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Keywords:

  • 16S rDNA;
  • anaerobic digestion;
  • biogas digester;
  • denaturing gradient gel electrophoresis (DGGE);
  • microbial community;
  • pig manure

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Aims:  To identify the bacterial and archaeal composition in a mesophilic biogas digester treating pig manure and to compare the consistency of two 16S rDNA-based methods to investigate the microbial structure.

Methods and results:  Sixty-nine bacterial operational taxonomic units (OTU) and 25 archaeal OTU were identified by sequencing two 16S rDNA clone libraries. Most bacterial OTU were identified as phyla of Firmicutes (47·2% of total clones), Bacteroides (35·4%) and Spirochaetes (13·2%). Methanoculleus bourgensis (29·0%), Methanosarcina barkeri (27·4%) and Methanospirillum hungatei (10·8%) were the dominant methanogens. Only 9% of bacterial and 20% of archaeal OTU matched cultured isolates at a similarity index of ≥97%. About 78% of the dominant bacterial (with abundance >3%) and 83% of archaeal OTU were recovered from the denaturing gradient gel electrophoresis (DGGE) bands of V3 regions in 16S rDNAs.

Conclusions:  In the digester, most bacterial and archaeal species were uncultured; bacteria belonging to Firmicutes, Bacteroides and Spirochaetes seem to take charge of cellulolysis, proteolysis, acidogenesis, sulfur-reducing and homoacetogenesis; the most methanogens were typical hydrogenotrophic or hydrogenotrophic/aceticlastic; DGGE profiles reflected the dominant microbiota.

Significance and Impact of the Study:  This study gave a first insight of the overall microbial structure in a rural biogas digester and also indicated DGGE was useful in displaying its dominant microbiota.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

As the biggest developing country in the world, China has a population of 1·3 billion. Among these people, 72·3% of them are living in the rural areas. Because of the rapid growth of industry and energy consumption in the city, energy shortage becomes a big problem [The Priority Programme for China’s Agenda 21; see the World Wide Web (WWW) site: http://www.acca21.org.cn/indexe8.html]. Furthermore, the demands of energy in the rural areas also increase daily. On the other hand, there is a great deal of waste biomass produced in the countryside, including a large amount of lignocellulose byproducts in agriculture and excrement of poultry and cattle, which creates problems for the rural environment. To improve the environmental and living conditions in the countryside and to create a sustainable development in rural economy (Gupta 2003), it is necessary to resolve the energy and sanitary problems in the rural areas.

Anaerobic fermentation of waste biomass not only generates biogas fuel for cooking, lighting and heating, but also reduces waste biomass, potentially providing a mutually beneficial situation for the environmental, social and cycling-economic development in the rural areas. The technology for constructing biogas digesters at different scales and for different applications for treating rural wastes is well established (Chynoweth et al. 1999), while the efficiency of biogas production needs to be improved urgently. The composition of the microbial community in a biogas digester directly determines its efficiency and biogas yield. The process of anaerobic conversion of different biomasses to methane usually includes four steps: hydrolysis, acidogensis, acetogenesis and methanogenesis, in which hydrolytic, fermentative bacteria, acetogens and methanogens play distinct roles, respectively (Pretty et al. 2002; Angenent et al. 2004).

Hydrolytic and acidogenic bacterial strains identified as Clostridium thermocellum, Clostridium leptum, Clostridium botulinum, Bacteroides termitidis, Desulfovibrio desulfuricans, Treponema palladium and Pirochaeta aurantia, acetogens such as Syntrophobacter wolinii and Syntrophomonas wolfei and archaeal methanogens belonging to Methanosaeta sp., Methanocorpusculum sp., Methanoculeus sp. and Methanobrevibacter sp. Methanobacterium sp., Methanosarcina sp. and Methanobrevibacter sp. were isolated from biogas digesters and anaerobic storages of animal manure in several studies (Boone and Bryant 1980; Zhao et al. 1986; Ney et al. 1990; Meher and Ranade 1993; Ohkuma and Kudo 1996; Whitehead and Cotta 1999; Cotta et al. 2003; Snell-Castro et al. 2005; Drake et al. 2006). A number of studies have indicated that only a small portion of the micro-organisms (0·1–25%) were cultured (Cotta et al. 2003).

Culture-independent approaches, mainly 16S rDNA-based methods, e.g. cloning and sequencing, D/TGGE (denaturing/temperature gradient gel electrophoresis) fingerprinting, FISH (fluorescence in situ hybridization), RFLP [restriction fragment length polymorphism, or ARDRA (amplified rDNA restriction analysis)] and T-RFLP (terminal-RFLP), are applied to elucidate the diversity and composition of microbial communities in anaerobic methanogenic digesters widely used for the treatment of municipal and industrial wastewater (Karakashev et al. 2005; Connaughton et al. 2006; Mladenovska et al. 2006; Cirne et al. 2007; Klocke et al. 2007; Sousa et al. 2007a; Ye et al. 2007). Connaughton et al. (2006) and Sousa et al. (2007a) reported changes over time of the bacterial and archeal population in an anaerobic digester, the former compared the biomass composition with the activity of the digester and the latter compared the composition of microbiota in the presence and absence of long chain fatty acids. Ye et al. (2007) reported changes in digesters operated at several pH. Several studies involved investigation of microbial compositions in anaerobic methanogenic digesters treating rural waste (Mladenovska et al. 2006; Cirne et al. 2007; Klocke et al. 2007). By TGGE and cloning library and sequencing, Mladenovska et al. (2003) found that the most dominant methanogens in lab-scale anaerobic digesters with cattle manure or a mixture of cattle manure with glycerol trioleate were phylogenetically related to Methanosarcina siciliae. The bacterial and archaeal composition identified by T-RFLP analysis of 16S rRNA genes were found to be identical in two thermophilic continuously stirred tank reactors (CSTR), treating nontreated manure and pretreated manure for 40 min at 140°C, respectively (Mladenovska et al. 2006). However, to our knowledge, no previous studies characterized the overall microbial communities in a biogas digester treating rural waste.

Compared with the sequencing analysis of 16S rDNA genes in clone libraries, DGGE profiles of the V3 region was simpler and less costly for analysing the structural variation between different microbial systems or the spatio-temporal dynamics of the same system (Muyzer et al. 1993). It could also be useful in tracing the variation of dominant microbial organism in a biogas digester. However, previous studies have indicated that DGGE profiles were not consistent with the results from the sequencing of clone libraries in different microbial systems (Krave et al. 2002; Freeman et al. 2008; Wakase et al. 2008). It is important to ensure whether the results from DGGE profiles are consistent with clone library analyses before they are applied in biogas digesters.

For the purpose of both basic research and biogas biotechnology, there is considerable interest in elucidating the microbial composition and metabolic diversity involved in biogas production, as well as setting up an applied and less costly method to trace the variation of microbial structure in anaerobic bio-digesters. In this study, the overall microbial communities in a mesophilic anaerobic biogas digester were investigated by analysing the diversity of 16S rDNA using DGGE and sequence analysis. Such an investigation for the composition of the microbial community in the biogas digesters would be the first step to elucidate the relationship between the efficiency of biogas production in the digesters and the structure and variation of the microbiota. The purpose of this study was twofold: (i) to characterize the microbial diversity of an anaerobic biogas digester using culture-independent methods and (ii) to compare the discriminatory power of DGGE vs 16S library screening.

Materials and methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Sample collection

A biogas slurry sample (3 l) was collected in June 2005 from a biogas digester in Qianwei Village, Chongming County, Shanghai, China, which was built in the early of 1980s and kept running for about 25 years. The digester has a volume of 600 m3 for anaerobic fermentation and produces 150 m3 biogas per day. The main fermentation substrate is pig manure from a nearby piggery breeding 1500 pigs. The digester can treat 1800 tonnes of pig manure from the piggery every year (Hao and Liu 2006).

DNA extraction

To remove extracellular DNA and soluble organic contaminants, the fresh slurry samples (50 ml) were washed thrice in five volumes of TENP buffer [50 mmol l−1 Tris-HCl, 20 mmol l−1 EDTA, 100 mmol l−1 NaCl, 0·01 g ml−1 polyvinylpyrrolidone (PVP), pH 10], twice in five volumes of sterile phosphate-buffered saline (PBS buffer, 137 mmol l−1 NaCl, 2·7 mmol l−1 KCl, 1·5 mmol l−1 KH2PO4, 8·1 mmol l−1 Na2HPO4 in distilled water, pH 7·4) and vortexed for 5 min and centrifuged for 10 min at 10 000g. The washed cell pellets were resuspended in a suitable volume of sterile PBS buffer containing glycerol at a final concentration of 20%, divided into 4-ml aliquots, and stored at −70°C until nucleic acid extraction.

After thawing and centrifugation of subsamples (1 ml), the supernatants were removed. The genomic DNA was then extracted using QIAamp DNA stool mini kit (Qiagen, Heidelberg, Germany) according to the manufacturer’s instruction with minor modification. The treated sample was carefully disrupted by Cell Disruptor Genie (Scientific Industries Inc., New York) in the 2·0-ml microcentrifuge tube after adding the extraction buffer. The purified DNA was quantified with a Biophotometer (Eppendorf), and stored at −20°C until use.

Establishment of bacterial and archaeal 16S rDNA libraries

Bacterial and archaeal clone libraries were generated from polymerase chain reaction (PCR)-amplified 16S rDNA using bacterial primers 27f (5′-GAG AGT TTG ATC CTG GCT CAG-3′) and 1495r (5′-CTA CGG CTA CCT TGT TAC GA-3′) (Bianciotto et al. 1996) and the archaeal primers 1Af (5′-TCY GKT TGA TCC YGS CRG AG-3′) and 1100Ar (5′-TGG GTC TCG CTC GTT G-3′) (Embley et al. 1992). Reaction mixtures (25 μl) contained 2·5 μl of 10 × PCR buffer (TaKaRa Inc., Dalian, China), 0·2 mmol l−1 each of deoxyribonucleotide triphosphates (dNTP), 0·5 μmol l−1 of each primer, 1 U of Ex Taq DNA polymerase (TaKaRa Inc.) and 5 ng of template DNA. PCR reactions were performed on a Flexigene thermal cycler (Techne Flexigene, Cambridge, UK). The PCR reaction for archaeal 16S rDNA was performed using the following programme: initial denaturation for 3 min at 94°C; 30 cycles of denaturation (1 min at 94°C), annealing (1 min at 55°C) and extension (2 min at 72°C) with a final extension of 72°C for 10 min. The optimized PCR amplification conditions for bacterial 16S rDNA were as follows: initial denaturation at 95°C for 1·5 min; 5 cycles of 95°C for 30 s, 60°C for 30 s, 72°C for 1·5 min; 5 cycles of 95°C for 30 s, 55°C for 30 s, 72°C for 1·5 min; 15 cycles of 95°C for 30 s, 50°C for 30 s, 72°C for 1·5 min and a final extension of 72°C for 10 min.

To minimize PCR artefacts, ‘reconditioning PCR’ was performed as described by Thompson et al. (2002) after the initial amplification of the bacterial and archaeal 16S rDNA as described before. The initial PCR-amplified reaction was diluted 10-fold in a fresh reaction mixture of the same composition and cycled thrice using this programme. ssDNA and heteroduplex DNA could be minimized by adding excess primer during the ‘reconditioning PCR’ (Zhang et al. 2005).

Cloning and sequencing

Exactly 100 μl of bacterial and archaeal 16S rDNA reconditioning PCR products were electrophoresed on 1·0% agarose and the bands of the correct size (c. >1·5 kb for bacteria, and 1·1 kb for archaea) were purified using 3S PCR Product Purification Kit V2·0 (Shenergy Biocolor Biological Science & Technology Co., China), respectively. Finally, the purified product was cloned into the pMD18-T plasmid vector (TaKaRa Inc.) following the manufacturer’s instructions. The ligated products were transformed into Escherichia coli TOP10 competent cell (Invitrogen) with ampicillin and blue/white screening, and positive clones were arrayed in 96-well plates and stored at −80°C for long-term storage. Plasmid inserts were checked by PCR amplification using the M13 PCR set. Exactly 310 bacterial and 192 archaeal positive insert-containing clones were randomly selected for gene sequencing. The template DNA was prepared from overnight cultures of selected clones using an alkaline miniprep kit (Qiagen), sequencing were performed on an ABI 3730 DNA sequencer with Big Dye terminator chemistry as specified by the manufacturer (Applied Biosystems).

16S rDNA V3 region amplification

The V3 regions of bacterial and archaeal 16S rDNA from the biogas slurry DNA extract were amplified by PCR and the amplified products were used for analysis by DGGE. The bacterial primer sets EubacVf (5′-CGC CCG CCG CGC GCG GCG GGC GGG GCG GGG GCA CGG GGG GCC TAC GGG AGG CAG CAG-3′) and Vr (5′-ATT ACC GCG GCT GCT GG-3′), archaeal primer sets PARCH340f (5′-CCC TAC GGG GYG CAS CAG-3′) and PARCH519r (5′-TTA CCG CGG CKG CTG-3′), GC clamp and PCR amplification used were the same as described by Muyzer et al. (1993) and Øvreås et al. (1997). Reconditioning PCR products (200 μl) were concentrated with two volumes of ethanol and finally dissolved in 20 μl of water for further DGGE analysis. PCR product was observed on agarose gel (2·0%) with 1 × TAE buffer (40 mmol l−1 Tris-HCl, 40 mmol l−1 acetate, 1·0 mmol l−1 EDTA) and ethidium bromide (0·5 μg ml−1) under ultraviolet (UV) light.

Denaturing gradient gel electrophoresis

DGGE of the PCR products was performed by the method described by Muyzer et al. (1993) with the DCode Universal Mutation Detection system (Bio-Rad Laboratories, Hercules, CA < USA). Denaturing gradient gel [1 mm thickness × 160 × 160 mm; 1 × TAE (40 mmol l−1 Tris base with 1·0 mmol l−1 EDTA and 20 mmol l−1 sodium acetate at pH 7·4), 8% acrylamide-bisacrylamide (37·5 : 1), and 25–60% (35–70% for archaea, based on our unpublished results of perpendicular DGGE) denaturant (100% = 7 mol l−1 urea with 40% formamide)] was poured with a gradient delivery system (model 475; Bio-Rad Laboratories). Electrophoresis was performed at a constant temperature of 60°C, first for 10 min at 25 V and then for 5 h at 200 V in 1 × TAE buffer. After the electrophoresis, the gels were stained with AgNO3 as described by the manufacturer.

Clone library construction of single DGGE bands

Each gel slice that contained an obvious DNA band was excised with a clean razor blade and placed in an 1·5-ml Eppendorf tube. The gel slice was crushed and incubated with 50 μl of TE buffer at 4°C overnight. The 3-μl supernatant was subjected to a second PCR under the same conditions as described before. The re-amplified PCR products were examined by DGGE to confirm that single bands were present at the same positions. The PCR products were then purified with Mini-DNA Rapid Purification Kit (BioDev, Beijing, China) and cloned into the pMD18-T Vector (TaKaRa Inc.) to construct the clone libraries. Five clones were picked from each library and sent to Invitrogen (Shanghai, China) for sequencing.

Sequence analysis

Sequences were edited manually to remove vector and ambiguous sequences at the ends by scanning of the individual chromatograms using Chromas software ver.2.23 (Technelysium, Shanghai, China). Chimeras were checked by the CHIMERA_CHECK programme (Cole et al. 2003) in Ribosomal Database Project (RDP) at first, and then were further firmed by Bellerophon programme on the Greengenes website (DeSantis et al. 2006). All reference sequences were obtained from the GenBank and RDP. Then all the sequences and their closest relatives were fitted into an alignment using the automated tools of the ARB software package (Ludwig et al. 2004). Aligned sequences were added to the ARB neighbour-joining tree (based on pairwise distances with Olsen correction) with the parsimony insertion tool as described by Ley et al. (2005). Sequences with internal regions of poor quality leading to alignment problems were excluded from further analysis. Dotur (Schloss and Handelsman 2005) was used to cluster sequences into operational taxonomic units (out) by % pairwise identity (%ID, using a furthest-neighbour algorithm and a precision of 0·03). The stability of tree branches was assessed by the bootstrap method using 1000 replicates.

Nucleotide sequence accession numbers

Bacterial nucleotide sequences obtained in this study are available in the GenBank database under accession numbers: EU358617EU358650 and EU358676EU358744. Archaeal nucleotide sequences obtained in this study are available in the GenBank database under accession numbers: EU358606EU358616 and EU358651EU358675.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Microbial community analysed by clone library-bacterial community

The 16S rDNA genes were amplified from the total DNA extracted from the biogas slurry sample with a bacterial primer set 27f/1495r and amplicons were ligated to pMD-18 T vector to construct a library. In total, 310 clones were randomly selected and sequenced. After eliminating low-quality (68 clones) sequences and chimeric sequences (53 clones), 189 sequences were used for the following analyses. The coverage of the library was 81·0%, indicating that the library was large enough for further analyses. The 189 sequences of 16S rDNA genes were classified into 69 OTU (Table 1). The abundances of all OTU in the library were less than 7%, where only nine OTU were more than 3%. The accession number, sources and described functions of their phylogenetically closest matched organisms are also listed in Table 1.

Table 1.   Taxonomic relationship of bacterial 16S rDNA sequences from Chongming biogas digester compared (Blast) with public databases (RDP, Greengenes and NCBI)
OTU (%)% of totalAccession numberPhylogenetically most closely related organism (accession no.) Sm (%)PhylumFunctional groupSource
  1. OTU, operational taxonomic unit.

BS016·9EU358676Uncultured anaerobic bacterium (AY953213)87FirmicutesUSA: swine lagoon
BS026·3EU358677Alkaliflexus imshenetskii (AJ784993)90BacteroidetesAcidogenicRussia: soda lake
BS036·3EU358678Petrimonas sulfuriphila (AY570690)90BacteroidetesAcidogenicCanada: biodegraded oil
BS045·3EU358679Proteiniphilum acetatigenes (AY742226)94BacteroidetesProteolyticChina: sludge of UASB reactor
BS054·8EU358680Spirochaeta sp. SPN1 (AJ698092)88SpirochaetesGermany: hindgut of the termite
BS064·2EU358681Uncultured Clostridiaceae (DQ069192)94FirmicutesUSA: SA Au mine
BS073·7EU358682Clostridium quinii (X76745)99FirmicutesAcidogenicUK: type strain DSM6736
BS083·2EU358683Uncultured bacterium (EF559197)99BacteroidetesFrance: mesophilic digester
BS093·2EU358684Ruminofilibacter xylanolyticum (DQ141183)91BacteroidetesChina: rumen
BS102·6EU358685Clostridium thermocellum (L09173)86FirmicutesCellulolyticDSM 1237
BS112·6EU358686Uncultured Clostridium sp. (DQ309375)93FirmicutesIndia: effluent treatment
BS122·1EU358687Treponema brennaborense (Y16568)91SpirochaetesAcidogenicFRG: dairy cow
BS132·1EU358688Treponema brennaborense (Y16568)91SpirochaetesAcidogenicFRG: dairy cow
BS142·1EU358689uncultured Fibrobacteres (EF454806)90FibrobacteresUSA: termite hindgut
BS152·1EU358690Clostridium thermocellum (L09173)88FirmicutesCellulolyticDSM 1237
BS162·1EU358691Paludibacter propionicigenes (AB078842)88BacteroidetesAcidogenicJapan: rice straw in paddy soil
BS172·1EU358692Uncultured Bacteroidetes(EF111167)89BacteroidetesColombia: bogota river
BS181·6EU358693Uncultured spirochete (EF562545)94SpirochaetesCanada: biodegraded oil
BS191·6EU358694Clostridium orbiscindens (Y18187)89FirmicutesAcidogenicDSM 6740
BS201·6EU358695Clostridium bartlettii (AY438672)92FirmicutesAcidogenicUSA: human feces
BS211·6EU358696Anaerovorax odorimutans (AJ251215)92FirmicutesAcidogenicGermany: strain NorPut
BS221·6EU358697Paludibacter propionicigenes(AB078842)88BacteroidetesAcidogenicJapan: rice straw in paddy soil
BS231·6EU358698uncultured Cytophaga sp. (EF562564)94BacteroidetesUSA: paper pulp column
BS241·1EU358699uncultured Verrucomicrobia (AM040118)86VerrucomicrobiaGermany: sandy sediments
BS251·1EU358700Tissierella praeacuta (X77848)96FirmicutesDSM 5675
BS261·1EU358701Clostridium orbiscindens (Y18187)87FirmicutesAcidogenicDSM 6740
BS271·1EU358702Uncultured Clostridiales (AB234509)89FirmicutesJapan: gut of termites
BS281·1EU358703Clostridium chartatabidum (X71850)99FirmicutesCellulolyticDSM 5482
BS291·1EU358704Tissierella praeacuta (X80833)93FirmicutesUK: type strain ATCC 25539
BS301·1EU358705Aminobacterium colombiense (AF069287)86FirmicutesAustralia: anaerobic sludge
BS311·1EU358706Uncultured Leptospiraceae (EF454914)88SpirochaetesUSA: termite hindgut
BS320·5EU358707Uncultured spirochete (EF562545)94SpirochaetesCanada: biodegraded oil
BS330·5EU358708Sphaerochaeta sp. RCcp2 (DQ833401)89SpirochaetesUSA:TCE-dechlorinating
BS340·5EU358709Spirochaeta sp. SPN1 (AJ698092)88SpirochaetesGermany: hindgut of the termite
BS350·5EU358710Xanthomonas vasicola (Y10755)95ProteobacteriaFRG: Strain LMG 736 T
BS360·5EU358711Uncultured planctomycete (DQ206406)98PlanctomycetesUSA: soda lake water
BS370·5EU358712Anaerovorax odorimutans (AJ251215)92FirmicutesAcidogenicGermany: strain NorPut
BS380·5EU358713Clostridium straminisolvens (AB125279)88FirmicutesCellulolyticJapan:cellulose-degrading
BS390·5EU358714Clostridiaceae bacterium 80Wc (AB078860)95FirmicutesJapan: rice straw in paddy soil
BS400·5EU358715Uncultured Clostridiales (AB234479)95FirmicutesJapan: gut of termites
BS410·5EU358716Sporobacter termitidis (Z49863)91FirmicutesHomoacetogenicAustralia: wood-feeding termite
BS420·5EU358717Desulfotomaculum guttoideum (Y11568)93FirmicutesAcetogenicDSM 4024
BS430·5EU358718Garciella nitratireducens (AY176772)89FirmicutesAcidogenicMexico: oilfield separator
BS440·5EU358719Streptococcus alactolyticus (AF201899)99FirmicutesAcidogenicDenmark: ATCC 43077
BS450·5EU358720Uncultured bacterium (AY976000)98FirmicutesUSA: human colon mucosal
BS460·5EU358721Clostridium nexile (X73443)93FirmicutesDSM 1787
BS470·5EU358722Tissierella praeacuta (X80833)93FirmicutesUK: type strain ATCC 25539
BS480·5EU358723Clostridium sp. (X75909)98FirmicutesUK: strain BN II
BS490·5EU358724Sporobacter termitidis (Z49863)92FirmicutesHomoacetogenicAustralia: wood-feeding termite
BS500·5EU358725Lactobacillus reuteri F275(CP000705)99FirmicutesAcidogenicDSM 20016
BS510·5EU358726Leuconostoc citreum(AF111949)100FirmicutesAcidogenicSouth Korea: fermented cabbage
BS520·5EU358727Guggenheimella bovis (AY272039)89FirmicutesProteolyticUSA: bovine dermatitis digitalis
BS530·5EU358728Desulfotomaculum thermocisternum (U33455)83FirmicutesAcetogenicNorway: hot North Sea oil
BS540·5EU358729Uncultured bacterium (EF559146)97FirmicutesFrance: mesophilic digester
BS550·5EU358730Gracilibacter thermotolerans (DQ117465)88FirmicutesAcidogenicUSA: acid sulfate wetland
BS560·5EU358731Moorella glycerini (U82327)81FirmicutesHomoacetogenicUSA: strain YS6
BS570·5EU358732Uncultured Thermoanaerobacteriales (AY684076)92FirmicutesGermany: methanogenic enrichment
BS580·5EU358733Uncultured bacterium (EF559145)97FirmicutesFrance: mesophilic digester
BS590·5EU358734Dethiobacter alkaliphilus (EF422412)94FirmicutesSulfur-reducingRussia: soda lakes
BS600·5EU358735Clostridium sp. (AB186360)88FirmicutesJapan: methanogenic bioreactor
BS610·5EU358736Uncultured bacterium (EF559145)99FirmicutesFrance: mesophilic digester
BS620·5EU358737Uncultured bacterium (CR933151)99FirmicutesFrance: naerobic sludge digester
BS630·5EU358738Alkaliflexus imshenetskii (AJ784993)90BacteroidetesAcidogenicRussia: soda lake
BS640·5EU358739Proteiniphilum acetatigenes (AY742226)89BacteroidetesProteolyticChina: sludge of UASB reactor
BS650·5EU358740Uncultured Bacteroidetes (AB234401)90BacteroidetesJapan: gut of termites
BS660·5EU358741Owenweeksia hongkongensis (AB125062)85BacteroidetesChina: strain UST20020801
BS670·5EU358742Uncultured Bacteroidetes (AF529128)89BacteroidetesUSA: trichloroethene-contaminated
BS680·5EU358743Bacteroidetes bacterium (AY548787)98BacteroidetesFinland: SRB reactor
BS690·5EU358744Alkaliflexus imshenetskii (AJ784993)89BacteroidetesAcidogenicRussia: soda lake

Only 14 of the 69 OTU in the bacterial library were matched to the closest related known sequences deposited in NCBI and RDP at a similarity index of more than 97%, which was regarded as an experiential index for differentiating species (Table 1). Twenty-nine of the 69 OTU were matched to the closest related known sequences deposited in the databases at a similarity index of between 90% and 96%, while 26 of the 69 OTU were matched to the closest related known sequences at a similarity index of between 80% and 90%. Nearly 80% of the bacteria in this digester may be new, previously undescribed species. Even among the 14 OTU most closely related to known sequences, only 6 OTU matched with the cultured bacterial strains, which were Leuconostoc citreum, Clostridium quinii, Clostridium chartatabidum, Streptococcus alactolyticus, Lactobacillus reuteri and Clostridium sp. The remaining eight OTU were from uncultured bacteria. These results indicated that more than 91% bacteria in this digester were uncultured.

Within the 69 OTU, 41 were classified as Firmicutes, 16 as Bacteroides and 8 as Spirochaetes. In a phylogenetic tree (Fig. 1), 24 OTU of Firmicutes were clustered with the pure cultures belonging to Clostridia, the most of which belonged to the family Clostridiaceae. Three Firmicutes OTU were assigned to different families of Bacilli. All of the 16 Bacteroides OTU clustered together and divided into four subgroups, belonging to family Porphyromonadaceae, Rikenellaceae and two uncultured Bacteroidetes. In the eight Spirochaetes OTU, two clustered together with genus Treponema, five with genus Spirochaeta and the remaining one with uncultured Leptospiracheae. The remaining four sequences were grouped together with uncultured Fibrobacteres, Xanthomonas vasicola, uncultured planctomycete and uncultured Verrucomicrobia, respectively.

image

Figure 1.  Phylogenetic tree of bacteria. The tree was constructed with the neighbour-joining method of the ARB programme package using nearly complete sequences of the 16S rRNA gene. Scale bar is 10% of the estimated difference in nucleotide sequence position. Aquifex pyrophilus was used as the outgroup.

Download figure to PowerPoint

The phylogenetically most closely matched bacteria were mostly detected from the intestine of pig or other animals, waste-water treatment plant (sludge or biofilm), anaerobic reactor or digester or landfill leachate and compost, which were all related to anaerobic fermentation (Table 1). The matching micro-organisms of most OTU belonged to phylum Firmicutes, Bacteroides and Spirochaetes, which were fermentative acidogens. Several OTU from Firmicutes were cellulolytic and those mainly from Bacteroides were proteolytic. The most closely matched micro-organisms of three OTU from Firmicutes were homoacetogens and that of another one was the sulfur-reducing bacteria (Table 1).

Microbial community analysed by clone library-archaeal community

The 16S rDNA genes amplified from the total DNA extracted from biogas slurry sample with a specific archaeal primer set of 1Af/1100Ar were ligated into the pMD-18 T vector to construct a library. Exactly 186 clones were used for the following analyses and the length of the amplified genes was approximately 1·1 kbp. All of the sequences were classified into 25 OTU (Table 2). The coverage of the library was 94·1%. OTU AS01, AS02 and AS04 accounted for 27·4%, 29·0% and 10·8% of the sequenced clones in the archaeal library, respectively.

Table 2.   Taxonomic relationship of archaeal 16S rDNA sequences from Chongming biogas digester compared (Blast) with public databases (RDP, Greengenes and NCBI)
OTU (%)% of totalAccession numberPhylogenetically most closely related organism (accession no.) Sm (%)GenusFunctional groupSource
AS0127·4EU358651Methanosarcina barkeri (AJ002476)97MethanosarcinaHydrogenotrophic/ aceticlasticNew Zealand: cow
AS0229·0EU358652Methanoculleus bourgensis (AB065298)97MethanoculleusHydrogenotrophicDSM 6216
AS030·5EU358653Methanospirillum hungatei (M60880)93MethanospirillumHydrogenotrophic
AS0410·8EU358654Methanospirillum hungatei (M60880)97MethanospirillumHydrogenotrophic
AS055·4EU358655Methanomicrobiales archaeon (DQ280483)97Methanogenium-USA: Skan bay
AS063·9EU358656Methanospirillum hungatei (M60880)92MethanospirillumHydrogenotrophic
AS071·1EU358657Methanogenium marinum (DQ177345) 95MethanogeniumHydrogenotrophicUSA: Skan bay
AS082·2EU358658Methanogenium marinum (DQ177344)89MethanogeniumHydrogenotrophicUSA: Skan bay
AS090·5EU358659Methanothrix soehngenii (X51423)99MethanosaetaAceticlasticNetherlands: Opfikon
AS100·5EU358660Uncultured archaeon (AY835414)92USA: sediment
AS110·5EU358661Methanogenium cariaci (M59130)94MethanogeniumHydrogenotrophicLibrary: DSM 1497
AS122·2EU358662Methanoculleus sp. (AJ550158)94HydrogenotrophicGermany: dm2
AS130·5EU358663Methanoculleus bourgensis (AB065298)90HydrogenotrophicDSM 6216
AS140·5EU358664Methanosarcina sp. HB-1 (AB288262)91Hydrogenotrophic/ aceticlasticJapan: sedimentary rock
AS151·6EU358665Methanosarcina mazei (NC_003901)90Hydrogenotrophic/ aceticlasticUSA: strain Go1
AS160·5EU358666Methanosarcina barkeri (AF028692)91Hydrogenotrophic/ aceticlasticFrance: Sar
AS170·5EU358667Methnosarcina siciliae (U89773)96MethanosarcinaHydrogenotrophic/ aceticlasticUSA: C2J
AS181·1EU358668Methanoculleus bourgensis (AY196674)93HydrogenotrophicAustralia: MS2
AS194·9EU358669Uncultured euryarchaeote (EF552190)98MethanosarcinaFrance: digester
AS200·5EU358670Methanospirillum hungatei (CP000254)92MethanospirillumHydrogenotrophicUSA: JF-1
AS211·6EU358671Methanospirillum sp. (AJ133792)92MethanospirillumHydrogenotrophicGermany: Bremen
AS220·5EU358672Uncultured Methanomicrobiales (AB353214)95Japan: mesophilic sludge
AS231·1EU358673Methanoculleus bourgensis (AB065298)94HydrogenotrophicDSM 6216
AS240·5EU358674Methanoculleus bourgensis (AB065298)95HydrogenotrophicDSM 6216
AS252·2EU358675Methanoculleus bourgensis (AY196674)94HydrogenotrophicAustralia: MS2

Only 6 of the 25 OTU had matches to most closely related sequences in the databases at a similarity more than 97% (Table 2), among which 5 OTU were matched to cultured isolates. Eighteen of the twenty-five OTU were matched to the closest related known sequences at a similarity index of between 90% and 96%; the remainders were matched to the closest related known sequences at a similarity index of 89%. More than 80·0% of the archaeal OTU in this digester may be uncultured.

All of the archaeal OTU were classified into Methanomicrobia of phylum Euryarchaeota and assigned to two branches: Methanomicrobiales and Methanosarcinales (Fig. 2). In branch Methanomicrobiales, four OTU were clustered with two isolates from genus of Methanogenium, one OTU was grouped to genus Methanoculleus, five to genus Methanospirillum and the remaining seven were divided into four different sub-branches, which might represent new genus or new families. Within the Methanosarcinales branch, three OTU clustered with genus Methanosarcina, one OTU belonged to genus Methanosaeta and the remaining four (AS10, AS14, AS15 and AS16) were clustered together but separated from genus Methanosarcina. It suggested that OTU AS10, AS14, AS15 and AS16 might represent a new genus of Methanosarcinaceae. However, the three most abundant OTU, AS02 (29·0%), AS01 (27·4%) and AS04 (10·8%), comprising more than 67% clones in the library, matched pure cultures of Methanoculleus bourgensis, Methanosarcina barkeri and Methanospirillum hungatei at a similarity index of 97%, respectively. This indicates that the four methanogens were the dominant archaeal species in this biogas digester.

image

Figure 2.  Phylogenetic tree of archaea. The tree was constructed using the neighbour-joining method of the ARB programme package using nearly complete sequences of 16S rRNA gene. Scale bar indicates 10% estimated difference in nucleotide sequence position. Methanococcus aeolicus was used as the outgroup.

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The phylogenetically assigned archaea OTU were divided into three functional groups. Most OTU, including the most and the third most abundant OTU, were hydrogenotrophic methanogens (Table 2). The most closely matched archaea to five OTU, including the second most abundant OTU, were hydrogenotrophic/aceticlastic methanogens, and one OTU was an aceticlastic methanogen.

Structure of the dominant microbiota in the digester analysed by DGGE fingerprinting and sequencing the DGGE bands

The structures of the dominant bacteria and archaea in the slurry sample were analysed by DGGE fingerprinting (Fig. 3). Twenty-four detected bands were found in the bacterial DGGE profile (CB1–CB24) and nine bands (CA1–CA9) in the archaeal profile (Fig. 3). These results demonstrated a higher diversity of bacteria than archaea in the digester.

image

Figure 3.  Denaturing gradient gel electrophoresis (DGGE) fingerprints of the bacterial (a) and archaeal (b) communities of biogas slurries obtained at Chongming in June 2005. UPGMA tree representing the genetic similarity of the microbial community profiles was obtained by polymerase chain reaction-DGGE.

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All of the detected bands from bacterial and archaeal DGGE fingerprinting were excised, amplified and cloned. The mobility of the inserted fragments of three clones randomly selected from each clone library (33 libraries in total) were checked via DGGE and compared with the original DGGE pattern. Clones of the inserts that migrated to the same locations as the original bands in the DGGE profile were sequenced. In total, 34 different sequences from the bacterial libraries and 11 sequences from archaeal libraries were obtained (Tables 3 and 4). Multi-fragments were found in a single band in at least 12 of the 33 recovered DGGE bands.

Table 3.   Bacterial 16S rDNA sequence similarities of excised bands that appear in Fig. 3
Band IDAccession numberPhylogenetically most closely related organism (accession no.)Lg (bp) Sm1 (%)Corresponding OTU no. Sm2 (%)Functional group
  1. Sm1, similarity between the sequences of denaturing gradient gel electrophoresis (DGGE) band and its phylogenetically closely related organism.

  2. Sm2, similarity between the sequences of DGGE band and its corresponding operational taxonomic unit (OTU).

CB1EU358617Proteiniphilum acetatigenes (AY742226)18995BS04, BS64100Proteolytic
CB2EU358618Eubacterium tortuosum (L34683)19583
CB3EU358619Petrimonas sulfuriphila (AY570690)18989BS0399Acidogenic
CB4-1EU358620Uncultured bacterium (EF559197)189100BS08100
CB4-2EU358621Alkaliflexus imshenetskii (AJ784993)16089BS02, 63, 69100Acidogenic
CB5-1EU358622Uncultured bacterium (EF686929)18997BS0898
CB5-2EU358623Uncultured bacterium (AY816908)19095
CB6-1EU358624Spirochaeta sp. grapes (AF357917)19487
CB6-2EU358625Uncultured bacterium (AJ937700)18995
CB7EU358626Uncultured bacterium (AB290394)18999
CB8EU358627Uncultured bacterium (AJ628010)18994BS23100
CB9-1EU358628Uncultured spirochete (EF562545)19496BS3299
CB9-2EU358629Clostridium orbiscindens(Y18187)17495Acidogenic
CB10-1EU358630Syntrophomonas zehnderi (DQ898277)19493Acetogenic
CB10-2EU358631Oscillospira guilliermondii (AB040499)171100
CB11-1EU358632Clostridium intestinale (X76740)16896
CB11-2EU358633Uncultured bacterium (AY816908)19097BS63, 6998
CB12EU358634Acetivibrio cellulolyticus (L35516)16999Cellulolytic
CB13-1EU358635Uncultured bacterium (EF559146)17198BS54100
CB13-2EU358636Clostridium thermocellum (L09173)16992Cellulolytic
CB14EU358637Succinivibrio dextrinosolvens (Y17600)17196Acidogenic
CB15EU358638Uncultured Clostridiaceae (DQ069192)169100BS06100
CB16-1EU358639Clostridium hydroxybenzoicum (L11305)16994
CB16-2EU358640Tissierella praeacuta (X80833)16996BS4799
CB17EU358641Clostridium chartatabidum (X71850)16998BS2898Cellulolytic
CB18EU358642Uncultured Verrucomicrobiales (AJ853598)19496  
CB19-1EU358643Anaerovorax odorimutans (AJ251215)17298BS2199Acidogenic
CB19-2EU358644Uncultured bacterium (AY980698)17296
CB20EU358645Clostridium quinii (X76745)169100BS07100Acidogenic
CB21-1EU358646Clostridium quinii (X76745)16998BS0798Acidogenic
CB21-2EU358647Tissierella praeacuta (X80833)16995BS2998
CB22EU358648Uncultured bacterium (EF559145)19595BS5899
CB23EU358649Spirochaeta sp. SPN1 (AJ698092)19495BS0599
CB24EU358650Dethiosulfovibrio acidaminovorans (AY005466)17297Sulfur-reducing
Table 4.   Archaeal 16S rDNA sequence similarities of excised bands that appear in Fig. 3
Band IDAccession numberPhylogenetically most closely related organism (accession no.)Lg (bp) Sm1 (%)Corresponding OTU no. Sm2 (%)Functional group
  1. Sm1, similarity between the sequences of denaturing gradient gel electrophoresis (DGGE) band and its phylogenetically closely related organism.

  2. Sm2, similarity between the sequences of DGGE band and its corresponding operational taxonomic unit (OTU).

CA1-1EU358606Methnosarcina siciliae (U89773)15298AS17100Hydrogenotrophic/ aceticlastic
CA1-2EU358607Methanosarcina barkeri (AJ002476)15297AS0198Hydrogenotrophic/ aceticlastic
CA2-1EU358608Methanoculleus bourgensis (AB065298)14897AS02, 18, 24, 2599Hydrogenotrophic
CA2-2EU358609Uncultured archaeon (AM712547)13298
CA3EU358610Uncultured Methanomicrobiales (AB353214)14896AS2299
CA4EU358611Uncultured Methanomicrobiales (AB353214)14897AS2299
CA5EU358612Methanoculleus bourgensis (AB065298)14898AS1399Hydrogenotrophic
CA6EU358613Methanoculleus bourgensis (AB065298)14898AS1399Hydrogenotrophic
CA7EU358614Methanospirillum sp. (AJ133792)14895AS3, 4, 6, 15, 20, 2199Hydrogenotrophic
CA8EU358615Uncultured Methanosarcinales (AB353215)15299AS1799Hydrogenotrophic/ aceticlastic
CA9EU358616Uncultured euryarchaeote (EF552190)15299AS1999

Nineteen of the bacterial sequences were assigned to Firmicutes, ten of the sequences were clustered to Bacteroidetes, three were grouped to Spirochaetes and one to Proteobacteria and Verrucomicrobiales, respectively (Table 3). Seven of the 34 bacterial sequences matched one isolate at a similarity index of ≥97%, the remainder matched isolates at a similarity index of <97% (Table 3), indicating that nearly 80% of the dominant bacteria in the biogas digester were unclassified.

In the DGGE profile, most of the fragments representing V3 regions of phylum Firmicutes were found to melt at high denaturant concentration areas, and that of phylum Bacteroidetes tended to melt at low denaturant concentration areas (Table 3 and Fig. 3).

Three archaeal sequences were assigned to genera Methanoculleus and Methanosarcina, respectively; one belonged to genus Methanospirillum, three grouped to order Methanomicrobiales and the remaining was grouped to order Methanosarcinales (Table 4). About half of the sequences were matched to uncultured taxa.

The consistency of dominant bacterial and archaeal composition revealed by clone library and DGGE profile analysis

Eighteen bacterial sequences representing 16 of the 24 recovered DGGE bands were also present in the clone library. On the other hand, seven of the nine OTU with abundance >3% were matched with the V3 sequences recovered from the DGGE bands (Table 3). This means that 78% dominant OTU (with abundance >3%) in the bacterial clone library could be also detected by DGGE profile analysis.

Ten archaeal sequences representing all of the nine recovered DGGE bands were in the clone library. Five of the dominant six archaeal OTU with abundance more than 3% were found to match with the V3 sequences recovered from DGGE bands. Exactly 83% of the dominant OTU (with abundance >3%) in the archaeal library were observed in the DGGE profile.

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Cloning and sequencing the full length of 16S rDNA has been frequently applied to elucidate the exact composition of a microbial community. However, DGGE profiles of the V3 regions can be employed to reflect the dominant community structure and to analyse the structural variation between different systems or the dynamics of the same system (Muyzer et al. 1993). In this study, sequencing of V3 regions from recovered DGGE bands and 16S rDNA from cloning libraries were both used to analyse the microbial community in an anaerobic digester. Both analyses indicated that the community was mainly composed of phyla Firmicutes, Bacteroides and Spirochaetes for bacteria, and orders Methanomicrobiales and Methanosarcinales for archaea. The analyses also indicated that the diversity of bacteria was higher than that of achaera. Importantly, 78% of dominant OTU (with abundance >3%) in the bacterial library and 83% of dominant OTU in the archaeal library could be detected in the DGGE profile. These results indicate that the DGGE profile in this study clearly reflected the dominant composition of the microbial community in the Qianwei biogas digester. Additionally, the major components of this bacterial community could be separately located on the different areas in the DGGE profile, e.g. Firmicutes tended to appear at the high denaturant concentration area and Bacteroidetes at low denaturant concentration areas in the DGGE profile. The analysis of DGGE profiles may be useful in displaying the dominant microbial composition in biogas digesters in our further studies. However, more OTU were identified by sequencing 16S rDNA libraries, indicating that DGGE may underperform in elucidating the diversity or the exact composition of a complex microbial community.

Anaerobic digesters are widely used to treat different wastes, e.g. brewery and pulp industry wastewater containing different carbohydrates, long-chain fatty acids, volatile fatty acids, methanethiol, terephthalate (Mata-Alvarez et al. 2000; Yadvika et al. 2004). Many studies focus on analyses of microbial community in such anaerobic digesters by using different approaches (Cotta et al. 2003; Snell-Castro et al. 2005; Peu et al. 2006). In these studies, the microbial structures in these systems varied greatly owing to the difference of the substrates used. Firmicutes, Nitrospira and Deferribacteres were found to be the predominant bacteria and Methanosaeta concilii was the dominant methanogenic archaea in an anaerobic digester treating wastewater from a beer brewery (Diaz et al. 2006). In an anaerobic digester treating long-chain fatty acids, species of Syntrophomonadaceae and Syntrophobacteraceae families, which oxidize fatty acids, were the predominant bacteria (Sousa et al. 2007b). When degrading methanethiol from paper mill wastewater, methylotrophic methanogens Methanomethylovorans hollandica were enriched (de Bok et al. 2006). In a laboratory methanogenic digester amended with glucose, Spirochaetes-, eubacterium- and propionibacterium-like bacteria were found to be dominant (Fernandez et al. 1999, 2000). Pig manure, which was mainly comprised of undigested biomass and some fatty acids, such as acetic acid and propionic acid, was the sole substrate in Qianwei biogas digester analysed in this study. Firmicutes (47·2%), Bacteroides (35·4%) and Spirochaetes (13·2%) were found to be the three most abundant bacterial phyla in this study. Within phylum Firmicutes, class Clostridia was the most dominant of the bacterial community (45·5% of the clones).

Several other studies have shown that within the bacterial and archaeal community of a pig manure slurry and a manure storage pit, Eubacterium, Clostidium, Bacillus–Lactobacillus–Streptococcus subdivision, Mycoplasma and the Flexibacter–Cytophaga–Bacteroides were the main components of the bacterial communities (Snell-Castro et al. 2005; Peu et al. 2006). Hydrogenotrophic methanogens, such as Methanoculleus, Methanogenium and Methanobrevibacter, dominated the archaeal communities (Whitehead and Cotta 1999; Tang et al. 2004; Hori et al. 2006). This agrees with the results from our digester, suggesting most archaea in our digester might originate from pig manure.

Most phylogenetically closely matched bacteria to the OTU identified in the biogas digester were found in anaerobic environments, such as the guts of animal or insects, sediments, anaerobic digesters and faeces (Table 1). Many phylogenetically closest matched taxa were uncultured organisms or function-unidentified organisms. The metabolic functions of their related OTU in the biogas digester were unknown. Among the function-identified bacteria, most were acidogenic, producing H2, CO2, formate, acetate and other fatty acids as well as a small amount of ethanol from cellobiose, d-fructose, N-acetylglucosamine, d-glucose, maltose, mannose and saccharose (Vandamme et al. 1999); several OTU related to the Clostridium sp. might be cellulolytic and the other three ones were related to proteolytic, both of which might take charge of decomposing polymers in the pig manure to monomers; Only three OTU might be homoacetogenic. This seems to be consistent with the fact that most archaeal OTU or the most abundant OTU were hydrogenotrophic methanogens, and only one OTU at low frequency was identified as the aceticlastic methanogen (Table 2).

The phylum Euryarchaeota was the major methanogenic archaeal group in anaerobic fermentation environments. Methanoculleus bourgensis, Methanosarcina barkeri, Methanospirillum hungatei and Methanomicrobiales archaeon were the most abundant methanogenic species in our digester (Table 2). Each of them showed some specific characteristics in methanogenic metabolism. Methanoculleus bourgensis was reported to use H2–CO2 or formate as a substrate for growth and methanogenesis, and is a hydrogenotrophic methanogen (Blotevogel et al. 1992). Methanospirillum hungatei produces methane only from H2–CO2 or formate, but not from acetate or ethanol and methanol, being a strictly hydrogenotrophic methanogen (Ferry et al. 1974). Methanosarcina barkeri could be used in different substrates to produce methane, including H2–CO2, methanol, mono-, di- and trimethylamines, acetate and CO (Bryant and Boone 1987), and is a hydrogenotrophic or aceticlastic methanogen.

Chimeric sequences of the full length of 16S rDNA were usually found while analysing the compositions of complex microbial communities (Ashelford et al. 2006). Therefore, several programmes were designed to identify chimeric sequences (Cole et al. 2003; DeSantis et al. 2006). Ashelford et al. (2006) reported that the average chance to falsely identify a sequence as chimeric by the Bellerophon programme was 7·2%. Fifty-three 16S rDNA sequences from bacteria were identified to be putatively chimeric. To ensure most of them to be assigned rightly, the putative chimeric sequences were confirmed by two different programmes, and some of them were further confirmed by PCR amplification. However, it was still possible that some of the 53 sequences be assigned as chimeric by wrong, and thus might underestimate the bacterial diversity in the biogas digester.

High diversities of microbial composition and metabolism were found in this microbiota in this study. It provides a pool of functional micro-organisms involved in biomass transformation. However, most micro-organisms, more than 91% bacteria and 80% of archaea, in this digester were uncultured. Presently it is impossible to elucidate the total metabolic process in the biogas digester using only the analysis of microbial composition. The recent application of metagenomic techniques suggest they may provide a new approach to obtain function genes related to biomass transformation within the assemblage. A lab anaerobic fermentation system and its control system are necessary in elucidating the relationship between the biogas-producing efficiency and microbiota composition under variable substrate conditions, with the addition of an inhibitor or accelerant, and/or adding specific micro-organisms for bio-augmentation.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

The authors are very grateful for the technical support from Dr Huirong Li at Key Laboratory for Polar Science of State Oceanic Administration, Polar Research Institute of China. They thank Dr Yongping Huang and Prof. Xiangxiong Zhu for their critical reading of the manuscript. They also thank Dr Hewson at the Department of Ocean Sciences, University of California Santa Cruz, for language polishing. This work was financially supported by Knowledge Innovation Key Program of the Chinese Academy of Sciences project (no. KSCX2-YW-G-022), the National High Technology Research and Development Key Program of China (863 Key Program, grant no. 2007AA021302) and the National Natural Science Foundation of China (grant no. 30700017).

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  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
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