Correspondence/Rebuttal pubs.acs.org/est
Response to Comment on “Enhanced Long-Term Nitrogen Removal and Its Quantitative Molecular Mechanism in Tidal Flow Constructed Wetlands”
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gene expression level.3 We are thus comfortable stating that using DNA-based quantification to assess the succession of the community structure is preferable because this method provides a broader perspective on the macroscale mechanisms affecting long-term nitrogen removal. As the comments suggested, the 15N isotope and gas measurements are valuable tools for distinguishing anammox from denitrification; however, the partitioning of the nitrogen loss were beyond the scope of this study and are thus not addressed in this response. At the molecular level, the oxidation of ammonia to nitrite is catalyzed by ammonia monooxygenase (AMO), which is specifically regulated by the amoA gene.13 However, the complexity increases with system scaling-up and the oxidation of ammonia to nitrite is affected both by internal controls (e.g., the amoA gene) and environmental factors (e.g., inhibitors) in a large-scale system. As mentioned in the study by Zhi et al.,14 the negative correlation between the ammonia transformation rate and amoA/(nxrA + anammox) is due to the inhibitory effect of accumulated nitrite. This result indicates that nitrogen transformation processes are closely interwoven in complex environmental settings,15−17 and a broader perspective is essential for revealing their coupled effects on nitrogen removal rates on a large scale. Although abiotic processes may contribute to nitrogen removal, our previous work showed that ammonia adsorption is a transient process that occurs mainly at the initial stage (in the first 35 days) and contributes little to long-term nitrogen removal.18 Although vegetation is considered an important component in CW systems favoring microbial immobilization and enrichment,19 the plants in our study had no direct effect on ammonia and nitrate removal, as demonstrated by our two unplanted control systems (unpublished data). This result was likely due to the low planting density and the shallow rooting depth. Thus, the microbial process was the dominant mechanism for long-term nitrogen removal. In fact, 89−96% of nitrogen removal in wetland systems has been attributed to microbial reactions.20,21 This evidence and established correlations can be used to identify the direct roles of functional genes in governing long-term nitrogen removal. We understand the concern regarding sampling. Unfortunately, sampling remains a challenge because it is nearly impossible to quantify the representativeness of a sample unless the entire population is analyzed, which is typically costprohibitive and impractical. Therefore, it is difficult to reach definitive conclusions for questions such as the degree of representativity and the number microbial samples required for a heterogeneous biosystem. In practice, the sampling problem is normally addressed based on the research objective (the scale and the resolution) and previous experience. In this study, the microbial sampling was designed to provide temporal data for
e wish to thank Yi Chen and Jan Vymazal for their constructive comments. We would like to take this opportunity to clarify our research objective and methodology. Whether RNA-based techniques are more suitable than DNAbased techniques in studying the role of microbial communities in environmental systems depends on the specific application, the characteristics of the bioreactor system, and the trade-off between the cost and the amount of information provided by each method. It is generally true that the analysis of mRNA, which is synthesized in response to changing environmental conditions, is useful for identifying active microbes and assessing in situ metabolic activity.1 However, this mRNAbased approach must be used with caution because the assessment of the gene expression level is likely to underestimate the total functioning of a microbial community.2,3 This underestimation is partly due to the presence of dormant and deceased cells, which are unaccounted for in the gene expression analysis but may contribute to ecosystem functioning.4 Dormant cells are a significant repository for system functioning and have the potential to restart once they adapt to the environment.5 Deceased cells provide a source of nutrients for other living microorganisms that drive ecosystem processes.6 Another technical problem with mRNA-based techniques is that high-quality environmental mRNA is difficult to extract in sufficient quantities and degrades rapidly.7,8 DNAbased quantification, which accounts for all active, dormant, and deceased cells, is a robust alternative for measuring the total microbial activity for the given environmental conditions.9 In addition, it is often necessary to consider the scales at which microbes interact with their surrounding environment. The highly variable and short-lived mRNA is useful mainly for identifying functionally active microbes by quantifying the gene expression dynamic before and after the application of a specific stimulus and on a time scale of hours.10 This short exposure time is often essential to minimize shifts in the community structure and to maintain the genetic background. To date, mRNA-based quantification has been used mainly to investigate the transcriptional activity of amoA genes in response to shortterm perturbations such as nutrient amendments,11 loading rate changes,12 and operation alteration.9 In this study, we chose to investigate long-term nitrogen removal in a large-scale bioreactor (80 L) over a broader scope rather than focus on the transcriptional activity in one specific process (e.g., ammonia oxidation) on the molecular scale. Thus, the complete chain of nitrogen removal processes was investigated in specifically designed constructed wetlands (CWs) with the following characteristics: (1) the systems were operated with set procedures for 245 days at a constant feeding rate to achieve a stable environment for the succession of microbial community structures and to minimize fluctuations in the microbial activity level and (2) a relatively longer hydraulic cycle was employed to support an approximately steady-state © XXXX American Chemical Society
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DOI: 10.1021/acs.est.5b03928 Environ. Sci. Technol. XXXX, XXX, XXX−XXX
Environmental Science & Technology
Correspondence/Rebuttal
metatranscriptomic approach to identify gene expression dynamics during extracellular electron transfer. Nat. Commun. 2013, 4, 1601. (11) Wei, X.; Yan, T.; Hommes, N. G.; Liu, X.; Wu, L.; McAlvin, C.; Klotz, M. G.; Sayavedra-Soto, L. A.; Zhou, J.; Arp, D. J. Transcript profiles of Nitrosomonas europaea during growth and upon deprivation of ammonia and carbonate. FEMS Microbiol. Lett. 2006, 257, 76−83. (12) Kuo, D. H.-W.; Robinson, K. G.; Layton, A. C.; Meyers, A. J.; Sayler, G. S. Transcription levels (amoA mRNA-based) and population dominance (amoA gene-based) of ammonia-oxidizing bacteria. J. Ind. Microbiol. Biotechnol. 2010, 37 (7), 751−757. (13) Rotthauwe, J. H.; Witzel, K. P.; Liesack, W. The ammonia monooxygenase structural gene amoA as a functional marker: molecular fine-scale analysis of natural ammonia-oxidizing populations. Appl. Environ. Microb. 1997, 63 (12), 4704−4712. (14) Zhi, W.; Yuan, L.; Ji, G.; He, C. Enhanced Long-Term Nitrogen Removal and Its Quantitative Molecular Mechanism in Tidal Flow Constructed Wetlands. Environ. Sci. Technol. 2015, 49 (7), 4575− 4583. (15) Ji, G.; Zhi, W.; Tan, Y. Association of nitrogen micro-cycle functional genes in subsurface wastewater infiltration systems. Ecol. Eng. 2012, 44, 269−277. (16) Ji, G.; Wang, R.; Zhi, W.; Liu, X.; Kong, Y.; Tan, Y. Distribution patterns of denitrification functional genes and microbial floras in multimedia constructed wetlands. Ecol. Eng. 2012, 44, 179−188. (17) Li, L.; He, C.; Ji, G.; Zhi, W.; Sheng, L. Nitrogen removal pathways in a tidal flow constructed wetland under flooded time constraints. Ecol. Eng. 2015, 81, 266−271. (18) Zhi, W.; Ji, G. Quantitative response relationships between nitrogen transformation rates and nitrogen functional genes in a tidal flow constructed wetland under C/N ratio constraints. Water Res. 2014, 64, 32−41. (19) Vymazal, J. Plants used in constructed wetlands with horizontal subsurface flow: a review. Hydrobiologia 2011, 674 (1), 133−156. (20) Lin, Y.-F.; Jing, S.-R.; Wang, T.-W.; Lee, D.-Y. Effects of macrophytes and external carbon sources on nitrate removal from groundwater in constructed wetlands. Environ. Pollut. 2002, 119 (3), 413−420. (21) Crites, R. W.; Middlebrooks, E. J.; Bastian, R. K. Natural Wastewater Treatment Systems; CRC Press: Boca Raton, FL, 2014.
characterizing the community dynamic of the entire wetland bed rather than high-resolution observations for spatial heterogeneity in the microbial community. Therefore, eight closely packed columns were buried in the center of each CW system to minimize the spatial variability in the sampling points. These highly permeable columns were exposed to the same hydraulic conditions as the overall wetland bed, thereby ensuring that the samples were representative of the system at the time they were collected. We acknowledge that this sampling method is not perfect in terms of spatial resolution, but it is a field-proven and practical method for obtaining multiple microbial samples over the long-term in large-scale systems.
Wei Zhi†,‡ Li Yuan† Guodong Ji*,† Chunguang He*,§
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Key Laboratory of Water and Sediment Sciences, Ministry of Education, Department of Environmental Engineering, Peking University, Beijing, 100871, China ‡ John and Willie Leone Department of Energy and Mineral Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States § State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, Northeast Normal University, Changchun, 130024, China
AUTHOR INFORMATION
Corresponding Authors
*E-mail:
[email protected]. *E-mail:
[email protected]. Phone: +86 (010)-62755914. Notes
The authors declare no competing financial interest.
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REFERENCES
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DOI: 10.1021/acs.est.5b03928 Environ. Sci. Technol. XXXX, XXX, XXX−XXX
