Genome sequencing of many model systems, including the human, yeast, Arabidopsis, and rice genomes, has been completed. As a complement to genomic techniques, proteomics has emerged to study the composition, structure, function, and interaction of the expressed proteins or proteome in a given cell, tissue, or organism. During the past several years, tremendous progress has been made with proteomic techniques in human and yeast, but progress has been slower in plants due to several factors. Proteomic techniques have been used in plants, especially in agriculturally important crops and weeds, to understand mechanisms of herbicide tolerance and weed resistance. Proteomic techniques, starting from protein extraction to protein identification, have been developed and advanced for large-scale proteomics studies, but limitations and problems for these techniques still exist for plant proteomics. Further technological improvements are needed to enhance quantitative and comparative large-scale proteomics studies. Applications of these techniques may help weed scientists to understand stress tolerance in crops and investigate weediness traits further. Proteomic techniques have unique strengths but also offer several challenges. Together with transcriptomics and metabolomics, these techniques for analyzing global patterns of gene expression offer new and novel techniques for better understanding biological questions of interest to weed scientists in the future.

Genome sequences and genome-wide transcript profiles are becoming increasingly available, opening a way to use this information in analyzing how groups of genes are connected in pathways or “regulons” that might explain how organisms accomplish the integration on an organismal level. We have begun to explore the large datasets that are available for transcripts of the best characterized plant model, Arabidopsis thaliana, setting up a gene network using clustering methods. A network, based on the Graphical Gaussian Model (GGM), describes coregulation of genes under a variety of external factors: abiotic, biotic, and chemical treatments. In its present structure, the network reveals coregulation for more than 7,000 genes in the Arabidopsis genome. The network appears to be particularly suited to reveal the regulatory structure of biochemical pathways and environmental stress responses. Examples describe network predictions centered on a trehalose-6-phosphate phosphatase, an Arabidopsis response regulator and EPSPS. Results from the statistical analysis and bioinformatics of large data sets provide hypotheses that must be checked by additional studies. However, networks, which should be expanded from transcripts to also include proteins and metabolites, can be expected to explain not only how the Arabidopsis gene network is structured, but also provide insight in how similar networks in weed species might deviate or show correspondence and overlap.

Genomics programs in the weed science community have not developed as rapidly as that of other crop, horticultural, forestry, and model plant systems. Development of genomic resources for selected model weeds are expected to enhance our understanding of weed biology, just as they have in other plant systems. In this report, we describe the development, characteristics, and information gained from an expressed sequence tag (EST) database for the perennial weed leafy spurge. ESTs were obtained using a normalized cDNA library prepared from a comprehensive collection of tissues. During the EST characterization process, redundancy was minimized by periodic subtractions of the normalized cDNA library. A sequencing success rate of 88% yielded 45,314 ESTs with an average read length of 671 nucleotides. Using bioinformatic analysis, the leafy spurge EST database was assembled into 23,472 unique sequences representing 19,015 unigenes (10,293 clusters and 8,722 singletons). Blast similarity searches to the GenBank nonredundant protein database identified 18,186 total matches, of which 14,205 were nonredundant. These data indicate that 77.4% of the 23,472 unique sequences and 74.7% of the 19,015 unigenes are similar to other known proteins. Further bioinformatics analysis indicated that 2,950, or 15.5%, of the unigenes have previously not been identified suggesting that some may be novel to leafy spurge. Functional classifications assigned to leafy spurge unique sequences using Munich Information Center for Protein or Gene Ontology were proportional to functional classifications for genes of arabidopsis, with the exception of unclassified or unknowns and transposable elements which were significantly reduced in leafy spurge. Although these EST resources have been developed for the purpose of constructing high-density leafy spurge microarrays, they are already providing valuable information related to sugar metabolism, cell cycle regulation, dormancy, terpenoid secondary metabolism, and flowering.