Predicting antibiotic resistance gene abundance in activated sludge using shotgun metagenomics and machine learning
Predicting antibiotic resistance gene abundance in activated sludge using shotgun metagenomics and machine learning
复制标题
使用鸟枪宏基因组学和机器学习预测活性污泥中抗生素抗性基因的丰度
DOI：
10.1016/j.watres.2021.117384
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发表时间：
2021
期刊：
影响因子：
12.8
通讯作者：
Li, Xu
中科院分区：
文献类型：
--
作者：
Sun, Yuepeng;Clarke, Bertrand;Clarke, Jennifer;Li, Xu
虽然污水处理厂（WWTPs）中活性污泥（AS）的微生物组在形成抗性组中起着至关重要的作用，但确定WWTPs中抗生素抗性基因（ARG）的潜在细菌宿主仍然具有挑战性。本研究的目的是探索使用机器学习方法，随机森林（RF），以确定从污水处理厂的活性污泥的宏基因组数据集的ARG和细菌类群之间的关联的强度的可行性。我们的研究结果表明，选择ARGs的丰度可以预测RF的使用丰富的属（EscheridatusAccumulibacter，Dechloromonas，Pesuelum，和Thauera等），（机会性）病原体和指示物（拟杆菌、梭菌和链球菌等），硝化菌（Nitrosomonas和Nitrospira等）作为解释变量。预测和观测到的ARGs丰度（Δ R（B）、泰特（O）、泰特（Q）等）之间的相关性当在测试数据集上验证时，范围从中等（0.400 < R2< 0.600）到强（R2> 0.600）。与属于其他两组的那些相比，（机会性）病原体和指示细菌组中的个体属与选择的ARG具有更积极的功能关系，这表明该组中的属（例如，拟杆菌属、梭菌属和链球菌属）可能是选择性ARG的宿主。此外，RF的（机会）病原体和指标作为解释变量被用来预测选择ARGs在一个全面的污水处理厂的丰度成功。诸如RF的机器学习方法可以潜在地识别ARG的细菌宿主，并揭示ARG与WWTP的AS中的微生物群落之间可能的功能关系。
While the microbiome of activated sludge (AS) in wastewater treatment plants (WWTPs) plays a vital role in shaping the resistome, identifying the potential bacterial hosts of antibiotic resistance genes (ARGs) in WWTPs remains challenging. The objective of this study is to explore the feasibility of using a machine learning approach, random forests (RF's), to identify the strength of associations between ARGs and bacterial taxa in metagenomic datasets from the activated sludge of WWTPs. Our results show that the abundance of select ARGs can be predicted by RF's using abundant genera (CandidatusAccumulibacter,Dechloromonas, Pesudomonas, andThauera, etc.), (opportunistic) pathogens and indicators (Bacteroides, Clostridium, andStreptococcus, etc.), and nitrifiers (NitrosomonasandNitrospira, etc.) as explanatory variables. The correlations between predicted and observed abundance of ARGs (erm(B),tet(O),tet(Q), etc.) ranged from medium (0.400 < R2< 0.600) to strong (R2> 0.600) when validated on testing datasets. Compared to those belonging to the other two groups, individual genera in the group of (opportunistic) pathogens and indicator bacteria had more positive functional relationships with select ARGs, suggesting genera in this group (e.g.,Bacteroides, Clostridium, andStreptococcus) may be hosts of select ARGs. Furthermore, RF's with (opportunistic) pathogens and indicators as explanatory variables were used to predict the abundance of select ARGs in a full-scale WWTP successfully. Machine learning approaches such as RF's can potentially identify bacterial hosts of ARGs and reveal possible functional relationships between the ARGs and microbial community in the AS of WWTPs.