Developing a rapid method for accurately predicting complex bacterial signalling networks, using Burkholderia pseudomallei as a test species

开发一种快速准确预测复杂细菌信号网络的方法，以假鼻疽伯克霍尔德氏菌作为测试物种细菌的生存取决于它们对环境压力、宿主免疫系统和抗生素攻击等潜在威胁的反应能力。威胁是使用传感器(激酶)检测到的，这些传感器触发反应并确保复活。一些细菌有数百个传感器，虽然这些传感器中的大多数独立工作(图1A)，但影响生存、毒力和抗生素耐药性等过程的更重要的决定很少基于单一信号做出。相反，必须评估多个不同的信号，这需要使用多个传感器一起工作的网络来检测多个不同的信号并做出正确的决定(图1B所示的多激酶网络)。在抗生素耐药性不断增加的时代，这些网络成为有吸引力的潜在药物靶点，因为它们不存在于人类体内，是细菌生存和毒力所必需的。然而，研究这些网络的一个主要障碍是确定哪些传感器参与其中。我们已经开发了一种生物信息学方法，使我们能够预测这些复杂的网络，因此将简化发现这些有希望的药物靶点的过程。这个项目的重点是我们在类鼻疽病原体假性伯克霍尔德氏菌中预测的一个独特的多激酶网络，它由两个对细胞生长至关重要的传感器激酶组成。它很可能是抗菌药物开发的极佳靶点。研究目的：1.通过相互作用分析(双杂交和磷酸化分析)证明传感器作为一个网络协同工作。2.确定此网络控制的内容及其重要性。3.使用结合分析和X-射线结晶学对感觉结构域的结构特征进行鉴定，以确定由这些蛋白激酶感受到的刺激。实现这些目标将首先表征一个重要的抗生素耐药病原体中的关键多激酶网络，其次，通过验证我们预测多激酶网络的方法将极大地加速发现这些有希望的潜在药物靶点的其他例子。轮换：1.史蒂夫·波特和里克·泰博尔：在伯克霍尔德里亚氏假性鼻炎中预测多激酶网络。获得的技能：特异性残留物的生物信息学分析、分子生物学和蛋白质-蛋白质相互作用分析。2.Ravi Acharya：假腮腺伯克霍尔德氏菌必需蛋白的结构生物学。获得的技能：蛋白质纯化和X射线结晶学。培训潜力：学生将发展广泛的对系统生物学研究重要的跨学科技能，包括：分子微生物学、蛋白质生物化学、结构生物学、生物信息学、序列分析和分子生物学。

Developing a rapid method for accurately predicting complex bacterial signalling networks, using Burkholderia pseudomallei as a test speciesBacterial survival depends upon their ability to respond to potential threats such as environmental stresses, attack by the host's immune system and antibiotics. Threats are detected using sensors (kinases), which trigger responses and ensuresurvival. Some bacteria have hundreds of sensors and while most of these sensors work independently (Figure 1A), the more important decisions affecting processes such as survival, virulence and antibiotic resistance, can rarely be made based on a single signal. Instead multiple different signals must be assessed and this requires the use of a network where multiple sensors work together to detect multiple different signals and to make the correct decision (a multikinase-network; Figure 1B). In the era of ever-increasing antibiotic resistance, these networks make attractive potential drug targets as they are absent from humans and are needed for bacterial survival and virulence. However, a major roadblock in researching these networks is determining which sensors participate in them. We have developed a bioinformatic method that allows us to predict these sophisticated networks and will therefore streamline the process of discovering these promising drug targets. This project is focussed on a unique multikinase-network that we have predicted in the melioidosis pathogen, Burkholderia pseudomallei, which comprises two sensor kinases that are essential for cell growth. It is likely to be an excellent target for the development of antimicrobial drugs. Research Objectives: 1. Show that the sensors work together as a network using interaction assays (two-hybrid and phosphorylation assays). 2. Determine what this network controls and why it is essential. 3. Identify the stimuli sensed by the kinases using binding assays and structural characterisation of the sensory domains using X-ray crystallography. Achieving these objectives will, firstly, characterise a crucial multikinase-network in an important antibiotic resistant pathogen and secondly, by validating our method for predicting multikinase-networks will greatly accelerate the discovery of other examples of these promising potential drug targets. Rotations: 1. Steve Porter and Rick Titball: Predicting multikinase-networks in Burkholderiapseudomallei. Skills gained: bioinformatic analysis of specificity residues, molecular biology and protein-protein interaction assays. 2. Ravi Acharya: Structural biology of the essential kinases in Burkholderia pseudomallei. Skills gained: protein purification and X-ray crystallography. Training potential: The student will develop a broad range of interdisciplinary skills important for systems biology research including: molecular microbiology, protein biochemistry, structural biology, bioinformatics, sequence analysis and molecular biology.