SuperPhy: predictive genomics for the bacterial pathogen Escherichia coli.

BACKGROUND: Predictive genomics is the translation of raw genome sequence data into a phenotypic assessment of the organism. For bacterial pathogens, these phenotypes can range from environmental survivability, to the severity of human disease. Significant progress has been made in the development of generic tools for genomic analyses that are broadly applicable to all microorganisms; however, a fundamental missing component is the ability to analyze genomic data in the context of organism-specific phenotypic knowledge, which has been accumulated from decades of research and can provide a meaningful interpretation of genome sequence data. RESULTS: In this study, we present SuperPhy, an online predictive genomics platform ( http://lfz.corefacility.ca/superphy/ ) for Escherichia coli. The platform integrates the analytical tools and genome sequence data for all publicly available E. coli genomes and facilitates the upload of new genome sequences from users under public or private settings. SuperPhy provides real-time analyses of thousands of genome sequences with results that are understandable and useful to a wide community, including those in the fields of clinical medicine, epidemiology, ecology, and evolution. SuperPhy includes identification of: 1) virulence and antimicrobial resistance determinants 2) statistical associations between genotypes, biomarkers, geospatial distribution, host, source, and phylogenetic clade; 3) the identification of biomarkers for groups of genomes on the based presence/absence of specific genomic regions and single-nucleotide polymorphisms and 4) in silico Shiga-toxin subtype. CONCLUSIONS: SuperPhy is a predictive genomics platform that attempts to provide an essential link between the vast amounts of genome information currently being generated and phenotypic knowledge in an organism-specific context.

Moo1 obesity quantitative trait locus in BTBR T+ Itpr3tf/J mice increases food intake.

The rising prevalence of obesity is one of the greatest health challenges facing the world today. Discovery of genetic factors affecting obesity risk will provide important insight to its etiology that could suggest new therapeutic approaches. We have previously identified the Modifier of obese 1 (Moo1) quantitative trait locus (QTL) in a cross between leptin-deficient BTBR T(+) Itpr3(tf)/J (BTBR) and C57BL/6J (B6) mice. Understanding the mechanism by which this locus acts will aid in the identification of candidate genes. Here we refined the location of this QTL and sought to determine the mechanism by which Moo1 affects body weight. We found that the effects of Moo1 also alter high fat diet-induced obesity in mice having functional leptin. In detailed metabolic analyses we determined that this locus acts by increasing food intake in BTBR mice, without affecting energy expenditure. The expression levels of the main molecular mediators of food intake in the hypothalamus were not altered, suggesting this locus affects an independent pathway, consistent with its identification in mice lacking functional leptin. Finally, we show that the increased adiposity resulting from Moo1 is sufficient to affect glucose tolerance. These studies show that the Moo1 obesity QTL affects food intake, likely through a novel mechanism, and indicate that modulation of the underlying pathway may not only ameliorate obesity but also its clinical consequences.