The Eukaryotic Pathogen, Vector and Host Informatics Resource (VEuPathDB, https://veupathdb.org) represents the 2019 merger of VectorBase with the EuPathDB projects. As a Bioinformatics Resource Center funded by the National Institutes of Health, with additional support from the Welllcome Trust, VEuPathDB supports >500 organisms comprising invertebrate vectors, eukaryotic pathogens (protists and fungi) and relevant free-living or non-pathogenic species or hosts. Designed to empower researchers with access to Omics data and bioinformatic analyses, VEuPathDB projects integrate >1700 pre-analysed datasets (and associated metadata) with advanced search capabilities, visualizations, and analysis tools in a graphic interface. Diverse data types are analysed with standardized workflows including an in-house OrthoMCL algorithm for predicting orthology. Comparisons are easily made across datasets, data types and organisms in this unique data mining platform. A new site-wide search facilitates access for both experienced and novice users. Upgraded infrastructure and workflows support numerous updates to the web interface, tools, searches and strategies, and Galaxy workspace where users can privately analyse their own data. Forthcoming upgrades include cloud-ready application architecture, expanded support for the Galaxy workspace, tools for interrogating host-pathogen interactions, and improved interactions with affiliated databases (ClinEpiDB, MicrobiomeDB) and other scientific resources, and increased interoperability with the Bacterial & Viral BRC.

MicrobiomeDB (http://microbiomeDB.org) is a data discovery and analysis platform that empowers researchers to fully leverage experimental variables to interrogate microbiome datasets. MicrobiomeDB was developed in collaboration with the Eukaryotic Pathogens Bioinformatics Resource Center (http://EuPathDB.org) and leverages the infrastructure and user interface of EuPathDB, which allows users to construct in silico experiments using an intuitive graphical 'strategy' approach. The current release of the database integrates microbial census data with sample details for nearly 14 000 samples originating from human, animal and environmental sources, including over 9000 samples from healthy human subjects in the Human Microbiome Project (http://portal.ihmpdcc.org/). Query results can be statistically analyzed and graphically visualized via interactive web applications launched directly in the browser, providing insight into microbial community diversity and allowing users to identify taxa associated with any experimental covariate.

The Eukaryotic Pathogen Genomics Database Resource (EuPathDB, http://eupathdb.org) is a collection of databases covering 170+ eukaryotic pathogens (protists & fungi), along with relevant free-living and non-pathogenic species, and select pathogen hosts. To facilitate the discovery of meaningful biological relationships, the databases couple preconfigured searches with visualization and analysis tools for comprehensive data mining via intuitive graphical interfaces and APIs. All data are analyzed with the same workflows, including creation of gene orthology profiles, so data are easily compared across data sets, data types and organisms. EuPathDB is updated with numerous new analysis tools, features, data sets and data types. New tools include GO, metabolic pathway and word enrichment analyses plus an online workspace for analysis of personal, non-public, large-scale data. Expanded data content is mostly genomic and functional genomic data while new data types include protein microarray, metabolic pathways, compounds, quantitative proteomics, copy number variation, and polysomal transcriptomics. New features include consistent categorization of searches, data sets and genome browser tracks; redesigned gene pages; effective integration of alternative transcripts; and a EuPathDB Galaxy instance for private analyses of a user's data. Forthcoming upgrades include user workspaces for private integration of data with existing EuPathDB data and improved integration and presentation of host-pathogen interactions.

FungiDB (http://FungiDB.org) is a functional genomic resource for pan-fungal genomes that was developed in partnership with the Eukaryotic Pathogen Bioinformatic resource center (http://EuPathDB.org). FungiDB uses the same infrastructure and user interface as EuPathDB, which allows for sophisticated and integrated searches to be performed using an intuitive graphical system. The current release of FungiDB contains genome sequence and annotation from 18 species spanning several fungal classes, including the Ascomycota classes, Eurotiomycetes, Sordariomycetes, Saccharomycetes and the Basidiomycota orders, Pucciniomycetes and Tremellomycetes, and the basal ‘Zygomycete’ lineage Mucormycotina. Additionally, FungiDB contains cell cycle microarray data, hyphal growth RNA-sequence data and yeast two hybrid interaction data. The underlying genomic sequence and annotation combined with functional data, additional data from the FungiDB standard analysis pipeline and the ability to leverage orthology provides a powerful resource for in silico experimentation.

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.

FungiDB (http://FungiDB.org) is a functional genomic resource for pan-fungal genomes that was developed in partnership with the Eukaryotic Pathogen Bioinformatic resource center (http:// EuPathDB.org). FungiDB uses the same infrastructure and user interface as EuPathDB, which allows for sophisticated and integrated searches to be performed using an intuitive graphical system. The current release of FungiDB contains genome sequence and annotation from 18 species spanning several fungal classes, including the Ascomycota classes, Eurotiomycetes, Sordariomycetes, Saccharomycetes and the Basidiomycota orders, Pucciniomycetes and Tremellomycetes, and the basal ‘Zygomycete’ lineage Mucormycotina. Additionally, FungiDB contains cell cycle microarray data, hyphal growth RNA-sequence data and yeast two hybrid interaction data. The underlying genomic sequence and annotation combined with functional data, additional data from the FungiDB standard analysis pipeline and the ability to leverage orthology provides a powerful resource for in silico

AmoebaDB (http://AmoebaDB.org) and MicrosporidiaDB (http://MicrosporidiaDB.org) are new functional genomic databases serving the amoebozoa and microsporidia research communities, respectively. AmoebaDB contains the genomes of three Entamoeba species (E. dispar, E. invadens and E. histolityca) and microarray expression data for E. histolytica. MicrosporidiaDB contains the genomes of Encephalitozoon cuniculi, E. intestinalis and E. bieneusi. The databases belong to the National Institute of Allergy and Infectious Diseases (NIAID) funded EuPathDB (http://EuPathDB.org) Bioinformatics Resource Center family of integrated databases and assume the same architectural and graphical design as other EuPathDB resources such as PlasmoDB and TriTrypDB. Importantly they utilize the graphical strategy builder that affords a database user the ability to ask complex multi-data-type questions with relative ease and versatility. Genomic scale data can be queried based on BLAST searches, annotation keywords and gene ID searches, GO terms, sequence motifs, protein characteristics, phylogenetic relationships and functional data such as transcript (microarray and EST evidence) and protein expression data. Search strategies can be saved within a user's profile for future retrieval and may also be shared with other researchers using a unique strategy web address.

TriTrypDB (http://tritrypdb.org) is an integrated database providing access to genome-scale datasets for kinetoplastid parasites, and supporting a variety of complex queries driven by research and development needs. TriTrypDB is a collaborative project, utilizing the GUS/WDK computational infrastructure developed by the Eukaryotic Pathogen Bioinformatics Resource Center (EuPathDB.org) to integrate genome annotation and analyses from GeneDB and elsewhere with a wide variety of functional genomics datasets made available by members of the global research community, often pre-publication. Currently, TriTrypDB integrates datasets from Leishmania braziliensis, L. infantum, L. major, L. tarentolae, Trypanosoma brucei and T. cruzi. Users may examine individual genes or chromosomal spans in their genomic context, including syntenic alignments with other kinetoplastid organisms. Data within TriTrypDB can be interrogated utilizing a sophisticated search strategy system that enables a user to construct complex queries combining multiple data types. All search strategies are stored, allowing future access and integrated searches. 'User Comments' may be added to any gene page, enhancing available annotation; such comments become immediately searchable via the text search, and are forwarded to curators for incorporation into the reference annotation when appropriate.

GiardiaDB (http://GiardiaDB.org) and TrichDB (http://TrichDB.org) house the genome databases for Giardia lamblia and Trichomonas vaginalis, respectively, and represent the latest additions to the EuPathDB (http://EuPathDB.org) family of functional genomic databases. GiardiaDB and TrichDB employ the same framework as other EuPathDB sites (CryptoDB, PlasmoDB and ToxoDB), supporting fully integrated and searchable databases. Genomic-scale data available via these resources may be queried based on BLAST searches, annotation keywords and gene ID searches, GO terms, sequence motifs and other protein characteristics. Functional queries may also be formulated, based on transcript and protein expression data from a variety of platforms. Phylogenetic relationships may also be interrogated. The ability to combine the results from independent queries, and to store queries and query results for future use facilitates complex, genome-wide mining of functional genomic data.

EuPathDB (http://EuPathDB.org; formerly ApiDB) is an integrated database covering the eukaryotic pathogens of the genera Cryptosporidium, Giardia, Leishmania, Neospora, Plasmodium, Toxoplasma, Trichomonas and Trypanosoma. While each of these groups is supported by a taxon-specific database built upon the same infrastructure, the EuPathDB portal offers an entry point to all these resources, and the opportunity to leverage orthology for searches across genera. The most recent release of EuPathDB includes updates and changes affecting data content, infrastructure and the user interface, improving data access and enhancing the user experience. EuPathDB currently supports more than 80 searches and the recently-implemented ‘search strategy’ system enables users to construct complex multi-step searches via a graphical interface. Search results are dynamically displayed as the strategy is constructed or modified, and can be downloaded, saved, revised, or shared with other database users.

PlasmoDB (http://PlasmoDB.org) is a functional genomic database for Plasmodium spp. that provides a resource for data analysis and visualization in a gene-by-gene or genome-wide scale. PlasmoDB belongs to a family of genomic resources that are housed under the EuPathDB (http://EuPathDB.org) Bioinformatics Resource Center (BRC) umbrella. The latest release, PlasmoDB 5.5, contains numerous new data types from several broad categories—annotated genomes, evidence of transcription, proteomics evidence, protein function evidence, population biology and evolution. Data in PlasmoDB can be queried by selecting the data of interest from a query grid or drop down menus. Various results can then be combined with each other on the query history page. Search results can be downloaded with associated functional data and registered users can store their query history for future retrieval or analysis.

OBJECTIVE—The global incidence of diabetes continues to increase. Cell replacement therapy and islet transplantation offer hope, especially for severely affected patients. Efforts to differentiate insulin-producing β-cells from progenitor or stem cells require knowledge of the transcriptional programs that regulate the development of the endocrine pancreas. RESEARCH DESIGN AND METHODS—Differentiation toward the endocrine lineage is dependent on the transcription factor Neurogenin 3 (Neurog3, Ngn3). We utilize a Neurog3–enhanced green fluorescent protein knock-in mouse model to isolate endocrine progenitor cells from embryonic pancreata (embryonic day [E]13.5 through E17.5). Using advanced genomic approaches, we generate a comprehensive gene expression profile of these progenitors and their immediate descendants. RESULTS—A total of 1,029 genes were identified as being temporally regulated in the endocrine lineage during fetal development, 237 of which are transcriptional regulators. Through pathway analysis, we have modeled regulatory networks involving these proteins that highlight the complex transcriptional hierarchy governing endocrine differentiation. CONCLUSIONS—We have been able to accurately capture the gene expression profile of the pancreatic endocrine progenitors and their descendants. The list of temporally regulated genes identified in fetal endocrine precursors and their immediate descendants provides a novel and important resource for developmental biologists and diabetes researchers alike.

The failure to expand functional pancreatic beta-cell mass in response to increased metabolic demand is a hallmark of type 2 diabetes. Lineage tracing studies indicate that replication of existing beta-cells is the principle mechanism for beta-cell expansion in adult mice. Here we demonstrate that the proliferative response of beta-cells is dependent on the orphan nuclear receptor hepatocyte nuclear factor-4alpha (HNF-4alpha), the gene that is mutated in Maturity-Onset Diabetes of the Young 1 (MODY1). Computational analysis of microarray expression profiles from isolated islets of mice lacking HNF-4alpha in pancreatic beta-cells reveals that HNF-4alpha regulates selected genes in the beta-cell, many of which are involved in proliferation. Using a physiological model of beta-cell expansion, we show that HNF-4alpha is required for beta-cell replication and the activation of the Ras/ERK signaling cascade in islets. This phenotype correlates with the down-regulation of suppression of tumorigenicity 5 (ST5) in HNF-4alpha mutants, which we identify as a novel regulator of ERK phosphorylation in beta-cells and a direct transcriptional target of HNF-4alpha in vivo. Together, these results indicate that HNF-4alpha is essential for the physiological expansion of adult beta-cell mass in response to increased metabolic demand.

EPConDB (http://www.cbil.upenn.edu/EPConDB) is a public web site that supports research in diabetes, pancreatic development and beta-cell function by providing information about genes expressed in cells of the pancreas. EPConDB displays expression profiles for individual genes and information about transcripts, promoter elements and transcription factor binding sites. Gene expression results are obtained from studies examining tissue expression, pancreatic development and growth, differentiation of insulin-producing cells, islet or beta-cell injury, and genetic models of impaired beta-cell function. The expression datasets are derived using different microarray platforms, including the BCBC PancChips and Affymetrix gene expression arrays. Other datasets include semi-quantitative RT-PCR and MPSS expression studies. For selected microarray studies, lists of differentially expressed genes, derived from PaGE analysis, are displayed on the site. EPConDB provides database queries and tools to examine the relationship between a gene, its transcriptional regulation, protein function and expression in pancreatic tissues.

ToxoDB ( http://ToxoDB.org ) is a genome and functional genomic database for the protozoan parasite Toxoplasma gondii . It incorporates the sequence and annotation of the T. gondii ME49 strain, as well as genome sequences for the GT1, VEG and RH (Chr Ia, Chr Ib) strains. Sequence information is integrated with various other genomic-scale data, including community annotation, ESTs, gene expression and proteomics data. ToxoDB has matured significantly since its initial release. Here we outline the numerous updates with respect to the data and increased functionality available on the website.

PANcreatic DERived factor is an islet-specific cytokine that promotes apoptosis in primary islets and islet cell lines. To elucidate the genetic mechanisms of PANDER-induced cell death we performed expression profiling using the mouse PancChip version 5.0 in conjunction with Ingenuity Pathway Analysis. Murine islets were treated with PANDER and differentially expressed genes were identified at 48 and 72 h post-treatment. 64 genes were differentially expressed in response to PANDER treatment. 22 genes are associated with cell death. In addition, the genes with the highest fold change were linked with cell death or apoptosis. The most significantly affected gene at 48 h was the downregulated cyclin-dependent kinase inhibitor 1A (CDKN1A or p21). Approximately half of the genes impacted at 72 h were linked to cell death. Cell death differentially expressed genes were confirmed by quantitative RT-PCR. Further analysis identified cell death genetic networks at both time points with 21 of the 22 cell death genes related in various biological pathways. Caspase-3 (CASP3) was biologically linked to CDKN1A in several genetic networks and these two genes were further examined. Elevated cleaved CASP3 levels in PANDER-treated β-TC3 insulinoma cells were found to abrogate CDKN1A expression. Levels of CDKN1A were not affected in the absence of cleaved CASP3. PANDER-induced downregulation of CDKN1A expression coupled with induced CASP3-activation may serve a central role in islet cell death and offers further insight into the mechanisms of cytokine-induced β-cell apoptosis.

The mouse PancChip, a microarray developed for studying endocrine pancreatic development and diabetes, represents over 13,000 cDNAs. After computationally assigning the cDNAs on the array to known genes, manual curation of the remaining sequences identified 211 novel transcripts. In microarray experiments, we found that 196 of these transcripts were expressed in total pancreas and/or pancreatic islets. Of 50 randomly selected clones from these 196 transcripts, 92% were confirmed as expressed by qRT-PCR. We evaluated the coding potential of the novel transcripts and found that 74% of the clones had low coding potential. Since the transcripts may be partial mRNAs, we examined their translated proteins for transmembrane or signal peptide domains and found that about 40 proteins had one of these predicted domains. Interestingly, when we investigated the novel transcripts for their overlap with noncoding microRNAs, we found that 1 of the novel transcripts overlapped a known microRNA gene.

After partial pancreatectomy (Ppx), substantial regeneration of the endocrine and exocrine pancreatic compartments has been shown in adult rodents. Exendin-4 (Ex-4) is a glucagon-like peptide-1 receptor agonist that augments endocrine beta-cell mass by stimulating neogenesis, proliferation, and cell survival. After Ppx, treatment with Ex-4 ameliorates hyperglycemia by stimulating beta-cell mass recovery. We utilized a cDNA microarray approach to identify genes differentially regulated during pancreatic regeneration after Ppx and/or Ex-4 administration. The pancreatic remnant after Ppx showed a large number of differentially regulated genes. In contrast, Ex-4 treatment resulted in a smaller number of differentially regulated genes. Of note, a common subset of genes regulated by Ex-4 and after Ppx was identified, including three members of the mitogenic Reg gene family, Reg2, -3gamma, and -3beta, as well as fragilis, a gene that maintains pluripotency during germ cell specification, and Serpin b1a, a member of an intracellular protease inhibitor family involved in cell survival. These observations were confirmed by real-time PCR. We determined that Reg3beta protein is also induced in the acinar pancreas after Ppx, suggesting a novel role for this factor in pancreatic growth or response to injury. Finally, comparison of transcription factor-binding sites present in the proximal promoters of these genes identified potential common transcription factors that may regulate these genes. Chromatin immunoprecipitation analyses confirmed Reg3gamma as a novel transcriptional target of Foxa2 (HNF3beta). Our data suggest molecular pathways that may regulate pancreatic growth and offer a unique set of candidate genes to target in the development of therapies aimed at improving pancreatic growth and function.