Methane production and methanogenic ... - Wiley Online Library

RESEARCH ARTICLE

Methane production and methanogenic archaeal communities in two types of paddy soil amended with different amounts of rice straw Qiong-Li Bao1, Ke-Qing Xiao1,2, Zheng Chen1, Huai-Ying Yao3 & Yong-Guan Zhu1,3 1 State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China; 2University of Chinese Academy of Sciences, Beijing, China; and 3Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, China

Correspondence: Yong-Guan Zhu, State Key Laboratory of Urban and Regional Ecology, Research Center for EcoEnvironmental Sciences, Chinese Academy of Sciences, 18 Shuangqing Road, Beijing 100085, China. Tel.: 86-10-62936940; fax: 86-10-62936940; e-mail: [email protected] Received 6 November 2013; revised 11 February 2014; accepted 12 February 2014. Final version published online 21 March 2014.

MICROBIOLOGY ECOLOGY

DOI: 10.1111/1574-6941.12305 Editor: Alfons Stams Keywords CH4; rice straw; methanogens; paddy soil.

Abstract Soil type and returning straw to the field are the important factors that regulate CH4 formation in paddy soil, and the variations of biogeochemical parameters and methanogens communities play important roles in the formation of CH4. In the present study, two paddy soil types [silt loam soil (JX) and silty clay loam soil (GD)] with different amounts of rice straw additions were incubated under anaerobic conditions to investigate the relationship between CH4 production, biogeochemical variations, and methanogenic archaeal communities. Straw incorporation significantly stimulated CH4 production in two soil types. CH4 production in JX soil was higher than the GD soil with equal straw addition. Significant differences between biogeochemical parameters and methanogenic archaeal communities were observed between two soil types. Straw addition increased archaeal 16S rRNA genes and mcrA genes copy numbers, especially in JX soil. Multiple regression analysis indicated that variations in H2, sulfate, Fe (II) concentrations, archaeal 16S rRNA genes and mcrA genes copy numbers, methanogens diversity index, and the relative abundance of Methanosarcinaceae and Methanobacteriaceae together influenced CH4 production in two soil types. These results indicated that methane production was influenced by the comprehensive effects of biotic and abiotic factors in paddy soils.

Introduction Methane (CH4) is a major contributor to global warming, second only to CO2. Rice paddy soils are a large source of CH4 (IPCC, 2007). CH4 is the final product of anaerobic methanogens. The production of CH4 in methanogens includes methanol, CO2 reduction, acetate cleavage, as well as methylated compounds (Angel et al., 2012). Methanogenic archaea, including acetoclastic and hydrogenotrophic methanogens, play a vital role for all biogenically produced CH4 in anoxic habitats. Acetate can be utilized by the acetoclastic methanogenic archaeal (Methanosarcinaceae and Methanosaetaceae). Methanosaetaceae seem to become prevalent in paddy soil when acetate concentrations are low, while Methanosarcinaceae are dominant at ª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

higher acetate concentrations (Fey & Conrad, 2000; Kr€ uger et al., 2005). H2/CO2 can be used by several groups of hydrogenotrophic Methanocellales, Methanomicrobiales, Methanosarcinales, and Methanobacteriales. CH4 formation from H2/CO2 is much larger (typically reaching 67%) than from acetate (generally 33%) (Conrad, 1999). To understand the responses of methanogenic community structure to substrates and other chemical factors, the methanogens have been examined through the archaeal 16S rRNA genes and mcrA genes as biomarkers in many studies on paddy soils (e.g. Juottonen et al., 2008; Freitag & Prosser, 2009). Returning rice straw to paddy soil is a common practice in agriculture for the amelioration of soil conditions, such as soil structure and soil organic carbon, thus to FEMS Microbiol Ecol 88 (2014) 372–385

373

Methane production in paddy soils with rice straw

enhance soil fertility and promote the agroecosystem into a virtuous cycle in the long term (Bird et al., 2002). However, many studies have shown that this measure could also increase CH4 emission into the atmosphere (Sch€ utz et al., 1989; Cai et al., 2001; Ma et al., 2009; Yan et al., 2009). The decomposition of organic matter also serves as an important electron source for the reduction in molecular oxygen, nitrate, iron (III), manganese (IV), and sulfate in anaerobic respiration process, and CH4 production is inhibited by the reduction in these electron acceptors (Yao et al., 1999). The members of the methanogenic microbial communities respond differently to the incorporation of organic residues. The size of the methanogenic communities generally increased during anoxic incubation with straw (Conrad et al., 2012). However, studies also found that rice straw addition selectively enhanced the growth of Methanosarcinaceae and Methanobacteriales, but suppressed rice cluster I (RC-I) methanogens and Methanomicrobiales (Conrad & Klose, 2006; Peng et al., 2008). Degrading rice straw seems to be colonized largely by Methanosarcina, Methanobacterium, and RC-I methanogens (Weber et al., 2001). Conrad et al. (2012) reported that the patterns of CH4 production were mainly dependent on the soil, but not on the straw types, and the methanogenic degradation of straw in different soils involved different methanogenic populations. Several pioneering studies have focused on the effects of rice straw utz application on CH4 emission in rice paddy soil (Sch€ et al., 1989; Cai et al., 2001; Ma et al., 2009; Yan et al., 2009). Exactly as present study, the CH4 emission from different types of rice paddy soil with different amounts of rice straw application has also been investigated (Naser et al., 2007), and the responses of the archaeal communities which based on 16S rRNA genes or mcrA genes have also been described abundantly (Peng et al., 2008; Ma et al., 2009, 2010; Conrad et al., 2012). However, the study which focused on the CH4 production in different types of rice paddy soil with different amounts of rice straw application combined with the methanogens responses has not yet been well known. Thus, we comparatively studied CH4 production in two types of paddy soil (silt loam soil and silty clay soil) amended with different amounts of rice straw, detected the temporal patterns of CH4 formation, the variations of

the biogeochemical parameters, and the methanogenic archaeal communities involved to address the following questions: (1) is there a difference in CH4 production when different soils are amended with different amounts of rice straw; (2) is the CH4 production in different soil involved different methanogenic communities; (3) how does the composition and abundance of the methanogenic archaeal community response to rice straw amounts in two types of paddy soil? In-depth knowledge of this research might be of particular importance for providing a theoretical basis for the control of CH4 production in rice paddy soil and developing models to predict CH4 emission from paddy soils at regional and global scales.

Material and methods Preparation and incubation of soil slurries

The rice field soil samples were taken from the plow layer (0–20 cm) of rice fields (28° 120 22.2″ N, 116° 560 02.2″ E) in Jiang xi province (abbreviated JX, belong to silt loam soil) and (20° 330 57.9″ N, 110° 040 25.2″ E) in Guang dong province (abbreviated GD, belong to silty clay loam soil) of China. The soils were sampled in the spring of 2010, and soil basic characteristics were shown in Table 1. The soil was air-dried and stored at ambient temperature. Prior to use, the soil samples were passed through 2 mm staining steel sieve to homogenize sample. Soil samples were pre-incubated for about 2 weeks to activate microorganisms. Rice straw was air-dried and crushed to powder. Sterilized glass bottles were filled with soil samples. Of 0, 0.075, 0.15, and 0.3 g rice straw powder (0, 0.5%, 1%, and 2% g g1 soild.w) were put into 100-mL glass bottles and mixed with soil completely. Levels of rice straw accorded to the references as Yang & Chang (1998), Watanabe et al. (1995) and Kludze & Delaune (1995) and compared to real application levels in the field. The application levels 0.5%, 1%, and 2% roughly correspond to 6, 12, and 24 t ha1, respectively. The sterilized and deionized anoxic water 22.5 mL were added into bottles (the ratio of water to soil was 1.5 : 1), the bottles were vigorously shaken manually to homogenize the soil slurries and capped with sterilized butyl stoppers, evacuated, and flushed with N2 four times. All bottles were incubated at 30 °C in the dark for about

Table 1. The experimental soil characteristics Soil sampling site

pH

Total C (g kg1)

Total N (g kg1

C/N

Total P (%)

Total K (%)

Soil types

JX GD

6.42 5.24

10.27 11.68

0.54 0.38

7.2 8.4

0.06 0.09

1.06 0.41

Silt loam soil Silty clay loam soil

Soil types according to FAO-UNESCO (1974). JX, Jiangxi; GD, Guangdong.

FEMS Microbiol Ecol 88 (2014) 372–385

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

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2 months stationary. Each experiment was carried out in quadruplicate. The treatments were named with JX/GD-Ctrl, JX/GD-0.5%RS, JX/GD-1%RS, and JX/GD2%RS, respectively. Within 10 days after incubation, measurements were carried out on each day, after that, the time intervals between measurements varied from 2 to 7 days. One set of bottles (n = 4) was used to track the partial pressures of CH4, CO2, and H2 which were accumulated during the incubation. Other sets of the bottles (n = 4) were used for chemical and microbial analysis of the soil slurries by destructive sampling. Measurements of gases and biogeochemical parameters

Before taking gas and porewater samples, bottles were shaken vigorously to achieve equilibrium between the gas and liquid phase. Gas samples (0.2 mL) were taken using a gas-tight pressure-lock syringe. Porewater samples (1 mL) were taken with sterile syringes equipped with long needles. The porewater was transferred to Eppendorf tubes and centrifuged for 15 min at 17 949 9 g at 4 °C. The supernatant was collected and stored at 20 °C until analysis. Prior to analysis, the samples were filtered through membrane filters (MS@ Nylon Syringe Filter, Diameter: 25 mm, Pore Size: 0.22 lm, Sangon, Shanghai). CH4 and CO2 were analyzed using GC (Shanghai Precision and Scientific Instrument, China) equipped with a flame ionization detector, a catalytic methanizer for CO2 (Chrompack, nickat replacement reator) and a 80-cm-long Propack QS 50/100 mesh column operated at 50 °C, N2 as carrier gas. A reductive gas detector (RGD2) (Trace Analytical, Menlo Park, CA) was used to determinate low H2 (< 10 Pa) with a molecular sieve column and synthetic air (80% N2 and 20% O2) as carrier gas. Gas samples were detected with a thermal conductivity detector with a molecular sieve column and N2 as carrier gas when H2 partial pressure was > 10 Pa. The Fe (II) iron concentrations were determined in soil slurry by the ferrozine reaction method described by Roy et al. (1997). A slurry aliquot (about 0.5 mL) was added to 4.5 mL 0.5 M HCl. The sample was mixed on a vortex for 1 min. The 100 lL of this mixture was mixed with 1 mL of ferrozin reagent (0.1% w/v ferrozin in 50 mM HEPES buffer pH 7.0) and centrifuged for 3 min at 10 000 9 g, and the extinction of the supernatant was measured at 562 nm with a spectrophotometer. The concentrations of nitrate and sulfate in porewater were analyzed by DX-120 ion chromatography (Dionex, America) (Bak et al., 1991; Yuan & Lu, 2009). The concentrations of acetate were measured with HPLC (Shimadzu LC-10A) using ShimPack RP-C18 ODS column (150 9 6.0 mm) under conditions described previously (Shen, 1998). ª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

Microbial DNA extraction

We selected four times (days 0, 13, 20, and 57) soil samples of all treatments which represented the crucial stage of CH4 production for microbial molecular analysis. Soil slurries were mixed thoroughly and then centrifuged at 4000 9 g for 15 min to remove the liquid. The soil samples were stored at 20 °C until DNA extraction using Fast DNA SPIN Kit for Soil (MP Biomedicals, Solon, OH). PCR amplification and T-RFLP determination of methanogenic archaeal 16S rRNA genes and mcrA genes

Amplification of methanogenic archaeal 16S rRNA genes fragments was carried out using primer sets Ar109f and Ar912r, with the reverse primer was labeled with 6-carboxyfluorescein for T-RFLP analysis (Lueders & Friedrich, 2000). The fragments of mcrA genes were amplified using primer pair of ME1 and ME2 with the ME1 primer labeled with 6-carboxyfluorescein for T-RFLP analysis (Daebeler et al., 2013). The purified PCR products of archaeal 16S rRNA genes were digested at 65 °C for 3 h by TaqI (Fermentas, Canada) (Lueders & Friedrich, 2000), and the products of mcrA genes were digested at 37 °C for 3 h by Sau96I (Fermentas) (Lueders et al., 2001). All the digestion products were size-separated using a 3730xl Genestic Analyzer (Applied Biosystems). The relative abundance of each T-RF was calculated as described recently by Xiao et al. (2013). Real-time PCR

The copy numbers of archaeal 16S rRNA genes and mcrA genes were determined using primer pairs Ar364f/Ar934r (Kemnitz et al., 2005) and ME1f/ME3r (Hales et al., 1996), respectively. Reactions were performed on a iCycler iQTMThermocycler (Bio-Rad). For archaeal 16S rRNA genes, the thermal cycles and fluorescence signal acquisition followed the same protocols as described by Kemnitz et al. (2005). For mcrA genes, the conditions were as described by Hales et al. (1996). The standard curves for the quantitative PCR were established using 16S rRNA genes and mcrA genes fragments cloned into plasmid pGEM-T Easy Vector (Promega). Cloning, sequencing, and phylogenetic analysis

The clone libraries were constructed for methanogenic archaeal 16S rRNA genes and mcrA genes fragments retrieved from the last sampling time (day 57) with 1% straw addition in two soils. The PCR amplification used



the same primers as indicated above without fluorescent labels. The PCR products of the correct size were purified and ligated into the pGEM-T Easy Vector (Promega, Madison, WI) according to the manufacturer’s instructions. Plasmids were transformed into Escherichia coli cells. 228 and 156 positive clones were randomly selected for archaeal 16S rRNA genes and mcrA genes, respectively. Phylogenetic trees were calculated from DNA sequences of 16S rRNA genes and amino acid sequences of mcrA genes with MEGA 4 using neighbor-joining method (Tamura et al., 2007). Clones with > 99% and > 97% (for archaeal 16S rRNA genes and mcrA genes, respectively) sequences similarity were considered to be the same OTU. Statistical analysis

Data were processed using EXCEL 2007 for the means and the standard errors. The data of T-RFLP were normally distributed with logarithmic transformation. Ordination analyses of T-RFLP profiles were performed using CANOCO 4.5 software (Microcomputer Power, Ithaca, NY). Microbial diversity was quantified using Shannon–Weiner diversity index (H) based on results from T-RFLP data. Relationships between CH4 production and environmental parameters were investigated using multiple regression analysis with SPSS (17.0) software (SPSS, Chacon). Nucleotide sequence accession numbers

The methanogenic archaeal 16S rRNA genes and mcrA genes nucleotide sequences have been deposited in the NCBI nucleotide sequences database under the following accession numbers for archaeal 16S rRNA genes sequences: KF228254–KF228258 and for mcrA genes sequences: KF22 8259–KF228299.

Results Changes in biogeochemical parameters

CH4 productions in both soils with different straw levels were shown in Fig. 1a. CH4 formation started with different lag times. The lag phases were followed by linear increase of CH4 within the period between day 5 and day 20 after incubation. The CH4 partial pressure reached 28.42 and 38.80 KPa in JX-2%RS and GD-2%RS treatments, respectively. After 20 days, the accumulation of CH4 maintained at a stable stage. A significant positive relationship between CH4 accumulation and straw addition was observed (Fig. 1b). CH4 productions were greater in JX soil than in GD soil in all corresponding treatments. FEMS Microbiol Ecol 88 (2014) 372–385

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Due to the decomposition of organic matter, vigorous CO2 production was observed right from the beginning of the incubation (Fig. 1c). The patterns of CO2 accumulation were similar to CH4 accumulation. The average CO2 accumulation in the stable stage in JX-Ctrl, JX-0.5%RS, JX-1% RS, and JX-2%RS treatments was 1.37, 1.41, 1.73, and 3.32 times of the corresponding treatments in GD soil. The H2 partial pressures were greater in JX soil than in GD soil at equal straw addition. Straw addition promoted H2 production significantly in both soils, and the production of H2 increased with the increasing straw addition (Fig. 1d). Very sharp peaks of the H2 partial pressures were observed with 2% straw addition, the highest values reached 170.55 and 137.52 Pa in JX and GD soils, respectively. The consumption of H2 was faster in JX soil than in GD soil at equal straw addition. Furthermore, the consumption of H2 increased with increasing straw addition, especially in GD soil. Similar to H2 production, acetate concentrations were significantly higher in JX soil than in GD soil at equal straw addition. Straw addition significantly promoted acetate production in both soils, and the production of acetate increased with increasing straw addition. The maximum acetate concentration was up to 194.94 and 157.06 mg kg1 with 2% straw addition in JX and GD soil, respectively. The consumption of acetate in all treatments completed within about two weeks and was faster than H2 consumption (Fig. 1e). No obvious variations in NO3 concentration were observed in all treatments except GD-Ctrl (Fig. 1f). The slightly greater NO3 concentrations (66.79 mg kg1) were observed with 2% straw addition in JX soil. However, for treatments with other levels of straw additions, NO3 concentration increased following the rapid initial decrease in both soils, and the increase of NO3 emerged earlier with higher straw addition. concentrations in JX soil were significantly The SO2 4 lower than in GD soil. Straw addition promoted SO2 4 reduction significantly in both soils. The faster reduction was observed with increasing straw addition, in SO2 4 especially in GD soil. The reduction of SO2 4 in JX-Ctrl completed within about seven weeks. However, SO2 4 concentration maintained at higher level in GD-Ctrl (2.80 mg kg1 on average) during the whole incubation period (Fig. 1g). In addition, significant increase in SO2 4 was observed at the initial incubation stage in GD soil. The increases in Fe (II) concentrations in JX soil were greater than in GD soil with equal straw addition. Straw addition promoted microbial Fe reduction, and Fe (II) concentrations increased significantly with increasing straw addition in both soils, especially in GD soil (Fig. 1h). The reductions of Fe (III) in both soils were completed within about 20 days. ª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

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(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h) Fig. 1. Methane productions (a), the correlation between CH4 productions and straw amounts (b), temporal changes of CO2 (c), H2 partial pressure (d), and the concentrations of acetate (e), nitrate (f), sulfate (g), Fe (II) (h) in two soil types with different amounts of rice straw addition. Data are mean SE (n = 4). JX, soil from Jiangxi province; GD, soil from Guangdong province; Ctrl, control; RS, rice straw. The abbreviations are same in below figures.

Variations of methanogenic archaeal community structure (based on 16S rRNA genes)

The structure of methanogenic archaeal community based on 16S rRNA genes analysis was determined four times (days 0, 13, 20, and 57) (Fig. 3). Significant differences in


methanogenic archaeal community structures were observed between the two soils from the T-RFLP profiles and PCA analysis (Figs 2 and 3a). The RDA analysis indicated that the amounts of straw and incubation time influenced the methanogenic archaeal community structure in both soils. In JX soil, 184 and 391 bp T-RFs were the most abundant, followed by 92 bp. The relative


377


abundances of 92 and 184 bp increased while 283 and 391 bp decreased with increasing straw amounts. 380 bp emerged and 391 bp decreased with incubation time (Supporting information, Fig. S1 JX). In GD soil, the TRFs of 92 and 184 bp were most abundant, followed by 391 bp. The relative abundances of 184 and 380 bp increased while 92 and 283 bp decreased with increasing straw amounts, and 391 and 380 bp increased while 92 and 84 bp decreased with incubation time (Fig. S1 GD). Clone library of the methanogenic archaeal 16S rRNA genes revealed that the archaeal communities were consisted mainly of Methanosarcinaceae and Methanomicrobia (Fig. S2). Most of the T-RFs detected in T-RFLP analysis could be assigned to phylogenetic groups of archaeal by comparing with the T-RFs found in silico in the clone sequences retrieved in this study. Due to the limitation of T-RFLP method, some of the T-RFs can come from more than one phylogenetic lineage. Analysis of sequence data indicated that the 92 bp T-RFs could be assigned to Methanomicrobiales, 391 bp to Methanocellales and Methanomicrobiales, 184 bp to Methanosarcinaceae, 84 bp to Methanobacteriales, 283 bp to Methanosaetaceae, and 380 bp to Methanocellales (Ma et al., 2010).

Variations of methanogenic archaeal community structure (based on mcrA genes)

Similar to methanogenic archaeal 16S rRNA genes analysis, significant differences in methanogens community structures were observed between the two soils from PCA analysis of the T-RFLP profiles targeting the mcrA genes (Figs 4 and 3b). The RDA analysis indicated that straw amounts and incubation time influenced the methanogenic archaeal community structure in both soils differently. In JX soil, the T-RF 136 bp was dominant. The relative abundances of 667 and 726 bp increased while 136 bp decreased slightly with increasing straw amounts. The T-RFs of 652 and 120 bp increased slightly with time (Fig. S3 JX). In GD soil, the most abundant T-RF 136 bp was followed by T-RFs of 667 and 726 bp, and the relative abundances of T-RFs of 667 and 726 bp increased while T-RFs of 120 and 136 bp decreased with increasing straw amounts. The T-RF 726 bp increased while the T-RFs of 136 and 639 bp decreased with incubation time (Fig. S3 GD). Clone library of the methanogenic mcrA genes showed that the methanogens were consisted mainly of

Fig. 2. Community structures of methanogenic archaeal based on T-RFLP analysis targeting archaeal 16S rRNA genes in different amounts of rice straw addition and sampling time in two soil types. Shown are the relative abundances (mean SE, n = 4) of terminal restriction fragments (T-RF) as digested by TaqI enzyme from PCR products.



378

Q.-L. Bao et al.

(a)

times (days 0, 13, 20, and 57) by quantitative PCR (Fig. 5a and b). The copy numbers of archaeal 16S rRNA genes and mcrA genes were greater in JX soil than in GD soil with equal straw addition and in the control. Both archaeal 16S rRNA genes and mcrA genes copy numbers increased with increasing straw addition and incubation time. The archaeal 16S rRNA genes copy numbers ranged from 2.59 9 107 to 3.74 9 109 and from 8.50 9 105 to 1.02 9 109 copies g1 dry soil in JX and GD soils, respectively. The mcrA genes copy numbers ranged from 2.49 9 106 to 2.17 9 108 and from 3.20 9 106 to 1.83 9 108 copies g1 dry soil in JX and GD soils, respectively. The archaeal 16S rRNA genes copy numbers were generally one order of magnitude greater than mcrA genes copy numbers in both soils. The patterns of the variations in archaeal 16S rRNA genes and mcrA genes copy numbers were significantly corrected with that of CH4 production in both soils. The correlation between CH4 production and mcrA genes copy numbers was more significant (Fig. 5c and d).

(b)

Fig. 3. Principal component analysis of T-RFLP profiles for archaeal 16S rRNA genes (a) and mcrA genes (b) retrieved from different treatments. Open and closed symbols denote JX and GD soil treatments, respectively. Different colors denote different rice straw amounts. Diamonds, triangles, stars, and circles indicate days 0, 13, 20, and 57, respectively. Values on the axes indicate the percentages of total variation explained by each axis.

Methanosarcinales, Methanocellaceae and Methanobacteriaceae, and only two clone sequence grouped with members of the Methanomicrobiales (Fig. S4). In silico analysis of mcrA genes sequences in this study and according to the published sequence information indicated that most of the T-RFs detected in T-RFLP analysis could be assigned to particular phylogenetic groups. Some of the T-RFs can come from more than one phylogenetic lineage. The 120, 136, and 652 bp T-RFs were indicative of Methanosarcinales, and T-RFs of 120, 667, and 726 bp were characteristic for Methanobacteriaceae. The sequences with a T-RF 88 bp belonged to Methanomicrobiales, and Methanocellaceae sequences exhibited T-RF 639 and 136 bp. A minor T-RF 494 bp, which was merely observed in JX soil, could not be identified unequivocally; therefore, the affiliation of this T-RF remains unclear. In addition, mcrA genes and 16S rRNA genes characteristic of the Methanosaetaceae were not detected in both soils.

Relationships between CH4 production and various variables

The multiple regression analysis showed that the variations of electron donors (acetate and H2) and acceptors (NO3, SO2 4 and microbial reducible Fe) could explain 94% (r2 = 0.94) of the observed variations in CH4 production, and H2 partial pressure, Fe (II) and SO2 4 concentrations were the significant variables. The methanogenic archaeal 16S rRNA genes copy numbers, archaeal diversity index, and relative abundances of the different T-RFs of methanogenic archaeal 16S rRNA genes could explain 77% (r2 = 0.77) of the observed variations in CH4 production, and archaeal 16S rRNA genes copy numbers were the most influence factors in the equation, followed by the relative abundance of Methanosarcinaceae (T-RF 184 bp). The mcrA genes copy numbers, methanogens diversity index, and the relative abundances of different T-RFs of mcrA genes could explain 88% (r2 = 0.88) of the observed variations in CH4 production. The mcrA genes copy numbers also were the most significant factor, followed by the relative abundance of Methanobacteriaceae (T-RFs 726 and 667 bp) and methangens diversity index. Other variables did not have a significant effect on CH4 production (Table 2).

Discussion Abundances of methanogenic archaeal 16S rRNA genes and mcrA genes

Biogeochemical parameters

The abundances of methanogenic archaeal 16S rRNA genes and methanogens mcrA genes were determined four

Our study showed differences in CH4 production patterns between two types of soil but also among the treatments




379

Fig. 4. Community structures of methanogenic archaeal based on T-RFLP analysis targeting mcrA genes in different amounts of rice straw addition and sampling time in two soil types. Shown are the relative abundances (mean SE, n = 4) of terminal restriction fragments (T-RF) as digested by Sau96I enzyme from PCR products.

with different amounts of rice straw. CH4 productions were greater in JX soil than in GD soil. Methane production was stimulated by the incorporation of rice straw in both soils, which is in agreement with previous studies (Denier van der Gon & Neue, 1995; Peng et al., 2008). The CH4 accumulation profiles in all treatments started with different lag phases in both soils. Lag phases were necessary after submerging soil for the sequential reduction of all electron acceptors such as NO3, Mn (IV), Fe (III), and SO2 4 before the reduction of CO2 to CH4 (Yao et al., 1999). Methane was then vigorously produced and eventually accumulated with a constant rate until the end of incubation, indicating that CH4 production and methanogenic consumption of H2 and acetate were in the steady state (Yao et al., 1999; Peng et al., 2008). Similarly, the production of the methanogenic precursors acetate and H2 were quite different in two soil types, and among the treatments with different amounts of rice straw. Greater H2, CO2, and acetate were observed in JX soil than in GD soil. A microbial assemblage including hydrolytic, fermenting, homoacetogenic, syntrophic, and FEMS Microbiol Ecol 88 (2014) 372–385

methanogenic microorganisms participated the anaerobic decomposition of organic residues (Conrad, 1999). Bacterial populations colonizing and decomposing rice straw occurred during the first two weeks of incubation (Weber et al., 2001). Members of the Clostridium (clusters I and III), Bacteroidetes, and Chlorobi are responsible for the decomposition of plant residues (Weber et al., 2001; Rui et al., 2009). Earlier studies on the dependence of organic matter decomposition on soil pH showed that different types of soil microorganisms have different pH optima for maximum growth and activity, the optimum pH range for decomposition of organic matter is 6.5–8.5, and bacteria and actinomycetes have pH optima near neutrality (Alexander, 1961; Parr & Papendick, 1978). In the present study, the pH in JX soil is neutrality almost, thus the microorganisms decomposed rice straw were more rapid than in GD soil probably. It is seemed that GD soil has a clay texture was another reason for low substrate (acetate and H2) production rates, because heavy soil texture seemed to inhibit soil microbial activity (Alexander, 1961). ª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

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(a)

(b)

(c)

(d)

Fig. 5. Abundance of methanogenic archaeal revealed by quantitative PCR analyses based on archaeal 16S rRNA genes (a) and mcrA genes (b) in all treatments at four sampling times in two soil types and the correlation between CH4 production and archaeal 16S rRNA genes copy numbers (c) or mcrA genes copy numbers (d).

The reductions of NO3 and SO2 influenced CH4 4 production in both soils. It seemed that the NO3 concentration in GD soil was greater than in JX soil as indicated by greater NO3 concentration in GD-ctrl, although the NO3 concentration variations between treatments with straw were so fast even within hours (Van Bodegom & Stams, 1999) that we failed to capture them exactly. Nitrate reducers in GD soil competed more electron donors with methanogens, and this may be the reason for lower CH4 production. Furthermore, the toxic effects of the denitrification intermediates (nitrite, NO, uber & N2O) on methanogens (Clarens et al., 1998; Kl€ Conrad, 1998) may also contribute to less CH4 production in GD soil. The SO2 4 concentrations increased significantly in GD soil after anoxic incubation possibly due to its release from the clays and hydrous aluminum oxides after flooding (Yao et al., 1999), and resulted in significantly higher SO2 4 concentrations than in JX soil. Thus, the complete reduction of greater SO2 4 in GD soil ª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

might compete for more electron donors with methanogens and thus possibly inhibited CH4 production. It has been suggested that sulfate reducing bacteria competed with methanogens for H2 and restrained the growth of the hydrogenotrophic rice cluster I or Methanomicrobiaceae group has been reported before (Scheid et al., 2003). Straw addition increased Fe (III) reduction significantly in both soils due to the supply of sufficient electron donors. Fe (II) productions (Fe (III) reduction) were greater in JX soil than in GD soil. It is well established that iron reduction can suppress CH4 production (J€ackel & Schnell, 2000; Roden & Wetzel, 2003) through the suppression of the Methanosarcina populations (Lueders & Friedrich, 2002). However, our study showed that CH4 produced was higher in JX soil than in GD soil. It seemed that the competition between iron reducers and methanogens for common electron donors had little effect on CH4 production when sufficient electron donors were supplied. Fe (III) reduction and methanogenesis can take FEMS Microbiol Ecol 88 (2014) 372–385

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Table 2. The multiple regression analysis for the dependent variables on CH4 production. Input variables were electron donors and acceptors, archaeal 16S rRNA genes and mcrA genes copy numbers, diversity index from T-RFLP, relative abundances of different T-RFs (%) of methanogenic archaeal 16s rRNA genes and mcrA genes. B is the regression coefficient, whereas b is the standardized regression coefficient CH4 production

r

2

P

Electron donors and acceptors 0.94 0.000 Acetate 0.42 0.02 H2 NO3 0.34 Fe(II) 0.000 0.000 SO2 4 Methanogenic archaeal 16S rRNA genes 0.77 0.000 Copy numbers 0.000 Diversity index 0.244 T-RF 92 bp 0.102 T-RF 184 bp 0.000 T-RF 283 bp 0.138 T-RF 380 bp 0.389 T-RF 391 bp 0.225 T-RF 715 bp 0.336 mcrA genes 0.87 0.000 Copy numbers 0.000 Diversity index 0.000 T-RF 726 bp 0.003 T-RF 667 bp 0.027 T-RF 652 bp 0.072 T-RF 639 bp 0.467 T-RF 494 bp 0.213 T-RF 136 bp 0.475 T-RF 120 bp 0.096 T-RF 88 bp 0.188

B

b

0.137 0.027 0.165 0.045 0.108

0.146 0.024 0.177 0.048 0.115

0.66 0.95 0.06 0.30 0.22 0.33 0.18 0.09

0.71 0.08 0.07 0.32 0.18 0.25 0.20 0.06

0.46 1.76 0.40 0.21 0.37 0.27 0.12 0.01 0.19 0.04

0.49 0.19 0.43 0.22 0.34 0.29 0.12 0.01 0.20 0.03

place simultaneously when electron donors are not limiting (Lovely & Phillips, 1987). Significant differences of other relevant soil properties such as soil type, pH, and K content between two soils were the possible reasons for different CH4 production (Table 1). K amendment effectively decreased CH4 emission by preventing a drop in soil Eh and inhibiting methanogenic communities and simultaneously stimulating methanotrophic bacterial growth (Jagadeesh Babu et al., 2006), and available K content influenced the CH4 production potentials of various soils (Mitra et al., 2002). The microbial communities involved in CH4 metabolism is not well adapted to low pH values (Dunfield et al., 1993). A near-neutral pH (6.9–7.2) is likely to be the optimum pH for CH4 formation (Wang et al., 1993). Additionally, CH4 entrapment was positively correlated with soil clay content (Wang et al., 1993). Sass & Fisher (1997) also reported that the sandy soils produce more CH4 than clay soils with similar carbon content. In the FEMS Microbiol Ecol 88 (2014) 372–385

present study, the JX soil with higher K content, nearneutral pH and less clay characteristics are possibly contributed to more CH4 production compared with GD soil. Responses of methanogenic microbial communities

Our study showed that the community structures of methanogens were quite different between the two soil types based on 16S rRNA genes and mcrA genes analyses. The different production of substrates acetate and H2 which probably influenced by different properties of soils, such as pH, K content, and soil type, was possible reasons for different community dynamics between two soils. Straw amounts and incubation time also influenced methanogenic archaeal community structures, and the variations of substrates H2 and acetate together with incubation time may be the key factors regulating the methanogenic archaeal community structures, which is in accordance with earlier studies (Chin et al., 2003; Conrad & Klose, 2006; Conrad et al., 2012). The methanogens belonged to the family of Methanosarcinaceae were the most abundant in both soils. Methanosarcina spp. have been described as fast-growing and substrate-versatile methanogens, and they can use acetate, methanol and H2-CO2 (Jetten et al., 1990, 1992). Methanosarcina species use H2 plus CO2 for proliferation during the degradation of straw has been detected before (Conrad & Klose, 2006). Methanocellaceae and Methanobacteriaceae were the second dominant groups in JX and GD soils, respectively. Methanobacteriaceae using H2 plus CO2 have frequently been isolated from rice field soil (Asakawa et al., 1993; Joulian et al., 1998) and proliferate preferentially when H2 is not limited (Conrad & Klose, 2006). Methanocellales represent a novel species of hydrogenotrophic methanogens and are widespread in methanogenic environments. They play a key role in CH4 production from root exudates of rice plant, especially in the partially oxic rhizophere of rice. (Lu & Conrad, 2005). Probably due to the unique genes encoding anti-oxidant enzymes and oxygen insensitive fermentation enzymes, Methanocellales are particular known for potentially being able to deal with toxic O2 species (Erkel et al., 2006), thus make Methanocellales better adapted to both oxic and anoxic conditions. Methanosarcinaceae and Methanocellales were also found to be the most prevalent groups after an extensive phase of methanogenesis from previous study (Angel et al., 2012). The Methanosaetaceae species were few in both soils, despite there were low concentrations of acetate at the end of incubation. Methanosaeta spp. has been described as slow-growing organisms utilizing acetate and has a much lower minimum threshold for acetate (Jetten et al., ª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

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1990, 1992). Thus, the mechanisms for such a phenomenon remain unclear. The T-RFLP profiles based on the archaeal 16S rRNA genes and mcrA genes exhibited different methanogens community structures in the same soil. The dominant species in two soils from T-RFLP profile which based on 16S rRNA genes were Methanocellales, Methanobacteriaceae, and Methanosarcinaceae. However, the dominant species in JX soil from the T-RFLP profile which based on mcrA genes were Methanosarcinaceae and Methanocellaceae, and the dominant species in GD soil were Methanosarcinaceae, Methanocellaceae and Methanobacteriaceae. This was probably caused by the potential PCR bias. T-RFLP fingerprinting technique is PCR based and possibly subjected to PCR-innate bias. PCR bias would result in less PCR products to become relatively abundant with increasing PCR cycle numbers (Suzuki & Giovannoni, 1996; Suzuki et al., 1998). Different annealing temperatures change primerbinding kinetics in a template mixture when primers with degenerate positions are used (Lueders & Friedrich, 2003). Obvious differences of methanogens community structures between 16S rRNA genes and mcrA genes targeted T-RFLP analysis possibly due to preferential amplification of certain templates which caused by the highly degenerate mcrA primer set. In addition, the abundant Methanobacteriaceae (120, 667, and 726 bp) in GD soil are likely due to the amplification of mcrA and mrtA genes simultaneously in PCR amplification. The methyl coenzyme-M reductase (MCR) is a specific marker for methanogens and the key enzyme of methanogenesis (Ermler et al., 1997). The operon encoding MCR-I (mcrBDCGA) is present in all methanogens (Reeve et al., 1997). However, members of Methanobacteriales also contain the methyl coenzyme-M reductase II (MCR-II, mrtBDGA). In rice paddy soil, the methanobacterial mcrA and mrtA sequences possibly originated from the same Methanobacteriaceae. Thus, the presence of both paralogous MCR genes origination from the same species causes biases when deduces methanogens community composition based on relative mcrA/mrtA gene abundances (Lueders et al., 2001). Incubation of rice straw under anoxic conditions resulted in an increase in methanogenic archaeal biomass irrespectively of the soil used. The increase of methanogens abundance was greater in JX soil than in GD soil with equal straw addition. It is probably due to the sufficient substrates (H2 and acetate) derived from the decomposition of organic matter (Conrad & Klose, 2006; Conrad et al., 2012). It indicated that microbial growth in the soil was restricted by energy substrate. The highest values of 16s RNA genes copy numbers reached to 3.74 9 10e9 and 1.02 9 109 copies g1 dry soil with 2% rice straw application in JX and GD soils, respectively, which were greater than previous report (Conrad et al., ª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

2012). The possible reason was greater rice straw (2% w/ w) was selected in our study, thus improved methanogens growth greatly. The variations in the genes copy numbers were key factors on the variations in CH4 production, especially the mcrA genes copy numbers. The explanation of the changes in abundance of methanogens which based on mcrA genes on the variations in CH4 production was seemingly more ideal than 16S rRNA genes as more significant correlation was observed between CH4 partial pressure and mcrA genes copy numbers. In addition, the archaeal 16S rRNA genes copy numbers were generally one order of magnitude higher than those of the mcrA genes in the present study. This is reasonable because the genome of methanogens contains two to four times more ribosomal RNA genes than mcrA genes (Erkel et al., 2006). In summary, our results revealed the effect of rice straw addition on CH4 production and methanogenic archaeal communities in two soil types and showed that CH4 production was mainly influenced by the amounts of rice straw, soil properties together with the variations of methanogenic archaeal communities induced by variations in substrates (e.g. H2 and acetate) in paddy soils. Our study provides a system approach to dissecting the complex interactions between soil properties, returning straw to the field and microbial community, and future efforts should be directed to predictive model based on the these dynamic processes.

Acknowledgements This project was financially supported by the National Natural Science Foundation of China Grant 41090282 and Key Projects of Natural Science Foundation of China Grant 41090280. We would like to thank Prof. Yahai Lu for measuring biogeochemical parameters.

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Supporting Information Additional Supporting Information may be found in the online version of this article:



Fig. S1. Redundancy analysis (RDA) for T-RFLP profiles of archaeal 16S rRNA genes analyzed in two soil types with different amounts of rice straw addition. Fig. S2. Phylogenetic tree of representative methanogenic archaeal 16S rRNA gene clone sequences retrieved from two soils with the 1% (w/w) straw addition at the end of experiment.


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Fig. S3. Redundancy analysis (RDA) for T-RFLP profiles of mcrA genes analyzed in two soil types with different amounts of rice straw addition. Fig. S4. Phylogenetic tree of representative methangens mcrA gene clone sequences retrieved from two soils with the 1% straw addition at the end of experiment.
