Operationalizing wastewater-based surveillance of multidrug-resistant bacteria

多药耐药生物(MDRO)对公众健康构成重大威胁。感染MDRO的人有 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。 感染性患者的分离株。这是昂贵的，劳动密集型的，而且没有考虑到无症状运输。 废水检测可以克服以患者为基础的监测带来的许多限制，方法是 成本效益高的人口一级数据获取，随后可用于建模和预测 传染性疾病的爆发。到目前为止，基于废水的检测已成功地用于监测 致病病毒，但在将这种方法应用于MDRO方面仍然存在障碍。而病原菌和 在污水处理厂中检测到抗生素耐药基因(Args)，有几个因素 目前限制了废水作为抗生素总体负担和多样性的标记的实用性和准确性 抵抗。在这里，我们的目标是通过以下方式更好地运作基于废水的元基因组学 了解耐多药细菌在污水流动过程中的动态及其相互关系 废水和临床检测MDRO之间的差异。首先，我们将设计废水MDRO模型系统 通过建造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 基因分型。这将解释生长速度的动态和潜在的跨物种ARG交换 污水流量，这可能会显著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 在主要医院中心和周围社区进行采样，以将废水中的MDRO与 临床MDRO和现有的患者监测队列。通过染色质连接的元基因组学和长- 阅读测序我们将阐明医院中的MDRO和社区污水之间的系统发育联系 与感染性患者分离株的差异，以及患者与 废水收集。最后，在目标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中的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。我们将把 污水流量参数和社区社会人口统计以及分子生物标记物数据，如 归一化系数，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发展提供信息。如果成功，则 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，以 防止抗生素多药耐药的传播。

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.