confirm sharing of the same causal variants between gene expression and trait in each genomic region. We then intersected the colocalized genes with known monogenic disease genes recorded in Online Mendelian Inheritance in Man (OMIM) and with genes annotated as drug targets in the Open Targets platform to identify promising drug targets. 80 eQTLs showed MR evidence of a causal effect, from which we prioritised 47 genes based on colocalization with the trait. We causally linked the expression of 23 genes with schizophrenia and a single gene each with anorexia, bipolar disorder and major depressive disorder within the psychiatric diseases and 9 genes with Alzheimer's disease, 6 genes with Parkinson's disease, 4 genes with multiple sclerosis and two genes with amyotrophic lateral sclerosis within the neurological diseases we tested. From these we identified five genes (ACE, GPNMB, KCNQ5, RERE and SUOX) as attractive drug targets that may warrant follow-up in functional studies and clinical trials, demonstrating the value of this study design for discovering drug targets in neuropsychiatric diseases.

Heritability Enrichment of Immunoglobulin G N-Glycosylation in Specific Tissues

Genome-wide association studies (GWAS) have identified over 60 genetic loci associated with immunoglobulin G (IgG) N-glycosylation; however, the causal genes and their abundance in relevant tissues are uncertain. Leveraging data from GWAS summary statistics for 8,090 Europeans, and large-scale expression quantitative trait loci (eQTL) data from the genotype-tissue expression of 53 types of tissues (GTEx v7), we derived a linkage disequilibrium score for the specific expression of genes (LDSC-SEG) and conducted a transcriptome-wide association study (TWAS). We identified 55 gene associations whose predicted levels of expression were significantly associated with IgG N-glycosylation in 14 tissues. Three working scenarios, i.e., tissue-specific, pleiotropic, and coassociated, were observed for candidate genetic predisposition affecting IgG N-glycosylation traits. Furthermore, pathway enrichment showed several IgG N-glycosylation-related pathways, such as asparagine N-linked glycosylation, N-glycan biosynthesis and transport to the Golgi and subsequent modification. Through phenome-wide association studies (PheWAS), most genetic variants underlying TWAS hits were found to be correlated with health measures (height, waist-hip ratio, systolic blood pressure) and diseases, such as systemic lupus erythematosus, inflammatory bowel disease, and Parkinson's disease, which are related to IgG N-glycosylation. Our study provides an atlas of genetic regulatory loci and their target genes within functionally relevant tissues, for further studies on the mechanisms of IgG N-glycosylation and its related diseases.

Insights Into the Proteomic Profiling of Extracellular Vesicles for the Identification of Early Biomarkers of Neurodegeneration

Extracellular vesicles (EVs) are involved in the development and progression of neurodegenerative diseases, including Alzheimer's and Parkinson's disease. Moreover, EVs have the capacity to modify the physiology of neuronal circuits by transferring proteins, RNA, lipids, and metabolites. The proteomic characterization of EVs (exosomes and microvesicles) from preclinical models and patient samples has the potential to reveal new proteins and molecular networks that affect the normal physiology prior to the appearance of traditional biomarkers of neurodegeneration. Noteworthy, many of the genetic risks associated to the development of Alzheimer's and Parkinson's disease affect the crosstalk between mitochondria, endosomes, and lysosomes. Recent research has focused on determining the role of endolysosomal trafficking in the onset of neurodegenerative diseases. Proteomic studies indicate an alteration of biogenesis and molecular content of EVs as a result of endolysosomal and autophagic dysfunction. In this review, we discuss the status of EV proteomic characterization and their usefulness in discovering new biomarkers for the differential diagnosis of neurodegenerative diseases. Despite the challenges related to the failure to follow a standard isolation protocol and their implementation for a clinical setting, the analysis of EV proteomes has revealed the presence of key proteins with post-translational modifications that can be measured in peripheral fluids.

When Friendship Turns Sour: Effective Communication Between Mitochondria and Intracellular Organelles in Parkinson's Disease

Parkinson's disease (PD) is a complex neurodegenerative disease with pathological hallmarks including progressive neuronal loss from the substantia nigra pars compacta and α-synuclein intraneuronal inclusions, known as Lewy bodies. Although the etiology of PD remains elusive, mitochondrial damage has been established to take center stage in the pathogenesis of PD. Mitochondria are critical to cellular energy production, metabolism, homeostasis, and stress responses; the association with PD emphasizes the importance of maintenance of mitochondrial network integrity. To accomplish the pleiotropic functions, mitochondria are dynamic not only within their own network but also in orchestrated coordination with other organelles in the cellular community. Through physical contact sites, signal transduction, and vesicle transport, mitochondria and intracellular organelles achieve the goals of calcium homeostasis, redox homeostasis, protein homeostasis, autophagy, and apoptosis. Herein, we review the finely tuned interactions between mitochondria and surrounding intracellular organelles, with focus on the nucleus, endoplasmic reticulum, Golgi apparatus, peroxisomes, and lysosomes. Participants that may contribute to the pathogenic mechanisms of PD will be highlighted in this review.

Proteomic Complexity in Parkinson's Disease: A Redox Signaling Perspective of the Pathophysiology and Progression

Parkinson's disease (PD) is a prevalent age-related neurodegenerative disorder that results in the progressive impairment of motor and cognitive functions. The majority of PD cases are sporadic, and only 5% of patients are associated with mutations in a few genes, which cause the early onset or familial PD. Environmental toxic substances and the individual genetic susceptibility play a role in sporadic cases, but despite significant efforts to treat and prevent the disease, the pathophysiological mechanisms leading to its onset and progress are not fully understood. In the last decade, genomic and proteomic studies have shown an increasing molecular complexity of sporadic PD, suggesting that a broad spectrum of biochemical pathways underlie its progression. Recent investigations and the literature review suggest the potential role of deregulation of the sensitive-cysteine proteome as a convergent pathogenic mechanism that may contribute to this complexity, opening new therapeutic opportunities.

A tissue-specific collaborative mixed model for jointly analyzing multiple tissues in transcriptome-wide association studies

Transcriptome-wide association studies (TWASs) integrate expression quantitative trait loci (eQTLs) studies with genome-wide association studies (GWASs) to prioritize candidate target genes for complex traits. Several statistical methods have been recently proposed to improve the performance of TWASs in gene prioritization by integrating the expression regulatory information imputed from multiple tissues, and made significant achievements in improving the ability to detect gene-trait associations. Unfortunately, most existing multi-tissue methods focus on prioritization of candidate genes, and cannot directly infer the specific functional effects of candidate genes across different tissues. Here, we propose a tissue-specific collaborative mixed model (TisCoMM) for TWASs, leveraging the co-regulation of genetic variations across different tissues explicitly via a unified probabilistic model. TisCoMM not only performs hypothesis testing to prioritize gene-trait associations, but also detects the tissue-specific role of candidate target genes in complex traits. To make full use of widely available GWASs summary statistics, we extend TisCoMM to use summary-level data, namely, TisCoMM-S2. Using extensive simulation studies, we show that type I error is controlled at the nominal level, the statistical power of identifying associated genes is greatly improved, and the false-positive rate (FPR) for non-causal tissues is well controlled at decent levels. We further illustrate the benefits of our methods in applications to summary-level GWASs data of 33 complex traits. Notably, apart from better identifying potential trait-associated genes, we can elucidate the tissue-specific role of candidate target genes. The follow-up pathway analysis from tissue-specific genes for asthma shows that the immune system plays an essential function for asthma development in both thyroid and lung tissues.

The Parkinson's Disease Genome-Wide Association Study Locus Browser

BACKGROUND

Parkinson's disease (PD) is a neurodegenerative disease with an often complex component identifiable by genome-wide association studies. The most recent large-scale PD genome-wide association studies have identified more than 90 independent risk variants for PD risk and progression across more than 80 genomic regions. One major challenge in current genomics is the identification of the causal gene(s) and variant(s) at each genome-wide association study locus. The objective of the current study was to create a tool that would display data for relevant PD risk loci and provide guidance with the prioritization of causal genes and potential mechanisms at each locus.

METHODS

We included all significant genome-wide signals from multiple recent PD genome-wide association studies including themost recent PD risk genome-wide association study, age-at-onset genome-wide association study, progression genome-wide association study, and Asian population PD risk genome-wide association study. We gathered data for all genes 1 Mb up and downstream of each variant to allow users to assess which gene(s) are most associated with the variant of interest based on a set of self-ranked criteria. Multiple databases were queried for each gene to collect additional causal data.

RESULTS

We created a PD genome-wide association study browser tool (https://pdgenetics.shinyapps.io/GWASBrowser/) to assist the PD research community with the prioritization of genes for follow-up functional studies to identify potential therapeutic targets.

CONCLUSIONS

Our PD genome-wide association study browser tool provides users with a useful method of identifying potential causal genes at all known PD risk loci from large-scale PD genome-wide association studies. We plan to update this tool with new relevant data as sample sizes increase and new PD risk loci are discovered. © 2020 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

Genetic Risk Profiling in Parkinson's Disease and Utilizing Genetics to Gain Insight into Disease-Related Biological Pathways

Parkinson's disease (PD) is a complex disorder underpinned by both environmental and genetic factors. The latter only began to be understood around two decades ago, but since then great inroads have rapidly been made into deconvoluting the genetic component of PD. In particular, recent large-scale projects such as genome-wide association (GWA) studies have provided insight into the genetic risk factors associated with genetically ''complex'' PD (PD that cannot readily be attributed to single deleterious mutations). Here, we discuss the plethora of genetic information provided by PD GWA studies and how this may be utilized to generate polygenic risk scores (PRS), which may be used in the prediction of risk and trajectory of PD. We also comment on how pathway-specific genetic profiling can be used to gain insight into PD-related biological pathways, and how this may be further utilized to nominate causal PD genes and potentially druggable therapeutic targets. Finally, we outline the current limits of our understanding of PD genetics and the potential contribution of variation currently uncaptured in genetic studies, focusing here on uncatalogued structural variants.

Meta-analysis of whole-exome sequencing data from two independent cohorts finds no evidence for rare variant enrichment in Parkinson disease associated loci

Parkinson disease (PD) is a complex neurodegenerative disorder influenced by both environmental and genetic factors. While genome wide association studies have identified several susceptibility loci, many causal variants and genes underlying these associations remain undetermined. Identifying these is essential in order to gain mechanistic insight and identify biological pathways that may be targeted therapeutically. We hypothesized that gene-based enrichment of rare mutations is likely to be found within susceptibility loci for PD and may help identify causal genes. Whole-exome sequencing data from two independent cohorts were analyzed in tandem and by meta-analysis and a third cohort genotyped using the NeuroX-array was used for replication analysis. We employed collapsing methods (burden and the sequence kernel association test) to detect gene-based enrichment of rare, protein-altering variation within established PD susceptibility loci. Our analyses showed trends for three genes (GALC, PARP9 and SEC23IP), but none of these survived multiple testing correction. Our findings provide no evidence of rare mutation enrichment in genes within PD-associated loci, in our datasets. While not excluding that rare mutations in these genes may influence the risk of idiopathic PD, our results suggest that, if such effects exist, much larger sequencing datasets will be required for their detection.

The multi-faceted role of mitochondria in the pathology of Parkinson's disease

Mitochondria are essential for neuronal function. They produce ATP to meet energy demands, regulate homeostasis of ion levels such as calcium and regulate reactive oxygen species that cause oxidative cellular stress. Mitochondria have also been shown to regulate protein synthesis within themselves, as well as within the nucleus, and also influence synaptic plasticity. These roles are especially important for neurons, which have higher energy demands and greater susceptibility to stress. Dysfunction of mitochondria has been associated with several neurodegenerative diseases, including Parkinson's disease, Alzheimer's disease, Huntington's disease, Glaucoma and Amyotrophic Lateral Sclerosis. The focus of this review is on how and why mitochondrial function is linked to the pathology of Parkinson's disease (PD). Many of the PD-linked genetic mutations which have been identified result in dysfunctional mitochondria, through a wide-spread number of mechanisms. In this review, we describe how susceptible neurons are predisposed to be vulnerable to the toxic events that occur during the neurodegenerative process of PD, and how mitochondria are central to these pathways. We also discuss ways in which proteins linked with familial PD control mitochondrial function, both physiologically and pathologically, along with their implications in genome-wide association studies and risk assessment. Finally, we review potential strategies for disease modification through mitochondrial enhancement. Ultimately, agents capable of both improving and/or restoring mitochondrial function, either alone, or in conjunction with other disease-modifying agents may halt or slow the progression of neurodegeneration in Parkinson's disease.

Post-GWAS knowledge gap: the how, where, and when

Genetic risk for complex diseases very rarely reflects only Mendelian-inherited phenotypes where single-gene mutations can be followed in families by linkage analysis. More commonly, a large set of low-penetrance, small effect-size variants combine to confer risk; they are normally revealed in genome-wide association studies (GWAS), which compare large population groups. Whereas Mendelian inheritance points toward disease mechanisms arising from the mutated genes, in the case of GWAS signals, the effector proteins and even general risk mechanism are mostly unknown. Instead, the utility of GWAS currently lies primarily in predictive and diagnostic information. Although an amazing body of GWAS-based knowledge now exists, we advocate for more funding towards the exploration of the fundamental biology in post-GWAS studies; this research will bring us closer to causality and risk gene identification. Using Parkinson's Disease as an example, we ask, how, where, and when do risk loci contribute to disease?

Genetic modifiers of risk and age at onset in GBA associated Parkinson's disease and Lewy body dementia

Parkinson's disease is a genetically complex disorder. Multiple genes have been shown to contribute to the risk of Parkinson's disease, and currently 90 independent risk variants have been identified by genome-wide association studies. Thus far, a number of genes (including SNCA, LRRK2, and GBA) have been shown to contain variability across a spectrum of frequency and effect, from rare, highly penetrant variants to common risk alleles with small effect sizes. Variants in GBA, encoding the enzyme glucocerebrosidase, are associated with Lewy body diseases such as Parkinson's disease and Lewy body dementia. These variants, which reduce or abolish enzymatic activity, confer a spectrum of disease risk, from 1.4- to >10-fold. An outstanding question in the field is what other genetic factors that influence GBA-associated risk for disease, and whether these overlap with known Parkinson's disease risk variants. Using multiple, large case-control datasets, totalling 217 165 individuals (22 757 Parkinson's disease cases, 13 431 Parkinson's disease proxy cases, 622 Lewy body dementia cases and 180 355 controls), we identified 1691 Parkinson's disease cases, 81 Lewy body dementia cases, 711 proxy cases and 7624 controls with a GBA variant (p.E326K, p.T369M or p.N370S). We performed a genome-wide association study and analysed the most recent Parkinson's disease-associated genetic risk score to detect genetic influences on GBA risk and age at onset. We attempted to replicate our findings in two independent datasets, including the personal genetics company 23andMe, Inc. and whole-genome sequencing data. Our analysis showed that the overall Parkinson's disease genetic risk score modifies risk for disease and decreases age at onset in carriers of GBA variants. Notably, this effect was consistent across all tested GBA risk variants. Dissecting this signal demonstrated that variants in close proximity to SNCA and CTSB (encoding cathepsin B) are the most significant contributors. Risk variants in the CTSB locus were identified to decrease mRNA expression of CTSB. Additional analyses suggest a possible genetic interaction between GBA and CTSB and GBA p.N370S induced pluripotent cell-derived neurons were shown to have decreased cathepsin B expression compared to controls. These data provide a genetic basis for modification of GBA-associated Parkinson's disease risk and age at onset, although the total contribution of common genetics variants is not large. We further demonstrate that common variability at genes implicated in lysosomal function exerts the largest effect on GBA associated risk for disease. Further, these results have implications for selection of GBA carriers for therapeutic interventions.

Two single nucleotide polymorphisms in IL13 and IL13RA1 from individuals with idiopathic Parkinson's disease increase cellular susceptibility to oxidative stress

The human genes for interleukin 13 (IL-13) and its receptor alpha 1 (IL-13Rα1) are in chromosomal regions associated with Parkinson's disease (PD). The interaction of IL-13 with its receptor increases the susceptibility of mouse dopaminergic neurons to oxidative stress. We identified two rare single SNPs in IL13 and IL13RA1 and measured their cytotoxic effects. rs148077750 is a missense leucine to proline substitution in IL13. It was found in individuals with early onset PD and no other known monogenic forms of the disease and is significantly linked with PD (Fisher's exact test: p-value = 0.01, odds ratio = 14.2). rs145868092 is a leucine to phenylalanine substitution in IL13RA1 affecting a residue critical for IL-13 binding. Both mutations increased the cytotoxic activity of IL-13 on human SH-SY5Y neurons exposed to sublethal doses of hydrogen peroxide, t-butyl hydroperoxide or RLS3, an inducer of ferroptosis. Our data show that both rs148077750 and rs145868092 conferred a gain-of-function that may increase the risk of developing PD.

PrGeFNE: Predicting disease-related genes by fast network embedding

Identifying disease-related genes is of importance for understanding of molecule mechanisms of diseases, as well as diagnosis and treatment of diseases. Many computational methods have been proposed to predict disease-related genes, but how to make full use of multi-source biological data to enhance the ability of disease-gene prediction is still challenging. In this paper, we proposed a novel method for predicting disease-related genes by using fast network embedding (PrGeFNE), which can integrate multiple types of associations related to diseases and genes. Specifically, we first constructed a heterogeneous network by using phenotype-disease, disease-gene, protein-protein and gene-GO associations; and low-dimensional representation of nodes is extracted from the network by using a fast network embedding algorithm. Then, a dual-layer heterogeneous network was reconstructed by using the low-dimensional representation, and a network propagation was applied to the dual-layer heterogeneous network to predict disease-related genes. Through cross-validation and newly added-association validation, we displayed the important roles of different types of association data in enhancing the ability of disease-gene prediction, and confirmed the excellent performance of PrGeFNE by comparing to state-of-the-art algorithms. Furthermore, we developed a web tool that can facilitate researchers to search for candidate genes of different diseases predicted by PrGeFNE, along with the enrichment analysis of GO and pathway on candidate gene set. This may be useful for investigation of diseases' molecular mechanisms as well as their experimental validations. The web tool is available at http://bioinformatics.csu.edu.cn/prgefne/.

Nuclear Factor-κB Dysregulation and α-Synuclein Pathology: Critical Interplay in the Pathogenesis of Parkinson's Disease

The loss of dopaminergic neurons of the nigrostriatal system underlies the onset of the typical motor symptoms of Parkinson's disease (PD). Lewy bodies (LB) and Lewy neurites (LN), proteinaceous inclusions mainly composed of insoluble α-synuclein (α-syn) fibrils are key neuropathological hallmarks of the brain of affected patients. Compelling evidence supports that in the early prodromal phases of PD, synaptic terminal and axonal alterations initiate and drive a retrograde degeneration process culminating with the loss of nigral dopaminergic neurons. This notwithstanding, the molecular triggers remain to be fully elucidated. Although it has been shown that α-syn fibrillary aggregation can induce early synaptic and axonal impairment and cause nigrostriatal degeneration, we still ignore how and why α-syn fibrillation begins. Nuclear factor-κB (NF-κB) transcription factors, key regulators of inflammation and apoptosis, are involved in the brain programming of systemic aging as well as in the pathogenesis of several neurodegenerative diseases. The NF-κB family of factors consists of five different subunits (c-Rel, p65/RelA, p50, RelB, and p52), which combine to form transcriptionally active dimers. Different findings point out a role of RelA in PD. Interestingly, the nuclear content of RelA is abnormally increased in nigral dopamine (DA) neurons and glial cells of PD patients. Inhibition of RelA exert neuroprotection against (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine) MPTP and 1-methyl-4-phenylpyridinium (MPP+) toxicity, suggesting that this factor decreases neuronal resilience. Conversely, the c-Rel subunit can exert neuroprotective actions. We recently described that mice deficient for c-Rel develop a PD-like motor and non-motor phenotype characterized by progressive brain α-syn accumulation and early synaptic changes preceding the frank loss of nigrostriatal neurons. This evidence supports that dysregulations in this transcription factors may be involved in the onset of PD. This review highlights observations supporting a possible interplay between NF-κB dysregulation and α-syn pathology in PD, with the aim to disclose novel potential mechanisms involved in the pathogenesis of this disorder.

Parkinson's disease treatment: past, present, and future

The substantial contributions of Dr. Gerald Stern to past and current treatments for Parkinson's disease patients are reviewed, which form the foundation for an evaluation of future options to control symptoms and halt progression of the disease. These opportunities will depend on a greater understanding of the relative contributions of the environment, genetic and epigenetic influences to disease onset, and promise to emerge as strategies for improving mitochondrial function, halting accumulation of synuclein and neuromelanin, in addition to refinement of stem cell and gene therapies. Such advances will be achieved through deployment of improved models for the disease.

Tensor decomposition of stimulated monocyte and macrophage gene expression profiles identifies neurodegenerative disease-specific trans-eQTLs

Recent human genetic studies suggest that cells of the innate immune system have a primary role in the pathogenesis of neurodegenerative diseases. However, the results from these studies often do not elucidate how the genetic variants affect the biology of these cells to modulate disease risk. Here, we applied a tensor decomposition method to uncover disease associated gene networks linked to distal genetic variation in stimulated human monocyte and macrophage gene expression profiles. We report robust evidence that some disease associated genetic variants affect the expression of multiple genes in trans. These include a Parkinson's disease locus influencing the expression of genes mediated by a protease that controls lysosomal function, and Alzheimer's disease loci influencing the expression of genes involved in type 1 interferon signaling, myeloid phagocytosis, and complement cascade pathways. Overall, we uncover gene networks in induced innate immune cells linked to disease associated genetic variants, which may help elucidate the underlying biology of disease.

Leucine-rich repeat kinase 2 and lysosomal dyshomeostasis in Parkinson disease

Over the last two decades, a number of studies have underlined the importance of lysosomal-based degradative pathways in maintaining the homeostasis of post-mitotic cells, and revealed the remarkable contribution of a functional autophagic machinery in the promotion of longevity. In contrast, defects in the clearance of organelles and aberrant protein aggregates have been linked to accelerated neuronal loss and neurological dysfunction. Several neurodegenerative disorders, among which Alzheimer disease (AD), Frontotemporal dementia, and Amyotrophic Lateral Sclerosis to name a few, are associated with alterations of the autophagy and endo-lysosomal pathways. In Parkinson disease (PD), the most prevalent genetic determinant, Leucine-rich repeat kinase 2 (LRRK2), is believed to be involved in the regulation of intracellular vesicle traffic, autophagy and lysosomal function. Here, we review the current understanding of the mechanisms by which LRRK2 regulates lysosomal-based degradative pathways in neuronal and non-neuronal cells and discuss the impact of pathogenic PD mutations in contributing to lysosomal dyshomeostasis.

Identification of biomarkers associated with Parkinson's disease by gene expression profiling studies and bioinformatics analysis

Parkinson's disease (PD) is associated with a selective loss of the neurons in the midbrain area called the substantia nigra pars compacta and the loss of projecting nerve fibers in the striatum. Predominant pathological hallmarks of PD are the degeneration of discrete neuronal populations and progressive accumulation of α-synuclein-containing intracytoplasmic inclusions called Lewy bodies and dystrophic Lewy neuritis. There is currently no therapy to terminate or delay the neurodegenerative process as the exact mechanisms underlying the pathogenesis of PD require further investigation. The identification and validation of novel biomarkers for the diagnosis of PD is a great challenge using contemporary approaches and optimizing sampling handling as well as interpretation using bioinformatics analysis. In this review, recent evidences associated with multi-omic data-sets and molecular mechanisms underlying PD are examined. A combined mapping of several transcriptional evidences could establish a patient-specific signature for early diagnose of PD though eligible systems biology tools, which can also help develop effective drug-based therapeutic approaches.

Informing disease modelling with brain-relevant functional genomic annotations

The past decade has seen a surge in the number of disease/trait-associated variants, largely because of the union of studies to share genetic data and the availability of electronic health records from large cohorts for research use. Variant discovery for neurological and neuropsychiatric genome-wide association studies, including schizophrenia, Parkinson's disease and Alzheimer's disease, has greatly benefitted; however, the translation of these genetic association results to interpretable biological mechanisms and models is lagging. Interpreting disease-associated variants requires knowledge of gene regulatory mechanisms and computational tools that permit integration of this knowledge with genome-wide association study results. Here, we summarize key conceptual advances in the generation of brain-relevant functional genomic annotations and amongst tools that allow integration of these annotations with association summary statistics, which together provide a new and exciting opportunity to identify disease-relevant genes, pathways and cell types in silico. We discuss the opportunities and challenges associated with these developments and conclude with our perspective on future advances in annotation generation, tool development and the union of the two.

Moving beyond neurons: the role of cell type-specific gene regulation in Parkinson's disease heritability

Parkinson’s disease (PD), with its characteristic loss of nigrostriatal dopaminergic neurons and deposition of α-synuclein in neurons, is often considered a neuronal disorder. However, in recent years substantial evidence has emerged to implicate glial cell types, such as astrocytes and microglia. In this study, we used stratified LD score regression and expression-weighted cell-type enrichment together with several brain-related and cell-type-specific genomic annotations to connect human genomic PD findings to specific brain cell types. We found that PD heritability attributable to common variation does not enrich in global and regional brain annotations or brain-related cell-type-specific annotations. Likewise, we found no enrichment of PD susceptibility genes in brain-related cell types. In contrast, we demonstrated a significant enrichment of PD heritability in a curated lysosomal gene set highly expressed in astrocytic, microglial, and oligodendrocyte subtypes, and in LoF-intolerant genes, which were found highly expressed in almost all tested cellular subtypes. Our results suggest that PD risk loci do not lie in specific cell types or individual brain regions, but rather in global cellular processes detectable across several cell types.