TriTrypDB: An integrated functional genomics resource for kinetoplastida.

Parasitic diseases caused by kinetoplastid parasites are a burden to public health throughout tropical and subtropical regions of the world. TriTrypDB (https://tritrypdb.org) is a free online resource for data mining of genomic and functional data from these kinetoplastid parasites and is part of the VEuPathDB Bioinformatics Resource Center (https://veupathdb.org). As of release 59, TriTrypDB hosts 83 kinetoplastid genomes, nine of which, including Trypanosoma brucei brucei TREU927, Trypanosoma cruzi CL Brener and Leishmania major Friedlin, undergo manual curation by integrating information from scientific publications, high-throughput assays and user submitted comments. TriTrypDB also integrates transcriptomic, proteomic, epigenomic, population-level and isolate data, functional information from genome-wide RNAi knock-down and fluorescent tagging, and results from automated bioinformatics analysis pipelines. TriTrypDB offers a user-friendly web interface embedded with a genome browser, search strategy system and bioinformatics tools to support custom in silico experiments that leverage integrated data. A Galaxy workspace enables users to analyze their private data (e.g., RNA-sequencing, variant calling, etc.) and explore their results privately in the context of publicly available information in the database. The recent addition of an annotation platform based on Apollo enables users to provide both functional and structural changes that will appear as 'community annotations' immediately and, pending curatorial review, will be integrated into the official genome annotation.

MaHPIC malaria systems biology data from Plasmodium cynomolgi sporozoite longitudinal infections in macaques.

Plasmodium cynomolgi causes zoonotic malarial infections in Southeast Asia and this parasite species is important as a model for Plasmodium vivax and Plasmodium ovale. Each of these species produces hypnozoites in the liver, which can cause relapsing infections in the blood. Here we present methods and data generated from iterative longitudinal systems biology infection experiments designed and performed by the Malaria Host-Pathogen Interaction Center (MaHPIC) to delve deeper into the biology, pathogenesis, and immune responses of P. cynomolgi in the Macaca mulatta host. Infections were initiated by sporozoite inoculation. Blood and bone marrow samples were collected at defined timepoints for biological and computational experiments and integrative analyses revolving around primary illness, relapse illness, and subsequent disease and immune response patterns. Parasitological, clinical, haematological, immune response, and -omic datasets (transcriptomics, proteomics, metabolomics, and lipidomics) including metadata and computational results have been deposited in public repositories. The scope and depth of these datasets are unprecedented in studies of malaria, and they are projected to be a F.A.I.R., reliable data resource for decades.

Correction to: Accessing Cryptosporidium Omic and Isolate Data via CryptoDB.org.

Correction to: Chapter 10 in: Susanne Warrenfeltz, Jessica C. Kissinger, and On Behalf of the EuPathDB Team.

Accessing Cryptosporidium Omic and Isolate Data via CryptoDB.org.

Cryptosporidium has historically been a difficult organism to work with, and molecular genomic data for this important pathogen have typically lagged behind other prominent protist pathogens. CryptoDB ( http://cryptodb.org/ ) was launched in 2004 following the appearance of draft genome sequences for both C. parvum and C. hominis. CryptoDB merged with the EuPathDB Bioinformatics Resource Center family of databases ( https://eupathdb.org ) and has been maintained and updated regularly since its establishment. These resources are freely available, are web-based, and permit users to analyze their own sequence data in the context of reference genome sequences in our user workspaces. Advances in technology have greatly facilitated Cryptosporidium research in the last several years greatly enhancing and extending the data and types of data available for this genus. Currently, 13 genome sequences are available for 9 species of Cryptosporidium as well as the distantly related Gregarina niphandrodes and two free-living alveolate outgroups of the Apicomplexa, Chromera velia and Vitrella brassicaformis. Recent years have seen several new genome sequences for both existing and new Cryptosporidium species as well as transcriptomics, proteomics, SNP, and isolate population surveys. This chapter introduces the extensive data mining and visualization capabilities of the EuPathDB software platform and introduces the data types and tools that are currently available for Cryptosporidium. Key features are demonstrated with Cryptosporidium-relevant examples and explanations.

FungiDB: An Integrated Bioinformatic Resource for Fungi and Oomycetes.

FungiDB (fungidb.org) is a free online resource for data mining and functional genomics analysis for fungal and oomycete species. FungiDB is part of the Eukaryotic Pathogen Genomics Database Resource (EuPathDB, eupathdb.org) platform that integrates genomic, transcriptomic, proteomic, and phenotypic datasets, and other types of data for pathogenic and nonpathogenic, free-living and parasitic organisms. FungiDB is one of the largest EuPathDB databases containing nearly 100 genomes obtained from GenBank, Aspergillus Genome Database (AspGD), The Broad Institute, Joint Genome Institute (JGI), Ensembl, and other sources. FungiDB offers a user-friendly web interface with embedded bioinformatics tools that support custom in silico experiments that leverage FungiDB-integrated data. In addition, a Galaxy-based workspace enables users to generate custom pipelines for large-scale data analysis (e.g., RNA-Seq, variant calling, etc.). This review provides an introduction to the FungiDB resources and focuses on available features, tools, and queries and how they can be used to mine data across a diverse range of integrated FungiDB datasets and records.

EuPathDB: The Eukaryotic Pathogen Genomics Database Resource.

Fighting infections and developing novel drugs and vaccines requires advanced knowledge of pathogen's biology. Readily accessible genomic, functional genomic, and population data aids biological and translational discovery. The Eukaryotic Pathogen Database Resources ( http://eupathdb.org ) are data mining resources that support hypothesis driven research by facilitating the discovery of meaningful biological relationships from large volumes of data. The resource encompasses 13 sites that support over 170 species including pathogenic protists, oomycetes, and fungi as well as evolutionarily related nonpathogenic species. EuPathDB integrates preanalyzed data with advanced search capabilities, data visualization, analysis tools and a comprehensive record system in a graphical interface that does not require prior computational skills. This chapter describes guiding concepts common across EuPathDB sites and illustrates the powerful data mining capabilities of some of the available tools and features.

EuPathDB: the eukaryotic pathogen genomics database resource.

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.

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.

Regulation of gene expression in ovarian cancer cells by luteinizing hormone receptor expression and activation.

BACKGROUND: Since a substantial percentage of ovarian cancers express gonadotropin receptors and are responsive to the relatively high concentrations of pituitary gonadotropins during the postmenopausal years, it has been suggested that receptor activation may contribute to the etiology and/or progression of the neoplasm. The goal of the present study was to develop a cell model to determine the impact of luteinizing hormone (LH) receptor (LHR) expression and LH-mediated LHR activation on gene expression and thus obtain insights into the mechanism of gonadotropin action on ovarian surface epithelial (OSE) carcinoma cells. METHODS: The human ovarian cancer cell line, SKOV-3, was stably transfected to express functional LHR and incubated with LH for various periods of time (0-20 hours). Transcriptomic profiling was performed on these cells to identify LHR expression/activation-dependent changes in gene expression levels and pathways by microarray and qRT-PCR analyses. RESULTS: Through comparative analysis on the LHR-transfected SKOV-3 cells exposed to LH, we observed the differential expression of 1,783 genes in response to LH treatment, among which five significant families were enriched, including those of growth factors, translation regulators, transporters, G-protein coupled receptors, and ligand-dependent nuclear receptors. The most highly induced early and intermediate responses were found to occupy a network impacting transcriptional regulation, cell growth, apoptosis, and multiple signaling transductions, giving indications of LH-induced apoptosis and cell growth inhibition through the significant changes in, for example, tumor necrosis factor, Jun and many others, supportive of the observed cell growth reduction in in vitro assays. However, other observations, e.g. the substantial up-regulation of the genes encoding the endothelin-1 subtype A receptor, stromal cell-derived factor 1, and insulin-like growth factor II, all of which are potential therapeutic targets, may reflect a positive mediation of ovarian cancer growth. CONCLUSION: Overall, the present study elucidates the extensive transcriptomic changes of ovarian cancer cells in response to LH receptor activation, which provides a comprehensive and objective assessment for determining new cancer therapies and potential serum markers, of which over 100 are suggested.

The luteinizing hormone receptor: insights into structure-function relationships and hormone-receptor-mediated changes in gene expression in ovarian cancer cells.

The luteinizing hormone receptor (LHR), one of the three glycoprotein hormone receptors, is necessary for critical reproductive processes, including gonadal steroidogenesis, oocyte maturation and ovulation, and male sex differentiation. Moreover, it has been postulated to contribute to certain neoplasms, particularly ovarian cancer. A member of the G protein-coupled receptor family, LHR contains a relatively large extracellular domain responsible for high affinity hormone binding; transmembrane activation then leads to G protein coupling and subsequent second messenger production. This review deals with recent advances in our understanding of LHR structure and structure-function relationships, as well as hormone-mediated changes in gene expression in ovarian cancer cells expressing LHR. Suggestions are also made for critical gaps that need to be filled as the field advances, including determination of the three-dimensional structure of inactive and active receptor, elucidation of the mechanism by which hormone binding to the extracellular domain triggers the activation of Gs, clarification of the putative roles of LHR in non-gonadal tissues, and the role, if any, of activated receptor in the development or progression of ovarian cancer.

Luteinizing hormone-induced up-regulation of ErbB-2 is insufficient stimulant of growth and invasion in ovarian cancer cells.

The effects of luteinizing hormone (LH), a gonadotropic hormone implicated in the development of ovarian cancer, are mediated by specific binding to its G protein-coupled receptor, the LH receptor (LHR). Activated LHR initiates second messenger responses, including cyclic AMP (cAMP) and inositol phosphate. Because cAMP increases expression of ErbB-2, a receptor tyrosine kinase whose overexpression in cancers correlates with poor survival, we hypothesized that LH may regulate ErbB-2 expression. Cell surface LHR expression in stable transformants of the ErbB-2-overexpressing ovarian cancer cell line SKOV3 was confirmed by PCR and whole-cell ligand binding studies. Second messenger accumulation in the LHR-expressing cells confirmed signaling through Gs and Gq. Western blots of total protein revealed that LHR introduction up-regulated ErbB-2 protein expression 2-fold and this was further up-regulated in a time- and dose-dependent manner in response to LH. Forskolin and 8Br-cAMP also up-regulated ErbB-2 in both LHR-expressing and mock-transfected cells, indicating that regulation of ErbB-2 is a cAMP-mediated event. Kinase inhibitor studies indicated the involvement of protein kinase A-mediated, protein kinase C-mediated, epidermal growth factor receptor-mediated, and ErbB-2-mediated mechanisms. The LH-induced up-regulation of ErbB-2 was insufficient to overcome the negative effects of LH on proliferation, invasion, and migration. A molecular signature for this nonaggressive phenotype was determined by Taqman array to include increased and decreased expression of genes encoding adhesion proteins and metalloproteinases, respectively. These data establish a role for LH and LHR in the regulation of ErbB-2 expression and suggest that, in some systems, ErbB-2 up-regulation alone is insufficient in producing a more aggressive phenotype.

Gene expression profiling of epithelial ovarian tumours correlated with malignant potential.

BACKGROUND: Epithelial ovarian tumours exhibit a range of malignant potential, presenting distinct clinical phenotypes. Improved knowledge of gene expression changes and functional pathways associated with these clinical phenotypes may lead to new treatment targets, markers for early detection and a better understanding of disease progression. RESULTS: Gene expression profiling (Affymetrix, U95Av2) was carried out on 18 ovarian tumours including benign adenomas, borderline adenocarcinomas of low malignant potential and malignant adenocarcinomas. Clustering the expression profiles of samples from patients not treated with chemotherapy prior to surgery effectively classified 92% of samples into their proper histopathological group. Some cancer samples from patients treated with chemotherapy prior to surgery clustered with the benign adenomas. Chemotherapy patients whose tumours exhibited benign-like expression patterns remained disease free for the duration of this study as indicated by continued normal serum CA-125 levels. Statistical analysis identified 163 differentially expressed genes: 61 genes under-expressed in cancer and 102 genes over-expressed in cancer. Profiling the functional categories of co-ordinately expressed genes within this list revealed significant correlation between increased malignant potential and loss of both IGF binding proteins and cell adhesion molecules. Interestingly, in several instances co-ordinately expressed genes sharing biological function also shared chromosomal location. CONCLUSION: Our findings indicate that gene expression profiling can reliably distinguish between benign and malignant ovarian tumours. Expression profiles of samples from patients pre-treated with chemotherapy may be useful in predicting disease free survival and the likelihood of recurrence. Loss of expression of IGF binding proteins as well as specific cell adhesion molecules may be a significant mechanism of disease progression in ovarian cancer. Expression levels in borderline tumours were intermediate between benign adenomas and malignant adenocarcinomas for a significant portion of the differentially expressed genes, suggesting that borderline tumours are a transitional state between benign and malignant tumours. Finally, genes displaying coordinated changes in gene expression were often genetically linked, suggesting that changes in expression for these genes are the consequence of regional duplications, deletions or epigenetic events.

Transcriptional profiling of initial differentiation events in human embryonic stem cells.

Currently, there are no differentiation strategies for human embryonic stem cells (hESCs) that efficiently produce one specific cell type, possibly because of lack of understanding of the genes that control signaling events prior to overt differentiation. sed HepG2 cell conditioned medium (MEDII), which induces early differentiation in mouse ES cells while retaining pluripotent markers, to query gene expression in hESCs. Treatment of adherent hESCs with 50% MEDII medium effected differentiation to a cell type with gene expression similar to primitive streak stage cells of mouse embryos. MEDII treatment up-regulates TDGF1 (Cripto), a gene essential for anterior-posterior axis and mesoderm formation in mouse embryos and a key component of the TGFB1/NODAL signaling pathway. LEFTYA, an antagonist of NODAL/TDGF1 signaling expressed in anterior visceral endoderm, is down-regulated with MEDII treatment, as is FST, an inhibitor of mesoderm induction via the related INHBE1 pathway. In summary, the TGFB1/NODAL pathway is important for primitive-streak and mesoderm formation and in using MEDII, we present a means for generating an in vitro cell population that maintains pluripotent gene expression (POU5F1, NANOG) and SSEA-4 markers while regulating genes in the TGFB1/NODAL pathway, which may lead to more uniform formation of mesoderm in vitro.