EPConDB: a web resource for gene expression related to pancreatic development, beta-cell function and diabetes

The Eukaryotic Pathogen Databases: a functional genomic resource integrating data from human and veterinary parasites

Over the past 20 years, advances in high-throughput biological techniques and the availability of computational resources including fast Internet access have resulted in an explosion of large genome-scale data sets "big data." While such data are readily available for download and personal use and analysis from a variety of repositories, often such analysis requires access to seldom-available computational skills. As a result a number of databases have emerged to provide scientists with online tools enabling the interrogation of data without the need for sophisticated computational skills beyond basic knowledge of Internet browser utility. This chapter focuses on the Eukaryotic Pathogen Databases (EuPathDB: http://eupathdb.org) Bioinformatic Resource Center (BRC) and illustrates some of the available tools and methods.

Glutathione peroxidase mimic ebselen improves glucose-stimulated insulin secretion in murine islets

AIMS

Glutathione peroxidase (GPX) mimic ebselen and superoxide dismutase (SOD) mimic copper diisopropylsalicylate (CuDIPs) were used to rescue impaired glucose-stimulated insulin secretion (GSIS) in islets of GPX1 and(or) SOD1-knockout mice.

RESULTS

Ebselen improved GSIS in islets of all four tested genotypes. The rescue in the GPX1 knockout resulted from a coordinated transcriptional regulation of four key GSIS regulators and was mediated by the peroxisome proliferator-activated receptor gamma coactivator 1 alpha (PGC-1α)-mediated signaling pathways. In contrast, CuDIPs improved GSIS only in the SOD1 knockout and suppressed gene expression of the PGC-1α pathway.

INNOVATION

Islets from the GPX1 and(or) SOD1 knockout mice provided metabolically controlled intracellular hydrogen peroxide (H2O2) and superoxide conditions for the present study to avoid confounding effects. Bioinformatics analyses of gene promoters and expression profiles guided the search for upstream signaling pathways to link the ebselen-initiated H2O2 scavenging to downstream key events of GSIS. The RNA interference was applied to prove PGC-1α as the main mediator for that link.

CONCLUSION

Our study revealed a novel metabolic use and clinical potential of ebselen in rescuing GSIS in the GPX1-deficient islets and mice, along with distinct differences between the GPX and SOD mimics in this regard. These findings highlight the necessities and opportunities of discretional applications of various antioxidant enzyme mimics in treating insulin secretion disorders. REBOUND TRACK: This work was rejected during standard peer review and rescued by Rebound Peer Review (Antioxid Redox Signal 16: 293-296, 2012) with the following serving as open reviewers: Regina Brigelius-Flohe, Vadim Gladyshev, Dexing Hou, and Holger Steinbrenner.

EuPathDB: the eukaryotic pathogen database

EuPathDB (http://eupathdb.org) resources include 11 databases supporting eukaryotic pathogen genomic and functional genomic data, isolate data and phylogenomics. EuPathDB resources are built using the same infrastructure and provide a sophisticated search strategy system enabling complex interrogations of underlying data. Recent advances in EuPathDB resources include the design and implementation of a new data loading workflow, a new database supporting Piroplasmida (i.e. Babesia and Theileria), the addition of large amounts of new data and data types and the incorporation of new analysis tools. New data include genome sequences and annotation, strand-specific RNA-seq data, splice junction predictions (based on RNA-seq), phosphoproteomic data, high-throughput phenotyping data, single nucleotide polymorphism data based on high-throughput sequencing (HTS) and expression quantitative trait loci data. New analysis tools enable users to search for DNA motifs and define genes based on their genomic colocation, view results from searches graphically (i.e. genes mapped to chromosomes or isolates displayed on a map) and analyze data from columns in result tables (word cloud and histogram summaries of column content). The manuscript herein describes updates to EuPathDB since the previous report published in NAR in 2010.

Synergizing genomic analysis with biological knowledge to identify and validate novel genes in pancreatic development

OBJECTIVE

This study investigated the utility of advanced computational techniques to large-scale genome-based data to identify novel genes that govern murine pancreatic development.

METHODS

An expression data set for mouse pancreatic development was complemented with high-throughput data analyzer to identify and prioritize novel genes. Quantitative real-time polymerase chain reaction, in situ hybridization, and immunohistochemistry were used to validate selected genes.

RESULTS

Four new genes whose roles in the development of murine pancreas have not previously been established were identified: cystathionine β-synthase (Cbs), Meis homeobox 1, growth factor independent 1, and aldehyde dehydrogenase 18 family, member A1. Their temporal expression during development was documented. Cbs was localized in the cytoplasm of the tip cells of the epithelial chords of the undifferentiated progenitor cells at E12.5 and was coexpressed with the pancreatic and duodenal homeobox 1 and pancreas-specific transcription factor, 1a-positive cells. In the adult pancreas, Cbs was localized primarily within the acinar compartment.

CONCLUSIONS

In silico analysis of high-throughput microarray data in combination with background knowledge about genes provides an additional reliable method of identifying novel genes. To our knowledge, the expression and localization of Cbs have not been previously documented during mouse pancreatic development.

When the human viral infectome and diseasome networks collide: towards a systems biology platform for the aetiology of human diseases

Background

Comprehensive understanding of molecular mechanisms underlying viral infection is a major challenge towards the discovery of new antiviral drugs and susceptibility factors of human diseases. New advances in the field are expected from systems-level modelling and integration of the incessant torrent of high-throughput "-omics" data.

Results

Here, we describe the Human Infectome protein interaction Network, a novel systems virology model of a virtual virus-infected human cell concerning 110 viruses. This in silico model was applied to comprehensively explore the molecular relationships between viruses and their associated diseases. This was done by merging virus-host and host-host physical protein-protein interactomes with the set of genes essential for viral replication and involved in human genetic diseases. This systems-level approach provides strong evidence that viral proteomes target a wide range of functional and inter-connected modules of proteins as well as highly central and bridging proteins within the human interactome. The high centrality of targeted proteins was correlated to their essentiality for viruses' lifecycle, using functional genomic RNAi data. A stealth-attack of viruses on proteins bridging cellular functions was demonstrated by simulation of cellular network perturbations, a property that could be essential in the molecular aetiology of some human diseases. Networking the Human Infectome and Diseasome unravels the connectivity of viruses to a wide range of diseases and profiled molecular basis of Hepatitis C Virus-induced diseases as well as 38 new candidate genetic predisposition factors involved in type 1 diabetes mellitus.

Conclusions

The Human Infectome and Diseasome Networks described here provide a unique gateway towards the comprehensive modelling and analysis of the systems level properties associated to viral infection as well as candidate genes potentially involved in the molecular aetiology of human diseases.

EuroDia: a beta-cell gene expression resource

Type 2 diabetes mellitus (T2DM) is a major disease affecting nearly 280 million people worldwide. Whilst the pathophysiological mechanisms leading to disease are poorly understood, dysfunction of the insulin-producing pancreatic beta-cells is key event for disease development. Monitoring the gene expression profiles of pancreatic beta-cells under several genetic or chemical perturbations has shed light on genes and pathways involved in T2DM. The EuroDia database has been established to build a unique collection of gene expression measurements performed on beta-cells of three organisms, namely human, mouse and rat. The Gene Expression Data Analysis Interface (GEDAI) has been developed to support this database. The quality of each dataset is assessed by a series of quality control procedures to detect putative hybridization outliers. The system integrates a web interface to several standard analysis functions from R/Bioconductor to identify differentially expressed genes and pathways. It also allows the combination of multiple experiments performed on different array platforms of the same technology. The design of this system enables each user to rapidly design a custom analysis pipeline and thus produce their own list of genes and pathways. Raw and normalized data can be downloaded for each experiment. The flexible engine of this database (GEDAI) is currently used to handle gene expression data from several laboratory-run projects dealing with different organisms and platforms.

Database URL: http://eurodia.vital-it.ch

LKB1 regulates pancreatic beta cell size, polarity, and function

Pancreatic beta cells, organized in the islets of Langerhans, sense glucose and secrete appropriate amounts of insulin. We have studied the roles of LKB1, a conserved kinase implicated in the control of cell polarity and energy metabolism, in adult beta cells. LKB1-deficient beta cells show a dramatic increase in insulin secretion in vivo. Histologically, LKB1-deficient beta cells have striking alterations in the localization of the nucleus and cilia relative to blood vessels, suggesting a shift from hepatocyte-like to columnar polarity. Additionally, LKB1 deficiency causes a 65% increase in beta cell volume. We show that distinct targets of LKB1 mediate these effects. LKB1 controls beta cell size, but not polarity, via the mTOR pathway. Conversely, the precise position of the beta cell nucleus, but not cell size, is controlled by the LKB1 target Par1b. Insulin secretion and content are restricted by LKB1, at least in part, via AMPK. These results expose a molecular mechanism, orchestrated by LKB1, for the coordinated maintenance of beta cell size, form, and function.

GeneSpeed Beta Cell: an online genomics data repository and analysis resource tailored for the islet cell biologist

OBJECTIVE

We here describe the development of a freely available online database resource, GeneSpeed Beta Cell, which has been created for the pancreatic islet and pancreatic developmental biology investigator community.

RESEARCH DESIGN AND METHODS

We have developed GeneSpeed Beta Cell as a separate component of the GeneSpeed database, providing a genomics-type data repository of pancreas and islet-relevant datasets interlinked with the domain-oriented GeneSpeed database.

RESULTS

GeneSpeed Beta Cell allows the query of multiple published and unpublished select genomics datasets in a simultaneous fashion (multiexperiment viewing) and is capable of defining intersection results from precomputed analysis of such datasets (multidimensional querying). Combined with the protein-domain categorization/assembly toolbox provided by the GeneSpeed database, the user is able to define spatial expression constraints of select gene lists in a relatively rigid fashion within the pancreatic expression space. We provide several demonstration case studies of relevance to islet cell biology and development of the pancreas that provide novel insight into islet biology.

CONCLUSIONS

The combination of an exhaustive domain-based compilation of the transcriptome with gene array data of interest to the islet biologist affords novel methods for multidimensional querying between individual datasets in a rapid fashion, presently not available elsewhere.

MiMiR--an integrated platform for microarray data sharing, mining and analysis

Background

Despite considerable efforts within the microarray community for standardising data format, content and description, microarray technologies present major challenges in managing, sharing, analysing and re-using the large amount of data generated locally or internationally. Additionally, it is recognised that inconsistent and low quality experimental annotation in public data repositories significantly compromises the re-use of microarray data for meta-analysis. MiMiR, the Microarray data Mining Resource was designed to tackle some of these limitations and challenges. Here we present new software components and enhancements to the original infrastructure that increase accessibility, utility and opportunities for large scale mining of experimental and clinical data.

Results

A user friendly Online Annotation Tool allows researchers to submit detailed experimental information via the web at the time of data generation rather than at the time of publication. This ensures the easy access and high accuracy of meta-data collected. Experiments are programmatically built in the MiMiR database from the submitted information and details are systematically curated and further annotated by a team of trained annotators using a new Curation and Annotation Tool. Clinical information can be annotated and coded with a clinical Data Mapping Tool within an appropriate ethical framework. Users can visualise experimental annotation, assess data quality, download and share data via a web-based experiment browser called MiMiR Online. All requests to access data in MiMiR are routed through a sophisticated middleware security layer thereby allowing secure data access and sharing amongst MiMiR registered users prior to publication. Data in MiMiR can be mined and analysed using the integrated EMAAS open source analysis web portal or via export of data and meta-data into Rosetta Resolver data analysis package.

Conclusion

The new MiMiR suite of software enables systematic and effective capture of extensive experimental and clinical information with the highest MIAME score, and secure data sharing prior to publication. MiMiR currently contains more than 150 experiments corresponding to over 3000 hybridisations and supports the Microarray Centre's large microarray user community and two international consortia. The MiMiR flexible and scalable hardware and software architecture enables secure warehousing of thousands of datasets, including clinical studies, from microarray and potentially other -omics technologies.