• Methanogen;
  • Regional biodiversity;
  • Methyl-coenzyme M reductase;
  • SSU rRNA;
  • Terminal restriction fragment length polymorphism analysis


  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Materials and methods
  5. 3. Results
  6. 4. Discussion
  7. Acknowledgements
  8. References

The methane production potential of rice soils, which are situated in different geographical regions, shows inherent variations and is catalyzed by archaeal methanogens. We therefore investigated the archaeal community structure in 11 rice field soils which represent a range of climatic conditions (temperate to subtropical zones) and soil properties. Retrieval of environmental partial SSU rDNA sequences from the rice soils of Shenyang (China) and Gapan (The Philippines) showed that the communities were different from each other. However, despite the differences in soil properties and geographical region the sequences clustered in similar phylogenetic groups to those obtained earlier from rice fields of Vercelli (Italy). The archaeal community structure in the other rice field soils was compared using terminal restriction fragment length polymorphism (T-RFLP) analysis targeting the SSU rRNA gene and the methyl-coenzyme M reductase α-subunit gene (mcrA). The relative abundance of each terminal restriction fragment (T-RF) was determined by fluorescence peak area integration. The 182-bp SSU rDNA T-RF (representing members of Methanosarcinaceae and rice cluster (RC) VI) was dominant (40–80% contribution) in Chinese soils (Zhenjiang, Changchun, Jurong, Beiyuan, Shenyang) and the Philippine soil of Gapan. The other Philippine soils (Luisiana, Guangzhou, Pila) and the Italian soils (Vercelli, Pavia) showed a dominant 389-bp T-RF (35–40% contribution), representing mainly the novel methanogenic RC-I. All the other T-RF (80, 88, 280, 375 and >800 bp) contributed <20%. Prolonged anoxic incubation (30–200 days) of the air-dried soils resulted in the production of CH4, which was in some soils preceded by a characteristic halt phase. T-RFLP analysis revealed that the soils with a methanogenic halt phase also showed dramatic archaeal population dynamics which were related to the length of the halt phase. Our results show that the archaeal communities in rice field soils of different geographical origin are highly related, but nevertheless exhibit individual patterns and dynamics, thus providing evidence for the active participation of the community members in energy and carbon flow.

1. Introduction

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Materials and methods
  5. 3. Results
  6. 4. Discussion
  7. Acknowledgements
  8. References

Methanogens are a highly specialized group of anaerobic microorganisms that belong phylogenetically to the domain Archaea. Though the metabolic process of formation and emission of CH4 in rice soils worldwide has been investigated extensively, relatively few reports are available on the populations of methanogenic Archaea present. These reports include isolation of methanogens from rice field soils [1–7] but also molecular characterization of the archaeal SSU rRNA genes in the soil or rhizosphere [5,6,8–12]. It has been demonstrated that a relatively large diversity of Archaea, including the members of the families Methanosarcinaceae, Methanosaetaceae, Methanomicrobiaceae, and Methanobacteriaceae (taxonomy according to [13]), are present in Italian rice soils. In addition, novel SSU rRNA sequences representing uncultivated microorganisms have been found in rice field soil. These novel sequences occur both in the kingdom Euryarchaeota, termed rice clusters (RC) I, II, III and V, and in the kingdom Crenarchaeota, termed RC-IV and RC-VI [6,9,11,12]. Most recently, the methanogenic community in Italian rice field soil was also characterized by the mcrA gene encoding the α-subunit of the methyl-coenzyme M reductase, the key catabolic enzyme of methanogens [14]. This study revealed the methanogenic geno- and phenotype of the novel RC-I lineage.

There is a need to characterize microbial communities phylogenetically in order to define the factors that regulate both their structure and function [15]. Methane production in rice soil ecosystems is considered to be accomplished by a complex community of hydrolytic, fermenting, syntrophic, homoacetogenic and methanogenic microorganisms [16]. The high variability of the CH4 production potential of rice soils is not completely accounted for by the differences in the soil chemical properties alone [17–19]. Potentially, the diversity and community structure of Archaea in rice soils may have important ecological significance. Therefore, our study was aimed at understanding the community structure of archaeal members in soils from rice fields other than the relatively well studied site at Vercelli, Italy. We used rice field soils representing a range of climatic conditions (temperate to subtropical zones) and soil organic carbon contents. These soils had been studied in detail regarding their potential to produce CH4 and its control by chemical characteristics [19–21]. We used terminal restriction fragment length polymorphism (T-RFLP) analysis of SSU rRNA and methyl-coenzyme M reductase genes (mcrA/mrtA) to identify archaeal and methanogenic phylotypes and compare their distribution and relative community composition in these soils.

2. Materials and methods

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Materials and methods
  5. 3. Results
  6. 4. Discussion
  7. Acknowledgements
  8. References

2.1 Soil sampling, analyses, and geographical regions

Soil samples were obtained from the different geographical sites, 3–4 months after rice harvest. Selected characteristics of soils and information on the geographical regions are provided in Table 1; other details are presented elsewhere [19]. Biogeochemical experiments using the same batch of soil samples have been described [19–21]. Samples were size-fractionated using stainless steel sieves to obtain soil particles between 0.1 and 1.0 mm diameter, and stored in darkness at 4°C prior to the experiments. The aerobic storage of dried soils is reported to have no significant effect on soil methane production capacity [22]. For studying methane production over time and the composition of the archaeal communities, soil samples (0.5 g) and 0.5 ml of anoxic, sterile water were placed into 2.0-ml replicate open polypropylene microcentrifuge tubes. Several tubes were then placed into replicate 120-ml serum bottles. The serum bottles were closed with butyl rubber stoppers and the head space was flushed with N2 for at least 30 min. Serum bottles were incubated statically in the dark at 30°C. At given time intervals (2 h after preparation of soil slurries (day 0), and after 30 days incubation), triplicate samples were withdrawn and stored at −20°C until further analysis. Another set of bottles with soil slurries (10 g of soil and 10 ml of anoxic sterile water) were incubated under anoxic conditions at 30°C and sampled after 200 days. Slurry samples (0.5 ml) from these vessels were collected after vortexing the serum bottles, and stored in microcentrifuge tubes at −20°C.

Table 1.  Selected characteristics of the soils from geographically different regions used in this study (data taken from [19,20])
OriginCountryOrganic carbon (mg g−1)Total nitrogen (mg g−1)Total free iron (μmol g−1)Maximum CH4 production (μmol g−1 day−1)End of halt phase (days)
LuisianaThe Philippines16.51.574220.405
PilaThe Philippines26.22.971283.080
GapanThe Philippines15.11.172610.479

2.2 DNA extraction, purification and quantification

DNA extraction was performed according to Moré et al. [23] with modifications as described by Henckel et al. [24]. Briefly, soil slurry samples (0.5 ml) were lysed in the presence of a sodium dodecyl sulfate solution using a bead-beating protocol as described previously [24]. Total soil community DNA was extracted, washed, pelleted, and resuspended in 100 μl of TE buffer (10 mM Tris base, 1 mM EDTA, pH 8.0). For further purification, DNA extracts were centrifuged at 2100×g for 2 min and passed through spin columns (Bio-Rad, Munich, Germany) filled with acid-washed polyvinyl polypyrrolidone (Sigma-Aldrich, Steinhein, Germany) in 30 mM potassium phosphate buffer (pH 8.0) as described by Berthelet et al. [25]. The purified extracts were clear and colorless, and were analyzed by standard gel electrophoresis. DNA concentrations of the purified extracts were determined spectrophotometrically.

2.3 Archaeal SSU rDNA amplification

From soil community DNA, archaeal SSU rDNA was amplified by PCR using primers Ar109f (ACK GCT CAG TAA CAC GT) and Ar912r (CTC CCC CGC CAA TTC CTT TA), 20 ng of DNA as template, and 28 cycles of PCR as described previously in detail [12].

2.4 Cloning, sequencing and phylogenetic analysis of SSU rDNA clones

Clone libraries of SSU rRNA genes were generated from community DNA samples of rice field soil (‘day 0’ after 2 h of incubation) from Shenyang (Shen-A clones) and Gapan (Gap-A clones). Archaeal SSU rDNA amplicons were cloned in the Escherichia coli JEM 109 using the pGEM-T Easy Vector System (Promega) according to the manufacturer's instructions (Promega, Mannheim, Germany). From each library, randomly selected clones were screened for positive inserts by PCR with the M13 primers and sequenced as described earlier [12]. Nucleotide sequences were assembled using SeqMan-II software (Dnastar, Madison, WI, USA) and checked for close relatives and taxonomic assignment using BLAST searches [26]. Phylogenetic analysis of the sequences, which were more than 650 bp in length, was performed using the ARB software package [27] as described previously in detail [6,9,12]. Clones were deposited with GenBank under accession numbers AF399284–AF399315 for Shen-A clones and AF399316–AF399345 for Gap-A clones.

2.5 Archaeal SSU rDNA T-RFLP analysis

T-RFLP was performed as described previously [11,12] using primer Ar912r labeled at the 5′ end with 6-carboxyfluorescein (FAM). Briefly, fluorescently labeled PCR amplicons (75 ng) were digested using TaqI, and subsequently analyzed using an automated sequencer (Model 373A, Applied Biosystems, Weiterstadt, Germany). T-RFLP patterns of each sample were evaluated using GeneScan analysis software (version 2.1, Applied Biosystems). Since the relative proportion of the integrated fluorescence of each terminal restriction fragment (T-RF) corresponds to the proportion of each amplicon in the PCR product [28,29], the relative abundance of amplicons was estimated and expressed as the percentage distribution of the different T-RF within each archaeal community fingerprint. By comparing the theoretical T-RF lengths of archaeal SSU rDNA clones obtained from China (Shenyang) and The Philippines (Gapan), the major T-RF were identified (Table 2) and compared to earlier analyses of Italian rice field soil [12].

Table 2.  Phylogenetic affiliation of SSU rDNA clones retrieved from three geographically different rice field soils with major archaeal lineages and frequency of clones falling into major TaqI-specific T-RF classes (OTUs)
T-RF (bp)PhylotypeSoils (number of clones)
  1. a Data taken from [12].


2.6 Statistical analysis

The area percentage (Ap) of each T-RF was calculated as described recently [30] as

  • image

in which ni represents the peak area of one distinct T-RF and N is the sum of all peak areas in individual T-RFLP electropherograms. A total of 70 different soil samples (four samples from day 0×10 different soils and three samples from day 30×10 different soils) were compared by T-RFLP. Excluded from statistical analysis were samples from day 200, which were not analyzed in replicate, and soil Changchun, which could only be analyzed for day 0 because of PCR inhibition in soil samples from other time points. All T-RF were included as dependent variables in a single multivariate analysis of variance (MANOVA) general linear model incorporating both regression and categorical effects. Sampling time, halt phase, halt phase×sampling time, organic carbon content, nitrogen content, total free iron, and methane production rate were effects. Parametric assumptions were checked by examination of probability and residual plots, and were satisfied after transformation by logarithm (T-RF 80 and 88), or inverse sine (all other T-RF) of the T-RF relative frequencies. For effects with significant multivariate test statistics (P<0.01), the univariate F-statistics (after Bonferroni adjustment for seven T-RF) and canonical loadings of each T-RF were examined. Statistical analyses were performed using Systat 10 (SPSS Inc., Chicago, IL, USA).

2.7mcrA gene amplification and T-RFLP analysis

mcrA fragments were amplified as described previously [14] using primer combinations MCRf (5′-TAY GAY CAR ATH TGG YT-3′) and MCRr (5′-ACR TTC ATN GCR TAR TT-3′) [31]. For T-RFLP analysis, PCR products were obtained using a FAM-labeled MCRf primer as described previously in detail [14]. Restriction digests with Sau96I (Promega) were performed using ∼100 ng of PCR amplicons. Digested amplicons were analyzed as described above.

3. Results

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Materials and methods
  5. 3. Results
  6. 4. Discussion
  7. Acknowledgements
  8. References

3.1 Phylogenetic analyses of SSU rDNA clones from rice field soils

Soil samples from different geographical sites exhibited a wide range of chemical characteristics and potential CH4 production rates [19,20] which are summarized in Table 1. Soils were incubated anoxically for 2 h (day 0), 30 days, and 200 days (selected soils) as previously described [19,20] to compare the community structure of Archaea in the different phases of methanogenesis after flooding of the soil. The archaeal community structure in the geographically different soils was analyzed by molecular methods targeting the SSU rRNA and mcrA genes. Total nucleic acids were extracted from these soils and purified. Using an Archaea-specific primer set we obtained PCR amplicons of the correct size from all soils, except from DNA extracts of a 30 days incubated soil sample from Changchun (China). For this particular soil sample, the archaeal community structure could not be analyzed, probably due to PCR-inhibiting substances still present in the DNA extract after purification.

Clone libraries of archaeal SSU rDNA were created from soil samples of Shenyang (China; Shen-A) and Gapan (The Philippines; Gap-A). The SSU rDNA insert of a total of 32 and 30 randomly selected clones from each soil, respectively, was sequenced (about 720 bp). Phylogenetic analyses showed that all clones fell within known eury- and crenarchaeotal lineages, i.e. major methanogenic groups such as the Methanosarcinaceae, Methanosaetaceae, Methanomicrobiaceae and Methanobacteriaceae, as well as the yet uncultured Archaea entitled RC-I, RC-II, RC-III, RC-IV, and RC-VI [6,9,11]. Representative clonal SSU rDNA sequences are shown in Fig. 1 together with close database relatives and with archaeal sequences from Italian rice soils [6,9,11,12].


Figure 1. Phylogenetic tree showing the placement of selected SSU rDNA clone sequences recovered from rice field soils Shenyang (Shen-A clones) and Gapan (Gap-A clones) within the Euryarchaeota and Crenarchaeota. Selected sequences of cultivated representatives (nearly full length rDNA) from archaeal lineages as well as environmental sequences (partial sequences) from Vercelli rice field soil (ABS, ARR, EtOH, H2, ST1, S15 and S30 clones [6,9–11]) and other environments were used as references to construct an evolutionary distance dendrogram (maximum likelihood method). SSU rDNA sequences of Aquifex pyrophilus and Thermotoga maritima as well as members of the Korarchaeota were used as outgroup references. The scale bar represents 10% sequence difference; GenBank accession numbers of sequences as indicated.

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3.2 T-RFLP analysis of the archaeal community structure

The SSU rDNA sequences retrieved from each clone library were analyzed by in silico digestion to identify restriction sites TaqI (Table 2). Ten different classes of T-RF were identified and affiliated to one or two of the 10 major phylogenetic groups of Archaea (Methanomicrobiaceae, Methanobacteriaceae, Methanosarcinaceae, Methanosaetaceae, RC-I to RC-VI). Characteristic group-specific T-RF predicted for Shenyang and Gapan SSU amplicons matched with previously defined groups for Italian rice field soil [11,12].

T-RFLP analysis was performed using the community DNA extracted from the different rice soils. Typical community fingerprints obtained by the T-RFLP analysis of SSU rRNA genes are shown in Fig. 2 (left panel) for the soils from Guangzhou (China), Gapan (The Philippines) and Pavia (Italy).


Figure 2. Typical T-RFLP electropherograms of (left panel) archaeal SSU rDNA (TaqI digest) and (right panel) mcrA/mrtA gene (Sau96 digest) amplicons from rice field slurry samples of Guangzhou (China), Gapan (The Philippines), and Pavia (Italy), after 30 days of incubation. The archaeal lineages represented in different T-RF are Methanobacteriaceae (MB), Methanomicrobiaceae (MM), Methanosarcinaceae (MS), Methanosaetaceae (MX), and RC-I to RC-VI. In addition to mcrA gene T-RF, a 470-bp mrtA fragment specific for Methanobacteriaceae is indicated. T-RF lengths of selected peaks in bp are indicated by numbers. RFU, relative fluorescence unit.

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The relative contribution of the different phylotypes to the soil archaeal community was quantified using peak area integration of SSU rDNA T-RF. We found that the relative peak area of a T-RF linearly increased with the increasing double-stranded DNA concentration of the amplicon digests, using the SSU rDNA of Methanosarcina barkeri as a standard (data not shown). Duplicate T-RFLP analyses using different DNA template concentrations (20–60 ng) obtained from the same soil community gave reproducible fingerprints with the same relative distribution of T-RF (data not shown). The relative proportion of each T-RF to the total was independent of the number of PCR amplification cycles (24–44), as found before for Italian rice field soil [32] and other environments [33].

Certain SSU rDNA T-RF may represent more than one phylotype [12], e.g. 182 bp (RC-VI and Methanosarcinaceae), >800 bp (RC-IV and Methanosarcinaceae). Therefore, the community structure of methanogens was analyzed in parallel by the more specific mcrA-targeted T-RFLP analysis (Fig. 2, right panel). In a previous study [14], the different T-RF of mcrA amplicons retrieved from Italian rice field soil were identified on the basis of a clone library of mcrA gene fragments (n=75) that had been sequenced and phylogenetically characterized. This phylogenetic classification was used to identify the major T-RF in fingerprints from the different soil samples shown in Fig. 2. In general, all major methanogenic groups (e.g. Methanosarcinaceae, Methanosaetaceae, Methanobacteriaceae, and RC-I) were detected by mcrA-targeted T-RFLP fingerprints. As in Italian rice field soil, mcrA genes characteristic of the Methanomicrobiaceae were not detected, but this lineage was present only at low SSU rDNA ratios as indicated by T-RFLP analysis (Fig. 2, left panel) and cloning of archaeal SSU rRNA genes from Shenyang and Gapan soils (Table 2).

3.3 Archaeal population dynamics

We used the seven major SSU rDNA T-RF (Table 2) for comparative analysis of the archaeal community in the different soils over time, i.e. 0, 30, and 200 days (some soils) of anoxic incubation (Fig. 3; Table 3). All seven major archaeal T-RF were detected in most soils, which had been incubated for only 2 h (day 0), indicating that these populations were either already present in the air-dried soil or that the PCR product originated from free soil DNA. Less than seven T-RF were detected in soils from Zhenjiang, Luisiana and Pila (Fig. 3). The most predominant T-RF in all soils analyzed were those of 182 bp (Methanosarcinaceae/RC-VI) and 389 bp (mainly RC-I) length. These had relative gene frequencies ranging from 22 to 54% and from 6 to 48%, respectively.


Figure 3. The composition of archaeal populations in slurries of rice field soils from different geographic regions after 2 h (day 0), 30 days, and 200 days of incubation as determined by T-RFLP analysis of SSU rDNA amplicons. Soils were sorted by the length of the halt phase before initiation of methane production (Table 1; [20]). For some soils only 0 and 30 day time points were analyzed. The figure shows the integrated fluorescence of each individual T-RF expressed as percentage relative gene frequency of the sum of all T-RF of an individual profile. Archaeal lineages represented by the different T-RF are abbreviated as in Fig. 2. Data are means of replicate analysis for day 0 (n=4) and day 30 (n=3); day 200 was not replicated. The absolute S.D. of single T-RF was between 0 and 20%.

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Table 3.  Estimation of the relative gene frequencies of methanogenic Archaea in rice field soils from different geographic regions after 30 days of anoxic incubation as derived from T-RFLP analyses of SSU rDNA(TaqI digest) and mcrA/mrtA genes (Sau96 digest)
Methanogenic groupMarker geneT-RF (bp)Country
   ChinaThe PhilippinesItaly
   Origin of soils (soil #)
   Zhenjiang (1)Guangzhou (3)Beijuan (4)Jurong (6)Shenyang (7)Luisiana (10)Pila (12)Gapan (13)Pavia (16)Vercelli (17)
  1. Gene frequencies were categorized into three classes according to the percentage peak area of individual T-RF relative to the sum of all T-RF of a T-RFLP profile: 1–14%, ‘+’; 15–29%, ‘++’; >30%, ‘+++’. ‘?’, undetected mcrA gene of members of the Methanomicrobiales.

MethanosarcinaceaeSSU rRNA182+++++++++++++++++ ++
 mcrA394++++++ ++++++++++
MethanosaetaceaeSSU rRNA280 ++++++++++++
 mcrA147 +++  +++ 
 mcrA419 +++++++++
RC-ISSU rRNA389++++++++++++++++++++++++++++
 mcrA238+++++ +++++
MethanomicrobialesSSU rRNA80 ++++ ++++
MethanobacterialesSSU rRNA88++++++++++
 mrtA470 ++ + ++++

The effects of different environmental factors (halt phase, organic carbon content, nitrogen content, total free iron, and methane production rate) on the archaeal community composition were tested by MANOVA. The multivariate test statistics showed that each single factor had some significant effect on the community pattern. Many of these factors are cross correlated and of course a causal relationship cannot be inferred from such a result. In any case, most of these effects were limited to only one or two minor T-RF. For example T-RF 80, which was present at only 0–5% total frequency (Fig. 3), was significantly affected by all factors except nitrogen content (P<0.01). Other significant effects were methane production rate on T-RF 389, organic carbon on T-RF 182, nitrogen content on T-RF 88, and sampling time on T-RF 375.

On the other hand, halt phase significantly affected both dominant T-RF 182 and 389 (in each case P<0.01). T-RF 80 and 280 were also significantly affected, but the effect was most evident for the dominant T-RF 182 and 389, based on canonical loadings of 0.698 and −0.495, respectively. An interaction of halt phase×sampling time was also significant on T-RF 182, 375, 389 and 800. Fig. 4 illustrates how the two major T-RF varied across soils with different halt phases. Soils with long halt phases tended initially to have a lower percentage of T-RF 182 and a correspondingly higher percentage of T-RF 389 compared to soils with short halt phases. At the later, day 30 sampling point, these differences had almost disappeared because the soils with long halt phases had developed communities more similar to those of the other soils. The statistical significance of the above conclusions was verified by dividing the soils into three groups (halt phase≤5 days, halt phase 9–13 days, and halt phase=23 days), performing an analysis of variance for each T-RF with sampling time and halt phase group as effects, and then doing post-hoc multiple Bonferroni contrasts for each halt phase group×sampling time pair. The pairwise comparison matrix demonstrated that T-RF 182 and 389 in soils with short halt phases did not change significantly from day 0 to 30, but did change in the soils with longer halt phases.


Figure 4. Influence of the halt phase on the relative gene frequencies of T-RF 182 bp and T-RF 389 bp on the first day of incubation (day 0) and after 30 days of incubation. Each point represents a separate soil and is the mean of three and four samples. Lines are least-square linear regressions.

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This observed pattern of relative SSU rRNA gene frequencies of the major archaeal groups, which appeared to be correlated to the length of the halt phase, was supported by the presence and estimated frequency of mcrA genes analyzed for soil samples after 30 days of incubation (Table 3). In general, gene frequencies of both marker genes, SSU rRNA and mcrA, were in good correlation and similar estimates were derived from both T-RFLP analyses. However, PCR/T-RFLP analyses of defined template mixtures with defined amounts of target DNA from pure cultures have revealed that methanobacterial mcrA genes (503-bp mcrA T-RF) tend to be over-represented (Lueders and Friedrich, unpublished).

The mcrA-targeted T-RFLP analysis clearly showed the presence of those methanogenic lineages which share a SSU rDNA T-RF with non-methanogenic lineages (e.g. Methanosarcinaceae/RC-VI). For example, mcrA genes of Methanosarcinaceae were abundant in soils with high levels of the 182-bp SSU rDNA T-RF (Table 3). Also the RC-I methanogens, represented by a characteristic 238-bp mcrA T-RF, were detected in all rice field soils regardless of geographical origin. Also the absence of the Methanosaeta-specific SSU rDNA T-RF in the Zhenjiang soil was verified by mcrA T-RFLP analysis.

4. Discussion

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Materials and methods
  5. 3. Results
  6. 4. Discussion
  7. Acknowledgements
  8. References

Rice field soil from Italy was found to contain a diverse archaeal community comprising most of the families of known methanogens [34], but also including phylogenetic lineages within the Euryarchaeota and the Crenarchaeota for which no microbial isolate exists [6,11,12]. One of these novel lineages, RC-I, was recently identified as methanogenic [14]. Here we have shown that rice field soils from China and The Philippines exhibit a similar archaeal diversity, despite the fact that they originate from different geographical regions. Sequences of archaeal SSU rRNA genes retrieved from Chinese and Philippine soils grouped with the same major phylotypes that had been identified in Italian rice field soil, including the uncultivated RC-I to RC-VI. The same lineages were identified in a total of 11 different rice field soils from China, The Philippines, and Italy by using T-RFLP fingerprinting targeting the genes of the SSU rRNA and the methyl-coenzyme M reductase (mcrA). The mcrA gene is only found in methanogenic Archaea, whereas the SSU rRNA approach targets also non-methanogenic Archaea. The similar community patterns found in the different rice field soils analyzed by three approaches, i.e. (1) sequencing and phylogenetic analysis of SSU rRNA genes, (2) T-RFLP fingerprinting of SSU rRNA genes, and (3) T-RFLP fingerprinting of mcrA genes, suggest that rice fields all over the world contain methanogenic communities which are similarly structured with respect to the major methanogenic archaeal lineages.

The worldwide distribution of major methanogenic phylotypes in rice field soils is very interesting with regard to global dispersal and colonization of Archaea. Using classical isolation procedures, Joulian et al. [5] reported that the genera Methanobacterium and Methanosarcina were dominant among cultivable organisms and Methanobacterium bryantii, in particular, was isolated from 12 of 13 soils from France, The Philippines, and USA. The atmosphere is one of the main media for dispersal and aerosol particles help to transport microorganisms attached to the surface of droplets [35,36]. In addition, surface and subsurface waters may play a significant role in transport [37]. The question remains, however, how obligately anaerobic microorganisms such as methanogens can be dispersed so widely, since these organisms do not possess classical resting stages (i.e. spores, cysts).

A worldwide distribution of the major methanogenic phylotypes is consistent with the observation of cosmopolitan phylogenetic clusters in other functional microbial groups, e.g. psychrophilic bacteria in ice [38] or pelagic freshwater bacteria [39]. This does not exclude, however, that the community structures with respect to taxonomic units of a level lower than that resolved by the molecular techniques applied might be different in the soils from the different geographic regions. It is worth mentioning that microbial communities were found to be composed of endemic ‘species’ provided that the genotypes were sufficiently resolved [40].

Molecular community fingerprinting by T-RFLP analysis is not only used for phylogenetic/taxonomic information but also for analysis of relative proportions of dominant phylotypes within the microbial community [28,33,41,42]. Although the rDNA-based T-RFLP analysis suffers from the lack of fine resolution at the species level, and also from biases related to cell lysis, DNA extraction and purification, and PCR amplification, it has many advantages for comprehensive sampling and rigorous comparative community analysis which can include process and biogeographical investigations [43,44]. We have used the T-RFLP fingerprinting technique for identification and quantification of the major archaeal phylotypes in different rice soils. We have quantified the relative amounts of the individual SSU rRNA gene T-RF as done before for different environments [28–30] and Italian rice field soil in particular [12,32,45,46]. These previous studies have shown that the quantification of relative amounts of individual SSU rRNA gene T-RF is highly reproducible. Our results confirm this conclusion. Since the distribution and abundance of characteristic T-RF in the different soils were not the same, the preferential amplification of certain sequences [47] or the kinetic bias effect [29] is not considered as a problem in PCR amplification and subsequent T-RFLP analysis in our studies. Osborn et al. [33] made similar observations during T-RFLP analysis of the PCB-polluted and pristine soil. Biases, however, related to primer mismatches or the use of degenerate primers, especially in mcrA-targeted T-RFLP analysis, cannot be excluded and are currently being investigated (Lueders and Friedrich, in preparation). Therefore, we estimated the mcrA gene abundance by T-RFLP analysis qualitatively rather than reporting absolute quantities.

Some of the soils (Zhenjiang, Beiyuan, Jurong, Shenyang, Gapan) showed a change in the relative composition of the archaeal community when they were incubated for extended periods under submerged and anoxic conditions indicating the existence of population dynamics. This dynamic may be connected to the temporal change in the activity of CH4 production. Although a change in CH4 production activity is observed in all rice field soils which generally produce CH4 in distinct phases [19], there are differences with respect to the duration of the individual phases, the reduction and methanogenic phases in particular. The reduction phase is dominated by reduction of ferric iron (and sulfate to a smaller extent) so that little CH4 is produced. The production of CH4, which is thermodynamically controlled, halts as long as the reduction of ferric iron proceeds. A pronounced halt phase is observed in many rice field soils [20], summarized in Table 1.

The archaeal population dynamics in the investigated soils appear to correlate with the duration of the halt phase, which fall into three groups: soils without or with a very short halt phase (Guangzhou, Luisiana, Pila, Pavia, Vercelli), soils with a halt phase between 9 and 13 days (Gapan, Jurong, Shenyang, Beijuan) and those with an extended halt phase of up to 23 days (Changchun, Zhenjang).

Soils without any halt phase or with only a short halt phase exhibited a very stable community structure over time, probably because the ratio of degradable organic matter to reducible iron allowed the continuous production of CH4 even during the phase of iron reduction [20]. In these soils, the archaeal population seemed to be rather stable and exhibited no dynamics, and was dominated mostly by members of RC-I and the Methanosarcinaceae. It should be noted, however, that this stability refers to the DNA of the populations and does not reflect their potential activities, which may well change with incubation time, and also does not reflect their actual activities (potential activities modified by substrate availability) which definitely do change with incubation time.

With increasing duration of the halt phase, soils exhibited an increasing proportion of relative SSU rRNA gene frequencies of Methanosarcinaceae with a concomitant decrease of the RC-I populations especially at day 0. During incubation these soils developed a community structure which was comparable to soils without halt phase. In soils with the longest halt phase the initial dominance of Methanosarcinaceae was most pronounced. The observed difference in community structure of soils with a prolonged halt phase before initiation of methanogenesis may be linked to site-intrinsic factors which favor the presence of Methanosarcina spp. in certain rice field soils, but the causes are unknown.

The relatively pronounced stability of the archaeal community structure in Italian rice field soil has been noticed before [12,32]. Only the relative contribution of Methanosarcinaceae was found to increase significantly (∼50%) in the initial phases of methanogenesis (0–13 days) after flooding of the soil. We might have overlooked this temporary change of the community structure since we did not analyze the initial phase of methanogenesis in this study. Enumeration by culturing methanogens also demonstrated relatively constant numbers over the season in the field and over incubation time in the laboratory [22,48]. Changes in incubation temperature, on the other hand, resulted in a pronounced change in the methanogenic community structure [11,46,49].

In summary, we found that the occurrence of major archaeal lineages was surprisingly constant among all the various rice field soils from different geographic regions. We hypothesize that differences, if they exist, are only on a taxonomic level at or below that of microbial ‘species’[50]. However, we also found that the relative composition of the archaeal communities was different, though only to some degree and among some of the soils tested. Furthermore, the composition changed with time of anoxic incubation, but only in those soils that showed pronounced changes in CH4 production during their reduction and methanogenic phases.


  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Materials and methods
  5. 3. Results
  6. 4. Discussion
  7. Acknowledgements
  8. References

We thank Bianca Wagner for excellent technical assistance. B.R. was supported by a BOYSCAST fellowship of the Department of Science and Technology, Government of India.


  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Materials and methods
  5. 3. Results
  6. 4. Discussion
  7. Acknowledgements
  8. References
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