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.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
2秒前
2秒前
2秒前
搜集达人应助ZY采纳,获得10
2秒前
2秒前
许许完成签到,获得积分10
3秒前
来可追发布了新的文献求助10
4秒前
坡区小旋风完成签到,获得积分10
4秒前
orixero应助早123采纳,获得10
4秒前
光崽是谁发布了新的文献求助10
4秒前
4秒前
JamesPei应助wzj采纳,获得10
5秒前
hua发布了新的文献求助10
5秒前
RL发布了新的文献求助10
5秒前
5秒前
5秒前
6秒前
7秒前
7秒前
zxcvvbnm完成签到 ,获得积分10
7秒前
野原x之助发布了新的文献求助10
8秒前
adeno发布了新的文献求助10
8秒前
9秒前
kyJYbs发布了新的文献求助10
9秒前
赘婿应助mdmdd采纳,获得10
10秒前
10秒前
js110完成签到,获得积分20
10秒前
10秒前
光崽是谁完成签到,获得积分10
11秒前
YJL发布了新的文献求助200
11秒前
zzzyyyppp发布了新的文献求助10
12秒前
量子星尘发布了新的文献求助150
12秒前
JamesPei应助木瓜木瓜采纳,获得10
13秒前
柳月萍发布了新的文献求助10
13秒前
xxxxc完成签到,获得积分10
13秒前
Velvet完成签到,获得积分10
14秒前
张恒发布了新的文献求助10
14秒前
完美世界应助活力的兔子采纳,获得10
14秒前
14秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Zeolites: From Fundamentals to Emerging Applications 1500
International Encyclopedia of Business Management 1000
Encyclopedia of Materials: Plastics and Polymers 1000
Architectural Corrosion and Critical Infrastructure 1000
Early Devonian echinoderms from Victoria (Rhombifera, Blastoidea and Ophiocistioidea) 1000
Hidden Generalizations Phonological Opacity in Optimality Theory 1000
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 内科学 生物化学 物理 计算机科学 纳米技术 遗传学 基因 复合材料 化学工程 物理化学 病理 催化作用 免疫学 量子力学
热门帖子
关注 科研通微信公众号,转发送积分 4933690
求助须知:如何正确求助?哪些是违规求助? 4201746
关于积分的说明 13054958
捐赠科研通 3975817
什么是DOI,文献DOI怎么找? 2178602
邀请新用户注册赠送积分活动 1194932
关于科研通互助平台的介绍 1106316