Guided multiplex analysis of microoxic fitness factors in P. aeruginosa

铜绿假单胞菌（Pa）是一种常见的病原体，引起广泛的不同感染， 导致巨大保健费用和不可接受的高发病率和死亡率。PA耐药性 是一个日益严重的问题。因此，新的Pa感染预防、消除和缓解策略是必要的。 needed.在许多感染环境中，包括生物膜、感染部位和多物种群落， Pa经常处于微氧环境中，微氧对Pa能量提出了独特的挑战 产生、催化剂和生物合成、信号传导和应激反应等过程。然而， 微氧适合度的决定因素知之甚少。为了增强我们识别相关基因的能力 在微氧适应性和最能揭示微氧适应性表型的条件下，我们将利用 我们先前的工作是利用公开的数据分析转录签名。爸爸一直 使用转录组学进行了广泛的研究，有超过4000个全转录组微阵列和RNA测序 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，安舒茨）已经使用这些转录组数据集来创建和验证 用于交叉实验比较的标准化纲要，并开发了机器学习 方法，以促进使用这些数据，以确定相关的表达模式的基因。 利用这些资源，我们可以观察到强大的共表达模式，这些模式可能在单个细胞中并不突出。 实验我们发现，已知的微氧适应基因在表达上显著相关， 模式与对方和一大套未表征的基因。在目标1中，我们将构建 候选微氧适应性基因中的突变体，分析它们在微氧和常氧中的适应性 条件下，并将使用单一突变体观察到的表型与来自Tn-Seq 在类似的条件下进行。根据我们的数据显示培养基成分和菌株 背景可以影响微氧适应基因的表达水平，即使当细胞密度和 O2的可用性是恒定的，在目标2中，我们将使用转录组纲要来确定 哪些微氧适应基因表达最高。我们将利用这些条件来评估 候选微氧适应性基因中突变体的表型。优先命中将通过以下方式进行验证： 并在今后的工作中进一步完善。在完成这些研究后，我们会 已经扩大了我们对微氧适应性决定因素的了解，我们将测试一种策略， 利用公共转录组学数据来鉴定可能 在其他系统和其他过程中广泛使用。

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.