The biological sciences have greatly advanced through the large scale physical mapping and sequencing of over twenty genomes including humans.1 These efforts have yielded genomic "parts lists" or lists of genes that govern the biochemical processes of life. Although these "parts lists" are of great utility, they often do not yield information concerning gene function or relationships. A significant number of gene functions can be inferred through similarity matching to gene sequences of known function determined through traditional empirical methods. This approach has been utilized in most species that have known genomes; however, there still remain a large number of predicted open reading frames (ORFs) that have no assigned function.2 As a result, many studies are now being directed toward the determination of the functional aspects of large numbers of genes or even total genomes. These studies are generally classified as functional genomics and serve as powerful discovery tools for understanding gene function.3 The ability to monitor large numbers of genes and gene products simultaneously allows the study of how whole organisms or systems response to genetic or environmental stimuli. These global studies are increasingly being referred to as systems biology4

FIGURE 1  An integrated functional genomic approach monitors quantitative and qualitative differences in the transcriptome, proteome, and metabolome as a means to study gene function and cellular responses to external stimuli. The large amount of information contained within the profile data is deposited into relational databases where it can be correlated, compared, and interrogated by bioinformatic tools to yield a better understanding of biology. Profile data are also emerging as unique means of annotating genome data. Confirmation of putative proteins through proteome analysis is one example.

Functional genomics seeks to determine gene function through the correlation of genes and gene products. (Fig. 1) These reverse genetic approaches are pursued by altering gene expression through genetic perturbation, followed by monitoring of the expression products to infer function. Genetic perturbation can be achieved by random or directed gene activation tagging, gene transfer, transient gene silencing, or transient gene over expression. Once expression has been altered, mRNA, protein and/or small molecule metabolite levels are quantified through various profiling approaches. Simultaneous profiling of large numbers of gene expression products has been termed transcriptomics, proteomics,5,6 and metabolomics.7-9 The quantitative and qualitative gene expression profiling information can be stored in relational databases so that it can be further interrogated, correlated, clustered, and compared to understand gene function. This correlated information can be used to better understand biology through the assembly of pathways. Once gene function has been elucidated, this information can be used to improve plants through genetic and metabolic engineering. Global profiling is also useful for studying cellular and systems responses to environmental challenges. Environmental challenges might include light, temperature, radiation, pathogens, biotic or abiotic elicitors. We propose that maximum biological information is obtained when an integrated functional genomics approach is employed that collects data across multiple levels of gene expression; however, many projects monitor gene expression at a single level, i.e. transcriptome, proteome, or metabolome, due to obvious limitations of time and/or resources.

The technological platforms being used to approach large scale profiling are rapidly evolving and maturing.Transcriptome profiling is being approached primarily through cDNA macro/micro arrays 10 and/or SAGE (serial analysis of gene expression). 11

We are profiling both proteins and metabolites using modern mass spectrometry coupled to high-end separation technologies. Our program has mass spectrometry at its core because of its selectivity and sensitivity. Not only does MS record a signal that a compound has been detected, but it also provides specific chemical information about the molecule including molecular weight and structural information. Specific techniques and their utility are outlined in the following sections.

Our group is conducting proteomic and metabolomic studies of the model legume, Medicago truncatula. These studies consist of profiling wildtype, mutants, specific phenotypes, and effects of biotic and abiotic stimuli. Legumes are a diverse and vital taxa of agricultural crops characterized by root nodules formed as a result of the symbiotic relationship with nitrogen-fixing rhizobium. This taxalogical class is composed of such plants as soybeans (Glycine max) which provide a major source of protein and oil for both humans and animals, and alfalfa (Medicago sativa) that serves as an important forage and soil conditioning crop. Because of the agricultural and commercial importance of this unique plant-microbe system and to better understand biological processes associated with legumes, a functional genomics project focused on the model legume, Medicago truncatula, common name Barrel medic is being pursued. This project incorporates the profiling of gene expression (genomics), protein expression (proteomics), and metabolite expression (metabolomics). Medicago truncatula has been chosen as a model legume because of its prolific nature, its small diploid genome (~5x10 8 bp) and its rapid generation time. 25

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