High density sampling reveals the spatiotemporal characteristics of microbial communities in a full-scale municipal wastewater treatment plant

Highlights

	● High density spatiotemporal sampling was adopted to investigate a municipal WWTP.
	● Spatially independent corridors showed high microbial community similarities.
	● Three distinct stable states of the microbial community were observed over a year.
	● Conserved function over microbial community succession was observed in the WWTP.

Abstract

Insights into the microbial communities in municipal wastewater treatment plants (WWTPs) are critical for the optimization of biological nutrient removal process. However, our understanding about the spatiotemporal characteristics of the microbial communities in WWTPs remains limited. In the present study, 264 samples were collected biweekly from four spatially independent corridors in a typical municipal WWTP. The annual compositional and metagenomic characteristics were investigated based on multiple ecological indicators using statistical tests. The results revealed that the microbial community compositions from the four corridors showed significantly high similarities, as revealed by the statistical analysis at the operational taxonomic unit (OTU) level. Consistent with the OTU level results, the functionality of the microbial communities in the four independent corridors also showed significant similarity. In comparison, the dynamics of the microbial community over the year showed two successional peaks of the microbial communities with the spatial similarity, and this resulted in three alternative stable states of the microbial communities in a calendar year. The microbial communities only drifted in July and November, suggesting an uneven community succession pattern driven by seasonal variation in environmental conditions. The functional characteristics were found to be relatively conservative compared to the microbial community succession, which revealed the decoupling between the composition and functionality of the microbial community in the municipal WWTP. The present study provides an in-depth overview of the microbial communities in a municipal WWTP and will be useful for the establishment of the connection between ecological characteristics and the operational stability of WWTPs.

Graphical abstract

Keywords

Activated sludge / Microbial community / Spatial similarity / Successional pattern / Metagenomics

References

[1] Anderson I, Abt B, Lykidis A, Klenk H P, Kyrpides N, Ivanova N. (2012). Genomics of aerobic cellulose utilization systems in Actinobacteria. PLoS One, 7(6): e39331

[2] BastianM H S, JacomyM (2009). Gephi: an open source software for exploring and manipulating networks. In: Proceedings of International AAAI Conference on Weblogs and Social Media. Menlo Park, California: Association for the Advancement of Artificial Intelligence

[3] Buchfink B, Reuter K, Drost H G. (2021). Sensitive protein alignments at tree-of-life scale using DIAMOND. Nature Methods, 18(4): 366–368

[4] Cao J, Zhang T, Wu Y, Sun Y, Zhang Y, Huang B, Fu B, Yang E, Zhang Q, Luo J. (2020). Correlations of nitrogen removal and core functional genera in full-scale wastewater treatment plants: Influences of different treatment processes and influent characteristics. Bioresource Technology, 297: 122455

[5] Chen G, Lin L, Wang Y, Zhang Z, Cao W, Zhang Y. (2023). Unveiling the interaction mechanisms of key functional microorganisms in the partial denitrification-anammox process induced by COD. Frontiers of Environmental Science & Engineering, 17(8): 103

[6] Chen S, Zhou Y, Chen Y, Gu J. (2018). FASTP: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics, 34(17): i884–i890

[7] Chen W, Wei J, Su Z, Wu L, Liu M, Huang X, Yao P, Wen D. (2022). Deterministic mechanisms drive bacterial communities assembly in industrial wastewater treatment system. Environment International, 168: 107486

[8] Cole J R, Wang Q, Fish J A, Chai B L, Mcgarrell D M, Sun Y N, Brown C T, Porras-Alfaro A, Kuske C R, Tiedje J M. (2014). Ribosomal Database Project: data and tools for high throughput rRNA analysis. Nucleic Acids Research, 42(D1): D633–D642

[9] Cui B, Zhang C, Fu L, Zhou D, Hou M. (2023). Current status of municipal wastewater treatment plants in North-east China: implications for reforming and upgrading. Frontiers of Environmental Science & Engineering, 17(6): 73

[10] Dottorini G, Wagner D S, Stokholm-Bjerregaard M, Kucheryavskiy S, Michaelsen T Y, Nierychlo M, Peces M, Williams R, Nielsen P H, Andersen K S, Nielsen P H. (2023). Full-scale activated sludge transplantation reveals a highly resilient community structure. Water Research, 229: 119454

[11] Edgar R C. (2013). UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nature Methods, 10(10): 996–998

[12] Fernández A, Huang S, Seston S, Xing J, Hickey R, Criddle C, Tiedje J. (1999). How stable is stable? Function versus community composition. Applied and Environmental Microbiology, 65(8): 3697–3704

[13] Fu L, Niu B, Zhu Z, Wu S, Li W. (2012). CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics, 28(23): 3150–3152

[14] Guo J, Li J, Chen H, Bond P L, Yuan Z. (2017). Metagenomic analysis reveals wastewater treatment plants as hotspots of antibiotic resistance genes and mobile genetic elements. Water Research, 123: 468–478

[15] Hyatt D, Chen G L, Locascio P F, Land M L, Larimer F W, Hauser L J. (2010). Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics, 11(1): 119

[16] Jiang X T, Ye L, Ju F, Li B, Ma L P, Zhang T. (2018a). Temporal dynamics of activated sludge bacterial communities in two diversity variant full-scale sewage treatment plants. Applied Microbiology and Biotechnology, 102(21): 9379–9388

[17] Jiang X T, Ye L, Ju F, Wang Y L, Zhang T. (2018b). Toward an intensive longitudinal understanding of activated sludge bacterial assembly and dynamics. Environmental Science & Technology, 52(15): 8224–8232

[18] Ju F, Zhang T. (2015). Bacterial assembly and temporal dynamics in activated sludge of a full-scale municipal wastewater treatment plant. ISME Journal, 9(3): 683–695

[19] Kanehisa M, Goto S. (2000). KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Research, 28(1): 27–30

[20] Kindaichi T, Yuri S, Ozaki N, Ohashi A. (2012). Ecophysiological role and function of uncultured Chloroflexi in an anammox reactor. Water Science and Technology, 66(12): 2556–2561

[21] Knoop S, Kunst S. (1998). Influence of temperature and sludge loading on activated sludge settling, especially on Microthrix parvicella. Water Science and Technology, 37(4−5): 27–35

[22] Li D H, Luo R B, Liu C M, Leung C M, Ting H F, Sadakane K, Yamashita H, Lam T W. (2016). MEGAHIT v1.0: a fast and scalable metagenome assembler driven by advanced methodologies and community practices. Methods, 102: 3–11

[23] Li R, Li Y, Kristiansen K, Wang J. (2008). SOAP: short oligonucleotide alignment program. Bioinformatics, 24(5): 713–714

[24] Louca S, Parfrey L W, Doebeli M. (2016). Decoupling function and taxonomy in the global ocean microbiome. Science, 353(6305): 1272–1277

[25] Louca S, Polz M F, Mazel F, Albright M B N, Huber J A, O’Connor M I, Ackermann M, Hahn A S, Srivastava D S, Crowe S A. . (2018). Function and functional redundancy in microbial systems. Nature Ecology & Evolution, 2(6): 936–943

[26] Luo J, Fang S, Huang W, Wang F, Zhang L, Fang F, Cao J, Wu Y, Wang D. (2022). New insights into different surfactants’ impacts on sludge fermentation: focusing on the particular metabolic processes and microbial genetic traits. Frontiers of Environmental Science & Engineering, 16(8): 106

[27] Philippot L, Griffiths B S, Langenheder S. (2021). Microbial community resilience across ecosystems and multiple disturbances. Microbiology and Molecular Biology Reviews, 85(2): e00026–20

[28] Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, Glöckner F. (2013). The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Research, 41(D1): D590–D596

[29] Shade A, Peter H, Allison S D, Baho D L, Berga M, Burgmann H, Huber D H, Langenheder S, Lennon J T, Martiny J B. . (2012). Fundamentals of microbial community resistance and resilience. Frontiers in Microbiology, 3: 417

[30] Tong J, Tang A, Wang H, Liu X, Huang Z, Wang Z, Zhang J, Wei Y, Su Y, Zhang Y. (2019). Microbial community evolution and fate of antibiotic resistance genes along six different full-scale municipal wastewater treatment processes. Bioresource Technology, 272: 489–500

[31] Wang Y, Ye J, Ju F, Liu L, Boyd J A, Deng Y, Parks D H, Jiang X, Yin X, Woodcroft B J. . (2021). Successional dynamics and alternative stable states in a saline activated sludge microbial community over 9 years. Microbiome, 9(1): 199

[32] Woo S G, Sewell H L, Criddle C S. (2022). Phylogenetic diversity of NO reductases, new tools for nor monitoring, and insights into N₂O production in natural and engineered environments. Frontiers of Environmental Science & Engineering, 16(10): 127

[33] Wu L, Ning D, Zhang B, Li Y, Zhang P, Shan X, Zhang Q, Brown M R, Li Z, Van Nostrand J D. . (2019). Global diversity and biogeography of bacterial communities in wastewater treatment plants. Nature Microbiology, 4(7): 1183–1195

[34] Xia Y, Wen X, Zhang B, Yang Y. (2018). Diversity and assembly patterns of activated sludge microbial communities: A review. Biotechnology Advances, 36(4): 1038–1047

[35] Yachi S, Loreau M. (1999). Biodiversity and ecosystem productivity in a fluctuating environment: the insurance hypothesis. Proceedings of the National Academy of Sciences of the United States of America, 96(4): 1463–1468

[36] Yang Y, Wang L, Xiang F, Zhao L, Qiao Z. (2020). Activated sludge microbial community and treatment performance of wastewater treatment plants in industrial and municipal zones. International Journal of Environmental Research and Public Health, 17(2): 436

[37] Yu J, Tang S N, Lee P K H. (2023). Universal dynamics of microbial communities in full-scale textile wastewater treatment plants and system prediction by machine learning. Environmental Science & Technology, 57(8): 3345–3356

[38] Zhang B, Sun C, Lin H, Liu W, Qin W, Chen T, Yang T, Wen X. (2023). Differences in distributions, assembly mechanisms, and putative interactions of AOB and NOB at a large spatial scale. Frontiers of Environmental Science & Engineering, 17(10): 122

[39] Zhang T, Shao M F, Ye L. (2012). 454 pyrosequencing reveals bacterial diversity of activated sludge from 14 sewage treatment plants. ISME Journal, 6(6): 1137–1147

[40] Zhu J J, Anderson P R. (2017). Effect of long-term successive storm flows on water reclamation plant resilience. Water Research, 111: 1–9

CRediT Authorship Contribution Statement

Zhaoyang Li: Data curation, methodology, validation, visualization, writing–original draft and editing. Liang Zhang: Conceptualization, funding acquisition, writing–review and editing, supervision, project administration. Jinghan Li: Investigation, data curation, visualization. Da Kang: Writing–review and editing. Jialin Li: Writing–review and editing. Shujun Zhang: resources, data curation, investigation. Xiaoyu Han: resources, data Curation. Bin Ma: Funding Acquisition, writing–review, project administration. Yongzhen Peng: Resource, Supervision, Project administration, Writing–Review.

Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (Grant Nos. 52122005 and U23A20675).

Conflict of Interests

Yongzhen Peng is Editorial Board Members of Frontiers of Environmental Science & Engineering. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Electronic Supplementary Material

Supplementary material is available in the online version of this article at https://doi.org/10.1007/s11783-025-1943-5 and is accessible for authorized users.

Open Access

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

RIGHTS & PERMISSIONS

2025 The Author(s) 2025. This article is published with open access at link.springer.com and journal.hep.com.cn