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August 2019

Microbes Environ. Vol. 32, No. 2, 180-183, 2017https://www.jstage.jst.go.jp/browse/jsme2 doi:10.1264/jsme2.ME16179

MOST VALUABLE PAPER OF THE YEAR 2017

Short Communication
Predominant but Previously-overlooked Prokaryotic Drivers of Reductive
Nitrogen Transformation in Paddy Soils, Revealed by Metatranscriptomics
Yoko Masuda1†, HideoMi itoH2*†, Yutaka sHiratori3, kazuo isobe1, sHigeto otsuka1, and keisHi senoo1
1Department of Applied Biological Chemistry, Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1–1–1

Yayoi, Bunkyo-ku, Tokyo 113–8657, Japan; 2Bioproduction Research Institute, National Institute of Advanced Industrial Scienceand Technology (AIST), Hokkaido Center, 2–17–2–1 Tsukisamu-higashi, Toyohira, Sapporo 062–8517, Japan; and3Niigata Agricultural Research Institute, 857 Nagakuramachi, Nagaoka, Niigata 940–0826, Japan(Received November 21, 2016—Accepted January 16, 2017—Published online April 22, 2017)

Waterlogged paddy soils possess anoxic zones in which microbes actively induce reductive nitrogen transformation (RNT).
In the present study, a shotgun RNA sequencing analysis (metatranscriptomics) of paddy soil samples revealed that most RNTgene transcripts in paddy soils were derived from Deltaproteobacteria, particularly the genera Anaeromyxobacter andGeobacter. Despite the frequent detection of the rRNA of these microbes in paddy soils, their RNT-associated genes haverarely been identified in previous PCR-based studies. This metatranscriptomic analysis provides novel insights into the diversity
of RNT microbes present in paddy soils and the ecological function of Deltaproteobacteria predominating in these soils.
Key words: paddy soils, metatranscriptomics, denitrification, dissimilatory nitrate reduction to ammonium, nitrogen fixation

Paddy soils are characterized by temporal anaerobic conditions caused by waterlogging, and the active occurrence ofanaerobic biogeochemical processes (9). Among these active
processes, biological reductive nitrogen transformation (RNT),
i.e., denitrification (NO3–→NO2–→NO→N2O→N2), dissimilatory nitrate reduction to ammonium (DNRA; NO3–→NO2–→NH4+), and nitrogen fixation (N2→NH4+) contribute to lessleaching of nitrogen pollutants (NO3–, NO2–, and N2O) into
the environment and the greater retention of nitrogen-based
nutrients (NH4+) for rice plants in waterlogged paddy soils
than in upland soils (8, 22). Therefore, the identification of microbial drivers of RNT in paddy soils is important for successful
rice production with minimal environmental nitrogen burden.
However, a comprehensive understanding of the RNT
microbial community has not yet been achieved. In order toinvestigate RNT microbes in paddy soils, genes encoding the
enzymes that catalyze each reaction have been assessed via
PCR-based culture-independent methods, as represented by aclone library analysis (13, 24). Recent studies based on bacterial
genomics reported that the diversity of microbes harboring
RNT genes is greater than previously considered; PCR-based
methods have underrepresented this diversity because of
mismatches in the sequences of the primers used (5, 10, 21),indicating the need for alternative methods without a PCR
bias. Furthermore, simultaneous assessments of microbes
involved in denitrification, DNRA, and nitrogen fixation in asingle paddy field have not yet been performed. Moreover,limited information is available on the transcriptional profiles
in situ of RNT microbes in paddy soils because of the small
number of field studies conducted based on soil RNA, which
directly implicates RNT microbial activity. In the present
study, we investigated RNT-associated microbial diversity inpaddy soils via a shotgun RNA sequencing analysis without
any prior PCR preparation (metatranscriptomics).
In order to obtain a more complete understanding of paddy
soils with various biogeochemical properties spatially andseasonally (9, 12), soil RNA extracted from paddy soils inshallow (S1, S3) and deep (S2, S4) layers under waterlogged(S1, S2) and drained (S3, S4) conditions (Fig. S1) were subjected
to a metatranscriptomic analysis using an Illumina MiSeq
sequencer (Illumina, San Diego, CA, USA). The sequencesof RNT genes were retrieved from the metatranscriptomic
libraries obtained and taxonomically annotated through atandem similarity search with the blat and blast programs(full methods in Supplementary information).
Four reactions crucial to denitrification are catalyzed by
the following enzymes: NO3– reductase (Nar), NO2– reductase (Nir), NO reductase (Nor), and N2O reductase (Nos). The
nar transcripts detected in all soil samples using metatranscriptomics were related to those of Deltaproteobacteria,
Betaproteobacteria, Alphaproteobacteria, Gammaproteobacteria,and Acidobacteria (Fig. 1A), suggesting that these bacterialgroups are involved in the reduction of NO3– to NO2–. The nirtranscripts were mostly derived from Betaproteobacteria,
Gammaproteobacteria, and Alphaproteobacteria (Fig. 1A),
the members of which include common denitrifiers (5); thesewere also frequently detected in the same paddy soils in ourprevious PCR-based survey (24). Furthermore, nor and nos
transcripts were predominantly detected in Deltaproteobacteria(Fig. 1A), the transcripts of which were rarely detected via previous PCR assays (2, 24). Successive denitrification steps wereconsidered to be associated with common denitrifiers, such as
Betaproteobacteria, Gammaproteobacteria, Alphaproteobacteria,and Actinobacteria, which harbor nir, nor, and/or nos (18).
However, the metatranscriptomic data obtained in the present
study suggested that the reduction of NO2– into NO was
driven by these denitrifiers, and that the reduction of NO andN2O was mainly progressed by non-denitrifiers such as* Corresponding author. E-mail: hideomi-itou@aist.go.jp,
Tel: +81–11–857–8979; Fax: +81–11–857–8915.
† These authors contributed equally to this work.

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181

Fig. 1. Microbial diversity of RNT gene transcripts and rRNA. Taxonomic distribution of nar, nir, nor, nos, nrf, and nif transcripts, and rRNA at
the phylum and proteobacterial class level (A), and deltaproteobacterial genus level (B). Sample IDs indicate data derived from paddy soils in shallow(S1, S3) and deep (S2, S4) layers under waterlogged (S1, S2) and drained (S3, S4) conditions. Data represent the mean of triplicates.

Deltaproteobacteria, Bacteroidetes, Acidobacteria, andVerrucomicrobia, which harbor nor and/or nos, but not nir(5). Thus, paddy soil denitrification appears to be a cooperative process by each nitrogen oxide reducer, i.e., NO2– reducers(denitrifiers) and NO/N2O reducers (non-denitrifiers), similar
to nitrification (NH4+→NO2–→NO3–) orchestrated by NH4+oxidizing bacteria/archaea and NO2–-oxidizing bacteria (6).
These inferences in the denitrification process may be verified
using co-culture experiments on denitrifiers and non-denitrifiers.
DNRA, another NO3– reduction process, is catalyzed by
Nar and NH4+-forming NO2– reductase (Nrf). Most of the nrf
transcripts belong to Deltaproteobacteria, while some belong
to Verrucomicrobia (Fig. 1A). Together with the frequent
detection of nar transcripts derived from Deltaproteobacteria
as described above, Deltaproteobacteria appear to mainly
contribute to DNRA dynamics in paddy soils. Although
DNRA has been geochemically detected in paddy soils (1,23), limited information is available on DNRA microbial
diversity. To the best of our knowledge, the present study isthe first to attempt to identify the key player groups in DNRA
in paddy soils.
Diazotrophs harboring nitrogenase (Nif) drive nitrogen
fixation. The taxonomic composition of nif transcripts was
dominated by Deltaproteobacteria (Fig. 1A), indicating that
Deltaproteobacteria represents a key player group in nitrogen
fixation. Rhizospheric Alphaproteobacteria, Betaproteobacteria,
and Gammaproteobacteria and phototrophic Cyanobacteria

were considered to be key diazotrophs in paddy soils (13, 18).
However, we detected more nif transcripts in Deltaproteobacteria
than in these well-known diazotrophs; our results were consistent with a recent metatranscriptomic analysis based on a
microcosm study on Italian paddy soils (11).
The abundance of RNT genes derived from Deltaproteobacteria,
as described above, was also demonstrated in a shotgun DNA
sequencing analysis (metagenomics) (Fig. S2A). Additionally,
the microbial community structure based on rRNA gene/transcript sequences showed that Deltaproteobacteria is amajor group in paddy soil microbes (Fig. 1A, S2A). These
results support Deltaproteobacteria being a key player groupdriving RNT in paddy soils.
Further analyses on Deltaproteobacteria at the genus level
revealed the consistent detection of RNT gene transcripts
in metatranscriptomic data derived from the genera
Anaeromyxobacter and Geobacter (Fig. 1B), as well as their
RNT genes in metagenomic data (Fig. S2B). These genera
represent obligate anaerobes and metal reducers predominatingin paddy soils (7, 20) and exhibit some RNT activities in vitro(summarized in Table S1). Although the nitrogen fixation
activity of Anaeromyxobacter has yet to be characterized, thegenomes of some Anaeromyxobacter spp. conserve a similar
nif cluster to that of Geobacter spp. exhibiting nitrogen fixation activity (Fig. S3). Together with the detection of the niftranscripts of Anaeromyxobacter in this study, it is plausiblethat Anaeromyxobacter spp. perform nitrogen fixation.
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182

However, in contrast to Anaeromyxobacter and Geobacter
rRNA genes, their RNT genes have rarely been detected inpaddy soil samples using PCR-based techniques (4, 24).
Thus, the putative role of these genera in the RNT process hasreceived little attention despite their predominance in paddy
soils. The limited coverage of RNT gene-specific PCR primers
used in previous studies may have led to the oversight ofthese genera (10, 17); additionally, the GC content may be
implicated because the nor/nos/nrf/nif of Anaeromyxobacter
spp. showed markedly higher GC contents than the rRNA
genes and nor/nos/nrf/nif of other bacteria (Table S2). Even
improved nos universal primers, which have enabled lower
rates of sequence mismatches, were unable to amplify
Anaeromyxobacter nos (10). Therefore, a metatranscriptomic
analysis represents a more effective approach to examine thediversity of functional microbes, without any PCR bias
arising from the high GC content of target genes as well as
primer limitations.
Anaeromyxobacter and Geobacter, which have frequently
been detected in Japanese, Chinese, and Italian paddy soils(4, 11, rRNA data in Fig. 1B), predominate more in paddy
soils than in upland soils, as confirmed by the present study(Fig. 2A, B; Table S3). Their universal distribution andpredominance in paddy soils support Anaeromyxobacter andGeobacter being the key RNT players in paddy soils.
Furthermore, the predominance of these genera was found inriver sediments (Fig. 2A, B; Table S3); the RNT genes ofAnaeromyxobacter were frequently and globally detected inupland soil environments in recent shotgun metagenomics
studies (14, 15), indicating the contribution of these bacteria
to RNT not only in paddy soils, but also in other environments. The further application of metatranscriptomics acrossdifferent environments will expand our knowledge on thediversity of RNT microbes in nature as well as the ecological
function of Deltaproteobacteria in soil environments.
Previous studies on paddy soils identified the predominanceof Deltaproteobacteria and their ecological roles in dissimilatory metal reduction, sulfur/sulfate reduction, and hydrogenproduction (7, 9, 19). Although genomic studies showed theubiquitous possession of the RNT genes of Deltaproteobacteria,
the association of RNT with Deltaproteobacteria has notbeen considered because of the rare detection of their RNT

Fig. 2. Distribution of Anaeromyxobacter and Geobacter in various
soil environments. Proportions of Anaeromyxobacter (A) and Geobacter(B) against all bacteria estimated by a quantitative PCR method. The
mean±SD is shown (n=3). The paddy soil sample P3 was collected fromthe same paddy field used for the metatranscriptomic analysis in thisstudy. Asterisks indicate significant differences (Mann-Whitney U test;*, p<0.01; **, p<0.001); NS, not significant. Details of soil samples andqPCR data are summarized in Table S3.

genes in soil environments through PCR-based analyses. The
present study revealed the novel ecological functions ofAnaeromyxobacter and Geobacter within Deltaproteobacteria
dominating in paddy soils, namely, RNT, denitrification support,
and NH4+ production via DNRA and nitrogen fixation (Fig. 3).
Acknowledgements
We thank the technical staff of Niigata Agricultural ResearchInstitute for their assistance with field management, and T. ShinanoFig. 3. Ecological functions of Anaeromyxobacter and Geobacter, belonging to Deltaproteobacteria, predominant in paddy soils, expanded bymetatranscriptomics in this study. Anaeromyxobacter and Geobacter, ubiquitously predominant in paddy soils, are key player groups in the reductionof iron and manganese, which actively progresses in paddy soils soon after waterlogging (3, 4, 7, 9, 11, 20, Fig. 1B, S2B). Metatranscriptomics inthis study suggested that these genera also associate with reductive nitrogen transformation, i.e., denitrification, DNRA, and nitrogen fixation.
Sketches of Anaeromyxobacter and Geobacter are based on electron microscope photographs published elsewhere (3, 16). †, NO2– reduction to NO
is driven by common denitrifiers within Alphaproteobacteria, Betaproteobacteria, and Gammaproteobacteria (5), not by Anaeromyxobacter andGeobacter within Deltaproteobacteria; ‡, Anaeromyxobacter reduce N2O to N2, whereas Geobacter do not (Table S1).

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Metatranscriptomics of paddy soils

183(Hokkaido Agricultural Research Center, NARO), T. Hasegawa, H.
Akiyama (Institute for Agro-Environmental Science, NARO), S.
Asakawa (Nagoya University), S. Obata (Emeritus Professor of Mie
University), and Y. Ono (Fukushima Agricultural Technology
Center) for collecting and providing soil samples. This study was
supported by JSPS KAKENHI Grant Numbers JP22248038,
JP25252013, JP15KT0014, and JP15K14675, and the Science andTechnology Research Promotion Program for Agriculture, Forestry,
Fisheries and Food Industry (27004C), Japan.

12. Kögel-Knabner, I., W. Amelung, Z. Cao, S. Fiedler, P. Frenzel, R.
Jahn, K. Kalbitz, A. Kölbel, and M. Schloter. 2010. Biogeochemistryof paddy soils. Geoderma 157:1–14.
13. Mårtensson, L., B. Díez, I. Wartiainen, W. Zheng, R. El-Shehawy, andU. Rasmussen. 2009. Diazotrophic diversity, nifH gene expression
and nitrogenase activity in a rice paddy field in Fujian, China. Plant
and Soil 325:207–218.
14. Nelson, M.B., A.C. Martiny, and J.B. Martiny. 2016. Global biogeography of microbial nitrogen-cycling traits in soil. Proc. Natl. Acad.
Sci. U.S.A. 113:8033–8040.
15. Orellana, L.H., L.M. Rodriguez-R, S. Higgins, J.C. Chee-Sanford,
R.A. Sanford, K.M. Ritalahti, F.E. Löffler, and K.T. Konstantinidis. 2014.
Detecting nitrous oxide reductase (nosZ) genes in soil metagenomes:
method development and implications for the nitrogen cycle. MBio.
5:e01193–14.
16. Sanford, R.A., J.R. Cole, and J.M. Tiedje. 2002. Characterization anddescription of Anaeromyxobacter dehalogenans gen. nov., sp. nov., anaryl-halorespiring facultative anaerobic myxobacterium. Appl. Environ.
Microbiol. 68:893–900.
17. Sanford, R.A., D.D. Wagner, Q. Wu, et al. 2012. Unexpected nondenitrifier nitrous oxide reductase gene diversity and abundance in soils.
Proc. Natl. Acad. Sci. U.S.A. 109:19709–19714.
18. Shapleigh, J.P. 2006. The denitrifying prokaryotes, p. 769–792. In M.
Dworkin, S. Falkow, E. Rosenberg, K.H. Schleifer, and E. Stackebrandt(ed.), The Prokaryotes. Springer, New York.
19. Shrestha, P.M., M. Kube, R. Reinhardt, and W. Liesack. 2009.
Transcriptional activity of paddy soil bacterial communities. Environ.
Microbiol. 11:960–970.
20. Weber, K.A., L.A. Achenbach, and J.D. Coates. 2006. Microorganisms
pumping iron: anaerobic microbial iron oxidation and reduction. Nat.
Rev. Microbiol. 4:752–764.
21. Wei, W., K. Isobe, T. Nishizawa, L. Zhu, Y. Shiratori, N. Ohte, K.
Koba, S. Otsuka, and K. Senoo. 2015. Higher diversity and abundanceof denitrifying microorganisms in environments than considered previously. ISME J. 9:1954–1965.
22. Xiong, Z.Q., T.Q. Huang, Y.C. Ma, G.X. Xing, and Z.L. Zhu. 2010.
Nitrate and ammonium leaching in variable- and permanent-charge
paddy soils. Pedosphere 20:209–216.
23. Yin, S.X., D. Chen, L.M. Chen, and R. Edis. 2002. Dissimilatory
nitrate reduction to ammonium and responsible microorganisms in
two Chinese and Australian paddy soils. Soil Biol. Biochem.
34(8):1131–1137.
24. Yoshida, M., S. Ishii, D. Fujii, S. Otsuka, and K. Senoo. 2012. Identificationof active denitrifiers in rice paddy soil by DNA- and RNA-based
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3. Childers, S.E., S. Ciufo, and D.R. Lovley. 2002. Geobacter metallireducens
accesses insoluble Fe (III) oxide by chemotaxis. Nature 416:767–769.
4. Ding, L.J., J.Q. Su, H.J. Xu, Z.J. Jia, and Y.G. Zhu. 2015. Long-term
nitrogen fertilization of paddy soil shifts iron-reducing microbialcommunity revealed by RNA-13C-acetate probing coupled withpyrosequencing. ISME J. 9:721–734.
5. Graf, D.R., C.M. Jones, and S. Hallin. 2014. Intergenomic comparisonshighlight modularity of the denitrification pathway and underpin theimportance of community structure for N2O emissions. PLoS One
9:e114118.
6. Hirsch, P.R., and T.H. Mauchline. 2015. The importance of the microbial
N cycle in soil for crop plant nutrition. Adv. Appl. Microbiol. 93:45–71.
7. Hori, T., A. Muller, Y. Igarashi, R. Conrad, and M.W. Friedrich. 2010.
Identification of iron-reducing microorganisms in anoxic rice paddy
soil by 13C-acetate probing. ISME J. 4:267–278.
8. Ishii, S., S. Ikeda, K. Minamisawa, and K. Senoo. 2011. Nitrogen
cycling in rice paddy environments: past achievements and futurechallenges. Microbes Environ. 26:282–292.
9. Itoh, H., S. Ishii, Y. Shiratori, K. Oshima, S. Otsuka, M. Hattori, and
K. Senoo. 2013. Seasonal transition of active bacterial and archaeal
communities in relation to water management in paddy soils. Microbes
Environ. 28:370–380.
10. Jones, C.M., D.R. Graf, D. Bru, L. Philippot, and S. Hallin. 2013. The
unaccounted yet abundant nitrous oxide-reducing microbial community:
a potential nitrous oxide sink. ISME J. 7:417–426.
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Microbes Environ. Vol. 33, No. 4, 428-434, 2018https://www.jstage.jst.go.jp/browse/jsme2 doi:10.1264/jsme2.ME18058

MOST VALUABLE PAPER OF THE YEAR 2018

Short Communication
Nitric Oxide Production from Nitrite Reduction and Hydroxylamine Oxidation
by Copper-containing Dissimilatory Nitrite Reductase (NirK) from the Aerobic
Ammonia-oxidizing Archaeon, Nitrososphaera viennensis
Shun KobayaShi1, DaiSuKe hira2, Keitaro yoShiDa3, MaSanori toyofuKu3, yoSuKe ShiDa4, Wataru ogaSaWara4,
taKaShi yaMaguchi5, nobuo araKi1, and MaMoru oShiKi1*
1Department of Civil Engineering, National Institute of Technology, Nagaoka College, Nagaoka, Japan; 2Department of AppliedLife Science, Faculty of Biotechnology and Life Science, Sojo University, Ikeda, Kumamoto, Japan; 3Graduate School of Life andEnvironmental Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan; 4Department of Bioengineering, Nagaoka University ofTechnology, Nagaoka, Niigata, Japan; and 5Department of Science of Technology Innovation, Nagaoka University of Technology,
Nagaoka, Japan(Received April 21, 2018—Accepted July 17, 2018—Published online October 12, 2018)

Aerobic ammonia-oxidizing archaea (AOA) play a crucial role in the global nitrogen cycle by oxidizing ammonia to nitrite,
and nitric oxide (NO) is a key intermediate in AOA for sustaining aerobic ammonia oxidation activity. We herein heterologously
expressed the NO-forming, copper-containing, dissimilatory nitrite reductase (NirK) from Nitrososphaera viennensis andinvestigated its enzymatic properties. The recombinant protein catalyzed the reduction of 15NO2– to 15NO, the oxidation ofhydroxylamine (15NH2OH) to 15NO, and the production of 14-15N2O from 15NH2OH and 14NO2–. To the best of our knowledge,
the present study is the first to document the enzymatic properties of AOA NirK.
Key words: nitrite reduction, hydroxylamine oxidation, nitrous oxide production, ammonia oxidizing archaea, Nitrososphaera viennensis

possess nirK, which is transcribed and expressed during aerobic
ammonia oxidation (8, 15, 20, 37). These findings suggestthat NirK are involved in NO formation in AOA cells.
However, NO2– reduction to NO by AOA NirK has neverbeen demonstrated.
Bacterial NirK have been characterized as homotrimeric
enzymes, and each subunit has 2 Cu-binding sites (Type 1
and 2 Cu-binding sites). Type 1 Cu-binding sites receive anelectron from an electron donor, such as type 1 Cu proteins(single-domain cupredoxins) and/or cytochrome c, and theelectron is then further transferred to a type 2 Cu-binding sitethat is the catalytic center of NirK (14, 25). Bacterial NirK
have been classified into 2 phylogenetically distinct groups(class 1 and class 2 groups) based on sequence similarities,
and the NirK of the class 1 group contains linker loop andtower loop regions in the amino acid sequence (3). AOA
NirK, including Ns. viennensis NirK, are affiliated with adistinct clade of bacterial class 1 and 2 groups (Fig. 1A).
Lund et al. (20) reported that AOA NirK may be further
classified into several phylogenetic clades showing specificgeographic distributions. Ns. viennensis NirK has amino acidresidues consistent with those of type 1 and 2 Cu-bindingsites (His106, His140, and His316 for type 1 Cu-binding sites andHis101, Cys141, His152, and Met157 for type 2 Cu-binding sites)
as well as the linker and tower loop regions, whereas the C
terminus has unusual extensions of ~26 residues (Fig. 1B).
These phylogenetic affiliations of and structural variations in
Ns. viennensis NirK raise concerns regarding its enzymatic
properties, such as specific enzymatic activity, affinity for
NO2–, and products of NO2– reduction.
Based on its unique sequence and lack of biochemical
information, the purpose of the present study was to charac-

Aerobic ammonia oxidation, a rate-limiting step of nitrification, drives the global nitrogen cycle (24, 40), which involves
aerobic ammonia-oxidizing archaea and bacteria (AOA
and AOB, respectively) and complete ammonia oxidizers(comammox) (9, 44). Of these, AOA primarily contribute to
aerobic ammonia oxidation in natural environments including
soil and open ocean (19, 31, 46). AOA are affiliated with thephylum Thaumarchaeota, which includes phylogenetically andphysiologically diverse members (6) and the soil-inhabitingarchaeon Nitrososphaera viennensis (41). The biochemistryof aerobic ammonia oxidation by AOA has received a great
deal of interest because ammonia oxidation to nitrite (NO2–)
proceeds in a different manner to that of AOB. AOA oxidize
ammonia to hydroxylamine by ammonia monooxygenase(Amo) as well as AOB (43), while hydroxylamine is further
oxidized to NO2– by an unidentified enzyme (17). All known
AOA genomes lack the gene encoding hydroxylamine
dehydrogenase (Hao), and the involvement of a copper-protein
complex has been proposed (40, 45). In parallel with theoxidation of ammonia to NO2–, AOA produce nitric oxide(NO) (22). NO is a key intermediate in AOA cells because
this highly reactive molecule is essential for sustaining aerobic
ammonia oxidation activity (17, 33, 36, 47). To date, the following
2 pathways have been reported as a source of prokaryotic NO
formation: NO2– reduction to NO by copper-containing andcytochrome cd1-type dissimilatory nitrite reductases (NirK
and NirS, respectively) (38) and NH2OH oxidation to NO byhydroxylamine oxidoreductase (Hao) (4, 21). Although neither
nirS nor hao are found in AOA genomes (6), AOA commonly* Corresponding author. E-mail: oshiki@nagaoka-ct.ac.jp;
Tel: +81–258–34–9277; Fax: +81–258–34–9277/9284.

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Fig. 1. Phylogeny (A) and sequence alignments (B) of prokaryotic NirK. A) A phylogenetic tree of prokaryotic NirK was constructed by the
maximum likelihood method with the Jones-Taylor-Thornton model using the protein sequence of multicopper oxidase type 3 of Nitrososphaera
viennensis (accession number; AIC14243.1) as an outgroup. Branching points that support a probability >80% in bootstrap analyses (based on 500
replicates) are shown as filled circles. The scale bar represents 10% sequence divergence. Sequence accession numbers are indicated in parentheses.
B) Protein sequence alignment of nirK. NirK sequences were aligned using ClustalW software. Circles and triangles correspond to the amino acidresidues of type 1 and 2 Cu-binding sites, respectively. Linker, Tower loop (3), and C-terminal extension regions are underlined. Abbreviations of
microorganisms are as follows: Nitrosomonas europaea is N. europaea, A. xylosoxidans is Achromobacter xylosoxidans, A. faecalis is Alcaligenes
faecalis, N. gonorrhoeae is Neisseria gonorrhoeae, S. thermophilus is Sphaerobacter thermophilus, and Ns. viennensis is Nitrososphaera viennensis.

terize Ns. viennensis NirK. Prior to the present study, we
aimed to isolate Ns. viennensis NirK from a batch culture ofNs. viennensis as a native enzyme. However, the activity of
aerobic ammonia oxidation often disappeared when we scaledup the cultures (data not shown). Additionally, a slow growthrate (μmax 0.024 h–1) (41) and low biomass concentration in
the culture (ca. 107~8 cells mL–1) further precluded the preparation of the biomass required for protein purification. Since
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430

recombinant NirK proteins have been successfully used topreviously examine several enzymatic properties (7, 16, 32),
the authors decided to heterologously express Ns. viennensis
NirK in Escherichia coli, and investigate its enzymatic
properties. The nirK gene located in the Ns. viennensis genome(accession number; CP007536.1) was cloned into the expression
vector pCold I (Takara Bio, Shiga, Japan) with the 6×His
tag using the Mighty cloning reagent set (Takara Bio), andtransformed into E. coli strain BL21(DE3) (Takara Bio). The
N-terminal region of Ns. viennensis NirK was predicted to be
the signal peptide sequence (Met1 to Ala24), and nirK without
the signal peptide sequence was amplified by PCR using
ExTaq polymerase (Takara Bio) and specific forward (5ʹGGCATATGGCCCCGACTGGTGTCACTAGACACTAT-3ʹ)and reverse (5ʹ-GGAAGCTTAACCAGAGGTGGTGTTGC
CACCGGAGG-3ʹ) oligonucleotide primers. The restriction
sites of NdeI and HindIII in the forward and reverse primers
above are underlined. Genomic DNA extracted from Ns.
viennensis cells (JCM19564) was used as the DNA templatefor PCR. The constructed plasmid was subjected to Sanger
sequencing, and no mutations were found in the sequence.
Regarding the expression of the recombinant protein in E. coli
cells, the expression culture was aerobically cultivated at 37°C
in Luria-Bertani media containing 100 ng μL–1 ampicillin.
When the OD600 of the culture increased to 0.4, the culture
was transferred to 15°C and held for 30 min, and protein
expression was then induced by adding isopropyl β-D-1thiogalactopyranoside (IPTG) at a final concentration of 0.1 mM.
After being incubated at 15°C for 24 h, cells were harvested
by centrifugation at 8,500×g at 4°C for 10 min. The harvested
cells were suspended in buffer containing 20 mM Tris HCl(pH 8), 200 mM NaCl, and 10% glycerol. The cells were
disrupted using a sonifier 250 (Branson) (output 20, duty 20%
for 60 s, 6 cycles), and centrifuged at 13,000×g at 4°C for 1 h.
The supernatant was recovered as a soluble protein fraction,and the recombinant protein was purified using His-tag affinity
chromatography. The recombinant protein was bound to His60
Ni Superflow resin (Takara Bio), and washed with washingbuffer containing 20 mM Tris HCl (pH 8), 200 mM NaCl,
10% glycerol, and 20 mM imidazole. The bound recombinant
protein was eluted with elution buffer containing 20 mM Tris
HCl (pH 8), 200 mM NaCl, 10% glycerol, and 300 mM
imidazole. Protein concentrations were measured using the
DC-protein assay kit (Bio-Rad, Hercules, CA, USA) withbovine serum albumin as previously described (26), and purity
was evaluated by sodium dodecyl sulfate-polyacrylamide gelelectrophoresis (SDS-PAGE) on a 10% polyacrylamide gel
as previously described (28). As shown in Fig. 2A, a single
protein band appeared at a molecular mass of 40 kDa, whichclosely matched the molecular mass deduced from aminoacid sequences of the recombinant protein (i.e., 39.7 kDa).
The protein band was excised from the gel, and subjected to amatrix-assisted laser desorption ionization-time of flight mass
spectrometry (MALDI-TOF MS) analysis after in-gel tryptic
digestion for protein identification (The detailed methodology
is described in the Supplementary text). The MALDI-TOF
MS analysis confirmed that the protein band corresponded toNs. viennensis NirK (Fig. S1). Regarding the reconstitutionof Cu-binding sites of the recombinant protein, the purified
recombinant protein was dialyzed against buffer containing

20 mM Tris HCl (pH 8), 300 mM NaCl, and 0.5 mM CuSO4
at 4°C for 57 h. The protein solution was dialyzed again using
the above Tris buffer without CuSO4 at 4°C for 6 h. The dialyzed
recombinant protein was concentrated using a Vivaspin column(MWCO; 30 kDa) (GE Healthcare Japan, Tokyo, Japan). The
recombinant protein was loaded onto a gel-filtration HiLoad
16/600 Superdex 200 pg column (GE Healthcare) to assess
the molecular mass of the recombinant protein, which was
105±1.3 kDa (Fig. 2B). Since the deduced molecular mass of
Ns. viennensis NirK was 39.7 kDa, the molecular mass obtainedby gel filtration indicated that the recombinant protein forms
a homotrimeric structure, similar to canonical NirK.
NirK have been characterized as metalloproteins showing
a blue or green color spectrum, and exhibit absorption peaks
at approximately 450 and/or 600 nm (3). Bacterial NirK,
which belong to the class 1 group, often show a maximum
absorption peak at approximately 450 nm, although an exception(Achromobacter xylosoxidans NirK) that shows a peak at 593 nm
has been previously reported (16). The purified recombinant
protein was pale blue in color, and showed an absorption
peak at 590 nm (Fig. 2C). This feature indicated that Ns.
viennensis NirK is affiliated with the subgroup of NirK showing
a blue color spectrum. The blue or green color spectrum of
NirK is derived from a copper atom in the type 1 Cu-bindingsite (14), while the type 2 Cu-binding site does not contributeto the UV or visible spectrum. The type 2 Cu-binding site
shows a characteristic electron spin resonance (ESR) spectrum(7, 16); therefore, an ESR analysis was performed using a
JES-FA200 spectrometer (JEOL, Tokyo, Japan) to test for thepresence of the type 2 Cu-binding site in the recombinant
protein. An axial type 2 Cu signal (g//=2.24, A//=18.31 mT,
and g⊥=2.06) was found in the ESR measurement (Fig. 2D),
indicating that the recombinant protein has a type 2 Cu-bindingsite coordinating with a copper atom. Additionally, we assessed
the copper content of the recombinant protein by inductively
coupled plasma mass spectrometry (ICP-MS). The copper
content was found to be 2.9 atoms per subunit of the recombinant protein, indicating that Cu was fully incorporated into
the recombinant protein. Overall, the recombinant protein shared
the structural and spectroscopic features of class 1 and 2
bacterial NirK, which is consistent with sequencing information.
The kinetics of NO2– reduction were examined by anoxically
incubating the recombinant protein at 25°C and pH 6.5 with
15NO – and artificial electron donors as previously described2(7). All of the buffers and stock solutions were preparedanoxically as previously described (27). Two milliliters of reaction
buffer (20 mM phosphate buffer, 0.1 to 1.6 mM Na15NO2–,
0.5 mM benzyl viologen (BV), and 0.24 mM sodium dithionite)
was dispensed into a 1-cm sealable quartz cuvette and placedin an anaerobic chamber in which the O2 concentration wasmaintained at lower than 1 ppm. BV was used as an artificial
electron donor because it has been employed to examine thekinetics of the NO2– reduction of bacterial NirK (7, 13). The
cuvette was set in a UV-VIS spectrometer UV-2700 (Shimadzu,
Kyoto, Japan), and the initial absorbance of the prepared reaction
mixture at a wavelength of 550 nm was approximately 2.0.
The reaction was initiated by adding the recombinant protein(50 μL containing 250 μg of protein) using a gastight syringe,
and the oxidation rate of reduced BV (molecular extinction
coefficient, 10.4 mM–1 cm–1) (13) was monitored at 550 nm.
7Copper-containing NO2– Reductase of AOA

431

Fig. 2. Characterization of recombinant Nitrososphaera viennensis NirK. A) SDS-PAGE of the recombinant protein purified by His-tag affinity
chromatography. B) Assessment of the molecular mass of the recombinant protein by gel filtration chromatography. Catalase from bovine liver (232 kDa),
lactate dehydrogenase (140 kDa), and bovine serum albumin (66 kDa) were used to prepare a standard calibration curve. C) UV-VIS absorption
spectra. The measurement was performed in a 20 mM Tris buffer (pH 8) containing 300 mM NaCl at 25°C. The solid line indicates the recombinant
protein (1 mL mL–1) oxidized with air. A 5×enlarged spectrum is also shown as a dashed line. D) ESR spectra. The measurement was performed
using the recombinant protein (4.9 mg mL–1) at –253°C.

The recombinant protein reduced NO2– by oxidizing BV,
whereas no significant BV oxidation was found in the cuvette
without the recombinant protein. The turnover number andKm value for NO2– reduction by the recombinant protein were
3.1 s–1 and 287 μM, respectively (Table 1), and the turnover

number and affinity constant were markedly lower and higher,
respectively, than those of other canonical NirK proteins,
including those from AOB. The product of NO2– reduction by
the recombinant protein was examined using phenazine
methosulfate (PMS) as the electron donor instead of BV.
8

Kobayashi et al.

432
Table 1.

Enzymatic properties of archaeal and bacterial copper-containing nitrite reductase (NirK). ND; not determined.

Organisms
Archaeal NirK
Nitrososphaera viennensis
NO2– reduction
NH2OH oxidation
Bacterial NirK (NO2– reduction)
Nitrosomonas europaea
Nitrosococcus oceani
Achromobacter xylosoxidans
Candidatus Jettenia caeni

MW*
(kDa)

Cu content†(atom per subunit)

Absorption(nm)105±1.5

2.9

590

96
114
110
101

ND
1.67
1.99
ND

450, 597
455, 575
595
449, 598

Activity‡
Turnover (s–1)

Km (μM)

Reference

3.1
0.039

287
97

This study
This study

288
1,600
172
319

ND
52
35
250

18
16
14, 32
7* Molecular weight (MW) of a trimeric NirK. The MW of Ca. Jettenia caeni NirK was calculated from amino acid sequences without a signal peptide
sequence. † Copper contents previously assessed by chemical analyses were shown. ‡ The following electron donors were used to evaluate the turnover number of NO2– reduction; methyl viologen for N. europaea and Nc. oceani, pseudoazurine for A. xylosoxidans, and benzyl viologen for Ns.
viennensis and Ca. Jettenia caeni NirK.

When BV was used as the electron donor, NO2– was reduced toNO, and further reduced to ammonia (approx. 60% of consumed
15NO –) as observed in a previous study in which the NO –2
2
reduction activity of A. xylosoxidans NirK was examinedusing methyl viologen (MV) as the electron donor (1). BV
and MV have low redox potentials (–350 and –440 mV,
respectively) (23), resulting in the reduction of NO to NH3;
therefore, PMS with a higher redox potential (+80 mV) wasused in the present study. The recombinant protein was incubatedas described above in a 1.8-mL gas-tight vial with the additionof 0.5 mM PMS and 5 mM ascorbic acid instead of BV anddithionite, and the production of 15N-labeled gaseous compounds(i.e., N2, NO, and N2O) in the headspace was examined by gas
chromatography mass spectrometry (GC/MS) as previously
described (27). The diluted gases of 15-15N2 (Cambridge Isotope
Laboratories, Tewksbury, MA, USA), 14NO, and 14-14N2O(GL Science, Tokyo, Japan) were also analyzed to prepare
standard curves for quantification. The recombinant protein
reduced 15NO2– with the oxidation of PMS, and 38 and 48%
of consumed 15NO2– were converted to 15NO and 15-15N2O,
respectively. This is direct evidence to show that the recombinant protein is a NO-forming nitrite reductase. We foundthat the production of 15-15N2O was equal to the production of
15NO, which likely results from the reduction of 15NO – to2
H15NO (i.e., NO2–+2e–+3H+ → HNO+H2O) and the chemical
formation of 15-15N2O from the formed H15NO (i.e., 2HNO →N2O+H2O) (35), as previously observed for a sulfide-linked
nitrite reductase (34).
Aside from NO2– reduction, NH2OH oxidation was also
investigated using the recombinant protein because NH2OH
is produced as an intermediate during aerobic ammonia
oxidation by AOA. The kinetics of NH2OH oxidation were
examined by aerobically incubating the recombinant protein(245 μg mL–1) at 30°C and pH 7.5 with 0.5 mM NH2OH, with
dissolved oxygen being available as an oxidant. The reaction
was initiated by the addition of NH2OH solution, and theconcentration of NH2OH was assessed colorimetrically (5).
The concentration of H2O2, which may be produced by theoxidase activity of NirK (12), was also evaluated colorimetricallyusing horseradish peroxidase (Wako, Osaka, Japan) and
3,3ʹ,5,5ʹ-tetramethylbenzidine (TMBZ) (Dojindo, Kumamoto,Japan) (2). As shown in Fig. S2, the recombinant proteinoxidized NH2OH with the production of H2O2. No NH2OH
oxidation or H2O2 production was observed when the incubation
was repeated without the addition of the recombinant protein.

The values for the turnover number and affinity constant for
NH2OH oxidation were 0.039 s–1 and 97 μM (Table 1),
respectively, and the value for the turnover number was twoorders of magnitude lower than that observed for NO2–
reduction; therefore, the recombinant protein catalyzed NO2–
reduction more efficiently. The addition of cytochrome cfrom equine heart (1 mg mL–1) or BV (0.5 mM) did not resultin an increase in the reaction rate or affinity for NH2OH oxidation.
The product of NH2OH oxidation by the recombinant protein
was examined in a 15NH2OH tracer experiment (29). The
recombinant protein was incubated in a 1.8-mL gas-tight vialwith the addition of 0.5 mM 15NH2OH (Cambridge Isotope
Laboratories) instead of 14NH2OH. After a 2-h incubation, theconcentrations of the 15N-labeled gaseous products wereassessed by GC/MS. The recombinant protein oxidized
15NH OH and produced 15NO, 15-15N O, and 15-15N gases
2
2
2
quantitatively (Fig. 3), whereas the production of NO2– andNH3 was not detectable (detection limits: 50 and 100 μM,
respectively). The oxidation of NH2OH to NO has beenFig. 3. NH2OH oxidation by recombinant Nitrososphaera viennensis
NirK. The recombinant protein was incubated at 30°C and pH 7.5 in
1.8-mL vials (volume of the headspace: 1.5 mL), with i) 0.5 mM
15NH OH, ii) 15NH OH and 14NO – (each 0.5 mM), or iii) 14NH OH and2222
15NO –. The production of N , NO, and N O in the headspace was222
examined by gas chromatography mass spectrometry (GC/MS). NH3
and NO2– concentrations were also measured; however, they were notdetectable during the incubation. During a 2-h incubation, i) 63±35(mean±SD), ii) 149±1, and iii) 120±1 nmol N of NH2OH were consumed
in the liquid phase, resulting in 75–137% of the 15N-labeled nitrogenmass balance in the vials. Error bars represent the SD derived from
triplicate incubations, and the graph bars represent the mean values.
NH2OH oxidation was not found in the vials without the addition of therecombinant protein.

9

Copper-containing NO2– Reductase of AOA

433

cytochrome c was not. To date, the biochemistry of AOA
cupredoxin has not been investigated using natural enzymes
and recombinant proteins, and our study provides basic
information that furthers our understanding of the biochemistryof AOA.

described in bacterial Hao (21); however, to the best of ourknowledge, this is the first description of NH2OH oxidation
by NirK. We also observed 15-15N2O production from
15NH OH oxidation, which likely resulted from the oxidation2of 15NH2OH to H15NO and abiotic coupling of H15NO, aspreviously described. Notably, 15-15N2 was the major productof 15NH2OH oxidation by the recombinant protein.
Hydroxylamine disproportionation (30) may not be responsiblefor 15-15N2 production because NH3 production was not detectablein the liquid phase. The molecular mechanisms underlying
the oxidation of 15NH2OH to 15-15N2 by the recombinant protein
warrant further studies.
We repeated the above incubation with the addition of
NH2OH and NO2– because both compounds are available in
AOA cells during aerobic ammonia oxidation. Therefore, theabove incubation was repeated with the addition of 15NH2OH
and 14NO2– (each 0.5 mM) or 14NH2OH and 15NO2– (Cambridge
Isotope Laboratories) (each 0.5 mM). In both cases, 14-15N2O
was the major product (Fig. 3), indicating that the recombinant
protein produces N2O by oxidizing NH2OH using NO2– as anelectron acceptor. N2O production by the denitrifier NirKfrom NH2OH and NO2– has been previously described (10),and the N-nitrosation reaction is involved in N2O production(39). Notably, Ns. viennensis cells produce N2O when they
are incubated aerobically with NH3 and NO2– (42), although
the Ns. viennensis genome lacks the gene encoding nitricoxide reductase (nor) that is involved in N2O production from
nitrifier-denitrification. Stieglmeier et al. (42) suggested the
involvement of Ns. viennensis NirK in the production of N2O
in an Ns. viennensis culture, and our results support thishypothesis. Although the catalytic efficiency of Ns. viennensis
NirK for NH2OH oxidation was markedly lower than that ofNO2– reduction (Table 1), Ns. viennensis NirK may act as an
NH2OH oxidase in Ns. viennensis cells and produce N2O
under oxic growth conditions. Aside from 14-15N2O production,
the production of 15NO and 15-15N2O was also observed when
the recombinant protein was incubated with 14NH2OH and
15NO – (Fig. 3).
2
Although the recombinant protein catalyzes NO2– reduction
and NH2OH oxidation, the catalytic efficiency of both reactions
was low, as shown in Table 1. AOA nirK transcripts areabundant in the transcriptome (8, 11, 20, 37), suggesting thestrong expression of AOA NirK in cells. NirK was the 225th
most abundant protein of the 1,503 proteins detected in theproteome of the late exponential phase of Ns. viennensis cells
aerobically oxidizing ammonia (15). The strong expressionof NirK appears to support the activity of NO2– reduction toNO as well as NH2OH oxidation to NO by the low efficiency
catalytic enzyme. Ns. viennensis NirK may function as abifunctional enzyme that supplies NO molecules from 2
different sources (i.e., NH2OH and NO2–), which provides Ns.
viennensis cells with a competitive advantage. In the present
study, the enzymatic kinetics of recombinant Ns. viennensis
NirK for NO2– reduction were examined using artificial electron
donors; further studies are needed to identify physiologicalelectron donors in Ns. viennensis cells. Bacterial NirK may
accept electrons supplied from single-domain cupredoxin andcytochrome c (14, 25). A number of genes encoding singledomain cupredoxin were found in the Ns. viennensis genome(Table S1), whereas the ortholog of the gene encoding

Acknowledgements
This work was supported by JSPS KAKENHI Grant numbers
17K15305, 16H02371, and 16H04442 to M.O., T.Y., and N.A.,
respectively. The authors acknowledge Ko Furukawa, Akihiro Suzuki,
and Hisashi Satoh for their technical assistance with the ESR,UV-VIS, and ICP-MS measurements, respectively.
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31. Prosser, J.I., and G.W. Nicol. 2008. Relative contributions of archaea
and bacteria to aerobic ammonia oxidation in the environment.
Environ. Microbiol. 10:2931–2941.

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Microbes Environ. Vol. 31, No. 2, 173-177, 2016

https://www.jstage.jst.go.jp/browse/jsme2 doi:10.1264/jsme2.ME16003

MOST CITED ORIGINAL PAPER IN THIS
THREE YEARS [2016-2018] (12 citations)Short Communication
Metagenomics Reveals a Novel Virophage Population in a Tibetan Mountain Lake
Seungdae Oh1,2, dOngwan YOO3, and wen-TSO Liu2*
1School of Civil and Environmental Engineering, Nanyang Technological University, Singapore, Singapore; 2Department of Civil

and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA; and 3Department of Pathobiology,
University of Illinois at Urbana-Champaign, Urbana, IL, USA(Received January 8, 2016—Accepted March 14, 2016—Published online May 3, 2016)

Virophages are parasites of giant viruses that infect eukaryotic organisms and may affect the ecology of inland water ecosystems. Despite the potential ecological impact, limited information is available on the distribution, diversity, and hosts of
virophages in ecosystems. Metagenomics revealed that virophages were widely distributed in inland waters with various
environmental characteristics including salinity and nutrient availability. A novel virophage population was overrepresented ina planktonic microbial community of the Tibetan mountain lake, Lake Qinghai. Our study identified coccolithophores andcoccolithovirus-like phycodnaviruses in the same community, which may serve as eukaryotic and viral hosts of the virophage
population, respectively.
Key words: virophage, metagenomics, microbial ecology, Tibetan lake

Lake (ALM) were investigated by analyzing their genomes
recovered from metagenomic datasets (29, 30). These studies
provided insights into the viral and eukaryotic hosts of virophages
and their co-evolution (e.g., horizontal gene transfer among
virophages, giant viruses, and eukaryotic hosts). Previous metagenomic studies documented the occurrence of virophages indiverse environments (fresh water, ocean, and soil) (5, 30)
and the phylogenetic diversity of virophages in marine environments and Antarctic lakes (29). However, it has not yet
been determined whether virophages are widely distributed ininland water environments, and, if so, what types of virophages
are present. Therefore, addressing these issues is important inorder to infer the ecological impact of virophages in inland
water environments, given that characterized virophages are
known to interact with different viral and eukaryotic hosts.
The metagenomic datasets of microbial communities sampledfrom the surface waters of ten lakes (Lanier, Mendota, Spark,
Trout, Damariscotta, Vattern, Ekoln, Erken, Qinghai, andYellowstone) and a river (Amazon) were collected (Table
S1). Two Antarctic lakes (Ace and Organic) and a freshwaterlagoon (Albufera) derived from seawater were also included.
Major capsid protein (MCP) sequences are often used toclassify viruses (3) and virophages (5, 24). Nine MCP sequencesfrom all of the available virophage genomes were aligned
using ClustalW (26). The sequence alignment identified aconserved region (amino acid positions 345–404; based on
Sputnik) in the MCP sequences (Fig. S1). The metagenomic
reads of the 13 datasets were searched against the conserved
region using BLASTx (2) with cut-offs of >30% amino acid
identity and >55 match length. The analysis revealed 134
virophage-like metagenomic reads originating from the
majority (n=8) of the metagenomic datasets, indicating thewidespread occurrence of virophages in inland waters withenvironmental gradients of salinity (freshwater to hypersaline)
and nutrient availability (oligotrophic to eutrophic) (Table S1).
The structure-based phylogenetic diversity of the virophages
was investigated using the 134 virophage-like sequences

Virophages are small (<100 nm), icosahedral, double-stranded
DNA viruses that replicate using the replication machinery of
giant viruses (7, 14, 19). Virophages parasitize giant viruses,
which is reflected in the formation of the abnormal particles
of giant viruses and the increased survival rates of eukaryotic
hosts when co-cultured (9, 14). Virophages have the ability toregulate members of Mimivirus, including Acanthamoeba
polyphaga mimivirus, Moumouvirus, and Megavirus chilensis,
which infect A. polyphaga (10). Acanthamoeba are commonly
found in soil, fresh water, and seawater and are predators ofvarious bacteria. Virophages control the populations of phototrophic algae by attenuating the infectivity of phycodnaviruses,
which, in turn, may affect algal blooms (29). Thus, elucidating
the distribution and host-virus interactions of virophages has
important implications for nutrient cycling and food webs innatural aquatic ecosystems.
Only a few studies are available on virophage isolates andtheir host-virus interactions. Sputnik, the first virophage
identified, was discovered with A. polyphaga mimivirus, which
was isolated from a water sample taken from a cooling towerin France (14). Sputnik and its closest relatives (Sputnik2 andSuptnik3, the genome sequences of which share >99%
nucleotide identity with the Sputnik genome) have a broad
range of viral hosts among Mimiviridae that infect Amoeba(6, 10, 15). Zamilon is closely related to Sputnik (76%genome sequence identity) and has the ability to multiply inmembers of Mimiviridae. Zamilon and the giant virus Mont1
were co-isolated in a soil sample from Tunisia (11). Mavirus
was isolated with the Cafeteria roenbergensis virus isolated
from coastal waters in the USA (9). The Mavirus infection
led to a decrease in the infectivity of the giant virus to theeukaryotic host, a marine phagotrophic flagellate.
Other virophages such as those found in Organic Lake
(OLV), Yellowstone Lake (YSLV1 through YSLV4), and Ace* Corresponding author. E-mail: wtliu@illinois.edu;
Tel: +1–217–333–8442; Fax: +1–217–333–0687.

12

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174

Fig. 1. Occurrence and diversity of virophage populations in diverse terrestrial aquatic environments. The phylogenetic tree was built based on the
maximum likelihood method with the Jones-Taylor-Thornton model using MEGA 6.0 (25). Bootstrap support values (higher than 50) from 100
replicates are shown on the nodes of the tree. The three lineages (Lineages I, II, and III) that were not closely related to the previously characterized
virophages were highlighted.

(Fig. 1). While 92 sequences were closely related to those of
the six previously characterized virophages (OLV, YSLV1
through 4, and ALM), the remaining 42 sequences corresponded
to three distinct lineages (Lineages I, II, and III). Most of the
newly identified virophage-like sequences (Lineages I and II)
were more closely related (38 to 62% amino acid identity) tophycodnavirus-associated virophages (YSLVs and OLV)
than Sputnik, Zamilon, and Mavirus.
Since many (n=26) of the virophage-like sequences ofLineage I identified in Qinghai were identical (100% identity)(Fig. 1), we attempted to reconstruct the genome sequence ofthe virophage population, designated as the Qinghai Lake
virophage (QLV). The Qinghai metagenomic dataset was
trimmed and assembled, as described previously (18). A
search of all assembled contig sequences against the nine
MCP sequences using BLASTx identified one MCPencoding contig (contig_0049). Qinghai metagenomic reads
were recruited to all assembled contigs using BLASTn with
cut-offs of >95% nucleotide identity and >50% query length
coverage, as described previously (16, 17). The read recruitment
analysis identified metagenomic reads that simultaneously
mapped on two distinct contigs. This approach was able toidentify three additional contigs that were consecutively
connected from contig_0049, based on the mapping patternsof the metagenomic reads. The metagenomic reads and fourcontigs were combined and re-assembled together using Newbler
2.8, which generated a single contig. The metagenomic reads
were successively overlapped between the end and beginningof the single contig (Fig. S2), indicating that the single contig
represents a circular genome, as with other virophages reported
previously. Metagenomic read recruitment showed 9 to 100×coverage (56× on average) at 95–100% identities over theentire contig region. More than 93% of the recruited reads

showed 98±2% identity, which was higher than the 90–95%
average identities frequently used for phage species demarcation(1). These results suggest that the reconstructed QLV genome
represents that of a species-like population, which has an
intrapopulation genetic variation of approximately 2% (Fig. S2).
The length and G+C content of the QLV genome were
23,379 bp and 33.2% (Fig. 2), respectively. These genomic
features were similar to those of other virophages reported
previously as genome sizes of 17 to 28 kbp and G+C contentsof 26 to 38% (9, 11, 14, 29, 30). The QLV genome encoded
25 open reading frames (ORFs) (Fig. 2 and Table S2). A gene
content analysis identified genes commonly conserved between
QLV and other virophage genomes: FtsK-HerA family ATPase(QLV1), cysteine protease (QLV6), MCP (QLV18), minor
capsid protein (QLV19), and DNA helicase/primase/polymerase(QLV23) genes. The gene products of the core genes are known
to carry out essential functions such as DNA replication andpackaging in the virophage life cycle (11, 30). Phylogenetic
trees were built using the full-length MCP, FtsK-HerA family
ATPase, and cysteine protease genes (Fig. S3). The phylogenetic
relationships observed based on the three different protein
sequences were congruent with those shown in Fig. 1. These
results confirmed the higher evolutionary relatedness of QLVto OLV-like virophages (OLV and YSLVs) than Sputnik andMavirus, which corroborated the conserved region of the
MCP sequences used in Fig. 1 serving as a robust genetic
marker for a phylogenetic analysis.
QLV shared 7 (41% of the average amino acid identity), 8(39%), 9 (40%), and 11 (46%) gene homologues with YSLV3,
OLV, YSLV1, and YSLV4, respectively. QLV showed <35%average amino acid identity and <6 gene homologues with
Sputnik, Zamilon, Mavirus, and ALM. QLV encoded 11
QLV-specific genes that were not found in other virophages
13Virophage Diversity in Inland Waters

175

Fig. 2. Circular map of the Qinghai Lake virophage (QLV) genome. Inwards: ORFs of the two DNA strands (red, blue, and black representing
core, specific, and others, respectively) and G+C content. Protein-coding genes on the genome were predicted using GeneMark.hmm with the heuristic
model (4). The protein-coding genes were functionally annotated by searching the amino acid sequences against the non-redundant protein database(nr) and metagenomic protein (env_nr) database, respectively, using BLASTp with >30% amino acid identity and >50% query length coverage.

coverage (56×) relative to that of the other contigs assembled(<30×). Thus, it was speculated that QLV were sampled with
their viral and/or eukaryotic hosts, in addition to those attached
to and collected with suspended solids.
The Qinghai metagenomic reads were searched against thesmall subunit ribosomal RNA (SSU rRNA) gene (V9 hypervariable region) database (13) using BLASTn with cut-offs of>70% nucleotide identity and >90% target length coverage.
More than 99% of the total SSU rRNA gene sequences
retrieved were bacterial or archaeal, whereas four chloroplast
16S rRNA and two 18S rRNA gene sequences were identified (Fig. S4). The chloroplast 16S rRNA gene sequenceswere closely related (>98% identity) to coccolithophores(more specifically, Isochrysis spp. and Emiliania huxleyi).
Coccolithophores are unicellular phytoplanktons that play a
key role in nutrient cycling and food webs in water environments (21, 28). While E. huxleyi form extensive blooms fromtropical to subpolar oceans, they are often found in oligotrophic environments. Some mechanisms (efficient cellular nitrogenutilization and ATP synthesis in nitrogen-limiting conditions)confer a fitness advantage to E. huxleyi in oligotrophic environments (22). Phytoplanktons have the ability to use alkaline
phosphatase, which hydrolyzes extracellular inorganic phosphate,
and this facilitates cellular uptake under phosphorous-limited
conditions. The alkaline phosphatase of E. huxleyi exhibits
maximum activity at approximately pH 9 (28). The surface
waters (depth of 0.5 meters) of Lake Qinghai are oligotrophic(<1 mg L–1 of total nitrogen and <0.02 mg L–1 of total phosphorus) and alkaline (pH 9.3) (27). While E. huxleyi arecommonly found in marine environments, they may have also
been favored in Lake Qinghai due to, at least in part, theoligotrophic and alkaline conditions. Furthermore, we performed a metagenomic survey to ascertain the presence ofwhen using cut-offs of 30% amino acid identity and 50%
query length coverage (Fig. 2). While many of the predictedfunctions of the QLV-specific genes were hypothetical,
QLV2 and QLV19 encoded a glycoprotein repeat domaincontaining protein and RecB-family recombinase, respectively (Table S2). RecB is a subunit of the RecBCD enzymethat salvages double strand breaks in DNA through recombinational DNA repair (8); however, its exact role in virophages
has not yet been investigated. Glycoproteins participate in theformation of the extracellular envelope, adhesion processes,
and protein-protein interactions between viruses and theirhosts (14, 29). A search of the QLV2 amino acid sequenceagainst the non-redundant protein database (nr) showed thebest hits (>48% amino acid identity) to phycodnaviruses(Paramecium bursaria and Acanthocystis turfacea Chlorellavirus), which are known to infect unicellular green algae.
Gene homologues conserved between virophages and their
giant viruses were subjected to genetic exchange through the
virophage-giant virus interaction (14, 29). Overall, the evolutionary relatedness of QLV to OLV-like virophages (Fig. 1
and S2) and the gene homology (QLV2) between QLV andphycodnaviruses (Table S2) collectively suggested that QLVpresumably prey on phycodnaviruses.
The occurrence of QLV in the planktonic microbial community of Lake Qinghai may be interpreted by considering
the filter size used for sampling and the size of virophages.
Microbial cells smaller than 5 μm (in length) and larger than
0.22 μm were collected from a surface water sample takenfrom Lake Qinghai in a previous study (18). Since virophages
are less than 0.1 μm in diameter, they may be underrepresented among the microbial cells collected using the above
sample preparation. However, the QLV genome was overrepresented in the metagenomic dataset in terms of genome14

Oh et al.

176

coccolithophore-infecting viruses within the same microbialcommunity. The alignment of MCP sequences from phycodnavirus genomes using ClustalW identified a conserved
region (amino acid positions 433–492; based on Organic
Lake phycodnavirus 1) in the MCP sequences. A search of
the Qinghai metagenomic reads against the conserved MCP
region using BLASTx with cut-offs of >30% identity and>90% target length coverage identified three MCP-encoding
metagenomic reads. The three MCP sequences showed
higher amino acid identities (31–40%) to E. huxleyi viruses
than other phycodnaviruses (Fig. S5). These results suggestthat phycodnavirus populations in the same microbial community were evolutionarily more closely related to E. huxleyi
viruses, the parasites of the E. huxleyi observed in Fig. S4.
A PCR and metagenomic approach recently revealed a
virophage population (DSLV1) in the surface waters of Lake
Dishui in East China (12). Since DSLV1 showed genomic
relatedness to YSLV3, it was associated with algae andalgae-infecting large dsDNA viruses. QLV showed 35–51%
amino acid identities on five gene homologues with DSLV1,
suggesting marked genomic divergence between two virophage
populations. The PCR amplification of DNA samples using
MCP gene-specific primers revealed the occurrence of the
DSLV1 population in Lake Dishui over a one-year period(12). Although PCR amplification is useful for identifying
virophages closely related to the reference virophage strainsused for primer design, it may not be useful for successfully
detecting environmental virophages with marked genomic
divergence (Fig. 1), as revealed in this study.
The development of a marker gene with high resolution
within a specific viral group is essential for examining its
diversity, distribution, and relative abundance in the environment, while viruses share no universal marker genes (e.g.,
16S rRNA genes in prokaryotes). A previous study searched
metagenomic sequences using a cut-off of <10–5 E-value
against “virophage-specific marker genes,” defined as thosethat had no hits to the nr database (30). In contrast, weselected a MCP gene as a genetic marker because it was oneof the five core genes conserved among all characterized
virophages that occurred in a single copy in a virophage
genome and contained a 60-amino-acid-long region with thelongest consecutive segment. We then searched raw metagenomic reads against the MCP alignment using cut-offs of>30% amino acid identity and >55 match length. Although
our marker gene survey used a target region (60 amino acids),
the amino acid length and cut-offs used were similar to thethreshold (29–31% identities in 55–60 amino acids) for
inferring structural homology between two proteins (23). In
contrast to the 10–3–10–6 E-value cut-offs used in previous
studies (5, 30), the 30% amino acid identity criterion was
employed in the present study because it is considered to be
more stringent and E-values change depending on the size ofthe database used (20). The 134 metagenomic reads (Fig. 1)
retrieved in the present study showed best hits to the nine
virophage MCP genes in the nr database, strongly suggestingthat the 134 reads retrieved were of a virophage origin.
It is important to note that the occurrence and diversity of
virophages (Fig. 1) in inland waters revealed in this study are
underestimated because many of the sample preparations(Table S1) for metagenome sequencing were conducted using

filter sizes larger than the typical size of virophages.
Nevertheless, our metagenomic study detected a large number of virophage sequences in metagenomic datasets frommany terrestrial aquatic environments using the robust genetic
marker developed in the present study. Notably, we uncovered
a novel virophage population particularly overrepresented in
the planktonic microbial community of Lake Qinghai (Fig. 1
and 2). Distinctive from previous studies, our bioinformatic
results detected eukaryotic and phycodnaviral populations aswell as virophages in the same microbial community, whichfurther implies that QLV are associated with coccolithophores and coccolithovirus-like phycodnaviruses. Therefore,
we encourage future experiments determining the infectivity
and host specificity of the novel virophage, which will contribute to more accurate assessments of the ecological consequences of the virophage population in the Tibetan mountainlake ecosystem.
Nucleotide sequence accession numbers
The genome sequence of QLV was deposited in GenBank
under the accession number KJ854379.1
Acknowledgements
This work was supported by a research grant from the National
University of Singapore and a start-up fund from the University of
Illinois at Urbana-Champaign to Wen-Tso Liu. Seungdae Oh waspartially supported by a start-up grant from Nanyang Technological
University.
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Microbes Environ. Vol. 31, No. 3, 204-212, 2016

https://www.jstage.jst.go.jp/browse/jsme2 doi:10.1264/jsme2.ME16024

MOST CITED REVIEW PAPERS IN THIS
THREE YEARS [2016-2018] (24 citations)

Minireview
Metagenomics and Bioinformatics in Microbial Ecology: Current Status and Beyond
SatoShi hiraoka1*, Ching-Chia Yang2, and Wataru iWaSaki1,2,3*
1Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, the University of Tokyo,

Chiba 277–8568, Japan; 2Department of Biological Sciences, Graduate School of Science, the University of Tokyo, Tokyo
113–0032, Japan; and 3Atmosphere and Ocean Research Institute, the University of Tokyo, Chiba 277–8564, Japan(Received February 12, 2016—Accepted May 4, 2016—Published online July 5, 2016)

Metagenomic approaches are now commonly used in microbial ecology to study microbial communities in more detail,including many strains that cannot be cultivated in the laboratory. Bioinformatic analyses make it possible to mine hugemetagenomic datasets and discover general patterns that govern microbial ecosystems. However, the findings of typical metagenomic and bioinformatic analyses still do not completely describe the ecology and evolution of microbes in their environments. Most analyses still depend on straightforward sequence similarity searches against reference databases. We herein
review the current state of metagenomics and bioinformatics in microbial ecology and discuss future directions for the field.
New techniques will allow us to go beyond routine analyses and broaden our knowledge of microbial ecosystems. We need toenrich reference databases, promote platforms that enable meta- or comprehensive analyses of diverse metagenomic datasets,devise methods that utilize long-read sequence information, and develop more powerful bioinformatic methods to analyze datafrom diverse perspectives.
Key words: metagenomics, bioinformatics, database, meta-analysis, long-read sequencer

High-throughput sequencing technologies have recently
popularized shotgun metagenomic and (typically 16S ribosomal
RNA [rRNA] gene) amplicon sequencing methods, which
identify members and/or functional genes at a greater scale
and in more detail. Their use in diverse environments hasrevealed the presence of extremophiles (27, 33, 92), uncovered relationships between microbes and human diseases (10,44, 55, 57, 85, 87, 96, 134, 147), and characterized the nutrition systems involved in symbiosis (68, 174, 177). Even more
applications of these methods are used in agriculture (93),food science and pharmaceuticals (32), and forensics (49, 79,
82, 182). Many large-scale metagenomic projects are now
generating comprehensive microbial sequence collections fordifferent environments (e.g., human-associated [116, 167],
soil [54, 171], and ocean environments [17, 142]). Since
microbial communities change as they interact with otherorganisms and as the environment changes, time-series analyses have also become common (21, 24, 77, 115, 172).
Several bioinformatic tools have been developed and popularized to analyze metagenomic and amplicon sequence
data. Web servers, such as MG-RAST (104), IMG/M (97),
EBI Metagenomics (69), and SILVAngs (135), and pipelines,
such as MEGAN (70), QIIME (25), and Mothur (145), now
allow researchers to perform integrated metagenomic analyses and visualize results without command-line operations orstrong computational knowledge. Since there are alreadyseveral introductory articles on these popular tools (36, 88,
103, 123, 146), we herein addressed how can we examine large
datasets in detail in order to obtain a deeper understanding of
the ecology and evolution of microbes in the environment
beyond existing approaches that are already popular (Fig. 1).

Data deluge in microbial ecology
Although microbes play fundamental roles in various
ecosystems, most have not yet been characterized in detail.
Bioinformatics, which aims to discover new biological concepts and laws based on large-scale data, is now expected toaccelerate discovery in unexamined areas of the microbial
universe. The data deluge has made bioinformatics indispensable in modern research; recent innovative technologies
are producing large amounts of data at an unprecedented
pace. Observations are key to science; for example, opticaland electron microscopies are important methods of observation combined with various staining methods. Among recent
observational technologies, high-throughput DNA sequencing
technologies have rapidly produced vast amounts of genetic
information at low cost, making available thousands of microbial genomes. These genome sequences provide a comprehensive catalog of the microbial genetic elements underlying
diverse microbial physiology, and also assist in weaving a
massive tapestry of microbial evolutionary histories (72, 154).
In microbial ecology, research has been hindered because
the majority of environmental microbes are uncultivable. A
large number of studies across diverse natural environments
have identified many microbial groups with no axenic culture(110, 113, 133, 138). In order to overcome this fundamental
difficulty, culture-independent approaches, including DNA
hybridization (e.g., microarray and fluorescent in situ
hybridization), DNA cloning, and PCR have been used todetect specific members and/or functional genes in microbialcommunities (3, 8, 9, 32, 34, 47, 56, 61, 74, 157, 174, 185).
* Corresponding authors. E-mails: hiraoka@cb.k.u-tokyo.ac.jp (S.H.)
and iwasaki@bs.s.u-tokyo.ac.jp (W.I.);
Tel: +81–3–5841–3047; Fax: +81–3–5841–4691.

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tions, discriminating between closely related species issometimes difficult, particularly in cases of highly conserved
genes (e.g., 16S rRNA genes). Additionally, genes that
undergo horizontal gene transfer (HGT) between different
taxa may cause incorrect taxonomic assignments. A more
fundamental issue is taxonomic bias in reference databases,
which leads to biased taxonomic assignments. A previous
study reported that taxonomic assignments markedly change
when different versions of reference databases are used (128).
Therefore, even in this era of data deluge, the further taxonomic enrichment of reference databases is key to theimprovement of reference-based methods. It is important tonote that this issue is more crucial in the analysis of fungal
and viral sequences because fewer reference sequences areavailable and their taxonomy is under debate. In order toovercome this obstacle, several projects are now attempting
to obtain a number of genomic sequences to enrich databases(58, 180). In cases in which amplicon sequencing data are
analyzed, the filtering of chimeric sequences formed during
PCR is very important for precise analyses (63). Several bioinformatic tools, such as AmpliconNoise (136), ChimeraSlayer(63), and UCHIME (41), have been proposed and commonlyused to remove chimeric sequences.
Reference-free methods may be used (e.g., CD-HIT [50],UCLUST [40], and UPARSE [42]) as an alternative to referencebased methods. These methods use clustering to group
marker genes, such as 16S rRNA, ribulose-1,5-bisphosphatecarboxylase/oxygenase (RuBisCO), ammonia monooxygenase (amoA), sulfate thioesterase/thiohydrolase (soxB), andmethyl-coenzyme M reductase genes (mcrA), into uniquerepresentative sequences that serve as operational taxonomic
units (OTUs) (27). 16S rRNA genes are used to study thegeneral composition of a microbial community, while
RuBisCO, amoA, soxB, and mcrA genes are typically used toinvestigate microbes that play critical roles in carbon, nitrogen, sulfur, and methane cycles, respectively. In addition to
traditional genes, useful marker genes may be found and usedby comprehensively profiling metagenomic datasets (159). In
reference-free methods, OTUs often cannot be assigned toknown taxa. In order to estimate phylogenetic information forthese OTUs, PhylOTU (150), pplacer (98), and PhyloSift (35)
couple reference-free methods with phylogenetic analyses.

Fig. 1. Schematic figure of metagenomic and bioinformatic analyses
in microbial ecology. The illustration covers topics that are alreadypopular, that need further development, and that will become importantin the future. At the bottom of the illustration, reference databases lay
foundations for various bioinformatic analyses.

Toward better taxonomic assignments
A fundamental step in microbial ecology is to describe thetaxonomic distribution of microbial community members.
Thus, the precise taxonomic assignment of sequencing reads
is one of the most important issues in the analysis of
metagenomic and amplicon sequencing data. Referencebased methods are frequently used for this purpose, in whichtaxonomic assignments are based on straightforward sequencesimilarity searches against reference genomes (e.g., RefSeq[163]) or 16S rRNA sequence databases (e.g., Greengenes[101], SILVA [135], RDP [31], and Ez-Taxon [29]). These
databases typically contain the sequences of previously isolated and taxonomically classified strains, whereas they also
contain environmental clone sequences. Many bioinformatic
tools, such as TANGO (6), MetaPhlAn (148), and Kraken(176), have been developed to improve the computational
efficiency, accuracy, and sensitivity of taxonomic assignments. Although these tools perform well for many applica-

Toward the cultivation-free reconstruction of genomic
sequences
Most metagenomic studies currently focus at the level ofindividual genes (“gene-centric” metagenomics [132]). In
contrast, in some pioneering research on “genome-centric”
metagenomics, microbial genomes that include those ofimportant uncultivated taxonomic groups were successfullyreconstructed by metagenomic binning and assembly fromvarious environments including oceans, groundwater, soil,
hypersaline lakes, and acid mine drainage (4, 18, 45, 95, 112,
168). Although amplification bias still poses a non-negligible
difficulty, single-cell genomic sequencing is expected toaccelerate direct genome reconstruction from environmental
samples (43, 90, 140), in which the combination of single cell
genomic and metagenomic approaches may be a promising
approach (102).
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Metagenomic assembly is an important step for revealing
the ecology and physiology of environmental microbes, inwhich the fundamental concepts of metagenomic assemblyfrom short-read sequences have already been described indetail (36, 88, 103, 123, 146). Several tools have beendeveloped for metagenomic assembly, and are classified into
reference-based (e.g., AMOS [130]) and de novo methods(e.g., MetaVelvet-SL [2], SPAdes [119], and IDBA-UD[127]). In the case of de novo assembly, users need to consider chimeric contigs because similar genetic regions may beshared by different genomes (100, 129, 170). In order toimprove the performance of de novo metagenomic assembly,
composition-based methods use specific sequence features ina metagenomic dataset to split reads into different species.
For example, CONCOCT (5), metaBAT (81), and MaxBin(178) bin sequences based on their tetra-nucleotide frequency
composition and coverages. These composition-based approaches
are computationally intensive, particularly in their memory
usage. Thus, a fast-clustering approach using matrix decomposition with streaming singular value decomposition may be
combined (30). On the other hand, sequence coverage information across different DNA extraction methods may also be
used to effectively split sequences into species because thenumbers of sequence reads from the same genome need to be
similar regardless of the extraction method (4). A related
approach bins co-abundant sequences across a series of metagenomic samples from similar environments (e.g., human gut
microbiome) to identify co-abundance gene groups (117).
Another information source that may improve the performanceof metagenomic assembly is long-range contiguity. The recent
development of methods to investigate long-range chromatininteractions (e.g., Carbon-Copy Chromosome Conformation
Capture [5C] [38] and Hi-C [11]) may also contribute tometagenomic assembly because these methods ligate
sequences from two different genomic regions that are in thesame cell (20). The Irys system (BioNano Genomics, San Diego,
USA), which also detects long-range contiguity with fluorescently labeled DNA, may be used to obtain long contigs (64).

tional gene abundance may be estimated using 16S rRNA
amplicon sequencing data (89, 121). This approach takes
advantage of closely related genomes being more likely tohave a similar gene content, and, thus, given the 16S rRNA
sequence, the gene content of its host genome may be estimated (at least, to some extent) if a closely related genome isalready sequenced. It is important to note that such estimations may become difficult when applied to microbial groups
with genomes that are rarely available and also that genomic
variations within closely related microbial groups cannot be
precisely considered. Despite these difficulties, this approachis very cost-effective and more easily applicable to largescale comparative analyses.
Toward a community-level analysis of genomic structuralvariations and dynamics
Operon structures, which are unique to prokaryotic
genomes, reflect the function of their encoded genes and need
to be associated with microbial ecological strategies. Thus, if
we observe systematic variations in the gene order (or genecluster structures) due to gene losses, fusions, duplications,inversions, translocations, and HGTs from an analysis of
metagenome data, these variations may provide important
clues for linking microbial communities to the environment(Fig. 2A). Although difficulties are sometimes associatedwith distinguishing variations under selection pressure fromthose because of population changes, MaryGold (118) is atool for the visual inspection of such variations. Variations in
the gene order for genes in the tryptophan pathway wereidentified within contigs assembled from the Sargasso Sea
metagenome (78). Since the availability of long sequencesthat encompass multiple genes greatly facilitates a gene-orderanalysis, DNA cloning may also be used if the targeted pathways are efficiently enriched by colony selection (51, 158).
On a larger scale, gene order may be affected by genome
replication mechanisms. Most prokaryotic genomes are
circular with one replication origin; thus, genes close to theorigin may physically exist in multiple copies, particularly
during an active growth phase. Thus, the detection of theseregions from metagenomic sequences may reveal the growth
dynamics of microbes in a community (86).
Among various sources of genomic variations and dynamics, HGT is of particular interest because it may help
microbes adapt quickly to different environments (120, 139,
156). Although many comparative genomic studies haveanalyzed HGT (26, 73, 153), its role in microbial communities has not yet been elucidated in detail. A classical approachto detect HGT within communities is based on DNA cloning,which is particularly effective if colony selection is applied tothe targeted genes (162, 175). In metagenomics, assembled
contigs may be used to comprehensively identify HGT eventsby analyzing phylogenetic incongruence and gene order differences (62, 125, 161). In addition, gene gains via plasmids
are also important driving forces that accelerate microbial
adaptation to their environment. In accordance with thishypothesis, plasmids are frequently reported to contain genesthat may contribute to fitness, such as detoxification genes(151, 152, 165) and antibiotic resistance genes (13). Notably,plasmid-specific metagenomics, or a plasmidome analysis, isToward a more reliable estimation of community metabolism
Microbial genomes are affected by the environment during
their evolution. Metabolic processes encoded in the genome,from biosynthesis to biodegradation, directly link microbialcommunities to the environment. Since most microbes areuncultivable, the direct estimation of community-scale
metabolic pathways is also targeted by a metagenomic analysis. The most straightforward approach is to conduct sequencesimilarity searches against pathway databases, such as KEGG
(80), MetaCyc (22), and SEED (124), and use the findings
obtained to annotate metabolic genes. Since many pathways
with component genes that are only partially found in given
metagenome data are typically detected with this naïve
approach, MAPLE (160), MinPath (183), MetaNetSam (75),and HUMAnN (1) quantitatively or probabilistically evaluate
whether these pathways likely function, enabling comparisons between samples. Significant biases in the databases ofknown pathways need to be taken into consideration wheninterpreting the findings of these methods. If shotgun metagenome data are unavailable, “virtual metagenomes” or func19

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Fig. 2. Schematic figures of genomic variations in environmental microbes. Each box represents a protein-coding gene, in which the letters indicate
homology. Boxes and thick lines of different brightnesses represent genes and genomic fragments, respectively, that originated from different
genomic areas or genomes. Dashed lines represent lost genes or expression. A: Types of genomic structural variations. B: Variations in regulatory
sequences. Mutations (black dots) and the horizontal transfer of intergenic regulatory sequences (thick lines in black) both affect the strength of gene
expression.

now being conducted to directly investigate environmental
plasmids without culturing or cloning (37, 173). For example,
the bovine rumen plasmidome was previously reported tocontain genes that may confer advantages to their hosts (19).
Rat gut (76) and wastewater (149) plasmidomes have also
been investigated.
In addition to genes, regulatory sequences in intergenic
regions are related to the function of nearby genes. Thus,
variations in the comprehensive set of regulatory sequences,or the regulome, for example, by promoter propagation, may
play important roles in microbial adaptation (99, 122). We
envision direct investigations of regulomes in microbialcommunities, or a “metaregulome” analysis, becoming an
attractive research field as technical advances occur in longread metagenomics. In a metaregulome analysis, variations
and/or possible transfers of regulatory sequences, in addition
to those of coding genes, may be identified from metagenomic datasets (Fig. 2B) (48, 122).

novel bacteriophage present in the majority of publishedhuman fecal metagenomes was recently reconstructed (39),and phage-bacteria ecological networks were suggested toprotect gut microflora from antibiotic stress (108). Since
viruses are classified into different types of DNA and RNA
viruses, different approaches must be combined for comprehensive analyses (169). The use of targeted sequence capture
techniques to efficiently increase the proportion of viral reads
in metagenomic samples may also be considered (179). The
largest limitation in bioinformatic analyses of viromes isinsufficient reference genome data. Similarity searches using
viral sequences often result in no significant hits, suggestingthat there are many unknown viruses. In order to overcome
this limitation, several bioinformatic tools have been developed and used for virome studies, such as ViromeScan (137)for taxonomy assignment and Metavir 2 (141) for viralgenome reconstruction. Another difficulty is that in contrast
to prokaryotes that have universal marker genes for a phylogenetic analysis (i.e., 16S rRNA), there is no such gene forviral studies. An analysis of clustered regularly interspaced
short palindromic repeats (CRISPRs) is a related emerging
field because these repeats represent previous exposures to(or attacks from) viruses (15, 109, 131). CRISPRs are foundin approximately 40% of bacteria and approximately 90% ofarchaea (59), and, thus, a metagenomic analysis of CRISPRs
will contribute to advancing the field toward a comprehensive
analysis of viral-microbial interactions.

Toward a comprehensive analysis of inter-species interactions
Inter-species interactions, including mutualism and parasitism, are of general interest in microbial ecology (16).
Using abundant information from large-scale metagenomic
datasets, co-occurrences (or anti-occurrences) among microbes,
hosts, and/or viruses have been studied, and, for example,
species interaction networks have been identified (12, 23, 28,
46, 53, 94, 114, 155, 164, 184). Recent large-scale projects
include the Tara Oceans project, which revealed interactions
among all three domains and viruses (17). Since environmental samples were revealed to contain environmental DNA
shed from large organisms in addition to microbial DNA(107), a combinatorial analysis of microbial and environmental DNA is expected to accelerate the analysis of interactions
between microbes and larger organisms.
The viral metagenome is called the metavirome or simply
the virome. Viruses also play fundamental roles in ecosystems;
therefore, a virome analysis is becoming an important field.
To date, viral communities in hypersaline (143, 144) andhuman gut (105) environments have been extensively studied,and antibiotic viruses have also been of interest (108). A

Toward a meta-analysis of metagenomes
Abundant metagenomic datasets containing dozens ofterabytes of sequence data are currently found in the Short
Read Archive database at NCBI, and its content is increasing
daily (84). Whereas each metagenomic dataset provides asnapshot of the microbial community at the time of sampling,a comprehensive analysis (or meta-analysis) of many datasets
is expected to reveal general patterns or laws that determine
how microbes interact with their environments and how theirgenomes have been shaped. It is important to note that different
datasets have been constructed with different experimental
methods and conditions.
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Regarding global correlations between environments andmicrobial genomes, correlations involving genomic GC
contents (66) and genome sizes (14) have been reported.
MetaMetaDB (181) was developed for a meta-analysis ofdifferent environments inhabited by a microbe and the factorsthat contribute to adaptation. This database may be used to
predict all possible habitats of microbes by searching for thepresence of microbes in metagenomic and 16S rRNA ampliconsequencing datasets derived from diverse environments.
Given a metagenomic or 16S rRNA amplicon sequencing
dataset, researchers may find environments with microbialcommunity structures that are similar to that dataset using
MetaMetaDB (181). A meta-analysis of metagenomic datasets was also performed to examine microbial adaptation toenvironments in terms of metabolic flexibility (52, 60) and toinvestigate specific functional genes that facilitate adaptation
to extreme habitats, such as heavy metal resistance genes (65,106) and salt-stress responsive genes (166). In a meta-analysis,
associations were found between membrane protein variations and oceanographic variables in a global ocean sampling
expedition (126). Microbial interactions between humans andthe indoor environment have also been investigated (91).

Concluding remarks

Toward metagenomics with long-read sequencers

We thank Tsukasa Fukunaga and Masamichi Takeshita for theirhelpful suggestions. The ideas and information described in thisreview were partly developed during research supported by theJapan Science and Technology Agency (CREST), the Japan Society
for the Promotion of Science (Grant Numbers 15J08604 and
221S0002), and the Canon Foundation.

Metagenomic and bioinformatic approaches are alreadycommon in microbial ecology and have been used to investigate whole communities containing many types of uncultivable microbes (Fig. 1). However, to date, most analyses havedepended on straightforward sequence similarity searches
against reference databases. This may not be satisfactory
because microbial genomes need to be the fundamental basis
for microbial ecology and evolution. The enrichment of reference sequences (for microbial taxa and functional genes) is
one of the fundamental issues for promoting various kinds ofanalyses. Platforms that enable a meta-analysis of diverse
metagenomic datasets will allow us to discover the hidden
laws of the microbial ecosystem from publicly available data.
Long-read sequence information will open up the possibilityof studies that focus on subjects that have not yet been examined in detail by using short-read sequences. Furthermore,
more powerful bioinformatic methods for analyzing datafrom diverse perspectives are required in order to advance
past routine metagenomic analyses.
Acknowledgements

Sequencers with the ability to produce long-read data are
currently being developed, such as the PacBio RS II (Pacific
Biosciences, Menlo Park, USA) and nanopore-based sequencers(Oxford Nanopore Technologies, Oxford, UK). Long reads
are already contributing to many types of bioinformatic
analyses, including the high-quality de novo assembly ofbacterial and viral genomes (7, 67) and the detection of
genomic structural variations, such as large-scale insertions/deletions or HGTs in microbial communities (71). Long
reads are expected to be helpful for reconstructing genomes
from metagenome data, directly observing genomic structuralvariations, and analyzing metaregulomes in various microbialcommunities. High-density microbial habitats, such as biofilms
and gut communities, may be interesting targets because theirgenomic structures may be changed by the frequent exchange
of genetic materials.
Long-read metagenomics will be an emerging field, butthere are still limitations to be considered. Although PacBio
RS II with P6-C4 chemistry may generate reads with anaverage length of approximately 15 kb, less than 50,000 reads
are generated per SMART cell (i.e., less than 1 Gb in each
SMART cell). This throughput is markedly smaller than thatof the so-called massively parallel sequencers (e.g.,
approximately 15 Gb in each run of MiSeq [Illumina]) andmay be insufficient for describing taxonomically diverse
microbial communities. In addition, the low accuracy of
PacBio RS II reads (approximately 85%) may hinder a bioinformatic analysis, unless highly redundant sequencing (e.g.,more than 50X coverage) is performed to reach high accuracy
in the ensemble. Along with the development of new bioinformatic methods, protocols also need to be optimized to
avoid DNA fragmentation during extraction (83, 111).

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Microbes Environ. Vol. 28, No. 2, 244–250, 2013https://www.jstage.jst.go.jp/browse/jsme2 doi:10.1264/jsme2.ME12189

MOST CITED ORIGINAL PAPER IN THISTEN YEARS [2009-2018] (91 citations)

Candidatus Methanogranum caenicola: a Novel Methanogen from theAnaerobic Digested Sludge, and Proposal of Methanomassiliicoccaceae
fam. nov. and Methanomassiliicoccales ord. nov., for a Methanogenic
Lineage of the Class Thermoplasmata
TAKAO IINO1*, HIDEYUKI TAMAKI2, SATOSHI TAMAZAWA2,3, YOSHIYUKI UENO4, MORIYA OHKUMA1,
KEN-ICHIRO SUZUKI5, YASUO IGARASHI6, and SHIN HARUTA7
1Japan Collection of Microorganisms, RIKEN BioResource Center, 3–1–1 Koyadai, Tsukuba, Ibaraki 305–0074, Japan;
2Bioproduction Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Central

6, 1–1–1 Higashi, Tsukuba, Ibaraki 305–8566, Japan; 3Graduate School of Life and Environmental Sciences, Universityof Tsukuba, 1–1–1 Ten-noudai, Tsukuba, Ibaraki 305–8572, Japan; 4Kajima Technical Research Institute, 2–19–1
Tobitakyu, Chofu, Tokyo 182–0036, Japan; 5NITE Biological Resource Center (NBRC), National Institute of Technology
and Evaluation (NITE), 2–5–8 Kazusakamatari, Kisarazu, Chiba 292–0818, Japan; 6Department of Biotechnology,
Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1–1–1 Yayoi, Bunkyo-ku, Tokyo 113–8657,
Japan; and 7Graduate School of Science and Engineering, Tokyo Metropolitan University, 1–1 Minami-Osawa,
Hachioji-shi, Tokyo 192–0397, Japan(Received October 11, 2012—Accepted January 21, 2013—Published online March 23, 2013)

The class Thermoplasmata harbors huge uncultured archaeal lineages at the order level, so-called Groups E2 andE3. A novel archaeon Kjm51a affiliated with Group E2 was enriched from anaerobic sludge in the present study.
Clone library analysis of the archaeal 16S rRNA and mcrA genes confirmed a unique archaeal population in theenrichment culture. The 16S rRNA gene-based phylogeny revealed that the enriched archaeon Kjm51a formed a distinct
cluster within Group E2 in the class Thermoplasmata together with Methanomassiliicoccus luminyensis B10T andenvironmental clone sequences derived from anaerobic digesters, bovine rumen, and landfill leachate. Archaeon Kjm51ashowed 87.7% 16S rRNA gene sequence identity to the closest cultured species, M. luminyensis B10T, indicating thatarchaeon Kjm51a might be phylogenetically novel at least at the genus level. In fluorescence in situ hybridization
analysis, archaeon Kjm51a was observed as coccoid cells completely corresponding to the archaeal cells detected,
although bacterial rod cells still coexisted. The growth of archaeon Kjm51a was dependent on the presence of methanol
and yeast extract, and hydrogen and methane were produced in the enrichment culture. The addition of 2-bromo
ethanesulfonate to the enrichment culture completely inhibited methane production and increased hydrogenconcentration, which suggested that archaeon Kjm51a is a methanol-reducing hydrogenotrophic methanogen. Taken
together, we propose the provisional taxonomic assignment, named Candidatus Methanogranum caenicola, for theenriched archaeon Kjm51a belonging to Group E2. We also propose to place the methanogenic lineage of the class
Thermoplasmata in a novel order, Methanomassiliicoccales ord. nov.
Key words: Methanogranum caenicola, methanogen, Thermoplasmata, rice cluster III, anaerobic digested sludge

Until recently, the class Thermoplasmata had consistedof mainly acidophilic, aerobic, mesophilic to thermophilic,and sulfur-reducing archaea such as genera Acidiplasma(15), Ferroplasma (14), Picrophilus (47), Thermoplasma(4), Thermogymnomonas (28), and Candidatus Aciduliprofundum boonei (50). Archaeal members of those genera
mainly inhabit extreme environments such as acidic andsolfataric fields. On the other hand, culture-independent
approaches have retrieved a diverse array of environmentalclones belonging to the class Thermoplasmata from ordinary
environments, and many of these clones form huge uncultured
archaeal lineages at the order level, so-called Groups E2 andE3 (6, 33, 39). Groups E2 and E3 consist of sublineages
such as Marine group II, deep-sea hydrothermal ventEuryarchaeotic group 1 and 2 (DHVE1 and DHVE2), andrice cluster III (RC-III), which is derived from the alimentary
canal (12, 19, 54), anaerobic digester (13), contaminatedaquifer (8), deep-sea hydrothermal vent (40, 55), marine
plankton (5, 7), and rice field soil (3, 17, 30). More recently,
a uniformly shaped pure culture B10T, given the nameMethanomassiliicoccus luminyensis, was isolated fromhuman feces, and revealed to be a methanol-reducing,mesophilic, slightly alkaliphilic methanogen belonging to theclass Thermoplasmata (9). These findings suggest that theclass Thermoplasmata is a phenotypically versatile taxon;
however, very little is known about the phylogenetic diversity
and ecological distribution of methanogens in the class
Thermoplasmata.
In our previous study, members of RC-III within GroupE2 in the Thermoplasmata as well as Methanoculleus,
Methanosarcina, and Methanothermobacter species havebeen detected from methanogenic bioreactors (1, 21, 43, 44,46). To obtain cultures of those methanogens, we conducted* Corresponding author. E-mail: iino@jcm.riken.jp;
Tel: +81–48–467–9564; Fax: +81–48–462–4618.

26

Novel Methanogen in the Class Thermoplasmata

245with a pT7Blue T-vector kit (Novagen, Madison, WI, USA). The
clonal DNAs were amplified from randomly selected recombinantsby direct PCR with M13 primers, and then used as templates forsequencing. A universal primer 907r (56) and T7 promoter primers
were used for sequencing the cloned 16S rRNA and mcrA genes,
respectively. The obtained sequences of all the 16S rRNA geneclones (~690 bp) and the mcrA gene clones (~475 bp) were comparedwith those in the GenBank database using the BLAST program(NCBI-BLAST, www.ncbi.nlm.nih.gov/BLAST), and aligned using
the CLUSTAL_X program. Sequence identity of 99% was used as
the cut-off value for grouping the sequences into different
operational taxonomic units (OTUs).

enrichment cultures from methanogenic digester sludge andeventually succeeded in enriching a novel methanogenbelonging to Group E2 in class Thermoplasmata. Thus, thispaper deals with the phylogenetic characterization of theenriched methanogen in Thermoplasmata and the provisional
characterization of the phenotypes.
Materials and Methods
Sampling
The anaerobic sludge was collected from a methanogenic packedbed reactor at Kajima Technical Research Institute on 16th December
2004. The reactor, which was packed with carbon fiber textile assupporting media (43–45), had been properly operated at 55°C andwas stably producing methane gas from garbage slurry as feedstock.
The garbage slurry was prepared from kitchen waste from thecompany cafeteria. It was diluted with an equal amount of water
after removing non-biodegradable materials and then pulverizedusing a homogenizer. The physicochemical properties of the slurry
were as follows: pH 5.2; chemical oxygen demand (COD), approx.
203 g L−1; and volatile suspended solids (VSS), approx. 104 g L−1.

Phylogenetic analyses
Almost full-length 16S rRNA gene and partial mcrA genesequences were determined for phylogenetic analysis. The following
primers were used for sequencing the PCR product of the archaeal
16S rRNA gene: A10F, Kjm700F (5'-TGGGGTAGGGGTAAAATCCT-3'), Kjm1000F (5'-ACTCACCAGGGGAGACTGTT-3'),
A500R (5'-GTGTTACCGCGGCKGCTGG-3'), Kjm700R (5'-GTG
GTCCTTCTAGGATTACA-3'), and A1400R, that of the bacterial
16S rRNA gene: U520F (5'-GTGCCAGCAGCCGCGG-3') andU1492R, and that of mcrA gene: MR1mod and ME2mod. Sequenceswere compared using the BLAST program with those available in
the DDBJ/EMBL/GenBank databases. Phylogenetic analyses werecarried out using the 16S rRNA gene sequence and deduced aminoacid sequence of the mcrA gene. The 16S rRNA gene sequenceswere aligned with an ARB data set using ARB software (32).
According to the previously described method (27), thirty-fivereference sequences of the phylogenetically related archaea andenvironmental clones were selected as authentic sequences locatedin the class Thermoplasmata. The data set of deduced McrA aminoacid sequences was aligned using the CLUSTAL_X program.
Phylogenetic trees were constructed by the neighbor-joining (NJ)
method with the CLUSTAL_X program (42, 57) and the maximumlikelihood (ML) method with MORPHY software version 2.3b3(10, 20). In addition, the posterior probabilities of branching points
were estimated by Bayesian inference using MrBayes 3.1 (23, 41).

Enrichment from the sludge
The basal medium was used with or without 0.01% (w/v) yeast
extract (Becton Dickinson, Franklin Lakes, NJ, USA), designated
YB and B media, respectively, in this study. Basal medium wascomposed of (L−1): 0.54 g NH4Cl, 0.14 g KH2PO4, 0.20 g
MgCl2·6H2O, 0.15 g CaCl2·2H2O, 2.5 g NaHCO3, and 1.0 mL
trace element solution (58) containing 4.0 mg Na2WO4·H2O andeliminating NaCl. Prior to inoculation, the pH of the medium wasadjusted to 7.0 with 6 N HCl, dissolved oxygen was removed byflushing with N2:CO2 (4:1, v/v), and 10 mL vitamin solution (L−1)(60) and 10 mL sterile stock solution of Na2S/cysteine-HCl solution(each 50.0 g L−1) (26) were added. H2:CO2 (4:1, v/v; approx. 150kPa), formate, acetate, or methanol (all at 10 mM) was added tothe basal medium as the sole substrate. For enrichment, 0.5 mL
anaerobic sludge was inoculated into 20 mL of each medium andincubated at 30°C for a week. A stable enrichment culture wasobtained after three cultivations in MYB medium, YB medium
supplied with methanol. The enrichment culture was maintained in
MYB medium by consecutive transfer monthly.

Fluorescence in situ hybridization
The enriched archaeon Kjm51a grew on the aforementioned
MYB medium for 8 d. The harvested cells were fixed in 4%
paraformaldehyde at 4°C for 2 h and stored in 99% ethanol–
phosphate-buffered saline (1:1). The fixed cells were incubated in
hybridization buffer (0.9 M NaCl, 0.01% sodium dodecyl sulfate,
20 mM Tris-HCl, pH 7.2, and an appropriate amount of formamide)containing fluorescently labeled probes (0.5 pmol µL−1). Afterincubation at 46°C for 10 h, the buffer was replaced with washingsolution (378 mM NaCl, 0.01% sodium dodecyl sulfate, 20 mM
Tris-HCl, pH 7.2, and 5 mM EDTA). The sample was incubated at
46°C for 20 min. and then stained with 1 µg mL−1 of 4',6-diamidino2-phenylindole (DAPI). The sample obtained was observed undera confocal laser scanning microscope (LSM710; Carl Zeiss
Microscopy, Tokyo, Japan). A specific oligonucleotide probetargeting the 16S rRNA gene of the enriched archaeon Kjm51a(RC281r2, 5'-AAGGCCCATACCCGTCATC-3') was designed
using the Probe Design tool of the ARB software package (32). The
overall Gibbs free energy of this probe and target sequence calculatedwith the mathFISH web server was −9.2 kcal mol−1 (62). The probes
were labeled with fluorescent dye, Alexa Fluor 555 (Japan Bio
Services, Saitama, Japan). Two domain-specific probes were also
used: EUB338 labeled with Alexa Fluor 647 for detection of almost
all bacteria (2), and ARC915 labeled with Alexa Fluor 488 fordetection of almost all archaea (53). The stringency of hybridization
was adjusted by adding formamide to the hybridization buffer (15%[v/v] for all the probes used in this study). More than 8,000 DAPIstained cells were counted to determine the ratio of ARC915hybridized cells to EUB338-hybridized cells.

Preparation of DNA, PCR amplification, and DNA sequencing
The genomic DNA was extracted from the enrichment culture
and purified as described previously (49). The archaeal and bacterial
16S rRNA genes were amplified by PCR using the following
primers: A10F (5'-TCYGGTTGATCCYGCCRG-3') and A1400R(5'-ACGGGCGGTGTGTGCAAG-3') for the domain Archaea,
U27F and U1492R (25) for the domain Bacteria. The PCR mixture(50 µL) contained 1×PCR buffer, 3.5 mM MgCl2, 10 mM
deoxynucleoside triphosphates (dNTPs), 1.25 U AmpliTaq Gold(each from Applied Biosystems, Foster City, CA, USA), and 0.4µM of each forward and reverse primer. Approximately 100 ng
genomic DNA was used as a template under the following cycling
conditions: initial AmpliTaq Gold activation at 95°C for 9 min,followed by 30 cycles of denaturation at 95°C for 30 s, annealing
at 56°C for 30 s, extension at 72°C for 1.5 min, and a final extension
step at 72°C for 5 min. The mcrA gene encoding the alpha-subunitof methyl-coenzyme M reductase was also partially amplified by
PCR with primers MR1mod and ME2mod (35) under almost thesame PCR conditions except for its cycle number (40 cycles) andtime of extension step (1 min) in the cycle. The PCR product was
purified using the QIAquick PCR purification kit (Qiagen, Hilden,
Germany), and sequenced using the BigDye terminator v3.1 cycle
sequencing kit with a 3130xl genetic analyzer (both from Applied
Biosystems).
Clone library
The purified archaeal 16S rRNA and mcrA genes were cloned
27

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IINO et al.

Physiological characteristics
Growth conditions were determined using MYB medium. Aerobic
and microaerobic conditions were prepared by the substitution of
air and the addition of 2% (v/v) oxygen, respectively, with filtration
through a 0.2 µm-pore membrane filter. Prior to inoculation, acetate,lactate, or pyruvate (all at 10 mM) were added as carbon sources
instead of yeast extract. 2-Bromo ethanesulfonate (BES, final
concentration 20 mM) was added as the inhibitor of methane
production. Then, 0.2 mL of the preculture of the enrichment was
inoculated into 20 mL fresh medium containing each substrate. The
culture was incubated at 30°C for two weeks. After the transfer
twice, hydrogen and methane concentrations in the headspaces ofserum bottles were determined with a gas chromatograph (GC-14A;
Shimadzu, Kyoto, Japan) equipped with a thermal conductivity
detector and a porapack Type Q 80–100, mesh 80–100 (Waters,
Tokyo, Japan). The analysis conditions were as follows; column
temperature, 60°C; injector temperature, 80°C; and detector temperature, 100°C; current, 80 mA; carrier gas, N2.
Fig. 1. Phylogenetic affiliation of the enriched archaeon Kjm51a
within Group E2 in the class Thermoplasmata on the basis of the 16S
rRNA gene sequences. The tree was constructed using the neighborjoining method. Solid circles at branching nodes indicate supporting
probabilities above 95% by all the phylogenetic analysis methods(NJ, ML, and Bayesian), and open circles indicate probabilities above
85% by two or more analyses. Bar, 0.02 substitutions per nucleotide
position.

Accession numbers
The 16S rRNA gene and mcrA gene sequences of the enrichedarchaeon Kjm51a have been deposited in the DDBJ/EMBL/NCBI,
and GenBank nucleotide sequence databases under accession
numbers AB749767 and AB749768, respectively.

Results
Methanogenic enrichment cultures from anaerobic sludge
A methanogenic enrichment culture was obtained fromanaerobic digester using MYB medium containing methanol
and yeast extract. The archaeal population in the culture
was analyzed using archaeal 16S rRNA gene- and mcrA
gene-specific primers. A total of 113 and 61 clones wereobtained, respectively. A sole phylotype was obtained in bothclone libraries, i.e., all the cloned 16S rRNA gene and mcrA
gene sequences in the two libraries were almost identical to
the sequence identities of 99.4–100% and 99.5–100%,
respectively.
An almost full-length 16S rRNA gene sequence (1,309 bp)
was determined for a novel archaeon, designated phylotype
Kjm51a, in the enrichment culture. In the phylogenetic treesof the 16S rRNA gene sequences constructed using NJ, ML,
and Bayesian methods, the enriched archaeon Kjm51a wasplaced into an uncultured archaeal lineage, Group E2, in theclass Thermoplasmata (6) (Fig. 1). The topologies of thetrees generated by the three phylogenetic analysis methods
were almost identical, and were supported by high bootstrap
values (99–100%). Archaeon Kjm51a was a member of RCIII, a sublineage within Group E2, and showed the highest
sequence similarities (91.3–96.2%) to the environmentalclones derived from anaerobic digesters, bovine rumen, andlandfill leachate (13, 22, 54, 61). The nearest cultivatedneighbor of archaeon Kjm51a was Methanomassiliicoccus
luminyensis B10T with 87.7% sequence identity. A partial
mcrA gene sequence (1,109 bases) was also determined forthe enriched archaeon Kjm51a. The NJ tree constructedusing the McrA amino acid sequence deduced from the mcrA
gene sequence demonstrated that archaeon Kjm51a formeda monophyletic cluster together with M. luminyensis B10T
and environmental clone sequences derived from anaerobic
bioreactor and bovine rumen (Fig. 2), and that the cluster
was apparently distinct from the four known methanogenic
lineages, the classes Methanobacteria, ‘Methanomicrobia’,Fig. 2. Deduced McrA amino acid sequence-based phylogeny
showing the phylogenetic relationships among the enriched archaeon
Kjm51a, its related archaeon and environmental clones, and otherknown methanogens. Solid circles at branching nodes indicate
supporting probabilities above 95%, and open circles indicate probabilities above 80%. Bar, 0.02 substitutions per nucleotide position.

Methanococci, and Methanopyri. The McrA amino acidsequence of the enriched archaeon Kjm51a showed 76.0%
identity with that of the closest species, M. luminyensis B10T.
Coccoid- and rod-shaped cells were observed under the
microscope. The cocci and rods were identified as archaealand bacterial cells, respectively, by fluorescence in situ
hybridization with archaeal and bacterial probes (Fig. 3A and
B). A ratio of archaeal cells to total cells was at least 3.5±1.4%
in the enrichment culture. Cocci were hybridized with a
Kjm51a-specific probe, but rods were not (Fig. 3C). Cells
hybridized with the Kjm51a-specific probe completely corresponded to those with the archaeal probe (Fig. 3B and D).
The bacterial rods in the enrichment culture were provisionally identified as Clostridium celerecrescens (sequence28

Novel Methanogen in the Class Thermoplasmata

247

Discussion
The novel archaeon Kjm51a was successfully enrichedfrom anaerobic sludge using MYB medium containing
methanol and yeast extract. The archaeon was not yet purified
in this study because C. celerecrescens was dominantly
isolated in the presence of yeast extract, although we madea great effort to isolate the archaeon using agar plate culture
and dilution-extinction culture. Clone library analysis demonstrated that cloned 16S rRNA and mcrA gene sequences
obtained from the enrichment culture were almost identicalin each. Furthermore, FISH analysis also showed that coccal
cells hybridized with a Kjm51a-specific probe completely
corresponded to those with an archaeal probe. These findings
strongly support the archaeal purity of the enriched archaeon
Kjm51a in MYB medium, although bacterial cells still
coexisted.
The enriched archaeon Kjm51a was a strictly anaerobic
and chemoheterotrophic cocci showing growth and methane
production in the presence of methanol, the inhibition of
methane production by BES, a well-known inhibitor ofmethanogenesis (18). Hydrogen production was also
observed in the absence of methanol, which indicated thatthe coexisting bacterium, C. celerecrescens, produced hydrogen by its fermentation (38). Inhibition of methane production
by BES resulted in increased hydrogen production. Thesephysiological properties suggest that the enriched archaeon
Kjm51a might be a methanol-reducing hydrogenotrophic
methanogen.
As reported for Methanosphaera stadtmanae (34),
Methanomicrococcus blatticola (51), and Methanosarcina
barkeri strain Fusaro (36), the enriched archaeon Kjm51a
is likely to produce methane by the hydrogen-dependent reduction of methanol through the following reaction: H2+
CH3OH → CH4+H2O (11, 29, 52, 59). Methanomassiliicoccus
luminyensis, a recently isolated methanogen from humanfeces, belonging to Group E2, also produced methane frommethanol in the presence of hydrogen (9). The genome of
M. luminyensis likely encodes only a partial methanogenesis
pathway (16). Most recently, archaeon MpT1 in Group E2
was enriched from termite guts as a methanogen, whichconverted methanol to methane (39). Methanol may be acommon substrate for methanogenesis in Group E2. Schink
and Zeikus reported that heterotrophic microbes anaerobically
produced methanol as a major end product from pectin, which
is a component of plant tissue (48). Biodegradation of plants
occurs in a wide variety of environments, such as the rumen,
rice field soil and anaerobic digester treating garbage, andGroup E2 methanogens may contribute to carbon flux.
The enriched archaeon Kjm51a is the first culture representative derived from an anaerobic methanogenic digesterin Group E2. In the phylogenetic trees constructed using 16SrRNA gene sequences, the archaeon Kjm51a and M.
luminyensis B10T were completely separated in Group E2
with their low sequence identity (87.7%), which was
sufficiently low to classify them into different genera. The
enriched archaeon Kjm51a and M. luminyensis B10T wereaffiliated with RC-III, one of the sublineages in Group E2.
RC-III was clearly and completely separated from the validly
described order Thermoplasmatales. Its monophyletic lineage

Fig. 3. Epifluorescence micrographs of in situ hybridization of theenrichment archaeon Kjm51a grown on MYB medium for a week. The
same microscopic field is shown after hybridization with a Kjm51aspecific probe (red), an archaeal probe ARC915 (green), and a bacterial
probe EUB338 (blue). A, blue color; B, green color; C, merge of red,green and blue colors; D, red color. Bars, 10 µm.

Fig. 4. Methane and hydrogen production in the enrichment culture
obtained from anaerobic sludge. Filled bars, methane; open squares,hydrogen. Data points and bars are the means and standard deviations,
respectively (n=3). Abbreviations: MYB, MYB medium supplied withmethanol in YB medium; YB, YB medium; MYB+BES, MYB
medium supplied with BES; ND, not detected.

identity: 99.9%, X71848) by bacterial 16S rRNA genesequence analysis.
Physiological property of the enriched archaeon Kjm51a
The enriched archaeon Kjm51a was strictly anaerobic andwas capable of growing in MYB medium under a N2/CO2(4:1 [v/v]) atmosphere, but could not grow under microaerobic or aerobic conditions. Both methanol and yeast extract
were required for the growth of archaeon Kjm51a. Acetate,lactate, and pyruvate were not utilized as carbon sources
instead of yeast extract. Metabolic products of the enrichment
culture with methanol and yeast extract were hydrogen andmethane, and that with yeast extract and without methanol
was hydrogen (Fig. 4). Growth of the enriched archaeon
Kjm51a in the presence of methanol and its methane
production were completely inhibited by the addition of BES.
The amount of hydrogen in the presence of BES wasapproximately three times higher than that in the absence of
BES.
29

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IINO et al.

pl. n. Methanomassiliicoccaceae family of the genus
Methanomassiliicoccus).
The family Methanomassiliicoccaceae is defined on thebasis of a phylogenetic tree constructed by phylogenetic
analysis of the 16S rRNA gene sequence of a single cultivatedrepresentative, of the enriched culture, and of environmentalclone sequences derived mainly from the alimentary canal,
anaerobic digester, landfill leachate, and rice field soil. The
type genus is Methanomassiliicoccus.

was strongly supported by the probability scores (>99%)
calculated using all the phylogenetic analysis methods. The
16S rRNA gene sequence of the enriched archaeon Kjm51aand M. luminyensis B10T had similarities of only 77.1 to80.3% with those of the known archaeal species in the order
Thermoplasmatales. These similarities are lower than the
85% similarity that is generally used as a cut-off value
for distinguishing lineages at the phylum, as suggested by
Hugenholtz et al. (24). Therefore, RC-III composed ofthe enriched culture Kjm51a and M. luminyensis B10T is adistinct order level lineage in the class Thermoplasmata.
Previously, Kemnitz et al. (30) reported that the RC-III
archaea might heterotrophically grow using peptides, basedon their enrichment culture experiment; however, taken
together with the recent study (6), our findings clearly
indicated that RC-III is a novel methanogenic lineage.
In conclusion, an archaeal representative enriched fromthe anaerobic methanogenic digester is a novel methanogenbelonging to RC-III within Group E2 in the class Thermoplasmata. According to the recommendations of Murray andStackebrandt (37), we propose the provisional taxonomic
assignment of Candidatus Methanogranum caenicola forthe enriched archaeon Kjm51a. Most recently, the order
Methanoplasmatales was provisionally proposed for the
deep-branching lineage accommodating M. luminyensis andthe enriched archaea MpT1 and MpM2 (39); however, thislineage should be proposed as the Methanomassiliicoccales
on the basis of Rule 47a of the Bacteriological Code to
avoid bacteriological confusion (31). Consequently, we
propose to rename the order ‘Methanoplasmatales’ as
Methanomassiliicoccales for the sublineage accommodating
M. luminyensis B10T and the enriched archaeon Kjm51a as
described below. The proposal of this novel order follows
the description of the new family Methanomassiliicoccaceae.
To purify the enriched archaeon Kjm51a and understand its
ecological role in the methanogenic environment, further
study via enrichment culture will be necessary.

Description of Methanomassiliicoccales ord. nov.
Methanomassiliicoccales (Me.tha.no.mas.si.li.i.coc.cal’es.
N.L. neut. n. Methanomassiliicoccus type genus of the order;-ales ending to denote an order; N.L. fem. pl. n. Methanomassiliicoccales order of the genus Methanomassiliicoccus).
The description is the same as that for the family
Methanomassiliicoccaceae. The type genus is Methanomassiliicoccus.
Acknowledgements
The authors thank Dr. Hiroyuki Imachi (Japan Agency for MarineEarth Science & Technology) for helpful discussions on several
aspects of the paper.
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Description of Candidatus Methanogranum caenicola
Methanogranum caenicola (Me.tha.no.gra’num. cae.ni.
co’la. N.L. n. methanum [from French n. méth(yle) andchemical suffix -ane], methane; N.L. pref. methano-,pertaining to methane; L. neut. n. granum, grain, kernel; N.L.
neut. n. Methanogranum, a methane-producing grain: L. n.
caenum, mud, sludge; L. suff. -cola [from L. n. incola],
inhabitant, dweller; N.L. n. caenicola, an inhabitant ofsludge).
Strictly anaerobic, chemoheterotrophic. Cells form cocci
occurring as single cells. Produce methane dependent onhydrogen and methanol. Represent a distinct phylogenetic
lineage in the class Thermoplasmata based on 16S rRNA
gene sequence analysis. Enriched from an anaerobic sludge
in a methanogenic digester.
Description of Methanomassiliicoccaceae fam. nov.
Methanomassiliicoccaceae (Me.tha.no.mas.si.li.i.coc.ca’ce.ae.
N.L. neut. n. Methanomassiliicoccus type genus of thefamily; -aceae ending to denote a family; N.L. fem.
30

Novel Methanogen in the Class Thermoplasmata

249
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32

MOST CITED ORIGINAL PAPER IN
THIS TEN YEARS [2009-2018] (116 citations)

Microbes Environ. Vol. 24, No. 3, 205–216, 2009http://wwwsoc.nii.ac.jp/jsme2/ doi:10.1264/jsme2.ME09151S

Minireview
Cultivation of Uncultured Chloroflexi Subphyla: Significance andEcophysiology of Formerly Uncultured Chloroflexi ‘Subphylum I’
with Natural and Biotechnological Relevance
TAKESHI YAMADA1, and YUJI SEKIGUCHI2*
1Department of Ecological Engineering, Toyohashi University of Technology, 1–1 Hibarigaoka, Tempaku, Toyohashi

441–8580, Japan; and 2Institute for Biological Resources and Functions, National Institute of Advanced Industrial
Science and Technology (AIST), 1–1–1 Higashi, Tsukuba 305–8566, Japan（Received May 31, 2009—Accepted July 30, 2009—Published online August 20, 2009）

Cultivation-independent molecular surveys have shown members of the bacterial phylum Chloroflexi to be ubiquitous in various natural and artificial ecosystems. Among the subphylum-level taxa of the Chloroflexi known to date,
the formerly uncultured ‘subphylum I’ had well been recognized as a typical group that contains a number of environmental gene clones with no culture representatives. In order to reveal their ecophysiology, attempts were made over the
past decade to domesticate them into laboratory cultures, and significant advances have been made in cultivatingstrains belonging to the group. The microorganisms characterized so far include seven species in six genera, i.e.,
Anaerolinea, Levilinea, Leptolinea, Bellilinea, Longilinea, and Caldilinea, and were proposed to represent two classes,
Anaerolineae and Caldilineae, providing solid insights into the phenotypic and genetic properties common to thegroup. Another subphylum-level uncultured group of the Chloroflexi, i.e., the class Ktedonobacteria, has also been
represented recently by a cultured strain. In addition to the results from these tangible cultures, data obtained fromfunctional analyses of uncultured Chloroflexi populations by assessing substrate uptake patterns are accumulating at anencouraging rate. In this review, recent findings on the ecological significance and possible ecophysiological roles of‘Chloroflexi subphylum I’ are discussed based on findings from both the characteristics of the cultured Chloroflexi andmolecular-based analyses.
Key words: Chloroflexi, Anaerolineae, Caldilineae, uncultured microorganism

such microbial groups have recently been cultured and characterized.
The phylum Chloroflexi, formerly known as ‘Green nonsulfur bacteria’, has been recognized as a typical ubiquitousbacterial taxon containing a number of diverse environmental 16S rRNA gene clones with a limited number of cultured
representatives (33, 63). Formerly, the phylum had been
divided into four major subphylum (class)-level taxa on thebasis of 16S rRNA/rRNA gene sequences, i.e. ‘subphyla I,
II, III, and IV (Fig. 1) (33), but the class Thermomicrobia has
been reclassified into the phylum as an additional subphylum(34). The phylogenetic depth of the phylum is comparablewith that of the phylum Proteobacteria (20). In addition to
the major five subphyla, other uncultured lineages at thesubphylum level were also identified (14, 63). Among thesubphyla, ‘subphylum III’, known as the class Chloroflexi,
has been best represented by cultured organisms belonging
to the genera Chloroflexus, Oscillochloris, Chloronema,
Heliothrix, Herpetosiphon, and Roseiflexus. These organisms
mostly possess filamentous morphotypes, and show photoheterotrophic and/or chemolithoheterotrophic growth undermesophilic or moderately thermophilic conditions. The class
Thermomicrobia also involves cultured organisms belonging

Introduction
Cultivation-independent molecular methods have provided new tools to study the microbial world, enabling us tounderstand the actual microbial diversity that traditional cultivation-based methods have never unveiled (32). With theapplication of these techniques, it has become evident thatthe majority of microorganisms in the environment areuncultured, and that the ecophysiology of these organisms
remains largely unknown. The finding of yet-to-be cultured
microorganisms have driven renewed efforts in the cultivation and isolation of such microbes, because the domestication (cultivation) of microorganisms into laboratory cultures
is still the best means to gain solid insights into metabolic
ability and detailed genomic traits of individual microbes. In
the past few years, new microorganisms have been successfully isolated that belong to uncultured taxa with environmental and biotechnological relevance, and the informationof their physiology in conjugation with phylogeny has been
updated (63). Chloroflexi subphyla are also examples where* Corresponding author. E-mail: y.sekiguchi@aist.go.jp; Tel: +81–
29–861–7866; Fax: +81–29–861–6400.

33

SpecialIssue

Special issue: Significance of culturing microbes in the omics era

YAMADA and SEKIGUCHISpecial

Issue

206

Fig. 1. Evolutionary distance dendrogram of the bacterial phylum Chloroflexi derived from a comparative analyses of 16S rRNA gene sequences,
showing the phylogenetic relationship of the subphyla (class)-level taxa of the phylum. The sequences were aligned, and the phylogenetic tree wasconstructed by the neighbor-joining (NJ) method with the ARB software package (52). The fidelity of the topology of the NJ tree was also confirmed by bootstrap resampling (based on 1,000 replicates) with the neighbor-joining (PAUP* 4.0 program package) and maximum likelihood(TREEFINDER program package) methods. Nodes highly supported with bootstrap values higher than 85% by both analyses are marked as circles(filled circles, >95%; open circles, 95–85%). Nodes without symbols were not highly resolved (<85%) as specific groups in either analysis. The bar
represents 10 nucleotide substitutions per 100 nucleotides.

been isolated and characterized that belongs to anotheruncultured lineage at the subphylum level in the Chloroflexi
phylum, representing a new class, Ktedonobacteria.
In this review, recent findings on the ecological significance and possible ecophysiological roles of the formerly
uncultured Chloroflexi subphyla are discussed based on findings from rRNA-based community analyses for the environment, as well as from the characteristics of recently cultured
Chloroflexi. In addition, recent studies on the ecophysiologyof these organisms in engineered systems through theevaluation of their substrate uptake pattern are described.
Special emphasis is placed on the ecology and function of
Chloroflexi ‘subphylum I’ members with natural and biotechnological relevance, particularly those found in waste/
wastewater treatment systems.

to the genera Thermomicrobium and Sphaerobacter (34),which are rod-shaped, moderately thermophilic or hyperthermophilic, chemoheterotrophic aerobes. In addition, Themobaculum terrenum, a moderately thermophilic chemolithoheterotrophic aerobe, represents a distinct lineage in thephylum, forming a new class-level taxon (9). The other threemajor subphyla (I, II, and IV) had been comprised solely of avariety of environmental clones except for purified, coccoidshaped organisms, ‘Dehalococcoides ethenogenes’ andrelated strains (e.g., (1, 53)) able to reductively dechlorinate
chlorinated compounds, being classified into ‘subphylum II’(class ‘Dehalococcoidetes’) (31, 33). ‘Subphylum I’ contains
the most diverse environmental clones among the four subphyla of the Chloroflexi; in the current 16S rRNA Ribosome
Database Project (RDP) database (release 10.11), ‘subphylum I’ phylotypes are most frequently represented
among the subphyla known to date and make up approximately >70% (>5,000 entries) of all the deposited sequences
relative to the Chloroflexi phylum. Although the past two
decades have seen a number of papers reporting the detectionof ‘Chloroflexi subphylum I’ in various ecosystems (seebelow), there had long been no description of cultivablemicrobes. However, in recent years, aerobic and anaerobic
strains have successfully been cultivated and characterizedthat belong to ‘subphylum I’. The microorganisms characterized so far are seven species in six genera in total, andwere proposed to represent two distinct classes Anaerolineae
and Caldilineae. In addition, an aerobic strain has recently

Ecological significance of Chloroflexi subphyla
In 1998, Hugenholtz et al. analyzed 16S rRNA genesequences of 5,224 cultured bacteria and 2,918 environmental gene clones retrieved from a wide range of natural andartificial ecosystems, revealing ubiquitous bacterial groups
to be those of the phyla Proteobacteria, Actinobacteria,
Firmicutes, Bacteroidetes, Planctomycetes, Acidobacteria,
Verrucomicrobia, and Chloroflexi, and the candidate phylum
OP11 (33). In the past decade, a flood of papers reporting themolecular detection of Chloroflexi phylotypes in a variety ofenvironments have been published, further supporting their34

Microorganisms Affiliated with the Classes Anaerolineae and Caldilineae

Relative abundance of Chloroflexi phylotypes in natural and artificial ecosystems as determined by 16S rRNA gene-based clone library
analyses

SubphylaDeepseafloor

Sea Lake○a○● ● - ●●

○

—

—

—

—

Subphylum IV

Subphylum I(Anaerolineae andCaldilineae)
Subphylum II(‘Dehalococcoidetes’)
Subphylum III(Chloroflexi)

Artificial habitats

Soil

River

Wastewater treatment

Microbial
Hot Freshwater Ocean Hypersaline
Lagoon Mine fuel cellspringlake
drainage
Agricultural Geothermal Meadow
Aerobic Anaerobic
system
●●●

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●●

—

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—

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—

—

○-●

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—

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—

○ - ●●● —

—

—

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New subphyla

○

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References

59

54 82

46

5

78

22

48

85, 99

29

50

97

79

39, 64——42, 45, 3, 24, 58,
56
64, 90a Frequency of clones assigned to a subphylum of Chloroflexi as a percentage of the total number of bacterial sequencs analyzed:—, 0%; ○, 0.1–5%; ●, 5–15%, ●●, 15–25%, ●●●, >25%.

phylotypes found were classified into ‘subphylum I’ as wellas other formerly and currently uncultured, previously unrecognized groups at the subphylum level, including theclass Ktedonobacteria (see below). These findings suggest awide range of temperature (0 to 65°C) and salinity for thehabitats of Chloroflexi.
Soil is believed to be one of the most complex environments for microbial life (15). Concerning Chloroflexi phylotypes in soil environments, a recent review by Janssen
showed the Chloroflexi to be one of the most dominant phyla
in soils: 32 previously published clone libraries for different
soil samples were re-evaluated and the mean contribution of
the Chloroflexi phylum to soil bacterial communities wasfound to be 3% (range: 0–16%) (40). Similarly, pyrosequencing of rRNA gene clone libraries (ca. 150,000 clones
in total) for different soils further supported the significanceand genetic diversity of the Chloroflexi population (22, 65).
Concerning the Chloroflexi phylum in oceanic and freshwater bacterioplanktons, environmental clones belonging to‘subphylum IV’ (formerly known as cluster SAR202) were
found in the Sargasso sea (4% of bacterial clones) (29), andsimilar phylotypes were detected in different ocean samples(6). Freshwater bacterioplankton also often contain the
Chloroflexi phylum (average: 1%, range: 0–4%), most ofthem affiliated with ‘subphyla I and IV’ (85, 99).
Chloroflexi phylotypes have also been found in abundancein artificial and engineered ecosystems, such as lagoons (e.g.,(97)), mine drainage (e.g., (73, 79)), anaerobic sludges for
waste and wastewater treatment (e.g., (2, 12, 17, 19, 25, 30,
43, 51, 57, 58, 64, 66, 71, 74, 76, 90, 94)), activated sludge
systems (e.g., (3, 8, 13, 25, 42, 45, 47, 55, 56, 60, 62, 77, 80,98)), and microbial fuel cell systems (39, 61). Among theseecosystems, waste/wastewater treatment facilities are perhaps the best-recognized habitat where Chloroflexi phylotypes reside in abundance. For example, Rivière et al. evaluated bacterial and archaeal community structures of sevenmesophilic (29–37°C), anaerobic (methanogenic) digesters
decomposing municipal sewage sludge and found the
Chloroflexi to be the most abundant bacterial phylum (average: 32% of all the bacterial clones analyzed, range: 15–45%)

ubiquity in natural and engineered environments (Table 1).
For example, Chloroflexi phylotypes were found as the mostnumerous bacterial group (nearly 80% of all bacterial geneclones analyzed) in an organic-rich deep subseafloor biosphere (38), where these phylotypes mainly fall into ‘subphyla I, II and IV’. Similarly, Chloroflexi are often one of themost dominating bacterial phyla in various deep subseafloor
sediments (e.g., (7), see also a recent review by Fry et al.
(27)), some of which were associated with methane hydratebearing sites (38, 59). Other natural environments where
Chloroflexi phylotypes were detected in abundance are hotsprings (e.g., (4, 48)), hypersaline microbial mats (e.g., (50)),
soil (e.g., agricultural soils (5, 75), geothermal soils (78), andlow-temperature meadow soils (14)), sediment (e.g., sea andlake (river) sediment (18, 35, 46, 54, 82, 89), and hydrothermally active sediment (79, 83)), chlorinated-solvent-contaminated aquifer sites (21, 26, 88)), oceans (e.g., (6, 29, 63,87)), and freshwater (e.g., (85, 99)).
Recently, Lau et al. analyzed the bacterial communities of
microbial mats from five hot springs (temperature: 60–65°C), indicating that 10–15% of the phylotypes detected
were related with the Chloroflexi phylum (48). These
Chloroflexi phylotypes fall into ‘subphyla I and III’. Similarly, Chloroflexi phylotypes were found in a hypersaline(salinity: 8%) microbial mat as the majority of the mat constituents (21–39% of the bacterial rRNA clones analyzed
were those of the Chloroflexi phylum) (50). These Chloroflexi
phylotypes, again, fall into the ‘subphyla I and III’. In thisstudy, quantitative rRNA-targeted dot blot hybridization was
conducted with an oligonucleotide probe specific for thephylum Chloroflexi (GNSB941 probe, Fig. 2, Table 2), estimating their abundance to be 22–41% of the total rRNA. Thesemicrobial mats were fueled by sunlight, and therefore photosynthetic bacteria of the class Chloroflexi (‘subphylum III’)
were found in association with uncultured ‘subphylum I’organisms. Costello et al. determined the bacterial community of a tundra wet meadow soil, where the annual soil temperature was 0.3°C and the temperature was stable throughout the year, revealing Chloroflexi phylotypes to make up
16% of the bacterial rRNA gene clones (14). In this case, the
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Sediment

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207

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208

Fig. 2. Evolutionary distance dendrogram of the class Anaerolineae derived from a comparative analyses of 16S rRNA gene sequences, showing
the phylogenetic positions of cultivated strains belonging to the class (indicated by arrow) and other related gene clones that were retrieved from avariety of environments. The sequences were aligned, and the phylogenetic tree was constructed by the NJ method with the ARB software package(52). The base tree was constructed with >1,200 nt sequences. Partial sequences of <1,200 nt (highlighted with dotted branches) were inserted
into the base tree using the parsimony insertion tool of the ARB program. 16S rRNA gene sequences of members affiliated with the phylum
Thermotogae were used as outgroups (not shown in the tree). The bar represents 10 nucleotide substitutions per 100 nucleotides. The fidelity ofthe topology of the NJ tree was also confirmed by bootstrap resampling (based on 1,000 replicates) with the neighbor-joining (PAUP* 4.0 program
package) and maximum likelihood (TREEFINDER program package) methods. Nodes highly supported with bootstrap values higher than 85% byboth analyses are marked as circles (filled circles, >95%; open circles, 95–85%). Nodes without symbols were not highly resolved (<85%) as
specific groups in either analysis. Probe specificity is shown to the right of the figure with the probe name; the bars indicate a perfect match between
the probe and target sequences.

20% of the total rRNA (12). Similarly, Narihiro et al. estimated the microbial diversity of twelve different types ofmesophilic (35–40°C), anaerobic (methanogenic) sludges
treating organic wastewaters, detecting Chloroflexi phylotypes as a predominant phylum (average: 12% of the bacterial clones analyzed, range: 0–32%) (58). The abundance of(64). All the Chloroflexi phylotypes detected were affiliatedwith ‘subphylum I’. Chouari et al. conducted quantitativerRNA-targeted dot blot hybridization with an oligonucleotide probe specific to the phylum Chloroflexi (GNSB1126
probe, Fig. 2, Table 2) for a mesophilic (33°C), anaerobic
digester for sewage sludge, estimating their abundance to be36

Microorganisms Affiliated with the Classes Anaerolineae and Caldilineae

209

Table 2. 16S rRNA-tageting oligonucleotide probes used for in situ detection of the Chloroflexi phylum and ‘subphylum I’

CFX1223
GNSB1126
CFX784
GNSB633
S-*OTU25to31-1406-a-A-18
S-*GNS-0667-a-A18
CFX197
CFX223virtually all members of the phylum
Chloroflexi
virtually all members of the phylum
Chloroflexi
members of the class Anaerolineae
members of the class Anaerolineae
Anaerolinea thermophila
uncultured phylotypes of the class
Anaerolineae
uncultured phylotypes of the class
Anaerolineae
uncultured phylotypes of the class
Anaerolineae
uncultured phylotypes of the class
Anaerolineae

Probe sequences (5'-3')Target site
(E. coli position)

LengthReferences(nt)

AAACCACACGCTCCGCT

941–957

17

28

CCATTGTAGCGTGTGTGTMG

1223–1242

20

8AACACACAGCGAGGG
ACCGGGGTCTCTAATCCCTAGCCCGCCAGTCTTGAACG

1112–1126
784–801
633–652

15
18
18

12
8
68

CCAGCTCCCATGACGTGA

1406–1423

1842

CACCCSGAATTCCACRTT

667–6841845

TCCCGGAGCGCCTGAACT

197–2141880

GGTGCTGGCTCCTCCCAG

223–24018

80Groups targeted by the probes are shown in Fig. 2.

these phylotypes varied depending on wastewater type, andall of them fell into ‘subphylum I’. Similar phylotypes (‘subphylum I’) have been frequently found in anaerobic sludgesthat had treated wastewaters containing compounds recalcitrant to biodegradation, such as phenol (11% of bacterial
clones) (24), phthalates (4–7%, (51, 90)), 4-methylbenzoate,(7%, (90)), 2,4-dinitroanisole and n-methyl-4-nitroaniline(36–42%, (3)).
Chloroflexi phylotypes are often present in activatedsludge systems; phylogenetic analysis of activated sludge
clones belonging to the Chloroflexi phylum indicated thatthey are affiliated with ‘subphyla I and III’ (8, 47). Theseclones were most abundant in submerged membrane bioreactors treating municipal wastewater (Chloroflexi phylum-specific probes, GNSB941 and CFX1223, were used for in situ
detection and the probe-reactive cells accounted for 14–26%
of the total, (56)). The phylotypes found in the systems again
fall into ‘subphyla I and III’. Nitrifying systems were also
shown to contain these Chloroflexi in abundance; Kindaichi
et al. found that phylotypes belonging to ‘subphylum I’ were
dominant (13% of bacterial clones analyzed) in nitrifying
biofilms formed in a submerged rotating disk reactor (45).
Chloroflexi cells were found in abundance in nitrifyingdenitrifying systems, in which uncultured ‘subphylum I’ cells(as detected using the probe S-*OTU25to31-1406-a-A-18,
Fig. 2) accounted for 16% of the cells (42). Another exampleof these engineered ecosystems is the microbial fuel cell.
The microbial fuel cell is a bio-electrochemical system thatgenerates electric power from organic matter, in which
Chloroflexi phylotypes are often found. For example, aphylotype affiliated with ‘subphylum I’ was one of the mostdominant (17% of bacterial clones) in a microbial fuel cellsystem fed with cellulose (39).
These molecular inventories of Chloroflexi phylotypes in awide range of natural and human-made ecosystems strongly
suggest the ecological significance and physiological breadthof these organisms, playing indispensable roles in such habitats.

Cultivation of uncultured Chloroflexi at the subphylum
level
The classes Anaerolineae and Caldilineae as ‘Chloroflexi
subphylum I’. Phylotypes affiliated with ‘subphylum I’ aredetected in a wide range of environments, and the group now
contains the most diverse rRNA gene sequences among
known subphyla with strong natural and biotechnologicalrelevance. To unveil their physiology, attempts were made tocultivate them, and the first pure culture representing thegroup, i.e., Anaerolinea thermophila, was obtained in 2001
from a thermophilic (55°C) anaerobic sludge treating organic
wastewater (71, 72). Simultaneously, the second tangible
organism of the group, Caldilinea aerophila, was obtained
from a hot spring (72). Later, a new thermophilic speciesof the genus Anaerolinea and four species of four genera,i.e., Levilinea, Leptolinea, Bellilinea, and Longilinea, weresuccessfully cultivated and characterized (92, 93). To ourknowledge, these seven species are only the cultivatedorganisms of ‘subphylum I’.
The morphology, physiology, and genetic properties ofcultivated strains of the two classes are shown in Table 3.
The strains are anaerobic or aerobic, mesophilic or moderately thermophilic, multicellular filamentous, chemolithoorganoheterotrophic organisms degrading carbohydrates andamino acids (or peptides). No growth was found in the dissimilatory reduction of nitrate and sulfate. It may not beappropriate to conclude that ‘subphylum I’ is comprised
solely of such heterotrophs, but based on the unveiled physiological traits of these microorganisms, common featuresthat make ‘subphylum I’ recalcitrant to isolation are likely tobe (1) a relatively slow growth rate compared to commonly
cultivable microbes and/or (2) the need to associate withother microbes (syntrophy) for efficient growth. In fact, theAnaerolinea-type anaerobes cultivated so far are all very
slow growers (doubling time: 45–92 hrs), and hence areeasily outcompeted by fast-growing heterotrophic anaerobes like Clostridia- and Thermoanaerobacter-type cells.
We actually found that irrelevant fast-growing microbes
immediately outcompeted Anaerolinea-type cells when we
37

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Target group

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YAMADA and SEKIGUCHI
Table 3.

Characteristics of cultivated species belonging to classes Anaerolineae and Caldilineae in the phylum Chloroflexi
Class
Caldilineae

Class Anaerolineae

SpecialIssue

Characteristic
Anaerolinea
thermophila

Anaerolinea
thermolimosa

Type strainstrain UNI-1T

strain IMO-1T strain KIBI-1T strain YMTK-2T strain GOMI-1T strain KOME-1T strain STL-6-O1T

Cell diameter (µm)
Temperature range (°C)
Optimum growthtemperature (°C)
pH range
Optimum growth pH
Doubling time (h)O2 respiration
Major cellular fatty acids

0.2–0.3
50–60
55

0.3–0.4
42–55
50

Levilinea
saccarolytica

0.4–0.5
25–50
37–40

Major quinone
DNA G+C content (mol%)

6.0–8.0
around 7.0
72 (48)*−
C16:0, C15:0,
C14:0
−
54.5

Utilization in the presenceof yeast extract of:
Tryptone
BetainPyruvate
GlucoseMannose
Galactose
Fructose
Arabinose
XyloseRibosePectinStarch

±
ND
±
+
+
+
+
±
±
±
±
+

+
−
+
+
+
+
+
+
+
+
±
±

+
±
+
+
±
±
+
−
+
+
±
−

Thermophilic
UASB sludge

Thermophilic
UASB sludge

Mesophilic
UASB sludge

71, 7293, 9493, 94Isolation source
Reference

6.5–7.5
6.0–7.2
around 7.0
around 7.0
48 (10)*56 (56)*−−ai-C17:0, i-C15:0, C14:0, i-C15:0,
C16:0
C16:0
−
−
53.3
59.5

Leptolinea
tardivitalis

Bellilinea
caldifistulae

Longilinea
arvoryzae

Caldilinea
aerophila

0.15–0.2
25–50
37

0.2–0.4
45–65
55

0.4–0.6
30–40
37

0.7–0.8
37–65
55

6.0–7.2
around 7.0
50 (50)*−Branched C17:0,
C16:0, C14:0−
48.2

6.0–8.5
around 7.0
45 (29)*−
C16:0, C14:0,
i-C15:0−54.7

5.0–7.5
around 7.0
92 (38)*−i-C15:0, ai-C15:0,
C14:0,−
54.5

7.0–9.0
around 7.5–8.0
5 (N.D)*+
C18:0, C16:0,
C17:0
MK-10
59.0

+
+
±
+
+
±
+
±
+
+
+
±

±
−
+
+
+
+
+
+
±
+
+
−

+
−
−
−
−
−
±
−
−
−
+
−

+
ND
+
+
−
ND
−
−
−
−
ND
+

Rice paddy
soil

Hot spring

92

72

Mesophilic
Thermophilic
UASB sludge anaerobic sludge
93, 9492* Doubling time in parentheses indicates that for sytrophic growth with hydrogenotrophic methanogens.
−, Negative; ±, variable; +, positive; ND, not determined. Only differences found among the strains are listed. All strains showed the following
characteristics: multicellular filamentous morphology; growth under anaerobic conditions (fermentation).

Another approach that eliminates irrelevant fast-growing
heterotrophic microbes (i.e., ‘subphylum I’ cells) is to establish primary enrichment cultures that allow the growth ofother slow growing bacteria, such as syntrophic propionateoxidizers (37). When we constructed methanogenic,
syntrophic propionate-degrading enrichment cultures, wefound that they contained GNSB941 probe-positive filamentous cells as a concomitant population (92). Therefore, theenrichment cultures were transferred to anaerobic media that
support the growth of Anaerolinea-type cells, resulting in thecultivation and isolation of two additional anaerobes of thegenera Bellilinea and Longilinea (92). In primary enrichment
cultures, they might survive on certain remnants fromthe propionate-oxidizing community. Similarly, Caldilinea
aerophila was isolated from a primary aerobic enrichment
culture to focus on the isolation of chemolithotrophic thermophiles (72). For the primary enrichment culture, thiosulfate was used as the sole energy source, and the cultured cells
were subsequently transferred to an aerobic organic medium,
resulting in the cultivation and isolation of C. aerophila.
Similar to Bellilinea and Longilinea, C. aerophila survived
on remnants from the community that formed during theattempted to isolate them (70, 71). This is probably the primary reason why many attempts to isolate ‘subphylum I’organisms have failed. Consequently, selecting appropriate
inocula, in which ‘subphylum I’-type cells are highly abundant, is one of the keys to success (71). In fact, the cultivation and isolation of Anaerolinea thermophila was possible
only when we used spine-like structures of sludge granules
as the inoculum, in which Anaerolinea cells were highly
concentrated. In the isolation, highly enriched portions ofAnaerolinea cells were found by fluorescence in situ hybridization (FISH) with the probe GNSB633 (Fig. 2, Table 2),
and were carefully washed and serially diluted in liquidmedium. In this case, the fast-growing anaerobes outgrew theAnaerolinea cells in lower dilutions, but the Anaerolinea
grew slowly in the highest dilution, in which growth was
also checked as determined by FISH with GNSB633. SuchrRNA-directed cultivation may be also important for cultivating uncultured cells. In fact, four strains of the genera
Anaerolinea, Levilinea and Leptolinea were successfully isolated by this approach, with rRNA-directed cultivation using
inocula that contain ‘subphylum I’ cells in abundance (71,
94).
38

211

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Microorganisms Affiliated with the Classes Anaerolineae and Caldilineae

Fig. 3. Evolutionary distance dendrogram of the class Caldilineae derived from a comparative analysis of 16S rRNA gene sequences, showing
the phylogenetic positions of Caldilinea aerophila (indicated by arrow) and other related gene clones that were retrieved from a variety of environments. The tree was constructed and marked as described in the legend of Fig. 2. The bar represents 5 nucleotide substitutions per 100 nucleotides.

on the 16S rRNA gene sequences of these cultured organisms and environmental gene sequences, and the group was
found not to be a monophyletic taxon (93), as suggested
by Hugenholtz and Stackebrandt (34). In particular, theCaldilinea cluster did not often form a clade with othermembers of Anaerolineae (93). Thus, it was concluded that
‘subphylum I’ should be phylogenetically subdivided into
at least two class-level taxa (Fig. 1), the class Anaerolineae
representing the majority of lineages in ‘subphylum I’ (Fig.
2) and the class Caldilineae representing a deeply branchedlineage relative to the Anaerolineae (Fig. 3). Physiologically,
distinct differences, such as aerobic respiration, were foundin the cultured members of the two classes (Table 3).
Whole-genome analyses of A. thermophila and C. aerophila
are now underway to unveil detailed genetic properties ofthese organisms (http://www.bio.nite.go.jp/ngac/e/projecte.html), which may also help to further assess the evolutional
relationship among the Chloroflexi subphyla.

primary enrichment, because C. aerophila cannot utilize
thiosulfate as an energy source. In contrast, inoculating theoriginal sample (hot spring microbial mat) directly into thesame aerobic, organic medium resulted in the cultivation of
typical fast growers like Thermus-type cells (72). These
cases also demonstrated that the elimination of fast-growers
from an inoculum is indispensable to the cultivation of‘subphylum I’.
Co-cultivation is an additional strategy. In the enrichmentof Anaerolinea-type anaerobes, hydrogenotrophic methanogens were added beforehand, which stimulated the growth of‘subphylum I’ anaerobes (94). Interestingly, some ‘subphylum I’ anaerobes produced hydrogen as an end productof fermentation, and grew more rapidly when co-cultivatedwith hydrogenotrophic methanogens (Table 3), indicatingthat they are “semi-syntrophic” bacteria requiring a hydrogen-scavenging partner for efficient growth (71, 72, 92, 93).
Because microorganisms rarely live in pure cultures, thisapproach may be generally applicable to uncultured strains,
whereby in situ conditions are appropriately mimicked, using
co-cultivation for example.
These results indicated that it is still feasible to apply traditional cultivation techniques to the isolation of unseen
microbes, if they are used thoughtfully in combination withmolecular tools, and with carefully selected inocula, whichcontain sufficient amounts of targeted cells. By using a combination of these approaches, more novel microbes belonging to ‘subphylum I’ may be obtained.
Concerning the higher taxonomy of these organisms, themonophyly of ‘subphylum I’ was evaluated in detail based

Other cultured subphyla of the Chloroflexi phylum.
Recently, a novel strain was isolated from a soil and named
Ktedobacter racemifer (11). The bacterium is a Gram-positive, aerobic, chemolithoorganoheterotrophic organism that
produces branched vegetative mycelia, growing well under
microaerobic conditions; this finding further expanded themorphological diversity of the phylum. Phylogenetically, thebacterium represents a new subphylum-level clade of the
Chloroflexi phylum, to which the name Ktedonobacteria
classis nov. has been given (11, 23). The subphylum (class)level clade contains a variety of environmental gene clones(>100 sequences in public databases) retrieved mainly from39Special

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Based on FISH using oligonucleotide probes for
Chloroflexi members (Fig. 2 and Table 2), the Anaerolineae
in anaerobic sludge were shown to be filamentous. For
example, FISH using a probe (GNSB 633) specific for
Anaerolinea thermophila showed that all of the probereactive cells in thermophilic sludges had a thin-filamentous
morphotype (70, 71). FISH experiments using GNSB633 ora Chloroflexi-specific probe (GNSB941) for various types ofanaerobic sludge also indicated that the probe-reactive cells
were all filamentous with a wide range of thicknesses (66, 70,
71, 94). Considering that almost all the Chloroflexi-related
16S rRNA gene sequences in anaerobic sludges are classified
into the class Anaerolineae, the filamentous morphology
observed with the GNSB941 probe may be a common trait of
the Anaerolineae lineage.
Interestingly, this filamentous morphotype was found to
be important for biotechnological reasons: Anaerolineae
members are considered important for the granulation ofsludge in UASB reactors, as well as the formation of fluffy
sludge (bulking) in similar treatment systems. The granulation of sludge (formation of sludge granules with good settleability) is the major premise for the start-up and stable operation of UASB reactors (67, 69). It was reported that the
granulation of thermophilic sludges was difficult to achieve
when volatile fatty-acid mixtures were used as the sole substrate, while the addition of sucrose or glucose to the influent
wastewater resulted in the formation of a granular sludge
with good settleability (84, 86). In thermophilic UASB reactors having well-settleable sludge granules, Anaerolineatype filamentous microbes predominated on the surface ofthe granules (70, 71, 94). Considering these findings together
with the physiological properties of the cultured Anaerolineae, Anaerolinea-type filaments are indispensable organisms to the granulation of thermophilic UASB sludges, forming a web-like coating on the surface of granules (70, 71, 94).
Besides their importance in thermophilic UASB reactors,
Anaerolinea-type organisms microorganism are a potential
causative agent for the bulking of granular sludges (71).
Once these filaments outgrew in the system, the sludge
became fluffy, resulting in flotation and washout of thesludge from the system (71, 95). Similar observations weremade in mesophilic anaerobic systems (71, 91, 95, 96).
Therefore it is important to control the growth of these filamentous Anaerolineae not only to enhance the formation of
granules but also to prevent the bulking of sludge granules.

soil samples (these sequences can be browsed on the recent
version of the greengene database (http://greengenes.lbl.gov/).
In addition to the class Ktedonobacteria, Davis et al. successfully isolated a strain belonging to a formerly uncultured
Chloroflexi clade at the subphylum level, called the
Ellin7237 lineage, from a soil sample using nontraditional
aerobic media solidified with gellan (16). The lineage contains a relatively small number of environmental genesequences mainly from soil environments (these sequences
can also be seen on the greengene database (http://greengenes.lbl.gov/). Although detailed physiological propertiesof the strain have not yet been published, the bacterium will
also provide new information on the genetic and phenotypic
traits of the newly cultured subphylum. Gellan-based methods were found to be more effective than conventional agarbased techniques for culturing uncultured strains (10, 41,44), resulting in the isolation of novel bacterial lineages,
even at the phylum level, using media solidified with gellan(16, 81). This approach may also be useful for further isolating and characterizing uncultured Chloroflexi phylotypes.
Ecophysiology and function of the Chloroflexi‘subphylum I’Members of the class Anaerolineae in anaerobic sludge.
The cultured Anaerolineae (the cultured members of thegenera Anaerolinea, Levilinea, Leptolinea, Bellilinea, andLongilinea) share common physiological and morphologicaltraits, such as anaerobic (fermentative) growth on carbohydrates and/or peptides (amino acids) and a multicellularfilamentous morphology. Considering that the class Anaerolineae contains a number of rRNA gene clone sequences
mainly retrieved from anoxic environments, most of whichwere obtained from anaerobic (methanogenic) sludges (2, 12,30, 51, 57, 58, 64, 66, 71, 74, 90, 94), the common physiological traits of the cultured Anaerolineae are likely to represent
those of organisms in the Anaerolineae, at least those of theAnaerolineaceae lineage (Fig. 2). For example, a layered
microbial structure of different trophic groups of anaerobes
was often found within granular sludges in upflow anaerobic
sludge blanket (UASB) systems treating organic wastewaters(e.g., (36, 70)), with the Anaerolineae-type populations often
found in the outer most layer of such sludge granules (66, 70,
71, 94). This unique architecture of sludge granules is considered to be a result of substrate profiles formed within the
granules (68). That is, because the methanogenic conversionof organic matter is driven by a food web of different trophic
groups of anaerobes, i.e., fermentative heterotrophs, protonreducing syntrophic bacteria, and methanogenic archaea(68), fermentative heterotrophs, that mainly utilize primary
substances in wastewaters such as carbohydrates, mainly
reside in the outer layer of granules. That Anaerolineae populations were found in the outer layers of sludge granules
suggests them to be heterotrophic degraders, decomposing
carbohydrates, for example. In addition, because yeast
extract and peptides are good substrates for cultured Anaerolineae (Table 3), and some Anaerolineaceae-type filaments
were also found inside of sludge granules (94), they may beable to act as degraders of cellular materials (like aminoacids) that are present inside sludge granules.

Members of the class Anaerolineae in activated sludge
systems. Although Anaerolineae strains have not yet beencultivated and isolated from oxic environments, phylotypesof the class have been frequently found in oxic systems likeactivated sludges (Fig. 2). FISH for the detection of Anaerolineae-type cells in activated sludges revealed that all of theprobe-reactive cells were filamentous (8, 45, 55, 56, 60, 80).
For example, Björnsson et al. used a probe (CFX784) specific for part of the class Anaerolineae and a probe (CFX109)
for the class Chloroflexi (‘subphylum III’) for in situ detection of these organisms in various activated sludge systems
and found that Anaerolineae in sludge samples were generally thin (<1 µm), smooth filaments, although they were lessabundant than filamentous Chloroflexi (‘subphylum III’)40

anaerobic heterotrophs decomposing carbohydrates andamino acids, and often need to be associated with other microbes (syntrophy) for growth. They are ubiquitous in naturaland artificial environments, and likely to play indispensable
roles in ecosystems. They are often closely associated withthe process performance of biological treatment systems, suchas granule and floc formation and/or sludge bulking. Theirecophysiology and function have been well establishedbased on information from cultured representatives, as well
as from molecular-based ecological analyses, including anassessment of their substrate uptake patterns with the MARFISH technique. This synergism between traditional (or rRNAdirected) cultivation and molecular ecological analyses may
be a promising strategy for further elucidating the function ofthese yet-to-be cultured lineages.
The cultivated Anaerolineae only make up part (the family
Anaerolineaceae in Fig. 2) of the Anaerolineae lineage, andthe class still contains surprisingly diverse, yet-to-be cultured
environmental clades even at the subclass level (Fig. 2). The
phylogenetic depth of the class is the highest among the
Chloroflexi phylum (approximate rRNA gene sequence
divergence of the class is 18%), possibly suggesting thepresence of more genetically (and phenotypically) diverse
Anaerolineae organisms than the cultured Anaerolineae
strains. Similarly, the recently cultured classes, Caldilineae
and Ktedonobacteria, contain only single cultured strains.
Other uncultured subphyla of the Chloroflexi phylum remain
to be characterized (63). To further unveil the function ofthese lineages that are less represented by cultured organisms, rRNA-directed cultivation and molecular ecologicalanalyses may be useful, and should be applied to environments where targeted populations are abundant. To efficiently isolate and cultivate these organisms, it may be necessary to employ newly developed cultivation approaches for
yet-to-be cultured microbes (10). More comprehensive studies of these Chloroflexi subphyla, involving cultivation,
molecular ecological analyses, (meta-) genomics, and transcriptomics, will allow us to gain deeper insight into theirfunctions, which may answer questions such as why theseorganisms are so abundant and ubiquitous in the environment.

cells as detected with CFX109 (8).
To reveal the ecophysiology of these Anaerolineae-typeorganisms in activated sludge systems, functional analyses
were conducted using microautoradiography-fluorescent insitu hybridization (MAR-FISH) (45, 49). For example, using
the MAR-FISH method, substrate uptake patterns of uncultured Chloroflexi, particularly those of the class Anaerolineae, were studied for autotrophic nitrifying biofilms
and revealed that the microbes aerobically utilized N-acetyl
glucosamine, a major structural component of bacterial cells,
as well as amino acids, implying that they metabolize cellular materials in the biofilms (45, 60). Similarly, Miura etal. (55, 56) found using MAR-FISH that filamentous cells of
the class Anaerolineae in membrane bioreactors (MBR) were
metabolically versatile and preferentially utilized N-acetyl
glucosamine and glucose under oxic and anoxic conditions.
Zang et al. observed that Anaerolineae-related cells incorporated decayed tritium-labeled bacterial cells in activatedsludges (98). Based on these observations, it may be concluded that Anaerolineae-type organisms in activated sludge
systems are likely to utilize carbohydrates, as well as toscavenge cellular materials formed in the systems, similar tothe cultured Anaerolineae in anaerobic sludges.
The Chloroflexi filaments in activated sludge may provide
a stabilizing backbone for sludge flocs, explaining one
important role of these organisms in the systems (8). Miuraet al. indicated that Anaerolineae-type filaments were
responsible for the degradation of soluble microbial products, including carbohydrates and cellular materials from
cells, consequently reducing membrane fouling potential inmembrane bioreactors (55, 56). Anaerolineae-type filaments
were also shown to be a causative agent for the filamentous
bulking in activated sludge systems as well. Recently, Speirs
et al. reported that Anaerolineae-type filamentous cells, asdetected with the probes CFX197 and CFX223 (Table 2 andFig. 2), are causative agents for aerobic filamentous bulking,which had long been recognized as Eikelboom Type 0092
(80). These studies demonstrate the importance of Anaerolinea organisms in activated sludge systems, drawing ananalogy between their functions in oxic and anoxic (methanogenic) biological waste/wastewater treatment systems.
The class Caldilineae in natural and biotechnologicalsystems. No detailed in situ studies have been conducted forthe class Caldilinea, i.e., no specific oligonucleotide probes
have been designed and no substrate-uptake properties have
yet been elucidated. Although 16S rRNA gene sequences
have been obtained from various ecosystems (Fig. 3), including hot springs (33), anaerobic sludges (76), aerobic sludges(13, 25, 56), geothermal soil (79), marine sediment (35),hydrothermal vents (83), rice paddy soils (75) and chlorinated-solvent-contaminated aquifers (88), the ecophysiology and functions of this class remain to be clarified.

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MICROBES AND ENVIRONMENTSEDITORIAL BOARD (2017-2018)

Editor-in-Chief

Ken Takai, Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokosuka, Japan

Senior Editors

Koki Toyota, Tokyo University of Agriculture and Technology, Koganei, Japan
Shin Haruta, Tokyo Metropolitan University, Hachioji, Japan
Satoshi Hanada, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan
Hiroyuki Futamata, Shizuoka University, Hamamatsu, Japan
Hiroyuki Ogata, Kyoto University, Kyoto, Japan
Kazuhiko Narisawa, Ibaraki University, Ami, Japan
Kiwamu Minamisawa, Tohoku University, Sendai, Japan
Nobuhiko Nomura, University of Tsukuba, Tsukuba, Japan

Managing Editors

Keizo Shimada (Manuscript Reception), Tokyo Metropolitan University, Hachioji, Japan
Hiroyuki Yamamoto (Manuscript Reception), Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokosuka, Japan
Akira Hiraishi (Manuscript Reception), Toyohashi University of Technology, Toyohashi, Japan
Yoshihiro Takaki (Online Journal), Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokosuka, Japan
Satoshi Nakagawa (Online Journal), Kyoto University, Kyoto, Japan
Shigeto Otsuka (Online Journal), The University of Tokyo, Tokyo, Japan
Takuro Nunoura (Journal Management), Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokosuka, Japan
Hiroyuki Imachi (Journal Management), Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokosuka, Japan
Yuki Morono (Manuscript Edition), Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Nankoku, Japan
Michinari Sunamura (Manuscript Edition), The University of Tokyo, Tokyo, Japan
Takashi Narihiro (Manuscript Edition), National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan
Yasuhiro Tanaka (Manuscript Edition), University of Yamanashi, Kofu, Japan
Akihiro Saito (Manuscript Edition), Shizuoka Institute of Science and Technology, Shizuoka, Japan
Noriaki Momma (Manuscript Edition), Institute for Horticultural Plant Breeding, Matsudo, Japan
Satoko Shinozaki-Yabana (Manuscript Edition), National Institutes for Cultural Heritage, Tokyo, Japan
Nana Yoshikane (Manuscript Edition), Saga University, Saga, Japan

Associate Editors

Yoshiteru Aoi, Hiroshima University, Higashi-Hiroshima, Japan
Chih-Yu Chiu, Academia Sinica, Taipei, Taiwan
Jang-Cheon Cho, Inha University, Incheon, Korea
Peter Dunfield, University of Calgary, Calgary, Canada
Craig Everroad, NASA Ames Research Center, Moffett Field, CA, USA
Manabu Fukui, Hokkaido University, Sapporo, Japan
Natsuko Hamamura, Kyushu University, Fukuoka, Japan
Yasufumi Hikichi, Kochi University, Nankoku, Japan
Shir-Ly Huang, National Central University, Jhongli City, Taiwan
Yuichi Hongoh, Tokyo Institute of Technology, Tokyo, Japan
Hiroyuki Imachi, Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokosuka, Japan
Satoshi Ishii, University of Minnesota, St. Paul, MN, USA
Shun’ichi Ishii, Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Nankoku, Japan
Tsukasa Ito, Gunma University, Kiryu, Japan
Elena P. Ivanova, Swinburne University of Technology, Melbourne, Australia
Wataru Iwasaki, The University of Tokyo, Tokyo, Japan
Yasuhiro Kasahara, Hokkaido University, Sapporo, Japan
Takafumi Kataoka, Fukui Prefectural University, Obama, Japan
Junichi Kato, Hiroshima University, Higashi-Hiroshima, Japan
Yoshitomo Kikuchi, National Institute of Advanced Industrial Science and Technology (AIST), Sapporo, Japan
Hiroyuki Kimura, Shizuoka University, Shizuoka, Japan
Tomonori Kindaichi, Hiroshima University, Higashi-Hiroshima, Japan
Staffan Kjelleberg, The University of New South Wales, Sydney, Australia
Ken-ichi Kucho, Kagoshima University, Kagoshima, JapanKen Kurokawa, National Institute of Genetics, Mishima, Japan
Mei-Chin Lai, National Chung Hsing University, Taichung, Taiwan
Shuang-Jiang Liu, Institute of Microbiology Chinese Academy of Sciences (IMCAS), Beijing, China
Wen-Tso Liu, University of Illinois at Urbana-Champaign, Champaign, IL, USA
Fumito Maruyama, Kyoto University, Kyoto, Japan
Shawn McGlynn, Tokyo Institute of Technology, Tokyo, Japan
Keisuke Miyauchi, Tohoku Gakuin University, Tagajo, Japan
This journal is partly subsidized by the Grant-in-Aid for Publication of Scientific Research Results from the Japan Society for the
Promotion of Science (JSPS).

c/o NakaNishi PriNtiNg Co.
Shimodachiuri Ogawa Higashi Kamikyoku, Kyoto 602-8048, Japan TEL: +81–75–441–3155, FAX: +81–75–417–2050

Ryo Miyazaki, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan
Keizo Nagasaki, Kochi University, Kochi, Japan
Yuji Nagata, Tohoku University, Sendai, Japan
Atsushi Nakabachi, Toyohashi University of Technology, Toyohashi, Japan
Satoshi Nakagawa, Kyoto University, Kyoto, Japan
Tatsunori Nakagawa, Nihon University, Kanagawa, Japan
Shin-ichi Nakano, Kyoto University, Otsu, Japan
Naohiro Noda, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan
Satoko Noda, University of Yamanashi, Kofu, Japan
Hideaki Nojiri, The University of Tokyo, Tokyo, Japan
Takuro Nunoura, Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokosuka, Japan
Ryo Ohtomo, Hokkaido Agricultural Research Center (NARO), Sapporo, Hokkaido
Shigeto Otsuka, The University of Tokyo, Tokyo, Japan
Joonhong Park, Yonsei University, Seoul, Korea
Michael J. Sadowsky, University of Minnesota, St. Paul, MN, USA
Keishi Senoo, The University of Tokyo, Tokyo, Japan
Toru Shigematsu, Niigata University of Pharmacy and Applied Sciences, Niigata, Japan
Masaki Shintani, Shizuoka University, Hamamatsu, Japan
Satoru Suzuki, Ehime University, Matsuyama, Japan
Shino Suzuki, Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Nankoku, Japan
Mio Takeuchi, National Institute of Advanced Industrial Science and Technology (AIST), Osaka, Japan
Hideyuki Tamaki, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan
Sen-Lin Tang, Academia Sinica, Taipei, Taiwan
Akinori Yabuki, Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokosuka, Japan
Naoko Yoshida, Nagoya Institute of Technology, Nagoya, Japan
Takashi Yoshida, Kyoto University, Kyoto, Japan
Susumu Yoshizawa, The University of Tokyo, Tokyo, Japan
Lian-Hui Zhang, Institute of Molecular and Cell Biology, Proteos, Singapore
Kazuhiko Saeki, Nara Womenʼs University, Nara, Japan
Shusei Sato, Tohoku University, Sendai, Japan

JAPANESE SOCIETY OF MICROBIAL ECOLOGY

President (2019–2020): Yoichi KAMAGATA, National Institute of Advanced Industrial Science and Technology (AIST)
Secretary (2019–2020): Seigo AMACHI, Chiba University
Business affairs (2019–2020): Hideyuki TAMAKI, National Institute of Advanced Industrial Science and Technology (AIST)
Business affairs (2019–2020): Shigeki YAMAMURA, National Institute for Environmental Studies
Business affairs (2019–2020): Wataru KITAGAWA, National Institute of Advanced Industrial Science and Technology (AIST)
Treasurer (2019–2020): Manabu KANNO, National Institute of Advanced Industrial Science and Technology (AIST)
Treasurer (2019–2020): Yuya SATO, National Institute of Advanced Industrial Science and Technology (AIST)
Treasurer (2019–2020): Taiki KATAYAMA, National Institute of Advanced Industrial Science and Technology (AIST)JAPANESE SOCIETY OF SOIL MICROBIOLOGY

President (2019–2020): Hiroyuki OHTA, Ibaraki University
Secretary (2019–2020): Kazuhiko NARISAWA, Ibaraki University
Treasurer (2019–2020): Shigenobu YOSHIDA, Central Region Agricultural Research Center, NARO

TAIWAN SOCIETY OF MICROBIAL ECOLOGY

President (2018–2020): Pi-Han WANG, Tunghai University
Vice President (2018–2020): Chang-Ping YU, National Taiwan University
International Business Affairs (2018–2020): Shiu-Mei LIU, National Taiwan Ocean University
International Business Affairs (2018–2020): Shir-Ly HUANG, National Yang-Ming University
Secretary-General (2018–2020): Chien-Sen LIAO, I-Shou University

JAPANESE SOCIETY OF PLANT MICROBE INTERACTIONS

Chair of Society: Kiwamu MINAMISAWA, Tohoku University
Secretary-General: Shusei SATO, Tohoku University
Treasurer: Hisayuki MITSUI, Tohoku University

JAPANESE SOCIETY FOR EXTREMOPHILES

President (2018–2019): Tairo OSHIMA, Kyowa-kako Co., Ltd
Vice President (2018–2019): Akira INOUE, Toyo University
Vice President (2018–2019): Toshihiro ITOH, Kitasato Research Center for Environmental ScienceMicrobes and Environments
Instructions for Authors
Microbes and Environments (M&E) is an international journal devoted tothe publication of original and minireview articles regarding all aspects ofresearch on microbial ecology and environmental microbiology. Articles candeal with culturable and non-culturable forms of any type of microorganism:
Bacteria, archaea, fungi, yeasts, protozoa, algae and viruses. M&E is an
open access journal (for online articles), published by Japanese Society ofMicrobial Ecology (JSME), Japanese Society of Soil Microbiology (JSSM),
and Taiwan Society of Microbial Ecology (TSME), and Japanese Society ofPlant Microbe Interactions (JSPMI), Japanese Society for Extremophiles (JSE).
1. Types of papers published
Four types of papers are published: Full-length Regular Papers, Short
Communications, Reviews, and Short Reviews. Regular Papers and Short
Communications must represent a report of original research thathas not been published before (except in the form of an abstract or as partof a published lecture, review, or thesis) and is not under consideration
for publication elsewhere. Reviews are normally invited. Authorsinterested in submitting a Review should write to the Editor-in-Chief(E-mail: kent@jamstec.go.jp) for approval before its submission. Short
Reviews are completely invited.
2. Copyright and agreement for publication
All authors of a manuscript must have agreed to its submission and are
responsible for its contents, including appropriate citations and acknowledgements. All authors also must have agreed that the corresponding author
has authority to act on their behalf in all matters pertaining to publication of
the manuscript. Unless the Copyright Transfer Agreement is executed, M&E
will not publish the article. The author guarantees that the manuscript will
not be published elsewhere in any other language without the written permission of the copyright holders. Copyright for supplemental material
remains with the authors, but a license permitting posting by M&E will besent, along with the article copyright transfer agreement, to the corresponding author for signing at the acceptance stage.
3. Submission procedure
All manuscripts must be submitted electronically via the online manuscript submission and peer review system at the following URL: http://
mc.manuscriptcentral.com/jsme2 (E-mail submissions will not be
accepted). A user ID and password are required and can be obtained on
the ﬁrst use of the system. Most of ﬁle types are supported, but the following
types are recommended: main document in Microsoft Word, ﬁgures in highresolution PPT, EPS, or TIFF. PDF ﬁle for main document is not acceptable.
Total ﬁle size (main document, ﬁgures, tables, and supplemental materials)
should not be exceeded 20 MB. Step-by-step instructions for submitting amanuscript via the online submission system are available. Authors in Japan
should give their address in Japanese as well as in English. The Editorial
Ofﬁce will conﬁrm the editorial style of a submitted manuscript. Authors
who are unsure of proper English usage should have their manuscripts
checked by someone proﬁcient in the English language.
4. Review process
Submitted manuscripts are sent to a Senior Editor with appropriate expertise,
who is responsible for making a final decision on its acceptability. The
Senior Editor will choose an Associate Editor who is in charge of sending
the manuscript to two referees for criticism and further communication withauthors. A manuscript returned to the author for revision should be resubmitted to the Associate Editor within 6 weeks. Otherwise it may be considered
withdrawn or treated as a new submission.
5. Accepted manuscripts
When the manuscript has been accepted, the Managing Editor will correct
the style and the format of the manuscript. The corresponding author will
receive a galley proof for one revision. If the galley proof is not returned
before the deadline, the paper may be printed without corrections.
6. Advance Publication online
M&E posts online PDF file versions of manuscripts that have been peer
reviewed, accepted and copy edited. Advance publication manuscripts areaccessible from the M&E website (https://www.jstage.jst.go.jp/browse/
jsme2/advpub/0/_contents). Supplemental material for publication is notposted until the ﬁnal, typeset article.
7. Preparation of manuscripts
Manuscripts should be written in clear and simple English and be doublespaced for all text on A4 size sheets. All pages must be numbered consecutively, starting with the title page. The lines of the text should benumbered to aid the reviewers. The arrangement of manuscripts forRegular Papers should be in the following order: Title, Author’s name andAddress, Abstract, Key words (no more than 5 words), Introduction, Materials
and Methods, Results, Discussion (or Results and Discussion), Acknowledgements, References, Tables, Figure legends and Figures. Use section headings (e.g. Introduction, Materials and Methods etc.) in the main text. The
Abstract should not exceed 250 words for a Regular Paper, and 100
words for a Short Communication.
Title page: The title page should include the title, the author’s full name,affiliations, address, and the running head. The title should be as concise
and informative as possible. Multiple authors with different addresses must
indicate their respective addresses with numerals. Place an asterisk after

the name of the author to whom correspondence regarding the manuscript
should be addressed. The abbreviated running head must not exceed 40
characters.
Main document: Subheadings can be used in the Materials and Methods andResults sections of Regular Papers. Use of footnotes is not recommended,
but abbreviations, when necessary, can be defined in a single footnote
inserted in the ﬁrst page of the text.
References: Arrange the citations in alphabetical order by ﬁrst author, andnumber consecutively. The references should not include personal communications, unpublished data or manuscripts in preparation (can be mentioned
parenthetically in the text). All listed references must be cited parentheticallyby number in the main text. Journal names should be abbreviated according
to the Chemical Abstracts Service Source Index (CASSI). Each citationmust be complete: Do not use ibid., idem., etc. Articles written by more than
10 authors should be edited to show the first three authors, and ‘et al’.
Acceptable styles are shown in the examples below. For additional styles
and formats, consult recent issues of M&E.
1. Saito, A., S. Ikeda, H. Ezura, and K. Minamisawa. 2007. Microbialcommunity analysis of the phytosphere using culture-independent methodologies. Microbes Environ. 22:93–105.
2. Katsuyama, C., N. Kondo, Y. Suwa, T. Yamagishi, M. Itoh, N. Ohte, H.
Kimura, K. Nagaosa, and K. Kato. 11 November 2008. Denitriﬁcation activity and relevant bacteria revealed by nitrite reductase gene fragments in soilof temperate mixed forest. Microbes Environ. doi:10.1264/jsme2.ME08541.
3. Sambrook, J., and D.W. Russell. 2001. Molecular Cloning: A Laboratory
Manual, 3rd ed. Cold Spring Harbor Laboratory Press, New York.
4. Hiraishi, A. 1989. Isoprenoid quinone proﬁles for identifying and classifying microorganisms in the environment, p. 663-668. In T. Hattori, Y.
Ishida, Y. Maruyama, R. Y. Morita, and A. Uchida (ed.), Recent
Advances in Microbial Ecology. Japan Scientiﬁc Societies Press, Tokyo.
Figures: All ﬁgures including drawings and photographs should be of
their intended publication size, and numbered consecutively. Indicate theﬁgure numbers and ﬁrst-author’s name in the margin. Titles and legends offigures must be typed separately. Graphs should be made from finisheddrawings not requiring additional artwork or typesetting. Photographs
should be glossy and rich in contrast. Give scales in microphotographs: Do
not report enlargement factors in legends. Figures in 1-column (maximum
width ca. 8.3 cm) of the printed page are strongly recommended. The maximum width for a 2-column ﬁgure should be 17.6 cm.
Tables: Each table must be typed on a separate page, and all tables shouldhave titles. Abbreviations, when necessary, should be deﬁned in table footnotes. The maximum width of a table on the printed page should be 17.6 cm,
and thus the author should prepare tables as concise as possible.
Supplemental material: Supplemental material may be posted by M&E for
any types of paper. M&E encourages that supplemental material be uploadedin the online submission system as a single PDF ﬁle or a single video imaging file. The maximum size permitted for an individual file is 10 MB.
Supplemental ﬁgures and tables should be referred to as Fig. S1 and Table
S1, respectively. A figure for large phylogenetic tree should be basicallysupplied as a supplemental material.
8. Publication Fees
Starting May 1st, 2018, authors are required to pay publication fees for
all accepted manuscripts. For a corresponding author who is a JSME/JSSM/TSME/JSPMI member, article processing charge is ¥60,000
per article (subject to change, without notice). For a nonmember
corresponding author, article processing charge is ¥140,000 per article(subject to change, without notice). NOTE: Only after confirmationof payment of page charges, the accepted article will proceed to
publication process. The cost of publishing colored ﬁgures needs to beborne by the authors. The costs are ¥50,000 for each printed color page.
The authors may choose a lower-cost color page only for publication in the
online version of M&E. In this publication style, the page in the printed
journal is in black and white, however the web page of the online journal isin full-color.
9. Permission
The corresponding author is responsible for obtaining permission from boththe original author(s) and the publishers (i.e., the copyright owner) to reproduce or modify ﬁgures and tables, and to reproduce text from previous publications. Statements indicating that the material is being reprinted with permission must be included in the relevant ﬁgure legend or table footnote of
the manuscript. Reprinted text must be enclosed in quotation marks, andthe permission statement must be included as running text or indicatedparenthetically.
The latest full version of Instructions for Authors is also available throughthe Internet at the following URL: http://www.microbes-and-environments.
jp/about/forauthors/
Copyright (C) 2019, Japanese Society of Microbial Ecology (JSME) /Japanese Society of Soil Microbiology (JSSM) / Taiwan Society of Microbial
Ecology (TSME) / Japanese Society of Plant Microbe Interactions (JSPMI).
All rights reserved

Microbes and Environments
DIGEST 2019
CONTENTS
MOST VALUABLE PAPER OF THE YEAR 2017
Predominant but Previously-overlooked Prokaryotic Drivers of Reductive Nitrogen
Transformation in Paddy Soils, Revealed by Metatranscriptomics

Yoko Masuda, HideoMi itoH, Yutaka sHiratori, kazuo isobe, sHigeto otsuka, and keisHi senoo
32(2): 180–183, 2017

1

MOST VALUABLE PAPER OF THE YEAR 2018
Nitric Oxide Production from Nitrite Reduction and Hydroxylamine Oxidation by
Copper-containing Dissimilatory Nitrite Reductase (NirK) from the Aerobic Ammonia-oxidizing
Archaeon, Nitrososphaera viennensis
sHun kobaYasHi, daisuke Hira, keitaro YosHida, Masanori toYofuku, Yosuke sHida,
Wataru ogasaWara, takasHi YaMagucHi, nobuo araki, and MaMoru osHiki
33(4) 428–434

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Metagenomics Reveals a Novel Virophage Population in a Tibetan Mountain Lakeseungdae oH, dongWan Yoo, and Wen-tso Liu
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MOST CITED REVIEW PAPER IN THIS THREE YEARS [2016-2018](24 citations)
Metagenomics and Bioinformatics in Microbial Ecology: Current Status and Beyond
satosHi Hiraoka, cHing-cHia Yang, and Wataru iWasaki
31(3) 204–212 201617

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Candidatus Methanogranum caenicola: a Novel Methanogen from the Anaerobic Digested
Sludge, and Proposal of Methanomassiliicoccaceae fam. nov. and Methanomassiliicoccales ord.
nov., for a Methanogenic Lineage of the Class Thermoplasmata
takao iino, HideYuki taMaki, satosHi taMazaWa, YosHiYuki ueno, MoriYa oHkuMa,
ken-icHiro suzuki, Yasuo igarasHi, and sHin Haruta
28(2) 244–250 2013

26

MOST CITED REVIEW PAPER IN THIS TEN YEARS [2009-2018](116 citations)Cultivation of Uncultured Chloroflexi Subphyla: Significance and Ecophysiology of Formerly
Uncultured Chloroflexi ‘Subphylum I’ with Natural and Biotechnological RelevancetakesHi YaMada, and Yuji sekigucHi
24(3) 205–216 200933