Mobile Genetic Elements and Clinical Outcomes in Staphylococcus aureus Bacteremia

金黄色葡萄球菌菌血症的移动遗传元件和临床结果

• 批准号: 10211124
• 负责人: Blake M Hanson
• 金额: $ 12.99万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-06 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10211124
• 关键词: Age; Antibiotic Resistance; Antibiotics; Antimicrobial Resistance; Automobile Driving; Bacteremia; Bacterial Genome; Body mass index; Bypass; Carbon; Centers for Disease Control and Prevention (U.S.); Central America; Cessation of life; Clinical; Clonal Expansion; Collection; Communities; Complex; DNA Insertion Elements; Data; Data Set; Detection; Development; Disease; Elements; Endocarditis; Environment; Evolution; Foundations; Gene Order; Genes; Genetic; Genetic Variation; Genome; Genomics; Geographic Locations; Geography; Health; Health Sciences; Horizontal Gene Transfer; Hospitals; Immune; Immune system; Individual; Infection; Laboratories; Light; Machine Learning; Measures; Methodology; Microbial Biofilms; Mobile Genetic Elements; Molecular Genetics; Outcome; Oxacillin; Patient Recruitments; Patient-Focused Outcomes; Patients; Phylogenetic Analysis; Phylogeny; Plasmids; Play; Population; Positioning Attribute; Public Health; Research; Research Personnel; Resistance; Resistance to infection; Role; Sampling; Science; Scientist; Sepsis; Single Nucleotide Polymorphism; Site; Skin Tissue; Societies; Soft Tissue Infections; South America; Staphylococcus aureus; Technology; Texas; Time; Training; Treatment Failure; United Nations; United States; United States National Institutes of Health; Universities; Vancomycin; Variant; Virulence; Virulence Factors; Work; beta-Lactams; career development; clinical epidemiology; clinically relevant; cohort; common treatment; comorbidity; design; fitness; genetic signature; genome sequencing; human pathogen; insight; machine learning method; methicillin resistant Staphylococcus aureus; microbial genomics; mortality; nanopore; novel; pathogen; predictive signature; pressure; random forest; reconstruction; resistance gene; tool; transmission process; whole genome

Project Summary Antimicrobial resistance (AMR) and its impact on health has been recognized as one of the most serious public health threats facing society. Significantly, the vast majority of acquired AMR genes are carried on mobile genetic elements (MGEs), which include plasmids, insertion sequences, and transposons. One of the most important hospital and community-associated pathogens harboring resistance genes is Staphylococcus aureus, which causes more than 80,000 infections and 11,000 deaths each year in the United States. S. aureus readily acquire MGEs, which encompass more than 20% of the genome for most strains, and these elements have been central to the establishment and broad spread of resistance to antibiotics such as oxacillin and vancomycin. Additionally, S. aureus is capable of causing a wide range of infections, including bacteremia, with a mortality rate up to 30%. Utilizing an unparalleled collection of S. aureus strains from a cohort of patients in Central and South America with bacteremia, I seek to develop a framework for the identification and comparison of circulating MGEs through the use of bacterial phylogenetics, clinical epidemiology, and machine learning. This dataset will serve as the foundation for my training to become an independent scientist. To begin interrogating this exceptional strain collection, we have generated Illumina short-read whole genome sequencing data on 1,087 S. aureus bacteremia isolates to identify MGEs, and will leverage novel ultra-long read sequencing methodologies to fully characterize the position and variations of these elements. The three aims within this proposal are designed to elucidate the role of MGEs in driving the genetic diversification of endemic S. aureus clades, and identify if they serve as adaptation hotspots when put under selective pressure from the host immune system or antibiotics. First, I will characterize the repertoire of MGEs within this large cohort of isolates and apply gene-order and Bayesian time-measured phylogenetics to identify the predominant MGEs within each clade and how frequently they are acquired and lost. Second, I will assess the MGE diversity within isolates collected serially from the same individual. I hypothesize these MGEs will be the primary variation points and will be more important than single nucleotide polymorphisms (SNPs) to the adaptation to selective pressures such as antibiotics. Third, I will identify the clinical (age, BMI, and present comorbidities) and genomic (SNPs, MGEs, and genes) features that are predictive of the 30-day mortality in the predominant clades of S. aureus within our dataset. I theorize these genetic and clinical signatures will be different for each clade as they possess different repertoires of MGEs. The Center for Antimicrobial Resistance and Microbial Genomics in The University of Texas Health Sciences Center at Houston has a firm commitment to understanding and reducing AMR and AMR infections. This provides an exceptional environment to conduct my research elucidating the role of MGEs in the clinical outcomes of S. aureus bacteremia, and to develop as an investigator and participate in the training offered to make the transition to independence.

项目摘要 抗菌药物耐药性（AMR）及其对健康的影响已被公认为最严重的公共卫生问题之一 社会面临的健康威胁。值得注意的是，绝大多数获得的AMR基因是在移动的上携带的。 遗传元件（MGE），其包括质粒、插入序列和转座子。一个最 携带耐药基因的重要医院和社区相关病原体是葡萄球菌 金黄色葡萄球菌，在美国每年导致超过80，000例感染和11，000例死亡。S. 金黄色葡萄球菌容易获得MGE，其包含大多数菌株基因组的20%以上，并且这些MGE 对苯唑西林等抗生素的耐药性的建立和广泛传播起着关键作用 和万古霉素。此外，S.金黄色葡萄球菌能够引起广泛的感染，包括菌血症， 死亡率高达30%利用无与伦比的S。来自患者队列的金黄色葡萄球菌菌株 在中美洲和南美洲的菌血症，我试图建立一个框架， 通过使用细菌系统发育学、临床流行病学和机器比较循环MGE 学习这个数据集将成为我成为独立科学家的基础。开始 通过对这个特殊菌株的研究，我们生成了Illumina短读全基因组 1,087株S.金黄色菌血症分离株来识别MGE，并将利用新型超长 阅读测序方法，以充分表征这些元素的位置和变化。三 该提案中的目标旨在阐明MGE在推动遗传多样性方面的作用， 特有的S. aureus进化枝，并确定它们是否在选择压力下作为适应热点 从宿主免疫系统或抗生素。首先，我将描述这个大型企业中的MGE曲目， 队列的分离株，并应用基因顺序和贝叶斯时间测量的遗传学，以确定主要的 每个进化枝内的MGE以及它们获得和丢失的频率。其次，我将评估MGE 从同一个体连续收集的分离物内的多样性。我假设这些MGE将是 主要变异点，并将比单核苷酸多态性（SNP）更重要， 适应选择性压力，如抗生素。第三，我将确定临床（年龄，BMI和目前 合并症）和基因组（SNP，MGE和基因）特征，这些特征可预测30天死亡率。 S.在我们的数据集中。我认为这些基因和临床特征 不同的进化枝，因为他们拥有不同的剧目的MGE。抗菌素耐药性中心 和微生物基因组学在得克萨斯大学健康科学中心在休斯敦有一个坚定的承诺 了解和减少AMR和AMR感染。这提供了一个特殊的环境， 我的研究阐明了MGE在S.金黄色葡萄球菌菌血症，并发展为 一名调查员，并参加为向独立过渡而提供的培训。