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In this study, genome-wide expression profiling based on Affymetrix ATH1 arrays was used to identify discriminating responses of Arabidopsis thaliana to five herbicides, which contain active ingredients targeting two different branches of amino acid biosynthesis. One herbicide contained glyphosate, which targets 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS), while the other four herbicides contain different acetolactate synthase (ALS) inhibiting compounds. In contrast to the herbicide containing glyphosate, which affected only a few transcripts, many effects of the ALS inhibiting herbicides were revealed based on transcriptional changes related to ribosome biogenesis and translation, secondary metabolism, cell wall modification and growth. The expression pattern of a set of 101 genes provided a specific, composite signature that was distinct from other major stress responses and differentiated among herbicides targeting the same enzyme (ALS) or containing the same chemical class of active ingredient (sulfonylurea). A set of homologous genes could be identified in Brassica napus that exhibited a similar expression pattern and correctly distinguished exposure to the five herbicides. Our results show the ability of a limited number of genes to classify and differentiate responses to closely related herbicides in A. thaliana and B. napus and the transferability of a complex transcriptional signature across species. Electronic supplementary material The online version of this article (doi:10.1007/s11103-009-9590-y) contains supplementary material, which is available to authorized users.

International audience; Nitrate acts as a potent signal to control global gene expression in Arabidopsis. Using an integrative bioinformatics approach we identified TGA1 and TGA4 as putative regulatory factors that mediate nitrate responses in Arabidopsis roots. We showed that both TGA1 and TGA4 mRNAs accumulate strongly after nitrate treatments in roots. Global gene expression analysis revealed 97% of the genes with altered expression in tga1 tga4 double mutant plants respond to nitrate treatments, indicating that these transcription factors have a specific role in nitrate responses in Arabidopsis root organs. We found TGA1 and TGA4 regulate the expression of nitrate transporter genes NRT2.1 and NRT2.2. Specific binding of TGA1 to its cognate DNA sequence on NRT2.1 and NRT2.2 promoters was confirmed by chromatin immunoprecipitation assays. The tga1 tga4 double mutant plants exhibit nitrate-dependent lateral and primary root phenotypes. Lateral root initiation is affected in both tga1 tga4 and nrt1.2 nrt2.2 double mutants, suggesting TGA1 and TGA4 regulate lateral root development at least partly via NRT2.1 and NRT2.2. Additional root phenotypes of tga1 tga4 double mutants indicate that these transcription factors play an important role in root developmental responses to nitrate. These results identify TGA1 and TGA4 as important regulatory factors of the nitrate response in Arabidopsis roots.

Data generation is no longer the limiting factor in advancing biological research. In addition, data integration, analysis, and interpretation have become key bottlenecks and challenges that biologists conducting genomic research face daily. To enable biologists to derive testable hypotheses from the increasing amount of genomic data, we have developed the VirtualPlant software platform. VirtualPlant enables scientists to visualize, integrate, and analyze genomic data from a systems biology perspective. VirtualPlant integrates genome-wide data concerning the known and predicted relationships among genes, proteins, and molecules, as well as genome-scale experimental measurements. VirtualPlant also provides visualization techniques that render multivariate information in visual formats that facilitate the extraction of biological concepts. Importantly, VirtualPlant helps biologists who are not trained in computer science to mine lists of genes, microarray experiments, and gene networks to address questions in plant biology, such as: What are the molecular mechanisms by which internal or external perturbations affect processes controlling growth and development? We illustrate the use of VirtualPlant with three case studies, ranging from querying a gene of interest to the identification of gene networks and regulatory hubs that control seed development. Whereas the VirtualPlant software was developed to mine Arabidopsis (Arabidopsis thaliana) genomic data, its data structures, algorithms, and visualization tools are designed in a species-independent way. VirtualPlant is freely available at www.virtualplant.org.