Exome-wide association study of levodopa-induced dyskinesia in Parkinson's disease.

Levodopa is the standard long-term dopamine replacement therapy to treat Parkinson's disease (PD) symptoms. With time, levodopa may induce debilitating dyskinesias (LID), the treatment of which represents a large clinically unmet need. However, time-to-LID onset varies between patients, reflecting a possible genetic component. We performed an hypothesis-free whole-exome sequencing (WES)-based screening of time-to-LID onset and attempted replication of previously published candidate gene studies. A WES association analysis was carried out in 134 PD patients in a meta-analytical framework. Replication was attempted in an independent study of 97 PD patients. Variants from previously reported candidate genes (OPRM1, COMT, BDNF) were also specifically examined. We significantly replicated, for the first time, an association of variant rs1799971 in the OPRM1 gene with time-to-LID onset. Furthermore, we identified two novel potentially functional variants, in the MAD2L2 (rs2233019) and MAP7 (rs35350783) genes, which were significantly associated at the discovery stage. In the replication study, the two variants showed direction-consistent effects but did not achieve the replication significance threshold. Our study provides the first WES results for time-to-LID onset, where we replicate association at OPRM1, and suggest new variants in MAD2L2 and MAP7 genes that are significant in discovery, but require larger datasets for replication. The results are being made publicly available to allow for independent external validation.

The arrhythmogenic cardiomyopathy-specific coding and non-coding transcriptome in human cardiac stromal cells.

BACKGROUND: Arrhythmogenic cardiomyopathy (ACM) is a genetic autosomal disease characterized by abnormal cell-cell adhesion, cardiomyocyte death, progressive fibro-adipose replacement of the myocardium, arrhythmias and sudden death. Several different cell types contribute to the pathogenesis of ACM, including, as recently described, cardiac stromal cells (CStCs). In the present study, we aim to identify ACM-specific expression profiles of human CStCs derived from endomyocardial biopsies of ACM patients and healthy individuals employing TaqMan Low Density Arrays for miRNA expression profiling, and high throughput sequencing for gene expression quantification. RESULTS: We identified 3 miRNAs and 272 genes as significantly differentially expressed at a 5% false discovery rate. Both the differentially expressed genes as well as the target genes of the ACM-specific miRNAs were found to be enriched in cell adhesion-related biological processes. Functional similarity and protein interaction-based network analyses performed on the identified deregulated genes, miRNA targets and known ACM-causative genes revealed clusters of highly related genes involved in cell adhesion, extracellular matrix organization, lipid transport and ephrin receptor signaling. CONCLUSIONS: We determined for the first time the coding and non-coding transcriptome characteristic of ACM cardiac stromal cells, finding evidence for a potential contribution of miRNAs, specifically miR-29b-3p, to ACM pathogenesis or phenotype maintenance.

A network-based meta-analysis for characterizing the genetic landscape of human aging.

Great amounts of omics data are generated in aging research, but their diverse and partly complementary nature requires integrative analysis approaches for investigating aging processes and connections to age-related diseases. To establish a broader picture of the genetic and epigenetic landscape of human aging we performed a large-scale meta-analysis of 6600 human genes by combining 35 datasets that cover aging hallmarks, longevity, changes in DNA methylation and gene expression, and different age-related diseases. To identify biological relationships between aging-associated genes we incorporated them into a protein interaction network and characterized their network neighborhoods. In particular, we computed a comprehensive landscape of more than 1000 human aging clusters, network regions where genes are highly connected and where gene products commonly participate in similar processes. In addition to clusters that capture known aging processes such as nutrient-sensing and mTOR signaling, we present a number of clusters with a putative functional role in linking different aging processes as promising candidates for follow-up studies. To enable their detailed exploration, all datasets and aging clusters are made freely available via an interactive website ( https://gemex.eurac.edu/bioinf/age/ ).

Exploring digenic inheritance in arrhythmogenic cardiomyopathy.

BACKGROUND: Arrhythmogenic cardiomyopathy (ACM) is an inherited genetic disorder, characterized by the substitution of heart muscle with fibro-fatty tissue and severe ventricular arrhythmias, often leading to heart failure and sudden cardiac death. ACM is considered a monogenic disorder, but the low penetrance of mutations identified in patients suggests the involvement of additional genetic or environmental factors. METHODS: We used whole exome sequencing to investigate digenic inheritance in two ACM families where previous diagnostic tests have revealed a PKP2 mutation in all affected and some healthy individuals. In family members with PKP2 mutations we determined all genes that harbor variants in affected but not in healthy carriers or vice versa. We computationally prioritized the most likely candidates, focusing on known ACM genes and genes related to PKP2 through protein interactions, functional relationships, or shared biological processes. RESULTS: We identified four candidate genes in family 1, namely DAG1, DAB2IP, CTBP2 and TCF25, and eleven candidate genes in family 2. The most promising gene in the second family is TTN, a gene previously associated with ACM, in which the affected individual harbors two rare deleterious-predicted missense variants, one of which is located in the protein's only serine kinase domain. CONCLUSIONS: In this study we report genes that might act as digenic players in ACM pathogenesis, on the basis of co-segregation with PKP2 mutations. Validation in larger cohorts is still required to prove the utility of this model.

Plasma and White Blood Cells Show Different miRNA Expression Profiles in Parkinson's Disease.

Parkinson's disease (PD) diagnosis is based on the assessment of motor symptoms, which manifest when more than 50% of dopaminergic neurons are degenerated. To date, no validated biomarkers are available for the diagnosis of PD. The aims of the present study are to evaluate whether plasma and white blood cells (WBCs) are interchangeable biomarker sources and to identify circulating plasma-based microRNA (miRNA) biomarkers for an early detection of PD. We profiled plasma miRNA levels in 99 L-dopa-treated PD patients from two independent data collections, in ten drug-naïve PD patients, and in unaffected controls matched by sex and age. We evaluated expression levels by reverse transcription and quantitative real-time PCR (RT-qPCR) and combined the results from treated PD patients using a fixed effect inverse-variance weighted meta-analysis. We revealed different expression profiles comparing plasma and WBCs and drug-naïve and L-dopa-treated PD patients. We observed an upregulation trend for miR-30a-5p in L-dopa-treated PD patients and investigated candidate target genes by integrated in silico analyses. We could not analyse miR-29b-3p, normally expressed in WBCs, due to the very low expression in plasma. We observed different expression profiles in WBCs and plasma, suggesting that they are both suitable but not interchangeable peripheral sources for biomarkers. We revealed miR-30a-5p as a potential biomarker for PD in plasma. In silico analyses suggest that miR-30a-5p might have a regulatory role in mitochondrial dynamics and autophagy. Further investigations are needed to confirm miR-30a-5p deregulation and targets and to investigate the influence of L-dopa treatment on miRNA expression levels.

Dintor: functional annotation of genomic and proteomic data.

BACKGROUND: During the last decade, a great number of extremely valuable large-scale genomics and proteomics datasets have become available to the research community. In addition, dropping costs for conducting high-throughput sequencing experiments and the option to outsource them considerably contribute to an increasing number of researchers becoming active in this field. Even though various computational approaches have been developed to analyze these data, it is still a laborious task involving prudent integration of many heterogeneous and frequently updated data sources, creating a barrier for interested scientists to accomplish their own analysis. RESULTS: We have implemented Dintor, a data integration framework that provides a set of over 30 tools to assist researchers in the exploration of genomics and proteomics datasets. Each of the tools solves a particular task and several tools can be combined into data processing pipelines. Dintor covers a wide range of frequently required functionalities, from gene identifier conversions and orthology mappings to functional annotation of proteins and genetic variants up to candidate gene prioritization and Gene Ontology-based gene set enrichment analysis. Since the tools operate on constantly changing datasets, we provide a mechanism to unambiguously link tools with different versions of archived datasets, which guarantees reproducible results for future tool invocations. We demonstrate a selection of Dintor's capabilities by analyzing datasets from four representative publications. The open source software can be downloaded and installed on a local Unix machine. For reasons of data privacy it can be configured to retrieve local data only. In addition, the Dintor tools are available on our public Galaxy web service at http://dintor.eurac.edu . CONCLUSIONS: Dintor is a computational annotation framework for the analysis of genomic and proteomic datasets, providing a rich set of tools that cover the most frequently encountered tasks. A major advantage is its capability to consistently handle multiple versions of tool-associated datasets, supporting the researcher in delivering reproducible results.

Identification of HNRNPK as regulator of hepatitis C virus particle production.

Hepatitis C virus (HCV) is a major cause of chronic liver disease affecting around 130 million people worldwide. While great progress has been made to define the principle steps of the viral life cycle, detailed knowledge how HCV interacts with its host cells is still limited. To overcome this limitation we conducted a comprehensive whole-virus RNA interference-based screen and identified 40 host dependency and 16 host restriction factors involved in HCV entry/replication or assembly/release. Of these factors, heterogeneous nuclear ribonucleoprotein K (HNRNPK) was found to suppress HCV particle production without affecting viral RNA replication. This suppression of virus production was specific to HCV, independent from assembly competence and genotype, and not found with the related Dengue virus. By using a knock-down rescue approach we identified the domains within HNRNPK required for suppression of HCV particle production. Importantly, HNRNPK was found to interact specifically with HCV RNA and this interaction was impaired by mutations that also reduced the ability to suppress HCV particle production. Finally, we found that in HCV-infected cells, subcellular distribution of HNRNPK was altered; the protein was recruited to sites in close proximity of lipid droplets and colocalized with core protein as well as HCV plus-strand RNA, which was not the case with HNRNPK variants unable to suppress HCV virion formation. These results suggest that HNRNPK might determine efficiency of HCV particle production by limiting the availability of viral RNA for incorporation into virions. This study adds a new function to HNRNPK that acts as central hub in the replication cycle of multiple other viruses.

Overexpression of blood microRNAs 103a, 30b, and 29a in L-dopa-treated patients with PD.

OBJECTIVE: The aims of the present study were to profile the expression of several candidate microRNAs (miRNAs) in blood from L-dopa-treated and drug-naive patients with Parkinson disease (PD) vs unaffected controls and to interpret the miRNA expression data in a biological context. METHODS: We analyzed RNAs from peripheral blood of 36 L-dopa-treated, 10 drug-naive patients with PD and unaffected controls matched 1:1 by sex and age. We evaluated expression by reverse transcription-quantitative real-time PCR, and we analyzed data using a 2-tailed paired t test. To detect miRNA targets, several miRNA resources were combined to generate an overall score for each candidate gene using weighted rank aggregation. RESULTS: Significant overexpression of miR-103a-3p (p < 0.0001), miR-30b-5p (p = 0.002), and miR-29a-3p (p = 0.005) in treated patients with PD was observed, and promising candidate target genes for these were revealed by an integrated in silico analysis. CONCLUSIONS: We revealed 3 candidate biomarkers for PD. miRNAs 30b-5p and 29a-3p replicated a documented deregulation in PD albeit opposite to published data, while for miR-103a-3p, we demonstrated for the first time an overexpression in treated patients with PD. Expression studies in patients and/or in isolated peripheral blood mononuclear cells before and after L-dopa administration are necessary to define the involvement of L-dopa treatment in the observed overexpression. Our in silico analysis to prioritize targets of deregulated miRNAs identified candidate target genes, including genes related to neurodegeneration and PD. Despite the preliminary character of our study, the results provide a rationale for further clarifying the role of the identified miRNAs in the pathogenesis of PD and for validating their diagnostic potential.

Identification of a set of endogenous reference genes for miRNA expression studies in Parkinson's disease blood samples.

BACKGROUND: Research on microRNAs (miRNAs) is becoming an increasingly attractive field, as these small RNA molecules are involved in several physiological functions and diseases. To date, only few studies have assessed the expression of blood miRNAs related to Parkinson's disease (PD) using microarray and quantitative real-time PCR (qRT-PCR). Measuring miRNA expression involves normalization of qRT-PCR data using endogenous reference genes for calibration, but their choice remains a delicate problem with serious impact on the resulting expression levels. The aim of the present study was to evaluate the suitability of a set of commonly used small RNAs as normalizers and to identify which of these miRNAs might be considered reliable reference genes in qRT-PCR expression analyses on PD blood samples. RESULTS: Commonly used reference genes snoRNA RNU24, snRNA RNU6B, snoRNA Z30 and miR-103a-3p were selected from the literature. We then analyzed the effect of using these genes as reference, alone or in any possible combination, on the measured expression levels of the target genes miR-30b-5p and miR-29a-3p, which have been previously reported to be deregulated in PD blood samples. CONCLUSIONS: We identified RNU24 and Z30 as a reliable and stable pair of reference genes in PD blood samples.

Profiling of Parkin-binding partners using tandem affinity purification.

Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting approximately 1-2% of the general population over age 60. It is characterized by a rather selective loss of dopaminergic neurons in the substantia nigra and the presence of α-synuclein-enriched Lewy body inclusions. Mutations in the Parkin gene (PARK2) are the major cause of autosomal recessive early-onset parkinsonism. The Parkin protein is an E3 ubiquitin ligase with various cellular functions, including the induction of mitophagy upon mitochondrial depolarizaton, but the full repertoire of Parkin-binding proteins remains poorly defined. Here we employed tandem affinity purification interaction screens with subsequent mass spectrometry to profile binding partners of Parkin. Using this approach for two different cell types (HEK293T and SH-SY5Y neuronal cells), we identified a total of 203 candidate Parkin-binding proteins. For the candidate proteins and the proteins known to cause heritable forms of parkinsonism, protein-protein interaction data were derived from public databases, and the associated biological processes and pathways were analyzed and compared. Functional similarity between the candidates and the proteins involved in monogenic parkinsonism was investigated, and additional confirmatory evidence was obtained using published genetic interaction data from Drosophila melanogaster. Based on the results of the different analyses, a prioritization score was assigned to each candidate Parkin-binding protein. Two of the top ranking candidates were tested by co-immunoprecipitation, and interaction to Parkin was confirmed for one of them. New candidates for involvement in cell death processes, protein folding, the fission/fusion machinery, and the mitophagy pathway were identified, which provide a resource for further elucidating Parkin function.

Specificity of linear motifs that bind to a common mitogen-activated protein kinase docking groove.

Mitogen-activated protein kinases (MAPKs) have a docking groove that interacts with linear "docking" motifs in binding partners. To determine the structural basis of binding specificity between MAPKs and docking motifs, we quantitatively analyzed the ability of 15 docking motifs from diverse MAPK partners to bind to c-Jun amino-terminal kinase 1 (JNK1), p38α, and extracellular signal-regulated kinase 2 (ERK2). Classical docking motifs mediated highly specific binding only to JNK1, and only those motifs with a sequence pattern distinct from the classical MAPK binding docking motif consensus differentiated between the topographically similar docking grooves of ERK and p38α. Crystal structures of four complexes of MAPKs with docking peptides, representing JNK-specific, ERK-specific, or ERK- and p38-selective binding modes, revealed that the regions located between consensus positions in the docking motifs showed conformational diversity. Although the consensus positions in the docking motifs served as anchor points that bound to common MAPK surface features and mostly contributed to docking in a nondiscriminatory fashion, the conformation of the intervening region between the anchor points mostly determined specificity. We designed peptides with tailored MAPK binding profiles by rationally changing the length and amino acid composition of intervening regions located between anchor points. These results suggest a coherent structural model for MAPK docking specificity that reveals how short linear motifs binding to a common kinase docking groove can mediate diverse interaction patterns and contribute to correct MAPK partner selection in signaling networks.

Functional characterization of human genes from exon expression and RNA interference results.

Complex biological systems comprise a large number of interacting molecules. The identification and detailed characterization of the functions of the involved genes and proteins are crucial for modeling and understanding such systems. To interrogate the various cellular processes, high-throughput techniques such as the Affymetrix Exon Array or RNA interference (RNAi) screens are powerful experimental approaches for functional genomics. However, they typically yield long gene lists that require computational methods to further analyze and functionally annotate the experimental results and to gain more insight into important molecular interactions. Here, we focus on bioinformatics software tools for the functional interpretation of exon expression data to discover alternative splicing events and their impact on gene and protein architecture, molecular networks, and pathways. We additionally demonstrate how to explore large lists of candidate genes as they also result from RNAi screens. In particular, our exemplary application studies show how to analyze the function of human genes that play a major role in human stem cells or viral infections.

Recruitment and activation of a lipid kinase by hepatitis C virus NS5A is essential for integrity of the membranous replication compartment.

Hepatitis C virus (HCV) is a major causative agent of chronic liver disease in humans. To gain insight into host factor requirements for HCV replication, we performed a siRNA screen of the human kinome and identified 13 different kinases, including phosphatidylinositol-4 kinase III alpha (PI4KIIIα), as being required for HCV replication. Consistent with elevated levels of the PI4KIIIα product phosphatidylinositol-4-phosphate (PI4P) detected in HCV-infected cultured hepatocytes and liver tissue from chronic hepatitis C patients, the enzymatic activity of PI4KIIIα was critical for HCV replication. Viral nonstructural protein 5A (NS5A) was found to interact with PI4KIIIα and stimulate its kinase activity. The absence of PI4KIIIα activity induced a dramatic change in the ultrastructural morphology of the membranous HCV replication complex. Our analysis suggests that the direct activation of a lipid kinase by HCV NS5A contributes critically to the integrity of the membranous viral replication complex.

DASMIweb: online integration, analysis and assessment of distributed protein interaction data.

In recent years, we have witnessed a substantial increase of the amount of available protein interaction data. However, most data are currently not readily accessible to the biologist at a single site, but scattered over multiple online repositories. Therefore, we have developed the DASMIweb server that affords the integration, analysis and qualitative assessment of distributed sources of interaction data in a dynamic fashion. Since DASMIweb allows for querying many different resources of protein and domain interactions simultaneously, it serves as an important starting point for interactome studies and assists the user in finding publicly accessible interaction data with minimal effort. The pool of queried resources is fully configurable and supports the inclusion of own interaction data or confidence scores. In particular, DASMIweb integrates confidence measures like functional similarity scores to assess individual interactions. The retrieved results can be exported in different file formats like MITAB or SIF. DASMIweb is freely available at http://www.dasmiweb.de.

DASMI: exchanging, annotating and assessing molecular interaction data.

MOTIVATION: Ever increasing amounts of biological interaction data are being accumulated worldwide, but they are currently not readily accessible to the biologist at a single site. New techniques are required for retrieving, sharing and presenting data spread over the Internet. RESULTS: We introduce the DASMI system for the dynamic exchange, annotation and assessment of molecular interaction data. DASMI is based on the widely used Distributed Annotation System (DAS) and consists of a data exchange specification, web servers for providing the interaction data and clients for data integration and visualization. The decentralized architecture of DASMI affords the online retrieval of the most recent data from distributed sources and databases. DASMI can also be extended easily by adding new data sources and clients. We describe all DASMI components and demonstrate their use for protein and domain interactions. AVAILABILITY: The DASMI tools are available at http://www.dasmi.de/ and http://ipfam.sanger.ac.uk/graph. The DAS registry and the DAS 1.53E specification is found at http://www.dasregistry.org/.

Integrating biological data--the Distributed Annotation System.

BACKGROUND: The Distributed Annotation System (DAS) is a widely adopted protocol for dynamically integrating a wide range of biological data from geographically diverse sources. DAS continues to expand its applicability and evolve in response to new challenges facing integrative bioinformatics. RESULTS: Here we describe the various infrastructure components of DAS and present a new extended version of the DAS specification. Version 1.53E incorporates several recent developments, including its extension to serve new data types and an ontology for protein features. CONCLUSION: Our extensions to the DAS protocol have facilitated the integration of new data types, and our improvements to the existing DAS infrastructure have addressed recent challenges. The steadily increasing numbers of available data sources demonstrates further adoption of the DAS protocol.

The implications of alternative splicing in the ENCODE protein complement.

Alternative premessenger RNA splicing enables genes to generate more than one gene product. Splicing events that occur within protein coding regions have the potential to alter the biological function of the expressed protein and even to create new protein functions. Alternative splicing has been suggested as one explanation for the discrepancy between the number of human genes and functional complexity. Here, we carry out a detailed study of the alternatively spliced gene products annotated in the ENCODE pilot project. We find that alternative splicing in human genes is more frequent than has commonly been suggested, and we demonstrate that many of the potential alternative gene products will have markedly different structure and function from their constitutively spliced counterparts. For the vast majority of these alternative isoforms, little evidence exists to suggest they have a role as functional proteins, and it seems unlikely that the spectrum of conventional enzymatic or structural functions can be substantially extended through alternative splicing.