Regional discrepancy of microbial community structure in activated sludge system from Chinese WWTPs based on high-throughput 16S rDNA sequencing

Zhang, Jinsen; Li, Guohua; Qi, Wei; Лю, Шуай; Shao, Yuting; Zhang, Jingbing; Qi, Lu; Wang, Hongchen

doi:10.1016/j.scitotenv.2021.151751

Cited by 15 publications

(7 citation statements)

References 39 publications

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“…The predominant phylum among the four sludge samples was Proteobacteria, with relative abundances of 24.26-28.31% (Figure 2), which are of great significance to the global carbon, nitrogen, phosphorus, and sulfur cycles [22]. This is consistent with previous studies, indicat-ing that Proteobacteria are widely spread in full-scale WWTPs [2,15,17]. Proteobacteria have five major classes, including Alphaproteobacteria, Betaproteobacteria, Deltaproteobacteria, Gammaproteobacteria, and Epsilonproteobacteria, which were generally detected in the other WWTPs [18].…”

Section: Bacterial Community Structuresupporting

confidence: 89%

“…Secondly, it is universally accepted that comammox survive selectively over other nitrifiers under an oligotrophic lifestyle, such as low ammonium concentrations [36]. Another possible explanation is that unknown ammonia oxidizing bacteria contribute to ammonia oxidation [15]. NO 3 − -N removal is governed by heterotrophic denitrifiers, which are responsible for reducing nitrate and nitrite to nitrogen gas under anoxic conditions [18].…”

Section: Functional Predictive Analysismentioning

confidence: 99%

“…A thorough understanding of the microbial community in full-scale WWTPs is crucial to provide intelligent management for operating and optimizing the processing effect to meet the stricter discharge standards [14]. One hundred ten activated sludge samples from 21 cities in the north and south of China were investigated using high-throughput 16S ribosomal DNA (rDNA) sequencing to explore the regional effects on microbial community structure [15]. The Global Water Microbiome Consortium (GWMC) recently carried out systematic investigations on microbial communities and identified core microbial taxa of global WWTPs [2].…”

Section: Introductionmentioning

confidence: 99%

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Bacterial Community Composition and Function in a Tropical Municipal Wastewater Treatment Plant

Xiang

et al. 2022

Water

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Bacterial diversity and community composition are of great importance in wastewater treatment; however, little is known about the diversity and community structure of bacteria in tropical municipal wastewater treatment plants (WWTPs). Therefore, in this study, activated sludge samples were collected from the return sludge, anaerobic sludge, anoxic sludge, and aerobic sludge of an A2O WWTP in Haikou, China. Illumina MiSeq high-throughput sequencing was used to examine the 16S ribosomal RNA (rRNA) of bacteria in the samples. The microbial community diversity in this tropical WWTP was higher than in temperate, subtropical, and plateau WWTPs. Proteobacteria, Bacteroidota, Patescibacteria, and Chloroflexi were the dominant phyla. Nitrification bacteria Nitrosomonas, and Nitrospira were also detected. Tetrasphaera, instead of Candidatus Accumulibacter, were the dominant polyphosphate accumulating organisms (PAOs), while, glycogen accumulating organisms (GAOs), such as Candidatus Competibacter and Defluviicoccus were also detected. The bacterial community functions predicted by PICRUSt2 were related to metabolism, genetic information processing, and environmental information processing. This study provides a reference for the optimization of tropical municipal WWTPs.

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Section: Bacterial Community Structuresupporting

confidence: 89%

Section: Functional Predictive Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Bacterial Community Composition and Function in a Tropical Municipal Wastewater Treatment Plant

Xiang

et al. 2022

Water

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“…Important factors that cannot be identified using traditional methods may be highlighted by ANN modeling. Conventional studies on AS systems have only focused on the correlation relationship between environmental factors and microbial communities(16, 17, 47), which limited the scope of consideration for key environmental factors. For example, some previous studies had shown apparent differences between the microbial communities of industrial sewage and municipal sewage(14, 48), which showed that the IndConInf variable might impact the AS system’s microbial community (49).…”

Section: Discussionmentioning

confidence: 99%

Predicting microbial community compositions in wastewater treatment plants using artificial neural networks

Liu

Nie

2022

Preprint

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Activated sludge (AS) of wastewater treatment plants (WWTP) is one of the world's largest artificial microbial ecosystems and the microbial community of the AS system is closely related to WWTP performance. However, how to predict its community structure is still unclear. Here, we used artificial neural networks (ANN) to predict the microbial compositions of AS systems collected from WWTPs located worldwide. We demonstrated that the microbial compositions of AS systems are predictable using our approach. The predictive accuracy R21:1 of Shannon-Wiener index reached 60.42%, and the average R21:1 of ASVs appearing in at least 10% of samples (ASVs>10%) and core taxa were 35.09% and 42.99%, respectively. We also found that the predictability of ASVs>10% was significantly positively correlated with their relative abundance and occurrence frequency, but significantly negatively correlated with potential migration rate. The typical functional groups such as nitrifiers, denitrifiers, polyphosphate-accumulating organisms (PAOs) and glycogen-accumulating organisms (GAOs), and filamentous organisms in AS systems could also be well recovered using an ANN model, with the R21:1 ranging from 32.62% to 56.81%. Furthermore, we found that industry wastewater source (IndConInf) had good predictive abilities, although its correlation with ASVs>10% in the Mantel test analysis was weak, which suggested important factors that cannot be identified using traditional methods may be highlight by the ANN model. Our results provide a better understanding of the factors affecting AS communities through the prediction of the microbial community of AS systems, which could lead to insights for improved operating parameters and control of community structure.

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“…Important factors that cannot be identi ed using traditional methods may be highlighted by ANN modeling. Conventional studies on AS systems have only focused on the correlation relationship between environmental factors and microbial communities (16,17,47), which limited the scope of consideration for key environmental factors. For example, some previous studies had shown apparent differences between the microbial communities of industrial sewage and municipal sewage (14,48), which showed that the IndConInf variable might impact the AS system's microbial community (49).…”

Section: Datasetsmentioning

confidence: 99%

Predicting microbial community compositions in wastewater treatment plants using artificial neural networks

Liu

Nie

2022

Preprint

View full text Add to dashboard Cite

Background: Activated sludge (AS) of wastewater treatment plants (WWTP) is one of the world’s largest artificial microbial ecosystems and the microbial community of the AS system is closely related to WWTP performance. However, how to predict its community structure is still unclear. Results: Here, we used artificial neural networks (ANN) to predict the microbial compositions of AS systems collected from WWTPs located worldwide. The predictive accuracy R21:1 of Shannon-Wiener index reached 60.42%, and the average R21:1 of amplicon sequence variants (ASVs) appearing in at least 10% of samples and core taxa were 35.09% and 42.99%, respectively. We also found that the predictability of ASVs was significantly positively correlated with their relative abundance and occurrence frequency, but significantly negatively correlated with potential migration rate. The typical functional groups such as nitrifiers, denitrifiers, polyphosphate-accumulating organisms (PAOs), glycogen-accumulating organisms (GAOs), and filamentous organisms in AS systems could also be well recovered using ANN models, with R21:1 ranging from 32.62% to 56.81%. Furthermore, we found that industry wastewater source (IndConInf) had good predictive abilities, although its correlation with ASVs in the Mantel test analysis was weak, which suggested important factors that cannot be identified using traditional methods may be highlight by the ANN model. Conclusions: We demonstrated that the microbial compositions and major functional groups of AS systems are predictable using our approach, and IndConInf has a significant impact on the prediction. Our results provide a better understanding of the factors affecting AS communities through the prediction of the microbial community of AS systems, which could lead to insights for improved operating parameters and control of community structure.

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Regional discrepancy of microbial community structure in activated sludge system from Chinese WWTPs based on high-throughput 16S rDNA sequencing

Cited by 15 publications

References 39 publications

Bacterial Community Composition and Function in a Tropical Municipal Wastewater Treatment Plant

Bacterial Community Composition and Function in a Tropical Municipal Wastewater Treatment Plant

Predicting microbial community compositions in wastewater treatment plants using artificial neural networks

Predicting microbial community compositions in wastewater treatment plants using artificial neural networks

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