Biochemical Analysis - Omics Group
Background
Omics research at JBEI enables a variety of cell wide studies as a part of the biofuels research program. Functional genomics workflows are applied to study the effect of: (1) exposure of deconstruction conditions (Ionic liquids, post saccharification mix from simple and complex cellulose sources); (2) accumulation of endogenous and exogenous target metabolites; and, (3) impact of biofuel pathways on model host microbes (E. coli, S. cerevisiae).
Additionally, functional genomics are extensively used by the Plant Feedstocks Division for molecular characterization of cell wall and by the Deconstruction Division in metaproteomic studies. In collaboration with the computational core, we also have powerful data analysis and integration tools.
Figure 1. Functional genomics studies provide the foundation for iterative improvement of fuel production strains via the identification inhibitory sources and limiting steps. (From Mukhopadhyay et al 2008)
Array Based Methods
We routinely use microarrays to evaluate cellular response to a wide variety of stresses, metabolic engineering and fuel candidate production at the transcript levels. We also use custom arrays to enable evaluation of fitness, bioprospecting for new genes and enzymes, and screen for specific activities. The microarray resources at JBEI is a Nimblegen based platform and is used extensively by other divisions at JBEI.
Proteomics
The proteomics team at JBEI works with researchers from each of the other divisions to characterize and quantify proteins in microbes and plants related to cellulosic biofuel production. Engineering of microorganisms for efficient production of high levels of biofuels requires a comprehensive understanding of both the biofuel production pathway and the native metabolism of the host. Metabolic imbalances often confound high-level biofuel production by the accumulation of toxic intermediates and redirection of metabolites to native pathways away from the biofuel target. Both targeted and shotgun proteomics are used to help the Fuels Synthesis division improve biofuel production from metabolically engineered microbes. Targeted proteomics is valuable in gauging the abundance of biofuel pathway proteins. Complementarily, shotgun proteomic analysis provides insight into the non-obvious perturbations to the system under biofuel producing conditions. Among other projects, we facilitate proteome characterization of plant organelles of interest to the Feedstocks Division. Shotgun proteomics via liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) is used to identify hundreds or thousands of proteins from isolated organelles. For the Deconstruction Division, de novo sequencing of novel enzymes isolated from microbial communities furthers efforts to improve saccharification of biomass.
Metabolomics
Metabolomics and metabolite assays form an essential part of functional genomics at JBEI. Equipped with very advanced instrumentation, we conduct Capillary electrophoresis and Liquid chromatography based mass spectrometry to analyze large sets of metabolites to obtain systems level understanding of cellular responses. We also conduct targeted studies of key biosynthetic pathway intermediates and target compounds.
Selected References
- Rutherford, B. J., R. H. Dahl, et al. (2010). "Functional genomic study of exogenous n-butanol stress in Escherichia coli." Applied and Environmental Microbiology 76(6): 1935-1945.
- Ito, J., C. J. Petzold, et al. (2010). "The Role of Proteomics in the Development of Cellulosic Biofuels." Current Proteomics 7(2): 121-134.
- Ouellet, M., P. D. Adams, et al. (2009). "A Rapid and Inexpensive Labeling Method for Microarray Gene Expression Analysis." BMC Biotechnology 9: 97.
- Fortman, J. L., S. Chhabra, et al. (2008). "Biofuel Alternatives to Ethanol: Pumping the Microbial Well." Trends in Biotechnology 26(7): 375-381.
- Mukhopadhyay, A., A. M. Redding, et al. (2008). "Importance of Systems Biology in Engineering Microbes for Biofuel Production." Current Opinion in Biotechnology 19(3): 228-234.
