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,
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Published by John Wiley & Sons Ltd. All rights reserved
196
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
197
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
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Published by John Wiley & Sons Ltd. All rights reserved
198
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
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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
199
Microbial dynamics and CH4 emissions in rice paddies
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
200
H.J. Lee et al.
(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
201
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
202
H.J. Lee et al.
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
FEMS Microbiol Ecol 88 (2014) 195–212
203
Microbial dynamics and CH4 emissions in rice paddies
(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,
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204
H.J. Lee et al.
(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
205
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
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Published by John Wiley & Sons Ltd. All rights reserved
206
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.
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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; Table
S2). We reasoned that the positive correlations between
methane flux and abundances of both Methylocaldum and
pmoA transcripts (indicative of a processes that consumes
methane) were probably artifacts of our restricted sampling intervals: methanotrophy is likely to respond to
methanogenesis, and hence to follow or track methane
fluxes. Pearson correlation coefficients and P values also
showed that mcrA gene and pmoA gene and transcripts
were positively correlated (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.
FEMS Microbiol Ecol 88 (2014) 195–212
Microbial dynamics and CH4 emissions in rice paddies
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
207
(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
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208
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.
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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.
FEMS Microbiol Ecol 88 (2014) 195–212