Regulatory sites for splicing in human basal ganglia are enriched for disease-relevant information

Genome-wide association studies have generated an increasing number of common genetic variants associated with neurological and psychiatric disease risk. An improved understanding of the genetic control of gene expression in human brain is vital considering this is the likely modus operandum for many causal variants. However, human brain sampling complexities limit the explanatory power of brain-related expression quantitative trait loci (eQTL) and allele-specific expression (ASE) signals. We address this, using paired genomic and transcriptomic data from putamen and substantia nigra from 117 human brains, interrogating regulation at different RNA processing stages and uncovering novel transcripts. We identify disease-relevant regulatory loci, find that splicing eQTLs are enriched for regulatory information of neuron-specific genes, that ASEs provide cell-specific regulatory information with evidence for cellular specificity, and that incomplete annotation of the brain transcriptome limits interpretation of risk loci for neuropsychiatric disease. This resource of regulatory data is accessible through our web server, http://braineacv2.inf.um.es/

Identification of novel risk loci, causal insights, and heritable risk for Parkinson's disease: a meta-analysis of genome-wide association studies

Background Genome-wide association studies (GWAS) in Parkinson's disease have increased the scope of biological knowledge about the disease over the past decade. We aimed to use the largest aggregate of GWAS data to identify novel risk loci and gain further insight into the causes of Parkinson's disease. Methods We did a meta-analysis of 17 datasets from Parkinson's disease GWAS available from European ancestry samples to nominate novel loci for disease risk. These datasets incorporated all available data. We then used these data to estimate heritable risk and develop predictive models of this heritability. We also used large gene expression and methylation resources to examine possible functional consequences as well as tissue, cell type, and biological pathway enrichments for the identified risk factors. Additionally, we examined shared genetic risk between Parkinson's disease and other phenotypes of interest via genetic correlations followed by Mendelian randomisation. Findings Between Oct 1, 2017, and Aug 9, 2018, we analysed 7·8 million single nucleotide polymorphisms in 37 688 cases, 18 618 UK Biobank proxy-cases (ie, individuals who do not have Parkinson's disease but have a first degree relative that does), and 1·4 million controls. We identified 90 independent genome-wide significant risk signals across 78 genomic regions, including 38 novel independent risk signals in 37 loci. These 90 variants explained 16–36% of the heritable risk of Parkinson's disease depending on prevalence. Integrating methylation and expression data within a Mendelian randomisation framework identified putatively associated genes at 70 risk signals underlying GWAS loci for follow-up functional studies. Tissue-specific expression enrichment analyses suggested Parkinson's disease loci were heavily brain-enriched, with specific neuronal cell types being implicated from single cell data. We found significant genetic correlations with brain volumes (false discovery rate-adjusted p=0·0035 for intracranial volume, p=0·024 for putamen volume), smoking status (p=0·024), and educational attainment (p=0·038). Mendelian randomisation between cognitive performance and Parkinson's disease risk showed a robust association (p=8·00 × 10−7). Interpretation These data provide the most comprehensive survey of genetic risk within Parkinson's disease to date, to the best of our knowledge, by revealing many additional Parkinson's disease risk loci, providing a biological context for these risk factors, and showing that a considerable genetic component of this disease remains unidentified. These associations derived from European ancestry datasets will need to be followed-up with more diverse data. Funding The National Institute on Aging at the National Institutes of Health (USA), The Michael J Fox Foundation, and The Parkinson's Foundation (see appendix for full list of funding sources)

Identification of candidate Parkinson disease genes by integrating genome-wide association study, expression, and epigenetic data sets

Importance Substantial genome-wide association study (GWAS) work in Parkinson disease (PD) has led to the discovery of an increasing number of loci shown reliably to be associated with increased risk of disease. Improved understanding of the underlying genes and mechanisms at these loci will be key to understanding the pathogenesis of PD. Objective To investigate what genes and genomic processes underlie the risk of sporadic PD. Design and Setting This genetic association study used the bioinformatic tools Coloc and transcriptome-wide association study (TWAS) to integrate PD case-control GWAS data published in 2017 with expression data (from Braineac, the Genotype-Tissue Expression [GTEx], and CommonMind) and methylation data (derived from UK Parkinson brain samples) to uncover putative gene expression and splicing mechanisms associated with PD GWAS signals. Candidate genes were further characterized using cell-type specificity, weighted gene coexpression networks, and weighted protein-protein interaction networks. Main Outcomes and Measures It was hypothesized a priori that some genes underlying PD loci would alter PD risk through changes to expression, splicing, or methylation. Candidate genes are presented whose change in expression, splicing, or methylation are associated with risk of PD as well as the functional pathways and cell types in which these genes have an important role. Results Gene-level analysis of expression revealed 5 genes (WDR6 [OMIM 606031], CD38 [OMIM 107270], GPNMB [OMIM 604368], RAB29 [OMIM 603949], and TMEM163 [OMIM 618978]) that replicated using both Coloc and TWAS analyses in both the GTEx and Braineac expression data sets. A further 6 genes (ZRANB3 [OMIM 615655], PCGF3 [OMIM 617543], NEK1 [OMIM 604588], NUPL2 [NCBI 11097], GALC [OMIM 606890], and CTSB [OMIM 116810]) showed evidence of disease-associated splicing effects. Cell-type specificity analysis revealed that gene expression was overall more prevalent in glial cell types compared with neurons. The weighted gene coexpression performed on the GTEx data set showed that NUPL2 is a key gene in 3 modules implicated in catabolic processes associated with protein ubiquitination and in the ubiquitin-dependent protein catabolic process in the nucleus accumbens, caudate, and putamen. TMEM163 and ZRANB3 were both important in modules in the frontal cortex and caudate, respectively, indicating regulation of signaling and cell communication. Protein interactor analysis and simulations using random networks demonstrated that the candidate genes interact significantly more with known mendelian PD and parkinsonism proteins than would be expected by chance. Conclusions and Relevance Together, these results suggest that several candidate genes and pathways are associated with the findings observed in PD GWAS studies

Defining the causes of sporadic Parkinson's disease in the global Parkinson's genetics program (GP2)

The Global Parkinson’s Genetics Program (GP2) will genotype over 150,000 participants from around the world, and integrate genetic and clinical data for use in large-scale analyses to dramatically expand our understanding of the genetic architecture of PD. This report details the workflow for cohort integration into the complex arm of GP2, and together with our outline of the monogenic hub in a companion paper, provides a generalizable blueprint for establishing large scale collaborative research consortia

Multi-ancestry genome-wide association meta-analysis of Parkinson?s disease

Although over 90 independent risk variants have been identified for Parkinson’s disease using genome-wide association studies, most studies have been performed in just one population at a time. Here we performed a large-scale multi-ancestry meta-analysis of Parkinson’s disease with 49,049 cases, 18,785 proxy cases and 2,458,063 controls including individuals of European, East Asian, Latin American and African ancestry. In a meta-analysis, we identified 78 independent genome-wide significant loci, including 12 potentially novel loci (MTF2, PIK3CA, ADD1, SYBU, IRS2, USP8, PIGL, FASN, MYLK2, USP25, EP300 and PPP6R2) and fine-mapped 6 putative causal variants at 6 known PD loci. By combining our results with publicly available eQTL data, we identified 25 putative risk genes in these novel loci whose expression is associated with PD risk. This work lays the groundwork for future efforts aimed at identifying PD loci in non-European populations

Correction: ANKK1 is found in myogenic precursors and muscle fibers subtypes with glycolytic metabolism.

[This corrects the article DOI: 10.1371/journal.pone.0197254.]

ANKK1 is found in myogenic precursors and muscle fibers subtypes with glycolytic metabolism

<div><p>Ankyrin repeat and kinase domain containing 1 (<i>ANKK1</i>) gene has been widely related to neuropsychiatry disorders. The localization of ANKK1 in neural progenitors and its correlation with the cell cycle has suggested its participation in development. However, ANKK1 functions still need to be identified. Here, we have further characterized the ANKK1 localization <i>in vivo</i> and <i>in vitro</i>, by using immunolabeling, quantitative real-time PCR and Western blot in the myogenic lineage. Histologic investigations in mice and humans revealed that ANKK1 is expressed in precursors of embryonic and adult muscles. In mice embryos, ANKK1 was found in migrating myotubes where it shows a polarized cytoplasmic distribution, while proliferative myoblasts and satellite cells show different isoforms in their nuclei and cytoplasm. <i>In vitro</i> studies of ANKK1 protein isoforms along the myogenic progression showed the decline of nuclear ANKK1-kinase until its total exclusion in myotubes. In adult mice, ANKK1 was expressed exclusively in the Fast-Twitch muscles fibers subtype. The induction of glycolytic metabolism in C2C12 cells with high glucose concentration or treatment with berberine caused a significant increase in the <i>ANKK1</i> mRNA. Similarly, C2C12 cells under hypoxic conditions caused the increase of nuclear ANKK1. These results altogether show a relationship between <i>ANKK1</i> gene regulation and the metabolism of muscles during development and in adulthood. Finally, we found ANKK1 expression in regenerative fibers of muscles from dystrophic patients. Future studies in ANKK1 biology and the pathological response of muscles will reveal whether this protein is a novel muscle disease biomarker.</p></div

ANKK1 participates in myogenic differentiation.

<p><b>(A)</b> ANKK1 (α-STk2) and ACTININ immunostaining of C2C12 cells during myogenic differentiation. D0: proliferative myoblast; D3: myoblasts in differentiation; D6: myotubes. <b>(B)</b> Quantification of nuclear ANKK1 fluorescence intensity (a.u.) (N = 3) <b>(C)</b> Western blot analysis of C2C12 subcellular fractions. β-ACTIN was used as control and α-LAMIN A/C as nuclear marker (N = 1). <b>(D)</b> Rhabdomyosarcoma (RD) myoblasts differentiation, <b>(E)</b> quantification of nuclear ANKK1 (N = 3) and <b>(F)</b> Western blot of subcellular fractions (N = 1). <b>(G)</b> Immunostaining of ANKK1 with PAX7 and MYOD in C2C12 cells, (left) and quantification of positive nuclei for ANKK1, PAX7 and MYOD along myogenic differentiation (right, N = 3). Images were taken from confocal optical sections that are representative for the group averages. Scale bar: 25 μm <i>p</i> < 0.05: *; <i>p</i> < 0.01: **; <i>p</i> < 0.001: ***. D: Days after induction of differentiation; a.u: arbitrary units; N: Nucleus; C: Cytoplasm.</p

ANKK1 is located in SCs and in a fiber subtype in adult muscles.

<p>ANKK1 (detected with α-STk, α-STk2 and α-STk3), PAX7 and MYOD immunostaining of mouse FDB isolated fibers and gastrocnemius sections of adult mice. Nuclei were stained with DAPI in IF and with hematoxylin in IHC. <b>(A)</b> ANKK1+ (α-STk2)/PAX7+ SCs. Boxed area in ANKK1 images are amplified [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0197254#pone.0197254.ref025" target="_blank">25</a>]. The green arrows show ANKK1− nuclei. <b>(B)</b> ANKK1+(α-STk2)/MYOD+ SCs showing ANKK1 cytoplasmic expression. <b>(C)</b> ANKK1+(α-STk3)/PAX7+ and <b>(D)</b> ANKK1+(α-STk3)/MYOD+ SCs showing ANKK1 cytoplasmic expression. Scale bar: 25 μm. <b>(E)</b> α-STk, α-STk2 and α-STk3 antibodies recognize the same pattern for ANKK1 in a serial of transversal gastrocnemius sections. <b>(F)</b> ANKK1 is expressed in the cytoplasm of some fibers and in SCs (black arrow). Scale bar: 50 μm. Images are representative for the group averages.</p

ANKK1 is expressed in regenerating fibers in neuromuscular dystrophies.

<p>ANKK1 and a mix of embryonic and neonatal myosin heavy chains (eMyHC/nMyHC) immunostaining of adult skeletal muscle sections. The samples were non-dystrophic (control) and dystrophic patients (DMD, LGDM2A, LGMD2B and FSHD). ANKK1 and eMyHC/nMyHC were studied in serial muscle sections. The black asterisk indicates the same fiber. The black arrows indicate ANKK1+ nuclei. Scale bar: 50 μm. All regenerating fibers from each tissue section were counted and analyzed. Images are representative for the group averages.</p