- Original article
- Open Access
Denitrification characterization of dissolved oxygen microprofiles in lake surface sediment through analyzing abundance, expression, community composition and enzymatic activities of denitrifier functional genes
AMB Express volume 9, Article number: 129 (2019)
The responses of denitrifiers and denitrification ability to dissolved oxygen (DO) concent in different layers of surface lake sediments are still poorly understood. Here, the optimal denitrification condition was constructed based on response surface methodology (RSM) to analyze the denitrification characteristics of surface sediments. The aerobic zone (AEZ), hypoxic zone (HYZ), up-anoxic zone (ANZ-1) and sub-anoxic zone (ANZ-2) were partitioned based on the oxygen contents, and sediments were collected using a customized-designed sub-millimeter scale sampling device. Integrated real-time quantitative PCR, Illumina Miseq-based sequencing and denitrifying enzyme activities analysis revealed that denitrification characteristics varied among different DO layers. Among the four layers, the DNA abundance and RNA expression levels of norB, nirS and nosZ were the highest at the aerobic layer, hypoxic layer and up-axoic layer, respectively. The hypoxia and up-anaerobic layer were the active nitrogen removal layers, since these two layers displayed the highest DNA abundance, RNA expression level and enzyme activities of denitrification functional genes. The abundance of major denitrifying bacteria showed significant differences among layers, with Azoarcus, Pseudogulbenkiania and Rhizobium identified as the main nirS, nirK and nosZ-based denitrifiers. Pearson’s correlation revealed that the response of denitrifiers to environmental factors differed greatly among DO layers. Furthermore, napA showed higher DNA abundance and RNA expression level in the aerobic and hypoxic layers than anaerobic layers, indicating that aerobic denitrifiers might play important roles at these layers.
Increased nitrogen (N, often in the form of nitrate) loading into aquatic environments has negative ecological and economic consequences on biodiversity and water quality (Dodds et al. 2009; Cardinale 2011). Denitrification processes in aquatic ecosystems act as a nitrate sink, transforming nitrate into gaseous products (N2, NO, N2O), which are then emitted into the atmosphere (Korom 1992; Verhoeven et al. 2006). Various metabolic enzymes, including nitrate reductases (Nar), nitrite reductases (Nir), nitric oxide reductases (Nor), and nitrous oxide reductases (Nos), catalyze the denitrification process (Zumft 1997). The denitrification functional genes narG, napA, nirS, nirK, norB and nosZ have commonly been used as biomarkers to elucidate the abundance, richness, and diversity of denitrifier communities (Tatti et al. 2015; Zhang et al. 2015; Yang et al. 2018).
Conventional biological denitrification requires hypoxic conditions with dissolved oxygen (DO) concentration less than 0.2 mg/L (Seitzinger et al. 2006). Since it was first discovered in the 1980s (Robertson and Kuenen 1984), aerobic denitrification at DO levels of 5.0–6.0 mg/L has attracted much attention because of its potential to overcome the disadvantages of conventional biological denitrification (Bai et al. 2019; Guo et al. 2016; Kim et al. 2008). Fluctuating oxygen concentrations, supply of nitrate, organic matter and other properties endow surface sediments (a few millimeters) a preferential place for denitrification (Santschi et al. 1990; Seitzinger et al. 2006). The denitrification characteristics in different habitats are always different, however, there is usually only one analysis method applied to investigate these (Yu et al. 2014; Saarenheimo et al. 2015; Tatti et al. 2015; Mao et al. 2017). Nevertheless, few studies have provided an integrated analysis of gene abundance, gene expression, enzyme activity and denitrifier community structure on a vertical scale within the micro-layers of lake sediment surfaces.
The global sedimentary denitrification rate has been found to be much lower (approximately 200 Tg a−1) than that of many existing measurement-based estimates (Devol 2015). This discrepancy may be as a result of scarcity of comprehensive measurements approaches. Hence, a comprehensive characterization of the denitrification process in lake surface sediments is needed to accurately evaluate the rate of denitrification yields and denitrification traits. Investigations are also important to understand the effects of DO content, temperature, pH and carbon source on denitrification (Strong et al. 2011; Kraft et al. 2014). Previous research have not reach a consensus in relation to that the effects of DO contents on different types of denitrification (Körner and Zumft 1989; Dalsgaard et al. 2014). Apart from DO, sediment physicochemical factors are also considered as important factors regulating lake denitrification (Saunders and Kalff 2001; Bruesewitz et al. 2011). However, there is little information regarding the interaction between denitrification characteristics and environmental factors among different DO sublayers.
Up to date, most studies utilized single-factor experiments; however, simultaneous changes in multiple environmental factors may impact nitrogen removal efficiency (Su et al. 2015). The conventional approach of assessing one factor at a time is not appropriate for this particular bioprocess because of potential interactions between independent variables. To overcome this problem, integration of multiple variables coupled with response surface methodology (RSM) should be used (Su et al. 2015). In the present study, surface sediments of a eutrophic lake and simulated artificial lake water were used to construct microcosms incubations. The optimal denitrification condition was constructed by adjusting the temperature, pH and organic carbon content (i.e., sawdust). Under the optimal conditions, different DO layers were sampled via a customised-designed sub-millimeter device to compare DNA abundance, RNA expression level and enzyme activity of denitrification enzymes. Moreover, the relationship between the sediment chemical factors and the denitrification processes within the vertical microecology was investigated. These results will help optimize conditions for nitrate removal from eutrophic water, and provide references for accurate assessment of denitrification ability of surface sediments.
Materials and methods
Preparation of sediments
Surface sediments were collected in October 2018 from Lake Dianchi, a eutrophic lake located in Kunming, P. R. China (24°40′–25°02′N, 102°36′–103°40′E), using the method described by Tian et al. (2015). Surface sediments were sealed in sterile plastic bags, transported to the laboratory, homogenized and then used for experiments.
Experimental design for determination of nitrate removal rate under various conditions
Three temperatures (5 °C, 15 °C and 25 °C), three pH values (5.5, 7.0 and 8.5) and three sawdust contents (0.1, 0.3 and 0.5 mg/110 g of sediment) were set in the present study. Response Surface Methodology (RSM) combined with the Box-Behnken Design (BBD) were applied to test the effects of these three factors on nitrate removal rate. In total, 17 rounds of assays were conducted. Detailed settings of environmental conditions for each round of tests are listed in Additional file 1: Tables S1, S2. For assays, PVC cylinders (30 mm in diameter × 110 mm in height) were used to mimic aquatic ecosystems. In each cylinder, 110 g of sediments were placed at the bottom and then 30 mL of artificial lake water [48.6 mg/L NaNO3, 5.1 mg/L MgSO4·7H2O, 3.8 mg/L NH4Cl, 5.6 mg/L K2HPO4, 4.4 mg/L KH2PO4 and 0.1 mL/L trace elements (Nancharaiah et al. 2008)] was gently added above sediments. The apparatus was incubated at corresponding temperature under dark in an incubator (Hengfeng Medical Devices Co., Ltd. China). For each condition, 21 PVC cylinders were prepared. Five millilitre of water was sampled to determine nitrite content from three cylinders each day as three replicates. Content of nitrite in overlying water was immediately analyzed by ICS5000 chromelenon7 (Thermofisher, USA).
The experiments were continued until the nitrite content in water was below 1 mg/L. All experiments were finished within 7 days. The denitrification efficiency was calculated as the daily decrease of nitrite content from the initial value to the final value (the nitrite content observed below 1 mg/L for the first time).
Sample preparation for determination of microbe indices under the optimal condition
To investigate expression levels of denitrification-related genes in different layers of sediments under the optimal environmental conditions, a special sub-millimeter sampling device was designed in the present study to accurately collect sediment samples at different depths (Fig. 1a). A series of different-sized microporous plates (0.2 mm thick, containing 256 pores with 3 mm diameter) were filled with sediments and then piled up. The size of upper plate was smaller than the lower one, forming a trapezoid structure. The upmost and nethermost plate was 8 cm × 8 cm and 11 cm × 11 cm in size, respectively. Overall, 20 microporous plates were stacked at the bottom of glass tanks (32 cm length × 20 cm width × 10 cm height), and then immersed in artificial lake water (total water depth was 8 cm). These tanks were incubated under dark at 25 °C in incubators. After stabilized for 2 days, changes of DO content in sediments along with depth were determined using an oxygen microsensor (Fig. 1b). Based on the DO contents, four layers of sediments were defined, including aerobic zone (AEZ, 0–1.8 mm depth, DO: 0.2–5.9 mg L−1), hypoxic zone, HAZ (1.8–2.2 mm depth, DO: 0–0.2 mg L−1); up-anoxic zone (ANZ-1, 2.2–2.6 mm depth, DO: 0 mg L−1) and sub-anoxic zone (ANZ-2, 2.6–3.0 mm depth, DO: 0 mg L−1). After incubation for 5 days, sediments were collected from these zones and stored at − 80 °C for biochemical and molecular analyses.
Analyses of chemical parameters in sediments
NH4+–N, NO3−–N and NO2−–N were extracted from sediments using 2 mol/L KCL solution at a ratio of 1: 5 (sediment: water) and measured using ICS5000 chromelenon7 (Thermofisher, USA). Frozen dried sediments were sieved and then analyzed for total organic carbon contents (TOC) using an Elementar vario TOC system (Elementar, Germany) and TN was analyzed by hydrochloric acid photometry method. All parameters were measured in triplicates.
Determination of activities of denitrification enzymes and electron transport system (ETS)
Methods for detecting denitrification enzyme activities and electron transport system (ETS) activity followed Su et al. (2019). Briefly, 5 g of sediments were suspended in 100 mM phosphate-buffered saline (PBS, pH 7.8) and then sonicated at 4 °C for 5 min to break cells. After centrifugation at 16,000 rpm for 10 min at 4 °C, the supernatants were collected for determination of NAR, NIR and NOS activities. The assay mixture (3 mL) included 10 mM PBS buffer (pH 7.8), 5 mM Na2S2O4, 10 mM methyl viologen, 1 mM denitrifying electron acceptor (NO3−, NO2− or N2O) and 1 mL of enzyme extract. After incubation at 25 °C under anaerobic conditions for 30 min, the increased or decreased NO2− concentration was determined at 540 nm to calculate NAR and NIR activities. The reduced N2O concentration was detected by a microsensor (MMM-Meter, Unisense, Denmark) to calculate NOS activities. Reduction from 2-(p-iodophenyl)-3-(p-nitrophenyl)-5- phenyl tetrazolium chloride (INTC) to formazan caused by enzyme extract was determined to express ETS activity.
Nucleic acid extraction and real-time quantitative PCR (RT-qPCR)
DNA was extracted from approximately 0.8 g of each sediment sample using an E.Z.N.A. Soil DNA Kit (Omega Bio-Tek, Norcross, GA, USA) according to the manufacturer’s instructions. Total RNA was extracted using the acid guanidium thiocyanate-phenol–chloroform (AGPC) method (Choi et al. 2018). After extraction, RNA was reversely transcribed into cDNA using Superscript II reverse transcriptase (Life Technologies Corporation, USA) in accordance with manufacturer’s instructions. Nucleic acid quality and concentration were examined by 1% agarose gel electrophoresis and spectrophotometry, respectively.
DNA levels and RNA transcriptional levels of 16 s rRNA, narG, norB, nirS, nirK, nosZ and napA were examined in the extracted DNA and RNA using the RT-qPCR method and then expressed as copies per gram of sediment.
The primers and conditions for RT-qPCR are provided in Additional file 1: Table S3. RT-qPCR experiments were performed on Bio-Rad qPCR machine (Hercules, CA, USA) using SYBR Green as the signal dye. Each 20-μL reaction mixture contained 1 μL of template DNA, 10 μL of iTaq Universal SYBR Green Supermix (Bio-Rad), 1 μL of 10 µM each primer, and 7 μL of water. Standard curves for each gene were obtained by tenfold serial dilution of standard plasmids containing target functional gene. Positive (plasmid DNA only) and negative (nuclease-free water) controls were prepared simultaneously. The ratio of DNA level to RNA transcriptional level for each gene was calculated and expressed as ratio of RNA to DNA (RNA: DNA).
PCR products of nirS, nirK and nosZ were amplified from DNA samples. The primers and conditions are presented in Additional file 1: Table S3. Amplicons were purified, pooled in equimolar concentrations for paired-end sequencing (2 × 300 bp) on an Illumina MiSeq platform (Illumina, San Diego, CA, USA) by LC-Bio Technology Company (Hangzhou, China) according to standard protocols. Operational taxonomic units (OTUs) were clustered with 97% similarity cutoff using UCHIME (version 7.1, http://drive5.com/uparse/), which also identified and removed chimeric sequences. Representative sequences were selected for each OTU, and taxonomy information of each representative sequence was obtained using the RDP Classifier (http://rdp.cme.msu.edu/) by blasting against the functional gene database (FGR, Fish et al. 2013). Beta diversity was calculated by analysis of similarities (ANOSIM) with weighted Unifrac in the R “vegan” package (v3.2.3).
Analysis of Variance (ANOVA) and Pearson’s correlation analysis were conducted using SPSS 16.0 software. Statistical significance was set at the P level of < 0.05. Figures were drawn using the Origin 8.0 program.
Results and discussion
Optimal environmental conditions for denitrification of water
Previous studies have investigated the effects of environmental factors on sediment denitrification (Huang et al. 2017; Myrstener et al. 2016; Saarenheimo et al. 2015). However, few studies have applied RSM to evaluate the interactive effects of environmental factors (temperature, pH and availability of organic C) on nitrate removal. In the present study, the interaction of temperature, sawdust content and pH on the removal of nitrate nitrogen caused by surface sediments were analyzed based on RSM. Additional file 1: Table S2 presents the determined nitrate removal rate under various conditions and Fig. 2 shows the response surface of the nitrate removal efficiency at different sawdust contents, pH values and temperatures. These results suggested that sawdust content, pH and temperature all significantly and positively affected nitrate removal rate in water (Fig. 2a, c).
ANOVA for response surface quadratic model revealed that F-value of the model was equal to 4.03 and the P value of the lack of fit was higher than 0.05 (Additional file 1: Table S4), suggesting that the as-obtained model was statically significant (Additional file 1: Table S4), which could be used to predict the optimal denitrification conditions. Besides, ANOVA revealed that temperate, sawdust content and their interaction all significantly affected nitrate removal efficiency (P = 0.0083, Additional file 1: Table S4). High temperature should accelerate growth of microorganisms and increase denitrification activities, thus increasing nitrate removal efficiency. In the present study, addition of sawdust promoted nitrate removal efficiency, which was consistent with previous findings (Wang and Chu 2016). The contribution of the three tested variables to denitrification efficiency followed the order temperature > sawdust content > pH, and the optimum condition for maximum nitrate removal were predicted as temperature = 25 °C, pH = 8.5 and sawdust content = 0.5 mg/110 g of sediment.
Abundance, transcriptional levels, enzyme activities of denitrifiers in surface sediments
Under the optimal nitrate removal condition, denitrifier abundance (at DNA level), transcriptional levels (at RNA level) and enzyme activities were compared among different layers of surface sediments (AEZ, HAZ, ANZ-1 and ANZ-2). All the tested genes were detected in all samples at both DNA and RNA levels, suggesting that the whole denitrification process took place in all these layers of sediments. However, the abundance and transcriptional levels of different denitrifying genes differed among layers. The order of the denitrifying gene abundance in AEZ, HAZ, ANZ-1 and ANZ-2 was norB > nirS > narG > nosZ > nirK, nirS > narG > nosZ > norB > nirK, nosZ > narG > nirS > nirK > norB, narG > nirS > nosZ > nirK > norB, respectively (Fig. 3a).
In addition, since napA is an indicator gene of aerobic denitrification (Marchant et al. 2017), we also compared its distribution among the four sediment layers. In this study, the DNA abundance and RNA transcriptional level of napA in the aerobic and hypoxic regions were significantly higher than those in the anoxic regions (Fig. 3b). Therefore, accurate understanding of the range of aerobic denitrification layer and activities of aerobic denitrification bacteria.
Denitrification was restricted to a narrow zone immediately below the aerobic–anaerobic interface in sediments and biofilms (Deutzmann et al. 2014). Previous studies have investigated areas of denitrification using microsensors to detect oxygen and nitrate concentrations in sediments. However, little information is available regarding determination of the dominant denitrification region in lake surface sediments based on abundance, expression, community composition and enzymatic activities of denitrifier functional genes (Christensen et al. 1989; Nielsen et al. 1990a, b). In the present study, among the four sediment layers, RNA transcriptional levels of narG, nirS, nirK, norB and nosZ were relatively higher in HYZ and ANZ-1 than those in AEZ and ANZ-2 (Fig. 3a). Besides, enzyme activities of NIR and NOS also showed similar trends. These results indicated that the hypoxic layer and the up-anaerobic layer were the active nitrogen removal layers.
Comparison of the DNA abundance and RNA transcriptional level of denitrification genes between the up- (HYZ-1) and sub-anoxic layers (HYZ-2) revealed a decreasing trend with depth, which might be due to the decreased total bacteria in the sub-anoxic layers. Lower copy number of 16S rRNA and EST activity were observed in HYZ-2 (Fig. 3b). This phenomenon was consistent with the decreases of total bacteria abundance with depth in other sediments (Qin et al. 2018). These findings further demonstrated that not all anoxic vertical profiles were active denitrification regions.
Vertical distribution for representative denitrifier communities in surface sediments
Blasting of nirS, nirK and nosZ sequences to FGR databases enabled taxonomic analyses of the denitrifier communities among different sediment layers, which has also been applied in other studies (such as Yang et al. 2018). ANOSIM revealed significant differences in denitrifier community structure among vertical profiles (nirS, R2 = 0.35, P = 0.02; nirK, R2 = 0.37, P = 0.04; nosZ, R2 = 0.67, P = 0). In total, 11,171, 7007 and 3063 OTUs were identified for nirS-type, nirK-type and nosZ-type denitrifiers, respectively. Dominant nirS, nirK and nosZ OTUs were identified to be the genera Azoarcus, Rhizobium and Pseudogulbenkiania in the four sediment layers (Fig. 4a–c), respectively. Each layer showed significant differences in the types and abundances of denitrifiers (Additional file 1: Table S5). Furthermore, in the aerobic layer, comparison of the abundance of the top five genus among nirS-type, nirK-type and nosZ-type denitrifiers revealed higher abundance of Dechloromonas and Azoarcus in AEZ than those in other three layers, and higher abundance of Pseudogulbenkiania than that in hypoxic layer. Previous studies reported a high abundance of Dechloromonas in agricultural soils and reservoirs (Coyotzi et al. 2017; Yu et al. 2014), as well as high levels of Azoarcus in oilfields, wastewater treatment plants, soils and sediments (Song et al. 2000; Wang et al. 2014a; Nazina et al. 2017), and high abundance of Pseudogulbenkiania in freshwater sediments and rice paddy soils (Tago et al. 2011; Guo et al. 2018). In addition, most of the isolated denitrifier strains in these genera were anaerobic strains. However, there was no much information pertaining to the isolation of aerobic denitrifier strains (Achenbach et al. 2001; Ishii et al. 2016; Yücel et al. 2019). These results indicated that a lot of aerobic denitrifier strains have not been isolated from the aerobic layer, which might be used for in situ restoration of eutrophic lake.
The abundance of Azoarcus was higher than other genera in the hypoxic layer. Previous studies showed that bacteria in the genus Azoarcus could use many aromatic hydrocarbons as carbon sources during denitrification processes (Zhou et al. 1995; Springer et al. 1998; Lee et al. 2013). Therefore, the genus Azoarcus might be the dominant denitrifying bacteria for nitrogen removal in low-DO sediment areas. Analysis of nosZ gene sequences showed that the abundance of Pseudogulbenkiania was higher than those of other bacteria in the anoxic layer. Previous studies presented some isolated strains of Pseudogulbenkiania and showed strong denitrification and N2O reduction activities in rice paddy soils (Tago et al. 2011; Yoshida et al. 2012). Therefore, Pseudogulbenkiania might be the most important N2O reducing microbes in the anoxic layer of surface sediments.
Relationship between denitrification traits and sediment physicochemical factors
To date, several studies have investigated the spatial changes of denitrifier traits in sediments (Devol 2015; Mao et al. 2017; Zhang et al. 2015). However, little is known about the relationship between denitrifier traits and physicochemical factors in surface sediments. In the present study, one-way ANOVA showed that the TN content (P < 0.01), TOC content (P < 0.01), NH4+–N content (P < 0.01), and NO3−–N content (P < 0.01) differed significantly among AEZ, HAZ, ANZ-1 and ANZ-2 (Additional file 1: Table S6). Pearson’s correlation revealed that the NH4+–N, NO3−–N and TOC content was significantly positively correlated with DNA abundance and RNA transcriptional level of denitrification genes (Fig. 4d). These results indicated that the response of denitrifiers to physicochemical factors varied in different DO layers. Similarly, Wang et al. (2014b) also revealed that physicochemical factors markedly affected the distribution of denitrification bacteria in bay sediments (Wang et al. 2014b). Besides, different genes revealed inconsistent relationship between physicochemical factors and the abundance of denitrification genes. Similar inconsistence was also reported in marine sediments (Gao et al. 2017).
In summary, following the RSM experiments, the optimal environmental conditions for best nitrate removal in water was predicted as 25C, pH 8.5 with 0.5 mg sawdust/110 g of sediment. Under the optimal environmental conditions, DNA abundance, RNA transcriptional levels and enzyme activities were compared among different layers of surface sediments, revealing that the activities of denitrification enzymes and key denitrifiers varied among layers with different DO contents. The as-obtained relationship between denitrification and environmental factors improved the understanding of their roles in geobiochemical cycles of Nitrogen.
Availability of data and materials
The raw Illumina reads obtained in the current study were deposited in the NCBI short-read archive under SRA Accession PRJNA525978 (https://www.ncbi.nlm.nih.gov/sra/PRJNA525978).
Achenbach LA, Michaelidou U, Bruce RA, Fryman J, Coates JD (2001) Dechloromonas agitata gen nov., sp. nov. and Dechlorosoma suillum gen nov., sp. nov., two novel environmentally dominant (per) chlorate-reducing bacteria and their phylogenetic position. Int J Syst Evol Microbiol 51(2):527–533. https://doi.org/10.1099/00207713-51-2-527
Bai H, Liao S, Wang A, Bai J, Shu W, Ye J (2019) High-efficiency inorganic nitrogen removal by newly isolated Pannonibacter phragmitetus B1. Bioresour Technol 271:91–99. https://doi.org/10.1016/j.biortech.2018.09.090
Bruesewitz DA, Hamilton DP, Schipper LA (2011) Denitrification potential in lake sediment increases across a gradient of catchment agriculture. Ecosystems 14(3):341–352. https://doi.org/10.1007/s10021-011-9413-2
Cardinale BJ (2011) Biodiversity improves water quality through niche partitioning. Nature 472(7341):86–89. https://doi.org/10.1038/nature09904
Choi C, Yoon S, Moon H, Bae Y, Kim C, Diskul-Na-Ayudthaya P, Ngu TV, Munir J, Han J, Park SB, Moon J, Song S, Ryu S (2018) mirRICH, a simple method to enrich the small RNA fraction from over-dried RNA pellets. RNA Biol. https://doi.org/10.1080/15476286.2018.1451723
Christensen PB, Nielsen LP, Revsbech NP, Sørensen J (1989) Microzonation of denitrification activity in stream sediments as studied with a combined oxygen and nitrous oxide microsensor. Appl Environ Microbiol 55(5):1234–1241
Coyotzi S, Doxey AC, Clark ID, Lapen DR, Van Cappellen P, Neufeld JD (2017) Agricultural soil denitrifiers possess extensive nitrite reductase gene diversity. Environ Microbiol 19(3):1189–1208. https://doi.org/10.1111/1462-2920.13643
Dalsgaard T, Stewart FJ, Thamdrup B, De BL, Revsbech NP, Ulloa O, Canfield DE, DeLong EF (2014) Oxygen at nanomolar levels reversibly suppresses process rates and gene expression in anammox and denitrification in the oxygen minimum zone off northern Chile. mBio 5:6. https://doi.org/10.1128/mbio.01966-14
Deutzmann JS, Stief P, Brandes J, Schink B (2014) Anaerobic methane oxidation coupled to denitrification is the dominant methane sink in a deep lake. Proc Natl Acad Sci USA 111(51):18273–18278. https://doi.org/10.1073/pnas.1411617111
Devol AH (2015) Denitrification, anammox, and N2 production in marine sediments. Annu Rev Mar Sci 7:403–423. https://doi.org/10.1146/annurev-marine-010213-135040
Dodds WK, Bouska WW, Eitzmann JL, Pilger TJ, Pitts KL, Riley AJ, Schloesser JT, Thornbrugh DJ (2009) Eutrophication of US freshwaters: analysis of potential economic damages. Environ Sci Technol 43(1):12–19. https://doi.org/10.1021/es801217q
Fish JA, Chai B, Wang Q, Sun Y, Brown CT, Tiedje JM, Cole JR (2013) FunGene: the functional gene pipeline and repository. Front Microb 4:291. https://doi.org/10.3389/fmicb.2013.00291
Gao M, Liu J, Qiao Y, Zhao M, Zhang XH (2017) Diversity and abundance of the denitrifying microbiota in the sediment of eastern China marginalseas and the impact of environmental factors. Microb Ecol 73(3):602–615. https://doi.org/10.1007/s00248-016-0906-6
Guo L, Zhao B, An Q, Tian M (2016) Characteristics of a novel aerobic denitrifying bacterium, Enterobacter cloacae Strain HNR. Appl Biochem Biotechnol 178(5):947–959. https://doi.org/10.1007/s12010-015-1920-8
Guo Q, Li N, Bing Y, Chen S, Zhang Z, Chang S, Chen Y, Xie S (2018) Denitrifier communities impacted by heavy metal contamination in freshwater sediment. Environ Pollut 242:426–432. https://doi.org/10.1016/j.envpol.2018.07.020
Huang Y, Li P, Chen G (2017) The production of cyanobacterial carbon under nitrogen-limited cultivation and its potential for nitrate removal. Chemosphere 190:1–8. https://doi.org/10.1016/j.chemosphere.2017.09.125
Ishii S, Joikai K, Otsuka S, Senoo K, Okabe S (2016) Denitrification and nitrate-dependent Fe(II) oxidation in various Pseudogulbenkiania Strains. Microbes Environ 31(3):293–298. https://doi.org/10.1264/jsme2
Kim M, Jeong S, Yoon SJ, Cho SJ, Kim YH, Kim MJ, Ryu EY, Lee S (2008) Aerobic denitrification of Pseudomonas putida AD-21 at different C/N ratios. J Biosci Bioeng 106(5):498–502. https://doi.org/10.1263/jbb.106.498
Körner H, Zumft WG (1989) Expression of denitrification enzymes in response to the dissolved oxygen level and respiratory substrate in continuous culture of Pseudomonas stutzeri. Appl Environ Microbiol 55(7):1670–1676
Korom SF (1992) Natural denitrification in the saturated zone: a review. Water Resour Res 28(6):1657–1668. https://doi.org/10.1029/92wr00252
Kraft B, Tegetmeyer HE, Sharma R, Klotz MG, Ferdelman TG, Hettich RL, Geelhoed JS, Strous M (2014) The environmental controls that govern the end product of bacterial nitrate respiration. Science 345:676–679. https://doi.org/10.1126/science.1254070
Lee DJ, Wong BT, Adav SS (2013) Azoarcus taiwanensis sp. nov., a denitrifying species isolated from a hot spring. Appl Microbiol Biot 98(3):1301–1307. https://doi.org/10.1007/s00253-013-4976-9
Mao G, Chen L, Yang Y, Wu Z, Tong T, Liu Y, Xie S (2017) Vertical profiles of water and sediment denitrifiers in two plateau freshwater lakes. Appl Microbiol Biotechnol 101(8):3361–3370. https://doi.org/10.1007/s00253-016-8022-6
Marchant HK, Ahmerkamp S, Lavik G, Tegetmeyer HE, Graf J, Klatt JM, Holtappels M, Walpersdorf E, Kuypers MMM (2017) Denitrifying community in coastal sediments performs aerobic and anaerobic respiration simultaneously. ISME J 11(8):1799–1812. https://doi.org/10.1038/ismej.2017.51
Myrstener M, Jonsson A, Ann-Kristin B (2016) The effects of temperature and resource availability on denitrification and relative N2O production in boreal lake sediments. J Environ Sci 47(9):82–90. https://doi.org/10.1016/j.jes.2016.03.003
Nancharaiah YV, Joshi HM, Hausner M, Venugopalan VP (2008) Bioaugmentation of aerobic microbial granules with Pseudomonas putida carrying TOL plasmid. Chemosphere 71(1):30–35. https://doi.org/10.1016/j.chemosphere.2007.10.062
Nazina TN, Shestakova NM, Semenova EM, Korshunova AV, Kostrukova NK, Tourova TP, Min L, Feng Q, Poltaraus AB (2017) Diversity of metabolically active bacteria in water-flooded high-temperature heavy oil reservoir. Front Microbiol 8:1. https://doi.org/10.3389/fmicb.2017.00707
Nielsen LP, Christensen PB, Revsbech NP, Sørensen J (1990a) Denitrification and oxygen respiration in biofilms studied with a microsensor for nitrous oxide and oxygen. Microb Eco 19(1):63–72. https://doi.org/10.1007/BF02015054
Nielsen LP, Christensen PB, Revsbech NP, Sorensen J (1990b) Denitrification and photosynthesis in stream sediment studied with microsensor and whole-core techniques. Limnol Oceanogr 35(5):1135–1144. https://doi.org/10.4319/lo.1918.104.22.1685
Qin H, Han C, Jin Z, Wu L, Deng H, Zhu G, Zhong W (2018) Vertical distribution and community composition of anammox bacteria in sediments of a eutrophic shallow lake. J Appl Microbiol 125(1):121–132. https://doi.org/10.1111/jam.13758 (Epub 2018 May 9)
Robertson LA, Kuenen JG (1984) Heterotrophic nitrification in Thiusphaeru puntotrophu: oxygen uptake and enzyme studies. J Gen Microbiol 134:857–863. https://doi.org/10.1099/00221287-134-4-857
Saarenheimo J, Tiirola MA, Rissanen AJ (2015) Functional gene pyrosequencing reveals core proteobacterial denitrifiers in boreal lakes. Front Microbiol. https://doi.org/10.3389/fmicb.2015.00674
Santschi P, Hihener P, Benoitt G, Buchholtzten M, Brink A (1990) Chemical processes at the sediment-water interface. Mar Chem 30:269–315. https://doi.org/10.1016/0304-4203(90)90076-O
Saunders D, Kalff J (2001) Denitrification rates in the sediments of Lake Memphremagog, Canada—USA. Water Res 35(8):1897–1904. https://doi.org/10.1016/s0043-1354(00)00479-6
Seitzinger S, Harrison JA, Bohlke JK, Bouwman AF, Lowrance R, Peterson B, Tobias C, Van Drecht G (2006) Denitrification across landscapes and waterscapes: a synthesis. Ecol Appl 16(6):2064–2090. https://doi.org/10.1890/1051-0761(2006)016%5b2064:DALAWA%5d2.0.CO;2
Song B, Palleroni NJ, Häggblom MM (2000) Isolation and characterization of diverse halobenzoate-degrading denitrifying bacteria from soils and sediments. Appl Environ Microbiol 66(8):3446–3453. https://doi.org/10.1128/aem.66.8.3446-3453.2000
Springer N, Ludwig W, Philipp B, Schink B (1998) Azoarcus anaerobius sp. nov, a resorcinol-degrading, strictly anaerobic, denitrifying bacterium. Int J Syst Bacteriol 48:953–956. https://doi.org/10.1099/00207713-48-3-953
Strong PJ, McDonald B, Gapes DJ (2011) Enhancing denitrification using a carbon supplement generated from the wet oxidation of waste activated sludge. Bioresour Technol 102(9):5533–5540. https://doi.org/10.1016/j.biortech.2010.12.025
Su JF, Zheng SC, Huang TL, Ma F, Shao SC, Yang SF, Zhang LN (2015) Characterization of the anaerobic denitrification bacterium Acinetobacter sp. SZ28 and its application for groundwater treatment. Bioresour Technol 192:654–659. https://doi.org/10.1016/j.biortech.2015.06.020
Su X, Chen Y, Wang Y, Yang X, He Q (2019) Impacts of chlorothalonil on denitrification and N2O emission in riparian sediments: microbial metabolism mechanism. Water Res 148:188–197. https://doi.org/10.1016/j.watres.2018.10.052
Tago K, Ishii S, Nishizawa T, Otsuka S, Senoo K (2011) Phylogenetic and functional diversity of denitrifying bacteria isolated from various rice paddy and rice-soybean rotation fields. Microbes Environ 26(1):30–35. https://doi.org/10.1264/jsme2.me10167
Tatti E, Goyer C, Burton DL, Wertz S, Zebarth BJ, Chantigny M, Filion M (2015) Tillage management and seasonal effects on denitrifier community abundance, gene expression and structure over winter. Microb Ecol 70(3):795–808. https://doi.org/10.1007/s00248-015-0591-x
Tian C, Wang C, Tian Y, Wu X, Xiao B (2015) Vertical distribution of Fe and Fe(III)-reducing bacteria in the sediments of Lake Donghu, China. Can J Microbiol 61(8):575–583. https://doi.org/10.1139/cjm-2015-0129
Verhoeven J, Arheimer B, Yin C, Hefting M (2006) Regional and global concerns over wetlands and water quality. Trends Ecol Evol 21(2):96–103. https://doi.org/10.1016/j.tree.2005.11.015
Wang J, Chu L (2016) Biological nitrate removal from water and wastewater by solid-phase denitrification process. Biotechnol Adv 34(6):1103–1112. https://doi.org/10.1016/j.biotechadv.2016.07.001
Wang Z, Zhang XX, Lu X, Liu B, Li Y, Long C, Li A (2014a) Abundance and diversity of bacterial nitrifiers and denitrifiers and their functional genes in tannery wastewater treatment plants revealed by high-throughput sequencing. PLoS ONE 9(11):e113603. https://doi.org/10.1371/journal.pone.0113603
Wang L, Zheng B, Nan B, Hu P (2014b) Diversity of bacterial community and detection of nirS- and nirK-encoding denitrifying bacteria in sandy intertidal sediments along Laizhou Bay of Bohai Sea, China. Mar Pollut Bull 88(1–2):215–223. https://doi.org/10.1016/j.marpolbul.2014.09.002
Yang Y, Hu Y, Wang Z, Zeng Z (2018) Variations of the nirS-, nirK-, and nosZ-denitrifying bacterial communities in a northern Chinese soil as affected by different long-term irrigation regimes. Environ Sci Pollut Res Int 25(14):14057–14067. https://doi.org/10.1007/s11356-018-1548-7
Yoshida M, Ishii S, Fujii D, Otsuka S, Senoo K (2012) Identification of active denitrifiers in rice paddy soil by DNA- and rna-based analyses. Microbes Environ 27:456–461. https://doi.org/10.1264/jsme2.me12076
Yu Z, Yang J, Liu L (2014) Denitrifier community in the oxygen minimum zone of a subtropical deep reservoir. PLoS ONE 9(3):e92055. https://doi.org/10.1371/journal.pone.0092055
Yücel O, Borgert SR, Poehlein A, Niermann K, Philipp B (2019) The 7α-hydroxysteroid dehydratase Hsh2 is essential for anaerobic degradation of the steroid skeleton of 7α-hydroxyl bile salts in the novel denitrifying bacterium Azoarcus sp. strain Aa7. Environ Microbiol 21(2):800–813. https://doi.org/10.1111/1462-2920.14508
Zhang L, Zeng G, Zhang J, Chen Y, Yu M, Lu L, Li H, Zhu Y, Yuan Y, Huang A, He L (2015) Response of denitrifying genes coding for nitrite (nirK or nirS) and nitrous oxide (nosZ) reductases to different physico-chemical parameters during agricultural waste composting. Appl Microbiol Biotechnol 99(9):4059–4070. https://doi.org/10.1007/s00253-014-6293-3
Zhou J, Fries MR, Chee-Sanford JC, Tiedje JM (1995) Phylogenetic analyses of a new group of denitrifiers capable of anaerobic growth on toluene and description of Azoarcus tolulyticus sp. nov. Int J Syst Bacteriol 45:500–506. https://doi.org/10.1099/00207713-45-3-500
Zumft WG (1997) Cell biology and molecular basis of denitrification. Microbiol Mol Biol Rev 61(4):533–616
This study was funded by the National Natural Science Foundation of China (Grant Numbers 31800112, 31800390, 31870450 and 31670465)
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Hong, P., Wu, X., Shu, Y. et al. Denitrification characterization of dissolved oxygen microprofiles in lake surface sediment through analyzing abundance, expression, community composition and enzymatic activities of denitrifier functional genes. AMB Expr 9, 129 (2019). https://doi.org/10.1186/s13568-019-0855-9
- Response surface methodology (RSM)
- DO concentration
- Lake surface sediment
- Denitrification traits