In Situ Studies of Arabidopsis thaliana Populations

During the last decade, natural variation of the genetic model plant Arabidopsis thaliana has been intensively reviewed and discussed in many fields of plant biological research. While numerous studies have analyzed and improved significantly the understanding of genotypic diversity, deriving explicit knowledge of molecular mechanisms leading to adaptation and phenotypic plasticity in a natural environment is still challenging. This is due to a high degree of variation of plasticity patterns within and among populations. In this context, our proposed study aims at developing an experimental workflow to functionally decipher naturally occurring molecular variation in selected Austrian populations of the vascular plant Arabidopsis thaliana. In combination with genetic analyses, we will characterize microenvironmental adaptation and phenotypic plasticity by intersecting molecular profiles of metabolites and proteins from individual Arabidopsis thaliana plants with a detailed description of biotic and abiotic habitat parameters. In preliminary work, we found these phenotypic variations of natural populations of Arabidopsis to be directly accessible by our bioanalytical metabolomics and proteomics platform in situ as well as in vitro. By mathematical analysis, comprising approaches of uni- and multivariate statistics as well as mathematical modeling, we aim at deriving correlations between metabolomic and proteomic data and in situ recorded environmental and ecosystem data. By this, our project builds up a comprehensive platform for identifying ecologically important adaptive traits and their regulatory dynamics. This will promote the molecular characterization of ecophysiological adaptation. The identification of such molecular adaptation strategies to environmental stresses in higher plants is central to the improvement of plant performance in changing environments by marker assisted plant breeding. Detailed characterization of environmental conditions will enable us to derive principles of genotype-environment- interactions by modeling and predicting molecular phenotypical information from data on genotype and environment.

During the last decade, natural variation of the genetic model plant Arabidopsis thaliana has been intensively reviewed and discussed in many fields of plant biological research. While numerous studies have analyzed and improved significantly the understanding of genotypic diversity, deriving explicit knowledge of molecular mechanisms leading to adaptation and phenotypic plasticity in a natural environment the in situ environment - is still challenging. This is due to a high degree of variation of plasticity patterns within and among populations in the context of environmental fluctuations. In this context, our study aimed at developing an experimental workflow to functionally decipher naturally occurring molecular variation in in situ populations of the vascular plant Arabidopsis thaliana. Firstly, we have identified 23 natural Arabidopsis thaliana populations in Austria and mapped their geographical coordinates. In combination with genetic analyses, we subsequently correlated microenvironmental fluctuation and phenotypic plasticity by intersecting molecular profiles of metabolites, proteins and transcripts from several Arabidopsis thaliana populations in situ with a detailed description of biotic and abiotic habitat parameters. We found these phenotypic variations of natural populations of Arabidopsis to be directly accessible by our bioanalytical metabolomics and proteomics platform in situ as well as in vitro. Here, we performed a large common garden experiment to distinguish habitat plasticity from genotypic plasticity. Natural populations showed a large intra-specific variation which we call breadth of biochemical reaction norm equivalent to molecular plasticity. By mathematical analysis, comprising approaches of uni- and multivariate statistics as well as mathematical modeling, we were able to derive correlations between metabolomic and proteomic data and in situ recorded environmental and ecosystem data. By this, our project builds up a comprehensive platform for identifying ecologically important adaptive traits and their regulatory dynamics. This extends existing genomic data on polymorphism and identifies distinct molecular mechanisms of ecophysiological adaptation. The identification of such molecular adaptation strategies to environmental stresses in higher plants is central to the improvement of plant performance in changing environments especially with a focus on global changing climate - by marker assisted plant breeding. Accordingly, the results of our study on Arabidopsis thaliana can be translated into crop plants. We have started with the translational research and investigate wheat and pearl millet and their strategies to heat and drought adaptation. Detailed characterization of environmental conditions will enable us to derive principles of genotype-environment- interactions by modeling and predicting molecular phenotypical information from data on genotype and environment.