AMRflows: antimicrobials and resistance from manufacturing flows to people: joined up experiments, mathematical modelling and risk analysis

AMRflows：抗菌剂和从制造流程到人类的耐药性：联合实验、数学建模和风险分析

• 批准号: NE/T013222/1
• 负责人: Jan-Ulrich Kreft
• 金额: $ 100.48万
• 依托单位: University of Birmingham
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FT013222%2F1
• 关键词: AMRflows; antimicrobials; resistance; manufacturing; flows

Antibiotics and other pharmaceuticals are released into rivers from multiple manufacturing sites at concentrations high enough to select for antibiotic resistance genes (ARGs). Such mixtures of antibiotics may select for new combinations of resistance genes, which is particularly concerning as this will further limit antibiotic treatment options. In addition, bacteria from treating manufacturing waste or domestic sewage and raw sewage entering rivers will mingle, facilitating horizontal gene transfer (HGT) of resistance genes carried on plasmids. However, the antibiotics will be diluted while being transported downstream, and some will be quickly degraded, and resistant bacteria may not survive so the question is how long is resistance selected and how long does it survive? Is resistance transmitted to other bacteria before they are lost? How far are resistant bacteria transport and what is the exposure of humans or livestock?In order to ask these questions, evaluate mitigation strategies and develop evidence-based global environmental standards, we will pursue a unique combined experimental and mathematical modelling programme including the following streams:(1) Measure concentrations of antibiotics and heavy metals, water chemistry, water levels and flow rates, water sediment exchange, abundance and diversity of antibiotic resistance genes and antibiotic-resistant bacteria. (2) Quantify transmission of resistance genes in bench-scale reactors. (3) Study selection in the river samples in bench-scale reactors under realistic, controlled conditions. (4) Study the risk of infection by resistant bacteria in tissue culture and Zebrafish laboratory models and the antibiotic dose required for treatment. (5) Build and test a mathematical model of antimicrobial resistance (AMR) dynamics on the small scale of a water sample, including degradation of antibiotics, growth and death of sensitive and resistant bacteria, selection of resistance as a function of antibiotic concentration, HGT of resistance. (6) Build and test a model of water flow for the river network; this will be on the large scale of rivers. (7) Combine the small-scale AMR dynamics and large-scale transport models into a model that can calculate the dilution of the compounds and track how long the chemicals and bacteria have been in the river water, sediments and floodplains and how far they spread to downstream populations and ecosystems.The combined model can evaluate whether interventions such as separate treatment of antibiotic manufacturing waste and domestic sewage would be effective in reducing resistance levels before putting this into practice. The environmental AMR pathways will be examined across two river systems. The Musi (Hyderabad) is more polluted with antibiotics than the Adyar (Chennai). Both are polluted by sewage. Their pollution flows to people via irrigation, drinking water production and spiritual cleansing. These rivers have phases of low flow with concentrated industrial waste and sewage and limited bacterial spread and high flows in the monsoon season, flooding communities with resistant bacteria.(8) Analyse the human health risks based on the predictions of the combined model and the experimental study in (4) and other information. The risk analysis will include the level of uncertainty in those risks and will contribute to the development of international environmental standards.These will be the two main outcomes to improve human, animal and environmental health, specifically (i) quantitative evidence for resistance (co)selection and transfer under in situ conditions in a more and less polluted river system and (ii) a truly novel combined AMR dynamics and transport modelling framework that can be used globally as a tool to track AMRflows.

抗生素和其他药物从多个生产基地释放到河流中，浓度高到足以选择抗生素抗性基因（ARG）。这种抗生素混合物可能会选择新的耐药基因组合，这特别令人担忧，因为这将进一步限制抗生素治疗选择。此外，来自处理生产废物或生活污水的细菌和进入河流的原污水将混合，促进质粒上携带的抗性基因的水平基因转移（HGT）。然而，抗生素在向下游运输的过程中会被稀释，有些会被迅速降解，耐药菌可能无法存活，所以问题是耐药性被选择多久，它能存活多久？耐药性是否会在其他细菌消失之前传播给它们？耐药细菌的传播距离有多远，人类或牲畜的暴露程度如何？为了提出这些问题、评估缓解策略并制定基于证据的全球环境标准，我们将实施一项独特的实验和数学建模相结合的计划，包括以下流程：（1）测量抗生素和重金属的浓度、水化学、水位和流速、水沉积物交换、抗生素耐药基因和抗生素耐药细菌的丰度和多样性。(2)量化实验室规模反应器中抗性基因的传播。(3)在实验室规模的反应器在现实的，受控的条件下，在河流样品的研究选择。(4)研究组织培养和斑马鱼实验室模型中耐药细菌感染的风险以及治疗所需的抗生素剂量。(5)在小规模水样中建立并测试抗菌素耐药性（AMR）动力学的数学模型，包括抗生素的降解、敏感和耐药细菌的生长和死亡、作为抗生素浓度的函数的耐药性选择、耐药性的HGT。(6)建立并测试一个河流网络的水流模型;这将是大规模的河流。(7)联合收割机将小规模AMR动力学和大规模运输模型结合成一个模型，该模型可以计算化合物的稀释度，并跟踪化学品和细菌在河水中的时间，该组合模型可以评估抗生素生产废物和生活污水的单独处理等干预措施是否有效，在付诸实践之前降低阻力水平。将在两个河流系统中检查环境AMR途径。穆西河（海得拉巴）比阿迪亚河（钦奈）受到的抗生素污染更严重。两者都被污水污染。他们的污染通过灌溉、饮用水生产和精神净化流向人们。这些河流有低流量阶段，集中的工业废物和污水，有限的细菌传播和季风季节的高流量，使社区充满抗药性细菌。(8)根据组合模型的预测和（4）中的实验研究等资料，分析人类健康风险。风险分析将包括这些风险的不确定程度，并将有助于制定国际环境标准，这将是改善人类、动物和环境健康的两个主要成果，具体而言，（i）在污染程度较高和较低的河流系统中原位条件下抗性（共）选择和转移的定量证据，以及（ii）一个真正新颖的AMR动态和运输建模框架，可在全球范围内用作跟踪AMR流量的工具。

项目成果

EMBRACE-WATERS statement: Recommendations for reporting of studies on antimicrobial resistance in wastewater and related aquatic environments.

Abrace-Waters声明：报告废水和相关水生环境中抗菌素耐药性研究的建议。

• DOI: 10.1016/j.onehlt.2021.100339
• 发表时间: 2021-12
• 期刊: One health (Amsterdam, Netherlands)
• 作者: Hassoun-Kheir N;Stabholz Y;Kreft JU;de la Cruz R;Dechesne A;Smets BF;Romalde JL;Lema A;Balboa S;García-Riestra C;Torres-Sangiao E;Neuberger A;Graham D;Quintela-Baluja M;Stekel DJ;Graham J;Pruden A;Nesme J;Sørensen SJ;Hough R;Paul M
• 通讯作者: Paul M

Towards a general model for predicting minimal metal concentrations co-selecting for antibiotic resistance plasmids

• DOI: 10.1101/2020.09.14.295766
• 发表时间: 2020-09
• 期刊: bioRxiv
• 作者: Sankalp Arya;Alexander Williams;S. Reina;C. Knapp;Jan-Ulrich Kreft;J. Hobman;D. Stekel

Exploring the impacts of physicochemical characteristics and heavy metals fractions on bacterial communities in four rivers.

探索理化特征和重金属组分对四条河流细菌群落的影响。

• DOI: 10.1016/j.jenvman.2022.116453
• 发表时间: 2023
• 期刊: Journal of environmental management
• 影响因子: 8.7
• 作者: Gupta S