A strategy for the identification of new abiotic stress determinants in Arabidopsis using web-based data mining and reverse genetics.

Since the sequencing of the Arabidopsis thaliana genome in 2000, plant researchers have faced the complex challenge of assigning function to thousands of genes. Functional discovery by in silico prediction or homology search resolved a significant number of genes, but only a minor part has been experimentally validated. Arabidopsis entry into the post-genomic era signified a massive increase in high-throughput approaches to functional discovery, which have since become available through publicly-available web-based resources. The present work focuses on an easy and straightforward strategy that couples data-mining to reverse genetics principles, to allow for the identification of new abiotic stress determinant genes. The strategy explores systematic microarray-based transcriptomics experiments, involving Arabidopsis abiotic stress responses. An overview of the most significant resources and databases for functional discovery in Arabidopsis is presented. The successful application of the outlined strategy is illustrated by the identification of a new abiotic stress determinant gene, HRR, which displays a heat-stress-related phenotype after a loss-of-function reverse genetics approach.