Gene Function and Pathway Analysis Using Systems Level Approaches in Prokaryotes

描述（由申请人提供）：我们解决了微生物学中的一个关键问题：如何将大量的基因组库解码成细菌细胞特性的蓝图。目前，序列信息获取速度与功能信息获取速度之间的差距阻碍了基因组资源的有效利用。我们已经填补了这个空白。我们正在开发和实施高通量表型方法，以加速确定基因功能，途径及其相互联系。因此，我们在系统分析和机械生物学之间的界面上工作。我们已经证明，化学基因组分析（在许多生长条件下对完整基因缺失文库的适应度进行定量分析）和Epistasis MAPs （E- MAPs；在基因组水平上对双突变和单突变表型进行比较）迅速加速了对大肠杆菌表型、途径和途径相互联系的发现。这项拨款中提出的工作大大扩展了我们的努力。首先，根据我们的证明，化学基因组分析提供了孤儿（功能未表征）基因和注释基因之间的高度相关性，我们现在将开发一个发现孤儿基因功能的管道。我们将通过分析更多的化学空间来扩展和改进高相关性关联，评估与其他大部分不重叠的功能关联（例如蛋白质-蛋白质相互作用）的关联，并将我们的多变量数据集整合到每个潜在的孤儿基因与注释基因相互作用的单个相互作用概率评分中。这个纲要将是一个强大的资源，既确定孤儿基因功能，并评估哪些基因功能的指标是最具信息和成本效益的功能表征。其次，我们将研究在我们的高通量筛选中发现的细胞分裂和肽聚糖合成之间难以捉摸的功能联系的分子基础。我们之前的工作表明，在细胞分裂过程中，PBP1B双功能肽聚糖合成机器与toll - pal在促进外膜收缩方面部分冗余。我们现在发现一个孤儿蛋白，YbgF，可能协调这两个机器，我们将追求分子，生化和细胞生物学的方法来探索如何协调完成。最后，我们将扩大我们的高通量表型方法枯草芽孢杆菌，关键的革兰氏阳性模式生物和厚壁菌门的成员，在人类肠道中普遍存在的两个主要门之一。我们将在枯草芽孢杆菌中实施化学基因组分析和E-MAP分析，并使用它来解剖基因功能和途径连接。由于革兰氏阳性和阴性生物在包膜结构、社会行为以及主要细胞过程（包括复制和代谢）的控制和执行方面存在差异，我们的开源数据集将丰富新颖的生物学。这项工作解决了阻碍基因组信息使用的“表型差距”，并展示了系统分析和机制研究在建立基因功能和过程之间高阶联系方面的联合力量。

DESCRIPTION (provided by applicant): We address a pivotal issue in microbiology: how to decipher the vast reservoir of genomes into a blueprint for the cellular properties of bacteria. Currently, the disparity between speed of acquisition of sequence and functional information impedes utilization of our genomic resources. We have stepped into this gap. We are developing and implementing high throughput phenotyping approaches to accelerate determination of gene functions, pathways and their interconnections. Thus, we function at the interface between systems analysis and mechanistic biology. We have already shown that chemical-genomic profiling (quantitative profiling of the fitness of the complete gene deletion library under many growth conditions) and Epistasis MAPs (E- MAPs; comparison of double vs single mutant phenotypes on a genome level) rapidly accelerates discovery of phenotypes, pathways and pathway interconnections in E. coli. The work proposed in this grant significantly expands our efforts. First, following on our demonstration that chemical genomic profiling provides high correlation associations between orphan (functionally uncharacterized) genes and annotated genes, we will now develop a pipeline for discovery of orphan gene function. We will expand and improve the high correlation associations by profiling more chemical space, assess associations with other, largely non overlapping measures of functional association (e.g. protein-protein interactions) and integrate our multivariate data sets into a single interaction probability score for each potential orphan-gene-to-annotated gene interaction. This compendium will be a powerful resource both for determining orphan gene function, and for assessing which metrics of gene function are most informative and cost-effective for functional characterization. Second, we will investigate the molecular underpinnings of an elusive functional link between cell division and peptidoglycan synthesis identified in our high-throughput screens. Our previous work showed that the PBP1B bifunctional peptidoglycan synthesis machine is partially redundant with Tol-Pal in promoting outer membrane constriction during cell division. We now find that an orphan protein, YbgF, may coordinate both machines, and we will pursue molecular, biochemical and cell biological approaches to explore how coordination is accomplished. Finally, we will expand our high throughput phenotyping approaches to B. subtilis, the key gram-positive model organism and a member of the Firmicutes, one of two major phyla ubiquitously present in the human gut. We will implement chemical-genomic profiling and E-MAP analysis in B. subtilis and use it to dissect gene function and pathway connections. As Gram-positive and negative organisms differ in their envelope structures, social behaviors and control and execution of major cellular processes, including replication and metabolism, our open-source dataset will be rich in novel biology. This work addresses the "phenotype gap" impeding the use of genomic information and demonstrates the combined power of systems analyses and mechanistic studies in establishing gene function and higher-order connections between processes.