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

拟议的研究将调查病原体和有益细菌与植物的相互作用，并将表征介导这些相互作用的潜在机制。细菌种类可以对植物生长产生巨大的影响，无论是积极的还是消极的-植物病原体可以大大降低植物生长和作物产量，而有益的细菌可以增加植物生长并减少环境胁迫的影响。有趣的是，病原体和有益细菌都有共同的相互作用机制。该研究项目的目标是描述病原体和有益细菌如何与植物相互作用的共性和差异。在这个项目中，比较基因组学和高通量测序将用于研究细菌与宿主生物的相互作用。McConkey实验室以前的研究结合了蛋白质组学和生物信息学领域的湿实验室和计算项目。蛋白质组学分析已经进行了各种目标，包括植物-细菌的相互作用，和计算项目包括蛋白质-蛋白质和蛋白质-碳水化合物识别的调查，最近的人类病原体和非病原体的基因组比较。后者的比较确定了许多已知和未知的蛋白质可能参与致病性，并提出了新的潜在目标，用于治疗疾病。这里将使用类似的方法来研究植物病原体。本研究的主要目标是：i）确定新的机制，并进一步表征植物-细菌相互作用的已知机制，以及ii）利用计算工具分析和整合高通量数据集。实验方法将包括蛋白质组和转录组分析细菌表达模式的变化，以响应宿主相互作用和环境条件，如潜在的宿主植物物种的存在。将使用iTraq肽标记和Q-Exactive Orbitrap质谱仪进行质谱分析来定量蛋白质表达。为了补充我们在蛋白质组学分析方面的现有专业知识，还将利用RNAseq进行基因表达，提供功能细胞中基因和蛋白质表达的更全面的图片。Illumina测序技术将用于收集RNA序列数据。一个MiSeq系统（生物系提供）将用于大部分数据收集，并可选择使用场外HiSeq 2500系统进行大型实验。蛋白质组学和转录组学数据集将进行比较和整合，提供表达变化的详细表征，这反过来又可用于识别宿主-细菌相互作用的潜在机制。除了使用湿实验室方法表征表达模式外，比较基因组学还将用于确定介导与植物相互作用的基因和蛋白质的分布范围，以及它们是否为有益菌和致病菌所共有，或者主要是特定于一个群体。拟议中的研究将结合联合收割机基因和蛋白质表达的高通量测序技术与大量的基因组数据，现在是可用的，并将使用这些信息来映射细菌和植物之间的相互作用机制。预计这项研究将确定病原体用于感染植物的新蛋白质。从长远来看，这可以用来改善作物管理方法，并开发所需的新方法来控制害虫。

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.