Systems Immunolobiology of Antibiotic-Persistent MRSA Infection

 描述（由申请方提供）：金黄色葡萄球菌菌血症（SAB）是一种常见的危及生命的血流感染，通常由耐甲氧西林菌株（MRSA）引起。值得紧急关注的是，高达30%的SAB患者即使使用金标准抗MRSA治疗（万古霉素[货车]或达托霉素（DAP））也未能获得抗生素治疗。这些患者具有持续性菌血症，这常常导致令人沮丧的临床结果。尽管根据体外CLSI折点，来自这些患者的MRSA分离株似乎对货车或DAP“敏感”，但这些抗生素未能清除血流感染。这种体内抗生素耐药性被称为抗生素持续性MRSA菌血症，或APMB。目前，对这些危及生命的感染几乎没有治疗选择。 有一个关键的，未得到满足的需要，了解独特的主机和病原体因素的交叉驱动APMB。阐明这些因素有望导致新的方法来前瞻性地识别有发展APMB风险的患者，以及预防或治疗这种通常具有破坏性的感染的新策略。重要的是，APMB代表了抗生素耐药性感染的一个独特子集，其由于“耐药性”或耐药性而不同于生物膜相关感染。 “复发性/复发性”分离株。APMB分离株是遗传稳定的，但在体内抗生素暴露诱导的高度适应性菌株。因此，持续性感染（APMB）的机制与耐药性感染不同。基于我们广泛的初步数据，我们假设APMB是病原体、宿主免疫反应和抗生素三者相互作用的结果。因此，我们将：1）在体外、离体和可区分的动物模型中分析野生型和突变型APMB菌株与宿主细胞和组分的相互作用，以解决S. aureus persistome驱动APMB; 2）利用我们的开创性S.金黄色葡萄球菌菌血症组（SABG）人类样品和匹配的临床分离株的生物储存库，基因组和转录分析，以及免疫表型分析，以确定货车或DAP治疗期间APMB的宿主遗传和免疫谱;和3）使用我们强大的基于系统的统计和计算免疫学方法来整合研究中的高通量基因组学和转录组学数据的结果，以模拟病原体-APMB唯一的主机签名。因此，我们将解决驱动APMB的病原体和宿主因素，以实现创新方法来预测，预防和治疗尽管抗生素治疗仍持续存在的MRSA血流感染。 这些迫切需要的进展将来自于对研究的反复完善，这些研究汇集了一个优秀研究团队的公认优势，以应用一种综合的、基于系统的方法。结果将产生强大的预测算法，用于临床评价，以改善对MRSA感染的干预措施。因此，通过优化协同作用的前沿方法和策略，我们在U 01项目中逐步集中的研究理想地响应了NIH和“抗菌素耐药性系统生物学”RFA（RFA-AI-14-064）的优先事项。

 DESCRIPTION (provided by applicant): Staphylococcus aureus bacteremia (SAB) is a common and life-threatening bloodstream infection that is often caused by methicillin-resistant strains (MRSA). Of urgent concern, up to 30% of SAB patients fail antibiotic treatment even when gold-standard anti-MRSA therapy (vancomycin [VAN] or daptomycin (DAP]) is used. These patients have persistent bacteremia, which frequently results in a dismal clinical outcome. Even though the MRSA isolates from these patients appear to be "susceptible" to VAN or DAP based upon in vitro CLSI breakpoints, these antibiotics fail to clear the bloodstream infection. Such in vivo antibiotic resistance is termed Antibiotic-Persistent MRSA Bacteremia, or APMB. At present, there are few therapeutic options for these life-threatening infections. There is a critical, unmet need to understand the unique intersection of host and pathogen factors driving APMB. Elucidating these factors holds promise to lead to new approaches to prospectively identify patients at risk for developing APMB, and novel strategies to prevent or treat this often devastating infection. Importantly, APMB represents a unique subset of antibiotic resistant infections that differ from biofilm-associated infections due to "antibiotic-tolerant" or "recalcitrant / relapsing" isolates. APMB isolates are genetically stable, but highly adaptive strains induced by in vivo antibiotic exposure. Thus, mechanisms of persistent infections (APMB) are distinct from antibiotic-tolerant infections. Based on our extensive preliminary data, we hypothesize that APMB results from a three-way interaction among the pathogen, host immune response and antibiotic. We further posit that conventional approaches to study this clinically important phenomenon may be insufficient to understand it. Therefore, we will: 1) analyze the interactions of wild-type and mutant APMB strains with host cells and constituents in vitro, ex vivo, and in discriminative animal models to resolve key genotypic & phenotypic determinants of the S. aureus persistome that drives APMB; 2) leverage our pioneering S. aureus Bacteremia Group (SABG) biorepository of human samples & matched clinical isolates, genomic & transcriptional analysis, and immunophenotyping to define host genetic and immune profiles of APMB during VAN or DAP treatment; and 3) use our powerful systems-based statistical and computational immunology approaches to integrate results of high-throughput genomics and transcriptomics data across studies to model the pathogen-host signatures unique to APMB. Therefore, we will resolve the pathogen and host factors that drive APMB to enable innovative approaches to predict, prevent and treat MRSA bloodstream infections that persist despite antibiotic treatment. These critically needed advances will derive from iterative refinement of studies that bring together proven strengths of an outstanding research team to apply an integrated, systems-based approach. The result will yield robust predictive algorithms for clinical evaluation for improved interventions against MRSA infections. Thus, through leading-edge methods and strategies that are optimized for synergy, our progressively focused studies in this U01 project are ideally responsive to the priorities of the NIH and this "Systems Biology of Antibacterial Resistance" RFA (RFA-AI-14-064).