Overview - Continued

The plastid is an excellent target for a functional genomics approach in Arabidopsis. From a purely practical point of view it has a large but manageable parts list of fewer than 4,500 proteins (e.g. http://ppdb.tc.cornell.edu; Sun et al, 2004), and our own analysis below) that are targeted to or synthesized within it. This manageable size allows the community of plastid researchers to consider a systematic functional genomics elucidation of protein function. Another advantage is that, despite the biosynthetic diversity and importance of products synthesized wholly or partly in the plastid, the total number of metabolic reactions is likely to be less than 1,000, based on the proportion of plastid proteins that are known or predicted enzymes or transporters. Thus, we are targeting a system whose functional complexity is similar to that of a prokaryotic microbe such as E. coli . Others have shown that microbial metabolism can be comprehensively defined (http://gcrg.ucsd.edu/organisms/ecoli.html) and even modeled in detail at the whole-network level (Overbeek, 2000; Overbeek et al., 2000l; Selkov et al., 2000; Bhattacharyya et al., 2002; Gerdes et al., 2003; Overbeek et al., 2003; Osterman & Overbeek, 2003;), an approach that has been used to accurately predict gene function. Because almost all the transcriptional and translational regulatory superstructure for plastid function is external to this organelle, our focus on the system of the plastid proteome arguably demarcates a simpler system than a whole prokaryotic organism and far simpler than a whole eukaryotic organism.

This relative simplicity suggests that we as a community will soon be able to measure the abundance of the transcripts, proteins and small molecules essential to plastid function, and from such data, build an integrative model of how these components function together. The genomic, transcriptional and proteomic dimensions of chloroplast-targeted proteins are either being described currently or have already been catalogued (Sun et al., 2004; Simpson & Stern, 2002; Maul et al., 2002; Stern et al., 2004; Peltier et al., 2002; Peltier et al., 2004; Zabrouskov et al., 2003). However, before computational tools of informatics and modeling can be used in a true "systems biology" approach to understanding plastid function, we must first connect individual genes to function via an array of functional genomic and direct biochemical assays, including measures of network-wide metabolic function. Only then is it reasonable to expect that we will be able to approach a predictive level of understanding of how gene products interact to produce the dynamic metabolic systems of the plastid.

Thus, an overall objective of this project will be to develop a knowledge-base that will set the stage for future comprehensive systems models of plastid metabolism and function. Although valuable in its own right for basic understanding, such models will also have tremendous practical implications for the genetic engineering of plastid-based reactions. Many if not most attempts to engineer plant metabolism for practical applications fail for reasons that are largely not understood. As is now routine for microbial engineering, manipulation of plant metabolism will be greatly enhanced when the full network of intersecting pathways can be quantitatively modeled to allow a predictive science of plant metabolic engineering to emerge (Sweetlove et al., 2003).

Project Objectives

Our long term goal as plant biochemists is to understand the metabolic functioning of the plastid. In this proposal we focus on the task of assigning function to the nuclear genes encoding plastid-targeted products.

Our specific objectives are to:

1. Use informatic and phenotypic approaches to significantly enrich the annotation of all nuclear-encoded genes whose products are known or predicted to be imported into the plastid.  

2. Identify those genes whose inactivation leads to a specific observable metabolic or morphological phenotype and experimentally assign functional attributes to these genes. The value of this phenotype-driven approach is well documented, and despite decades of forward genetic screens in Arabidopsis, the function of the vast majority of genes remain experimentally undefined.

3. Use flux analysis to map primary metabolism in the developing seed and determine patterns of plasticity and rigidity in this network. Metabolic flux mapping can both illuminate individual gene function in the context of the network of central metabolism and answer general, fundamental questions about seed metabolism.

4. Generate hypotheses about gene function using statistical tools to reveal connections among metabolic phenotypes and correlations with gene and other expression measurements. The quantitative results of the primary and secondary analyses will be subjected to statistical analysis and whenever possible will be related to existing transcriptional datasets (e.g. AtGenExpress) and proteomic and/or metabolomic data sets as these become available. This will reveal correlations that suggest mechanisms and regulation of integrated biochemical functions that single functional analyses or one level of expression cannot be expected to show.

5. Build an easy-to-use, publicly accessible database integrated into a website to disseminate the information openly and without IP restrictions.

6. Educate current and future plant scientists in studying gene function at the genome-wide level. Because of the range of experimental and theoretical expertise that the PIs bring to the project, their close proximity on a single campus, and the integrated management plan of this project, the PIs, students, postdoctoral associates, and technical support staff will learn multiple approaches and their integration. By introducing faculty from primarily-undergraduate institutions and from local high schools as well as undergraduates to this collaborative, integrative approach to biological enquiry, we will impact a broad range of students and citizens in systems-wide approaches and cutting-edge tools for plant genomics and biochemistry.

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