Characterization of Methanogenic Archaea Communities in Biogas Reactors by Quantitative PCR

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

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

3 TABLE OF CONTENTS

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

4 TABLE OF CONTENTS

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

5 ABBREVIATIONS

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

6 ABBREVIATIONS

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

7 ABBREVIATIONS

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

8 ABBREVIATIONS

UV Ultraviolet VFA Volatile fatty acid X-Gal 5-bromo-4-chloro-3-indolyl- β-D-galactopyranoside

9 ABSTRACT

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.

10 ABSTRACT

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.

11 ZUSAMMENFASSUNG

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.

12 ZUSAMMENFASSUNG

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.

13 INTRODUCTION

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.

14 INTRODUCTION

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).

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

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.

15 INTRODUCTION

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.

16 INTRODUCTION

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.

17 INTRODUCTION

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.

Organic material Proteins, carbohydrates, lipids

Hydrolysis Hydrolytic Bacteria

Monomers and Oligomers Amino acids, sugars, fatty acids

Acidogenesis Fermentative Bacteria

Carboxylic acids Alcohols

Acetogenic Bacteria

Acetate Carbon dioxide Hydrogen

Methanogenesis Methanogenic Archaea

Biogas Methan, carbon dioxide

Fig. 1 Four-stage pathway of anaerobic digestion from particulate organic material to methane (modified after Weiland 2010).

18 INTRODUCTION

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).

19 INTRODUCTION

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).

20 INTRODUCTION

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).

21 INTRODUCTION

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.

22 INTRODUCTION

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).

23 INTRODUCTION

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).

24 INTRODUCTION

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).

25 INTRODUCTION

107 106 105 104 103 102 101 Copy number

Fluorescence Fluorescence Threshold

A 10 20 30 Cycle number

30 value

value 20

T T

C C

B 101 102 103 104 105 106 107 Copy number [log]

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 101 to 107 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).

26 INTRODUCTION

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.

27 INTRODUCTION

94°C

Denaturation

54°C Annealing

60°C

Polymerization

Excitation

SYBR Green I Dye Primer

DNA with target gene sequence

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.

28 INTRODUCTION

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).

R R

94°C Q Q

Q Denaturation

54°C Annealing

R R

R Q 60°C Q

Polymerization

R Q Excitation

R Q TaqMan probe with reporter (R) and quencher (Q) Primer

DNA with target gene sequence

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.

29 INTRODUCTION

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.

30 INTRODUCTION

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.

31 INTRODUCTION

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.

32 INTRODUCTION

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).

33 INTRODUCTION

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).

γ α β

β´ α´ F430

γ´

Fig. 5 Ribbon diagram of the methyl-coenzyme M reductase (MCR) with all subunits and the structure of F430 (Shima et al. 2002).

34 INTRODUCTION

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.

35 INTRODUCTION

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?

36 MATERIAL AND METHODS

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).

37 MATERIAL AND METHODS

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).

38 MATERIAL AND METHODS

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).

39 MATERIAL AND METHODS

)

-1

)

-1

[rpm]

Stirring

Without stirring

35 (Continuously)

50 - 100 (15 min h

50 - 100 (15 min [0.5 h]

]

-1

3.0

2.2

2.7

2.0

1.9

2.1

2.0

2.1

13.0

1.5 - 9.5

[g l [g

Start (Control)

3.3 (Overloaded)

4.2 (Overloaded)

Organic loading rate loading Organic

Feeding

Continuously

Discontinuously

Co-substrate

Pig manure (68%)

Pig manure (50%)

Cattle manure (100%)

Mähnert (2007). Mähnert

d) d)

Primary substrate Primary

Maize silage (32%)

Maize silage (50%)

Maize silage (100%)

Blume (2008); (2008); Blume

Fodder beet silage (100%)

c) c)

Rye silage (91%), straw (9%) silage straw (91%), Rye

[l]

the the analyzed laboratoryscale biogas reactors.

Size reactor of

Linke and Schönberg (2009); (2009); Schönberg and Linke

b) b)

[week]

Sampling

1 (7.day)

10 (67.day)

10 (66.day)

10 (64.day)

9 (63.day)

8 (56.day)

7 (49.day)

6 (42.day)

5 (35.day)

3 (21.day)

Maincharacteristics of

Linke et al. (2009); (2009); al. Linke et

System 4 System

System 3 System

System 2 System

System 1 System

Reactor

Table a) a)

40 MATERIAL AND METHODS

[%]

51.7

61.9

63.0

55.4

52.0

50.0

47.8

46.1

47.3

48.4

48.0

content

0.1

0.7

[%]

56.0

26.0

55.0

59.0

56.0

55.0

42.4

47.5

48.8

51.4

52.2

51.7

51.6

52.0

content

]

-1

oDW

0.610

0.750

0.680

0.220

0.670

0.800

0.400

0.840

0.700

0.890

0.770

0.001

0.007

0.424

0.475

0.488

0.514

0.522

0.517

0.631

0.224

Biogas yield Biogas

]

-1

9.93

7.05

6.74

2.35

2.23

2.62

5.88

1.68

2.24

5.03

1.46

0.17

0.10

0.02

0.05

9.43

6.90

[g l [g

17.07

10.93

17.03

17.63

16.23

16.85

Volatile fatty acids Volatile fatty

]

-1

3.37

6.80

[g kg [g

Total N Total

]

-1

-N

2.85

2.47

2.71

2.27

2.39

2.19

1.70

1.82

2.55

0.57

2.19

0.38

3.80

[g kg [g

Mähnert (2007). Mähnert

d) d)

7.88

8.16

8.39

6.10

7.85

7.98

8.32

7.84

8.38

7.73

8.01

5.37

5.46

5.61

7.39

7.55

7.51

7.62

7.61

7.68

8.14

7.65

pH value

Blume (2008); (2008); Blume

c) c)

Operation mode Operation

Mesophilic (37°C)

Thermophilic (55°C)

Linke and Schönberg (2009); (2009); Schönberg and Linke

b) b)

; ;

[week]

Sampling

1 (7 day)

10 (67.day)

10 (66.day)

10 (64.day)

9 (63.day)

8 (56.day)

7 (49.day)

6 (42.day)

5 (35.day)

3 (21.day)

Physicaland chemical parametersof the analyzed laboratory scale biogas reactors.

Linke et al. (2009) al. et Linke

System 4 System

System 3 System

System 2 System

System 1 System

Reactor

a) a)

Table NA = not analyzed. oDW = organic dry weight. dry = organic oDW analyzed. NA = not

41 MATERIAL AND METHODS

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).

42 MATERIAL AND METHODS

Dry

Wet

Wet Wet

Fermentation

Operation mode Operation

Mesophilic (40°C)

Mesophilic (41°C)

Mesophilic (39°C)

Mesophilic (43°C)

Mesophilic (39°C)

Mesophilic (41°C)

Mesophilic (39°C)

Mesophilic (40°C) Mesophilic (37°C) Mesophilic (39°C)

]

-1

-3

3.8

4.0

2.1

3.4

3.9

5.4

3.1

3.3 3.8 3.9

m [kg Organic loading rate loading Organic

Water (6%)

Water (3%)

Co-substrate

Cattle dung (4%)

Pig manure (6%)

Pig manure (57%)

Pig manure (50%)

Pig manure (54%)

Cattle dung (18%)

Pig manure (74%)

Cattle manure (76%)

Cattle manure (54%) Cattle manure (72%)

(41%) Cattle sewage

Turkey hen dung (2%) Turkey Turkey hen dung (9%) Turkey

Triticale (100%)

Grass silage (5%)

Maize silage (9%)

Grains of rye (2%) Grains of rye

Grains of rye (6%) Grains of rye

Maize silage (13%)

Maize silage (37%)

Maize silage (82%)

Maize silage (40%)

Maize silage (39%)

Maize silage (28%) Maize silage (38%)

Maize silage (45.7%)

Grains of barley (12%) Grains of barley

Maize cob silage (11%)

Renewable raw material Anhalt; S = Sachsen = S Anhalt; -

]

1950

2640

1000

2700

1300

2326

1000 3600

1000

120 (AF) 4x 120 (FR) 4x Size reactor of

Vorpommern; SA = Sachsen = SA Vorpommern;

MV MV

MV Location

Sampling

26.07.2007

25.07.2007

09.11.2006

23.04.2007

23.05.2007

28.08.2006

20.07.2006

29.03.2007

24.07.2006 04.05.2006

Parametersthe of analyzedbiogas plants.

BA10

BA9

BA8

BA7

BA6

BA5

BA4

BA3

BA2

BA1

Biogas plant Biogas

Table BB = Brandenburg; MV = Mecklenburg = MV Brandenburg; = BB

43 MATERIAL AND METHODS

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:

-1 VN = (pm*Vm*TN) (Tm*pN)

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.

44 MATERIAL AND METHODS

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.

45 MATERIAL AND METHODS

(5)

(2)

(4)

(2)

(4)

(2)

(1)

(2)

(4)

(3)

(1)

(2)

(2) (1)

Institution

Format

DNA template

DNA template DNA template

Actively growing cultureActively growing

Actively growing cultureActively growing Actively growing cultureActively growing cultureActively growing

DSM 30168

DSM 1053

Mh1 DSM 1125

DSM 1535

DSM 1825

DSM 863

DSM 3045

DSM 1498

DSM 1224

DSM 4140

DSM 800

DSM 1311

DSM 3647

DSM 8687 DSM 2139

barkeri

mazei mazei

mazei mazei barkeri

Species

Methanospirillum hungatei

Methanosaeta conciliiMethanosaeta

Methanosarcina Methanosarcina

Methanosarcina

Methanofollis liminatans

Methanococcus vannielii

Methanobacterium bryantiiMethanobacterium

Methanoculleus marisnigri

Methanoculleus bourgensis

Methanosarcina thermophila

Methanobacterium formicicum formicicum Methanobacterium

Methanobrevibacter arboriphilus

thermautotrophicus Methanothermobacter

Pectobacterium ssp. carotovorum carotovorum Bornim e.V., Potsdam e.V., Bornim

tsdam

und Zellkulturen GmbH, Braunschweig Zellkulturen GmbH, und

Family

Methanosaetaceae

Enterobacteriaceae

Methanococcaceae

Methanosarcinaceae Methanobacteriaceae

Methanomicrobiaceae Institut für Agrartechnik PoAgrartechnik für Institut -

acterialand archaeal cultures respective or genomicDNA used this in study.

Enterobacteriales

Methanosarcinales Methanosarcinales

Methanomicrobiales

Methanococcales

Methanobacteriales

Order

Table

(1) Lehrstuhl für Mikrobielle Ökologie, Limnologie und Allgemeine Mikrobiologie, University of Konstanz of University Allgemeine Mikrobiologie, und Limnologie Ökologie, für LehrstuhlMikrobielle (1) Mikroorganismen von Deutsche (2) Sammlung Regensburg of und University Archaeenzentrum, für LehrstuhlMikrobiologie (3) Potsdam Institut, Wegener Alfred Forschung, Geowissenschaften/Periglaziale (4) Bioverfahrenstechnik, (5) Leibniz

46 MATERIAL AND METHODS

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 -1 proteinase K, 1.2% SDS (w/v) and 1.2 mmol l 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).

47 MATERIAL AND METHODS

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.

48 MATERIAL AND METHODS

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.

49 MATERIAL AND METHODS

One PCR mixture contained 10 ng DNA-template, 1 × PCR-Buffer, 0.2 mmol l-1 of -1 -1 each dNTP, 3 mmol l MgCl2, 0.2 µmol l 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.

50 MATERIAL AND METHODS

References Toth et al. 2001 Jensen et al. 1993 Weisburg et al. 1991 Nettmann et al. 2008 Nettmann et al. 2008 Nettmann et al. 2008 Nettmann et al. 2008 Nettmann et al. 2008 Nettmann et al. 2008 Nettmann et al. 2008 Nettmann et al. 2008

[AF169245] [AF169245]

[AY196674] [AY196674] [X16932] [X16932] [NC007355] [NC007355]

target Microbial

number] [NCBI accession Methanosaeta conciliiMethanosaeta conciliiMethanosaeta

Methanosarcina barkeri Methanosarcina barkeri . The . obtained amplicon was subsequently used for

Methanoculleus bourgensis Methanoculleus bourgensis Methanobacterium formicicum formicicum Methanobacterium formicicum Methanobacterium

GC [%] 50.0 50.0 47.5 50.0 50.0 60.0 55.6 55.0 50.0 60.0

m T [°C] 57.3 57.3 56.3 57.3 57.3 61.4 56.0 59.4 57.3 50.6

880 [bp] 1247 1433 1445 1579

Amplicon size Amplicon

[5´ → 3´] [5´ Sequence

CAAGG CATCC ACCGT CAAGG

CATCA GTCCG GAGAC CAT GAGAC CATCA GTCCG CTACG GCTAC CTTGT TACGA CTTGT GCTAC CTACG ACGCA TTCCA GCTTC ATGAG GCTTC ACGCA TTCCA GCTAT TTACT AGAGG CTGCC TTACG CCTTG CCTAC GGCTA CAGAG GTTAC CCTGC TTGAT CTCAG CATGG TTGAT AGAGT TAAGC CATGC AAGTC GAACG AAGTC TAAGC CATGC GCCGA CTGCG TGGAT TAGGA

Tvectors. -

F primer F primer F primer F primer F primer R primer R primer R primer R primer R primer Function

PCR PCR primerstargeting the 16S rRNA genes of different methanogenicreference species

= melting temperature melting =

primer = reverse primer R primer, forward = primer

Primer Metforfw1 Metforrev3 Metboufw1 Metbourev2 Metconfw1 Metconrev4 Metbarfw1 Metbarrev3 16S for L1

Table cloning intopGEM F T primer deduced the within content cytosine and = GC guanidine

51 MATERIAL AND METHODS

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).

52 MATERIAL AND METHODS

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:

-1 NDNA = (cDNA*NA) (lplasmid*mmol,bp)

with NDNA as the number of DNA copies per µl solution after DNA isolation, cDNA as -1 the DNA concentration measured via fluorescent DNA quantification [g µl ], NA as the Avogadro constant, lplasmid as the plasmid length [number of base pairs], and -1 mmol,bp as the average molar mass of a base pair [660 g mol ]. 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.

53 MATERIAL AND METHODS

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.

54 MATERIAL AND METHODS

References

Yu et al. 2005a Yu

Yu et al. 2005a Yu Yu et al. 2005a Yu

theused primer and probesets is

Bacteria

Archaea

Target group Target

Methanosaetaceae

Methanobacteriales Methanomicrobiales Methanosarcinaceae

[%]

44.4

61.1

50.0

61.2

59.1

50.0

45.4

54.2

58.0

55.6

61.1

47.4

47.6

61.9

64.7

62.5 65.0 40.0

[°C]

59.9

66.7

61.2

62.1

70.0

61.0

61.5

70.2

63.8

63.2

67.2

60.7

70.8

63.4

62.3 70.1

61.0 PCR. The denomination of -

408

164

506

343

468 273 [bp]

size Amplicon

[5´ → 3´] [5´

Sequence

GCCAT GCACC WCCTC GCACC T GCCAT

ACTCC TACGG GAGGC AG GAGGC ACTCC TACGG

CCTAC GGCAC CRACM AC CCTAC GGCAC

TACCG TCGTC CACTC CTT TCGTC TACCG

TAGCG ARCAT CGTTT ACG ARCAT CGTTT TAGCG

ATCGR TACGG GTTGT GGG GTTGT TACGG ATCGR

AGCAC CACAA CGCGT GGA AGCAC CACAA CGCGT

TTAGC AAGGG CCGGG CAA CCGGG AAGGG TTAGC

GAAAC CGYGA TAAGG GGA GGA TAAGG CGYGA GAAAC

CGWAG GGAAG CTGTT AAGT CTGTT GGAAG CGWAG ATTAG ATACC CSBGT AGTCC ATTAG

CACCA RGGAC TAATC CTYGA

TaqMan = TaqMan probe TaqMan = TaqMan

AGGAA TTGGC GGGGG AGCAC GGGGG TTGGC AGGAA

CACCT AACGC RCATH GTTTA C RCATH GTTTA CACCT AACGC

GACTA CCAGG GTATC TAATC C TAATC GTATC CCAGG GACTA

TGCCA GCAGC CGCGG TAATA C CGCGG GCAGC TGCCA

, ACGGC AAGGG ACGAA AGCTA GG ACGAA AGCTA AAGGG ACGGC CGAAA GCTG AGGRA CAGTG TYCGA

TaqMan

F primer

R primer

R primer Function

2005a).

(

Bacfw

Mscfw

Archfw

Bacrev

Primer

Mscrev

Mbacfw

Archrev

Msaetfw

Mmicrfw

Mbacrev

Msaetrev

Mmicrrev

BacTaqman

MscTaqman

ArchTaqman

MbacTaqman MsaetTaqman

MmicrTaqman

Characteristicsthe of primer and probesets for amplifying the 16SrRNA geneby Q

= melting temperature melting =

Msc

Mst

MMB

MBT

BAC

ARC

Set

Table according to al.Yu et F primer = forward primer, R primer = reverse primer = reverse primer R primer, forward = F primer T primer deduced the within content cytosine and = GC guanidine

55 MATERIAL AND METHODS

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):

-1 LOD = I + 3SI*(slope)

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:

-1 LOQ = I + 10SI*(slope)

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.

56 MATERIAL AND METHODS

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.

57 MATERIAL AND METHODS

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:

-1 NE/NT = nt (nsample + npure culture)

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.

58 MATERIAL AND METHODS

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.

59 MATERIAL AND METHODS

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, -1 -1 -1 0.2 mmol l of each dNTP, 3 mmol l MgCl2, 0.2 µmol l 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.

60 MATERIAL AND METHODS

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).

61 MATERIAL AND METHODS

This study This study This study This study This study This study This study This study This study This study This study This study This study This study References

Hales et al. 1996 Hales et al. 1996

[NC000916] [NC000916]

[AF169245] [AF169245] [AY196674] [AY196674] [NC009051] [NC009051] [X16932] [X16932] [NC007355] [NC007355] [NC009634] [NC009634]

target Microbial [NCBI accession number] [NCBI accession

conciliiMethanosaeta conciliiMethanosaeta Methanosarcina barkeri Methanosarcina barkeri Methanococcus vannielii Methanococcus vannielii Methanoculleus marisnigri Methanoculleus marisnigri Methanoculleus bourgensis Methanoculleus bourgensis formicicum Methanobacterium formicicum Methanobacterium

thermautotrophicus Methanothermobacter thermautotrophicus Methanothermobacter

GC [%] 60.0 55.0 50.0 50.0 50.0 50.0 60.0 60.0 50.0 55.0 50.0 55.0 50.0 60.0 46.7 42.0

m T [°C] 61.4 59.4 57.3

57.3 57.3 57.3 61.4 61.4 57.3 59.4 57.3 59.4 57.3 61.4 55.9 55.6

407 368 468 778 [bp] 1643 1537 1391 1592

Amplicon size Amplicon

A gene A for used cloning.

mcr

[5´ → 3´] [5´ Sequence

CCTGC AGCGT CGAAT TCTCT CGAAT AGCGT CCTGC CACTG ACGAT ATCCT GGATG ACGAT ATCCT CACTG CTGGA CACTT GGTGG TTACA TCCGA GACGA CCGAA TTCTA GACGA CCGAA TCCGA AGTTA GGACC ACGTA GTTCG ACGTA GGACC AGTTA AGAGC ACCTT CTCGC GAACT TGACG GAAGG CTCAT ACTGT GTAGT TGGCG CCTCT CAGCT CCTCT TGGCG GTAGT CGGTA TGGAC TACAT CAAGG TGGAC CGGTA CACAG GTTCG GTCAA TGGAA AGCAG GCACG AACTC TCTGA AGCAG GCACG CAGCT CGCCG ATAAG ACCGT CGCCG CAGCT GAAGC TACAT GTCCG GCGGT TACAT GTCCG GAAGC ATGAA CTCGC GGATG GCACC GGATG ATGAA CTCGC GCMAT GCARA THGGW ATGTC THGGW GCARA GCMAT

T GRTAG GTTDG TCATK GCRTA or or amplificationthe of

F primer F primer F primer F primer F primer F primer F primer F primer R primer R primer R primer R primer R primer R primer R primer R primer Function

Primersets f

= melting temperature melting =

Primer Mcrforfw1 Mcrforrev6 Mcrboufw4 Mcrbourev5 Mcrconfw1 Mcrconrev1 Mcrbarfw1 Mcrbarrev3 Mcrthefw Mcrtherev Mcrvanfw Mcrvanrev Mcrmarfw Mcrmarrev ME1 ME2

Table F primer = forward primer, R primer = reverse primer = reverse primer R primer, forward = F primer T primer deduced the within content cytosine and = GC guanidine

62 MATERIAL AND METHODS

1996

This study

References Hales et al. 1996 Hales et al.

Target group Target

Methanogens

Methanosaetaceae

Methanobacteriales Methanomicrobiales

Methanosarcinaceae (except Methanosaetaceae)

[%]

60.0

47.7

54.8

63.2

52.2

64.7

45.0

50.0 42.0 46.7

[°C]

61.4

59.3

60.8

61.0

62.4

57.6

55.3

55.6 55.6 55.9

191

373 778 [bp]

size Amplicon

PCR. -

forQ

[5´ → 3´] [5´

Sequence

A gene A used

CCNCA CTGGT CCTGN AG CCTGN CCNCA CTGGT TACAT GTCWG GTGGT GTNG GTGGT TACAT GTCWG

AGGT GACGA CTACC AGGGC

mcr

GTCGT AACCR TAGAA WCCNA GTCGT

AGCTA CATGT CCGGN GGNGT CCGGN CATGT AGCTA

GCMAT GCARA THGGW ATGTC THGGW GCARA GCMAT

TCATK GCRTA GTTDG GRTAG T GRTAG GTTDG TCATK GCRTA

CTGGT GACCR ACGTT CATTG C CATTG ACGTT GACCR CTGGT

ACNAT GATGG ANGAC CACTT NG ACNAT GATGG ACNTC GTT CCAGG GTCNT GAGAA

F primer

R primer

R primer Function

ME2

ME1

Primer

MicrfwV1

SaetfwV1

MicrrevV1

SaetrevV1

MbacfwV2

MbacrevV2 MBARKFW

MBARKREV

Primersets for amplificationthe of

= melting temperature melting =

MSarc

MSaet

MMIC

MBAC

Set

Table F primer = forward primer, R primer = reverse primer = reverse primer R primer, forward = F primer T primer deduced the within content cytosine and = GC guanidine

63 MATERIAL AND METHODS

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).

64 RESULTS

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.

65 RESULTS

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).

66 RESULTS

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.

67 RESULTS

The DNA yield was quantified by fluorescence spectrometry (Table 10). The highest -1 amount of DNA (259.00 ± 18.30 µg mlreactor sample ) was obtained with the SDS-based cell lysis method (protocol D). All other DNA isolation protocols resulted in nearly the -1 same DNA concentrations (approx. 20.00 µg mlreactor sample ). 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.

bp M A B C D E F G H I M

21226

1584

564

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).

68 RESULTS

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.

DNA amount and purity parameters Protocol 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

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 -1 solutions, which showed amplification (e.g. 18.80 ± 3.87 µg mlreactor sample -1 (protocol C) and 12.50 ± 4.99 µg mlreactor sample (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.

69 RESULTS

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.

Dilution series of Protocol gDNA A B C D E F G H I

1 × 100 + + + - + - + - + 1 × 101 + + + + + + + + + 1 × 102 + + + + + + + + + 1 × 103 + + + + + + + + + 1 × 104 + + + + + + + + +

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.

Primer set 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

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.

70 RESULTS

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).

71 RESULTS

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).

72 RESULTS

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).

73 RESULTS

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. 8 8 -1 Values from 1.17 × 10 to 2.90 × 10 gene copies mlreactor sample were acquired with the primer set of MMB in DNA solutions of the extraction protocols D-I, while only 6 6 -1 2.64 × 10 to 9.43 × 10 gene copies mlreactor sample 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.

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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.

Protocol Primer set 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%

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.

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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.

C N Primer set N T E N /N T Mean SD Mean SD E T 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

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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 5 7 were spiked with 10 -10 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.

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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.

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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 4 6 influenced CT values of the spiked plasmid standard curve were in a range of 10 -10

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 3 2 (Mscreactor sample = 1.36 × 10 ± 3.65 × 10 ).

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.

Primer set 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

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.

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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.

sample + SSD Ratio of 16S rRNA gene copy numbers [ ] SSD Primer set SSD 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

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).

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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.

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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.

Primer set ARC MMB Msc

(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

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.

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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.

Primer set ARC MMB Msc

(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

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.

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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.

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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 -1 showed a steady increase reaching a maximum of 3.6 and 1.8 lN l 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 -1 -1 -1 -1 0.37 lN g DOM day at an OLR of 2.4 g DOM l day (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 -1 -1 9.5 g DOM l day . 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.

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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.

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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.

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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.

Primer set 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

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 7 -1 and 10 copies mlreactor sample , 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 8 -1 16S rRNA gene copy numbers increased to final 10 copies mlreactor sample . 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.

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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.

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Slightly higher concentrations were found when very high OLRs were applied. 7 -1 Methanosaetaceae increased up to about 10 16S rRNA copies mlreactor sample 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.

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

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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 -1 mlreactor sample ) (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 -1 5 gene copies mlreactor sample in the first three samples while only 2.81 × 10 gene -1 copies mlreactor sample 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 9 -1 phase (1.12 × 10 gene copies mlreactor sample ). 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 -1 5 -1 copies mlreactor sample ). A value of 1.59 × 10 gene copies mlreactor sample 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.

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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. 12 -1 8 BAC-set = 10 gene copies mlreactor sample , ARC-set = 10 gene copies -1 mlreactor sample ) (Fig. 17).

92 RESULTS

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 -3 -1 8 -1 OLR of 3.0 kg m d (3.04 × 10 gene copies mlreactor sample ).

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.

93 RESULTS

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 5 -1 and 4.05 × 10 gene copies mlreactor sample 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.

94 RESULTS

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.

Primer set 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

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).

95 RESULTS

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 -1 copies mlreactor sample ). Nearly similar proportions of 16S rRNA copies were detected 9 -1 with the primer sets MMB, MBT and Msc (approx. 10 gene copies mlreactor sample ).

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.

96 RESULTS

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. 8 -1 A number of 1.88 × 10 gene copies mlreactor sample 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.

97 RESULTS

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 11 11 -1 1.43 × 10 and 3.06 × 10 gene copies mlreactor sample . 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.

98 RESULTS

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.

99 RESULTS

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.

100 RESULTS

biogas

Mst

0.718

0.985

0.930

0.986

0.718

0.985

0.881

0.992

0.881

0.992

47.480

44.150

47.480

43.850

-4.278

-3.501

-4.278

-3.644

1.043

0.977

1.042

0.986

1.043

0.977

1.039

0.979

1.039

0.979

Msc

33.870

33.930

33.870

34.360

-3.222

-3.225

-3.222

-3.232

Methanobacteriales primer

0.713

0.997

0.903

0.966

0.713

0.997

0.784

0.993

0.784

0.993

MBT

45.000

43.627

45.000

42.580

-4.278

-3.580

-4.278

-3.979

Primer set

0.793

0.981

1.073

0.972

0.793

0.981

0.818

0.964

0.818

0.964

MMB

48.860

46.916

48.860

48.530

-3.943

-3.159

-3.943

-3.851

numbers in reactor samples of 10

0.769

0.995

0.882

0.994

0.769

0.995

0.776

0.995

0.776

0.995

ARC

41.710

42.010

41.710

43.710

-4.035

-3.641

-4.035

-4.010

rRNA gene copy

0.834

0.993

0.923

0.994

0.834

0.993

0.801

0.967

0.801

0.967

BAC

44.350

42.171

44.350

44.230

-3.798

-3.522

-3.798

-3.913

Methanosaetaceaeprimer set.

universal Bacteria primer set, MBT

BA 8 BA

BA 6 BA

BA 4 BA

BA 2 BA

BA 10 BA

Mst

0.718

0.985

0.718

0.985

0.718

0.985

0.881

0.992

0.881

0.992

47.480

43.850

-4.278

-3.644

primerset, Mst

1.043

0.977

1.043

0.977

1.043

0.977

1.039

0.979

1.039

0.979

Msc

33.870

34.360

-3.222

-3.232

0.713

0.997

0.713

0.997

0.713

0.997

0.784

0.993

0.784

0.993

MBT

45.000

42.580

-4.278

-3.979

universal Archaea primer set, BAC

Methanosarcinaceae

Primer set

0.793

0.981

0.793

0.981

0.793

0.981

0.818

0.964

0.818

0.964

MMB

48.860

48.530

-3.943

-3.851

0.769

0.995

0.769

0.995

0.769

0.995

0.776

0.995

0.776

0.995

ARC

41.710

43.710

-4.035

-4.010

0.834

0.993

0.834

0.993

0.834

0.993

0.801

0.967

0.801

0.967

BAC

44.350

44.230

-3.798

-3.913

PCR PCR reaction parameters of the standard curves used for estimating the 16S

Methanomicrobialesprimer set, Msc

)

Intercept