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Phage Phenomics: Physiological Approaches to Characterize Novel Viral Proteins

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...

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Detalles Bibliográficos
Autores principales: Sanchez, Savannah E., Cuevas, Daniel A., Rostron, Jason E., Liang, Tiffany Y., Pivaroff, Cullen G., Haynes, Matthew R., Nulton, Jim, Felts, Ben, Bailey, Barbara A., Salamon, Peter, Edwards, Robert A., Burgin, Alex B., Segall, Anca M., Rohwer, Forest
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MyJove Corporation 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4544906/
https://www.ncbi.nlm.nih.gov/pubmed/26132888
http://dx.doi.org/10.3791/52854
Descripción
Sumario: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.