Synergizing Ecotoxicology and Microbiome Data Is Key for Developing Global Indicators of Environmental Antimicrobial Resistance

被引:0
|
作者
Makumbi, John P. [1 ,2 ]
Leareng, Samuel K. [2 ,3 ]
Pierneef, Rian E. [1 ]
Makhalanyane, Thulani P. [1 ,2 ,3 ]
机构
[1] Univ Pretoria, Dept Biochem Genet & Microbiol, Pretoria, South Africa
[2] Stellenbosch Univ, Ctr Epidem Response & Innovat, Sch Data Sci & Computat Thinking, Stellenbosch, South Africa
[3] Stellenbosch Univ, Fac Sci, Dept MicroBiol, Stellenbosch, South Africa
基金
新加坡国家研究基金会;
关键词
Antimicrobial resistance; Antimicrobial resistant bacteria (ARB); Antimicrobial resistance genes (ARGs); Bacteria; Microbiomes; Risk assessment; ANTIBIOTIC-RESISTANCE; SELECTION; HEALTH; RISK;
D O I
10.1007/s00248-024-02463-3
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
The One Health concept recognises the interconnectedness of humans, plants, animals and the environment. Recent research strongly supports the idea that the environment serves as a significant reservoir for antimicrobial resistance (AMR). However, the complexity of natural environments makes efforts at AMR public health risk assessment difficult. We lack sufficient data on key ecological parameters that influence AMR, as well as the primary proxies necessary for evaluating risks to human health. Developing environmental AMR 'early warning systems' requires models with well-defined parameters. This is necessary to support the implementation of clear and targeted interventions. In this review, we provide a comprehensive overview of the current tools used globally for environmental AMR human health risk assessment and the underlying knowledge gaps. We highlight the urgent need for standardised, cost-effective risk assessment frameworks that are adaptable across different environments and regions to enhance comparability and reliability. These frameworks must also account for previously understudied AMR sources, such as horticulture, and emerging threats like climate change. In addition, integrating traditional ecotoxicology with modern 'omics' approaches will be essential for developing more comprehensive risk models and informing targeted AMR mitigation strategies.
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页数:14
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