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

这项拟议的研究将调查病原体和有益细菌与植物的相互作用，并将表征调节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，无论是正性还是负性植物病原菌都可以极大地降低植物的生长和作物产量，而有益的细菌可以促进植物的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。这项研究项目的目标是描述病原体和有益细菌与植物相互作用的共性和差异。在这个项目中，将使用比较基因组学和高通量测序来研究细菌与宿主生物的相互作用。麦康基实验室之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在包括植物-细菌相互作用在内的各种目标上进行，计算项目包括研究蛋白质-蛋白质和蛋白质-碳水化合物识别，最近还对人类病原体和非病原体进行了基因组比较。后一种比较确定了许多可能与致病性有关的已知和未知蛋白质，并提出了治疗疾病的新的潜在靶点。这里将使用类似的方法来研究植物病原体。这项研究的主要目标是i)识别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集成高通量数据集。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，以响应宿主的相互作用和环境条件，例如潜在宿主植物的存在。蛋白质的表达将通过多肽的iTRAQ标记和使用Q-Exactive Orbitrap质谱仪的质谱分析进行量化。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景。Illumina测序技术将用于收集RNA序列数据。大多数数据收集将使用MiSeq系统(在生物系可用)，并可选择使用场外HiSeq 2500系统进行非常大的实验。蛋白质组学和转录组数据集将被比较和整合，提供表达变化的详细特征，这反过来可以用来确定宿主-细菌相互作用的潜在机制。除了使用湿实验室方法描述表达模式外，还将使用比较基因组学来确定与植物相互作用的基因和蛋白质是如何广泛分布的，以及它们是有益细菌和病原细菌共同的，还是主要针对一个群体。这项拟议的研究将把基因和蛋白质表达的高通量测序技术与现有的大量基因组数据结合起来，并将使用这些信息来绘制细菌和植物之间相互作用的机制。预计这项研究将识别病原体用来感染植物的新蛋白质。从长远来看，这可用于改进作物管理方法和开发必要的虫害控制新方法。

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.