151. Phage Phenomics: Physiological Approaches to Characterize Novel Viral Proteins
- Author
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Forest Rohwer, Matthew Haynes, Cullen G. Pivaroff, Peter Salamon, Tiffany Y. Liang, Anca M. Segall, Ben Felts, Jason E. Rostron, Daniel A. Cuevas, Barbara A. Bailey, Alex B. Burgin, Savannah E. Sanchez, Jim Nulton, and Robert Edwards
- Subjects
Phage display ,General Chemical Engineering ,Immunology ,Genomics ,viral metagenome ,Genome, Viral ,Biology ,Genome ,Homology (biology) ,General Biochemistry, Genetics and Molecular Biology ,03 medical and health sciences ,Viral Proteins ,Phenomics ,phage ,Escherichia coli ,Bacteriophages ,Gene ,030304 developmental biology ,Genetics ,0303 health sciences ,General Immunology and Microbiology ,030306 microbiology ,General Neuroscience ,phenomics ,Multi-phenotype Assay Plates (MAPs) ,Phenotype ,metabolomics ,Open reading frame ,Issue 100 ,continuous culture - Abstract
Current investigations into phage-host interactions are dependent on extrapolating knowledge from (meta)genomes. Interestingly, 60 - 95% of all phage sequences share no homology to current annotated proteins. As a result, a large proportion of phage genes are annotated as hypothetical. This reality heavily affects the annotation of both structural and auxiliary metabolic genes. Here we present phenomic methods designed to capture the physiological response(s) of a selected host during expression of one of these unknown phage genes. Multi-phenotype Assay Plates (MAPs) are used to monitor the diversity of host substrate utilization and subsequent biomass formation, while metabolomics provides bi-product analysis by monitoring metabolite abundance and diversity. Both tools are used simultaneously to provide a phenotypic profile associated with expression of a single putative phage open reading frame (ORF). Representative results for both methods are compared, highlighting the phenotypic profile differences of a host carrying either putative structural or metabolic phage genes. In addition, the visualization techniques and high throughput computational pipelines that facilitated experimental analysis are presented.
- Published
- 2015
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