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The complete genome sequence of the rumen methanogen Methanobrevibacter millerae SM9


Methanobrevibacter millerae SM9 was isolated from the rumen of a sheep maintained on a fresh forage diet, and its genome has been sequenced to provide information on the phylogenetic diversity of rumen methanogens with a view to developing technologies for methane mitigation. It is the first rumen isolate from the Methanobrevibacter gottschalkii clade to have its genome sequence completed. The 2.54 Mb SM9 chromosome has an average G + C content of 31.8 %, encodes 2269 protein-coding genes, and harbors a single prophage. The overall gene content is comparable to that of Methanobrevibacter ruminantium M1 and the type strain of M. millerae (ZA-10T) suggesting that the basic metabolism of these two hydrogenotrophic rumen methanogen species is similar. However, M. millerae has a larger complement of genes involved in methanogenesis including genes for methyl coenzyme M reductase II (mrtAGDB) which are not found in M1. Unusual features of the M. millerae genomes include the presence of a tannase gene which shows high sequence similarity with the tannase from Lactobacillus plantarum, and large non-ribosomal peptide synthase genes. The M. millerae sequences indicate that methane mitigation strategies based on the M. ruminantium M1 genome sequence are also likely to be applicable to members of the M. gottschalkii clade.


Ruminant livestock such as cattle and sheep produce methane as a product of enteric fermentation and ruminant-derived methane accounts for almost 30 % of New Zealand’s anthropogenic greenhouse gas emissions. Methane is produced by methanogenic archaea, and sequencing of 16S rRNA gene amplicons has shown that members of the orders Methanobacteriales and Methanomassiliicoccales are the dominant methanogens in the rumens of farmed New Zealand ruminants [1, 2]. Among the Methanobacteriales two different Methanobrevibacter species (or clades of very closely related species) constitute the bulk of the population. These two clades are the Methanobrevibacter gottschalkii clade ( M. gottschalkii , M. millerae and M. thaueri ) and the Methanobrevibacter ruminantium clade ( M. olleyae and M. ruminantium ) with a mean abundance of 42.4 and 32.9 % respectively [2]. These Methanobrevibacter species produce methane hydrogenotrophically using hydrogen or formate formed during the fermentation of ingested feed by other members of the rumen microbiota [1]. To mitigate emissions of methane from ruminants into the atmosphere, strategies are being developed to reduce the number or activity of methanogens in the rumen. These mitigation strategies include the development of vaccines and inhibitors based on genome sequences of key methanogens [3]. We have previously used the genome sequence of the type strain of M. ruminantium to identify methane mitigation targets [4] and here we present the genome sequence of M. millerae SM9, a rumen representative of the M. gottschalkii clade.

Organism information

Classification and features

Methanobrevibacter millerae SM9 was isolated from the rumen of a sheep maintained on a fresh forage diet [5]. SM9 cells are Gram positive, non-motile coccobacilli occurring singly or in pairs (Fig. 1). Although originally described as Methanobrevibacter sp. [5] or M. smithii [6], the 16S rRNA from SM9 is 99 % similar to the M. millerae type strain ZA-10T (DSM 16643) [7] and as such SM9 can be considered as a strain of M. millerae (Fig. 2). Additional characteristics of M. millerae SM9 are shown in Table 1.

Fig. 1
figure 1

Morphology of M. millerae SM9. Micrograph of M. millerae SM9 cells captured at 100× magnification using UV illumination to show F420 fluorescence

Fig. 2
figure 2

Phylogenetic tree highlighting the position of M. millerae SM9 relative to the type strains of the other species within the genus Methanobrevibacter. The evolutionary history was inferred by using the Maximum Likelihood method based on the General Time Reversible model [38]. The tree with the highest log likelihood (−4507.7026) is shown. The percentage of trees in which the associated taxa clustered together is shown next to the branches. Initial tree(s) for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using the Maximum Composite Likelihood (MCL) approach, and then selecting the topology with superior log likelihood value. A discrete Gamma distribution was used to model evolutionary rate differences among sites [5 categories (+G, parameter = 0.2484)]. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. The analysis involved 15 nucleotide sequences. All positions with less than 95 % site coverage were eliminated. That is, fewer than 5 % alignment gaps, missing data, and ambiguous bases were allowed at any position. There were a total of 1206 positions in the final dataset. Evolutionary analyses were conducted in MEGA6 [39]. Species with strain genome sequencing projects registered in the Genomes Online Database (GOLD) [40] are labeled with an asterisk

Table 1 Classification and general features of Methanbtevibacter millerae SM9 [41]

Genome sequencing information

Genome project history

Methanobrevibacter millerae SM9 was selected for genome sequencing on the basis of its phylogenetic position relative to other methanogens belonging to the family Methanobacteriaceae , and falls within the M. gottschalkii clade of rumen methanogens. The genome sequence of SM9 is being used to underpin the development of technologies to mitigate methane emissions from ruminant livestock. A summary of the genome project information is shown in Table 2 and Additional file 1: Table S1. The 2.73 Mb draft genome sequence of M. millerae ZA-10T (JGI IMG/ER genome ID 2593339167) was produced by the Hungate1000 project [8] and used for comparison with SM9.

Table 2 Project information

Growth conditions and genomic DNA preparation

SM9 was grown in BY medium [9] with added SL10 Trace Elements solution (1 ml l−1) [10], selenite/tungstate solution (final concentrations of selenite and tungstate were 3 and 4 μg l−1 respectively) [11] and Vitamin 10 solution (0.1 ml added to 10 ml culture before inoculation) [4]. Hydrogen was supplied as the energy source by pumping the culture vessels to 180 kPa over pressure with an 80:20 mixture of H2:CO2. Genomic DNA was extracted from freshly grown cells using a modified version of a liquid N2 freezing and grinding method as described previously [12], and purified using the Qiagen Genomic-Tip 500 Maxi kit (Qiagen, Hilden, Germany). Genomic DNA was precipitated by the addition of 0.7 vol isopropanol, and collected by centrifugation at 12,000 × g for 10 min at room temperature. The supernatant was removed, and the DNA pellet was washed in 70 % ethanol, re-dissolved in TE buffer (10 mM Tris–HCl, 1 mM EDTA pH 7.5) and stored at −20 °C until required.

Genome sequencing and assembly

The complete genome sequence of SM9 was determined using pyrosequencing of a paired-end 454 GS-FLX sequence library and a mate-pair 454 GS FLX with Titanium chemistry sequence library (Macrogen, Korea). Pyrosequencing reads provided 213× coverage of the genome and were assembled using the Newbler assembler version 2.0 (Roche 454 Life Sciences, USA). The assembly process resulted in 52 contigs across 1 scaffold. Gap closure was managed using the Staden package [13] and gaps were closed using additional Sanger sequencing by standard and inverse PCR based techniques. A total of 169 additional reactions were used to close gaps and to improve the quality of the genome sequence to ensure correct assembly and to resolve any remaining base-conflicts. Assembly validation was confirmed by pulsed-field gel electrophoresis (data not shown) as described previously [14], using the enzyme MluI which cuts the SM9 chromosome at 16 sites.

Genome annotation

A GAMOLA/ARTEMIS [15, 16] software suite was used to manage genome annotation. Protein-encoding open reading frames were identified using the ORF-prediction program Glimmer [17] and BLASTX [18, 19]. A manual inspection was performed to verify or, if necessary, redefine the start and stop codons of each ORF. Assignment of protein function to ORFs was performed manually using results from the following sources; BLASTP [18] to both a non-redundant protein database provided by the National Centre for Biotechnology Information [20] and Clusters of Orthologous Groups database [21]. HMMER [22] was used to identify protein motifs to both the PFAM [23] and TIGRFAM [24] libraries. TMHMM [25], ( was used to predict transmembrane sequences, and SignalP, version 4.1 [26] was used for the prediction of signal peptides. Ribosomal RNA genes were detected on the basis of BLASTN searches to a custom GAMOLA ribosomal database. Transfer RNA genes were identified using tRNAscan-SE [27]. The genome sequence was prepared for NCBI submission using Sequin [28], and the adenine residue of the start codon of the Cdc6-1 replication initiation protein (sm9_0001) gene was chosen as the first base for the genome. Synteny plots were generated using the program MUMmer, version 3.07 [29]. Only scaffold sequence information greater than 50 kb from the draft genome of M. millerae ZA-10T (JGI IMG/ER genome ID 2593339167) was used in the syntheny analysis. The number of shared and unique genes between SM9 and ZA-10T was calculated based on OrthoMCL analysis [30].

Genome properties

The genome of M. millerae SM9 consists of a single 2,543,538 base pair (bp) circular chromosome with an average G + C content of 31.8 %. A total of 2370 genes were predicted, of which 2269 were protein-coding genes. The properties and statistics of the SM9 genome are summarized in Tables 3 and 4, and the nucleotide sequence has been deposited in GenBank under accession number CP011266. The SM9 genome contains an integrated 49 kb prophage (sm9_0421-sm9_0483). Most of the genes in this region are predicted to encode hypothetical proteins together with an integrase, a MCM family protein, a terminase, restriction-modification system components and a predicted endoisopeptidase that may function as a lytic enzyme (sm9_0468). There is no homology between this prophage region and that found in the genome of Methanobrevibacter ruminantium M1 [4]. The genome atlas for M. millerae SM9 is shown in Fig. 3.

Table 3 Genome statistics
Table 4 Number of genes associated with the general COG functional categories
Fig. 3
figure 3

Genome atlas for M. millerae SM9. The circles from the outside represent: (1) forward and reverse coding domain sequences, the colour coding of the CDS represent different Clusters of Orthologous Groups categories; (2) rRNA and tRNA; (3) % GC plot; (4) GC skew [(GC)/(G + C)]

Insights from the genome sequence

The genome of M. millerae SM9 shows a high level of synteny (Fig. 4a) with that of M. millerae ZA-10T. Comparison of the ORFeome of SM9 with that of ZA-10 shows a core genome of 1783 genes with 486 unique genes in SM9 and 600 in ZA-10.

Fig. 4
figure 4

Synteny analysis. Alignment of the M. millerae SM9 genome against the draft genome of M. millerae ZA-10T (a) and the complete genome of M ruminantium M1 (b). Whenever the two sequences agree, a coloured line or dot is plotted. If the two sequences were perfectly identical, a single line would go from the bottom left to the top right. Units displayed in base-pairs

Although the genomes of M. millerae SM9 and M. ruminantium M1 do not show significant synteny (Fig. 4b), their gene contents are comparable suggesting that the basic metabolism of these two hydrogenotrophic rumen methanogen species is similar. However, there are important differences between the methanogenesis genes from the two species. M. millerae SM9 has the same set of methanogenesis genes as M. ruminantium M1, but also has several genes not found in M1 including an additional gene cluster containing the methyl coenzyme M reductase II (mrtAGDB) genes together with a second copy of F420-dependent methylenetetrahydromethanopterin dehydrogenase (mtd), and a second set of formate dehydrogenase genes (flpABD). Compared to M1, SM9 also has additional copies of the methanogenesis genes hmd, hdrABC and mtrH and the methanogenesis marker proteins 5 and 8. The two M. millerae strains have the same complement of methanogenesis genes but the mrtAGDB-mtd and flpABD genes are not co-located in strain ZA-10. It is possible that the difference in methanogenesis genes may allow M. ruminantium and M. millerae to occupy different niches within the rumen environment [4], and explain why both groups are always found in ecological studies of rumen methanogens [31]. Genome sequences from further strains belonging to the M. gottschalkii and M. ruminantium clades are required to determine if these differences are common features of the two groups.

The biosynthetic genes for most cofactors are conserved between the SM9 and M1 strains with the exceptions being biotin, cobalamin and coenzyme M. M1 encoded genes for biotin biosynthesis of bacterial origin [4], but these are not present in SM9 or ZA-10, although both M. millerae strains contain a BioY transporter believed to be responsible for biotin uptake. Many of the cobalamin biosynthesis genes in M1 were clustered and of bacterial origin, whereas SM9 and ZA-10 also have a full complement of cobalamin biosynthesis genes but their organization is different and they are spread throughout the genome. M1 is unable to synthesise coenzyme M because it lacks key genes, but SM9 and ZA-10 have the five genes necessary (comA, comB, comC, comD and comE) for coenzyme M synthesis.

The pseudomurein biosynthesis genes found in SM9 and ZA-10 are similar to those reported for M1, and their genomes also encode genes for the production of several different cell wall associated polysaccharides. Unique genes in strain ZA-10 include a cluster of four genes that have no methanogen matches. These are IE19DRAFT_01711-4 and include genes encoding phosphoenolpyruvate mutase and phosphonopyruvate decarboxylase whose location suggests they could be involved in modification of cell wall polysaccharides. Both strains contain numerous adhesin-like proteins but while the the role of these is not known it seems likely that they are important for methanogen ecology in the rumen [32]. Many of these proteins are very large (sm9_1600 is predicted to encode a protein of 7805 amino acid residues) and their production likely represents a considerable metabolic burden on the cell.

Tannins are polyphenolic secondary metabolites found in a variety of plants used as forages for ruminants, and are known to have significant effects on animal nutrition [33]. One of these effects is to reduce methane production [34] and tannins have been shown to have direct inhibitory effects on methanogens belonging to the genus Methanobrevibacter [35]. Some microorganisms are resistant to tannins and encode the enzyme tannin acyl hydrolase (tannase) which catalyses the hydrolysis of the galloyl ester bond of tannins. The best studied bacterial tannases are those from Lactobacillus plantarum which have been biochemically and structurally characterized [36, 37], but tannases have not been reported from methanogens. Both M. millerae genomes contain genes (sm9_1028 and IE19DRAFT_01487) predicted to encode signal peptide-containing proteins with high sequence identity (50 %) to TanALp from L. plantarum . These proteins contains the conserved sequence motifs involved in catalysis and substrate-binding that have been identified in TanALp [36]. We hypothesize that strains of M. millerae have the ability to produce an extracellular tannase which enables them to tolerate tannins encountered in the rumen, and that this protein has been acquired by horizontal gene transfer from another member of the rumen microbial community. A Blast search of the predicted tannase from SM9 also shows homology with predicted proteins from a number of rumen bacteria sequenced in the Hungate1000 project [8] including organisms belonging to the phyla Actinobacteria ( Corynebacterium and Slackia sp.) and Firmicutes ( Butyrivibrio , Oribacterium , Pseudobutyrivibrio and Streptococcus ). In all cases the residues important for activity are conserved.

The SM9 genome has two non-ribosomal peptide synthase genes (sm9_0755 and _0760 predicted to encode proteins of 2605 and 2394 amino acids) located close together, convergently transcribed, separated by transporters and bounded by transposases. The predicted protein from sm9_0755 is similar (81 % amino acid identity) to the one predicted to be encoded by mru_0068 from M. ruminantium M1 [4]. In contrast the ZA-10 genome has three non-ribosomal peptide synthase genes (IE19DRAFT_00420, _00763 and _01910 predicted to encode proteins of 4187, 4390 and 2573 amino acids) that differ from those found in SM9. The predicted protein from IE19DRAFT_00420 is a close match (89 % amino acid identity) to the one predicted to be encoded by mru_0351 from M1 [4].


The species M. millerae belongs to the Methanobrevibacter gottschalkii clade of rumen methanogens and the availability of genome sequences for strains SM9 and ZA-10 provide valuable information for developing methane mitigation strategies targeting this group. While the M. millerae genome is largely similar to that of M. ruminantium M1 it is notable that strains SM9 and ZA-10 have a larger complement of methanogenesis genes. The M. gottschalkii and M. ruminantium clades are the dominant hydrogenotrophic methanogens in the rumen and these differences in methanogenesis genes may allow them to occupy different niches in the rumen environment. Genome sequences from additional rumen strains will establish if the observations based on these representatives are characteristic of the two clades. Both M. millerae genomes contain a tannase of bacterial origin which may represent an adaptation to the rumen environment as tannin containing plants are an important component of fresh forages, and tannins are known to have an inhibitory effect on methanogens. The overall similarity between the genomes of M. millerae and M. ruminantium M1 suggests that the strategies based on the M1 genome should be generally applicable to methanogens belonging to the M. gottschalkii clade.


H2 :


CO2 :

Carbon dioxide

N2 :



Tris Ethylenediaminetetraacetic acid


  1. Janssen PH, Kirs M. Structure of the archaeal community of the rumen. Appl Environ Microbiol. 2008;74:3619–25.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Seedorf H, Kittelmann S, Janssen PH. Few highly abundant operational taxonomic units dominate within rumen methanogenic archaeal species in New Zealand sheep and cattle. Appl Environ Microbiol. 2015;81:986–95.

    Article  CAS  PubMed  Google Scholar 

  3. Leahy SC, Kelly WJ, Ronimus RS, Wedlock N, Altermann E, Attwood GT. Genome sequencing of rumen bacteria and archaea and its application to methane mitigation strategies. Animal. 2013;7 Suppl 2:235–43.

    Article  PubMed  Google Scholar 

  4. Leahy SC, Kelly WJ, Altermann E, Ronimus RS, Yeoman CJ, Pacheco DM, et al. The genome sequence of the rumen methanogen Methanobrevibacter ruminantium reveals new possibilities for controlling ruminant methane emissions. PLoS ONE. 2010;5:e8926.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Joblin KN. Ruminal acetogens and their potential to lower ruminant methane emissions. Aust J Agric Res. 1999;50:1307–13.

    Article  Google Scholar 

  6. Skillman LC, Evans PN, Strömpl C, Joblin KN. 16S rDNA directed PCR primers and detection of methanogens in the bovine rumen. Lett Appl Microbiol. 2006;42:222–8.

    Article  CAS  PubMed  Google Scholar 

  7. Rea S, Bowman JP, Popovski S, Pimm C, Wright AD. Methanobrevibacter millerae sp. nov. and Methanobrevibacter olleyae sp. nov., methanogens from the ovine and bovine rumen that can utilize formate for growth. Int J Syst Evol Microbiol. 2007;57:450–6.

    Article  CAS  PubMed  Google Scholar 

  8. Creevey CJ, Kelly WJ, Henderson G, Leahy SC. Determining the culturability of the rumen bacterial microbiome. Microb Biotechnol. 2014;7:467–9.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Joblin KN, Naylor GE, Williams AG. Effect of Methanobrevibacter smithii on xylanolytic activity of anaerobic ruminal fungi. Appl Environ Microbiol. 1990;56:2287–95.

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Widdel F, Kohring G, Mayer F. Studies on dissimilatory sulfate-reducing bacteria that decompose fatty acids III. Characterization of the filamentous gliding Desulfonema limicola gen. nov. sp. nov., and Desulfonema magnum sp. nov. Arch Microbiol. 1983;134:286–94.

    Article  CAS  Google Scholar 

  11. Tschech A, Pfennig N. Growth yield increase linked to caffeate reduction in Acetobacterium woodii. Arch Microbiol. 1984;137:163–7.

    Article  CAS  Google Scholar 

  12. Kelly WJ, Leahy SC, Li D, Perry R, Lambie SC, Attwood GT, et al. The complete genome sequence of the rumen methanogen Methanobacterium formicicum BRM9. Stand Genomic Sci. 2014;9:15.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Staden R, Beal KF, Bonfield JK. The Staden package, 1998. Methods Mol Biol. 2000;132:115–30.

    CAS  PubMed  Google Scholar 

  14. Leahy SC, Kelly WJ, Li D, Li Y, Altermann E, Lambie SC, et al. The complete genome sequence of Methanobrevibacter sp. AbM4. Stand Genomic Sci. 2013;8:215–27.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Altermann E, Klaenhammer T. GAMOLA: a new local solution for sequence annotation and analyzing draft and finished prokaryotic genomes. OMICS. 2003;7:161–9.

    Article  CAS  PubMed  Google Scholar 

  16. Rutherford K, Parkhill J, Crook J, Horsnell T, Rice P, Ranjandream MA, Barrell B. Artemis: sequence visualization and annotation. Bioinformatics. 2000;16:944–5. doi:10.1093/bioinformatics/16.10.944.

    Article  CAS  PubMed  Google Scholar 

  17. Delcher AL, Harmon D, Kasif S, White O, Salzberg S. Improved microbial gene identification with GLIMMER. Nucleic Acids Res. 1999;27:4636–41. doi:10.1093/nar/27.23.4636.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Altschul SF, Gish W, Miller W, Myers E, Lipman D. Basic local alignment search tool. J Mol Biol. 1990;215:403–10. doi:10.1016/S0022-2836(05)80360-2.

    Article  CAS  PubMed  Google Scholar 

  19. Gish W, States D. Identification of protein coding regions by database similarity search. Nat Genet. 1993;3:266–72. doi:10.1038/ng0393-266.

    Article  CAS  PubMed  Google Scholar 

  20. NCBI Resource Coordinators. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 2013;41:D8–20.

    Article  Google Scholar 

  21. Tatusov RL, Galperin M, Natale D, Koonin E. The COG database: a tool for genome-scale analysis of protein functions and evolution. Nucleic Acids Res. 2000;28:33–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Eddy SR. Profile hidden Markov models. Bioinformatics. 1998;14:755–63.

    Article  CAS  PubMed  Google Scholar 

  23. Punta M, Coggill PC, Eberhardt RY, Mistry J, Tate J, Boursnell C, Pang N, Forslund K, Ceric G, Clements J, Heger A, Holm L, Sonnhammer EL, Eddy SR, Bateman A, Finn RD. The Pfam protein families data-base. Nucleic Acids Res. 2012;40:D290–301.

    Article  CAS  PubMed  Google Scholar 

  24. Haft DH, Selengut JD, Richter RA, Harkins D, Basu MK, Beck E. TIGRFAMs and genome properties in 2013. Nucleic Acids Res. 2013;41:D387–95.

    Article  CAS  PubMed  Google Scholar 

  25. Krogh A, Larsson B, von Heijne G, Sonnhammer E. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol. 2001;305:567–80.

    Article  CAS  PubMed  Google Scholar 

  26. Petersen TN, Brunak S, von Heijne G, Nielsen H. SignalP 4.0: discriminating signal peptides from transmembrane regions. Nat Methods. 2011;8:785–6.

    Article  CAS  PubMed  Google Scholar 

  27. Lowe TM, Eddy S. tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res. 1997;25:955–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Benson DA, Cavanaugh M, Clark K, Karsch-Mizrachi I, Lipman DJ, Ostell J, et al. GenBank. Nucleic Acids Res. 2013;41:D36–42.

    Article  CAS  PubMed  Google Scholar 

  29. Kurtz S, Phillippy A, Delcher AL, Smoot M, Shumway M, Antonescu C, et al. Versatile and open software for comparing large genomes. Genome Biol. 2004;5:R12.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Li L, Stoeckert Jr CJ, Roos DS. OrthoMCL: identification of ortholog groups for eukaryotic genomes. Genome Res. 2003;13:2178–89.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Henderson G, Cox F, Ganesh S, Jonker A, Young W, Global Rumen Census Collaborators, et al. Rumen microbial community composition varies with diet and host, but a core microbiome is found across a wide geographical range. Sci Rep. 2015;5:14567.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Ng F, Kittelmann S, Patchett ML, Attwood GT, Janssen PH, Rakonjac J, et al. An adhesin from hydrogen-utilizing rumen methanogen Methanobrevibacter ruminantium M1 binds a broad range of hydrogen-producing microorganisms. Environ Microbiol. 2015; doi:10.1111/1462-2920.13155.

  33. Min BR, Barry TN, Attwood GT, McNabb WC. The effect of condensed tannins on the nutrition and health of ruminants fed fresh temperate forages: a review. Animal Feed Sci Technol. 2003;106:3–19.

    Article  CAS  Google Scholar 

  34. Cieslak A, Szumacher-Strabel M, Stochmal A, Oleszek W. Plant components with specific activities against rumen methanogens. Animal. 2013;7 Suppl 2:253–65.

    Article  PubMed  Google Scholar 

  35. Tavendale MH, Meagher LP, Pacheco D, Walker N, Attwood GT, Sivakumaran S. Methane production from in vitro rumen incubations with Lotus pedunculatus and Medicago sativa, and effects of extractable condensed tannin fractions on methanogenesis. Animal Feed Sci Technol. 2005;123–124:403–19.

    Article  Google Scholar 

  36. Ren B, Wu M, Wang Q, Peng X, Wen H, McKinstry WJ, Chen Q. Crystal structure of tannase from Lactobacillus plantarum. J Mol Biol. 2013;425:2737–51.

    Article  CAS  PubMed  Google Scholar 

  37. Jiménez N, Esteban-Torres M, Mancheño JM, de Las RB, Muñoz R. Tannin degradation by a novel tannase enzyme present in some Lactobacillus plantarum strains. Appl Environ Microbiol. 2014;80:2991–7.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Nei M, Kumar S. ReaMolecular evolution and phylogenetics. New York: Oxford University Press; 2000.

    Google Scholar 

  39. Tamura K, Stecher G, Peterson D, Filipski A, Kumar S. MEGA6: Molecular Evolutionary Genetics Analysis version 6.0. Mol Biol Evol. 2013;30:2725–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Reddy TBK, Thomas A, Stamatis D, Bertsch J, Isbandi M, Jansson J, et al. The Genomes OnLine Database (GOLD) v. 5: a metadata management system based on a four level (meta)genome project classification. Nucleic Acids Res. 2015;43:D1099–106.

    Article  CAS  PubMed  Google Scholar 

  41. Field D, Garrity G, Gray T, Morrison N, Selengut J, Sterk P, et al. Towards a richer description of our complete collection of genomes and metagenomes “Minimum Information about a Genome Sequence” (MIGS) specification. Nat Biotechnol. 2008;26:541–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Woese CR, Kandler O, Wheelis ML. Towards a natural system of organisms: proposal for the do-mains Archaea, Bacteria, and Eucarya. Proc Natl Acad Sci USA. 1990;87:4576–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Garrity GM, Phylum HJG, AII. Euryarchaeota phy. nov. In: Garrity GM, Boone DR, Castenholz RW, editors. Bergey’s manual of systematic bacteriology, vol. 1. 2nd ed. New York: Springer; 2001. p. 211–355.

    Chapter  Google Scholar 

  44. Boone DR, Class I. Methanobacteria class. nov. In: Garrity GM, Boone DR, Castenholz RW, editors. Bergey’s Manual of Systematic Bacteriology, vol. 1. 2nd ed. New York: Springer; 2001. p. 213–34.

    Chapter  Google Scholar 

  45. Balch WE, Fox GE, Magrum LJ, Woese CR, Wolfe RS. Methanogens: reevaluation of a unique biological group. Microbiol Rev. 1979;43:260–96.

    CAS  PubMed  PubMed Central  Google Scholar 

  46. List Editor. Validation List no. 6. Validation of the publication of new names and new combinations previously effectively published outside the IJSB. Int J Syst Bacteriol. 1981;31:215–218.

  47. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium Nat Genet. 2000;25:25–9.

    CAS  PubMed  Google Scholar 

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The draft genome sequence of M. millerae DSM 16443T was generated by the Hungate1000 project supported by the New Zealand Government in support of the objectives of the Global Research Alliance on Agricultural Greenhouse Gases and the US Department of Energy Joint Genome Institute Community Science Program (CSP 612).


The SM9 genome sequencing project was funded by the New Zealand Pastoral Greenhouse Gas Research Consortium (PGgRc) and the New Zealand Agricultural Greenhouse Gas Research Centre (NZAGRC).

Authors’ contributions

WJK, GTA, EA, SCL conceived and designed the experiments. DMP, DL, SCL performed the sequencing and assembly experiments. WJK, EA, SCL performed the genome annotation and comparative studies. WJK, SCL wrote the manuscript. All authors commented on the manuscript before submission. All authors read and approved the final manuscript.

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The authors declare that they have no competing interests.

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Correspondence to Sinead C. Leahy.

Additional file

Additional file 1: Table S1.

Associated MIGS record for M. millerae SM9, which links to the SIGS supplementary content website. (DOC 70 kb)

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Kelly, W.J., Pacheco, D.M., Li, D. et al. The complete genome sequence of the rumen methanogen Methanobrevibacter millerae SM9. Stand in Genomic Sci 11, 49 (2016).

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  • Methanogen
  • Methane
  • Rumen
  • Methanobrevibacter millerae