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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

Characterization of methanogenic Archaea communities in ... · Characterization of methanogenic Archaea communities in biogas reactors by quantitative PCR vorgelegt von Ingo Bergmann

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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).

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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

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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

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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.

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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

10

10

6.8

10

10 ±

 1.0

10

10

2.4

10

10  ±

 1.0

10

95

.30

×10

7  ±

 1.8

10

7N

D1

.40

×10

6  ±

 2.3

10

5

BA

23

.53

×10

10  ±

3.7

10

90

6.5

10

90 ±

 9.6

10

80

1.1

10

90  ±

 4.8

10

84

.33

×10

7  ±

 2.4

10

76

.63

×10

8  ±

 3.7

10

72

.75

×10

5  ±

 3.8

10

4

BA

38

.61

×10

10  ±

4.1

10

90

2.1

10

10 ±

 4.7

10

90

6.8

10

90  ±

 3.3

10

91

.00

×10

8  ±

 5.0

10

71

.14

×10

9  ±

 8.7

10

73

.18

×10

5  ±

 1.2

10

4

BA

42

.02

×10

10  ±

3.2

10

90

2.6

10

90 ±

 3.2

10

80

1.2

10

90  ±

 1.2

10

84

.60

×10

7  ±

 7.0

10

61

.66

×10

8  ±

 2.3

10

7N

D

BA

51

.33

×10

11  ±

7.2

10

90

9.7

10

90 ±

 1.9

10

90

1.2

10

10  ±

 3.0

10

94

.58

×10

7  ±

 5.8

10

6N

DN

D

BA

61

.33

×10

10  ±

2.1

10

90

1.7

10

90 ±

 2.1

10

80

7.9

10

80  ±

 8.0

10

73

.02

×10

7  ±

 4.6

10

61

.09

×10

8  ±

 1.5

10

7N

D

BA

72

.64

×10

10  ±

2.9

10

99

1.6

10

90  ±

 2.4

10

80

1.0

10

99  ±

 2.2

10

82

.10

×10

8  ±

 7.0

10

6N

D5

.43

×10

5  ±

 1.1

10

5

BA

8A

F1

.61

×10

11  ±

1.6

10

10

1.0

10

11  ±

 1.2

10

10

1.4

10

11  ±

 5.2

10

97

.67

×10

8  ±

 3.4

10

73

.28

×10

9  ±

 2.1

10

81

.45

×10

7  ±

 9.0

10

5

BA

8F

R1.5

10

11  ±

4.7

10

90

1.1

10

11  ±

 1.4

10

10

2.2

10

11  ±

 9.1

10

96

.42

×10

8  ±

 1.6

10

75

.26

×10

9  ±

 2.7

10

82

.37

×10

7  ±

 1.0

10

6

BA

95

.94

×10

10  ±

1.7

10

10

1.0

10

10  ±

 3.7

10

90

7.4

10

99  ±

 5.4

10

91

.72

×10

8  ±

 2.1

10

7N

D4

.78

×10

6  ±

 7.2

10

5

BA

10

3.7

10

10  ±

9.1

10

90

6.8

10

90  ±

 5.3

10

80

1.8

10

99  ±

 7.6

10

82

.18

×10

8  ±

 8.1

10

75

.40

×10

9  ±

 7.1

10

88

.71

×10

5  ±

 2.6

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).

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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!

181

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.