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.

多重耐药微生物（MDRO）对公众健康构成重大风险。 MDRO 感染是 与高死亡率和医疗费用有关，特别是与医院获得性肺炎有关。 目前控制和预防这些病原体传播的方法主要集中在临床测试上 传染性患者分离株。这是成本高昂、劳动密集型的，而且无法考虑到无症状携带者。 废水检测可以克服基于患者的监测带来的许多限制，方法是： 具有成本效益的人口水平数据采集，随后可用于建模和预测 传染病爆发。迄今为止，基于废水的测试已成功用于监测 病原病毒，但将这种方法应用于 MDRO 仍然存在障碍。虽然致病菌和 在废水处理厂中检测到抗生素抗性基因（ARG），有几个因素 目前限制了废水作为抗生素总体负荷和多样性标记的效用和准确性 反抗。在这里，我们的目标是通过以下方式更好地实施基于宏基因组废水的流行病学 了解废水流动过程中多重耐药细菌的动态以及相互关系 废水与 MDRO 临床检测之间的关系。首先，我们将设计废水MDRO模型系统 通过构建推流反应器并测试水力停留时间等流动参数的影响， pH、温度以及抗生素压力对 MDRO 和 ARG 的流行率和多样性的影响 基因型。这将解释生长速率的动态以及物种间潜在的 ARG 交换 废水流量，这可能会显着影响基于废水的监测模型的准确性。 这些生物反应器模型系统将使未来的实验能够测试与特定 MDRO 相关的条件 物种或废水流。在目标 2 中，我们将利用我们正在进行的纵向废水处理 在主要医院中心和周边社区进行采样，将废水中的 MDRO 与 临床 MDRO 和现有患者监测队列。通过染色质相关的宏基因组学和长 读测序我们将阐明医院和社区废水中 MDRO 之间的系统发育联系 具有传染性的患者分离株，以及患者与患者之间 MDRO 进化模式的潜在差异 废水收集。最后，在目标 3 中，我们将探讨基于废水的不同方法 流行病学模型来估计特定社区的 MDRO 负担。我们将对比线性和 采用动态数学建模方法的非线性加性回归模型。我们将纳入 废水流参数和社区社会人口统计数据以及分子生物标志物数据， 标准化因子以提高模型精度。风险评估技术将应用于这些 废水模型为未来公共卫生决策工具的开发提供信息。如果成功的话， 这项研究的结果将使废水监测成为一种工具，为有针对性的缓解战略提供信息 防止抗生素多重耐药性的传播。