• Open Access

Oligonucleotide primers, probes and molecular methods for the environmental monitoring of methanogenic archaea

Authors


E-mail y.sekiguchi@aist.go.jp; Tel. (+81) 29 861 7866; Fax (+81) 29 861 6400.

Summary

For the identification and quantification of methanogenic archaea (methanogens) in environmental samples, various oligonucleotide probes/primers targeting phylogenetic markers of methanogens, such as 16S rRNA, 16S rRNA gene and the gene for the α-subunit of methyl coenzyme M reductase (mcrA), have been extensively developed and characterized experimentally. These oligonucleotides were designed to resolve different groups of methanogens at different taxonomic levels, and have been widely used as hybridization probes or polymerase chain reaction primers for membrane hybridization, fluorescence in situ hybridization, rRNA cleavage method, gene cloning, DNA microarray and quantitative polymerase chain reaction for studies in environmental and determinative microbiology. In this review, we present a comprehensive list of such oligonucleotide probes/primers, which enable us to determine methanogen populations in an environment quantitatively and hierarchically, with examples of the practical applications of the probes and primers.

Introduction

Methanogenic archaea (methanogens) are strictly anaerobic microorganisms producing methane as a result of their anaerobic respiration (Schink, 1997; Thauer, 1998). For methanogenesis, they can utilize a limited number of substrates such as carbon dioxide, acetate and methyl-group-containing compounds under anoxic conditions (Liu and Whitman, 2008). Most of the known methanogens are hydrogenotrophs reducing carbon dioxide to form methane; among them, formate is also often utilized as the electron donor instead of hydrogen. Some of the hydrogenotrophic methanogens can also utilize secondary alcohols such as 2-propanol as the electron donor. Acetate is an important intermediate substance in the anaerobic decomposition of organic matter, and is generally exclusively utilized by limited groups of methanogens to form methane under anoxic conditions, where external electron acceptors other than carbon dioxide are unavailable. Methyl-group-containing compounds, such as methanol and methylamines, are also utilized by some methanogens through disproportionation of methyl groups.

Methanogens are frequently found in anoxic environments, such as rice paddy fields (Iino et al., 2010; Sakai et al., 2010), wetlands (Cadillo-Quiroz et al., 2009; Bräuer et al., 2010), permafrost (Krivushin et al., 2010; Shcherbakova et al., 2010), landfills (Laloui-Carpentier et al., 2006), subsurfaces (Doerfert et al., 2009; Mochimaru et al., 2009) and ruminants (Frey et al., 2009), which are known to be the major sources of atmospheric methane. It has been estimated that the annual global emission of methane is 500–600 Tg, and atmospheric methane concentration has risen threefold over the past 200 years (Liu and Whitman, 2008). With the increased interests in global climate change and environmental issues, studies on the diversity and ecophysiological functions of methanogens in such environments have been extensively conducted using cultivation-dependent and cultivation-independent approaches (Liu and Whitman, 2008). In addition to such environments, methanogens play key roles in fields of anaerobic digestion technology, which is widely used as a means for treating municipal and industrial waste/wastewater containing high levels of organic compounds (Sekiguchi, 2006; Narihiro and Sekiguchi, 2007; Talbot et al., 2008; Tabatabaei et al., 2010). Methanogens are often critical components of such bioconversion systems, resulting in the recovery of gaseous methane from those wastes as reusable energy resource. To better manage the bioconversion systems and achieve a higher efficiency in removing organic compounds in wastes, methanogens in these systems have been extensively studied and the quantitative monitoring of such methanogenic populations in these systems has been conducted (Narihiro and Sekiguchi, 2007).

To explore the ecological significance of methanogens in these natural and engineered ecosystems, identification and quantification techniques for different methanogen groups are indispensable. For the purpose, analyses of membrane lipid (Weijers et al., 2004; Strapoc et al., 2008), autofluorescence (Neu et al., 2002; Tung et al., 2005; Mochimaru et al., 2007), activity measurement (Lehmann-Richter et al., 1999; Weijers et al., 2004) and immunoenzymatic profiling (Visser et al., 1991; Sorensen and Ahring, 1997) have been used. In addition to these methods, cultivation-independent, nucleic acid-based analysis by using oligonucleotide probe/primers, such as membrane hybridization, fluorescence in situ hybridization (FISH), gene cloning, quantitative polymerase chain reaction (qPCR), and cleavage method with ribonuclease H (RNase H) were most widely and frequently used as means to detect and quantify methanogens more specifically and accurately. In this review, we present a catalogue of previously developed oligonucleotide probes/primers targeting genes of methanogens. Particular emphasis is placed on the probes/primers for 16S rRNA, 16S rRNA gene and the gene for the α-subunit of methyl coenzyme M reductase (mcrA), which are generally used for the taxonomic classification of methanogens (Friedrich, 2005; Liu and Whitman, 2008).

Phylogeny of methanogens

All the methanogens isolated and characterized to date have been classified into the phylum Euryarchaeota of the domain Archaea (Garrity et al., 2007). They are assigned into 33 genera of the classes ‘Methanomicrobia’, Methanobacteria, Methanococci and Methanopyri (Fig. 1, Table 1). The class ‘Methanomicrobia’ is the most phylogenetically and physiologically diverse group of methanogens consisting of three orders (Methanosarcinales, Methanomicrobiales and Methanocellales); 23 genera belonging to seven families (Fig. 1, Table 1). Within the order Methanosarcinales, the genera Methanosarcina and Methanosaeta are known to play a key role in the conversion of acetate into methane in various anaerobic environments, and the rest are known to metabolize relatively broad ranges of substrates, such as hydrogen, methanol and methylamines (Garrity and Holt, 2001). Known members of the order Methanomicrobiales are all hydrogenotrophs, and some of them are often observed in anaerobic environments as important hydrogen scavengers (Liu and Whitman, 2008). Members of the class Methanobacteria, consisting of the families Methanobacteriaceae and Methanothermaceae, are recognized as important hydrogenotrophs that have also been widely found in anaerobic ecosystems (Garrity and Holt, 2001). Methanobacteriaceae comprises four genera, Methanobacterium, Methanosphaera, Methanobrevibacter and Methanothermobacter. The class Methanococci includes the families Methanococcaceae and Methanocaldococcaceae, which are widely distributed in natural ecosystems such as marine sediments and deep sea geothermal sediments (Liu and Whitman, 2008). The class Methanopyri consists of solely the genus Methanopyrus, a hyperthermophilic, hydrogenotrophic methanogen isolated from the deep-sea hydrothermal field (Takai et al., 2008).

Figure 1.

Phylogeny and taxonomy of methanogens. The neighbour-joining tree was constructed on the basis of 16S rRNA gene sequences using the ARB package (Ludwig et al., 2004) with the data set (Yarza et al., 2008) provided from silva databases (http://silva.mpi-bremen.de/), showing representative species of methanogens that have been described to date.

Table 1.  Oligonucleotide probes and primers targeting the 16S rRNA gene of methanogens.
Target groupProbe nameProbe sequence (5′–3′)aApplicationProbe length (mer)Reference
  • a. 

    IUPAC Ambiguity Codes: Y = C or T, R = A or G, K = G or T, S = C or G, W = A or T, M = A or C, H = A or C or T, V = A or C or G

  • b. 

    Arch f2 probe covers members of the orders Methanomicrobiales, Methanosarcinales and Methanococcales.

  • c. 

    SMPL623 probe covers members of the Methanoplanus limicola and M. endosynbiosus.

  • d. 

    SMPP1252 probe covers members of the Methanoplanus petrolearius and Methanolacinia.

  • e. 

    F6SC393 probe covers members of the genera Methanocorpusculum and Methanocalculus.

  • f. 

    EelMS240 probe targets for members of the genera Methanolobus, Methanohalophilus, Methanococcoides and Methanomethylovorans.

  • g. 

    R15F probe covers members of the genera Methanomethylovorans and Methanosarcina and Methanolobus psychrophilus.

  • h. 

    MS821m probe covers members of the genera Methanimicrococcus and Methanosarcina.

  • i. 

    FMCMT1044 probe covers members of the family Methanocaldococcaceae and genus Methanopyrus.

  • MH, membrane hybridization.

Most methanogensArch f2bTTCYGGTTGATCCYGCCRGAPCR (forward)20Skillman et al. (2004)
Arch r1386GCGGTGTGTGCAAGGAGCPCR (reverse)18Skillman et al. (2004)
A1fTCYGKTTGATCCYGSCRGAGPCR (forward), DGGE20Embley et al. (1992)
A1100rTGGGTCTCGCTCGTTGPCR (forward), DGGE16Embley et al. (1992)
Met83FACKGCTCAGTAACACPCR (forward)15Wright and Pimm (2003)
Met86FGCTCAGTAACACGTGGPCR (forward)16Wright and Pimm (2003)
Met448FGGTGCCAGCCGCCGCsequencing15Wright and Pimm (2003)
Met1027FGTCAGGCAACGAGCGAGACCsequencing20Wright and Pimm (2003)
Met1340RCGGTGTGTGCAAGGAGPCR (reverse)16Wright and Pimm (2003)
109fACKGCTCAGTAACACGTPCR (forward)17Grosskopf et al. (1998)
146fGGSATAACCYCGGGAAACPCR (forward)18Marchesi et al. (2001)
1324rGCGAGTTACAGCCCWCRAPCR (reverse)18Marchesi et al. (2001)
ARC344fACGGGGYGCAGCAGGCGCGAPCR (forward), DGGE20Casamayor et al. (2001)
25fCYGGTYGATYCTGCCRGPCR (forward)17Dojka et al. (1998)
1391rGACGGGCGGTGTGTRCAPCR (reverse)17Barns et al. (1994)
A24fTCYGKTTGATCCYGSCRGAPCR (forward), DGGE19Yu et al. (2008)
A357fCCCTACGGGGCGCAGCAGPCR (forward), DGGE18Yu et al. (2008)
A329rTGTCTCAGGTTCCATCTCCGPCR (reverse), DGGE20Yu et al. (2008)
A348rCCCCRTAGGGCCYGGPCR (reverse), DGGE15Yu et al. (2008)
A693rGGATTACARGATTTCPCR (reverse), DGGE15Yu et al. (2008)
Met630FGGATTAGATACCCSGGTAGTqPCR (forward), DGGE20Hook et al. (2009)
Met803RGTTGARTCCAATTAAACCGCAqPCR (reverse), DGGE21Hook et al. (2009)
A1040fGAGAGGWGGTGCATGGCCPCR (forward), DGGE18Reysenbach and Pace (1995)
ARC344TCGCGCCTGCTGCICCCCGTMH20Raskin et al. (1994b)
ARC915GTGCTCCCCCGCCAATTCCTPCR (reverse), DGGE, MH, FISH20Raskin et al. (1994b)
MER1GGGCACGGGTCTCGCTPCR (reverse)16Hales et al. (1996)
Class Methanomicrobia1068RATGCTTCACAGTACGAACPCR (reverse)18Banning et al. (2005)
CMSMM1068mGGATGCTTCACAGTACGAACRNase H20Narihiro et al. (2009b)
Order Methanocellales     
Family Methanocellaceae     
Genus MethanocellaSANAE1136GTGTACTCGCCCTCCTCGFISH18Sakai et al. (2007)
Order MethanomicrobialesMG1200CGGATAATTCGGGGCATGCTGMH, FISH21Raskin et al. (1994b)
MG1200mCCGGATAATTCGGGGCATGCTGRNase H22Narihiro et al. (2009b)
M(SA/MI)355GTAAAGTTTTCGCGCCTGMH18Ovreås et al. (1997)
MMB282FATCGRTACGGGTTGTGGGqPCR (forward)18Yu et al. (2005)
MMB749FTYCGACAGTGAGGRACGAAAGCTGqPCR (probe)24Yu et al. (2005)
MMB832RCACCTAACGCRCATHGTTTACqPCR (reverse)21Yu et al. (2005)
Family Methanomicrobiaceae     
Genus Methanoculleus298FGGAGCAAGAGCCCGGAGTqPCR (forward)18Franke-Whittle et al. (2009a)
586RCCAAGAGACTTAACAACCCAqPCR (reverse)20Franke-Whittle et al. (2009a)
F2SC668TCCTACCCCCGAAGTACCCCTCRNase H22Narihiro et al. (2009b)
F2SC732TCGAAGCCGTTCTGGTGAGGCGRNase H22Narihiro et al. (2009b)
AR934FAGGAATTGGCGGGGGAGCACqPCR (forward)20Shigematsu et al. (2003)
MCU1023TAQGAATGATTGCCGGGCTGAAGACTCqPCR (probe)24Shigematsu et al. (2003)
MG1200bCCGGATAATTCGGGGCATGCTGqPCR (reverse)22Shigematsu et al. (2003)
Species M. thermophilusMc412fCTGGGTGTCTAAAACACACCCAAqPCR (forward)23Hori et al. (2006)
Mc578rATTGCCAGTATCTCTTAGqPCR (reverse)18Hori et al. (2006)
SMCUT1253GCCTTTCGGCGTCGATACCCRNase H20Narihiro et al. (2009b)
Genus MethanofollisF3SC984CATATCGCTGTCCTACCCGGRNase H20Narihiro et al. (2009b)
Genus MethanogeniumGMG1128CGTTCCGGAGAACAAGCTAGRNase H20Narihiro et al. (2009b)
Genus MethanomicrobiumGMM829CTCGTAGTTACAGGCACACCFISH, RNase H20Yanagita et al. (2000)
Genus Methanoplanus     
Species M. limicolaSMPL623cTTCTCTTAAACGCCTGCAGGRNase H20Narihiro et al. (2009b)
Species M. endosynbiosusSMPL623cTTCTCTTAAACGCCTGCAGGRNase H20Narihiro et al. (2009b)
Species M. petroleariusSMPP1252dCTTCTCAGTGTCGTTGCTCARNase H20Narihiro et al. (2009b)
Genus MethanolaciniaSMPP1252dCTTCTCAGTGTCGTTGCTCARNase H20Narihiro et al. (2009b)
Family Methanospirillaceae     
Genus MethanospirillumF7SC1260TATCCTCACCTCTCGGTGTCRNase H20Narihiro et al. (2009b)
MSP1025TAQGAATGATAGTCGGGATGAAGACTCTAqPCR (probe)26Tang et al. (2005)
Genus Methanosphaerula     
Genus MethanolineaNOBI109fACTGCTCAGTAACACGTqPCR (forward)17Imachi et al. (2008)
NOBI633GATTGCCAGTTTCTCCTGqPCR (reverse), FISH18Imachi et al. (2008)
Family Methanocorpusculaceae     
Genus MethanocorpusculumF6SC393eGACAGGCACTCAGGGTTTCCRNase H20Narihiro et al. (2009b)
GMCP489GCCCTGCCCTTTCTTCACATRNase H20Narihiro et al. (2009b)
Family incertae sedis     
Genus MethanocalculusF6SC393eGACAGGCACTCAGGGTTTCCRNase H20Narihiro et al. (2009b)
GMCL488CCCCGCCCTTTCTCCTGGTGRNase H20Narihiro et al. (2009b)
Genus Methanoregula     
Species M. boonei6A8 644TCTTCCGGTCCCTAGCCTGCCAFISH22Bräuer et al. (2006)
Species M. formicicaSMSP129TATCCCCTTCCATAGGGTAGATTFISH23Yashiro et al. (2009)
Order MethanosarcinalesMSMX860GGCTCGCTTCACGGCTTCCCTMH21Raskin et al. (1994b)
MSSH859TCGCTTCACGGCTTCCCTFISH18Boetius et al. (2000)
MSr r859TCGCTTCACGGCTTCCCTGPCR (reverse)19Skillman et al. (2004)
MSMX860mGCTCGCTTCACGGCTTCCCTRNase H20Narihiro et al. (2009b)
MSL812FGTAAACGATRYTCGCTAGGTqPCR (forward)20Yu et al. (2005)
MSL860FAGGGAAGCCGTGAAGCGARCCqPCR (probe)21Yu et al. (2005)
MSL1159RGGTCCCCACAGWGTACCqPCR (reverse)17Yu et al. (2005)
Family Methanosaetaceae     
Genus MethanosaetaMX825TCGCACCGTGGCCGACACCTAGCMH, FISH23Raskin et al. (1994b)
MX825mixTCGCACCGTGGCYGACACCTAGCRNase H23Narihiro et al. (2009b)
MX1361ACGTATTCACCGCGTTCTGTFISH20Crocetti et al. (2006)
S-G-Msae-0332-a-A-22TTAGGTCCGGGATGCXCCACGTMH, FISH22Zheng and Raskin (2000)
Mst702FTAATC CTYGA RGGAC CACCAqPCR (forward)20Yu et al. (2005)
Mst753FACGGC AAGGG ACGAA AGCTA GGqPCR (probe)22Yu et al. (2005)
Mst862RCCTAC GGCAC CRACM ACqPCR (reverse)17Yu et al. (2005)
MS1bCCGGCCGGATAAGTCTCTTGAqPCR (forward)21Shigematsu et al. (2003)
SAE761TAQACCAGAACGGACCTGACGGCAAGGqPCR (probe)24Shigematsu et al. (2003)
SAE835RGACAACGGTCGCACCGTGGCCqPCR (reverse)21Shigematsu et al. (2003)
S-F-Msaet-0387-S-a-21GATAAGGGRAYCTCGAGTGCYqPCR (forward)21Sawayama et al. (2004)
S-F-Msaet-0540-A-a-31AGACCCAATAAHARCGGTTACCACTCGRGCCqPCR (probe)31Sawayama et al. (2004)
S-F-Msaet-0573-A-a-17GGCCGRCTACAGACCCTqPCR (reverse)17Sawayama et al. (2004)
Species M. conciliiRotcl1CTCCCGGCCTCGAGCCAGACFISH20Zepp Falz et al. (1999)
MS1CCGGATAAGTCTCTTGAMH17Rocheleau et al. (1999)
MS2CTGAATGAGAGCGCTTTCTTTMH21Rocheleau et al. (1999)
MS5GGCCACGGTGCGACCGTTGTCGMH, FISH22Rocheleau et al. (1999)
MMX1273GGTTTTAGGAGATTCCCGTCRNase H20Narihiro et al. (2009b)
GTMS393mACCCAGCACTCGAGGTCCCCRNase H20Narihiro et al. (2009b)
Species M. thermophilaMs413fCAGATGTGTAAAATACATCTGTTqPCR (forward)23Hori et al. (2006)
Ms578rTCTGGCAGTATCCACCGAqPCR (reverse)18Hori et al. (2006)
TMX745CCCTTGCCGTCGGATCCGTTRNase H20Narihiro et al. (2009b)
Family MethanosarcinaceaeMS1414CTCACCCATACCTCACTCGGGMH, FISH21Raskin et al. (1994b)
EelMS240fCTATCAGGTTGTAGTGGGFISH18Boetius et al. (2000)
Msc380FGAAACCGYGATAAGGGGAqPCR (forward)18Yu et al. (2005)
Msc492FTTAGCAAGGGCCGGGCAAqPCR (probe)18Yu et al. (2005)
Msc828RTAGCGARCATCGTTTACGqPCR (reverse)18Yu et al. (2005)
R15FgGCTACACGCGGGCTACAATGAqPCR (forward)21Zhang et al. (2008a)
FMSC394ATGCTGGCACTCGGTGTCCCRNase H20Narihiro et al. (2009b)
MS821mhGCCATGCCTGACACCTAGCGRNase H20Narihiro et al. (2009b)
Genus MethanimicrococcusGMIB1254CACCTTTCGGTGTAGTTGCCRNase H20Narihiro et al. (2009b)
Genus MethanosarcinaMS821CGCCATGCCTGACACCTAGCGAGCMH, FISH24Raskin et al. (1994b)
SARCI551GACCCAATAATCACGATCACFISH20Sorensen and Ahring (1997)
SARCI645TCCCGGTTCCAAGTCTGGCFISH19Sorensen and Ahring (1997)
MB1TTTGGTCAGTCCTCCGGMH17Rocheleau et al. (1999)
MB3CCAGACTTGGAACCGMH15Rocheleau et al. (1999)
MB4TTTATGCGTAAAATGGATTMH, FISH19Rocheleau et al. (1999)
240FCCTATCAGGTAGTAGTGGGTGTAATqPCR (forward)25Franke-Whittle et al. (2009a)
589RCCCGGAGGACTGACCAAAqPCR (reverse)18Franke-Whittle et al. (2009a)
MB1bCGGTTTGGTCAGTCCTCCGGqPCR (forward)20Shigematsu et al. (2003)
SAR761TAQACCAGAACGGGTTCGACGGTGAGGqPCR (probe)24Shigematsu et al. (2003)
SAR835RAGACACGGTCGCGCCATGCCTqPCR (reverse)21Shigematsu et al. (2003)
S-G-Msar-0450-S-a-19TAGCAAGGGCCGGGCAAGAqPCR (forward)19Sawayama et al. (2006)
S-P-Msar-0540-A-a-31AGACCCAATAATCACGATCACCACTCGGGCCqPCR (probe)31Sawayama et al. (2006)
S-G-Msar-0589-S-a-20ATCCCGGAGGACTGACCAAAqPCR (reverse)20Sawayama et al. (2006)
Genus MethanococcoidesGMCO441ACATGCCGTTTACACATGTGRNase H20Narihiro et al. (2009b)
Genus MethanohalobiumGMHB842TCGGCACTAGGAACGGCCGTRNase H20Narihiro et al. (2009b)
Genus MethanohalophilusGMHP1258CCGTCACTTTTCAGTGTAGGRNase H20Narihiro et al. (2009b)
Genus MethanolobusGMLB834TGAAACGGTCGCACCGTCCCAGRNase H22Narihiro et al. (2009b)
Species M. psychrophilusR15FGCTACACGCGGGCTACAATGAqPCR (forward)21Zhang et al. (2008a)
R15RAATTTAGGTTCGAACACGGCATGAAqPCR (reverse)25Zhang et al. (2008a)
Genus Methanomethylovorans     
Genus MethanosalsumGMSS261GTCGGCTAGCAGGTACCTTGRNase H20Narihiro et al. (2009b)
Family Methermicoccaceae     
Genus Methermicoccus     
Class Methanobacteria     
Order MethanobacterialesMB310CTTGTCTCAGGTTCCATCTCCGMH22Raskin et al. (1994b)
MB311ACCTTGTCTCAGGTTCCATCTCCFISH23Crocetti et al. (2006)
Mbac f331CTTGTCTCAGGTTCCATCTCPCR20Skillman et al. (2004)
MB1174TACCGTCGTCCACTCCTTCCTCMH, FISH22Raskin et al. (1994b)
MBT857FCGWAGGGAAGCTGTTAAGTqPCR (forward)19Yu et al. (2005)
MBT929FAGCACCACAACGCGTGGAqPCR (probe)18Yu et al. (2005)
MBT1196RTACCGTCGTCCACTCCTTqPCR (reverse)18Yu et al. (2005)
1401RKTTTGGGTGGYGTGACGGGCPCR (reverse)20Banning et al. (2005)
Family MethanobacteriaceaeMB1175mCCGTCGTCCACTCCTTCCTCRNase H20Narihiro et al. (2009b)
MEB859AGGGAAGCTGTTAAGTCCFISH18Boetius et al. (2000)
Genus MethanobrevibacterfMbb1CTCCGCAATGTGAGAAATCGPCR20Skillman et al. (2004)
GMB406GCCATCCCGTTAAGAATGGCRNase H20Narihiro et al. (2009b)
Species M. ruminantiumMBR1001TCAGCCTGGTAATCATACAFISH19Yanagita et al. (2000)
Species M. smithiiForwardCCGGGTATCTAATCCGGTTCqPCR (forward)20Armougom et al. (2009)
ReverseCTCCCAGGGTAGAGGTGAAAqPCR (reverse)20Armougom et al. (2009)
ProbeCCGTCAGAATCGTTCCAGTCAGqPCR (probe)22Armougom et al. (2009)
Genus MethanobacteriumfMbiumCGTTCGTAGCCGGCYTGAPCR18Skillman et al. (2004)
GMBA755TGGCTTTCGTTACTCACCRNase H18Narihiro et al. (2009b)
S-F-Mbac-0398-S-a-20CCCAAGTGCCACTCTTAACGqPCR (forward)20Sawayama et al. (2006)
S-G-Mbac-0526-A-a-33AAYGGCCACCACTTGAGCTGCCGGTGTTACCGCqPCR (reverse)33Sawayama et al. (2006)
S-G-Mbac-0578-A-a-22AGACTTATCAARCCGGCTACGAqPCR (probe)22Sawayama et al. (2006)
Genus MethanosphaeraGMSP838CCGGAACAACTCGAGGCCATRNase H20Narihiro et al. (2009b)
Genus MethanothermobacterMt392fACTCTTAACGGGGTGGCTTTTqPCR (forward)21Hori et al. (2006)
Mt578rTCATGATAGTATCTCCAGCqPCR (reverse)19Hori et al. (2006)
410FCTCTTAACGGGGTGGCTTTTqPCR (forward)20Franke-Whittle et al. (2009a)
667RCCCTGGGAGTACCTCCAGCqPCR (reverse)19Franke-Whittle et al. (2009a)
GMTB541AAAAGCGGCTACCACTTGAGCTRNase H22Narihiro et al. (2009b)
S-F-Mbac-0398-S-a-20CCCAAGTGCCACTCTTAACGqPCR (forward)20Sawayama et al. (2006)
S-G-Mthb-0549-S-a-32CGGACGCTTTAGGCCCAATAAAAGCGGCTACCqPCR (probe)32Sawayama et al. (2006)
S-G-Mthb-0589-A-a-25GGGATTTCACCAGAGACTTATCAGqPCR (reverse)25Sawayama et al. (2006)
Family Methanothermaceae     
Genus MethanothermusFMTH1183TACGGACCTACCGTCGCCCGCARNase H22Narihiro et al. (2009b)
Class Methanococci     
Order MethanococcalesM(CO/BA)377CCCCCGTCGCACTTKCGTGMH19Ovreås et al. (1997)
Mcc rWASTVGCAACATAGGGCACGGPCR (reverse)21Skillman et al. (2004)
MCC495FTAAGGGCTGGGCAAGTqPCR (forward)16Yu et al. (2005)
MCC686FTAGCGGTGRAATGYGTTGATCCqPCR (probe)22Yu et al. (2005)
MCC832RCACCTAGTYCGCARAGTTTAqPCR (reverse)20Yu et al. (2005)
1202RCCAGGRGATTCGGGGCATGCPCR (reverse)20Banning et al. (2005)
Family MethanocaldococcaceaeS-F-Mcc-1109-b-A-20GCAACATGGGGCRCGGGTCTMH20Nercessian et al. (2004)
MC504GGCTGCTGGCACCGGACTTGCCCAFISH24Crocetti et al. (2006)
FMCMT1044iGTCAACCTGGCCTTCATCCTGCRNase H22Narihiro et al. (2009b)
Genus Methanocaldococcus     
Genus Methanotorris     
Family MethanococcaceaeMC1109GCAACATAGGGCACGGGTCTMH20Raskin et al. (1994b)
Genus MethanococcusGMC728ACCCGTTCCAGACAAGTGCCTTRNase H22Narihiro et al. (2009b)
GMC231ACTACCTAATCGAGCGCAGTCCRNase H22Narihiro et al. (2009b)
GMC416TTGATAAAAGCCCATGCTGTGCRNase H22Narihiro et al. (2009b)
Genus MethanothermococcusGMTL416TAGAAAAGCCTACGCAGTGCRNase H20Narihiro et al. (2009b)
Class Methanopyri     
Order Methanopyrales     
Family Methanopyraceae     
Genus MethanopyrusFMCMT1044iGTCAACCTGGCCTTCATCCTGCRNase H22Narihiro et al. (2009b)
S-G-Mp-0431-a-A-20TTACACCCCGGTACAGCCGCMH20Nercessian et al. (2004)
GMPK1331GGTTACTACCGATTCCACCTTCRNase H22Narihiro et al. (2009b)

The isolation and characterization of novel methanogens from various ecosystems are ongoing, and the descriptions of such methanogens have been carried out at an encouraging rate. Recently, hydrogenotrophic methanogens, which are novel at high taxonomic levels (Methanocella paludicola and Methanocella arvoryzae), have been isolated, and the novel order Methanocellales was proposed (Sakai et al., 2008; 2010). These methanogens have long been considered as the uncultivable methanogen group (Rice cluster I), and responsible for the major part of methanogenesis in rice paddy soil (Conrad et al., 2006). In addition, novel hydrogenotrophic methanogens associated with previously uncultivated phylogenetic groups of the order Methanomicrobiales (formerly known as E1/E2 or Fen cluster) were isolated from anaerobic bioreactors (Imachi et al., 2008; Yashiro et al., 2009) and wetlands (Cadillo-Quiroz et al., 2009; Bräuer et al., 2010). Novel strains of the genera Methanofollis (Imachi et al., 2009), Methanolobus (Doerfert et al., 2009; Mochimaru et al., 2009), Methanospirillum (Iino et al., 2010) and Methanobacterium (Krivushin et al., 2010; Shcherbakova et al., 2010) have also been reported recently.

Despite these efforts in cultivating as yet uncultivable methanogens present in environments, there are still a vast number of uncultivable archaeal taxa that may have similar metabolic functions as those of known methanogens. For example, 16S rRNA gene types assigned into the WSA2 (or ArcI) group were frequently retrieved from methanogenic waste/wastewater treatment systems (Sekiguchi and Kamagata, 2004; Chouari et al., 2005). The WSA2 group is considered to be an archaeal taxon at the class level with no cultured representatives (Hugenholtz, 2002). However, Chouari and colleagues have found that WSA2-related cells can be enriched using formate- or hydrogen-containing culture media, suggesting that they harbour methanogenic activity (Chouari et al., 2005). Another example similar to the Rice Cluster I group is Rice Cluster II (RC-II). Members of the RC-II group were also considered to be methanogens, because the 16S rRNA gene clones affiliated with this group were frequently observed in methanogenic enrichment cultures containing ethanol as an electron donor, and because the RC-II group is a lineage within the phylogenetic radiation of the orders Methanosarcinales and Methanomicrobiales (Lehmann-Richter et al., 1999). As can be noted from these examples, there is no doubt that the actual biodiversity of methanogens will be much expanded in the future as the number of isolated and described methanogens continues to increase. However, in this review, we mainly focus on the quantitative monitoring tools for previously cultured methanogens.

Oligonucleotide probes/primers for 16S rRNA and its gene

16S rRNA and its gene are the most frequently used biomarkers for the determination of methanogenic populations in environments. 16S rRNA gene-targeted probes/primers frequently used for identifying methanogens are listed in Table 1. To entirely describe methanogenic populations in ecosystems of interest, 16S rRNA gene-targeted primer sets for a wide range of methanogen taxa, such as 146f/1324r (Marchesi et al., 2001) and Met83F (Met86F)/Met1340R (Wright and Pimm, 2003), were developed. In addition, a number of oligonucleotide probes/primers for specifically and hierarchically detecting methanogens at different taxonomic levels were designed to resolve different methanogen populations in waste/wastewater treatment anaerobic sludges (Rocheleau et al., 1999; Zheng and Raskin, 2000; Hori et al., 2006; Ariesyady et al., 2007; Franke-Whittle et al., 2009a; Narihiro et al., 2009a,b), the rumen (Yanagita et al., 2000; Skillman et al., 2004), subseafloor sediments (Boetius et al., 2000; Nercessian et al., 2004), sediments (Falz et al., 1999), the human gut (Armougom et al., 2009) and wetlands (Bräuer et al., 2006; Zhang et al., 2008a,b) (Table 1). Nowadays, almost all of the known culturable methanogens can be detected using these probes/primers at the class, order, family genus and even species levels; at the genus level, it should be noted that the probes/primers targeting for the genera Methermicoccus, Methanomethylovorans, Methanocaldococcus and Methanotorris are lacking.

Oligonucleotide probes/primers for mcrA gene

The 16S rRNA gene has been best used for the identification of methanogens in environments. However, because archaeal 16S rRNA genes other than those of methanogens can also often be detected using PCR primer sets for a wide range of methanogen taxa, it has limitation in exclusively describing the population structure of methanogens. Therefore, there is a need to detect methanogens on the basis of functional genes that are found to be unique in methanogenesis. Such a functional gene frequently used is mcrA. Methyl coenzyme M reductase (mcr) is the terminal enzyme involved in methanogenesis, which reduces the methyl group bond of methyl coenzyme M with the release of methane (Friedrich, 2005). Because the α-subunit of mcr (mcrA) and its isoenzyme gene (mrtA) are highly conserved among methanogens, and that these genes are almost exclusively found in methanogens, mcrA/mrtA-based detection of methanogens has been used. The phylogeny of methanogens determined using mcrA/mrtA (or translated amino acid) sequences is in good accordance with those determined using 16S rRNA gene sequences (Friedrich, 2005). Previously reported, frequently used probes/primers for mcrA/mrtA are categorized into three primer sets, namely, MCR (Springer et al., 1995), ME (Hales et al., 1996) and ML (Luton et al., 2002) (Table 2). The targeted regions of the forward primers of these sets are considerably different, whereas those of the reverse primers are almost the same. The MCR primer set was originally designed to determine the phylogeny of the family Methanosarcinaceae (Springer et al., 1995). The ME primer set was designed to describe methanogenic populations in wetlands (Hales et al., 1996), for which the difficulty in amplifying mcrA/mrtA relevant to Methanosarcinaceae and Methanobacteriaceae was pointed out later (Lueders et al., 2001; Juottonen et al., 2006). The ML primer set was developed on the basis of the mcrA sequences obtained from five orders, comprising Methanosarcinales, Methanomicrobiales, Methanobacteriales, Methanococcales and Methanopyrales (Luton et al., 2002). Four other primer sets and probes for specific taxonomic groups have also been developed recently (Table 2).

Table 2.  Oligonucleotide PCR primers and probes targeting the mcrA gene.
Probe/primer nameNameDirection/ApplicationProbe sequence (5′–3′)Probe length (mer)ReferenceSpecificity
  1. The probes/primers listed here.

PCR primer      
 Set 1MCRfForwardTAYGAYCARATHTGGYT17Springer et al. (1995)Most methanogens
MCRrReverseACRTTCATNGCRTARTT17  
 Set 2ME1ForwardGCMATGCARATHGGWATGTC20Hales et al. (1996)Most methanogens
ME2ReverseTCATKGCRTAGTTDGGRTAGT21  
 Set 3MLfForwardGGTGGTGTMGGATTCACACARTAYGCWACAGC32Luton et al. (2002)Most methanogens
MLrReverseTTCATTGCRTAGTTWGGRTAGTT23  
 Set 4ME1ForwardGCMATGCARATHGGWATGTC20Hales et al. (1996)Most methanogens
ME2bReverseTCCTGSAGGTCGWARCCGAAGAA23Shigematsu et al. (2004) 
 Set 5MrtA_forForwardAAACAATCAACCACGCACTC20Scanlan et al. (2008)Methanosphaera stadtmanae
MrtA_revReverseGTGAGCCCAATCGAAGGA18  
 Set 6METH-fForwardRTRYTMTWYGACCARATMTG20Colwell et al. (2008)Most methanogens
METH-rReverseYTGDGAWCCWCCRAAGTG18  
 Set 7mlasForwardGGTGGTGTMGGDTTCACMCARTA24Steinberg and Regan (2008)Most methanogens
mcrA-revReverseCGTTCATBGCGTAGTTVGGRTAGT24  
 Set 8ME3MFForwardATGTCNGGTGGHGTMGGSTTYAC23Nunoura et al. (2008)Most methanogens
ME3MF-eForwardATGAGCGGTGGTGTCGGTTTCAC23  
ME2r'ReverseTCATBGCRTAGTTDGGRTAGT21  
Probe      
 ME3Clone screeningGGTGGHGTMGGWTTCACACA20Hales et al. (1996)Most of methanogens
SAE716TAQTaqMan probeAGGCCTTCCCCACTCTGCTTGAGGAT26Shigematsu et al. (2004)Genus Methanosaeta
SAR716TAQTaqMan probeAGAAATTCCCAACAGCCCTTGAAGAC26Shigematsu et al. (2004)Genus Methanosarcina
MCU716TAQTaqMan probeAGCAGTACCCGACCATGATGGAGGAC26Shigematsu et al. (2004)Genus Methanoculleus
mbac-mcrATaqMan probeARGCACCKAACAMCATGGACACWGT25Steinberg and Regan (2009)Family Methanobacteriaceae
mrtATaqMan probeCCAACTCYCTCTCMATCAGRAGCG24Steinberg and Regan (2009)Family Methanobacteriaceae
mcpTaqMan probeAGCCGAAGAAACCAAGTCTGGACC24Steinberg and Regan (2009)Family Methanocorpusculaceae
mspTaqMan probeTGGTWCMACCAACTCACTCTCTGTC25Steinberg and Regan (2009)Family Methanospirillaceae
FenTaqMan probeAAVCACGGYGGYMTCGGMAAG21Steinberg and Regan (2009)Genus Methanoregula
msaTaqMan probeCCTTGGCRAATCCKCCGWACTTG23Steinberg and Regan (2009)Family Methanosaetaceae
msarTaqMan probeTCTCTCWGGCTGGTAYCTCTCCATGTAC28Steinberg and Regan (2009)Genus Methanosarcina
McvME0FISHGGAAAAATTCGAAGAAGATC20Kubota et al. (2006)Methanococcus vannielii
McvME3rFISHTGTGTGAAACCTACGCCACC20Kubota et al. (2006)Methanococcus vannielii
McvME1rFISHGACATTCCAATCTGCATTGC20Kubota et al. (2006)Methanococcus vannielii

Assessing the biodiversity of methanogens in complex communities by PCR detection and cloning of methanogen genes

Some of the noted primers for 16S rRNA and methyl coenzyme M reductase genes have often been used for the detection and identification by PCR to explore the diversity of methanogens in environmental samples (Table 3). For example, the 146f/1324r primer set for most of all the known methanogens was designed for the 16S rRNA gene clone analysis of deep sediment gas hydrate deposit, and the results showed that gene clones (phylotypes) affiliated with Methanosarcina and Methanobrevibacter predominated in the sediments (Marchesi et al., 2001). Similarly, some of these primers shown in Table 1 have been used for PCR to profile methanogen populations by denaturing gradient gel electrophoresis (DGGE) (e.g. (Casamayor et al., 2001; 2002; Yu et al., 2005; 2006; 2008). As examples, Wright and Pimm (2003) developed PCR and sequencing primers for the 16S rRNA gene of methanogens, and used them for the ribotyping of members of the classes ‘Methanomicrobia’ and Methanobacteria. The detection of methanogens by PCR in lamb rumen samples was performed using methanogen-specific primers targeting different taxonomic levels (Skillman et al., 2004). Banning and colleagues (2005) designed novel reverse primers to provide specific amplification of the 16S rRNA genes of ‘Methanomicrobia’ (Methanomicrobiales and Methanosarcinales), Methanobacteriales and Methanococcales, and successfully used them for the identification of methanogenic population structures in lake sediments.

Table 3.  Examples of oligonucleotide primer sets for PCR-based analyses for methanogens.
Type of sampleApplicationTarget geneTarget groupProbe set (forward/reverse/probe)aReference
Anaerobic processqPCR16S rRNAMethanomicrobialesMMB282F/MMB832R/MMB749FYu et al. (2005)
  MethanosarcinalesMSL812F/MSL1159R/MSL860F 
  MethanobacterialesMBT857F/MBT1196R/MBT929F 
  MethanococcalesMCC495F/MCC832R/MCC686F 
  MethanosarcinaceaeMsc380F/Msc828R/Msc492F 
  MethanosaetaMst702F/Mst862R/Mst753F 
qPCRmcrAMethanocorpusculaceaemlas/mcrA-rev/mcpSteinberg and Regan (2009)
  Methanospirillaceaemlas/mcrA-rev/msp 
  Methanosaetaceaemlas/mcrA-rev/msa 
  Methanobacteriaceaemlas/mcrA-rev/mbac-mcrA 
  Methanobacteriaceaemlas/mcrA-rev/mrtA 
  Methanoregulamlas/mcrA-rev/Fen 
  Methanosarcinamlas/mcrA-rev/msar 
qPCR16S rRNAMethanoculleus298F/586RFranke-Whittle et al. (2009a)
  Methanosarcina240F/589R 
  Methanothermobacter410F/667R 
qPCR16S rRNAMethanoculleus thermophilusMc412f/Mc578rHori et al. (2006)
  Methanosaeta thermophilaMs413f/Ms578r 
  MethanothermobacterMt392f/Mt578r 
qPCR16S rRNAMethanosaetaMS1b/SAE835R/SAE761TAQShigematsu et al. (2003)
  MethanosarcinaMB1b/SAR835R/SAR761TAQ 
  MethanoculleusAR934F/MG1200b/MCU1023TAQ 
qPCRmcrAMethanosaetaME1/ME2b/SAE716TAQShigematsu et al. (2004)
  MethanosarcinaME1/ME2b/SAR716TAQ 
  MethanoculleusME1/ME2b/MCU716TAQ 
qPCR16S rRNAMethanosaetaS-F-Msaet-0387-S-a-21/S-F-Msaet-0540-A-a-31/S-F-Msaet-0573-A-a-17Sawayama et al. (2004)
qPCR16S rRNAMethanosarcinaS-G-Msar-0450-S-a-19/S-P-Msar-0540-A-a-31/S-G-Msar-0589-S-a-20Sawayama et al. (2006)
  MethanobacteriumS-F-Mbac-0398-S-a-20/S-G-Mbac-0526-A-a-33/S-G-Mbac-0578-A-a-22 
  MethanothermobacterS-F-Mbac-0398-S-a-20/S-G-Mthb-0549-S-a-32/S-G-Mthb-0589-A-a-25 
qPCR16S rRNAMethanospirillumAR934F/MG1200b/MSP1025TAQTang et al. (2005)
qPCR16S rRNAMethanolineaNOBI109f/NOBI633Imachi et al. (2008)
PCR-cloning16S rRNAMost methanogens109f/UNIV1492rbNarihiro et al. (2009a)
PCR-cloning16S rRNAMost methanogens25f/1391rAriesyady et al. (2007)
   25f/UNIV1492rb 
   109f/UNIV1492rb 
Anaerobic process, wetlandPCR-cloningmcrAMost methanogensmlas/mcrA-revSteinberg and Regan (2008)
WetlandPCR-cloningmcrAMost methanogensME1/ME2Hales et al. (1996)
qPCR16S rRNAMethanolobus psychrophilusR15F/R15RZhang et al. (2008a)
RumenqPCR, DGGE16S rRNAMost methanogensMet630F/Met803RHook et al. (2009)
PCR, DGGE16S rRNAMost methanogensA357f/A693rYu et al. (2008)
   A24f/A329r 
   A24f/A348r 
PCR-typing16S rRNAMost methanogensArch f2/Arch r1386Skillman et al. (2004)
  MethanosarcinalesArch f2/MSr r859 
  MethanobacterialesMbac f331/Arch r1386 
  MethanobacteriumfMbium/Arch r1386 
  MethanococcalesArch f2/Mcc r 
  MethanobrevibacterfMbb1/Arch r1386 
qPCR16S rRNAMethanobrevibacter smithiiforward/reverse/probeArmougom et al. (2009)
Gastrointestinal tractPCR-cloningmcrAMost methanogensMrtA_for/MrtA_revScanlan et al. (2008)
Deep sea sedimentsPCR-cloning16S rRNAMost methanogens146f/1324rMarchesi et al. (2001)
qPCRmcrAMost methanogensMETH-f/METH-rColwell et al. (2008)
qPCRmcrAMost methanogensME3MF and ME3MF-e/ME2r'Nunoura et al. (2008)
Lake sedimentPCR-cloning16S rRNAMethanomicrobia355Fc/1068RBanning et al. (2005)
  Methanobacteriales109f/1401R 
  Methanococcales344Fd/1202R 
Sulfurous lakePCR, DGGE16S rRNAMost methanogensARC344f/ARC915Casamayor et al. (2001)
LandfillPCR-cloningmcrAMost methanogensMLf/MLrLuton et al. (2002)
Ciliate endosymbiontPCR, DGGE16S rRNAMost methanogensA1f/A1100rEmbley et al. (1992)
Rice paddy soilPCR, DGGE16S rRNAMost methanogens109f/ARC915Grosskopf et al. (1998)
Pure culturesPCR-ribotyping16S rRNAMost methanogensMet83F (or Met86F)/Met1340RWright and Pimm (2003)
PCR-cloningmcrAMost methanogensMCRf/MCRrSpringer et al. (1995)

Massive parallel sequencing of PCR-amplified 16S rRNA genes using next generation sequencers (such as the FLX pyrosequencers) allows us to obtain a huge number of community sequence tags (for example c. 10 000–100 000 16S pyrotags for each sample), which is more than any Sanger-based cloning study to date, and have been used for characterizing archaeal populations (including methanogens) in hydrothermal chimneys (Brazelton et al., 2010a,b). The methodological advancements of 16S rRNA gene pyrosequencing include higher resolution (more sequences) for gene-based community structure analysis, analysis of multiple related samples and use of metadata (Tringe and Hugenholtz, 2008). Because of these advancements, as well as recent development of analytical tools for massive sequence data such as QIIME (Caporaso et al., 2010), the method may be further used for characterizing diversity of methanogens in ecosystems.

Similarly, the primers for methyl coenzyme M reductase genes have often been used for PCR detection and identification to exclusively explore the diversity of methanogens in samples. For example, the MCR set was used to elucidate the diversity of methanogens in various environments with PCR-based cloning (Kemnitz et al., 2004; Dhillon et al., 2005; Alain et al., 2006) and T-RFLP analyses (Ramakrishnan et al., 2001; Kemnitz et al., 2004). Such cloning analyses were also conducted using the ME (Hales et al., 1996; Nercessian et al., 1999; Galand et al., 2002; 2005; Tatsuoka et al., 2004) and ML primer sets (Luton et al., 2002; Castro et al., 2004; Juottonen et al., 2005; Nercessian et al., 2005; Ufnar et al., 2007; Smith et al., 2008). Comparative studies using these three primer sets have indicated that the ML primer set is more efficient for retrieving phylogenetically diverse methanogens in the wetland than others (Juottonen et al., 2006; Jerman et al., 2009). Owing to this advantage, the ML set has been used extensively to determine the diversity of methanogens in various anaerobic ecosystems. In addition, it has been noted that these mcrA-targeted primer sets (especially ME-related primer set) were also used for the quantitative detection of anaerobic methanotrophic archaea (ANME) in methane seep sediments (Inagaki et al., 2004; Nunoura et al., 2006; 2008). This is due to the fact that anaerobic methane oxidation represented by the ANME group is considered to proceed with mcr-type enzymes (Thauer and Shima, 2008). Detailed information about the mcrA-based qPCR for ANMEs is described below.

Polymerase chain reaction-based molecular techniques, such as PCR-cloning, pyrosequencing, DGGE and T-RFLP are adequate to gain entire community composition and diversity of methanogens in ecosystems. Based on the frequency of retrieval of phylotypes in gene library (or relative intensity of DGGE or T-RF bands in electropherogram), relative abundance of phylotypes of interest can be inferred. However, it should be noted that entire microbial community structure analysis based on bulk cell lysis, DNA extraction, PCR and cloning are often suspect because of several biases involved in each of the steps (Dahllof, 2002). Therefore, one should be careful to discuss on the abundance of phylotypes in samples based solely on the data obtained by these methods. More reliable methods to carry out quantitative detection of different groups of methanogens in samples would be to use the following quantitative molecular techniques.

Identification and quantification of methanogens in complex communities by membrane hybridization method

Quantitative membrane hybridization of labelled DNA probes to community rRNAs has been applied to various environmental rRNAs for the quantitative detection of specific groups of microbes present in complex communities (Stahl et al., 1988; Raskin et al., 1994a). RNA-dependent community analysis is known to indicate the in situ activity of individual members in ecosystems, because of the reasons that RNA synthesis is known to reflect the in situ growth rates of organisms (Poulsen et al., 1993; Amann et al., 1995), and that the turnover of RNA is thought to be much higher than that of DNA. Therefore, rRNA-dependent molecular techniques like the present one provide precise information about the dynamic nature of individual microbes in systems. In 1994, Raskin and colleagues carried out the first leading studies on the development of eight oligonucleotide probes for the quantitative detection of methanogens in anaerobic wastewater treatment sludges (Stahl and Amann, 1991; Raskin et al., 1994a,b). In these studies, they established the group-specific oligonucleotide probes targeting Methanomicrobiales (probes MG1200 and MSMX860), Methanobacteriaceae (probes MB310 and MB1174) and Methanococcales (probe MC1109). Because of the importance of methane production from acetate in anaerobic bioreactors, specific probes for aceticlastic methanogens, such as the members of Methanosarcinaceae (probes MS1414 and MS821) and Methanosaeta (probe MX825), were also developed.

These probes have been successfully applied to the quantification of methanogens in laboratory- and full-scale anaerobic bioreactors based on rRNA (Raskin et al., 1995; Griffin et al., 1998; Liu et al., 2002; McMahon et al., 2004; Zheng et al., 2006). Although membrane hybridization enables the sensitive quantification of individual species of rRNA molecules, this method requires several laborious experimental steps, often radioactively labelled DNA probes, and reference rRNA samples as external standards for each experiment. Thus, the method itself may be replaced by similar but much rapid and simpler methods, such as real-time RT-PCR and RNase H methods. However, the probes used for membrane hybridization experiments may be also used as probes/primers in other experiments shown below.

FISH for methanogens

Whole-cell FISH based on 16S rRNA is now commonly used to detect specific groups of microbes and to quantify populations of interest in environments by direct counting under a microscope (Amann et al., 1995). In addition, FISH is used for visualizing the spatial distribution of the population of interest in biofilms, such as those of methanogens in sludge granules in methanogenic wastewater treatment systems (Sekiguchi et al., 1999). Basically, the probes developed for membrane hybridization of methanogen 16S rRNAs or reverse primers for PCR amplification of methanogen 16S rRNA genes can directly be used as oligonucleotide probes for in situ hybridization studies, the probes previously designed by Raskin and colleagues (Raskin, et al., 1994b) have frequently be used for the purpose of FISH studies as well. These probes have been used for the quantitative detection of methanogens using the FISH technique in various anaerobic ecosystems, such as peat bog (e.g. Horn et al., 2003), aquifer (e.g. Kleikemper et al., 2005), landfills (e.g. Laloui-Carpentier et al., 2006) and anaerobic wastewater treatment processes (e.g. Sekiguchi et al., 1999; Plumb et al., 2001; Boonapatcharoen et al., 2007; Chen et al., 2009). Recently, the improvement of the specificity and sensitivity of the probes designed by Raskin and colleagues (1994b) has been reported. Crocetti and colleagues (2006) refined the experimental conditions of such probes for FISH analysis to accurately and sensitively detect methanogens.

In addition to the quantification, the probes (Table 1) have also been used for investigating the localization of methanogens in biofilms (sludge granules) [e.g. (Rocheleau et al., 1999; Sekiguchi et al., 1999; Plumb et al., 2001; Zheng et al., 2006; Vavilin et al., 2008; Chen et al., 2009)]. In anaerobic sludge granules, hydrogenotrophic methanogens are often juxtaposed with syntrophic substrate-degrading bacteria, such as syntrophic propionate-oxidizing bacteria such as members of the genera Syntrophobacter and Pelotomaculum; such close proximity between syntrophic bacteria and methanogens has been observed by FISH with confocal laser scanning microscopy (Harmsen et al., 1995; 1996; Sekiguchi et al., 1999; Imachi et al., 2000). Anaerobic ciliates often posses endosymbiotic methanogens within their cells, and the distribution of such methanogens in eukaryotic cells has been observed by the FISH method [e.g. (Embley et al., 1992; Shinzato et al., 2007)].

Although FISH is a powerful method for visualizing the cells of interest, there are some drawbacks in detecting cells; one of such problems is concerned with the penetration of oligonucleotide probes into the cells (Amann et al., 1995). For methanogens, FISH staining is often difficult for some Methanobacterium and Methanobrevibacter cells, for which oligonucleotide probes do not penetrate into their cells (Sekiguchi et al., 1999; Yanagita et al., 2000; Nakamura et al., 2006). To solve this problem, fixed cells were subjected to freeze-thaw cycles before hybridization, resulting in the improvement of probe penetration (Sekiguchi et al., 1999). Another way to solve this problem is the use of recombinant pseudomurein endoisopeptidase, which increases the permeability of oligonucleotide probes into cells, and allows a better visualization of methanogens in anaerobic granular sludge and the endosymbiotic methanogens in the anaerobic ciliate Trimyema compressum (Nakamura et al., 2006). An improved protocol of catalysed reporter deposition-FISH for methanogens with recombinant pseudomurein endoisopeptidase has also been reported, which can increase fluorescence signal intensity in FISH for detecting cells with a low rRNA content (Kubota et al., 2008).

Recently, mcrA-based in situ detection of methanogens has been performed using the two-pass tyramide signal amplification-FISH approach combined with locked nucleic acids (Kubota et al., 2006; Kawakami et al., 2010). These attempts were, at this point, only partially successful in detecting methanogen cells, because mcrA is generally present as a single copy gene on their chromosome, which results in a low sensitivity of detection.

qPCR

Quantitative PCR of 16S rRNA gene and mcrA has also been used to quantify the abundance of methanogens in recent years. Examples of qPCR primer and probe sets for different taxa of methanogens are listed in Table 3. For example, the primers Met630F/Met803R were developed for the SYBR green-based real-time qPCR for almost all the known methanogens in the rumen of the dairy cow (Hook et al., 2009). Yu and colleagues (2005) designed TaqMan-based qPCR probes/primer sets (six sets in total) for each of the orders Methanomicrobiales, Methanosarcinales, Methanobacteriales and Methanococcales, as well as the families Methanosaetaceae and Methanosarcinaceae. They applied a part of these sets to quantifying aceticlastic methanogens in methanogenic sludges for treating sewage sludges, cheese whey wastewater and synthetic wastewater, and revealed that the population of aceticlastic methanogens is affected by the acetate concentration in the wastewaters (Yu et al., 2006). qPCR detection using specific primers for particular groups of methanogens of interest, such as Methanoculleus (Shigematsu et al., 2003; Hori et al., 2006; Franke-Whittle et al., 2009a), Methanolinea (Imachi et al., 2008), Methanospirillum (Tang et al., 2005), Methanosaeta (Shigematsu et al., 2003; Sawayama et al., 2004; Hori et al., 2006), Methanosarcina (Shigematsu et al., 2003; Sawayama et al., 2006; Franke-Whittle et al., 2009a), Methanolobus (Zhang et al., 2008a,b), Methanobrevibacter (Armougom et al., 2009), Methanobacterium (Sawayama et al., 2006) and Methanothermobacter (Hori et al., 2006; Sawayama et al., 2006; Franke-Whittle et al., 2009a) have also been reported to date (Table 3).

For the qPCR detection of mcrA, the ME primer set was used for the quantification of methanogenic and methanotrophic populations in methane seep sediments (Inagaki et al., 2004; Nunoura et al., 2006). Afterwards, Nunoura and colleagues (2008) slightly modified the ME primer series, and showed that the mixture of the ME3MF and ME3MF-e forward primers and the ME2' reverse primer is most suitable for the qPCR detection of the methanogens and ANMEs in the environments. The results showed that a significant amount of methanogens and ANMEs was found in anaerobically digested sludge and methane seep sediments. The ML primer set was also used for the quantitation of methanogenic archaeal populations in the rumen (Denman et al., 2007) and human subgingival plaque (Vianna et al., 2008). Moreover, Steinberg and Regan (2008; 2009) developed the mlas/mcrA-rev primer set, which is a derivative of the ML primer set, for the clone library construction and qPCR analyses of methanogens in oligotrophic fen and anaerobic digester sludge. In addition, the genus-specific TaqMan probes for the mcrA-based quantitative detection of the Methanosaeta, Methanosarcina and Methanoculleus resident in acetate-fed chemostats, and the results showed that dilution rate is a key factor in the acetate bioconversion pathway (Shigematsu et al., 2004).

Quantitative PCR method provides sensitive, quantitative data of gene of interest with a sufficiently high dynamic range of quantification (Zhang and Fang, 2006). Therefore, in addition to the use of digital PCR (Ottesen et al., 2006), qPCR may be further used for quantitative monitoring of methanogen taxa of interests in complex microbial communities. However, it should be noted that the method is PCR-based and hence their data can be suspect because of biases involved in DNA extraction and primer/probe mismatches.

Assessing methanogen population by RNase H method

Although the above-mentioned quantitative methods such as membrane hybridization and qPCR are becoming general means to determine the abundance of the population of interest in a complex microbial community, there is a need to develop more simple and rapid techniques that meet the needs for real-time monitoring of the population of interest in a complex community. Recently, a simple and rapid quantification method, namely, the RNase H method, has been developed (Uyeno et al., 2004). This method is based on the sequence-specific cleavage of 16S rRNA with ribonuclease H (RNase H) and oligonucleotide (scissor) probes. RNAs from a complex community were first mixed with an oligonucleotide and subsequently digested with RNase H. Because RNase H specifically degrades the RNA strand of RNA : DNA hybrid heteroduplexes, the targeted rRNAs are cleaved at the hybridization site in a sequence-dependent manner and are consequently cut into two fragments. In contrast, non-targeted rRNAs remain intact under the same conditions. For the detection of cleaved rRNAs, the resulting RNA fragment patterns can be resolved by gel electrophoresis using RNA-staining dyes. The relative abundance of the targeted species of 16S rRNA fragments in total 16S rRNA can also be quantified by determining the signal intensity of individual 16S rRNA bands in an electropherogram (without the use of external standards). Because this method does not require an external RNA standard for each experiment, as is required in membrane hybridization, and because the present method is relatively easy to perform within a short time (i.e. within 2–3 h), this technique may provide direct, rapid and easy means of the quantitative detection of particular groups of anaerobes based on their rRNA, such as those of methanogens as well.

This method has been successfully applied to the quantification of active methanogens in anaerobic biological treatment processes (Uyeno et al., 2004; Sekiguchi et al., 2005; Narihiro et al., 2009b). In general, oligonucleotide probes used in FISH and membrane hybridization methods can directly be used as scissor probes in the RNase H method. Recently, a total of 40 probes, including newly designed and previously reported probes listed in Table 1, have been optimized for the specific quantification of methanogens at different taxonomic levels for use in the RNase H method and have been applied to quantitative and comprehensive detection of methanogens in various types of anaerobic biosystems (Narihiro et al., 2009b). As a result, methanogen populations were identified at different taxonomic levels and were influenced by the process temperature and wastewater compositions. Because of the reasons that this method is based on rRNA and that the RNA (rRNA) level is often dependent on the in situ activity of individual cells as described above, this method may be used for real-time monitoring of active methanogens and other important bacteria in engineered ecosystems such as waste/wastewater treatment systems to better control such bioreactors.

Stable isotope probing (SIP)-based detection of active methanogen populations in environments

To identify metabolically active populations in environments, SIP of DNA (Radajewski et al., 2000) and RNA (Manefield et al., 2002) has been used in recent years. In principle, SIP technology is based on the incorporation of 13C-labelled substrates into the nucleic acids. The separation of isotopically labelled (active) fractions from unlabeled (inactive) fractions is generally performed with density gradient centrifugation. The substrate-assimilated microorganisms in the labelled fractions are identified by a set of PCR-based molecular techniques such as gene cloning, DGGE and other methods. Therefore, for the purpose of identifying active methanogens that are responsible for particular metabolisms in environments, the probes/primers listed in Tables 1 and 2 can be used.

As examples, active methanogen populations involved in the syntrophic propionate oxidation in anoxic soil were analysed on the basis of rRNA-SIP, and it was found that the members of the genera Methanobacterium, Methanosarcina and Methanocella play a key role in scavenging hydrogen/formate/acetate in syntrophic association with propionate-oxidizing bacteria (Lueders et al., 2004). Conrad and coworkers have studied the detection of active methanogen populations using DNA-SIP combined with 13C-labelled CO2, and the results of T-RFLP profiling and phylogenetic analysis for clonal 16S rRNA gene fragments suggest that members of the RC-I group (Methanocellales) serve as important methanogens in rice paddy fields (Lu and Conrad, 2005; Lu et al., 2005). The active methanogenic populations in enrichment culture of municipal solid waste digester residues spiked with 13C-labelled substrates (such as cellulose, glucose and sodium acetate) were determined by DNA-SIP followed by cloning analysis (Li et al., 2009).

Other methods and future perspectives

DNA microarray platform, like PhyloChip, is becoming an important tool for parallel detection of different community members of microbes in ecosystems. For high throughput and comprehensive detection of methanogens in parallel, ANAEROCHIP (Franke-Whittle et al., 2009b) and GeoChip (Wang et al., 2009) have been developed recently. The primers/probes summarized in this review may be integrated into such a platform for parallel and hierarchical detection of methanogens. These primer/probes for methanogens can also be used in novel PCR-based techniques, such as the hierarchical oligonucleotide primer extension method (Wu and Liu, 2007), which has recently been developed for quantitative, multiplex detection of targeted microbial genes among PCR-amplified genes. SIP technology has been noted as an important pretreatment step for functional microbial community analyses, such as Raman microscopy-FISH (Huang et al., 2007; 2009) and metagenomic approaches (Kalyuzhnaya et al., 2008; Sul et al., 2009). Moreover, recent advances in analytical chemistry, such as isotope ratio mass spectrometry (Penning et al., 2006; Vavilin et al., 2008) and secondary ion mass spectrometry (Orphan et al., 2001), hold great promise for the highly sensitive determination of targeted microbes. Thus, in addition to describing the diversity of methanogens in particular environments of interest on the basis of DNA and RNA, such function-related analyses of methanogens may become important in the fields of environmental, determinative and applied microbiology.

As described in this minireview, a vast number of probe/primers have been developed for describing and quantifying methanogen populations, covering most parts of the known culturable methanogens described so far. A variety of molecular methods have also been developed that are used in combination with the probe/primers. Because these molecular methods have their own advancements and drawbacks, researchers need to select appropriate combinations of methods and probe/primers depending on what the researchers need to know. For details, recent reviews may be helpful for the selection of molecular techniques to be used (Talbot et al., 2008; Tabatabaei et al., 2010). In molecular ecology, multiple approaches are best to gain a complete picture of methanogen populations in environments. Therefore, the use of appropriate (multiple) molecular techniques in combinations with other non-molecular based methods like membrane lipid, autofluorescence, activity measurement and immunoenzymatic profiling should be considered. It should also be noted that there are still a number of uncultivated methanogens in various environments, and that they should be further isolated and characterized in detail. Monitoring tools for such uncultured methanogens remain to be developed to further increase in the coverage of methanogens present in environments.

Acknowledgements

This work was supported by the Environment Research and Technology Development Fund (S2-03) and the Global Environment Research Fund (RF-076) of the Ministry of the Environment, Japan.

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