Responses of Methanogen mcrA Genes and Their Transcripts to an Alternate Dry/Wet Cycle of Paddy Field Soil

doi:10.1128/AEM.06934-11

Appl Environ Microbiol. 2012 January; 78(2): 445–454.

doi: 10.1128/AEM.06934-11

PMCID: PMC3255761

Responses of Methanogen mcrA Genes and Their Transcripts to an Alternate Dry/Wet Cycle of Paddy Field Soil

Ke Ma,^a^b Ralf Conrad,^b and Yahai Lu^a

^aCollege of Resources and Environmental Sciences, China Agricultural University, Beijing, China

^bDepartment of Biogeochemistry, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany

Corresponding author.

Address correspondence to Yahai Lu, Email: yhlu/at/cau.edu.cn.

Received September 20, 2011; Accepted November 10, 2011.

This article has been cited by other articles in PMC.

Abstract

Intermittent drainage can substantially reduce methane emission from rice fields, but the microbial mechanisms remain poorly understood. In the present study, we determined the rates of methane production and emission, the dynamics of ferric iron and sulfate, and the abundance of methanogen mcrA genes (encoding the alpha subunit of methyl coenzyme M reductase) and their transcripts in response to alternate dry/wet cycles in paddy field soil. We found that intermittent drainage did not affect the growth of rice plants but significantly reduced the rates of both methane production and emission. The dry/wet cycles also resulted in shifts of soil redox conditions, increasing the concentrations of ferric iron and sulfate in the soil. Quantitative PCR analysis revealed that both mcrA gene copies and mcrA transcripts significantly decreased after dry/wet alternation compared to continuous flooding. Correlation and regression analyses showed that the abundance of mcrA genes and transcripts positively correlated with methane production potential and soil water content and negatively correlated with the concentrations of ferric iron and sulfate in the soil. However, the transcription of mcrA genes was reduced to a greater extent than the abundance of mcrA genes, resulting in very low mcrA transcript/gene ratios after intermittent drainage. Furthermore, terminal restriction fragment length polymorphism analysis revealed that the composition of methanogenic community remained stable under dry/wet cycles, whereas that of metabolically active methanogens strongly changed. Collectively, our study demonstrated a stronger effect of intermittent drainage on the abundance of mcrA transcripts than of mcrA genes in rice field soil.

INTRODUCTION

Irrigated rice fields, whose water regime is fully controlled by farmers, account for 51 and 93% of the global and Chinese rice production area, respectively (44, 45). This type of rice field represents an important anthropogenic biological source of atmospheric CH₄, accounting for ca. 10% of the global CH₄ emission (6). The production of CH₄ by methanogenic archaea is the final step of the organic matter decomposition in anoxic environments. Methanogens in rice field soil comprise mainly Methanobacteriales, Methanomicrobiales, Methanosarcinales, and the novel order Methanocellales (previously known as rice cluster I) (36). The measurement campaigns of CH₄ fluxes in the past 3 decades have revealed that intermittent drainage as a water management practice is the most promising approach to attenuate CH₄ emission from irrigated rice fields (13, 40, 44). The microbial mechanisms, however, have been poorly characterized.

In a Japanese rice field study, it was found that the structure of methanogenic archaea remained surprisingly stable under drainage conditions, even in the dry seasons when no methane was produced (46, 48). A stable structure of the methanogenic community was also revealed in our previous study on a typical Chinese rice field soil under drainage conditions (27). Roling (35) showed that the rate of CH₄ production was controlled by the cellular activity of methanogens rather than their cell numbers. Thus, it might be assumed that analysis at the DNA level may not be fully adequate to link the methanogenic community with the methane production (47). Alternatively, different techniques have been developed in the past decade to link the biogeochemical processes to microbial identity, such as DNA/RNA-SIP (29, 33), protein-SIP (15), and NanoSIMS (20). Recently, the transcriptional analysis (mRNA) of functional genes has been applied to detect the metabolically active organisms responsible for biogeochemical processes such as methane oxidation (4, 17), ammonium oxidation (30), and nitrate reduction (21). The mcrA gene, encoding the alpha subunit of methyl coenzyme M reductase (MCR), is thought to be highly conserved and specific for methanogens (12, 26, 43). Several studies have used this gene as biomarker to determine methanogen community in paddy soil, boreal mire, peat soil, and anaerobic digesters (10, 16, 26, 41). We hypothesized that analyses of both mcrA genes and transcripts could be useful to evaluate the structure and function of methanogen communities in the paddy field soil.

Over the past decade, several investigations have been performed to determine the effect of drainage on CH₄ production in rice field soil (34, 51). Here we presented a comprehensive microcosm experiment in which two water regimes were established, i.e., continuous flooding and dry/wet alternation. The rates of CH₄ emission in situ, the CH₄ production potential in vitro, the dynamics of ferric iron and sulfate, and the abundance and transcript of methanogen mcrA genes were analyzed to obtain a mechanistic understanding for the effect of intermittent drainage on the methanogenic community dynamics in rice field soil.

MATERIALS AND METHODS

Planted rice microcosm.

Soil was collected from a rice field at the experimental farm of the China National Rice Research Institute at Hangzhou in autumn 2007 after the rice harvest (23). Soil was dried, crushed, sieved (2 mm mesh size), mixed and stored at room temperature. The characteristics of this soil have been described previously (32, 49). An indica rice cultivar (Oryza sativa, Jinzao 47) with a short growth period (105 days) but with a high grain yield was used. There were 48 cylindrical plastic pots (height, 18 cm; diameter, 14 cm) prepared in total for rice planting. Each pot included 1.4 kg of rice field soil and was flooded for 4 days prior to planting (we defined the flooding day here as day 0). Urea and K₂HPO₄ fertilizers were applied at rates of 30 mg of N kg soil⁻¹ and 12.5 mg of K kg soil⁻¹, and a healthy 4-days old rice seedling was transplanted into each pot. Urea was applied a second time as topdressing at a rate of 30 mg of N kg soil⁻¹ on day 42 corresponding to the early tillering stage of rice. All planted pots were flooded continuously till day 54 and then were divided into two groups for the treatments of water management practices. Half of the pots were continuously flooded during the entire growth period with a surface water depth of 2 to 3 cm (referred as the FL treatment). The other half of the pots were drained for 8 days (day 55 to 62), followed by 10 days of reflooding (days 63 to 72) and 8 days of redrainage (days 73 to 80), and were then kept flooded till day 90 (referred as the DR treatment). During the rice growth stage, the height and tiller number of rice were recorded once a week.

Methane flux measurement and soil pore water analysis.

The rate of CH₄ flux was measured by a static chamber once or twice a week from day 28 until the end following the procedure described previously (28). Difluoromethane (CH₂F₂), a specific inhibitor of CH₄ oxidation, was used to inhibit CH₄ oxidation (18). The fluxes in the absence or presence of CH₂F₂ were defined as the CH₄ emission rate and the CH₄ production rate, respectively (18). Both the emission rate and the production rate were calculated from the linear increase in the CH₄ mixing ratio and are expressed as mmol of CH₄ day⁻¹ m⁻².

Soil pore water was sampled weekly as described previously (28). The concentration of sulfate in the pore water was analyzed by ion chromatography (3), and the concentration of ferrous iron was analyzed by a colorimetric assay with ferrozine reagent (1, 22).

Collection of soil samples.

Soil was destructively sampled on days 54, 57, 62, 67, 72, 76, 80, and 90 (eight times in total). For soil sampling, rice shoots were cut from the plant base and the dry biomass of shoots was determined after drying in the oven at 75°C for 3 day. Subsamples of soil were collected from four compartments as follows. First, a very thin layer of yellowish top soil (0 to 5 mm in depth from the surface) was taken as the surface soil. Second, the subsoil which did not contain roots was taken as bulk soil. Third, the soil attached to the roots was taken as rhizosphere soil. Fourthly, the root material with the tightly adhering soil was washed thoroughly with 100 ml of sterile double-distilled water, the soil suspension was centrifuged at 5,000 rpm, and the precipitate was taken as the washed root material. The fresh samples from each compartment were frozen within 15 min using liquid nitrogen and stored at −80°C. Extractable ferrous iron and sulfate in each fresh sample were determined as described above.

Measurement of methane production potential (MPP).

Within 1 h after destructive soil sampling, the CH₄ production potential of fresh soil samples was determined by short-term incubation (18). In brief, 2.0 g of fresh soil was placed into sterile 25-ml glass tubes containing 2 ml of N₂-flushed demineralized water. The tubes were closed with butyl stoppers, flushed with N₂ for 5 min, and then incubated for 250 h at 30°C under dark. The rates of CH₄ production potentials were calculated from the slopes of the regression lines of the production curve and expressed in nmol of CH₄ h⁻¹ g (dry weight) (gdw) of soil⁻¹.

Nucleic acid extraction and purification.

The total DNA and RNA contents of soil samples were coextracted using a published protocol (31) with some modifications. In brief, 0.5 g of soil was extracted with 700 μl of TPMS buffer (50 mM Tris-HCl [pH 7.0], 1.7% [wt/vol] polyvinylpyrrolidone K25, 20 mM MgCl₂, 1% [wt/vol] sodium dodecyl sulfate) once and then subsequently extracted twice with 700 μl of phenol-based lysis buffer (5 mM Tris-HCl [pH 7.0], 5 mM Na₂EDTA, 1% [wt/vol] sodium dodecyl sulfate, 6% [vol/vol] water-saturated phenol). The supernatants from the three extraction steps were combined and further extracted with 500 μl of water-saturated phenol, 500 μl of phenol-chloroform-isoamyl alcohol (25:24:1 [vol/vol/vol]), and 500 μl of chloroform-isoamyl alcohol (24:1 [vol/vol]). The total nucleic acids were precipitated with 3 volumes of cold ethanol and 1/10 volume of 3 M sodium acetate (pH 5.2), washed with 500 μl of cold 70% ethanol, dried for about 10 min, and finally dissolved in 100 μl of TE buffer (10 mM Tris-HCl, 1 mM EDTA [pH 8.0]).

For the analysis of DNA, no further purification steps were carried out. For the analysis of RNA, DNA was hydrolyzed in a total volume of 40 μl containing 15 μl of extracted nucleic acids, 4 μl of RQ1 RNase-Free DNase 10× reaction buffer, 5 μl of RQ1 RNase-free DNase, and 0.2 μl of recombinant RNasin RNase inhibitor (Promega, Mannheim, Germany) by incubation at 37°C for 1 h. RNA was further purified using an RNeasy minikit (Qiagen, Hilden, Germany) according to the manufacturer's instructions. The quality and purity of DNA and RNA were checked by 1% agarose gel electrophoresis and NanoDrop1000 spectrophotometry (NanoDrop Technologies, Wilmington, DE).

cDNA synthesis.

The ImProm-II reverse transcription system (Promega) was used to synthesize the complete cDNA by the following procedures: (i) a reaction tube including 2 μl of purified RNA and 1 μl of random primer was incubated at 70°C for 5 min, followed by chilling 5 min on ice; (ii) a reverse transcription reaction mixture was prepared by combining 4 μl of ImProm-II 5× reaction buffer, 2 μl of MgCl₂, 1 μl of deoxynucleoside triphosphate mix, 1 μl of recombinant RNasin RNase inhibitor, 0.5 μl of bovine serum albumin (Roche, Mannheim, Germany), and 1 μl of ImProm-II reverse transcriptase (Promega) and then added to each reaction tube; and (iii) the tubes were incubated at 25°C for 5 min, followed by 42°C for 1 h, 15°C for 15 min and then stored at −20°C. To verify the absence of DNA, a control reaction was performed with nuclease-free water instead of reverse transcriptase.

PCR, cloning, sequencing, and phylogenetic analysis.

PCR amplification of mcrA genes and mcrA transcripts was carried out using the primers MCRf and MCRr (26). Each 50-μl PCR contained 1× MasterAmp PCR PreMix B (Epicentre Technologies, Madison, WI), 0.5 μl of each primer (Sigma, Munich, Germany), 0.5 μl of bovine serum albumin, and 5 U Taq DNA polymerase (Promega). Then, 2 μl of DNA (a 10:1 dilution of crude extracts) and 2 μl of cDNA were used as the PCR templates for the mcrA gene and transcript analyses, respectively.

One clone library of mcrA transcripts retrieved from the bulk soil sample of FL treatment on day 80 was generated using the pGEM-T Easy Vector system (Promega) according to the manufacturer's instruction, and 33 randomly selected clones were sequenced. Phylogenetic trees were constructed using the neighbor-joining program MEGA3.1 according to the protocol described earlier (28). The gene sequences were deposited into the GenBank nucleotide sequence database under accession numbers JN030360 to JN030392.

T-RFLP analysis.

Terminal restriction fragment length polymorphism (T-RFLP) analyses were used to determine the composition of methanogenic communities. PCR amplification used the primers described above, except that the forward primer was labeled with 6-carboxyfluorescein (26). The purified PCR products were digested by Sau96I at 37°C for 3 h (Fermentas, Burlington, Ontario, Canada). The analysis used here matched a protocol described previously (28). The percent fluorescence intensity represented by a single T-RF was calculated relative to the total fluorescence intensity of all T-RFs.

Quantitative analysis of mcrA genes and transcripts.

Quantitative PCR of copies of mcrA genes and transcripts was done in an iCycler instrument (Bio-Rad, Munich, Germany) using the mlas/mcrA-rev primer pair (42). The reaction solution in a total volume of 25 μl contained 12.5 μl of SybrGreen Jumpstart Taq ReadyMix (Sigma), 0.25 μM concentrations of the primers, 0.5 μl of bovine serum albumin, and 5 μl of DNA (25:1 dilution) for gene copy quantification or cDNA (5:1 dilution) for transcript quantification. The thermal cycles and fluorescence signal acquisition followed a protocol as described earlier (41). The DNA standard was prepared from the purified plasmid DNA of an mcrA clone with the concentration ranging from 1.0 × 10¹ to 1.0 × 10⁸ copies μl⁻¹ as described earlier (28). The transcript standard was prepared from the in vitro transcript of a mcrA clone using the Riboprobe in vitro transcription systems (Promega) according to the manufacturer's protocol. The details followed the procedure described in a recent study (8). The concentration of transcript standard ranged from 1.0 × 10¹ to 1.0 × 10⁶ in vitro transcripts μl⁻¹. Each measurement was performed in three replicates.

Statistical analysis.

The ordination analysis of T-RFLP patterns of mcrA gene and transcript was done using CANOCO for Windows 4.5 software (Microcomputer Power, Ithaca, NY), and analysis of variance was performed to test significant differences between treatments using the SAS program (SAS Institute, Cary, IN).

RESULTS

Plant growth.

Rice plants were grown for 90 days in the greenhouse. The height and tiller number of plants reached a maximum at the late tillering stage. The shoot biomass increased continuously, with the total amounts at harvest close to the local records under field conditions (see Fig. S1 in the supplemental material). There was no difference in rice growth between the FL and DR treatments, indicating that intermittent drainage did not affect rice growth.

Methane dynamics.

Methane emissions from FL treatment gradually increased from a rate of 26.2 mmol of CH₄ day⁻¹ m⁻² on day 28 to a maximum of 103 mmol of CH₄ day⁻¹ m⁻² on day 61 (the late tillering stage) and thereafter remained relatively stable, ranging from 80 to 95.5 mmol of CH₄ day⁻¹ m⁻² (Fig. 1a). Intermittent drainage (DR treatment) resulted in a completely different emission pattern (Fig. 1a). Just 2 days after drainage (day 56) the emission rate decreased rapidly to 8.3 mmol of CH₄ day⁻¹ m⁻², which was approximately 1 order of magnitude lower than the rate in FL treatment. After another 5 days of drainage (day 61), the emission rate decreased to close to zero. The irrigation (reflooding) on day 71 slightly increased the emission rate, which, however, decreased again after the second drainage. The temporal pattern of CH₄ production, which was estimated using difluoromethane, highly resembled the pattern of CH₄ emission, being substantially reduced after dry/wet cycles (Fig. 1b).

Fig 1

Patterns of methane emission rate in the absence of CH₂F₂ (a) and methane production rate in the presence of CH₂F₂ (b) under the treatments of continuous flooding (FL) and alternate dry/wet cycle (DR) (n = 3). Downward arrows and upward arrows indicate (more ...)

The rates of the methane production potential (MPP) were measured using short-term anaerobic incubation. For the FL treatment, the MPP differed among the four different soil compartments (Fig. 2), with the highest rate observed in the rhizosphere soil (61.0 ± 16.1 nmol of CH₄ h⁻¹ gdw⁻¹), followed by the washed root material (48.7 ± 8.2 nmol of CH₄ h⁻¹ gdw⁻¹), the bulk soil (21.8 ± 3.6 nmol of CH₄ h⁻¹ gdw⁻¹), and the surface soil (6.6 ± 2.7 nmol of CH₄ h⁻¹ gdw⁻¹). The MPP rates in the bulk soil were significantly inhibited by intermittent drainage but slightly increased after reflooding (Fig. 2b). The average MPP rate was only 1.6 ± 2.2 nmol of CH₄ h⁻¹ gdw⁻¹ in the DR treatment compared to 21.8 ± 3.6 nmol of CH₄ h⁻¹ gdw⁻¹ in the FL treatment (Fig. 2b). The rhizosphere soil and the washed root material exhibited similar inhibitory patterns (Fig. 2c and d). In contrast, the MPP rates of the surface soil were higher in the DR treatment than in the FL treatment, especially during the second dry/wet cycle (Fig. 2a).

Fig 2

Rates of methane production potential in surface soil (a), bulk soil (b), rhizosphere soil (c), and washed root material (d) in the FL and DR treatments under anaerobic incubation conditions (n = 3).

Iron and sulfate dynamics.

For the FL treatment, the Fe(II) concentration in pore water gradually decreased from 3.4 to 1.7 mM over the experimental period (see Fig. S2a in the supplemental material). Alternate dry/wet cycles significantly reduced the Fe(II) concentration in the DR treatment, which only slightly recovered after reflooding events. The extractable Fe(II) in the FL treatment was relatively stable for all soil subsamples (Fig. 3a, b, c, and d). However, the concentrations in the DR treatment changed greatly, decreasing after drainage and then recovering after reflooding (Fig. 3b and c). The extractable Fe(II) concentration in the second drainage stage was lower than that in the first drainage, indicating an enhanced effect of repeated drainages on the iron transformation (Fig. 3c). The extractable Fe(II) in the washed root material and the surface soil was less affected by drainage and reflooding compared to the bulk and rhizosphere soils (Fig. 3a and d).

Fig 3

Concentrations of extractable Fe(II) in surface soil (a), bulk soil (b), rhizosphere soil (c), and washed root material (d) and concentrations of extractable sulfate in surface soil (e), bulk soil (f), rhizosphere soil (g), and washed root material (h) (more ...)

Intermittent drainage also affected the dynamics of sulfate in the soil. The sulfate concentration in pore water was close to the detection limit over most of the experimental period in the FL treatment. The sulfate concentration substantially increased after drainage, followed by a rapid decrease after each reflooding in the DR treatment (see Fig. S2b in the supplemental material). The effect of intermittent drainage on the extractable sulfate highly resembled that of extractable Fe(II) but in the opposite direction (Fig. 3e, f, g, and h), i.e., sulfate rapidly increased after drainage and decreased after reflooding. Distinct to extractable Fe(II), however, extractable sulfate in the surface soil was also strongly affected by intermittent drainage (Fig. 3e).

Dynamics of methanogenic community.

T-RFLP analyses of mcrA genes and mcrA transcripts were performed to investigate the dynamics of the methanogenic populations. In total 10 different T-RFs (145, 234, 390, 404, 407, 417, 423, 468, 504, and 507 bp) were detected across all samples. A clone library was constructed using the mcrA transcripts of the bulk soil from the FL treatment on day 80 (see Fig. S3 in the supplemental material). The phylogenetic analysis showed that the methanogenic community consisted mainly of Methanocellaceae (30%), Methanosaetaceae (40%), Methanosarcinaceae (27%), and Methanobacteriaceae (3%). These organisms were characterized by the T-RFs of 234, 145, 390, and 407 bp, respectively.

The T-RFLP patterns of mcrA genes retrieved from the bulk soil are illustrated in Fig. 4. The Methanocellaceae (234 bp) and Methanosarcinaceae (390 bp) represented the predominant mcrA genes of methanogens; this pattern did not change much over the experimental period (Fig. 4a) and was not significantly affected by intermittent drainage (Fig. 4b). In contrast, the T-RFLP patterns of mcrA transcripts, which represented the structure of active methanogens, exhibited a strong variation in both the FL and the DR treatments (Fig. 4c and d). In the DR treatment, a few minor T-RFs, such as the 504-bp T-RF, increased compared to the FL treatment (Fig. 4d). Interestingly, Methanocellaceae became highly abundant at the end of alternate dry/wet cycles (day 90). This was corroborated by the correspondence analysis of the T-RFLP profiles showing the patterns of the day 90 sample as distinct from the others (as shown in Fig. 5b, circled in red).

Fig 4

Structure of methanogenic community in bulk soil revealed by T-RFLP analyses based on mcrA genes in FL (a) and DR (b) treatments and mcrA transcripts in FL (c) and DR (d) treatments (n = 3). RA, relative abundance.

Fig 5

Correspondence analyses of T-RFLP profiles generated at the mcrA gene level (DNA) (a) and mcrA transcript level (mRNA) (b). The eigenvalues of the first and second axes in the two-dimensional ordination diagrams are as follows: λ₁ = 0.721 and (more ...)

The T-RFLP profiles in rhizosphere soil, washed root material, and surface soil are shown in the supplemental material (see Fig. S4 to S6 in the supplemental material). The patterns did not differ significantly between the rhizosphere (see Fig. S4 in the supplemental material) and the washed root material (see Fig. S5 in the supplemental material). In these samples, Methanocellaceae (234 bp) and Methanosarcinaceae (390 bp) were dominant in the mcrA gene profiles, but the relative abundances of 468 and 504 bp were slightly greater compared to the bulk soils (see Fig. S4a and S5a in the supplemental material). According to the published sequence information, both 468 and 504 bp are probably affiliated with Methanobacteriaceae (5, 7, 26). The dry/wet alternation did not affect the mcrA gene profiles (see Fig. S4b and S5b in the supplemental material). However, the mRNA transcript profiles were influenced. In particular, the transcripts of Methanocellaceae (234 bp) and Methanosarcinaceae (390 bp) became less dominant, while that of other methanogens obviously increased (see Fig. S4d and S5d in the supplemental material). The composition of methanogens in the surface soil was different from that in the other soil compartments (see Fig. S6 in the supplemental material). This was clearly illustrated by correspondence analyses using both DNA and mRNA data (Fig. 5). In addition, because of the low concentrations of mcrA transcripts in the surface soils, the reverse transcription and PCR amplification assay failed for the DR treatment (see Fig. S6d in the supplemental material), indicating that intermittent drainage strongly inhibited the methanogenic activity in the surface soil.

Taking all T-RFLP profiles for mcrA genes (DNA level) together, the correspondence analysis revealed that intermittent drainage did not affect the composition of the methanogenic communities (Fig. 5a), but obviously changed the profiles of mcrA transcripts (mRNA level) representing the structure of the active methanogens (Fig. 5b).

Quantification of mcrA genes and transcripts.

The mcrA gene copy number in the surface soil remained constant in the FL treatment but fluctuated in the DR treatment (Fig. 6a). Similarly, the gene copy number in the bulk soil was stable in the FL treatment, ranging from 5.73 × 10⁹ to 1.18 × 10¹⁰ copies g of soil⁻¹. However, the gene copy number in the DR treatment decreased, especially after the second drainage, to a value of 9.62 × 10⁸ copies g of soil⁻¹, which was about 1 order of magnitude lower than in the FL treatment (Fig. 6b). A similar effect was detected in the rhizosphere soil (Fig. 6c) and the washed root material (Fig. 6d). All of these results indicated that the second dry/wet cycle had a stronger effect on the growth of methanogenic archaea than did the first cycle.

Fig 6

Abundances of methanogens revealed by quantitative PCR analyses based on mcrA genes in surface soil (a), bulk soil (b), rhizosphere soil (c), and washed root material (d) and mcrA transcripts in surface soil (e), bulk soil (f), rhizosphere soil (g), and (more ...)

Quantitative PCR of mcrA transcripts was done by using the cDNA as templates. The dry/wet alternation affected the mcrA transcripts to a much greater extent than the abundance of the mcrA genes. For the bulk soil in the FL treatment (Fig. 6f), the mcrA transcripts ranged from 3.4 × 10¹⁰ to 6.7 × 10¹⁰ transcripts g of soil⁻¹, but the transcripts in the DR treatment decreased to 1.9 × 10⁹ transcripts g of soil⁻¹ at 3 days after drainage. At the end of the first drainage, the transcript number was 2 orders of magnitude lower in the DR treatment than in the FL treatment. After 10 days of reflooding, the mcrA transcripts had recovered to the same level as in the FL treatment. However, after the second drainage, the mcrA transcripts reduced to a larger extent and did not recover to the level as in the FL treatment (Fig. 6f). The quantitative PCR results in rhizosphere soil (Fig. 6g) and washed root material (Fig. 6 h) also showed that the abundance of active methanogens was significantly suppressed in the DR treatment, indicating a strong inhibition of intermittent drainage on the methanogenic activity.

The mcrA transcript/gene ratios (38, 53) are considered as a potential indicator of in situ methanogenic activity in environmental samples (10). In the FL treatment, the average ratio of transcript to gene was 6.7 and 12.4 for the bulk soils and rhizosphere soils, respectively (see Table S1 and Fig. S7b and c in the supplemental material). In contrast, the ratios for most samples during the drainage periods in the DR treatment were <1, reconfirming that the inhibitory effects of alternate dry/wet cycles were stronger at the transcript level than at the gene abundance level.

In addition, the abundance of mcrA genes and mcrA transcripts differed among the soil compartments, with rhizosphere soils showing the highest numbers, followed by bulk soil and surface soil (see Table S1 in the supplemental material; Fig. 6). Compared to the bulk and the rhizosphere soils, the mcrA gene copy number in the surface soils were about 1 order of magnitude lower, and the transcript number was 2 orders of magnitude lower. The washed root material had higher mcrA gene copy numbers than the rhizosphere soils, but the transcripts numbers were lower, possibly due to the rapid degradation of mRNA during the washing procedure. Therefore, the ratio of transcript to gene in washed root material was only one-fifth of the rhizosphere soils.

Correlation and regression analysis.

The correlation analysis showed (Table 1; see also Fig. S7 to S9 in the supplemental material) that the mcrA gene copy numbers in the bulk soil were positively correlated with the methane emission rate (r = 0.613, P < 0.001), the MPP rate (r = 0.428, P = 0.004), and extractable Fe(II) concentrations (r = 0.615, P < 0.001) but not with the extractable sulfate concentrations (r = −0.108, P = 0.493). The mcrA transcript numbers were positively correlated with the methane emission rate (r = 0.831, P < 0.001), the MPP rates (r = 0.814, P < 0.001), and the concentrations of extractable Fe(II) (r = 0.877, P < 0.001), and negatively correlated with the concentrations of extractable sulfate (r = −0.471, P = 0.001). The correlation coefficients of mcrA transcript analysis were greater than those of mcrA gene analysis. Similarly, a significant correlation was also detected for the rhizosphere soil, whereas no significant correlation was obtained for the surface soil. When the data for all three soil compartments were analyzed together (washed root material was excluded because the washing treatment caused a change in the biogeochemical parameters), the relationships between methanogenic abundance and biogeochemical parameters were still significant at both gene and transcript levels (Table 1; see also Fig. S10 in the supplemental material). Furthermore, we found that both mcrA gene copy numbers (r = 0.647, P < 0.001 for the bulk soil; r = 0.541, P < 0.001 for the rhizosphere soil) and mcrA transcript numbers (r = 0.736, P < 0.001 for the bulk soil; r = 0.777, P < 0.001 for the rhizosphere soil) were correlated positively with the soil water content.

Table 1

Correlation analysis and linear regression analysis to describe the relationship between mcrA genes or transcripts and biogeochemical parameters

DISCUSSION

Our study demonstrates a strong inhibition of dry/wet alternation on CH₄ production and emission, similar to findings observed in previous studies (2, 23, 37, 39, 44, 50). To obtain a mechanistic understanding of the drainage effect, we analyzed soil biogeochemistry, as well as the abundance and composition of methanogenic community, in the samples from four soil compartments. To the best of our knowledge, this is the first comprehensive investigation of methanogenic functional genes and their transcription in association with CH₄ emission and production. Furthermore, we analyzed the dynamics of these genes in the context of the turnover of ferric ion and sulfate and with other soil environmental factors in rice field soil.

In the permanent flooding treatment, the abundance of mcrA gene copies was relatively stable over the growing period of rice plants (Fig. 6). This stability of methanogen populations has also been observed previously (19, 27, 46). In the drainage treatment, however, the growth of methanogenic populations, as revealed by mcrA gene abundance, was suppressed in four soil compartments during the second dry/wet cycle. The first dry/wet cycle did not significantly affect the mcrA gene copies, probably due to insufficient drainage (27). Apparently, methanogenic populations can survive the moderate drainage or aeration of paddy field soil.

However, the mcrA transcripts in the DR treatment substantially decreased during both the first and the second dry/wet cycles. On the other hand, it stayed relatively stable in the FL treatment. In the first drainage, the mcrA transcripts in the bulk and rhizosphere soils decreased by 2 orders of magnitude. The transcript/gene ratio decreased to close to zero on day 62 (Fig. 7b and c), indicating that the expression of the mcrA gene was completely repressed by the drainage treatment. This inhibition coincided with the suppression of methane production (Fig. 1b). A recent study of peat soil showed that the transcript/gene ratio of the mcrA gene correlated linearly with the CH₄ flux (11). Hence, it may be a general characteristic that transcription of a functional gene is a more sensitive descriptor of activity in environmental samples than the abundance of the gene.

Fig 7

Ratios of mcrA transcript to gene in surface soil (a), bulk soil (b), rhizosphere soil (c), and washed root material (d) (n = 3).

Correlation analysis revealed that both mcrA gene and transcript abundance were significantly correlated positively with methane production potentials, the concentration of extractable Fe(II), and the soil water content and negatively with the concentration of extractable sulfate in the bulk and rhizosphere soils. The correlation coefficients were more significant for the transcripts than the gene copies. This result indicated that the transcripts explained the inhibitory effect of dry/wet alternation on methanogenic dynamics better than the gene abundance. The negative correlations of mcrA transcript and gene abundance with the concentrations of extractable Fe(III) and sulfate suggested that the iron or sulfate reducers activated by Fe(III) and sulfate regenerated and probably outcompeted the methanogens for substrates. In addition, O₂ stress could also repress the mcrA transcription, although it recovered to some extent after reflooding (52).

Alternate dry/wet cycles did not influence the structure of the methanogenic community, a finding consistent with several earlier studies demonstrating the stable structure of methanogenic archaea in rice field soil under different conditions (17, 27, 46, 51). A recent study using the traditional culture method also showed no effects of water management on the methanogenic community (14). However, DNA analysis and cultivation might only show the existence of methanogens and not their activity in rice field soil. In contrast, the T-RFLP profiles of the mcrA transcripts showed that alternate dry/wet cycles altered the composition of the metabolically active methanogens.

The transcription of some methanogens such as Methanobacteriaceae (T-RF of 504 bp), which were low in abundance under continuous flooding, was stimulated by drainage and reflooding. Of specific interest, the transcripts of Methanocellaceae increased in abundance in the bulk soil at the end after the alternate dry/wet cycles. A recent study targeting mcrA transcripts in Japanese rice field soil also showed that Methanocellaceae survived better during the dry season (47). Furthermore, the mcrA transcripts of these organisms in the slurry incubation were more resistant to oxygen exposure than other methanogens (52). It is currently unclear why Methanocellales can cope with the soil aeration. However, according to the genome sequence available for one strain (9), these organisms possess a unique set of genes encoding antioxidant enzymes and oxygen-insensitive fermentation enzymes, which probably facilitate them to be better adapted under alternate dry/wet conditions.

The rates of CH₄ emission gradually increased with time and were highly correlated with the rates of CH₄ production (r = 0.978, P < 0.001). These parameters were also highly correlated with the methane production potentials in soil compartments (i.e., bulk soil, rhizosphere soil, and roots) except for surface soil. Interestingly, the CH₄ production potential in the FL treatment was greater in the rhizosphere and the washed root material than in the bulk soil, indicating that methanogenesis was active in these environments, albeit the potentially oxic conditions. This was in line with previous findings that root-derived substrates served as major carbon and energy sources for CH₄ production despite the possibility of O₂ release into the rhizosphere soil (24, 25). The temporal patterns of methane production potentials were very similar among the bulk soil, rhizosphere soil, and washed root material and were strongly inhibited by intermittent drainage. The dry/wet alternation also markedly reduced the concentration of Fe(II) [hence the increase in Fe(III)] and increased the concentration of sulfate in the soil. These results were in agreement with previous laboratory incubation experiments showing that short-term aeration or O₂ exposure significantly increased the concentrations of Fe(III) and sulfate (34, 51). Correlation analyses indicated that methane production potential in all soil compartments was positively correlated with the concentration of Fe(II) [hence negatively with Fe(III)] and negatively correlated with the concentration of sulfate. These results indicated that alternate dry/wet cycling inhibited CH₄ production because of the regeneration of Fe(III) and sulfate, which in turn allowed iron- and sulfate-reducing bacteria to outcompete methanogens for the substrates H₂ and acetate.

In conclusion, our study showed that intermittent drainage significantly reduced the rates of CH₄ production and emission in rice field soil by primarily suppressing transcription in some members of the methanogenic archaeal community, whereas the abundance of methanogens was comparatively little affected. The inhibitory effects were probably related to the regeneration of Fe(III) and sulfate, which activated iron and sulfate reducers and hence outcompeted methanogens for available substrates. Our study indicated that the analysis of functional genes at the mRNA level is more powerful than at the DNA level if the biogeochemical process is to be linked to microbial identity in the environments.

Supplementary Material

Supplemental material

Click here to view.

ACKNOWLEDGMENTS

This study was partly supported by the Natural Science Foundation of China (40830534), the National Basic Research Program of China (2011CB100505), and the Max Planck Institute in the form of partner lab program. K.M. was also supported by the Chinese Government Graduate Student Overseas Study Program of the China Scholarship Council.

Footnotes

Published ahead of print 18 November 2011

Supplemental material for this article may be found at http://aem.asm.org/.

REFERENCES

1. Achtnich C, Bak F, Conrad R. 1995. Competition for electron-donors among nitrate reducers, ferric iron reducers, sulfate reducers, and methanogens in anoxic paddy soil. Biol. Fertil. Soils 19:65–72.

2. Adhya TK, et al. 1994. Methane emission from flooded rice fields under irrigated conditions. Biol. Fertil. Soils 18:245–248.

3. Bak F, Pfennig N. 1991. Microbial sulfate reduction in littoral sediment of Lake Constance. FEMS Microbiol. Ecol. 85:31–42.

4. Chen Y, Dumont MG, Cebron A, Murrell JC. 2007. Identification of active methanotrophs in a landfill cover soil through detection of expression of 16S rRNA and functional genes. Environ. Microbiol. 9:2855–2869. [PubMed]

5. Chin KJ, Lueders T, Friedrich MW, Klose M, Conrad R. 2004. Archaeal community structure and pathway of methane formation on rice roots. Microb. Ecol. 47:59–67. [PubMed]

6. Conrad R. 2009. The global methane cycle: recent advances in understanding the microbial processes involved. Environ. Microbiol. Rep. 1:285–292.

7. Conrad R, Klose M, Noll M, Kemnitz D, Bodelier PLE. 2008. Soil type links microbial colonization of rice roots to methane emission. Glob. Change Biol. 14:657–669.

8. Dumont MG, Pommerenke B, Casper P, Conrad R. 2011. DNA-, rRNA-, and mRNA-based stable isotope probing of aerobic methanotrophs in lake sediment. Environ. Microbiol. 13:1153–1167. [PubMed]

9. Erkel C, Kube M, Reinhardt R, Liesack W. 2006. Genome of Rice Cluster I archaea: the key methane producers in the rice rhizosphere. Science 313:370–372. [PubMed]

10. Freitag TE, Prosser JI. 2009. Correlation of methane production and functional gene transcriptional activity in a peat soil. Appl. Environ. Microbiol. 75:6679–6687. [PMC free article] [PubMed]

11. Freitag TE, Toet S, Ineson P, Prosser JI. 2010. Links between methane flux and transcriptional activities of methanogens and methane oxidizers in a blanket peat bog. FEMS Microbiol. Ecol. 73:157–165. [PubMed]

12. Friedrich MW. 2005. Methyl-coenzyme M reductase genes: unique functional markers for methanogenic and anaerobic methane-oxidizing Archaea. Method. Enzymol. 397:428–442. [PubMed]

13. Fumoto T, Yanagihara T, Saito T, Yagi K. 2010. Assessment of the methane mitigation potentials of alternative water regimes in rice fields using a process-based biogeochemistry model. Glob. Change Biol. 16:1847–1859.

14. Hadi A, Inubushi K, Yagi K. 2010. Effect of water management on greenhouse gas emissions and microbial properties of paddy soils in Japan and Indonesia. Paddy Water Environ. 8:319–324.

15. Jehmlich N, Schmidt F, von Bergen M, Richnow HH, Vogt C. 2008. Protein-based stable isotope probing (Protein-SIP) reveals active species within anoxic mixed cultures. ISME J. 2:1122–1133. [PubMed]

16. Juottonen H, Tuittila ES, Juutinen S, Fritze H, Yrjala K. 2008. Seasonality of rDNA- and rRNA-derived archaeal communities and methanogenic potential in a boreal mire. ISME J. 2:1157–1168. [PubMed]

17. Kolb S, Knief C, Dunfield PF, Conrad R. 2005. Abundance and activity of uncultured methanotrophic bacteria involved in the consumption of atmospheric methane in two forest soils. Environ. Microbiol. 7:1150–1161. [PubMed]

18. Kruger M, Frenzel P, Conrad R. 2001. Microbial processes influencing methane emission from rice fields. Glob. Change Biol. 7:49–63.

19. Kruger M, Frenzel P, Kemnitz D, Conrad R. 2005. Activity, structure and dynamics of the methanogenic archaeal community in a flooded Italian rice field. FEMS Microbiol. Ecol. 51:323–331. [PubMed]

20. Li T, et al. 2008. Simultaneous analysis of microbial identity and function using NanoSIMS. Environ. Microbiol. 10:580–588. [PMC free article] [PubMed]

21. Liu BB, Morkved PT, Frostegard A, Bakken LR. 2010. Denitrification gene pools, transcription and kinetics of NO, N₂O, and N₂ production as affected by soil pH. FEMS Microbiol. Ecol. 72:407–417. [PubMed]

22. Lovley DR, Phillips EJP. 1986. Organic-matter mineralization with reduction of ferric iron in anaerobic sediments. Appl. Environ. Microbiol. 51:683–689. [PMC free article] [PubMed]

23. Lu WF, et al. 2000. Methane emissions and mitigation options in irrigated rice fields in southeast China. Nutr. Cycl. Agroecosys. 58:65–73.

24. Lu YH, Conrad R. 2005. In situ stable isotope probing of methanogenic archaea in the rice rhizosphere. Science 309:1088–1090. [PubMed]

25. Lu YH, Wassmann R, Neue HU, Huang CY. 2000. Dynamics of dissolved organic carbon and methane emissions in a flooded rice soil. Soil Sci. Soc. Am. J. 64:2011–2017.

26. Lueders T, Chin KJ, Conrad R, Friedrich M. 2001. Molecular analyses of methyl-coenzyme M reductase alpha-subunit (mcrA) genes in rice field soil and enrichment cultures reveal the methanogenic phenotype of a novel archaeal lineage. Environ. Microbiol. 3:194–204. [PubMed]

27. Ma K, Lu YH. 2011. Regulation of microbial methane production and oxidation by intermittent drainage in rice field soil. FEMS Microbiol. Ecol. 75:446–456. [PubMed]

28. Ma K, Qiu QF, Lu YH. 2010. Microbial mechanism for rice variety control on methane emission from rice field soil. Glob. Change Biol. 16:3085–3095.

29. Manefield M, Whiteley AS, Griffiths RI, Bailey MJ. 2002. RNA stable isotope probing, a novel means of linking microbial community function to phylogeny. Appl. Environ. Microbiol. 68:5367–5373. [PMC free article] [PubMed]

30. Nicol GW, Leininger S, Schleper C, Prosser JI. 2008. The influence of soil pH on the diversity, abundance and transcriptional activity of ammonia oxidizing archaea and bacteria. Environ. Microbiol. 10:2966–2978. [PubMed]

31. Noll M, Matthies D, Frenzel P, Derakshani M, Liesack W. 2005. Succession of bacterial community structure and diversity in a paddy soil oxygen gradient. Environ. Microbiol. 7:382–395. [PubMed]

32. Peng JJ, Lu Z, Rui J, Lu YH. 2008. Dynamics of the methanogenic archaeal community during plant residue decomposition in an anoxic rice field soil. Appl. Environ. Microbiol. 74:2894–2901. [PMC free article] [PubMed]

33. Radajewski S, Ineson P, Parekh NR, Murrell JC. 2000. Stable-isotope probing as a tool in microbial ecology. Nature 403:646–649. [PubMed]

34. Ratering S, Conrad R. 1998. Effects of short-term drainage and aeration on the production of methane in submerged rice soil. Glob. Change Biol. 4:397–407.

35. Roling WFM. 2007. Do microbial numbers count? Quantifying the regulation of biogeochemical fluxes by population size and cellular activity. FEMS Microbiol. Ecol. 62:202–210. [PubMed]

36. Sakai S, et al. 2009. Cultivation of methanogens under low-hydrogen conditions by using the coculture method. Appl. Environ. Microbiol. 75:4892–4896. [PMC free article] [PubMed]

37. Sass RL, Fisher FM, Wang YB. 1992. Methane emission from rice fields: the effect of floodwater management. Global Biogeochem. Cy. 6:249–262.

38. Sharkey FH, Banat IM, Marchant R. 2004. Detection and quantification of gene expression in environmental bacteriology. Appl. Environ. Microbiol. 70:3795–3806. [PMC free article] [PubMed]

39. Sigren LK, Lewis ST, Fisher FM, Sass RL. 1997. Effects of field drainage on soil parameters related to methane production and emission from rice paddies. Global Biogeochem. Cycles 11:151–162.

40. Smith P, et al. 2008. Greenhouse gas mitigation in agriculture. Philos. Trans. R. Soc. Lond. B Biol. Sci. 363:789–813. [PMC free article] [PubMed]

41. Steinberg LM, Regan JM. 2009. mcrA-targeted real-time quantitative PCR method to examine methanogen communities. Appl. Environ. Microbiol. 75:4435–4442. [PMC free article] [PubMed]

42. Steinberg LM, Regan JM. 2008. Phylogenetic comparison of the methanogenic communities from an acidic, oligotrophic fen and an anaerobic digester treating municipal wastewater sludge. Appl. Environ. Microbiol. 74:6663–6671. [PMC free article] [PubMed]

43. Thauer RK. 1998. Biochemistry of methanogenesis: a tribute to Marjory Stephenson. Microbiology 144(Pt 9):2377–2406. [PubMed]

44. Wassmann R, et al. 2000. Characterization of methane emissions from rice fields in Asia. III. Mitigation options and future research needs. Nutr. Cycl. Agroecosys. 58:23–36.

45. Wassmann R, et al. 2000. Characterization of methane emissions from rice fields in Asia. II. Differences among irrigated, rainfed, and deepwater rice. Nutr. Cycl. Agroecosys. 58:13–22.

46. Watanabe T, Kimura M, Asakawa S. 2006. Community structure of methanogenic archaea in paddy field soil under double cropping (rice-wheat). Soil Biol. Biochem. 38:1264–1274.

47. Watanabe T, Kimura M, Asakawa S. 2009. Distinct members of a stable methanogenic archaeal community transcribe mcrA genes under flooded and drained conditions in Japanese paddy field soil. Soil Biol. Biochem. 41:276–285.

48. Watanabe T, Kimura M, Asakawa S. 2007. Dynamics of methanogenic archaeal communities based on rRNA analysis and their relation to methanogenic activity in Japanese paddy field soils. Soil Biol. Biochem. 39:2877–2887.

49. Wu LQ, Ma K, Lu YH. 2009. Prevalence of betaproteobacterial sequences in nifH gene pools associated with roots of modern rice cultivars. Microb. Ecol. 57:58–68. [PubMed]

50. Yagi K, Tsuruta H, Kanda K, Minami K. 1996. Effect of water management on methane emission from a Japanese rice paddy field: automated methane monitoring. Global Biogeochem. Cycles 10:255–267.

51. Yuan YL, Conrad R, Lu YH. 2009. Responses of methanogenic archaeal community to oxygen exposure in rice field soil. Environ. Microbiol. Rep. 1:347–354.

52. Yuan YL, Conrad R, Lu YH. 2011. Transcriptional response of methanogen mcrA genes to oxygen exposure of rice field soil. Environ. Microbiol. Rep. 3:320–328.

53. Yun JJ, et al. 2006. Genomic DNA functions as a universal external standard in quantitative real-time PCR. Nucleic Acids Res. 34:e85. [PMC free article] [PubMed]

Articles from Applied and Environmental Microbiology are provided here courtesy of
American Society for Microbiology (ASM)