An avalanche of sequencing and genomic data has a potential to revolutionize our understanding of microbial biology and transform medical research. Accurate reconstructions of bacterial metabolism often provide the most direct route toward understanding the biology of sequenced species, their growth and environmental properties, and possible interactions with other species in metagenomic communities. Reconstructed metabolic networks can also guide the development of new antibacterial therapeutics. In this application we propose to use the GLOBUS framework to build an integrated, accurate, and fully probabilistic system for the annotation of bacterial metabolism;we will integrate into the framework key data modalities that, according to our preliminary results, will significantly improve the method's coverage and accuracy. Specifically, we will a.) integrate protein structural information into GLOBUS, using analyses of enzyme active sites;b.) extend the GLOBUS framework to accommodate metabolomics data by joint sampling of metabolomics and protein annotations;and c.) integrate into the annotation framework phenotypic information, including growth on multiple nutrient sources measured by the widely used BiOLOG platform for high-throughput bacterial phenotyping. We will implement and make the developed methodology publically available through a transparent web-based portal. This will make it possible for other researchers to use the GLOBUS methodology to annotate any sequenced bacterial species of interest. The portal will be transparent, enabling users to identify the sources of the predictions We will also obtain relevant phenotypic information from our experimental collaborators and use GLOBUS to generate accurate probabilistic annotations for all major bacterial species (~50 bacteria) that are pathogenic to humans.

Public Health Relevance

The GLOBUS methodology developed in the previous funding period represents a major conceptual innovation over currently existing approaches for metabolic annotations. Building on the work performed previously, we propose in the renewal application to significantly expand the developed methodology, add key data modalities (protein structure, metabolomics, phenotypes), and apply the method to all major bacterial species that are pathogenic to humans. Comprehensive phenotypic data (~2000 growth condition per species for ~50 species) will be obtained from our experimental collaborators, and used to significantly improve metabolic annotations for all major bacterial human pathogens.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Modeling and Analysis of Biological Systems Study Section (MABS)
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Sledjeski, Darren D
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Columbia University (N.Y.)
Internal Medicine/Medicine
Schools of Medicine
New York
United States
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Plata, Germán; Henry, Christopher S; Vitkup, Dennis (2015) Long-term phenotypic evolution of bacteria. Nature 517:369-72
Plata, Germán; Vitkup, Dennis (2014) Genetic robustness and functional evolution of gene duplicates. Nucleic Acids Res 42:2405-14
Hu, Jie; Locasale, Jason W; Bielas, Jason H et al. (2013) Heterogeneity of tumor-induced gene expression changes in the human metabolic network. Nat Biotechnol 31:522-9
Gilman, Sarah R; Chang, Jonathan; Xu, Bin et al. (2012) Diverse types of genetic variation converge on functional gene networks involved in schizophrenia. Nat Neurosci 15:1723-8
Plata, German; Fuhrer, Tobias; Hsiao, Tzu-Lin et al. (2012) Global probabilistic annotation of metabolic networks enables enzyme discovery. Nat Chem Biol 8:848-54
Gilman, Sarah R; Iossifov, Ivan; Levy, Dan et al. (2011) Rare de novo variants associated with autism implicate a large functional network of genes involved in formation and function of synapses. Neuron 70:898-907
Plata, Germán; Gottesman, Max E; Vitkup, Dennis (2010) The rate of the molecular clock and the cost of gratuitous protein synthesis. Genome Biol 11:R98
de Hoon, Michiel J L; Eichenberger, Patrick; Vitkup, Dennis (2010) Hierarchical evolution of the bacterial sporulation network. Curr Biol 20:R735-45
Hsiao, Tzu-Lin; Revelles, Olga; Chen, Lifeng et al. (2010) Automatic policing of biochemical annotations using genomic correlations. Nat Chem Biol 6:34-40
Chastanet, Arnaud; Vitkup, Dennis; Yuan, Guo-Cheng et al. (2010) Broadly heterogeneous activation of the master regulator for sporulation in Bacillus subtilis. Proc Natl Acad Sci U S A 107:8486-91

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