ABSTRACT
Vaccine design for rapidly changing viruses is based on empirical surveillance of strains circulating in a given season to assess those that will most likely spread during the next season. The choice of which strains to include in the vaccine is critical, as an erroneous decision can lead to a nonimmunized human population that will then be at risk in the face of an epidemic or, worse, a pandemic. Here, we present the first steps toward a very general phylogenetic approach to predict the emergence of novel viruses. Our genomic model builds upon natural features of viral evolution such as selection and recombination / reassortment, and incorporates episodic bursts of evolution and or of recombination. As a proof-of-concept, we assess the performance of this model in a retrospective study, focusing: (i) on the emergence of an unexpected H3N2 influenza strain in 2007, and (ii) on a longitudinal design. Based on the analysis of hemagglutinin (HA) and neuraminidase (NA) genes, our results show a lack of predictive power in both experimental designs, but shed light on the mode of evolution of these two antigens: (i) supporting the lack of significance of recombination in the evolution of this influenza virus, and (ii) showing that HA evolves episodically while NA changes gradually.
Subject(s)
Antigenic Variation/genetics , Hemagglutinin Glycoproteins, Influenza Virus/genetics , Influenza A Virus, H3N2 Subtype/genetics , Influenza A Virus, H3N2 Subtype/immunology , Neuraminidase/genetics , Antigenic Variation/immunology , Biological Evolution , Evolution, Molecular , Genetic Variation , Hemagglutinin Glycoproteins, Influenza Virus/immunology , Humans , Influenza Vaccines/immunology , Influenza, Human/epidemiology , Influenza, Human/genetics , Influenza, Human/virology , Neuraminidase/immunology , Recombination, GeneticABSTRACT
StemBase is a database of gene expression data obtained from stem cells and derivatives mainly from mouse and human using DNA microarrays and Serial Analysis of Gene Expression. Here, we describe this database and indicate ways to use it for the study the expression of particular genes in stem cells or to search for genes with particular expression profiles in stem cells, which could be associated to stem cell function or used as stem cell markers.
Subject(s)
Biomarkers/analysis , Databases, Genetic , Gene Expression Profiling/methods , Gene Expression/physiology , Oligonucleotide Array Sequence Analysis/methods , Stem Cells/physiology , Animals , Humans , MiceABSTRACT
BACKGROUND: Currently one of the largest online repositories for human and mouse stem cell gene expression data, StemBase was first designed as a simple web-interface to DNA microarray data generated by the Canadian Stem Cell Network to facilitate the discovery of gene functions relevant to stem cell control and differentiation. FINDINGS: Since its creation, StemBase has grown in both size and scope into a system with analysis tools that examine either the whole database at once, or slices of data, based on tissue type, cell type or gene of interest. As of September 1, 2008, StemBase contains gene expression data (microarray and Serial Analysis of Gene Expression) from 210 stem cell samples in 60 different experiments. CONCLUSION: StemBase can be used to study gene expression in human and murine stem cells and is available at http://www.stembase.ca.
ABSTRACT
The cell wall of mycobacteria includes an unusual outer membrane of extremely low permeability. While Escherichia coli uses more than 60 proteins to functionalize its outer membrane, only two mycobacterial outer membrane proteins (OMPs) are known. The porin MspA of Mycobacterium smegmatis provided the proof of principle that integral mycobacterial OMPs share the beta-barrel structure, the absence of hydrophobic alpha-helices and the presence of a signal peptide with OMPs of gram-negative bacteria. These properties were exploited in a multi-step bioinformatic approach to predict OMPs of M. tuberculosis. A secondary structure analysis was performed for 587 proteins of M. tuberculosis predicted to be exported. Scores were calculated for the beta-strand content and the amphiphilicity of the beta-strands. Reference OMPs of gram-negative bacteria defined threshold values for these parameters that were met by 144 proteins of unknown function of M. tuberculosis. Two of them were verified as OMPs by a novel two-step experimental approach. Rv1698 and Rv1973 were detected only in the total membrane fraction of M. bovis BCG in Western blot experiments, while proteinase K digestion of whole cells showed the surface accessibility of these proteins. These findings established that Rv1698 and Rv1973 are indeed localized in the outer membrane and tripled the number of known OMPs of M. tuberculosis. Significantly, these results provide evidence for the usefulness of the bioinformatic approach to predict mycobacterial OMPs and indicate that M. tuberculosis likely has many OMPs with beta-barrel structure. Our findings pave the way to identify the set of proteins which functionalize the outer membrane of M. tuberculosis.