Operationalizing wastewater-based surveillance of multidrug-resistant bacteria

实施基于废水的多重耐药细菌监测

• 批准号: 10449747
• 负责人: Medini Annavajhala
• 金额: $ 12.05万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-08 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10449747
• 关键词: Affect; Antibiotic Resistance; Antibiotic-resistant organism; Antibiotics; Antimicrobial Resistance; Biological Models; Biomedical Engineering; Bioreactors; Blood Circulation; Carbapenems; Cephalosporin Resistance; Cephalosporins; Chromatin; Clinical; Clinical Data; Collection; Communities; Data; Decision Making; Detection; Development; Disease Outbreaks; Early Diagnosis; Future; Gene Exchanges; Genetic; Genotype; Growth; Guidelines; Health Care Costs; Horizontal Gene Transfer; Hospitals; Individual; Infection; Link; Literature; Lung infections; Medical center; Metagenomics; Methods; Modeling; Multi-Drug Resistance; Multiple Bacterial Drug Resistance; Neighborhoods; Non-linear Models; Nosocomial pneumonia; Outcome; Patient Isolators; Patients; Pattern; Phylogenetic Analysis; Plants; Plug-in; Population; Population Heterogeneity; Population Surveillance; Prevalence; Public Health; Reporting; Research; Resistance; Risk; Risk Assessment; Sampling; Series; Stream; Surveillance Modeling; Target Populations; Techniques; Temperature; Testing; Time; Viral; bacterial resistance; base; carbapenem resistance; clinically relevant; cohort; cost; cost effective; data acquisition; design; epidemiological model; experimental study; gene interaction; gut colonization; high risk; improved; mathematical model; molecular marker; mortality; multi-drug resistant pathogen; novel; pathogen; pathogenic bacteria; pathogenic virus; patient population; pressure; prevent; primary outcome; research clinical testing; residence; resistance allele; resistance gene; resistance mechanism; sociodemographics; tool; transmission process; trend; wastewater epidemiology; wastewater samples; wastewater sampling; wastewater surveillance; wastewater testing

Multidrug-resistant organisms (MDRO) pose a significant risk to public health. Infections with MDRO are associated with high mortality rates and healthcare costs, particularly related to hospital-acquired pneumonia. Current approaches to control and prevent transmission of these pathogens focus primarily on clinical testing of infectious patient isolates. This is costly, labor-intensive, and fails to account for asymptomatic carriage. Wastewater testing can overcome many of the limitations posed by patient-based surveillance by enabling cost-effective population-level data acquisition, which can subsequently be used to model and forecast infectious outbreaks. To date, wastewater-based testing has been successfully used for surveillance of pathogenic viruses, but barriers remain in applying this approach to MDRO. While pathogenic bacteria and antibiotic resistance genes (ARGs) have been detected in wastewater treatment plants, several factors currently limit the utility and accuracy of wastewater as a marker for overall burden and diversity of antibiotic resistance. Here, we aim to better operationalize metagenomic wastewater-based epidemiology by understanding the dynamics of multidrug-resistant bacteria during wastewater flow, as well as the relationship between wastewater and clinical detection of MDRO. First, we will design wastewater MDRO model systems by constructing plug-flow reactors and testing the effects of flow parameters such as hydraulic retention time, pH, and temperature, as well as antibiotic pressure, on the prevalence and diversity of MDRO and ARG genotypes. This will account for dynamics in growth rates and potential ARG exchange across species along the wastewater flow, which could significantly affect the accuracy of wastewater-based surveillance models. These bioreactor model systems will enable future experiments testing conditions relevant to specific MDRO species or wastewater streams. In Aim 2, we will take advantage of our ongoing longitudinal wastewater sampling at a major hospital center and the surrounding community to correlate MDRO in wastewater with clinical MDRO and existing patient surveillance cohorts. Through chromatin-linked metagenomics and long- read sequencing we will elucidate phylogenetic links between MDRO in hospital and community wastewater with infectious patient isolates, and potential differences in evolutionary patterns of MDRO in patient versus wastewater collections. Lastly, in Aim 3 we will interrogate different approaches to wastewater-based epidemiological modeling to estimate MDRO burden in a given community. We will contrast linear and nonlinear additive regression models with dynamic mathematical modeling approaches. We will incorporate wastewater flow parameters and community sociodemographics as well as molecular biomarker data, as normalization factors to improve model accuracy. Risk assessment techniques will be applied to these wastewater models to inform development of future public health decision making tools. If successful, the results of this study would enable wastewater surveillance as a tool to inform targeted mitigation strategies to prevent the spread of antibiotic multidrug-resistance.

抗耐药的生物（MDRO）对公共卫生构成了重大风险。 MDRO感染是 与高死亡率和医疗保健成本有关，特别是与医院获得的肺炎有关。 当前控制和防止传播这些病原体的方法主要集中在临床测试上 传染病患者分离株。这是代价高昂的，劳动力密集的，无法解释无症状的运输。 废水测试可以克服基于患者的监视所带来的许多限制 具有成本效益的人群级数据获取，随后可以用于建模和预测 传染性暴发。迄今为止，基于废水的测试已成功用于监视 致病性病毒，但障碍仍在将这种方法应用于MDRO中。而致病细菌和 在废水处理厂已经检测到抗生素抗性基因（ARGS），这是几个因素 目前限制废水的效用和准确性，作为抗生素总体负担和多样性的标志 反抗。在这里，我们的目的是通过 了解废水流动期间多药耐药细菌的动力学以及关系 在废水和MDRO的临床检测之间。首先，我们将设计废水MDRO模型系统 通过构造插头流动反应堆并测试流参数的影响，例如液压保留时间， pH和温度以及抗生素压力，在MDRO和ARG的流行率和多样性上 基因型。这将说明增长率的动态和跨物种跨物种的潜在交流 废水流，这可能会严重影响基于废水的监视模型的准确性。 这些生物反应器模型系统将使未来的实验测试与特定MDRO相关的条件 物种或废水流。在AIM 2中，我们将利用我们正在进行的纵向废水 在一个主要的医院中心和周围社区取样，以将MDRO与废水相关联 临床MDRO和现有的患者监视队列。通过染色质连接的宏基因组学和长期 阅读测序我们将阐明医院和社区废水之间的MDRO之间的系统发育联系 具有感染性患者分离株以及患者MDRO进化模式的潜在差异 废水收集。最后，在AIM 3中，我们将询问基于废水的不同方法 流行病学建模以估计给定社区的MDRO负担。我们将对比线性和 具有动态数学建模方法的非线性添加回归模型。我们将合并 废水流参数和社区社会人口统计学以及分子生物标志物数据，作为 提高模型准确性的归一化因素。风险评估技术将应用于这些技术 废水模型为未来公共卫生决策工具的开发提供信息。如果成功， 这项研究的结果将使废水监视作为一种工具，以告知有针对性的缓解策略 防止抗生素多药的耐药性。