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Description :
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The Shewanella Federation, a multi-institutional consortium assembled by DOE, is applying high-throughput approaches for measuring gene and proteome expression of the microbe Shewanella
oneidensis MR-1. The federation seeks to achieve a systems-level understanding of how this respiration-versatile microorganism regulates energy and material flow and uses its electron-transport system to reduce metals and nitrate, processes relevant to DOE missions. The group conducts integrated and coordinated investigations that incorporate many facets of biological research and technologies across a number of disciplines and, hence, serve as a model for systems biology studies within the Genomics:GTL program. Federation members share information and resources and collaborate on projects consisting of a few investigators focused on a defined topic and on larger experiments combining their capabilities to address complex scientific questions. Combining Computational and Experimental Approaches to Enhance Shewanella Genome Annotation Genomics, the study of all the genetic sequences in living organisms, has leaned heavily on the blueprint metaphor. A large part of the blueprint unfortunately has been unintelligible, requiring a way to link genomic features to what’s happening in the cell. The Shewanella Federation has taken a significant step toward improving the interpretation of the blueprint for S. oneidensis MR-1. Federation members have applied a powerful new approach that integrates experimental and computational analyses to ascribe cellular function to genes that had been termed “hypothetical”— sequences that appear in the genome but whose biological expression and purpose previously were unknown. This approach currently offers the most-comprehensive “functional annotation,” a way of assigning biological function to the mystery sequences and ranking them based on their similarity to genes known to encode proteins. Before this study, 1988 (nearly 40%) of the predicted 4931 genes in S. oneidensis were considered hypothetical. To gain insight into whether the sequences in fact produced proteins and the importance and function of any expressed hypothetical genes, a rigorous experimental approach was used. This approach involved growing the cells under a range of conditions to elicit expression of as many genes as possible, followed by comprehensive comparative analyses using a wide assortment of databases. Highthroughput proteome and transcriptome analyses of MR-1 cells grown under a variety of conditions revealed that 538 of the hypothetical genes were expressed (proteins and mRNA) under at least one condition. The analyses confirmed that these are true genes used for one or more cellular processes. Searches were undertaken to determine if existing databases could provide high-confidence insights into putative functions for these expressed genes (initially hypothetical). Of the 538 genes, 97% were identified as having homologs in other genomes, and general functional assignments were possible for 256 of them. Given the current amount and quality of experimental data in public genome databases, however, assigning exact biochemical function was possible for only 16 genes. These results and other arguments (Roberts 2004; Roberts et al. 2004) point to the need for new methods for understanding gene, protein, and, ultimately, organism function. The ability to rank hypothetical sequences according to their likelihood to encode proteins will be vital for any further experimentation and, eventually, for predicting biological function. The method not only portends a way to fill in the blanks in any organism’s genome but also to compare the genomes of different organisms and their evolutionary relationships. In many cases, it is not known if a computationally annotated gene expresses a protein. With growing confidence that many hypothetical genes are expressing proteins, follow-on analyses now can be used to establish the role these proteins play. Reference E. Kolker et al., “Global Profiling of Shewanella oneidensis MR-1: Expression of Hypothetical Genes and Improved Functional Annotations,” Proc. Natl. Acad. Sci. USA 102, 2099-2104 (2005). |
