Machine learning assisted in situ synthesis of functional microbiomes towards sustainable wastewater treatment
Abstract Emerging contaminants (ECs) in wastewater pose significant threats to eco-systems, calling for upgrading of conventional wastewater treatment technologies to tackle this challenge. The in situ synthesis of functional microbiomes is a novel and promising method for sustainable control of ECs...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Communications Earth & Environment |
| Online Access: | https://doi.org/10.1038/s43247-025-02489-6 |
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| _version_ | 1849767241829056512 |
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| author | Shubo Zhang Sai Gong Ran Yin Jinfeng Wang Yanru Wang Hongqiang Ren |
| author_facet | Shubo Zhang Sai Gong Ran Yin Jinfeng Wang Yanru Wang Hongqiang Ren |
| author_sort | Shubo Zhang |
| collection | DOAJ |
| description | Abstract Emerging contaminants (ECs) in wastewater pose significant threats to eco-systems, calling for upgrading of conventional wastewater treatment technologies to tackle this challenge. The in situ synthesis of functional microbiomes is a novel and promising method for sustainable control of ECs. However, challenges remain due to complexity of the microbiome. We herein developed a machine learning-assisted framework for in situ synthetic microbiome that encompasses multidimensional key species identification, community-level quantification and assessment, and determination of optimal microhabitat conditions. Altogether 1068 activated sludge samples from 177 biological wastewater treatment processes (BWWTPs) across China were collected to validate framework. The framework enabled in situ synthesis of microbiomes, significantly enhancing abundance of functional species capable of degrading sulfamethoxazole, a representative EC. The optimal microhabitat conditions for synthesis were also elucidated. The framework together with the protocol developed in this study can be applied in many other scenarios for in situ synthesis of functional microbiomes with specific degradation capabilities. |
| format | Article |
| id | doaj-art-bbee9c9bb8424f3fa21f675f4aecb6c9 |
| institution | DOAJ |
| issn | 2662-4435 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Earth & Environment |
| spelling | doaj-art-bbee9c9bb8424f3fa21f675f4aecb6c92025-08-20T03:04:17ZengNature PortfolioCommunications Earth & Environment2662-44352025-07-016111110.1038/s43247-025-02489-6Machine learning assisted in situ synthesis of functional microbiomes towards sustainable wastewater treatmentShubo Zhang0Sai Gong1Ran Yin2Jinfeng Wang3Yanru Wang4Hongqiang Ren5State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing UniversityState Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing UniversityState Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing UniversityState Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing UniversityState Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing UniversityState Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing UniversityAbstract Emerging contaminants (ECs) in wastewater pose significant threats to eco-systems, calling for upgrading of conventional wastewater treatment technologies to tackle this challenge. The in situ synthesis of functional microbiomes is a novel and promising method for sustainable control of ECs. However, challenges remain due to complexity of the microbiome. We herein developed a machine learning-assisted framework for in situ synthetic microbiome that encompasses multidimensional key species identification, community-level quantification and assessment, and determination of optimal microhabitat conditions. Altogether 1068 activated sludge samples from 177 biological wastewater treatment processes (BWWTPs) across China were collected to validate framework. The framework enabled in situ synthesis of microbiomes, significantly enhancing abundance of functional species capable of degrading sulfamethoxazole, a representative EC. The optimal microhabitat conditions for synthesis were also elucidated. The framework together with the protocol developed in this study can be applied in many other scenarios for in situ synthesis of functional microbiomes with specific degradation capabilities.https://doi.org/10.1038/s43247-025-02489-6 |
| spellingShingle | Shubo Zhang Sai Gong Ran Yin Jinfeng Wang Yanru Wang Hongqiang Ren Machine learning assisted in situ synthesis of functional microbiomes towards sustainable wastewater treatment Communications Earth & Environment |
| title | Machine learning assisted in situ synthesis of functional microbiomes towards sustainable wastewater treatment |
| title_full | Machine learning assisted in situ synthesis of functional microbiomes towards sustainable wastewater treatment |
| title_fullStr | Machine learning assisted in situ synthesis of functional microbiomes towards sustainable wastewater treatment |
| title_full_unstemmed | Machine learning assisted in situ synthesis of functional microbiomes towards sustainable wastewater treatment |
| title_short | Machine learning assisted in situ synthesis of functional microbiomes towards sustainable wastewater treatment |
| title_sort | machine learning assisted in situ synthesis of functional microbiomes towards sustainable wastewater treatment |
| url | https://doi.org/10.1038/s43247-025-02489-6 |
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