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题名

Isolating and characterizing translationally active fraction of anammox microbiota using bioorthogonal non-canonical amino acid tagging

作者
通讯作者Wu,Ren'an; Xia,Yu
发表日期
2021-08-15
DOI
发表期刊
ISSN
1385-8947
卷号418
摘要

Deciphering active and inactive populations is crucial to the understanding of how microorganisms function in their native environment. In this study, we propose a rapid, sensitive and cost-effective approach to isolating and characterizing translationally active and inactive fractions of anammox consortia using a combination of bioorthogonal non-canonical amino acid tagging (BONCAT), fluorescence activated cell sorting (FACS) and 16S rRNA gene sequencing methods. The approach can detect translationally active anammox bacteria as early as after 8 h incubation in the presence of 50 µM homopropargylglycine (HPG). Overall, the relative abundance of translationally active taxa obtained by BONCAT displayed good correlation with measurement from RNA-based method, while the active community structure of the two methods was distinct from each other. Integration of the two approaches will be particularly useful for providing insight into the active members of microbial communities. Based on BONCAT-method, we firstly report that about 70% cells of anammox biomass were translationally active, and the community structure of the active fraction is significantly distinct from that of the inactive fraction. The fold-change differences between translationally active and inactive fractions demonstrated population-wide and taxon-specific translational heterogeneity within anammox consortia. Surprisingly, more than a quarter of anammox bacteria were translationally inactive even in suitable habitat, which may be attributed to the fiercely intraspecies competition when density-dependent resources become limited. Together, our BONCAT application offers new insight into the ecophysiology of anammox consortia and lays the foundation for expanding the approach into other biological wastewater treatment systems.

关键词
相关链接[Scopus记录]
收录类别
SCI ; EI
语种
英语
学校署名
第一 ; 通讯
WOS记录号
WOS:000655662700005
EI入藏号
20211310134034
EI主题词
Amino acids ; Bacteria ; Biological water treatment ; Cost effectiveness ; Flow cytometry ; Genes ; Nitrogen removal ; RNA
EI分类号
Water Treatment Techniques:445.1 ; Industrial Wastes Treatment and Disposal:452.4 ; Biological Materials and Tissue Engineering:461.2 ; Optical Devices and Systems:741.3 ; Organic Compounds:804.1 ; Industrial Economics:911.2 ; Optical Variables Measurements:941.4
ESI学科分类
ENGINEERING
Scopus记录号
2-s2.0-85103013002
来源库
Scopus
引用统计
被引频次[WOS]:6
成果类型期刊论文
条目标识符http://sustech.caswiz.com/handle/2SGJ60CL/222601
专题工学院_环境科学与工程学院
工学院
作者单位
1.School of Environmental Science and Engineering,College of Engineering,Southern University of Science and Technology,Shenzhen,518055,China
2.Laboratory of High-Resolution Mass Spectrometry Technologies,Dalian Institute of Chemical Physics,Chinese Academy of Sciences,Dalian,116023,China
3.State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control,School of Environmental Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
4.Guangdong Provincial Key Laboratory of Soil and Groundwater Pollution Control,School of Environmental Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
第一作者单位环境科学与工程学院;  工学院
通讯作者单位环境科学与工程学院;  工学院
第一作者的第一单位环境科学与工程学院;  工学院
推荐引用方式
GB/T 7714
Chen,Liming,Zhao,Bixi,Li,Xiang,et al. Isolating and characterizing translationally active fraction of anammox microbiota using bioorthogonal non-canonical amino acid tagging[J]. CHEMICAL ENGINEERING JOURNAL,2021,418.
APA
Chen,Liming,Zhao,Bixi,Li,Xiang,Cheng,Zhanwen,Wu,Ren'an,&Xia,Yu.(2021).Isolating and characterizing translationally active fraction of anammox microbiota using bioorthogonal non-canonical amino acid tagging.CHEMICAL ENGINEERING JOURNAL,418.
MLA
Chen,Liming,et al."Isolating and characterizing translationally active fraction of anammox microbiota using bioorthogonal non-canonical amino acid tagging".CHEMICAL ENGINEERING JOURNAL 418(2021).
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