Journal of Biotechnology 136 (2008) 77–90

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Journal of Biotechnology

journa l homepage: www.e lsev ier .com/ locate / jb io tec

The metagenome of a biogas-producing microbial community of aproduction-scale biogas plant fermenter analysed by the454-pyrosequencing technology

Andreas Schlutera,∗, Thomas Bekelb, Naryttza N. Diazb, Michael Dondrupb, Rudolf Eichenlaubc,Karl-Heinz Gartemannc, Irene Krahna, Lutz Krauseb,g, Holger Kromekeb, Olaf Krused,Jan H. Mussgnugd, Heiko Neuwegerb, Karsten Niehause, Alfred Puhlera, Kai J. Runteb,Rafael Szczepanowskia, Andreas Taucha, Alexandra Tilkera, Prisca Viehover f, Alexander Goesmannb

a Lehrstuhl fur Genetik, Universitat Bielefeld, D-33594 Bielefeld, Germanyb Centrum fur Biotechnologie (CeBiTec), Universitat Bielefeld, D-33594 Bielefeld, Germanyc Lehrstuhl fur Gentechnologie/Mikrobiologie, Universitat Bielefeld, D-33594 Bielefeld, Germanyd Algenbiotechnologie, Universitat Bielefeld, D-33594 Bielefeld, Germanye Proteom und Metabolomforschung, Universitat Bielefeld, D-33594 Bielefeld, Germanyf Lehrstuhl fur Genomforschung, Universitat Bielefeld, D-33594 Bielefeld, Germanyg Nestle Research Center, BioAnalytical Science Department, CH-1000 Lausanne 26, Switzerland

a r t i c l e i n f o

Article history:Received 12 March 2008Received in revised form 16 April 2008Accepted 8 May 2008

Keywords:Biogas fermentationMethanogenesisMethane production454-PyrosequencingMetagenome

a b s t r a c t

Composition and gene content of a biogas-producing microbial community from a production-scale biogasplant fed with renewable primary products was analysed by means of a metagenomic approach applyingthe ultrafast 454-pyrosequencing technology. Sequencing of isolated total community DNA on a GenomeSequencer FLX System resulted in 616,072 reads with an average read length of 230 bases accounting for141,664,289 bases sequence information. Assignment of obtained single reads to COG (Clusters of Orthol-ogous Groups of proteins) categories revealed a genetic profile characteristic for an anaerobic microbialconsortium conducting fermentative metabolic pathways. Assembly of single reads resulted in the forma-tion of 8752 contigs larger than 500 bases in size. Contigs longer than 10 kb mainly encode house-keepingproteins, e.g. DNA polymerase, recombinase, DNA ligase, sigma factor RpoD and genes involved in sugarand amino acid metabolism. A significant portion of contigs was allocated to the genome sequence ofthe archaeal methanogen Methanoculleus marisnigri JR1. Mapping of single reads to the M. marisnigri JR1genome revealed that approximately 64% of the reference genome including methanogenesis gene regionsare deeply covered. These results suggest that species related to those of the genus Methanoculleus play adominant role in methanogenesis in the analysed fermentation sample. Moreover, assignment of numer-ous contig sequences to clostridial genomes including gene regions for cellulolytic functions indicates thatclostridia are important for hydrolysis of cellulosic plant biomass in the biogas fermenter under study.

Metagenome sequence data from a biogas-producing microbial community residing in a fermenter of abiogas plant provide the basis for a rational approach to improve the biotechnological process of biogas

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production.

. Introduction

Renewable resources for energy production come more andore into public focus because of problems caused by the pre-

ictable shortage of fossil fuels in the next decades and by

∗ Corresponding author at: Lehrstuhl fur Genetik, Universitat Bielefeld, Postfach00131, D-33501 Bielefeld, Germany. Tel.: +49 521 106 2036; fax: +49 521 106 5626.

E-mail address: Andreas.Schlueter@Genetik.Uni-Bielefeld.DE (A. Schluter).

To(Ycmrie

168-1656/$ – see front matter © 2008 Elsevier B.V. All rights reserved.oi:10.1016/j.jbiotec.2008.05.008

© 2008 Elsevier B.V. All rights reserved.

lobal warming due to CO2 release from burning of fossil fuels.hese problems can partly be circumvented by the productionf biogas from plant or waste material in a biological processAngelidaki and Ellegaard, 2003; Daniels, 1992; Weiland, 2003;advika et al., 2004). Anaerobic degradation of plant biomass

arried out in biogas plants can be subdivided into differentetabolic steps. First, plant compounds including cell wall mate-

ial such as cellulose and xylan are hydrolysed and convertednto mono-, di- and oligosaccharides (Bayer et al., 2004; Cirnet al., 2007; Lynd et al., 2002). This hydrolysis step is conducted

7 Biotec

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8 A. Schluter et al. / Journal of

ainly by cellulolytic Clostridia and Bacilli, but is often inefficientnder anaerobic conditions. Sugar intermediates are fermentedo organic acids (acidogenesis) which in turn are converted tocetate, CO2 and H2 by bacteria performing secondary fermenta-ions (Drake et al., 1997, 2002; Myint et al., 2007; Shin and Youn,005). The final methanogenesis step is conducted by Archaeahich are restricted to a limited spectrum of input substrates

acetate, CO2 and H2, some C1 compounds like formate and alco-ols) that can be used for methane formation (Deppenmeier etl., 1996). Hydrolysis, acidogenesis, and acetogenesis are con-ucted by members of the Eubacteria. Several biochemical reactionsre thermodynamically only possible in close interaction of ateast two different bacterial partners (e.g. syntrophic H2 feeding)Schink, 1997, 2006). The enzymology of methanogenic path-ays has been analysed in detail for model systems (Blaut,

994; Deppenmeier, 2002; Ferry, 1992, 1999; Reeve, 1992; Reevet al., 1997; Schnurer et al., 1999). However, the compositionnd interactions within a biogas-producing microbial commu-ity, and the contribution of a specific bacterium to the overallrocess are mainly unknown. Moreover, the influence of physico-hemical parameters on population structure and efficiency ofiogas formation is still under investigation (Karakashev et al.,005; Shigematsu et al., 2004, 2006). Thus, a rational approacho improve the performance of biogas plants is impossible at the

oment.The composition of biogas-producing microbial communities

ommonly is determined via construction of 16S-rDNA cloneibraries and subsequent sequencing of 16S-rDNA ampliconsHuang et al., 2002; Klocke et al., 2007; McHugh et al., 2003;

ladenovska et al., 2003). Moreover, Polymerase Chain Reactioningle Strand Conformation Polymorphism (PCR-SSCP) followedy sequencing of obtained DNA-molecules was also used to elu-idate community structures in biogas reactors (Chachkhiani et al.,004). Another valuable marker for the analysis of methanogenicommunities is the mcrA gene encoding a key-enzyme of methano-enesis, namely the �-subunit of methyl-coenzyme M reductaseMCR). Many methanogenic communities were analysed by usinghe mcrA gene as a phylogenetic marker (Lueders et al., 2001; Lutont al., 2002; Friedrich, 2005; Juottonen et al., 2006; Rastogi et al.,007).

Development of second-generation ultrafast sequencing tech-ologies such as 454-pyrosequencing led to the realisationf cost-effective large-scale environmental shotgun sequencingrojects. Metagenomics became a versatile approach for explo-ation of different habitats for the structure, gene content andunction of the respective autochthonous microbial communities.he number of metagenome projects using ultrafast sequenc-ng techniques is constantly increasing (Angly et al., 2006;dwards et al., 2006; Gill et al., 2006; Turnbaugh et al., 2006).ioinformatics for the interpretation of metagenomic data has co-rdinately been improved (Raes et al., 2007). Recently, a novelene finding algorithm that allows for exploitation of the lim-ted information contained in the 250 nucleotides reads generatedy 454-pyrosequencing for the prediction of coding sequencesas developed (Krause et al., 2006). Moreover, design of bioin-

ormatics strategies and tools for metagenomic data processingacilitates insights in community structures and gene contentf microbial consortia from different habitats (Krause et al.,008a,b).

Here, insight into the metagenome of a biogas-producing

icrobial community residing in the main fermenter of a

roduction-scale biogas plant is presented. Obtained nucleotideequence data were analysed at the single read and contig levelor their genetic information content by applying different bioin-ormatics approaches.

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hnology 136 (2008) 77–90

. Materials and methods

.1. Total community DNA preparation from a fermentationample of a biogas reactor

A fermentation sample was taken from the first biogas fermenterf an agricultural biogas plant located in Bielefeld-JollenbeckGermany) in August 2007. The sample was stored in entirelylled, screw capped bottles and transferred to the laboratory. Thenalysed 500 kW biogas plant consists of two fermenters andstorage reservoir and was continuously fed with maize silage

63%), green rye (35%) and low amounts of chicken manure (appr.%). The substrate was fermented at appr. 41 ◦C at a pH-value of.7. The retention period of the substrate was 40–60 days. Fur-her data for the analysed bioreactor were: volatile organic acids,739 mg acid L−1; total anorganic carbon, 15,159 mg CaCO3 L−1 and628 mg acetic acid L−1. The biogas plant started to operate inecember 2005.

First microscopic analysis of the fermentation sample was car-ied out within 2 h upon sampling. Samples were diluted withhree parts of sterile tap-water. The diluted fermentation sludgeas stained for 20 min by the addition of 2 �g/ml 4′,6′-diamidino--phenylindole hydrochloride (DAPI). Bacteria were visualised byNikon eclips 80i epi-fluorescence microscope equipped with anlan Apo 60× (na 1.2) objective and DAPI filter settings (EX 340-380;M 400; BA 435-485). Photos were taken using a Nikon camera inutomatic mode.

A 20 g aliquot of the fermentation sample was used for totalommunity DNA preparation by applying a CTAB (cetyltrimethy-ammonium bromide) containing DNA extraction buffer asescribed previously (Entcheva et al., 2001; Henne et al.,999; Zhou et al., 1996). The obtained DNA pellet was resus-ended in 8 ml TE buffer. One milliliter of the total genomicNA preparation was purified on 10 MicroSpin S-400 HR

ephacryl columns (GE Healthcare, Munchen, Germany). Afterhis final purification step ten DNA-eluates were pooled andubjected to precipitation using 40 �l NaCl (5 M) and 2 mlthanol (−20 ◦C). After centrifugation (11,500 rpm, 10 min) theNA-pellet was resuspended in 100 �l TE buffer. DNA con-entration was estimated by means of the NanoDrop 2000nstrument (NanoDrop Technologies, Wilmington, USA) and anal-sed by gelelectrophoresis. The applied method yielded a highlyure genomic DNA (A260/A280 = 1.8) with a concentration of44 ng/�l.

.2. Sequencing of the biogas reactor total community DNAreparation on a Genome Sequencer FLX System

Sequencing of the genomic DNA derived from the biogaseactor sample was done by applying the whole-genome-shotgunequencing approach on the Genome Sequencer FLX System (Rochepplied Science, Mannheim, Germany). Approximately 5 �g of theNA-preparation were used to generate a whole-genome-shotgun

ibrary according to the protocol supplied by the manufacturer.fter titration, 3.5 DNA-copies per bead were used for the mainequencing run. After emulsion PCR and subsequent bead recovery,00,000 DNA-beads were loaded onto each half of the PicoTiter-late and subjected to sequencing. Obtained metagenome sequence

e/pub/supplements/SchlueterEtAl Metagenome JournalBiotech008.zip.

The Genome Sequencer De Novo Assembler Software (Rochepplied Science, Mannheim, Germany) was used for assembly of

he obtained nucleotide sequence reads.

Biotec

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acatfTg2pmmtained huge amounts of differently shaped bacteria, but showedno nuclei characteristic for eukaryotic cells (see Fig. 1). Isolationof total community DNA from the fermentation sample using aCTAB-based method including a final gel filtration purification step

A. Schluter et al. / Journal of

.3. Classification of metagenome single reads according to COGategories

To characterise the gene content of the biogas reactor sample,ll reads were functionally annotated by means of the Clustersf Orthologous Groups of proteins database (COG) (Tatusov et al.,000, 2001). COGs were identified in the biogas reactor sampleased on a BLASTx search of reads vs. the COG database using the

-w 15’ frameshift option and an E-value cut-off of 10−8. Reads weressigned to the COG category of their best BLAST hit.

.4. Characterisation of metagenome reads and assembledontigs by means of the SAMS system

The Sequence Analysis and Management System SAMShttp://www.cebitec.uni-bielefeld.de/groups/brf/software/samsnfo/) was originally developed for quality supervision of genomeequencing projects. In addition to the quality assessment ofhotgun nucleotide sequence data, SAMS is well suited for thenalysis of individual sequence fragments. In this study the systemas used to characterise short metagenome contigs. Similar to

he annotation of predicted chromosomal coding regions, individ-al short sequences are analysed and functionally annotated inAMS using an automated approach. For this purpose, a functionrediction is computed by interpreting the combined results oftandard bioinformatics tools such as BLAST and InterPro (Muldernd Apweiler, 2007; Mulder et al., 2007). This approach leads toonsistent annotation data, assigning gene names, gene products,C numbers and functional protein categories (COGs). For theunctional annotation of short metagenome contigs, the analysisipeline was applied with four different BLAST tools: BLAST2x vs.he NCBI NR protein database (E-value cut-off of 10−5), BLAST2xs. the SWISSPROT protein database (E-value cut-off of 10−5),LAST2x vs. the COG protein database (E-value cut-off of 10−5) andLAST2x vs. the KEGG Database (E-value cut-off of 10−5).

.5. Gene identification and automatic functional annotation ofong contigs

For the functional annotation of long assembled contigs fromhe metagenome of the fermentation sample, the GenDB (Meyer etl., 2003) genome annotation system was employed. For predictionf coding sequences the Reganor Pipeline (Linke et al., 2006) andismo (Krause et al., 2007) were used. Additionally, search for RNAsnd tRNA-scan-SE were applied to identify rRNA and tRNA regions.or each predicted CDS, the automatic Metanor pipeline was usedo compute consistent function assignments based on the followinget of tools: BLAST2n vs. the NCBI nucleotide database; BLAST2p vs.he NCBI protein database; BLAST2p vs. the KEGG protein database;SI-BLAST vs. the Swissprot database, PSI-BLAST vs. COG, HMMerearches vs. Pfam and TIGRFAM and InterPro. Moreover, the follow-ng tools were computed: RPSBLAST vs. CDD, TMHMM, SignalP, andelix-turn-helix.

.6. Identification of cellulosome genes on assembled contigs

To search for contigs encoding cellulosome proteins, all pro-ein sequences associated with the annotation term ‘Cellulosome’rom species of the order Clostridiales were collected from the NCBIequence database and imported into a BLAST database. A tBLASTn

earch for all contigs of the metagenome data set from the biogasermenter against the cellulosome protein database was carried outnd the best matching contigs were identified. Information on theuality of the computed alignment (length, percentage of identicalmino acids, E-value) and the total number of contigs that match

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hnology 136 (2008) 77–90 79

he same reference protein with an E-value better than 1e−5 wasxtracted from the BLAST results.

.7. Mapping of metagenome reads to the Methanoculleusarisnigri JRI genome

Metagenome sequence reads were aligned to the completelyequenced M. marisnigri JR1 genome based on a BLASTn searchf reads vs. the genome sequence of that strain downloaded fromhe GenBank Database (Accession No.: NC 009051). A cut-off valuef 1e−10 was set. The coverage of the M. marisnigri referenceequence by reads obtained from the biogas fermentation sam-le was visualised using the ReadMapper software (Krause et al.,008b). ReadMapper aligns reads to reference sequences usingLASTn. Resulting alignments are visualised in a coverage plot.

. Results and discussion

.1. Metagenome sequencing of a biogas-producing microbialommunity residing in an agricultural biogas fermenter by meansf the 454-pyrosequencing technology

To analyse a biogas-producing microbial community residing inbiogas fermenter in terms of its structure, gene content, metabolicapabilities and the role of specific organisms for biogas formation,metagenomic approach using the ultrafast 454-pyrosequencing

echnology was accomplished. A fermentation sample was takenrom the first fermenter of a 500 kW agricultural biogas plant.he bioreactor had been continuously fed with maize silage (63%),reen rye (35%) and low amounts of chicken manure (around%) and operates at mesophilic temperatures (around 41 ◦C) at aH-value of about 7.7. To get a first insight into the microbial com-unity, the fermentation sample was analysed by fluorescenceicroscopy. DAPI staining of DNA revealed that the sample con-

ig. 1. Microscopic analysis of the microbial community residing in a fermentationample obtained from an agricultural biogas plant. The sample was taken from theain fermenter and analysed within 2 h after sampling. The sample was stained

or DNA with DAPI and analysed by fluorescence microscopy. The sample containeduge amounts of differently shaped bacteria, but showed no nuclei characteristic

or eukaryotic cells. The bar represents 10 �m.

80 A. Schluter et al. / Journal of Biotec

Table 1Statistics of the 454-sequencing and assembly approach

Number of reads 616,072Number of bases 141,664,289 basesAverage read length 229.9 basesNumber of large contigs 8,752Number of bases in large contigs 11,797,906 basesAverage large contig size 1,348 basesLargest contig 31,533 basesNumber of all contigs 57,108NP

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umber of bases in all contigs 22,724,756 basesercentage of assembled bases (%) 16.04%

ielded highly pure genomic DNA. The applied total communityNA extraction method was described previously for the analysis ofethanogenic population structures in a variety of anaerobic biore-

ctors (McHugh et al., 2003). The purified total community DNA wassed for library construction and subsequent pyrosequencing on aenome Sequencer FLX System. One sequencing run of the sam-le resulted in 616,072 reads with an average read length of about30 bases accounting for 141,664,289 bases sequence information.or the first time, metagenomic sequence reads generated by a sec-nd generation sequencing technology were assembled by meansf the GS De Novo Assembler Software supplied by Roche Appliedcience (Mannheim, Germany). This approach resulted in the for-ation of 8752 large contigs (>500 nt) with an average size of 1348

ases. Forty contigs are larger than 10 kb in size and the longestontig has a length of 31,533 bp. Statistical data summarising theequencing approach and the assembly output are given in Table 1.

.2. Classification of metagenome single reads according to COGategories (clusters of orthologous groups of proteins)

To get insight into biological processes operating in the micro-ial community residing in the biogas reactor sample, sequencesere annotated on a single read basis according to Clusters ofrthologous Groups of proteins categories (COGs) thus assigningredicted functions to coding sequences. A total of 180,998 sample

tfS2

ig. 2. Categorisation of biogas fermenter metagenome sequence reads according to Cluste, translation, ribosomal structure and biogenesis; A, RNA processing and modification;nd dynamics; D, cell cycle control, cell division, chromosome partitioning; V, defense miogenesis; N, cell motility; W, extracellular structures; U, intracellular trafficking, secrehaperones; C, energy production and conversion; G, carbohydrate transport and metabolis, coenzyme transport and metabolism; I, lipid transport and metabolism; P, inorganic ioatabolism; R, general function prediction only; S, function unknown. The abbreviation E

hnology 136 (2008) 77–90

eads (∼29%) were assigned to one or more COG functional cat-gories. COG categorisation of the analysed data set is shown inig. 2.

Among all functional COG categories, ‘Energy production andonversion (C)’ and ‘Carbohydrate transport and metabolism (G)’re of particular interest since during the conversion of biomassnto methane, carbohydrates are broken down into simpler com-ounds in the absence of oxygen. COGs in the functional category

Energy production and conversion (C)’ are mainly related to genesnvolved in fermentation metabolism (see Table 2). Assignments tocetate kinase (COG0282) and phosphotransacetylase (COG0280),nzymes catalysing the final steps in acetate production, werebserved which is in agreement with the fact that acetate is a sub-trate for methanogenesis. Conversely to these results, most aerobicespiration related COGs (e.g. pyruvate dehydrogenase complex orytochrome C oxidase) were scarcely detected correlating to thenaerobic conditions during substrate fermentation in biogas reac-ors.

Enzymes related to the carbohydrate metabolism involvedn processing of monosaccharides and disaccharides such as-arabinose isomerase, l-fucose isomerase, galactokinase, and glu-uronate isomerase were identified among others (see Table 2).n addition, some COGs represent enzymes having functions inoly- and oligosaccharide breakage. These are mainly members ofhe glycosyl hydrolase family. In particular, the COG-group ‘pre-icted glycosyl hydrolase’ should be mentioned in this context.ylan is one of the components of the plant cell wall and cane hydrolysed to xylose. Remarkably, COGs associated with xylanr xylose degradation, were identified in the category ‘Carbohy-rate transport and metabolism (G)’ (see Table 2). Moreover, hitso ABC-type xylose transport system permease components werebserved for the bioreactor metagenome, indicating that xyloses metabolised in the analysed bioreactor. It is important to note

ion is in accordance with the finding that many species in theermentation sample belong to the class Clostridia (see below).pecies of this class are known to form endospores (Paredes et al.,005).

rs of Orthologous Groups of proteins (COGs). Categories are abbreviated as follows:K, transcription; L, replication, recombination and repair; B, chromatin structureechanisms; T, signal transduction mechanisms; M, cell wall/membrane/envelopetion, and vesicular transport; O, posttranslational modification, protein turnover,m; E, amino acid transport and metabolism; F, nucleotide transport and metabolism;n transport and metabolism; Q, secondary metabolites biosynthesis, transport andGT stands for Environmental Gene Tag.

A. Schluter et al. / Journal of Biotec

Table 2Selected clusters of orthologous groups of proteins (COGs) identified in the analysedmetagenome of the biogas-producing microbial consortium

COGcategory/COGno.

Description

Posttranslational modification, protein turnover, chaperons (O)COG1180 Pyruvate-formate lyase-activating enzyme

Energy production and conversion (C)COG0543 2-Polyprenylphenol hydroxylase and

related flavodoxin oxidoreductasesCOG1882 Pyruvate-formate lyaseCOG0282 Acetate kinaseCOG0280 Phosphotransacetylase

Carbohydrate transport and metabolism (G)COG2160 l-Arabinose isomeraseCOG2407 l-Fucose isomerase and related proteinsCOG0153 GalactokinaseCOG1904 Glucuronate isomeraseCOG3693 �-1, 4-xylanaseCOG2115 Xylose isomeraseCOG3507 �-XylosidaseCOG0726 Predicted xylanase/chitin deacetylaseCOG4213 ABC-type xylose transport system,

periplasmic componentCOG4214 ABC-type xylose transport system,

permease component

Amino acid transport and metabolism (E)COG3404 Methenyl tetrahydrofolate cyclohydrolase

Nucleotide transport and metabolism (F)COG2759 Formyltetrahydrofolate synthetase

Coenzyme transport and metabolism (H)COG0192 S-Adenosylmethionine synthetaseCOG0190 5, 10-methylene-tetrahydrofolate

dehydrogenase/methenyl tetrahydrofolatecyclohydrolase

G

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eneral function prediction only (R)COG3858 Predicted glycosyl hydrolase

.3. Allocation of assembled contig sequences to microbialenomes

The Sequence Analysis and Management System SAMS was usedo analyse the genetic information content of assembled contigsrom the sequenced biogas fermenter total community DNA. All7,108 contigs were uploaded into SAMS and different BLAST anal-ses were conducted (BLAST2x vs. COG, BLAST2x vs. KEGG, BLAST2xs. SP and BLAST2x vs. nr). Observations obtained from the differentLAST tools are stored in SAMS and can be categorised and retrieved

or certain criteria. COG-analysis resulted in 17,110 hits (30% of allontigs) to entries of the COG database. Contig hits to COG cate-ories confirm results obtained from the corresponding single readnalysis (see Section 3.2.).

In a second approach contig sequences were mapped to micro-ial genome sequences of the non-redundant database (nr) byeans of the BLASTx algorithm. A total of 29,596 contigs could be

ffiliated to specific microbial genome sequences based on a bestLAST hit approach. Counting of best hit species entries for all con-igs led to the bar chart shown in Fig. 3. This approach led to thellocation of contigs to 956 different microbial species. Among thedentified species those belonging to the classes Clostridia (52%)nd Bacteroidetes (16%) dominate, followed by Bacilli (12%) and

ethanomicrobiales (10%) (see Table 3). Dominance of members of

lostridia, Bacilli and Bacteroidetes was also described for the micro-ial community of a biogas-producing reactor fed with fodder beetilage as mono-substrate (Klocke et al., 2007). Likewise, 16S-rDNAequences obtained from bacteria of an anaerobic thermophilic

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hnology 136 (2008) 77–90 81

atch digester inoculated with cattle manure were affiliated to dif-erent Bacillus, Bacteroides and Clostridium species (Chachkhiani etl., 2004). Many Clostridia possess the capability of anaerobic diges-ion of cellulosic material and therefore play an important role forhe hydrolysis step of plant biomass (Lynd et al., 2002; Desvaux,005; Ohmiya et al., 2005; Schwarz et al., 2004; Shiratori et al.,006).

It appeared that 4529 contig sequences were assigned to the. marisnigri JR1 genome (see Fig. 3). The reference species is

n archaeal methanogen belonging to the taxonomic class Metha-omicrobia (Maestrojuan et al., 1990). Members of the genusethanoculleus were previously isolated from marine sediments

ut they were also found to be dominant in different biore-ctor environments. For example, Methanoculleus sp. was foundn the archaeal community residing in a thermophilic anaerobicigester that was inoculated with sludge from garbage wastewa-er treatment (Hori et al., 2006) and in the leachate of a full-scaleecirculating landfill (Huang et al., 2002). Moreover, a dominantrchaeal SSCP (Single Strand Conformation Polymorphism) peakhylogenetically close to the 16S-rDNA sequence of Methanoculleushermophilicus was observed for a thermophilic batch digestered with cattle manure (Chachkhiani et al., 2004). Methanoculleuspecies produce methane via the hydrogenotrophic pathway buthey can also use different secondary alcohols as electron donorsor methanogenesis (Maestrojuan et al., 1990), which might explainheir dominance in biogas-producing communities residing inioreactors. In addition to hits to the order Methanomicrobiales,ontig matches to two reference species, namely Methanosarcinacetivorans and Methanosarcina barkei, of the order Methanosarci-ales were identified. Methanogens of this order are able to produceethane from acetate and thus use the aceticlastic methanogenesis

athway (Maeder et al., 2006).About 1000 contig sequences were allocated to the Clostridium

hermocellum genome. The reference organism possesses cellu-olytic and ethanogenic activity and produces a highly efficientnzyme-complex for the degradation of polysaccharides in biomassZverlov et al., 2003, 2005). A great number of extracellular cellu-ases are organised in a large multienzyme complex, the so-calledellulosome (Bayer et al., 2004). In fact, deduced amino acidequences of several contigs produced significant alignments to. thermocellum cellulosome enzymes of the dockerin type I andellulosome anchoring proteins containing cohesin regions (seeable 4). Due to the specific interaction between cohesin andockerin modules, presence of the dockerin sequence indicateshe localisation of the corresponding protein in the cellulosomeMechaly et al., 2000; Salamitou et al., 1994). Other reference organ-sms identified by the analysis shown in Fig. 3 are also known toossess cellulolytic activity. These are for example Moorella ther-oacetica (Karita et al., 2003), Clostridium cellulolyticum (Gaudin et

l., 2000), Caldicellulosiruptor saccharolyticus (Bagi et al., 2007) andlostridium phytofermentans (Warnick et al., 2002). Very recently,

t could be shown that inoculation of cattle manure with bacte-ia of the genus Caldicellusiruptor resulted in an increase of thepecific methane yield (Nielsen et al., 2007). Significant intensi-cation of biogas production was also observed when differentubstrates including wastewater sludge and dried plant biomassere inoculated with a hydrogen-producing strain of the speciesaldicellulosiruptor saccharolyticus (Bagi et al., 2007). Table 4 sum-arises matches of contig-encoded gene products to clostridial

ellulosome enzymes and cellulosome-associated proteins. These

esults indicate that species of the class Clostridia presumably playn important role for the degradation of cellulosic biomass in thenalysed fermentation sample and in some cases provide sub-trates, that can be used for methanogenesis performed by specieselated to the genus Methanoculleus. Moreover, clostridia are known

82 A. Schluter et al. / Journal of Biotechnology 136 (2008) 77–90

F requew ase. Th

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ig. 3. Allocation of assembled contig sequences to microbial genome sequences. Fere obtained by best BLASTx hits to the NCBI GenBank nucleotide sequence datab

o conduct secondary fermentations (Demain et al., 2005; Mitchell,992, 1998; Paredes et al., 2005).

The thirdmost hits were found for Thermosinus carboxidi-orans which is an anaerobic facultatively carboxydotrophic,ydrogenogenic bacterium (Sokolova et al., 2004). T. carboxydivo-ans produces metabolites (acetate, hydrogen and carbon dioxide)hat can directly be converted to methane by methanogenicrchaea. Carboxydothermus hydrogenoformans (see Fig. 3) also ishydrogenogen that grows on carbon monoxide and produces

2 and CO2 as waste products (Wu et al., 2005). Likewise, otherpecies identified by BLAST analysis of contig sequences potentiallyre cabable of producing metabolites that indirectly or directly

ontribute to biogas formation from plant material. Pelotomacu-um thermopropionicum (Kosaka et al., 2008), Thermoanaerobacterthanolicus (Erbeznik et al., 2004) and Victivallis vadensis, origi-ally isolated from human faeces (Zoetendal et al., 2003), shoulde mentioned in this context.

5bT

ncy of contig matches was determined for 50 abundant microbial species. Resultse x-axis denotes number of contig matches.

Although numerous contigs assembled from the metagenomeata set were assigned to the species listed in Table 3, this does notecessarily mean that these species represent part of the analysediogas-producing community. It is rather very likely that bacte-ia closely related to the respective reference species belong tohe microbial consortium residing in the bioreactor. Some char-cteristic features of the metabolic types described above arenalysed in more detail in an accompanying paper (Krause et al.,008b).

.4. Annotation of large contigs assembled from the biogasermenter metagenome

Forty contigs longer than 10 kb in size, altogether comprising46,405 nucleotides sequence information, resulted from assem-ly using the Genome Sequencer De Novo Assembler Software.he longest contig is 31,533 bps in size. Several predicted coding

A. Schluter et al. / Journal of Biotechnology 136 (2008) 77–90 83

Table 3Allocation of assembled contig sequences to microbial genome sequences

Species Phylum Class Order Countsa

Methanoculleus marisnigri JR1 Euryarchaeota Methanomicrobia Methanomicrobiales 4529Methanospirillum hungatei JF-1 Euryarchaeota Methanomicrobia Methanomicrobiales 203Candidatus Methanoregula boonei 6A8 Euryarchaeota Methanomicrobia Methanomicrobiales 160Methanosarcina acetivorans C2A Euryarchaeota Methanomicrobia Methanosarcinales 144Methanosarcina barkeri str. fusaro Euryarchaeota Methanomicrobia Methanosarcinales 123

Clostridium thermocellum ATCC 27405 Firmicutes Clostridia Clostridiales 995Thermosinus carboxydivorans Nor1 Firmicutes Clostridia Clostridiales 665Pelotomaculum thermopropionicum SI Firmicutes Clostridia Clostridiales 621Alkaliphilus metalliredigens QYMF Firmicutes Clostridia Clostridiales 583Desulfotomaculum reducens MI-1 Firmicutes Clostridia Clostridiales 497Clostridium cellulolyticum H10 Firmicutes Clostridia Clostridiales 483Alkaliphilus oremlandii OhILAs Firmicutes Clostridia Clostridiales 475Caldicellulosiruptor saccharolyticus DSM 8903 Firmicutes Clostridia Clostridiales 375Clostridium phytofermentans ISDg Firmicutes Clostridia Clostridiales 365Carboxydothermus hydrogenoformans Z-2901 Firmicutes Clostridia Clostridiales 359Desulfitobacterium hafniense Y51 Firmicutes Clostridia Clostridiales 332Clostridium kluyveri DSM 555 Firmicutes Clostridia Clostridiales 206Desulfitobacterium hafniense DCB-2 Firmicutes Clostridia Clostridiales 193Clostridium beijerinckii NCIMB 8052 Firmicutes Clostridia Clostridiales 187Clostridium tetani E88 Firmicutes Clostridia Clostridiales 185Clostridium novyi NT Firmicutes Clostridia Clostridiales 155Clostridium acetobutylicum ATCC 824 Firmicutes Clostridia Clostridiales 151Ruminococcus obeum ATCC 29174 Firmicutes Clostridia Clostridiales 133Ruminococcus gnavus ATCC 29149 Firmicutes Clostridia Clostridiales 130Ruminococcus torques ATCC 27756 Firmicutes Clostridia Clostridiales 121Eubacterium ventriosum ATCC 27560 Firmicutes Clostridia Clostridiales 120Halothermothrix orenii H 168 Firmicutes Clostridia Halanaerobiales 629Moorella thermoacetica ATCC 39073 Firmicutes Clostridia Thermoanaerobacteriales 542Thermoanaerobacter tengcongensis MB4 Firmicutes Clostridia Thermoanaerobacteriales 458Thermoanaerobacter pseudoethanolicus ATCC 33223 Firmicutes Clostridia Thermoanaerobacteriales 341Thermoanaerobacter sp. X514 Firmicutes Clostridia Thermoanaerobacteriales 222

Symbiobacterium thermophilum IAM 14863 Firmicutes Bacilli Lactobacillales 433Geobacillus thermodenitrificans NG80-2 Firmicutes Bacilli Bacillales 165Bacillus halodurans C-125 Firmicutes Bacilli Bacillales 161Bacillus sp. NRRL B-14911 Firmicutes Bacilli Bacillales 152Geobacillus kaustophilus HTA426 Firmicutes Bacilli Bacillales 151Bacillus coagulans 36D1 Firmicutes Bacilli Bacillales 121

Parabacteroides distasonis ATCC 8503 Bacteroidetes Bacteroidetes Bacteroidales 600Parabacteroides merdae ATCC 43184 Bacteroidetes Bacteroidetes Bacteroidales 507Bacteroides vulgatus ATCC 8482 Bacteroidetes Bacteroidetes Bacteroidales 437Bacteroides capillosus ATCC 29799 Bacteroidetes Bacteroidetes Bacteroidales 266Bacteroides thetaiotaomicron VPI-5482 Bacteroidetes Bacteroidetes Bacteroidales 238Bacteroides fragilis YCH46 Bacteroidetes Bacteroidetes Bacteroidales 203Porphyromonas gingivalis W83 Bacteroidetes Bacteroidetes Bacteroidales 175Bacteroides caccae ATCC 43185 Bacteroidetes Bacteroidetes Bacteroidales 186

Syntrophomonas wolfei subsp. wolfei str. Goettingen Synergistetes – Syntrophomonadaceae (family) 457

Victivallis vadensis ATCC BAA-548 Lentisphaerae Victivallases (no rank) Victivallaceae (family) 299

Thermotoga lettingae TMO Thermotogae Thermotogae Thermotogales 130Petrotoga mobilis SJ95 Thermotogae Thermotogae Thermotogales 123

Treponema denticola ATCC 35405 Spirochaetes Spirochaetes Spirochaetales 126

s. Ress

swaGhp

ctmaoi

cpurDsc

a Frequency of contig matches was determined for 50 abundant microbial specieequence database.

equences on contigs were found to be split due to frame shiftshich mostly occurred in homopolymeric sequences. These regions

re difficult to resolve by the 454-pyrosequencing technology. The+ C content of most contigs is around 60%. One contig (01695)as a very low G + C content of 45.7% and seems to contain somehage-related genes.

Genes encoding putative sporulation/competence functionsould be identified on different contigs (e.g. contig00542 and con-

ig00533) indicating that corresponding sequences derived from

embers of the endospore-forming Firmicutes. Since deducedmino acid sequences of these genes only show moderate similarityf about 70% to corresponding gene products of reference organ-sms, it was not possible to reliably assign underlying contigs to a

mpHtm

ults were obtained from best BLASTx hits searching the NCBI GenBank nucleotide

ertain species/genus. Moreover, many large 10 kb contigs encodeutative house keeping proteins such as DNA polymerase III (� sub-nit PolC), recombinase A (RecA), DNA ligase (LigA), ferric uptakeegulation protein Fur and RNA polymerase sigma factor RpoD.educed amino acid sequences of these proteins show moderate

imilarity (69–90%) to corresponding gene products of differentlostridial species.

A coding sequence probably involved in degradation of macro-

olecules was predicted on a large contig. The deduced gene

roduct is 68% similar to an �-amylase from Halothermothrix orenii168 and therefore could play a role in starch degradation. Addi-

ionally, different serine proteases were predicted, some of whichight be extracellular. These enzymes could be involved in degra-

84 A. Schluter et al. / Journal of Biotechnology 136 (2008) 77–90

Table 4Contigs encoding cellulosome or cellulosome-associated functions

Gene product Gene ID/organism Contig Lengtha Identity (%)b E-Value Totalc

�-l-Arabinofuranosidase ArfA gi|25989577 contig27920 81 56 2e−24 10C. cellulovorans

�-l-Arabinofuranosidase B gi|125712765 contig00171 447 57 1e−150 8C. thermocellum ATCC 27405

�-1,4-Glucanase gi|37651957 contig56254 73 77 5e−33 1C. thermocellum

�-Galactosidase/�-L-arabinopyranosidaseBgaA

gi|25989579 contig37918 81 35 5e−09 2C. cellulovorans

�-Glucosidase A gi|33242570 contig56545 442 55 1e−140 6C. cellulovorans

Carbohydrate binding family 6 gi|125714906 contig14816 127 65 8e−53 5C. thermocellum ATCC 27405

Carbohydrate binding family 6 gi|118662241 contig18218 116 73 1e−52 1C. cellulolyticum H10

Carbohydrate binding family 6 gi|125715716 contig44474 78 37 7e−14 1C. thermocellum ATCC 27405

Carbohydrate binding family 6 gi|125714904 contig25323 130 40 5e−18 3C. thermocellum ATCC 27405

Carbohydrate binding family 6 gi|125714011 contig03322 76 38 6e−08 2C. thermocellum ATCC 27405

Carbohydrate binding family 6 gi|125714907 contig00588 481 39 3e−94 23C. thermocellum ATCC 27405

Carbohydrate binding family 6 gi|118663315 contig55370 523 33 5e−68 10C. cellulolyticum H10

Carbohydrate binding family 6 gi|125712993 contig41239 144 35 5e−15 6C. thermocellum ATCC 27405

Carbohydrate binding family 6 gi|125714905 contig37629 105 35 3e−13 1C. thermocellum ATCC 27405

Carbohydrate binding family 6 gi|125714903 contig51044 141 30 3e−09 2C. thermocellum ATCC 27405

Carbohydrate binding family 6 gi|118664995 contig40846 65 63 2e−23 1C. cellulolyticum H10

Carbohydrate binding family 6 gi|118663311 contig04982 62 48 9e−15 3C. cellulolyticum H10

Carbohydrate binding family 6 gi|125712993 contig41239 144 35 5e−15 6C. thermocellum ATCC 27405

Cellulosome enzyme, dockerin type I gi|125712793 contig27937 80 30 1e−07 1C. thermocellum ATCC 27405

Cellulosome enzyme, dockerin type I gi|118662674 contig32535 52 37 6e−07 1C. cellulolyticum H10

Cellulosome enzyme, dockerin type I gi|125712986 contig56685 161 40 2e−35 15C. thermocellum ATCC 27405

Cellulosome enzyme, dockerin type I gi|118664929 contig54863 88 39 1e−08 4C. cellulolyticum H10

Cellulosome enzyme, dockerin type I gi|125714619 contig52733 71 42 7e−06 1C. thermocellum ATCC 27405

Cellulosome enzyme, dockerin type I gi|125713005 contig07191 108 51 4e−29 4C. thermocellum ATCC 27405

Cellulosome-anchoring protein precursor gi|543808 contig44848 64 31 5e−06 1C. thermocellum ATCC 27405

Endo-�-1,4-glucanase gi|144770 contig22208 61 41 4e−07 1C. thermocellum

Endoglucanase B precursor(Endo-1,4-�-glucanase B) (Cellulase B)

gi|121814 contig30815 68 41 2e−10 1C. cellulovorans

Glycoside hydrolase, clan GH-D gi|118663690 contig50862 71 37 7e−07 2C. cellulolyticum H10

Glycoside hydrolase, family 10 gi|125714689 contig07257 316 36 3e−54 11C. thermocellum ATCC 27405

A. Schluter et al. / Journal of Biotechnology 136 (2008) 77–90 85

Table 4 (Continued )

Gene product Gene ID/organism Contig Lengtha Identity (%)b E-Value Totalc

Glycoside hydrolase, family 11 gi|118662526 contig01643 81 41 3e−08 1C. cellulolyticum H10

Glycoside hydrolase, family 18 gi|118663878 contig03357 366 33 1e−47 1C. cellulolyticum H10

Glycoside hydrolase, family 5 gi|125713540 contig47564 57 49 4e−12 1C. thermocellum ATCC 27405

Glycoside hydrolase, family 5 gi|118664931 contig22770 139 31 2e−15 2C. cellulolyticum H10

Glycoside hydrolase, family 8 gi|118665052 contig56849 376 41 2e−85 6C. cellulolyticum H10

Glycosyl hydrolase 53 gi|125714139 contig03592 69 49 9e−17 1C. thermocellum ATCC 27405

Lipolytic enzyme, G-D-S-L gi|125715844 contig19803 79 44 3e−13 3C. thermocellum ATCC 27405

Pectate lyase/Amb allergen gi|118665462 contig53213 132 36 5e−16 1C. cellulolyticum H10

Pectate lyase/Amb allergen gi|125714889 contig16438 72 35 2e−07 2C. thermocellum ATCC 27405

Peptidase S8 and S53, subtilisin, kexin,sedolisin

gi|125715839 contig01891 255 38 3e−28 4C. thermocellum ATCC 27405

PKD domain containing protein gi|118663624 contig04119 141 35 1e−14 8C. cellulolyticum H10

Polysaccharide deacetylase gi|118663600 contig55682 59 42 1e−09 1C. cellulolyticum H10

Protein of unknown function DUF1680 gi|118662240 contig15719 138 32 5e−18 8C. cellulolyticum H10

Proteinase inhibitor I4, serpin gi|125712940 contig56040 53 40 3e−06 1C. thermocellum ATCC 27405

Proteinase inhibitor I4, serpin gi|125712939 contig55045 186 31 3e−15 3C. thermocellum ATCC 27405

Putative nicotinic acidphosphoribosyltransferase

gi|52222507 contig18472 126 66 7e−49 6C. cellulovorans

Ricin B lectin gi|125713404 contig47389 58 53 7e−12 1C. thermocellum ATCC 27405

Scaffolding dockerin binding protein A(SdbA)

gi|1531592 contig07475 143 32 5e−10 5C. thermocellum NCIB 10682

S-layer protein gi|296881 contig08675 174 34 5e−22 4C. thermocellum

Thrombospondin-like gi|118663885 contig00927 231 30 3e−13 4C. cellulolyticum H10

Xylanase gi|3721552 contig05546 198 38 2e−26 7C. thermocellum F1

Xylanase gi|4850306 contig08334 362 38 5e−56 4C. thermocellum F1

Xylanase U; XYLU gi|2920659 contig24764 98 36 2e−11 6C. thermocellum

Xylanase V; XYLV gi|2920658 contig22668 69 35 1e−07 1C. thermocellum

nly gi

dnphtBp

go

a Alignment length in amino acids.b Percentage of identity of the aligned region.c Number of matching contigs (alignment length and percentage of identity are o

ation of proteins and peptides. Four genes encoding enzymesecessary for histidine utilisation (hutH, hutU, orf2 and hutI) are

resent on contig00565. The corresponding gene products areistidine ammonia lyase (73% similarity to Thermoanaerobacterengcongensis MB4), an urocanate hydratase (87% similarity toacillus cereus subsp. cytotoxis NVH 391-98), an imidazolonepro-ionase (70% similarity to Alkaliphilus oremlandii OhILAs) and a

pctdp

ven for the best hit contig).

lutamate formimidoyltransferase (76% similarity to Thermoanaer-bacter tengcongensis). In addition, this contig also encodes a

utative sn-glycerol-3-phosphate uptake system consisting of theomponents UgpB, UgpA and UgpE. These results provide evidencehat assembly of contig00565 could be correct. Many more pre-icted genes on contigs encode conserved hypothetical proteins,utative transporters and regulatory proteins without obvious

86 A. Schluter et al. / Journal of Biotechnology 136 (2008) 77–90

Fig. 4. Mapping of metagenome sequence reads to the Methanoculleus marisnigri JR1 genome. Metagenome sequence reads obtained from the biogas fermentation samplew sion N −10

L arisnn ars inr ters re

dy

atfbwtccs

3m

4pts

tGiairmbMscrigbA

ere aligned to the complete M. marisnigri JR1 genome sequence (GenBank Accesength of vertical bars indicates the local coverage at a given genome position. M. mon-covered regions are visualised in red. Aligned reads are highlighted as green bespectively, not covered or lowly covered by metagenome reads are labelled by let

irect connection to the degradation process operating in the anal-sed biogas fermenter.

To summarise, the low percentage of assembled nucleotides inll contigs (appr. 16%) and the low number of contigs larger in sizehan 10 kb indicate that the metagenome sequencing approach isar from saturation. Accordingly, reconstruction of single micro-ial genomes from the obtained metagenome nucleotide sequencesas not possible. A reliable taxonomic grouping of larger con-

igs was possible only at higher taxonomic levels like order andlass. Most large contigs seem to originate from members of thelass Clostridia and some contigs show high similarity to Bacillipecies.

.5. Mapping of metagenome reads to the genome of M.arisnigri JR1

BLAST analysis of metagenome contig sequences revealed that529 contigs were assigned to the M. marisnigri JR1 genome, com-rising a total amount of 2.8 million bps. To get an overview onhe coverage of the M. marisnigri JR1 genome by metagenomeequence reads from the biogas-producing microbial community,

stpie

o.: NC 009051) by means of the tool ReadMapper using a cut-off value of 1e .igri JR1 genome sub-regions covered by metagenome reads are coloured in green;the lower part of the plot. Some larger M. marisnigri JR1 genome regions that are,ferring to Table 5.

hese reads were mapped to the reference genome (unpublished,enBank Accession No. CP000562) by means of the bioinformat-

cs tool ReadMapper (see Fig. 4). The high number of reads thatlign to the M. marisnigri JR1 genome with nucleotide sequencedentities over 90% indicates that Archaea closely related to theeference microorganism are very abundant in the analysed fer-entation sample. Nevertheless, there are significant differences

etween the M. marisnigri JR1 genome and the genomes of theethanoculleus group methanogens in the fermentation sample

ince certain parts of the reference sequence (appr. 36%) are notovered by metagenome reads. Seven larger M. marisnigri JR1egions amounting to a total of 205.8 kb were found to be absentn the metagenome sequence data set (Table 5). Lack of theseene regions might be explained by differences in gene contentetween strains and species closely related to the reference strain.nalysis of the M. marisnigri JR1 regions that are missing in the

equenced metagenome of the biogas fermentation sample showedhat regions A, B and E have a low G + C content (see Table 5). Generoducts encoded in regions A and B were predicted to be involved

n the biosynthesis of extracellular polysaccharides (EPSs) (Parolist al., 1996) suggesting that members of the Methanoculleus group

A. Schluter et al. / Journal of Biotechnology 136 (2008) 77–90 87

Table 5Methanoculleus marisnigri JR1 genome regions lowly covered by metagenome nucleotide sequence reads

Region Position Size (kb) GC content CDSs Encoded functions

A 173,600–186,000 12.4 44.7 7 Probably involved in biosynthesis of anextracellular polysaccharide

B 657,200–679,520 22.3 50.5 17 Probably involved in biosynthesis of anextracellular polysaccharide; duplicatedasparagine synthetase

C 839,728–907,680 68.0 62.1 64 Integrase (pseudogene)D 1,080,784–1,134,104 53.3 58.8 ∼35 Restriction/modification systemE 1,460,000–1,480,000 20.0 47.3 ∼8 Restriction/modification systemF 0.0G 57.6

iatomEli

mbs

3

hetrqftafemcPsdmrftamspa

3

cptfri

mtcsiaigacti

samtrtmfagoihftgtnutraw(Mndrosba

1,738,480–1,757,576 19.1 62,371,872–2,382,536 10.7

n the fermentation sample differ from the reference strain in thisspect. Region G also carries genes possibly involved in the syn-hesis of EPS or surface structures, but has an average GC contentf 57.6%, which is similar to the GC content of the complete M.arisnigri JR1 genome. Region D and the low GC content regionboth contain restriction/modification genes. The other regions

isted in Table 5 do neither carry genes for which a high variabilitys expected nor regions of atypical G + C content.

In conclusion, the methanogen community in the analysed fer-entation sample from the biogas reactor seems to be dominated

y members related to the genus Methanoculleus. Methanoculleuspecies use the hydrogenotrophic pathway for methane formation.

.6. Methanogenesis genes were identified on contig sequences

Since M. marisnigri is a methanogen performing theydrogenotrophic pathway for methane production (Maestrojuant al., 1990), the question arose whether assembled contigs fromhe metagenome sequence data set contain methanogenesis geneselated to corresponding genes of M. marisnigri. To answer thisuestion, known methanogenesis gene products were extractedrom the annotated M. marisnigri JR1 genome database entry andBLASTn searches (E-value cutoff e−10) were accomplished usingssembled contigs as input sequences. Highly similar sequencesor all methanogenesis-related gene products were found to bencoded on contigs from the metagenome data set. The bestatching contigs together with additional information about

overage, sequence identity, and E-value are shown in Table 6.roduction of methane via the hydrogenotrophic pathway involveseven enzymatical steps starting with the reduction of carbonioxide to the methyl level and proceeding with the reduction ofethyl-coenzyme to methane (Ferry, 1999; Reeve et al., 1997). The

esults shown in Table 6 indicate that genes for all enzymaticalunctions of the hydrogenotrophic methanogenesis pathway seemo be present in the biogas-producing microbial community of thenalysed fermentation sample. High sequence similarity of the M.arisnigri JR1 methanogenesis gene products to corresponding

equences deduced from the metagenome sample of the biogaslant confirms that species related to the genus Methanoculleusre dominant in the bioreactor under study.

.7. Concluding remarks

Insight into the metagenome of a biogas-producing microbialommunity residing in a fermenter of a production-scale biogas

lant was obtained by means of the ultrafast 454-pyrosequencingechnology followed by sequence data interpretation using bioin-ormatics strategies. COG classification of nucleotide sequenceeads and assembled contigs revealed a genetic profile character-stic for an anaerobic microbial consortium running fermentative

vnabp

∼25 Mainly hypothetical coding sequences∼12 Some gene products predicted to be

involved in EPS or surface protein synthesis

etabolic pathways. Moreover, assignment of numerous sequenceso COGs related to polysaccharide digestion and decomposition ofellulosic material indicates that many species in the fermentationample are engaged in hydrolysis of plant material. Since hydrolysiss the rate limiting step in degradation of plant biomass (Noike etl., 1985) it would be worthwhile to learn more about microorgan-sms and their metabolic features involved in this process. Someenetic traces of organisms dominating the hydrolysis step werelready identified in the metagenome data set. Future work willoncentrate on isolation of corresponding bacteria and analysis ofheir genomic properties with the objective to optimise initial stepsn the decomposition of substrates for biogas production.

Moreover, putative key organisms involved in intermediateteps of biomethanogenesis were identified. Here, syntrophicssociations with methanogens seem to be of importance, i.e.etabolites produced by some secondary fermenters poten-

ially feed methanogenic Archaea. Interestingly, species closelyelated to those of the genus Methanoculleus are dominant amonghe methanogens. Methanoculleus species are known to produce

ethane via the hydrogenotrophic pathway. Accordingly, methaneormation from hydrogen and carbon dioxide is of importance in thenalysed fermentation sample. The hydrogenotrophic methano-enesis pathway presumably is accompanied by syntrophic acetatexidation leading to the formation of hydrogen and carbon diox-de (Ahring, 1995) which in turn can be converted to methane byydrogen-utilising methanogens. Thus, the acetate pool indirectlyeeds biogas production even if there are only few aceticlas-ic methanogens residing in the community. Nevertheless, someenetic traits identified in the metagenome data set point towardshe occurrence of species clustering within the order Methanosarci-ales. These species potentially generate methane from acetatesing the aceticlastic pathway. Since the final products of aceticlas-ic methanogenesis are methane and carbon dioxide high metabolicates involving this pathway could lead to increased carbon dioxidemounts in the produced biogas. Restricted methanogen diversityas observed for other biogas-producing microbial communities

Chachkhiani et al., 2004; Klocke et al., 2007; Liu et al., 2002;ladenovska et al., 2003; Rastogi et al., 2007). For example, Metho-

omicrobiales are dominant in a biogas reactor fed with cattleung under low temperatures (Rastogi et al., 2007). Underrep-esentation of putative acetate-utilising methanogens has beenbserved for several anaerobic digestors (Leclerc et al., 2004). Inome cases, occurrence of certain groups of methanogenes coulde correlated to the concentrations of key metabolites (Griffin etl., 1998; Hori et al., 2006; Karakashev et al., 2005). Acetate and

olatile fatty acids (VFA) levels, for example, influence the commu-ity structure of methanogens (Griffin et al., 1998; Karakashev etl., 2005). Metabolome studies of samples obtained from the sameiogas reactor that was probed for metagenome sequencing are inrogress. Determination of key metabolite concentrations might

88 A. Schluter et al. / Journal of Biotechnology 136 (2008) 77–90

Table 6Assembled contigs encoding methanogenesis functions related to those of Methanoculleus marisnigri JR1

Enzyme EC no. Contig hit Length (aa) Identity (%) E-Value

Formylmethanofuran dehydrogenasea 1.2.99.5 contig51197 265 (69) 92.34 (81.16) 1e−135Methyl-coenzyme M reductase (subunit) 2.8.4.1 contig55617 426 92.72 0.0Methylenetetrahydromethanopterin reductase 1.5.99.11 contig53724 275 83.64 1e−130Methylenetetrahydromethanopterin dehydrogenase 1.5.99.9 contig53698 241 92.53 1e−126Tetrahydromethanopterin S-methyltransferase, subunit H 2.1.1.86 contig01080 307 92.83 0.0Tetrahydromethanopterin S-methyltransferase, subunit A 2.1.1.86 contig01076 141 93.62 6e−75Tetrahydromethanopterin S-methyltransferase, subunit B 2.1.1.86 contig49686 81 95.06 3e−40Tetrahydromethanopterin S-methyltransferase, subunit C 2.1.1.86 contig49686 60 75.00 6e−23Tetrahydromethanopterin S-methyltransferase, subunit D 2.1.1.86 contig01082 54 96.30 2e−26Tetrahydromethanopterin S-methyltransferase, subunit E 2.1.1.86 contig01072 214 76.64 2e−85Coenzyme-B sulfoethylthiotransferase 2.8.4.1 contig55617 426 92.96 0.0Formylmethanofuran dehydrogenase, subunit C 1.2.99.5 contig49707 266 80.45 1e−124Formylmethanofuran-tetrahydromethanopterin formyltransferase 2.3.1.101 contig54116 233 89.27 1e−123Coenzyme F420-reducing hydrogenase, � subunit 1.12.98.1 contig51799 148 93.24 2e−83M 7C .1

rtdsssift

amitscote

prp

A

FtRcBMGBAFbm(b

R

A

A

A

B

B

BC

C

D

D

D

D

D

D

D

E

E

E

F

F

F

G

ethenyltetrahydromethanopterin cyclohydrolase 3.5.4.2oenzyme F420-reducing hydrogenase, � subunit 1.12.98

a Frameshift splits high scoring sequence pairs (HSPs) of matching contig.

eveal dependencies between the methanogen community struc-ure and the metabolite profile. In addition, complete sequencing ofominant Methanoculleus strains from the analysed fermentationample would give insights into the adaptive properties of thesetrains with respect to the fermented substrate. As shown in thistudy, a great genome portion of dominant methanogens alreadys represented in the established metagenome data set. Anotheruture challenge will be the identification of syntrophic associa-ions (Schink, 2006) involved in biomethanogenic degradation.

Definitely, 142 megabases metagenome sequence informationre not enough to cover the whole complexity of the autochthonousicrobial community residing in the bioreactor. Further sequenc-

ng runs would be required to discover less abundant geneticraits. Moreover, it would be informative to compare metagenomeequences representing different phases of the fermentation pro-ess to follow development, succession and dynamic interactionsf and within the microbial community, preferably in correlationo metabolome data and operational physical and chemical param-ters.

Finally, community-specific genetic profiles of biogas-roducing microbial consortia will certainly help to developational strategies for optimisation of the biogas productionrocess technology.

cknowledgements

LK was supported by the Bundesministerium fur Bildung undorschung (BMBF) project 0313805A. HN and LK would like to thankhe International Graduate School in Bioinformatics and Genomeesearch for providing financial support. TB acknowledges finan-ial support from Degussa GmbH and the Bundesministerium furildung und Forschung (BMBF), SysMAP project (grant 0313704).D and KR were financially supported by the BMBF through theenoMik-Plus network (grant 0313805A). AG acknowledges theMBF for financial support. NND was supported by the Germancademic Exchange Service (DAAD). The Bioinformatics Resourceacility (BRF) team is acknowledged for their support on runningioinformatics software at the Center for Biotechnology. Further-ore, the authors thank S. Eschenbacher, V. Klassen and P. Grimm

BIOGAS NORD AG) for arranging and organising sampling at the

iogas plant and assistance in sample drawing.

eferences

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G

contig16902 80 78.75 8e−32contig53288 202 94.06 1e−110

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