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

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 ; Sakai ), wetlands (Cadillo‐Quiroz ; Bräuer ), permafrost (Krivushin ; Shcherbakova ), landfills (Laloui‐Carpentier ), subsurfaces (Doerfert ; Mochimaru ) and ruminants (Frey ), 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 ; Tabatabaei ). 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 ; Strapoc ), autofluorescence (Neu ; Tung ; Mochimaru ), activity measurement (Lehmann‐Richter ; Weijers ) and immunoenzymatic profiling (Visser ; 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 ). 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 ).

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 ) with the data set (Yarza ) 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 group	Probe name	Probe sequence (5′–3′)a	Application	Probe length (mer)	Reference
Most methanogens	Arch f2b	TTCYGGTTGATCCYGCCRGA	PCR (forward)	20	Skillman et al. (2004)
	Arch r1386	GCGGTGTGTGCAAGGAGC	PCR (reverse)	18	Skillman et al. (2004)
	A1f	TCYGKTTGATCCYGSCRGAG	PCR (forward), DGGE	20	Embley et al. (1992)
	A1100r	TGGGTCTCGCTCGTTG	PCR (forward), DGGE	16	Embley et al. (1992)
	Met83F	ACKGCTCAGTAACAC	PCR (forward)	15	Wright and Pimm (2003)
	Met86F	GCTCAGTAACACGTGG	PCR (forward)	16	Wright and Pimm (2003)
	Met448F	GGTGCCAGCCGCCGC	sequencing	15	Wright and Pimm (2003)
	Met1027F	GTCAGGCAACGAGCGAGACC	sequencing	20	Wright and Pimm (2003)
	Met1340R	CGGTGTGTGCAAGGAG	PCR (reverse)	16	Wright and Pimm (2003)
	109f	ACKGCTCAGTAACACGT	PCR (forward)	17	Grosskopf et al. (1998)
	146f	GGSATAACCYCGGGAAAC	PCR (forward)	18	Marchesi et al. (2001)
	1324r	GCGAGTTACAGCCCWCRA	PCR (reverse)	18	Marchesi et al. (2001)
	ARC344f	ACGGGGYGCAGCAGGCGCGA	PCR (forward), DGGE	20	Casamayor et al. (2001)
	25f	CYGGTYGATYCTGCCRG	PCR (forward)	17	Dojka et al. (1998)
	1391r	GACGGGCGGTGTGTRCA	PCR (reverse)	17	Barns et al. (1994)
	A24f	TCYGKTTGATCCYGSCRGA	PCR (forward), DGGE	19	Yu et al. (2008)
	A357f	CCCTACGGGGCGCAGCAG	PCR (forward), DGGE	18	Yu et al. (2008)
	A329r	TGTCTCAGGTTCCATCTCCG	PCR (reverse), DGGE	20	Yu et al. (2008)
	A348r	CCCCRTAGGGCCYGG	PCR (reverse), DGGE	15	Yu et al. (2008)
	A693r	GGATTACARGATTTC	PCR (reverse), DGGE	15	Yu et al. (2008)
	Met630F	GGATTAGATACCCSGGTAGT	qPCR (forward), DGGE	20	Hook et al. (2009)
	Met803R	GTTGARTCCAATTAAACCGCA	qPCR (reverse), DGGE	21	Hook et al. (2009)
	A1040f	GAGAGGWGGTGCATGGCC	PCR (forward), DGGE	18	Reysenbach and Pace (1995)
	ARC344	TCGCGCCTGCTGCICCCCGT	MH	20	Raskin et al. (1994b)
	ARC915	GTGCTCCCCCGCCAATTCCT	PCR (reverse), DGGE, MH, FISH	20	Raskin et al. (1994b)
	MER1	GGGCACGGGTCTCGCT	PCR (reverse)	16	Hales et al. (1996)
Class Methanomicrobia	1068R	ATGCTTCACAGTACGAAC	PCR (reverse)	18	Banning et al. (2005)
	CMSMM1068m	GGATGCTTCACAGTACGAAC	RNase H	20	Narihiro et al. (2009b)
Order Methanocellales
Family Methanocellaceae
Genus Methanocella	SANAE1136	GTGTACTCGCCCTCCTCG	FISH	18	Sakai et al. (2007)
Order Methanomicrobiales	MG1200	CGGATAATTCGGGGCATGCTG	MH, FISH	21	Raskin et al. (1994b)
	MG1200m	CCGGATAATTCGGGGCATGCTG	RNase H	22	Narihiro et al. (2009b)
	M(SA/MI)355	GTAAAGTTTTCGCGCCTG	MH	18	Ovreås et al. (1997)
	MMB282F	ATCGRTACGGGTTGTGGG	qPCR (forward)	18	Yu et al. (2005)
	MMB749F	TYCGACAGTGAGGRACGAAAGCTG	qPCR (probe)	24	Yu et al. (2005)
	MMB832R	CACCTAACGCRCATHGTTTAC	qPCR (reverse)	21	Yu et al. (2005)
Family Methanomicrobiaceae
Genus Methanoculleus	298F	GGAGCAAGAGCCCGGAGT	qPCR (forward)	18	Franke‐Whittle et al. (2009a)
	586R	CCAAGAGACTTAACAACCCA	qPCR (reverse)	20	Franke‐Whittle et al. (2009a)
	F2SC668	TCCTACCCCCGAAGTACCCCTC	RNase H	22	Narihiro et al. (2009b)
	F2SC732	TCGAAGCCGTTCTGGTGAGGCG	RNase H	22	Narihiro et al. (2009b)
	AR934F	AGGAATTGGCGGGGGAGCAC	qPCR (forward)	20	Shigematsu et al. (2003)
	MCU1023TAQ	GAATGATTGCCGGGCTGAAGACTC	qPCR (probe)	24	Shigematsu et al. (2003)
	MG1200b	CCGGATAATTCGGGGCATGCTG	qPCR (reverse)	22	Shigematsu et al. (2003)
Species M. thermophilus	Mc412f	CTGGGTGTCTAAAACACACCCAA	qPCR (forward)	23	Hori et al. (2006)
	Mc578r	ATTGCCAGTATCTCTTAG	qPCR (reverse)	18	Hori et al. (2006)
	SMCUT1253	GCCTTTCGGCGTCGATACCC	RNase H	20	Narihiro et al. (2009b)
Genus Methanofollis	F3SC984	CATATCGCTGTCCTACCCGG	RNase H	20	Narihiro et al. (2009b)
Genus Methanogenium	GMG1128	CGTTCCGGAGAACAAGCTAG	RNase H	20	Narihiro et al. (2009b)
Genus Methanomicrobium	GMM829	CTCGTAGTTACAGGCACACC	FISH, RNase H	20	Yanagita et al. (2000)
Genus Methanoplanus
Species M. limicola	SMPL623c	TTCTCTTAAACGCCTGCAGG	RNase H	20	Narihiro et al. (2009b)
Species M. endosynbiosus	SMPL623c	TTCTCTTAAACGCCTGCAGG	RNase H	20	Narihiro et al. (2009b)
Species M. petrolearius	SMPP1252d	CTTCTCAGTGTCGTTGCTCA	RNase H	20	Narihiro et al. (2009b)
Genus Methanolacinia	SMPP1252d	CTTCTCAGTGTCGTTGCTCA	RNase H	20	Narihiro et al. (2009b)
Family Methanospirillaceae
Genus Methanospirillum	F7SC1260	TATCCTCACCTCTCGGTGTC	RNase H	20	Narihiro et al. (2009b)
	MSP1025TAQ	GAATGATAGTCGGGATGAAGACTCTA	qPCR (probe)	26	Tang et al. (2005)
Genus Methanosphaerula
Genus Methanolinea	NOBI109f	ACTGCTCAGTAACACGT	qPCR (forward)	17	Imachi et al. (2008)
	NOBI633	GATTGCCAGTTTCTCCTG	qPCR (reverse), FISH	18	Imachi et al. (2008)
Family Methanocorpusculaceae
Genus Methanocorpusculum	F6SC393e	GACAGGCACTCAGGGTTTCC	RNase H	20	Narihiro et al. (2009b)
	GMCP489	GCCCTGCCCTTTCTTCACAT	RNase H	20	Narihiro et al. (2009b)
Family incertae sedis
Genus Methanocalculus	F6SC393e	GACAGGCACTCAGGGTTTCC	RNase H	20	Narihiro et al. (2009b)
	GMCL488	CCCCGCCCTTTCTCCTGGTG	RNase H	20	Narihiro et al. (2009b)
Genus Methanoregula
Species M. boonei	6A8 644	TCTTCCGGTCCCTAGCCTGCCA	FISH	22	Bräuer et al. (2006)
Species M. formicica	SMSP129	TATCCCCTTCCATAGGGTAGATT	FISH	23	Yashiro et al. (2009)
Order Methanosarcinales	MSMX860	GGCTCGCTTCACGGCTTCCCT	MH	21	Raskin et al. (1994b)
	MSSH859	TCGCTTCACGGCTTCCCT	FISH	18	Boetius et al. (2000)
	MSr r859	TCGCTTCACGGCTTCCCTG	PCR (reverse)	19	Skillman et al. (2004)
	MSMX860m	GCTCGCTTCACGGCTTCCCT	RNase H	20	Narihiro et al. (2009b)
	MSL812F	GTAAACGATRYTCGCTAGGT	qPCR (forward)	20	Yu et al. (2005)
	MSL860F	AGGGAAGCCGTGAAGCGARCC	qPCR (probe)	21	Yu et al. (2005)
	MSL1159R	GGTCCCCACAGWGTACC	qPCR (reverse)	17	Yu et al. (2005)
Family Methanosaetaceae
Genus Methanosaeta	MX825	TCGCACCGTGGCCGACACCTAGC	MH, FISH	23	Raskin et al. (1994b)
	MX825mix	TCGCACCGTGGCYGACACCTAGC	RNase H	23	Narihiro et al. (2009b)
	MX1361	ACGTATTCACCGCGTTCTGT	FISH	20	Crocetti et al. (2006)
	S‐G‐Msae‐0332‐a‐A‐22	TTAGGTCCGGGATGCXCCACGT	MH, FISH	22	Zheng and Raskin (2000)
	Mst702F	TAATC CTYGA RGGAC CACCA	qPCR (forward)	20	Yu et al. (2005)
	Mst753F	ACGGC AAGGG ACGAA AGCTA GG	qPCR (probe)	22	Yu et al. (2005)
	Mst862R	CCTAC GGCAC CRACM AC	qPCR (reverse)	17	Yu et al. (2005)
	MS1b	CCGGCCGGATAAGTCTCTTGA	qPCR (forward)	21	Shigematsu et al. (2003)
	SAE761TAQ	ACCAGAACGGACCTGACGGCAAGG	qPCR (probe)	24	Shigematsu et al. (2003)
	SAE835R	GACAACGGTCGCACCGTGGCC	qPCR (reverse)	21	Shigematsu et al. (2003)
	S‐F‐Msaet‐0387‐S‐a‐21	GATAAGGGRAYCTCGAGTGCY	qPCR (forward)	21	Sawayama et al. (2004)
	S‐F‐Msaet‐0540‐A‐a‐31	AGACCCAATAAHARCGGTTACCACTCGRGCC	qPCR (probe)	31	Sawayama et al. (2004)
	S‐F‐Msaet‐0573‐A‐a‐17	GGCCGRCTACAGACCCT	qPCR (reverse)	17	Sawayama et al. (2004)
Species M. concilii	Rotcl1	CTCCCGGCCTCGAGCCAGAC	FISH	20	Zepp Falz et al. (1999)
	MS1	CCGGATAAGTCTCTTGA	MH	17	Rocheleau et al. (1999)
	MS2	CTGAATGAGAGCGCTTTCTTT	MH	21	Rocheleau et al. (1999)
	MS5	GGCCACGGTGCGACCGTTGTCG	MH, FISH	22	Rocheleau et al. (1999)
	MMX1273	GGTTTTAGGAGATTCCCGTC	RNase H	20	Narihiro et al. (2009b)
	GTMS393m	ACCCAGCACTCGAGGTCCCC	RNase H	20	Narihiro et al. (2009b)
Species M. thermophila	Ms413f	CAGATGTGTAAAATACATCTGTT	qPCR (forward)	23	Hori et al. (2006)
	Ms578r	TCTGGCAGTATCCACCGA	qPCR (reverse)	18	Hori et al. (2006)
	TMX745	CCCTTGCCGTCGGATCCGTT	RNase H	20	Narihiro et al. (2009b)
Family Methanosarcinaceae	MS1414	CTCACCCATACCTCACTCGGG	MH, FISH	21	Raskin et al. (1994b)
	EelMS240f	CTATCAGGTTGTAGTGGG	FISH	18	Boetius et al. (2000)
	Msc380F	GAAACCGYGATAAGGGGA	qPCR (forward)	18	Yu et al. (2005)
	Msc492F	TTAGCAAGGGCCGGGCAA	qPCR (probe)	18	Yu et al. (2005)
	Msc828R	TAGCGARCATCGTTTACG	qPCR (reverse)	18	Yu et al. (2005)
	R15Fg	GCTACACGCGGGCTACAATGA	qPCR (forward)	21	Zhang et al. (2008a)
	FMSC394	ATGCTGGCACTCGGTGTCCC	RNase H	20	Narihiro et al. (2009b)
	MS821mh	GCCATGCCTGACACCTAGCG	RNase H	20	Narihiro et al. (2009b)
Genus Methanimicrococcus	GMIB1254	CACCTTTCGGTGTAGTTGCC	RNase H	20	Narihiro et al. (2009b)
Genus Methanosarcina	MS821	CGCCATGCCTGACACCTAGCGAGC	MH, FISH	24	Raskin et al. (1994b)
	SARCI551	GACCCAATAATCACGATCAC	FISH	20	Sorensen and Ahring (1997)
	SARCI645	TCCCGGTTCCAAGTCTGGC	FISH	19	Sorensen and Ahring (1997)
	MB1	TTTGGTCAGTCCTCCGG	MH	17	Rocheleau et al. (1999)
	MB3	CCAGACTTGGAACCG	MH	15	Rocheleau et al. (1999)
	MB4	TTTATGCGTAAAATGGATT	MH, FISH	19	Rocheleau et al. (1999)
	240F	CCTATCAGGTAGTAGTGGGTGTAAT	qPCR (forward)	25	Franke‐Whittle et al. (2009a)
	589R	CCCGGAGGACTGACCAAA	qPCR (reverse)	18	Franke‐Whittle et al. (2009a)
	MB1b	CGGTTTGGTCAGTCCTCCGG	qPCR (forward)	20	Shigematsu et al. (2003)
	SAR761TAQ	ACCAGAACGGGTTCGACGGTGAGG	qPCR (probe)	24	Shigematsu et al. (2003)
	SAR835R	AGACACGGTCGCGCCATGCCT	qPCR (reverse)	21	Shigematsu et al. (2003)
	S‐G‐Msar‐0450‐S‐a‐19	TAGCAAGGGCCGGGCAAGA	qPCR (forward)	19	Sawayama et al. (2006)
	S‐P‐Msar‐0540‐A‐a‐31	AGACCCAATAATCACGATCACCACTCGGGCC	qPCR (probe)	31	Sawayama et al. (2006)
	S‐G‐Msar‐0589‐S‐a‐20	ATCCCGGAGGACTGACCAAA	qPCR (reverse)	20	Sawayama et al. (2006)
Genus Methanococcoides	GMCO441	ACATGCCGTTTACACATGTG	RNase H	20	Narihiro et al. (2009b)
Genus Methanohalobium	GMHB842	TCGGCACTAGGAACGGCCGT	RNase H	20	Narihiro et al. (2009b)
Genus Methanohalophilus	GMHP1258	CCGTCACTTTTCAGTGTAGG	RNase H	20	Narihiro et al. (2009b)
Genus Methanolobus	GMLB834	TGAAACGGTCGCACCGTCCCAG	RNase H	22	Narihiro et al. (2009b)
Species M. psychrophilus	R15F	GCTACACGCGGGCTACAATGA	qPCR (forward)	21	Zhang et al. (2008a)
	R15R	AATTTAGGTTCGAACACGGCATGAA	qPCR (reverse)	25	Zhang et al. (2008a)
Genus Methanomethylovorans
Genus Methanosalsum	GMSS261	GTCGGCTAGCAGGTACCTTG	RNase H	20	Narihiro et al. (2009b)
Family Methermicoccaceae
Genus Methermicoccus
Class Methanobacteria
Order Methanobacteriales	MB310	CTTGTCTCAGGTTCCATCTCCG	MH	22	Raskin et al. (1994b)
	MB311	ACCTTGTCTCAGGTTCCATCTCC	FISH	23	Crocetti et al. (2006)
	Mbac f331	CTTGTCTCAGGTTCCATCTC	PCR	20	Skillman et al. (2004)
	MB1174	TACCGTCGTCCACTCCTTCCTC	MH, FISH	22	Raskin et al. (1994b)
	MBT857F	CGWAGGGAAGCTGTTAAGT	qPCR (forward)	19	Yu et al. (2005)
	MBT929F	AGCACCACAACGCGTGGA	qPCR (probe)	18	Yu et al. (2005)
	MBT1196R	TACCGTCGTCCACTCCTT	qPCR (reverse)	18	Yu et al. (2005)
	1401R	KTTTGGGTGGYGTGACGGGC	PCR (reverse)	20	Banning et al. (2005)
Family Methanobacteriaceae	MB1175m	CCGTCGTCCACTCCTTCCTC	RNase H	20	Narihiro et al. (2009b)
	MEB859	AGGGAAGCTGTTAAGTCC	FISH	18	Boetius et al. (2000)
Genus Methanobrevibacter	fMbb1	CTCCGCAATGTGAGAAATCG	PCR	20	Skillman et al. (2004)
	GMB406	GCCATCCCGTTAAGAATGGC	RNase H	20	Narihiro et al. (2009b)
Species M. ruminantium	MBR1001	TCAGCCTGGTAATCATACA	FISH	19	Yanagita et al. (2000)
Species M. smithii	Forward	CCGGGTATCTAATCCGGTTC	qPCR (forward)	20	Armougom et al. (2009)
	Reverse	CTCCCAGGGTAGAGGTGAAA	qPCR (reverse)	20	Armougom et al. (2009)
	Probe	CCGTCAGAATCGTTCCAGTCAG	qPCR (probe)	22	Armougom et al. (2009)
Genus Methanobacterium	fMbium	CGTTCGTAGCCGGCYTGA	PCR	18	Skillman et al. (2004)
	GMBA755	TGGCTTTCGTTACTCACC	RNase H	18	Narihiro et al. (2009b)
	S‐F‐Mbac‐0398‐S‐a‐20	CCCAAGTGCCACTCTTAACG	qPCR (forward)	20	Sawayama et al. (2006)
	S‐G‐Mbac‐0526‐A‐a‐33	AAYGGCCACCACTTGAGCTGCCGGTGTTACCGC	qPCR (reverse)	33	Sawayama et al. (2006)
	S‐G‐Mbac‐0578‐A‐a‐22	AGACTTATCAARCCGGCTACGA	qPCR (probe)	22	Sawayama et al. (2006)
Genus Methanosphaera	GMSP838	CCGGAACAACTCGAGGCCAT	RNase H	20	Narihiro et al. (2009b)
Genus Methanothermobacter	Mt392f	ACTCTTAACGGGGTGGCTTTT	qPCR (forward)	21	Hori et al. (2006)
	Mt578r	TCATGATAGTATCTCCAGC	qPCR (reverse)	19	Hori et al. (2006)
	410F	CTCTTAACGGGGTGGCTTTT	qPCR (forward)	20	Franke‐Whittle et al. (2009a)
	667R	CCCTGGGAGTACCTCCAGC	qPCR (reverse)	19	Franke‐Whittle et al. (2009a)
	GMTB541	AAAAGCGGCTACCACTTGAGCT	RNase H	22	Narihiro et al. (2009b)
	S‐F‐Mbac‐0398‐S‐a‐20	CCCAAGTGCCACTCTTAACG	qPCR (forward)	20	Sawayama et al. (2006)
	S‐G‐Mthb‐0549‐S‐a‐32	CGGACGCTTTAGGCCCAATAAAAGCGGCTACC	qPCR (probe)	32	Sawayama et al. (2006)
	S‐G‐Mthb‐0589‐A‐a‐25	GGGATTTCACCAGAGACTTATCAG	qPCR (reverse)	25	Sawayama et al. (2006)
Family Methanothermaceae
Genus Methanothermus	FMTH1183	TACGGACCTACCGTCGCCCGCA	RNase H	22	Narihiro et al. (2009b)
Class Methanococci
Order Methanococcales	M(CO/BA)377	CCCCCGTCGCACTTKCGTG	MH	19	Ovreås et al. (1997)
	Mcc r	WASTVGCAACATAGGGCACGG	PCR (reverse)	21	Skillman et al. (2004)
	MCC495F	TAAGGGCTGGGCAAGT	qPCR (forward)	16	Yu et al. (2005)
	MCC686F	TAGCGGTGRAATGYGTTGATCC	qPCR (probe)	22	Yu et al. (2005)
	MCC832R	CACCTAGTYCGCARAGTTTA	qPCR (reverse)	20	Yu et al. (2005)
	1202R	CCAGGRGATTCGGGGCATGC	PCR (reverse)	20	Banning et al. (2005)
Family Methanocaldococcaceae	S‐F‐Mcc‐1109‐b‐A‐20	GCAACATGGGGCRCGGGTCT	MH	20	Nercessian et al. (2004)
	MC504	GGCTGCTGGCACCGGACTTGCCCA	FISH	24	Crocetti et al. (2006)
	FMCMT1044i	GTCAACCTGGCCTTCATCCTGC	RNase H	22	Narihiro et al. (2009b)
Genus Methanocaldococcus
Genus Methanotorris
Family Methanococcaceae	MC1109	GCAACATAGGGCACGGGTCT	MH	20	Raskin et al. (1994b)
Genus Methanococcus	GMC728	ACCCGTTCCAGACAAGTGCCTT	RNase H	22	Narihiro et al. (2009b)
	GMC231	ACTACCTAATCGAGCGCAGTCC	RNase H	22	Narihiro et al. (2009b)
	GMC416	TTGATAAAAGCCCATGCTGTGC	RNase H	22	Narihiro et al. (2009b)
Genus Methanothermococcus	GMTL416	TAGAAAAGCCTACGCAGTGC	RNase H	20	Narihiro et al. (2009b)
Class Methanopyri
Order Methanopyrales
Family Methanopyraceae
Genus Methanopyrus	FMCMT1044i	GTCAACCTGGCCTTCATCCTGC	RNase H	22	Narihiro et al. (2009b)
	S‐G‐Mp‐0431‐a‐A‐20	TTACACCCCGGTACAGCCGC	MH	20	Nercessian et al. (2004)
	GMPK1331	GGTTACTACCGATTCCACCTTC	RNase H	22	Narihiro et al. (2009b)

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

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

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

SMPP1252 probe covers members of the Methanoplanus petrolearius and Methanolacinia.

F6SC393 probe covers members of the genera Methanocorpusculum and Methanocalculus.

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

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

MS821m probe covers members of the genera Methanimicrococcus and Methanosarcina.

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

MH, membrane hybridization.

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 ). 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 ). 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 ; Yashiro ) and wetlands (Cadillo‐Quiroz ; Bräuer ). Novel strains of the genera Methanofollis (Imachi ), Methanolobus (Doerfert ; Mochimaru ), Methanospirillum (Iino ) and Methanobacterium (Krivushin ; Shcherbakova ) 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 ). 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 ). 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 ). 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 ) 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 ; Zheng and Raskin, 2000; Hori ; Ariesyady ; Franke‐Whittle ; Narihiro ), the rumen (Yanagita ; Skillman ), subseafloor sediments (Boetius ; Nercessian ), sediments (Falz ), the human gut (Armougom ) and wetlands (Bräuer ; Zhang ) (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 ), ME (Hales ) and ML (Luton ) (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 ). The ME primer set was designed to describe methanogenic populations in wetlands (Hales ), for which the difficulty in amplifying mcrA/mrtA relevant to Methanosarcinaceae and Methanobacteriaceae was pointed out later (Lueders ; Juottonen ). 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 ). 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 name	Name	Direction/Application	Probe sequence (5′–3′)	Probe length (mer)	Reference	Specificity
PCR primer
Set 1	MCRf	Forward	TAYGAYCARATHTGGYT	17	Springer et al. (1995)	Most methanogens
	MCRr	Reverse	ACRTTCATNGCRTARTT	17
Set 2	ME1	Forward	GCMATGCARATHGGWATGTC	20	Hales et al. (1996)	Most methanogens
	ME2	Reverse	TCATKGCRTAGTTDGGRTAGT	21
Set 3	MLf	Forward	GGTGGTGTMGGATTCACACARTAYGCWACAGC	32	Luton et al. (2002)	Most methanogens
	MLr	Reverse	TTCATTGCRTAGTTWGGRTAGTT	23
Set 4	ME1	Forward	GCMATGCARATHGGWATGTC	20	Hales et al. (1996)	Most methanogens
	ME2b	Reverse	TCCTGSAGGTCGWARCCGAAGAA	23	Shigematsu et al. (2004)
Set 5	MrtA_for	Forward	AAACAATCAACCACGCACTC	20	Scanlan et al. (2008)	Methanosphaera stadtmanae
	MrtA_rev	Reverse	GTGAGCCCAATCGAAGGA	18
Set 6	METH‐f	Forward	RTRYTMTWYGACCARATMTG	20	Colwell et al. (2008)	Most methanogens
	METH‐r	Reverse	YTGDGAWCCWCCRAAGTG	18
Set 7	mlas	Forward	GGTGGTGTMGGDTTCACMCARTA	24	Steinberg and Regan (2008)	Most methanogens
	mcrA‐rev	Reverse	CGTTCATBGCGTAGTTVGGRTAGT	24
Set 8	ME3MF	Forward	ATGTCNGGTGGHGTMGGSTTYAC	23	Nunoura et al. (2008)	Most methanogens
	ME3MF‐e	Forward	ATGAGCGGTGGTGTCGGTTTCAC	23
	ME2r'	Reverse	TCATBGCRTAGTTDGGRTAGT	21
Probe
	ME3	Clone screening	GGTGGHGTMGGWTTCACACA	20	Hales et al. (1996)	Most of methanogens
	SAE716TAQ	TaqMan probe	AGGCCTTCCCCACTCTGCTTGAGGAT	26	Shigematsu et al. (2004)	Genus Methanosaeta
	SAR716TAQ	TaqMan probe	AGAAATTCCCAACAGCCCTTGAAGAC	26	Shigematsu et al. (2004)	Genus Methanosarcina
	MCU716TAQ	TaqMan probe	AGCAGTACCCGACCATGATGGAGGAC	26	Shigematsu et al. (2004)	Genus Methanoculleus
	mbac‐mcrA	TaqMan probe	ARGCACCKAACAMCATGGACACWGT	25	Steinberg and Regan (2009)	Family Methanobacteriaceae
	mrtA	TaqMan probe	CCAACTCYCTCTCMATCAGRAGCG	24	Steinberg and Regan (2009)	Family Methanobacteriaceae
	mcp	TaqMan probe	AGCCGAAGAAACCAAGTCTGGACC	24	Steinberg and Regan (2009)	Family Methanocorpusculaceae
	msp	TaqMan probe	TGGTWCMACCAACTCACTCTCTGTC	25	Steinberg and Regan (2009)	Family Methanospirillaceae
	Fen	TaqMan probe	AAVCACGGYGGYMTCGGMAAG	21	Steinberg and Regan (2009)	Genus Methanoregula
	msa	TaqMan probe	CCTTGGCRAATCCKCCGWACTTG	23	Steinberg and Regan (2009)	Family Methanosaetaceae
	msar	TaqMan probe	TCTCTCWGGCTGGTAYCTCTCCATGTAC	28	Steinberg and Regan (2009)	Genus Methanosarcina
	McvME0	FISH	GGAAAAATTCGAAGAAGATC	20	Kubota et al. (2006)	Methanococcus vannielii
	McvME3r	FISH	TGTGTGAAACCTACGCCACC	20	Kubota et al. (2006)	Methanococcus vannielii
	McvME1r	FISH	GACATTCCAATCTGCATTGC	20	Kubota et al. (2006)	Methanococcus vannielii

The probes/primers listed here.

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 ). 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 ; Yu ). 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 ). 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 sample	Application	Target gene	Target group	Probe set (forward/reverse/probe)a	Reference
Anaerobic process	qPCR	16S rRNA	Methanomicrobiales	MMB282F/MMB832R/MMB749F	Yu et al. (2005)
			Methanosarcinales	MSL812F/MSL1159R/MSL860F
			Methanobacteriales	MBT857F/MBT1196R/MBT929F
			Methanococcales	MCC495F/MCC832R/MCC686F
			Methanosarcinaceae	Msc380F/Msc828R/Msc492F
			Methanosaeta	Mst702F/Mst862R/Mst753F
	qPCR	mcrA	Methanocorpusculaceae	mlas/mcrA‐rev/mcp	Steinberg and Regan (2009)
			Methanospirillaceae	mlas/mcrA‐rev/msp
			Methanosaetaceae	mlas/mcrA‐rev/msa
			Methanobacteriaceae	mlas/mcrA‐rev/mbac‐mcrA
			Methanobacteriaceae	mlas/mcrA‐rev/mrtA
			Methanoregula	mlas/mcrA‐rev/Fen
			Methanosarcina	mlas/mcrA‐rev/msar
	qPCR	16S rRNA	Methanoculleus	298F/586R	Franke‐Whittle et al. (2009a)
			Methanosarcina	240F/589R
			Methanothermobacter	410F/667R
	qPCR	16S rRNA	Methanoculleus thermophilus	Mc412f/Mc578r	Hori et al. (2006)
			Methanosaeta thermophila	Ms413f/Ms578r
			Methanothermobacter	Mt392f/Mt578r
	qPCR	16S rRNA	Methanosaeta	MS1b/SAE835R/SAE761TAQ	Shigematsu et al. (2003)
			Methanosarcina	MB1b/SAR835R/SAR761TAQ
			Methanoculleus	AR934F/MG1200b/MCU1023TAQ
	qPCR	mcrA	Methanosaeta	ME1/ME2b/SAE716TAQ	Shigematsu et al. (2004)
			Methanosarcina	ME1/ME2b/SAR716TAQ
			Methanoculleus	ME1/ME2b/MCU716TAQ
	qPCR	16S rRNA	Methanosaeta	S‐F‐Msaet‐0387‐S‐a‐21/S‐F‐Msaet‐0540‐A‐a‐31/S‐F‐Msaet‐0573‐A‐a‐17	Sawayama et al. (2004)
	qPCR	16S rRNA	Methanosarcina	S‐G‐Msar‐0450‐S‐a‐19/S‐P‐Msar‐0540‐A‐a‐31/S‐G‐Msar‐0589‐S‐a‐20	Sawayama et al. (2006)
			Methanobacterium	S‐F‐Mbac‐0398‐S‐a‐20/S‐G‐Mbac‐0526‐A‐a‐33/S‐G‐Mbac‐0578‐A‐a‐22
			Methanothermobacter	S‐F‐Mbac‐0398‐S‐a‐20/S‐G‐Mthb‐0549‐S‐a‐32/S‐G‐Mthb‐0589‐A‐a‐25
	qPCR	16S rRNA	Methanospirillum	AR934F/MG1200b/MSP1025TAQ	Tang et al. (2005)
	qPCR	16S rRNA	Methanolinea	NOBI109f/NOBI633	Imachi et al. (2008)
	PCR‐cloning	16S rRNA	Most methanogens	109f/UNIV1492rb	Narihiro et al. (2009a)
	PCR‐cloning	16S rRNA	Most methanogens	25f/1391r	Ariesyady et al. (2007)
				25f/UNIV1492rb
				109f/UNIV1492rb
Anaerobic process, wetland	PCR‐cloning	mcrA	Most methanogens	mlas/mcrA‐rev	Steinberg and Regan (2008)
Wetland	PCR‐cloning	mcrA	Most methanogens	ME1/ME2	Hales et al. (1996)
	qPCR	16S rRNA	Methanolobus psychrophilus	R15F/R15R	Zhang et al. (2008a)
Rumen	qPCR, DGGE	16S rRNA	Most methanogens	Met630F/Met803R	Hook et al. (2009)
	PCR, DGGE	16S rRNA	Most methanogens	A357f/A693r	Yu et al. (2008)
				A24f/A329r
				A24f/A348r
	PCR‐typing	16S rRNA	Most methanogens	Arch f2/Arch r1386	Skillman et al. (2004)
			Methanosarcinales	Arch f2/MSr r859
			Methanobacteriales	Mbac f331/Arch r1386
			Methanobacterium	fMbium/Arch r1386
			Methanococcales	Arch f2/Mcc r
			Methanobrevibacter	fMbb1/Arch r1386
	qPCR	16S rRNA	Methanobrevibacter smithii	forward/reverse/probe	Armougom et al. (2009)
Gastrointestinal tract	PCR‐cloning	mcrA	Most methanogens	MrtA_for/MrtA_rev	Scanlan et al. (2008)
Deep sea sediments	PCR‐cloning	16S rRNA	Most methanogens	146f/1324r	Marchesi et al. (2001)
	qPCR	mcrA	Most methanogens	METH‐f/METH‐r	Colwell et al. (2008)
	qPCR	mcrA	Most methanogens	ME3MF and ME3MF‐e/ME2r'	Nunoura et al. (2008)
Lake sediment	PCR‐cloning	16S rRNA	Methanomicrobia	355Fc/1068R	Banning et al. (2005)
			Methanobacteriales	109f/1401R
			Methanococcales	344Fd/1202R
Sulfurous lake	PCR, DGGE	16S rRNA	Most methanogens	ARC344f/ARC915	Casamayor et al. (2001)
Landfill	PCR‐cloning	mcrA	Most methanogens	MLf/MLr	Luton et al. (2002)
Ciliate endosymbiont	PCR, DGGE	16S rRNA	Most methanogens	A1f/A1100r	Embley et al. (1992)
Rice paddy soil	PCR, DGGE	16S rRNA	Most methanogens	109f/ARC915	Grosskopf et al. (1998)
Pure cultures	PCR‐ribotyping	16S rRNA	Most methanogens	Met83F (or Met86F)/Met1340R	Wright and Pimm (2003)
	PCR‐cloning	mcrA	Most methanogens	MCRf/MCRr	Springer et al. (1995)

The primer sequences were shown in Tables 1 and 2.

UNIV1492r reverse primer was originally referred from Lane (1991) as an universal primer.

355F forward primer was originally referred as M(SA/MI)355 probe developed by Ovreås ) as shown in Table 1.

344F forward primer was originally referred as ARC344 probe developed by Raskin and colleagues (1994b) as shown in Table 1.

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 ). 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 ), 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 ; Dhillon ; Alain ) and T‐RFLP analyses (Ramakrishnan ; Kemnitz ). Such cloning analyses were also conducted using the ME (Hales ; Nercessian ; Galand ; Tatsuoka ) and ML primer sets (Luton ; Castro ; Juottonen ; Nercessian ; Ufnar ; Smith ). 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 ; Jerman ). 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 ; Nunoura ). 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 ; Raskin ). 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 ; Amann ), 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 ). 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 ; Griffin ; Liu ; McMahon ; Zheng ). 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 ). 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 ). 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, ) 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 ), aquifer (e.g. Kleikemper ), landfills (e.g. Laloui‐Carpentier ) and anaerobic wastewater treatment processes (e.g. Sekiguchi ; Plumb ; Boonapatcharoen ; Chen ). 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 ; Sekiguchi ; Plumb ; Zheng ; Vavilin ; Chen )]. 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 ; Sekiguchi ; Imachi ). 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 ; Shinzato )].

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 ). For methanogens, FISH staining is often difficult for some Methanobacterium and Methanobrevibacter cells, for which oligonucleotide probes do not penetrate into their cells (Sekiguchi ; Yanagita ; Nakamura ). To solve this problem, fixed cells were subjected to freeze‐thaw cycles before hybridization, resulting in the improvement of probe penetration (Sekiguchi ). 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 ). 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 ).

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 ; Kawakami ). 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 ). 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 ). qPCR detection using specific primers for particular groups of methanogens of interest, such as Methanoculleus (Shigematsu ; Hori ; Franke‐Whittle ), Methanolinea (Imachi ), Methanospirillum (Tang ), Methanosaeta (Shigematsu ; Sawayama ; Hori ), Methanosarcina (Shigematsu ; Sawayama ; Franke‐Whittle ), Methanolobus (Zhang ), Methanobrevibacter (Armougom ), Methanobacterium (Sawayama ) and Methanothermobacter (Hori ; Sawayama ; Franke‐Whittle ) 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 ; Nunoura ). 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 ) and human subgingival plaque (Vianna ). 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 ).

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 ), 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 ). 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 ; Sekiguchi ; Narihiro ). 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 ). 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 ) and RNA (Manefield ) 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 ). 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 ). 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 ).

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 ) and GeoChip (Wang ) 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 ) and metagenomic approaches (Kalyuzhnaya ; Sul ). Moreover, recent advances in analytical chemistry, such as isotope ratio mass spectrometry (Penning ; Vavilin ) and secondary ion mass spectrometry (Orphan ), 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 ; Tabatabaei ). 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.