Laboratory of Infectious Disease Epidemiology, KAUST Smart-Health Initiative and Biological and Environmental Science and Engineering (BESE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.
KAUST Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.
Bioinformatics. 2023 Jul 1;39(7). doi: 10.1093/bioinformatics/btad400.
Wastewater treatment plants (WWTPs) harbor a dense and diverse microbial community. They constantly receive antimicrobial residues and resistant strains, and therefore provide conditions for horizontal gene transfer (HGT) of antimicrobial resistance (AMR) determinants. This facilitates the transmission of clinically important genes between, e.g. enteric and environmental bacteria, and vice versa. Despite the clinical importance, tools for predicting HGT remain underdeveloped.
In this study, we examined to which extent water cycle microbial community composition, as inferred by partial 16S rRNA gene sequences, can predict plasmid permissiveness, i.e. the ability of cells to receive a plasmid through conjugation, based on data from standardized filter mating assays using fluorescent bio-reporter plasmids. We leveraged a range of machine learning models for predicting the permissiveness for each taxon in the community, representing the range of hosts a plasmid is able to transfer to, for three broad host-range resistance IncP plasmids (pKJK5, pB10, and RP4). Our results indicate that the predicted permissiveness from the best performing model (random forest) showed a moderate-to-strong average correlation of 0.49 for pB10 [95% confidence interval (CI): 0.44-0.55], 0.43 for pKJK5 (0.95% CI: 0.41-0.49), and 0.53 for RP4 (0.95% CI: 0.48-0.57) with the experimental permissiveness in the unseen test dataset. Predictive phylogenetic signals occurred despite the broad host-range nature of these plasmids. Our results provide a framework that contributes to the assessment of the risk of AMR pollution in wastewater systems.
The predictive tool is available as an application at https://github.com/DaneshMoradigaravand/PlasmidPerm.
污水处理厂 (WWTP) 中存在着密集多样的微生物群落。它们不断接收抗菌残留物和耐药菌株，因此为水平基因转移 (HGT) 的抗菌抗性 (AMR) 决定因素提供了条件。这促进了临床重要基因在肠道细菌和环境细菌之间的传播，反之亦然。尽管具有临床重要性，但预测 HGT 的工具仍未得到充分发展。
在这项研究中，我们研究了通过部分 16S rRNA 基因序列推断的水循环微生物群落组成，在多大程度上可以预测质粒允许性，即细胞通过共轭接受质粒的能力，这是基于使用荧光生物报告质粒进行标准化过滤交配实验的数据。我们利用一系列机器学习模型来预测群落中每个分类群的允许性，代表质粒能够转移到的宿主范围，针对三种广泛宿主范围的抗性 IncP 质粒 (pKJK5、pB10 和 RP4)。我们的结果表明，来自表现最佳模型（随机森林）的预测允许性与实验允许性在未见测试数据集之间表现出中等至强的平均相关性，pB10 为 0.49 [95%置信区间 (CI)：0.44-0.55]，pKJK5 为 0.43 (0.95% CI：0.41-0.49)，RP4 为 0.53 (0.95% CI：0.48-0.57)。尽管这些质粒具有广泛的宿主范围，但仍然存在预测的系统发育信号。我们的结果提供了一个框架，有助于评估废水系统中 AMR 污染的风险。
预测工具可作为应用程序在 https://github.com/DaneshMoradigaravand/PlasmidPerm 上使用。
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