RESEARCH ARTICLE

Methane emission and dynamics of methanotrophic and methanogenic communities in a flooded rice field ecosystem Hyo Jung Lee1, Sang Yoon Kim2, Pil Joo Kim2, Eugene L. Madsen3 & Che Ok Jeon1 1

Department of Life Science, Chung-Ang University, Seoul, Korea; 2Division of Applied Life Science, Gyeongsang National University, Jinju, Korea; and 3Department of Microbiology, Cornell University, Ithaca, NY, USA

Correspondence: Che Ok Jeon, School of Biological Sciences, Chung-Ang University, 84, HeukSeok-Ro, Dongjak-Gu, Seoul 156756, Korea. Tel.: +82 2 820 5864; fax: +82 2 825 5206; e-mail: [email protected] Received 31 July 2013; revised 3 January 2014; accepted 3 January 2014. Final version published online 5 February 2014. DOI: 10.1111/1574-6941.12282 Editor: Gary King

MICROBIOLOGY ECOLOGY

Keywords microbial communities; rice paddy; methane emission; methanogen; methanotroph.

Abstract Methane emissions, along with methanotrophs and methanogens and soil chemical properties, were investigated in a flooded rice ecosystem. Methane emission increased after rice transplantation (from 7.2 to 552 mg day 1 m 2) and was positively and significantly correlated with transcripts of pmoA and mcrA genes, transcript/gene ratios of mcrA, temperature and total organic carbon. Methane flux was negatively correlated with sulfate concentration. Methanotrophs represented only a small proportion (0.79–1.75%) of the total bacterial 16S rRNA gene reads: Methylocystis (type II methanotroph) decreased rapidly after rice transplantation, while Methylosinus and unclassified Methylocystaceae (type II) were relatively constant throughout rice cultivation. Methylocaldum, Methylobacter, Methylomonas and Methylosarcina (type I) were sparse during the early period, but they increased after 60 days, and their maximum abundances were observed at 90–120 days. Of 33 218 archaeal reads, 68.3–86.6% were classified as methanogens. Methanosaeta, Methanocella, Methanosarcina and Methanobacterium were dominant methanogens, and their maximum abundances were observed at days 60–90. Only four reads were characteristic of anaerobic methanotrophs, suggesting that anaerobic methane metabolism is negligible in this rice paddy system. After completing a multivariate canonical correspondence analysis of our integrated data set, we found normalized mcrA/pmoA transcript ratios to be a promising parameter for predicting net methane fluxes emitted from rice paddy soils.

Introduction Methane (CH4), the second most important greenhouse gas after carbon dioxide (CO2), is responsible for about 18% of human-induced radiative forcing (Bridgham et al., 2013). Because the CH4 molecule has 25 times the global warming potential of the CO2 molecule, small changes of CH4 in the atmosphere significantly contribute to global warming (Bridgham et al., 2013). Rice paddies, which are cultivated worldwide on 155 million hectares, contribute approximately 5–19% to annual atmospheric CH4 emissions and are considered important anthropogenic CH4 sources along with landfills, livestock, fossil fuel production and biomass burning (Ma et al., 2010). Moreover, an increase in rice paddy area by 35% worldwide may be required to meet nutritional needs of the FEMS Microbiol Ecol 88 (2014) 195–212

increasing world population in the next two decades (Nguyen & Ferrero, 2006). Methane flux to the atmosphere from many ecosystems is governed by complex communities of diverse microorganisms, including hydrolytic, fermenting, syntrophic, methanogenic and methanotrophic microorganisms (Conrad, 2007). CH4 emissions are determined mainly by the net balance between the activities of methanogens and methanotrophs. Therefore, numerous prior studies of rice paddy ecosystems have focused on methanogens and methanotrophs, as a strategy for obtaining a better understanding of CH4 metabolism (Conrad, 1996, 2007; Liesack et al., 2000; Shrestha et al., 2010; Bridgham et al., 2013; Mills et al., 2013; Watanabe et al., 2013). Methanogens comprise phylogenetically diverse taxa belonging to the phylum Euryarchaeota. Many families [e.g. Methanobacteriaceae, ª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

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Methanomicrobiaceae, Methanosaetaceae, Methanosarcinaceae and Methanocellaceae (formerly Rice cluster I)] have been identified from rice paddy soils as ecologically important methanogens (Großkopf et al., 1998; Fey & Conrad, 2000; Chin et al., 2004; Daebeler et al., 2013). Methane is metabolized aerobically as well as anaerobically. Aerobic methanotrophs are found within the Proteobacteria and Verrucomicrobia; the former can be broadly divided into two physiologically and phylogenetically different groups: type I and type II methanotrophs (Trotsenko & Murrell, 2008). Anaerobic oxidation of methane (AOM) is carried out by a syntrophic consortium consisting of anaerobic methanotrophic archaea (ANME) and sulfate-reducing bacteria (Orphan et al., 2001). Alternatively, anaerobic methanotrophy has been shown to be coupled to denitrification (Ettwig et al., 2010) and both iron and manganese reduction (Beal et al., 2009). The composition and abundances of methanogens and methanotrophs in rice paddy soils are dynamic. Populations are anticipated to respond to environmental factors, especially those associated with rice-cultivation practices such as soil management, continuous cropping, proximity to the rice plant (rhizosphere vs. bulk soil), maturity of the rice plant, fertilizers, geographical locations, rice cultivars, latitude and flooding. These same factors affect CH4 emissions from rice paddies (Fey & Conrad, 2000; Henckel et al., 2000; Mohanty et al., 2007; Conrad et al., 2008, 2009; Wu et al., 2009; Krause et al., 2010, 2012; Shrestha et al., 2010; Ho et al., 2011; L€ uke et al., 2011; Ma et al., 2012; Reim et al., 2012; Watanabe et al., 2013). Because CH4 emissions from rice paddies are a result of multiple simultaneous processes (such as CH4 formation, oxidation and transport), integrated investigations on methanogens, methanotrophs and environmental factors are required to more clearly understand CH4 fluxes emerging from rice paddies (Conrad, 2002). Numerous studies investigated the diversity of methanogens and methanotrophs and the effects of environmental factors on CH4 emissions in rice paddies (Bosse & Frenzel, 1997; Henckel et al., 1999; Liesack et al., 2000; Eller & Frenzel, 2001; Horz et al., 2001; Peng et al., 2008; L€ uke et al., 2010, 2014; Ma et al., 2010; Shrestha et al., 2010; Ahn et al., 2012; Singh et al., 2012) and terminal-restriction fragment length polymorphism (T-RFLP) analysis of 16S rRNA genes has been mainly used to analyse the diversity of methanogens and methanotrophs. However, because T-RFLP produces limited information about microbial communities, relationships between CH4 emissions and the integrated composition of methanogenic and methanotrophic communities are still not clear. Pyrosequencing has the potential to better characterize the composition of microbial communities dwelling in natural habitats, ª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

H.J. Lee et al.

perhaps revealing rare populations such as methanotrophs that comprise as little as 1% of the total population (Sogin et al., 2006; Roesch et al., 2007). In the present study, a barcoded pyrosequencing approach was applied to investigate the communities of Bacteria, Archaea, methanotrophs and methanogens in conjunction with the analysis of soil chemical properties in a flooded rice field ecosystem throughout an entire rice-cropping period. Additionally, we measured CH4 emission rates and expression levels of genes encoding particulate methane monooxygenase alpha subunit (pmoA) and methyl-coenzyme M reductase alpha subunit (mcrA), key enzymes for aerobic methane oxidation and methanogenesis, respectively, in rice paddies.

Materials and methods Rice field experiments

Rice field experiments were carried out at the research farm of Gyeongsang National University located in Sacheon, South Korea (35°10′90″N, 128°11′84″E) following the Korean standard rice cultivation guidelines (RDA, 1999). The rice paddy soil had a silt loam soil texture (20% clay, 55% silt, 25% sand) and had been tilled once a year for the previous decade. The rice paddy was treated by a regular tillage practice (plowing and harrowing) and flooded with water up to about 5 cm above the soil surface. After 1 week of flooding, chemical fertilizers corresponding to 55 kg-N (as urea), 45 kg-P2O5 (as super phosphate) and 40.6 kg-K2O (as potassium chloride) per hectare were applied, and 21-day-old seedlings of Korean rice cultivar ‘Nampyeongbyeo’ (Oryza sativa, Japonica type) were transplanted with spacing of 30 9 15 cm (three plants per hill) on 4 June 2011. The day of the rice transplantation was marked as day 0. Tillering fertilizer corresponding to 22 kg-N ha 1 (as urea) and panicle fertilizer corresponding to 33 kg-N ha 1 (as urea) and 17.4 kg K2O ha 1 (as potassium chloride) were applied at 14 and 42 days, respectively. Water level was maintained at ~5-cm depth during the rice growing period (until 90 days), and the rice paddy was not irrigated until the end of the experiment. The rice plants were harvested at 136 days (17 October 2011). Measurement of CH4 emission rates

CH4 emissions from the rice paddies were periodically measured using a closed chamber method as described previously (Ali et al., 2009; Lee et al., 2010). Briefly, air gas samples were collected from three square-shaped glass chambers (62 9 62 9 112 cm) covering eight rice hills using 50-mL gas-tight syringes at 0, 15 and 30 min after FEMS Microbiol Ecol 88 (2014) 195–212

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Microbial dynamics and CH4 emissions in rice paddies

closing the top of the chamber. Gas samplings were carried out three times (08:00, 12:00, 16:00) per day to obtain average CH4 emissions. CH4 concentrations were measured by a gas chromatograph (GC-2010, Shimadzu, Tokyo, Japan) equipped with a Porapak NQ column (Q 80–100 mesh) and a flame ionization detector. The temperatures of column, injector and detector were 100, 200 and 200 °C, respectively. Helium and H2 were used as carrier and burning gases, respectively. CH4 emission rates from the rice paddies were calculated from the increase of CH4 concentration for specific time intervals within the chambers as described previously (Lee et al., 2010). Soil sampling and analysis of soil properties

Every 30 days, soil samples (0–10 cm depth) were collected in triplicate from areas adjacent to the plant rhizosphere (~3 cm from rice hills) using soil core samplers with a diameter of 2.5 cm and a depth of approximately 10 cm (all soil samples were taken from different rice hills). The sampling time was 14:00–16:00 h, showing the uger et al., 2001). Each maximum CH4 emission rates (Kr€ soil sample was mixed well to avoid heterogeneity, and immediately frozen in a dry ice/ethanol bath, and then stored at 80 °C until further analyses. Soil pH values were measured according to ASTM International standard method D4972 (ASTM International, 2007). Ammonia concentrations were analysed colorimetrically by flow injection analysis (FIA Star 5000, Sweden). Nitrite, nitrate and sulfate concentrations were measured using an ICS1000 ion chromatograph (Dionex, Sunnyvale, CA) after the soil samples were resuspended in distilled water (1 : 2, w/v). Total organic carbon (TOC) and total nitrogen (TN) were analysed using an elemental analyzer (Flash EA 1112, CE Instruments, Italy), and the concentration of available phosphate was analysed using the Lancaster method (RDA, 1988). Temperature information was obtained from Korea Meteorological Administration (KMA; http://www.kma.go.kr). Quantitative PCR of 16S rRNA, pmoA and mcrA genes in extracted soil DNA

The abundances of Bacteria, Archaea, aerobic methanotrophs and methanogens in the rice paddies were estimated during the rice cultivation using quantitative realtime PCR (qPCR) according to the method described previously with some modifications (Lee et al., 2012). Briefly, 100 ng of salmon testes DNA (Sigma, St Louis, MO) was added to 0.5 g of the soil samples as an exogenous and internal standard, and then the total genomic DNA was extracted using a FastDNA Spin kit (MPbio, Santa Ana, CA) according to the manufacturer’s instructions. FEMS Microbiol Ecol 88 (2014) 195–212

For the measurement of the 16S rRNA gene copies of Bacteria and Archaea, two qPCR primer sets, bac1114F/bac1275R and arch349F/arch806R (Lee et al., 2012), targeting 16S rRNA genes of Bacteria and Archaea, respectively, were used. The abundances of aerobic methanotrophs and methanogens were also quantified by qPCR using two primer sets, A189f/mb661r (Kolb et al., 2003) and ML-F/ML-R (Luton et al., 2002), targeting pmoA and mcrA, respectively. The qPCR amplifications were conducted in triplicate as described previously (Jung et al., 2011). Sample-to-sample variations caused by different genomic DNA recoveries and PCR amplification efficiencies were normalized on the basis of qPCR results using the primer set Sketa2-F (5′-GGTTTCCGCAGCTG GG-3′)/R (5′-CCGAGCCGTCCTGGTCTA-3′), targeting the internal transcribed spacer region 2 of the rRNA gene operon in salmon testes DNA, as described previously (Haugland et al., 2005). Standard curves were generated for the calculations of gene copies on the basis of the numbers of pCR2.1 vectors (Invitrogen, Carlsbad, CA) carrying bacterial and archaeal 16S rRNA genes and pmoA and mcrA genes as described previously (Lee et al., 2012; Jung et al., 2013). The gene copy numbers in each rice paddy soil were calculated on a dry weight basis of soil by measuring dry weight of the rice paddy soil samples used. Quantitative reverse transcriptase PCR for the expressional analysis of pmoA and mcrA genes

For the analysis of pmoA and mcrA gene expressions, quantitative reverse transcriptase real-time PCR (qRT-PCR) was performed as described previously with some modifications (Lee et al., 2011). Briefly, to avoid mRNA degradation, 10 volumes of RNAlater-ICE (Ambion, Austin, TX) were added to 2 g of the soil samples and the samples were stored overnight at 20 °C. Total RNA from the stored soil samples was extracted using the RNA Power Soil Total RNA Isolation Kit (Mo Bio Laboratories,, Carlsbad, CA) based on the manufacturer’s instructions and the total RNA was treated with RNase-free DNase I (Qiagen, Valencia, CA). qRT-PCR using the two primer sets, A189f/ mb661r and ML-F/ML-R, was carried out with the iScript One-Step RT-PCR kit with SYBR Green (Bio-Rad, Hercules, CA) in a C1000 Thermal Cycler (Bio-Rad) in triplicate as described previously (Lee et al., 2011). The copy numbers of pmoA and mcrA gene transcripts were calculated on a dry weight basis of soil using the standard curves generated in the qPCR as done above. PCR amplifications for pyrosequencing

The same amounts of soil samples from the triplicates were well mixed to represent the overall microbial ª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

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communities in the rice paddies. Total genomic DNA from the mixed soil samples was extracted using a Fast-DNA Spin Kit (MPbio) according to the manufacturer’s instructions. For bacterial and archaeal 16S rRNA, pmoA and mcrA gene amplifications, Bac9F/Bac541R (Lee et al., 2012), Arc344F/Arc927R (Jurgens et al., 1997; Sørensen et al., 2004), A189f/A682r (L€ uke & Frenzel, 2011) and ML-f/mcrA-rev (Zeleke et al., 2013) were used, respectively, where X denotes unique 7–10 barcode sequences inserted between the 454 Life Sciences adaptor A sequence and the common linkers, AC and GA (Supporting information, Table S1). All PCR amplifications were performed in a 50-lL C1000 thermal cycler (Bio-Rad) containing a Taq polymerase mixture (Solgent, Seoul, South Korea), 1 lL template DNA and 20 pmol of each primer. Cycling regimes were as follows: 94 °C for 5 min (one cycle); 94 °C for 45 s, 60 °C (Bacteria) or 55 °C (Archaea, pmoA and mcrA) for 45 s, and 72 °C for 45 s (30 cycles); and 72 °C for 10 min (one cycle). The PCR products were purified using a PCR purification kit (Bioneer, Seoul, South Korea) and quantified using the Qubit dsDNA BR assay kit (Invitrogen) according to the manufacturer’s instructions. A composite DNA sample was prepared by pooling equal amounts of purified PCR amplicons from each sample and then analysed using a 454 GS-FLX Titanium system (Roche, Mannheim, Germany) at Macrogen (Seoul, South Korea). Data analysis of pyrosequencing reads

Pyrosequencing reads obtained were processed using the RDP pyrosequencing pipeline (http://pyro.cme.msu.edu/; Cole et al., 2009). Pyrosequencing reads were sorted to specific samples based on their unique barcodes, and the barcodes were then removed. Pyrosequencing reads with more than two ‘N’s (undetermined nucleotide) and/or a shorter than 300 bp read length were excluded from further analyses. For 16S rRNA gene sequences, putative chimeric reads were removed by the chimera.slayer command within the MOTHUR program (v. 1.31.2) (Schloss et al., 2009) and rarefaction curves were generated using the RDP pyrosequencing pipeline at a 97% similarity level. For pmoA and mcrA gene sequences, putative chimeric and frame shifting reads were removed using the USEARCH 6.0 and FRAMEBOT programs in the RDP functional gene pipeline, respectively. Rarefaction curves were generated using amino acid sequences of pmoA and mcrA genes derived from the FRAMEBOT program in the RDP functional gene pipeline at 93% (pmoA) and 89% (mcrA) identity cutoff values, respectively, which have been considered to be affiliated with a methanotroph and methanogen species, respectively (Steinberg & Regan, 2008; L€ uke & Frenzel, 2011). To compare microbial diversities ª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

H.J. Lee et al.

among samples, the numbers of 16S rRNA gene sequences and amino acid sequences of pmoA and mcrA genes were normalized to the lowest yield of reads among compared samples by randomly deleting sequencing reads from the sequencing fasta files using a perl script called selector.pl (Giongo et al., 2010). Operational taxonomic units (OTUs), Shannon–Weaver (Shannon & Weaver, 1963) and Chao1 biodiversity (Chao, 1987) indices, and evenness for 16S rRNA gene sequences and amino acid sequences of pmoA and mcrA from original and normalized sequencing reads were calculated using the pyrosequencing and functional gene pipelines in RDP, respectively. For bacterial and archaeal 16S rRNA gene sequences, taxonomic assignments were performed at the phylum, class and genus levels using the nearest-neighbor method within the MOTHUR program based on the SILVA database (v.102) (Pruesse et al., 2007). For pmoA and mcrA gene sequences, representative amino acid sequences of pmoA and mcrA were obtained through the complete linkage clustering in the RDP functional gene pipeline at 93% (pmoA) and 89% (mcrA) cutoff values and were assigned to their taxonomic affiliations by BLASTP comparisons to the GenBank nonredundant protein (nr) database and selections of the top BLASTP hits. The bacterial and archaeal communities of soil samples were compared using UniFrac analysis (Lozupone & Knight, 2005) based on the phylogenetic relationships of representative sequences derived from the respective soil samples. The representative sequences were selected using CD-HIT (Li & Godzik, 2006) with an identity cutoff of 97% and were aligned using NAST (DeSantis et al., 2006a) based on the greengenes database (DeSantis et al., 2006b), with a minimum alignment length of 300 bp and a minimum identity of 75%. Neighbor-joining trees were constructed using the PHYLIP software (ver. 3.68) with the Kimura two-parameter model (Felsenstein, 2002) and were used as input files for the weighted hierarchical clustering and principal coordinate analysis (PCoA). The weighted hierarchical clustering and PCoA were carried out using the sequencing data sets both before and after removing singletons as described by Zhou et al. (2011). Statistical analysis

To investigate the correlations among rice paddy soil samples, microbial communities and environmental factors, a multivariate canonical correspondence analysis (CCA) was performed using the package ‘VEGAN’ (Oksanen et al., 2011) in the R programming environment (http://cran.r-project.org). First, a biplot analysis was conducted between rice paddy soil samples and bacterial and archaeal communities that were classified at the FEMS Microbiol Ecol 88 (2014) 195–212

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genus level. Following the biplot analysis, environmental factors of Table 1 were introduced onto the ordination biplot, which was plotted as a CCA triplot. Pearson correlation coefficients and P values between methane emissions and various parameters including chemical properties of rice paddy soil (Table 1), pmoA and mcrA gene and transcript abundances, and pmoA and mcrA transcript/gene ratios during the rice cultivation period were calculated using the PASW Statistics 18 software (SPSS, Chicago, IL). Nucleotide sequence accession number

The pyrosequencing data of the 16S rRNA, pmoA and mcrA genes are publicly available in the NCBI Short Read Archive (SRA) under accession no. SRA068865.

Results Chemical properties of rice paddy soil and methane emission rates

Chemical properties of the rice paddy soil including pH, TOC, TN, sulfate, and available phosphate and CH4 emission rates were monitored every 30 days during the rice cultivation period (Table 1). After rice transplantation (day 0), pH values and TOC increased slowly until day 90, but the values decreased after irrigation stopped (120 and 150 days). Concentrations of TN and available phosphate were relatively constant during the entire rice cultivation. Ammonium concentrations decreased rapidly after 30 days and were maintained at low concentrations until rice harvest. Nitrite and nitrate concentrations were below detection levels during the entire cultivation period. Sulfate concentrations decreased rapidly during the flowering and heading stages (60 and 90 days after rice transplantation), but they increased quickly after rice harvest (150 days),

possibly reflecting an influx of oxygen and subsequent microbially mediated oxidation of sulfide. CH4 emission rates increased rapidly after rice transplantation, and the maximum CH4 emission rate was observed at 90 days. CH4 emission rates decreased quickly after irrigation practices stopped (120 days). Abundances of total Bacteria, Archaea, methanotrophs and methanogens in rice paddy soil

A qPCR approach based on 16S rRNA gene copies was applied to enumerate total Bacteria and Archaea in rice paddy soil during rice cultivation (Fig. 1a and b). After rice transplantation, the 16S rRNA gene copies of Bacteria decreased from ~3.5 9 109 to ~2.2 9 109 copies soil-gdw 1 (soil gram dry weight) at 30 days. Thereafter, the bacterial 16S rRNA gene copies increased to the highest value of ~8.6 9 109 copies soil-g-dw 1 at 90 days and then decreased again to ~2.4 9 109 copies soil-g-dw 1 at 150 days. The decrease and increase of the 16S rRNA gene copies of Archaea also occurred after rice transplantation, but the 16S rRNA gene copies of Archaea were observed at a maximum of ~4.2 9 107 copies soil-g-dw 1 at 120 days. Archaeal 16S rRNA gene copies decreased again to ~1.7 9 107 copies soil-g-dw 1 at 150 days. Total aerobic methanotrophs and methanogens in rice paddy soil during rice cultivation were also enumerated using qPCR based on the copies of pmoA and mcrA genes, respectively (Fig. 1c and d). Like bacterial and archaeal 16S rRNA gene copies, the decrease and increase of pmoA and mcrA genes also occurred after rice transplantation, and the maximum copies of pmoA and mcrA genes were observed at ~1.04 9 108 and ~2.34 9 107 copies soil-g-dw 1, respectively, at 90 days. The copy numbers of pmoA and mcrA genes also decreased to ~2.1 9 107 and ~1.2 9 107 copies soil-g-dw 1 (150 days), respectively, after irrigation practices stopped.

Table 1. Chemical properties of rice paddy soil and methane emission rates during the rice cultivation period Sampling time (day)*

pH

0 30 60 90 120 150

6.61 6.69 6.75 6.89 6.07 6.33

Total organic carbon (g-C)†      

0.08 0.10 0.09 0.08 0.11 0.13

9.3 9.9 10.6 11.5 10.5 9.7

     

0.9 0.2 0.2 0.1 0.3 0.1

Total nitrogen (g-N)† 0.98 1.05 1.05 1.11 1.10 1.02

     

0.10 0.02 0.02 0.03 0.02 0.01

Ammonia (mg-N)† 9.35 10.03 3.09 2.90 2.54 5.25

     

0.25 0.02 0.1 0.26 0.05 0.13

Sulfate (mg-S)† 17.8 15.0 2.1 5.2 5.1 28.3

     

Available P2O5 (mg)† 1.4 2.3 0.5 1.7 1.3 0.7

34.8 26.6 30.2 25.2 30.8 28.4

     

5.2 0.9 4.9 7.1 1.9 4.2

CH4 emission (mg day 1 m 2) 7.2 238.9 469.3 552.2 290.8 NA

    

0.6 68.5 82.0 127.5 75.9

Temp. (°C)‡ 18.2 24.0 26.1 25.7 17.3 13.0

NA, not analysed. *Sampling times of 0, 30, 60/90, 120 and 150 days indicate corresponding transplanting, tillering, flowering/heading, maturing and after harvesting stages, respectively. † The concentrations indicate the amounts of chemicals per 1 kg dried soil. ‡ The values represent mean temperatures for 10 days before sampling.

FEMS Microbiol Ecol 88 (2014) 195–212

ª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

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(a)

(b)

(c)

(d)

Fig. 1. Changes in the 16S rRNA gene copy numbers for total Bacteria (a) and Archaea (b) and the copy numbers of genes coding for particulate methane monooxygenase alpha subunit (pmoA, c) and methyl-coenzyme M reductase alpha subunit (mcrA, d) in the rice paddy soil during the rice cultivation period. All measurements were performed independently in triplicate. Bars indicate standard errors (n = 3). g-dw, gram-dry weight.

Expression of pmoA and mcrA genes in rice paddy soil

The pmoA and mcrA transcripts of methanotrophs and methanogens, respectively, were monitored in rice paddy soil during rice cultivation using qRT-PCR (Fig. 2a and b). Transcriptional levels of pmoA and mcrA genes increased rapidly after rice transplantation, and their maximum expressional levels were observed at 90 days. Expression levels of pmoA and mcrA genes decreased rapidly after irrigation practices stopped (120 and 150 days). The transcript/gene ratios of pmoA and mcrA were calculated using gene and transcript copy numbers of pmoA and mcrA (Fig. 2c and d). The mcrA transcript/gene ratios increased rapidly to a maximum of ~2.02 until 60 days after rice transplantation, and then decreased gradually until rice harvest. Probably in response methane formation, the pmoA transcript/gene ratios increased rapidly to their highest ª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

value of ~1.29 at 30 days after rice transplantation, and then decreased to ~0.34. After irrigation practices stopped, the pmoA transcript/gene ratio increased again (120 days). Bacterial and archaeal community analysis

To investigate the changes of bacterial and archaeal communities during rice cultivation, a barcoded pyrosequencing approach was applied. A total of 37 731 and 36 978 sequencing reads were generated from bacterial and archaeal PCR amplicons, respectively. After removing low-quality and chimera reads and trimming the PCR primers, a total of 24 366 and 33 218 high-quality reads with average read lengths of 488 and 514 bases were obtained for bacterial and archaeal sequences, respectively, and their statistical diversities in each rice paddy soil sample were calculated (Table 2). Failure to approach an asymptote in the rarefaction curves of the bacterial sequencing reads indicated that bacterial communities in the rice FEMS Microbiol Ecol 88 (2014) 195–212

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Microbial dynamics and CH4 emissions in rice paddies

(a)

(b)

(c)

(d)

Fig. 2. Transcript abundances (a,b) and transcript/gene ratios (c,d) of pmoA and mcrA in the rice paddy soil during the rice cultivation period. All measurements were performed independently in triplicate and error bars represent standard deviations. The ratios were calculated using copy numbers of pmoA and mcrA genes (Fig. 1c and d) and transcripts (Fig. 2a and b).

paddy soil were highly diverse (Fig. S1). In contrast, asymptotes were nearly approached in the rarefaction curves of the archaeal sequencing reads, which indicated that archaeal communities in the rice paddy soil were less diverse than the bacterial communities, and the archaeal sequencing reads were relatively sufficient in describing archaeal populations. Although the statistical diversity indices (particularly Chao1 and Shannon) are influenced by the number of sequencing reads obtained, these indices clearly showed that bacterial diversity decreased quickly after rice transplantation (30 days) and that diversity increased gradually during the flowering and heading stages of rice growth (60–90 days) (Table 1). On the other hand, after rice transplantation, archaeal diversities decreased steadily until 90 days and increased quickly after irrigation practices stopped (120 days). These trends in community diversity were also supported by diversity indices calculated from the normalized sequencing reads and rarefaction curves (Fig. S1). FEMS Microbiol Ecol 88 (2014) 195–212

The high-quality 16S rRNA gene sequencing reads of Bacteria and Archaea were classified at both the phylum and the class levels to investigate the changes of bacterial and archaeal communities during rice cultivation (Fig. 3). The bacterial classification at the phylum level showed that approximately 80% of the bacterial reads fell into only four phyla, Proteobacteria, Chloroflexi, Acidobacteria and Actinobacteria, during the entire rice cultivation period (Fig. 3a). More than 31 phyla, including Bacteroidetes, Gemmatimonadetes, Firmicutes, Nitrospirae, Planctomycetes and Cyanobacteria, were found as minor components. The class-level analysis of the bacterial sequencing reads showed that Anaerolineae, Caldilineae, Alphaproteobacteria, Betaproteobacteria, Deltaproteobacteria, Actinobacteria and Acidobacteria were dominant in the rice paddy soil (Fig. 3b). Only approximately 2.5% of the sequences from each sample remained unclassified at the class level. Changes of the bacterial relative abundances were not pronounced during the rice cultivation period. ª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

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Table 2. Summary of pyrosequencing data and statistical analysis of bacterial and archaeal microbial communities in rice paddy soil*

Sampling time (day) Bacteria 0 30 60 90 120 150 Archaea 0 30 60 90 120 150

Normalized‡

No. of high-quality reads

Avg. read length (bp)

Original OTUs

Shannon

Chao1†

5698 5143 2901 3510 3539 3575

457 474 467 466 470 469

3191 2177 1533 1896 2044 1996

7.64 7.06 6.93 7.14 7.28 7.22

9830 4963 3413 4494 5498 5443

     

6400 6263 4282 4409 6308 5556

514 515 515 515 511 513

252 233 182 174 444 300

4.31 4.16 4.05 3.87 4.73 4.30

373 310 291 286 639 531

     

Evenness

OTUs

Shannon

Chao1†

852 428 325 415 417 549

0.95 0.92 0.94 0.95 0.95 0.95

1794 1379 1553 1578 1696 1674

7.18 6.69 6.93 6.98 7.10 7.06

5613 3404 3413 3769 4416 4724

     

648 389 325 387 463 532

0.96 0.93 0.94 0.95 0.95 0.95

72 50 79 80 82 122

0.78 0.76 0.78 0.75 0.78 0.75

199 190 182 166 356 268

4.17 4.11 4.05 3.84 4.63 4.20

290 295 291 270 515 444

     

54 45 79 63 75 94

0.79 0.78 0.78 0.75 0.79 0.75

Evenness

*OTUs, operational taxonomic units. Diversity indices of the bacterial and archaeal communities in each sample were calculated using the RDP pipeline based on the 16S rRNA gene sequences at a 97% cutoff value. † Chao1values were calculated at a 95% confidence level. ‡ Bacterial and archaeal sequencing reads were normalized to 2901 and 4282 reads, respectively.

Classification of the archaeal sequencing reads showed that members of the class Methanomicrobia belonging to the phylum Euryarchaeota represented the predominant populations in rice paddy soil during the entire rice cultivation period (Fig. 3c and d). Halobacteria and Methanobacteria (belonging to the phylum Euryarchaeota) and Soil_Crenarchaeotic_Group (SCG) and South_African_ Gold_Mine_Gp_1 (SAGMG-1) (belonging to the phylum Crearchaeota) were also detected as prominent classes from the rice paddy soil. The predominant class, Methanomicrobia, increased gradually until 90 days after rice transplantation, but its relative abundance decreased quickly at 120 days and increased again at 150 days. The relative abundance of SCG decreased gradually after rice transplantation. Interestingly, Halobacteria and Methanococci were minor populations after rice transplantation, but their relative abundances increased noticeably after irrigation practices stopped (120 days, Fig. 3d). After rice transplantation, the relative abundance of Methanobacteria increased, and its maximum abundance was observed at day 60. On the other hand, SAGMG-1 was one of the dominant classes at the time of rice transplantation, but its abundance decreased very quickly and almost disappeared after 60 days of transplantation. To understand CH4 production and metabolism via microbial populations in rice paddy soil, the bacterial and archaeal sequencing reads were further classified at the genus level, and only the genera known as possible methanotrophs and methanogens are indicated in Fig. 4 (a and b), respectively. Only a low proportion (0.79–1.75%) of total bacterial sequencing reads in each rice paddy soil ª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

sample was assigned to methanotrophs associated with CH4 metabolism (Fig. 4a). The relative abundance of methanotrophs decreased gradually after rice transplantation, but their relative abundance began to increase after 60 days, and the maximum abundance was observed at 120 days. The relative abundance of methanotrophs decreased again at 150 days. The relative abundance of overall type II methanotrophs was relatively constant during the entire rice cultivation period, while the relative abundance of overall type I methanotrophs had a profile similar to that of the total methanotrophs, although the abundances of type I methanotrophic groups fluctuated (Fig. 4a). Methylocystis, Methylosinus and unclassified Methylocystaceae (belonging to type II methanotrophs) were generally dominant during the entire rice cultivation period. Methylocystis decreased quickly after rice transplantation, while Methylosinus and unclassified Methylocystaceae were relatively constant during rice cultivation, although their abundances fluctuated depending on the sampling time. Relative abundances of Methylocaldum, Methylobacter, Methylomonas and Methylosarcina belonging to type I methanotrophs were low during the early rice cultivation period (Fig. 4a). However, Methylocaldum and Methylobacter increased rapidly after 60 days and their maximum relative abundances were observed at 90 days. Methylomonas and Methylosarcina increased very quickly after irrigation practices stopped (120 days), and their maximum relative abundances were observed at 120 days. Most of the methanotrophs, especially Methylomonas and Methylosarcina, decreased very quickly after rice harvest (150 days), while

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(a)

(b)

(c)

(d)

Fig. 3. Bacterial (a,b) and archaeal (c,d) taxonomic compositions showing the changes of bacterial and archaeal communities in the rice paddy soil during the rice cultivation period. Bacterial and archaeal 16S rRNA gene sequences were classified at the phylum (a,c) and class (b,d) levels using the MOTHUR program based on the SILVA database. Other taxa shown in (a) and (b) represent phyla or classes, showing percentages of < 0.4% and < 1% for the total reads in all of the samples, respectively. Abbreviations: MBG, Marine_Benthic_Group_A; MCG, Miscellaneous_Crenarchaeotic_Group; SAGMG-1, South_African_Gold_Mine_Gp_1; SCG: Soil_Crenarchaeotic_Group.

the relative abundance of Methylomicrobium and unclassified Methylocystaceae increased even after rice harvest. Approximately 68.3–86.6% of the archaeal sequencing reads were classified as putative methanogens, with the potential to contribute to CH4 production in rice paddy soil (Fig. 5b). After rice transplantation, the relative abundance of methanogens increased gradually, and their maximum relative abundance was observed at 90 days. The relative abundance of methanogens decreased rapidly after irrigation practices stopped (120 days), but they increased again at 150 days. Members of Methanosaeta were the most dominant methanogens during the entire rice cultivation period (32.7–52.0% of the total methanogens), and their maximum relative abundance was observed at 90 days. Methanocella, Methanosarcina and Methanobacterium were detected as the prominent methanogens from the rice paddy soil, and their maximum relative FEMS Microbiol Ecol 88 (2014) 195–212

abundances were observed during the flowering and heading stages (60 and 90 days; Fig. 4b). Interestingly, Methanococcus and Candidatus_Methanoregula increased very quickly after irrigation practices stopped, and their maximum relative abundances were observed at 120 days. However, members of Methanococcus and Candidatus_Methanoregula were not detected from the rice paddy soil samples of 150 days. On the other hand, sequencing reads belonging to ANME were hardly detected from the archaeal reads during the entire rice cultivation period. Of 33 218 archaeal sequences obtained, only four sequencing reads were detected, two on day 0 and two in the day 150 sample. The diversity and community shifts of methanotrophs and methanogens were analysed using pmoA and mcrA gene sequences for a more in-depth analysis. A total of 11 361 and 31 593 high-quality pyrosequencing reads were generated from pmoA and mcrA gene amplicons, ª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

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(a)

(b)

(c)

(d)

Fig. 4. Changes in methanotrophs (a,c) and methanogens (b,d) detected from the rice paddy soil during the rice cultivation period. Bacterial and archaeal 16S rRNA gene sequencing reads were classified at the genus level using the MOTHUR software based on the SILVA database and only the genera known as methanotrophs (a) and methanogens (b) among all bacterial and archaeal genera were shown. The sequencing reads of pmoA and mcrA genes were assigned to their taxonomic affiliations of methanotrophs (c) and methanogens (d), respectively, by the complete linkage clustering in the RDP functional gene pipeline and BLASTP comparisons in GenBank.

respectively, after removing low-quality, putative chimeric and frame shifting reads, and their statistical diversities in each rice paddy soil sample were calculated (Table 3). Because only methanotrophs and methanogens among Bacteria and Archaea were analysed, asymptotes were approached in the rarefaction curves of the pmoA and mcrA gene sequences (Fig. S2), meaning that pmoA and mcrA gene sequences were probably sufficient for describing methanotroph and methanogen communities. The statistical diversity changes of pmoA and mcrA genes during the rice cultivation period did not perfectly match trends shown for bacterial and archaeal 16S rRNA genes (Tables 2 and 3). After rice transplantation, the diversities of methanotrophs gradually decreased until 90 days and then increased quickly after irrigation practices stopped (120 days). On the other hand, after rice transplantation, the diversities of methanogens steadily increased during the rice cultivation period, except at 90 days. The comª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

munity changes of methanotrophs and methanogens during the rice cultivation period were monitored based on pmoA and mcrA gene sequences (Fig. 4c and d). The taxonomic affiliations of methanotrophs based on the pmoA genes were quite different from those based on 16S rRNA genes, although major methanotrophs were similar. Discrepancies may have been caused by primer bias for pmoA amplification and/or by incomplete pmoA gene sequence information (Saidi-Mehrabad et al., 2013). However, taxonomic affiliations of methanogens based on the mcrA genes were quite similar to the results based on 16S rRNA gene sequences. Type II methanotrophs, including Methylosinus/Methylocystis and methanotroph K3–16, were predominant compared with type I methanotrophs, including Methylococcus, during the entire rice cultivation period, which was in accordance with the community shifts based on 16S rRNA gene sequences. Members of Methanosaeta, Methanocella, Methanosarcina FEMS Microbiol Ecol 88 (2014) 195–212

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Microbial dynamics and CH4 emissions in rice paddies

(a)

(b)

(c)

(d)

Fig. 5. The weighted UniFrac clustering (a,b) and PCoA (c,d) results showing the relationships of bacterial (a,c) and archaeal (b,d) communities from each rice paddy soil sample. The scale bars in trees represent the weighted UniFrac distances.

Table 3. Summary of pyrosequencing data and statistical analysis of methanotrophs and methanogens using amino acid sequences of pmoA and mcrA in rice paddy* Sampling time (day)

No. of high quality reads

Methanotrophs (pmoA) 0 1967 30 2020 60 1550 90 1692 120 2076 150 2056 Methanogens (mcrA) 0 5162 30 5250 60 5131 90 5195 120 6276 150 4579

Avg. read length (bp)

Normalized‡

Original OTUs

Shannon

Chao1†

Evenness

OTUs

Shannon

Chao1†

465 464 459 462 467 464

102 92 65 62 95 89

3.40 2.95 2.46 2.30 2.66 2.79

161 152 79 73 115 110

     

63 61 20 16 23 26

0.73 0.65 0.59 0.56 0.58 0.62

84 70 65 53 68 70

3.26 2.86 2.46 2.21 2.27 2.42

113 79 79 63 83 89

     

41 21 20 14 22 21

0.73 0.67 0.59 0.56 0.54 0.57

444 443 443 443 443 443

77 91 93 86 104 85

3.40 3.49 3.61 3.51 3.58 3.46

88 112 131 101 125 96

     

22 30 55 24 30 19

0.78 0.77 0.80 0.79 0.77 0.78

70 79 73 68 77 85

3.42 3.41 3.43 3.27 3.39 3.46

81 92 86 81 86 96

     

20 24 23 21 22 19

0.81 0.76 0.80 0.80 0.78 0.78

Evenness

*OTUs, operational taxonomic units. Diversity indices of methanotrophs and methanogens in each sample were calculated using the RDP functional gene pipeline based on the pmoA and mcrA gene sequences at 93% and 89% cutoff values, respectively. † Chao1values were calculated at a 95% confidence level. ‡ pmoA and mcrA sequencing reads were normalized to 1550 and 4579 reads, respectively.

and Methanobacterium were predominant during the entire rice cultivation period, consistent with the results based on 16S rRNA gene sequences. Statistical analyses and a single, composite parameter to predict methane emissions

The bacterial and archaeal community changes in the rice paddy soil during the rice cultivation period were statistiFEMS Microbiol Ecol 88 (2014) 195–212

cally assessed using the weighted UniFrac clustering and PCoA. The UniFrac clustering analysis showed that the bacterial community [when the rice was transplanted (day 0)] was distinct from community compositions at other sampling times (Fig. 5a). In contrast, the archaeal communities could be grouped into three clusters (early growing stage, 0–30 days; flowering and heading stage, 60–90 days; harvesting stage, 120–150 days) (Fig. 5b). The PCoA results (Fig. 5c and d) confirmed the relationships ª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

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found by the UniFrac clustering analysis. The bacterial community of the day 0 soil sample was clearly distinguished from those of the other soil samples by PC1. The PCoA results also showed that the bacterial and archaeal communities derived at each rice paddy sampling date were distributed into the PC1 and PC2 regions, indicating that bacterial and archaeal communities changed gradually over the rice cultivation period. Recently, Zhou et al. (2011) demonstrated that microbial community analyses can suffer from low reproducibility due to data distortion associated with singletons when sequencing reads are not sufficient to represent the whole microbial community. Therefore, the weighted UniFrac clustering and PCoA were also performed using the sequence reads after the removal of singletons; the results were unaffected by the presence of singletons (data not shown). The CCA triplot analysis showed relationships between bacterial and archaeal community compositions and key experimental/environmental parameters (sampling times, methane emissions, pH, phosphate, sulfate, TOC, TN, ammonia and temperature) for the entire rice cultivation period (Fig. 6). The triplot analysis indicated that CH4 emissions were positively associated with TOC, TN, temperature, and the genera Methylocaldum, Methanosaeta and Methanobacterium, while they were negatively associated with sulfate concentration. Pearson correlation coefficients and P values also showed that methane emissions

Fig. 6. Canonical correspondence analysis (CCA) showing the relationships among the rice soil samples, relative abundances of bacterial and archaeal communities classified at the genus level, and environmental factors. Only the genera known as methanotrophs and methanogens among all bacterial and archaeal genera are shown on the CCA triplot.

ª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

H.J. Lee et al.

had significantly positive correlations with TOC, temperature, pmoA and mcrA transcript abundances, and mcrA transcript/gene ratios (P < 0.05, Pearson correlation coefficients > 0.8), whereas sulfate concentration showed a significantly negative correlation with methane emission (P < 0.05, Pearson correlation coefficients 0.8, data not shown). The statistical trends described above that emerged from this integrated study led us to focus on relationships between genes known to produce methane (mcrA) and consume methane (pmoA). We computed normalized ratios for these two key metabolic activities: mcrA transcripts/mcrA genes divided by pmoA transcripts/pmoA genes (from Fig. 2c and d). We reasoned that normalization was necessary to compensate for possible differential extraction and PCR amplification biases. When we plotted total methane fluxes (from Table 1) against normalized mcrA/pmoA transcript ratios, we obtained a linear relationship (Fig. 7; R2 = 0.8791). Outlier points from the day 0 sample – probably reflecting soil-disturbance artifacts associated with planting and establishing the young

Fig. 7. Relationship between net methane flux from field rice paddy soils (from Table 1) and normalized ratios of two key metabolic activities: mcrA transcripts/mcrA genes divided by pmoA transcripts/ pmoA genes (from Fig. 2c and d). Data points reflect three normalized ratios derived from triplicated samples for each average methane emission rate, taken on days 30, 60, 90 and 120 from field samples of the growing rice crop. Data from day 0 were omitted due to soil-disturbance artifacts.

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rice plants – were omitted from the analysis. In essence, the data displayed in Fig. 7 show evidence for the simple hypothesis that net release of methane from rice paddies is governed by the balance between actively expressed methane-production genes vs. methane-destruction genes. We realize that the trend line shown in Fig. 7 is only a single data set; therefore, it does not fully establish normalized ratios of mcrA and pmoA transcripts as a definitive index for estimating methane emissions. Future confirmation of the trend in Fig. 7 will be required to achieve greater statistical robustness for this potentially promising index.

Discussion To the best of our knowledge, this is the first study to analyse the communities of methanogens and methanotrophs using pyrosequencing with concomitant measurements of CH4 emission rates, soil chemical properties, and expression levels of pmoA and mcrA genes throughout the entire rice cropping period. In particular, we were able to investigate the phylogenetic information as well as dynamics of methanotrophs and methanogens, relatively rare populations, in a flooded rice field ecosystem by high-throughput sequencing of bacterial and archaeal 16S rRNA, pmoA and mcrA genes during the entire rice cropping period. The profiles of CH4 emissions from rice paddies and soil chemical properties were generally in good accordance with previous reports (Table 1) (Yao et al., 1999; Eller & Frenzel, 2001; Ali et al., 2008; Ma et al., 2010; Kim et al., 2013). The TOC increase during the rice growing period (0–90 days) was expected because it is known that TOC, the major driver for CH4 production by methanogens, is derived mainly from rice organic root exudates and decaying root debris in rice paddies (Lu et al., 2000; Lu & Conrad, 2005). The pH value is also a well-known factor influencing carbon metabolism and methane production and it has been reported that the increase of pH and temperature is generally correlated with the increase of CH4 emissions in soil (Wang et al., 1993; Ye et al., 2012). After rice transplantation, CH4 emissions from rice paddies increased very quickly until the end of the rice growing stage (90 days), which is likely to be attributable to the increase of TOC, soil pH and temperature. Low redox potential is crucial for CH4 production from rice paddies because methanogens are strict anaerobes. Tillage and rice transplantation practices can raise the redox potential in rice paddies via O2 introduction. Subsequently, it is expected that rice paddies gradually progress into strictly anoxic conditions characterized by sulfate reduction (sulfate concentrations decreased; Table 1). After the rice growing stage FEMS Microbiol Ecol 88 (2014) 195–212

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(90 days), irrigation ceased, and pH and TOC decreased (Table 1), as expected with the increase of redox potential (Ali et al., 2009). The CCA plot and Pearson correlation coefficients also showed that methane emissions had significantly positive correlations with TOC and temperature and had significantly negative correlation with sulfate concentration (Fig. 6 and Table S2). The analysis of microbial communities showed that only 0.79–1.75% of the total bacterial reads in each rice paddy sample were classified as methanotrophs (Fig. 4a), similar to previous results (Eller & Frenzel, 2001). Although the relative abundance of methanotrophs decreased throughout the first 60 days, the absolute abundances of both Bacteria and methanotrophs increased at 60 days compared with day 0 (Fig. 1a and c, respectively), suggesting that the aerobic methanotrophs had limited (surface, oxygen-exposed) habitat for growth, compared with many of the other anaerobic and facultative bacterial groups. A striking increase of the relative abundance of methanotrophs was observed at 120 days, probably caused by the increase of oxygen concentration imposed by cessation of irrigation practices under the relatively high CH4 concentration. The relative abundance of type II methanotrophs was quite constant during rice cultivation, while type I methanotrophs showed a pronounced shift during rice cultivation (Fig. 4), which was in accordance with previous report (e.g. Henckel et al., 2000) and the notion that type II methanotrophs can form desiccation- and heat-resistant resting cells more easily than type I methanorophs (Ho et al., 2013). The relative abundance of type I methanotrophs was high at 90 and 120 days, coinciding with a high methane emission rate and cessation of irrigation practices (Fig. 4a). The relative abundance of Methylocaldum, a genus composed of thermophilic methanotrophs, increased very rapidly at approximately 90 days, perhaps due to high temperatures during the summer growing season in Korea (Bodrossy et al., 1997; Medvedkova et al., 2009). Growth of the genera Methylomonas and Methylosarcina was especially prominent at 120 days, showing relatively high CH4 emissions under the unflooded condition, suggesting that members of the genera Methylomonas and Methylosarcina might require relatively higher oxygen and CH4 concentrations than other methanotrophs for CH4 oxidation (Reim et al., 2012). The relative abundance of Methylocystis belonging to type II methanotrophs was highest at transplantation time, showing very low methane emissions (Fig. 4a), which suggested that members of Methylocystis might have a low minimum threshold concentration (Km value) in methane oxidation. Recently, it has been shown that ANME may be important contributors to methane processing in many habitats (Shima & Thauer, 2005; Maignien et al., 2013). However, in this ª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

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study, sequencing reads classified as ANME were below detection or at trace levels (only four of 33 218 sequencing reads) throughout the rice cultivation period, which indicates that anaerobic methane oxidation is a negligible metabolic process in rice paddies. The class SCG (called Crenarchaeota group 1.1b), having a possibly ammonia-oxidizing ability (Spang et al., 2010), decreased gradually after rice transplantation (Fig. 3d), which might have been caused by a likely transition to strictly anaerobic conditions during the middle stages of the rice crop (Table 1). The relative abundance of methanogens (Fig. 4b) was well matched to the profiles of CH4 emissions, methanogen abundance and mcrA gene expression (Table 1, Figs 1d, 2b and 3d). Like previous results (Großkopf et al., 1998; Ahn et al., 2012; Ma et al., 2012; Ke et al., 2014), the class Methanomicrobia was the predominant methanogen group found throughout the entire rice cultivation period (Fig. 3d), suggesting that members of Methanomicrobia may play important roles for methanogenesis in rice paddies. Methanosaeta, known as an effective acetate utilizer for CH4 production at low acetate concentrations (Jetten et al., 1990; Großkopf et al., 1998; Chin et al., 2004; Narayanan et al., 2009), were predominant during the entire rice cultivation period (Fig. 4b and d), indicating that acetate may also be a major carbon and energy source for methanogenesis in rice paddies. However, members of Methanosaeta have lower Km values for acetate than members of Methanosarcina, which might explain why Methanosaeta was more abundant than Methanosarcina in rice paddies (Jetten et al., 1990; Großkopf et al., 1998). After irrigation practices stopped, the relative abundance of Methanosaeta, Methanocella and Methanosarcina decreased, while Candidatus_Methanoregula and Methanococcus increased (Fig. 4b), suggesting that members of Methanosaeta, Methanocella and Methanosarcina may be more sensitive to oxygen than members of Candidatus_ Methanoregula and Methanococcus (Yuan et al., 2009). Because it is known that members of Methanoregula are acidiphilic methanogens utilizing H2/CO2 for CH4 production (Br€auer et al., 2006, 2011), the increase of Candidatus_Methanoregula suggested that H2/CO2 is also likely to be an important carbon and energy source for CH4 production at 120 days. Additionally, the increase of Candidatus_Methanoregula might have been favored by the low pH value at 120 days (Table 1). The abundance of Bacteria, Archaea, methanotrophs and methanogens generally increased after rice transplantation, although a small decrease in abundance occurred at day 30 (Fig. 1), possibly due to the increase of TOC and methane production (Table 1). However, interestingly, although the maximum abundance of methanogens (68.3–86.6% of the total Archaea) was observed at 90 days, the maximum abundance of Archaea was observed at ª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

H.J. Lee et al.

120 days (Fig. 1b). This observation was probably caused by the striking growth of Halobacteria, not a methanogen, at 90 days (Fig. 3d). A dry soil environment (consistent with cessation of irrigation on day 120) has been reported previously to be favorable to halobacteria (Timonen & Bomberg, 2009). Although the decrease of methanogens and methanotrophs related to the CH4 metabolisms in rice paddies was observed at 30 days, the expression levels of key CH4 metabolic genes, mcrA as well as pmoA, were in good accordance with the profile of CH4 emissions from the rice paddies even under flooded conditions during the rice growing stage (Table 1 and Fig. 2a and b). In our view, methanotrophy (here assessed by abundances of both 16S rRNA genes and pmoA in DNA and mRNA) is likely to follow (i.e. respond to) methanogenesis (here assessed by abundances of both 16S rRNA genes and mcrA in DNA and mRNA). The transcript/gene ratios of pmoA and mcrA have previously been shown to indicate potential in situ activities of methanotrophs and methanogens (Freitag et al., 2010). Data in Table 1, Fig. 2(d) and Table S2 indicate that the changes of the mcrA transcript/gene ratios were closely linked to methane emissions. Predictably, methanotrophy appears to have responded to methanogenesis. After rice transplantation, the transcript/gene ratios of pmoA increased rapidly with the increase of methane emissions. However, after 30 days, although methane emissions still increased, the transcript/gene ratios of pmoA decreased very quickly until day 90 (Table 1 and Fig. 2c) – perhaps due to the restriction of aerobic conditions. At day 120, transcriptional levels of the mcrA gene decreased more rapidly than those of the pmoA gene, which may have been caused by the change of rice paddy soil into aerobic conditions after irrigation practices stopped. While individually monitored expression of mcrA and pmoA genes plays an obvious mechanistic role contributing to methanogenesis in the rice paddy system, it was the dual parameter, normalized mcrA/pmoA ratios, that we found to be the best predictor of actual methane flux from the rice paddy system (Fig. 7).

Acknowledgements These efforts were supported by the ‘National Research Foundation of Korea (No. 2013R1A2A2A07068946)’ grant funded by the Korean Government (MEST), Republic of Korea. E.L.M. was supported by NSF grant DEB-0841999.

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Supporting Information Additional Supporting Information may be found in the online version of this article: Fig. S1. Rarefaction curves showing bacterial (a) and archaeal (b) diversities in the rice paddy during the rice

ª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

H.J. Lee et al.

cultivation period. Operational taxonomic units (OTUs) were calculated via the RDP pipeline at a 3% 16S rRNA gene sequence dissimilarity. Fig. S2. Rarefaction curves showing pmoA (a) and mcrA (b) diversities in the rice paddy during the rice cultivation period. Operational taxonomic units (OTUs) of amino acid sequences of pmoA and mcrA were calculated using the RDP functional pipeline at 93% and 89% identities, respectively. Table S1. List of adapter and barcode sequences in the PCR primer sets used in this study. Table S2. Pearson correlation coefficients and P values between methane emission and various parameters during the rice cultivation period.

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