Differences in distributions, assembly mechanisms, and putative interactions of AOB and NOB at a large spatial scale

doi:10.1007/s11783-023-1722-0

Front. Environ. Sci. Eng.

2023, Vol. 17

Issue (10) : 122 https://doi.org/10.1007/s11783-023-1722-0

RESEARCH ARTICLE

Differences in distributions, assembly mechanisms, and putative interactions of AOB and NOB at a large spatial scale

Bing Zhang^1,², Chenxiang Sun², Huimin Lin¹, Wei Liu², Wentao Qin^2,³, Tan Chen¹, Ting Yang¹, Xianghua Wen²(

)

¹. College of Life and Environmental Sciences, Minzu University of China, Beijing 100081, China
². Environmental Simulation and Pollution Control State Key Joint Laboratory, School of Environment, Tsinghua University, Beijing 100084, China
³. Institute of Plant Protection, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China

Download: PDF(2791 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

● Nitrifiers in WWTP were investigated at large spatial scale.

● AOB populations varied greatly but NOB populations were similar among cities.

● Drift dominated both AOB and NOB assembling processes.

● DO did not show a significant effect on NOB.

● NOB tended to cooperate with AOB and non-nitrifying microorganisms.

Ammonia-oxidizing bacteria (AOB) and nitrite-oxidizing bacteria (NOB) play crucial roles in removing nitrogen from sewage in wastewater treatment plants (WWTPs) to protect water resources. However, the differences in ecological properties and putative interactions of AOB and NOB in WWTPs at a large spatial scale remain unclear. Hence, 132 activated sludge (AS) samples collected from 11 cities across China were studied by utilizing 16S rRNA gene sequencing technology. Results indicated that Nitrosomonas and Nitrosospira accounted for similar ratios of the AOB community and might play nearly equal roles in ammonia oxidation in AS. However, Nitrospira greatly outnumbered other NOB genera, with proportions varying from 94.7% to 99.9% of the NOB community in all WWTPs. Similar compositions and, hence, a low distance–decay turnover rate of NOB (0.035) across China were observed. This scenario might have partly resulted from the high proportions of homogenizing dispersal (~13%). Additionally, drift presented dominant roles in AOB and NOB assembling mechanisms (85.2% and 81.6% for AOB and NOB, respectively). The partial Mantel test illustrated that sludge retention time and temperature were the primary environmental factors affecting AOB and NOB communities. Network results showed that NOB played a leading role in maintaining module structures and node connections in AS. Moreover, most links between NOB and other microorganisms were positive, indicating that NOB were involved in complex symbioses with bacteria in AS.

Keywords Activated sludge Spatial distributions Microbial assembly Co-occurrence patterns Nitrifying bacteria

Corresponding Author(s): Xianghua Wen

About author:

*These authors equally shared correspondence to this manuscript.

Issue Date: 28 April 2023

Cite this article:

Bing Zhang,Chenxiang Sun,Huimin Lin, et al. Differences in distributions, assembly mechanisms, and putative interactions of AOB and NOB at a large spatial scale[J]. Front. Environ. Sci. Eng., 2023, 17(10): 122.

URL:

https://academic.hep.com.cn/fese/EN/10.1007/s11783-023-1722-0
https://academic.hep.com.cn/fese/EN/Y2023/V17/I10/122

Fig.1 Occurrence of nitrifying bacteria populations in WWTPs at different spatial scales. (a) AOB populations across China; (b) NOB populations across China; (c) compositions of the main AOB populations at the city level; (d) compositions of the main NOB populations at the city level. Abbreviations for the sampled cities: BJ for Beijing, DL for Dalian, HB for Harbin, JN for Jinan, QD for Qingdao, XA for Xi’an, CD for Chengdu, CQ for Chongqing, CS for Changsha, SZ for Shenzhen, and XM for Xiamen.

Fig.2 The distance–decay pattern of AOB and NOB across China. The blue dots and orange dots represent AOB and NOB, respectively. The blue line represents the linear regression for AOB (y = −0.0918x −0.2879; r = 0.25; P < 0.001). The orange line represents the linear regression for NOB (y = −0.0692x −0.4221; r = 0.13; P < 0.001).

Fig.3 Assembling processes of nitrifiers in AS. DR, HeS, HoS, DL, and HD represent drift, heterogeneous selection, homogeneous selection, dispersal limitation, and homogeneous dispersal, respectively.

Fig.4 Correlation analysis between environmental factors and the whole AOB/NOB community in AS based on partial Mantel test. Each line indicates a correlation between AOB/NOB and each environmental factor. Line width corresponds to the partial Mantel’s r value, and the edge color denotes the statistical significance. Pairwise correlations among these environmental factors are shown with a color gradient denoting Pearson’s correlation coefficient. TMLSS, temperature of AS; Cond., conductivity in the aeration tank; Inf., influent.

Fig.5 Ecological network in AS where nodes are colored based on taxonomy. The size of a node is scaled by its node degree. The red links between nodes indicate significant negative correlations, whereas the blue links between nodes indicate significant positive correlations.

1	Y Cao, M C Van Loosdrecht, G T Daigger. (2017). Mainstream partial nitritation-anammox in municipal wastewater treatment: status, bottlenecks, and further studies. Applied Microbiology and Biotechnology, 101(4): 1365–1383 https://doi.org/10.1007/s00253-016-8058-7
2	H Daims, S Lucker, M Wagner. (2016). A new perspective on microbes formerly known as nitrite-oxidizing bacteria. Trends in Microbiology, 24(9): 699–712 https://doi.org/10.1016/j.tim.2016.05.004
3	Y Deng, Y H Jiang, Y Yang, Z He, F Luo, J Zhou. (2012). Molecular ecological network analyses. BMC Bioinformatics, 13: 113 https://doi.org/10.1186/1471-2105-13-113
4	J Dolinšek, I Lagkouvardos, W Wanek, M Wagner, H Daims. (2013). Interactions of nitrifying bacteria and heterotrophs: identification of a micavibrio-like putative predator of Nitrospira spp. Applied and Environmental Microbiology, 79(6): 2027–2037 https://doi.org/10.1128/AEM.03408-12
5	R S Etienne, H Olff. (2004). A novel genealogical approach to neutral biodiversity theory. Ecology Letters, 7(3): 170–175 https://doi.org/10.1111/j.1461-0248.2004.00572.x
6	L J Gilarranz, B Rayfield, G Linan-Cembrano, J Bascompte, A Gonzalez. (2017). Effects of network modularity on the spread of perturbation impact in experimental metapopulations. Science, 357(6347): 199–201 https://doi.org/10.1126/science.aal4122
7	J L Green, A J Holmes, M Westoby, I Oliver, D Briscoe, M Dangerfield, M Gillings, A J Beattie. (2004). Spatial scaling of microbial eukaryote diversity. Nature, 432(7018): 747–750 https://doi.org/10.1038/nature03034
8	J S Griffin, G F Wells. (2017). Regional synchrony in full-scale activated sludge bioreactors due to deterministic microbial community assembly. ISME Journal, 11(2): 500–511 https://doi.org/10.1038/ismej.2016.121
9	C Gruber-Dorninger, M Pester, K Kitzinger, D F Savio, A Loy, T Rattei, M Wagner, H Daims. (2015). Functionally relevant diversity of closely related nitrospira in activated sludge. ISME Journal, 9(3): 643–655 https://doi.org/10.1038/ismej.2014.156
10	P Han, D Wu, D Sun, M Zhao, M Wang, T Wen, J Zhang, L Hou, M Liu, U Klumper, Y Zheng, H P Dong, X Liang, G Yin. (2021). N2O and NOy production by the comammox bacterium nitrospira inopinata in comparison with canonical ammonia oxidizers. Water Research, 190: 116728 https://doi.org/10.1016/j.watres.2020.116728
11	C Jiang, S Xu, R Wang, S Feng, S Zhou, S Wu, X Zeng, S Wu, Z Bai, G Zhuang, X Zhuang. (2019). Achieving efficient nitrogen removal from real sewage via nitrite pathway in a continuous nitrogen removal process by combining free nitrous acid sludge treatment and DO control. Water Research, 161: 590–600 https://doi.org/10.1016/j.watres.2019.06.040
12	J Johnston, T Lapara, S Behrens. (2019). Composition and dynamics of the activated sludge microbiome during seasonal nitrification failure. Scientific Reports, 9(1): 4565 https://doi.org/10.1038/s41598-019-40872-4
13	E R Jones, M T van Vliet, M Qadir, M F Bierkens. (2021). Country-level and gridded estimates of wastewater production, collection, treatment and reuse. Earth System Science Data, 13(2): 237–254 https://doi.org/10.5194/essd-13-237-2021
14	Y M Kim, H U Cho, D S Lee, D Park, J M Park. (2011). Influence of operational parameters on nitrogen removal efficiency and microbial communities in a full-scale activated sludge process. Water Research, 45(17): 5785–5795 https://doi.org/10.1016/j.watres.2011.08.063
15	G Liu, J Wang. (2013). Long-term low DO enriches and shifts nitrifier community in activated sludge. Environmental Science & Technology, 47(10): 5109–5117 https://doi.org/10.1021/es304647y
16	I D Ofiţeru, M Lunn, T P Curtis, G F Wells, C S Criddle, C A Francis, W T Sloan. (2010). Combined niche and neutral effects in a microbial wastewater treatment community. Proceedings of the National Academy of Sciences of the United States of America, 107(35): 15345–15350 https://doi.org/10.1073/pnas.1000604107
17	S Rahimi, O Modin, I Mijakovic. (2020). Technologies for biological removal and recovery of nitrogen from wastewater. Biotechnology Advances, 43: 107570 https://doi.org/10.1016/j.biotechadv.2020.107570
18	B E Rittmann, P L Mccarty (2001). Environmental Biotechnology: Principles and Applications. Boston: McGraw-Hill
19	D Seuntjens, M Han, F M Kerckhof, N Boon, A Al-Omari, I Takacs, F Meerburg, Mulder C De, B Wett, C Bott. et al.. (2018). Pinpointing wastewater and process parameters controlling the AOB to NOB activity ratio in sewage treatment plants. Water Research, 138: 37–46 https://doi.org/10.1016/j.watres.2017.11.044
20	S Siripong, B E Rittmann. (2007). Diversity study of nitrifying bacteria in full-scale municipal wastewater treatment plants. Water Research, 41(5): 1110–1120 https://doi.org/10.1016/j.watres.2006.11.050
21	J C Stegen, X J Lin, J K Fredrickson, X Y Chen, D W Kennedy, C J Murray, M L Rockhold, A Konopka. (2013). Quantifying community assembly processes and identifying features that impose them. ISME Journal, 7(11): 2069–2079 https://doi.org/10.1038/ismej.2013.93
22	C Sun, B Zhang, Z Chen, W Qin, X Wen. (2020). Sludge retention time affects the microbial community structure: a large-scale sampling of aeration tanks throughout China. Environmental Pollution, 261: 114140 https://doi.org/10.1016/j.envpol.2020.114140
23	C Sun, B Zhang, D Ning, Y Zhang, T Dai, L Wu, T Li, W Liu, J Zhou, X Wen. (2021). Seasonal dynamics of the microbial community in two full-scale wastewater treatment plants: diversity, composition, phylogenetic group based assembly and co-occurrence pattern. Water Research, 200: 117295 https://doi.org/10.1016/j.watres.2021.117295
24	L Wang, S Qiu, J Guo, S Ge. (2021). Light irradiation enables rapid start-up of nitritation through suppressing nxrb gene expression and stimulating ammonia-oxidizing bacteria. Environmental Science & Technology, 55(19): 13297–13305 https://doi.org/10.1021/acs.est.1c04174
25	X Wang, X Wen, Y Deng, Y Xia, Y Yang, J Zhou (2016). Is there a distance-decay relationship in biological wastewater treatment plants? Applied and Environmental Microbiology, 82(16): 4860–4866 https://doi.org/10.1128/AEM.01071-16
26	L Wu, D Ning, B Zhang, Y Li, P Zhang, X Shan, Q Zhang, M R Brown, Z Li, Nostrand J D Van. et al.. (2019). Global diversity and biogeography of bacterial communities in wastewater treatment plants. Nature Microbiology, 4(7): 1183–1195 https://doi.org/10.1038/s41564-019-0426-5
27	Y Xia, M Hu, X Wen, X Wang, Y Yang, J Zhou. (2016). Diversity and interactions of microbial functional genes under differing environmental conditions: insights from a membrane bioreactor and an oxidation ditch. Scientific Reports, 6(1): 18509 https://doi.org/10.1038/srep18509
28	T Ya, Z Wang, J Liu, M Zhang, L Zhang, X Liu, Y Li, X Wang. (2023). Responses of microbial interactions to elevated salinity in activated sludge microbial community. Frontiers of Environmental Science & Engineering, 17(5): 60 https://doi.org/10.1007/s11783-023-1660-x
29	Q Yao, D C Peng. (2017). Nitrite oxidizing bacteria (NOB) dominating in nitrifying community in full-scale biological nutrient removal wastewater treatment plants. AMB Express, 7(1): 25 https://doi.org/10.1186/s13568-017-0328-y
30	B Zhang, D Ning, J D Van Nostrand, C Sun, Y Yang, J Zhou, X Wen. (2020a). Biogeography and assembly of microbial communities in wastewater treatment plants in China. Environmental Science & Technology, 54(9): 5884–5892 https://doi.org/10.1021/acs.est.9b07950
31	B Zhang, D Ning, J D Van Nostrand, C Sun, Y Yang, J Zhou, X Wen. (2021). The call for regional design code from the regional discrepancy of microbial communities in activated sludge. Environmental Pollution, 273: 116487 https://doi.org/10.1016/j.envpol.2021.116487
32	B Zhang, D Ning, Y Yang, J D Van Nostrand, J Zhou, X Wen. (2020b). Biodegradability of wastewater determines microbial assembly mechanisms in full-scale wastewater treatment plants. Water Research, 169: 115276 https://doi.org/10.1016/j.watres.2019.115276
33	B Zhang, T Yang, C Sun, X Wen. (2022). Drivers of microbial beta-diversity in wastewater treatment plants in China. Journal of Environmental Sciences (China), 115: 341–349 https://doi.org/10.1016/j.jes.2021.07.028
34	Y Zhao, H Wang, W Dong, Y Chang, G Yan, Z Chu, Y Ling, Z Wang, T Fan, C Li. (2020). Nitrogen removal and microbial community for the treatment of rural domestic sewage with low C/N ratio by A/O biofilter with Arundo donax as carbon source and filter media. Journal of Water Process Engineering, 37: 101509 https://doi.org/10.1016/j.jwpe.2020.101509
35	J Zhou, D Ning (2017). Stochastic community assembly: Does it matter in microbial ecology? Microbiology and Molecular Biology Reviews, 81(4): e00002–17 https://doi.org/10.1128/MMBR.00002-17

[1]

FSE-23022-OF-ZB_suppl_1

Download

[1]	Ziqi Zhao, Meng Li, Wansong Huang, Nuowei Guo, Qian Zhang. Evaluation of activated sludge properties’ changes in industrial-wastewater pre-treatment: role of residual aluminum hydrolyzed species with different polymerization degree[J]. Front. Environ. Sci. Eng., 2023, 17(6): 75-.
[2]	Tao Ya, Zhimin Wang, Junyu Liu, Minglu Zhang, Lili Zhang, Xiaojing Liu, Yuan Li, Xiaohui Wang. Responses of microbial interactions to elevated salinity in activated sludge microbial community[J]. Front. Environ. Sci. Eng., 2023, 17(5): 60-.
[3]	Hongcan Cui, Ronghua Xu, Zhong Yu, Yuanyuan Yao, Shaoqing Zhang, Fangang Meng. Tank-dependence of the functionality and network differentiation of activated sludge community in a full-scale anaerobic/anoxic/aerobic municipal sewage treatment plant[J]. Front. Environ. Sci. Eng., 2023, 17(3): 36-.
[4]	Xinrui Yuan, Kangping Cui, Yihan Chen, Shiyang Wu, Yao Zhang, Tong Liu. Response of antibiotic and heavy metal resistance genes to the co-occurrence of gadolinium and sulfamethoxazole in activated sludge systems[J]. Front. Environ. Sci. Eng., 2023, 17(12): 154-.
[5]	Weishuai Li, Jingang Huang, Zhuoer Shi, Wei Han, Ting Lü, Yuanyuan Lin, Jianfang Meng, Xiaobing Xu, Pingzhi Hou. Machine learning enabled prediction and process optimization of VFA production from riboflavin-mediated sludge fermentation[J]. Front. Environ. Sci. Eng., 2023, 17(11): 135-.
[6]	Tingting Zhu, Zhongxian Su, Wenxia Lai, Jiazeng Ding, Yufen Wang, Yingxin Zhao, Yiwen Liu. Evaluating the impact of sulfamethoxazole on hydrogen production during dark anaerobic sludge fermentation[J]. Front. Environ. Sci. Eng., 2023, 17(1): 7-.
[7]	Jingyang Luo, Shiyu Fang, Wenxuan Huang, Feng Wang, Le Zhang, Fang Fang, Jiashun Cao, Yang Wu, Dongbo Wang. New insights into different surfactants’ impacts on sludge fermentation: Focusing on the particular metabolic processes and microbial genetic traits[J]. Front. Environ. Sci. Eng., 2022, 16(8): 106-.
[8]	Tingwei Gao, Kang Xiao, Jiao Zhang, Wenchao Xue, Chunhai Wei, Xiaoping Zhang, Shuai Liang, Xiaomao Wang, Xia Huang. Techno-economic characteristics of wastewater treatment plants retrofitted from the conventional activated sludge process to the membrane bioreactor process[J]. Front. Environ. Sci. Eng., 2022, 16(4): 49-.
[9]	Ling Wang, Chunxue Yang, Sangeetha Thangavel, Zechong Guo, Chuan Chen, Aijie Wang, Wenzong Liu. Enhanced hydrogen production in microbial electrolysis through strategies of carbon recovery from alkaline/thermal treated sludge[J]. Front. Environ. Sci. Eng., 2021, 15(4): 56-.
[10]	Luman Zhou, Chengyang Wu, Yuwei Xie, Siqing Xia. Biogenic palladium prepared by activated sludge microbes for the hexavalent chromium catalytic reduction: Impact of relative biomass[J]. Front. Environ. Sci. Eng., 2020, 14(2): 27-.
[11]	Lanhe Zhang, Jing Zheng, Jingbo Guo, Xiaohui Guan, Suiyi Zhu, Yanping Jia, Jian Zhang, Xiaoyu Zhang, Haifeng Zhang. Effects of Al³⁺ on pollutant removal and extracellular polymeric substances (EPS) under anaerobic, anoxic and oxic conditions[J]. Front. Environ. Sci. Eng., 2019, 13(6): 85-.
[12]	Siqi Li, Min Zheng, Shuang Wu, Yu Xue, Yanchen Liu, Chengwen Wang, Xia Huang. The impact of ultrasonic treatment on activity of ammonia-oxidizing bacteria and nitrite-oxidizing bacteria in activated sludge[J]. Front. Environ. Sci. Eng., 2019, 13(6): 82-.
[13]	Yuanyuan Zhang, Masashi Kuroda, Shunsuke Arai, Fumitaka Kato, Daisuke Inoue, Michihiko Ike. Biological removal of selenate in saline wastewater by activated sludge under alternating anoxic/oxic conditions[J]. Front. Environ. Sci. Eng., 2019, 13(5): 68-.
[14]	Aoshuang Jing, Tao Liu, Xie Quan, Shuo Chen, Yaobin Zhang. Enhanced nitrification in integrated floating fixed-film activated sludge (IFFAS) system using novel clinoptilolite composite carrier[J]. Front. Environ. Sci. Eng., 2019, 13(5): 69-.
[15]	Yanqing Duan, Aijuan Zhou, Kaili Wen, Zhihong Liu, Wenzong Liu, Aijie Wang, Xiuping Yue. Upgrading VFAs bioproduction from waste activated sludge via co-fermentation with soy sauce residue[J]. Front. Environ. Sci. Eng., 2019, 13(1): 3-.

Viewed

Full text

Abstract

Cited

Shared

Discussed