Antimicrobial resistance mechanisms in the presence of sub-inhibitory antibiotics and the microbiome

背景：在过去的二十年里，已经清楚的是，囊性纤维化患者的多种疾病（如肺部感染）是由微生物群落（微生物群）引起的，而不是孤立的单一病原体。这些多微生物群落可能与自身和治疗性抗生素相互作用，但在大多数多微生物疾病中，最终会导致抗菌素耐药性和疾病。这个博士学位将挑战与一种微生物隔离工作的教条，了解抗菌素耐药性和毒力。它将利用分子基因组和下一代测序工具来梳理病原体，微生物群和抗生素之间发生的复杂相互作用。目标。该研究的总体目标是建立一个综合的分子理解微生物群落如何相互作用，以推动抗菌素耐药性和疾病。学生将实现以下目标：目标1。了解简单的混合群落如何在广泛使用的实验室模型中相互作用。通过将优势病原体（如假单胞菌）与次要微生物群成员（如念珠菌）混合，我们已经证明了细菌运动性，生物膜形成，抗菌药物敏感性和毒力因子产生（例如，脂肪酶和蛋白酶），发生了显著变化。使用耐药病原体和微生物群的良好表征面板，学生将它们成对地相互作用，并根据驱动耐药性或疾病的结果将其扩展到不同的多微生物群落。介导更强抗菌剂耐药性和毒力的多种微生物相互作用的分子解开将从分子上详细研究产生新型相互作用（如增加生物膜形成或抗菌剂耐药性）的优势和次级微生物群的组合。转录组学（RNA测序），蛋白质组学和代谢产物的生产将被用来确定基因组途径，促进在多微生物相互作用中的毒力或抗菌素耐药性的增加。抗生素药敏试验的混合模型在临床实验室中只对单一病原体进行分离试验，以确定感染时应使用哪种抗生素。这些测试的结果对于治疗多微生物CF肺部感染没有用。学生将使用上述“混合”建模经验，结合最新的微生物群分析工具，开发新的多微生物敏感性检测方法，最终将其应用于CF痰液。这将确定特定的抗生素或抗生素组合是否在不同的社区中表现更好，从而推动抗生素耐药性。

BACKGROUND.Over the last two decades it has become clear that multiple diseases such as lung infections in people with cystic fibrosis are caused by a community of microorganisms (the microbiota), rather than a single pathogen in isolation. These polymicrobial communities may interact with themselves and with therapeutic antibiotics, but in most polymicrobial diseases end up driving antimicrobial resistance and disease. This PhD will challenge the dogma of working with one microorganism in isolation in terms of understanding antimicrobial resistance and virulence. It will harness molecular genomic and next-generation sequencing tools to tease apart the complex interactions that occur between pathogens, microbiota and antibiotics. AIM. The overall aim of the research will be to build an integrated molecular understanding of how microbial communities interact to drive antimicrobial resistance and disease. The student will carry out the following objectives:OBJECTIVE 1. UNDERSTAND HOW SIMPLE MIXED COMMUNITIES INTERACT IN WIDELY USED LABORATORY MODELS.By mixing a dominant pathogen such as Pseudomonas with a secondary microbiota member such as Candida, we have shown that behaviour in assays such as bacterial motility, biofilm formation, antimicrobial susceptibility, and virulence factor production (eg. lipases and proteases), altered dramatically. Using well characterised panels of resistant pathogens and microbiota, the student will interact them in pairs, and expand this to diverse polymicrobial communities depending on outcomes which drive resistance or disease.OBJECTIVE 2. MOLECULAR UNRAVELLING OF POLYMICROBIAL INTERACTIONS WHICH MEDIATE GREATER ANTIMICROBIAL RESISTANCE AND VIRULENCE.Combinations of dominant and secondary microbiota producing novel interactions such as increased biofilm formation or antimicrobial resistance will be studied in molecular detail. Transcriptomics (RNA-sequencing), proteomics and metabolite production will be used to identify the genomic pathways that facilitate the increases in virulence or antimicrobial resistance within the polymicrobial interaction.OBJECTIVE 3. DEVELOPING MIXED MODELS OF ANTIMICROBIAL SUSCEPTIBILITY TESTING.Only a single pathogen in isolation is tested in clinical laboratories to determine which antibiotics should be given for an infection. The outcome of these tests are not useful for treating polymicrobial CF lung infections. Student will use the "mixed" modelling experience from above, in combination with the latest microbiota analysis tools to develop new polymicrobial susceptibility testing assays, ultimately applying these to CF sputum. This will determine if particular antibiotics or combinations of antibiotics perform better on different communities driving antimicrobial resistance.