Overview

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) and even modeled in detail at the whole-network level (Overbeek, 2000; Overbeek et al., 200l; Selkov et al., 2000; Bhattacharyya et al., 2002; Gerdes 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).

References Cited:

Bhattacharyya A, Stilwagen S, Ivanova N, D'Souza M, Bernal A, Lykidis A, Kapatral V, Anderson I, Larsen N, Los T, Reznik G, Selkov E Jr, Walunas TL, Feil H, Feil WS, Purcell A, Lassez JL, Hawkins TL, Haselkorn R, Overbeek R, Predki PF, Kyrpides NC. Whole-genome comparative analysis of three phytopathogenic Xylella fastidiosa strains. Proc Natl Acad Sci U S A. (2002) Sep 17;99(19):12403-8. doi: 10.1073/pnas.132393999

Gerdes SY, Scholle MD, Campbell JW, Balázsi G, Ravasz E, Daugherty MD, Somera AL, Kyrpides NC, Anderson I, Gelfand MS, Bhattacharya A, Kapatral V, D'Souza M, Baev MV, Grechkin Y, Mseeh F, Fonstein MY, Overbeek R, Barabási AL, Oltvai ZN, Osterman AL. Experimental determination and system level analysis of essential genes in Escherichia coli MG1655. J Bacteriol. (2003) Oct;185(19):5673-84. doi: 10.1128/JB.185.19.5673-5684.2003

Maul JE, Lilly JW, Cui L, dePamphilis CW, Miller W, Harris EH, Stern DB. The Chlamydomonas reinhardtii plastid chromosome: islands of genes in a sea of repeats. Plant Cell. (2002) Nov;14(11):2659-79. doi: 10.1105/tpc.006155

Osterman A, Overbeek R. Missing genes in metabolic pathways: a comparative genomics approach. Curr Opin Chem Biol. (2003) Apr;7(2):238-51. doi: 10.1016/S1367-5931(03)00027-9

Overbeek R. Genomics: what is realistically achievable? Genome Biol. (2000);1(2):COMMENT2002. doi: 10.1186/gb-2000-1-2-comment2002

Overbeek R, Larsen N, Pusch GD, D'Souza M, Selkov E Jr, Kyrpides N, Fonstein M, Maltsev N, Selkov E. WIT: integrated system for high-throughput genome sequence analysis and metabolic reconstruction. Nucleic Acids Res. (2000) Jan 1;28(1):123-5. doi: 10.1093/nar/28.1.123

Peltier JB, Emanuelsson O, Kalume DE, Ytterberg J, Friso G, Rudella A, Liberles DA, Söderberg L, Roepstorff P, von Heijne G, van Wijk KJ. Central functions of the lumenal and peripheral thylakoid proteome of Arabidopsis determined by experimentation and genome-wide prediction. Plant Cell. (2002) Jan;14(1):211-36. doi: 10.1105/tpc.010304

Peltier JB, Ytterberg AJ, Sun Q, van Wijk KJ. New functions of the thylakoid membrane proteome of Arabidopsis thaliana revealed by a simple, fast, and versatile fractionation strategy. J Biol Chem. (2004) Nov 19;279(47):49367-83. doi: 10.1074/jbc.M406763200

Schlesinger, W. H. in Biogeochemistry, An Analysis of Global Change 127-165 (Academic Press, San Diego, CA, 1997).

Selkov E, Overbeek R, Kogan Y, Chu L, Vonstein V, Holmes D, Silver S, Haselkorn R, Fonstein M. Functional analysis of gapped microbial genomes: amino acid metabolism of Thiobacillus ferrooxidans. Proc Natl Acad Sci U S A. (2000) Mar 28;97(7):3509-14. doi: 10.1073/pnas.97.7.3509

Simpson CL, Stern DB. The treasure trove of algal chloroplast genomes. Surprises in architecture and gene content, and their functional implications. Plant Physiol. (2002) Jul;129(3):957-66. doi: 10.1104/pp.010908

Stern DB, Hanson MR, Barkan A. Genetics and genomics of chloroplast biogenesis: maize as a model system. Trends Plant Sci. (2004) Jun;9(6):293-301. doi: 10.1016/j.tplants.2004.04.001

Sun Q, Emanuelsson O, van Wijk KJ. Analysis of curated and predicted plastid subproteomes of Arabidopsis. Subcellular compartmentalization leads to distinctive proteome properties. Plant Physiol. (2004) Jun;135(2):723-34. doi: 10.1104/pp.104.040717

Sweetlove LJ, Last RL, Fernie AR. Predictive metabolic engineering: a goal for systems biology. Plant Physiol. (2003) Jun;132(2):420-5 doi: 10.1104/pp.103.022004

Zabrouskov V, Giacomelli L, van Wijk KJ, McLafferty FW. A new approach for plant proteomics: characterization of chloroplast proteins of Arabidopsis thaliana by top-down mass spectrometry. Mol Cell Proteomics. (2003) Dec;2(12):1253-60. doi: 10.1074/mcp.M300069-MCP200