Operationalizing wastewater-based surveillance of multidrug-resistant bacteria

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相关，特别是与医院获得性肺炎相关。 目前控制和预防这些病原体传播的方法主要集中在临床检测上 感染性患者分离株。这是昂贵的，劳动密集型的，并且不能解释无症状携带。 废水检测可以克服基于患者的监测所带来的许多限制， 具有成本效益的人口数据采集，随后可用于建模和预测 传染病爆发迄今为止，基于废水的检测已成功用于监测 病原性病毒，但在将这种方法应用于MDRO方面仍然存在障碍。而致病菌和 抗生素抗性基因（ARGs）已在污水处理厂中检测到，几个因素 目前限制了废水作为抗生素总体负荷和多样性的标记的实用性和准确性 阻力在这里，我们的目标是更好地操作宏基因组废水为基础的流行病学， 了解废水流动过程中多药耐药细菌的动态，以及它们之间的关系， 废水和MDRO临床检测之间的联系首先，设计废水MDRO模型系统 通过构建活塞流反应器并测试流动参数如水力停留时间的影响， pH、温度以及抗生素压力对MDRO和ARG的患病率和多样性的影响 基因型这将解释生长率的动态和沿着物种间潜在的ARG交换 废水流量，这可能会大大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测试条件相关的具体MDRO 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 在一个主要的医院中心和周围社区取样，将废水中的MDRO与 临床MDRO和现有患者监测队列。通过染色质连接的宏基因组学和长- 我们将阐明医院和社区废水中MDRO之间的系统发育联系 感染性患者分离株，以及患者与 废水收集。最后，在目标3中，我们将探讨不同的废水处理方法。 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 非线性可加回归模型与动态数学建模方法。我们将合并 废水流量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， 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。

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.