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Characterization of methanogenic Archaea communities in
biogas reactors by quantitative PCR
vorgelegt von
Ingo Bergmann
aus Parchtitz auf Rügen
Von der Fakultät III - Prozesswissenschaften
der Technischen Universität Berlin
zur Erlangung des akademischen Grades
Doktor der Naturwissenschaften
Dr. rer. nat.
genehmigte Dissertation
Promotionsausschuss:
Vorsitzender: Prof. Dr.-Ing. Sven-Uwe Geißen
Berichter: Prof. Dr. rer. nat. Ulrich Szewzyk
Berichter: PD Dr. tech. Elisabeth Grohmann
Berichter: Dr. rer. nat. Michael Klocke
Tag der wissenschaftlichen Aussprache: 19.09.2011
Berlin 2012
D-83
Zwei Dinge sind zu unserer Arbeit nötig:
Unermüdliche Ausdauer und die Bereitschaft,
etwas, in das man viel Zeit und Arbeit gesteckt hat,
wieder wegzuwerfen…
Albert Einstein
TABLE OF CONTENTS
3
Table of contents
1. Abbreviations .................................................................................... 6
2. Abstract ........................................................................................... 10
3. Zusammenfassung ......................................................................... 12
4. Introduction ..................................................................................... 14
4.1 The politics .................................................................................................. 14
4.2 The practice ................................................................................................. 16
4.3 The process ................................................................................................. 17
4.3.1 The four-stage pathway of anaerobic degradation of organic material to
biogas ........................................................................................................... 17
4.3.2 Methanogenesis .................................................................................. 19
4.4 The producers ............................................................................................. 20
4.5 The technique ............................................................................................. 23
4.5.1 The basics of Q-PCR applications ....................................................... 24
4.5.2 Fluorescent probes and dyes ............................................................... 27
4.5.3 Application of Q-PCR in environmental microbiology .......................... 30
4.5.4 Factors for a successful Q-PCR run .................................................... 31
4.5.5 The target genes .................................................................................. 33
4.6 The aims ...................................................................................................... 35
5. Materials and Methods .................................................................... 37
5.1 Model biogas reactors ................................................................................ 37
5.1.1 System 1 .............................................................................................. 37
5.1.2 System 2 .............................................................................................. 37
5.1.3 System 3 .............................................................................................. 38
5.1.4 System 4 .............................................................................................. 38
5.2 Agricultural biogas plants .......................................................................... 42
5.3 Physical and chemical analyses of the biogas and the reactor content 44
5.3.1 Determination of the pH value ............................................................. 44
5.3.2 Calculation of the gas composition ...................................................... 44
5.3.3 Determination of the acid composition ................................................. 44
TABLE OF CONTENTS
4
5.4 DNA-based analysis of the archaeal community structure ..................... 45
5.4.1 Used strains ......................................................................................... 45
5.4.2 DNA extraction and purification ........................................................... 45
5.4.3 DNA quantification ............................................................................... 48
5.4.4 Analysis of the DNA purity ................................................................... 49
5.4.5 Quantitative real-time PCR (Q-PCR) ................................................... 49
6. Results ............................................................................................. 65
6.1 Establishment and application of a Q-PCR assay for the detection of
methanogenic Archaea in biogas plants by the use of the 16S rRNA gene 65
6.1.1 Optimization of the PCR conditions of the group-specific 16S rRNA
gene assays for quantitative real-time PCR .................................................. 65
6.1.2 Influence of DNA isolation on Q-PCR-based quantification of
methanogenic Archaea in biogas fermenters ................................................ 67
6.1.3 Accuracy of the real-time PCR assays and influence of PCR interfering
substances on Q-PCR-based quantification of methanogenic Archaea in
biogas fermenters ......................................................................................... 75
6.1.4 Application of the 16S rRNA gene real-time PCR assays for analyzing
the composition and development of the methanogenic Archaea in meso- and
thermophilic biogas reactors ......................................................................... 84
6.2 Development of group-specific primer sets for the detection of
methanogenic Archaea in biogas plants by the use of the metabolic
mcrA gene ....................................................................................................... 107
7. Discussion ..................................................................................... 123
7.1 Evaluation and optimization of the PCR conditions for amplifying the
16S rRNA gene by using the real-time PCR assay of Yu et al. (2005a) ...... 123
7.2 Design and testing of group-specific Q-PCR primers based on the
mcrA gene for the quantification of methanogenic communities .............. 125
7.3 The influence of different DNA isolation methods on the quantification
of methanogenic Archaea in biogas reactors by real-time PCR................. 127
7.4 Influences of PCR interfering substances on Q-PCR-based
quantification of methanogens in biogas reactors ...................................... 131
TABLE OF CONTENTS
5
7.5 Determination of methanogenic Archaea abundances in
semi-continuous fermentation and acidification by overloading in a
short-run experiment ...................................................................................... 136
7.6 Methanogenic population dynamics in semi-continuous fermentation
and acidification by overloading under mesophilic and thermophilic
conditions in a long-run experiment ............................................................. 139
7.7 Determination of the methanogenic community in biogas reactors with
different substrates for anaerobic digestion under mesophilic and
thermophilic conditions ................................................................................. 143
7.8 Determination of the methanogenic Archaea in agricultural biogas
plants ............................................................................................................... 145
8. Outlook .......................................................................................... 148
References ......................................................................................... 149
List of figures .................................................................................... 168
List of tables ...................................................................................... 171
Publication list .................................................................................. 174
Funding .............................................................................................. 178
Acknowledgments ............................................................................ 179
Appendix............................................................................................ 182
ABBREVIATIONS
6
1. Abbreviations
A Adenine
ABI Applied Biosystems Instruments
AF Anaerobic filter
approx. Approximately
ARC Archaea
BA Biogas plant
BAC Bacteria
BB Brandenburg
bp Base pair
C Cytosine
Carrez I Potassium hexacyanoferrate(II)-3-hydrate
Carrez II Zinc sulphate-7-hydrate
cf. Confer
CSTR Continuously stirred tank reactor
CT Threshold cycle number
CTAB Cetyl trimethyl ammonium bromide
DGGE Denaturing gradient gel electrophoresis
DNA Deoxyribonucleic acid
dNTP Deoxynucleoside triphosphate
DOM Dry organic matter
dsDNA Double-stranded deoxyribonucleic acid
e.g. Exempli gratia (for example)
EDTA Ethylenediaminotetraacetic acid
et al. Et alii (and others)
F primer Forward primer
FAM 6-Carboxyfluoroscein
Fig. Figure
FISH Fluorescence in-situ hybridization
FNR Fachagentur Nachwachsende Rohstoffe e.V.
FR Hydrolysis reactor
FRET Fluorescence resonance energy transfer
ABBREVIATIONS
7
g Gravitational acceleration
G Guanidine
GC Gas chromatography
gDNA genomic deoxyribonucleic acid
IPTG Isopropyl β-D-1-thiogalactopyranoside
JOE 2,7-Dimethoxy-4,5-dichloro-6-carboxyfluorescein
LB Lysogeny broth
LOD Limit of detection
LOQ Limit of quantification
Mb. Methanobacterium
Mbb. Methanobrevibacter
Mbp Mega base pair
MBT Methanobacteriales
Mbt. Methanothermobacter
Mc. Methanococcus
MCC Methanococcales
Mcc. Methanococcoides
Mcd. Methanocaldococcus
MCR Methyl-coenzyme M reductase enzyme complex
Mcr. Methanocorpusculum
mcrA Methyl-coenzyme M reductase sububit α
Mcu. Methanoculleus
Mf. Methanofollis
Mg. Methanogenium
Mha. Methanohalophilus
Mm. Methanomicrobium
MMB Methanomicrobiales
Mml. Methanomethylovorans
Mpr. Methanosphaera
Mpy. Methanopyrus
Msa. Methanosaeta
Msc Methanosarcinaceae
Msp. Methanospirillum
ABBREVIATIONS
8
Msr. Methanosarcina
Mss. Methanosalsum
Mst Methanosaetaceae
Mtc. Methanothermococcus
Mth. Methanothermus
Mts. Methanotorris
MV Mecklenburg-Vorpommern
NA Not analyzed
ND Not detected
OLR Organic loading rate
OTU Operational taxonomic unit
PCR Polymerase chain reaction
PET Polyethylene terephthalate
Q-PCR Quantitative real-time PCR
R primer Reverse primer
rDNA Ribosomal deoxyribonucleic acid
RFLP Restriction fragment length polymorphism
RNA Ribonucleic acid
rpm Revolutions per minute
rRNA Ribosomal ribonucleic acid
RT Retention time
S Sachsen
SA Sachsen-Anhalt
SD Standard deviation
SDS Sodium dodecyl sulphate
SSD Dilution of the standard series
ssp. Subspecies
T Thymine
TAMRA 6-Carboxytetramethylrhodamine
TET Tetrachloro-6-carboxyfluorescein
Tm Melting temperature
T-RFLP Terminal restriction fragment length polymorphism
Tris Trishydroxymethylaminomethane
ABBREVIATIONS
9
UV Ultraviolet
VFA Volatile fatty acid
X-Gal 5-bromo-4-chloro-3-indolyl- β-D-galactopyranoside
ABSTRACT
10
2. Abstract
Energy production from renewable raw material is of increasing importance. Biogas is
one of the major renewable energy sources ensuring an adequate energy supply for
the next generations. Beside technical optimization and upgrading of biogas reactors
and plants, detailed information about the diversity and composition of the
participating microbial community structure is indispensable for optimizing the
biogas-forming process. Furthermore, precise knowledge about the metabolic activity
of the microorganisms and their optimal growth conditions are of utmost importance
to ensure the maximal degradation of the substrates to biogas.
The main objective of this study was the establishment of a highly sensitive and
culture-independent approach for the detection and quantification of methanogenic
Archaea in biogas reactors and plants at the taxonomic level of orders and families.
The method of choice was the quantitative real-time PCR (Q-PCR).
Initially, DNA extraction was optimized for samples taken from biogas reactors
because Q-PCR results are strongly influenced by the DNA quality. Harsh DNA
extraction with bead-beating cell lysis was most efficient while soft DNA extraction led
to a discrimination of certain taxonomic groups like Methanosaetaceae. Hence, a
combined mechanic and chemical cell lysis was used for isolating DNA from biogas
reactor samples.
After finding the most efficient DNA extraction protocol, primer sets which were
developed for the detection of the 16S rRNA gene by Yu et al. (2005a) were
optimized. Therefore, an adaptation of the PCR protocol for the ABI system was
successfully conducted. Subsequently, this molecular genetic approach was used for
the analysis of the quantitative distribution of methanogenic Archaea in biogas
fermenters and plants.
A great variability in the composition of the methanogenes was observed at
mesophilic conditions. Depending on the chosen substrate hydrogenotrophic or
acetotrophic methanogenes were most abundant in laboratory CSTRs.
ABSTRACT
11
By contrast, the hydrogenotrophic Methanomicrobiales and Methanobacteriales were
always the dominant methanogenes in samples taken from mesophilic working
agricultural biogas plants.
At thermophilic conditions the hydrogenotrophic methanogenes always represented
the process dominating group.
By analyzing the methanogenic community structure during the continuous increase
of the organic loading rate (OLR) at mesophilic conditions, a shift from an
acetotrophic to a hydrogenotrophic dominated population structure was observed.
Methanosaetaceae might be taken as a biological indicator for early process
instability because of the sudden vanishing of this methanogenic group by increasing
propionic and acetic acid concentrations.
Beside the 16S rRNA gene, the facultative expressed methyl-coenzyme M reductase
subunit α gene (mcrA gene) was chosen as target gene for Q-PCR analysis. Primer
sets were derived for Methanomicrobiales, Methanobacteriales, Methanosarcinaceae
and Methanosaetaceae. Afterwards, the developed primer sets were tested for their
suitability of quantifying methanogens in biogas reactor samples. At the phylogenetic
level of families an establishment of specific primer sets became feasible while
cross-amplification of non target organisms was observed by testing the specifity of
the order-specific primer sets.
The results of this study prove the importance of Q-PCR as an accurate and
time-saving molecular genetic approach for determining abundancies of
methanogenic Archaea in biogas reactor samples. This work is an indispensable
pre-requisite for metabolic activity measurements of methanogenes by combining the
Q-PCR assays of the mcrA and the 16S rRNA gene for RNA analysis.
ZUSAMMENFASSUNG
12
3. Zusammenfassung
Die Energiegewinnung aus nachwachsenden Rohstoffen gewinnt zunehmend an
Bedeutung. Zu einer der wichtigsten erneuerbaren Energiequellen zählt das Biogas,
welches eine gesicherte Energieversorgung für die Zukunft gewährleisten kann.
Neben der technischen Optimierung von Biogasreaktoren und –anlagen sind
detaillierte Kenntnisse über die Diversität und die Zusammensetzung der mikrobiellen
Lebensgemeinschaft unabdingbar, um den Biogasbildungsprozess zu optimieren.
Zudem sind genaue Angaben über die optimalen Wachstumsbedingungen und den
Stoffwechsel der Mikroorganismen notwendig, um einen vollständigen Abbau des
Substrates zu Biogas zu sichern.
Das Ziel dieser Arbeit war eine hoch sensitive und kultivierungsunabhängige
Methode für die Detektion und Quantifizierung von methanogenen Archaea in
Biogasanlagen auf der taxonomischen Ebene von Ordnungen und Familien zu
etablieren. Als Nachweismethode diente die quantitative real-time PCR (Q-PCR).
Da die Ergebnisse der Q-PCR maßgeblich von der Qualität der isolierten DNA
abhängig sind, wurde zunächst ein optimiertes DNA-Isolierungsprotokoll für die
Umweltproben, die aus den Biogasreaktoren und –anlagen stammten, erstellt. Die
größte Effizienz der DNA-Extraktion konnte mit der mechanischen
Aufschlussmethode über Keramik- und Kieselerdepartikel erreicht werden. Eine
Diskriminierung im Zellaufschluss wurde hingegen bei Anwendung der chemischen
Lyse beobachtet, mit welcher es nicht möglich war, Zellen der Methanosaetaceae
aufzubrechen. Ein kombinierter Zellaufschluss aus mechanischer und chemischer
Lyse konnte für Proben aus Biogasreaktoren als die optimale
DNA-Isolierungsmethode angesehen werden.
Nach der Etablierung der optimalen DNA-Extraktionsmethode wurden Primer Sets,
basierend auf der Grundlage des 16S rRNA Gens, welche durch Yu et al. (2005a)
entwickelt wurden, optimiert. Nach der Adaptation der PCR Protokolle an das
ABI System konnte diese molekulargenetische Methode zur Quantifizierung der
methanogenen Archaea in Biogasreaktoren genutzt werden.
ZUSAMMENFASSUNG
13
Unter mesophilen Bedingungen konnte eine große Variabilität innerhalb der
Zusammensetzung der methanogenen Archaea festgestellt werden. In klassischen
Rührkesselreaktoren dominierten, in Abhängigkeit vom eingesetzten Substrat,
sowohl die hydrogenotrophen als auch die acetotrophen Methanbildner. In mesophil
betriebenen Biogasanlagen waren hingegen immer die hydrogenotrophen
methanogenen Archaea der Ordnungen Methanomicrobiales und
Methanobacteriales vorherrschend.
Unter thermophilen Bedingungen konnte ebenfalls ausschließlich eine Dominanz an
Vertretern der hydrogenotrophen Methanbildner festgestellt werden.
Im Verlauf einer Belastungssteigerung eines mesophilen Rührkesselreaktors konnte
eine Verschiebung der acetotrophen methanogenen Lebensgemeinschaft zu einer
hydrogenotroph dominierenden beobachtet werden. Als biologischer Marker für eine
beginnende Prozessinstabilität kann die Gruppe der Methanosaetaceae angesehen
werden. Mit steigenden Propionsäure- und Essigsäurekonzentrationen konnten
Vertreter dieser methanogenen Familie nicht mehr nachgewiesen werden.
Neben dem 16S rRNA Gen wurde als zweites Zielgen für die Q-PCR das reguliert
exprimierte Methyl-Coenzym M Reduktase Untereinheit α Gen (mcrA Gen)
verwendet. Es wurden Primer Sets für Vertreter der Methanomicrobiales,
Methanobacteriales, Methanosarcinaceae and Methanosaetaceae entwickelt und auf
ihre Anwendbarkeit überprüft. Eine erfolgreiche Etablierung konnte für die Primer
Sets, welche auf Familienebene abgeleitet wurden, erreicht werden. Durch das
Auftreten von Kreuzamplifikationen konnte keine Spezifität für die Primer Sets,
welche auf Ordnungsebene abgeleitet wurden, erreicht werden.
Mit dieser Arbeit konnte gezeigt werden, dass die Q-PCR eine akkurate und
zeitsparende Methode ist, um methanogene Archaea in Reaktorsystemen
nachzuweisen. Zudem legt sie den Grundstein für Stoffwechselaktivitätsanalysen auf
der Grundlage der RNA-Analytik durch den kombinierten Einsatz des mcrA und des
16S rRNA Gen Nachweisassays.
INTRODUCTION
14
4. Introduction
4.1 The politics
In the World Energy Outlook of 2009 an increase of nearly 40% is projected for the
world primary energy demand between 2007 and 2030 (IEA 2009). Due to the
limitation of fossil fuels on earth and the risk of global warming by an increased
discharge of greenhouse gases into the atmosphere, the industrial development of
renewable energy sources seems to be indispensable.
In 2009, the German Bundestag adopted the second amendment of the renewable
energy law (EEG). As main objective an increase of the amount of renewable energy
up to 30% was defined for the total gross electricity consumption until 2020.
Currently, 16.1% of the gross electricity consumption is derived from renewable
energy (AGEE-Stat 2010).
Besides windpower, hydropower and solar energy, biogas production plays a crucial
role for accomplishing the determined objectives of the EEG. Biogas is a
multifunctional renewable energy source which is used for a variation of different
applications. In addition to the replacement of fossil fuels in power and heat
production it can also be applied as a gaseous vehicle fuel or as a feedstock for
producing chemicals and materials (Weiland 2010). Therefore, the importance of
optimizing the biogas building process is obvious.
In 2009, 4,500 agricultural biogas plants were operated with an electrical power of
1,650 MW in Germany, and it is supposed that 800 new biogas plants will be put into
operation until the end of 2010 (Table 1, AGEE-Stat 2010). Because of the exploding
increase of operating biogas plants in the last decade, a lot of studies were carried
out for upgrading technical standards and operation modes of digesters (Kalyuzhnyi
et al. 1998, Castrillon et al. 2002, Kaparaju and Rintala 2006, Liu et al. 2009, Mumme
et al. 2010). However, current knowledge of biogas reactors and plants is still not
sufficient, and many technical and microbial aspects and their interactions have not
been investigated yet. Concerning process optimization, this especially applies to the
functional composition and the diversity and stability of the microbial community
during the fermentation of renewable resources.
INTRODUCTION
15
Table 1 Development of operating biogas plants and their installed electrical power in Germany between 1999 and 2010.
a) Assumption for 2010 (AGEE-Stat 2010).
Even if a number of studies have been published concerning the determination of the
microbial diversity in biogas reactors supplied with renewable raw material (Klocke et
al. 2008, Nettmann et al. 2008, Krakat et al. 2010, Wang et al. 2010), a detailed
knowledge of the microbial community structure and its dynamics is still lacking.
Therefore, researchers denote the microbial composition and its interacting
processes as a “black box process” up to the present day (Opperer 2009).
The recent study should make a contribution to extend the knowledge of the
microbial ecology in biogas reactors and plants. Besides the diversity of the microbial
community structure the quantification of the most abundant taxonomic groups of
microorganisms in biogas fermenters is of prime importance.
By means of this knowledge, microorganisms could be determined which are most
important for unhampered and continuous substrate degradation in anaerobic
digesters. Moreover, derived from the obtained quantification results, conclusions
could be drawn on the main metabolic pathways occurring in biogas reactors and
plants.
Year Number of operating Installed electrical power
biogas plants [MW]
1999 0850 0049
2000 1043 0078
2001 1360 0111
2002 1608 0160
2003 1760 0190
2004 2010 0247
2005 2690 0665
2006 3280 0950
2007 3711 1270
2008 4099 1435
2009 4500 1650
2010a)
5300 1950
INTRODUCTION
16
4.2 The practice
Since the increased importance of using biogas as a renewable energy source, a
number of different reactor types, operational modes and technical standards were
developed for optimizing the biogas building process.
In the following the main characteristics of the most frequently applied processes are
summarized. Two process types are used which can be classified in wet und dry
fermentation (Schattauer and Weiland 2004). Both types can be operated at
mesophilic and thermophilic conditions. Temperatures for mesophilic operating
biogas reactors range between 38°C and 42°C whereas thermophilic conditions are
obtained at temperatures varying between 50°C and 55°C (Weiland 2010).
The applied substrates for anaerobic degradation in biogas fermenters can be
divided into three categories. All wet fermentation processes use animal manure as
sole substrate or in addition with cosubstrates such as renewable raw materials for
biomethanization. The applied amount of the solid fraction is below 10% w/v.
Contrary to this, monofermentation of energy crops is conducted for all dry digestion
processes whereby the amount of the total solid fraction varies between 15% w/v
and 35% w/v in the biogas reactor (Weiland 2010). Three different ways of substrate
supply are known for loading biogas fermenters (Scholwin et al. 2006). For wet
fermentation processes substrates are loaded continuously, e.g. once in a day or
semi continuously meaning that the loading is organized in special time intervals. A
discontinuous substrate supply is mostly applied for dry digestion processes.
Most of the digesters are built up as one-phase reactors meaning that the whole
process of anaerobic degradation takes place in one single biogas fermenter. For
achieving a more efficient degradation of the substrates two-phase reactor systems
were developed where the hydrolysis stage and the metabolic pathway of
methanogenesis occur in two separate biogas fermenters.
INTRODUCTION
17
Besides the separation of process phases the number of process stages differs
(Schattauer and Weiland 2004). The most common one is the two-stage digestion
system where a high-loaded biogas reactor is connected to a low-loaded one in
series. The obtained digestate of the high-loaded biogas reactor is transferred into
the low-loaded one for ensuring high degradation rates of the substrates.
4.3 The process
4.3.1 The four-stage pathway of anaerobic degradation of organic material to biogas
The anaerobic digestion of particulate organic material to biogas is a complex, in its
principles well-known degradation pathway (Zinder 1993, Conrad 1999). In general,
the process of biogas formation is described as a four-stage pathway: hydrolysis,
acidogenesis, acetogenesis and methanogenesis (Hayes et al. 1987). In each stage
groups of microorganisms are involved which are partly related to each other in
syntrophy.
Usually the first step of metabolic conversion of the substrates (hydrolysis) is
described as the rate-limiting step during anaerobic digestion (Veeken and Hamelers
1999, Wang et al. 2010). Hydrolytic Bacteria decompose proteins, carbohydrates and
lipids into amino acids, sugars and fatty acids by the excretion of hydrolytic enzymes
such as protease, amylase, cellulase or lipase (Boone and Mah 1987, Weiland
2010). In the subsequent acidogenesis, the obtained metabolic products are
converted by fermentative Bacteria to produce several volatile fatty acids as well as
alcohols, ammonia, CO2 and H2. Most of the participating microorganisms in the first
two stages of anaerobic digestion belong to the taxonomic groups of Clostridia,
Bacilli, Bacteroidetes and Actinobacteria (Souidi et al. 2007, Krause et al. 2008,
Zverlov et al. 2009). For biogas production from renewable resources the cellulolytic
Bacteria play a key role in anaerobic digestion because they ensure a most efficient
degradation of the applied biomass (Lynd et al. 2002, Zverlov et al. 2009).
During acetogenesis – the third stage of anaerobic digestion – higher volatile fatty
acids and alcohols are converted into acetate and H2 by acetogenic Bacteria.
INTRODUCTION
18
Most of the representatives of this group grow in symbiosis with hydrogenotrophic
methanogens because of energetic reasons (Nettmann 2009 for review). Therefore,
most of the hydrogen-producing acetogenic Bacteria can not be grown in pure
cultures which hampers a good and detailed characterization of those
microorganisms (Weiland 2010). The dependence of the symbiotic interaction
between hydrogenotrophic methanogens and acetogenic Bacteria is mainly caused
by the hydrogen concentration. Only at a low partial pressure of hydrogen both
metabolic pathways reach optimal degradation rates of the substrates.
Fig. 1 Four-stage pathway of anaerobic digestion from particulate organic material to methane (modified after Weiland 2010).
Organic material Proteins, carbohydrates, lipids
Monomers and Oligomers Amino acids, sugars, fatty acids
Carboxylic acids Alcohols
Carbon dioxide Hydrogen
Acetate
Biogas Methan, carbon dioxide
Hydrolysis Hydrolytic Bacteria
Acidogenesis Fermentative Bacteria
Acetogenic Bacteria
Methanogenesis Methanogenic Archaea
INTRODUCTION
19
Hence, the metabolic dependence between acetotrophic Bacteria and
hydrogenotrophic methanogens can be described as an interspecies hydrogen
transfer (Schink 1997). Typical representatives of this group belong to the orders of
Syntrophomonas, Syntrophobacter, Clostridium and Acetobacterium (Hattori 2008,
Weiland 2010).
In the terminal step of anaerobic digestion (methanogenesis) CO2 and H2, acetate or
methyl-group containing compounds can directly be converted into methane by
methanogenic Archaea.
In the following the metabolic pathways of methanogenesis are described more in
detail because the main objective of this study was to quantify methanogens at
different phylogenetic levels in biogas reactors and plants.
4.3.2 Methanogenesis
The three main substrates which can be utilized by methanogens are CO2, acetate
and methyl-group containing compounds (Shima et al. 2002).
The hydrogenotrophic methanogenesis is the most common metabolic pathway
where CO2 and H2 are converted to methane. Besides H2, most of the
hydrogenotrophs can also use formate as the major electron donor (Garrity and Holt
2001). In this case, the formate dehydrogenase oxidizes four molecules of formate to
CO2 before one molecule of CO2 is decomposed to methane. During
hydrogenotrophic methanogenesis the CO2 is stepwise reduced to methane by
special coenzymes (methanofuran, tetrahydromethanopterin, coenzyme M) through
the formyl, methylene and methyl levels. The key enzyme of this process is the
methyl-coenzyme M reductase which reduces methyl-coenzyme M to methane
whereby the oxidized coenzyme M forms a heterodisulfide complex with coenzyme B
(Duin and McKee 2008). Conclusively, this complex is reduced in two terminal
reactions for generating the thiols for the formation of the next methane molecule. In
addition, a minor group of hydrogenotrophs has the ability for using secondary
alcohols, cyclopentanol and ethanol as electron donors (Bleicher et al. 1989, Widdel
and Wolfe 1989).
INTRODUCTION
20
In the second type of methanogenesis, the acetotrophic methanogenesis, acetate is
directly converted to methane. Here, the carboxyl-group of the acetate is oxidized to
CO2 whereby the methyl-group is reduced to methane (Ferry 1997). Two major
pathways of acetate degradation are known which only differ in the first step. One
group of acetotrophic methanogens, the Methanosarcinaceae, uses the acetate
kinase phosphotransacetylase system for activating acetate to acetyl-coenzyme A. In
case of Methanosaetaceae, the second group of acetate converters, the adenosine
monophosphate-forming acetyl-coenzyme A synthetase is responsible for this
reaction (Smith and Ingram-Smith 2007).
Only a small group of methanogens is able to utilize methyl-group containing
compounds such as methanol, methylated amines and methylated sulfides for
methane production (Garrity and Holt 2001). During this metabolic pathway the
methyl-groups of the methylated compounds are first transferred to the cognate
corrinoid protein and afterwards to coenzyme M.
Besides the three main metabolic pathways for methane formation, a CO metabolism
could be determined for Methanothermobacter thermautotrophicus, Methanosarcina
barkeri and Methanosaeta acetivorans (Lessner et al. 2006). Nevertheless, these
species produce most of the methane by using the “classical” pathways of
hydrogenotrophic and acetotrophic methanogenesis, respectively.
4.4 The producers
The phylogenetic tree of life, based on sequence comparison of the 16S and
18S rRNA gene sequences, divides all living individuals on earth in three domains:
Archaea, Bacteria and Eucaryota (Woese et al. 1990, Madigan et al. 2006). The
methanogens, a phylogenetic highly diverse group, are assigned to the phyla of the
Euryarchaeota within the domain of the Archaea. All so far described and
characterized methanogens are classified in five orders: Methanobacteriales,
Methanomicrobiales, Methanosarcinales, Methanococcales and Methanopyrales
(Garrity and Holt 2001).
INTRODUCTION
21
Representatives of the Methanobacteriales, Methanomicrobiales and
Methanosarcinales have already been detected in high amounts in digesters and
biogas plants whereby methanogens belonging to the taxonomic order of
Methanococcales seem to play a relatively minor role in anaerobic digesters
(McHugh et al. 2003, Li et al. 2008, Cardinali-Rezende et al. 2009). Up to now,
members of the Methanopyrales could not be detected in biogas fermenters.
In the following all five orders are shortly characterized. Representatives of the order
Methanobacteriales are rod-shaped or coccoid methanogens which are nonmotile.
They are widely distributed in anaerobic habitats such as aquatic sediments, soils,
solfatara fields or gastrointestinal tracts of animals (Garrity and Holt 2001). In
anaerobic digesters they seem to play a major role under thermophilic conditions
(Leven et al. 2007, Krakat et al. 2010). For methane formation they use CO2 or
methyl compounds as the main substrate whereby H2, formate and secondary
alcohols serve as electron donors. Therefore, all Methanobacteriales are
hydrogenotrophic methanogens.
The second strictly hydrogenotrophic order which is commonly detected in
fermenters and biogas reactors is the group of the Methanomicrobiales (Souidi et al.
2007, Klocke et al. 2008, Kröber et al. 2009, O´Reilly et al. 2010). Their cells are
coccoid or rod-shaped and they occupy nearly the same habitats as the members of
the Methanobacteriales.
The absence of the hydrogenotrophic Methanopyrales in digesters can easily be
explained because this taxonomic order only could be found in marine hydrothermal
systems with temperatures ranging from 84°C to 110°C (Garrity and Holt 2001). The
cells of these methanogens are rod-shaped and motile. One morphological
characteristic are the flagella which are arranged as polar tufts.
Finally the order of Methanococcales comprises strictly hydrogenotrophic
methanogens. They gain their energy by producing methane out of CO2 using H2 or
formate as the electron donors. Thus far, the presence of this methanogenic group
could not be clearly established in biogas fermenters (Hugh et al. 2003).
INTRODUCTION
22
Mostly they have been isolated from marine sediments. Cells are coccoid and they
are motile.
The widest range of substrate utilization can be found among the methanogens of
the order Methanosarcinales. This taxonomic group is divided into two families,
Methanosarcinaceae and Methanosaetaceae. Most of the representatives of the
Methanosarcinaceae have the ability to utilize CO2, methylated compounds as well
as acetate. Hence, they are often described as mixotrophic methanogens because
they use all metabolic pathways of methanogenesis. Members of this methanogenic
family play a crucial role in methane formation during anaerobic degradation in
biogas fermenters (Mladenovska et al. 2006, Narihiro et al. 2009). Different species
of Methanosarcinaceae have already been detected in digesters operating under
psychrophilic, mesophilic and thermophilic conditions (Collins et al. 2003, Hori et al.
2006, Sousa et al. 2007, Patil et al. 2010). Moreover, they have been isolated from
habitats such as marine and freshwater sediments, hypersaline sediments or the
rumen of ungulates (Garrity and Holt 2001). Cells of these methanogenic Archaea
are irregularly formed and they are typically arranged in cell aggregates.
The second family of Methanosarcinales, the Methanosaetaceae, is a strict
acetotrophic group of methanogens. They are nonmotile and cells are formed as
sheathed rods. Methanosaetaceae were often detected in biogas reactors working
under a mesophilic temperature regime (McHugh et al. 2003, Laloui-Carpentier et al.
2006). As it was stated by Smith and Ingram-Smith (2007), Methanosaetaceae
belong to one of the most important methane producers on earth.
INTRODUCTION
23
4.5 The technique
Traditional microbiological techniques such as the roll-tube method or the most
probable number estimation were carried out for the first determinations of
methanogenic Archaea in environmental samples (Kataoka et al. 1991, Asakawa et
al. 1998, Hofman-Bang et al. 2003). Those culture-dependent approaches give a first
insight in the methanogenic community structure in anaerobic habitats whereby the
validity of the obtained results is limited (Hofman-Bang et al. 2003). To give an
example of the limitation of these techniques, Wagner et al. (1993) demonstrated that
only 1-15% of the total microbial community could be detected in activated sludge
samples by using culture-dependent methods.
With the development of molecular genetic techniques, new tools were applicable to
examine diversity and abundances of microorganisms in environmental samples.
Most of these molecular genetic approaches are based on PCR (Amann et al. 1995,
Leclerc et al. 2004, Deng et al. 2008). Basically sequencing and PCR-based
techniques can be separated into two main sections: the qualitative and the
quantitative approach.
The main objectives of the qualitative approach are diversity, dynamic range or
genetic potential investigations of microbial communities within the investigated
habitat. A broad range of studies was published regarding the diversity of
methanogenic Archaea in biogas reactors with the construction of clone libraries
combined with PCR-based restriction fragment length polymorphism (PCR-RFLP)
(You et al. 2000, Chen et al. 2004, Nettmann et al. 2008, Krakat et al. 2010).
Furthermore, several studies on the dynamic range of methanogenic Archaea were
conducted by the use of denaturing gradient gel electrophoresis (DGGE) or terminal
restriction fragment length polymorphism (T-RFLP) (Conrad and Klose 2006,
Schwarz et al. 2007, Wang et al. 2010). Currently, metagenomic approaches with the
application of the 454-pyrosequencing technology are the most innovative platforms
determining the composition and gene content of the microbial community in biogas
plants (Schlüter et al. 2008, Kröber et al. 2009).
INTRODUCTION
24
For the culture-independent quantification of particular methanogenic populations in
environmental samples Q-PCR is the method of choice. With this approach an
accurate quantification of the analyzed target gene copies is feasible whereby the
reliability of Q-PCR results is strongly dependent on the quality of the extracted
genomic DNA (Takai and Horikoshi 2000, Dionisi et al. 2003). Because of several
advantages of this molecular genetic approach, this technique was used as the main
method in this study. Even if the Q-PCR was the method of choice in this thesis,
polyphasic approaches should always be applied for quantifying microorganisms in
environmental samples (Braun et al. 2000, Collins et al. 2006, Sousa et al. 2007).
Therefore, fluorescence in-situ hybridization (FISH) – a cell-based approach – can be
helpful for verifying Q-PCR data (Sekiguchi et al. 1998, Krakat et al. 2010).
4.5.1 The basics of Q-PCR applications
Since Higuchi et al. (1993) added ethidium bromide to a conventional PCR for
monitoring the amplification of a target gene by increasing fluorescence intensities,
Q-PCR applications became one of the most used approaches for quantifying
microorganisms in environmental samples. Nowadays, this technique is widely
applied in the fields of food and veterinary microbiology, environmental microbiology
and clinical diagnostics (Klein 2002, Bach et al. 2003, Chua and Bhagwat 2009, Lee
et al. 2009).
The principle of the Q-PCR is very similar to that of a conventional PCR. The target
gene is amplified over a defined number of PCR cycles which follow the three typical
steps of temperature change for an optimal amplification: denaturation, annealing
and polymerization. The conventional PCR allows only end-point detection whereby
the concentration of the amplified target is monitored after each PCR cycle in Q-PCR
applications using a fluorescent dye or probe. The detected change in fluorescence
intensity reflects the concentration of the amplified gene in real-time (Klein 2002,
Zhang and Fang 2006).
INTRODUCTION
25
Two major types of Q-PCR approaches are known, the relative and the absolute
quantification method (Wong and Medrano 2005). Both Q-PCR modifications are
applicable for quantifying DNA and RNA, respectively.
Relative quantification is applied for the detection of changes in the expression of a
specific functional gene. Mostly, the expression of this gene is observed in relation to
a constitutive expressed housekeeping gene (Thellin et al. 1991, Pfaffl et al. 2004).
Typical housekeeping genes with a presumed stable expression are e.g. the rRNA
genes, the β-actin gene or the gene of the glyceraldehyde-3-phosphate
dehydrogenase (Thellin et al. 1999, Wong and Medrano 2005). Since it was shown
that even the most frequently applied housekeeping genes are slightly influenced in
expression by different treatments normalization based on a set of housekeeping
genes seems to be preferable (Vandesompele et al. 2002b). The two most frequently
applied relative quantification strategies are the comparative ∆∆CT method or the
Pfaffl model for evaluating the obtained Q-PCR results (Livak and Schmittgen 2001,
Pfaffl 2001, Pfaffl et al. 2002, Raymaekers et al. 2009).
Absolute quantification which was used in this study is performed by the standard
curve method (Rutledge and Cote 2003). The determination of unknown
concentrations of the target gene is based on the relationship between the defined
copy number of the standard and their corresponding fluorescence intensity
(cf. Fig. 2A, 2B). As standard double-stranded DNA, single-stranded DNA or cDNA
can be used (Wong and Medrano 2005). In this study the plasmid DNA standard was
used for quantifying the target gene in genomic DNA extracted from biogas reactor
samples. The advantage of this specific standard is that it can be prepared in high
amounts and it is easy to handle. Furthermore, this type of standard is highly
reproducible which underlines the convenience of using this standard for Q-PCR
applications. One disadvantage of that standard is the difference between the
chemical background of the pure plasmid standard and the one of the environmental
sample. Therefore, spiking experiments are commonly regarded as indispensable for
a reliable quantification (Lebuhn et al. 2003, Yu et al. 2005b).
INTRODUCTION
26
Threshold
Cycle number
Flu
ore
sce
nce
107 106 105 104 103 102 101
10 20 30
CT
va
lue
Copy number [log]
101 103 107102 104 105
Copy number
106
10
20
30
CT
A
B
Threshold
Cycle number
Flu
ore
sce
nce
107 106 105 104 103 102 101
10 20 30
CT
va
lue
Copy number [log]
101 103 107102 104 105
Copy number
106
10
20
30
CT
A
B
Fig. 2 Principle of a Q-PCR application using the standard curve method for absolute quantification. (A) Fluorescence intensity changes during amplification of the target gene by using seven standard solutions from 10
1 to 10
7 target gene copy numbers (black curves) per reaction and one environmental
sample (red curve). (B) CT values of the standard curve for absolute quantification (black dots) and one environmental sample (red dot). CT = Threshold cycle number.
The theoretical process of a Q-PCR can be divided in four main phases: the linear
ground phase, the early exponential phase, the log-linear phase and the plateau
phase (Fig. 2A) (Tichopad et al. 2003). In the first phase the Q-PCR starts and the
fluorescence emission has not been raised above the background. During the
second phase the amount of fluorescence increases resulting in a threshold which is
significantly higher than the background. This point is known as the threshold cycle
number (CT value). In the log-linear phase the Q-PCR reaches its optimal
amplification value and at the plateau phase the efficiency of the PCR reaction
decreases because of limited reaction components (Wong and Medrano 2005).
INTRODUCTION
27
4.5.2 Fluorescent probes and dyes
Fluorescence intensity measurements serve as a basis for all Q-PCR applications.
The principles of fluorescent detection can be divided into non-specific and specific
detection. Both detection methods were applied in the recent study.
The non-specific detection system uses double-stranded DNA (dsDNA) binding dyes
for determining the amount of the amplified target gene after each PCR cycle. The
most commonly used dsDNA binding dyes are SYBR Green I, BEBO and thiazole
orange (Benveniste et al. 1996, Bengtsson et al. 2003, Harasawa et al. 2005,
Steinberg and Regan 2009). In this study, the most frequently applied dsDNA binding
dye, the SYBR Green I, was used for all Q-PCR applications performed with the
non-specific detection method. The principle of the Q-PCR process by using SYBR
Green I as the fluorescent dye follows a stepwise reaction scheme (Fig. 3). At the
beginning of the PCR run the SYBR Green I dye is associated at the minor groove
side of the supplied genomic DNA template. The initial fluorescence is set to the
background fluorescence of the Q-PCR. During denaturation the SYBR Green I dye
is released, resulting in a drastically reduced fluorescence. In the annealing and
polymerization step primer-binding and the generation of the PCR product occur.
During the last step the SYBR Green I dye binds again to the dsDNA. The increase
of the now detected fluorescence is proportional to the amplification of the target
gene.
This type of detection method offers a number of advantages because it is a cheap
and easy to handle approach which can be used for any kind of PCR primer sets
(Malinen et al. 2003, Zhang and Fang 2006). The limitations of this detection method
can be seen in the non-specific binding of the dsDNA binding dye (Raymaekers et al.
2009). The formation of primer dimers and the amplification of non-target DNA
fragments would strongly influence the obtained Q-PCR results.
Therefore, a dissociation curve analysis is often applied after performing a Q-PCR
run with dsDNA binding dyers (Woo et al. 1998, Harasawa et al. 2005). With this
additional approach the presence of primer dimer structures and non-target PCR
products can be verified by comparing melting temperatures of the formed PCR
products.
INTRODUCTION
28
Denaturation
94°C
54°C Annealing
Polymerization
60°C
SYBR Green I Dye
Primer
DNA with target gene sequence
Excitation
Fig. 3 Principle of Q-PCR by using SYBR Green I as the fluorescent dye for absolute quantification.
The second principle of fluorescent detection uses specifically designed probes for
quantifying target genes in a sample. A large number of different fluorescent probes
was developed in recent years such as the TaqMan probe, molecular beacons, the
light-up probe, scorpion primers or LUX primers (Bustin 2000, Thelwell et al. 2000,
Taveau et al. 2002, Wong and Medrano 2005, Pillay et al. 2006).
All so far known fluorescent probes can be categorized in hybridization, hydrolysis
and hairpin probes (Wong and Medrano 2005).
In this study the hydrolysis TaqMan probe was used. A TaqMan probe can be
described as a double-labelled single stranded oligonucleotide which is
complementary to a specific region of the target gene.
INTRODUCTION
29
The 5´-end of the oligonucleotide is attached to a reporter dye while the 3´-end is
labelled with a quencher dye. Typical reporter fluorophores are FAM
(6-carboxyfluoroscein), TET (tetrachloro-6-carboxyfluorescein) and JOE
(2,7-dimethoxy-4,5-dichloro-6-carboxyfluorescein). The most commonly used
quencher dye is TAMRA (6-carboxytetramethylrhodamine). The immediate proximity
of both dyes results in a quenched emission of the reporter dye induced by
fluorescence resonance energy transfer (FRET) (Förster 1948).
Denaturation
94°C
Denaturation
94°C
54°C Annealing54°C Annealing
Polymerization
60°C
Polymerization
60°CQ
R
Q
R
Q
R
Q
R
QR TaqMan probe with reporter (R) and quencher (Q)
Primer
DNA with target gene sequence
Excitation
QR QR TaqMan probe with reporter (R) and quencher (Q)
Primer
DNA with target gene sequence
Excitation
QR QR
Q
R
Q
R
Q
R
Q
R
Fig. 4 Principle of Q-PCR by using the TaqMan fluorescent probe for absolute quantification.
During the Q-PCR process the TaqMan probe hybridizes with the complementary
region of the target gene (Fig. 4). In the polymerization step the DNA polymerase
cleaves the reporter from the probe resulting in a distinct increase of the reporter
fluorescence because of the termination of FRET.
INTRODUCTION
30
With this detection method an interfering influence on the Q-PCR process by primer
dimer and non-target PCR product formation can be excluded. Therefore, this
method is more precise and accurate than applications using fluorescent dyes.
However, the difficulty of designing suitable probes for the specific detection method
can be seen as one disadvantage of this detection system (Zhang and Fang 2006).
4.5.3 Application of Q-PCR in environmental microbiology
The application of Q-PCR for quantifying microorganisms in environmental samples
was used in this study because this technique offers a number of advantages
compared to other quantification approaches.
First it can be stated that this culture-independent method is very sensitive, permitting
analyses of very small amounts of DNA and RNA (Freeman et al. 1999, Bar et al.
2003, Lebuhn et al. 2004). For clinical diagnostics a technical sensitivity of detecting
less than five copies of the target gene per reaction volume could be verified
(Klein 2002). Furthermore, advantages of this specific method can be seen in terms
of absence of post-PCR manipulations and the high throughput capacity
(Vandesompele et al. 2002a). Moreover, Q-PCR applications are characterized by a
wide dynamic range of quantification up from seven to eight decades (Klein 2002).
Muller et al. (2002) stated that this technique has a tremendous potential for high
throughput analysis of gene expression in research and diagnostics. By the use of
differently labelled fluorescent probes multiplex Q-PCR applications become feasible
(Rensen et al. 2006, Yuan et al. 2009). This provides the possibility for a
simultaneous detection of microorganisms from different phylogenetic levels during
one Q-PCR run.
Even if the Q-PCR is a good working tool for quantifying microorganisms in a wide
range of habitats some major prerequisites such as the purity of the applied DNA
solution have to be complied by using this method for analyzing the quantity of
microorganisms belonging to different taxonomic levels in environmental samples.
INTRODUCTION
31
4.5.4 Factors for a successful Q-PCR run
Biogas reactor samples are normally rich in inhibitors and other PCR-interfering
substances, such as humic acids, that are produced as by-products of bacterial
fermentation. For this purpose, the application of an optimized protocol is an
essential prerequisite for the successful extraction of DNA from biogas reactor
samples. Additionally, only a highly efficient and complete cell disruption ensures the
detection of all or, at least, most of the taxonomic groups from the microbial
community. In principle, two different approaches for purification of microbial DNA
from environmental samples are currently used. On the one hand, the microbial cells
can be purified from the environmental background prior to cell lysis (Bourrain et al.
1999). In this case, microorganisms, which are strongly attached to organic
compounds, will be discriminated in varying amounts. To avoid this pitfall, an
alternative direct DNA extraction that disrupts microbial cells directly within the
environmental sample is usually preferable (Roh et al. 2006). Besides an optimal
DNA yield, quality and purity of the DNA solutions are of extraordinary importance for
the application of molecular genetic studies. One of the main PCR inhibitors
co-extracted during DNA preparation are the humic acids (Zhou et al. 1996, Min et al.
2006, Weiß et al. 2007). These contaminants directly inhibit DNA polymerases and
other enzymes involved in subsequent DNA analysis (Dionisi et al. 2003) and,
therefore, need to be effectively removed prior to PCR. Several DNA extraction
methods, including chemical, enzymatic or mechanical cell disruption, have been
established for various environmental samples (e.g. soil, compost, activated sludge)
containing high amounts of humic acids (Yeates et al. 1998, Martin-Laurent et al.
2001, Yang et al. 2007, Zheng et al. 2008). However, the influence of the type of cell
lysis and DNA extraction for the results obtained by molecular genetic approaches,
like quantitative Q-PCR, PCR-RFLP, PCR-RAPD or PCR-DGGE analyses, have
been covered in only a few studies (Purohit et al. 2001, Stach et al. 2001, Yang et al.
2007). However, in the case of the increasing importance of Q-PCR for quantification
of genes in environmental samples, analyzing the impact of DNA isolation on the
Q-PCR-based evaluation of the methanogenic microbial consortia within humic acid
rich biogas reactors is a matter of particular interest.
INTRODUCTION
32
The DNA extraction efficiency can be determined by several spiking and recovery
experiments. Lebuhn et al. (2004) supplied cells of pathogen analogues such as an
avirulent poliovirus into a biogas reactor sample for reducing the error due to
differences in DNA extraction efficiencies. Coyne et al. (2005) used an exogenous
plasmid DNA standard which was added into the extraction buffer for verifying
extraction efficiencies. Another often applied approach for estimating the efficiency of
the DNA isolation method is the use of an artificial construct competitor DNA which is
spiked into the extraction buffer (Widada et al. 2002). Such DNA standard is as
similar to the target DNA as possible and it is amplified with the same primer set.
A second group of spiking experiments is applied for analyzing the amplification
efficiency of a Q-PCR run which can be directly influenced by co-extracted
PCR-interfering substances. The internal inhibitor control is a good working tool for
estimating the influence of PCR-inhibitory substances on Q-PCR efficiencies (Behets
et al. 2007). Here, a known amount of purified DNA of the analyzed microbial target
is added to the PCR mixture as a positive amplification control. The interference of
DNA amplification efficiency due to a competition of the internal positive control and
the target DNA for the same primer and probe set can be seen as a disadvantage of
this approach (Behets et al. 2007).
To overcome these limitations, exogenous internal positive controls (IPC) were
developed (Coyne et al. 2005, Hartman et al. 2005). IPCs use their own primer and
probe set for amplifying the PCR product. Hence, an optimal amplification rate has to
be ensured for both amplified targets by using the same PCR protocol.
Conclusively, it can be stated that spiking and recovery as well as spiking
experiments after DNA extraction are most essential for the interpretation of obtained
Q-PCR results by analyzing samples from humic acid rich habitats.
INTRODUCTION
33
4.5.5 The target genes
The 16S rRNA gene – a gene for the determination of phylogenetic relationships. In
this study, the 16S rRNA gene was used as the target gene for quantifying
methanogenic Archaea in biogas reactors and plants. This constitutively expressed
gene is the most commonly applied target gene for determining phylogenetic and
evolutionary relationships among Bacteria and Archaea (Corless et al. 2000,
Hofman-Bang et al. 2003, Deng et al. 2008).
Several reasons can be adduced for this application. First the 16S rRNA gene is one
of the key elements in microbial cells and its evolution and function is comparable in
all microorganisms (Hofman-Bang et al. 2003). Moreover, the 16S rRNA gene
contains conserved as well as variable gene sequence regions (Zhang and Fang
2006). Conserved regions can be used for designing group-specific primer and probe
sets for determining all microorganisms at higher phylogenetic levels such as families
or orders in environmental samples. The variable regions within the 16S rRNA gene
are used for classifying the microorganisms at lower taxonomic levels. To date many
16S rRNA gene sequences have been deposited in genetic databases e.g. GenBank
or ARB which illustrate the popularity for using the 16S rRNA gene as target.
In Q-PCR applications, the 16S rRNA gene is often used as a housekeeping gene
standard because its expression level is less likely to vary under conditions which
affect the expression of the mRNA of functional genes (Bustin 2000).
Besides the suitability of the 16S rRNA gene for Q-PCR applications there are some
major concerns which have to be considered by using this specific gene for
quantification. The 16S rRNA gene is mostly present in multiple copies in the genome
which leads to an increased possibility for overestimating the amounts of those
individuals which have a large number of 16S rRNA gene copies in the genome
(Farrelly et al. 1995, Corless et al. 2000). Therefore, a transfer from the number of
16S rRNA genes obtained from Q-PCR to the real individual number is not feasible
(Hallam et al. 2003). Moreover, metabolic activity of the microorganisms can not be
determined by 16S rRNA gene analysis because the expression of this gene is barely
influenced by changing growth conditions (Bustin 2000, Wong and Medrano 2005).
INTRODUCTION
34
For those investigations expression studies of functional genes which are directly
involved in the metabolic pathway have to be carried out.
The methyl-coenzyme M reductase MCR – the key enzyme of methane formation.
The second gene which was chosen for quantifying methanogens in biogas reactor
samples is the methyl-coenzyme M reductase subunit α gene (mcrA gene). This
gene is part of the operon encoding the enzyme complex of the methyl-coenzyme M
reductase (MCR) which is often described as the key enzyme of methanogenesis
(Inagaki et al. 2004, Rastogi et al. 2008).
MCR consists of three different subunits (α2, β2, γ2) whereby the genes of these
subunits are organized in a single transcription unit (cf. Fig. 5) (Springer et al. 1995,
Shima et al. 2002). Moreover, MCR contains two molecules of F430. Because of the
uniqueness and ubiquitous distribution of this enzyme complex the appertaining
genes are well suitable tools for the specific detection of methanogens (Luton et al.
2002). Furthermore, MCR is only present in methanogens and methanotrophic
Archaea which underlines the suitability of this specific enzyme for phylogenetic and
metabolic investigations (Nunoura et al. 2008, Steinberg and Regan 2009).
Fig. 5 Ribbon diagram of the methyl-coenzyme M reductase (MCR) with all subunits and the structure of F430 (Shima et al. 2002).
α
α´
γ
γ´
β
β´
F430
INTRODUCTION
35
From the MCR complex the mcrA gene was chosen as the functional marker
because it is highly conserved and it shows mostly congruent phylogeny to the
16S rRNA gene (Springer et al. 1995, Steinberg and Regan 2009).
During the last years, many research groups have already used the mcrA gene for
the detection of methanogens in a wide range of habitats such as rice fields,
hypereutrophic lake sediments, peats, guts of termites and the rumen (Ohkuma et al.
1995, Hales et al. 1996, Earl et al. 2003, Denman et al. 2007). Currently the
mcrA gene becomes more and more popular for Q-PCR applications which shows
the great potential of this metabolic gene for investigating the composition and the
dynamic range of methanogens at different taxonomic levels in environmental
samples in future.
4.6 The aims
The main aim of the recent study is the development of a culture-independent,
molecular genetic approach for quantifying the methanogenic community structure in
biogas fermenters and plants. Therefore, Q-PCR analysis is used.
Initially, a 5´-nuclease assay which was developed for the detection of methanogenic
communities in environmental samples by Yu et al. (2005a) will be optimized for
samples taken from biogas reactors and plants. The Q-PCR assay is based on the
16S rRNA gene – the mostly used target for analyzing phylogenetic relationships
between individuals belonging to the domains of Bacteria and Archaea.
Afterwards a second Q-PCR assay is developed where the mcrA gene is functioned
as target gene. A Q-PCR assay on the basis of a regulated expressed gene offers
the possibility for metabolic activity measurements by RNA analysis. Relative
quantification by Q-PCR becomes feasible where the expression of the regulated
expressed mcrA gene is compared to the expression of the relatively constant
transcribed 16S rRNA gene, which functioned as a house-keeping gene.
INTRODUCTION
36
Conclusively, the Q-PCR assay based on 16S rRNA gene will be applied for
answering the following questions:
(1) Is the application of the Q-PCR technique a good working tool for quantifying
methanogenic Archaea in reactor samples? Do preparative factors such as
the type of the chosen DNA extraction method have an effect on the detected
amounts of the methanogenic Archaea in biogas plants?
(2) How does the methanogenic community react on overloading of the biogas
reactor and are there key organisms that can be used as a biological key
control parameter for estimating the condition of the biomethanization
process?
(3) Is there a difference in the population dynamic of the methanogenic
community during acidification of a biogas reactor which is operated under
short- and long-time conditions, respectively?
(4) Does the temperature regime have an effect on the presence or absence of
certain methanogenic groups?
(5) Does the chosen substrate have an influence on the composition of the
methanogens in biogas reactors?
(6) How are the methanogenic Archaea composed in agricultural biogas plants?
Is the detected methanogenic community structure comparable to those
determined in laboratory scale CSTRs?
(7) Who is the most abundant methanogen in a biogas fermenter or plant – the
acetotrophic or the hydrogenotrophic methanogen?
MATERIAL AND METHODS
37
5. Materials and Methods
5.1 Model biogas reactors
Four different types of wet fermentation reactor systems were used for sampling. All
laboratory scale reactors were operated at the Leibniz-Institut für Agrartechnik
Potsdam-Bornim e.V. (ATB). Technical characteristics of the biogas reactors are
listed in Table 2 and 3.
5.1.1 System 1
To determine the influence of DNA isolation on Q-PCR-based quantification of
methanogenic Archaea in biogas fermenters a fermentation using maize silage and
pig manure as substrates was conducted in a single-stage continuously stirred tank
reactor (CSTR). The reactor was maintained at mesophilic conditions by an external
heating coil connected to a thermostat (Lauda Dr. R. Wobster GmbH & Co. KG,
Lauda-Königshofen, Germany). The content of the reactor was stirred via a special
agitator: a two-bladed plane in combination with an anchor stirrer to avert the
swimming layer. The stirrer was controlled by a time switch (Heidolph Electro GmbH
& Co. KG, Kelheim, Germany).
The feeding took place with a dosing pump (12 times per day in 2 hour intervals). For
collecting the biogas a gas bag was connected to the reactor. The gas composition
was analyzed by a TG05/05 gas meter (Ritter, Bochum, Germany).
5.1.2 System 2
A two-phase solid state fermentation was built-up consisting of a hydrolysis reactor
(bioleaching reactor) and a fixed film bed methane reactor (anaerobic filter). For
circulation of the process liquid two different cycles were used. The dissolved organic
compounds which were gained during percolation in the hydrolysis reactor were
collected in a hydrolyzate reservoir (material = acryl glass, working capacity = 60 l).
MATERIAL AND METHODS
38
One part of the process liquid was led back into the hydrolysis reactor while another
part was transferred into the fixed film bed methane reactor (material = acryl glass,
working capacity = 32 l) by a continuous volume flow of 1 l h-1. Furthermore, one part
of the newly composed process liquid in the methane reactor was fed back into the
hydrolysis reactor (1 l h-1).
The generated biogas was collected in 100 l biogas bags (TECOBAG, Tesseraux
GmbH, Bürstadt, Germany). The gas composition was calculated automatically once
a day. Therefore, the biogas was extracted by the SSM 6000 gas analysis device
(Pronova Analysentechnik GmbH & Co. KG, Berlin, Germany) and pumped through
the gas meter (Ritter, Bochum, Germany).
The samples for DNA extraction were collected from the process liquid of the fixed
film bed methane reactor.
5.1.3 System 3
For analyzing the methanogenic population dynamics during a semi-continuous,
short-time biogas fermentation and acidification by overloading, a laboratory scale
CSTR was built up. The reactor consisted of a PET double wall reactor, an OST
basic mixer (IKA, Staufen, Germany), a Fisherbrand FBH 600 thermostat (Fisher
Scientific, Schwerte, Germany) and a TG05/05 gas meter (Ritter, Bochum,
Germany). The thermostat was connected to the reactor’s water jacket via rubber
tubes to provide a constant temperature in the reactor. To avoid air intrusion the
in-feed and the drain outlet were capped by plugs. The produced biogas was
collected in a gas bag which was connected to the reactor. The analysis of the
biogas was carried out as in System 1.
5.1.4 System 4
All long-time biogas fermentation experiments were carried out in CSTRs. The
temperature regimes of the digesters were maintained by a water jacket which was
heated by a thermostat. The reactor content was circulated by time switch-regulated
OST basic mixers (IKA, Staufen, Germany).
MATERIAL AND METHODS
39
The stirrer consisted of two-bladed planes which were situated at different heights of
the stirring staff. In reactors which utilized foot beet silage as substrate two in
succession reduced metal plates were fixed above the reactor content to densify the
occurred layer of foam. The produced biogas was stored in gas bags. It was
analyzed automatically by a gas analyzer (Pronova, Berlin, Germany) and by a
TG05/05 gas meter (Ritter, Bochum, Germany).
MATERIAL AND METHODS
40
Tab
le 2
Ma
in c
hara
cte
ristics o
f th
e a
naly
zed lab
ora
tory
scale
bio
gas r
eacto
rs.
a) Lin
ke e
t al. (
2009);
b) Lin
ke a
nd S
chönberg
(2009);
c) B
lum
e (
2008);
d) M
ähnert
(2007).
Re
ac
tor
Sa
mp
lin
gS
ize
of
rea
cto
rP
rim
ary
su
bs
tra
teC
o-s
ub
str
ate
Fe
ed
ing
Org
an
ic l
oa
din
g r
ate
Sti
rrin
g
[we
ek
][l
][g
l-1
d-1
][r
pm
]
Sys
tem
1a)
56
60
Ma
ize
sila
ge
(5
0%
)P
ig m
an
ure
(5
0%
)C
on
tin
uo
usly
13
.03
5 (
Co
ntin
uo
usly
)
Sys
tem
2b)
2
3
1R
ye
sila
ge
(9
1%
), s
tra
w (
9%
)D
isco
ntin
uo
usly
With
ou
t stirr
ing
Sys
tem
3c)
1
(7
.da
y)
9M
aiz
e s
ilag
e (
32
%)
Pig
ma
nu
re (
68
%)
Co
ntin
uo
usly
1.5
- 9
.53
5 (
Co
ntin
uo
usly
)
3
(2
1.d
ay)
5
(3
5.d
ay)
6
(4
2.d
ay)
7
(4
9.d
ay)
8
(5
6.d
ay)
9
(6
3.d
ay)
10
(6
4.d
ay)
10
(6
6.d
ay)
10
(6
7.d
ay)
Sys
tem
4d)
26
8M
aiz
e s
ilag
e (
10
0%
)C
on
tin
uo
usly
2.1
50
- 1
00
(1
5 m
in [
0.5
h]-1
)
Sys
tem
4d)
38
8F
od
de
r b
ee
t sila
ge
(1
00
%)
Co
ntin
uo
usly
2.0
50
- 1
00
(1
5 m
in [
0.5
h]-1
)
Sys
tem
4d)
38
8M
aiz
e s
ilag
e (
10
0%
)C
on
tin
uo
usly
2.1
50
- 1
00
(1
5 m
in [
0.5
h]-1
)
Sys
tem
4d)
38
8F
od
de
r b
ee
t sila
ge
(1
00
%)
Co
ntin
uo
usly
2.1
50
- 1
00
(1
5 m
in [
0.5
h]-1
)
Sys
tem
4d)
36
8C
att
le m
an
ure
(1
00
%)
Co
ntin
uo
usly
1.9
50
- 1
00
(1
5 m
in [
0.5
h]-1
)
Sys
tem
4d)
1
8M
aiz
e s
ilag
e (
10
0%
)C
on
tin
uo
usly
Sta
rt (
Co
ntr
ol)
50
- 1
00
(1
5 m
in h
-1)
26
2.0
50
- 1
00
(1
5 m
in [
0.5
h]-1
)
34
2.7
50
- 1
00
(1
5 m
in [
0.5
h]-1
)
46
4.2
(O
ve
rlo
ad
ed
)5
0 -
10
0 (
15
min
[0
.5 h
]-1)
Sys
tem
4d)
4
8M
aiz
e s
ilag
e (
10
0%
)C
on
tin
uo
usly
Sta
rt (
Co
ntr
ol)
50
- 1
00
(1
5 m
in h
-1)
38
2.2
50
- 1
00
(1
5 m
in [
0.5
h]-1
)
44
3.0
50
- 1
00
(1
5 m
in [
0.5
h]-1
)
49
3.3
(O
ve
rlo
ad
ed
)5
0 -
10
0 (
15
min
[0
.5 h
]-1)
MATERIAL AND METHODS
41
Tab
le 3
Physic
al a
nd c
he
mic
al para
mete
rs o
f th
e a
naly
zed lab
ora
tory
scale
bio
ga
s r
eacto
rs.
a) Lin
ke e
t al. (
2009);
b) Lin
ke a
nd S
chönberg
(2009);
c) B
lum
e (
2008);
d) M
ähnert
(2007).
NA
= n
ot
analy
zed.
oD
W =
org
anic
dry
weig
ht.
Reacto
rS
am
plin
gO
pera
tio
n m
od
ep
H v
alu
eN
H4-N
To
tal N
Vo
lati
le f
att
y a
cid
sB
iog
as y
ield
CH
4 c
on
ten
tC
O2 c
on
ten
t
[week]
[g k
g-1
][g
kg
-1]
[g l
-1]
[m3 k
go
DW
-1]
[%]
[%]
Syste
m 1
a)
56
Mesophili
c (
37°C
)7
.65
3.8
06
.80
6.9
00
.22
45
2.0
48
.0
Syste
m 2
b)
2
T
herm
ophili
c (
55°C
)8
.14
0.3
83
.37
9.4
30
.63
15
1.6
48
.4
Syste
m 3
c)
1 (7
day)
Mesophili
c (
37°C
)7
.68
NA
NA
0.0
50
.51
75
1.7
47
.3
3 (
21.d
ay)
7.6
1N
AN
A0
.02
0.5
22
52
.24
6.1
5 (
35.d
ay)
7.6
2N
AN
A0
.10
0.5
14
51
.44
7.8
6 (
42.d
ay)
7.5
1N
AN
A0
.17
0.4
88
48
.85
0.0
7 (
49.d
ay)
7.5
5N
AN
A1
.46
0.4
75
47
.55
2.0
8 (
56.d
ay)
7.3
9N
AN
A5
.03
0.4
24
42
.45
5.4
9 (
63.d
ay)
5.6
1N
AN
A1
6.8
50
.00
70
.76
3.0
10 (
64.d
ay)
5.4
6N
AN
A1
6.2
3N
AN
AN
A
10 (
66.d
ay)
5.3
7N
AN
A1
7.6
30
.00
10
.16
1.9
10 (
67.d
ay)
5.3
7N
AN
A1
7.0
30
.00
10
.15
1.7
Syste
m 4
d)
26
Mesophili
c (
37°C
)8
.01
2.1
9N
A2
.24
0.7
70
55
.0N
A
Syste
m 4
d)
38
Mesophili
c (
37°C
)7
.73
0.5
7N
A1
.68
0.8
90
55
.0N
A
Syste
m 4
d)
38
Therm
ophili
c (
55°C
)8
.38
2.5
5N
A5
.88
0.7
00
56
.0N
A
Syste
m 4
d)
38
Therm
ophili
c (
55°C
)7
.84
1.8
2N
A1
0.9
30
.84
05
6.0
NA
Syste
m 4
d)
36
Therm
ophili
c (
55°C
)8
.32
1.7
0N
A2
.62
0.4
00
59
.0N
A
Syste
m 4
d)
1
Mesophili
c (
37°C
)N
AN
AN
AN
AN
AN
AN
A
26
7.9
82
.19
NA
2.2
30
.80
05
5.0
NA
34
7.8
52
.39
NA
2.3
50
.67
05
5.0
NA
46
6.1
02
.27
NA
17
.07
0.2
20
26
.0N
A
Syste
m 4
d)
4
Therm
ophili
c (
55°C
)N
AN
AN
AN
AN
AN
AN
A
38
8.3
92
.71
NA
6.7
40
.68
05
6.0
NA
44
8.1
62
.47
NA
7.0
50
.75
0N
AN
A
49
7.8
82
.85
NA
9.9
30
.61
0N
AN
A
MATERIAL AND METHODS
42
5.2 Agricultural biogas plants
In 2006 and 2007 a number of different agricultural biogas plants were sampled as
described by Nettmann (2009). The biogas reactors were chosen with respect to
following criterions: a stable and continuous operation mode of the biogas plant had
to be assured, and the substrates which were used for anaerobic digestion had to be
kept constant for at least one year.
The chosen biogas plants varied among each other in substrate composition,
operation mode, reactor volume, organic loading rate and retention time (Table 4). All
biogas plants were driven under mesophilic conditions (37°C).
A conventional wet fermentation was carried out in the biogas plants of BA1-BA6,
BA9 and BA10. By means of these plants the influence of the substrate composition
on the diversity of the methanogens was analyzed. Additionally, biogas plant BA1
was probed twice (04.05.2006 and 24.07.2006) to verify if a shift was distinguished in
the methanogenic community structure over a certain time period.
The effect on the allocation of the methanogenic Archaea by using renewable raw
material as mono-substrate was determined by sampling BA7. Only in the start-up
phase of the biogas plant the feeding stock of maize silage and grains of barley was
inoculated with cattle manure. For assuring the fed substrates being semi-fluid, water
was added as co-substrate during the anaerobic digestion process.
In case of BA8 both compartments, the hydrolysis reactor (FR) and the anaerobic
filter (AF), were sampled to analyze the subjection of methanogens concerning to the
reactor operation mode and the reactor type of a two-stage dry fermentation biogas
plant.
All process parameters and results of the chemical analyses were collected by the
co-operation partners and plant operators. The collected data material concerning
the ten biogas plants is summarized on special data sheets part of the thesis of
Nettmann (2009).
MATERIAL AND METHODS
43
Tab
le 4
Para
mete
rs o
f th
e a
na
lyzed b
iog
as p
lants
.
Bio
ga
s p
lan
tS
am
pli
ng
Lo
ca
tio
nS
ize
of
rea
cto
rR
en
ew
ab
le r
aw
ma
teri
al
Co
-su
bs
tra
teO
rga
nic
lo
ad
ing
ra
teO
pe
rati
on
mo
de
Fe
rme
nta
tio
n
[m3]
[kg
m-3
d-1
]
BA
10
4.0
5.2
00
6B
B1
00
0M
aiz
e s
ilag
e (
9%
)P
ig m
an
ure
(7
4%
)3
.9M
eso
ph
ilic (
39
°C)
We
t
24
.07
.20
06
Ma
ize
co
b s
ilag
e (
11
%)
Gra
ins o
f ry
e (
6%
)
BA
22
9.0
3.2
00
7B
B3
60
0M
aiz
e s
ilag
e (
38
%)
Ca
ttle
se
wa
ge
(4
1%
)3
.8M
eso
ph
ilic (
37
°C)
We
t
Ca
ttle
du
ng
(1
8%
)
Wa
ter
(3%
)
BA
32
0.0
7.2
00
6M
V1
00
0M
aiz
e s
ilag
e (
45
.7%
)P
ig m
an
ure
(5
4%
)3
.3M
eso
ph
ilic (
40
°C)
We
t
BA
42
0.0
7.2
00
6M
V2
32
6M
aiz
e s
ilag
e (
28
%)
Ca
ttle
ma
nu
re (
72
%)
3.1
Me
so
ph
ilic (
39
°C)
We
t
BA
52
8.0
8.2
00
6M
V1
30
0M
aiz
e s
ilag
e (
39
%)
Pig
ma
nu
re (
50
%)
5.4
Me
so
ph
ilic (
41
°C)
We
t
Tu
rke
y h
en
du
ng
(9
%)
BA
62
3.0
5.2
00
7S
A2
70
0M
aiz
e s
ilag
e (
40
%)
Pig
ma
nu
re (
57
%)
3.9
Me
so
ph
ilic (
39
°C)
We
t
BA
72
3.0
4.2
00
7B
B1
00
0M
aiz
e s
ilag
e (
82
%)
Wa
ter
(6%
)3
.4M
eso
ph
ilic (
43
°C)
We
t
Gra
ins o
f b
arl
ey (
12
%)
BA
80
9.1
1.2
00
6S
12
0 (
AF
)T
ritica
le (
10
0%
) -
2.1
Me
so
ph
ilic (
39
°C)
Dry
4x 1
20
(F
R)
BA
92
5.0
7.2
00
7M
V2
64
0M
aiz
e s
ilag
e (
37
%)
Ca
ttle
ma
nu
re (
54
%)
4.0
Me
so
ph
ilic (
41
°C)
We
t
Pig
ma
nu
re (
6%
)
Tu
rke
y h
en
du
ng
(2
%)
BA
10
26
.07
.20
07
SA
19
50
Ma
ize
sila
ge
(1
3%
)C
att
le m
an
ure
(7
6%
)3
.8M
eso
ph
ilic (
40
°C)
We
t
Gra
ss s
ilag
e (
5%
)C
att
le d
un
g (
4%
)
Gra
ins o
f ry
e (
2%
)
BB
= B
randenburg
; M
V =
Meckle
nburg
-Vorp
om
mern
; S
A =
Sachsen-A
nhalt; S
= S
achsen
MATERIAL AND METHODS
44
5.3 Physical and chemical analyses of the biogas and the reactor content
5.3.1 Determination of the pH value
The pH value was determined by the use of a calibrated laboratory pH meter
(Wissenschaftlich-Technische Werkstätten GmbH, Weilheim, Germany).
5.3.2 Calculation of the gas composition
For the calculation of the actual volumes (VN) of methane (CH4), carbon dioxide
(CO2) and oxygen (O2) based on norm temperature (TN = 273.15 K) and norm
pressure (pN = 1013.25 mbar), the measured produced gas volume (Vm), the actual
gas temperature (Tm) and the atmospheric pressure (pm) were determined. The
following equation (ideal gas law) was used for calculation:
VN = (pm*Vm*TN) (Tm*pN)-1
5.3.3 Determination of the acid composition
At every sampling day a volume of 50 ml reactor content was taken in a PET bottle
and stored at -20°C. After finishing the lab-scale experiment regarding anaerobic
digestion all samples were analyzed with a Fisons GC 8000 gas chromatograph
(Fisons Instruments GmbH, Mainz-Kastel, Germany) for acid composition. The gas
chromatograph was supplied with a flame ionization detector (Thermo Fisher
Scientific, CE Instruments, Milan, Italy).
The concentrations of the following organic acids and alcohols were detected: lactid
acid, acetic acid, propionic acid, butyric and isobutyric acid, valeric and isovaleric
acid, caproic acid, ethanol and propanol.
For analyzing the acids and alcohols the collected samples were defrosted and a
5 ml aliquot was taken. Subsequently, 1 ml of Carrez I solution
(K4[Fe(CN)6] × 3 H2O), 1 ml of Carrez II solution (ZnSO4 × 7 H2O), 0.5 ml of
phosphoric acid (85 %) and 2.5 ml of distilled water were added.
MATERIAL AND METHODS
45
After centrifugation (10 min, 5,000 × g) the clear supernatant was passed through a
0.2 µm membrane filter (GHP Acrodisc Life Science, Port Washington, USA) into
glass tubes. Then the gas chromatography process was conducted.
5.4 DNA-based analysis of the archaeal community structure
5.4.1 Used strains
All bacterial and archaeal strains used for this study are listed in Table 5.
5.4.2 DNA extraction and purification
For DNA extraction 200 ml of the reactor content were carried over into four 50 ml
tubes. After centrifugation (2 min, 200 × g; 20°C) samples were homogenized in a
Whirl-Pak bag (Carl Roth GmbH & Co. KG, Karlsruhe, Germany). These bags were
fit up with special PE-filters which repressed rough organic materials. Subsequently
the permeate of the reactor sample was used for DNA isolation. Several protocols
were tested for DNA isolation consisting of different approaches for cell lysis and
DNA clean-up as described in the following.
Mechanical cell lysis with the FastPrep - 24 System (DNA extraction protocols A, B,
C). 100 µl of the homogenate were mixed with 1 ml of sodium-phosphate buffer
(0.1 mol l-1, pH = 7.0). Cell pellets were obtained by centrifugation (2 min,
14,000 × g). After discarding the supernatant the pellets were resuspended in 1 ml of
0.85% KCl solution and another centrifugation step followed (2 min, 14,000 × g). The
genomic DNA was isolated with the FastDNA Spin Kit for soil (MP Biomedicals,
Heidelberg, Germany) according to manufacturer’s guidelines (DNA extraction
protocol A).
A modified DNA isolation protocol of the FastDNA Spin Kit for soil (MP Biomedicals,
Heidelberg, Germany) was used for the second DNA extraction approach (DNA
extraction protocol B) according to Lebuhn et al. (2003). After adding the binding
matrix suspension to the lysed cells of the environmental sample the tubes were
inverted for 5 min to allow the binding of the DNA.
MATERIAL AND METHODS
46
Tab
le 5
Bacte
ria
l an
d a
rch
aea
l culture
s o
r re
spective g
eno
mic
DN
A u
sed in t
his
stu
dy.
(1)
Le
hrs
tuh
l fü
r M
ikro
bie
lle Ö
ko
log
ie,
Lim
no
log
ie u
nd
Allg
em
ein
e M
ikro
bio
log
ie,
Univ
ers
ity o
f K
on
sta
nz
(2)
Deu
tsch
e S
am
mlu
ng v
on M
ikro
org
an
ism
en u
nd
Ze
llku
ltu
ren
Gm
bH
, B
rau
nsch
we
ig
(3)
Le
hrs
tuh
l fü
r M
ikro
bio
log
ie u
nd
Arc
ha
eenze
ntr
um
, U
niv
ers
ity o
f R
eg
ensb
urg
(4)
Ge
ow
isse
nsch
aft
en/P
erig
lazia
le F
ors
ch
un
g, A
lfre
d W
eg
en
er
Institu
t, P
ots
dam
(5)
Bio
ve
rfa
hre
nste
ch
nik
, L
eib
niz
-In
stitu
t fü
r A
gra
rte
ch
nik
Pots
da
m-B
orn
im e
.V., P
ots
da
m
Ord
er
Fam
ily
Sp
ecie
sF
orm
at
Insti
tuti
on
Meth
an
ob
acte
riale
sM
eth
an
obacte
riaceae
Meth
anobre
vib
acte
r arb
oriphilu
s D
SM
1125
Actively
gro
win
g culture
(1)
Meth
anobacte
rium
form
icic
um
DS
M 1
535
DN
A tem
pla
te(2
)
Meth
anobacte
rium
bry
antii
DS
M 8
63
Actively
gro
win
g culture
(2)
Meth
anoth
erm
obacte
r th
erm
auto
trophic
us D
SM
1053
Actively
gro
win
g culture
(1)
Meth
an
oco
ccale
sM
eth
an
ococcaceae
Meth
anococcus v
annie
lii D
SM
1224
Actively
gro
win
g culture
(3)
Meth
an
om
icro
bia
les
Meth
an
om
icro
bia
ceae
Meth
anoculle
us m
arisnig
ri D
SM
1498
Actively
gro
win
g culture
(3)
Meth
anoculle
us b
ourg
ensis
DS
M 3
045
Actively
gro
win
g culture
(4)
DN
A tem
pla
te(2
)
Meth
anofo
llis lim
inata
ns D
SM
4140
Actively
gro
win
g culture
(2)
Meth
anospir
illum
hungate
i M
h1
Actively
gro
win
g culture
(1)
Meth
an
osarc
inale
s
Meth
an
osarc
inaceae
Meth
anosarc
ina
bark
eri
DS
M 8
00
DN
A tem
pla
te(2
)
Meth
anosarc
ina
bark
eri
DS
M 8
687
Actively
gro
win
g culture
(4)
Meth
anosarc
ina
maze
i D
SM
3647
Actively
gro
win
g culture
(2)
Meth
anosarc
ina
maze
i D
SM
1311
Actively
gro
win
g culture
(4)
Meth
anosarc
ina therm
ophila
DS
M 1
825
Actively
gro
win
g culture
(2)
Meth
an
osaeta
ceae
Meth
anosaeta
concili
i D
SM
2139
DN
A tem
pla
te(2
)
En
tero
bacte
riale
sE
nte
robacte
riaceae
Pecto
bacte
rium
caro
tovo
rum
ssp. caro
tovo
rum
DS
M 3
0168
Actively
gro
win
g culture
(5)
MATERIAL AND METHODS
47
After centrifugation (10 s, 14,000 × g) the supernatant was discarded, and the
binding matrix was resuspended (500 µl SEWS-M buffer). Then 600 µl of the mixture
were transferred to a Spin filter and centrifuged at 14,000 × g for 1 min. Afterwards
the Spin filter was washed twice with 500 µl of the SEWS-M buffer. For drying the
matrix of residual wash solution the SPIN filter was centrifuged (2 min, 14,000 × g).
The binding matrix was gently resuspended in 100 µl of warmed DES solution
(55°C). After 5 min of incubation the eluted DNA was removed from the binding
matrix (1 min, 14,000 × g) and stored at 4°C.
In case of DNA extraction protocol C a homogenization of the biogas reactor sample
was conducted according to the protocol of the FastDNA Spin Kit for soil (refer DNA
extraction protocol A) in the FastPrep Instrument for 40 s at a speed setting of 6.0.
Then the homogenized samples were adjusted to a final concentration of 0.3 mg ml-1
proteinase K, 1.2% SDS (w/v) and 1.2 mmol l-1 CaCl2. After incubation (45 min, 65°C)
the lysates were centrifuged (10 min, 6,000 × g). Supernatants were adjusted
to ≥ 0.7 mol l-1 NaCl and ≥ 2% CTAB followed by further incubation (20 min, 65°C).
One volume phenol-chloroform-isoamyl alcohol (25:24:1) was added, the samples
were mixed and phases were separated by centrifugation (5 min, 1,000 × g).
Aqueous phases were collected and the chloroformation step was repeated with
chloroform-isoamyl alcohol (24:1). Subsequently, one tenth of the supernatants’
volumes of a 3 mol l-1 sodium acetate solution (pH = 5.2) was added followed by
isopropanol DNA precipitation (one volume). Samples were centrifuged (10 min,
20,000 × g), supernatants were discarded, and pellets were washed twice in 70%
ethanol. After drying the pellets were resuspended in 10 mmol l-1 Tris/HCl buffer
(pH = 8) and stored at 4°C.
Chemical cell lysis by SDS (DNA extraction protocols D, E). For chemical cell lysis by
SDS (sodium dodecyl sulphate) the DNA isolation protocol of Henne et al. (1999)
was used. Both applied extraction approaches (DNA extraction protocols D and E)
only differed in the purification step after total genomic DNA isolation: in protocol D
the preparation was used without any further purification for all PCR applications. In
protocol E the DNA was purified on MicroSpin S-400 HR sephacryl columns
(GE Healthcare, München, Germany).
MATERIAL AND METHODS
48
Chemical enzymatic cell lysis with lysozyme (DNA extraction protocols F, G) Sample
preparation and DNA isolation were conducted according to Nettmann et al. (2008).
This included an enzymatic cell lysis with lysozyme, proteinase K and SDS as
detergent, purification steps with CTAB and chloroform-isoamyl alcohol (24:1) and a
subsequently isopropanol precipitation. In case of protocol F, the DNA was used
without any further purification for subsequent PCR analysis. This extraction method
was used as the standard protocol for all extractions of environmental DNA from the
biogas plants and lab-scale experiment reactors. For protocol G, the obtained DNA
was purified on MicroSpin S-400 HR sephacryl columns (refer protocol E).
Combined physical and enzymatic cell lysis (DNA extraction protocols H, I). Total
genomic DNA was isolated by the method of Nettmann et al. (2008) (refer protocol F)
with the following modification: after treating the cell suspension with lysozyme
(0.3 mg ml-1) the samples were incubated at 37°C for 60 min, followed by three
cycles of freezing in liquid nitrogen for 1 min and heating in a water bath at 65°C until
the sample was thawed completely. As before, one part of the resulted DNA
preparation was used directly for PCR applications (extraction protocol H) while the
other part was purified on sephacryl columns again (extraction protocol I).
5.4.3 DNA quantification
The DNA concentrations were determined with the NanoDrop ND-3300
fluorospectrometer (NanoDrop Technologies, Wilmington, USA). Hence, DNA was
marked with the intercalating fluorescence dye PicoGreen (Quant-iT PicoGreen
dsDNA Assay Kit, Invitrogen, Carlsbad, USA). The fluorescence was detected at
525 nm (Amplitude of fluctuation (AF): 20 nm) after an excitation with light of
wavelength 470 nm (AF: 10 nm). As template for the standard curves, calf thymus
DNA was used. According to the Standard Curve Protocol, standard series were
created. Then the DNA amounts of the environmental samples were determined.
Therefore a dilution series of the isolated genomic DNA was prepared (1:500,
1:1000, and 1:1500). Every concentration was measured in triplicate.
MATERIAL AND METHODS
49
5.4.4 Analysis of the DNA purity
Optical densities of DNA preparations were measured with a Unicam UV1-100
spectrophotometer (Nicolet Instruments GmbH; Offenbach; Germany). To
characterize the intensity of contamination of the DNA solution by proteins the ratio of
absorbance signals at 260 and 280 nm was calculated. If the determined ratios
exceeded the value of 1.8 the sample was scored as “contaminated”. Another value
reflecting the carbohydrate, phenol and aromatic compound contaminations is the
absorbance ratio of 260 nm and 230 nm. Ratios between 1.5 and 1.8 are indicative of
pure DNA (Weiss et al. 2007).
5.4.5 Quantitative real-time PCR (Q-PCR)
Amplification of the 16S rRNA gene with conventional PCR technique. Genomic DNA
of four type species of the Archaea-genera Methanoculleus (M. bourgensis
DSM 3045), Methanobacterium (M. formicicum DSM 1535), Methanosarcina
(M. barkeri DSM 800) and Methanosaeta (M. concilii DSM 2139) were ordered by the
Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH (DSMZ,
Braunschweig, Germany) to construct the standard curves (cf. Table 5). The strain
Pectobacterium carotovorum ssp. carotovorum DSM 30168 cultivated at the
Leibniz-Institut für Agrartechnik Potsdam-Bornim e.V. was used as reference for the
domain of Bacteria. The latter strain was grown in Nutrient Broth (Carl Roth GmbH &
Co. KG, Karlsruhe, Germany) at 30°C for 24 h without shaking. Cells were harvested
and DNA was isolated according to Pospiech and Neumann (1995).
Species-specific primers were designed (Table 6) located up- and downstream of the
sequences recognized by the real-time PCR primer set. These primers were used to
amplify the 16S rRNA gene of the archaeal and bacterial microorganisms. The
primers were purchased from MWG Biotech (Ebersberg, Germany). All PCRs were
performed on a Biometra T gradient 96 (Whatman Biometra, Göttingen, Germany).
The temperature profile for PCR reactions was as follows: initial denaturation of 5 min
at 94°C; 10 cycles of 1 min at 94°C, 1 min at 55°C and 1 min at 72°C; 25 cycles of
1 min at 94°C, 1 min at 52°C and 1 min at 72°C; final elongation at 72°C for 10 min.
MATERIAL AND METHODS
50
One PCR mixture contained 10 ng DNA-template, 1 × PCR-Buffer, 0.2 mmol l-1 of
each dNTP, 3 mmol l-1 MgCl2, 0.2 µmol l-1 of each primer and 1 U Taq-Polymerase.
The total reaction volume was 20 µl. PCR products were purified with the Cycle Pur
Kit Classic Line (PEQLAB Biotechnologie GmbH, Erlangen, Germany).
Ligation and transformation of the 16S rRNA gene fragments. The purified 16S rRNA
gene amplicons were cloned into the pGEM-T Vector System as described in the
manufacturer’s protocol (Promega, Mannheim, Germany). After a successful
insertion of the 16S rRNA gene fragments into the pGEM-T vectors, those were
transformed into high efficient competent cells of E. coli JM 109 (Promega,
Mannheim, Germany). 50-100 µl of each transformation culture was plated out onto
LB/ampicillin/IPTG/X-Gal agar. Afterwards the plates were incubated for 14 h
at 37°C. The next day positive clones were picked and cultured overnight in 5 ml LB
Broth (Luria/Miller) (Carl Roth GmbH & Co. KG, Karlsruhe, Germany) added with
ampicillin (50 µg ml-1). One volume (1 ml) of the cell suspension was added to one
volume (1 ml) of glycerine solution (50%). Then these glycerine cultures were stored
at -80°C. The rest of the cell suspension was used for plasmid isolation.
Plasmid isolation. Two different plasmid isolation kits were used:
(1) peqGOLD Plasmid Miniprep Kit I Classic-Line (PEQLAB Biotechnologie GmbH,
Erlangen, Germany)
(2) Mini NucleoSpin Plasmid Kit (Macherey-Nagel GmbH & Co. KG, Düren,
Germany)
The plasmids were extracted according to manufacturer’s guidelines. Plasmids were
stored at –20°C.
Test-PCR and restriction control. To test the isolated plasmids for positive
recombination a test-PCR and a restriction digest control were carried out.
The PCR was performed on a Biometra T gradient 96 (Whatman Biometra,
Göttingen, Germany), and the following program was applied: an initial denaturation
at 94°C for 2 min; 30 cycles of denaturation at 94°C for 30 s, annealing at 47°C
for 1 min, extension at 70°C for 2 min; end of extension at 70°C for 10 min and
cooling to 4°C.
MATERIAL AND METHODS
51
Tab
le 6
PC
R p
rim
ers
targ
eting
the
16
S r
RN
A g
enes o
f diffe
rent
meth
an
oge
nic
refe
rence s
pecie
s. T
he
obta
ined a
mplic
on
was s
ubse
que
ntly u
se
d for
clo
nin
g into
pG
EM
-T v
ecto
rs.
F p
rim
er
= f
orw
ard
prim
er,
R p
rim
er
= r
evers
e p
rim
er
Tm =
me
ltin
g tem
pera
ture
GC
= g
uanid
ine a
nd c
yto
sin
e c
onte
nt
within
the d
educed p
rim
er
Pri
mer
Fu
ncti
on
Seq
uen
ce
Am
plico
n s
ize
Tm
GC
Mic
rob
ial ta
rget
Refe
ren
ces
[5´
→ 3
´][b
p]
[°C
][%
][N
CB
I accessio
n n
um
ber]
Metforf
w1
F p
rim
er
TA
AG
C C
AT
GC
AA
GT
C G
AA
CG
12
47
57
.35
0.0
Meth
anobacte
rium
form
icic
um
[A
F169245]
Nettm
ann
et al. 2
008
Metforr
ev3
R p
rim
er
AC
GC
A T
TC
CA
GC
TT
C A
TG
AG
57
.35
0.0
Meth
anobacte
rium
form
icic
um
[A
F169245]
Nettm
ann
et al. 2
008
Metb
ou
fw1
F p
rim
er
TA
GG
A T
GG
AT
CT
GC
G G
CC
GA
88
06
1.4
60
.0M
eth
anoculle
us b
ourg
ensis
[A
Y196674]
Nettm
ann
et al. 2
008
Metb
ou
rev2
R p
rim
er
CA
TC
A G
TC
CG
GA
GA
C C
AT
56
.05
5.6
Meth
anoculle
us b
ourg
ensis
[A
Y196674]
Nettm
ann
et al. 2
008
Metc
onfw
1F
prim
er
CT
GC
C A
GA
GG
TT
AC
T G
CT
AT
14
33
57
.35
0.0
Meth
anosaeta
concili
i [X
16
932]
Nettm
ann
et al. 2
008
Metc
onre
v4
R p
rim
er
CC
TA
C G
GC
TA
CC
TT
G T
TA
CG
59
.45
5.0
Meth
anosaeta
concili
i [X
16
932]
Nettm
ann
et al. 2
008
Metb
arf
w1
F p
rim
er
TT
GA
T C
CT
GC
CA
GA
G G
TT
AC
14
45
57
.35
0.0
Meth
anosarc
ina b
ark
eri [
NC
00
73
55
]N
ettm
ann
et al. 2
008
Metb
arr
ev3
R p
rim
er
CT
AC
G G
CT
AC
CT
TG
T T
AC
GA
57
.35
0.0
Meth
anosarc
ina b
ark
eri [
NC
00
73
55
]N
ettm
ann
et al. 2
008
16S
for
F p
rim
er
AG
AG
T T
TG
AT
CA
TG
G C
TC
AG
15
79
56
.34
7.5
Weis
burg
et al. 1
991
L1
R p
rim
er
CA
AG
G C
AT
CC
AC
CG
T5
0.6
60
.0Jensen e
t al. 1
993
Toth
et al. 2
001
MATERIAL AND METHODS
52
The primer pair SP6 [5´-CATTT AGGTG ACACT ATAG-3´]/ T7 [5´-TAATA CGACT
CACTA TAGGG 3´] was used which flank the specific 16S rRNA gene fragment
within the plasmid. The PCR solution consisted of 2 µl of 10 × PCR buffer, 2 µl of
dNTP (10 mM), 1.6 µl of MgCl2 (25 mM), 1 µl of the forward and the reverse primer
(10 µM), 10.4 µl of sterile water, 1 µl Taq DNA Polymerase (1 U/µl) and 1 µl of
plasmid DNA.
According to manufacturer’s guidelines, the restriction enzymes NcoI and SalI
(Fermentas, St. Leon-Rot, Germany) were used for the restriction digest control. A
1.2% agarose gel electrophoresis was carried out to verify if the plasmids had
inserted the right 16S rRNA gene fragment.
Gel electrophoresis. To examine the results of DNA extraction, restriction digest and
conventional PCR agarose gel electrophoresis was applied. The agarose was
supplied by Biozyme Scientific GmbH (Hessisch Oldendorf, Germany). For the
separation of nucleic acids with lengths of 500 to 2000 bp, 1.2% agarose gels
charged with ethidium bromide (30 µl l-1) were used. As DNA length standard the
Lambda DNA/EcoRI + HindIII marker (MBI Fermentas, St. Leon Rot, Germany) was
used.
3% agarose gels were poured for the separation of DNA fragments which were
smaller than 500 bp. Therefore, the DNA length standard pUC19/MspI marker
(MBI Fermentas, St. Leon Rot, Germany) was applied. The agarose gels were
documented using the Gene Snap-Gene Bio Imaging System (Syngene/Merck
Eurolab, Darmstadt, Germany).
Sequencing. The sequencing of the 16S rRNA gene amplicons was performed by
MWG Biotech (Ebersberg, Germany). Up to 800 bp of the PCR-insert were
sequenced. The obtained sequences were compared to previously published ones
using the nucleotide-nucleotide BLAST of the NCBI GenBank
(http://www.ncbi.nlm.nih.gov/Genbank/index.html).
Linearization of the plasmids for Q-PCR standard curves. For linearization of the
plasmids the restriction enzyme ScaI was used according to manufacturer’s
guidelines (New England Biolabs Inc., USA).
MATERIAL AND METHODS
53
The DNA concentration of the purified, linearized plasmids was measured and
calculated with the NanoDrop ND-3300 fluorospectrometer (NanoDrop Technologies,
Wilmington, USA).
Calculation of plasmid copy numbers. Isolated DNA copy numbers were calculated
with the following equation:
NDNA = (cDNA*NA) (lplasmid*mmol,bp)-1
with NDNA as the number of DNA copies per µl solution after DNA isolation, cDNA as
the DNA concentration measured via fluorescent DNA quantification [g µl-1], NA as
the Avogadro constant, lplasmid as the plasmid length [number of base pairs], and
mmol,bp as the average molar mass of a base pair [660 g mol-1]. Standard series
concentrations were set from 101 to 109 target DNA copies per PCR mixture.
Amplification of the 16S rRNA gene by Q-PCR. The Q-PCR was performed on an
ABI 7300 System (Applied Biosystems, Darmstadt, Germany). For all Q-PCR
observations, using the 16S rRNA gene as a target gene, the detection method of the
5´-nuclease assay (TaqMan) was used. PCR primers and TaqMan FAM/TAMRA
probes were provided by MWG Biotech (Ebersberg, Germany). For analyses the
primer sets of ARC, BAC, MMB, MBT, Msc and Mst were used (cf. Table 7, Yu et al.
2005a, Yu et al. 2006).
Two different Q-PCR protocols differing in the composition of the PCR mixture and
the PCR conditions were applied.
(1) All Q-PCR conditions (PCR mixture and cycler program) were performed
according to Yu et al. (2005a).
(2) According to manufacturer’s conditions (Applied Biosystems, Darmstadt,
Germany) the PCR mixture was optimized.
MATERIAL AND METHODS
54
Amplification was carried out in a final volume of 25 µl containing 2 µl (1 ng)
of genomic DNA, 2.25 µl (final concentration, 900 nM) of the forward and the
reverse primer, 0.5 µl (final concentration, 200 nM) of the TaqMan probe,
12.5 µl of the TaqMan Universal PCR Master Mix (Applied Biosystems,
Darmstadt, Germany) and 5.5 µl of sterile water.
The following PCR conditions were used: an initial DNA denaturation step of
10 min at 95°C and 45 cycles of denaturation at 95°C for 15 s, annealing
at 50°C (Mst-set, Msc-set), 54°C (MBT-set) or 57°C (BAC-set) for 30 s and
extension at 60°C for 1 min. The annealing step was cancelled by the primer
sets of MMB and ARC. All PCRs were performed in triplicate.
Then results were analyzed with the 7300 Real-Time PCR System Sequence
Detection Software Version 1.3 (Applied Biosystems, Darmstadt, Germany).
At first the calibration curves were generated. The concentration of the
defined standards (1 × 102 to 1 × 109 copies) was plotted against the cycle
number which is a detected fluorescence signal above the threshold value.
This value was defined in every Q-PCR run by a logarithmic fluorescence
intensity of 0.05. Then the number of copies of the 16S rRNA gene in the
unknown samples could be detected.
Analysis of Q-PCR efficiencies. After creating standard curves, the PCR efficiency
was calculated with the following equation:
PCR efficiency = 10(-1/slope of the standard curve)-1
Solid standard curves have normally a slope between -3.9 and -3.0 corresponding to
PCR efficiencies ranging between 0.800 and 1.150 (Zhang and Fang 2006). The
stability index of standard curves has to reach a value of R2 = 0.950 for absolute
quantification. The intercept of the standard curve describes the value of PCR cycles
which is theoretically essential to detect one single DNA fragment of the target gene
in the environmental sample.
MATERIAL AND METHODS
55
Tab
le 7
Chara
cte
ristics o
f th
e p
rim
er
and p
rob
e s
ets
fo
r am
plif
yin
g t
he 1
6S
rR
NA
gene
by Q
-PC
R.
The d
en
om
ination o
f th
e u
se
d p
rim
er
and p
rob
e s
ets
is
accord
ing
to Y
u e
t a
l. (
2005
a).
F p
rim
er
= f
orw
ard
prim
er,
R p
rim
er
= r
evers
e p
rim
er,
Ta
qM
an =
TaqM
an p
robe
Tm =
me
ltin
g tem
pera
ture
GC
= g
uanid
ine a
nd c
yto
sin
e c
onte
nt
within
the d
educed p
rim
er
Se
tP
rim
er
Fu
nc
tio
nS
eq
ue
nc
eA
mp
lic
on
siz
e
Tm
GC
Ta
rge
t g
rou
pR
efe
ren
ce
s
[5´
→ 3
´][b
p]
[°C
][%
]
AR
CA
rch
fwF
prim
er
AT
TA
G A
TA
CC
CS
BG
T A
GT
CC
27
36
1.0
40
.0A
rch
ae
aY
u e
t a
l. 2
00
5a
Arc
hT
aq
ma
nT
aq
Ma
nA
GG
AA
TT
GG
C G
GG
GG
AG
CA
C7
0.1
65
.0Y
u e
t a
l. 2
00
5a
Arc
hre
vR
prim
er
GC
CA
T G
CA
CC
WC
CT
C T
62
.36
2.5
Yu
et
al. 2
00
5a
BA
CB
acfw
F p
rim
er
AC
TC
C T
AC
GG
GA
GG
C A
G4
68
63
.46
4.7
Ba
cte
ria
Yu
et
al. 2
00
5a
Ba
cT
aq
ma
nT
aq
Ma
nT
GC
CA
GC
AG
C C
GC
GG
TA
AT
A C
70
.86
1.9
Yu
et
al. 2
00
5a
Ba
cre
vR
prim
er
GA
CT
A C
CA
GG
GT
AT
C T
AA
TC
C6
0.7
47
.6Y
u e
t a
l. 2
00
5a
MB
TM
ba
cfw
F p
rim
er
CG
WA
G G
GA
AG
CT
GT
T A
AG
T3
43
60
.74
7.4
Me
tha
no
ba
cte
ria
les
Yu
et
al. 2
00
5a
Mb
acT
aq
ma
nT
aq
Ma
nA
GC
AC
CA
CA
A C
GC
GT
GG
A6
7.2
61
.1Y
u e
t a
l. 2
00
5a
Mb
acre
vR
prim
er
TA
CC
G T
CG
TC
CA
CT
C C
TT
63
.25
5.6
Yu
et
al. 2
00
5a
MM
BM
mic
rfw
F p
rim
er
AT
CG
R T
AC
GG
GT
TG
T G
GG
50
66
3.8
58
.0M
eth
an
om
icro
bia
les
Yu
et
al. 2
00
5a
Mm
icrT
aq
ma
nT
aq
Ma
nT
YC
GA
CA
GT
G A
GG
RA
CG
AA
A G
CT
G7
0.2
54
.2Y
u e
t a
l. 2
00
5a
Mm
icrr
ev
R p
rim
er
CA
CC
T A
AC
GC
RC
AT
H G
TT
TA
C6
1.5
45
.4Y
u e
t a
l. 2
00
5a
Mst
Msa
etf
wF
prim
er
TA
AT
C C
TY
GA
RG
GA
C C
AC
CA
16
46
1.0
50
.0M
eth
an
osa
eta
ce
ae
Yu
et
al. 2
00
5a
Msa
etT
aq
ma
nT
aq
Ma
nA
CG
GC
AA
GG
G A
CG
AA
AG
CT
A G
G7
0.0
59
.1Y
u e
t a
l. 2
00
5a
Msa
etr
ev
R p
rim
er
CC
TA
C G
GC
AC
CR
AC
M A
C6
2.1
61
.2Y
u e
t a
l. 2
00
5a
Msc
Mscfw
F p
rim
er
GA
AA
C C
GY
GA
TA
AG
G G
GA
4
08
61
.25
0.0
Me
tha
no
sa
rcin
ace
ae
Yu
et
al. 2
00
5a
MscT
aq
ma
nT
aq
Ma
nT
TA
GC
AA
GG
G C
CG
GG
CA
A6
6.7
61
.1Y
u e
t a
l. 2
00
5a
Mscre
vR
prim
er
TA
GC
G A
RC
AT
CG
TT
T A
CG
59
.94
4.4
Yu
et
al. 2
00
5a
MATERIAL AND METHODS
56
Calculation of the limit of detection and the limit of quantification. Assuming a normal
distribution of measured CT-values, the limit of detection (LOD) and the limit of
quantification were calculated from the residual standard deviation of the standard
curves. The following formula was used to calculate the LOD (LOD):
LOD = I + 3SI*(slope)-1
with I as the Intercept of the standard curve and SI as the residual standard deviation
of the Intercept.
The limit of quantification (LOQ) was calculated with the following equation:
LOQ = I + 10SI*(slope)-1
with I as the Intercept of the standard curve and SI as the residual standard deviation
of the Intercept.
Spiking experiments. The effect of DNA extraction, sample matrixes and detection
limits of the methanogenic Archaea on Q-PCR was analyzed in two different spiking
experiment basic approaches.
For spiking experiment (1) reactor samples of System 2 were used.
(1) Methanoculleus bourgensis DSM 3045 and Methanosarcina barkeri DSM 8687,
purchased from the Deutsche Sammlung von Mikroorganismen und Zellkulturen
GmbH (DSMZ, Braunschweig, Germany), were provided by Dirk Wagner
(Geowissenschaften - Periglaziale Forschung, Alfred Wegener Institut, Potsdam,
Germany). The determination of total cell counts of the archaeal cell suspensions
was carried out by using the Multisizer™ Coulter Counter® (Beckman Coulter GmbH,
Krefeld, Germany) with a 30 µM aperture in triplicate. Additionally, the cell volume of
each measured cell was recorded. To obtain a measurement concentration
between 1 and 10% the cell suspensions were diluted with an IsoFlow Sheath Fluid
electrolyte solution (Beckman Coulter GmbH, Krefeld, Germany). Then a final volume
of 50 µl was measured. The cell size and cell number was recorded between 0.4
and 18.0 µm.
MATERIAL AND METHODS
57
The region of interest for analyses of the cell volume lay in a range
between 0.103 µm3 and 9.937 µm3. The aperture current was set to –400 µA by an
actual gain of four. After determining the total cell numbers of the pure cultures the
reactor samples were spiked with defined cell numbers of the archaeal strains.
Initially aliquots of 1 ml reactor sample content were prepared. From the first samples
DNA was isolated directly (nonspiked control). A proportion of 109, 108, 107
and 106 cells of actively grown culture of Methanoculleus bourgensis DSM 3045 was
added to the second group of samples, respectively and afterwards DNA extraction
was carried out. The remaining reactor samples were pooled with 108, 107
and 106 cells of actively grown culture of Methanosarcina barkeri DSM 8687 before
DNA isolation was conducted. For all DNA extractions protocol G was applied.
Furthermore, for spiking the extracted genomic DNA (gDNA) of the reactor sample
with defined amounts of gDNA of pure methanogenic cultures, DNA of both archaeal
strains was isolated. All DNA extractions were performed in triplicate.
Afterwards DNA extraction and quantification Q-PCR was carried out. Therefore, the
primer sets of ARC, MMB and Msc were used. All Q-PCR procedures were
performed with the standard protocol. The following samples were analyzed by one
single Q-PCR run:
(a) plasmid standard series (concentrations were set from 101 to 108 target DNA
copies per PCR mixture),
(b) plasmid standard dilution series (concentrations were set from 101 to
108 target DNA copies per PCR mixture) spiked with 1000 pg of genomic DNA
of the reactor sample,
(c) reactor sample dilution series (concentrations were set from 102 to 104 pg of
genomic DNA per PCR mixture),
(d) DNA standard dilution series of the pure cultures (concentrations were set
from 0.10 to 102 ng of genomic DNA per PCR mixture),
(e) reactor sample spiked with 106 to 109 cells of archaeal pure culture before
DNA extraction.
MATERIAL AND METHODS
58
For the spiking experiment (2) the reactor sample of System 3 was used.
(2) The influence of interfering substances on the measurements was investigated by
another spiking experiment. Therefore, respective 16S rRNA gene copy numbers
were quantified by Q-PCR within the DNA sample of day 35 in presence (or absence)
of varying amounts of added reference DNA (dilutions of the plasmid standard series,
SSD). SSDs were increased from 101 to 108 copy numbers (SSD1 – SSD8),
respectively.
All Q-PCR procedures were performed with the standard protocol. Results were
analyzed with the 7300 Real-Time PCR System Sequence Detection Software
Version 1.3 (Applied Biosystems, Darmstadt, Germany). Identical fluorescence
threshold and baseline settings were used for comparability of the results.
For analysing the effect of the DNA extraction method on Q-PCR the theoretical
number of detected gene copies was compared to the estimated number of detected
gene copies by Q-PCR.
Therefore, the following equation was used:
NE/NT = nt (nsample + npure culture)-1
with NE as the estimated number of detected gene copies, NT as the theoretical
number of detected gene copies, nt as the estimated copy number of the reactor
samples which were spiked with cells of archaeal pure culture before DNA extraction,
nsample as the detected copy number of the sample and npure culture as the total copy
number of the spiked pure culture of the added archaeal cell suspension. With this
computation the loss of copy numbers, caused by the chosen DNA extraction
protocol, was determined.
MATERIAL AND METHODS
59
To evaluate whether the DNA solutions of the reactor samples contained inhibitory
compounds to Q-PCR different experimental set-ups were used:
(1) comparison of the slopes of the plasmid standard dilution series to the
dilution series of the DNA solution of the reactor sample - comparable slope
values indicate the absence of PCR inhibiting effects,
(2) comparison of the sum of the detected copy numbers of the plasmid
standard dilution series and the reactor sample to the detected copy numbers
of the plasmid standard dilution series spiked with 1000 pg of extracted DNA
solution from the reactor sample - an uninterrupted Q-PCR run will result in
comparable numbers of detected copy numbers in both series,
To check if the choice of the used standard (plasmid standard dilution series,
archaeal genomic DNA standard dilution series) influenced the PCR efficiency of the
Q-PCR run the slopes of both dilution series were compared.
Design of group-specific primer sets for Q-PCR using methyl-coenzyme M reductase
subunit alpha (mcrA) gene sequences. This study focused on the design and
characterization of group-specific primer sets based on the methyl-coenzyme M
reductase subunit alpha (mcrA) gene. In accordance to the developed methanogenic
group-specific 16S rRNA gene assays by Yu et al. (2005a), four primer sets were
designed to detect the following order-level and familiy-level methanogenic Archaea:
Methanobacteriales (MBAC-set), Methanomicrobiales (MMIC-set),
Methanosarcinaceae (MSarc-set) and Methanosaetaceae (MSaet-set).
Genome and partial mcrA sequences were selected from the NCBI database
(http://www.ncbi.nlm.nih.gov/Genbank/index.html) to align and compare these
published nucleotide sequences. All used sequences for designing group-specific
primers are listed in Table I a-c (Appendix). The MEGA software (MEGA version 4.0,
Tamura et al. 2007) was applied for all sequence comparisons. The primer set for
each target group was designed based on regions of identity within the
mcrA sequences. Each region of identity within the multiple alignment for a
group-specific mcrA primer was investigated manually.
MATERIAL AND METHODS
60
The following criteria, suggested by Applied Biosystems (Darmstadt, Germany) were
noted to design an optimal primer set: an amplicon length between 50-150 bases to
reach the optimum of PCR efficiency, an optimal primer length of 20 bases, a Tm
ranging between 50°C and 60°C and a GC-content of 30% to 80%. The last five
nucleotides at the 3´-end contained no more than two G+C residues.
After deducing the optimal primer set from the regions of identity those primers were
checked with the primer software (Primer Designer version 2.2, Scientific and
Educational Software, Durham, USA) for hairpin structures and primer dimerization.
Primers which showed a strong possibility of self-complementarity or formation of
dimers were excluded. At last the specifity of each primer set was examined by
nucleotide-nucleotide BLAST (http://blast.ncbi.nlm.nih.gov/Blast.cgi).
Amplification of the mcrA gene with conventional PCR technique. Species-specific
primers for the mcrA gene (Table 8) were deduced which were located up- and
downstream of the target region recognized by the Q-PCR primer set.
PCR was carried out with a Biometra T gradient 96 (Whatman Biometra, Göttingen,
Germany) using a standard temperature profile: 5 min at 94°C; 10 cycles of 1 min
at 94°C, 1 min at 55°C and 1 min at 72°C; 25 cycles of 1 min at 94°C, 1 min at 52°C
and 1 min at 72°C (where the elongation step was continuously prolonged for 15 s
per cycle) and at 72°C for 10 min.
PCR mixtures were prepared as follows: 10 ng DNA-template, 1 × PCR-Buffer,
0.2 mmol l-1 of each dNTP, 3 mmol l-1 MgCl2, 0.2 µmol l-1 of each primer and
1 U Taq-Polymerase. The total reaction volume was 20 µl.
In case of missing sequence information of some methanogenic Archaea where no
species-specific primers could be evaluated, an universal mcrA primer set (Hales et
al. 1996) was used to amplify a 778 bp fragment of the mcrA gene. Therefore, the
following PCR protocol was used: initial denaturation at 94°C for 3 min followed by
35 cycles of 94°C for 45 s, 50°C for 45 s and 72°C for 1.5 min. The thermal extension
step was 72°C for 5 min. The reaction mixture of 20 µl contained 2 µl of 10 × PCR
buffer, 2 µl of dNTP (10 mM), 1.6 µl of MgCl2 (25 mM), 1 µl of the ME1 and
ME2 primer (10 µM), 10.4 µl of sterile water, 1 µl Taq DNA Polymerase (1 U/µl) and
1 µl of genomic DNA.
MATERIAL AND METHODS
61
After amplification of the mcrA fragments a 1.2% agarose gel electrophoresis was
carried out to check if the PCRs were effective. Then the PCR products were purified
with the QIAquick PCR Purification kit (Qiagen, Hilden, Germany).
Ligation, transformation and plasmid isolation of the mcrA region. For creating
plasmids for Q-PCR which consist of the pGEM-T vector and the species-specific
mcrA amplicon all working steps were conducted like previously described.
Amplification of the mcrA gene by quantitative PCR. All Q-PCR runs for analyzing the
mcrA gene depended on real-time PCR with SYBR Green I and melting curve
analysis. SYBR Green I is the most commonly used double-stranded DNA (dsDNA)
binding dye in Q-PCR because of its high affinity to dsDNA.
PCR amplifications were achieved using an ABI 7300 System (Applied Biosystems,
Darmstadt, Germany). The thermocycling consisted of an initial incubation at 95°C
for 10 min followed by 45-50 cycles of a denaturation at 95°C for 15 s, an annealing
step at 53°C (ME-set, MBAC-set) or 55°C (MMIC-set, MSaet-set) and an extension
at 72°C for 1 min. The annealing step was cancelled by the primer set of MSarc. All
PCR mixtures (25 µl) contained 12.5 µl of the SYBR Green PCR Master Mix,
1 µl (400 nM) of each primer, 8.5 µl distilled water and 2 µl of a defined amount of
plasmid DNA (102-108 copies per reaction). The characteristics of the deduced primer
sets for amplifying the mcrA fragment are listed Table 9. All PCRs were carried out in
triplicate.
The results of the PCR amplification were recorded and interpreted using the
7300 Real-Time PCR System Sequence Detection Software Version 1.3 (Applied
Biosystems, Darmstadt, Germany). The CT values were used to calculate and plot a
linear regression line. The quality parameters of the standard curve were judged with
the slope and the correlation coefficient (R2).
MATERIAL AND METHODS
62
Tab
le 8
Pri
mer
sets
for
am
plif
ication
of th
e m
crA
gene
used f
or
clo
nin
g.
F p
rim
er
= f
orw
ard
prim
er,
R p
rim
er
= r
evers
e p
rim
er
Tm =
me
ltin
g tem
pera
ture
GC
= g
uanid
ine a
nd c
yto
sin
e c
onte
nt
within
the d
educed p
rim
er
Pri
mer
Fu
ncti
on
Seq
uen
ce
Am
plico
n s
ize
Tm
GC
Mic
rob
ial ta
rget
Refe
ren
ces
[5´
→ 3
´][b
p]
[°C
][%
][N
CB
I accessio
n n
um
ber]
Mcrf
orf
w1
F p
rim
er
CA
CT
G A
CG
AT
AT
CC
T G
GA
TG
40
75
7.3
50
.0M
eth
anobacte
rium
form
icic
um
[A
F169245]
This
stu
dy
Mcrf
orr
ev6
R p
rim
er
AG
TT
A G
GA
CC
AC
GT
A G
TT
CG
57
.35
0.0
Meth
anobacte
rium
form
icic
um
[A
F169245]
This
stu
dy
Mcrb
oufw
4F
prim
er
CG
GT
A T
GG
AC
TA
CA
T C
AA
GG
36
85
7.3
50
.0M
eth
anoculle
us b
ourg
ensis
[A
Y196674]
This
stu
dy
Mcrb
oure
v5
R p
rim
er
CA
GC
T C
GC
CG
AT
AA
G A
CC
GT
61
.46
0.0
Meth
anoculle
us b
ourg
ensis
[A
Y196674]
This
stu
dy
Mcrc
onfw
1F
prim
er
GA
AG
C T
AC
AT
GT
CC
G G
CG
GT
46
86
1.4
60
.0M
eth
anosaeta
concili
i [X
16
932]
This
stu
dy
Mcrc
onre
v1
R p
rim
er
GT
AG
T T
GG
CG
CC
TC
T C
AG
CT
61
.46
0.0
Meth
anosaeta
concili
i [X
16
932]
This
stu
dy
Mcrb
arf
w1
F p
rim
er
TG
GA
A G
TC
AA
GT
TC
G C
AC
AG
16
43
57
.35
0.0
Meth
anosarc
ina b
ark
eri [
NC
00
73
55
]T
his
stu
dy
Mcrb
arr
ev3
R p
rim
er
AG
CA
G G
CA
CG
AA
CT
C T
CT
GA
59
.45
5.0
Meth
anosarc
ina b
ark
eri [
NC
00
73
55
]T
his
stu
dy
Mcrt
hefw
F p
rim
er
TT
AC
A C
AC
TT
GG
TG
G C
TG
GA
15
37
57
.35
0.0
Meth
anoth
erm
obacte
r th
erm
auto
trophic
us [
NC
00
09
16
]T
his
stu
dy
Mcrt
here
vR
prim
er
GA
AC
T C
TC
GC
AG
AG
C A
CC
TT
59
.45
5.0
Meth
anoth
erm
obacte
r th
erm
auto
trophic
us [
NC
00
09
16
]T
his
stu
dy
Mcrv
anfw
F p
rim
er
CT
CA
T A
CT
GT
GA
AG
G T
GA
CG
13
91
57
.35
0.0
Meth
anococcus v
annie
lii [
NC
00
96
34
]T
his
stu
dy
Mcrv
anre
vR
prim
er
CC
TG
C A
GC
GT
CG
AA
T T
CT
CT
59
.45
5.0
Meth
anococcus v
annie
lii [
NC
00
96
34
]T
his
stu
dy
Mcrm
arf
wF
prim
er
TC
CG
A G
AC
GA
CC
GA
A T
TC
TA
15
92
57
.35
0.0
Meth
anoculle
us m
arisnig
ri [
NC
00
90
51
]T
his
stu
dy
Mcrm
arr
ev
R p
rim
er
AT
GA
A C
TC
GC
GG
AT
G G
CA
CC
61
.46
0.0
Meth
anoculle
us m
arisnig
ri [
NC
00
90
51
]T
his
stu
dy
ME
1F
prim
er
GC
MA
T G
CA
RA
TH
GG
W A
TG
TC
77
85
5.9
46
.7H
ale
s e
t al. 1
996
ME
2R
prim
er
TC
AT
K G
CR
TA
GT
TD
G G
RT
AG
T5
5.6
42
.0H
ale
s e
t al. 1
996
MATERIAL AND METHODS
63
Tab
le 9
Pri
mer
sets
for
am
plif
ication
of th
e m
crA
gene
used
for
Q-P
CR
.
F p
rim
er
= f
orw
ard
prim
er,
R p
rim
er
= r
evers
e p
rim
er
Tm =
me
ltin
g tem
pera
ture
GC
= g
uanid
ine a
nd c
yto
sin
e c
onte
nt
within
the d
educed p
rim
er
Set
Pri
mer
Fu
ncti
on
Seq
uen
ce
Am
plico
n s
ize
Tm
GC
Targ
et
gro
up
Refe
ren
ces
[5´
→ 3
´][b
p]
[°C
][%
]
ME
ME
1F
prim
er
GC
MA
T G
CA
RA
TH
GG
W A
TG
TC
77
85
5.9
46
.7M
eth
anogen
sH
ale
s e
t al. 1
99
6
ME
2R
prim
er
TC
AT
K G
CR
TA
GT
TD
G G
RT
AG
T5
5.6
42
.0(e
xcept M
eth
anosaeta
ceae)
Hale
s e
t al. 1
996
MB
AC
Mbacfw
V2
F p
rim
er
TA
CA
T G
TC
WG
GT
GG
T G
TN
G3
73
55
.65
0.0
Meth
anobacte
riale
sT
his
stu
dy
Mbacre
vV
2R
prim
er
GT
CG
T A
AC
CR
TA
GA
A W
CC
NA
55
.34
5.0
This
stu
dy
MM
ICM
icrf
wV
1F
prim
er
AC
NA
T G
AT
GG
AN
GA
C C
AC
TT
NG
19
15
9.3
47
.7M
eth
anom
icro
bia
les
This
stu
dy
Mic
rrevV
1R
prim
er
CC
NC
A C
TG
GT
CC
TG
N A
G5
7.6
64
.7T
his
stu
dy
MS
aet
Saetf
wV
1F
prim
er
AG
CT
A C
AT
GT
CC
GG
N G
GN
GT
77
61
.46
0.0
Meth
anosaeta
ceae
This
stu
dy
Saetr
evV
1R
prim
er
GA
GA
A G
TC
NT
CC
AG
G A
CN
TC
GT
T6
2.4
52
.2T
his
stu
dy
MS
arc
MB
AR
KF
WF
prim
er
CT
AC
C A
GG
GC
GA
CG
A A
GG
T7
96
1.0
63
.2M
eth
anosarc
inaceae
This
stu
dy
MB
AR
KR
EV
R p
rim
er
CT
GG
T G
AC
CR
AC
GT
T C
AT
TG
C6
0.8
54
.8T
his
stu
dy
MATERIAL AND METHODS
64
Melting curve analysis of the mcrA amplicons. A melting curve analysis followed after
finishing the SYBR Green I real-time PCR to ensure a sufficient quality of the
amplified products. In environmental samples of high microbial diversity most primer
pairs produce unspecific products. These products greatly influence the results of the
Q-PCR. According to manufacturer’s guidelines (Applied Biosystems, Darmstadt,
Germany) the amplification from a specific Q-PCR product was displayed with
a Tm of > 82°C while primer-dimer structures had a characteristically lower
Tm of ~ 75°C.
The melting curve was generated by heating the sample to 95°C for 15 s followed by
cooling down to 60°C. Then the sample was slowly heated (0.1°C s-1) to 95°C while
the fluorescence was detected in 0.3°C intervals. An abrupt decrease of fluorescence
was observed at the melting temperature of the specific DNA fragment. The first
derivative of the rate of change in fluorescence was plotted against the temperature
to visualize the melting temperature peak of the fragment.
The melting points of all mcrA fragments were calculated automatically with the
7300 Real-Time PCR System Sequence Detection Software Version 1.3 (Applied
Biosystems, Darmstadt, Germany).
RESULTS
65
6. Results
6.1 Establishment and application of a Q-PCR assay for the detection of
methanogenic Archaea in biogas plants by the use of the 16S rRNA gene
6.1.1 Optimization of the PCR conditions of the group-specific 16S rRNA gene
assays for quantitative real-time PCR
A culture-independent approach for the detection of methanogenic Archaea in biogas
plants and reactors was determined. The quantitative real-time PCR is an effective
and highly valuable tool to describe the archaeal methanogenic diversity within an
environmental sample. In 2005, Yu et al. (2005a) investigated group-specific primer
and probe sets based on the 16S rRNA gene to describe the methanogenic
community structure in anaerobic processes and environmental samples. These
specific primer assays were established to have a good working tool for analysing the
biogas-producing microflora within biogas reactors and plants. Referring to the
diversity studies of the methanogenic community structure in biogas plants analyzed
by PCR-RFLP and clone library construction conducted by Nettmann (2009) the
primer and probe sets of the following taxonomic groups were optimized:
Methanobacteriales (MBT-set), Methanomicrobiales (MMB-set), Methanosarcinaceae
(Msc-set) and Methanosaetaceae (Mst-set). No optimization of the MCC-set
(Methanococcales) was carried out because most of the individuals of this taxonomic
group prefer more extreme habitats (hyperthermophilic, barophilic, psychrophilic
environments). Additionally, no OTUs were detected by PCR-RFLP analysis
combined with clone library construction (Nettmann 2009). As a sum parameter of all
Bacteria and Archaea which were present in the reactor sample, the primer sets of
BAC (Bacteria) and ARC (Archaea) were determined.
Firstly the universal primer and probe set of the domain Archaea was tested with the
suggested PCR mixture and thermocycling conditions as described by Yu et
al. (2005a). The CT values in correlation to the concentration of the defined standards
are shown in Fig. 6A.
RESULTS
66
Fig. 6 Standard curves of the primer sets (A) ARC-set with the applied PCR conditions according to Yu et al. (2005a) (open circles) and the suggested PCR mixture and thermocycling conditions of Applied Biosystems (Darmstadt, Germany) (filled circles), (B) BAC-set, (C) MMB-set, (D) MBT-set, (E) Msc-set and (F) Mst-set with the PCR conditions suggested by Applied Biosystems without (open circles) and with (filled circles) optimized annealing temperature generated by an analysis of the amplification of the 16S rRNA gene by a dilution series of the primer set specific plasmid (refer “MATERIALS AND METHODS”). Lines represent the linear regression of the respective standard curve. The mean values and standard deviation the 16S rRNA gene copy numbers were performed from triplicate within the same run. CT = Threshold cycle number.
As result, no optimal standard curve for quantifying 16S rRNA copy numbers in
reactor samples was generated. Because of the suboptimal amplification of the
16S rRNA gene a second PCR protocol was carried out according to manufacturer’s
conditions (Applied Biosystems, Darmstadt, Germany).
RESULTS
67
Here, the amplification of the 16S rRNA gene was optimal with a slope of 3.0 and a
corresponding PCR efficiency of 1.15 (“MATERIALS AND METHODS”). This protocol was
applied for all remaining primer sets. Due to different amplicon lengths of the
16S rRNA gene fragment an additional annealing step was included in the cycling
protocols for the BAC, MBT, Msc and Mst assays to obtain an optimal plasmid
standard curve (Fig. 6).
All optimized PCR protocols are listed in the “MATERIALS AND METHODS” section.
6.1.2 Influence of DNA isolation on Q-PCR-based quantification of methanogenic
Archaea in biogas fermenters
Nine different combined cell disruption and DNA purification techniques were tested
by using material taken from the same reactor sample. All important information
concerning the reactor sample and the different applied protocols (protocol A-I) are
given in the “MATERIALS AND METHODS” section. After DNA extraction and purification
quality parameters of the isolated DNA were determined followed by Q-PCR based
on the 16S rRNA gene for analysing the methanogenic community structure. The
aims of these experiments were: (i) to test currently available protocols for their
applicability to samples from biogas reactors, and, (ii) to analyze how the chosen
DNA extraction method influenced the data obtained concerning the diversity of
methanogens within the biogas reactor sample.
Quality parameters of the isolated DNA from the biogas reactor sample. The colour of
the DNA solutions obtained varied between the different DNA extraction protocols.
Visually clear solutions were obtained with all FastPrep DNA extraction methods
(protocols A-C), while the combined lysozyme and SDS-based cell disruption
protocols yielded yellow (protocols F and H) to pale yellow (protocols G and I)
solutions. DNA extraction according to protocol D resulted in a brown DNA solution.
However, the colour changed to yellow when the crude DNA was purified with
sephacryl columns (protocol E). All applied DNA extraction protocols yielded high
molecular gDNA (Fig. 7). No differences in DNA size were detected after gel
electrophoresis but some slight shearing of DNA was observed during the DNA
extraction of protocols A-C and F-I.
RESULTS
68
The DNA yield was quantified by fluorescence spectrometry (Table 10). The highest
amount of DNA (259.00 ± 18.30 µg mlreactor sample-1) was obtained with the SDS-based
cell lysis method (protocol D). All other DNA isolation protocols resulted in nearly the
same DNA concentrations (approx. 20.00 µg mlreactor sample-1). When the crude DNA
solution was subsequently purified by sephacryl columns the DNA yield decreased by
one log cycle.
The purity of the extracted DNA was estimated spectrophotometrically for all DNA
preparations except for the brown-yellow DNA solutions obtained by protocols D
and E.
Fig. 7 DNA preparations of a biogas reactor sample. The preparations were obtained by different DNA extraction protocols (A-I). From extraction protocols A-C and F-G 5 µl of originally extracted DNA solution were applied, whereas only 1 µl of undiluted DNA solution was loaded on the agarose gel of the DNA solutions D and E. M = DNA length standard (Lambda DNA/EcoRI+HindIII).
Here, the influence of high humic substance concentrations compromised the
photometrical determination (Weiß et al. 2007). The A260/A280 ratio of the DNA
solutions varied between 1.51 and 2.19, indicating a nearly sufficient removal of
protein contamination (Table 10). DNA extracted with the FastPrep System showed
slightly better A260/A280 values compared to those where the cell lysis was induced
with chemical and enzymatic techniques. However, a different picture was obtained
for the removal of phenol, carbohydrate and humic acid contaminations. DNA
solutions, where lysozyme and SDS were applied for cell disruption, showed only
small or even no contamination (A260/A230 ~ 1.8). In contrast, the A260/A230 values of
DNA preparations obtained by the FastPrep System (0.17 - 0.44) showed a huge
discrepancy to the optimal A260/A230 value as mentioned in literature (approx. 1.8).
M A B C D E F G H I Mbp
21226
1584
564
M A B C D E F G H I Mbp
21226
1584
564
RESULTS
69
A further purification of the crude DNA extracts by sephacryl columns resulted in only
minor improvements in the A260/A230 and A260/A280 values.
Table 10 Comparison of the analyzed DNA amounts and the tests for co-extraction of contaminants by using different DNA extraction protocols (A-I). The DNA amount was measured fluorometrically. The purity parameter concerning carbohydrate, phenol and aromatic compound contaminations was calculated by the ratio of the absorption at λ = 260 and λ = 230 (A260/A230). For verifying the isolated DNA for protein contamination, the ratio of the absorption at λ = 260 to λ = 280 was evaluated (A260/A280). The means, ± standard deviation, are given from three or four independent measurements, respectively. ND = not determined.
Applicability of purified DNA for conventional PCR. Independent of the applied
extraction protocol, all DNA preparations were accessible for PCR amplification of
bacterial 16S rRNA gene (Table 11). In some cases (protocols D, F, H) the PCR
amplification was only successful if the DNA template was diluted. These results
point to contamination of those DNA preparations with PCR inhibitors because the
undiluted DNA concentrations which were used, were almost equal to those of DNA
solutions, which showed amplification (e.g. 18.80 ± 3.87 µg mlreactor sample-1
(protocol C) and 12.50 ± 4.99 µg mlreactor sample-1 (protocol F)). However, a further
purification as applied in protocols E, G and I, resulted in DNA solutions accessible
for PCR amplification even for undiluted templates.
DNA yield A260/A280 A260/A230
(µg ml-1
)
A 041.80 ± 01.99 1.77 ± 0.14 0.17 ± 0.04
B 033.20 ± 03.76 2.19 ± 0.37 0.44 ± 0.15
C 018.80 ± 03.87 1.76 ± 0.11 0.30 ± 0.18
D 259.00 ± 18.30 ND ND
E 020.10 ± 23.80 ND ND
F 012.50 ± 04.99 1.51 ± 0.15 1.83 ± 0.74
G 007.14 ± 00.33 1.69 ± 0.04 1.72 ± 0.18
H 011.80 ± 03.27 1.63 ± 0.09 2.31 ± 0.57
I 007.81 ± 00.67 1.57 ± 0.11 1.67 ± 0.22
DNA amount and purity parametersProtocol
RESULTS
70
Table 11 PCR amplification of bacterial 16S rRNA gene using dilution series of DNA samples obtained by different extraction protocols (A-I) as templates. (+) successful amplification, (-) absence of amplicons of the 16S rRNA gene by using universal Bacteria primers (Toth et al. 2001, Weisburg et al. 1991). gDNA = genomic DNA.
Applicability of extracted DNA for quantitative real-time PCR. First, the characteristics
of Archaea-specific Q-PCR were determined using defined DNA templates
(Table 12). On the basis of the slopes of the resulting standard curves the full PCR
efficiencies were calculated. These efficiency values ranged from 0.785 to 1.088,
which indicated that the analysis of the detected 16S rRNA gene copy numbers of
the reactor samples was feasible. The stability indexes of the standard curves
corroborated this conclusion (R2 > 0.973).
Table 12 Parameters of the standard curves for 16S rRNA gene targeting Q-PCR. The following primer sets were used: ARC = universal Archaea primer set, BAC = universal Bacteria primer set, MBT = Methanobacteriales primer set, MMB = Methanomicrobiales primer set, Msc = Methanosarcinaceae primer set, Mst = Methanosaetaceae primer set.
Two different benchmarks were chosen to interpret the Q-PCR results. On the one
hand the detected 16S rRNA gene copy numbers were referred to one nanogram of
gDNA (analysis method 1), and on the other hand the number of 16S rRNA gene
copies were calculated in one millilitre of the reactor sample (analysis method 2).
Firstly, analysis method 1 was used for evaluating the Q-PCR data. The results of the
Archaea-specific Q-PCR using the different DNA preparations from a biogas reactor
sample as template are shown in Fig. 8.
BAC ARC MMB MBT Msc Mst
Slope (Average) -3.128 -3.686 -3.974 -3.400 -3.469 -3.226
Slope (R2) 00.984 00.980 00.982 00.984 00.973 00.993
Full efficiency 01.088 00.868 00.785 00.968 00.942 01.042
Intercept 39.360 46.971 51.009 39.710 42.331 35.844
Primer set
A B C D E F G H I
1 × 100 + + + - + - + - +
1 × 101 + + + + + + + + +
1 × 102 + + + + + + + + +
1 × 103 + + + + + + + + +
1 × 104 + + + + + + + + +
ProtocolDilution series of
gDNA
RESULTS
71
Fig. 8 16S rRNA gene copy numbers for Bacteria and methanogenic Archaea in the biogas fermentation as determined by Q-PCR. On the x-axis the applied DNA extraction methods are given while the y-axis reflects the 16S rRNA gene copy number, which was detected in 1 ng of genomic DNA (gDNA). Columns and error bars represent means and the standard deviation of 3 or 4 independent DNA samples, respectively. ND = not detected.
Initially, it should be noted that in most cases the highest number of 16S rRNA gene
copies was detected in DNA preparations resulting from extraction protocols F-I
(lysozyme and SDS-based cell lysis).
RESULTS
72
DNA preparations, which were acquired after mechanical cell lysis with ceramic and
silica particles (protocols A-C), showed significantly lower 16S rRNA gene copy
numbers with almost all applied primer sets. Comparable results were obtained by
the use of the SDS-based DNA isolation protocols D and E. In addition, the number
of detected 16S rRNA gene copies increased after purification of crude DNA with
sephacryl columns.
The detected 16S rRNA gene copy numbers resulting from a Q-PCR with the
universal Bacteria primer set ranged between 1.73 × 105 (protocol D) and 1.74 × 106
(protocol I) copies per nanogram gDNA. These 16S rRNA gene copy numbers were
approximately 10 to 100-fold higher than those determined by Archaea-specific
Q-PCR.
Regarding the 16S rRNA gene copy numbers by the use of family or order-specific
Q-PCR primers and probes, comparable 16S rRNA gene copy numbers in all DNA
solutions were observed with the Msc primer set (approx. ten 16S rRNA gene copies
per nanogram gDNA). With the MBT primer set, in DNA preparations with
SDS-based cell lysis (protocol D and E), the number of detected 16S rRNA gene
copies ranged between 200 and 400, while a 5-fold larger value was obtained in the
remaining DNA solutions. Surprisingly, in some cases (protocols D and E), the Mst
primer set did not result in sufficient Q-PCR amplification, in contrast to other DNA
preparations, where the Mst set revealed the presence of about 101 16S rRNA gene
copies per nanogram gDNA. The highest variability in detected copy numbers was
obtained with the MMB primer set. Only 102 16S rRNA gene copies per nanogram
gDNA were calculated in DNA preparations using the FastPrep System. In the case
of DNA purified after SDS-based cell disruption (protocol D and E), approximately
103 16S rRNA gene copies per nanogram gDNA were obtained in crude DNA
solution, while the number increased by one log after purification. The largest number
of MMB-specific 16S rRNA gene copies was detected in DNA preparations derived
from SDS and lysozyme lysed cells (protocols F-I).
A slightly different picture was obtained by the use of analysis method 2 (Fig. 9).
Here, the main differences could be detected between the DNA solutions obtained by
mechanical cell lysis and all other remaining DNA isolation methods (protocols D-I).
RESULTS
73
Fig. 9 16S rRNA gene copy numbers for Bacteria and methanogenic Archaea in the biogas fermentation as determined by Q-PCR. On the x-axis the applied DNA extraction methods are given while the y-axis reflects the 16S rRNA gene copy number, which was detected in 1 ml of the reactor sample. Columns and error bars represent means and the standard deviation of 3 or 4 independent DNA samples, respectively. ND = not detected.
For the Bacteria- and Archaea-specific primer set a 5-fold larger number of 16S rRNA
gene copies was calculated for all enzymatic and SDS-based cell lysis protocols
(protocols D-I) compared to those of the FastPrep-isolated ones (protocols A-C).
RESULTS
74
Contrary to analysis method 1, no differences were observed with the MBT primer set
in the detected number of 16S rRNA gene copies between DNA preparations with
SDS-based cell lysis (protocol D and E) and all remaining DNA suspensions.
Values from 1.17 × 108 to 2.90 × 108 gene copies mlreactor sample-1 were acquired with
the primer set of MMB in DNA solutions of the extraction protocols D-I, while only
2.64 × 106 to 9.43 × 106 gene copies mlreactor sample-1 were calculated for DNA
solutions with mechanical cell lysis (protocols A-C).
In accordance with analysis method 1 comparable numbers of the 16S rRNA gene
copies were reached in all DNA solutions which were analyzed with the order-specific
primer set of Methanosarcinaceae.
Furthermore, no differences of the obtained results of analysis method 1 and 2 could
be observed by using the Mst primer set. Interestingly, the subsequent purification of
DNA solutions with sephacryl columns had no influence on the detected number of
16S rRNA gene copies in all applied primer sets.
The relative percentages of group-specific 16S rRNA gene copy numbers for
methanogenic Euryarchaeota as determined by Q-PCR are given in Table 13. By
application of the DNA extraction protocols D-I, the most prevalent methanogenic
order of the probed CSTR seemed to be the order Methanomicrobiales (82-95% of
the total 16S rRNA gene copy number). The second largest group (4-17% of the total
16S rRNA gene copy number) was formed by members of the order
Methanobacteriales, while only small numbers of 16S rRNA gene copies were
detected for the families Methanosaetaceae and Methanosarcinaceae (each < 1% of
the total 16S rRNA gene copy number) of the order Methanosarcinales.
In contrast, the application of DNA preparations obtained by using the FastPrep
System (protocols A-C) resulted in totally different percentages of Q-PCR determined
16S rRNA gene copy numbers for the order Methanomicrobiales (MMB primer set)
and Methanobacteriales (MBT primer set). Hence, the ratio of MBT to MMB
16S rRNA gene copy numbers detected was exactly the opposite of that obtained
based on the other DNA preparation protocols.
RESULTS
75
Table 13 Taxonomic allocation of the methanogenic Archaea within a CSTR as determined by Q-PCR analyses. Percentages represent the ratio of the number of group-specific 16S rRNA gene copy numbers detected to the sum of all 16S rRNA gene copy numbers of the group-specific primer sets. Abbreviations of the used primer sets were according to Table 12. ND = not detected.
6.1.3 Accuracy of the real-time PCR assays and influence of PCR interfering
substances on Q-PCR-based quantification of methanogenic Archaea in biogas
fermenters
Different standard spiking approaches were applied to determine the DNA extraction
efficiency and the accuracy of the real-time PCR assays for analyzing reactor
samples from digesters and biogas plants.
Investigation of the DNA extraction efficiency by spike-and-recovery controls. To
estimate the loss of DNA during nucleic acid extraction, the reactor samples were
spiked with defined volumes of cells of Methanosarcina barkeri and Methanoculleus
bourgensis, respectively, before cell lysis (cf. “MATERIALS AND METHODS” section).
Usually an appropriate surrogate for spike-and-recovery controls has to be absent
from the native sample. However, for ensuring that the lysis of the supplement has an
equal effectiveness compared to target cells and that it contains DNA that is
extracted and recovered with an efficiency equivalent to that of the targeted cells,
strains of methanogenic Archaea which can also be found in the reactor sample were
chosen.
A summary of the validation of DNA extraction of the real-time PCR assays ARC,
MMB and Msc is shown in Table 14. The recovery of DNA was calculated by the
comparison between the theoretical and estimated copy numbers of the target.
A B C D E F G H I
MMB 15% 9% 18% 82% 95% 91% 95% 88% 92%
MBT 84% 90% 81% 17% 04% 08% 04% 11% 07%
Mst <1% <1% <1% ND ND <1% <1% <1% <1%
Msc <1% <1% <1% <1% <1% <1% <1% <1% <1%
ProtocolPrimer set
RESULTS
76
As prerequisites for the evaluation of the theoretical copy number the following
assumptions were established: (1) the number of isolated 16S rRNA gene copies of
the reactor sample is comparable in all samples, (2) the added cell suspension of the
methanogenic culture was uniformly distributed in the reactor sample, (3) the lysis
efficiency of the added cell suspension was assumed as one and (4) the number of
detectable 16S rRNA gene copy numbers in one genome is three for Methanosarcina
barkeri [NC007355.1] and one for Methanoculleus bourgensis [NC009051.1].
Correlating with higher amounts of cells used for spiking, an increased number of
16S rRNA gene copies was recovered with the primer sets ARC and MMB. With the
primer set ARC recovery rates were most often higher than the theoretical number
and were up to 2.23. A comparable result was observed with the primer set MMB.
Here, the NE/NT values were in the range between 0.96 and 4.56. The recovery of
16S rRNA gene copy numbers with the primer set Msc was lower compared to the
other primer sets and estimated to be between 0.61 and 0.89, resulting in an
underestimation of the participating Methanosarcinaceae within the biogas reactor.
Table 14 Summary of the validation of DNA extraction. The following primer sets were used: ARC = universal Archaea primer set, MMB = Methanomicrobiales primer set and Msc = Methanosarcinaceae primer set. NT = theoretical number of detected gene copies, CT = threshold cycle number, SD = standard deviation, NE = estimated number of detected gene copies; NE/NT = recovery rate of the 16S rRNA gene copy numbers.
Mean SD Mean SD
ARC 314857 22.90 0.34 0701730 130960 2.23
198697 24.95 0.13 0213164 015294 1.07
188665 25.87 0.11 0125159 007546 0.66
MMB 346237 19.39 0.15 1580000 132754 4.56
121437 22.25 0.00 0309827 000191 2.55
094637 24.41 0.22 0091297 010769 0.96
090685 23.94 0.36 0120442 025488 1.01
Msc 097356 25.53 0.33 0087129 015605 0.89
012396 29.78 0.11 0007605 000464 0.61
002364 32.05 0.14 0002070 000172 0.88
Primer set NT NE/NT
C T NE
RESULTS
77
Evaluation of the real-time PCR assays with the different primer sets. The accuracy
of the real-time PCR assays of the primer sets ARC, MMB and Msc was validated by
quantifying known numbers of the 16S rRNA gene added into the DNA solution of the
reactor sample. The number of 16S rRNA gene copies in the non-spiked DNA
solution of the reactor sample was determined by the use of the plasmid standard
curve. The obtained value of the 16S rRNA gene copies was used as the theoretical
background of the spiked plasmid standard curve.
If the PCR assays worked precisely and the DNA solutions did not have significant
inhibition in each of the three tested PCR assays, the following results could be
expected: Firstly, the CT values of the spiked plasmid standard curve which lie in the
range of the CT value of the pure sample have to decrease compared to the
non-spiked plasmid standard curve because these values are influenced directly by
the number of the 16S rRNA gene copies of the reactor sample.
On the one hand all CT values of the spiked plasmid standard curve which are 1.5 log
units above the CT value of the pure sample have to correspond with the CT values of
the pure sample because the added number of 16S rRNA gene copies of the plasmid
standard curve is too low for a direct influence.
On the other hand it can be assumed that all spiked samples which are 1.5 log units
below the CT value of the reactor sample have to correlate with the CT value of the
pure plasmid standard curve because of the neglecting number of 16S rRNA gene
copies of the reactor sample.
The previously described behaviour of an accurate real-time PCR assay was
observed with all three tested primer sets (Fig. 10).
For the ARC-set 1.87 × 105 16S rRNA gene copy numbers per reaction were
detected in the DNA solution of the non-spiked reactor sample. The samples which
were spiked with 105-107 16S rRNA gene copies showed a decreased CT value
compared to those of the pure plasmid standard curve meaning that the number of
added 16S rRNA gene copies influenced the CT value directly. No effect was
observed in samples which were spiked with 101-104 and 108 16S rRNA gene copies,
respectively.
RESULTS
78
Fig. 10 Comparison of spiked (open circles) and non-spiked (filled circles) standard curves of the primer sets (A) ARC, (B) MMB and (C) Msc by an analysis of the amplification of the 16S rRNA gene by a dilution series of the primer set specific plasmid (see “MATERIALS AND METHODS”). Fine lines represent the linear regression of the respective standard curve while bold lines represent the detected CT value of the non-spiked reactor sample. The mean values and standard deviation of the 16S rRNA gene copy numbers were performed from triplicate within the same run. CT = Threshold cycle number.
Samples with added plasmid standard concentrations of 101-104 showed comparable
CT values to the pure reactor sample while the CT values of samples which were
spiked with 108 16S rRNA gene copies correlated with those of the non-spiked
plasmid standard curve.
RESULTS
79
The detected 16S rRNA gene copy number resulting from Q-PCR with the MMB-set
ranged between 7.32 × 104 and 1.02 × 105 copies per reaction. Consequently the
influenced CT values of the spiked plasmid standard curve were in a range of 104-106
16S rRNA gene copy numbers per reaction. For the Msc-set decreased CT values
were obtained for samples with 103-105 16S rRNA gene copy numbers per reaction
(Mscreactor sample = 1.36 × 103 ± 3.65 × 102).
The influence of interfering substances on the measurements was investigated by
another spiking experiment. Therefore, respective 16S rRNA gene copy numbers
were quantified by Q-PCR in the DNA sample of day 35 (see “MATERIALS AND
METHODS” section) in presence (or absence) of varying amounts of added reference
DNA (dilutions of the standard series, SSD). SSDs were increased from 101 to 108
copy numbers (SSD1-SSD8), respectively. The six specific standard curves reaction
parameters are given in Table 15.
Table 15 Quantitative real-time PCR (Q-PCR) reaction parameters of the standard curves used for the second spiking experiment. The following primer sets were used: ARC = universal Archaea primer set, BAC = universal Bacteria primer set, MBT = Methanobacteriales primer set, MMB = Methanomicrobiales primer set, Msc = Methanosarcinaceae primer set, Mst=Methanosaetaceae primer set.
The resulting ratios of the detected group-specific 16S rRNA gene copies in the
sample of total microbial DNA of day 35, spiked with the SSDs to those of the SSDs
without sample DNA, were calculated (Table 16). Ratios equal or close to one
indicate the absence of any inhibition effects. Ratios of 16S rRNA gene copies of
Bacteria are close to one, meaning that quantification was not influenced by addition
of 101-108 copies of different DNA. Important influences of Archaea and
Methanomicrobiales 16S rRNA gene copies were recorded when spiked with
SSD4-SSD8 and SSD6-SSD8 leading to ratios up to 16 and 9, respectively.
BAC ARC MMB MBT Msc Mst
Slope (Average) -3.420 -3.720 -3.800 -3.690 -3.880 -3.760
Slope (R2) 00.970 00.986 00.990 00.990 00.989 00.990
Full efficiency 00.961 00.857 00.833 00.868 00.845 00.810
Intercept 44.430 49.160 51.930 45.350 46.990 47.260
Primer set
RESULTS
80
No inhibitory effect on Q-PCR analyses was determined when the DNA sample of
day 35 was spiked with Methanobacteriales 16S rRNA gene copies. Regarding the
analysis of Methanosaetaceae, the appraised value of 16S rRNA gene copy numbers
in samples which were spiked with SSD1-SSD2 decreased by one log cycle
compared to those which were obtained in the pure sample. No influence of the DNA
sample background was observed in samples spiked with SSD3-SSD7.
Table 16 Ratios of 16S rRNA gene copy numbers determined by group-specific quantitative real-time PCR (Q-PCR) using total microbial DNA derived from the biogas reactor sample of day 35 spiked with a standard DNA dilution series (SSD) and the SSD without addition of foreign DNA, respectively, as templates. SSD is given as number of added 16S rRNA gene copies per 1 ng DNA sample.
Further, it can be seen from Table 16 that the Methanosarcinaceae copy number in
the mixture with SSD1-SSD2 was found to be one-tenth of that of the pure sample.
When the respective 16S rRNA gene concentration of the applied SSDs was higher
than that of the DNA sample, the detected 16S rRNA gene copies were in the range
of the added SSDs of Methanosarcinaceae.
Comparison of efficiencies of Q-PCR using the plasmid standards or the
reactor-derived DNA as templates. To know more about the reaction efficiency of the
reactor sample a dilution series of the genomic DNA solution was prepared (see
“MATERIALS AND METHODS” section). After a successful Q-PCR run the slope of the
regression line from the dilution series of the reactor sample was compared to the
one of the plasmid standard curve.
As it can be seen in Fig. 11, the regression lines of the reactor sample and the
plasmid standard run parallel by the use of the ARC-set and the MMB-set, meaning
that comparable slopes with their corresponding PCR efficiencies were obtained
(Table 17).
101
102
103
104
105
106
107
108
BAC 104016 8918 1129 98 22 02 01 1
ARC 002405 0343 0052 11 10 13 16 7
MBT 000451 0061 0003 01 01 01 01 2
MMB 000047 0003 0001 01 01 02 03 9
Mst 000083 0009 0001 01 01 02 01 6
Msc 000002 0002 0001 01 01 01 01 1
Ratio of 16S rRNA gene copy numbers [
Primer setSSD
]sample + SSD
SSD
RESULTS
81
Fig. 11 Comparison of the dilution series of the reactor sample (open circles) and the plasmid standard curves (filled circles) using the primer sets (A) ARC, (B) MMB and (C) Msc by an analysis of the amplification of the 16S rRNA gene (see “MATERIALS AND METHODS”). Lines represent the linear regression of the respective standard curve and the dilution series of the reactor sample, respectively. The mean values and standard deviation of the 16S rRNA gene copy numbers were performed from triplicates within the same run. CT = Threshold cycle number.
In contrast to these results, the regression line of the reactor sample varied
significantly from the calibration curve of the plasmid standard by amplifying the
family-specific 16S rRNA gene of Methanosarcinaceae. Here, the PCR efficiency of
the reactor sample decreased by a value of nearly 20% in comparison to the plasmid
standard. The sharp decline of the regression line of the reactor sample indicated
that the background of the reactor sample negatively influenced the Q-PCR run by
the application of the Msc-set.
RESULTS
82
Table 17 Parameters of the dilution series of the reactor sample (gDNARS) and the plasmid standard curves (Plasmid) for 16S rRNA gene targeting Q-PCR. The following primer sets were used: ARC = universal Archaea primer set, MMB = Methanomicrobiales primer set, Msc = Methanosarcinaceae primer set. gDNARS = genomic DNA of the reactor sample.
In all primer sets the calculated regression lines for both PCR-template types were
stable because the coefficient of determination (R2) ranged between 0.972
and 1.000.
Comparison of two different standards used for absolute quantification by Q-PCR.
Standards for absolute quantification are based on known concentrations of DNA
standard molecules. Four main possibilities are known for generating solid standard
curves to produce reproducible, high specific Q-PCR data. As templates recombinant
DNA (plasmids), genomic DNA, purified PCR products or RT-PCR products are
used.
In this study, the standards of recombinant DNA and genomic DNA were compared
for determining their applicability for absolute quantification of methanogenic Archaea
in biogas fermenters. Therefore, the PCR assays of the group-specific primer sets for
Archaea (ARC-set), Methanomicrobiales (MMB-set) and Methanosarcinaceae
(Msc-set) were used. Both kinds of standards have their advantages and
disadvantages. The precise knowledge of the concentration and fragment length, the
easily prepared high yields and the stability of the plasmid standard are indicative of
choosing recombinant DNA as standard whereas the process of standard production
is very time-consuming (cloning, transformation, plasmid isolation, linearization of the
plasmids). Opposing to this, the genomic DNA standard is time-saving developed. A
second advantage of the genomic DNA standard is that the matrix of the standard is
comparable to those of the reactor sample.
(Plasmid) (gDNARS) (Plasmid) (gDNARS) (Plasmid) (gDNARS)
Slope (Average) -3.984 -3.888 -3.874 -3.675 -3.911 -4.718
Slope (R2) 00.993 00.972 00.994 01.000 00.998 00.996
Full efficiency 00.782 00.808 00.812 00.871 00.802 00.629
Intercept 46.216 37.589 43.904 35.491 44.991 46.635
Primer set
ARC MMB Msc
RESULTS
83
The disadvantages of this standard can be seen in the stability of the genomic DNA
and the difficulties in optimal primer binding during a Q-PCR run. Furthermore, the
genome size of the applied standard has to be known.
For determining the two different standards, parameters of the standard curves were
compared. For the ARC-set, nearly identical PCR efficiencies of 0.782 and 0.855 for
the plasmid standard and the genomic DNA standard, respectively, were derived
from the slopes (Table 18). The PCR efficiencies which were calculated for the
calibration curves of the MMB-set differed by a value of 0.233 and both are in the
range for obtaining reliable Q-PCR results (see “MATERIALS AND METHODS” section).
Table 18 Parameters of genomic DNA standard curves (gDNAC) and the plasmid standard curves (Plasmid) for 16S rRNA gene targeting Q-PCR. The following primer sets were used: ARC = universal Archaea primer set, MMB = Methanomicrobiales primer set, Msc = Methanosarcinaceae primer set. gDNAC = genomic DNA of the methanogenic culture.
A different picture was obtained evaluating the standard curves of the Msc-set. A
solid standard curve was observed with the plasmid standard while the calibration
curve of the genomic DNA standard showed an inefficient amplification of the
16S rRNA gene with the genomic DNA standard (plasmid standard, full
efficiency = 0.802; genomic DNA standard, full efficiency = 0.557). By comparing
both kinds of standard curves in each of the primer sets the CT values of the genomic
DNA standard curves were significantly higher than those of the plasmid standard
(Fig. 12). This indicates that the Q-PCR runs with the genomic DNA standard showed
a delayed reaction by contrast with the plasmid standard.
(Plasmid) (gDNAC) (Plasmid) (gDNAC) (Plasmid) (gDNAC)
Slope (Average) -3.984 -3.726 -3.874 -3.218 -3.911 -5.203
Slope (R2) 00.993 00.989 00.994 00.973 00.998 00.997
Full efficiency 00.782 00.855 00.812 01.045 00.802 00.557
Intercept 46.216 51.435 43.904 46.192 44.991 66.871
Primer set
ARC MMB Msc
RESULTS
84
Fig. 12 Comparison of the standard curves using genomic DNA of methanogenic cultures (open circles) and plasmids (filled circles) as DNA template. The following primer sets were applied for an analysis of the amplification of the 16S rRNA gene: (A) ARC, (B) MMB and (C) Msc (see “MATERIALS
AND METHODS”). Lines represent the linear regression of the respective standard curve and the dilution series of the reactor sample, respectively. The mean values and standard deviation of the 16S rRNA gene copy numbers were performed from triplicate within the same run. CT = Threshold cycle number.
6.1.4 Application of the 16S rRNA gene real-time PCR assays for analyzing the
composition and development of the methanogenic Archaea in meso- and
thermophilic biogas reactors
Based on the optimized Q-PCR assays, evaluations of the methanogenic Archaea
were conducted concerning their abundances and development in biogas reactors
and biogas plants.
RESULTS
85
Initially, the Q-PCR assays were used to determine the methanogenic population
dynamics within a mesophilic CSTR during semi-continuous biogas fermentation by
overloading in a short-run analysis (operational time = 10 weeks).
Hence, reactor samples from long-term laboratory-scale experiments (operational
time = 51 weeks) were examined for investigating the effect of increased organic
loading rates (OLRs) on the development of the composition of methanogens under
mesophilic and thermophilic conditions. Furthermore, the influence of different
substrates (maize silage, food beet silage, cattle slurry) on the methanogenic
population structure was determined.
Conclusively, the Q-PCR assays were used to describe the quantitative composition
of the methanogenic Archaea in ten biogas plants varying in the supplement of liquid
manures and renewable raw materials as substrates.
Determination of methanogenic population dynamics during semi-continuous biogas
fermentation by overloading. The main objective was to address the influence of
acidification due to increase of OLRs on the different groups of methanogenic
microorganisms present in a laboratory scale biogas reactor. Initially, results of the
continuous short-run experiment are described. Here, the acidification of the biogas
reactor was reached at day 67.
At first the reactor performance was regarded. Biogas as well as methane production
showed a steady increase reaching a maximum of 3.6 and 1.8 lN l-1 digester volume
per day at an OLR of 4.1 g DOM l-1 day-1. Biogas and methane yields related to the
amount of DOM per current feed reached maximal values of 0.73 and
0.37 lN g-1 DOM day-1 at an OLR of 2.4 g DOM l-1 day-1 (Fig. 13). After reaching a
critical OLR of about 7.5 g DOM l-1 day-1 on day 59, the biogas yield dropped
dramatically, suggesting digester’s imbalance. Overloading was continued until gas
production rates had fallen below 15 lN biogas per day on day 63, at a final OLR of
9.5 g DOM l-1 day-1. On the last day, 0.8 lN biogas per day was produced.
Disregarding digester’s overload, and by this, the incomplete degradation of the final
feed charges, biogas production amounted to 0.39 lN biogas including
0.18 lN methane per gram DOM.
RESULTS
86
Fig. 13 Gas production and feeding rate during the fermentation. Symbols represent the daily gas yield (filled circles), the daily methane yield (plus) and organic loading rate (filled triangles) applied each day.
At day 42, OLR was 3.7 g DOM l-1 day-1 resulting in a retention time (RT) of lower
than 17 days. From that day onwards, RTs of the feed charges exceeded the time in
which digester performance and methane yield will have remained stable. The
methane content of the biogas was almost constant during the first 50 days of the
experiment, ranging from initial 55 to 48%. Then, it decreased substantially to 36%
on day 60, before dropping to almost zero.
Results of the analyses of organic acids and the pH are given in Fig. 14A and 14B.
As long as OLR was ≤ 4.5 g DOM l-1 day-1, total acid concentration remained below
0.2 g l-1. From day 49 onwards, when OLRs exceeded 4.5 g DOM l-1 day-1, total acid
concentration increased from ≤ 0.2 to 1.5 g l-1 and to final 17.6 g l-1 on day 66.
Thereof, propionic and acetic acid concentrations were 1.2 and 0.3 g l-1 on day 49
and 6.1 and 8.0 g l-1 at the end of the experiment, respectively. The concentration of
propionic acid increased earlier than that of acetic acid. The pH was stable for the
first 8 weeks (7.8-7.4) and then decreased to 5.6 within one week, reaching a final
value of 5.4.
RESULTS
87
Fig. 14 Chemical composition of the process fluid of the fermentation determined by gas chromatography and pH measurements. Symbols represent (A) the total organic acid concentration (filled circles) and pH value (filled rectangles); (B) the acetic acid concentration (plus), the propionic acid concentration (filled triangles) and the propionic to acetic acid ratio (filled squares).
Already as OLRs of 3.7 and 4.5 g DOM l-1 day-1 were applied from days 42 and 49
on, the propionic to acetic acid ratio increased from almost zero to about 0.15
and 3.50, respectively (Fig. 14B). A maximum ratio of 13.34 was reached after
providing 4.5 g DOM maize silage l-1 day-1 for one more week. Subsequently, it
dropped to nearly 7% of the maximum within 7 days. It is apparent from Fig. 14A, B
that the propionic to acetic acid ratio increased before the pH dropped dramatically.
RESULTS
88
After the detailed characterization of the physical and chemical parameters of the
laboratory-scale biogas fermenter during a semi-continuous biogas fermentation by
overloading, Q-PCR results on the abundances of the methanogenic Archaea were
considered.
Reaction parameters of all Q-PCR curves lie in the ranges that were generally
assumed to produce reliable results (Table 19).
Table 19 Parameters of the standard curves for Q-PCR used for estimating the 16S rRNA gene copy numbers in reactor samples during semi-continuous biogas fermentation and acidification by overloading in a short-run experiment. The following primer sets were used: ARC = universal Archaea primer set, BAC = universal Bacteria primer set, MBT = Methanobacteriales primer set, MMB = Methanomicrobiales primer set, Msc = Methanosarcinaceae primer set, Mst = Methanosaetaceae primer set.
The limit of detection ranged between 101 and 102 16S rRNA gene copies for all
applied primer sets. During the first 5 weeks when OLRs were raised to
3.3 g DOM l-1 day-1, 16S rRNA gene copy numbers of all primer sets increased
(Fig. 15). Bacteria and Archaea remained at levels of about 1010
and 107 copies mlreactor sample-1, respectively. Thus, Archaea 16S rRNA gene copies
constituted only a diminutive percentage of the overall 16S rRNA gene copies
determined in the samples (0.1-1.2%). Simultaneously, Methanobacteriales
16S rRNA gene copy numbers increased to final 108 copies mlreactor sample-1. After
OLRs had been shifted to 3.7 g DOM l-1 day-1, Methanobacteriales copy numbers
were the highest to be found among the methanogens. No or few
Methanomicrobiales copy numbers (≤ 106) compared to those of the
Methanobacteriales were detected at low OLRs.
BAC ARC MMB MBT Msc Mst
Slope (Average) -3.260 -3.890 -3.720 -3.380 -3.930 -3.770
Slope (R2) 00.986 00.991 00.995 00.968 00.982 00.993
Full efficiency 01.025 00.808 00.859 00.978 00.797 00.841
Intercept 42.870 49.340 49.420 42.280 47.770 47.900
Primer set
RESULTS
89
Fig. 15 Methanogenic population dynamics determined by Q-PCR of 16S rRNA gene copy numbers for Bacteria and methanogenic Archaea during biogas fermentation and acidification by overloading. On the x-axis the day of sampling is given while the y-axis reflects the 16S rRNA gene copy number, which was detected in 1 ml of the reactor sample. Columns and error bars represent means and the standard deviation of 3 independent DNA samples. ND = not detected.
RESULTS
90
Slightly higher concentrations were found when very high OLRs were applied.
Methanosaetaceae increased up to about 107 16S rRNA copies mlreactor sample-1
throughout the initial 5 weeks and completely vanished from the digester after
OLRs ≥ 4.5 g DOM l-1 day-1 were applied from day 49 onwards. This coincided with
the first time the acetate and propionate concentration increased noticeably.
Additionally, Methanosarcinaceae were detected in low copy numbers only on day 7
and 35 (OLR of 1.8 and 3.3 g DOM l-1 day-1, respectively).
The influence of acidification on the abundances of the taxonomic groups of
methanogenic Archaea was analyzed in a second approach.
Continuous long-term experiments – up to an operational time of 51 weeks – were
carried out in laboratory-scale biogas reactors with an increasing feed supply of
maize silage under mesophilic and thermophilic conditions. All sampled biogas
reactors were part of a project funded by the Agency of Renewable Resources (FNR,
grant 22011402). Detailed information concerning the biogas reactor performances
and the kinetics of biogas production are published in Mähnert 2007.
For Q-PCR analysis samples were taken at four different organic loading rate stages
(start-up phase, moderate OLR of approx. 2 kg m-3 d-1, increased OLR of
approx. 3 kg m-3 d-1, acidification after overloading). First, the results of the
mesophilic laboratory-scale digester were evaluated.
A comparison of the parameters of the standard curves, used to calculate the
detected 16S rRNA gene copy numbers in the samples, showed that all plasmid
standard curves were suitable for absolute quantification (Table 20, see “MATERIALS
AND METHODS” section).
Table 20 Q-PCR reaction parameters of the standard curves used for estimating the 16S rRNA gene copy numbers in reactor samples during semi-continuous biogas fermentation and acidification by overloading in a long-term experiment. The following primer sets were used: ARC = universal Archaea primer set, BAC = universal Bacteria primer set, MBT = Methanobacteriales primer set, MMB = Methanomicrobiales primer set, Msc = Methanosarcinaceae primer set, Mst = Methanosaetaceae primer set.
BAC ARC MMB MBT Msc Mst
Slope (Average) -3.460 -3.660 -3.600 -3.440 -3.410 -3.780
Slope (R2) 00.970 00.980 00.960 00.990 00.980 00.990
Full efficiency 00.945 00.880 00.896 00.953 00.965 00.839
Intercept 47.560 44.390 53.200 40.290 41.680 45.510
Primer set
RESULTS
91
Comparable 16S rRNA gene copy numbers were observed from the start phase to an
OLR of 2.7 kg m-3 d-1 with the BAC-set (3.31 × 1012 to 8.89 × 1012 gene copies
mlreactor sample-1) (Fig. 16). During acidification the number of detected gene copies
decreased by two log cycles. A nearly similar picture was obtained with the ARC-set.
Here, the number of detected gene copies varied between 8.07 × 108 and 5.69 × 109
gene copies mlreactor sample-1 in the first three samples while only 2.81 × 105 gene
copies mlreactor sample-1 were calculated for the overloaded stage.
Turning to the group-specific methanogenic primer sets, the highest number of
Methanomicrobiales-related 16S rRNA gene copies was determined during the start
phase (1.12 × 109 gene copies mlreactor sample-1). From this time on the abundances of
detected 16S rRNA gene copy numbers decreased continuously. At the end of the
fermentation process the number of gene copies was out of the detection limit. As
opposed to this, 16S rRNA gene copies of the order Methanobacteriales were
observed in all four sampling stages. Up to an OLR of 2.7 kg m-3 d-1 the number of
detectable 16S rRNA gene copy numbers was almost identical (approx. 107 gene
copies mlreactor sample-1). A value of 1.59 × 105 gene copies mlreactor sample
-1 was obtained
for the acidification stage.
Representatives of the family Methanosaetaceae were only detected during the start
phase while Methanosarcinaceae-specific 16S rRNA gene copy numbers were
evaluated with a continuous 10-fold decrease in every sample stage from the
beginning until the end of the fermentation.
Statistic analysis was used to compare alterations in the 16S rRNA gene copy
numbers of the hydrogenotrophic (MMB-set, MBT-set) and acetotrophic (Mst-set,
Msc-set) methanogens. The ratio of hydrogenotrophic to acetotrophic methanogens
increased constantly from the start to the acidification stage (0.60-2.26), meaning that
the composition of the methanogens shifted from an acetotrophic dominated
community structure to a hydrogenotrophic one. Besides the ratio from
hydrogenotrophic to acetotrophic methanogens, the Archaea to Bacteria ratio was
calculated. Herein, a slightly decrease of the Archaea to Bacteria ratio was observed
during ongoing fermentation.
RESULTS
92
Fig. 16 Quantification of the 16S rRNA gene copy numbers for Bacteria and methanogenic Archaea during biogas fermentation with organic loading rates (OLR) of (A) < 2.0 kg m
-3 d
-1, (B) 2.0 kg m
-3 d
-1,
(C) 2.7 kg m-3
d-1
and (D) 4.2 kg m-3
d-1
at mesophilic conditions. On the x-axis the used primer sets are given while the y-axis reflects the 16S rRNA gene copy number, which was detected in 1 ml of the reactor sample. The following primer sets were used: ARC = universal Archaea primer set, BAC = universal Bacteria primer set, MBT = Methanobacteriales primer set, MMB = Methanomicrobiales primer set, Msc = Methanosarcinaceae primer set, Mst = Methanosaetaceae primer set. Columns and error bars represent means and the standard deviation of 3 independent DNA samples, respectively. ND = not detected.
After careful consideration of the development of the methanogenic Archaea
community composition by an OLR increase under mesophilic conditions, results of
the thermophilic biogas reactors were evaluated.
Regarding the 16S rRNA gene copy numbers by the BAC- and ARC-set, comparable
16S rRNA gene copy numbers were determined in all OLR treatment stages (approx.
BAC-set = 1012 gene copies mlreactor sample-1, ARC-set = 108 gene copies
mlreactor sample-1) (Fig. 17).
RESULTS
93
At the start-up phase of the fermentation the Methanomicrobiales population was the
most abundant Archaea order while the increase of maize silage in the feedstock led
to reduction of this methanogenic group. During the continuous increase of OLR the
Methanomicrobiales were not present at detectable values after week 44. A
near-constant value of Methanobacteriales-specific 16S rRNA gene copies was
calculated throughout the whole experiment. The highest abundance was found at an
OLR of 3.0 kg m-3 d-1 (3.04 × 108 gene copies mlreactor sample-1).
Fig. 17 Quantification of the 16S rRNA gene copy numbers for Bacteria and methanogenic Archaea during biogas fermentation with organic loading rates (OLR) of (A) < 2.0 kg m
-3 d
-1, (B) 2.2 kg m
-3 d
-1,
(C) 3.0 kg m-3
d-1
and (D) 3.3 kg m-3
d-1
at thermophilic conditions. On the x-axis the used primer sets are given while the y-axis reflects the 16S rRNA gene copy number, which was detected in 1 ml of the reactor sample. The following primer sets were used: ARC = universal Archaea primer set, BAC = universal Bacteria primer set, MBT = Methanobacteriales primer set, MMB = Methanomicrobiales primer set, Msc = Methanosarcinaceae primer set, Mst = Methanosaetaceae primer set. Columns and error bars represent means and the standard deviation of 3 independent DNA samples, respectively. ND = not detected.
RESULTS
94
The strictly acetotrophic methanogens of Methanosaetaceae were only detected at
the start stage of the biogas-building process. The highest variability in the numbers
of 16S rRNA gene copies was obtained with the Msc-set. First, the number of gene
copies showed a decrease from the start phase to an OLR of 2.2 kg m-3 d-1. From
this time on the number of detected 16S rRNA gene copies increased continuously.
Regarding the ratio of hydrogenotrophic to acetotrophic methanogens, a dominance
of the participating hydrogenotrophs was observed in all sampling stages. The
Archaea to Bacteria ratio decreased slowly from 5 × 10-3 to 1 × 10-3.
By comparison of the two applied temperature regimes (mesophilic, thermophilic) the
following results were achieved. With the BAC-set the calculated number of
16S rRNA gene copies in the thermophilic reactors was almost as high as the
obtained values under mesophilic conditions while more archaeal copy numbers
were detected in the thermophilic reactors compared to the mesophilic ones. A
decrease of the Methanomicrobiales population was observed during the operational
time for both temperature regimes of the biogas digesters whereby the number of
detected copies dropped faster in the thermophilic CSTRs. Nearly comparable values
of detected gene copies were obtained by the use of the MBT-set. Only in the
acidification stage the number of detected gene copies varied between 1.26 × 108
and 4.05 × 105 gene copies mlreactor sample-1 for the thermophilic and mesophilic
digesters, respectively. No differences were observed for the abundances of
Methanosaetaceae-specific 16S rRNA gene copies. Representatives of the family
Methanosarcinaceae showed contrasting growth tendencies to the applied
temperature regime. A continuous decrease of the Methanosarcinaceae population
was observed under mesophilic conditions while an increase of detectable 16S rRNA
gene copies was determined in thermophilic biogas reactors.
The ratio of hydrogenotrophic to acetotrophic methanogens differed strongly under
the applied working temperatures of the digesters. A shift from an acetotrophic to a
hydrogenotrophic dominated population structure was detected in the mesophilic
CSTR whereas under thermophilic conditions the hydrogenotrophic methanogens
always represented the process dominating group.
RESULTS
95
Determination of the methanogenic population structure in semi-continuous biogas
fermentation by the use of different substrates. The influence of different substrates
on the composition of methanogenic Archaea was examined by the following
experiments. Reactor samples were collected from laboratory-scale biogas
fermenters operating at mesophilic and thermophilic conditions which were built up
and conducted during the FNR-funded project 22011402, as previously described
(Mähnert 2007). For ensuring that biogas production was optimal at sampling day all
reactor samples were taken at OLRs of 1.9 - 2.1 kg m-3 d-1. The substrates fodder
beet silage and maize silage were applied. In addition, the fermentation of cattle
manure was tested under thermophilic conditions.
In this test series the number of 16S rRNA gene copies of the domain Archaea was
not determined with the ARC-set. Instead of this approach, the amount of archaeal
gene copies was calculated by summing up the detected gene copies of the primer
sets MMB, MBT, Mst und Msc.
Table 21 summarizes the quality parameters of the standard curves used for
quantification. The slopes of linear regression curves calculated over a 9-log range
were comparable to the theoretical optimum of -3.33 and showed that amplification
rates were efficient. Furthermore, R2 values ranged between 0.960 and 0.990,
indicating that the Q-PCR systems were highly linear.
Table 21 Q-PCR reaction parameters of the standard curves used for estimating the 16S rRNA gene copy numbers in reactor samples during semi-continuous biogas fermentation by the application of different substrates. The following primer sets were used: BAC = universal Bacteria primer set, MBT = Methanobacteriales primer set, MMB = Methanomicrobiales primer set, Msc = Methanosarcinaceae primer set, Mst = Methanosaetaceae primer set. NA = not analyzed.
Concerning to the mesophilic biogas reactors the following results were achieved.
Initially, it can be noted that comparable numbers of bacterial 16S rRNA gene copies
were obtained by using fodder beet silage and maize silage as substrates (Fig. 18).
BAC ARC MMB MBT Msc Mst
Slope (Average) -3.300 NA -3.600 -3.840 -3.520 -3.760
Slope (R2) 00.960 NA 00.960 00.960 00.990 00.970
Full efficiency 01.009 NA 00.896 00.821 00.923 00.845
Intercept 40.510 NA 53.200 45.110 40.560 44.160
Primer set
RESULTS
96
Fig. 18 Quantification of the 16S rRNA gene copy numbers for Bacteria and methanogenic Archaea during biogas fermentation by the use of the substrates (A) fodder beet silage and (B) maize silage at mesophilic conditions. On the x-axis the used primer sets are given while the y-axis reflects the 16S rRNA gene copy number, which was detected in 1 ml of the reactor sample. The following primer sets were used: BAC = universal Bacteria primer set, MBT = Methanobacteriales primer set, MMB = Methanomicrobiales primer set, Msc = Methanosarcinaceae primer set, Mst = Methanosaetaceae primer set. The amount of the archaeal 16S rRNA gene copies (ARC) was calculated by summing up the detected gene copies of the primer sets MMB, MBT, Mst and Msc. Columns and error bars represent means and the standard deviation of 3 independent DNA samples, respectively. ND = not detected.
In laboratory-scale fermenters which used fodder beet silage as mono-substrate, the
acetotrophic Methanosaetaceae were the dominant methanogens (5.57 × 109 gene
copies mlreactor sample-1). Nearly similar proportions of 16S rRNA copies were detected
with the primer sets MMB, MBT and Msc (approx. 109 gene copies mlreactor sample-1).
A completely different picture was obtained in the reactors fed with maize silage. The
taxonomic group of the Methanosaetaceae which was most abundant in biogas
reactors supplied with fodder beet silage was not present at detectable levels. Here,
H2-oxidizing Methanobacteriales and Methanomicrobiales dominated the digesters
Archaea population. In case of Methanosarcinaceae a 10-fold lower number of
16S rRNA gene copies was detected in biogas reactors of maize silage compared to
those of fodder beet silage.
A higher value of the Archaea to Bacteria ratio was observed in biogas fermenters by
fodder beet silage supply in relation to reactors fed with maize silage.
RESULTS
97
Under thermophilic conditions the composition of the methanogenic Archaea varied
slightly between the three types of substrates. In biogas reactors of fodder beet
silage the Methanobacteriales were the prevalent representative of the methanogens
with a relative percentage of 94.1% of all detected archaeal 16S rRNA gene copy
numbers (Fig 19). The second largest group was formed by the Methanomicrobiales.
A number of 1.88 × 108 gene copies mlreactor sample-1 was assigned to this taxonomic
order. Only small proportions of the acetotrophic Methanosaetaceae and
Methanosarcinaceae were found with the family-specific primer sets.
Fig. 19 Quantification of the 16S rRNA gene copy numbers for Bacteria and methanogenic Archaea during biogas fermentation by the use of the substrates (A) fodder beet silage, (B) maize silage and (C) cattle manure at thermophilic conditions. On the x-axis the used primer sets are given while the y-axis reflects the 16S rRNA gene copy number, which was detected in 1 ml of the reactor sample. The following primer sets were used: BAC = universal Bacteria primer set, MBT = Methanobacteriales primer set, MMB = Methanomicrobiales primer set, Msc = Methanosarcinaceae primer set, Mst = Methanosaetaceae primer set. The amount of the archaeal 16S rRNA gene copies (ARC) was calculated by summing up the detected gene copies of the primer sets MMB, MBT, Mst and Msc. Columns and error bars represent means and the standard deviation of 3 independent DNA samples, respectively. ND = not detected.
RESULTS
98
By the use of maize silage as sole-substrate the group of methanogens was
uniformly represented by the Methanobacteriales.
Even in biogas reactors which were supplied with cattle manure as the main
substrate the Methanobacteriales were the dominant order (99.7%). In contrast to the
fermentations of maize silage, Methanomicrobiales-specific 16S rRNA gene copy
numbers were detected in cattle manure fed reactors. No acetotrophic methanogens
were determined in CSTRs which were supplied with cattle manure and maize silage,
respectively. Conclusively it can be stated that the hydrogenotrophic methanogens
dominated in all biogas fermenters under thermophilic conditions.
Concerning the Archaea to Bacteria ratio a 5-fold higher value was calculated for
biogas fermentations using cattle manure as substrate in relation to digesters which
were supplied with fodder beet or maize silage.
By the comparison of the reactors of fodder beet silage supply under mesophilic and
thermophilic conditions the following characteristics were determined. No differences
were obtained by analyzing the proportion of bacterial 16S rRNA gene copy numbers
in the reactor samples. The number of detected gene copies ranged between
1.43 × 1011 and 3.06 × 1011 gene copies mlreactor sample-1. All analyzed methanogenic
taxonomic groups were verified in both reactor types. While the number of detected
16S rRNA gene copies of the MBT-set was comparable in both temperature regimes,
the determined number of gene copies with the primer sets MMB, Msc and Mst was
much lower in biogas reactors under thermophilic conditions. For both acetotrophic
families a 3-fold higher value of gene copies was detected in biogas reactors of a
mesophilic temperature range. Comparing the composition of the methanogenic
community structure of both temperature regimes, it appears that increasing
temperatures result in a shift from an acetotrophic dominated population to a
hydrogenotrophic one.
Only minor differences were obtained in the methanogenic community structure in
biogas reactors which were supplied with maize silage. Under mesophilic as well as
under thermophilic conditions the Methanobacteriales were the predominant
methanogenic order.
RESULTS
99
Representatives of the Methanomicrobiales and Methanosarcinaceae were only
detected under mesophilic conditions, meaning that the diversity of methanogens
decreased by an increase of the working temperature of the biogas fermenters.
Determination of methanogenic community structure in agricultural biogas plants. To
analyze the quantitative composition of methanogenic Archaea in biogas plants, ten
full-scale biogas plants varying in substrate composition, operation mode, reactor
volume, organic loading rate and retention time were chosen for sampling (see
“MATERIALS AND METHODS” section).
Besides the estimation of solid standard curves by verifying the Q-PCR reaction
parameters (Table 22) two additional calculations derived from the slope, the
intercept and the residual standard deviation of the regression line were evaluated for
a more accurate interpretation of the obtained Q-PCR data.
Initially, the limit of detection (LOD) was determined. It can be described as the
lowest variation of detectable fluorescence caused by gene copies in the sample
which can be differentiated from the background fluorescence of the no template
control. For all Q-PCR assays which were used in this study comparable LOD values
were obtained (Table 23). The detection of 16S rRNA gene copy numbers varied
between one to eight gene copies whereby the highest LOD was observed for the
standard curve of the MMB-set (LOD = 7.90 gene copies). Thus it can be assumed
that the LOD was only slightly influenced by the background of the reaction mixture.
The limit of quantification (LOQ) was the second parameter which was calculated for
estimating the precision of the standard curve. It can be defined as the lowest value
of detectable gene copies at which a solid quantification is feasible. With regards to
the determination of the LOQ, it can be stated that the LOQ reached values between
two and 982 gene copies. Here, too, the calibration curve of the MMB-set showed the
highest inaccuracy compared to the remaining primer sets. By the use of the primer
sets BAC, ARC, MMB, MBT and Mst the number of detected 16S rRNA gene copies
of the reactor samples were above the LOQ value meaning that an accurate,
absolute quantification by Q-PCR was ensured.
RESULTS
100
Concerning the Msc-set a different picture was obtained. In reactor samples where
an amplification of the 16S rRNA gene was observed, the number of detected gene
copies ranged between the LOD and LOQ value. Deduced from these results, the
presence of Methanosarcinaceae in the sampled biogas plants could only be vague
confirmed.
After testing the precision of the standard curves used for absolute quantification the
composition of the methanogenic community structure was determined.
The distribution of the detected 16S rRNA gene copies and the number of detected
genomes in one millilitre of the reactor sample are given in Fig. 20 and Table 24,
respectively. As prerequisite for the calculation of genomes the average number of
16S rRNA gene copies in one genome was set to seven for the domain Bacteria and
to three for the domain Archaea and all group-specific methanogenic primer sets
(NCBI Entrez Genome Project database (http://www.ncbi.nlm.nih.gov/genomeprj)).
Regarding Q-PCR results, Methanomicrobiales 16S rRNA gene copy numbers were
mostly the highest to be found among the methanogens. With the exception of BA10,
the distribution of gene copies ranged between 61% and 99% which indicates the
dominance of this taxonomic group. Representatives of the order Methanobacteriales
were determined in all biogas plants while the highest number was observed in
reactor samples of BA7 (17% of all detected 16S rRNA gene copy numbers). In
BA10, the acetotrophic family Methanosaetaceae was absolutely dominant in terms
of the 16S rRNA gene concentration (73% of the determined methanogenic
population). Smaller values of Methanosaetaceae-specific 16S rRNA gene copies
were found for the biogas plants BA2, BA3, BA4, BA6 and BA8. The archaeal
methanogens of Methanosarcinaceae presented only a minor fraction (< 1%) of the
participating methanogens. In seven of the sampled biogas plants this taxonomic
group was observed whereby the presence of Methanosarcinaceae can not exactly
be confirmed because of the obtained results determined with limit of quantification
analysis.
RESULTS
101
Tab
le 2
2 Q
-PC
R r
eactio
n p
ara
mete
rs o
f th
e s
tand
ard
curv
es u
sed f
or
estim
ating t
he 1
6S
rR
NA
gen
e c
opy n
um
bers
in r
eacto
r sa
mple
s o
f 10
bio
gas
pla
nts
. T
he f
ollo
win
g p
rim
er
sets
were
used:
AR
C =
univ
ers
al A
rchaea p
rim
er
set, B
AC
= u
niv
ers
al B
acte
ria p
rim
er
set, M
BT
= M
eth
an
ob
acte
ria
les p
rim
er
set, M
MB
= M
eth
ano
mic
rob
iale
s p
rim
er
set, M
sc =
Meth
anosarc
inacea
e p
rim
er
se
t, M
st
= M
eth
anosae
tacea
e p
rim
er
set.
BA
1B
AC
AR
CM
MB
MB
TM
sc
Mst
BA
2B
AC
AR
CM
MB
MB
TM
sc
Mst
Slo
pe (
Avera
ge)
-3.9
13
-4.0
10
-3.8
51
-3.9
79
-3.2
32
-3.6
44
-3.9
13
-4.0
10
-3.8
51
-3.9
79
-3.2
32
-3.6
44
Slo
pe (
R2)
00
.96
70
0.9
95
00
.96
40
0.9
93
00
.97
90
0.9
92
00
.96
70
0.9
95
00
.96
40
0.9
93
00
.97
90
0.9
92
Fu
ll e
ffic
ien
cy
00
.80
10
0.7
76
00
.81
80
0.7
84
01
.03
90
0.8
81
00
.80
10
0.7
76
00
.81
80
0.7
84
01
.03
90
0.8
81
Inte
rce
pt
44
.23
04
3.7
10
48
.53
04
2.5
80
34
.36
04
3.8
50
44
.23
04
3.7
10
48
.53
04
2.5
80
34
.36
04
3.8
50
BA
3B
A 4
Slo
pe (
Avera
ge)
-3.9
13
-4.0
10
-3.8
51
-3.9
79
-3.2
32
-3.6
44
-3.9
13
-4.0
10
-3.8
51
-3.9
79
-3.2
32
-3.6
44
Slo
pe (
R2)
00
.96
70
0.9
95
00
.96
40
0.9
93
00
.97
90
0.9
92
00
.96
70
0.9
95
00
.96
40
0.9
93
00
.97
90
0.9
92
Fu
ll e
ffic
ien
cy
00
.80
10
0.7
76
00
.81
80
0.7
84
01
.03
90
0.8
81
00
.80
10
0.7
76
00
.81
80
0.7
84
01
.03
90
0.8
81
Inte
rce
pt
44
.23
04
3.7
10
48
.53
04
2.5
80
34
.36
04
3.8
50
44
.23
04
3.7
10
48
.53
04
2.5
80
34
.36
04
3.8
50
BA
5B
A 6
Slo
pe (
Avera
ge)
-3.7
98
-4.0
35
-3.9
43
-4.2
78
-3.2
22
-4.2
78
-3.7
98
-4.0
35
-3.9
43
-4.2
78
-3.2
22
-4.2
78
Slo
pe (
R2)
00
.99
30
0.9
95
00
.98
10
0.9
97
00
.97
70
0.9
85
00
.99
30
0.9
95
00
.98
10
0.9
97
00
.97
70
0.9
85
Fu
ll e
ffic
ien
cy
00
.83
40
0.7
69
00
.79
30
0.7
13
01
.04
30
0.7
18
00
.83
40
0.7
69
00
.79
30
0.7
13
01
.04
30
0.7
18
Inte
rce
pt
44
.35
04
1.7
10
48
.86
04
5.0
00
33
.87
04
7.4
80
44
.35
04
1.7
10
48
.86
04
5.0
00
33
.87
04
7.4
80
BA
7B
A 8
Slo
pe (
Avera
ge)
-3.7
98
-4.0
35
-3.9
43
-4.2
78
-3.2
22
-4.2
78
-3.5
22
-3.6
41
-3.1
59
-3.5
80
-3.2
25
-3.5
01
Slo
pe (
R2)
00
.99
30
0.9
95
00
.98
10
0.9
97
00
.97
70
0.9
85
00
.99
40
0.9
94
00
.97
20
0.9
66
00
.98
60
0.9
86
Fu
ll e
ffic
ien
cy
00
.83
40
0.7
69
00
.79
30
0.7
13
01
.04
30
0.7
18
00
.92
30
0.8
82
01
.07
30
0.9
03
01
.04
20
0.9
30
Inte
rce
pt
44
.35
04
1.7
10
48
.86
04
5.0
00
33
.87
04
7.4
80
42
.17
14
2.0
10
46
.91
64
3.6
27
33
.93
04
4.1
50
BA
9B
A 1
0
Slo
pe (
Avera
ge)
-3.7
98
-4.0
35
-3.9
43
-4.2
78
-3.2
22
-4.2
78
-3.7
98
-4.0
35
-3.9
43
-4.2
78
-3.2
22
-4.2
78
Slo
pe (
R2)
00
.99
30
0.9
95
00
.98
10
0.9
97
00
.97
70
0.9
85
00
.99
30
0.9
95
00
.98
10
0.9
97
00
.97
70
0.9
85
Fu
ll e
ffic
ien
cy
00
.83
40
0.7
69
00
.79
30
0.7
13
01
.04
30
0.7
18
00
.83
40
0.7
69
00
.79
30
0.7
13
01
.04
30
0.7
18
Inte
rce
pt
44
.35
04
1.7
10
48
.86
04
5.0
00
33
.87
04
7.4
80
44
.35
04
1.7
10
48
.86
04
5.0
00
33
.87
04
7.4
80
Pri
mer
set
Pri
mer
set
RESULTS
102
Tab
le 2
3 L
imit o
f d
ete
ction
(LO
D)
and l
imit o
f qua
ntification (
LO
Q)
derived f
rom
the s
tan
dard
curv
es o
f th
e a
pp
lied p
rim
er
sets
. T
he f
ollo
win
g p
rim
er
sets
w
ere
used:
AR
C =
univ
ers
al
Arc
haea
pri
mer
set,
BA
C =
un
ivers
al
Bacte
ria
pri
mer
set,
M
BT
= M
eth
an
obacte
ria
les
prim
er
set,
MM
B =
Meth
an
om
icro
bia
les p
rim
er
set,
Msc =
Meth
an
osarc
inacea
e p
rim
er
set,
Mst
= M
eth
anosae
tacea
e p
rim
er
set.
CT =
thre
sho
ld c
ycle
nu
mber.
BA
1B
AC
AR
CM
MB
MB
TM
sc
Ms
tB
A 2
BA
CA
RC
MM
BM
BT
Ms
cM
st
LO
D (
CT)
04
3.0
84
3.5
50
47
.13
42
.35
33
.68
43
.56
04
3.0
84
3.5
50
47
.13
42
.35
33
.68
43
.56
LO
D (
Co
py n
um
be
r)0
04
.60
01
.25
00
6.8
60
1.3
60
3.0
60
1.5
30
04
.60
01
.25
00
6.8
60
1.3
60
3.0
60
1.5
3
LO
Q (
CT)
04
0.4
04
3.1
60
43
.86
41
.83
32
.08
42
.88
04
0.4
04
3.1
60
43
.86
41
.83
32
.08
42
.88
LO
Q (
Co
py n
um
be
r)1
59
.59
02
.10
62
1.0
80
2.7
64
1.9
60
4.0
91
59
.59
02
.10
62
1.0
80
2.7
64
1.9
60
4.0
9
BA
3B
A 4
LO
D (
CT)
04
3.0
84
3.5
50
47
.13
42
.35
33
.68
43
.56
04
3.0
84
3.5
50
47
.13
42
.35
33
.68
43
.56
LO
D (
Co
py n
um
be
r)0
04
.60
01
.25
00
6.8
60
1.3
60
3.0
60
1.5
30
04
.60
01
.25
00
6.8
60
1.3
60
3.0
60
1.5
3
LO
Q (
CT)
04
0.4
04
3.1
60
43
.86
41
.83
32
.08
42
.88
04
0.4
04
3.1
60
43
.86
41
.83
32
.08
42
.88
LO
Q (
Co
py n
um
be
r)1
59
.59
02
.10
62
1.0
80
2.7
64
1.9
60
4.0
91
59
.59
02
.10
62
1.0
80
2.7
64
1.9
60
4.0
9
BA
5B
A 6
LO
D (
CT)
44
.10
41
.55
48
.14
44
.91
33
.13
46
.97
44
.10
41
.55
48
.14
44
.91
33
.13
46
.97
LO
D (
Co
py n
um
be
r)0
1.3
80
1.2
40
2.6
50
1.1
20
3.3
60
1.9
00
1.3
80
1.2
40
2.6
50
1.1
20
3.3
60
1.9
0
LO
Q (
CT)
43
.53
41
.19
46
.46
44
.68
31
.40
45
.79
43
.53
41
.19
46
.46
44
.68
31
.40
45
.79
LO
Q (
Co
py n
um
be
r)0
2.9
40
2.0
62
5.3
40
1.4
75
7.7
60
8.3
10
2.9
40
2.0
62
5.3
40
1.4
75
7.7
60
8.3
1
BA
7B
A 8
LO
D (
CT)
44
.10
41
.55
48
.14
44
.91
33
.13
46
.97
41
.96
41
.80
04
5.6
30
42
.34
33
.48
43
.61
LO
D (
Co
py n
um
be
r)0
1.3
80
1.2
40
2.6
50
1.1
20
3.3
60
1.9
00
1.3
60
1.3
70
07
.90
00
5.1
10
2.0
90
2.2
6
LO
Q (
CT)
43
.53
41
.19
46
.46
44
.68
31
.40
45
.79
41
.47
41
.31
04
2.6
40
39
.34
32
.44
42
.36
LO
Q (
Co
py n
um
be
r)0
2.9
40
2.0
62
5.3
40
1.4
75
7.7
60
8.3
10
2.7
70
2.8
69
81
.97
22
9.7
21
1.6
91
5.1
1
BA
9B
A 1
0
LO
D (
CT)
44
.10
41
.55
48
.14
44
.91
33
.13
46
.97
44
.10
41
.55
48
.14
44
.91
33
.13
46
.97
LO
D (
Co
py n
um
be
r)0
1.3
80
1.2
40
2.6
50
1.1
20
3.3
60
1.9
00
1.3
80
1.2
40
2.6
50
1.1
20
3.3
60
1.9
0
LO
Q (
CT)
43
.53
41
.19
46
.46
44
.68
31
.40
45
.79
43
.53
41
.19
46
.46
44
.68
31
.40
45
.79
LO
Q (
Co
py n
um
be
r)0
2.9
40
2.0
62
5.3
40
1.4
75
7.7
60
8.3
10
2.9
40
2.0
62
5.3
40
1.4
75
7.7
60
8.3
1
Pri
me
r s
et
Pri
me
r s
et
RESULTS
103
Fig. 20 Relative frequency of detected 16S rRNA gene copy numbers for the methanogenic, archaeal groups of the Methanomicrobiales (light grey), Methanobacteriales (grey), Methanosaetaceae (dark grey) and Methanosarcinaceae (black) in 10 sampled biogas plants. On the x-axis the sampled biogas plants are given while the y-axis reflects the percentage distribution of detected 16S rRNA gene copy numbers of the methanogenic Archaea.
Table 25 Percentage distribution of the detected 16S rRNA gene copy number of the hydrogenotrophic (Methanomicrobiales, Methanobacteriales) and acetotrophic (Methanosaetaceae, Methanosarcinaceae) methanogens in one nanogram of genomic DNA.
After evaluating the allocation of each specific methanogenic group in the reactor
samples, the percentage of acetotrophic to hydrogenotrophic methanogens was
observed (Table 25). In almost all biogas plants the hydrogenotrophic methanogens
were detected as the most abundant Archaea (63-100%).
BA1 BA2 BA3 BA4 BA5 BA6 BA7 BA8AF BA8FR BA9 BA10
Hydrogenotrophic 999% 663% 086% 088% 100% 088% 099% 098% 098% 099% 027%
Acetotrophic < 1% 337% 014% 012% - 012% < 1% 002% 002% < 1% 073%
Biogas plant
RESULTS
104
Tab
le 2
4 N
um
ber
of
dete
cte
d g
eno
mes i
n o
ne m
illili
tre o
f th
e r
eacto
r sam
ple
by t
he a
pp
licatio
n o
f all
pri
mer
sets
. T
he f
ollo
win
g p
rim
er
sets
were
use
d:
AR
C =
univ
ers
al A
rcha
ea p
rim
er
set, B
AC
= u
niv
ers
al B
acte
ria p
rim
er
set, M
BT
= M
eth
ano
bacte
riale
s p
rim
er
set, M
MB
= M
eth
anom
icro
bia
les p
rim
er
set,
Msc =
Meth
anosarc
inaceae
prim
er
set, M
st
= M
eth
an
osaeta
cea
e p
rim
er
set.
Me
ans a
nd s
tand
ard
devia
tio
n w
ere
dete
rmin
ed f
rom
3 i
ndepe
nde
nt
DN
A
sam
ple
s.
SD
= s
tand
ard
de
via
tion, N
D =
not
dete
rmin
ed.
BA
CA
RC
MM
BM
BT
Mst
Msc
(Mean
± S
D) G
en
om
es/m
l(M
ean
± S
D) G
en
om
es/m
l(M
ean
± S
D) G
en
om
es/m
l(M
ean
± S
D) G
en
om
es/m
l(M
ean
± S
D) G
en
om
es/m
l(M
ean
± S
D) G
en
om
es/m
l
BA
12
.90
×10
11 ±
6.3
2×
10
10
6.8
5×
10
10 ±
1.0
8×
10
10
2.4
6×
10
10 ±
1.0
4×
10
95
.30
×10
7 ±
1.8
9×
10
7N
D1
.40
×10
6 ±
2.3
3×
10
5
BA
23
.53
×10
10 ±
3.7
0×
10
90
6.5
5×
10
90 ±
9.6
1×
10
80
1.1
1×
10
90 ±
4.8
7×
10
84
.33
×10
7 ±
2.4
4×
10
76
.63
×10
8 ±
3.7
9×
10
72
.75
×10
5 ±
3.8
5×
10
4
BA
38
.61
×10
10 ±
4.1
0×
10
90
2.1
1×
10
10 ±
4.7
6×
10
90
6.8
9×
10
90 ±
3.3
2×
10
91
.00
×10
8 ±
5.0
7×
10
71
.14
×10
9 ±
8.7
0×
10
73
.18
×10
5 ±
1.2
0×
10
4
BA
42
.02
×10
10 ±
3.2
9×
10
90
2.6
1×
10
90 ±
3.2
1×
10
80
1.2
1×
10
90 ±
1.2
2×
10
84
.60
×10
7 ±
7.0
9×
10
61
.66
×10
8 ±
2.3
0×
10
7N
D
BA
51
.33
×10
11 ±
7.2
8×
10
90
9.7
9×
10
90 ±
1.9
9×
10
90
1.2
1×
10
10 ±
3.0
4×
10
94
.58
×10
7 ±
5.8
6×
10
6N
DN
D
BA
61
.33
×10
10 ±
2.1
6×
10
90
1.7
2×
10
90 ±
2.1
1×
10
80
7.9
3×
10
80 ±
8.0
1×
10
73
.02
×10
7 ±
4.6
6×
10
61
.09
×10
8 ±
1.5
1×
10
7N
D
BA
72
.64
×10
10 ±
2.9
3×
10
99
1.6
1×
10
90 ±
2.4
6×
10
80
1.0
4×
10
99 ±
2.2
4×
10
82
.10
×10
8 ±
7.0
0×
10
6N
D5
.43
×10
5 ±
1.1
3×
10
5
BA
8A
F1
.61
×10
11 ±
1.6
1×
10
10
1.0
0×
10
11 ±
1.2
3×
10
10
1.4
7×
10
11 ±
5.2
2×
10
97
.67
×10
8 ±
3.4
8×
10
73
.28
×10
9 ±
2.1
5×
10
81
.45
×10
7 ±
9.0
8×
10
5
BA
8F
R1.5
5×
10
11 ±
4.7
2×
10
90
1.1
8×
10
11 ±
1.4
7×
10
10
2.2
0×
10
11 ±
9.1
7×
10
96
.42
×10
8 ±
1.6
9×
10
75
.26
×10
9 ±
2.7
4×
10
82
.37
×10
7 ±
1.0
1×
10
6
BA
95
.94
×10
10 ±
1.7
9×
10
10
1.0
0×
10
10 ±
3.7
6×
10
90
7.4
1×
10
99 ±
5.4
1×
10
91
.72
×10
8 ±
2.1
4×
10
7N
D4
.78
×10
6 ±
7.2
7×
10
5
BA
10
3.7
6×
10
10 ±
9.1
7×
10
90
6.8
8×
10
90 ±
5.3
9×
10
80
1.8
0×
10
99 ±
7.6
4×
10
82
.18
×10
8 ±
8.1
4×
10
75
.40
×10
9 ±
7.1
4×
10
88
.71
×10
5 ±
2.6
4×
10
5
Pri
mer
set
Bio
gas p
lan
t
RESULTS
105
Even in four of the ten biogas plants the hydrogenotrophs formed a uniform group of
the methanogenic flora. For methanogens belonging to the acetate utilizing group,
the percentage distribution of detected 16S rRNA gene copy numbers varied
between < 1% and 37% indicating that these methanogens were almost
underrepresented in the sampled biogas plants. With a percentage of 78%, a
dominance of the acetotrophic methanogens was only determined in reactor samples
of BA10.
Besides the analysis of the group-specific methanogenic primer sets, the primer sets
of Archaea and Bacteria were applied for determining the number of detected
archaeal 16S rRNA gene copies in relation to those of the bacterial ones (Table 26).
The ARC/BAC ratio ranged between 3% (BA5 and BA7) and 33% (BA8FR).
Interestingly, the highest ARC/BAC values were obtained for the two-stage dry
fermentation biogas plant BA8 which leads to the assumption that this reactor type
influenced the accumulation of archaeal community positively.
Table 26 Percentage distribution of the detected archaeal 16S rRNA gene copy numbers in relation to those of the domain Bacteria. ARC = number of detected 16S rRNA gene copies of the domain Archaea in one nanogram of genomic DNA, BAC = number of detected 16S rRNA gene copies of the domain Bacteria in one nanogram of genomic DNA.
To go further into a question if the detected number of 16S rRNA gene copies is
feasible to be found in the applied amount of genomic DNA per PCR, the total
amount of genomic DNA for all detected Archaea and Bacteria was compared to the
total applied amount of genomic DNA per PCR. Therefore, the following prerequisites
were determined: (1) the average of the bacterial genome was set to 6.58 Mbp while
for the archaeal genome an average size of 3.67 Mbp was assumed and (2) the
minimum and maximum of 16S rRNA gene copies within the bacterial genome were
set to one and 15, respectively whereas one to four 16S rRNA gene copies were
defined as the minimal and maximal number of gene copies of the archaeal genome.
All information concerning the genomes was derived from the NCBI database (NCBI
Entrez Genome Project database, URL: http://www.ncbi.nlm.nih.gov/genomeprj).
BA1 BA2 BA3 BA4 BA5 BA6 BA7 BA8AF BA8FR BA9 BA10
ARC/BAC 10% 08% 11% 06% 03% 06% 03% 28% 33% 07% 08%
Biogas plant
RESULTS
106
In Table 27 the total amount of detected genomic DNA for Bacteria and Archaea
ranged between 1-2% in all samples. With respect to this result, it can be assumed
that only a small percentage of the participating microbial community structure was
detected by Q-PCR analysis. However, this calculation can only be seen as a rough
parameter for the determination of total genomic DNA for Bacteria and Archaea
because of several prerequisites which were assumed for this calculation. The main
objective of this evaluation is that the number of detected 16S rRNA gene copies of
Bacteria and Archaea which was detected in one nanogram of genomic DNA is
possible.
Table 27 Percentage distribution of the total amount of genomic DNA for the detected Archaea and Bacteria in relation to the total amount of genomic DNA (1 ng) per Q-PCR. Amin = percentage distribution of the total amount of genomic DNA for all detected genomes assuming that every genome is provided with the highest possible number of 16S rRNA gene copies. Amax = percentage distribution of the total amount of genomic DNA for all detected genomes assuming that every genome is provided with the lowest possible number of 16S rRNA gene copies.
a)
Sum of the determined amounts of genomic DNA for all detected Archaea and Bacteria in relation to the total amount of genomic DNA (1 ng) per Q-PCR.
Amin Amax Amin Amax Amin Amax
BA1 < 1% < 2% < 1% < 1% < 1% < 2%
BA2 < 1% < 1% < 1% < 1% < 1% < 1%
BA3 < 1% < 1% < 1% < 1% < 1% < 1%
BA4 < 1% < 1% < 1% < 1% < 1% < 1%
BA5 < 1% < 2% < 1% < 1% < 1% < 2%
BA6 < 1% < 1% < 1% < 1% < 1% < 1%
BA7 < 1% < 1% < 1% < 1% < 1% < 1%
BA8AF < 1% < 2% < 1% < 1% < 1% < 2%
BA8FR < 1% < 2% < 1% < 1% < 1% < 2%
BA9 < 1% < 1% < 1% < 1% < 1% < 1%
BA10 < 1% < 1% < 1% < 1% < 1% < 1%
Bacteria Archaea Totala)
Biogas plant
RESULTS
107
6.2 Development of group-specific primer sets for the detection of
methanogenic Archaea in biogas plants by the use of the metabolic mcrA gene
With the establishment of the Q-PCR assays based on the 16S rRNA gene,
community structure and population dynamics of the methanogenic Archaea were
observed in CSTRs and biogas plants.
Even if these Q-PCR assays are suitable working tools for analyzing methanogenic
communities in environmental samples, the validity of a 16S rRNA gene based
Q-PCR assay is limited. Regarding the fact that the number of 16S rRNA gene
copies in genomes of methanogens differs, a calculation of the detected copy
number to the total number of microbial cells is not exactly feasible. Species with
more than one 16S rRNA gene in the genome might be overrepresented in
comparison to species with only one 16S rRNA gene. In addition, it is impossible to
obtain information concerning the physiological activity of the methanogens.
Targeting a gene of an enzyme complex which is directly involved in methanogenesis
offers the possibility for metabolic activity analyses based on messenger RNA.
Therefore, a unique, ubiquitous enzyme complex which can be found in all
methanogenic Archaea has to be chosen for deriving group-specific primer sets. The
methyl-coenzyme M reductase is the terminal enzyme complex in methanogenesis,
and it exists in all representatives of the methanogens. Hence, this enzyme complex
is ideally suited for the development of methanogenic group-specific primer sets. The
MCRI complex consists of three different subunits (α, β, γ) while every subunit exists
in the complex in duplicate. Each subunit is encoded by a specific gene offering the
potential to serve as a target for Q-PCR assays.
For the development of group-specific primer sets all available sequence information
has to be checked. Concerning the mcr genes most sequence information is
published for the methyl-coenzyme M reductase subunit alpha (mcrA) gene (NCBI
Genebank database, URL: http://www.ncbi.nlm.nih.gov/Genbank/index.html).
Therefore, this methanogenic gene was used for designing group-specific primer sets
referring to the 16S rRNA gene based Q-PCR assays according to Yu et al. (2005a).
RESULTS
108
Hence, four primer sets (MMIC-set = Methanomicrobiales-specific,
MBAC-set = Methanobacteriales-specific, MSaet-set = Methanosaetaceae-specific
and MSarc-set = Methanosarcinaceae-specific) were designed for detecting the
methanogenic groups in reactor samples. Besides a universal mcrA primer set,
developed for detecting methanogens in environmental samples by conventional
PCR technique was assigned to the Q-PCR platform for creating a sum parameter for
almost all participating methanogens in biogas plants. Therefore, the primer set ME
published by Hales et al. (1996) was chosen.
Design of group-specific primer sets based on the methyl-coenzyme M reductase
subunit alpha gene (mcrA). All used sequences for designing group-specific primers
are listed in Table I a-c (Appendix). By the comparison of the mcrA gene sequences,
no conserved regions were determined at the order level. Therefore, all methanogens
belonging to one methanogenic order were subdivided into their natural living
environments. Sequences of methanogens where an occurrence in the biogas
reactor seems to be highly improbable e. g. extreme barophilic methanogens were
excluded for the development of the primers. With this assumption the possibility for
designing Q-PCR primer sets for the methanogenic orders of Methanomicrobiales
and Methanobacteriales was given. All derived primer sets are summarized in
Table 9 (see “MATERIALS AND METHODS” section).
After the determination of suitable group-specific primer sets the specifity of each
primer set for non-target and target organisms with potential positive and negative
false detection was analyzed (Table 28-30). Except for the primer set MBAC, no
theoretical cross amplification was obtained by in silico comparison of the derived
primer sequences to all reference sequences listed in Table I a-c (Appendix). With
respect of the MSaet-set false negative results were determined for all remaining
primer sets.
The sizes of the mcrA amplicons ranged between 77 and 373 bp. Because of the
varying amplicon lengths different thermocycling protocols were applied to the
respective group-specific primer set (see “MATERIALS AND METHODS” section).
RESULTS
109
Tab
le 2
8 S
pecifity o
f th
e p
rim
er
sets
ME
and M
BA
C b
y e
valu
ating t
he r
esu
lts f
or
pote
ntial
fals
e p
ositiv
e a
nd p
ote
ntia
l fa
lse n
eg
ative
am
plif
ication.
The
num
ber
in s
quare
d b
rackets
repre
sents
the n
um
ber
of
mis
matc
hes b
etw
een t
he r
DN
A o
f th
e s
train
an
d t
he f
orw
ard
(F
) an
d r
evers
e (
R)
prim
er
of
the
corr
espondin
g p
rim
er
set.
Se
tS
tra
ins
be
lon
gin
g t
o t
his
gro
up
Po
ten
tia
l fa
lse
-po
sit
ive
re
su
lts
Po
ten
tia
l fa
lse
-ne
ga
tive
re
su
lts
ME
MB
AC
, M
MIC
, M
Sa
rcN
one
Mcu
. m
arisn
igri J
R-1
[F
1,
R2
]
Mc.
ma
rip
alu
dis
S2
Msp
. h
un
ga
tei
JF
1 [
R1
]
Mc.
ae
olic
us D
SM
43
04
Mcr.
la
bre
an
um
Z [
R1
]
Mc.
vo
lta
e p
sM
b.
arr
hu
se
nse
H2
-LR
[F
1]
Mc.
va
nn
ielii
SB
Mp
r. s
tad
tma
na
e D
SM
30
91
[F
3]
Mtc
. th
erm
olit
ho
tro
ph
icu
s D
SM
20
95
, JC
M 1
05
49
Mc.
ma
rip
alu
dis
S2
[R
1]
Mtc
. o
kin
aw
en
sis
DS
M 1
42
08
, IH
-1M
tc.
the
rmo
lith
otr
op
hic
us J
CM
10
54
9 [
F1
]
Mcd
. ja
nn
asch
ii D
SM
26
61
M
cd
. ja
nn
asch
ii D
SM
26
61
[F
1]
Mcd
. in
fern
us D
SM
11
81
2,
SL
48
, S
L4
7M
ts.
form
icic
us
Mc-S
-70
[F
1]
Mts
. ig
ne
us D
SM
56
66
, JC
M 1
18
34
Msr.
ace
tivo
ran
s C
2A
[R
1]
Mts
. fo
rmic
icu
s M
c-S
-70
Mp
y.
ka
nd
leri A
V1
9 [
F3
, R
2]
Mp
y.
ka
nd
leri A
V1
9,
DS
M 6
32
4U
ncu
ltu
red
arc
ha
eo
n c
lon
e A
TB
-EN
-56
77
-M0
15
[F
1]
All
un
cu
ltu
red
arc
ha
eo
n c
lon
es
MB
AC
Mb
. fo
rmic
icu
m D
SM
13
12
, D
SM
15
35
Mc.
ma
rip
alu
dis
S2
Mb
. fo
rmic
icu
m D
SM
15
35
[F
1]
Mb
. b
rya
ntii
DS
M 8
63
Mc.
vo
lta
e p
sM
b.
bry
an
tii
DS
M 8
63
[F
1,
R1
]
Mb
. a
rrh
use
nse
H2
-LR
Mcd
. in
fern
us D
SM
11
81
2M
b.
arr
hu
se
nse
H2
-LR
[F
1,
R1
]
Mb
. th
erm
ag
gre
ga
ns
DS
M 3
26
6
Un
cu
ltu
red
arc
ha
eo
n c
lon
e A
TB
-EN
-55
95
-M0
08
Mb
. iv
an
ovii
DS
M 2
61
1 [
F1
]
Mb
. iv
an
ovii
DS
M 2
61
1U
ncu
ltu
red
arc
ha
eo
n c
lon
e A
TB
-EN
-44
82
-M1
12
Mb
. b
eiji
ng
en
se
DS
M 1
59
99
[F
2]
Mb
. b
eiji
ng
en
se
DS
M 1
59
99
Mb
b.
ora
lis D
SM
72
56
[R
2]
Mb
b.
rum
ina
ntiu
m D
SM
10
93
M
bb
. sm
ith
ii D
SM
86
1 [
F1
, R
2]
Mb
b.
arb
orip
hilu
s D
SM
11
25
, D
SM
70
56
Mb
b.
arb
orip
hilu
s D
SM
11
25
, D
SM
70
56
[F
1,
R1
]
Mb
b.
ora
lis D
SM
72
56
M
pr.
sta
dtm
an
ae
DS
M 3
09
1 [
F2
]
Mb
b.
sm
ith
ii D
SM
86
1M
bt.
th
erm
au
totr
op
hic
us d
elta
H [
F1
]
Mp
r. s
tad
tma
na
e D
SM
30
91
M
bt.
wo
lfe
ii D
SM
29
70
[F
1]
Mb
t. t
he
rma
uto
tro
ph
icu
s d
elta
HM
th.
so
cia
bili
s D
SM
34
96
[F
1]
Mb
t. t
he
rmo
ph
ilus D
SM
65
29
Un
cu
ltu
red
arc
ha
eo
n c
lon
e A
TB
-EN
-55
95
-M0
20
[R
1]
Mb
t. t
he
rmo
fle
xu
s D
SM
72
68
Mb
t. w
olfe
ii D
SM
29
70
Mth
. so
cia
bili
s D
SM
34
96
Un
cu
ltu
red
arc
ha
eo
n c
lon
e A
TB
-EN
-55
95
-M0
20
Un
cu
ltu
red
arc
ha
eo
n c
lon
e A
TB
-EN
-44
82
-M0
05
Un
cu
ltu
red
arc
ha
eo
n c
lon
e A
TB
-EN
-45
70
-M0
10
Un
cu
ltu
red
arc
ha
eo
n c
lon
e A
TB
-EN
-10
44
7-M
12
2
RESULTS
110
Tab
le 2
9 S
pecifity o
f th
e p
rim
er
sets
MM
IC a
nd M
Sa
et
by e
va
luating
the
results f
or
pote
ntia
l fa
lse
positiv
e a
nd p
ote
ntial fa
lse n
eg
ati
ve
am
plif
ication.
The
num
ber
in s
quare
d b
rackets
repre
sents
the n
um
ber
of
mis
matc
hes b
etw
een t
he r
DN
A o
f th
e s
train
an
d t
he f
orw
ard
(F
) an
d r
evers
e (
R)
prim
er
of
the
corr
espondin
g p
rim
er
set.
Se
tS
tra
ins
be
lon
gin
g t
o t
his
gro
up
Po
ten
tia
l fa
lse
-po
sit
ive
re
su
lts
Po
ten
tia
l fa
lse
-ne
ga
tive
re
su
lts
MM
ICM
m.
mo
bile
DS
M 1
53
9N
one
Mcu
. b
ou
rge
nsis
DS
M 2
77
2 [
F1
]
Mcu
. b
ou
rge
nsis
DS
M 3
04
5,
DS
M 6
21
6,
DS
M 2
77
2U
ncu
ltu
red
arc
ha
eo
n c
lon
e A
TB
-EN
-39
60
-M0
30
[F
2,
R1
]
Mcu
. th
erm
op
hilu
s D
SM
26
24
, D
SM
23
73
Un
cu
ltu
red
arc
ha
eo
n c
lon
e A
TB
-EN
-13
93
6-M
11
6 [
F1
, R
1]
Mcu
. m
arisn
igri J
R-1
Un
cu
ltu
red
arc
ha
eo
n c
lon
e A
TB
-EN
-45
31
-M0
08
[F
2,
R1
]
Mcu
. p
alm
ole
i D
SM
42
73
Un
cu
ltu
red
arc
ha
eo
n c
lon
e A
TB
-EN
-56
42
-M0
13
[F
2]
Mcu
. ch
iku
go
en
sis
DS
M 1
34
59
Mf.
lim
ina
tan
s D
SM
41
40
Mg
. o
rga
no
ph
ilum
DS
M 3
59
6
Mcr.
pa
rvu
m D
SM
38
23
Mcr.
ag
gre
ga
ns D
SM
30
27
Mcr.
ba
va
ricu
m D
SM
41
79
Mcr.
la
bre
an
um
Z
Msp
. h
un
ga
tei
JF
1,
DS
M 8
64
Un
cu
ltu
red
arc
ha
eo
n c
lon
e A
TB
-EN
-57
46
-M0
17
Un
cu
ltu
red
arc
ha
eo
n c
lon
e A
TB
-EN
-39
60
-M0
30
Un
cu
ltu
red
arc
ha
eo
n c
lon
e A
TB
-EN
-44
96
-M0
64
Un
cu
ltu
red
arc
ha
eo
n c
lon
e A
TB
-EN
-45
73
-M0
67
Un
cu
ltu
red
arc
ha
eo
n c
lon
e A
TB
-EN
-13
93
6-M
11
6
Un
cu
ltu
red
arc
ha
eo
n c
lon
e A
TB
-EN
-45
31
-M0
08
Un
cu
ltu
red
arc
ha
eo
n c
lon
e A
TB
-EN
-56
42
-M0
13
Un
cu
ltu
red
arc
ha
eo
n c
lon
e A
TB
-EN
-10
20
9-M
11
2
Un
cu
ltu
red
arc
ha
eo
n c
lon
e A
TB
-EN
-97
79
-M1
44
Un
cu
ltu
red
arc
ha
eo
n c
lon
e A
TB
-EN
-97
59
-M1
48
MS
ae
tM
sa
. co
ncili
i D
SM
36
71
, V
eA
c9
None
None
Msa
. h
aru
nd
ina
ce
a 8
A,
6A
Msa
. th
erm
op
hila
PT
RESULTS
111
Tab
le 3
0 S
pecifity o
f th
e p
rim
er
set
MS
arc
by e
valu
ating t
he r
esults f
or
po
tentia
l fa
lse p
ositiv
e a
nd p
ote
ntial
fals
e n
eg
ative a
mplif
icatio
n.
The n
um
ber
in
square
d b
rackets
repre
sents
the n
um
ber
of
mis
matc
he
s b
etw
een t
he r
DN
A o
f th
e s
train
and t
he f
orw
ard
(F
) and r
evers
e (
R)
prim
er
of
the c
orr
espond
ing
prim
er
set.
Se
tS
tra
ins
be
lon
gin
g t
o t
his
gro
up
Po
ten
tia
l fa
lse
-po
sit
ive
re
su
lts
Po
ten
tia
l fa
lse
-ne
ga
tive
re
su
lts
MS
arc
Msr.
ace
tivo
ran
s C
2A
None
Mcc.
bu
rto
nii
DS
M 6
24
2 [
F3
, R
1]
Msr.
ma
zei
Go
1,
DS
M 2
05
3,
DS
M 4
55
6,
DS
M 9
19
5M
cc.
ala
ske
nse
DS
M 1
72
73
[F
3]
Msr.
ba
rke
ri f
usa
roM
ha
. m
ah
ii D
SM
52
19
[F
3]
Msr.
la
cu
str
is M
MM
ml. t
he
rmo
ph
ila L
2F
AW
[F
2]
Msr.
th
erm
op
hila
DS
M 1
82
5M
ml. h
olla
nd
ica
ZB
[F
3]
Mcc.
bu
rto
nii
DS
M 6
24
2M
ss.
zhili
na
e D
SM
40
17
[F
5]
Mcc.
ala
ske
nse
DS
M 1
72
73
Un
cu
ltu
red
arc
ha
eo
n c
lon
e A
TB
-EN
-57
46
-M0
02
[R
1]
Mh
a.
ma
hii
DS
M 5
21
9U
ncu
ltu
red
arc
ha
eo
n c
lon
e A
TB
-EN
-39
60
-M0
12
[F
1]
Mm
l. t
he
rmo
ph
ila L
2F
AW
Un
cu
ltu
red
arc
ha
eo
n c
lon
e A
TB
-EN
-44
96
-M0
15
[F
1,
R1
]
Mm
l. h
olla
nd
ica
ZB
Mss.
zhili
na
e D
SM
40
17
Un
cu
ltu
red
arc
ha
eo
n c
lon
e A
TB
-EN
-57
46
-M0
02
Un
cu
ltu
red
arc
ha
eo
n c
lon
e A
TB
-EN
-39
60
-M0
12
Un
cu
ltu
red
arc
ha
eo
n c
lon
e A
TB
-EN
-44
96
-M0
15
RESULTS
112
Specifity of the ME-set. By using twelve archaeal strains and ten uncultured
archaeon clones from the sampled biogas plants (BA1, BA3, BA7, BA8AF, BA9), the
specifity of the ME-set was verified (see “MATERIALS AND METHODS” section). Except
for Methanospirillum hungatei Mh1, all mcrA genes were amplified. The nucleotide
sequence of the mcrA gene from this methanogen includes a mismatch in one of the
primers (R[1]) which resulted in a false negative amplification (Fig. 21).
Unexpectedly, the mcrA fragment of Methanoculleus marisnigri DSM 1498 was
amplified even if two mismatches were determined by aligning the nucleotide
sequence of the mcrA fragment with the primer sequences of the ME-set.
Comparing the results of Q-PCR parameters full efficiencies from 0.781 to 1.001
were obtained (Table 31). The coefficient of determination ranged between 0.994
and 0.987. Therefore, almost all standard curves could be described as solid
calibration curves for quantifying the mcrA gene.
Fig. 21 Comparison of the standard curves obtained by specific detection of the twelve archaeal and ten uncultured archaeon DNAs from biogas plants using the Q-PCR assay with the universal mcrA primer set of ME. On the x-axis the applied number of mcrA gene copies is given while the y-axis reflects the threshold cycle values (CT values). Bold lines represent the linear regression of all standard curves with positive amplification results while fine lines represent the linear regression of all standard curves with a false negative amplification result. Open symbols (circles = Methanospirillum hungatei Mh1) correspond the mean values of detected CT values by false negative amplification. The mean CT values and standard deviation were performed in triplicate in the same Q-PCR run.
RESULTS
113
Tab
le 3
1 Q
-PC
R r
eaction p
ara
mete
rs o
f th
e s
tan
dard
curv
es b
y a
mplif
yin
g t
he
mcrA
gen
e w
ith t
he M
E-s
et, M
BA
C-s
et
an
d M
MIC
-set. M
E =
un
ivers
al
mcrA
pri
mer
set, M
BA
C =
Meth
ano
bacte
riale
s p
rim
er
set, M
MIC
= M
eth
anom
icro
bia
les p
rim
er
set.
NA
= n
ot
ana
lyzed. N
D =
not
dete
cte
d.
Us
ed
str
ain
s/c
lon
es
Slo
pe
R2
Eff
icie
nc
yIn
terc
ep
tS
lop
eR
2E
ffic
ien
cy
Inte
rce
pt
Slo
pe
R2
Eff
icie
nc
yIn
terc
ep
t
Me
tha
no
ba
cte
ria
les
Me
tha
no
ba
cte
ria
ce
ae
Mb
b.
arb
orip
hilu
s D
SM
11
25
-3
.71
00
.96
70
.86
04
6.3
20
-3.4
50
0.9
70
0.9
49
42
.10
0N
DN
DN
DN
D
Mb
. fo
rmic
icu
m D
SM
15
35
-3.9
10
0.9
78
0.8
02
52
.85
0-3
.72
00
.97
90
.85
74
7.1
10
ND
ND
ND
ND
Mb
. b
rya
ntii
DS
M 8
63
-3
.33
00
.97
00
.99
74
3.0
20
-3.4
90
0.9
86
0.9
34
42
.98
0N
DN
DN
DN
D
Mb
t. t
he
rma
uto
tro
ph
icu
s D
SM
10
53
-3
.60
00
.98
20
.89
64
4.0
90
-3.3
70
0.9
82
0.9
80
39
.07
0N
DN
DN
DN
D
Un
cu
ltu
red
arc
ha
eo
n A
TB
-EN
-44
82
-M0
05
-3.9
40
0.9
45
0.7
94
51
.02
0N
AN
AN
AN
AN
DN
DN
DN
D
Un
cu
ltu
red
arc
ha
eo
n A
TB
-EN
-10
44
7-M
12
2-3
.77
00
.96
90
.84
24
8.1
20
NA
NA
NA
NA
ND
ND
ND
ND
Me
tha
no
co
cc
ale
s
Me
tha
no
co
cc
ac
ea
e
Mc.
va
nn
ielii
DS
M 1
22
4
-3.9
40
0.9
82
0.7
94
47
.86
0-3
.50
00
.97
10
.93
14
7.5
20
ND
ND
ND
ND
Me
tha
no
mic
rob
iale
s
Me
tha
no
mic
rob
iac
ea
e
Mcu
. m
arisn
igri D
SM
14
98
-3
.55
00
.97
40
.91
35
2.2
80
-3.1
30
0.9
54
1.0
87
48
.79
0-3
.45
00
.97
30
.94
94
1.6
70
Mcu
bo
urg
en
sis
DS
M 3
04
5
-3.8
20
0.9
79
0.8
27
50
.35
0N
DN
DN
DN
D-3
.89
00
.98
50
.80
74
6.1
40
Mf.
lim
ina
tan
s D
SM
41
40
-3
.54
00
.97
20
.91
64
3.8
90
ND
ND
ND
ND
-3.2
90
0.9
84
1.0
13
37
.94
0
Msp
. h
un
ga
tei
Mh
1N
DN
DN
DN
D-3
.32
00
.96
91
.00
14
4.2
90
-3.3
70
0.9
82
0.9
80
39
.03
0
Un
cu
ltu
red
arc
ha
eo
n A
TB
-EN
-57
46
-M0
17
-3.8
30
0.9
80
0.8
24
52
.70
0N
AN
AN
AN
A-3
.96
00
.99
40
.78
95
5.6
70
Un
cu
ltu
red
arc
ha
eo
n A
TB
-EN
-39
60
-M0
30
-3.9
90
0.9
44
0.7
81
55
.78
0N
AN
AN
AN
A-3
.92
00
.99
40
.79
95
9.5
60
Un
cu
ltu
red
arc
ha
eo
n A
TB
-EN
-56
42
-M0
13
-3.9
80
0.9
51
0.7
83
54
.45
0N
AN
AN
AN
A-3
.50
00
.98
30
.93
14
4.3
20
Un
cu
ltu
red
arc
ha
eo
n A
TB
-EN
-97
79
-M1
44
-3.9
00
0.9
75
0.8
05
53
.46
0N
AN
AN
AN
A-3
.54
00
.98
70
.91
64
8.0
40
Un
cu
ltu
red
arc
ha
eo
n A
TB
-EN
-97
59
-M1
48
-3.8
80
0.9
71
0.8
10
51
.41
0N
AN
AN
AN
A-3
.23
00
.96
71
.04
04
0.9
10
Me
tha
no
sa
rcin
ale
s
Me
tha
no
sa
rcin
ac
ea
e
Msr.
ba
rke
ri D
SM
80
0-3
.31
00
.97
21
.00
14
3.3
70
ND
ND
ND
ND
ND
ND
ND
ND
Msr.
ma
zei
DS
M 3
64
7-3
.70
00
.98
30
.86
34
2.7
30
-3.2
80
0.9
53
1.0
18
46
.33
0N
DN
DN
DN
D
Msr.
th
erm
op
hila
DS
M 1
82
5-3
.78
00
.98
70
.83
94
2.4
90
-3.2
80
0.9
59
1.0
18
45
.78
0N
DN
DN
DN
D
Un
cu
ltu
red
arc
ha
eo
n A
TB
-EN
-39
79
-M0
02
-3.7
90
0.9
59
0.8
36
48
.57
0N
AN
AN
AN
A-3
.96
00
.99
20
.78
95
3.1
60
Un
cu
ltu
red
arc
ha
eo
n A
TB
-EN
-56
37
-M0
12
-3.7
60
0.9
73
0.8
45
44
.69
0N
AN
AN
AN
A-3
.76
00
.96
60
.84
55
4.4
70
Un
cu
ltu
red
arc
ha
eo
n A
TB
-EN
-56
77
-M0
15
-3.8
30
0.9
72
0.8
24
50
.22
0N
AN
AN
AN
A-3
.81
00
.99
30
.83
05
4.4
90
Me
tha
no
sa
eta
ce
ae
Msa
. co
ncili
i D
SM
21
39
NA
NA
NA
NA
ND
ND
ND
ND
ND
ND
ND
ND
Pri
me
r s
et
ME
MB
AC
MM
IC
RESULTS
114
Tab
le 3
2 Q
-PC
R
reaction
para
mete
rs
of
the
sta
ndard
curv
es
by
am
plif
yin
g
the
mcrA
ge
ne
with
the
MS
aet-
set
an
d
MS
arc
-set.
MS
aet =
Meth
anosa
eta
cea
e p
rim
er
set, M
sarc
= M
eth
anosarc
inaceae
pri
mer
set.
ND
= n
ot
dete
rmin
ed
.
Us
ed
str
ain
s/c
lon
es
Slo
pe
R2
Eff
icie
nc
yIn
terc
ep
tS
lop
eR
2E
ffic
ien
cy
Inte
rce
pt
Me
tha
no
ba
cte
ria
les
Me
tha
no
ba
cte
ria
ce
ae
Mb
b.
arb
orip
hilu
s D
SM
11
25
N
DN
DN
DN
DN
DN
DN
DN
D
Mb
. fo
rmic
icu
m D
SM
15
35
ND
ND
ND
ND
ND
ND
ND
ND
Mb
. b
rya
ntii
DS
M 8
63
N
DN
DN
DN
DN
DN
DN
DN
D
Mb
t. t
he
rma
uto
tro
ph
icu
s D
SM
10
53
N
DN
DN
DN
DN
DN
DN
DN
D
Un
cu
ltu
red
arc
ha
eo
n A
TB
-EN
-44
82
-M0
05
ND
ND
ND
ND
ND
ND
ND
ND
Un
cu
ltu
red
arc
ha
eo
n A
TB
-EN
-10
44
7-M
12
2N
DN
DN
DN
DN
DN
DN
DN
D
Me
tha
no
co
cc
ale
s
Me
tha
no
co
cc
ac
ea
e
Mc.
va
nn
ielii
DS
M 1
22
4
ND
ND
ND
ND
ND
ND
ND
ND
Me
tha
no
mic
rob
iale
s
Me
tha
no
mic
rob
iac
ea
e
Mcu
. m
arisn
igri D
SM
14
98
-3
.29
00
.94
51
.01
34
6.4
30
ND
ND
ND
ND
Mcu
bo
urg
en
sis
DS
M 3
04
5
ND
ND
ND
ND
ND
ND
ND
ND
Mf.
lim
ina
tan
s D
SM
41
40
N
DN
DN
DN
DN
DN
DN
DN
D
Msp
. h
un
ga
tei
Mh
1N
DN
DN
DN
DN
DN
DN
DN
D
Un
cu
ltu
red
arc
ha
eo
n A
TB
-EN
-57
46
-M0
17
ND
ND
ND
ND
ND
ND
ND
ND
Un
cu
ltu
red
arc
ha
eo
n A
TB
-EN
-39
60
-M0
30
ND
ND
ND
ND
ND
ND
ND
ND
Un
cu
ltu
red
arc
ha
eo
n A
TB
-EN
-56
42
-M0
13
ND
ND
ND
ND
ND
ND
ND
ND
Un
cu
ltu
red
arc
ha
eo
n A
TB
-EN
-97
79
-M1
44
ND
ND
ND
ND
ND
ND
ND
ND
Un
cu
ltu
red
arc
ha
eo
n A
TB
-EN
-97
59
-M1
48
ND
ND
ND
ND
ND
ND
ND
ND
Me
tha
no
sa
rcin
ale
s
Me
tha
no
sa
rcin
ac
ea
e
Msr.
ba
rke
ri D
SM
80
0N
DN
DN
DN
D-3
.22
00
.97
81
.04
44
1.6
80
Msr.
ma
zei
DS
M 3
64
7N
DN
DN
DN
D-3
.24
00
.99
61
.03
53
5.3
10
Msr.
th
erm
op
hila
DS
M 1
82
5N
DN
DN
DN
D-3
.19
00
.98
61
.05
83
2.7
90
Un
cu
ltu
red
arc
ha
eo
n A
TB
-EN
-39
79
-M0
02
ND
ND
ND
ND
ND
ND
ND
ND
Un
cu
ltu
red
arc
ha
eo
n A
TB
-EN
-56
37
-M0
12
ND
ND
ND
ND
ND
ND
ND
ND
Un
cu
ltu
red
arc
ha
eo
n A
TB
-EN
-56
77
-M0
15
ND
ND
ND
ND
ND
ND
ND
ND
Me
tha
no
sa
eta
ce
ae
Msa
. co
ncili
i D
SM
21
39
3.7
20
0.9
82
0.8
57
42
.82
0N
DN
DN
DN
D
Pri
me
r s
et
MS
ae
tM
Sa
rc
RESULTS
115
In fact, most of the calculated PCR efficiency values ranged between 0.840 and
0.890 which is caused by the amplicon length of the mcrA fragment (amplicon
size = 778 bp). As it is shown in Fig. 21, the CT-values within a dilution step of a
defined number of mcrA gene copies varied among all tested strains. Differences in
primer hybridization might be one reason for this finding.
To ensure the quality of the amplified PCR products a melting curve analysis was
performed after the Q-PCR run (Table 33). All melting curves resulted in one specific
peak meaning that only one PCR product was amplified. No primer-dimer formations
were observed by the evaluation of the dissociation curve analysis. Interestingly, a
variation of the melting curve maxima was determined at the order-specific level. For
Methanobacteriales, the melting curve maxima of the mcrA fragment ranged between
79-84°C while higher temperature values were obtained for the Methanosarcinales
(84-86°C) and Methanomicrobiales (85-88°C). Slightly varying nucleotide sequences
and GC-contents of the mcrA fragment are responsible for the measured differences
in the melting curve maxima.
Specifity of the MBAC-set. Initially 13 archaeal strains were tested with the
MBAC-set. Four of the tested strains belong to the target order Methanobacteriales.
As result of the Q-PCR, all representatives of the Methanobacteriales were detected
with the MBAC-set. The slopes of the standard curves reached values between
-3.370 and -3.720 with R2 > 0.970 (Table 31). Besides all tested strains for
Methanobacteriales, a positive amplification result of the mcrA fragment was
obtained for the methanogenic Archaea of Methanococcus vannielii DSM 1224,
Methanoculleus marisnigri DSM 1498, Methanospirillum hungatei Mh1,
Methanosarcina mazei DSM 3647 and Methanosarcina thermophila DSM 1825.
Here, the slopes varied between -3.130 and -3.500. The signal intensity of the
amplification curves of the false positive tested strains was comparable to those of
the Methanobacteriales strains (Fig. 22).
More than three mismatches were obtained by sequence alignment of the developed
primers and the sequences of the false positive tested strains.
RESULTS
116
From this it follows that the number of mismatches in the applied primers has to be
increased for non-target methanogens to reduce the possibility of false positive
detection.
Table 33 Melting curve maxima of the Q-PCR products of the mcrA gene. ME = universal mcrA primer set, MBAC = Methanobacteriales primer set, MMIC = Methanomicrobiales primer set, MSaet = Methanosaetaceae primer set, MSarc = Methanosarcinaceae primer set. NA = not analyzed. ND = not determined.
Used strains/clones
ME MBAC MMIC MSaet MSarc
(°C) (°C) (°C) (°C) (°C)
Methanobacteriales
Methanobacteriaceae
Mbb. arboriphilus DSM 1125 79.1 77.1 ND ND ND
Mb. formicicum DSM 1535 82.7 81.6 ND ND ND
Mb. bryantii DSM 863 80.9 78.2 ND ND ND
Mbt. thermautotrophicus DSM 1053 84.6 84.1 ND ND ND
Uncultured archaeon ATB-EN-4482-M005 82.7 NA ND ND ND
Uncultured archaeon ATB-EN-10447-M122 84.2 NA ND ND ND
Methanococcales
Methanococcaceae
Mc. vannielii DSM 1224 84.9 79.2 ND ND ND
Methanomicrobiales
Methanomicrobiaceae
Mcu. marisnigri DSM 1498 87.9 83.7 86.3 86.6 ND
Mcu bourgensis DSM 3045 87.9 ND 86.9 ND ND
Mf. liminatans DSM 4140 88.4 ND 82.9 ND ND
Msp. hungatei Mh1 ND 84.3 83.2 ND ND
Uncultured archaeon ATB-EN-5746-M017 86.4 NA 86.1 ND ND
Uncultured archaeon ATB-EN-3960-M030 87.7 NA 85.7 ND ND
Uncultured archaeon ATB-EN-5642-M013 84.6 NA 83.9 ND ND
Uncultured archaeon ATB-EN-9779-M144 86.2 NA 85.9 ND ND
Uncultured archaeon ATB-EN-9759-M148 85.8 NA 84.8 ND ND
Methanosarcinales
Methanosarcinaceae
Msr. barkeri DSM 800 85.0 ND ND ND 80.2
Msr. mazei DSM 3647 85.4 82.7 ND ND 81.0
Msr. thermophila DSM 1825 86.0 82.7 ND ND 80.0
Uncultured archaeon ATB-EN-3979-M002 86.3 NA 85.9 ND ND
Uncultured archaeon ATB-EN-5637-M012 84.0 NA 85.8 ND ND
Uncultured archaeon ATB-EN-5677-M015 86.2 NA 85.9 ND ND
Methanosaetaceae
Msa. concilii DSM 2139 NA ND ND 81.5 ND
Primer set
RESULTS
117
The results of the dissociation curve analysis confirmed the previously described
findings. The melting temperature of the PCR products was in a range of 77.1-84.3°C
for all strains with an expected or false positive amplification result which indicates
that the primer hybridization occurred at the same region of the mcrA gene.
Therefore, it has to be noted that the MBAC-set is non-specific for the
representatives of the Methanobacteriales. Primer sets at family level should be
designed to determine methanogens of this order because the development of an
order-specific primer set seems to be difficult due to the high heterogeneity of the
mcrA sequences.
After determination of the non-specifity of the MBAC-set, further Q-PCR tests using
uncultured archaeon clones from biogas plants were not conducted.
Fig. 22 Comparison of the standard curves obtained by specific detection of the 13 archaeal DNAs using the Q-PCR assay of the MBAC-set. On the x-axis the applied number of mcrA gene copies is given while the y-axis reflects the threshold cycle values (CT values). Bold lines represent the linear regression of all standard curves with positive amplification results while fine lines represent the linear regression of all standard curves with a false positive amplification result. Dotted lines display the negative amplification result of non-target methanogens. Filled symbols (circles = Methanococcus vannielii DSM 1224, squares = Methanoculleus marisnigri DSM 1498, triangles up = Methanospirillum hungatei Mh1, triangles down = Methanosarcina mazei DSM 3647 and diamonds = Methanosarcina thermophila DSM 1825) correspond the mean values of detected CT values by false positive amplification. The mean CT values and standard deviation were performed in triplicate in the same Q-PCR run.
RESULTS
118
Specifity of the MMIC-set. The specifity test of the MMIC-set was performed with
13 archaeal strains and ten uncultured archaeon clones. In the Q-PCR test all strains
of the target order Methanomicrobiales were detected (Fig. 23). As it was expected,
no amplification of the mcrA fragment was observed for all non-target methanogenic
strains by the use of 101-106 mcrA gene copies per reaction volume. Surprisingly,
slightly cross-amplification was observed for almost all non-target methanogens by
the application of higher mcrA gene copy concentrations.
Concerning the uncultured archaeon clones a different picture was obtained. Two of
these clones were classified as potential false negative. One mismatch was observed
between the forward primer and the corresponding DNA sequence of clone
ATB-EN-5642-M013 and one mismatch of each primer was determined for clone
ATB-EN-3960-M030, respectively. As expected the clone ATB-EN-3960-M030
showed comparable results to the non-target methanogenic strains meaning that the
primer hybridization was inefficient. In contrast, the mismatch in the forward primer of
clone ATB-EN-5642-M013 had no effect on the amplification rate of the mcrA gene
and a solid standard curve was obtained (slope = -3.500, R2 = 0.983). A false
negative amplification was observed for clone ATB-EN-5746-M017 even if no
mismatches were verified after aligning the mcrA fragment with the primers of the
MMIC-set. Interestingly, false positive amplification was observed for the archaeon
clones of ATB-EN-3979-M002, ATB-EN-5637-M012 and ATB-EN-5677-M015 which
were assigned to Methanosarcinaceae after nucleotide sequence alignment with all
so far known genome projects of the methanogens. The amplification of the
mcrA fragment was unexpected because five mismatch positions were obtained by
comparing the DNA sequences of the clones with the forward primer of the
MMIC-set. The amplification intensities ranged between the positive detected and the
non-target methanogenic strains which is an indication for slightly cross-amplification.
Regarding the results of the dissociation curve analysis, melting temperatures of
82.9-86.9°C were obtained for all strains of the target order. The determined values
for the melting curve maxima of uncultured archaeon clones varied between 85.8°C
and 85.9°C. In all tested methanogenic strains and archaeon clones no primer-dimer
formation could be observed.
RESULTS
119
Even if the MMIC-set showed a higher specifity for the target genes compared to the
MBAC-set, the primer set is not suitable for absolute quantification.
Fig. 23 Comparison of the standard curves obtained by specific detection of the 13 archaeal and ten uncultured archaeon DNAs from biogas plants using the Q-PCR assay of the MMIC-set. On the x-axis the applied number of mcrA gene copies is given while the y-axis reflects the threshold cycle values (CT values). Bold lines represent the linear regression of all standard curves with positive amplification results while fine lines represent the linear regression of all standard curves with a false positive and false negative amplification result, respectively. Dotted lines display the negative amplification result of non-target methanogens. Filled symbols (circles = ATB-EN-3979-M002, squares = ATB-EN-5637-M012 and triangles = ATB-EN-5677-M015) correspond the mean values of detected CT values by false positive amplification and open symbols (circles = ATB-EN-3960-M030 and squares = ATB-EN-5746-M017) correspond the mean values of detected CT values by false negative amplification. The mean CT values and standard deviation were performed in triplicate in the same Q-PCR run.
Specifity of the MSaet-set. With the MSaet-set the same strains and uncultured
archaeon clones were tested. A solid standard curve was obtained for Methanosaeta
concilii DSM 2139 (Table 32, Fig. 24). With the exception of Methanoculleus
marisnigri DSM 1498, no false positive amplification was observed. From 107 to
109 mcrA gene copies per reaction the detected fluorescence intensity was above the
detection limit for Methanoculleus marisnigri DSM 1498, which indicates slight
cross-amplification.
The melting curve analysis confirmed this finding. A characteristic peak was detected
at a melting temperature of 86.6°C. However, mcrA genes from Methanoculleus
marisnigri DSM 1498 were not as effective detected as those of Methanosaeta
concilii DSM 2139.
RESULTS
120
The weaker detection signal was caused by two mismatches in the forward and six
mismatches in the reverse primer determined by sequence alignment. The slightly
false positive detection of Methanoculleus marisnigri DSM 1498 was unexpected
because the closely related strain of Methanoculleus bourgensis DSM 3045 showed
no cross-amplification with the MSaet-set. The type strain of Methanoculleus
marisnigri was isolated from sediments of the Black Sea which leads to the
assumption that this representative of the genus Methanoculleus is rarely present in
biogas plants.
Therefore, the MSaet-set should be applicable for quantifying the mcrA gene of
Methanosaetaceae in anaerobic digesters and biogas plants.
Fig. 24 Comparison of the standard curves obtained by specific detection of the 13 archaeal and ten uncultured archaeon DNAs from biogas plants using the Q-PCR assay of the MSaet-set. On the x-axis the applied number of mcrA gene copies is given while the y-axis reflects the threshold cycle values (CT values). Bold lines represent the linear regression of all standard curves with positive amplification results while fine lines represent the linear regression of all standard curves with a false positive amplification result. Dotted lines display the negative amplification result of non-target methanogens. Filled symbols (circles = Methanoculleus marisnigri DSM 1498) correspond the mean values of detected CT values by false positive amplification. The mean CT values and standard deviation were performed in triplicate in the same Q-PCR run.
Specifity of the MSarc-set. No false positive amplification was found by in silico
sequence comparison of the primers for the MSarc-set with reference
mcrA sequences. By testing 13 archaeal strains and ten uncultured archaeon clones
this finding was confirmed (Fig. 25).
RESULTS
121
The archaeal clones ATB-EN-3979-M002, ATB-EN-5637-M012 and
ATB-EN-5677-M015 were classified as false negative because one and two
mismatches were obtained by comparison of the oligonucleotides with the DNA
sequences of the uncultured archaeon clones, respectively. As expected, all three
clones showed no amplification in the Q-PCR test. Regarding the results of the three
tested strains of Methanosarcinaceae, solid standard curves were obtained whereas
the amplification of the mcrA gene of Methanosarcina barkeri DSM 800 showed a
slightly delayed reaction compared to Methanosarcina mazei DSM 3647 and
Methanosarcina thermophila DSM 1825 (Table 32, Fig. 25).
Fig. 25 Comparison of the standard curves obtained by specific detection of the 13 archaeal and ten uncultured archaeon DNAs from biogas plants using the Q-PCR assay of the MSarc-set. On the x-axis the applied number of mcrA gene copies is given while the y-axis reflects the threshold cycle values (CT values). Bold lines represent the linear regression of all standard curves with positive amplification results while fine lines represent the linear regression of all standard curves with a false negative amplification result. Dotted lines display the negative amplification result of non-target methanogens. Open symbols (circles = ATB-EN-3979-M002, squares = ATB-EN-5637-M012 and triangles = ATB-EN-5677-M015) correspond the mean values of detected CT values by false negative amplification. The mean CT values and standard deviation were performed in triplicate in the same Q-PCR run.
Because of the short amplicon length of the PCR product (79 bp), the melting
temperature ranged between 80-81°C (Table 33). The derived primer set for
Methanosarcinaceae was target-specific for all tested methanogenic strains. The
observed specifity of the primer set was in accordance with the obtained results of
the potential false positive and false negative amplification analysis.
RESULTS
122
However, the primer set was designed prior to sequencing of the archaeal clones of
the biogas plants. Hence, these sequences were not considered for primer design.
Therefore, the Methanosarcinaceae-specific primer set should be slightly modified for
detecting all uncultured Methanosarcinaceae-like archaeon clones and all so far
known Methanosarcinaceae strains.
Applicability of the primer sets. In conclusion, four group-specific primer sets were
designed and one primer set (ME = universal mcrA primer set), which was developed
by Hales et al. (1996) for conventional PCR-technique, was transferred to the Q-PCR
platform.
Specifity tests of the order-specific primer sets of Methanobacteriales (MBAC-set)
and Methanomicrobiales (MMIC-set) showed that these primer sets were not
applicable for further Q-PCR analyses. The primer sets of ME and MSaet showed
highest specifity for their target groups and over- or underestimation due to false
positive amplification proved to be insignificant in each primer set. In case of the
MSarc-set an amplification of the mcrA fragment was feasible for all tested
methanogenic strains of Methanosarcinaceae. Slight modifications of the MSarc-set
are suggested to combine the detection of Methanosarcinaceae-like archaeon
species and all so far known and classified representatives of the
Methanosarcinaceae.
DISCUSSION
123
7. Discussion
The aim of the present study was to develop and establish a detection method for
quantifying methanogenic Archaea in digesters and biogas plants. The method of
choice is the quantitative real-time PCR which has a great potential to be used as a
reliable, specific and sensitive tool for determining the composition of methanogenic
population structures during the biogas-building process.
Firstly, a group-specific real-time PCR assay which was designed and evaluated by
Yu et al. (2005a) was established and optimized for the detection of methanogenic
communities in biogas plants. These primer and probe sets developed on the basis
of the constitutively expressed 16S rRNA gene provide the possibility to get a first
glance of the methanogenic archaeal flora in biogas plants whereby metabolic activity
measurements based on the analysis of messenger RNA are limited. Therefore, a
second group-specific real-time PCR assay dependent on a metabolic gene of the
methanogenesis metabolism was generated. As target the mcrA gene – an
ubiquitous gene of the methanogens which encodes one peptide of the terminal
enzyme complex MCRI – was chosen.
7.1 Evaluation and optimization of the PCR conditions for amplifying the
16S rRNA gene by using the real-time PCR assay of Yu et al. (2005a)
The need for comparing Q-PCR results from different instruments and platforms and
thus the reliability and biological significance of those results has become more and
more important over the last decade. The transfer of real-time PCR assays from one
detection system to another is often described as an easy and straightforward way
(Silvy et al. 2005, Christensen et al. 2006, Zhang et al. 2007, Arikawa et al. 2008).
Therefore, all Q-PCR parameters of the used primer sets which were performed on
the LightCycler 1.2 (Roche Diagnostics, Mannheim, Germany) by Yu et al. (2005a)
for amplifying the group-specific methanogenic 16S rRNA gene were transferred to
the platform of the ABI 7300 System (Applied Biosystems, Darmstadt, Germany).
DISCUSSION
124
By application of the published PCR mixture and thermocycling profiles of Yu et al.
(2005a), no optimal plasmid standard curves were generated for absolute
quantification (“RESULTS”, chapter 6.1.1). This study showed that the use of a specific
platform can significantly affect the results. This is in accordance with Hermann et al.
(2006) who compared the whole-amplicon melting analysis of the β-globin gene for
genotyping homozygous variants and scanning for heterozygotes by the use of nine
available instruments. With the LightCycler 1.2. and LightCycler 2.0 (Roche
Diagnostics, Mannheim, Germany) PCR products with small Tm differences
(Tm > 0.25°C) could be separated while with the ABI 7000 and ABI 7900 System only
amplicons with melting temperature differences of Tm > 0.5°C and Tm > 1.0°C could
be differentiated, respectively. Hence, the LightCycler System showed a higher
precision and accuracy compared to the real-time PCR instruments of Applied
Biosystems. Even if the real-time PCR instruments are not primarily intended for
melting curve analysis, this study indicates that slight variations in instrument
accuracy can strongly influence the validity of the obtained results.
In addition, Eckert et al. (2003) demonstrated that the ABI 7700 System only
provided comparable quantification results with the LightCycler System if PCR
mixtures were used as recommended by the manufacturer. This leads to the
assumption that the influence of the chemical composition of the PCR mixture is
crucial for obtaining optimal evaluable Q-PCR results. Deduced from this study, it can
be stated that comparable sensitivity and specifity of the PCR instruments can only
be guaranteed if all instructions and recommendations of the manufacturers are
complied. This hypothesis was confirmed by the results in the present study. When
PCR conditions were changed according to the manufacturer’s guidelines of Applied
Biosystems, solid standard curves were obtained for all primer sets (“RESULTS”,
chapter 6.1.1).
Besides the type of instrument platform and the composition of the reaction mixture,
the type of DNA template can influence the PCR efficiency. In the present study
linearized plasmids were applied for constructing standard curves while Yu et al.
(2005a) used genomic DNA as template. The influence of the chosen standard will
be discussed in detail in chapter 7.3.
DISCUSSION
125
Conclusively, it can be stated that transfer of Q-PCR assays from one instrument
platform to another is feasible in most cases, if the suggested PCR protocol is
adjusted accordingly to the respective platform.
7.2 Design and testing of group-specific Q-PCR primers based on the
mcrA gene for the quantification of methanogenic communities
The quantification of methanogens belonging to different phylogenetic groups has
been often conducted by Q-PCR. Most of these studies used the 16S rRNA gene as
target (Yu et al. 2005a, Denman et al. 2007, Watanabe et al. 2007, Frank-Whittle et
al. 2009). Even if the 16S rRNA gene is ideally suited for phylogenetic relationship
studies, there are some major concerns using this specific gene for quantification
analyses.
Firstly, the 16S rRNA gene is highly conserved, meaning that sequence differences
are rare between closely related methanogens. Therefore, the design of
group-specific primers at family and genera levels may be difficult. Another limiting
factor for the application of this specific gene is the varying number of 16S rRNA
genes in the genome of analyzed methanogens. Furthermore, inaccuracies in
quantification results might be caused by a transfer of 16S rRNA genes between
dissimilar species (Springer et al. 1995).
Since Springer et al. (1995) showed that the mcrA gene can be used as a
phylogenetic marker for detecting methanogenic Archaea, this gene became most
important for proving methanogenic communities in different habitats (Luton et al.
2002, Inagaki et al. 2004, Steinberg and Regan 2008). Four primer sets based on the
mcrA gene were developed in this study to distinguish the methanogens concerning
their two main metabolic pathways – hydrogenotrophic and acetotrophic
methanogenesis – in biogas plants. The hydrogenotrophic methanogens could be
determined by the MBAC- and MMIC-set while the aceticlasts could be verified with
the MSaet- and MSarc-set. As a sum parameter for all hydrogenotrophic
methanogens a primer set (ME-set), developed by Hales et al. (1996), for
conventional PCR was transferred to the Q-PCR system based on SYBR Green I
detection.
DISCUSSION
126
Initially, the application of the ME-set on the Q-PCR platform is discussed. After the
optimal Q-PCR conditions were set for amplifying the mcrA fragment, the application
of this specific primer set is feasible for quantifying the hydrogenotrophs in biogas
reactors. A discrimination of methanogenic species with the ME-set could not be
obtained. Nunoura et al. (2008) demonstrated a negative amplification result of the
mcrA gene for Methanobacterium formicicum and Methanosarcina barkeri by
application of this primer set. In contrast to this, an amplification of the mcrA gene
was feasible for both methanogenic species in this study. Reasons for differences in
the amplification behaviour might be caused by varying PCR conditions. This
conclusion is supported by the findings of Juottonen et al. (2006) who stated that
Methanosarcinaceae could not be recovered with the ME-set in their study while
other research groups could retrieve this taxonomic group (Lueders et al. 2001,
Inagaki et al. 2004).
By testing the specifity of the ME-set the obtained standard curves of different
methanogenic species were not coplanar arranged to each other which is an
indication for variable binding affinities of the primers. This is a general disadvantage
of degenerate primers. Selective amplification was often observed for primer sets at
higher phylogenetic levels (Polz and Cananaugh 1998, Juottonen et al. 2006). Even
if this specific PCR-based limitation can not be excluded by generating group-specific
primer sets, degenerate primers are commonly regarded as indispensable for the
characterization and quantification of the majority of target organisms. Therefore, the
ME-set is well applicable for quantifying hydrogenotrophic methanogens in biogas
plants.
The design of order-specific primers for Methanomicrobiales and Methanobacteriales
was attended by some difficulties. Sequence alignments conducted for both
methanogenic groups separately, showed a high heterogeneity. Therefore, the
non-specific detection method based on the fluorescent dye SYBR Green I was
chosen for all Q-PCR applications using the mcrA gene as target. This detection
method needs only two regions of sequence similarity for primer design whereby an
additional region of sequence homology is essential for all specific detection methods
by deriving group-specific fluorescent probes.
DISCUSSION
127
Hence, the design of order-specific Q-PCR primers for Methanobacteriales and
Methanomicrobiales was only feasible if degenerate bases were used (“MATERIALS
AND METHODS”, chapter 5.4.5).
Regarding the results of both tested primer sets cross amplification could be
observed. Hence, the MMIC- and MBAC-set were not applicable for absolute
quantification by Q-PCR. One reason for cross amplification was the number of
mismatches for non-target individuals. In this study the minimum of mismatches for
non-target organisms was set to three. Juottonen et al. (2006) showed that target
organisms could even be detected with five to six mismatches in the primer sequence
which indicates that the number of mismatches has to be increased for guaranteeing
non-amplification. Further investigations have to be conducted for quantifying both
methanogenic phylogenetic orders. Designing TaqMan probes for ME-set could be
one possibility.
Contrary to this, the MSaet and MSarc primer sets are applicable for quantifying
representatives of these taxonomic groups (“RESULTS”, chapter 6.2). With the use of
the primer sets ME, MSaet and MSarc the methanogens can be differentiated in
hydrogenotrophic and acetotrophic methanogens. The ME-set detects all
hydrogenotrophic methanogens (including Methanosarcinaceae), and with the primer
sets of MSaet and MSarc the acetotrophic methanogens could be detected.
However, prior to analysis of reactor samples, further investigations concerning the
optimization of these three primer sets have to be conducted by spiking experiments.
7.3 The influence of different DNA isolation methods on the quantification of
methanogenic Archaea in biogas reactors by real-time PCR
Highly pure DNA extracted from environmental samples is the most important
pre-requirement for all molecular genetic techniques. Analysis procedures, such as
quantitative real-time PCR, PCR-RFLP or PCR-DGGE, are commonly used to
assess the microbial diversity and their dynamics in a broad range of environments.
DISCUSSION
128
However, an unbiased presentation of the total bacterial and archaeal diversity can
only be reached if the microbial genomic DNA is extracted from the environmental
sample in high quality and equally from all taxonomic groups present in the sample.
Harsh DNA extraction by bead beating is often described as most efficient for
analyzing the diversity of the microbial community structure in environmental samples
such as soil or sludge (Yeates et al. 1998, Roh et al. 2006). In the present study, the
DNA yield of DNA solutions, which were extracted with the FastPrep System, ranged
between 18.80 and 41.80 µg mlreactor sample-1 (“RESULTS”, chapter 6.1.2). These results
are in agreement with the findings of Weiss et al. (2007), who compared the
effectiveness of different DNA isolation protocols for municipal biogas plant samples.
There, the DNA concentrations varied between 2.76 and 63.90 µg mloriginal sample-1.
This showed that cell lysis efficiencies from environmental samples taken from similar
habitats were comparable. Although the DNA yield is a useful parameter of effective
cell disruption, it cannot be claimed that an increased DNA yield results in a higher
microbial diversity in an environmental sample (Stach et al. 2001).
To analyze the methanogenic diversity, Q-PCR assays were used to determine the
quantity and diversity of 16S rRNA gene copy numbers of the taxonomic groups
Methanomicrobiales, Methanobacteriales, Methanosaetaceae and
Methanosarcinaceae. Firstly, it can be assessed that all targeted methanogenic
groups were detected if mechanical cell disruption was applied. However, if soft
extraction methods were applied, the number of detected 16S rRNA gene copies
almost increased. Hereafter, some reasons for these findings are discussed in detail.
On the one hand, the mechanical cell lysis with ceramic and silica beads yielded a
larger number of broken maize cells, which changed the ratio from plant to microbial
DNA in favour of maize DNA.
On the other hand, PCR efficiency decreased in DNA solutions obtained with the
FastPrep System because of the large quantity of maize cell wall polysaccharides
(Lei et al. 2006). Optical density measurements allow a first characterization of the
DNA solutions, even if these calculated ratios can be considered only as rough
estimations for DNA purity (Sambrook and Russell 2001).
DISCUSSION
129
Although no protein contamination (A260/A280 ratio) in the harshly extracted DNA
samples (protocols A-C) was detected, a major deviation of the A260/A230 ratios
(~ 0.5) from the optimal value of 1.5-1.8 (Weiss et al. 2007) was obtained, even if
these DNA solutions appeared clear by visual consideration. One potential
explanation for these values could be contamination with carbohydrates. The
appearance of carbohydrates might increase as a result of more efficient lysis of the
maize cells during the mechanical cell disruption process. Lei et al. (2006) showed
that mechanical cell lysis with beads and the use of guanidine thiocyanate in the lysis
buffer led to most efficient DNA yields from maize cells. Additionally, cell walls of
maize are rich in neutral and acidic polysaccharides, which are known PCR inhibitors
and can be easily co-extracted (Holden et al. 2003, Porcar et al. 2007). Another
explanation for these values could be that chaotrophic substances, such as
guanidinium chloride in the buffer solution, might reduce the A260/A230 ratios.
The lower 16S rRNA gene copy numbers in harsh-extracted DNA solutions can also
be caused by shearing of DNA (Weiss et al. 2007). Khandka et al. (1997) showed a
positive correlation between the level of degradation of the DNA and the extent of
reduction in the amplification of 600, 800 and 1000 bp fragments. These findings
could explain the low 16S rRNA gene copy number of the Methanomicrobiales in all
harsh-extracted DNA solutions. The fragment length of the 16S rRNA gene, which is
amplified with the MMB primer set, is about 506 bp. Thereby, this PCR product was
the longest one compared to all other 16S rRNA gene fragment products amplified
with Q-PCR in this study. It can be hypothesized that a longer amplicon size of the
PCR product results in decreased detection by Q-PCR because of a shattered target
gene nucleotide sequence. In addition, the efficiency of amplification is not
necessarily influenced in the same way by PCR inhibitory compounds for different
amplicons. This was shown by Cankar et al. (2006), who quantified genetically
modified organisms in food samples. Therefore, it might be possible that PCR
repressive compounds, which reduce the PCR efficiency of the MMB primer set, had
less influence on the PCR efficiency of MBT and Msc primer sets. The slightly
increased detection of 16S rRNA gene copy numbers by MBT primer set using the
harsh cell lysis approaches supports this hypothesis.
DISCUSSION
130
Another explanation for the increased values of detected 16S rRNA gene copy
numbers of the Methanobacteriales could be the higher efficiency of cell lysis with
mechanical cell disruption. Most representatives of this specific methanogenic group
live in extreme habitats (Garrity and Holt 2001). Therefore, the cell walls are
comparatively robust and cells can only be broken with mechanical cell lysis.
Results of soft cell lysis procedures differed strongly from those which were obtained
by mechanical cell lysis. Here, the DNA yield ranged between 7.14 and
259.00 µg mlreactor sample-1. The largest concentrations of DNA were observed with
DNA extraction protocol D (SDS-based cell lysis). The DNA yields were significantly
higher compared to those of harsh DNA extractions. Other investigations which
compared soft and harsh DNA extraction methods for environmental samples support
these findings. Min et al. (2006) compared four soil DNA extraction methods and
indicated that the SDS-high strength salt method gave larger DNA yields than
mechanical cell lysis by glass beads, and Yang et al. (2007) showed that combined
SDS and lysozyme treatment of the bacterial cell walls in compost should be
preferred to physical treatments.
By comparison of the DNA yield obtained before and after purification with sephacryl
columns, it can be stated that the DNA yield of the purified samples decreased by
one log. An exception was determined using chemical cell lysis by SDS. Here, the
DNA yield was reduced by a factor of 25 (protocols D and E). Two reasons can be
responsible for this finding. First, the extracted DNA contained a very high yield of
cationic substances, since, due to positive charges, DNA was bound to these
substances and this caused less available DNA. Second, the high amount of humic
acid compounds led to blocking of the sephacryl matrix. Residues on top of the filter
hinder the passage of genomic DNA through the filter resulting in decreased DNA
yields. An increase in humic acids in DNA solutions resulted in higher A260/A230 ratios
(protocol H (yellow DNA solution), 2.31 ± 0.57 and protocol I (slightly yellow solution),
1.67 ± 0.22). This indicates that the chemical and biological compounds which
influence the absorption spectra of the DNA solutions from the FastPrep System
differ from those which were isolated by chemical, enzymatic and physical cell
disruption.
DISCUSSION
131
Even if the number of 16S rRNA gene copies was mostly higher by soft cell
lysis-based methods compared to mechanical cell disruption, not all methanogenic
groups were detected by Q-PCR.
No 16S rRNA genes of the methanogenic family Methanosaetaceae were detected
with the SDS-based DNA extraction method (protocol D), which indicates that soft
cell lysis approaches were not as effective as harsh ones. This observation confirmed
with the results of Heuer and Smalla (1997) and Stach et al. (2001), who showed that
the microbial diversity determined by means of PCR-DGGE and PCR-SSCP analysis
in soil was significantly higher in DNA solutions isolated by harsh extraction
compared to those of soft extraction. This implies that soft cell lysis leads to a
discrimination of certain taxonomic groups. Hence, harsh extraction seems to be
more effective for diversity studies than soft isolation protocols (lysozyme, SDS,
sonication).
In conclusion, this study showed that the approach used for DNA preparation strongly
influences the results of subsequent Q-PCR-based quantification of microbial groups.
However, further efforts will be indispensable for developing a less time-consuming
DNA extraction protocol applicable to biogas reactor samples. Therefore, the bead
beating cell lysis should be optimised because of higher cell lysis efficiencies.
Generally, internal standard procedures should be performed in order to recognise
the specific influence of the sample matrix on PCR efficiency. If applicable, additional
cell-based approaches, such as fluorescence in situ hybridization with oligonucleotide
probes and microscopic cell counting (e.g. Burggraf et al. 1994), should also be
applied to substantiate Q-PCR results.
7.4 Influences of PCR interfering substances on Q-PCR-based quantification of
methanogens in biogas reactors
For the estimation of cell lysis efficiency for DNA isolation, spike and recovery
experiments are indispensable. Therefore, a number of different reference standards
are known for analyzing the loss of DNA during nucleic acid extraction such as cells
of target organisms or closely related species and cells containing target DNA or
competitor DNA constructs (Coyne et al. 2005).
DISCUSSION
132
In this study cells of target organisms (Methanosarcina barkeri and Methanoculleus
bourgensis) were used as spike and recovery controls. The advantage of these spike
and recovery controls is that the cell lysis efficiency of these microorganisms is
comparable to those of the target cells of the reactor sample. However, the
competition of primers and probes during Q-PCR can be seen as a disadvantage of
this approach.
Surprisingly, more 16S rRNA gene copy numbers were detected with the primer sets
of ARC and MMB than those predicted from the number of spiked cells (“RESULTS”,
chapter 6.1.3). This finding is in accordance with Koike et al. (2007). Here, the
recovery rates of the 16S rRNA gene varied between 1.16 and 2.24. These values
could be explained by varying extraction efficiencies for the methanogenic Archaea
of the reactor sample. A second possibility for the increased recovery rates might be
a non-uniform distribution of the methanogens in the reactor sample. The more small
particles of organic material are in the reactor sample, the more methanogens could
be found because of the preference for microorganisms to adhere to organic
materials by producing extracellular polymeric substances (Böckelmann et al. 2003).
An overestimation of microorganisms in environmental samples was not only
observed by DNA spike and recovery experiments based on the 16S rRNA gene.
Sen et al. (2007) developed a Q-PCR assay for the detection of Helicobacter pylori in
drinking water by the use of a urease subunit gene (ureA). They detected more gene
copies than the theoretical calculated number. They suggested that the applied cells
used as spike and recovery controls were in process of DNA replication and cell
division, resulting in an increase of the copy numbers. This reason can be excluded
with the utmost probability for the results of the recent study because only slight
variations in cell size were obtained by quantification of the cell number of the two
methanogenic cell cultures with the Multisizer (“MATERIALS AND METHODS”).
In contrast to the spike and recovery experiment of the ARC- and MMB-set, the
recovery values of the primer set Msc ranged between 0.61 and 0.89 which
corresponded to cell lysis efficiencies of 61-89% (“RESULTS”, chapter 6.1.3).
Therefore, the number of Methanosarcinaceae could have been underestimated in
this study.
DISCUSSION
133
One explanation for this finding might be that the cell lysis of this methanogenic
group was affected by the structure of its cell wall. The cell wall of
Methanosarcina sp. is more solid in comparison to other methanogens like
Methanospirillum sp. and Methanomicrobium sp. (Garrity and Holt 2001). A layer of
heteropolysaccharides with sulphate groups is directly located above the protein shell
of the cell wall. This special layer might hamper the cell lysis efficiencies of the
Methanosarcinaceae.
A gene-specific variation in extraction efficiency like it was observed by Koike et al.
(2007) could be excluded because for all primer sets the 16S rRNA gene was used.
However, varying cell lysis efficiencies for the different taxonomic units of
methanogens led to a varying number of target genes in the sample. Regarding the
choice of group-specific primer sets, one has to reckon that primers vary in their
effectiveness to attach to target regions within different taxonomic groups (Housley et
al. 2006). Thus, several species might be excluded during the PCR (Cardinale et al.
2004). Especially primers which should cover a wide range of different species like
those for ARC used in this study might lack an equal efficiency for cross-species
determination.
On the whole recovery rates varied strongly in prior reports. Stoeckel et al. (2009)
obtained very low recovery values which ranged between 2.2% and 5.5% for plasmid
and chromosomal DNA whereas much higher values were found by Lebuhn et al.
(2004) (13-66%) and Koike et al. (2007) (20-200%). These findings underline the
importance of spike and recovery experiments for estimating the real amount of
microorganisms in an environmental sample dependent on the chosen DNA
extraction method.
Conclusively, it can be stated that spike and recovery controls of target organisms
are a useful tool for estimating cell lysis efficiencies. Despite all that an estimation of
cell lysis seems to be difficult by varying isolated DNA amounts from the reactor
sample. For further investigations the addition of closely related species that are not
present in the reactor sample seems to be useful.
DISCUSSION
134
Besides the determination of cell lysis efficiencies the influence of interfering
substances which can be co-extracted in the DNA isolation process has to be
investigated. Certain substances such as humic acids are known inhibitors of PCRs.
In the recent study absolute quantification of methanogens in reactor samples should
be performed with standard curves using linearized plasmids with the target gene
sequence. Even if this approach has been often applied (Galluzzi et al. 2004,
Saengkerdsub et al. 2007), some pitfalls have to be considered. The most important
prerequisite which has to be ensured is the consistency of the amplification
efficiencies of plasmid DNA of the standard and the genomic DNA extracted from the
reactor sample. If this prerequisite is not met, Q-PCRs with different results will be
obtained (Kolb et al. 2003).
Hence, two independent, slightly varying experiments were carried out where known
copy numbers of the 16S rRNA gene were added into the DNA solution of the reactor
sample. In both spiking experiments, no inhibitory effects could be determined on
Q-PCR analyses (“RESULTS”, chapter 6.1.3). This indicates that nearly all interfering
substances which inhibit Q-PCR resulting in false negative results were removed
during DNA preparation. Thus, the quantification of the 16S rRNA gene in reactor
samples is feasible by using plasmids with the target gene sequence as standards. In
case of the second spiking experiment some additional findings were obtained. As
previously described all spiked SSDs were recovered in the Q-PCRs. In most
instances, the expected values were slightly exceeded. In contrast to these findings,
a PCR inhibition would lead to lower concentrations of PCR products, and thus, to
lower 16S rRNA gene copy numbers than expected. Even if all added concentrations
of SSDs were detected in the DNA sample extracted from the biogas reactor, the
increased concentrations of 16S rRNA gene copy numbers using the primer sets of
Archaea and Methanomicrobiales were unexpected. The reasons for these findings
remain unclear.
Yu et al. (2005b) applied a nearly identical approach for evaluating whether the
genomic DNA extracts contained factors that were inhibitory to PCR. They showed
that this method is ideally suited for determining the level of purity of genomic DNA
solutions.
DISCUSSION
135
Unfortunately, investigations concerning to influences of PCR interfering substances
on Q-PCR were often not conducted in earlier reports on quantifying the abundance
of microorganisms in environmental samples which exacerbates the interpretation of
these Q-PCR results (Becker et al. 2000). In further Q-PCR analyses spiking
experiments should always be applied for a better interpretation of the obtained data.
The purity of DNA solutions used for Q-PCR analysis in this study was confirmed by
the comparison of efficiencies of the dilution series of the plasmid standard and the
reactor sample. Comparable PCR efficiencies were obtained for the plasmid standard
and the reactor sample by using the primer sets of ARC and MMB, indicating an
undisturbed Q-PCR run (“RESULTS”, chapter 6.1.3). An influence of PCR-inhibitory
substances on Q-PCR was obtained by the application of the Msc-set where
efficiency decreased from 0.802 of the plasmid standard to 0.629 of the reactor
sample. Reasons for these findings could be the increase of PCR interfering
substances in the lower diluted concentrations of the reactor sample. This result
underlines the hypothesis that the potential for tolerating low concentrations of
PCR-inhibitory substances varies by the use of different primer sets. Even if the
quantification for Methanosarcinaceae is effected by higher concentrations of DNA
per PCR, no influence could be observed for DNA concentrations less than 1 ng.
Hence, the determination of 16S rRNA gene copies of Methanosarcinaceae was
feasible for all reactor samples analyzed in this study.
When abundances of microbial species are quantified, Q-PCR standards are mostly
performed by using either plasmids with known numbers of target genes or genomic
DNA of a type strain (Saito et al. 2002, O´Reilly et al. 2009, Steinberg and Regan
2009). Even if these standards are often applied for Q-PCR analysis comparable
studies of both standard types are rare. In this study both standard procedures were
performed and compared to analyze if the chosen standard has an effect on the
Q-PCR results.
DISCUSSION
136
A reaction delay was observed for all Q-PCRs using the genomic DNA standard. This
indicates that effectiveness of primer binding varied between both standard types
meaning that PCR conditions have to be optimized for every applied standard.
Reasons for different primer binding efficiencies are caused due to the structure of
both template types. Where an optimal primer binding can be expected for linearized
plasmids, an insufficient primer binding by using genomic DNA samples might be
caused by varying structure properties of the genomic DNA (Ghosh and Bansal
2003).
In summary it can be stated that both standard types are applicable for quantifying
target genes in reactor samples whereas optimized PCR conditions have to be found
for each respective chosen standard. For ensuring that the standard and the reactor
sample work with comparable PCR efficiencies a comparison of the Q-PCR
parameters for both DNA solutions should be performed to obtain optimal Q-PCR
results.
7.5 Determination of methanogenic Archaea abundances in semi-continuous
fermentation and acidification by overloading in a short-run experiment
During the first seven weeks, both the low acid concentrations and the stable pH of
the reactor offered favourable conditions for methanogenesis. In this study, biogas
yields were estimated based on the current OLR and gas production. These
estimates are in the range of the results presented by Mähnert et al. (2007) and
Souidi et al. (2007), who also investigated mesophilic biogas reactors operated with
maize silage. Furthermore, the biogas yield amounted to approx. two-thirds of the
benchmark proposed for maize silage (KTBL 2005). Possibly, the applied overloading
conditions decreased the average biogas yield. The methane content closely
corresponded to the values proposed by KTBL (2005). Current publications have
focused on the association between acid concentration and digester’s performance.
Chynoweth et al. (1999) reported that fermentation was inhibited when the acid
content exceeded 10 g l-1. The severe drop in pH and methane production observed
in the present study when total acid concentrations of 6.8 and 16.9 g l-1 were
measured on days 57 and 63 certainly affirmed this report.
DISCUSSION
137
In this study, this ratio increased prior to the occurrence of the drop in the pH. Hence,
monitoring the propionic to acetic acid ratio allows an earlier detection of digester’s
imbalance than the pH. That regard, Marchaim and Krause (1993) and Nielsen et al.
(2007) observed that immediately after raising the OLR, the ratio of propionic to
acetic acid increased, indicating an overload effect prior to changes in pH or in
methane production. Methanosaetaceae outcompete Methanobacteriales,
Methanomicrobiales and Methanosarcinaceae during the first 5 weeks.
Methanosaetaceae are known to be competitive aceticlastic methanogens in
environments with low acetate concentrations (Griffin et al. 1998; Yu et al. 2006).
This is in accordance with the findings of this study because Methanosaetaceae was
predominant whilst acid concentrations were low. Interestingly, Methanosaetaceae
16S rRNA gene copies were no longer found after the propionic and acetic acid
concentrations had increased to 1.2 and 0.3 g l-1 on day 49 and the propionic to
acetic acid ratio had risen to 3.5, respectively (“RESULTS”, chapter 6.1.4, Fig. 14
and 15). Consequently, one major outcome of this study is the finding that process
instability of the digester was accompanied with the disappearance of
Methanosaetaceae.
Inhibition of methanogenesis was also found to be accompanied by an increase in
propionate production (van Nevel and Demeyer 1977). Furthermore, Marchaim and
Krause (1993) pointed out the great potential of the propionic to acetic acid ratio as
indicator of important changes within anaerobic digesters, as quoted before. This
might be related to an inhibition of several methanogenic groups, such as inhibition of
Methanosaetaceae found in this study. Certainly, other factors, such as the
ammonium concentration, not investigated here, may also contribute to the decrease
in Methanosaetaceae. To solve this, it is recommendable to conduct a similar study
in which the critical phase is investigated in more detail.
Although detected in only two of ten CSTR samples, Methanosarcinaceae 16S rRNA
gene copy numbers were extremely high. In contrast, Methanosarcinaceae were
found to be rarely represented or not present in continuously fed, mesophilic
digesters fed with triticale silage and municipal solid waste with sludge (McMahon et
al. 2004; Klocke et al. 2008).
DISCUSSION
138
Whether the late absence of aceticlastic methanogens caused an increase of
syntrophic acetate oxidation by syntrophic bacteria, as assumed by Hansen et al.
(1999), Schnurer et al. (1999) and Karakashev et al. (2006), could not be
determined. However, a significant growth of such bacteria from day 56 onwards is
highly improbable, because acetic acid did not appear to be degraded during the
terminal phase (“RESULTS”, chapter 6.1.4, Fig. 13 and 14B).
A re-emergence of Methanomicrobiales was recorded on day 63, when considerable
numbers of its 16S rRNA gene were found 2 weeks after Methanomicrobiales had
disappeared. It can be hypothesized that this was caused by the prior increase in
acid concentration, or the drop in pH. Apparently, Methanomicrobiales were not
detected at all, or were very poorly represented in the samples with a pH higher
than 7.0. This reflects to the pH optimum for most species of Methanomicrobiales of
6.1–7.0 (Garrity and Holt 2001).
For Methanobacteriales, growth conditions were optimal (Garrity and Holt 2001).
From the sixth week on, Methanobacteriales copy numbers were highest,
outcompeting Methanomicrobiales, Methanosarcinaceae and Methanosaetaceae.
Comparing results from Figs 14 and 15, it is apparent that neither increased propionic
to acetic acid ratios, nor high total acid concentrations, nor a low pH had a negative
impact on the abundance of Methanobacteriales. Among the analysed methanogens,
only several species of Methanobacteriales are able to grow at a pH of about 5
(Garrity and Holt 2001). Regarding their acetate tolerance found here, a study based
on rRNA analysis showed that elevated acetate concentrations up to 8 g l-1 strongly
increased the activity of Methanobacteriales (McMahon et al. 2004). Simultaneously,
Archaea and most methanogens were inhibited.
The shift from Methanosaetaceae to Methanobacteriales during prolonged
fermentation is in accordance with findings of Karakashev et al. (2006). They
demonstrated that the volatile fatty acid concentration significantly influences the
predominant methanogens. When, at first, the propionic to acetic acid ratio and
secondly the total acid concentration notably increased on days 42 and 49, the most
severe changes in methanogenic community structure occurred.
DISCUSSION
139
The very low percentage of Archaea (0.1-1.2%) of the overall 16S rRNA gene found
in this study suggests that the domain was not well represented. This contradicts
expectations, because other studies revealed abundances of Archaea which varied
between 17 and 34% in comparably conditioned digesters (Liu et al. 2002; Klocke et
al. 2008; Nettmann et al. 2008).
Concerning the estimation of cells based on 16S rRNA gene, it was previously shown
that 16S rRNA gene copy numbers in cells differ substantially. Klappenbach et al.
(2001), Vezzi et al. (2005) and Samuel et al. (2007) found 16S rRNA gene copies in
the genome of chosen species of methanogens and Bacteria, ranging from 1 to 15.
This implies that alterations in cell numbers of the groupings might not have been as
distinctive as those of the corresponding 16S rRNA genes. Bacteria might not have
been present in that superior number as their 16S rRNA gene copies suggest. Other
investigations that aim at the characterization of the methanogenic composition, as
have been done by various authors, may help to tackle this issue (Shigematsu et al.
2004; Conrad 2005; Calli et al. 2006; Lessner et al. 2006; Nettmann et al. 2008;
Rastogi et al. 2008; Zhang et al. 2008). This aims at the comparison of detected copy
numbers for the housekeeping gene with other gene transcripts which encode
methanogenspecific enzymes (e.g. mcrA-gene).
In conclusion, a high variability in the composition of the methanogenic flora was
observed during the continuous increase of OLRs which was provoked by the
acidification of the digester. In this context, Methanosaetaceae might be taken as
biological indicator for process instability.
7.6 Methanogenic population dynamics in semi-continuous fermentation and
acidification by overloading under mesophilic and thermophilic conditions in a
long-run experiment
After an in-depth study of changes in the archaeal community structure during
mesophilic methanization by overloading in a short-time experiment, results of a
long-time fermentation with comparable operational conditions shall be discussed.
DISCUSSION
140
As it was observed in the short-time experiment, representatives of the family
Methanosaetaceae could only be detected in the start-up phase of the anaerobic
digestion. Here, too, the acetate concentration can be seen as an influence factor for
the presence or absence of this methanogenic group because only at the very
beginning of the fermentation low acetate concentrations could be determined
(“DISCUSSION”, chapter 7.5). A dramatic decrease of Methanosaetaceae following the
start-up was often observed by analyzing the methanogenic community structure
during semi-continuous fermentations (Griffen et al. 1998, Pender et al. 2004, Qu et
al. 2009).
Besides the acetate concentration, higher ammonium concentrations inhibit the
growth of Methanosaetaceae. From the second sampling until acidification the
concentration of NH4-N varied between 2.19 and 2.39 g l-1 (Mähnert 2007). A growth
limitation of Methanosaeta concilii was observed by Steinhaus et al. (2007) at
ammonium concentrations above 1.1 g l-1. Therefore, both, the increase of the
acetate concentration and the high ammonium concentration are responsible for the
non-detection of Methanosaetaceae ongoing from sampling week 26.
Methanosarcinaceae are known for tolerating higher acetate and ammonium
concentrations compared to Methanosaetaceae (Batstone et al. 2002, Karakashev et
al. 2006, Yu et al. 2006, Lee et al. 2009). Hence, this methanogenic group was
detected during the whole fermentation process while the number of 16S rRNA gene
copies decreased slightly from the start-up to acidification. This result is in contrast to
the short-time experiment where detectable amounts of Methanosarcinaceae could
only be verified at OLRs less than 3.0 kg m-3 d-1. One feasible explanation for this
difference could be a variation in substrate availability. Lee et al. (2009) showed that
Methanosarcinaceae communities had different biokinetic characteristics in digesters
which only varied in the carbohydrate to protein ratio. Even if maize silage was used
as the main substrate in the long- as well as in the short-time experiment of this
study, slight variations in the carbohydrate to protein ratio can not be excluded.
DISCUSSION
141
The hydrogenotrophic orders Methanomicrobiales and Methanobacteriales reacted
differently upon increase of OLRs in the long-time experiment. While the number of
16S rRNA gene copies decreased for the Methanomicrobiales, a consistent value of
gene copies was detected for the Methanobacteriales during the fermentation
process (“RESULTS”, chapter 6.1.4). Two reasons can be adduced for this finding.
Initially, the predominance of one hydrogenotrophic species can directly be
influenced by the accumulation of propionate. For the sampled CSTR a sudden
increase of the propionate concentration was determined from week 36
to 46 (Mähnert 2007). As it was shown by Hori et al. (2006), increasing propionate
concentrations in anaerobic digesters can result in a shift from a Methanomicrobiales
(Methanoculleus sp.) dominated methanogenic community structure to a
Methanobacteriales (Methanothermobacter sp.) dominated one. Moreover, they
stated the possibility that the VFA concentration which is closely related to the
dissolved hydrogen concentration can play a crucial role for the dominance of one
specific hydrogenotrophic order during anaerobic digestion because the affinity to the
hydrogen concentration varies within the hydrogenotrophic methanogens.
Interestingly, the shift from Methanomicrobiales to Methanobacteriales by a
continuous increase of the OLR could not be determined in the short-time
experiment.
To analyze if the temperature has an effect on the development of the methanogenic
community structure during biogas fermentation and acidification by overloading, a
long-time experiment was carried out under thermophilic conditions.
Different reports showed that representatives of Methanosaetaceae are often missing
under a thermophilic temperature regime (Petersen and Ahring 1991, Pender et al.
2004, Krakat et al. 2010). These findings were confirmed by the obtained Q-PCR
results in the recent study. As it has been already described for the
mesophilic-operating CSTRs, only a minor fraction of Methanosaetaceae was verified
at the very beginning of the fermentation process. Therefore, all explanations which
were adduced for the disappearance of Methanosaetaceae under mesophilic
conditions can be assigned to thermophilic digesters as well. Furthermore, Chen et
al. (1983) indicated that utilization of acetate may be difficult for acetotrophic
methanogens under a thermophilic temperature regime.
DISCUSSION
142
That temperature can not be seen as the only factor affecting the presence or
absence of Methanosaetaceae in biogas reactors was demonstrated by McHugh et
al. (2003) and Bourque et al. (2008). They found Methanosaetaceae in anaerobic
digesters with a working temperature of 55°C.
A dominance of Methanosarcinaceae was often established by quantifying
methanogens in bioreactors under thermophilic conditions (Mladenovska et al. 2006,
Leven et al. 2009). Even if this taxonomic group was not predominant in CSTRs
analyzed in this study, slightly varying amounts were detected during the whole
fermentation process. The consistency of Methanosarcinaceae could be caused by
the ability of conducting acetotrophic as well as hydrogenotrophic methanogenesis.
At higher temperatures an increased formation of methane from H2/CO2 rather than
acetate was shown by Fey and Conrad (2000). They used the stable carbon isotope
signatures of CO2 to quantify the relative contribution of these two methanogenic
pathways. Therefore, it can be assumed that the production of methane from H2/CO2
is the preferred metabolic pathway under thermophilic conditions for this taxonomic
group. However, the importance of the hydrogenotrophic methanogenesis for
Methanosarcinaceae in biogas reactors has not been determined extensively.
Moreover, Zinder (1993) hypothesized that Methanosarcinaceae are unable to
compete for hydrogen with other hydrogenotrophic methanogens. Further
investigations have to solve the question, how Methanosarcinaceae regulate their
metabolic pathway in presence of representatives of Methanobacteriales and
Methanomicrobiales under thermophilic conditions.
The findings of Fey and Conrad (2000) that the hydrogenotrophic methanogenesis is
preferred under thermophilic conditions go conform to the obtained results of this
study. The majority of methanogens belonged to the hydrogenotrophic orders of
Methanobacteriales and Methanomicrobiales whereby the growth of both taxonomic
groups differed strongly during thermophilic digestion. As it was observed under
mesophilic conditions, a shift from a Methanomicrobiales dominated
hydrogenotrophic community structure to a Methanobacteriales dominated one was
detected. Reasons for these fluctuations were discussed above.
DISCUSSION
143
In summary, the development of the methanogenic community structure was
comparable in both temperature regimes. This indicates that increased OLRs
achieved a similar adaptation of the methanogenic flora to changing physical and
chemical parameters of the reactor content.
7.7 Determination of the methanogenic community in biogas reactors with
different substrates for anaerobic digestion under mesophilic and thermophilic
conditions
By the application of different substrates used for biomethanization variations in the
obtained biogas and methane yield were determined (Mähnert 2007). This
investigation leads to the assumption that the bacterial and archaeal activity varies
with the substrates. Plant material such as so called “energy” crops as main or sole
substrate is of raising importance in biogas production. Therefore, in this study, the
methanogenic community structure was analyzed in biogas reactors utilizing fodder
beet silage, maize silage and cattle manure.
Comparing the results on the population structure of methanogens main differences
could be verified by using different kind of substrates (“RESULTS”, chapter 6.1.4). The
contents of volatile fatty acids, acetate, propionate and ammonium as well as the
temperature regime and the pH value were determined as the main factors affecting
the presence or absence of one specific methanogenic group. Hence, it can be
stated that the substrate chosen for anaerobic digestion has an indirect influence on
the composition of methanogens in biogas reactors.
The process of biogas formation is categorized in a four-stage pathway where the
acetotrophic and hydrogenotrophic methane formation are the terminal steps
(“INTRODUCTION”). Hydrolytic, fermentative and acetogenic Bacteria utilize the organic
material to the chemical compounds and substrates which are required for
methanogenesis. Therefore, the hydrolytic Bacteria are those microorganisms which
are directly influenced by the applied substrate. Jeroch et al. (2008) investigated the
potential of plant material on anaerobic digestion.
DISCUSSION
144
They stated that the organic dry weight, the amount of water-soluble carbohydrates
and the buffering capacity of the plant material are the main criteria for the biological
degradability. Consequently, the first three stages of the biogas formation process
are responsible for the physical and chemical characteristics of the bioreactor content
where methanogenesis occurred.
Comparing the methanogenic composition in biogas reactors using the same
substrate under mesophilic and thermophilic conditions, a general reduction of the
methanogenic diversity was observed upon temperature increase. This is in
accordance with Leven et al. (2007) who provided a higher number of OTUs in a
clone library of a mesophilic working reactor compared to those of a thermophilic
biogas digester. Besides the reduction of methanogenic diversity at higher
temperatures, an increase of the hydrogenotrophic methanogens accompanied by a
decrease of acetotrophic ones was observed in biogas reactors which were fed with
fodder beet silage and maize silage, respectively (“RESULTS”, chapter 6.1.4). This
phenomenon has been reported by a number of different studies. Here,
hydrogenotrophic methanogens showed a much higher tolerance to stressed reactor
conditions than acetoclastics (Schnurer et al. 1999, Pender et al. 2004).
Conclusively, it can be summarized that the use of different substrates showed an
indirect influence on the composition of the methanogens in biogas reactors.
Hydrolytic Bacteria form the basis for the biological degradation of the supplied
substrates used for biogas production. The metabolic products generated by all
hydrolytic, fermentative and acetogenic Bacteria are responsible for the
characteristics of the bioreactor content which mainly influence whether
hydrogenotrophic or acetotrophic methanogenesis is preferred.
DISCUSSION
145
7.8 Determination of the methanogenic Archaea in agricultural biogas plants
After the determination of the methanogenic community structure in laboratory scale
biogas fermenters under varying conditions, the composition of the methanogenic
Archaea was examined in reactor samples of agricultural biogas plants. One of the
major questions for this investigation was if the detected methanogenic community
structure is comparable to those determined in laboratory-scale CSTRs.
Among the methanogenic Archaea, representatives of Methanomicrobiales were the
most prevalent taxonomic group in nine of the ten sampled biogas plants. This is in
accordance with recent studies where Methanomicrobiales were the predominant
order in mesophilic as well as in thermophilic operated biogas plants (Kröber et al.
2009, Weiß et al. 2009). Therefore, it can be stated that the Methanomicrobiales are
mainly responsible for the methane production in agricultural biogas plants. However,
studies by several authors have revealed that approx. 70% of the methane generated
is derived from acetate (Mackie and Bryant 1981). This leads to the assumption that
a high amount of acetate which is produced by acetogenic Bacteria has to be
converted to CO2/H2 by syntrophic acetate-oxidizing Bacteria (Ahring 1995). FISH
analyses by Hori et al. (2006) showed that Methanoculleus sp. lay adjacent to
road-shaped bacteria. This finding implies on the one hand that these bacteria have
the ability to convert acetate into the basic substrates for hydrogenotrophic
methanogenesis. On the other hand a low partial pressure of hydrogen which
improves the unfavourable thermodynamic potential of the acetate oxidation
(ΔG0’ = + 104.6 kJ mol-1) can be ensured by Methanoculleus sp.. So far, only few
bacterial species are known to degrade acetate to CO2/H2 in syntrophy with
hydrogenotrophic methanogens (Nettmann et al. 2010). Interestingly,
Methanoculleus sp. was also be assigned as the predominant genus in an
agricultural biogas fermenter operating at mesophilic conditions by using
454-pyrosequencing technology (Schlüter et al. 2008). This investigation underlines
the importance of this methanogenic genus for methane formation in biogas plants.
DISCUSSION
146
Representatives of the Methanobacteriales could be detected in low abundances in
all ten sampled biogas plants. This indicates that this methanogenic group seems to
play a minor role in producing biogas in these agricultural habitats. No correlation
between the VFA concentration and the predominance of one hydrogenotrophic
group could be determined as it was suggested by Hori et al. (2006). Therefore,
besides the VFA concentration other chemical, physical and biological parameters
have to be responsible for the presence or absence of one specific hydrogenotrophic
methanogenic group.
The physiological parameters of syntrophic bacteria which influence the composition
of the hydrogenotrophic methanogens in biogas reactors could be one important
aspect. Up to now, only little is known about the association of syntrophic Bacteria
and their methanogens. Therefore, these interactions have to be further investigated
leading to a better understanding of the factors which are crucial for the composition
of the microbial community structure in biogas plants.
Even if the hydrogenotrophic methanogens were most abundant in almost all biogas
plants, acetate converting methanogens could be detected as well. While
Methanosaetaceae were detected in six of the sampled biogas plants by Q-PCR
analysis, the occurrence of Methanosarcinaceae could only be assumed because the
detected number of 16S rRNA gene copies lay below the limit of quantification
(“RESULTS”, chapter 6.1.4). Furthermore, the primer set for the detection of
Methanosarcinaceae was slightly influenced by the background of the reactor sample
which led to the assumption that the presence of this methanogenic group might be
underestimated. This hypothesis was confirmed by Nettmann et al. (2010). They
analyzed the allocation of the methanogenic community of the biogas plants sampled
in this study by FISH and 16S rRNA gene clone libraries. As an example, in biogas
plant BA9 less than 1% of all detected archaeal 16S rRNA gene copies could be
assigned to Methanosarcinaceae by Q-PCR whereas 4% and 30% of the whole
methanogenic community could be allocated to this methanogenic family by the
PCR-RFLP analysis combined with clone library analysis and FISH analysis,
respectively.
DISCUSSION
147
These findings underline the importance of using polyphasic approaches for the
determination of the microbial community structure in environmental samples for
ensuring the validity of the obtained results because every applied method has its
own known limitations and pitfalls (Baker et al. 2003; Juottonen et al. 2005).
Conclusively, it can be stated that the hydrogenotrophic methanogenesis is the main
metabolic pathway for methane formation in biogas plants while acetate degradation
by methanogens seems to be inferior.
OUTLOOK
148
8. Outlook
With the optimized conditions for DNA extraction, DNA purification and the real-time
PCR protocol for detecting the 16S rRNA gene in biogas reactor samples, an optimal
molecular genetic tool is given for analyzing the development of methanogenic
Archaea in biogas reactors over time.
Besides the chemical parameters, Q-PCR results are useful for the right estimation if
a biogas reactor works under stable conditions or if the biogas-forming process
becomes instabile. Methanosaetaceae seems to be a biological indicator for process
instability in mesophilic reactor types. Therefore, especially this methanogenic group
is of utmost interest for further investigations.
As ist was shown by Nettmann et al. (2010) representatives of the so far uncultivated
potential methanogens of the CA-11 and ARC-I are also present in the methanogenic
community structure of biogas plants. Hence, a design of two new primer sets for the
CA-11 and ARC-1 group might be useful for limiting the number of 16S rRNA gene
copies numbers which were detected with the Archaea-specific primer set but which
could not be allocated to one of the classified methanogenic groups.
Metabolic activity measurements based on the mcrA gene become feasible with the
application of the primer sets ME, MSarc and MSaet. The possibility of following the
two main metabolic pathways for methane formation allows a deeper understanding
of the biogas-forming process in biogas plants. With the ME-set all hydrogenotrophic
methanogens, including the Methanosarcinaceae, are detected. Representatives of
the strictly acetotrophic Methanosaetaceae are detected with the MSaet-set and
Methanosarcinaceae, which are able to produce biogas with both metabolic
pathways, are detected with the MSarc-set.
Hence, the first most important objectives are the development of an optimal RNA
isolation protocol for samples taken from biogas reactors and plants and the finding
of the optimal reverse transcriptase for transcribing the messenger RNA into
complementary DNA.
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LIST OF FIGURES
168
List of figures
Fig. 1 Four-stage pathway of anaerobic digestion from particulate organic material to methane
(modified after Weiland 2010) ............................................................................................................... 18
Fig. 2 Principle of a Q-PCR application using the standard curve method for absolute quantification 26
Fig. 3 Principle of Q-PCR by using SYBR Green I as the fluorescent dye for absolute quantification . 28
Fig. 4 Principle of Q-PCR by using the TaqMan fluorescent probe for absolute quantification ............ 29
Fig. 5 Ribbon diagram of the methyl-coenzyme M reductase (MCR) with all subunits and the structure
of F430 (Shima et al. 2002) ..................................................................................................................... 34
Fig. 6 Standard curves of the primer sets ARC-set with the applied PCR conditions according to Yu et
al. (2005a) and the suggested PCR mixture and thermocycling conditions of Applied Biosystems,
BAC-set, MMB-set, MBT-set, Msc-set and Mst-set with the PCR conditions suggested by Applied
Biosystems without and with optimized annealing temperature generated by an analysis of the
amplification of the 16S rRNA gene by a dilution series of the primer set specific plasmid.................. 66
Fig. 7 DNA preparations of a biogas reactor sample ............................................................................ 68
Fig. 8 16S rRNA gene copy numbers for Bacteria and methanogenic Archaea in the biogas
fermentation as determined by Q-PCR (detected in 1 ng of DNA) ....................................................... 71
Fig. 9 16S rRNA gene copy numbers for Bacteria and methanogenic Archaea in the biogas
fermentation as determined by Q-PCR (detected in 1 ml of the reactor sample) ................................. 73
Fig. 10 Comparison of spiked and non-spiked standard curves of the primer sets ARC, MMB and Msc
by an analysis of the amplification of the 16S rRNA gene by a dilution series of the primer set specific
plasmid .................................................................................................................................................. 78
Fig. 11 Comparison of the dilution series of the reactor samples and the plasmid standard curves
using the primer sets ARC, MMB and Msc by an analysis of the amplification of the 16S rRNA gene 81
Fig. 12 Comparison of the standard curves using genomic DNA of methanogenic cultures and
plasmids as DNA template .................................................................................................................... 84
LIST OF FIGURES
169
Fig. 13 Gas production and feeding rate during the fermentation ........................................................ 86
Fig. 14 Chemical composition of the process fluid of the fermentation determined by gas
chromatography and pH measurements ............................................................................................... 87
Fig. 15 Methanogenic population dynamics determined by Q-PCR of 16S rRNA gene copy numbers
for Bacteria and methanogenic Archaea during biogas fermentation and acidification by overloading 89
Fig. 16 Quantification of the 16S rRNA gene copy numbers for Bacteria and methanogenic Archaea
during biogas fermentation with organic loading rates (OLR) of < 2.0 kg m-3
d-1
, 2.0 kg m-3
d-1
,
2.7 kg m-3
d-1
and 4.2 kg m-3
d-1
at mesophilic conditions ..................................................................... 92
Fig. 17 Quantification of the 16S rRNA gene copy numbers for Bacteria and methanogenic Archaea
during biogas fermentation with organic loading rates (OLR) of < 2.0 kg m-3
d-1
, 2.2 kg m-3
d-1
,
3.0 kg m-3
d-1
and 3.3 kg m-3
d-1
at thermophilic conditions ................................................................... 93
Fig. 18 Quantification of the 16S rRNA gene copy numbers for Bacteria and methanogenic Archaea
during biogas fermentation by the use of the substrates fodder beet silage and maize silage at
mesophilic conditions ............................................................................................................................ 96
Fig. 19 Quantification of the 16S rRNA gene copy numbers for Bacteria and methanogenic Archaea
during biogas fermentation by the use of the substrates fodder beet silage, maize silage and cattle
manure at thermophilic conditions ......................................................................................................... 97
Fig. 20 Relative frequency of detected 16S rRNA gene copy numbers for the methanogenic, archaeal
groups of the Methanomicrobiales, Methanobacteriales, Methanosaetaceae and Methanosarcinaceae
in 10 sampled biogas plants ................................................................................................................ 103
Fig. 21 Comparison of the standard curves obtained by specific detection of the twelve archaeal and
ten uncultured archaeon DNAs from biogas plants using the Q-PCR assay with the universal
mcrA primer set of ME ......................................................................................................................... 112
Fig. 22 Comparison of the standard curves obtained by specific detection of the 13 archaeal DNAs
using the Q-PCR assay of the MBAC-set ........................................................................................... 117
Fig. 23 Comparison of the standard curves obtained by specific detection of the 13 archaeal and ten
uncultured archaeon DNAs from biogas plants using the Q-PCR assay of the MMIC-set ................. 119
LIST OF FIGURES
170
Fig. 24 Comparison of the standard curves obtained by specific detection of the 13 archaeal and ten
uncultured archaeon DNAs from biogas plants using the Q-PCR assay of the MSaet-set ................ 120
Fig. 25 Comparison of the standard curves obtained by specific detection of the 13 archaeal and ten
uncultured archaeon DNAs from biogas plants using the Q-PCR assay of the MSarc-set ................ 121
LIST OF TABLES
171
List of tables
Table 1 Development of operating biogas plants and their installed electrical power in Germany
between 1999 and 2010 ........................................................................................................................ 15
Table 2 Main characteristics of the analyzed laboratory scale biogas reactors ................................... 40
Table 3 Physical and chemical parameters of the analyzed laboratory scale biogas reactors ............ 41
Table 4 Parameters of the analyzed biogas plants ............................................................................... 43
Table 5 Bacterial and archaeal cultures or respective genomic DNA used in this study...................... 46
Table 6 PCR primers targeting the 16S rRNA genes of different methanogenic reference species .... 51
Table 7 Characteristics of the primer and probe sets for amplifying the 16S rRNA gene by Q-PCR ... 55
Table 8 Primer sets for amplification of the mcrA gene used for cloning ............................................. 62
Table 9 Primer sets for amplification of the mcrA gene used for Q-PCR ............................................. 63
Table 10 Comparison of the analysed DNA amounts and the tests for co-extraction of contaminants by
using different DNA extraction protocols (A-I) ....................................................................................... 69
Table 11 PCR amplification of bacterial 16S rRNA gene using dilution series of DNA samples obtained
by different extraction protocols (A-I) as templates ............................................................................... 70
Table 12 Parameters of the standard curves for 16S rRNA gene targeting Q-PCR ............................ 70
Table 13 Taxonomic allocation of the methanogenic Archaea within a CSTR as determined by Q-PCR
analyses ................................................................................................................................................. 75
Table 14 Summary of the validation of DNA extraction ........................................................................ 76
Table 15 Quantitative real-time PCR (Q-PCR) reaction parameters of the standard curves used for the
second spiking experiment .................................................................................................................... 79
LIST OF TABLES
172
Table 16 Ratios of 16S rRNA gene copy numbers determined by group-specific quantitative real-time
PCR (Q-PCR) using total microbial DNA derived from the biogas reactor sample of day 35 spiked with
a standard DNA dilution series (SSD) and the SSD without addition of foreign DNA, respectively, as
templates ............................................................................................................................................... 80
Table 17 Parameters of the dilution series of the reactor sample (gDNARS) and the plasmid standard
curves (Plasmid) for 16S rRNA gene targeting Q-PCR......................................................................... 82
Table 18 Parameters of genomic DNA standard curves (gDNAC) and the plasmid standard curves
(Plasmid) for 16S rRNA gene targeting Q-PCR .................................................................................... 83
Table 19 Parameters of the standard curves for Q-PCR used for estimating the 16S rRNA gene copy
numbers in reactor samples during semi-continuous biogas fermentation and acidification by
overloading in a short-run experiment ................................................................................................... 88
Table 20 Q-PCR reaction parameters of the standard curves used for estimating the 16S rRNA gene
copy numbers in reactor samples during semi-continuous biogas fermentation and acidification by
overloading in a long-term experiment .................................................................................................. 90
Table 21 Q-PCR reaction parameters of the standard curves used for estimating the 16S rRNA gene
copy numbers in reactor samples during semi-continuous biogas fermentation by the application of
different substrates ................................................................................................................................ 95
Table 22 Q-PCR reaction parameters of the standard curves used for estimating the 16S rRNA gene
copy numbers in reactor samples of 10 biogas plants ........................................................................ 101
Table 23 Limit of detection (LOD) and limit of quantification (LOQ) derived from the standard curves of
the applied primer sets ........................................................................................................................ 102
Table 24 Number of detected genomes in one millilitre of the reactor sample by the application of all
primer sets ........................................................................................................................................... 104
Table 25 Percentage distribution of the detected 16S rRNA gene copy number of the
hydrogenotrophic (Methanomicrobiales, Methanobacteriales) and acetotrophic (Methanosaetaceae,
Methanosarcinaceae) methanogens in one nanogram of genomic DNA ........................................... 103
Table 26 Percentage distribution of the detected archaeal 16S rRNA gene copy numbers in relation to
those of the domain Bacteria ............................................................................................................... 105
LIST OF TABLES
173
Table 27 Percentage distribution of the total amount of genomic DNA for the detected Archaea and
Bacteria in relation to the total amount of genomic DNA (1 ng) per Q-PCR ....................................... 106
Table 28 Specifity of the primer sets ME and MBAC by evaluating the results for potential false
positive and potential false negative amplification .............................................................................. 109
Table 29 Specifity of the primer sets MMIC and MSaet by evaluating the results for potential false
positive and potential false negative amplification .............................................................................. 110
Table 30 Specifity of the primer set MSarc by evaluating the results for potential false positive and
potential false negative amplification ................................................................................................... 111
Table 31 Q-PCR reaction parameters of the standard curves by amplifying the mcrA gene with the
ME-set, MBAC-set and MMIC-set ....................................................................................................... 113
Table 32 Q-PCR reaction parameters of the standard curves by amplifying the mcrA gene with the
MSaet-set and MSarc-set .................................................................................................................... 114
Table 33 Melting curve maxima of the Q-PCR products of the mcrA gene ........................................ 116
PUBLICATION LIST
174
Publication list
Articles in peer-reviewed journals
SCHAIBLE R, BERGMANN I, SCHUBERT H (2011) Genetic structure of sympatrical sexually and
parthenogenetically reproducing population of Chara canescens (Charophyta). ISRN Ecol.
Article ID 501838 (2011): 1-13
BLUME F, BERGMANN I, NETTMANN E, SCHELLE H, REHDE G, KLOCKE M (2010) Quantitative analysis of
methanogenic population dynamics and effects of the biogas yield in a continuous operated
mesophilic biogas fermenter and during over-acidification. J Appl Microbiol 109 (2): 441-450
BERGMANN I, NETTMANN E, MUNDT K, LINKE B, KLOCKE M (2010) 16S rRNA gene based determination
of methanogenic Archaea abundances in a mesophilic biogas plant. Can J Microbiol 56 (5): 440-444
NETTMANN E, BERGMANN I, MUNDT K, PRAMSCHÜFER S, PLOGSTIES V, HERRMANN C, KLOCKE M (2010)
Polyphasic analyses of methanogenic population in agricultural biogas plants. Appl Environ
Microbiol 76 (8): 2540-2548
BERGMANN I, MUNDT K, SONTAG M, BAUMSTARK I, NETTMANN E, KLOCKE M (2010) Influence of DNA
isolation on Q-PCR based quantification of methanogenic Archaea in biogas fermenters. Syst Appl
Microbiol 33 (2): 78-84
SCHAIBLE R, BERGMANN I, SCHUBERT H (2009) A survey of sexually reproducting female and male
populations of Chara canescens (Charophyta) in the National Park Neusiedler See-Seewinkel
(Austria). Crytogamie Algol 30 (4): 279-294
SCHAIBLE R, BERGMANN I, BÖGLE M, SCHOOR A, SCHUBERT H (2009) Genetic characterisation of
sexually and parthenogenetically reproductive populations of Chara canescens (Charophyceae) using
AFLP, rbcL, and SNP markers. Phycol 48 (2): 105-117
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 (6): 1835-1850
BERGMANN I, GEIß-BRUNSCHWEIGER U, HAGEMANN M, SCHOOR A (2008) Salinity tolerance of the
chlorophyll b-synthesizing Cyanobacterium Prochlorothrix hollandica strain SAG 10.89. Microb Ecol 55
(4): 685-696
PUBLICATION LIST
175
KLOCKE M, NETTMANN E, BERGMANN I, MUNDT K, SOUIDI K, MUMME J, LINKE B (2008) Methanogenic
Archaea within green biomass utilizing two-phase biogas reactors. Syst Appl Microbiol 31 (3): 190-205
GEIß U, BERGMANN I, BLANK M, SCHUMANN R, HAGEMANN M, SCHOOR A (2003) Detection of
Prochlorothrix in brackish waters by specific amplification of pcb genes. Appl Environ Microbiol 69
(10): 6243-6249
Publications in selected volumes
KLOCKE M, NETTMANN E, BERGMANN I (2009) Monitoring der methanbildenden Mikroflora in Praxis-
Biogasanlagen im ländlichen Raum: Analyse des Ist-Zustandes und Entwicklung eines quantitativen
Nachweissystems. Bornimer Agrartechnische Berichte 67: 1-90
KLOCKE M, MUNDT K, NETTMANN E, BERGMANN I, SOUIDI K, LINKE B (2008) Diversity of methanogenic
Archaea in biogas reactors. Biospektrum 2008 p.: 96
SOUIDI K, MUMME J, MUNDT K, NETTMANN E, BERGMANN I, LINKE B, KLOCKE M (2007) Microbial diversity
in a biogas-producing co-fermentation of maize silage and bovine manure. Agr Eng Res 13: 197-206
Contribution to conferences
KLOCKE M, NETTMANN E, BERGMANN I (2009) Mikrobielle Diversität in Biogasreaktoren bei der
Vergärung von Nachwachsenden Rohstoffen. In: Fachagentur Nachwachsende Rohstoffe (FNR) und
Kuratorium für Technik und Bauwesen in der Landwirtschaft (KTBL) [Eds.]: Biogas in der
Landwirtschaft - Stand und Perspektiven. Proceedings of the Conference, Weimar, Germany,
15.-16. September 2009.
KLOCKE M, MUNDT K, NETTMANN E, SOUIDI K, BERGMANN I, MUMME J, SCHÖNBERG M, LINKE B (2008)
Diversity of methanogenic Archaea in silage-utilizing two-phase biogas reactors. In: Euro-pean Society
of Agricultural Engineers [Ed.]: Proceedings of the International Conference on Agricultural
Engineering & Industry Exhibition AgEng2008 - Agricultural and Biosystems Engineering for a
Sustainable World, Hersonissos, Crete, Greece, 23.-25. June 2008. Book of abstracts: P-144,
Conference proceedings CD: 1143216 [13 pages].
PUBLICATION LIST
176
NETTMANN E, MERZ P, MUNDT K, BERGMANN I, LINKE B, KLOCKE M (2008): 16S rDNA and mcrA based
analysis of the methanogenic Archaea in an agricultural biogas plant reveals a predomination of
hydrogenotrophic methanogens. In: European Society of Agricultural Engineers [Ed.]: Proceedings of
the International Conference on Agricultural Engineering & Industry Exhibition AgEng2008 -
Agricultural and Biosystems Engineering for a Sustainable World, Hersonissos, Crete, Greece,
23.-25. June 2008. Book of abstracts: P-106, Conference proceedings CD: 1176428 [19 pages].
NETTMANN E, BERGMANN I, KLOCKE M (2009) Methanogene Archaea in landwirtschaftlichen
Biogasanlagen. In: Bayerische Landesanstalt für Landwirtschaft (LfL): Biogas Science 2009,
Proceedings of the Conference, Erding, Germany, 02.-04. Dezember 2009, p.: 303-319.
BERGMANN I, NETTMANN E, HAUSDORF L, SOUIDI K, KLOCKE M Detection and quantification of
methanogenic archaea in digestors utilizing different substrates. Joint Annual Conference of the VAAM
and GBM, Frankfurt/Main, Germany, 09.-11. March 2008.
BERGMANN I, NETTMANN E, HAUSDORF L, KLOCKE M (2008): Detection and quantification of
methanogenic archaea in digestors of different substrate primary products and residues. Biospektrum
2008 p.: 88.
BERGMANN I, NETTMANN E, MUNDT K, LINKE B, KLOCKE M 16S rDNA and mcrA based analyses of the
methanogenic archaea in agricultural biogas plant reveals a predomination of hydrogenotrophic
methanogens. Joint Annual Conference of the VAAM and GBM, Frankfurt/Main, Germany,
09.-11. March 2008.
NETTMANN E, MERZ P, MUNDT K, BERGMANN I, LINKE B, KLOCKE M (2008) 16S rDNA and mcrA based
analyses of the methanogenic Archaea in agricultural biogas plants reveals a predomination of
hydrogenotrophic methanogens. Biospektrum 2008 p.: 94.
BERGMANN I, GEIß-BRUNSCHWEIGER U, HAGEMANN M, SCHOOR A (2007) Prochlorophytes in the open
Baltic Sea? – Salinity tolerance of Prochlorothrix hollandica. Baltic Sea Science Congress,
Rostock/Warnemünde, Germany, 19.-23. March 2007.
SCHAIBLE R, BERGMANN I, BÖGLE M, SCHUBERT H (2007) Studies about the Parthenogenesis of Chara
canescens. Baltic Sea Science Congress, Rostock/Warnemünde, Germany, 19.-23. March 2007.
KLOCKE M, NETTMANN E, MUNDT K, SOUIDI K, BERGMANN I, LINKE B (2007) Diversity of methanogenic
Archaea in biogas reactors. 13th European Congress on Biotechnology, Barcelona, Spain,
16.-19. September 2007.
PUBLICATION LIST
177
KLOCKE M, MUMME J, MUNDT K, SOUIDI K, NETTMANN E, BERGMANN I, LINKE B (2007) Methanbildende
Archaea in zweistufigen Biogasreaktoren bei der Vergärung von Triticale-Silage. Energiepflanzen im
Aufwind - Fachtagung zur Produktion von Biogaspflanzen und Feldholz, Potsdam, Germany,
12.-13. June 2007.
SOUIDI K, MUMME J, MUNDT K, NETTMANN E, BERGMANN I, LINKE B, KLOCKE M (2007) Analyse der
mikrobiellen Diversität in Biogasreaktoren. Energiepflanzen im Aufwind - Fachtagung zur Produktion
von Biogaspflanzen und Feldholz, Potsdam, Germany, 12.-13. June 2007.
FUNDING
178
Funding
This study was supported by research grants from the German Federal Ministry of
Food, Agriculture and Consumer Protection (BMELV)/ Agency of Renewable
Resources (FNR) (grants 22011804 and 22018306) and the German Federal Ministry
of Education and Research (BMBF)/ Project Management Jülich (PtJ)
(grant 03SF0317M).
ACKNOWLEDGMENTS
179
Acknowledgments
Now I would like to thank all the people who helped and supported me during my
years of dissertation.
First, I would like to thank Dr. Michael Klocke for his supervisory support. I really
enjoyed the informative and fruitful discussions. When I had a specific scientific
question, he was always friendly and willing to offer good advice. Moreover, I would
like to thank Dr. Michael Klocke for his patience when things went a little bit slow
sometimes.
I would like to thank Prof. Dr. Ulrich Szewzyk for his contribution and willingness to
supervise this thesis. He gave me a lot of interesting causes of thoughts with his
expert knowledge and crucial advices.
A very special thanks goes to PD Dr. Elisabeth Grohmann who supervised and
reviewed my doctorate thesis very carefully. Especially during the time of writing the
dissertation she gave me invaluable advices and helped me in difficult expressions of
the English language.
Furthermore I would like to thank Prof. Dr. Bernhard Schink, PD Dr. Dirk Wagner and
Prof. Dr. Michael Thomm and their staffs for the allocation of actively grown
methanogenic cultures. Dr. Michael Lebuhn and Prof. Dr. Michael Pfaffl I would like
to thank for answering my questions concerning the Q-PCR applications.
Another special thanks goes to Dr. Monika Heiermann who arranged for me an
interims financing between two projects. With her help a continuous employment
contract became feasible for the whole time of the doctorate thesis at the
Leibniz-Institut für Agrartechnik Potsdam-Bornim e.V..
ACKNOWLEDGMENTS
180
For the friendly and nice working atmosphere I would like to thank all the people of
the ATB who worked with me. Special thanks go to all employees of the department
bioengineering and here especially to Kerstin Mundt and Mario Sontag for their
wonderful technical support. I would like to thank all PhD students of the ATB for the
nice seminars, get-togethers and social events. It was always a welcome change
after long-working days. Especially I would like to thank Ingo, Antje R., Mandy, Lena,
Antje F., Angelika, Christiane, Anika und Kristina. A special thank goes to Dr. Edith
Nettmann because of the wonderful team work in a common project. She always
motivated me when things went wrong or became complicated. Besides the PhD
students I also would like to thank my graduands Frank Blume and Anika Rögner.
We had a wonderful and nice time together and many thanks for applying good work
habits for the whole time.
Without the support of my friends this dissertation would not be the same. I would like
to thank my HGW flat share friends Conny, Xav, Marcel and Peter, my “biological”
friends Beate, Steffi, Nicole, Christine, Dor, Claudi and especially Suse “Starlet”, my
friends and colleagues from Rostock Arne, Ralf, Bianca, Manfred, Conny, Daniel,
Marco, Zhenya, Grit and Claudi and my friends from Southern Germany Patrick and
Roman.
Uno speciale ringraziamento va ai coniugi Marleen e Furio Calvani, i quali mi hanno
aiutato e sostenuto durante il mio lungo percorso di dottorato. Ho sempre ritrovato
nuove forze e vigore attraverso i miei viaggi in Italia e pomeriggi passati a bere del
caffè in loro compagnia, momenti che mi hanno dato nuove motivazioni a proseguire
nella mia tesi di dottorato. Vorrei anche ringraziare tutto il resto della famiglia Calvani
e tutti i miei amici italiani per i bellissimi momenti passati in Italia e per la loro
fantastica ospitalità.
Der wohl wichtigste und größte Dank geht an meine Familie und Verwandten, die
mich während der Promotionszeit stets unterstützt, in allen Situationen begleitet und
mir immer Mut zugesprochen haben. Ohne die vielen aufbauenden Worte und die
schönen Stunden mit meinen Eltern, meinen Großeltern und meiner Schwester wäre
diese Arbeit nicht zustande gekommen. – Ich danke Euch von ganzem Herzen!
182
Appendix
Buffer, solutions and bacterial media
1 × TE-buffer 1 mM EDTA (pH = 8.0)
10 mM Tris-HCl (pH = 7.5)
5 × Loading dye 0.1 M ethylenediaminotetraacetic acid
(EDTA, M = 292.25 g mol-1)
40 % glycerol
0.1 % sodium dodecyl sulfate (SDS)
0,025 % bromphenol blue
50 × TAE-buffer 2 M Tris
0.05 M ethylenediaminotetraacetic acid
(EDTA, M = 292.25 g mol-1)
1 M acetic acid
Ethidium bromide staining solution 0,025 M ethidium bromide
LB -medium 10 g l-1 bacto-tryptone (pH = 7.4)
5 g l-1 NaCl
5 g l-1 yeast extract
50 mg l-1 ampicillin
LB/ampicillin/IPTG/X-Gal agar plates 40 g l-1 LB-agar
0.5 mM isopropyl-beta-thiogalactopyranoside
(IPTG)
50 mg l-1 ampicillin
80 mg l-1 5-bromo-4-chloro-indoly-β-D-
galactoside (X-Gal)
183
Lysozyme 10 mg ml-1 lysozyme
10 mM Tris-HCl (pH = 8)
PBS-buffer (pH = 7.0) 136 mM NaCl
10 mM Na2HPO4
2.7 mM KCl
1.8 mM KH2PO4
Proteinase K 10 mg ml-1 Proteinase K
50 mM Tris-HCl (pH = 8)
1.5 mM Calciumacetate
184
Table I a Summary of all mcrA gene sequences which were used for creating group-specific primer sets. All data sequences were selected from the NCBI database (http://www.ncbi.nlm.nih.gov/Genbank/index.html).
Organism Strain/Clone Accesssion number of the NCBI database
Methanobacterium
formicicum DSM 1312, DSM 1535 AF414051, EF465108
bryantii DSM 863 AF313806
arrhusense H2-LR AY386125
thermaggregans DSM 3266 AY289750
ivanovii DSM 2611 EF465107
beijingense DSM 15999 EF465106
Methanobrevibacter
ruminantium DSM 1093 AF414046
arboriphilus DSM 1125, DSM 7056 AF414035, AB300777
oralis DSM 7256 DQ251045
smithii DSM 861 DQ251046
Methanosphaera DSM 3091
stadtmanae NC007681
Methanothermobacter
thermautotrophicus delta H NC000916
thermophilus DSM 6529 AY289752
thermoflexus DSM 7268 AY303950
wolfeii DSM 2970 AY289748
Methanothermus
sociabilis DSM 3496 AY289747
Methanococcus
maripaludis S2 NC005791
aeolicus DSM 4304 AY354034
voltae ps MVMCR1
vannielii SB NC009634
Methanothermococcus
thermolithotrophicus DSM 2095, JCM 10549 AF414048, AB353226
okinawensis DSM 14208, IH-1 AY354033, AB353229
Methanocaldococcus
jannaschii DSM 2661 NC000909
infernus DSM 11812, SL48, SL47 AY354035, AY354032, AY354031
Methanotorris
igneus DSM 5666, JCM 11834 AF414039, AB353228
formicicus Mc-S-70 AB353227
185
Table I b Summary of all mcrA gene sequences which were used for creating group-specific primer sets. All data sequences were selected from the NCBI database (http://www.ncbi.nlm.nih.gov/Genbank/index.html).
Organism Strain/Clone Accesssion number of the NCBI database
Methanomicrobium
mobile DSM 1539 AF414044
Methanoculleus
bourgensis DSM 3045, DSM 6216, DSM 2772 AF414036, AB300786, AB300785
thermophilus DSM 2624, DSM 2373 AF313804, AB300783
marisnigri JR-1 NC009051
palmolei DSM 4273 AB300784
chikugoensis DSM 13459 AB300779
Methanofollis
liminatans DSM 4140 AF414041
Methanogenium
organophilum DSM 3596 AB353222
Methanocorpusculum
parvum DSM 3823 AF414045
aggregans DSM 3027 AF414034
bavaricum DSM 4179 AF414049
labreanum Z NC008942
Methanospirillum
hungatei JF1, DSM 864 AF313805, AF414038
Methanosarcina
acetivorans C2A, NC002097
mazei Go1, DSM 2053, DSM 4556, DSM 9195 NC003901, AF414043, AB300782, AB300778
barkeri fusaro NC007355
lacustris MM AY260438
thermophila DSM 1825 AB353225
Methanococcoides
burtonii DSM 6242 NC007955
alaskense DSM 17273 AB353221
Methanohalophilus
mahii DSM 5219 AB353223
Methanomethylovorans
thermophila L2FAW AY672820
hollandica ZB AY260437
Methanosalsum
zhilinae DSM 4017 AB353224
Methanosaeta
concilii DSM 3671, VeAc9 AF414037, AF313803
harundinacea 8A, 6A AY970348
thermophila PT NC008553
Methanopyrus
kandleri AV19, DSM 6324 NC003551, AF414042
186
Table I c Summary of all mcrA gene sequences which were used for creating group-specific primer sets. All data sequences were selected from the NCBI database (http://www.ncbi.nlm.nih.gov/Genbank/index.html).
Organism Strain/Clone Accesssion number of the NCBI database
Uncultured archaeon clone ATB-EN-5746-M017 FJ226628
Uncultured archaeon clone ATB-EN-3960-M030 FJ226641
Uncultured archaeon clone ATB-EN-4496-M064 FJ226671
Uncultured archaeon clone ATB-EN-4573-M067 FJ226674
Uncultured archaeon clone ATB-EN-13936-M116 FJ226706
Uncultured archaeon clone ATB-EN-4531-M008 FJ226619
Uncultured archaeon clone ATB-EN-5642-M013 FJ226624
Uncultured archaeon clone ATB-EN-10209-M112 FJ226705
Uncultured archaeon clone ATB-EN-9779-M144 FJ226737
Uncultured archaeon clone ATB-EN-9759-M148 FJ226741
Uncultured archaeon clone ATB-EN-5595-M020 FJ226631
Uncultured archaeon clone ATB-EN-4482-M005 FJ226616
Uncultured archaeon clone ATB-EN-4570-M010 FJ226621
Uncultured archaeon clone ATB-EN-10447-M122 FJ226715
Uncultured archaeon clone ATB-EN-3979-M002 FJ226613
Uncultured archaeon clone ATB-EN-5637-M012 FJ226623
Uncultured archaeon clone ATB-EN-5677-M015 FJ226626
187
Eidesstattliche Erklärung
Die vorliegende Dissertation habe ich selbst angefertigt und sämtliche von mir
benutzten Hilfsmittel, persönliche Mitteilungen oder Quellen sind in der vorliegenden
Arbeit angegeben.
Die vorgelegte Dissertation habe ich noch nicht als Prüfungsarbeit für eine staatliche
oder andere wissenschaftliche Prüfung eingereicht. Ebenso habe ich nicht die
gleiche, eine in wesentlichen Teilen ähnliche oder eine andere Abhandlung bei einer
anderen Hochschule als Dissertation eingereicht.
Personen, die mich bei der Erstellung der Dissertation unterstützt haben, sind in der
Danksagung (“Acknowledgments“) genannt.