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Publikationen - Molekulare Signalverarbeitung

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Publikation

Ryan, P. T.; Ó’Maoiléidigh, D. S.; Drost, H.-G.; Kwaśniewska, K.; Gabel, A.; Grosse, I.; Graciet, E.; Quint, M.; Wellmer, F.; Patterns of gene expression during Arabidopsis flower development from the time of initiation to maturation BMC Genomics 16, 488, (2015) DOI: 10.1186/s12864-015-1699-6

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.
Publikation

Quint, M.; Melchinger, A. E.; Dußle, C. M.; Lübberstedt, T.; Breeding for virus resistance in maize Genetika 32, 529-545, (2000)

Sugarcane mosaic virus (SCMV) is an important disease in maize, which is emerging in Germany since 1983. Using this pest as a model for the inheritance of oligogenic traits, we clarified the genetic ba­sis for resistance in early maturing European maize germplasm. Screening of 122 adapted European inbred lines identified three completely resistant lines, which were used for further analyses. The genetics of SCMV resis­tance was investigated by allelism tests in field experiments combined with QTL and bulked segregant analyses (BSA) on the marker level. QTL analyses revealed the presence of two major genes Scm1 and Scm2 plus three minor QTL. Involvement of Scm1 and Scm2 in the inheritance of SCMV resistance could be confirmed by BSA in a second cross. Breeders can make use of tightly linked STS markers for marker-assisted selection (MAS) as well as our SCMV resistant flint lines to improve their elite germplasm. Currently, recurrent backcrossing with phenotypic selection is the most appropriate and cost effective breeding method. With de­creasing costs of DNA chip technology, MAS can be competitive with phenotypic selection in the near future. Further objectives of our research are the isolation and cloning of Scm1 and Scm2. To achieve this goal we follow two different approaches. (1) Positional cloning based on more than 500 AFLP primer combinations resulted in Scm1/Scm2 specific markers with a resolution of approximately 0.2 cM in the respective re­gions. (2) Resistance gene analogues (RGAs), cosegregating with the tar­get genes are used to identify further candidate genes for transformation experiments.
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