Arabidopsis 2010: Deriving the Gene Circuitry and Network Motifs of the Arabidopsis Defense Response

为了确定基因网络的基因功能，将从拟南芥白粉病感染的时间和空间大尺度mRNA谱中提取植物防御病原体的电路。激光捕获显微解剖将用于从不同的植物细胞群（包括受感染的表皮细胞和邻近的表皮和叶肉细胞）中以采样率和足够的时间点分离RNA，以捕获潜在的生物过程。为了分析这些mRNA表达数据，将开发新的统计方法。例如，时间对齐算法将用于在实验之间同步数据，并评估响应的改变时间模式，包括时间延迟和周期压缩/扩展。对植物防御回路进行建模，可以对影响感染进展的调节因子和下游产品进行严格评估。连续表示将用于推导响应电路，允许人们发现与不同功能响应相关的网络节点和子节点。预测基因功能的实验测试将集中在使用SA生物合成突变体阐明的水杨酸（SA）影响网络亚节点上的基因。此外，定义这些网络和节点允许发现和评估网络基元，例如用于解决不同环境和物种中常见的生物学“问题”的前馈回路。这些空间和时间解析的表达数据，统计和计算方法和工具，以及识别的防御电路和网络基序都将对拟南芥群落具有重要价值。以前从整个叶片样本和少数时间点收集的数据集无法解决防御反应的复杂性。这些努力将产生许多关于基因功能和调控的预测。模型驱动实验将重点关注sa影响节点和子节点中的基因。这些基因不是先天已知的，但将从建模工作中确定。可能的基因包括：参与诱导ICS1和PR1簇的转录因子，推测的SA糖基转移酶，参与从异酸盐合成SA的第二种酶的候选基因，以及推测的负责SA依赖性抑制PDF1.2簇的转录抑制因子。表达数据将存储在公共数据库中，如集成微阵列数据库系统（Integrated Microarray Database System），该系统是2010年国家科学基金会拟南芥奖授予X. Dong及其合作项目的一部分，并将免费提供使用。此外，开发的算法和计算工具将可供下载，并将作为工具包含在Bioconductor（一个免费的微阵列分析平台）中。该研究将通过为分子遗传和生化数据提供正式的数学框架，从而更全面地了解植物与病原体的相互作用。所阐明的植物-病原体相互作用的策略可能适用于其他宿主-病原体相互作用。特别是，已确定的功能控制模块可能在不同的病理系统中共享。为分析和建模工作开发的信息和统计方法和工具将广泛提供给植物界和一般科学界。此外，年轻数学家、工程师和实验生物学家的密切合作和交叉培训产生了真正的跨学科科学家，他们独特地定位于使用定量和基于系统的方法来解决生物学问题。

In order to determine the gene function for a network of genes, the circuitry of plant defense against pathogen will be derived from temporal and spatial large-scale mRNA profiling of powdery mildew infection of Arabidopsis. Laser capture microdissection will be utilized to isolate RNA from distinct populations of plant cells (including infected epidermal cells and neighboring epidermal and mesophyll cells) at a sampling rate and with sufficient time points to capture the underlying biological processes. To analyze this mRNA expression data, novel statistical approaches will be developed. For example, time-alignment algorithms will be used to synchronize data among experiments and evaluate altered temporal patterns of response including time delays and period compression/expansion. Modeling the circuitry of plant defense allows for the rigorous assessment of the regulatory factors and downstream products impacting the progression of infection. Continuous representations will be used to derive the circuitry of response, allowing one to uncover network nodes and subnodes associated with distinct functional responses. Experimental testing of predicted gene function will focus on genes in subnodes of salicylic acid (SA)-impacted networks elucidated using an SA biosynthetic mutant. In addition, defining these networks and nodes allows for discovery and assessment of network motifs such as feed-forward loops used to solve a common biological "problem" in diverse circumstances and species. This spatially and temporally resolved expression data, the statistical and computational approaches and tools, and the identified defense circuitry and network motifs will all be of significant value to the Arabidopsis community. Previous datasets collected from whole leaf samples and few time points were unable to resolve much of the complexity of the defense response. Numerous predictions of gene function and regulation will emerge from these efforts. Model-driven experiments will focus on genes in SA-impacted nodes and subnodes. These genes are not known a priori, but will be determined from the modeling efforts. Likely genes include: transcription factor(s) involved in the induction of ICS1 and PR1 clusters, a putative SA glucosyltransferase, candidate genes for the second enzyme involved in the synthesis of SA from isochorismate, and a putative transcriptional repressor responsible for the SA-dependent repression of the PDF1.2 cluster. The expression data will be deposited in public databases such as the Integrated Microarray Database System being developed as part of the NSF Arabidopsis 2010 award to X. Dong and co-PIs and will be freely available for use. In addition, developed algorithms and computational tools will be available for download and will be included as tools in Bioconductor, a free microarray analysis platform. Information about this project is available at http://plantbio.berkeley.edu:16080/~wildermuth/.Broader Impacts This research will result in a more comprehensive understanding of plant-pathogen interactions by providing a formal mathematical framework for molecular genetic and biochemical data. The strategies elucidated for this plant-pathogen interaction are likely applicable to other host-pathogen interactions. In particular, identified functional control modules are likely to be shared across pathosystems. The informatic and statistical methodologies and tools developed for the analysis and modeling efforts will be made widely available to both the plant and general scientific communities. In addition, the intimate collaboration and cross-training of young mathematicians, engineers, and experimental biologists yields truly interdisciplinary scientists uniquely positioned to address biological questions using quantitative and systems-based approaches.