Combining high-throughput protein and gene expression analysis for investigation of bacterial-plant interactions

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The proposed research will investigate the interactions of pathogens and beneficial bacteria with plants, and will characterize underlying mechanisms that mediate these interactions. Bacterial species can have a huge impact on plant growth, both positive and negative – plant pathogens can greatly reduce plant growth and crop yield, whereas beneficial bacteria can increase plant growth and reduce the effects of environmental stresses. Interestingly, both pathogens and beneficial bacteria share common mechanisms of interaction. The target of this research project is to characterize commonalities and differences in how pathogens and beneficial bacteria interact with plants. In this project, comparative genomics and high-throughput sequencing will be used to investigate bacterial interactions with host organisms. Previous research in the McConkey lab has combined wet-lab and computational projects in the areas of proteomics and bioinformatics. Proteomics analyses have been conducted on a variety of targets including plant-bacterial interactions, and computational projects have included investigations of protein-protein and protein-carbohydrate recognition, and most recently a genomic comparison of human pathogens and non-pathogens. The latter comparison identified numerous known and unknown proteins potentially involved in pathogenicity, and suggested novel potential targets for treating disease. A similar approach will be used here to investigate plant pathogens. The major goals of this research are i) to identify novel mechanisms and further characterize known mechanisms of plant-bacteria interactions, and ii) to utilize computational tools for analysis and integration of high-throughput data sets. Experimental methods will include proteome and transcriptome analysis of changes in bacterial expression patterns in response to host interactions and environmental conditions, such as the presence of a potential host plant species. Protein expression will be quantified using iTraq labeling of peptides and mass spectrometry analysis using a Q-Exactive Orbitrap mass spectrometer. To complement our existing expertise in proteomics analyses, gene expression using RNAseq will also be utilized, providing a more comprehensive picture of gene and protein expression in the functioning cell. Illumina sequencing technology will be used to collect RNA sequence data. A MiSeq system (available in the Department of Biology) will be used for most data collection, with the option of using an off-site HiSeq 2500 system available for very large experiments. Proteomics and transcriptomics data sets will be compared and integrated, providing a detailed characterization of changes in expression, which in turn can be used to identify the underlying mechanisms of host-bacterial interactions. In addition to the characterization of expression patterns using wet-lab methods, comparative genomics will be used to determine how widespread genes and proteins mediating interactions with plants, and whether they are common to both beneficial and pathogenic bacteria, or mostly specific to one group. The proposed research will combine high-throughput sequencing technologies for gene and protein expression with the vast amount of genomic data that is now available, and will use this information to map mechanisms of interaction between bacteria and plants. It is expected that this research will identify novel proteins used by pathogens to infect plants. In the longer term this can be used to improve methods for crop management and develop needed new methods for pest control.