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Neural network establishes co-occurrence links between transformation products of the contaminant and the soil microbiome.
Xiang, Yuhui; Yu, Yansong; Wang, Jiahui; Li, Weiwei; Rong, Yu; Ling, Haibo; Chen, Zhongbing; Qian, Yiguang; Han, Xiaole; Sun, Jie; Yang, Yuyi; Chen, Liang; Zhao, Chao; Li, Juying; Chen, Ke.
Afiliación
  • Xiang Y; Key Laboratory of Resources Conversion and Pollution Control of the State Ethnic Affairs Commission, College of Resources and Environmental Science, South-Central Minzu University, Wuhan 430074, PR China.
  • Yu Y; Key Laboratory of Resources Conversion and Pollution Control of the State Ethnic Affairs Commission, College of Resources and Environmental Science, South-Central Minzu University, Wuhan 430074, PR China.
  • Wang J; Key Laboratory of Resources Conversion and Pollution Control of the State Ethnic Affairs Commission, College of Resources and Environmental Science, South-Central Minzu University, Wuhan 430074, PR China.
  • Li W; Hubei Key Laboratory of Pollution Damage Assessment and Environmental Health Risk Prevention and Control, Hubei Provincial Academy of Eco-Environmental Sciences, Wuhan 430074, PR China.
  • Rong Y; Hubei Key Laboratory of Pollution Damage Assessment and Environmental Health Risk Prevention and Control, Hubei Provincial Academy of Eco-Environmental Sciences, Wuhan 430074, PR China.
  • Ling H; Hubei Key Laboratory of Pollution Damage Assessment and Environmental Health Risk Prevention and Control, Hubei Provincial Academy of Eco-Environmental Sciences, Wuhan 430074, PR China.
  • Chen Z; Department of Applied Ecology, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Praha 16500, Czech Republic.
  • Qian Y; Research Center for Environmental Ecology and Engineering, School of Environmental Ecology and Biological Engineering, Wuhan Institute of Technology, Wuhan 430205, PR China.
  • Han X; Key Laboratory of Resources Conversion and Pollution Control of the State Ethnic Affairs Commission, College of Resources and Environmental Science, South-Central Minzu University, Wuhan 430074, PR China.
  • Sun J; Key Laboratory of Resources Conversion and Pollution Control of the State Ethnic Affairs Commission, College of Resources and Environmental Science, South-Central Minzu University, Wuhan 430074, PR China.
  • Yang Y; Key Laboratory of Aquatic Botany and Watershed Ecology, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, PR China.
  • Chen L; Key Laboratory of Aquatic Botany and Watershed Ecology, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, PR China.
  • Zhao C; Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, PR China.
  • Li J; Shenzhen Key Laboratory of Environmental Chemistry and Ecological Remediation, College of Chemistry and Environmental Engineering, Shenzhen University, Shenzhen 518060, PR China. Electronic address: jyli@szu.edu.cn.
  • Chen K; Key Laboratory of Resources Conversion and Pollution Control of the State Ethnic Affairs Commission, College of Resources and Environmental Science, South-Central Minzu University, Wuhan 430074, PR China. Electronic address: kechen@mail.scuec.edu.cn.
Sci Total Environ ; 924: 171287, 2024 May 10.
Article en En | MEDLINE | ID: mdl-38423316
ABSTRACT
It remains challenging to establish reliable links between transformation products (TPs) of contaminants and corresponding microbes. This challenge arises due to the sophisticated experimental regime required for TP discovery and the compositional nature of 16S rRNA gene amplicon sequencing and mass spectrometry datasets, which can potentially confound statistical inference. In this study, we present a new strategy by combining the use of 2H-labeled Stable Isotope-Assisted Metabolomics (2H-SIAM) with a neural network-based algorithm (i.e., MMvec) to explore links between TPs of pyrene and the soil microbiome. The links established by this novel strategy were further validated using different approaches. Briefly, a metagenomic study provided indirect evidence for the established links, while the identification of pyrene degraders from soils, and a DNA-based stable isotope probing (DNA-SIP) study offered direct evidence. The comparison among different approaches, including Pearson's and Spearman's correlations, further confirmed the superior performance of our strategy. In conclusion, we summarize the unique features of the combined use of 2H-SIAM and MMvec. This study not only addresses the challenges in linking TPs to microbes but also introduces an innovative and effective approach for such investigations. Environmental Implication Taxonomically diverse bacteria performing successive metabolic steps of the contaminant were firstly depicted in the environmental matrix.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Suelo / Microbiota Idioma: En Revista: Sci Total Environ Año: 2024 Tipo del documento: Article Pais de publicación: Países Bajos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Suelo / Microbiota Idioma: En Revista: Sci Total Environ Año: 2024 Tipo del documento: Article Pais de publicación: Países Bajos