ABSTRACT
Legumes provide significant sources of fatty acids and proteins for human and animal nutrition; they also have non-food uses, for example in producing industrial feedstocks and combustible fuels (Thelen and
Department of Molecular Systems Biology, University of Vienna, Althanstrasse 14, 1090 Vienna, Austria; e-mail: stefanie.wienkoop@univie.ac.at
Ohlrogge 2002). Conditions in almost all cultivated land are suboptimal for plant growth. Understanding the connections between initial responses and the downstream events that constitute successful adjustment to its fl uctuating environment is one of the challenges of genome-based systems biology in plant research. The complete genome sequences of rice (International Rice Genome Sequencing Project 2005) and Arabidopsis (Arabidopsis Genome Initiative 2000), which are non-leguminous model species, are available and provide insight into many fundamental aspects of plant biology; however, they do not address some important aspects of legume biology. Legumes are important for maintenance of human health and as crops for sustainable agriculture due to their ability to fi x atmospheric nitrogen via their symbiosis with soil rhizobia. These bacteria colonize legume roots in specialized organs called nodules. Two model species of legumes, Lotus japonicus (Udvardi et al. 2005) and Medicago truncatula (Retzel et al. 2007), have been the focus of genome sequencing and functional genomics projects. However, functional genomics studies on pea (Pisum sativum) are still in their infancy, because this species has genome duplications, self-incompatibility, and a long generation time. In this case, the proteomics approach may be a powerful tool for analyzing the functions of the plant genes/proteins. Gaining an understanding of the biological function of any novel gene is a more ambitious goal than merely determining its sequence. At present, the wealth of information on nucleotide sequences that is being generated through genome projects far outweighs that which is currently available on the amino acid sequences of known proteins (Lockhart and Winzeler 2000; Pandey and Mann 2000). However, proteomic and metabolomic approaches have been demonstrated to be complementary to genomic data (May et al. 2008). Bioinformatic modeling approaches revealed the identifi cation of missing enzymatic links and allowed for the reconstruction of draft metabolic networks . Although in general, genome sequence data and inferred protein-sequence data can be used to identify proteins and to follow temporal changes in protein expression in an organism, new tools have been developed enabling also genome sequence independent analysis. These techniques are based on high accuracy of the new generation of mass spectrometers such as the so called mass accuracy precursor alignment strategy (MAPA, see also Section 8.2.2). Currently, cool season grain legumes such as pea are barely used for proteomic studies due to the above mentioned reasons. However, Medicago one of the model plant for legume crops has been the subject of several proteomic and metabolic studies. In this chapter an overview of some of the most common MS based strategies for systems biology will be presented. Technical requirements for targeted and non-targeted proteomic and metabolomic deliverables and current status of legumes research will be addressed.
