SPP 1530: Flowering Time Control - from Natural Variation to Crop Improvement

WP 1: Gene expression networks and signaling pathways

As a result of speciation and adaptation a broad genetic variation for FTi genes can be expected. The genetic basis of FTi variation shall be studied in models with small genomes and cultivated species with more complex genomes. The studies will comprise all kinds of phenological development.

The points of integration between pathways are still largely unknown. In Arabidopsis, an unexpected influence of autonomous pathway components on the circadian clock has been unraveled (see “Integration of different pathways and environmental factors to one output”). In turn, a circadian clock-controlled RNA-binding protein was found to promote flowering through a negative effect on FLC abundance. Links like this between floral transition and the circadian clock beyond photoperiodic timekeeping will be further characterized through genetic interaction studies and the identification of downstream targets (goal No. 1). Gene expression networks underlying flowering induction in Arabidopsis will be identified by studying the binding sites of important transcription factors. GA signaling will be connected with other flower-promoting pathways with an ultimate goal to establish a mathematic model for flowering induction in Arabidopsis and other species. The effects of epigenetic DNA and histone modification will be studied to transfer this knowledge to crop species. 

Cereals and Brassica crops are typical of facultative biennials whose evolution is strongly related to FTi gene variation. Integrative projects will exploit sequence information from models as well as crops (wheat, barley, rapeseed) to describe genetic variation for key regulators of FTi (goal No. 2). Moreover, new genes will be identified using deep transcriptome sequencing and gene array techniques. During plant cultivation, tissue samples will be taken and used to study quantitative gene expression of individual major genes or on a genome-wide scale. This measure will be taken on transcript, proteome and metabolome levels.

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