Developing Computational Methods for Surveillance of Antimicrobial Resistant Agents

开发监测抗菌药物的计算方法

• 批准号: 10053321
• 负责人: Christina Boucher
• 金额: $ 42.23万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-11-26 至 2023-10-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10053321
• 关键词: Affect; Agriculture; Algorithms; Antibiotic Resistance; Antibiotics; Antimicrobial Resistance; Attention; Bacteria; Base Pairing; Big Data; Bioinformatics; Clinical; Collaborations; Combating Antibiotic Resistant Bacteria; Computing Methodologies; DNA; Data; Data Analyses; Data Compression; Data Set; Development; Disease Outbreaks; Effectiveness of Interventions; Florida; Food production; Foundations; Genes; Genomics; Goals; Graph; Hospitalization; Infection; International; Investigation; Length; Location; Measures; Memory; Metagenomics; Methods; Monitor; Noise; Organism; Pathogenicity; Plasmids; Prevention; Public Health; Research; Resistance; Risk; Sampling; Shotguns; Structure; Surveillance Methods; System; Techniques; Time; Translating; Update; Work; antibiotic resistant infections; bacterial resistance; base; combinatorial; drug resistant pathogen; effective intervention; foodborne outbreak; genetic variant; hazard; improved; large datasets; machine learning algorithm; method development; microbial; microbiome analysis; microbiome research; multiple datasets; novel; pathogen; petabyte; reconstruction; research and development; resistance gene; spatiotemporal; standard care

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

项目摘要 抗菌素抵抗是一个关键的公共卫生问题。估计具有耐药病原体的感染 每年还会导致住院的日期再造成800万个住院日 易感药物的感染。正在查看抗生素（在临床和农业环境中） 作为这些感染的先驱，因此是一个主要的公共卫生问题，尤其是随着爆发的趋势 更频繁和严重。但是，描述与使用抗生素有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。承诺阐明这种风险的途径是 使用shot弹枪宏基因组学来识别通过系统时空的样品中的AMR基因 监视。这项拟议工作的目的是开发算法，以提供这种手段 分析。该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，调查团队将开发理论基础并应用MET- 通过使用shot弹枪宏基因组学研究AMR所需的OD。拟议工作的主要重点是 开发可以处理非常大数据集的算法。为了实现这种可伸缩性，我们将创建新颖的手段 创建，压缩，重建和更新以某种方式核能数据的非常大的de bruijn图 需要研究AMR。此外，我们将通过提出新的新数据来开拓AMR的研究 使用数据的算法问题和解决方案。例如，识别特异性基因在A中的位置 尚未提出使用长阅读数据的宏基因组学样本或研究。那，算法思想和 该项目中开发的技术不仅会推进对AMR的研究，还会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佛罗里达州农业和临床环境收集的样本。 对初步和新数据的分析将使我们可以包括（1）与抗小鼠相关的公共风险 在一致性中使用BIAL； （2）用于减少抗性细菌的干预措施的有效性，最后（3） 允许抗性细菌生长，繁殖和进化的因素。 A – 1