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 (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,
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.
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.
Cardiomyocyte hypertrophy is associated with changes in gene expression. Extracellular signal-regulated kinases 1/2 (ERK1/2) and RhoA activated by hypertrophic agonists (e.g. endothelin-1) regulate ...gene expression and are implicated in the response, but their relative significance in regulating the cardiomyocyte transcriptome is unknown. Our aim was to establish the significance of ERK1/2 and/or RhoA in the early cardiomyocyte transcriptomic response to endothelin-1.
Cardiomyocytes were exposed to endothelin-1 (1 h) with/without PD184352 (to inhibit ERK1/2) or C3 transferase (C3T, to inhibit RhoA). RNA expression was analyzed using microarrays and qPCR. ERK1/2 signaling positively regulated approximately 65% of the early gene expression response to ET-1 with a small (approximately 2%) negative effect, whereas RhoA signaling positively regulated approximately 10% of the early gene expression response to ET-1 with a greater (approximately 14%) negative contribution. Of RNAs non-responsive to endothelin-1, 66 or 448 were regulated by PD184352 or C3T, respectively, indicating that RhoA had a more significant effect on baseline RNA expression. mRNAs upregulated by endothelin-1 encoded a number of receptor ligands (e.g. Ereg, Areg, Hbegf) and transcription factors (e.g. Abra/Srf) that potentially propagate the response.
ERK1/2 dominates over RhoA in the early transcriptomic response to endothelin-1. RhoA plays a major role in maintaining baseline RNA expression but, with upregulation of Abra/Srf by endothelin-1, RhoA may regulate changes in RNA expression over longer times. Our data identify ERK1/2 as a more significant node than RhoA in regulating the early stages of cardiomyocyte hypertrophy.
Abstract
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.
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.
FungiDB (https://fungidb.org) serves as a valuable online resource that seamlessly integrates genomic and related large-scale data for a wide range of fungal and oomycete species. As an integral part ...of the VEuPathDB Bioinformatics Resource Center (https://veupathdb.org), FungiDB continually integrates both published and unpublished data addressing various aspects of fungal biology. Established in early 2011, the database has evolved to support 674 datasets. The datasets include over 300 genomes spanning various taxa (e.g. Ascomycota, Basidiomycota, Blastocladiomycota, Chytridiomycota, Mucoromycota, as well as Albuginales, Peronosporales, Pythiales, and Saprolegniales). In addition to genomic assemblies and annotation, over 300 extra datasets encompassing diverse information, such as expression and variation data, are also available. The resource also provides an intuitive web-based interface, facilitating comprehensive approaches to data mining and visualization. Users can test their hypotheses and navigate through omics-scale datasets using a built-in search strategy system. Moreover, FungiDB offers capabilities for private data analysis via the integrated VEuPathDB Galaxy platform. FungiDB also permits genome improvements by capturing expert knowledge through the User Comments system and the Apollo genome annotation editor for structural and functional gene curation. FungiDB facilitates data exploration and analysis and contributes to advancing research efforts by capturing expert knowledge for fungal and oomycete species.
Some species responded successfully to prehistoric changes in climate 1, 2, while others failed to adapt and became extinct 3. The factors that determine successful climate adaptation remain poorly ...understood. We constructed a reference genome and studied physiological adaptations in the Alpine marmot (Marmota marmota), a large ground-dwelling squirrel exquisitely adapted to the “ice-age” climate of the Pleistocene steppe 4, 5. Since the disappearance of this habitat, the rodent persists in large numbers in the high-altitude Alpine meadow 6, 7. Genome and metabolome showed evidence of adaptation consistent with cold climate, affecting white adipose tissue. Conversely, however, we found that the Alpine marmot has levels of genetic variation that are among the lowest for mammals, such that deleterious mutations are less effectively purged. Our data rule out typical explanations for low diversity, such as high levels of consanguineous mating, or a very recent bottleneck. Instead, ancient demographic reconstruction revealed that genetic diversity was lost during the climate shifts of the Pleistocene and has not recovered, despite the current high population size. We attribute this slow recovery to the marmot’s adaptive life history. The case of the Alpine marmot reveals a complicated relationship between climatic changes, genetic diversity, and conservation status. It shows that species of extremely low genetic diversity can be very successful and persist over thousands of years, but also that climate-adapted life history can trap a species in a persistent state of low genetic diversity.
•The Alpine marmot is among the least genomically diverse animal species•Its diversity was lost during consecutive ice-age climate-related events•An extreme lifestyle hampered the subsequent recovery of genetic variation•Alpine marmots show why large populations can coexist with very low genetic variation
Despite being highly abundant and well adapted, Gossmann et al. report that the Alpine marmot is among the least genetically diverse animal species. The low diversity is found to be the consequence of consecutive, climate-related events, including long-term extreme niche adaptation, that also greatly retarded the recovery of its genetic diversity.
Proteomics data can supplement genome annotation efforts, for example being used to confirm gene models or correct gene annotation errors. Here, we present a large‐scale proteogenomics study of two ...important apicomplexan pathogens: Toxoplasma gondii and Neospora caninum. We queried proteomics data against a panel of official and alternate gene models generated directly from RNASeq data, using several newly generated and some previously published MS datasets for this meta‐analysis. We identified a total of 201 996 and 39 953 peptide‐spectrum matches for T. gondii and N. caninum, respectively, at a 1% peptide FDR threshold. This equated to the identification of 30 494 distinct peptide sequences and 2921 proteins (matches to official gene models) for T. gondii, and 8911 peptides/1273 proteins for N. caninum following stringent protein‐level thresholding. We have also identified 289 and 140 loci for T. gondii and N. caninum, respectively, which mapped to RNA‐Seq‐derived gene models used in our analysis and apparently absent from the official annotation (release 10 from EuPathDB) of these species. We present several examples in our study where the RNA‐Seq evidence can help in correction of the current gene model and can help in discovery of potential new genes. The findings of this study have been integrated into the EuPathDB. The data have been deposited to the ProteomeXchange with identifiers PXD000297and PXD000298.
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