Developing Computational Methods for Surveillance of Antimicrobial Resistant Agents

项目摘要 抗菌素耐药性是一个严重的公共卫生问题。据估计，感染抗药性病原体 每年造成额外800万天的住院天数超过 易感病原体感染。抗生素的使用(在临床和农业环境中)正在被观察到 作为这些感染的前兆，因此是一个主要的公共卫生问题--特别是当疫情 更加频繁和严重。然而，描述与抗生素使用相关的危害的科学fic证据 由于无法量化这些做法的风险，因此缺乏。阐明这种风险的一个有希望的途径是 用鸟枪式元基因组学鉴定系统时空样本中的AMR基因 监视系统。这项拟议工作的目标是开发算法，以提供这样一种手段 分析。算法需要可扩展到非常大的数据集，因此，将需要开发 并使用简洁的数据结构。 为了实现这一目标，调查小组将发展理论基础和应用方法。 消耗臭氧层物质需要通过使用鸟枪式元基因组学来研究AMR。拟议工作的一个主要重点是 开发可以处理非常大的数据集的算法。为了实现这种可伸缩性，我们将创建新的方法 以一种方式创建、压缩、重建和更新非常大的De Bruijn图 需要研究AMR。此外，我们将通过长阅读数据，提出新的研究AMR的先锋 使用数据的算法问题和解决方案。例如，识别特定的fic基因在 使用长读取数据的宏基因组学样本还没有提出或研究过。因此，算法思想和 该项目开发的技术不仅将促进AMR的研究，而且将为不断增长的领域做出贡献 大数据分析和泛基因组学。 最后，我们计划将我们的方法应用于从佛罗里达州的农业和临床环境中收集的样本。 对初步数据和新数据的分析将使我们能够得出以下结论：(1)与抗微生物相关的公共风险- 在农业上的使用；(2)用于减少耐药细菌的干预措施的有效性，最后，(3) 使耐药细菌生长、繁衍和进化的因素。 A-1

PROJECT ABSTRACT Antimicrobial resistance is a critical public health issue. Infections with drug resistant pathogens are estimated to cause an additional eight million hospitalization days annually over the hospitalizations that would be seen for infections with susceptible agents. The use of antibiotics (in both clinical and agricultural settings) is being viewed as precursor for these infections and thus, is a major public health concern—particularly as outbreaks become more frequent and severe. However, scientific evidence describing the hazards associated with antibiotic use is lacking due to inability to quantify the risk of these practices. One promising avenue to elucidate this risk is to use shotgun metagenomics to identify the AMR genes in samples taken through systematic spatiotemporal surveillance. The goal of this proposed work is to develop algorithms that will provide such a means for analysis. The algorithms need to be scalable to very large datasets and thus, will require the development and use succinct data structures. In order to achieve this goal, the investigative team will develop the theoretical foundations and applied meth- ods needed to study AMR through the use of shotgun metagenomics. A major focus of the proposed work is developing algorithms that can handle very large datasets. To achieve this scalability, we will create novel means to create, compress, reconstruct and update very large de Bruijn graphs that metagenomics data in a manner needed to study AMR. In addition, we will pioneer the study of AMR through long read data by proposing new algorithmic problems and solutions that use data. For example, identifying the location of specific genes in a metagenomics sample using long read data has not been proposed or studied. Thus, the algorithmic ideas and techniques developed in this project will not only advance the study of AMR, but contribute to the growing domain of big data analysis and pan-genomics. Lastly, we plan to apply our methods to samples collected from both agricultural and clinical settings in Florida. Analysis of preliminary and new data will allow us to conclude about (1) the public risk associated with antimicro- bial use in agriculture; (2) the effectiveness of interventions used to reduce resistant bacteria, and lastly, (3) the factors that allow resistant bacteria to grow, thrive and evolve. A–1