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The rapid spread of Zika virus (ZIKV) has caused much concern in the global health community, due in part to a link to fetal microcephaly and other neurological illnesses. While an increasing amount of ZIKV genomic sequence data is being generated, an understanding of the virus molecular biology is still greatly lacking. A significant step towards establishing ZIKV proteomics would be the compilation of all proteins produced by the virus, and the resultant virus genotypes. Here we report for the first time such data, using new computational methods for the annotation of mature peptide proteins, genotypes, and recombination events for all ZIKV genomes. The data is made publicly available through the Virus Pathogen Resource at www.viprbrc.org.

The Influenza Research Database (IRD) is a U.S. National Institute of Allergy and Infectious Diseases (NIAID)-sponsored Bioinformatics Resource Center dedicated to providing bioinformatics support for influenza virus research. IRD facilitates the research and development of vaccines, diagnostics and therapeutics against influenza virus by providing a comprehensive collection of influenza-related data integrated from various sources, a growing suite of analysis and visualization tools for data mining and hypothesis generation, personal workbench spaces for data storage and sharing, and active user community support. Here, we describe the recent improvements in IRD including the use of cloud and high performance computing resources, analysis and visualization of user-provided sequence data with associated metadata, predictions of novel variant proteins, annotations of phenotype-associated sequence markers and their predicted phenotypic effects, hemagglutinin (HA) clade classifications, an automated tool for HA subtype numbering conversion, linkouts to disease event data and the addition of host factor and antiviral drug components. All data and tools are freely available without restriction from the IRD website at https://www.fludb.org.

The BioHealthBase Bioinformatics Resource Center (BRC) (http://www.biohealthbase.org) is a public bioinformatics database and analysis resource for the study of specific biodefense and public health pathogens-Influenza virus, Francisella tularensis, Mycobacterium tuberculosis, Microsporidia species and ricin toxin. The BioHealthBase serves as an extensive integrated repository of data imported from public databases, data derived from various computational algorithms and information curated from the scientific literature. The goal of the BioHealthBase is to facilitate the development of therapeutics, diagnostics and vaccines by integrating all available data in the context of host-pathogen interactions, thus allowing researchers to understand the root causes of virulence and pathogenicity. Genome and protein annotations can be viewed either as formatted text or graphically through a genome browser. 3D visualization capabilities allow researchers to view proteins with key structural and functional features highlighted. Influenza virus host-pathogen interactions at the molecular/cellular and systemic levels are represented. Host immune response to influenza infection is conveyed through the display of experimentally determined antibody and T-cell epitopes curated from the scientific literature or as derived from computational predictions. At the molecular/cellular level, the BioHealthBase BRC has developed biological pathway representations relevant to influenza virus host-pathogen interaction in collaboration with the Reactome database (http://www.reactome.org).

The Systems Biology for Infectious Diseases Research program was established by the U.S. National Institute of Allergy and Infectious Diseases to investigate host-pathogen interactions at a systems level. This program generated 47 transcriptomic and proteomic datasets from 30 studies that investigate in vivo and in vitro host responses to viral infections. Human pathogens in the Orthomyxoviridae and Coronaviridae families, especially pandemic H1N1 and avian H5N1 influenza A viruses and severe acute respiratory syndrome coronavirus (SARS-CoV), were investigated. Study validation was demonstrated via experimental quality control measures and meta-analysis of independent experiments performed under similar conditions. Primary assay results are archived at the GEO and PeptideAtlas public repositories, while processed statistical results together with standardized metadata are publically available at the Influenza Research Database (www.fludb.org) and the Virus Pathogen Resource (www.viprbrc.org). By comparing data from mutant versus wild-type virus and host strains, RNA versus protein differential expression, and infection with genetically similar strains, these data can be used to further investigate genetic and physiological determinants of host responses to viral infection.

The Virus Pathogen Resource (ViPR; www.viprbrc.org) and Influenza Research Database (IRD; www.fludb.org) have developed a metadata-driven Comparative Analysis Tool for Sequences (meta-CATS), which performs statistical comparative analyses of nucleotide and amino acid sequence data to identify correlations between sequence variations and virus attributes (metadata). Meta-CATS guides users through: selecting a set of nucleotide or protein sequences; dividing them into multiple groups based on any associated metadata attribute (e.g. isolation location, host species); performing a statistical test at each aligned position; and identifying all residues that significantly differ between the groups. As proofs of concept, we have used meta-CATS to identify sequence biomarkers associated with dengue viruses isolated from different hemispheres, and to identify variations in the NS1 protein that are unique to each of the 4 dengue serotypes. Meta-CATS is made freely available to virology researchers to identify genotype-phenotype correlations for development of improved vaccines, diagnostics, and therapeutics.

Author(s): Squires, R Burke; Noronha, Jyothi; Hunt, Victoria; Garcia-Sastre, Adolfo; Macken, Catherine; Baumgarth, Nicole; Suarez, David; Pickett, Brett E; Zhang, Yun; Larsen, Christopher N; Ramsey, Alvin; Zhou, Liwei; Zaremba, Sam; Kumar, Sanjeev; Deitrich, Jon; Klem, Edward; Scheuermann, Richard H | Abstract: BackgroundThe recent emergence of the 2009 pandemic influenza A/H1N1 virus has highlighted the value of free and open access to influenza virus genome sequence data integrated with information about other important virus characteristics.DesignThe Influenza Research Database (IRD, http://www.fludb.org) is a free, open, publicly-accessible resource funded by the U.S. National Institute of Allergy and Infectious Diseases through the Bioinformatics Resource Centers program. IRD provides a comprehensive, integrated database and analysis resource for influenza sequence, surveillance, and research data, including user-friendly interfaces for data retrieval, visualization and comparative genomics analysis, together with personal log in-protected 'workbench' spaces for saving data sets and analysis results. IRD integrates genomic, proteomic, immune epitope, and surveillance data from a variety of sources, including public databases, computational algorithms, external research groups, and the scientific literature.ResultsTo demonstrate the utility of the data and analysis tools available in IRD, two scientific use cases are presented. A comparison of hemagglutinin sequence conservation and epitope coverage information revealed highly conserved protein regions that can be recognized by the human adaptive immune system as possible targets for inducing cross-protective immunity. Phylogenetic and geospatial analysis of sequences from wild bird surveillance samples revealed a possible evolutionary connection between influenza virus from Delaware Bay shorebirds and Alberta ducks.ConclusionsThe IRD provides a wealth of integrated data and information about influenza virus to support research of the genetic determinants dictating virus pathogenicity, host range restriction and transmission, and to facilitate development of vaccines, diagnostics, and therapeutics.

Genetic drift of influenza virus genomic sequences occurs through the combined effects of sequence alterations introduced by a low-fidelity polymerase and the varying selective pressures experienced as the virus migrates through different host environments. While traditional phylogenetic analysis is useful in tracking the evolutionary heritage of these viruses, the specific genetic determinants that dictate important phenotypic characteristics are often difficult to discern within the complex genetic background arising through evolution. Here we describe a novel influenza virus sequence feature variant type (Flu-SFVT) approach, made available through the public Influenza Research Database resource ( www.fludb.org ), in which variant types (VTs) identified in defined influenza virus protein sequence features (SFs) are used for genotype-phenotype association studies. Since SFs have been defined for all influenza virus proteins based on known structural, functional, and immune epitope recognition properties, the Flu-SFVT approach allows the rapid identification of the molecular genetic determinants of important influenza virus characteristics and their connection to underlying biological functions. We demonstrate the use of the SFVT approach to obtain statistical evidence for effects of NS1 protein sequence variations in dictating influenza virus host range restriction.

Enterovirus D68 (EV-D68) caused a severe respiratory illness outbreak in the United States in 2014. Reports of acute flaccid myelitis (AFM)/paralysis (AFP) in several independent epidemiological clusters of children with detectable EV-D68 have raised concerns that genetic changes in EV-D68 could be causing increased disease severity and neurological symptoms. To explore the potential link between EV-D68 genetic variations and symptom changes, we performed a series of comparative genomic analyses of EV-D68 2014 outbreak isolate sequences using data and analytical tools in the Virus Pathogen Resource (ViPR; www.viprbrc.org). Our results suggest that (1) three distinct lineages of EV-D68 were co-circulating in 2013 and 2014; (2) isolates associated with AFM/AFP belong to a single phylogenetic subclade – B1; (3) the majority of isolates from the B1 subclade have 21 unique substitutions that distinguish them from other isolates, including amino acid substitutions in the VP1, VP2, and VP3 capsid proteins and the 3D RNA-dependent RNA polymerase, and nucleotide substitutions in the internal ribosome entry sequence (IRES); (4) at 12 of these positions, B1 isolates carry the same residues observed at equivalent positions in paralysis-causing enteroviruses, including poliovirus, EV-D70 and EV-A71. Based on these results, we hypothesize that unique B1 substitutions may be responsible for the apparent increased incidence of neuropathology associated with the 2014 outbreak.

Psoriasis is a disease characterized by hyperproliferation of epidermal cells. This is the first study of the effects. of antipsoriasis drugs on the growth of epidermis-derived cells in cell culture. GP18 cells, a clonal strain of epithelial-like cells derived from guinea pig epidermis and selected for rapid growth and epithelial morphology, responded as expected to metabolic inhibitors with known mechanisms of action. Cytosine arabinoside and daunorubicine inhibited thymidine incorporation into DNA, as did actinomycin D. Cycloheximide and puromycin inhibited amino acid incorporation into protein. All inhibited cell proliferation at a 2 μM concentration. The antipsoriasis drugs (in order of potency: methotrexate, anthralin, mycophenolic acid, azaribine [its active metabolite, azauridine, was used], and hydroxyurea) all inhibited thymidine (or deoxyuridine) incorporation into DNA, but not amino acid incorporation into protein. They also inhibited cell proliferation when applied to GP18 cells at concentrations of 0.5 to 50 μg/ml. Anthralin, which was studied in more detail, was also found to be most potent against rapidly growing cells, to inhibit growth irreversibly, and to inhibit utilization of thymidine and deoxyuridine equally. The similar type of activity shown by all of these drugs with GP18 cells indicates that in vitro cultured cell systems could be useful tools in identifying new topically active drugs potentially useful as antipsoriasis agents.

The phenomenon of selective translation of T4 template RNA by ribosomes from T4-infected cells, or factors derived therefrom, has been extended to studies on the initiation of protein synthesis. A high-salt extract derived from T4-infected ribosomes inhibits the formation of initiation complexes of MS2 and Escherichia coli template RNA with uninfected ribosomes while efficiently supporting the formation of initiation complexes with T4 template RNA. T4 factors also permit T5 template RNA to bind to E. coli ribosomes, which indicates that the T4 selective effect is not exclusive for T4 templates. Other evidence indicates that T4 factors do not alter the process of polypeptide chain elongation.