Understanding drought tolerance with cell-type specific-data sets and computational approaches

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The publication 21 years ago of the genome sequence of Arabidopsis thaliana was a landmark event in plant biology. The sequence has benefitted plant research immensely [1]. Of the 27,655 predicted gene products in the Araport 11 version of the genome, however, only ~42% have a GO term assigned to them based on direct experimental data, and around 36% still do not have any associated GO Molecular Function or Biological Process terms [2]. Although large amounts of gene expression data have been generated, new methods for the isolation of RNA from specific cell types are allowing unprecedented understanding of biological processes at the resolution of specific cells, which is important to uncover responses not discernible in "bulk tissue" samples. To fully take advantage of these plant biology data sets and methods, I am proposing a two-pronged bioinformatic and biology-based approach. The first objective is to develop bioinformatic tools to enable researchers (including those in my lab) to make use of the vast amount of information being generated. Dozens of such tools are already available at my lab's Bio-Analytic Resource website at bar.utoronto.ca, including a suite of ePlants [3,4], which have an intuitive interface for browsing data from the kilometre to nanometre scale. The BAR receives 4M page views per month from researchers worldwide, in part because we have created connections between these scales for hypothesis generation. We are interested in generating more connections between levels and across species - for instance, are there similar hotspots of non-synonymous changes in homologs from different species and can we use increasingly accurate structure prediction methods to understand the functional context of these hotspots? A wet-lab component will aim to understand this variation in the context of abiotic stress signaling. The second objective of my proposed research will leverage new cell-type-specific data sets my lab has generated from guard cells of plants subjected to drought stress, where we observed 3,973 differentially-expressed genes across any timepoint [5]. Thermal imaging of prioritized T-DNA mutants suggests that roughly 25% of these candidates are involved in drought response. We will investigate several of these candidates at the molecular level to understand how they fit into drought response pathways, and will also undertake translational experiments in agriculturally-important plants. The significance of this research will be considerable: cyberinfrastructure developed by my lab will enable new insights based on large data sets, will leverage the value of these publicly-funded data sets, and will permit a better understanding of plants. This is critical to feeding 9 billion people by 2050. We are excited to continue to develop not only the Arabidopsis ePlant but other ePlants for cultivated species. The in planta part of this project will result in novel stress-tolerance genes that can be deployed agriculturally.