Investigating and Modeling the Regulation of Extracellular Antibiotic Resistance Gene Bioavailability by Naturally Occurring Nanoparticles

被引:14
作者
Chowdhury, Nadratun N. [1 ]
Hicks, Ethan [1 ]
Wiesner, Mark R. [1 ]
机构
[1] Duke Univ, Dept Civil & Environm Engn, Durham, NC 27708 USA
基金
美国国家科学基金会;
关键词
antimicrobial resistance; extracellular DNA; nanoparticles; particle aggregation; horizontal gene transfer; BACILLUS-SUBTILIS; WATER TREATMENT; PLASMID DNA; HUMIC ACIDS; TRANSFORMATION; DEGRADATION; PROTECTION; BINDING; SAND; MONTMORILLONITE;
D O I
10.1021/acs.est.2c02878
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Extracellular antibiotic resistance genes (eARGs) are widespread in the environment and can genetically transform bacteria. This work examined the role of environmentally relevant nanoparticles (NPs) in regulating eARG bioavailability. eARGs extracted from antibiotic-resistant B. subtilis were incubated with nonresistant recipient B. subtilis cells. In the mixture, particle type (either humic acid coated nanoparticles (HASNPs) or their micron-sized counterpart (HASPs)), DNase I concentration, and eARG type were systematically varied. Transformants were counted on selective media. Particles decreased bacterial growth and eARG bioavailability in systems without nuclease. When DNase I was present (>= 5 mu g/mL), particles increased trans-formation via chromosomal (but not plasmid-borne) eARGs. HASNPs increased transformation more than HASPs, indicating that the smaller nanoparticle with greater surface area per volume is more effective in increasing eARG bioavailability. These results were also modeled via particle aggregation theory, which represented eARG-bacteria interactions as transport leading to collision, followed by attachment. Using attachment efficiency as a fitting factor, the model predicted transformant concentrations within 35% of experimental data. These results confirm the ability of NPs to increase eARG bioavailability and suggest that particle aggregation theory may be a simplified and suitable framework to broadly predict eARG uptake.
引用
收藏
页码:15044 / 15053
页数:10
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