Stability of a biogas-producing bacterial, archaeal and fungal community degrading food residues

R E S EA RCH AR T I C L E

Stability of a biogas-producing bacterial, archaeal and fungalcommunity degrading food residues

Frank R. Bengelsdorf1, Ulrike Gerischer2, Susanne Langer1, Manuel Zak3 & Marian Kazda3

1Institute for Microbiology and Biotechnology, University of Ulm, Ulm, Germany; 2Theoretical and Computational Biophysics Department, Max

Planck Institute for Biophysical Chemistry, G€ottingen, Germany; and 3Institute for Systematic Botany and Ecology, University of Ulm, Ulm,

Germany

Correspondence: Frank Bengelsdorf, Institut

f€ur Mikrobiologie und Biotechnologie, Albert-

Einstein-Allee 11, D-89081 Ulm, Universit€at

Ulm, Germany. Tel.: +49 731 50 22713;

fax: +49 731 50 22719;

e-mail: [email protected]

Received 27 October 2011; revised 7

November 2012; accepted 2 December

2012.

DOI: 10.1111/1574-6941.12055

Editor: Alfons Stams

Keywords

biowaste; biogas plant; 16S/18S rRNA gene;

Saccharomyces; denaturating gradient gel

electrophoresis.

Abstract

The resident microbiota was analyzed in a mesophilic, continuously operating

biogas plant predominantly utilizing food residues, stale bread, and other waste

cosubstrates together with pig manure and maize silage. The dominating bacte-

rial, archaeal, and eukaryotic community members were characterized by two

different 16S/18S rRNA gene culture-independent approaches. Prokaryotic 16S

rRNA gene and eukaryotic 18S rRNA gene clone libraries were constructed and

further analyzed by restriction fragment length polymorphism (RFLP), 16S/18S

rRNA gene sequencing, and phylogenetic tree reconstruction. The most domi-

nant bacteria belonged to the phyla Bacteriodetes, Chloroflexus, and Firmicutes.

On the family level, the bacterial composition confirmed high differences

among biogas plants studied so fare. In contrast, the methanogenic archaeal

community was similar to that of other studied biogas plants. Furthermore, it

was possible to identify fungi at the genus level, namely Saccharomyces and

Mucor. Both genera, which are important for microbial degradation of complex

compounds, were up to now not found in biogas plants. The results revealed

their long-term presence as indicated by denaturating gradient gel electropho-

resis (DGGE). The DGGE method confirmed that the main members of the

microbial community were constantly present over more than one-year period.

Introduction

Organic residues (biowaste) accumulate in large amounts

as leftovers of food processing and households and are

therefore available as a substrate for biogas production

[methane (CH4) and carbon dioxide (CO2)] in anaerobic

reactors (Wiesenthal et al., 2006). Household waste is reg-

ularly collected at public expense, and the amount that is

presently incinerated or landfilled is in principle accessible

for energy recovery (Wiesenthal et al., 2006). Biowaste

has a considerable advantage compared with renewable

primary products (i.e. maize silage, fodder beat, green

rye), as there is no competition between the use of the

substrate for the production of biogas and the use of the

substrate as food.

In general, the fermentation process is characterized by

the three phases: hydrolysis sometimes referred to as pri-

mary fermentation (Schink, 1997), acidogenesis/acetogen-

esis sometimes referred to as secondary fermentation

(Schink, 1997), and methane production (methanation),

which occur simultaneously. The influence of the compo-

sition and diversity of the microbial community on the

stability of the biogas-forming process and on the biogas

yield is of great interest (Weiland, 2010). So far, several

studies focused on the microbial diversity in biogas plants

supplied with renewable primary products and liquid

manure as substrates (Cirne et al., 2007; Schl€uter et al.,

2008; Kr€ober et al., 2009; Liu et al., 2009; Nettmann

et al., 2010). In contrast, this work analyzed the composi-

tion, the diversity as well as the stability of the microbial

community, over the time of operation of a mesophilic,

continuously operated biogas plant. The biogas plant was

predominantly supplied with sanitized food residues and

stale bread. According to availability, substrates such as

pig slurry, maize silage, potato peelings, and grain husk

were supplied in minor and variable proportions.

The microorganisms that break down the organic com-

pounds initiate biogas production by producing the

FEMS Microbiol Ecol && (2012) 1–12 ª 2012 Federation of European Microbiological SocietiesPublished by Blackwell Publishing Ltd. All rights reserved

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major substrates for the methanogens (Ahring, 2003).

Besides the known fermentative bacteria, microorganisms

from the eukaryotic domain are also important candi-

dates of the primary and secondary fermentation pro-

cesses. For instance, it is well known that obligate

anaerobic protozoa are part of the ruminal microbiota

(Williams & Coleman, 1997). The presence of episymbi-

otic methanogens in ruminal ciliated protozoa was pro-

ven in the early 1980s (Ushida, 2011). Furthermore,

anaerobic fungi are known to form co-cultures with ru-

minal methanogenic archaea as a result of their high

hydrogen production (Orpin & Joblin, 1997). The role of

ruminal fungi in the degradation of plant fibers has been

studied extensively (Akin & Borneman, 1990; Wubah

et al., 1993; Krause et al., 2003; Tripathi et al., 2007). The

fungi attach to the most lignified tissues of plant material

(Akin & Rigsby, 1987) and penetrate the plant tissue in a

way that cellulolytic bacteria can gain access to the inte-

rior. Fungal penetration then leads to faster and more

complete decomposition of fodder that enters the rumen

(Orpin & Joblin, 1997). The same could be true for a

biogas plant, but until recently, not much is known about

the occurrence of fungi and other eukaryotes within a

biogas plant.

Most of the latest studies focused on the methanogenic

archaea present in biogas plants (Nettmann et al., 2010;

Fotidis et al., 2012; Kampmann et al., 2012a). Certainly,

this is due to the fact that methane-generating microor-

ganisms (methanogens) play a key role in the biogas-

forming process (Weiland, 2010). Methanogens are

generally divided into two groups, hydrogenotrophic and

acetoclastic methanogens. Members of the former use

CO2 as a carbon source for their metabolism and H2 or

formate as an electron donor, whereas members of the

latter metabolize primarily C1 compounds and acetate

(Whitman et al., 2006). Recently, it has been shown that

members of the hydrogenotrophic group dominate the

biogas-forming process (Nettmann et al., 2010; Kamp-

mann et al., 2012a). This is in accordance with the results

published by Laukenmann et al. (2010). They used stable

carbon isotope labeling (d13C) to identify the pathway of

methane production. As a result, they found that only a

minor part of the produced methane derives from acetate

(Laukenmann et al., 2010).

The first topic of this work was to analyze the compo-

sition and diversity of the microbial community of the

mentioned mesophilic biogas plant supplied with bio-

waste. The dominating bacterial, archaeal, and eukaryotic

community members were characterized using different

culture-independent approaches. Therefore, clone libraries

were constructed containing either prokaryotic 16S rRNA

gene fragments or eukaryotic 18S rRNA gene fragments.

Furthermore, the enumeration of the microorganisms was

carried out using epifluorescence microscopy. To detect

all groups of microorganisms in the reactor content, dif-

ferent staining dyes were used that intercalate into the

DNA of microorganisms.

The principal objective of this work was to analyze the

stability of the microbial community of the mentioned

biogas plant. So far, very little is known about the stabil-

ity of the microbial communities in full-scale biogas reac-

tors (Weiss et al., 2008), especially under substrate

variation. To investigate the stability, the composition of

the microbial community was monitored and described

as a function of time. The hypothesis was that the micro-

bial community of a full-scale biogas reactor is stable over

time despite variations in the substrate composition. The

stability of the microbial community was analyzed by

denaturating gradient gel electrophoresis (DGGE) that

allows among others a comparison of molecular DNA

fingerprints of microbial communities as a function of

time (Muyzer et al., 1993).

Materials and methods

Sampling and specifications of the biogas plant

A biogas plant located in Southern Germany near Aulen-

dorf in Baden-W€urttemberg utilizing predominantly sani-

tized food residues was chosen to investigate the variation

in microbial community during its operation. The plant

consists of three reactors with 350, 450, and 1200 m3

total volumes and a production capacity of 380 kWh.

Before sampling, operation of the plant was stable in

terms of substrate feeding, biogas production, and engine

performance for at least 1 year. The methane content

accounts for 55–60% (v/v) of the produced biogas. Tem-

perature in the reactor ranged from 37 to 44 °C depend-

ing on the season and average pH of 7.9. Food residues

and stale bread were constantly fed into the plant; all

other substrates such as pig slurry, maize silage, potato

peelings, and grain husk were supplied according to avail-

ability, with a mean hydraulic residence time of 100 days.

Food residues were leftovers from commercial kitchens or

factory canteens. It is mandatory in Germany that food

residues used in the plant were previously pasteurized

(70 °C, 1 h) and homogenized by an accredited waste

management company (Pig Food GmbH, Aulendorf, Ger-

many). The slurry (usually pH of 3.7) was delivered to

the biogas plant.

The 350 m3 biogas reactor that was investigated had an

organic loading rate of 3.5 to 4.0 kg VS m�3 d�1 (VS,

volatile solids). The concentration of volatile fatty acids

in the reactor was approximately 0.8–1.0 g L�1. The main

acid produced was acetate. Extensive description of the

process parameters and analytical methods is given

ª 2012 Federation of European Microbiological Societies FEMS Microbiol Ecol && (2012) 1–12Published by Blackwell Publishing Ltd. All rights reserved

2 F.R. Bengelsdorf et al.

elsewhere (Zak, 2012). This reactor was sampled several

times during the period from December 2008 to May

2011 (Table 1). The reactor content was stirred for

10 min prior to each sampling. Samples of warm reactor

content were taken through a provided outlet, whereas

the first cold liters from the pipe were discarded. In four

parallels, a total of 20 mL of reactor content was mixed

with 20 mL of 99.8% ethanol in a 50-mL Falcon tube,

transported on ice, and stored at �20 °C for genomic

DNA extraction. For verification of cell density by fluo-

rescence microscopy, two extra samples were drawn from

the reactor at March 15, 2010, and May 10, 2010. Reactor

content was fixed as described before and further pro-

cessed according to the protocol of Daims et al. (2005).

Fluorescence microscopy

To determine the number of cells, fixed reactor content

was diluted (100–200 fold). The cell suspension and

100 mg of sterile glass beads (∅ 0.1 mm) were filled in a

2-mL microtube (Sarstedt, N€umbrecht, Germany) and

homogenized using the RiboLyser (Hybaid Ltd, Middle-

sex, UK) at 4 m s�1 for 20 s. The homogeneous cell sus-

pension (15 lL) was dropped onto each well of a teflon-

coated slide (8 wells, ∅ 6 mm; Menzel GmbH & Co KG,

Karlsruhe, Germany) and incubated for 15 min at 60 °C.Cells were stained with 20 lL of 10 000-fold diluted

SYBR® Gold solution (Molecular Probes Inc., Eugene)

(Yamada et al., 2008) for 10 min at room temperature

(RT), flushed with cold double-distilled H2O, and imme-

diately dried with compressed air. An analogous proce-

dure was carried out with 4′,6′-diamidino-2-phenylindole

(DAPI, 2.5 mg mL�1). Acetate solution (0.5 M, pH 3.5)

containing acridine orange (AO, 3 lL mL�1) was used

for cell staining as well. Twenty microlitre of AO acetate

solution was applied per well, incubated for 5 min (RT),

flushed, and dried. To reduce background radiation, every

stained well was washed twice with 40 lL of pure acetate

solution for 1 min (RT). Fluorescence microscopy was

also used to detect methanogenic archaea using their

coenzyme F420 native autofluorescence (Solera et al.,

2001). In this case, the unstained cell suspension was

stimulated using fluorescence excitation at 420 nm. Fluo-

rescence was detected using a Leica microscope (Leica,

Solms, Germany). The microscope was equipped with a

100-W mercury high-pressure bulb (HBO 103W/2) and

corresponding filter cubes to detect the dyes. Digital

images were taken with an AxioCam MRc 5 (D) (Carl

Zeiss, Jena, Germany) and the software AxioVision Rel.

4.8. Ten microscopic pictures (randomly chosen, magnifi-

cation of 1000) were made per well. Cells in the picture

were only counted if approximately 600 cells were stained

by specific dyes or 6 cells showed autofluorescence of the

coenzyme F420. The following formula was used to calcu-

late the total cell number (TCn) per mL reactor content:

TCn = A/Ami 9 Cn 9 Df. A is the total area of a well

(28.27 mm2), Ami is the area of microscopic image

(0.015552 mm2), Cn is the average cell number per

microscopic image, and Df is the dilution factor per milli-

liter reactor content. The sum of the average cell counts

determined by specific dyes and coenzyme F420 autofluo-

rescence served as a basis for the calculation of the distri-

bution of methanogenic cells.

DNA isolation and purification

Initial treatment of 2 mL of fixed reactor content was

carried out as described by Nettmann et al. (2008). After-

ward, the genomic DNA was extracted following the pro-

tocol of the ‘High Pure PCR Template Preparation Kit’

from Roche (Mannheim, Germany). Furthermore, the

RNA was removed by RNase digestion (20 ng mL�1) for

20 min at 37 °C, followed by a standard phenol–chloro-form extraction and ethanol precipitation. Yields of the

genomic DNA extracted were determined photometrically

with the Ultrospec 3100 pro (GE Healthcare Bioscience

AB, Uppsala, Sweden).

Amplification of 16S rRNA gene and 18S rRNA

gene

The prepared genomic DNA of sample no. 3 (Table 1) was

used as PCR template. PCR was performed using a Ther-

mocycler (MWG-Biotech AG, Ebersberg, Germany). All

employed primers (Table S1) were synthesized by biomers.

net GmbH (Ulm, Germany). Bacterial 16S rRNA gene

fragments were amplified using the primer set 27F and

1492r (Lane, 1991) with the DF Taq DNA polymerase

Table 1. List of samples from the 350 m3 biogas reactor of the plant

near Aulendorf.

Sample

number Date of sampling Quality pH Annotation

1 December 19, 2008 raw rc 7.8 DGGE sample

2 February 27, 2009 raw rc 8.0 DGGE sample

3 March 05, 2009 raw rc 7.9 Internal reference

and DGGE sample

4 May 26, 2009 raw rc 8.2 DGGE sample

5 August 08, 2009 raw rc 8.0 DGGE sample

6 February 12, 2010 raw rc 8.0 DGGE sample

7 March 15, 2010 raw rc Microscopically cell

density analysis

8 May 10, 2010 raw rc Microscopically cell

density analysis

raw rc, raw reactor content; DGGE, denaturing gradient gel electro-

phoresis.


Microbial community in a mesophilic anaerobic reactor 3

(5 U lL�1; Genaxxon, Ulm, Germany). Temperature steps

were 95 °C for 5 min, a loop of 35 cycles of 94 °C for

45 s, 55 °C for 1 min, 72 °C for 1.5 min, and a final step

of 72 °C for 7 min. Archaeal 16S rRNA gene was ampli-

fied using the primer set 8aF and 1512uR (Eder et al.,

1999) with the High Fidelity PCR Enzyme Mix (5 U lL�1;

Fermentas, St. Leon-Rot, Germany). Temperature steps

were 96 °C for 5 min, 10 cycles of 96 °C for 45 s, 54 °Cfor 1 min, and 72 °C for 3 min, 25 cycles of 94 °C for

30 s, 59 °C for 1 min, and 72 °C for 3 min, and a final

incubation at 72 °C for 10 min. Eukaryotic 18S rRNA

gene was amplified using the primer set Euk1a and

Euk516r-GC (Scanlan & Marchesi, 2008) with the DF Taq

DNA polymerase. 18S rRNA gene fragments were gener-

ated with the following protocol: initial denaturation at

95 °C for 5 min, 8 cycles of 94 °C for 30 s, 55 °C for 30 s,

and 72 °C for 1 min, 20 cycles of 92 °C for 30 s, 52 °C for

30 s, and 72 °C for 1 min, followed by final extension at

72 °C for 10 min.

Construction and analysis of clone libraries

Different libraries of clones for bacteria, archaea, and

eukaryotes were constructed using the amplified 16S/18S

rRNA gene fragments. Those fragments were cloned into

either the pDrive cloning vector (QIAGEN GmbH, Hil-

den, Germany) or pJET1.2/blunt cloning vector according

to the instructions provided by the manufacturer. Cold

competent Escherichia coli DH5a cells were used for heat-

shock transformation of plasmid DNA (Inoue et al.,

1990). Colony PCR (Woodman, 2005) was carried out to

recover the cloned DNA fragment from the recombinant

plasmid. For restriction fragment length polymorphism

(RFLP) analysis, PCR fragments were combined with an

appropriate restriction enzyme mix and enzyme buffer.

Electrophoresis to separate restriction fragments was car-

ried out using 2% agarose gels. The resulting RFLP band-

ing patterns were compared visually. In case of identical

RFLP banding patterns, one representative DNA fragment

was chosen for sequencing (Eurofins MWG Operon,

Ebersberg, Germany). The resulting DNA sequence and

the corresponding RFLP banding pattern were defined as

an operational taxonomic unit (OTU). Representative

sequences were used for phylogenetic classification of the

corresponding microorganisms.

Phylogenetic analysis

All reference sequences used to analyze the 16S or 18S

rRNA gene sequences were retrieved from GenBank

(NCBI database) and the Ribosomal Database Project

(RDP). Considering the fact that the closest BLAST hit to

a query sequence is usually not the nearest neighbor

(Koski & Golding, 2001), two or three related sequences

with high similarity (97%) were chosen as references for

further analysis. All obtained nucleotide sequences were

checked for chimeric artifacts with the Mallard software

tool (version 1.02) (Ashelford et al., 2006). For each

deduced data set of bacterial, archaeal, or eukaryotic 16S/

18S rRNA gene sequences, three multiple sequence align-

ments (MSAs) were calculated using ClustalW2 (Larkin

et al., 2007), MUSCLE (Edgar, 2004), or MAFFT (Katoh

& Toh, 2008). The resulting MSAs were checked visually.

The reconstruction of phylogenetic trees based on each of

these three MSAs was performed with the program MrBa-

yes 3.1.2 (Ronquist & Huelsenbeck, 2003). The default

settings recommended in the manual for tree reconstruc-

tion were used for all analyses.

DGGE

In the DGGE approach, samples from the biogas reactor

(Table 1) were compared considering the banding pattern

of amplifiable 16S/18S rRNA gene fragments. The primer

set 357F-GC and 518R (Muyzer et al., 1993) was used to

amplify bacterial DGGE PCR products (200 bp) with DF

Taq DNA polymerase and 5% DMSO as an additive. The

corresponding protocol equates to the one described

above for 18S rRNA gene fragments from eukaryotes. Ar-

chaeal DGGE PCR products (180 bp) were generated

using a nested PCR. Nearly full-length archaeal 16S rRNA

gene fragments were used as templates with the primer

set SA1f-GC, SAf2-GC, and PARCH519r and DF Taq

DNA polymerase. The following program was used: initial

denaturation at 95 °C for 5 min, 5 cycles of 94 °C for

30 s, 53.5 °C for 30 s, and 72 °C for 1 min, 30 cycles of

92 °C for 30 s, 53.5 °C for 30 s, and 72 °C for 1 min,

followed by final extension at 72 °C for 10 min. Eukary-

otic DGGE PCR products (500 bp) were generated as

described above.

The DGGE methodology and the protocol were derived

from the protocol of Zwart and Bok (http://omega.rc.

unesp.br/mauricio/curso/bibliografia/25/415/protocolo_

DGGE.pdf) described in Bodelier et al. (2005). DGGE

analysis of 16S/18S rRNA gene fragments was performed

in 8% (w/v) polyacrylamide gels containing denaturing

gradients of 25–50% (bacteria), 35–60% (archaea), and

25–55% (eukaryotic). One-hundred percent denaturing

conditions corresponded to 7 M urea and 40% formam-

ide. Electrophoresis was performed at 40 V and 60 °C for

15.5 h.

Based on sequencing data from the bacterial clone

library, an internal classification standard (ICS) was

developed for DGGE analysis (Nicol et al., 2003; Smalla

et al., 2007). ICS1 consists of five different 16S rRNA

gene fragments mixed in equal concentrations, while ICS2



consists of four different 16S rRNA gene fragments. A

DGGE was performed to verify that 16S rRNA gene frag-

ments of the ICS run at identical positions as fragments

amplified from a genomic DNA. Therefore, bands were

excised, re-amplified, and sequenced according to the

protocol of O’Sullivan et al. (2008).

Gel image analysis

Similarity analysis of DGGE banding pattern was carried

out with the Bio-1D software package (version 12.06, Vil-

ber Lourmat). Therefore, the digital DGGE banding pat-

tern image was used for comparison and clustering. Lanes

were defined by hand, and corresponding band positions

were recognized from a corrected intensity chart. The dice

coefficient was calculated to produce a similarity matrix

(Smalla et al., 2007). The corresponding dendrogram was

created using UPGMA (unweighted pairwise grouping

method with mathematical averages) with a tolerance

position value of 2% (Lliros et al., 2010).

Nucleotide sequences accession numbers

All nucleotide sequences obtained in this study were

deposited in the NCBI GenBank database. Bacterial and

archaeal 16S rRNA gene sequences are available under the

accession numbers JF421645 to JF421664 and JF421665 to

JF421673, while fungal 18S rRNA gene sequences are

available under the accession numbers JF421674 to

JF421682.

Results

Microscopical analysis

Micrographs that showed fluorescent cells were used to

determine the number of prokaryotic organisms. Using

87 micrographs (309 DAPI, 279 AO, 309 SYBR® Gold),

the average prokaryotic cells in the reactor were calcu-

lated to be 1.44 (� 0.3) 9 1010 mL�1. Independent of

the dye used, the magnitude of cells counted was in the

same range (1010 cells mL�1). Furthermore, 75 micro-

graphs of autofluorescent methanogenic cells (coenzyme

F420) showed on average, 3.5 (� 0.78) 9 108 archaea

mL�1 were in the reactor, accounting for 2.3% of

the total prokaryotic cells. Additionally, 26 micrographs

of DAPI stained cells showed on average 2.03

(� 1.05) 9 108 cells mL�1 in the reactor of presumably

fungal origin (Fig. 1). In the micrographs, the fungal cells

showed a more intensive fluorescent and were on average

seven times bigger than prokaryotic cells. Fungal cell had

a mean circumference of 7.7 (�1.8) lm with an expanse

of 2.8 (�1.0) lm2.

Molecular and phylogenetic analysis

In total, 120 randomly selected clones of the clone

libraries tested positive for expected DNA fragments: 62

clones contained bacterial 16S rRNA gene fragments

(54% positive clones within the respective library), 38

clones contained archaeal 16S rRNA gene fragments

(39%), and 20 clones contained eukaryotic 18S rRNA

gene fragments (30%).

According to the respective RFLP banding pattern, rep-

resentative whole DNA fragments were sequenced: 43 of

the bacterial and archaeal 16S rRNA gene fragments as

well as 16 of the eukaryotic 18S rRNA gene fragments.

All nonchimeric representative sequences were used for

phylogenetic classification. The sequences were also used

to classify DNA fragments showing identical RFLP band-

ing pattern (OTUs) according the representative 16S

rRNA gene sequence. Thus, OTUs were of the same fam-

ily (Table S2). Bacterial 16S rRNA gene sequences were

classified using the RDP Classifier (95% confidence

threshold) at least to the family level. Clostridiaceae,

Erysipelotrichaceae, Lactobacillaceae, Ruminococcaceae,

Thermoanaerobacteraceae, Caldilineaceae, and Porphyromo-

nadaceae within the phyla Firmicutes, Chloroflexi, and

Bacteroidetes were identified. Comparing the RFLP band-

ing pattern, it was found that Caldilineaceae, Clostridia-

ceae, Erysipelotrichaceae, and Ruminococcaceae were

dominating the bacterial OTUs in the clone library (Table

S2). The RFLP results also showed the archaeal commu-

nity was less diverse than the bacterial community. The

archaeal 16S rRNA gene clone library was dominated by

OTUs assigned to the family Methanomicrobiaceae

especially with the two species Methanoculleus sp. and

Fig. 1. Epifluorescence micrograph showing cells of biogas reactor

content. Cells were stained with DAPI. Cells marked by arrows are

presumable of fungal origin.



Methanoculleus bourgensis (Table S2). Only two clones

contained archaeal 16S rRNA gene from the genus Met-

hanosarcina (RDP Classifier, 95% confidence threshold)

belonging to the family Methanosarcinaceae.

For bacterial phylogenetic tree reconstruction, 20 bacte-

rial 16S rRNA gene sequences obtained from the clone

library as well as 40 16S rRNA gene sequences retrieved

from RDP were used. The corresponding phylogenetic

tree (Fig. 2) is based on the MAFFT alignment and is in

accordance with the current taxonomic classification in

terms of a correct arrangement of all sequences. Lactoba-

cillus delbrueckii, Proteiniphilum acetatigenes, Alkaliphilus

oremlandii, and Clostridium isatidis were identified at spe-

cies level with 97% sequence identity. To root the tree,

two archaeal 16S rRNA gene sequences were used as out-

group. The trees based on MUSCLE or ClustalW align-

ments computed with MrBayes 3.1.2 were not correct in

terms of sequence arrangements for this specific 16S

rRNA gene sequence set (data not shown). Nine

sequences obtained from the archaeal clone library and

13 reference sequences chosen from RDP were used to

reconstruct the phylogenetic tree. The resulting tree (Fig.

S1) is also based on a MAFFT alignment. Two bacterial

16S rRNA gene sequences were used as outgroup. Recon-

structed trees based on MUSCLE and ClustalW align-

ments showed an analogous structure (data not shown).

Methanoculleus bourgensis was identified at species level

with 97% sequence identity. Nine 18S rRNA gene

sequences from the eukaryotic clone library and 14 refer-

ence sequences chosen from the NCBI GenBank were

used for reconstruction of the phylogenetic tree. The cor-

responding tree (Fig. 3) was reconstructed based on MA-

FFT alignment showing 18S rRNA gene sequences related

to the genera Mucor, Saccharomyces, and two so far

uncultured fungi. These fungal 18S rRNA gene sequences

were classified into the orders Mucoromycotina, Saccharo-

mycotina, and Pucciniomycotina. Analyses with MUSCLE

and ClustalW revealed analogous results (data not

shown).

DGGE

Six samples were drawn from the biogas reactor over a

period of 1 year (Table 1) to monitor the composition of

the microbial community (bacteria, archaea, and eukary-

otes) by DGGE. The ICS was developed for DGGE analy-

sis based on sequenced 16S rRNA gene fragments of the

bacterial clone library. By sequence alignment and com-

parison, it was confirmed that 16S rRNA gene fragments

from the ICS were homologous to 16S rRNA gene frag-

ments within the banding pattern obtained from genomic

DNA extracts (data not shown). Thus, the ICS was used

to analyze whether those bacterial families were constantly

present in all samples drawn. As shown in Fig. 4, almost

each band of the ICS was found in banding patterns of

the reactor samples (lanes 1 to 6), demonstrating that the

following members of the bacterial community were pres-

ent throughout the whole sampling time (1 year): Erysipe-

lotrichaceae (42 Bac), Porphyromonadaceae (5 and 33

Bac), Ruminococcaceae (7 Bac oFk), and Clostridiaceae (6

Bac and 7 Bac). The bands for Lactobacillaceae (13 Bac)

and Caldilineaceae (1 Bac) were absent in the banding

pattern of lanes 5 and 6 (Fig. 4). Each banding pattern

showed on average 17 (�2) bands that could not be iden-

tified by means of the ICS. Considering this, the diversity

of the bacterial community was at least 2.5 times higher

compared with the seven families described in the bacte-

rial phylogenetic tree (Fig. 2).

To compute similarities between the six analyzed band-

ing patterns, a homology dendrogram (Fig. S2) was

deduced from Fig. 4. On average, 25 independent distinct

bands were detectable in each banding pattern (lanes 3 to

8). Thereof, 16 to 18 independent bands were perma-

nently present in all lanes. The homology dendrogram of

the banding patterns displayed that these lanes were at

least 75–80% similar to each other (Fig. S2). Although an

identical composition of 16S rRNA gene fragments in the

ICS2, the UPGMA method computed an only 80%

homology value. Lanes 1 and 10 represent the ICS 1, and

due to some inconsistent bands such as 20 Bac (Fig. 4),

the value of homology was even lower (70%). Consider-

ing the DGGE banding patterns and the homology den-

drogram, the bacterial community remains similar over

the period of 1 year. The banding patterns of the archaeal

16S rRNA gene fragments from all six reactor samples

were highly similar to each other and were composed of

three bands (Fig. S3). Three archaeal 16S rRNA gene frag-

ments (letters a, b, c in Fig. S3) were excised from the

gel. Sequencing led to the 16S rRNA gene sequences from

Methanosarcina sp., Methanoculleus sp., and M. bourgen-

sis, respectively. The banding patterns of the eukaryotic

18S rRNA gene fragments were very similar for all six

samples (Fig. 5). 18S rRNA gene fragments for DGGE

experiments were amplified with the same primer set

used for the assembly of the clone library (Table S1), and

therefore, the eukaryotic community in the biogas reactor

was dominated by fungi (Fig. 3). The presence of these

fungi did not change over the time period of 1 year

(Fig. 5).

Discussion

The biogas plant located near Aulendorf in Baden-

W€urttemberg was chosen for the investigation of the

microbial community, because of its diverse substrate

supply. The plant is predominantly fed with sanitized



Fig. 2. Phylogenetic tree based on bacterial 16S rRNA gene sequences (approximately 1500 bp per sequence) from sample no. 3 of biogas

reactor content. Estimation is based on Bayesian inference and MAFFT alignment.



food residues and stale bread. The use of these substrates

is in contrast to most other biogas plants in Germany,

which were predominantly supplied with renewable bio-

mass such as maize silage (Biogas, 2012).

Three different staining dyes that intercalate into DNA

were used to ensure the microscopic detection of all

groups of microorganisms in the biogas reactor. The total

cell number of the microbial community was in the range

of 1010 cells mL�1 reactor content, independent of stain-

ing with either AO, DAPI, or SYBR® GOLD. Using DAPI

staining, Nettmann et al. (2010) reported total cell num-

bers in the range of 108 cells mL�1 for six different biogas

plants, and Krakat et al. (2010) found approximately

1010 cells mL�1 reactor content in a 6-L laboratory-scale

reactor. Concerning the methanogenic archaea, our

results differ also from the mentioned studies. Krakat

et al. (2010) detected 16–20% methanogenic archaea of

total cell number in the laboratory-scale reactor. In con-

trast, Nettmann et al. (2010) detected 3–7% methanogen-

ic archaea of total cell numbers in biogas plants, which is

closer to the corresponding 2.3% found in this study.

Cells of presumably fungal origin differ clearly from

prokaryotic cells (Fig. 1). They are characterized by a

more intensified fluorescence compared with prokaryotic

Fig. 3. Phylogenetic tree based on eukaryotic, particularly fungi 18S

rRNA gene sequences (approximately 530 bp per sequence) from

sample no. 3 of biogas reactor content. Estimation is based on

Bayesian inference and MAFFT alignment.

Fig. 4. DGGE profiles of bacterial 16S rRNA gene fragments (200 bp)

amplified from genomic DNA extracted from samples of the biogas

reactor. Lanes: ICS 1, mix of sequenced 16S rRNA gene fragments

from Firmicutes; ICS 2, mix of sequenced 16S rRNA gene fragments

from Chloroflexi and Bacteroidetes. Lanes: 1 to 6: samples from the

biogas reactor in the order 4, 1, 2, 3, 5, 6 (Table 1).

Fig. 5. DGGE profiles of eukaryotic 18S rRNA gene fragments

(530 bp) amplified from genomic DNA extracted from samples of the

biogas reactor. Lanes: 1 to 6, samples from the biogas reactor in the

order 1, 2, 3, 4, 5, 6 (Table 1).



cells, which is certainly due to the higher DNA content in

the nucleus of fungi cells. The mean circumference and

expanse of those fungal-like cells were on average above

the usual size of prokaryotic cells (circumference 7.09,

expanse 2.69). Their estimated abundance was in the

same range as the methanogenic archaea (of

108 cells mL�1), but two magnitudes below the total cell

number. Still, such differences in estimating microbial

abundances using microscopically methods underline the

need to further investigate the cell numbers of bacteria,

archaea, and fungi in biogas reactors.

A further attention of this study was to identify the

dominating community members within the anaerobic

reactor. As in other mesophilic biogas plants, the bacte-

rial community (Fig. 2) was dominated by the phyla

Firmicutes and Bacteroidetes (Schl€uter et al., 2008; Liu

et al., 2009). However, the composition of bacterial fam-

ilies in the investigated biogas reactor clearly differs to

that of previous described reactors (Schl€uter et al., 2008;

Weiss et al., 2008; Goberna et al., 2009; Liu et al., 2009).

Only a few of the bacterial species found in this study

were also reported in the mentioned studies, including

P. acetatigenes, A. oremlandii, and Lactobacillus sp. Only

members belonging to the genus Clostridium seem to be

ubiquitous in all biogas plants. The methanogenic com-

munity of the reactor was dominated by the genus

Methanoculleus (Fig. S2, Table S2). Seven different stud-

ies (Schn€urer et al., 1999; Weiss et al., 2008; Cardinali-

Rezende et al., 2009; Goberna et al., 2009; Kr€ober et al.,

2009; Liu et al., 2009; Nettmann et al., 2010) showed

that the genus Methanoculleus predominates microbial

communities of full-scale biogas reactors. Nettmann

et al. (2010) pointed out that the hydrogenotrophic

genus Methanoculleus was the predominant group of

methanogens in five of six mesophilic biogas reactors

supplied with liquid manure (pig and cattle) and renew-

able raw materials (maize silage). In the analytical proce-

dure when constructing clone libraries, the numbers of

relevant fragments were low (Table S2). This is due to

the fact that cloned DNA fragments showing identical

RFLP banding pattern were described as an OTU. RFLP

analyses were performed from the respective clone

library until no further OTU was found. The OTUs

obtained from each clone library were considered to be

the dominating members of the microbial community.

Furthermore, the cloning and sequencing approach of

DNA fragments does not detect members of the micro-

bial community that appear at low abundance (Sogin

et al., 2006).

This study provides the first qualitative and quantita-

tive assessment of fungi in a full-scale biogas reactor. Ear-

lier studies by Ravella et al. (2010) showed that a

laboratory-scale biogas reactor is an appropriate habitat

for fungi as viable strains of different yeasts could be iso-

lated under aerobic conditions. The origin of the fungal

cells found in the present study remains unclear. It is

possible that they were brought into the biogas reactor by

the substrate stale bread, or alike. Saccharomyces, Mucor,

and fungi from the subphylum Pucciniomycotina were

detected (Fig. 3), and their presence was confirmed over

a time span of 15 months (Fig. 5). The fact that the bio-

gas reactor investigated here is not utilizing any residues

from cattle breeding makes it obvious that fungi are sus-

taining members of the microbial community. The

applied methods used for detection (clone library and

microscopy) of fungi do not prove any enzymatic activity

of the cells. Furthermore, the primers used for amplifica-

tion of 18S rRNA gene amplified a high number of chi-

meric fragments (Table S2). For subsequent analysis of

fungal communities in biogas reactors, it is recommended

to directly amplify the fungal internal transcribed spacer

(ITS) regions.

So far, there is little known about abundance and func-

tion of fungi in microbial communities of biogas reactors.

Fliegerov�a et al. (2010) evaluated the diversity of anaero-

bic fungi within cow manure obtained from input

homogenizing tank of a biogas plant. The authors sug-

gested that fungi possibly contribute to hydrolytic pro-

cesses inside a biogas reactor. In the anaerobic biological

treatment, anaerobic fungi are known to enhance the bio-

gas yield (Proch�azka et al., 2012), and yeasts, to accelerate

the decomposition rate of substrate (Olcay & Kocasoy,

2004) in laboratory-scale reactors. Such a consequence is

already known for probiotic yeasts as feed additive for

cattle in ruminal fermentation, especially cultures of Sac-

charomyces cerevisiae and their extracts (Wallace, 1994;

Tripathi et al., 2008). Lila et al. (2004) showed that living

cells of S. cerevisiae increased the numbers of total viable

bacteria and especially cellulolytic bacteria in the rumen

of cows, but had no effect on the concentration of hydro-

gen and methane in rumen gases. S. cerevisiae in biogas

plants could have similar effects regarding an increase in

cellulolytic bacteria and thereby the decomposition of cel-

lulose-rich substrates. This means the decomposition of

cellulose-rich substrates could be increased in full-scale

biogas reactors using yeasts. Members of the fungal genus

Mucor can be found in soil and environmental samples

worldwide (Ribes et al., 2000). Mucoralean fungi (Santi-

ago & Motta, 2008), especially Mucor sp. (Alves et al.,

2005), are characterized by a high protease activity that

could improve the digestion of organic residues in biogas

plants. The other fungal subphylum Pucciniomycotina

includes more than 8000 described species; thus, all fur-

ther conclusions would be highly speculative. In sum-

mary, it is important to point out that the presence of

fungi in a biogas reactor as reported here could improve



and broaden the potential of various substrates for biogas

production.

DGGE experiments monitored the bacterial and

archaeal 16S rRNA gene as well as eukaryotic 18S rRNA

gene composition and showed a homogeneous microbial

community over a 15-month period (Figs 4 and 5, Figs

S2 and S3). Concluding from DGGE results (Figs 4 and

S2), the bacterial community does not show a stable

composition over time. However, the chosen 16S rRNA

gene fragments for the ICS represented the dominating

bacteria in the biogas reactor, and occurrence of the

respective bands within the six different DGGE patterns

were expected. This was true for all bands classified on

family level of the ICS (Fig. 4) and supported by the

corresponding homology dendrogram (Fig. S2). There-

fore, the bacterial community remains similar (75–80%)

over the period of 1 year and was only affected in a

minor degree by variations in the substrate supply. The

archaeal and fungal communities (Fig. 5, Fig. S3) stayed

highly similar within the investigated period. Altogether,

DGGE results propose that the majority of the micro-

bial community was not (archaea and fungi) or just in

a minor degree (bacteria) affected by variations in the

substrate composition. The only available long-term

study that reported similar analysis was performed for a

thermophilic biogas plant (Weiss et al., 2008). Over

more than 2 years of investigation, a stable microbial

composition was observed. Additionally, Kampmann

et al. (2012b) used other noncultural detection systems

(SSCP) and suggested also stability of the bacterial

phyla Bacteroidetes and Firmicutes during the digestion

of defined substrates in 200-liter biogas reactors. DGGE

results were assessed only qualitatively, because the

intensity of single bands in the DGGE pattern is no

reliable measure for quantification (Weiss et al., 2008).

In general, the DGGE method of Muyzer et al. (1993)

has some notable limitations. On one hand, bands in

the same position in different lanes can belong to dif-

ferent microorganisms as shown in Fig. 4 (ICS 1 and 2,

see 1 Bac and 13 Bac as well as 6 Bac and 33 Bac),

and on the other hand, one 16S rRNA gene fragment

can occur in double bands in DGGE profiles (Janse

et al., 2004).

This study revealed that the composition of the micro-

bial community (bacteria, archaea, and fungi) in a full-

scale biogas reactor utilizing various substrates was con-

sistent over more than 1-year period (Figs 4 and 5, Figs

S3). It underlines that due to various interactions between

its members, the anaerobic ecosystem of the reactor has

highly resilient and can withstand changes in substrate

feeding. The role of anaerobic fungi in the complex deg-

radation of cellulose-rich substrates should receive more

attention in future biogas research.

Acknowledgements

The Baden-W€urttemberg Stiftung, Biomass to Energy

program (Project No, Bio 06) funded this study. We are

very thankful to Prof. P. D€urre at the Ulm University for

providing laboratory space. We gratefully acknowledge

Mrs. M. K€oster for her support during epifluorescence

analysis and Mrs. E. Salzer as well as Mr. S. Br€andel for

their excellent technical service. We are very thankful to

Mr. A. Allgayer for providing access to his biogas plant.

The authors are also very grateful to Prof. C. Gliesche

and Mr. M. Barsch for providing computing capacity to

reconstruct the phylogenetic trees. We would like to

thank Dr. M. Othman and Dr. T. Huynh for critical read-

ing of the manuscript.

References

Ahring BK (2003) Perspectives for anaerobic digestion. Adv

Biochem Eng Biotechnol 81: 1–30.Akin DE & Borneman WS (1990) Role of rumen fungi in fiber

degradation. J Dairy Sci 73: 3023–3032.Akin DE & Rigsby LL (1987) Mixed fungal populations and

lignocellulosic tissue degradation in the bovine rumen. Appl

Environ Microbiol 53: 1987–1995.Alves MH, de Campos-Takaki GM, Okada K, Pessoa IHF &

Milanez AI (2005) Detection of extracellular protease in

Mucor species. Rev Iberoam Micol 22: 114–117.Ashelford KE, Chuzhanova NA, Fry JC, Jones AJ &

Weightman AJ (2006) New screening software shows

that most recent large 16S rRNA gene clone libraries

contain chimeras. Appl Environ Microbiol 72:

5734–5741.Biogas (2012) Ausgangsstoffe. pp. 18, Fachagentur

Nachwachsende Rohstoffe e. V. (FNR) (www.FNR.de)

Bestell-Nr. 175: 1-42.

Bodelier PLE, Meima-Franke M, Zwart G & Laanbroek HJ

(2005) New DGGE strategies for the analyses of

methanotrophic microbial communities using different

combinations of existing 16S rRNA-based primers. FEMS

Microbiol Ecol 52: 163–174.Cardinali-Rezende J, Debarry R, Colturato L, Carneiro E,

Chartone-Souza E & Nascimento A (2009) Molecular

identification and dynamics of microbial communities in

reactor treating organic household waste. Appl Microbiol

Biotechnol 84: 777–789.Cirne DG, Lehtom€aki A, Bj€ornsson L & Blackall LL (2007)

Hydrolysis and microbial community analyses in two-stage

anaerobic digestion of energy crops. J Appl Microbiol 103:

516–527.Daims H, Stoecker K & Wagner M (2005) Fluorescence in

situ hybridization for the detection of prokaryotes.

Advanced Methods in Molecular Microbial Ecology (Osborn

AM & Smith CJ, eds), pp. 213–239. Bios-Garland,Abingdon, UK.



Eder W, Ludwig W & Huber R (1999) Novel 16S rRNA gene

sequences retrieved from highly saline brine sediments of

Kebrit Deep, Red Sea. Arch Microbiol 172: 213–218.Edgar RC (2004) MUSCLE: multiple sequence alignment with

high accuracy and throughput. Nucleic Acids Res 32:

1792–1797.Fliegerov�a K, Mr�azek J, Hoffmann K, Z�abransk�a J & Voigt K

(2010) Diversity of anaerobic fungi within cow manure

determined by ITS1 analysis. Folia Microbiol 55: 319–325.Fotidis IA, Karakashev D, Kotsopoulos TA, Martzopoulos

GG & Angelidaki I (2012) Effect of ammonium and

acetate on methanogenic pathway and methanogenic

community composition. FEMS Microbiol Ecol 83: 38–48.Goberna M, Insam H & Franke-Whittle IH (2009) Effect of

biowaste sludge maturation on the diversity of thermophilic

bacteria and archaea in an anaerobic reactor. Appl Environ

Microbiol 75: 2566–2572.Inoue H, Nojima H & Okayama H (1990) High efficiency

transformation of Escherichia coli with plasmids. Gene 96:

23–28.Janse I, Bok J & Zwart G (2004) A simple remedy against

artifactual double bands in denaturing gradient gel

electrophoresis. J Microbiol Methods 57: 279–281.Kampmann K, Ratering S, Baumann R, Schmidt M, Zerr W &

Schnell S (2012a) Hydrogenotrophic methanogens dominate

in biogas reactors fed with defined substrates. Syst Appl

Microbiol 35: 404–413.Kampmann K, Ratering S, Kramer I, Schmidt M, Zerr W &

Schnell S (2012b) Unexpected stability of Bacteroidetes and

Firmicutes communities in laboratory biogas reactors fed

with different defined substrates. Appl Environ Microbiol 78:

2106–2119.Katoh K & Toh H (2008) Recent developments in the MAFFT

multiple sequence alignment program. Brief Bioinform 9:

286–298.Koski LB & Golding GB (2001) The closest BLAST hit is often

not the nearest neighbor. J Mol Evol 52: 540–542.Krakat N, Westphal A, Schmidt S & Scherer P (2010)

Anaerobic digestion of renewable biomass: thermophilic

temperature governs population dynamics of methanogens.

Appl Environ Microbiol 76: 1842–1850.Krause DO, Denman SE, Mackie RI, Morrison M, Rae AL,

Attwood GT & McSweeney CS (2003) Opportunities to

improve fiber degradation in the rumen: microbiology,

ecology, and genomics. FEMS Microbiol Rev 27: 663–693.Kr€ober M, Bekel T, Diaz NN et al. (2009) Phylogenetic

characterization of a biogas plant microbial community

integrating clone library 16S-rDNA sequences and

metagenome sequence data obtained by 454-

pyrosequencing. J Biotechnol 142: 38–49.Lane DJ (1991) 16S/23S rRNA sequencing. Nucleic Acid

Techniques in Bacterial Systematics (Stackebrandt E &

Goodfellow M, eds), pp. 115–175. Wiley, New York.

Larkin MA, Blackshields G, Brown NP et al. (2007) Clustal W

and Clustal X version 2.0. Bioinformatics 23: 2947–2948.

Laukenmann S, Polag D, Heuwinkel H, Greule M, Gronauer

A, Lelieveld J & Keppler F (2010) Identification of

methanogenic pathways in anaerobic digesters using stable

carbon isotopes. Eng Life Sci 10: 509–514.Lila ZA, Mohammed N, Yasui T, Kurokawa Y, Kanda S &

Itabashi H (2004) Effects of a twin strain of Saccharomyces

cerevisiae live cells on mixed ruminal microorganism

fermentation in vitro. J Anim Sci 82: 1847–1854.Liu FH, Wang SB, Zhang JS, Zhang J, Yan X, Zhou HK, Zhao

GP & Zhou ZH (2009) The structure of the bacterial and

archaeal community in a biogas digester as revealed by

denaturing gradient gel electrophoresis and 16S rDNA

sequencing analysis. J Appl Microbiol 106: 952–966.Lliros M, Gich F, Plasencia A, Auguet J, Darchambeau F,

Casamayor EO, Descy J & Borrego C (2010) Vertical

distribution of ammonia-oxidizing crenarchaeota and

methanogens in the epipelagic waters of Lake Kivu

(Rwanda-Democratic Republic of the Congo). Appl Environ

Microbiol 76: 6853–6863.Muyzer G, de Waal EC & Uitterlinden AG (1993) Profiling of

complex microbial populations by denaturing gradient gel

electrophoresis analysis of polymerase chain reaction-

amplified genes coding for 16S rRNA. Appl Environ

Microbiol 59: 695–700.Nettmann E, Bergmann I, Mundt K, Linke B & Klocke M

(2008) Archaea diversity within a commercial biogas plant

utilizing herbal biomass determined by 16S rDNA and mcrA

analysis. J Appl Microbiol 105: 1835–1850.Nettmann E, Bergmann I, Pramschufer S, Mundt K, Plogsties

V, Herrmann C & Klocke M (2010) Polyphasic analyses of

methanogenic archaea communities in agricultural biogas

plants. Appl Environ Microbiol 76: 2540–2548.Nicol GW, Glover LA & Prosser JI (2003) The impact of

grassland management on archaeal community structure in

upland pasture rhizosphere soil. Environ Microbiol 5:

152–162.Olcay O & Kocasoy G (2004) Acceleration of the

decomposition rate of anaerobic biological treatment.

J Environ Sci Heal A 39: 1083–1093.Orpin CG & Joblin KN (1997) The rumen anaerobic fungi.

The Rumen Microbial Ecosystem (Hobson PN & Stewart CS,

eds), pp. 145–184. Chapman and Hall, New York.

O’Sullivan LA, Webster G, Fry JC, Parkes RJ & Weightman AJ

(2008) Modified linker-PCR primers facilitate complete

sequencing of DGGE DNA fragments. J Microbiol Methods

75: 579–581.Proch�azka J, Mr�azek J, Strosov�a L, Fliegerov�a K, Z�abransk�a J &

Doh�anyos M (2012) Enhanced biogas yield from energy

crops with rumen anaerobic fungi. Eng Life Sci, 12: 343–351.Ravella SR, James SA, Bond CJ, Roberts IN, Cross K, Reiter A

& Hobbs PJ (2010) Cryptococcus shivaji sp. nov.: a novel

basidiomycetous yeast isolated from biogas reactor. Curr

Microbiol 60: 12–16.Ribes JA, Vanover-Sams CL & Baker DJ (2000) Zygomycetes in

human disease. Clin Microbiol Rev 13: 236–301.



Ronquist F & Huelsenbeck JP (2003) MrBayes 3: Bayesian

phylogenetic inference under mixed models. Bioinformatics

19: 1572–1574.Santiago ALCM & Motta CM (2008) Isolation of Mucorales

from processed maize (Zea mays L.) and screening for

protease activity. Braz J Microbiol 39: 698–700.Scanlan PD & Marchesi JR (2008) Micro-eukaryotic diversity

of the human distal gut microbiota: qualitative assessment

using culture-dependent and -independent analysis of faeces.

ISME J 2: 1183–1193.Schink B (1997) Energetics of syntrophic cooperation in

methanogenic degradation. Microbiol Mol Biol Rev 61: 262–280.

Schl€uter A, Bekel T, Diaz NN et al. (2008) The metagenome of

a biogas-producing microbial community of a production-

scale biogas plant fermenter analysed by the 454-

pyrosequencing technology. J Biotechnol 136: 77–90.Schn€urer A, Zellner G & Svensson BH (1999) Mesophilic

syntrophic acetate oxidation during methane formation in

biogas reactor. FEMS Microbiol Ecol 29: 249–261.Smalla K, Oros-Sichler M, Milling A, Heuer H, Baumgarte S,

Becker R, Neuber G, Kropf S, Ulrich A & Tebbe CC (2007)

Bacterial diversity of soils assessed by DGGE, T-RFLP and

SSCP fingerprints of PCR-amplified 16S rRNA gene

fragments: Do the different methods provide similar results?

J Microbiol Methods 69: 470–479.Sogin ML, Morrison HG, Huber JA, Welch DM, Huse SM,

Neal PR, Arrieta JM & Herndl GJ (2006) Microbial diversity

in the deep sea and the underexplored ‘rare biosphere’.

PNAS 103: 12115–12120.Solera R, Romero LI & Sales D (2001) Analysis of the methane

production in thermophilic anaerobic reactors: use of

autofluorescence microscopy. Biotechnol Lett 23: 1889–1892.Tripathi VK, Sehgal JP, Puniya AK & Singh K (2007)

Hydrolytic activities of anaerobic fungi from wild blue bull

(Boselaphus tragocamelus). Anaerobe 13: 36–39.Tripathi MK, Karim SA, Chaturvedi OH & Verma DL

(2008) Effect of different liquid cultures of live yeast

strains on performance, ruminal fermentation and

microbial protein synthesis in lambs. J Anim Physiol Anim

Nutr 92: 631–639.Ushida K (2011) Symbiotic methanogens and rumen ciliates.

(Endo)Symbiotic Methanogenic Archaea, Vol. 19 (Hackstein

JHP, ed), pp. 25–34. Springer, Berlin/Heidelberg.

Wallace RJ (1994) Ruminal microbiology, biotechnology, and

ruminant nutrition: progress and problems. J Anim Sci 72:

2992–3003.Weiland P (2010) Biogas production: current state and

perspectives. Appl Microbiol Biotechnol 85: 849–860.

Weiss A, J�erome V, Freitag R & Mayer H (2008) Diversity of

the resident microbiota in a thermophilic municipal biogas

plant. Appl Microbiol Biotechnol 81: 163–173.Whitman W, Bowen T & Boone D (2006) The Methanogenic

Bacteria. The Prokaryotes, Vol. 3 (Dworkin M, Falkow S,

Rosenberg E, Schleifer K & Stackebrandt E, eds), pp. 165–207. Springer, New York.

Wiesenthal T, Mourelatou A, Petersen J & Taylor P (2006)

How much bioenergy can Europe produce without harming

the environment? Eur Environ Agency 7: 1–70.Williams AG & Coleman GS (1997) The rumen protozoa. The

Rumen Microbial Ecosystem (Hobson PN & Stewart CS,

eds), pp. 73–139. Chapman and Hall, New York.

Woodman ME (2005) Direct PCR of intact bacteria (colony

PCR). Current Protocols in Microbiology, Vol. 9 (Coico R,

Kowalik T, Quarles J, Stevenson B & Taylor R, eds), pp.

A.3D.1–A.3D.6. John Wiley & Sons, Inc., Hoboken (USA).

Wubah DA, Akin DE & Bomeman WS (1993) Biology, fiber-

degradation, and enzymology of anaerobic zoosporic fungi.

Crit Rev Microbiol 2: 99–115.Yamada N, Suzumura M, Tsurushima N & Harada K (2008)

Impact on bacterial activities of ocean sequestration of

carbon dioxide into bathypelagic layers. OCEANS 2008 -

MTS/IEEE Kobe Techno-Ocean 1–3.Zak M (2012) Einfluss pflanzlicher Biofilmtr€ager auf die

Verg€arung von Speiseresten. PhD Thesis, Institute for

Systematic Botany and Ecology, University of Ulm, Ulm,

Germany.

Supporting Information

Additional Supporting Information may be found in the

online version of this article:

Fig. S1. Phylogenetic tree based on archaeal 16S rDNA

sequences (approximately 1500 bp per sequence) from

sample no. 3 of biogas reactor content.

Fig. S2. Homology dendrogram deduced from Fig. 4

computed using Gel Analyser software (biocompare®).

Fig. S3. DGGE profiles of archaeal 16S rDNA fragments

(180 bp) amplified from genomic DNA extracted from

samples of the biogas reactor.

Table S1. Primers used for amplification of 16S/18 rDNA

fragments of bacteria (27F, 1492r), archaea (8aF, 1512uR)

and eukaryotes (Euk1a, Euk516r-GC) to construct clone

libraries.

Table S2. 16S and 18S rDNA sequences from the respec-

tive clone libraries.



Stability of a biogas-producing bacterial, archaeal and fungal community degrading food residues

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