The utility of Functional Genomics in the characterization of Clostridium thermocellum
Thursday, May 1, 2014: 11:00 AM
Grand Ballroom D-E, lobby level (Hilton Clearwater Beach)
Charlotte M. Wilson1, Dawn M. Klingeman2, Courtney M. Johnson2, Miguel Rodriguez Jr.2, Stanton L. Martin3, Tzu-Ming Chu3, Russ D. Wolfinger3, Loren J. Hauser2, Miram L. Land2, Mustafa H. Syed2, Sagar M. Utturkar4, Punita Manga4, Richard J. Giannone2, Robert L. Hettich2, Arthur Ragauskas5, Timothy Tschaplinski2, Jonathan R. Mielenz2 and Steven D. Brown2, (1)Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, (2)Biosciences Division and BioEnergy Science Center, Oak Ridge National Laboratory, Oak Ridge, TN, (3)SAS Institute Inc, Cary, NC, (4)Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN, (5)School of Chemistry and Biochemistry, BioEnergy Science Center/Georgia Institute of Technology, Atlanta, GA
Clostridium thermocellum is a candidate consolidated bioprocessing biocatalyst for the production of bioethanol.  Functional genomics as part of systems biology approach has been used to gain a global perspective on C. thermocellum cellular processes.  At the genome level, reduced costs of sequencing technologies permit near routine genome sequencing and the resequencing of strains of interest.  This has allowed the metabolic machinery inherent to the C. thermocellum to be identified and has permitted the full characterization, at the genomic level, of strains that have gone through various genetic modifications.  Coupled with this is the continual update to genome annotations as the algorithms for gene calling and functional prediction have occurred.  An accurate genome annotation is necessary as other systems biology techniques rely on this for a reference.  With the move from the traditional microarray platform to an RNAseq platform, we have employed both transcriptome platforms determining genes involved in C. thermocellum growth on solid substrates. We found similar results between the platforms although RNASeq had the advantage of a greater sensitivity due to a greater dynamic range of data collection.  A further advantage is the option of revisiting an RNAseq database as gene prediction algorithms continue to improve.  Other techniques that can be used as part of a functional genomics approach to strain characterization include the incorporation of other omic’s technologies such as proteomics and metabolomics to provide a more complete snapshot of the cell at any given time, which will be discussed.