Guided multiplex analysis of microoxic fitness factors in P. aeruginosa

铜绿假单胞菌微氧适应因子的引导多重分析

• 批准号: 10740163
• 负责人: DEBORAH A HOGAN
• 金额: $ 20.43万
• 依托单位: DARTMOUTH COLLEGE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10740163
• 关键词: Accounting; Acute Disease; Anabolism; Binding Proteins; Biological; Biological Assay; Bronchiectasis; Candidate Disease Gene; Catabolism; Cell Density; Cells; Centers for Disease Control and Prevention (U.S.); Chronic Obstructive Pulmonary Disease; Collaborations; Collection; Colorado; Communities; Complement; Cystic Fibrosis; Data; Data Set; Deposition; Drug resistance; Environment; Future; Gene Expression; Gene Expression Profile; Gene Expression Profiling; Generations; Genes; Genetic; Genetic Diseases; Growth; Health Care Costs; Hemerythrin; Individual; Infection; Infection prevention; Knowledge; Label; Laboratories; Light; Machine Learning; Microbial Biofilms; Modeling; Morbidity - disease rate; Nitrates; Operon; Oxidants; Oxygen; Pathogenesis; Pathway interactions; Pattern; Phenotype; Physiology; Play; Process; Proteins; Pseudomonas; Pseudomonas aeruginosa; Pseudomonas aeruginosa infection; Publishing; Resources; Role; Signal Transduction; Site; System; Testing; Therapeutic; Universities; Work; biological adaptation to stress; candidate identification; chronic infection; density; environmental change; experimental study; fitness; gene environment interaction; genome database; improved; innovation; insight; mortality; multidrug-resistant Pseudomonas aeruginosa; mutant; nanomolar; novel; pathogen; public repository; respiratory; therapeutic target; tool; trait; transcriptome; transcriptome sequencing; transcriptomics; transposon sequencing

Pseudomonas aeruginosa (Pa), a common pathogen, causes a wide range of diverse infections that lead to enormous health care costs and unacceptably high morbidity and mortality. Pa drug resistance is an increasing problem. Thus, new Pa infection prevention, elimination and mitigation strategies are needed. In many infection contexts, including biofilms, infection sites, and multispecies communities, Pa is often in microoxic environments and microoxia imposes unique challenges to Pa energy generation, catabolism, and biosynthesis, signaling, and stress responses among other processes. Yet, microoxic fitness determinants are poorly understood. To enhance our ability to identify genes involved in microoxic fitness and conditions that will best reveal microoxic fitness phenotypes, we will leverage our prior work on the analysis of transcriptional signatures using publicly available data. Pa has been extensively studied using transcriptomics with over 4000 whole transcriptome microarray and RNA-seq datasets in public repositories. The Hogan Lab in collaboration with the laboratory of Dr. Casey Greene (University of Colorado, Anschutz) has used these transcriptome datasets to create and validate normalized compendia for cross-experiment comparisons and developed machine learning approaches to facilitate the use of these data to identify genes with correlated expression patterns. Using these resources, we can observe robust co-expression patterns that may not stand out in single experiments. We found that known microoxic fitness genes are significantly correlated in expression patterns with each other and with a large set of uncharacterized genes. In Aim 1, we will construct mutants in candidate microoxic fitness genes, analyze their fitness in microoxic and normoxic conditions, and compare the phenotype observed using single mutants to the results from a Tn-Seq performed in similar conditions. In light of our data that show that medium composition and strain background can influence the expression level of microoxic fitness genes even when cell density and O2 availability are constant, in Aim 2, we will use transcriptome compendia to identify conditions in which microoxic fitness genes are most highly expressed. We will use those conditions to assess the phenotypes of mutants in candidate microoxic fitness genes. Prioritized hits will be validated by complementation and further characterized in future work. Upon completion of these studies, we will have expanded our knowledge of microoxic fitness determinants, and we will have tested a strategy for leveraging public transcriptomics data for identifying candidate genes and assay conditions that might be broadly useful in other systems and for other processes.

铜绿假单胞菌（PA）是一种常见的病原体，引起多种多种感染， 导致巨大的医疗保健成本以及高度高的发病率和死亡率。 PA耐药性 是一个越来越多的问题。因此，新的PA感染预防，消除和缓解策略是 需要。在许多感染环境中，包括生物膜，感染部位和多人社区， PA通常在微氧环境中，微氧对PA能量构成了独特的挑战 在其他过程中产生，分解代谢和生物合成，信号传导和应力反应。然而， 微毒素适应性决定因素知之甚少。增强我们识别涉及基因的能力 在最能揭示微氧化健身表型的微毒性适应性和条件下，我们将利用 我们先前使用可公开数据分析转录签名的工作。 PA一直是 使用具有4000多个整个转录组微阵列和RNA-Seq的转录组学广泛研究 公共存储库中的数据集。霍根实验室与凯西·格林博士的实验室合作 （科罗拉多大学，安索兹大学）使用了这些转录组数据集来创建和验证 用于跨体验比较和开发机器学习的标准化汇编 促进这些数据以识别具有相关表达模式的基因的方法。 使用这些资源，我们可以观察到强大的共表达模式，这些模式可能不会单一脱颖而出 实验。我们发现已知的微毒素适应性基因在表达中显着相关 彼此之间的模式以及大量未表征的基因。在AIM 1中，我们将构建 候选微毒素适应性基因中的突变体，分析其在微毒素和常氧中的适应性 条件，并比较使用单个突变体观察到的表型与TN-Seq的结果 在类似条件下进行。鉴于我们的数据表明中等成分和应变 即使细胞密度和 O2的可用性是恒定的，在AIM 2中，我们将使用转录组汇编来识别 哪些微氧化适应性基因表达最高。我们将使用这些条件来评估 候选微毒性适应基因中突变体的表型。优先的命中将由 互补并在未来的工作中进一步表征。完成这些研究后，我们将 已经扩展了我们对微毒素健康决定因素的了解，我们将测试一种策略 利用公共转录组学数据来识别候选基因和测定条件 在其他系统和其他过程中广泛有用。