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: 10517284
• 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 2024-10-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10517284
• 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; Predisposition; Prevention; Public Health; Research; Resistance; Risk; Sampling; Shotguns; Structure; Surveillance Methods; System; Techniques; Time; Translating; United States Department of Agriculture; Update; Work; antibiotic resistant infections; antimicrobial; bacterial resistance; 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 抗生素耐药性是一个重要的公共卫生问题。估计耐药病原体感染 导致每年住院天数比预计住院天数增加 800 万天 易感物质感染。正在审查抗生素的使用（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——特别是当疫情爆发时 更加频繁和严重。然而，科学证据描述了与抗生素使用相关的危害 由于无法量化这些做法的风险而缺乏。阐明这种风险的一个有希望的途径是 使用鸟枪法宏基因组学来识别通过系统时空采集的样本中的 AMR 基因 监视。这项工作的目标是开发算法，为 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，研究小组将开发理论基础和应用方法。 ods 需要通过使用鸟枪法宏基因组学来研究 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，我们将创造新颖的方法 以某种方式创建、压缩、重建和更新宏基因组数据的非常大的 de Bruijn 图 需要研究AMR。此外，我们将通过提出新的方法，通过长读数据开创 AMR 研究 使用数据的算法问题和解决方案。例如，识别特定基因的位置 使用长读数据的宏基因组样本尚未被提出或研究。因此，算法思想和 该项目开发的技术不仅将推进 AMR 的研究，还将为不断发展的领域做出贡献 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得出以下结论：（1）与抗微生物药物相关的公共风险 农业双重用途； (2) 用于减少耐药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

项目成果

KARGAMobile: Android app for portable, real-time, easily interpretable analysis of antibiotic resistance genes via nanopore sequencing.

• DOI: 10.3389/fbioe.2022.1016408
• 发表时间: 2022
• 期刊: FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY
• 影响因子: 5.7
• 作者: Barquero, Alexander;Marini, Simone;Boucher, Christina;Ruiz, Jaime;Prosperi, Mattia
• 通讯作者: Prosperi, Mattia

Evaluating the Diagnostic Paradigm for Group A and Non-Group A Streptococcal Pharyngitis in the College Student Population.

• DOI: 10.1093/ofid/ofab482
• 发表时间: 2021-11
• 期刊: Open forum infectious diseases
• 影响因子: 4.2
• 作者: Rich SN;Prosperi M;Klann EM;Codreanu PT;Cook RL;Turley MK
• 通讯作者: Turley MK

Exploring Prediction of Antimicrobial Resistance Based on Protein Solvent Accessibility Variation.

探索基于蛋白质溶剂可及性变化的抗菌耐药性的预测。

• DOI: 10.3389/fgene.2021.564186
• 发表时间: 2021
• 期刊: Frontiers in genetics
• 影响因子: 3.7
• 作者: Marini S;Oliva M;Slizovskiy IB;Noyes NR;Boucher C;Prosperi M
• 通讯作者: Prosperi M

Quantifying Health Outcome Disparity in Invasive Methicillin-Resistant Staphylococcus aureus Infection using Fairness Algorithms on Real-World Data

使用真实世界数据的公平算法量化侵袭性耐甲氧西林金黄色葡萄球菌感染的健康结果差异

• 发表时间: 2023
• 期刊: Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
• 作者: Inyoung Jun;Sara Ser;Scott A. Cohen;Jie Xu;Robert J. Lucero;Jiang Bian;M. Prosperi
• 通讯作者: M. Prosperi

Development of a Prediction Model for Antibiotic-Resistant Urinary Tract Infections Using Integrated Electronic Health Records from Multiple Clinics in North-Central Florida.

• DOI: 10.1007/s40121-022-00677-x
• 发表时间: 2022-10
• 期刊: INFECTIOUS DISEASES AND THERAPY
• 影响因子: 5.4
• 作者: Rich, Shannan N.;Jun, Inyoung;Bian, Jiang;Boucher, Christina;Cherabuddi, Kartik;Morris, J. Glenn, Jr.;Prosperi, Mattia
• 通讯作者: Prosperi, Mattia