Predicting the emergence of antibiotic resistance through multi-omics approaches and Immune System-surveillan

通过多组学方法和免疫系统监测预测抗生素耐药性的出现

• 批准号: 9241341
• 负责人: Jose Bento
• 金额: $ 220.9万
• 依托单位: BOSTON COLLEGE
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-03-10 至 2021-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9241341
• 关键词: Acinetobacter; Acinetobacter baumannii; Affect; Antibiotic Resistance; Antibiotic Therapy; Antibiotic susceptibility; Antibiotics; Antimicrobial Resistance; Bacteria; Bacterial Genome; Cells; Communities; Complex; Computer Simulation; Cues; Data; Development; Drug usage; Effectiveness; Equilibrium; Evolution; Experimental Models; Failure; Fingerprint; Genes; Genetic Transcription; Goals; Gram-Positive Bacteria; Growth; Immune; Immune response; Immune system; Immunity; Immunocompetence; Immunocompromised Host; Immunologic Monitoring; Immunotherapy; In Vitro; Infection; Joints; Knowledge; Lead; Learning; Maps; Methods; Microbial Biofilms; Modeling; Mus; Mutation; Pathway interactions; Periodicity; Pharmaceutical Preparations; Phenotype; Plasticizers; Play; Pneumonia; Population; Probability; Regulator Genes; Resistance; Role; Route; Source; Streptococcus pneumoniae; Stress; Surface; Testing; Time; Treatment Failure; Validation; bacterial genetics; bacterial resistance; base; clinically relevant; cost; cytokine; design; dosage; effective therapy; experience; fitness; flexibility; genetic element; genome-wide; immunological status; in vivo; knockout gene; macrophage; minimal risk; mouse model; mutant; mutation screening; novel; novel strategies; pathogen; predictive marker; pressure; prevent; prototype; public health relevance; resistance mechanism; resistance mutation; response; ribosome profiling; screening; stem; success; synergism; tool; transcriptome sequencing; transmission process

 DESCRIPTION (provided by applicant): The rise of antibiotic resistance in bacterial populations reflects inadequate counterselection by the antibiotic itself, by host immunity, or by fitness costs of the resistance mechanism. Our inability to control resistance stems from limited understanding of these three forces of selection and especially of the interplay between antibiotic dosage, how bacteria populations respond to antibiotics, and host immunity. This project maps these interactions in high definition for the bacterial pathogens Streptococcus pneumoniae and Acinetobacter baumannii, both of which are serious threats and can cause antibiotic-resistant pneumonia. Specifically, in this project: 1) RNA-seq is applied to construct transcriptional networks; 2) Two complementary, genome-wide methods of screening mutations are used to map the phenotypic networks that affect resistance. One screening method called Tn-seq screens all possible gene knockout mutations for their roles in antibiotic susceptibility and resistance. The other method, experimental evolution, enables mutants to arise naturally and compete for success in the antibiotic condition. These interactions will be studied in vitro, using standard culture and under resistance-inducing conditions, including biofilms on plastic surfaces that are often the source of nosocomial A. baumannii infections. Mutant responses to antibiotic selection will also be studied in vivo, in which bacterial populations infect mice that re treated with antibiotic but vary in their immune competence; 3) The state-of-the-immune-system (sIS) will be profiled during mouse infections to identify sIS fingerprints reflecting both immune success and failure, when the bacterial population evades both antibiotic and immune pressure. All of these methods will be applied to define the bacterial-adaptive and host-response pathways for 20 different clinically relevant antibiotics; 4) Ribo-seq is used to evaluate evolved strains and the manner in which their interactions with the immune system changes over time. Finally, all of these bacterial- networks, immune-states and responses will be integrated into a joint model that will be learnt using a novel plug-and-play toolbox for fast prototyping of data driven solutions. This model will be validated by testing the likelihood of the emergence of resistance through: 1) selection in the presence of five novel antibiotics, 2) selection in the presence of two different resistant pathogens, and 3) through selection in the presence of three different immunocompromised hosts. Therefore, the ultimate goal of this project is to design a `plug-and-play learning toolbox' that is able to forecast the likelihood of the emergence of antibiotic resistance and prioritize antibiotic and immune therapies that enable more rapid, effective treatment with minimal risk of treatment failure.

 描述（由申请方提供）：细菌群体中抗生素耐药性的上升反映了抗生素本身、宿主免疫或耐药机制的适应性成本的反选择不足。我们无法控制耐药性源于对这三种选择力量的有限理解，特别是抗生素剂量之间的相互作用，细菌种群对抗生素的反应以及宿主免疫力。该项目以高清晰度绘制了细菌病原体肺炎链球菌和鲍曼不动杆菌的这些相互作用，这两种细菌病原体都是严重的威胁，并可导致耐药性肺炎。具体而言，在该项目中：1）RNA-seq用于构建转录网络; 2）使用两种互补的全基因组筛选突变的方法来绘制影响抗性的表型网络。一种名为Tn-seq的筛选方法筛选了所有可能的基因敲除突变，以确定它们在抗生素敏感性和耐药性中的作用。另一种方法，实验进化，使突变体自然产生，并在抗生素条件下竞争成功。这些相互作用将在体外进行研究，使用标准培养和耐药性诱导条件下，包括塑料表面上的生物膜，通常是医院A的来源。鲍曼不动杆菌感染。还将在体内研究对抗生素选择的突变体应答，其中细菌群体感染用抗生素重新治疗但其免疫能力不同的小鼠; 3）在小鼠感染期间将分析免疫系统状态（sIS）以鉴定反映免疫成功和失败的sIS指纹，当细菌群体逃避抗生素和免疫压力时。所有这些方法都将用于确定20种不同临床相关抗生素的细菌适应性和宿主反应途径; 4）Ribo-seq用于评估进化菌株及其与免疫系统相互作用随时间变化的方式。最后，所有这些细菌网络，免疫状态和反应将被整合到一个联合模型中，该模型将使用一个新的即插即用工具箱进行学习，用于数据驱动解决方案的快速原型设计。将通过以下方式测试出现耐药性的可能性来验证该模型：1）在存在五种新型抗生素的情况下进行选择，2）在存在两种不同耐药病原体的情况下进行选择，以及3）在存在三种不同免疫功能低下宿主的情况下进行选择。因此，该项目的最终目标是设计一个“即插即用的学习工具箱”，能够预测出现抗生素耐药性的可能性，并优先采用抗生素和免疫疗法，以便能够进行更迅速、有效的治疗，同时将治疗失败的风险降到最低。