Abundance and diversity of denitrifying bacterial communities associated with N2O emission under long-term organic farming

European Journal of Soil Biology 97 (2020) 103153

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European Journal of Soil Biology journal homepage: www.elsevier.com/locate/ejsobi

Abundance and diversity of denitrifying bacterial communities associated with N2O emission under long-term organic farming

T

Hui Hana, Chen Chena, Mohan Baia, Ting Xua,b, Hefa Yangc, Aiming Shic, Guo-chun Dinga,b,∗, Ji Lia,b,∗∗ a College of Resources and Environmental Science, Beijing Key Laboratory of Biodiversity and Organic Farming, China Agricultural University, Yuanmingyuan West Road No. 2, 100193, Beijing, China b Organic Recycling Institute at Suzhou, China Agricultural University and Suzhou ViCheck Biotechnology, Changli Road No. 366, 215128, Suzhou, China c Quzhou Experimental Station of China Agricultural University, Quzhou County, 057250, Hebei province, China

A R T I C LE I N FO

A B S T R A C T

Handling editor: Christoph Tebbe

Nitrous oxide (N2O), a powerful greenhouse gas, is produced and consumed through denitrifying bacteria. However, the abundance, diversity and succession of denitrifying bacteria and their association with farming systems are largely unknown. Therefore, the objectives of the present study were: (1) to unravel the influence of long-term organic farming on diversity of nirK-, nirS- and nosZ-type denitrifying microorganisms; and (2) to analyze the association of diversity and abundance of these denitrifying microorganism with the emission of N2O from soil. The abundance and diversity of denitrifying bacteria with nirK, nirS and nosZ were compared among organic (ORG), integrated (INT), and conventional (CON) vegetable greenhouses located in northern China over four growing seasons using quantitative PCR and high-throughput DNA sequencing of PCR-amplified products. As compared to CON, the abundance of nosZ gene was significantly higher in the ORG, in which the N2O emission was 35% lower. In general, both abundance and diversity of nitrite reducing populations (nirK- and nirS-type) was influenced by growing seasons rather than farming systems. Additionally, dominant denitrifying populations displayed irregular, periodical, or persistent succession patterns. Co-occurrence network revealed that nirK-, nirS- and nosZ-type denitrifying bacteria formed microbial hubs associated with soil temperature, NO3−-N content, or pH. In summary, long-term organic farming enriched N2O reducing bacterial populations, while having little effect on nitrite reducing populations which mainly succeeded seasonally.

Keywords: Organic farming Denitrifying bacteria Abundance Diversity High-throughput sequencing

1. Introduction Nitrous oxide (N2O) is a powerful greenhouses gas, which is 298 times more potent than carbon dioxide, and serves as the dominant ozone-depleting agent [1]. Globally, agricultural soils contribute to about 55% of the total N2O emission in 2005 and the value is expected to increase to 59% in 2030 [2]. The N2O emission is more intensive in greenhouses used for vegetable production than in crop fields, partially due to the higher input (3–5 times) of N fertilizer [3]. In addition to N2O emission, other environmental problems such as acidification, salinization and accumulation of heavy metals in greenhouse soil were also severe in greenhouses [4,5]. Previous studies suggested that agricultural managements associated with organic farming were able to

mitigate soil degradations. A recent comparison between organic to non-organic systems under field conditions revealed a 40.2% reduction for the organic treatments [6]. Similarly, significantly lower N2O emissions were found in a long-term organic vegetable greenhouse [7]. Denitrification, which potentially involves the activity of many different bacterial taxa, is one of the major N2O producing processes in soil [8,9]. Bacterial taxa with the capacity to reduce N2O can be found among the major phyla, including Alpha-, Beta-, Gamma-, Epsilonproteobacteria, Bacteroidetes and so on [10]. Under greenhouse conditions, approximately 58% of N2O emissions were derived from denitrification [11]. Agricultural practices such as chemical or organic fertilization, tillage, and irrigation are key factors influencing denitrifying microorganisms [9,12]. The potential denitrification activity

∗ Corresponding author. College of Resources and Environmental Science, Beijing Key Laboratory of Biodiversity and Organic Farming, China Agricultural University, Yuanmingyuan West Road No. 2, 100193, Beijing, China. ∗∗ Corresponding author. College of Resources and Environmental Science, Beijing Key Laboratory of Biodiversity and Organic Farming, China Agricultural University, Yuanmingyuan West Road No. 2, 100193, Beijing, China. E-mail addresses: [email protected] (G.-c. Ding), [email protected] (J. Li).

https://doi.org/10.1016/j.ejsobi.2020.103153 Received 30 October 2019; Received in revised form 14 January 2020; Accepted 16 January 2020 1164-5563/ © 2020 Elsevier Masson SAS. All rights reserved.

European Journal of Soil Biology 97 (2020) 103153

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2.3. Soil analyses and DNA extraction

and abundances of bacterial nirS, nirK and nosZ genes were often higher in soils treated with organic fertilizer than those treated with chemical fertilizers [9,12,13]. However, increases of denitrifying microorganisms and potential denitrification rates did not necessarily result in higher N2O emission [14]. The extent of in situ N2O release from agricultural soils was suggested to be associated with the community structure of bacterial denitrifiers [15]. These findings indicated that both quantity and compositions of denitrifying microorganisms are important to govern their functions and thus, the emission of N2O. Thus, we hypothesized that long-term organic farming will alter the abundance and structure of denitrifying bacterial communities associated with N2O emission in soil, because organic farming can improve soil nutrients and change soil pH which were correlated with denitrifiers [12]. The specific objectives of the present study were: (1) to unravel the influence of long-term organic farming on diversity of nirK-, nirS- and nosZ-type denitrifying microorganisms; and (2) to analyze the association of diversity and abundance of these denitrifying microorganism with the emission of N2O from soil.

Soil sampling was performed at depths of 0–20 cm and samples from each plot in each farming system were composites of five cores (2 cm in diameter) taken in a zigzag pattern, with distances of about 1 m between each core insertion. Three replicates per farming system were taken at 19 times of sampling (totally 57 samples for each system). Samples for inorganic N (ammonium N: NH4+-N, nitrate N: NO3−-N) determine and DNA extraction were sieved through a 2-mm mesh and stored at −20 °C for 3–5 days before analysis. Samples for soil total N (STN), soil organic matter (SOM) and soil pH were air dried and sieved through 0.25-mm (for STN, SOM) and 1-mm (for pH) meshes, and then stored at room temperature for a month before analysis. Analysis of STN, SOM, soil pH, soil temperature, water content, bulk density, and total pore volume was performed in accordance with the procedure outlined by Bao [18]. Soil NH4+-N and NO3−-N was extracted with 100 mL of 0.01 M CaCl2 and analyzed by a flow analyzer (TRAACS 2000, Bran and Luebbe, Norderstedt, Germany). Soil total DNA was extracted from 0.5 g of fresh soil (ranges of water content in CON, INT and ORG were 20.34–24.13%, 22.84–27.42%, 25.64–32.76%, respectively) using the E.Z.N.A.™ Soil DNA Kit (Omega Bio-tek, Inc., Norcross, GA, U.S.A.).

2. Materials and methods 2.1. Site description

2.4. Quantitative real-time PCR analyses of nirK, nirS, nosZ, and bacterial 16S rRNA genes

A long-term greenhouse experiment was set up in March 2002 at the Quzhou experiment station (36°52′N, 115°01′E) of China Agricultural University, Handan, Hebei province, China. The climate is warm, semi humid and consists of rainy summers and dry-cold winters. The soil was classified as silt loam (sand: 18–20%, clay: 14–20% and silt: 60–68%) [16].

Real-time PCR (qPCR) was performed to quantify nirK, nirS, nosZ, and bacterial 16S rRNA gene with primers as follows using a Bio-Rad iQ-5 platform (Bio-Rad, California, USA): nirK876/nirK1040 for nirK gene [19], cd3aF/R3cd for nirS gene [20], nosZ2F/nosZ2R for nosZ gene [21], and 1369F/1492R for bacterial 16S rRNA gene [22]. Standard curves were constructed using a 10-fold series dilution of the plasmids carrying the respective target genes for five gradients. The qPCR reaction and thermal profile is shown in Table 2. The melting curves were generated with continuous fluorescence acquisition from 60 to 95 °C at a rate of 5 °C/s. The amplification efficiency for nirK (R2 > 0.99), nirS (R2 > 0.99), nosZ (R2 > 0.99), and bacterial 16S rRNA gene (R2 > 0.99) ranged between 85% and 110%.

2.2. Experimental design The experiment included organic (ORG), integrated (INT) and conventional (CON) farming systems (systems were different not only fertilizer types but also pest and disease control) using three side-byside greenhouses. Each greenhouse was divided into three plots as three replications. The ORG system was carried out according to the International Federation of Organic Agriculture Movements (IFOAM), with chicken- and cow-manure derived compost (165 t ha−1 year−1), using physical and biological methods for plant protection. The CON system follows the local method for greenhouse vegetable production by using chemical fertilizers (2625 kg ha−1 year−1 urea, 3000 kg ha−1 year−1 calcium superphosphate and 2400 kg ha−1 year−1 potassium chloride), pesticides, chicken- and cow-manure (46.8 t ha−1 year−1). The INT system combined 50% of chemical fertilizers (1312.5 kg ha−1 year−1 urea, 1500 kg ha−1 year−1 calcium superphosphate and 1200 kg ha−1 year−1 potassium chloride) with 50% of compost (82.5 t ha−1 year−1), using biological methods for general plant protection and low-toxic chemical pesticides for serious situations. Compost was made from chicken- and cow-manure with inoculants of VT1000 and aerobic fermentation was performed for 25–30 days. Temperature of window was over 55 °C for more than 5 days. Types, rates and application time of pesticides was shown in Table 1. The average annual temperature of ORG, INT and CON is 22.1 °C, 22.6 °C, and 22.1 °C, respectively, and mean irrigation rate was about 13,650 m3 ha−1 year−1. Double-cropping rotation has been performed since 2002, and vegetable varieties remained the same for all three systems. During the experiment (September 26, 2013 to August 29, 2015), cauliflower (Brassica oleracea L. var. botrytis L., density: 960) and celery (Apium graveolens L., density: 11,000) were transplanted into three systems in the autumn of 2013 and 2014, respectively. Eggplant (Solanum melongena L., density: 865 (2014) and 888 (2015)) was transplanted in the spring of 2014 and 2015. Row spacing was 50 cm. The emission of N2O from the same field was described in a previous study [17].

2.5. Illumina MiSeq high-throughput sequencing Diversities of different denitrifying bacteria were analyzed by high throughput sequencing of the nirK, nirS and nosZ genes fragments, which were amplified with the following primers: nirK1F/nirK5R for nirK gene [23], cd3aF/R3cd for nirS gene [20], nosZ-F [24] and nosZ1622R [20] for nosZ gene. Specifics of PCR assay for functional gene sequencing are shown in Table 2. Purified PCR products were quantified using the NanoDrop spectrophotometer (NanoDrop ND-2000c Technologies, Inc., Wilmington, DE, U.S.A.) and pooled with an equal molar amount for each sample. High throughput sequencing was performed using the Illumina Miseq 2000 sequencer (Illumina, San Diego, CA, U.S.A.). 2.6. Statistical analysis The copy numbers of nirK, nirS, nosZ and bacterial 16S rRNA gene were transformed to log10 values. Data were compared using the least significant difference at a probability level of 0.05 using the one-way ANOVA with R program (version 3.1.2) (http://www.r-project.org/). High throughput sequencing of PCR amplicons of nirK, nirS, and nosZ were analyzed as follows: forward and reverse sequences were assembled using software Mothur 1.39 using the default setting [27], and they were assigned to each sample based on barcodes and primers. To identify the translation frames, a standalone BLASTX analysis (1e10) was performed with sequenced functional gene as query and the corresponding database made from the curated nirK, nirS, and nosZ 2

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Table 1 Types, rates and application date of pesticides in four growing seasons. Growing seasons

CON

INT

ORG

Cauliflower

November 26, 2013: Kocide 600 times.

November 26, 2013: Kocide 600 times.

November 26, 2013: Kocide 600 times.

Eggplant

May 01, 2014: Gray mold lore 400 times; May 07, 2014: Gray mold lore 400 times; May 18, 2014: Emamectin Benzoate 400 times, acetamiprid 600 times; June 01, 2014: Emamectin Benzoate 400 times, acetamiprid 600 times; June 13, 2014: Emamectin Benzoate 400 times, acetamiprid 600 times; June 21, 2014: Avermectin 300 times; July 05, 2014: Avermectin 300 times; July 15, 2014: Emamectin Benzoate 300 times, acetamiprid 400 times; July 26, 2014: Emamectin Benzoate 300 times, acetamiprid 400 times; August 10, 2014: Emamectin Benzoate 300 times, acetamiprid 400 times.

May 01, 2014: Gray mold lore 400 times; May 07, 2014: Gray mold lore 400 times; May 24, 2014: Emamectin Benzoate 400 times, acetamiprid 600 times; June 01, 2014: Emamectin Benzoate 400 times, acetamiprid 600 times; June 13, 2014: Emamectin Benzoate 400 times, acetamiprid 600 times; June 21, 2014: Avermectin 300 times; July 05, 2014: Avermectin 300 times; July 15, 2014: Emamectin Benzoate 300 times, acetamiprid 400 times; July 26, 2014: Emamectin Benzoate 300 times, acetamiprid 400 times; August 10, 2014: Emamectin Benzoate 300 times, acetamiprid 400 times.

May 01, 2014: brassinolide 600 times; May 07, 2014: Bamboo vinegar 200 times, Matrine 300 times; May 13, 2014: Bamboo vinegar 200 times, Matrine 300 times; May 18, 2014: Bamboo vinegar 200 times, Matrine 300 times; June 01, 2014: Bamboo vinegar 200 times, Matrine 300 times; June 13, 2014: Bamboo vinegar 200 times, Matrine 300 times; June 21, 2014: Bamboo vinegar 200 times, Matrine 300 times; July 05, 2014: Bamboo vinegar 200 times, Matrine 300 times; July 15, 2014: Bamboo vinegar 200 times, Matrine 300 times; July 26, 2014: Bamboo vinegar 200 times, Matrine 300 times; August 10, 2014: Bamboo vinegar 200 times, Matrine 300 times.

Celery

November 16, 2014: Kocide 600 times (25 g).

November 16, 2014: Kocide 600 times (25 g).

November 16, 2014: Kocide 600 times (25 g); December 21, 2014: Kocide 600 times (25 g).

Eggplant

May 20, 2015: Kocide 600 times (25 g).

May 20, 2015: Kocide 600 times (25 g).

May 20, 2015: Kocide 600 times (25 g).

with CON (average: 28.73 and 22.03 mg kg−1 dry soil, respectively). The soil pH values were lower in ORG (average: 7.51) than those in CON (average: 7.73), and the soil temperature in the three systems shown the following trend: CON > INT > ORG.

gene sequences downloaded from the RDP database (http://rdp.cme. msu.edu/). Only sequences with no stop codon within their deduced amino acid sequences (AAs) were included for further analysis. AAs of the remaining sequences were further subjected to hmmscan analysis to identify nirK, nirS, and nosZ genes sequences. AAs of nirK, nirS, and nosZ were further assigned to OTUs (> 95% sequences identity) using the program VSEARCH to construct an OTU table [28,29]. Alpha diversity index was calculated for each sample using the same number of sequences reads by the R-addon package vegan. UPGMA cluster analysis was performed to compare the community compositions based on the relative abundance of OTUs using Bray-Curtis distance. Redundancy analysis (RDA) was carried out using R program (version 3.1.2) (http:// www.r-project.org). The most dominant OTUs in each sample were selected for heatmap analysis. Network analysis was performed to detect denitrifying bacterial modules using the software Gephi (version 0.9.1) [30]. All statistical analyses and plots were performed with R program (version 3.1.2) (http://www.r-project.org/). Tools mentioned above have been implemented into a galaxy instance (www.freebioinfo. org). All sequences retrieved in this study were deposited at the NCBI SRA (https://www.ncbi.nlm.nih.gov/sra/?term=) under the accession numbers PRJNA545880, PRJNA545927, and PRJNA546063.

3.2. The abundance of denitrifying microorganisms in three farming systems Bacterial abundance as indicated by qPCR analysis of bacterial 16S rRNA gene was significantly higher in ORG than that in CON (Fig. 2a), although they fluctuated dramatically over the entire sampling time (p < 0.001, Fig. S1a), with values ranging from 3.16 × 107 to 1.47 × 109, 8.32 × 107 to 3.55 × 109, and 9.68 × 107 to 4.77 × 109 copies g−1 dry soil in CON, INT and ORG, respectively. Increased bacterial abundance was often detected after basal fertilization, especially when the bacterial abundance was low (2.82 × 108 to 4.47 × 108) (Fig. S1a). No significant effects of systems on the abundances of nirK and nirS were detected (Fig. 2b and c). Similar to bacterial 16S rRNA gene, the abundances of nirK and nirS genes also fluctuated over sampling times (nirK: 1.38 × 106 to 5.13 × 107, 9.85 × 105 to 5.94 × 107, and 1.34 × 106 to 7.30 × 107 copies g−1 dry soil in CON, INT and ORG, respectively; nirS: 4.98 × 105 to 3.87 × 107, 1.10 × 106 to 2.55 × 107, and 1.30 × 106 to 7.52 × 107 copies g−1 dry soil in CON, INT and ORG, respectively. Figs. S1b and S1c). Moreover, nirK abundances were often higher in samples taken from soil with low temperature, while the opposite trend was observed for nirS (Figs. S1b and S1c), indicating that temperature may serve as a selective pressure between nirK- and nirS-type of nitrite reducing bacteria. Abundances of nosZ gene fluctuated less than those of nirK and nirS genes (Fig. S1d), ranging from 1.41 × 106 to 2.54 × 108, 2.41 × 106 to 3.28 × 108, and 6.31 × 106 to 2.27 × 108 copies g−1 dry soil in CON, INT and ORG, respectively. Interestingly, the average abundance of nosZ gene was significantly higher in ORG than in INT and CON (Fig. 2d).

3. Results 3.1. Soil physicochemical characteristics The physicochemical properties of soil were considerably different among three systems (Fig. 1a–e, results of available P and available K are shown in Table S1. Data of STN and water-filled pore space (WFPS, %) were published in another study [17]. Detailed information on STN, SOM and soil pH are shown in Table S2). In particular, clearly higher STN, SOM, WFPS, and NH4+-N content were observed in ORG (average: 3.08 g kg−1, 47.27 g kg−1, 64.58%, and 7.18 mg kg−1 dry soil, respectively) than those in CON (average: 1.57 g kg−1, 22.85 g kg−1, 55.52%, and 4.02 mg kg−1 dry soil, respectively). The NO3−-N content was also slightly higher (p = 0.302) in ORG compared 3

European Journal of Soil Biology 97 (2020) 103153

[21]

Touchdown PCR: 95 °C for 5 min; 6 cycles of 30 s at 95 °C, 30 s at 65-60 °C (−1 °C each cycle), 30 s at 72 °C; 34 cycles of 15 s at 95 °C, 15 s at 60 °C, 30 s at 72 °C. Touchdown PCR: 94 °C for 2 min; 10 cycles of 30 s at 94 °C, 30 s at 57–52.5 °C(-0.5 °C each cycle), 40 s at 72 °C; 25 cycles of 30 s at 94 °C, 30 s at 55 °C, 40 s at 72 °C; 7 min at 72 °C. Touchdown PCR: 50 °C for 2 min, 95 °C for 2 min; 6 cycles of 30 s at 95 °C, 30 s at 65-60 °C (−1 °C each cycle), 30 s at 72 °C; 24 cycles of 15 s at 95 °C, 30 s at 62 °C, 30 s at 72 °C. 98 °C for 2 min; 35 cycles of 98 °C for 10 s, 60 °C for 30 s, 72 °C for 20 s; 72 °C for 5 min nirS

25 μL Premix Taq™ (Takara), 4 μL of each primer (10 μM), 5 μL of BSA (5 mg mL−1), 1 μL of DNA template, 11 μL sterilized deionized and free of nucleases water.

3.4. Soil physicochemical properties associated with abundance and community of denitrification genes Co-occurrence of network analysis was applied to indicate the interaction between denitrifying bacteria and soil physicochemical properties and five hubs were identified (Fig. 5). The first hub (green) was mixed with denitrifying bacteria from Alpha-, Beta- and Gammaproteobacteria (see Table S3), which were positively correlated with soil temperature and the content of NO3−-N. Interestingly, three OTUs (Azovibrio restrictus and Skermanella aerolata) of nirS gene were positively correlated with its abundance. The second hub (purple) mainly included nirK- and nosZ-type denitrifying bacteria from Alpha-, Betaand Gamma-proteobacteria and they were positively correlated with soil AK, WFPS, STN and SOM (see Table S3). The copy numbers of nosZ gene were also positively correlated with those OTUs affiliated to Thauera sp. MZ1T, Pseudomonas, Pseudogulbenkiania sp. KS32B and Lysobacter sp. A03 which were all nosZ-denitrifiers. The third hub (blue) was positively correlated with pH and contained denitrifying bacteria from Alpha-, Beta- and Gamma-proteobacteria (see Table S3). The fourth hub (red) also mainly consisted of nirK- and nosZ-type denitrifying bacteria from Alpha- and Beta-proteobacteria and they were positively correlated with soil temperature (see Table S3). Five OTUs (Bosea thiooxidans, Bradyrhizobium sp. URHA0002 and Ensifer shofinae) were also positively correlated with the copy numbers of nirK gene.

nosZ

), 1.6 μL of DNA nirK

25 μL Premix Taq™ (Takara), 1.6 μL of each primer (10 μM), 4 μL of BSA (5 mg mL template, 16.2 μL sterilized deionized and free of nucleases water. The same with nirK gene.

−1

To acquire an in-depth understanding of the diversity and community successions of denitrifying microorganisms, amplicon sequencing for nirK, nirS, and nosZ genes was used in this study. In total 97,883, 392,481, and 309,619 AAs were acquired for the nirK, nirS, and nosZ genes, respectively. These sequences were assigned into 8,475, 10,839, and 104,931 OTUs (> 95% similarity). BLASTP (P < 1e-10) analysis indicated that the nirK genes were affiliated to Alpha-proteobacteria (87.5%) and Beta-proteobacteria (12.5%); the nirS and nosZ genes were affiliated to Beta-proteobacteria (50% for nirS and 72.3% for nosZ), Gamma-proteobacteria (33.3% for nirS and 10.8% for nosZ), and Alphaproteobacteria (16.7% for nirS and 16.9% for nosZ). No clear effects of farming system on the Chao1 diversity index of all three genes were detected (Fig. 3a–c). NMMD analysis shown that community compositions of these denitrifying bacteria mainly changed over sampling time, but they were less affected by farming system (Fig. 4a–c). Variation partition of the community composition further shown that the growing stages could explain 36%, 53%, and 15% of the variation for the nirK, nirS, and nosZ genes, respectively, whereas the farming system only explained 4%, 4%, and 1% of the variation for the nirK, nirS, and nosZ genes, respectively. Dominant nirK OTUs (Clusters 5 and 6) occurred in most soil samples (> 71% for cluster 6 and 23–83% for Cluster 5) (Fig. S2), suggesting that they were the core denitrifying microbiomes. The OTUs in Clusters 1–4 were mainly dominant for a transient period (Fig. S2). These results indicated that the nirK populations exhibited more irregular patterns of oscillation. Succession of the dominant nirS OUTs over the growing season was also observed among different farming systems (Fig. S3). The nirS population in Cluster 2 (> 89% similarity to Azovibrio restrictus, Bradyrhizobium oligotrophicum, and Balneatrix alpica) were dominant in most soils (Fig. S3). Interestingly, six of the nirS OTUs (> 88% similarity to Azovibrio restrictus, Skermanella aerolata, and Balneatrix alpica) periodically appeared as dominant populations (Fig. S3), suggesting that some of the nirS populations fluctuated cyclically. Succession of the most dominant nosZ OUTs over the growing season occurred periodically except for Clusters 1 and 2, whereas the OTUs in Cluster 5 were dominant in most soils (Fig. S4).

PCR

nosZ

[26]

[23] Touchdown PCR: 50 °C for 2 min, 95 °C for 2 min; 6 cycles of 30 s at 95 °C, 30 s at 65-60 °C (−1 °C each cycle), 30 s at 72 °C; 24 cycles of 15 s at 95 °C, 30 s at 62 °C, 30 s at 72 °C. nirS

[23]

[19] Touchdown PCR: 50 °C for 2 min, 95 °C for 15 min; 6 cycles of 15 s at 95 °C, 30 s at 63-58 °C(-1 °C each cycle), 30 s at 72 °C; 34 cycles of 15 s at 95 °C, 30 s at 61 °C, 30 s at 72 °C. nirK

[25]

[22] 16S rRNA

2.5 μL dNTPs, 0.25 μL Taq (2.5U), 2.5 μL 10 × Buffer, 1.25 μL of BSA (5 mg mL−1), 0.125 μL probe, 0.3 μL 1369F (10 μM), 0.25 μL 1492R (10 μM), 2.5 μL of DNA template (10-fold dilution), 15.325 μL sterilized deionized and free of nucleases water. 12.5 μL SYBR Premix Ex Taq (TaKaRa Biotech, Dalian, China), 0.6 μL of each primer, 1 μL of BSA (5 mg mL−1), 2.5 μL of DNA template (10-fold dilution), 7.8 μL sterilized deionized and free of nucleases water. 12.5 μL SYBR Premix Ex Taq (TaKaRa Biotech, Dalian, China), 0.8 μL of each primer, 1 μL of BSA (5 mg mL−1), 2 μL of DNA template (10-fold dilution), 7.9 μL sterilized deionized and free of nucleases water. The same with nirS gene.

94 °C for 5 min; 40 cycles of 10 s at 95 °C, 1 min at 56 °C, 1 min at 60 °C.

3.3. Diversity of microbial populations associated with denitrification in three farming systems

qPCR

Reaction system (25 μL for qPCR, 50 μL for PCR) Target genes

Table 2 Specifics of qPCR and PCR assay for 16S rRNA gene and functional genes.

Cycling conditions

References

H. Han, et al.

4

European Journal of Soil Biology 97 (2020) 103153

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Fig. 1. Soil physiochemical characteristics (a–d) and surface layer mean temperature (e) under three farming systems. Values of each system in each figure are means of 57 replicates from 19 samplings. Different letters in each figure are significantly different according to LSD at p < 0.05. CON: the conventional system; INT: the integrated system; ORG: the organic system.

4. Discussion

that denitrifying microorganisms were enriched in soil that was organically managed. This result was consistent with other studies in which the abundances of nosZ, nirK and nirS gene were higher in the treatment with organic fertilizer than that with mineral fertilizer [13,34]. However, the result seemed to be in disagreement with lower emission of N2O measured in the ORG in the same study site [7,17] as increased nirK or nirS activities frequently result in higher N2O emission [15]. It is worth noting that the nosZ gene that encodes N2O reductase was statistically higher in ORG than that in CON. Thus, it is possible that consumption of N2O was also elevated in ORG; although further studies are required to confirm this. The ratio of nosZ/(nirS + nirK) was suggested as a good indicator for estimating the N2O emissions [31]. In the present study, the ratio was statistically higher (p < 0.05) in ORG (3.38) than that in CON (2.14) and INT (2.03). However, it is worth nothing that relative abundances of the nirS and nosZ gene were comparable among three systems and that of nirK was significantly lower in ORG than that in CON and these results are still in agreement with the metagenomics analysis [35]. The abundance rather than the fraction of these denitrifying bacteria might be more indicative of their activity in soil. We also found that temperature was negatively correlated with

The role of N-cycling microorganisms in the emission of N2O has been well established [15,31]; however, it is still not understood how their diversity is shaped by agricultural managements, which is especially relevant to better understand the draw-backs or benefits of ecological intensifications in agroecosystems. Because microbial communities are often sensitive to environmental perturbations [32], studies based on few observations may be limited in deciphering the pattern of complex microbial successions in soil, where microbial diversity is extremely high. Several environmental benefits such as increase in organic matter and suppression of soil borne disease associated with organic farming have been reported previously [14,33]. 4.1. The influence of organic farming on the abundance of denitrifying microorganisms The abundance of nosZ gene were significantly higher in the ORG than those in the CON, and the abundances of nirK (p = 0.281) and nirS (p = 0.222) were slightly higher in ORG than those in CON, suggesting 5

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Fig. 2. Gene copy numbers per gram dry soil of bacterial 16S rRNA gene (a), nirK gene (b), nirS gene (c) and nosZ gene (d) in different systems. Values of each system in each figure are means of 57 replicates from 19 samplings, and different letters are significantly different according to LSD (p < 0.05). CON: the conventional system; INT: the integrated system; ORG: the organic system.

Although further studies at mutlitrophic levels are needed, it is possible that these dominant denitrifying bacteria were killed after their flush. Interestingly, few OTUs which may escape the hunting, were persistent in most samples. Co-occurrence network analysis was a useful tool to indicate potential interaction among complex microbial communities [42]. Previously, microbial interactions in soil under organic farming were suggested to be more intensive than those from conventional farming systems [43]. Using this approach, we discovered that different denitrifying microorganism formed microbial hubs (Fig. 5), suggesting that they may work cooperatively. OTUs in the first hub were also positive correlated with soil temperature and majority of them were affiliated to Alpha-proteobacteria. These results were in agreement with the assumption that proteobacterial phyla were more sensitive to soil temperature [44]. The content of NO3−-N was positively correlated with 19 OTUs (6 OTUs of nirK gene, 5 OTUs of nirS gene, 8 OTUs of nosZ gene). Under anaerobic conditions, nitrate can serve as an electron accepter for denitrifying bacteria [45]. Some OTU of nirK- and nosZ-type denitrifiers were also correlated with pH, which was suggested to be key factor influencing bacterial alpha-diversity in soil or the ratio between ammonia oxidizing Archaea and ammonia oxidizing Bacteria [46,47]. It is known that optimum pH for growth was different among different species of denitrifying bacteria [48–50]. Taken together, these results indicated that different denitrifying bacteria may work cooperatively and vary in their physiological properties. However, it should be noted that due to the spatial resolution as applied in this study when working with DNA extracted from 0.5 g of soil, co-occurrence of taxa is not necessarily directly linked to interactions, but only an indication, thus requiring additional experimental conformation. Here, it is also to worth to mention that nitrogen cycling in soil involves several steps and a complex microbial community with different functional potentials and activities, such as those involved in nitrate and nitric oxide reduction [51], dissimilatory nitrate reduction

nirK and positively correlated with nirS gene abundance, indicating that temperature may serve as a selective pressure between nirK- and nirStype of nitrite reducing bacteria, which partly explained the fluctuation. Other studies also suggested that these two nitrite reducing bacteria occupy different niches (nirK-type bacteria peaked when temperature was low and nirS-type bacteria peaked when nitrate or pH was high) [31,36]. Moreover, nitrification could also contribute to the N2O in the soil. In a previous study, relative abundance of genes in ammonia oxidation in ORG was significantly lower than those in CON [35], which may also be a reason for lower N2O in ORG. 4.2. The influence of organic farming on the diversity of microbial populations associated with denitrification An in-depth analysis of denitrifying microorganisms could help decipher the patterns of microbial succession in complex microbial communities. The detected diversity was higher than that observed in previous studies using clone library [37,38] and this result suggested that the diversity of denitrifying bacteria was underestimated. Here nirK genes were mainly affiliated to three gene clusters (nirK cluster IIII) and nirS genes were mainly affiliated to two clusters (nirS cluster I and II). NosZ genes shared close similarities with those from clade I. Similar findings were also reported in other studies [10,39]. These denitrifying bacteria were likely prevalent in arable soils. A majority of denitrifying genes were similar to those from Proteobacteria, several members of which were suggested be copiotrophic [40]. In addition, some nirK populations exhibit more irregular pattern of oscillation while those of nirS or nosZ were periodically appeared as dominant populations (Figs. S2–S4). Succession of dominant denitrifying populations irregularly or periodically was in agreement with the assumption of “killing the winner”, which suggested that dominant bacterial populations may be predated by protozoans or killed by viruses [41]. 6

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Fig. 3. Chao1 index of nirK- (a), nirS- (b) and nosZ- (c) type denitrifier in three systems. The same letter in each figure indicates no significant difference according to LSD at p < 0.05. CON: the conventional system; INT: the integrated system; ORG: the organic system.

to ammonium [52,53] as well as those nitrifiers [54] possibly also play key roles on nitrogen cycling and thus the emission of N2O. Previously, the relative abundance of bacteria-carrying periplasmic nitrate reductases encoded by napA gene were higher for ORG as revealed by a metagenomic analysis [35]. In addition to DNA-based analysis, transcriptomics and proteomics could deepen the understanding on the functions of microorganisms associated with nitrogen cycling. However, effects of organic farming on the dynamics and functions of these populations were remained to be explored. In conclusion, long-term organic farming enriched the nitrous oxide reducing bacterial populations, while having little effect on nitrite reducing populations which mainly succeeded seasonally.

Fig. 4. NMMD of community composition of nirK- (a), nirS- (b) and nosZ- (c) denitrifiers in three systems in different samplings. CON: the conventional system; INT: the integrated system; ORG: the organic system. Different colors refer different sampling times. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Declaration of competing interest None. 7

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Fig. 5. Correlations between denitrifying bacteria and soil physicochemical properties under three systems. Each color stands for a different microbial hub and connections indicate significant (p < 0.05) co-occurrence between the taxa. The unit of qPCR is log (copies g−1 dry soil). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Acknowledgments

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