Investigating Unknown Regions of E. coli Metabolism

Metabolic models are useful for a number of applications in the world of biology. If one understands the biochemistry involved in every part of an organism, it could be possible to predict how the organism would behave at the molecular level in particular environments. Once one model is understood sufficiently, the concepts could be applied to other organisms and groups of organisms. This has possible applications in medicine and environmental science concerning microbiomes. The base model could also be used as a foundation for further experimentation and metabolic modeling projects on more complex or less understood organisms. Our work focuses on the state-of-the-art metabolic model for E. coli, since this is a well studied model organism. However, the model currently only accounts for conservation of mass and the presence of pathways connecting metabolites. In order to be more useful, the model would have to incorporate a number of biological patterns. One such pattern is transcriptomics: the analysis of gene expression over the entire genome when the organism is in different environments. Transcriptomics is of particular interest as it involves, to some degree, all parts of the metabolic network. However, the usefulness of these data depend upon the diversity of the experiment pool. The current transcriptomic data set for E. coli contains about 1200 experiments, representing fewer than 100 unique conditions. We have conducted initial algorithmic development and analysis to identify parts of the metabolic network not yet perturbed in transcriptomic data. The goal of this project is to design a set of unique conditions and gather expression data. These data will be used to improve upon the initial algorithms and to eventually supplement the transcriptome data set with enough information to improve the current computer model of E. coli K12

Isolation of 18 Novel Mycobacteriophages and Genomic Analyses of Krueger and Phrappuccino

Eighteen new mycobacteriophages were isolated from soil samples collected around the state of Michigan and parts of the United States. All phages were capable of infecting Mycobacterium smegmatis and were isolated through either enrichment or direct plating at 25°C. A variety of plaque morphologies were produced based on size, shape, and clarity; both lytic and temperate phages appear represented in this collection. Two mycobacteriophages, Krueger and Phrappuccino, were chosen for complete genome sequencing and comparative genomic analyses. The predominant plaque produced by Krueger at 32°C was circular and 2 mm in diameter. The predominant plaque produced by Phrappuccino at 32°C was 1 mm in diameter, and took 48 hours to appear. Complete genome sequence for Krueger revealed relationships to members of the novel Subcluster K6, while Phrappuccino was not closely related to any known phage and is currently classified as a Singleton. The genome of Krueger is 60.3 Kb, 66.5% GC, and contains 101 genes, including 1 tRNA(Lys-TTT) gene; the genome of Phrappuccino is 136.3 Kb, 67.4% GC, and contains 200 genes. While Phrappuccino is a Singleton, there is strong evidence at the morphological (Myoviridae) and genomic levels for a relationship to Cluster C phages. Despite this relationship, Phrappuccino does not carry any tRNA genes. Forty (39.6%) and thirty-six (18%) protein coding genes were assigned functions in Krueger and Phrappuccino, respectively, based on comparative analyses. A detailed analysis of the complete genome sequences and comparison with sequenced mycobacteriophages is the subject of the second semester of this yearlong course and is presented