Transcript profiling to analyse gene expression during male meiosis in petunia hybrida

Meiosis is a key feature of eukaryotic sexual reproduction. So far, the molecular and functional analysis of meiosis is relatively underdeveloped in plants, but the flood of genomics data from yeast research and the availability of large mutant collections cause a growing interest in molecular studies of plant meiosis. We have developed a strategy to follow gene expression during male meiosis in Petunia hybrida, exploring the limits of the cDNA-AFLP technology. This experiment was a continuation of a previous study, in which we have demonstrated that cDNA-AFLP transcript profiling is a suitable method to analyse gene expression during male and female gametogenesis in Petunia. Using DAPI-staining of enzyme-digested spread pollen mother cells to determine the meiotic stages, we observed that the meiocytes develop rather synchronously through meiosis. The transcript profiling experiment focused on the analysis of modulated gene expression during meiosis. About 8,000 transcript tags, estimated to represent 35% of the total transcriptome, were generated, 475 of which exhibited a modulated gene expression pattern. Among these, we find many components of meiotic processes, indicating that our screening was effective and demonstrating the high level of sensitivity of cDNA-AFLP transcript profiling. A thorough characterisation of these fragments based on sequence homology was performed. A functional classification of these tagged genes demonstrated that many components of meiosis have been encountered, overall covering the complete process of meiosis. We could confirm the high level of evolutionary conservation of genes involved in meiotic recombination and DNA repair. In contrast, less conserved processes, such as synapsis, might well be present in our collection, but cannot be recognized on the basis of a homology search. Although incomplete, our data set provides a starting point for the further unraveling of meiosis at a molecular level in plants, allowing a knowledge-based selection of tags for further characterisation.