Neural network establishes co-occurrence links between transformation products of the contaminant and the soil microbiome

• Published in: The Science of the total environment Vol. 924; p. 171287
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 10.05.2024
• Subjects: Co-occurrence network; Microbiome; Neural network; Organic contaminants; Stable isotope; Transformation products; Transformation products; SIAM; Organic contaminants; MS; Neural network; Co-occurrence network; DNA-SIP; HRMS; Stable isotope; UPLC; TPs; LC; m/z; ESI; GC; Microbiome
• ISSN: 0048-9697; 1879-1026
• DOI: 10.1016/j.scitotenv.2024.171287

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. [Display omitted] •A new strategy is proposed to link TPs of contaminants and microbes.•The strategy combines the use of 2H-SIAM, and a neural network-based algorithm.•The obtained links are reliable and confirmed by multidimensional evidence.•The strategy has unique features compared to the other approaches.