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Predicting antibiotic resistance gene abundance in activated sludge using shotgun metagenomics and machine learning

基因组 猎枪 丰度(生态学) 霰弹枪测序 抗生素 活性污泥 抗性(生态学) 化学 微生物学 基因 抗生素耐药性 环境科学 生物 生态学 环境工程 污水处理 生物化学 DNA测序
作者
Yuepeng Sun,Bertrand Clarke,Jennifer Clarke,Xu Li
出处
期刊:Water Research [Elsevier BV]
卷期号:202: 117384-117384 被引量:76
标识
DOI:10.1016/j.watres.2021.117384
摘要

• Metagenomic datasets on activated sludge were analyzed using random forests (RF's). • ARGs showed associations with abundant taxa, pathogens/indicators, and nitrifiers. • Individual pathogens/indicators exhibited positive relationships with select ARGs. • The RF's developed could predict the abundance of ARGs in a full-scale WWTP. • Coupling metagenomics and RF's offered a means to predict bacterial hosts of ARGs. 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 ( Candidatus Accumulibacter, Dechloromonas, Pesudomonas , and Thauera , etc.), (opportunistic) pathogens and indicators ( Bacteroides, Clostridium , and Streptococcus , etc.), and nitrifiers ( Nitrosomonas and Nitrospira , 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 < R 2 < 0.600) to strong (R 2 > 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 , and Streptococcus ) 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.
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