Identification of genetic modifiers of age-at-onset for familial Parkinson's disease

Genetic and Epigenetic Study of Monozygotic Twins Affected by Parkinson’s Disease

Background: Genetic and epigenetic modifiers of age at onset of Parkinson’s disease (PD) are largely unknown. It remains unclear whether DNA methylation (DNAm) age acceleration is linked to age at onset in PD patients of different ethnicities with a similar genetic background. We aim to characterize the clinical, genomic and epigenomic features of three pairs of Chinese monozygotic twins discordant for PD onset by up to 10 years. Methods: We conducted whole genome sequencing, multiplex ligation-dependent probe amplification and genome-wide DNAm array to evaluate the three pairs of Chinese monozygotic twins discordant for age at onset of PD (families A–C). Results: We identified two heterozygous PRKN mutations (exon 2–4 deletion and p.Met1Thr) in PD affected members of one family. Somatic mutation analyses of investigated families did not reveal any variants that could explain the phenotypic discordance in the twin pairs. Of note, our epigenetic study revealed that the twins with earlier-onset had a trend of faster DNAm age acceleration than the later-onset/asymptomatic twins, but without statistical significance. Conclusion: The link between DNAm age acceleration and PD onset in Chinese patients should be interpreted with cautious, and need to be further verified in an extended PD cohort with similar genetic background.

Promising biomarkers and therapeutic targets for the management of Parkinson's disease: recent advancements and contemporary research

Parkinson's disease (PD) is one of the progressive neurological diseases which affect around 10 million population worldwide. The clinical manifestation of motor symptoms in PD patients appears later when most dopaminergic neurons have degenerated. Thus, for better management of PD, the development of accurate biomarkers for the early prognosis of PD is imperative. The present work will discuss the potential biomarkers from various attributes covering biochemical, microRNA, and neuroimaging aspects (α-synuclein, DJ-1, UCH-L1, β-glucocerebrosidase, BDNF, etc.) for diagnosis, recent development in PD management, and major limitations with current and conventional anti-Parkinson therapy. This manuscript summarizes potential biomarkers and therapeutic targets, based on available preclinical and clinical evidence, for better management of PD.

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.

Epigenetic Clock Acceleration Is Linked to Age at Onset of Parkinson's Disease

BACKGROUND

Aging is the strongest risk factor for Parkinson's disease (PD), which is a clinically heterogeneous movement disorder with highly variable age at onset. DNA methylation age (DNAm age) is an epigenetic clock that could reflect biological aging.

OBJECTIVES

The aim was to evaluate whether PD age at onset is associated with DNAm-age acceleration (difference between DNAm age and chronological age).

METHODS

We used the genome-wide Infinium MethylationEPIC array to assess DNAm age in discovery (n = 96) and replication (n = 182) idiopathic PD cohorts and a unique longitudinal LRRK2 cohort (n = 220) at four time points over a 3-year period, comprising 91 manifesting and 129 nonmanifesting G2019S carriers at baseline. Cox proportional hazard regression and multivariate linear regression were used to evaluate the relation between DNAm-age acceleration and PD age at onset, which was highly variable in manifesting G2019S carriers (36-75 years) and both idiopathic PD cohorts (26-77 and 35-81 years).

RESULTS

DNAm-age acceleration remained steady over the 3-year period in most G2019S carriers. It was strongly associated with age at onset in the LRRK2 cohort (P = 2.25 × 10^-15 ) and discovery idiopathic PD cohort (P = 5.39 × 10^-9 ), suggesting that every 5-year increase in DNAm-age acceleration is related to about a 6-year earlier onset. This link was replicated in an independent idiopathic PD cohort (P = 1.91 × 10^-10 ). In each cohort, the faster-aging group has an increased hazard for an earlier onset (up to 255%).

CONCLUSIONS

This study is the first to demonstrate that DNAm-age acceleration is related to PD age at onset, which could be considered in disease-modifying clinical trials. Future studies should evaluate the stability of DNAm-age acceleration over longer time periods, especially for phenoconverters from nonmanifesting to manifesting individuals. © 2022 International Parkinson and Movement Disorder Society.

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.

Novel insights into the role of circular RNAs in Parkinson disease: An emerging renaissance in the management of neurodegenerative diseases

Parkinson's disease (PD), as a debilitating neurodegenerative disease, particularly affects the elderly population, and is clinically identified by resting tremor, rigidity, and bradykinesia. Pathophysiologically, PD is characterized by an early loss of dopaminergic neurons in the Substantia nigra pars compacta, accompanied by the extensive aggregation of alpha-synuclein (α-Syn) in the form of Lewy bodies. The onset of PD has been reported to be influenced by multiple biological molecules. In this context, circular RNAs (circRNAs), as tissue-specific noncoding RNAs with closed structures, have been recently demonstrated to involve in a set of PD's pathogenic processes. These RNA molecules can either up- or downregulate the expression of α-Syn, as well as moderating its accumulation through different regulatory mechanisms, in which targeting microRNAs (miRNAs) is considered the most common pathway. Since circRNAs have prominent structural and biological characteristics, they could also be considered as promising candidates for PD diagnosis and treatment. Unfortunately, PD has become a global health concern, and a large number of its pathogenic processes are still unclear; thus, it is crucial to elucidate the ambiguous aspects of PD pathophysiology to improve the efficiency of diagnostic and therapeutic strategies. In line with this fact, the current review aims to highlight the interplay between circRNAs and PD pathogenesis, and then discusses the diagnostic and therapeutic potential of circRNAs in PD progression. This study will thus be the first of its kind reviewing the relationship between circRNAs and PD.

Multi-modality machine learning predicting Parkinson's disease

Personalized medicine promises individualized disease prediction and treatment. The convergence of machine learning (ML) and available multimodal data is key moving forward. We build upon previous work to deliver multimodal predictions of Parkinson's disease (PD) risk and systematically develop a model using GenoML, an automated ML package, to make improved multi-omic predictions of PD, validated in an external cohort. We investigated top features, constructed hypothesis-free disease-relevant networks, and investigated drug-gene interactions. We performed automated ML on multimodal data from the Parkinson's progression marker initiative (PPMI). After selecting the best performing algorithm, all PPMI data was used to tune the selected model. The model was validated in the Parkinson's Disease Biomarker Program (PDBP) dataset. Our initial model showed an area under the curve (AUC) of 89.72% for the diagnosis of PD. The tuned model was then tested for validation on external data (PDBP, AUC 85.03%). Optimizing thresholds for classification increased the diagnosis prediction accuracy and other metrics. Finally, networks were built to identify gene communities specific to PD. Combining data modalities outperforms the single biomarker paradigm. UPSIT and PRS contributed most to the predictive power of the model, but the accuracy of these are supplemented by many smaller effect transcripts and risk SNPs. Our model is best suited to identifying large groups of individuals to monitor within a health registry or biobank to prioritize for further testing. This approach allows complex predictive models to be reproducible and accessible to the community, with the package, code, and results publicly available.

Accumulation of systematic TPM1 mediates inflammation and neuronal remodeling by phosphorylating PKA and regulating the FABP5/NF-κB signaling pathway in the retina of aged mice

The molecular mechanisms underlying functional decline during normal brain aging are poorly understood. Here, we identified the actin‐associated protein tropomyosin 1 (TPM1) as a new systemic pro‐aging factor associated with function deficits in normal aging retinas. Heterochronic parabiosis and blood plasma treatment confirmed that systemic factors regulated age‐related inflammatory responses and the ectopic dendritic sprouting of rod bipolar (RBC) and horizontal (HC) cells in the aging retina. Proteomic analysis revealed that TPM1 was a potential systemic molecule underlying structural and functional deficits in the aging retina. Recombinant TPM1 protein administration accelerated the activation of glial cells, the dendritic sprouting of RBCs and HCs and functional decline in the retina of young mice, whereas anti‐TPM1 neutralizing antibody treatment ameliorated age‐related structural and function changes in the retina of aged mice. Old mouse plasma (OMP) induced glial cell activation and the dendritic outgrowth of RBCs and HCs in young mice, and yet TMP1‐depleted OMP failed to reproduce the similar effect in young mice. These results confirmed that TPM1 was a systemic pro‐aging factor. Moreover, we demonstrated that systematic TPM1 was an immune‐related molecule, which elicited endogenous TPM1 expression and inflammation by phosphorylating PKA and regulating FABP5/NF‐κB signaling pathway in normal aging retinas. Interestingly, we observed TPM1 upregulation and the ectopic dendritic sprouting of RBCs and HCs in young mouse models of Alzheimer's disease, indicating a potential role of TPM1 in age‐related neurodegenerative diseases. Our data indicate that TPM1 could be targeted for combating the aging process.

Screening of potential biomarkers in peripheral blood of patients with depression based on weighted gene co-expression network analysis and machine learning algorithms

BACKGROUND

The prevalence of depression has been increasing worldwide in recent years, posing a heavy burden on patients and society. However, the diagnostic and therapeutic tools available for this disease are inadequate. Therefore, this research focused on the identification of potential biomarkers in the peripheral blood of patients with depression.

METHODS

The expression dataset GSE98793 of depression was provided by the Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/gds). Initially, differentially expressed genes (DEGs) were detected in GSE98793. Subsequently, the most relevant modules for depression were screened according to weighted gene co-expression network analysis (WGCNA). Finally, the identified DEGs were mapped to the WGCNA module genes to obtain the intersection genes. In addition, Gene Ontology (GO), Disease Ontology (DO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses were conducted on these genes. Moreover, biomarker screening was carried out by protein-protein interaction (PPI) network construction of intersection genes on the basis of various machine learning algorithms. Furthermore, the gene set enrichment analysis (GSEA), immune function analysis, transcription factor (TF) analysis, and the prediction of the regulatory mechanism were collectively performed on the identified biomarkers. In addition, we also estimated the clinical diagnostic ability of the obtained biomarkers, and performed Mfuzz expression pattern clustering and functional enrichment of the most potential biomarkers to explore their regulatory mechanisms. Finally, we also perform biomarker-related drug prediction.

RESULTS

Differential analysis was used for obtaining a total of 550 DEGs and WGCNA for obtaining 1,194 significant genes. Intersection analysis of the two yielded 140 intersection genes. Biological functional analysis indicated that these genes had a major role in inflammation-related bacterial infection pathways and cardiovascular diseases such as atherosclerosis. Subsequently, the genes S100A12, SERPINB2, TIGIT, GRB10, and LHFPL2 in peripheral serum were identified as depression biomarkers by using machine learning algorithms. Among them, S100A12 is the most valuable biomarker for clinical diagnosis. Finally, antidepressants, including disodium selenite and eplerenone, were predicted.

CONCLUSION

The genes S100A12, TIGIT, SERPINB2, GRB10, and LHFPL2 in peripheral serum are viable diagnostic biomarkers for depression. and contribute to the diagnosis and prevention of depression in clinical practice.

The Parkinson's-disease-associated mutation LRRK2-G2019S alters dopaminergic differentiation dynamics via NR2F1

Increasing evidence suggests that neurodevelopmental alterations might contribute to increase the susceptibility to develop neurodegenerative diseases. We investigate the occurrence of developmental abnormalities in dopaminergic neurons in a model of Parkinson's disease (PD). We monitor the differentiation of human patient-specific neuroepithelial stem cells (NESCs) into dopaminergic neurons. Using high-throughput image analyses and single-cell RNA sequencing, we observe that the PD-associated LRRK2-G2019S mutation alters the initial phase of neuronal differentiation by accelerating cell-cycle exit with a concomitant increase in cell death. We identify the NESC-specific core regulatory circuit and a molecular mechanism underlying the observed phenotypes. The expression of NR2F1, a key transcription factor involved in neurogenesis, decreases in LRRK2-G2019S NESCs, neurons, and midbrain organoids compared to controls. We also observe accelerated dopaminergic differentiation in vivo in NR2F1-deficient mouse embryos. This suggests a pathogenic mechanism involving the LRRK2-G2019S mutation, where the dynamics of dopaminergic differentiation are modified via NR2F1.

CoCoA-diff: counterfactual inference for single-cell gene expression analysis

Finding a causal gene is a fundamental problem in genomic medicine. We present a causal inference framework, CoCoA-diff, that prioritizes disease genes by adjusting confounders without prior knowledge of control variables in single-cell RNA-seq data. We demonstrate that our method substantially improves statistical power in simulations and real-world data analysis of 70k brain cells collected for dissecting Alzheimer's disease. We identify 215 differentially regulated causal genes in various cell types, including highly relevant genes with a proper cell type context. Genes found in different types enrich distinctive pathways, implicating the importance of cell types in understanding multifaceted disease mechanisms.

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.

Atremorine in Parkinson's disease: From dopaminergic neuroprotection to pharmacogenomics

Atremorine is a novel bioproduct obtained by nondenaturing biotechnological processes from a genetic species of Vicia faba. Atremorine is a potent dopamine (DA) enhancer with powerful effects on the neuronal dopaminergic system, acting as a neuroprotective agent in Parkinson's disease (PD). Over 97% of PD patients respond to a single dose of Atremorine (5 g, p.o.) 1 h after administration. This response is gender-, time-, dose-, and genotype-dependent, with optimal doses ranging from 5 to 20 g/day, depending upon disease severity and concomitant medication. Drug-free patients show an increase in DA levels from 12.14 ± 0.34 pg/ml to 6463.21 ± 1306.90 pg/ml; and patients chronically treated with anti-PD drugs show an increase in DA levels from 1321.53 ± 389.94 pg/ml to 16,028.54 ± 4783.98 pg/ml, indicating that Atremorine potentiates the dopaminergic effects of conventional anti-PD drugs. Atremorine also influences the levels of other neurotransmitters (adrenaline, noradrenaline) and hormones which are regulated by DA (e.g., prolactin, PRL), with no effect on serotonin or histamine. The variability in Atremorine-induced DA response is highly attributable to pharmacogenetic factors. Polymorphic variants in pathogenic (SNCA, NUCKS1, ITGA8, GPNMB, GCH1, BCKDK, APOE, LRRK2, ACMSD), mechanistic (DRD2), metabolic (CYP2D6, CYP2C9, CYP2C19, CYP3A4/5, NAT2), transporter (ABCB1, SLC6A2, SLC6A3, SLC6A4) and pleiotropic genes (APOE) influence the DA response to Atremorine and its psychomotor and brain effects. Atremorine enhances DNA methylation and displays epigenetic activity via modulation of the pharmacoepigenetic network. Atremorine is a novel neuroprotective agent for dopaminergic neurons with potential prophylactic and therapeutic activity in PD.

Kapd Is Essential for Specification of the Dopaminergic Neurogenesis in Zebrafish Embryos

To define novel networks of Parkinson's disease (PD) pathogenesis, the substantia nigra pars compacta of A53T mice, where a death-promoting protein, FAS-associated factor 1 was ectopically expressed for 2 weeks in the 2-, 4-, 6-, and 8-month-old mice, and was subjected to transcriptomic analysis. Compendia of expression profiles and a hierarchical clustering heat map of differentially expressed genes associated with PD were bioinformatically generated. Transcripts level of a particular gene was fluctuated by 20, 60, and 0.75 fold in the 4-, 6-, and 8-month-old mice compared to the 2 months old. Because the gene contained Kelch domain, it was named as Kapd (Kelch-containing protein associated with PD). Biological functions of Kapd were systematically investigated in the zebrafish embryos. First, transcripts of a zebrafish homologue of Kapd, kapd were found in the floor plate of the neural tube at 10 h post fertilization (hpf), and restricted to the tegmentum, hypothalamus, and cerebellum at 24 hpf. Second, knockdown of kapd caused developmental defects of DA progenitors in the midbrain neural keel and midbrain? hindbrain boundary at 10 hpf. Third, overexpression of kapd increased transcripts level of the dopaminergic immature neuron marker, shha in the prethalamus at 16.5 hpf. Finally, developmental consequences of kapd knockdown reduced transcripts level of the markers for the immature and mature DA neurons, nkx2.2, olig2, otx2b, and th in the ventral diencephalon of the midbrain at 18 hpf. It is thus most probable that Kapd play a key role in the specification of the DA neuronal precursors in zebrafish embryos.

Shared genetic links between amyotrophic lateral sclerosis and obesity-related traits: a genome-wide association study

Epidemiological and clinical studies have suggested comorbidities between amyotrophic lateral sclerosis (ALS) and obesity-related traits. However, little is known about their shared genetic architecture. To examine whether genetic enrichment exists between ALS and obesity-related traits and to identify shared risk loci, we analyzed summary statistics from genome-wide association studies using the conditional false discovery rate statistical framework, and further conducted functional enrichment analysis. Robust genetic enrichment was observed for ALS conditional on body mass index, body fat percentage, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and type 2 diabetes. Nine shared genetic loci were identified, among which 6 were replicated in a second ALS cohort, including C9orf72, G2E3, SCFD1, ATXN3, CLCN3 and GGNBP2. We further identified GGNBP2 as a novel ALS risk gene, by integrating summary data-based Mendelian randomization analysis. Functional enrichment analysis indicated that the shared risk genes were involved in 2 pathways, namely membrane trafficking and vesicle-mediated transport. These results provide a better understanding for the pleiotropy of ALS and have implications for future therapeutic trials.

Analysis of master transcription factors related to Parkinson's disease through the gene transcription regulatory network

INTRODUCTION

This study investigated the relationships between differentially co-expressed gene pairs or links (DCLs) and transcription factors (TFs) in the gene transcription regulatory network (GTRN) to clarify the molecular mechanisms underlying the pathogenesis of Parkinson's disease (PD).

MATERIAL AND METHODS

Microarray dataset GSE7621 from Gene Expression Omnibus (GEO) was used to identify differentially expressed genes (DEGs) and perform Gene Ontology (GO) enrichment analysis. Differentially co-expressed genes (DCGs) and DCLs were identified by the DCGL package in R soft-ware. DCLs that were potentially related to the regulation mechanisms, and corresponding TFs, were identified using the DR sort function in the DCGL V2.0 package. The GTRN was constructed with these DCLs-TFs, and visualized with Cytoscape software.

RESULTS

A total of 131 DEGs, including 77 up-regulated DEGs and 54 down-regulated DEGs, were identified, which were mainly enriched for plasma membrane, cell activities, and metabolism. We found that ICAM1-LTBP and CTHRC1-UTP3 might alter gene regulation relationships in PD. The GTRN was constructed with DCLs-TFs, including 348 nodes (118 TFs and 230 DCGs) and 1045 DCLs. These TFs (AHR, SP1, PAX5, etc.) could regulate many target genes (e.g. ICAM1 and LTBP) in the GTRN of PD.

CONCLUSIONS

ICAM1 and LTBP may play a role in PD symptom development and pathology, and might be regulated by important TFs (AHR, SP1, PAX5, etc.) identified in the GTRN of PD. These findings may help elucidate the molecular mechanisms underlying PD and find a novel drug target for this disease.

Common Factors in Neurodegeneration: A Meta-Study Revealing Shared Patterns on a Multi-Omics Scale

Neurodegenerative diseases such as Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), and amyotrophic lateral sclerosis (ALS) are heterogeneous, progressive diseases with frequently overlapping symptoms characterized by a loss of neurons. Studies have suggested relations between neurodegenerative diseases for many years (e.g., regarding the aggregation of toxic proteins or triggering endogenous cell death pathways). We gathered publicly available genomic, transcriptomic, and proteomic data from 177 studies and more than one million patients to detect shared genetic patterns between the neurodegenerative diseases on three analyzed omics-layers. The results show a remarkably high number of shared differentially expressed genes between the transcriptomic and proteomic levels for all conditions, while showing a significant relation between genomic and proteomic data between AD and PD and AD and ALS. We identified a set of 139 genes being differentially expressed in several transcriptomic experiments of all four diseases. These 139 genes showed overrepresented gene ontology (GO) Terms involved in the development of neurodegeneration, such as response to heat and hypoxia, positive regulation of cytokines and angiogenesis, and RNA catabolic process. Furthermore, the four analyzed neurodegenerative diseases (NDDs) were clustered by their mean direction of regulation throughout all transcriptomic studies for this set of 139 genes, with the closest relation regarding this common gene set seen between AD and HD. GO-Term and pathway analysis of the proteomic overlap led to biological processes (BPs), related to protein folding and humoral immune response. Taken together, we could confirm the existence of many relations between Alzheimer's disease, Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis on transcriptomic and proteomic levels by analyzing the pathways and GO-Terms arising in these intersections. The significance of the connection and the striking relation of the results to processes leading to neurodegeneration between the transcriptomic and proteomic data for all four analyzed neurodegenerative diseases showed that exploring many studies simultaneously, including multiple omics-layers of different neurodegenerative diseases simultaneously, holds new relevant insights that do not emerge from analyzing these data separately. Furthermore, the results shed light on processes like the humoral immune response that have previously been described only for certain diseases. Our data therefore suggest human patients with neurodegenerative diseases should be addressed as complex biological systems by integrating multiple underlying data sources.

Complex genetic dependencies among growth and neurological phenotypes in healthy children: Towards deciphering developmental mechanisms

The genetic mechanisms of childhood development in its many facets remain largely undeciphered. In the population of healthy infants studied in the Growing Up in Singapore Towards Healthy Outcomes (GUSTO) program, we have identified a range of dependencies among the observed phenotypes of fetal and early childhood growth, neurological development, and a number of genetic variants. We have quantified these dependencies using our information theory-based methods. The genetic variants show dependencies with single phenotypes as well as pleiotropic effects on more than one phenotype and thereby point to a large number of brain-specific and brain-expressed gene candidates. These dependencies provide a basis for connecting a range of variants with a spectrum of phenotypes (pleiotropy) as well as with each other. A broad survey of known regulatory expression characteristics, and other function-related information from the literature for these sets of candidate genes allowed us to assemble an integrated body of evidence, including a partial regulatory network, that points towards the biological basis of these general dependencies. Notable among the implicated loci are RAB11FIP4 (next to NF1), MTMR7 and PLD5, all highly expressed in the brain; DNMT1 (DNA methyl transferase), highly expressed in the placenta; and PPP1R12B and DMD (dystrophin), known to be important growth and development genes. While we cannot specify and decipher the mechanisms responsible for the phenotypes in this study, a number of connections for further investigation of fetal and early childhood growth and neurological development are indicated. These results and this approach open the door to new explorations of early human development.

Network Protein Interaction in Parkinson's Disease and Periodontitis Interplay: A Preliminary Bioinformatic Analysis

Recent studies supported a clinical association between Parkinson's disease (PD) and periodontitis. Hence, investigating possible interactions between proteins associated to these two conditions is of interest. In this study, we conducted a protein-protein network interaction analysis with recognized genes encoding proteins with variants strongly associated with PD and periodontitis. Genes of interest were collected via the Genome-Wide Association Studies (GWAS) database. Then, we conducted a protein interaction analysis, using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, with a highest confidence cutoff of 0.9 and sensitivity analysis with confidence cutoff of 0.7. Our protein network casts a comprehensive analysis of potential protein-protein interactions between PD and periodontitis. This analysis may underpin valuable information for new candidate molecular mechanisms between PD and periodontitis and may serve new potential targets for research purposes. These results should be carefully interpreted, giving the limitations of this approach.

Genome-wide identification of m6A-associated single-nucleotide polymorphisms in Parkinson's disease

N⁶-methyladenosine (m⁶A)-associated single nucleotide polymorphisms (SNPs) play a vital role in several neurological diseases. However, little is known about the relationship between m⁶A modification and Parkinson's disease (PD). We investigated potential functional variants of m⁶A-SNPs from large-scale genome-wide association studies (GWAS) in PD patients. The candidate m⁶A-SNPs were further assessed by expression quantitative trait loci (eQTL) analysis and differential gene expression analysis. We identified 12 m⁶A-SNPs that were significantly associated with PD risk. Further, eQTL and expression analyses identified five of these m⁶A-SNPs (rs75072999 of GAK, rs1378602, rs4924839 and rs8071834 of ALKBH5, and rs1033500 of C6orf10) that were associated with altered gene expression in PD. Our results suggest that m⁶A-SNPs could play a role in PD risk. Future studies are needed to confirm these PD-associated m⁶A-SNPs and elucidate their mechanisms.

Prediction of age at onset in Parkinson's disease using objective specific neuroimaging genetics based on a sparse canonical correlation analysis

The age at onset (AAO) is an important determinant in Parkinson’s disease (PD). Neuroimaging genetics is suitable for studying AAO in PD as it jointly analyzes imaging and genetics. We aimed to identify features associated with AAO in PD by applying the objective-specific neuroimaging genetics approach and constructing an AAO prediction model. Our objective-specific neuroimaging genetics extended the sparse canonical correlation analysis by an additional data type related to the target task to investigate possible associations of the imaging–genetic, genetic–target, and imaging–target pairs simultaneously. The identified imaging, genetic, and combined features were used to construct analytical models to predict the AAO in a nested five-fold cross-validation. We compared our approach with those from two feature selection approaches where only associations of imaging–target and genetic–target were explored. Using only imaging features, AAO prediction was accurate in all methods. Using only genetic features, the results from other methods were worse or unstable compared to our model. Using both imaging and genetic features, our proposed model predicted the AAO well (r = 0.5486). Our findings could have significant impacts on the characterization of prodromal PD and contribute to diagnosing PD early because genetic features could be measured accurately from birth.

Parkinson's disease age at onset genome-wide association study: Defining heritability, genetic loci, and α-synuclein mechanisms

BACKGROUND

Increasing evidence supports an extensive and complex genetic contribution to PD. Previous genome-wide association studies (GWAS) have shed light on the genetic basis of risk for this disease. However, the genetic determinants of PD age at onset are largely unknown.

OBJECTIVES

To identify the genetic determinants of PD age at onset.

METHODS

Using genetic data of 28,568 PD cases, we performed a genome-wide association study based on PD age at onset.

RESULTS

We estimated that the heritability of PD age at onset attributed to common genetic variation was ∼0.11, lower than the overall heritability of risk for PD (∼0.27), likely, in part, because of the subjective nature of this measure. We found two genome-wide significant association signals, one at SNCA and the other a protein-coding variant in TMEM175, both of which are known PD risk loci and a Bonferroni-corrected significant effect at other known PD risk loci, GBA, INPP5F/BAG3, FAM47E/SCARB2, and MCCC1. Notably, SNCA, TMEM175, SCARB2, BAG3, and GBA have all been shown to be implicated in α-synuclein aggregation pathways. Remarkably, other well-established PD risk loci, such as GCH1 and MAPT, did not show a significant effect on age at onset of PD.

CONCLUSIONS

Overall, we have performed the largest age at onset of PD genome-wide association studies to date, and our results show that not all PD risk loci influence age at onset with significant differences between risk alleles for age at onset. This provides a compelling picture, both within the context of functional characterization of disease-linked genetic variability and in defining differences between risk alleles for age at onset, or frank risk for disease. © 2019 International Parkinson and Movement Disorder Society.

Mitochondria function associated genes contribute to Parkinson's Disease risk and later age at onset

Mitochondrial dysfunction has been implicated in the etiology of monogenic Parkinson's disease (PD). Yet the role that mitochondrial processes play in the most common form of the disease; sporadic PD, is yet to be fully established. Here, we comprehensively assessed the role of mitochondrial function-associated genes in sporadic PD by leveraging improvements in the scale and analysis of PD GWAS data with recent advances in our understanding of the genetics of mitochondrial disease. We calculated a mitochondrial-specific polygenic risk score (PRS) and showed that cumulative small effect variants within both our primary and secondary gene lists are significantly associated with increased PD risk. We further reported that the PRS of the secondary mitochondrial gene list was significantly associated with later age at onset. Finally, to identify possible functional genomic associations we implemented Mendelian randomization, which showed that 14 of these mitochondrial function-associated genes showed functional consequence associated with PD risk. Further analysis suggested that the 14 identified genes are not only involved in mitophagy, but implicate new mitochondrial processes. Our data suggests that therapeutics targeting mitochondrial bioenergetics and proteostasis pathways distinct from mitophagy could be beneficial to treating the early stage of PD.

A proteomic analysis of differentiating dopamine neurons derived from human embryonic stem cells

Human embryonic stem cells (hESC) are being exploited for potential use in cell transplantation due to their capacity for self-renewal and pluripotency. Dopamine (DA) neurons derived from hESC represent a promising source of cell replacement therapy for Parkinson’s disease (PD). While gene expression on the transcriptome level has been extensively studied, limited information is available for the proteome-level changes associated with DA neuron differentiation. Here we analyzed the proteome of differentiating DA neurons to search for the potential biomarkers to assess the efficiency of differentiation. Although the proteome profile of DA neurons did not exhibit significant changes, a number of cytoskeletal proteins including nuclear lamin, tropomyosin 1, and myosin light chain 1 were specifically up-regulated during differentiation. Expression analysis of the respective genes was also consistent with the proteome results. In addition, these differentially expressed proteins form protein interaction network with several PD-related proteins suggesting that they may play roles in PD pathogenesis as well as the maturation of DA neurons.

Family-based association tests for rare variants with censored traits

We propose a set of family-based burden and kernel tests for censored traits (FamBAC and FamKAC). Here, censored traits refer to time-to-event outcomes, for instance, age-at-onset of a disease. To model censored traits in family-based designs, we used the frailty model, which incorporated not only fixed genetic effects of rare variants in a region of interest but also random polygenic effects shared within families. We first partitioned genotype scores of rare variants into orthogonal between- and within-family components, and then derived their corresponding efficient score statistics from the frailty model. Finally, FamBAC and FamKAC were constructed by aggregating the weighted efficient scores of the within-family components across rare variants and subjects. FamBAC collapsed rare variants within subject first to form a burden test that followed a chi-squared distribution; whereas FamKAC was a variant component test following a mixture of chi-squared distributions. For FamKAC, p-values can be computed by permutation tests or for computational efficiency by approximation methods. Through simulation studies, we showed that type I error was correctly controlled by FamBAC for various variant weighting schemes (0.0371 to 0.0527). However, FamKAC type I error rates based on approximation methods were deflated (max 0.0376) but improved by permutation tests. Our simulations also demonstrated that burden test FamBAC had higher power than kernel test FamKAC when high proportion (e.g. ≥ 80%) of causal variants had effects in the same direction. In contrast, when the effects of causal variants on the censored trait were in mixed directions, FamKAC outperformed FamBAC and had comparable or higher power than an existing method, RVFam. Our proposed framework has the flexibility to accommodate general nuclear families, and can be used to analyze sequence data for censored traits such as age-at-onset of a complex disease of interest.

Exploring genetic modifiers of Gaucher disease: The next horizon

Gaucher disease is an autosomal recessive lysosomal storage disorder resulting from mutations in the gene GBA1 that lead to a deficiency in the enzyme glucocerebrosidase. Accumulation of the enzyme's substrates, glucosylceramide and glucosylsphingosine, results in symptoms ranging from skeletal and visceral involvement to neurological manifestations. Nonetheless, there is significant variability in clinical presentations amongst patients, with limited correlation between genotype and phenotype. Contributing to this clinical variation are genetic modifiers that influence the phenotypic outcome of the disorder. In this review, we explore the role of genetic modifiers in Mendelian disorders and describe methods to facilitate their discovery. In addition, we provide examples of candidate modifiers of Gaucher disease, explore their relevance in the development of potential therapeutics, and discuss the impact of GBA1 and modifying mutations on other more common diseases like Parkinson disease. Identifying these important modulators of Gaucher phenotype may ultimately unravel the complex relationship between genotype and phenotype and lead to improved counseling and treatments.

Plasticity-related gene 3 (LPPR1) and age at diagnosis of Parkinson disease

Objective

To identify modifiers of age at diagnosis of Parkinson disease (PD).

Methods

Genome-wide association study (GWAS) included 1,950 individuals with PD from the NeuroGenetics Research Consortium (NGRC) study. Replication was conducted in the Parkinson's, Genes and Environment study, including 209 prevalent (PAGE_P) and 517 incident (PAGE_I) PD cases. Cox regression was used to test association with age at diagnosis. Individuals without neurologic disease were used to rule out confounding. Gene-level analysis and functional annotation were conducted using Functional Mapping and Annotation of GWAS platform (FUMA).

Results

The GWAS revealed 2 linked but seemingly independent association signals that mapped to LPPR1 on chromosome 9. LPPR1 was significant in gene-based analysis (p = 1E-8). The top signal (rs17763929, hazard ratio [HR] = 1.88, p = 5E-8) replicated in PAGE_P (HR = 1.87, p = 0.01) but not in PAGE_I. The second signal (rs73656147) was robust with no evidence of heterogeneity (HR = 1.95, p = 3E-6 in NGRC; HR = 2.14, p = 1E-3 in PAGE_P + PAGE_I, and HR = 2.00, p = 9E-9 in meta-analysis of NGRC + PAGE_P + PAGE_I). The associations were with age at diagnosis, not confounded by age in patients or in the general population. The PD-associated regions included variants with Combined Annotation Dependent Depletion (CADD) scores = 10–19 (top 1%–10% most deleterious mutations in the genome), a missense with predicted destabilizing effect on LPPR1, an expression quantitative trait locus (eQTL) for GRIN3A (false discovery rate [FDR] = 4E-4), and variants that overlap with enhancers in LPPR1 and interact with promoters of LPPR1 and 9 other brain-expressed genes (Hi-C FDR < 1E-6).

Conclusions

Through association with age at diagnosis, we uncovered LPPR1 as a modifier gene for PD. LPPR1 expression promotes neuronal regeneration after injury in animal models. Present data provide a strong foundation for mechanistic studies to test LPPR1 as a driver of response to damage and a therapeutic target for enhancing neuroregeneration and slowing disease progression.

No evidence for DNM3 as genetic modifier of age at onset in idiopathic Parkinson's disease

Parkinson's disease (PD) is a disorder with highly variable clinical phenotype. The identification of genetic variants modifying age at onset and other traits is of great interest because it may provide insight into disease mechanisms and potential therapeutic targets. A variant in the DNM3 gene (rs2421947) has been reported as a genetic modifier of age at onset in LRRK2-associated PD. To test the possible effect of genetic variation in DNM3 on age at onset in idiopathic PD, we examined rs2421947 in a total of 5918 patients with PD from seven data sets. We also assessed the potential effect of all common variants in the DNM3 locus. There was no significant association between rs2421947 and age at onset in any of the individual studies. Meta-analysis of the seven studies was nonsignificant and the between-study heterogeneity was minimal. No other common variants within the DNM3 locus affected age at onset. In conclusion, we find no evidence of an association between DNM3 variants and age at onset in idiopathic PD.

Advances in the discovery of genetic risk factors for complex forms of neurodegenerative disorders: contemporary approaches, success, challenges and prospects

Neurodegenerative diseases constitute a large proportion of disorders in elderly, majority being sporadic in occurrence with ∼5-10% familial. A strong genetic component underlies the Mendelian forms but nongenetic factors together with genetic vulnerability contributes to the complex sporadic forms. Several gene discoveries in the familial forms have provided novel insights into the pathogenesis of neurodegeneration with implications for treatment. Conversely, findings from genetic dissection of the sporadic forms, despite large genomewide association studies and more recently whole exome and whole genome sequencing, have been limited. This review provides a concise account of the genetics that we know, the pathways that they implicate, the challenges that are faced and the prospects that are envisaged for the sporadic, complex forms of neurodegenerative diseases, taking four most common conditions, namely Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis and Huntington disease as examples. Poor replication across studies, inability to establish genotype-phenotype correlations and the overall failure to predict risk and/or prevent disease in this group poses a continuing challenge. Among others, clinical heterogeneity emerges as the most important impediment warranting newer approaches. Advanced computational and system biology tools to analyse the big data are being generated and the alternate strategy such as subgrouping of case-control cohorts based on deep phenotyping using the principles of Ayurveda to overcome current limitation of phenotype heterogeneity seem to hold promise. However, at this point, with advances in discovery genomics and functional analysis of putative determinants with translation potential for the complex forms being minimal, stem cell therapies are being attempted as potential interventions. In this context, the possibility to generate patient derived induced pluripotent stem cells, mutant/gene/genome correction through CRISPR/Cas9 technology and repopulating the specific brain regions with corrected neurons, which may fulfil the dream of personalized medicine have been mentioned briefly. Understanding disease pathways/biology using this technology, with implications for development of novel therapeutics are optimistic expectations in the near future.

Overexpression of PGC-1α in aging muscle enhances a subset of young-like molecular patterns

PGC-1α is a transcriptional co-activator known as the master regulator of mitochondrial biogenesis. Its control of metabolism has been suggested to exert critical influence in the aging process. We have aged mice overexpressing PGC-1α in skeletal muscle to determine whether the transcriptional changes reflected a pattern of expression observed in younger muscle. Analyses of muscle proteins showed that Pax7 and several autophagy markers were increased. In general, the steady-state levels of several muscle proteins resembled that of muscle from young mice. Age-related mtDNA deletion levels were not increased by the PGC-1α-associated increase in mitochondrial biogenesis. Accordingly, age-related changes in the neuromuscular junction were minimized by PGC-1α overexpression. RNA-Seq showed that several genes overexpressed in the aged PGC-1α transgenic are expressed at higher levels in young when compared to aged skeletal muscle. As expected, there was increased expression of genes associated with energy metabolism but also of pathways associated with muscle integrity and regeneration. We also found that PGC-1α overexpression had a mild but significant effect on longevity. Taken together, overexpression of PGC-1α in aged muscle led to molecular changes that resemble the patterns observed in skeletal muscle from younger mice.

Identification of potential 'lifestyle-responsive' epigenomic biomarkers in healthy women aged 18-40

CONTEXT

Human health is complex and multifaceted; there is a need for biomarkers that reflect the multidimensional nature of health.

OBJECTIVE

To identify potential epigenomic biomarkers of health in women aged 18-40 participating in a six-month lifestyle intervention, next level health.

MATERIALS AND METHODS

Methylation data were obtained by reduced representation bisulphite sequencing of 21 female intervention participants as well as three non-participants. The Differential Methylation Analysis Package (DMAP) was used to investigate inter- and intra-individual variability and to identify potential targets of transient epigenetic control in the population studied.

RESULTS

Eleven genes were identified as significantly differentially methylated post- intervention in all 21 participants. 1884 genomic locations were found to be differentially methylated amongst the total female population studied representing potential epigenomic biomarkers.

CONCLUSIONS

The ability to demonstrate epigenetic changes arising from a lifestyle intervention can provide key information on the relationship between gene regulation, human behaviour and health.

Immunochemical analysis of the expression of SV2C in mouse, macaque and human brain

The synaptic vesicle glycoprotein 2C (SV2C) is an undercharacterized protein with enriched expression in phylogenetically old brain regions. Its precise role within the brain is unclear, though various lines of evidence suggest that SV2C is involved in the function of synaptic vesicles through the regulation of vesicular trafficking, calcium-induced exocytosis, or synaptotagmin function. SV2C has been linked to multiple neurological disorders, including Parkinson's disease and psychiatric conditions. SV2C is expressed in various cell types-primarily dopaminergic, GABAergic, and cholinergic cells. In mice, it is most highly expressed in nuclei within the basal ganglia, though it is unknown if this pattern of expression is consistent across species. Here, we use a custom SV2C-specific antiserum to describe localization within the brain of mouse, nonhuman primate, and human, including cell-type localization. We found that the immunoreactivity with this antiserum is consistent with previously-published antibodies, and confirmed localization of SV2C in the basal ganglia of rodent, rhesus macaque, and human. We observed strongest expression of SV2C in the substantia nigra, ventral tegmental area, dorsal striatum, pallidum, and nucleus accumbens of each species. Further, we demonstrate colocalization between SV2C and markers of dopaminergic, GABAergic, and cholinergic neurons within these brain regions. SV2C has been increasingly linked to dopamine and basal ganglia function. These antisera will be an important resource moving forward in our understanding of the role of SV2C in vesicle dynamics and neurological disease.

Parkinson's disease susceptibility variants and severity of Lewy body pathology

INTRODUCTION

Meta-analyses of genome-wide association studies (GWAS) have established common genetic risk factors for clinical Parkinson's disease (PD); however, associations between these risk factors and quantitative neuropathologic markers of disease severity have not been well-studied. This study evaluated associations of nominated variants from the most recent PD GWAS meta-analysis with Lewy body disease (LBD) subtype (brainstem, transitional, or diffuse) and pathologic burden of LB pathology as measured by LB counts in five cortical regions in a series of LBD cases.

METHODS

547 autopsy-confirmed cases of LBD were included and genotyped for 29 different GWAS-nominated PD risk variants. LB counts were measured in middle frontal (MF), superior temporal (ST), inferior parietal (IP), cingulate (CG), and parahippocampal (PH) gyri.

RESULTS

None of the variants examined were significantly associated with LB counts in any brain region or with LBD subtype after correcting for multiple testing. Nominally significant (P < 0.05) associations with LB counts where the direction of association was in agreement with that observed in the PD GWAS meta-analysis were observed for variants in BCKDK/STX1B (MF, ST, IP) and SNCA (ST). Additionally, MIR4697 and BCKDK/STX1B variants were nominally associated with LBD subtype.

CONCLUSION

The lack of a significant association between PD GWAS variants and severity of LB pathology is consistent with the generally subtle association odds ratios that have been observed in disease-risk analysis. These results also suggest that genetic factors other than the susceptibility loci may determine quantitative neuropathologic outcomes in patients with LBD.

New Genes Causing Hereditary Parkinson's Disease or Parkinsonism

PURPOSE OF REVIEW

This article reviews was to review genes where putative or confirmed pathogenic mutations causing Parkinson's disease or Parkinsonism have been identified since 2012, and summarizes the clinical and pathological picture of the associated disease subtypes.

RECENT FINDINGS

Newly reported genes for dominant Parkinson's disease are DNAJC13, CHCHD2, and TMEM230. However, the evidence for a disease-causing role is not conclusive, and further genetic and functional studies are warranted. RIC3 mutations have been reported from one family but not yet encountered in other patients. New genes for autosomal recessive disease include SYNJ1, DNAJC6, VPS13C, and PTRHD1. Deletions of a region on chromosome 22 (22q11.2del) are also associated with early-onset PD, but the mode of inheritance and the underlying causative gene remain unclear. PODXL mutations were reported in autosomal recessive PD, but their roles remain to be confirmed. Mutations in RAB39B cause an X-linked Parkinsonian disorder. Mutations in the new dominant PD genes have generally been found in medium- to late-onset Parkinson's disease. Many mutations in the new recessive and X-chromosomal genes cause severe atypical juvenile Parkinsonism, but less devastating mutations in these genes may cause PD.

The impact of obesity on neurodegenerative diseases

Neurodegenerative diseases are a growing health concern. The increasing incidences of these disorders have a great impact on the patients' quality of life. Although the mechanisms of neurodegenerative diseases are still far from being clarified, several studies look for new discoveries about their pathophysiology and prevention. Furthermore, evidence has shown a strong correlation between obesity and the development of Alzheimer's disease (AD) and Parkinson's disease (PD). Metabolic changes caused by overweight are related to damage to the central nervous system (CNS), which can lead to neural death, either by apoptosis or cell necrosis, as well as alter the synaptic plasticity of the neuron. This review aims to show the association between neurodegenerative diseases, focusing on AD and PD, and metabolic alterations.

Parkinson's Disease: From Pathogenesis to Pharmacogenomics

Parkinson's disease (PD) is the second most important age-related neurodegenerative disorder in developed societies, after Alzheimer's disease, with a prevalence ranging from 41 per 100,000 in the fourth decade of life to over 1900 per 100,000 in people over 80 years of age. As a movement disorder, the PD phenotype is characterized by rigidity, resting tremor, and bradykinesia. Parkinson's disease -related neurodegeneration is likely to occur several decades before the onset of the motor symptoms. Potential risk factors include environmental toxins, drugs, pesticides, brain microtrauma, focal cerebrovascular damage, and genomic defects. Parkinson's disease neuropathology is characterized by a selective loss of dopaminergic neurons in the substantia nigra pars compacta, with widespread involvement of other central nervous system (CNS) structures and peripheral tissues. Pathogenic mechanisms associated with genomic, epigenetic and environmental factors lead to conformational changes and deposits of key proteins due to abnormalities in the ubiquitin-proteasome system together with dysregulation of mitochondrial function and oxidative stress. Conventional pharmacological treatments for PD are dopamine precursors (levodopa, l-DOPA, l-3,4 dihidroxifenilalanina), and other symptomatic treatments including dopamine agonists (amantadine, apomorphine, bromocriptine, cabergoline, lisuride, pergolide, pramipexole, ropinirole, rotigotine), monoamine oxidase (MAO) inhibitors (selegiline, rasagiline), and catechol-O-methyltransferase (COMT) inhibitors (entacapone, tolcapone). The chronic administration of antiparkinsonian drugs currently induces the "wearing-off phenomenon", with additional psychomotor and autonomic complications. In order to minimize these clinical complications, novel compounds have been developed. Novel drugs and bioproducts for the treatment of PD should address dopaminergic neuroprotection to reduce premature neurodegeneration in addition to enhancing dopaminergic neurotransmission. Since biochemical changes and therapeutic outcomes are highly dependent upon the genomic profiles of PD patients, personalized treatments should rely on pharmacogenetic procedures to optimize therapeutics.