Publikationen - Molekulare Signalverarbeitung

BackgroundThe formation of flowers is one of the main model systems to elucidate the molecular mechanisms that control developmental processes in plants. Although several studies have explored gene expression during flower development in the model plant Arabidopsis thaliana on a genome-wide scale, a continuous series of expression data from the earliest floral stages until maturation has been lacking. Here, we used a floral induction system to close this information gap and to generate a reference dataset for stage-specific gene expression during flower formation.ResultsUsing a floral induction system, we collected floral buds at 14 different stages from the time of initiation until maturation. Using whole-genome microarray analysis, we identified 7,405 genes that exhibit rapid expression changes during flower development. These genes comprise many known floral regulators and we found that the expression profiles for these regulators match their known expression patterns, thus validating the dataset. We analyzed groups of co-expressed genes for over-represented cellular and developmental functions through Gene Ontology analysis and found that they could be assigned specific patterns of activities, which are in agreement with the progression of flower development. Furthermore, by mapping binding sites of floral organ identity factors onto our dataset, we were able to identify gene groups that are likely predominantly under control of these transcriptional regulators. We further found that the distribution of paralogs among groups of co-expressed genes varies considerably, with genes expressed predominantly at early and intermediate stages of flower development showing the highest proportion of such genes.ConclusionsOur results highlight and describe the dynamic expression changes undergone by a large number of genes during flower development. They further provide a comprehensive reference dataset for temporal gene expression during flower formation and we demonstrate that it can be used to integrate data from other genomics approaches such as genome-wide localization studies of transcription factor binding sites.

Natural accessions of many species harbor a wealth of genetic variation visible in a large array of phenotypes. Although expression level polymorphisms (ELPs) in several genes have been shown to contribute to variation in diverse traits, their general impact on adaptive variation has likely been underestimated. At present, ELPs have predominantly been correlated to quantitative trait loci (eQTLs) that occupy central hubs in signaling networks, which pleiotropically affect numerous traits. To increase the sensitivity of detecting minor effect eQTLs or those that act in a trait-specific manner, we emphasize the need for more systematic approaches. This requires, but is not limited to, refining experimental designs such as reduction of tissue complexity and combinatorial methods including a priori defined networks.