{"title":"MnOxGeneTool:一个鉴定和定量Mn(II)氧化基因的综合工具,揭示Mn(II)氧化基因的系统发育多样性和环境驱动因素","authors":"Yuhan Wang, Zhengkai Pan, Yuyu Shi, Yaohui Bai, Jinsong Liang, Aijie Wang, Jiuhui Qu","doi":"10.1021/acs.est.5c01235","DOIUrl":null,"url":null,"abstract":"Manganese (Mn) oxides are crucial for degrading organic pollutants and driving biogeochemical cycles. Microorganisms drive Mn(II) oxidation, but traditional cultivation-dependent identification methods are inefficient and error-prone. To overcome these limitations, we developed MnOxGeneTool, a bioinformatics tool for identifying and quantifying Mn(II)-oxidizing genes from genomic and metagenomic data. MnOxGeneTool consists of three main components: (1) a curated database of known Mn(II)-oxidizing proteins and their homologues, (2) a hidden Markov models (HMMs) database derived from this protein data set, and (3) a computational pipeline that integrates bioinformatics tools (e.g., HMMER and BLASTX) to identify and quantify Mn(II)-oxidizing genes. We assessed the accuracy and sensitivity of these HMMs through cross-validation, demonstrating their effectiveness in identifying Mn(II)-oxidizing genes in bacterial genomes. Using MnOxGeneTool, we explored the phylogenetic diversity of Mn(II)-oxidizers and identified 824 bacterial genera containing Mn(II)-oxidizing genes, significantly expanding previous knowledge in this field. Additionally, we analyzed metagenomic data from various environments to explore environmental drivers of Mn(II)-oxidizing genes, identifying two potential drivers: oligotrophic conditions and alkaline environments. These findings enable targeted discovery of novel Mn(II)-oxidizers and genetic determinants through identification of their ecological niches and expression optima, thereby expanding MnOxGeneTool’s predictive coverage of uncatalogued Mn(II)-oxidizing proteins. By providing an innovative bioinformatics tool that enables efficient identification and quantification of Mn(II)-oxidizing genes from both genomic and metagenomic data, this study offers significant advancements in the research of biogenic Mn(II) oxidation. The tool is available at https://github.com/wyh19990121/MnOxGeneTool.","PeriodicalId":36,"journal":{"name":"环境科学与技术","volume":"31 1","pages":""},"PeriodicalIF":10.8000,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"MnOxGeneTool: A Comprehensive Tool for Identifying and Quantifying Mn(II)-Oxidizing Genes, Revealing Phylogenetic Diversity and Environmental Drivers of Mn(II)-Oxidizers\",\"authors\":\"Yuhan Wang, Zhengkai Pan, Yuyu Shi, Yaohui Bai, Jinsong Liang, Aijie Wang, Jiuhui Qu\",\"doi\":\"10.1021/acs.est.5c01235\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Manganese (Mn) oxides are crucial for degrading organic pollutants and driving biogeochemical cycles. Microorganisms drive Mn(II) oxidation, but traditional cultivation-dependent identification methods are inefficient and error-prone. To overcome these limitations, we developed MnOxGeneTool, a bioinformatics tool for identifying and quantifying Mn(II)-oxidizing genes from genomic and metagenomic data. MnOxGeneTool consists of three main components: (1) a curated database of known Mn(II)-oxidizing proteins and their homologues, (2) a hidden Markov models (HMMs) database derived from this protein data set, and (3) a computational pipeline that integrates bioinformatics tools (e.g., HMMER and BLASTX) to identify and quantify Mn(II)-oxidizing genes. We assessed the accuracy and sensitivity of these HMMs through cross-validation, demonstrating their effectiveness in identifying Mn(II)-oxidizing genes in bacterial genomes. Using MnOxGeneTool, we explored the phylogenetic diversity of Mn(II)-oxidizers and identified 824 bacterial genera containing Mn(II)-oxidizing genes, significantly expanding previous knowledge in this field. Additionally, we analyzed metagenomic data from various environments to explore environmental drivers of Mn(II)-oxidizing genes, identifying two potential drivers: oligotrophic conditions and alkaline environments. These findings enable targeted discovery of novel Mn(II)-oxidizers and genetic determinants through identification of their ecological niches and expression optima, thereby expanding MnOxGeneTool’s predictive coverage of uncatalogued Mn(II)-oxidizing proteins. By providing an innovative bioinformatics tool that enables efficient identification and quantification of Mn(II)-oxidizing genes from both genomic and metagenomic data, this study offers significant advancements in the research of biogenic Mn(II) oxidation. 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MnOxGeneTool: A Comprehensive Tool for Identifying and Quantifying Mn(II)-Oxidizing Genes, Revealing Phylogenetic Diversity and Environmental Drivers of Mn(II)-Oxidizers
Manganese (Mn) oxides are crucial for degrading organic pollutants and driving biogeochemical cycles. Microorganisms drive Mn(II) oxidation, but traditional cultivation-dependent identification methods are inefficient and error-prone. To overcome these limitations, we developed MnOxGeneTool, a bioinformatics tool for identifying and quantifying Mn(II)-oxidizing genes from genomic and metagenomic data. MnOxGeneTool consists of three main components: (1) a curated database of known Mn(II)-oxidizing proteins and their homologues, (2) a hidden Markov models (HMMs) database derived from this protein data set, and (3) a computational pipeline that integrates bioinformatics tools (e.g., HMMER and BLASTX) to identify and quantify Mn(II)-oxidizing genes. We assessed the accuracy and sensitivity of these HMMs through cross-validation, demonstrating their effectiveness in identifying Mn(II)-oxidizing genes in bacterial genomes. Using MnOxGeneTool, we explored the phylogenetic diversity of Mn(II)-oxidizers and identified 824 bacterial genera containing Mn(II)-oxidizing genes, significantly expanding previous knowledge in this field. Additionally, we analyzed metagenomic data from various environments to explore environmental drivers of Mn(II)-oxidizing genes, identifying two potential drivers: oligotrophic conditions and alkaline environments. These findings enable targeted discovery of novel Mn(II)-oxidizers and genetic determinants through identification of their ecological niches and expression optima, thereby expanding MnOxGeneTool’s predictive coverage of uncatalogued Mn(II)-oxidizing proteins. By providing an innovative bioinformatics tool that enables efficient identification and quantification of Mn(II)-oxidizing genes from both genomic and metagenomic data, this study offers significant advancements in the research of biogenic Mn(II) oxidation. The tool is available at https://github.com/wyh19990121/MnOxGeneTool.
期刊介绍:
Environmental Science & Technology (ES&T) is a co-sponsored academic and technical magazine by the Hubei Provincial Environmental Protection Bureau and the Hubei Provincial Academy of Environmental Sciences.
Environmental Science & Technology (ES&T) holds the status of Chinese core journals, scientific papers source journals of China, Chinese Science Citation Database source journals, and Chinese Academic Journal Comprehensive Evaluation Database source journals. This publication focuses on the academic field of environmental protection, featuring articles related to environmental protection and technical advancements.