Developing Computational Methods for Surveillance of Antimicrobial Resistant Agents

项目摘要 抗生素耐药性是一个关键的公共卫生问题。估计耐药病原体感染 导致每年额外的800万住院日， 感染易感病原体。抗生素的使用（在临床和农业环境中） 作为这些感染的前兆，因此是一个主要的公共卫生问题，特别是当爆发成为 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证据 由于无法量化这些做法的风险，因此缺乏这方面的信息。阐明这种风险的一个有希望的途径是 使用鸟枪宏基因组学来识别通过系统时空分析获得的样本中的AMR基因， 监视这项工作的目标是开发算法，将提供这样一种手段， 分析.这些算法需要可扩展到非常大的数据集，因此需要开发 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， ods需要通过使用鸟枪宏基因组学来研究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。为了实现这种可扩展性，我们将创造新的手段， 创建、压缩、重建和更新非常大的de Bruijn图， 需要研究AMR。此外，我们将通过提出新的方法， 使用数据的算法问题和解决方案。例如，确定特定基因在一个 尚未提出或研究使用长读段数据的宏基因组学样品。因此，算法思想和 本项目开发的技术不仅将促进AMR的研究，而且有助于不断增长的领域 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）与抗微生物药物相关的公共风险， 农业中的细菌使用;（2）用于减少耐药细菌的干预措施的有效性，最后，（3） 这些因素使耐药细菌得以生长、茁壮成长和进化。 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