An Integrated Study on Microbial Community in Anaerobic Digestion Systems

DISSERTATION

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University

Yueh-Fen Li

Graduate Program in Environmental Science

The Ohio State University

2013

Dissertation Committee:

Dr. Zhongtang Yu, Advisor

Dr. Brian Ahmer

Dr. Richard Dick

Dr. Olli Tuovinen

Copyrighted by

Yueh-Fen Li

2013

Abstract

Anaerobic digestion (AD) is an attractive microbiological technology for both waste treatment and energy production. Microorganisms are the driving force for the whole transformation process in anaerobic digesters. However, the microbial community underpinning the AD process remains poorly understood, especially with respect to community composition and dynamics in response to variations in feedstocks and operations. The overall objective was to better understand the microbiology driving anaerobic digestion processes by systematically investigating the diversity, composition and succession of microbial communities, both bacterial and archaeal, in anaerobic digesters of different designs, fed different feedstocks, and operated under different conditions.

The first two studies focused on propionate-degrading bacteria with an emphasis on syntrophic propionate-oxidizing bacteria. Propionate is one of the most important intermediates and has great influence on AD stability in AD systems because it is inhibitory to methanogens and it can only be metabolized through syntrophic propionate- oxidizing acetogenesis under methanogenic conditions. In the first study (chapter 3), primers specific to the propionate-CoA transferase gene (pct) were designed and used to construct clone libraries, which were sequenced and analyzed to investigate the diversity and distribution of propionate-utilizing bacteria present in the granular and the liquid portions of samples collected from four digesters of different designs, fed different

ii feedstocks, and operated at different temperatures. The results suggest that propionate metabolism can be affected by feedstocks and partitions differently between the solid and the liquid phases in digesters. Additionally, cluster-specific real-time PCR assays were developed and used in quantifying the abundance of different types of propionate- metabolizing bacteria. The second study (chapter 4) focused on developing a propionate- specific whole-cell bacterial biosensor that can serve as an alternative tool to measure concentrations and bioavailability of propionate in digesters. The biosensor was based on the transcription fusion of luxCDABE operon under the control of the promoter PprpB.

The biosensor was shown to be specific to propionate and to report signal in a dose- dependent manner. The utility of the biosensor was also tested on several digester samples following dilution.

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Temporal succession in the microbial communities and methanogen populations were also examined. Moreover, canonical correspondence analysis (CCA) was used to identify correlation between microbial populations and the environmental/performance factors of the digesters. This study demonstrated that a distinct microbiome was formed at each of the two operation temperatures, with a greater microbial diversity and evenness observed during the mesophilic SS-AD than during the thermophilic SS-AD and suggested that syntrophic acetate oxidation coupled with hydrogenotrophic methanogenesis may be an important pathway for biogas production from acetate in the SS-AD. Because the same seed sludge was used to start up both the mesophilic and the thermophilic SS-ADs, the difference in the microbial communities observed in these two digesters might be attributed to both selection and adaptive diversification. The last study of my research

(chapter 7) investigated the community successions and population dynamics in the thermophilic and the mesophilic digesters of a lab-scale temperature-phased anaerobic digester (TPAD) system fed a mixture of dairy manure and whey for co-digestion using

Illumina sequencing. The TPAD system was intentionally fed with increasing amounts of readily digestible substrates (whey) to mimic organic overloading and to create an opportunity to examine how organic overloading affects different bacteria and methanogens. CCA was used to elucidate the correlation between microbial groups and the digester conditions/performance. The results showed that the microbial composition was affected by the organic overloading and revealed dramatic successions from a stable community structure to another distinct one, especially in the thermophilic digester. The

CCA revealed that temperature and pH were the most influential environmental factors that explained much of the variations of the microbial communities in this TPAD system.

Collectively, this series of studies advanced our understanding of the microbial community that underpins the AD process and elucidated possible roles that some bacterial and methanogens play in anaerobic digesters. The pct-specific qPCR and the propionate biosensor may also be useful tools in future studies on AD processes and in improving operation of anaerobic digesters.

Dedication

To my father and mother.

Acknowledgements

First, I sincerely thank my advisor Dr. Zhongtang Yu for his support in all ways.

Without his acceptance of me being in his lab, his generous support and great guidance, my study and the completion of this dissertation would not be possible. I would also like to show my appreciation to my committee members: Drs. Brian Ahmer, Richard Dick, and Olli Tuovinen. Thank you for the constructive suggestions that improve my research.

My special appreciation to Dr. Ahmer for his kindly sharing of bacterial strains, vectors, instruments, and for the assistance from his lab members.

Secondly, I would like to thank all the current and past lab members, especially

Neslon, Lingling, and Jill. Thank you for your technical support and assistance on my research. I would like to thank my friends in the department of Animal Sciences, TWSA,

NTUAA, ESGP, and Buckeye table tennis club, especially Paonan, Judy, Danni,

Cressman, Josie, Anita, Po-Hsu, Riu, and Maureen. Your accompany and friendship make my study in OSU and life in Columbus wonderful and enjoyable most of the time.

Last, my greatest appreciation to my parents, brother, sister, family members, and to a special friend Andre. Thank you for being exemplary, thank you for being strong, thank you for being endless supportive and encouraging, and thank you for always being there for me.

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Vita

2000...... National Kinmen Senior High School, Taiwan 2004...... B.S. Bioenvironmental Systems Engineering, National Taiwan University, Taiwan 2006...... M.S. Bioenvironmental Systems Engineering, National Taiwan University, Taiwan 2006 to 2008 ...... Research Assistant/Lab Manager, Bioenvironmental Systems Engineering, National Taiwan University, Taiwan 2008 to present ...... Graduate Research/Administrative Associate, Environmental Science Graduate Program, The Ohio State University

Publications

Li YF, Wei S, Yu Z. 2013. Feedstocks affect the diversity and distribution of propionate CoA-transferase genes (pct) in anaerobic digesters. Microb Ecol. 66(2): 351-362.

Li YF, Li FY, Ho CL, Liao VH. 2008. Construction and comparison of fluorescence and bioluminescence bacterial biosensors for the detection of bioavailable toluene and related compounds. Environ Pollut. 152(1): 123-129.

Fields of Study

Major Field: Environmental Science Focus: Microbial Ecology

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Table of Contents

Abstract ...... ii Dedication ...... vi Acknowledgements ...... vii Vita ...... viii Table of Contents ...... ix List of Tables ...... xi List of Figures ...... xii CHAPTER 1 Introduction...... 1 CHAPTER 2 Review of Literature ...... 6 2.1 Anaerobic digester for waste treatment ...... 6 2.2 Techniques and technologies used in studying the microbiome in AD ...... 18 2.3 Statistical analysis, data representation, and visualization ...... 26 2.4 Summary ...... 29 CHAPTER 3 Effect of Feedstocks on the Diversity and Distribution of Propionate CoA-Transferase Genes (pct) in Anaerobic Digesters ...... 31 3.1 Abstract ...... 31 3.2 Introduction ...... 32 3.3 Materials and Methods ...... 35 3.4 Results ...... 41 3.5 Discussion ...... 46 CHAPTER 4 Development of a whole-cell bacterial biosensor for the detection and measurement of propionate ...... 59 4.1 Abstract ...... 59 4.2 Introduction ...... 60 4.3 Materials and Methods ...... 63

4.4 Results ...... 69 4.5 Discussion ...... 71 CHAPTER 5 Spatial and Temporal Variations of Microbial Community in a Mixed Plug-Flow Loop Reactor (MPFLR) Fed with Dairy Manure ...... 80 5.1 Abstract ...... 80 5.2 Introduction ...... 81 5.3 Materials and Methods ...... 83 5.4 Results ...... 89 5.5 Discussion ...... 96 CHAPTER 6 Comparison of the Microbial Communities in Solid-State Anaerobic Digesters (SS-ADs) Operated at Mesophilic and Thermophilic Temperatures ...... 111 6.1 Abstract ...... 111 6.2 Introduction ...... 112 6.3 Materials and Methods ...... 114 6.4 Results ...... 117 6.5 Discussion ...... 128 CHAPTER 7 Comparison of Microbiota in the Temperature-phased Anaerobic Digestion (TPAD) System Before and After Feedstock Overloading ...... 141 7.1 Abstract ...... 141 7.2 Introduction ...... 142 7.3 Materials and Methods ...... 145 7.4 Results ...... 150 7.5 Discussion ...... 163 CHAPTER 8 General Discussion ...... 179 Works Cited ...... 184

List of Tables

Table 3.1 Primers designed in this study ...... 52

Table 3.2 BLASTx results of putative pct sequences obtained from the anaerobic digesters ...... 53

Table 4.1 PCR primers used in this study ...... 75

Table 5.1 Primers used in this study ...... 102

Table 5.2 Concentrations of volatile fatty acid (VFA) and content of total solid (TS) and volatile solid (VS)...... 103

Table 5.3 Distribution of the taxa with notable difference in relative abundance (%) among samples ...... 104

Table 6.1 Alpha diversity analysis on the SS-AD samples ...... 133

Table 6.2 Abundance of methanogens (shown as percentage in each sample) in the SS-ADs ...... 134

Table 6.3 Relative Abundance (shown as the percentage in the individual sample) of the major OTUs (containing > 1% of the total sequences) in the SS-AD system ...... 135

Table 7.1 Alpha diversity analysis on the TPAD samples...... 170

Table 7.2 Archaea relative abundance (shown in percentage of the sample) in the TPAD system ...... 171

List of Figures

Fig. 3.1 Comparison of tree topology between 16S rRNA gene sequences and Pct amino acid sequences ...... 54

Fig. 3.2 Phylogenetic tree of deduced amino acid sequences from the cloned pct gene with nearest neighbor reference sequences ...... 55

Fig. 3.4 Principal coordinate analysis of putative pct from different AD samples using UniFrac program ...... 57

Fig. 3.5 The abundance (logs of gene copies per microgram of metagenomic DNA) of each cluster of putative pct genes in different AD samples quantified by qPCR ...... 58

Fig. 4.1 Schematic diagram of the biosensor plasmid ...... 76

Fig. 4.2 Responses of the plasmid-based biosensor to propionate at different concentration and after different exposure duration ...... 77

Fig. 4.3 Responses of the plasmid-based biosensor to the individual VFAs tested ...... 78

Fig. 4.4 Responses of the biosensors to increased propionate concentrations spiked into samples collected from a UASB (A), an MPFLR (B), and a CSTR (C) ..... 79

Fig. 5.1 Scheme of mixed plug-flow loop reactor (MPFLR). A) Aerial view and sampling locations ...... 105

Fig. 5.2 DGGE banding profiles of A) archaea and B) bacteria of the samples collected from the influents (I), locations along the course (T1, T2, and T3), and the effluents (E) of the MPFLR digester ...... 106

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Fig. 5.3 Major bacterial phyla (represented by > 1% total bacterial sequences) in each sample ...... 107

Fig. 5.4 Heatmap of the Top 197 OTUs with most counts (> 50 reads) in the MPFLR samples ...... 108

Fig. 5.5 PCoA plot of the bacterial community in the MPFLR samples based on the pyrosequencing analysis ...... 109

Fig. 5.6 Quantification of five commonly observed genera of methanogens in AD using qPCR ...... 110

Fig. 6.1 Principal coordinate analysis (PCoA) of the microbial community in the mesophilic (open diamond) and thermophilic (dark diamond) SS-ADs using weighted Unifrac distance matrix ...... 136

Fig 6.2 Major phyla (abundance > 1% in at least one sample) observed in the SS- ADs ...... 137

Fig. 6.3 Distribution of the OTUs that could be identified to the genus level in the SS-ADs ...... 138

Fig 6.4 Results of canonical correspondence analysis on A) phyla level, B) abundant OTUs (containing reads more than 0.05% of total reads) clustered to genus level, and C) abundant OTUs ...... 139

Fig. 7.1 DGGE profiles of archaea (A) and bacteria (B) in the two digesters of the TPAD system ...... 172

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Fig. 7.4 Major bacterial phyla (abundance > 1% in at least one sample) in the TPAD system ...... 175

Fig. 7.5 Distribution of OTUs in the TPAD system. G1-G6, groups of OTUs ...... 176

Fig. 7.6 Results of constrained correspondence analysis on A) phyla level, B) abundant OTUs (containing reads more than 0.1% of total reads), and C) OTUs classifed to genus level ...... 177

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CHAPTER 1 Introduction

The increasing demand for energy promotes development of alternative energy. In the meantime, large amounts of different kinds of wastes are produced from human activities such as industry, agriculture, and from societies. These wastes create a challenge to protect the environment from being polluted, but they also represent a huge source of energy. Thus, effective treatment and management of various industrial, agricultural, and household wastes are needed to both prevent environmental pollution and to recover the energy from wastes. Anaerobic digestion (AD) is a biological process that converts organic matters to methane and carbon dioxide in an anaerobic environment

(Pullammanappallil et al., 1998). Although the technology has been used for decades, AD has received renewed attention because of the pursuit of new sources of energy and of reduced impact from human activities on the environment.

Anaerobic digestion has several advantages as an attractive method of wastes treatment and energy production: low operational cost, energy production, pathogen control, and environment-friendly operations (Suryawanshi et al., 2010). As for waste treatment, when compared to traditional aerobic wastewater treatment methods, AD is less expensive because (1) no aeration is needed and (2) less sludge is produced so reducing the cost of sludge handling and disposal compared to typical aerobic biological

1 treatment. For these reasons, there are at least 1500 wastewater treatment plants worldwide that use AD (Kleerebezem and Macarie, 2003). A much larger number of biogas plants use AD to produce energy, particularly in Europe. In Sweden alone, 1,363

GWh worth of biogas was produced in 2009 (www.biogasportalen.se).

As a renewable energy, the methane-rich biogas produced by AD can be used for heat production and power generation. In addition to being a carbon neutral energy production process, AD can also reduce greenhouse gas emission from certain wastes, such as livestock manure. Anaerobic digestion systems, ranging from household scale to commercial large scale, have been developed and implemented in many places of the world. For example, household-scale anaerobic digester systems are widely applied in some suburbs of China (Tan et al., 2010). In these areas, the waste products from humans and animals, and agricultural by-products, such as rice stalks, are fed into anaerobic digesters. During the AD process, the biogas is collected in a storage tank and used as the energy for heating, cooking and lighting, while the digested sludge is used as fertilizer.

This is an economic and self-sufficient system that benefits both human and the environment. Most of the large anaerobic digester plants are located in Europe. The energy produced from large-scale anaerobic digesters has reached 6.1 million tons of oil equivalents (Mtoe) a year in 2007, with a projected increase of more than 20% annually

(EurObserv‟er, 2008; Weiland, 2010).

As AD being implemented as a technology to generate energy, microbiologist and engineers have been making great research effort to improve efficiency and stability to maximize stable and reliable energy supply. Both efficiency and stability of AD 2 processes, to a large extent, depend on the microbial communities and their metabolic activities in anaerobic digesters. As in other environments, the microbial community in anaerobic digesters can be affected by a number of factors, such as temperature, pH, availability and digestibility of substrates (i.e., organic loading and types of feedstocks).

A better understanding of the composition and dynamics of microbial communities in anaerobic digesters under different feeding and operational conditions is needed for improved efficiency and stability of AD processes. I conducted a series of studies on the microbial communities in both lab-scale and commercial-scale digesters to help advance our knowledge on AD processes.

In the first two studies, the focus was propionate-related bacteria. Propionate is an important intermediate that has great influence on AD stability in AD systems. Thus, propionate-metabolizing bacteria, especially syntrophic propionate-oxidizing acetogens, are important to stable operation of anaerobic digesters. In the first study (chapter 3), primers specific to the propionate-CoA transferase gene (pct) were designed and used to construct clone libraries, which were sequenced and analyzed to investigate the diversity and distribution of propionate-utilizing bacteria present in granular and liquid portions of samples collected from four digesters of different designs, fed different feedstocks, and operated at different temperatures. Additionally, cluster-specific real-time PCR assays were developed that can quantify the pct clusters found in this study. The second study

(chapter 4) focused on developing a propionate-specific whole-cell bacterial biosensor that can serve as an alternative tool to measure concentration and bioavailability of propionate. The biosensor was developed for potential real-time monitoring of propionate

3 levels in AD systems to provide early warning of propionate accumulation, which often results from organic overloading.

The second half of my research focused on the microbial ecology in different AD systems, particularly on community composition and succession in response to changes in feedstock, organic loading and operation. One study (chapter 5) investigated the spatial and temporal patterns of microbial composition within a full-scale mixed plug-flow loop reactor (MPFLR) treating dairy manure using DGGE, 454 pyrosequencing, and genus- specific quantitative PCR. In another study (chapter 6), the community comparison in two lab-scale solid-state anaerobic digesters (SS-AD) fed the same feedstock (i.e., corn stover) but operated at different temperature (mesophilic vs. thermophilic temperature) was examined and compared using Illumina sequencing of 16S rRNA genes. Temporal succession in the microbial communities and methanogen populations were also examined. Moreover, canonical correspondence analysis (CCA) was used to identify correlation between microbial populations and the environmental/performance factors of the digesters. The last study of my research (chapter 7) investigated the community successions and population dynamics in the thermophilic and the mesophilic digesters of a lab-scale temperature-phased anaerobic digester (TPAD) system fed a mixture of dairy manure and whey for co-digestion using Illumina sequencing. The TPAD system was intentionally fed with increasing amounts of readily digestible substrates (whey) to mimic organic overloading and to create an opportunity to examine how organic overloading affects different bacteria and methanogens. CCA was also applied to elucidate the correlation between microbial groups and the digester conditions/performance.

CHAPTER 2 Review of Literature

2.1 Anaerobic digester for waste treatment

Microbial processes of anaerobic digestion

The anaerobic digestion process is driven by a complex microbiome present in anaerobic digesters and can be conceptually divided into four steps: hydrolysis, acidogenesis, syntrophic acetogenesis, and methanogenesis as shown in Figure 2.1 (IWA

Task Group for Mathematical Modeling of Anaerobic Digestion Processes, 2002; Bitton,

2005). Each of these sequential steps is carried out by a guild of microorganisms, and it is important to maintain a balance in reaction rate among the four steps to ensure a stable

AD process. The four sequential steps are further introduced as follows.

In the hydrolysis step, polymeric substrates that cannot be directly transported across cellular membrane by microorganisms, such as polysaccharides, lipids, and proteins, are hydrolyzed by hydrolases secreted by hydrolytic bacteria. These extracellular hydrolytic enzymes include cellulase, xylanase, pectinase, amylase, lipase, and protease. The hydrolysis step „solubilize‟ polymers to monomeric or oligomers, such as glucose and cellobiose from cellulose, xylose from hemicellulose, amino acids from proteins, and fatty acids and glycerol from lipids. Hydrolytic bacteria as a guild are phylogenetically diverse, but Firmicutes and Bacteroides are the two phyla that contain the largest number of species of hydrolytic bacteria. In anaerobic digesters, hydrolytic 6 species have been found in the genera Acetivibrio, Clostridium, Bacteroides, and

Thermotoga (in the phylum Thermotogae) and many others (Liebl, 2001; O'Sullivan et al., 2005; Cirne et al., 2007; Zverlov et al., 2010; Sträuber et al., 2012). Hydrolytic bacteria in general have fast growth and are less sensitive to changes in environment conditions, such as pH and temperature. Except for recalcitrant substrates, such as lignocellulose, the hydrolysis step is not rate limiting of anaerobic digestion (Noike et al.,

1995; Mata-Alvarez et al., 2000; Vidal et al., 2000). All hydrolytic bacteria in anaerobic digesters can utilize the hydrolysis products as growth substrates, primarily through fermentation, to produce volatile fatty acids (VFA).

Acidogenesis is basically a fermentation step in which hydrolytic products are fermented to VFA, with acetate, propionate, butyrate, valerate, and isobutyrate as the major VFAs. Carbon dioxide, hydrogen, and ammonia are also produced during acidogenesis. Acetogens include hydrolytic bacteria and fermentative bacteria that do not produce extracellular hydrolases. Bacteroidetes, Chloroflexi, Firmicutes, Proteobacteria, and OP9 are phyla that contain many known species of acidogens. Non-hydrolytic acidogens have been found in Bifidobacterium (in the phylum Actinobacteria),

Lactobacillus (in the phylum Firmicutes), Anaerolinaceae (in the phylum Chloroflexi), and a few thermophilic bacteria in the phylum Thermotogae (Stiles and Holzapfel, 1997;

Balk et al., 2002; Dong et al., 2000; Yamada et al., 2006). Acidogenesis is generally rapid, and it can cause accumulation of VFA and sharp pH drop when digesters are over fed with readily digestible feedstocks, such as waste whey. Because accumulation of

VFA can cause digester failure, early warning systems of VFA accumulation are highly sought after.

Although some of the end-products from hydrolysis and acidogenesis, such as acetate, H2 and CO2, can be directly utilized by methanogens for biogas production, other intermediates, such as fatty acids, propionate, and butyrate, isobutyrate, valerate, isovalerate, and ethanol, cannot be utilized by methanogens and need to be further degraded and transformed into the substrates of the following step, methanogenesis.

Syntrophic acetogenesis is that step in which the aforementioned intermediates are further degraded/oxidized to form acetate, H2 and CO2. Syntrophic oxidation of propionate is particularly important because nearly 30% of the electrons generated from complex substrates flow through propionate during anaerobic digestion (Speece et al.,

2006). Most of the medium- and long-chain fatty acids resulted from lipid hydrolysis are also oxidized to acetate, H2 and CO2 through syntrophic acetogenesis. Syntrophic acetogenesis is thermodynamically unfavorable unless the H2 partial pressure is kept below 10-4 atm (McCarty and Smith, 1986; Lowe et al., 1993). In anaerobic digesters, hydrogenotrophic methanogens live in close proximity of syntrophic acetogens and consume the hydrogen released from the latter. This syntrophic relationship makes syntrophic acetogenesis thermodynamics feasible. This syntrophy (Schink, 1997) is based on hydrogen transfer from hydrogen-producing and hydrogen-consuming microorganisms, which is referred to as interspecies hydrogen transfer (Boone, 1985;

Stams and Plugge, 2009).

Syntrophic acetogens found in the digesters include species in the genera

Smithllela, Syntrophobacter, and Pelotomaculum for propionate oxidation (Liu et al.,

1999; de Bok et al., 2001) and in the genera Syntrophus and Syntrophomonas for oxidation of butyrate and long chain fatty acids (Jackson et al., 1999; Imachi et al., 2007;

Sousa et al., 2007). Syntrophic acetogenesis is a very important step in maintaining stable and robust AD operation because some of the aforementioned volatile fatty acids (VFA), particularly propionate, are potent inhibitors of methanogens even at neutral pH (Barredo and Evison, 1991; Pullammanappallil et al., 1998; Demirel and Yenigün, 2002; Nielsen et al., 2007). Studies on syntrophic acetogens have been difficult because they cannot be cultured alone. Genomics and metagenomics provided new opportunities to further study this important guild of bacteria in anaerobic digesters and other methanogenic habitats.

The final step, methanogenesis, is carried out by methanogens, a specialized group of archaea. Based on their substrates, methanogens can be divided into three groups: i) acetoclastic methanogens that use the acetoclastic pathway to produce methane and carbon dioxide from acetate, ii) hydrogenotrophic methanogens that use hydrogen to reduce carbon dioxide to methane via the hydrogenotrophic pathway, and iii) methylotrophic methanogens that produce methane from C1 compounds, such as methanol, methylamines, and methyl sulfides, through the methylotrophic methanogenesis. Methanogens have also been divided into three classes (Anderson et al.,

2009). Class I and Class II are classified as hydrogenotrophic methanogens that utilize formate, H2 and CO2 and they are of great importance in the AD process owing to its ability to scavenge H2 and to keep the partial hydrogen pressure low (as mentioned

9 above, low hydrogen pressure is a requirement for the syntrophic acetogenesis to proceed to oxidize short chain fatty acids).

Common hydrogenotrophic methanogens found in the anaerobic digesters include

Methanoculleus, Methanobacterium, Methanobrevibacter, Methanospirillum, and

Methanothermobacter (Cuzin et al., 2001; Savant et al., 2002; Leclerc et al., 2004; Hori et al., 2006). Class III methanogens possess the ability to utilize substrates other than formate, H2 and CO2, such as acetate, methanol, and other C1 compounds. In anaerobic digesters, about two-third of the methane is produced from acetate, and about one-third produced from H2 and CO2 (Zinder, 1993). In most anaerobic digesters, only minimal amount of methane is produced from methanol, methylamines, or methyl sulfides.

Acetoclastic methanogens are found in Methanosaeta, a genus of obligate acetoclastic methanogens, and Methanosarcina, a genus of facultative acetoclastic methanogens.

Methanosaeta can use only acetate as the substrate, and it has slow growth rate but a high affinity for acetate so it propagates and dominates when acetate concentration is low.

Methanosaeta can have filamentous morphology and play important role in granulogenesis of anaerobic granules or aggregates. Most Methanosarcina species can utilize H2 and CO2, methanol, methylamine, and methyl sulfides in addition to acetate

(Westerman et al., 1989; Conklin et al., 2006). In addition to its broader range of substrates, Methanosarcina has higher growth rate and lower affinity for acetate so it can outcompete Methanosaeta in digesters where acetate concentration is high (Westerman et al., 1989; Conklin et al., 2006; Hori et al., 2006). Compared to other bacteria in AD, methanogens grow the slowest and are more sensitive to environmental disturbances,

10 such as pH decline accumulation of VFA or ammonia (Chen et al., 2008; Liu and

Whitman, 2008).

An alternative pathway for methane production from acetate has also been found in some anaerobic digesters when operated under certain conditions. This pathway couples syntrophic oxidation of acetate to H2 and CO2 by syntrophic acetate-oxidizing bacteria and conversion of H2 and CO2 to methane by hydrogenotrophic methanogens

(Zinder and Koch, 1984). This pathway is not a major pathway for biogas production in most digesters because syntrophic acetate-oxidizing bacteria are not as competitive as acetoclastic methanogens (Rui et al., 2011). However, under conditions that inhibit acetoclastic methanogens, such as high ammonia concentration and high operation temperature, this pathway is important to biogas production (Schnurer et al., 1999;

Hattori, 2008; Schnurer and Nordberg, 2008; Westerholm et al., 2012; Lü et al., 2013;

Hao et al., 2011; Rui et al., 2011). In addition, researches have shown that long hydraulic retention time and the absence of Methanosaeta could also promote shift from acetoclastic methanogenesis to this alternative pathway (Shigematsu et al., 2004;

Karakashev et al., 2006).

Anaerobic Digester Design

The waste currently tested and treated using anaerobic digesters (AD) includes but not limited to agriculture waste, waste produced from livestock farms, food processing waste, household waste, and municipal sludge. To achieve better digestion efficiency, co-digestion of wastes with different characteristics is also performed.

Because of the broad range of wastes that could be treated by anaerobic digestion, 11 different designs and configurations of AD have been developed to optimize the capability of treating different types of feedstock.

Continuous stirred-tank reactor (CSTR) is a tank reactor with continuous mixing, using mechanical propellers, hydraulic jets, or biogas jets, so the feedstock fed is completely mixed with the sludge inside the tank. The biogas produced is collected from the top and the sludge exits through an overflow weir placed close to the top of the reactor. The microbial biomass, bacteria and methanogens, exist as disperse cells or small aggregates. Due to its simplicity, this is a common AD design and has been implemented widely for treatment of waste in many industries. Because of the complete mixing, the microbial biomass flows out with the digestate effluent, and thus it is not possible to retain high density of microbial biomass in digesters. As a result, CSTR suffers from low digestion efficiency and high risk of washout of microbial biomass at high hydraulic loading rates. Recycling of a small portion of the effluent can compensate these two limitations, but recycling increases operational cost.

Upflow anaerobic sludge blanket (UASB) reactor is a design that decouples hydraulic retention time (HRT) from solid retention time (SRT) to overcome the limitations of CSTR. In a UASB reactor, which is typically vertical, feedstock is fed from the bottom and exits from the top to create an upward hydraulic flow in the digester. This hydraulic upflow is maintained at such a linear velocity that microbial biomass (both bacteria and methanogens) forms anaerobic granules of up to 4 mm in size and stays in the lower portion of UASB reactor. Because the anaerobic granules, also called anaerobic sludge bed, are retained and separated from the effluent, UASB reactors can be operated 12 at high hydraulic loading rates (volume of feedstock/volume of digester/d). Although

UASB reactors were efficient, they cannot be used to digest feedstock that contains a large amount of suspended solid. The anaerobic granules are responsible for most of the microbial activities of the anaerobic process. Researches on microbial composition in the granules have observed close proximity between syntrophic bacteria and methanogens

(Imachi et al., 2000; De Bok et al., 2004; Ishii et al., 2005). However, mechanistic understanding of granulogenesis remains limited. In addition, little is known on how feedstock and operation affect microbial composition of anaerobic granules.

Among the several designs of anaerobic digesters implemented on US dairy farms, the mixed plug-flow loop reactor (MPFLR), which is designed and implemented by DVO, Inc. (Chilton, WI), is the most common AD technology. An MPFLR is basically a U-shaped tank digester. Influent enters an MPFLR digester at one end, flows forward and loops back as a “plug”, and finally exits from the other end (Yu et al., 2010).

The digester content is continuously mixed by biogas in the direction perpendicular to the plug-flow of the digester content. A portion of the effluent is typically recycled to inoculate the influent to improve AD performance. According to a recent survey conducted in May 2013 by USEPA, 65 out of the 167 anaerobic digesters operated on the

US dairy farms use the MPFLR design (http://www.epa.gov/agstar/downloads/ digesters_dairy.xlsx). The popularity of MPFLR digesters stemmed from its simplicity in construction and maintenance and its reliability in operation. No study has been reported that examined the microbial community in this type of digesters.

A temperature-phased anaerobic digester (TPAD) system is a system with two anaerobic digesters connected in series but operated at different temperatures, with the first digester operated at thermophilic temperature (50-60 °C), while the second operated at mesophilic temperature (30-35 °C). The advantage of this system is that the hydrolysis and acidogenesis stages is physically separated from the acetogenesis and methanogenesis stage, thus the environmental conditions of the individual digester can be adjusted to meet the need of different guilds of microorganisms. Generally, the process of hydrolysis of cellulolytic materials and other polymers and subsequent acidogenesis primarily occur in the first thermophilic digester, while the second stage mesophilic digester provides permissive environment for syntrophic acetogenesis and methanogenesis, both of which are more sensitive to the environmental factors (such as pH, temperature, and VFA) to occur (Ge et al., 2011). Although energy input is relatively high because of the need to maintain elevated temperature in the thermophilic digester, TPAD systems can produce improved efficiency and stability of AD. TPAD digesters also produce class A biosolid by killing off most pathogens.

Based on total solid (TS) content of influent feedstock, AD can be divided into two major types: liquid-state AD (L-AD) and solid-state AD (SS-AD) (Guendouz et al.,

2010). Wastes that contain low TS content, such as animal manure slurry, food- processing wastewater, and other high-strength wastewater with TS content below 15%, are generally subjected to L-AD. On the other hand, for feedstocks that have high solid content (>15% total solid), such as crop residues, organic fraction of municipal solid wastes (OFMSW), and food wastes with low water content, SS-AD is advantageous and

14 preferred because it eliminates the need to dilute the feedstocks to fluid slurry and produces low-moisture digestate, which is much easier to handle. Besides, SS-AD requires smaller reactor volumes per unit mass of feedstock, less energy for heating and mixing, and produces less digestate (Li et al., 2011). Therefore, although most of the AD systems in use are L-AD systems, the above advantages recently spearheaded research and implementation of SS-AD systems, particularly in Europe, to digest feedstock of high solid content, such as crop residues and OFMSW. Similar microbiological processes, that is hydrolysis, acidogenesis, syntrophic acetogenesis, and methanogenesis, probably occur in SS-AD, but the microbial community underpinning SS-AD likely differ from that of L-

AD. However, few studies have been conducted to investigate the microbial communities in SS-AD, and this knowledge gap needs to be filled to improve efficiency and stability of SS-AD.

Anaerobic Digester Stability

Process stability is crucial when it comes to the AD operation. Most of the AD in operation is operated conservatively in terms of organic loading rate and hydraulic loading rate to ensure the AD is not overloaded and functions stably. A failure of operation costs not only money and takes a long time, up to several months, to reestablish the microbial consortia and restart the process, it can also affect or disrupt operation of core business, such as manufacturing of food-processing companies. More importantly, reliability is probably the most paramount when choosing an energy supply method. As a result, how to improve AD stability without compromising biogas yield has always been

15 a research focus. A number of factors have been identified that could cause disturbance or even operation failure of AD (Chen et al., 2008).

VFAs are key intermediates during the entire anaerobic digestion process produced by acidogens. When digesters are overloaded with organic feedstocks, VFAs can accumulate because consumption of VFA by acetoclastic methanogens and syntrophic acetogens is slow while VFA production by acidogens is rapid. Because of their adverse effect on the growth of methanogens (Barredo and Evison, 1991;

Pullammanappallil et al., 1998; Demirel and Yenigün, 2002; Nielsen et al., 2007), accumulation of VFAs can upset or fail AD. Such adverse effect of VFA on methanogens is largely caused by acidification. Some VFA, particularly propionate, is also inhibitory even at neutral pH. When recalcitrant feedstocks, such as municipal sludge and dairy manure, are digested, VFA accumulation rarely occurs. When readily digestible feedstocks, such as waste whey or other food-processing waste streams, are digested,

VFA can easily accumulate if the feeding is aggressive. Yet, aggressive feeding is desired to increase biogas yields. Therefore, detection tools are needed to provide early warning of VFA accumulation. Studies are also needed to understand how different microbial populations are involved in response to VFA accumulation.

Ammonia concentration also has an impact on the microbial composition and in turn affects AD stability (Hansen et al, 1998; Westerholm et al., 2012; Fotidis et al.,

2013; Lü et al., 2013). Ammonia was mainly produced from the hydrolysis of protein- rich feedstocks such as manure and nitrogen-rich food waste. Ammonia inhibition results from intracellular pH change, increase of maintenance energy requirement, and 16 potassium imbalance (Whittmann et al., 1995; Gallert et al., 1998). It has been shown that relatively high ammonia concentrations can be tolerated if the microbial communities are allowed to adapt gradually (Chen et al., 2008). Among the microorganisms inside the AD system, methanogens are more susceptible to elevated ammonia concentration than bacteria (Kayhanian, 1994). Some studies reported that hydrogenotrophic methanogens are more resistant to ammonia than their acetoclastic cousins (Westerholm et al., 2011;

Zhang et al., 2013), but one study reported the opposite (Fotidis et al., 2013). Further studies are needed to resolve this discrepancy.

Anaerobic digesters can be operated at thermophilic (50-60 °C), mesophilic (30-

35 °C), and psychrophilic (10-15 °C) temperatures after a stable microbial community is established. Because temperature has the greatest effect on microbial metabolism and growth, the above three types of digesters have different stability. In general, thermophilic digesters have a lower microbial diversity and they are more susceptible to variations in feeding and other operational parameters than the other two types of digesters. Although thermophilic digesters have better performance in terms of rate of digestion, biogas yield, and reduction of pathogens, their high susceptibility to feeding and operation hurdles the applications of thermophilic digesters (Kim et al., 2002).

Consequently, most of the full-scale ADs in use are operated at mesophilic temperature.

As mentioned above, however, new system design, such as TPAD, can use and benefit from thermophilic digesters.

As AD is looked upon as a source of bioenergy, efficient biogas production from a wide array of feedstocks in a stable and reliable manner is highly sought after.

However, the vast diversity of the microbial communities underpinning anaerobic digestion and the complexity of the interactions between different trophic groups and with the environmental conditions in digesters remain poorly understood. The metabolism and lifestyle of most of the microbes in anaerobic digesters also remain unknown. Molecular biology techniques and -omic technologies provided new tools to comprehensively investigate the microbial communities in anaerobic digesters.

2.2 Techniques and technologies used in studying the microbiome in AD

Traditionally, cultivation-dependent methods are the major way to investigate the microbial composition inside AD, and a number of species of bacteria and archaea have been successfully isolated from AD systems. Studies on pure or axenic cultures obtained from the cultivation methods provided valuable knowledge not only on the characteristics and fundamental metabolism of individual species but also on the syntrophic interspecies interactions. However, owing to the fact that most of the microorganisms are not cultivatable in the lab with current cultivation methods, a small portion of the microbial communities are known, and the ecology information revealed by cultivation-based methods is restricted. Thanks to the advancement and development in molecular biology techniques and technologies, many cultivation-independent methods are available and have become the preferable methods to study the microbial ecology in AD. Most of these contemporary techniques and technologies analyze marker genes, and 16S ribosomal

RNA (rRNA) gene has been almost exclusively used as marker gene in studies of microbial communities, either community composition or population dynamics.

The 16S rRNA gene is a phylogenetic marker for both bacteria and archaea. This gene is composed of nine hypervariable regions interspersed with conserved regions (Van de Peer et al., 1996).The reasons it was chosen as the marker gene over other rRNA genes (i.e., 5S or 23S rRNA gene, about 120 bp and 3,000 bp, respectively) is because its length (about 1,600 bp) is long enough to reveal the phylogenetic information needed to distinguish different bacteria and archaea but short enough to be easily sequenced. In addition, the mosaic structure of both the hypervariable regions and the conversed regions allows design of probes and primers specific at different taxonomic levels.

Furthermore, because of the common acceptance of 16S rRNA as the marker for phylogenetic studies of microbiomes, several large public databases dedicated to 16S rRNA genes have been constructed. These databases, including Greengenes

(http://greengenes.lbl.gov/), Silva (http://www.arb-silva.de/), and RDP (http://rdp.cme. msu.edu/), greatly facilitate archiving and phylogenetic analysis of 16S rRNA genes.

A variety of 16S rRNA-based techniques have been developed and applied to microbial ecology studies in the area of AD. Cloning library of PCR-amplified 16S rRNA gene, a fragment or the entire gene, followed by traditional sequencing has been used for decades in microbial community studies. Domain-specific primers allow broad analysis of a community, while primers targeting a lower taxon, such as a genus, allow detailed analysis of a taxon. Most of the information on microbial composition in anaerobic digesters was produced by this approach (Nelson et al., 2011). However, because this 19 traditional sequencing approach sequences individual clones one by one, which is costly and time consuming, it does not support detailed analysis to reveal the true microbial composition and diversity, especially when multiple samples are analyzed. Recent advancement and decreasing cost of the next-generation sequencing (NGS) technologies

(see below) made the above traditional method obsolete.

Microbial community profiling is also commonly used to evaluate and compare different microbiomes. The ones that have been used include terminal restriction fragment length polymorphism (T-RFLP), single strand conformation polymorphism

(SSCP), and denaturing gradient gel electrophoresis (DGGE). T-RFLP entails amplification of a region of the 16S rRNA gene using primers labeled with a fluorescent dye at 5‟ end, digestion using one or two restriction endonuclease, and sizing one or both terminal restriction fragments using Sanger sequencers (Sanger et al., 1977). Different

16S rRNA gene amplicons with different occurrence and locations of the cutting sites of the restriction endonuclease used produce different terminal restriction fragments. The terminal restriction fragments can be quantified based on the intensity of the fluorescence signal, but such quantification is not accurate because of PCR bias. T-RFLP fingerprinting has been used to investigate both the archaeal (Akuzawa et al., 2011;

Zhang et al., 2013) and bacterial communities (Ryan et al., 2008; Akuzawa et al., 2011;

Westerholm et al., 2011) in different AD systems.

SSCP is another method to profile microbial community. It is based on the principle that single-strand DNA fragments of the same length but different sequences can form different secondary structures, which affect migration during gel electrophoresis 20 and allow separation of different fragments. Typically, a region of the 16S rRNA is amplified by PCR. Then one strand is digested. The remaining DNA strand is allowed to fold and resolved on polyacrylamide gels (Orita et al., 1989). SSCP has been used to investigate the methanogenic community in several AD systems (Leclerc et al., 2001;

Chachkhiani et al., 2004; Leclerc et al., 2004; Hori et al., 2006), but it is not used as commonly as DGGE. Similar to SSCP, DGGE is also based on migration differences among 16S rRNA gene amplicons of the same length but different sequences. Different from SSCP, DGGE analyzes double stranded DNA fragments. It is the different denaturing behaviors of different amplicons that render different migrations in polyacrylamide gels containing a denaturant gradient. Because of its technical simplicity, rapidness, and low cost, DGGE has been widely used for microbial community studies in

AD systems even after NGS technologies are increasingly affordable (Rosa et al., 2009;

Shin et al., 2010; Lv et al., 2013; Zamanzadehn et al., 2013). It should also be noted that a recent study showed that DGGE profiles concurred with the detailed community profiles of microbial communities in several digesters that were determined using 454 pyrosequencing (Nelson, 2011). In studies that involve a large number of samples,

DGGE can be a useful tool to profile all the samples to identify representative samples to be further analyzed by deep sequencing (Nelson, 2011).

The major limitation of SSCP, T-RFLP, and DGGE lies on the difficulty to obtain sequence information of the 16S rRNA gene fragments. To putatively identify the bands on either SSCP or DGGE gels, a set of amplicons from known species can serve as references, but such identification is not reliable and was only used many years ago. As

21 for DGGE, although individual DNA bands in the gel could be excised out, re-amplified and sequenced directly or after cloning, it has been reported that one band could contain more than one DNA sequences, making it difficult to reliably identify the bacteria or methanogens represented by individual bands. For T-RFLP, terminal restriction fragments are typically compared to databases to infer possible source of bacteria or methanogens. However, individual terminal restriction fragments nearly always match multiple species of microorganisms. Nevertheless, despite of their limitations, these three profiling methods can be still useful to obtain a snapshot of the microbial communities from large number of samples.

Although SSCP, DGGE, and T-RFLP are useful tools in profiling the microbial community in the AD systems, they do not support accurate quantification of individual populations. By determining accumulation of PCR amplicons in real time during PCR cycles, real-time quantitative PCR (qPCR) can provides accurate quantification (relative or absolute) of the gene of interest. By comparing with a standard curve prepared with serial dilutions of known DNA target concentration, absolute quantification can be achieved. Relative quantification of a target group of microbes is performed using a reference target, mostly total 16S rRNA or its gene. Primers and/or probes have been designed to target Bacteria and Archaea as domains or specific taxonomic groups, such as the order-specific primers for commonly witnessed methanogens in AD (Yu et al., 2005), or genera-specific primers for Methanoculleus, Methanosarcina, and

Methanothermobacter (Franke-Whittle et al., 2009). Genus-specific primers targeting

16S rRNA of hydrolytic bacteria Clostridium, Peptostreptococcus, and syntrophic

22 acetogen Syntrophomonas were also used in qPCR to investigate the spatial distribution of these genera in different compartments of a plug flow digester (Talbot et al., 2010).

Another target-specific method, fluorescence in situ hybridization (FISH), has also been used in detection and quantification of microbial populations in AD. The advantage of

FISH is its ability to localize the specific group of interest. FISH has been used to identify and elucidate spatial arrangement of different trophic groups in anaerobic granules formed in AD systems (Angenent et al., 2004; Yamada et al., 2005).

Furthermore, a method that combines microautoradiography and FISH (MAR-FISH) was successfully developed and used in investigating the microbial species that are involved in syntrophic propionate oxidation and non-acetoclastic acetate oxidation in the AD sludge (Ariesyady et al., 2007; Ho et al., 2013).

Although the 16S rRNA gene has been the most commonly used phylogenetic marker in detailing the bacterial and methanogenic diversity of anaerobic digesters, several functional genes have also been explored as alternative markers. For example, the mcrA gene that encodes the α subunit of methyl CoM reductase of the methanogenesis pathway has been widely used in studying methanogen as a guild (Lueders et al., 2001;

Luton et al., 2002). A recent study used T-RFLP fingerprinting in analyzing mcrA transcripts and revealed the relationship between ammonia concentration and the abundance of different methanogens (Zhang et al., 2013). The fhs gene and the acsB gene, which encode the formyltetrahydrofolate synthetase and the acetyl-CoA synthase, respectively, of the homoacetogenesis pathway, have also been proved as useful markers in investigating homoacetogens in anaerobic environment (Leaphart and Lovell, 2001;

Ryan et al., 2008; Gagen et al., 2010; Akuzawa et al., 2011). Functional genes as phylogenetic markers have several advantages because they not only support interpretation of the phylogenetic diversity, but also enable detailed studies of particular microbial guilds, such as sulfate-reducing bacteria, with respect to their distribution, population dynamics, and in situ metabolic activities (Luton et al., 2002). Additionally, functional genes are mostly single copy genes. Thus, quantification based on functional genes will be more accurate than that based on 16S rRNA genes, which can exist in varying numbers in different species. Phospholipid fatty acid (PLFA) profiling has been used in other habitats, but its application to the microbiome in digesters is rare. Only one study has been reported in the literature that investigated changes of microbial community under different operation conditions in AD (Schwarzenauer and Illmer,

2012).

The development of the so-called next-generation sequencing (NGS) technologies makes it possible to reveal the complete (or nearly complete) composition and diversity of microbiomes in AD system by employing massive parallel sequencing. Next generation sequencing (NGS) technologies can produces millions of sequencing reads in a single instrument run. Unique barcodes (a short unique sequence region) can be used to

„label‟ individual samples so that multiple samples can be sequenced simultaneously in a single instrument run. The two most popular NGS technologies in use are 454 pyrosequencing on the FLX Titanium platform (Roche) and the Illumina sequencing on

HiSeq and MiSeq platforms (Illumina). Pyrosequencing produces longer sequencing reads but only about one million reads, while Illumina sequencing can produce several

24 dozen to several hundred million reads (depending on the platform and the chemistry used). Although differing in sequencing principle, a recent study suggested that both methods were suitable in quantitative analysis of microbial communities and produced similar results on the same microbial community (Luo et al., 2012). By directly comparing the sequencing datasets obtained from these two sequencing methods on the same microbial community, the authors reported that the results obtained from the two platform agreed on over 90% of the assembled contigs and 89% of the unassembled reads as well as on the estimated gene and genome abundance in the samples (Luo et al., 2012).

Studies using both technologies on AD microbial communities have successfully demonstrated the usefulness and power of their applications (Zhang et al., 2009; Werner et al., 2011; Lee et al., 2012, 2013; Cho et al., 2013; Smith et al., 2013). Many bioinformatics software tools have also been developed to handle the NGS data, such as

QIIME (Caporaso et al., 2010) and MOTHUR (Schloss et al., 2009) for 16S rRNA gene sequences and MEGAN (Huson et al., 2011) and MG-RAST (Meyer et al., 2008) for functional metagenomic sequencing data. However, it should be kept in mind that biases are generated from DNA preparation to sequencing data processing and analysis. These biases can be generated before the actual sequencing, such as DNA extraction, primer selection, and PCR amplification. During sequencing, large amounts of artificial sequencing reads are also produced by both NGS platforms (Niu et al, 2010; Kunin et al,

2010). The final results can also be affected by processing and analyzing of sequencing data, especially the phylogenetic software tools used and the parameters chosen. It should be pointed out that PCR amplicons of 16S rRNA genes, rather than community DNA, are

25 sequenced in nearly all studies reported in the literature. Because PCR has amplification bias, quantitative interpretation of prevalence of individual sequencing reads as relative abundance of the microbes represented is not reliable.

2.3 Statistical analysis, data representation, and visualization

Diversity and diversity indices

Diversity is an important indicator of stability of an ecosystem. Whittaker proposed the concept and definition of alpha diversity, beta diversity, and gamma diversity (Whittaker, 1972). Alpha diversity refers to the richness and evenness within a community, while beta diversity compares the richness and evenness among different communities. As for gamma diversity, it is an overall diversity of a defined region and represented as geographical diversity (Hunter, 2002). These diversity terms and evaluation methods were initially developed for and applied to ecosystems at macro scale, such as forest and fishery. When applied to microbial ecology, alpha and beta diversities are commonly accepted to describe the diversity in individual samples and among samples.

Several methods have been developed to evaluate the alpha diversity of a community. The simplest one is species richness (S), which is the observed number of species in a given community. It is easy and straightforward when applied to macro ecosystems (such as forest) because individual species, often a small number, can be morphologically identified and counted. However, species richness cannot be determined because no robust and reliable species concept is available for microbes and often it is

26 difficult to ascertain if all the „species‟ in a community have been accounted for. A practical taxonomic unit equivalent to species of microorganisms is operational taxonomic unit (OTU), which is typically defined based on a sequence similarity of 16S rRNA genes. However, true OTU richness is still difficult to determine because not all the OTUs present in a sample can be identified. In most studies, thus, „true‟ OTU richness is estimated using rarefaction (Sanders, 1968; Hurlbert et al., 1971), Chao1

(Chao, 1984), or ACE (Chao and Lee, 1992). Different models are used in each method and the estimates often differ considerably. Another challenge associated with determining OTU richness stems from huge difference in the number of sequences

(ranging from 1 to hundred thousands of sequences) representing each OTUs. Given the high error rates of sequencing data from NGS technologies, OTUs represented by a small number of sequences are discarded as artificial ones. However, the cutoff values used in all the studies were arbitrary. For alpha diversity, the Simpson diversity index (D) and

Shannon diversity index (H) are commonly used. Both indices take into account of the richness and evenness of the species (OTUs) detected. Simpson diversity index is in a scale from 0 to 1, with a value of 0 representing a completely homogenous community, while a value of 1 representing a completely heterogeneous community (Simpson, 1949).

Shannon diversity index, first proposed by Claude Shannon (Shannon, 1948), is another index of alpha diversity. In addition, equability index (EH) indicates the evenness of a community, with a value of 0 representing a community containing only one species

(OTU) and a value of 1 representing a community consisting of species with the same abundance.

Beta diversity is the comparison of diversity between samples. Instead of a single value like alpha diversity index, beta diversity is calculated as the distance (or dissimilarity) between a pair of samples. Thus, if more than two samples are compared, a distance (or dissimilarity) matrix will be generated to represent the beta diversity. Many methods have been developed to generate distance (or dissimilarity) matrix, such as

Bray-Curtis, Ochiai, and Unifrac. The first two methods are non-phylogenetic, with the

Bray-Curtis method taking into account the abundance of species (or OTUs), while the

Ochiai method only comparing presence or absence of species (or OTUs). Unifrac is a method to generate distance matrix based on phylogenetic trees constructed from the species present in the communities to be compared. It is specifically developed for 16S rRNA sequences (Lozupone et al., 2006) and widely used in analysis of beta diversity of microbiomes.

Multivariate analysis/ Ordination methods

Multivariate analyses have been widely used in ecological studies of communities of plants, fish, and bacteria to help elucidate the relationship between abundance of organisms and the habitat environments (see review by Ramette, 2007). Multivariate analysis of large ecological datasets allows reduction of data complexity so that major patterns and correlation between populations and environmental factors can be established. In environmental microbiology, principal coordinate analysis (PCoA) has been increasingly used in visualizing beta diversity and revealing the relationship between bacterial community composition and source of pollutants (Ibekwe et al., 2012) or geological distributions (Cao et al., 2011; Hong et al., 2011). However, its usage on

28 microbial diversity analysis of the microbial communities in AD is still limited. Only the research by Werner and his colleagues applied this ordination method to investigate and compare the microbial community in 112 samples taken from different full-scale AD systems (Werner et al., 2011). Canonical correspondence analysis (CCA), another ordination method, can help explain the variations of community structure patterns in AD by revealing the potential correlations between microbial communities or specific groups of microbes with environmental variables and/or AD performance. It has been used often in ecological studies of other environments, but its application to studies on microbial communities in AD is only recent (Supaphol et al., 2011; Werner et al., 2011; Wang et al., 2012). Although multivariate analysis is a powerful tool to elucidate the correlation between microbial community and the environmental factors, extra caution should be taken when interpreting the results and over-interpretation should be avoided because the synthetic variables, axes, or clusters derived do not necessarily and always have biological meanings (James & McCulloch, 1990).

2.4 Summary

29 and dynamics of microbial communities in AD can be better understood in the context of feedstocks, design and operation of anaerobic digesters.

CHAPTER 3 Effect of Feedstocks on the Diversity and Distribution of Propionate CoA- Transferase Genes (pct) in Anaerobic Digesters

3.1 Abstract

Anaerobic digestion (AD) is an attractive microbiological technology for both waste treatment and energy production. Syntrophic acetogenic bacteria are an important guild because they are essential to maintaining efficient and stable AD operation.

However, this guild is poorly understood due to difficulties to culture them. In this study, we developed specific PCR assays targeting the propionate-CoA transferase genes (pct) to investigate their diversity and distribution in several mesophilic anaerobic digesters and a bench-scale temperature-phased AD (TPAD) system. Phylogenetic analysis of sequenced pct amplicons revealed the occurrence of Syntrophobacter fumaroxidans and 6 other clusters of putative pct genes. Principal coordinate analysis (PCoA) showed that pct diversity and abundance were largely correlated to the feedstocks of the digesters, while little difference was seen between the granular and the liquid fractions of each digester or between the two digesters of the TPAD system. Cluster-specific qPCR analysis revealed major impact of feedstocks and fractions on the abundance of pct genes. Readily- fermentable substrates such as sugar- or starch-rich feedstocks selected for pct genes

(cluster I) related to Syntrophobacter, while manure feedstock selected for pct clusters that were related to pct of Clostridium spp. These results suggest that propionate 31 metabolism can be affected by feedstocks and partition differently between solid and liquid phases in digesters. The PCR assays developed in this study may serve as a tool to investigate propionate-oxidizing bacteria in anaerobic digesters and other anaerobic environments.

3.2 Introduction

Anaerobic digestion (AD) is an attractive dual-function technology widely implemented to both treat various organic wastes and wastewaters and harness the energy therefrom. The AD process is driven by a complex microbiome present in anaerobic digesters and can be conceptually divided into four steps: hydrolysis, acidogenesis, syntrophic acetogenesis, and methanogenesis (Bitton, 2005). Syntrophic acetogenesis is an important step responsible for converting a number of acidogenesis products, including propionate, butyrate, isopropionate, isobutyrate, valerate, isovalerate, and ethanol to the substrates of methanogenesis, i.e. acetate, H2 and CO2. Syntrophic oxidation of propionate is particularly important because nearly 30% of the electrons generated from complex substrates flow through propionate during anaerobic digestion

(Speece et al., 2006). Most of the medium- and long-chain fatty acids resulted from lipid hydrolysis are also oxidized to acetate, H2 and CO2 through syntrophic acetogenesis and eventually converted to methane and CO2 in anaerobic digesters. In addition, syntrophic acetogenesis is the most important step in maintaining stable and robust AD operation because some of the aforementioned volatile fatty acids (VFA), particularly propionate, are potent inhibitors of methanogens even at neutral pH (Barredo and Evison, 1991).

Although playing an important role in the AD process, syntrophic acetogens are poorly understood, largely due to difficulties in isolating and culturing them in laboratory media because they cannot be cultured as a single pure culture. Genomic studies provided valuable insight into the physiology of these bacteria; however, by far only the genomes of four species of syntrophic bacteria have been sequenced: Syntrophus aciditrophicus

(McInerney et al., 2007), Syntrophus wolfei (Sieber et al., 2010), Syntrophobacter fumaroxidans (unpublished, GenBank accession number: CP000478.1), and

Pelotomaculum thermopropionicum (Kosaka et al., 2006). The limited availability of pure cultures of syntrophic species limited genomic studies on syntrophic acetogens.

Hence, cultivation-independent DNA-based techniques, particularly 16S rRNA genes- based (McMahon et al., 2004; Chouari et al., 2005; Roest et al., 2005; Shin et al., 2010), including fluorescence in situ hybridization (FISH) (Harmsen et al., 1996; Sekiguchi et al., 1999; Shimada et al., 2011), are the primary methods used in studying the diversity and populations of certain syntrophic acetogens. Microautoradiography (MAR)-FISH has also been used (Ariesyady et al., 2007) in studying the ecophysiology of syntrophic acetogens. However, because only a limited number of specific probes or primers are available for the study of syntrophs, little is understood about their phylogenetic and functional diversity, distribution, and population dynamics in any AD process, a knowledge gap that hinders improvement of both stability and efficiency of AD processes.

Although the 16S rRNA gene has been the most commonly used phylogenetic marker in detailing the bacterial and methanogenic diversity of anaerobic digesters,

33 several functional genes have also been explored as alternative markers. For example, the mcrA gene that encodes the α subunit of methyl CoM reductase of the methanogenesis pathway has been widely used in studying methanogens as a guild (Lueders et al., 2001;

Luton et al., 2002). The fhs gene and the acsB gene, which encode the formyltetrahydrofolate synthetase and the acetyl-CoA synthase, respectively, of the homoacetogenesis pathway, have also been proved as useful markers in investigating homoacetogens in anaerobic environment (Leaphart and Lovell, 2001; Gagen et al.,

2010). Functional genes as phylogenetic markers have several advantages because they not only support interpretation of the phylogenetic diversity, but also enable detailed studies of particular microbial guilds, such as sulfate-reducing bacteria, with respect to their distribution, population dynamics, and in situ metabolic activities (Luton et al.,

2002). However, marker genes specific for syntrophic propionate-utilizing acetogens remain underexplored.

In syntrophic propionate-oxidizing bacteria, propionate is oxidized through either the randomizing methylmalonyl-CoA (MMC) pathway (Houwen et al., 1990; Plugge et al., 1993) or a non-randomizing pathway via a 6-carbon intermediate metabolite (de Bok et al., 2001). The MMC pathway was found to be the primary pathway for propionate oxidation in most syntrophic propionate oxidizers, while the non-randomizing pathway of propionate oxidation has so far only been found in Smithella propionica in co-cultures with methanogens. In the first step of the MMC pathway, propionate is activated by the addition of a CoA group from the CoA donor acetyl-CoA. This reaction is catalyzed by propionate-CoA transferase (encoded by pct), which is categorized under the EC 2.8.3.1

34 propionate-CoA transferase family. Therefore, we hypothesized that the pct gene can be a useful functional marker gene for propionate-oxidizing bacteria including syntrophic acetogens. To this end, the pct gene was explored in this study as a potential marker for the syntrophic propionate-oxidizing bacteria in anaerobic environment. The first goal of this study was to develop degenerate primers to examine the diversity of the pct gene in different anaerobic digesters, while our second goal was to design specific primers from the pct sequences obtained to investigate the distribution and population dynamic of propionate-oxidizing bacteria as affected by feedstock, digester design, and operation to better understand the ecology of syntrophic propionate-oxidizing bacteria in anaerobic digesters.

3.3 Materials and Methods

Samples collection and DNA extraction

Samples from three types of digesters were analyzed in this study: one upflow anaerobic sludge blanket (UASB) digester fed the processing waste streams from a commercial jam and jelly manufacturer (referred to as digester S), one pilot-scale sand bed filer (SBF) digester (with a 30 m3 working volume) operated on a corn and potato chip manufacturer to treat a feedstock consisting of waste materials and off-spec products

(referred to as digester N), the same pilot SBF digester operated on a Swiss cheese manufacturing company to treat the processing wastes therefrom (referred to as digester

B), and one bench-scale completely mixed temperature-phase anaerobic digester (TPAD) system fed dairy manure slurry. The operation of digesters S, N, and B have been

35 reported in a previous study (Nelson et al., 2012). Briefly, digester S had been operated for over 15 years on the jelly and jam wastes when it was sampled for this study. The sludge sample from digester S was used as the seed inoculum for digester N, which was sampled after 7 month operation on the wastes of the chip manufacturer. Then the sludge from digester N was subsequently used as the seed inoculum for digester B, which was sampled after 8 months of operation on the processing wastewater from the cheese manufacturer. All the digesters were operated at mesophilic temperature, with digester N and digester S operated at 35 °C specifically (unfortunately, other operations data were not provided to us by the companies). The bacterial and archaeal diversity of the above digesters has been analyzed using 16S rRNA clone libraries (Nelson et al., 2012). The

TPAD digester system consisted of two digesters with the thermophilic digester operated at 55 °C (TPAD-T) and the mesophilic digester at 37 °C (TPAD-M). The TPAD system was batch-fed a dairy manure slurry with a total solid content of 10% with an organic loading rate of 6 g VS/L/day. The hydraulic retention time was 5 and 10 days for the thermophilic and the mesophilic digesters, respectively.

Except for the samples from the TPAD digester, the microbial biomass from the granular (G) and liquid (L) fractions was separated (Nelson et al., 2012). Total metagenomic DNA was extracted from each sample (0.5 g for G portion, 0.5 ml for L portion and TPAD samples) using the repeated bead beating plus column purification

(RBB+C) method (Yu and Morrison, 2004b). The integrity of the extracted DNA was evaluated using agarose gel (1.0%) electrophoresis, while the concentrations were

36 quantified using a NanoDrop 1000 spectrophotometer (Thermo Scientific, Wilmington,

DE).

Primer design and diversity analysis of pct sequences

All the Pct amino acid sequences available in GenBank were retrieved. These included the Pct sequences from three syntrophic propionate-oxidizing acetogens species

(Syntrophus aciditrophicus SB, accession number: YP_460490.1; S. fumaroxidans

MPOB, accession number: YP_845291.1; and the three copies Pct of Pelotomaculum thermopropionicum SI, accession numbers: YP_001212321.1, YP_001212091.1, and

YP_001211062.1). The Pct sequences were aligned using ClustalW (Thompson et al.,

1994) to obtain the conserved regions of the Pct protein. Due to the inability to find well- conserved regions from the alignment of all the Pct sequences, degenerate primers were then designed from the alignment of the 6 Pct sequences of known syntrophic propionate- oxidizing acetogens using CODEHOP (Rose et al., 2003). In order to obtain long pct gene sequences, we selected a primer set that yielded the longest amplicon yet met the general criteria for primer design. The forward primer selected (pct-F) was based on the conserved region EKRFLET (amino acid 39 to 45) while the reverse primer (pct-R) was based on the conserved region VTERAVF (amino acid 458 to 464) of the Pct of S. fumaroxidans MPOB. This primer set (Table 3.1) yielded an amplicon approximately

1300 bp in length. The primer specificity was verified by in silico analysis against the

NCBI Reference Sequences (RefSeq) database.

Metagenomic DNA extracted from each sample (SG, SL, BG, BL, NG, NL,

TPAD-M, and TPAD-T) were used (200 ng each) in each PCR reaction (50 µl), which 37 contained 1X PCR buffer (200 mM Tris-HCl [pH 8.4] and 500 mM KCl), 1.75 mM

MgCl2, 670 ng/µl bovine serum albumin (BSA), 0.2 mM dNTPs, 0.5 µM each primer, and 1.25 U of Platinum Taq DNA polymerase (Invitrogen, Carlsbad, CA). The PCR thermal program was as follows: initial denaturation at 95 °C for 5 min, followed by 35 cycles of denaturation at 95 °C for 30 s, annealing at 62°C for 45 s, and extension at 72

°C for 90 s. The PCR was ended with a final extension step at 72 °C for 10 min. The PCR products were verified by agarose gel (1.0%) electrophoresis and the expected bands

(corresponding to approximately 1,300 bp DNA) were excised and purified using a

QIAquick gel extraction kit (Qiagen, Valencia, CA) following the manufacturer‟s instructions. The recovered DNA products were cloned using a TOPO TA cloning Kit

(Invitrogen, Carlsbad, CA) per manufacturer‟s instructions. From each library, 48 clones were randomly selected and sequenced from both directions using the M13 primers and the BigDye® Terminator v3.1 Cycle Sequencing Kit (Life Technologies, Carlsbad, CA).

Construction and comparison of phylogenetic trees of Pct amino acids and 16S rRNA genes of bacterial isolates

Sequences of Pct amino acids and 16S rRNA gene recovered from the same strain were retrieved from GenBank. The Pct amino acid sequences were aligned using

Expresso (http://tcoffee.crg.cat), which aligns multiple sequences based on sequence and structure information of proteins, and ClustalX 2.12, which aligns sequences based on sequence similarity. Neighbor-joining (Saitou and Nei, 1987) trees and UPGMA clustering trees of aligned Pct amino acid sequences were constructed in ClustalX 2.12 with default setting. Maximum-likelihood trees were also constructed from the aligned

Pct amino acid sequences using RAxML and the Jones Taylor Thornton model of amino acid substitutions with a γ rate of substitution and 25 discrete rate categories (Jones et al.,

1992) in ARB (Ludwig et al., 2004) as described by Gagen et al. (2010). The 16S rRNA genes were aligned first using ClustalX 2.12 and then SINA aligner (http://www.arb- silva.de/aligner/). Neighbor-joining, UPGMA, and Maximum-likelihood trees of the aligned 16S rRNA genes were constructed as described for the Pct amino acid sequences.

The topologies of the phylogenetic trees of Pct amino acid sequences were compared to those of the16S rRNA genes using the MetaTree program (Nye, 2008). The

UPGMA tree of Pct amino acid sequences aligned by ClustalX and the UPGMA tree of

16S rRNA genes aligned by SINA aligner shared the highest similarity (50.3% similarity) of tree topology, and these two trees were chosen for further comparison.

Analysis of the pct sequences recovered from digesters

The DNA sequences obtained from the pct clone libraries were processed using the CLC Main Workbench (http://www.clcbio.com). First, the low-quality reads and vector sequence region were trimmed off, and then the sequences were assembled into contigs using the default setting and checked manually. Then the DNA sequences were translated to amino acids sequences. Both the nucleotide and amino acid sequences were compared against the NCBI RefSeq collection using BLASTx and BLASTp, respectively. The sequences that exhibited the highest sequence similarity with the cloned pct sequences were downloaded as reference sequences, combined with the latter and aligned using ClustalW. A UPGMA tree was constructed from the aligned pct sequences as described previously. The pct diversity was compared among the samples collected 39 from different digesters with different design, operation and feedstocks using principal coordinate analysis (PCoA) implemented in the UniFrac program (Lozupone and Knight,

2005).

Quantitative PCR of pct gene

Specific qPCR was performed to determine the distribution and abundance of the pct genes in the different digesters. Seven primer sets (Table 3.1) targeting different main clusters of pct genes were designed based on the conserved regions of the pct sequences obtained from the clone libraries. To confirm the specificity of each primer set, one clone library each was constructed from PCR amplicons generated from digester samples NG and TPAD-M, and eight random clones from each library were sequenced using a

GenomeLab™ GeXP sequencer (Beckman Coulter, Brea, CA). All the sequenced amplicons turned out to be the target sequences for each specific primer set. After verifying the primer specificity, qPCR assays including melting curve analysis were performed on a Mx3000 real-time PCR system (Stratagene, La Jolla, CA) as described previously (Wang et al., 2012) using respective qPCR standards prepared from 10-fold serial dilutions (102 - 108 copies/reaction) of the plasmids carrying the target sequence

2 (R > 0.99 for all standard curves). Samples with a Ct value below that of the no-template control were considered as below the detection limit.

Statistical analysis

The data were first log10 transformed to improve normality and analyzed using the

GLM Procedure of SAS 9.2 (SAS Institute, Cary, NC, USA). Least-square means were

40 calculated for all the data sets. Mean separation was conducted by using Fisher‟s protected least significant difference test, with significant difference declared at P ≤ 0.05.

Accession numbers

The unique putative pct sequences obtained in the present study have been deposited in GenBank under the accession numbers of KC175645 to KC175907.

3.4 Results

Comparison of the tree topologies between 16S rRNA gene and Pct amino acid sequences

Topologies of trees constructed with 16S rRNA gene and Pct amino acid sequences were compared to determine if pct gene is as conserved as 16S rRNA genes

(Fig. 3.1). The topologies of the two trees were about 50.3% similar. Most of the Pct sequences were grouped into their cognate bacterial classes that were defined from corresponding 16S rRNA gene sequences. However, some Pct sequences were grouped into other classes than the classes where their corresponding 16S rRNA genes were placed. Furthermore, more than one copy of pct genes were found in some bacteria, such as Clostridium botulinum, Coprococcus catus GD/7, Anaerostipes caccae DSM 14662, P. thermopropionicum, and Thauera sp. MZ1T. Although some copies within a strain shared high sequence similarity and were grouped closely in the tree, others were rather different and were grouped into different clusters across different classes. For example, two copies of pct genes each were found in Ralstonia eutropha JMP134,

Verminephrobacter eiseniae EF01-2, and Escherichia coli IHE3034. Taxonomically, the first two bacteria belong to the class β-Proteobacteria while the third one belongs to the

41 class γ-Proteobacteria. One copy of the Pct sequences each from these three bacteria was grouped into their phylogenetic group as their corresponding 16S rRNA gene was grouped (Clusters F and E in Fig. 3.1), whereas the other copy was grouped with the Pct sequences from α-Proteobacteria (Cluster D in Fig. 3.1). Moreover, there were three heterogeneous copies of Pct found in the syntrophic bacterium P. thermopropionicum, and they only shared 51% amino acid sequence similarity. One copy (accession number:

BAE54375.1) was highly similar (79% similarity) to the Pct from another syntrophic bacterium S. fumaroxidans MPOB, which has two identical copies of Pct. The second Pct copy (accession number: BAF58693.1) was most similar (64% similarity) to the Pct from

Syntrophus aciditrophicus, while the third Pct copy (accession number: BAF59722.1) was most similar (67%) to the Pct from Clostridium botulinum. These results showed some evolutionary conservation of pct genes, but also suggested horizontal pct gene transfers among bacteria. pct diversity in the anaerobic digesters

To investigate the diversity of pct genes in the digester samples as affected by digester design, operation, and feedstock, a pair of degenerate PCR primers (pct-F and pct-R, Table 3.1) was designed based on the Pct sequences that are available in GenBank.

We purposely picked a primer pair that allows for the longest possible amplicon (about

1,300 bp of the about 1,600 bp pct gene) so that more phylogenetic information can be obtained from the amplicons, and internal primers can be designed for other analysis including qPCR. Using this primer set, 357 putative pct sequences were obtained from the libraries constructed from 8 different anaerobic digester samples. The BLASTx

42 search results of the highest similarity were summarized in Table 3.2. The conserved domain of acyl CoA:acetate/3-ketoacid CoA-transferase (COG4670), which is the feature sequence of propionate CoA-transferases, was found in 263 of the 357 putative pct sequences recovered. Among them, 99 sequences are highly similar (94 – 100% identity of amino acid sequence) to the pct gene of S. fumaroxidans MPOB. This group of pct genes was only found in digesters S and N, with numerically more being found in the liquid fraction of these two digesters. Another group of pct sequences, which share 70-

72% amino acid sequence identity with the pct gene of Syntrophus aciditrophicus SB, were detected in the liquid fraction of digester S and digesters N and B, and this group of pct sequences was abundant in the liquid fraction of digester B. Sequences that were similar to the pct sequences of Alkaliphilus metalliredigens QYMF and

Tepidanaerobacter sp. Re1 were mainly found in granules of digesters S and N.

Sequences that are similar (64-81% amino acid identity) to the pct of the genus

Clostridium were also found, but interestingly only in the two digesters of the TPAD system. Some of the directly recovered sequences did not exhibit high similarity to any of the pct genes of the isolated bacteria (Table 3.2), and these sequences were particularly copious in digester B. Overall, the putative pct sequences seemed to be distributed differently among the digesters, especially digesters fed different feedstocks and between the liquid and the granular fractions.

Clusters of pct genes identified from the digesters

The deduced amino acid sequences of the putative pct sequences were grouped into 7 major clusters (Fig. 3.2). Cluster I was the largest one comprising 99 sequences

43 that are identical or very similar (94-100% identical) to the pct of S. fumaroxidans MPOB.

This cluster also shared on average about 70% identity to one of the three pct genes of P. thermopropionicum. Cluster I was predominant in digesters S and N, especially in their liquid fractions (66% and 71% of the clones in each sample, respectively). Cluster II is distantly related to the pct from some β-Proteobacteria and was distributed in the granules of samples S and N. Clusters III and IV were grouped with the pct gene from the genus Clostridium, and all of them were retrieved from the TPAD system. Cluster V appeared to be related to the pct of Syntrophus aciditrophicus SB with an amino acid sequence identity of 72%. The second copy of the pct from P. thermopropionicum

(accession number: YP_001212091) was also grouped with this cluster on the tree. This cluster was detected in the liquid fraction of digester S, and both fractions of digester N and B, with numerically more being in the liquid fraction of digester B. Cluster VI is associated with the pct gene of Tepidanaerobacter acetatoxydans, which is syntrophic acetate-oxidizing bacterium with unknown ability to oxidize propionate or other VFA in a syntrophic manner (Westerholm et al., 2011). This Cluster was found in digester S and

N, with the majority in the granular fractions. Cluster VII is closely related to the pct of

Clostridium sp. D5 and was only found in the TPAD systems. Interestingly, pct from this

Clostridium strain is only distantly related to the pct of other Clostridium species.

One representative putative Pct sequence from each cluster and the characterized

Pct amino acid sequences from bacterial isolates were aligned to confirm the presence of the featured regions of Pct sequences. The conserved regions of the catalytic glutamate in the EXGXXG region and the conserved sequences motif GXGGF for the oxyanion hole,

44 the key characteristics of propionate CoA transferase (Rangarajan et al. 2005; Tielens et al., 2010; Lindenkamp et al., 2012), were present in all seven clusters of the putative Pct sequences we obtained (Fig. 3.3).

Principal coordinate analysis

The pct gene diversity was compared among different samples by PCoA analysis using both weighted and unweighted UniFrac (Fig. 3.4). The granular and liquid fractions from the same digesters were clustered together and tended to be separated along PC1, which explained more than 50% of the total variation. The samples from the mesophilic and the thermophilic digesters of the TPAD system were also grouped closely, but separated from the samples of the other digesters. Along PC2, which explained 43.7% of the total variations, samples from digesters N and S were separated from those from digester B and the two digesters of the TPAD system. These results showed that the diversity of pct genes among the samples was strongly affected by the feedstocks.

However, no obvious separation was seen between the granular and liquid fractions of each sample. The difference in operational temperatures between the mesophilic and the thermophilic digesters did not appear to affect the pct diversity.

Distribution of putative pct genes

To further investigate the distribution of the putative pct genes in the samples collected, cluster-specific qPCR assays were used to quantify the abundance of the pct gene clusters in all the digester samples (Fig. 3.5). In digester S, Cluster VI pct genes were the most abundant in the granular fraction but were not detected in the liquid fraction. Cluster I dominated other pct clusters in both fractions of digester S, but the two 45 fractions differed in distribution of Clusters II and V, with Cluster II being only found in the granular fraction while Cluster V only in the liquid fractions. The two fractions of digester N had high abundance of Clusters I, V, and VI. The two fractions of digester N also differed in the distribution of minor Clusters II and III. Only two clusters of pct genes, Clusters V and I, were found in the sample B. Cluster V pct genes were the most abundant in both the granular and liquid fractions of digester B, while the liquid fraction had a greater abundance of Cluster I pct gene than the granular fraction. The mesophilic and the thermophilic digesters of the TPAD system had similar distribution of the pct clusters except the abundance of Cluster III being higher in the thermophilic than the mesophilic digesters. When compared to the data from clone library, the pct distribution determined by qPCR showed similar trend. In some samples, however, several clusters were found in the qPCR assays but not in the clone libraries sequenced, such as the presence of Cluster I in digester B, Cluster III and VI in the liquid fraction of digester N, and Cluster V in the thermophilic digester of the TPAD system.

3.5 Discussion

Syntrophic oxidation (and possibly non-syntrophic utilization) of propionate and other acetogenesis products that cannot be utilized by methanogens is an important step in anaerobic digestion to ensure both digestion efficiency and process stability. In this study, a two-step DNA-based phylogenetic approach was used to investigate the functional diversity and distribution of syntrophic acetogens to circumvent the difficulty to culture these bacteria. One pair of PCR primers designed from conserved regions of

46 available pct gene sequences of known syntrophic acetogens allowed finding of a large number of putative pct genes, while cluster-specific primer sets were designed and used to examine the abundance of different pct genes in several digesters representing different designs and fed different feedstocks.

Approximately 38% of the pct clones exhibited high similarity (>94% amino acid sequence similarity) to the Pct of obligate syntrophic acetogens (Table 3.2). S. fumaroxidans, a syntrophic propionate-degrading sulfate-reducing bacterium (Harmsen et al., 1998), is particularly well represented by the sequence dataset, suggesting its predominance and competitiveness in the anaerobic digesters. The ability to utilize sulfate might be an important attribute for its predominance in anaerobic digesters because sulfate can be found in most feedstock of anaerobic digesters. The rest of the pct genes exhibited greatest sequence similarity to the pct genes of other bacteria that are not obligate syntrophic acetogens. However, all these putative pct genes have the conserved domain of acyl CoA:acetate/3-ketoacid CoA-transferase (COG4670), which is characteristic of propionate-CoA transferases. More specifically, the conserved regions of the catalytic glutamate in the EXGXXG region and the conserved sequences motif

GXGGF for the oxyanion hole, the key characteristics of propionate-CoA transferase

(Rangarajan et al. 2005; Tielens et al., 2010; Lindenkamp et al., 2012), were found in all seven clusters of the putative Pct sequences we obtained. Because all the recovered pct genes were of partial length, functional Pct could not be expressed and enzymatically confirmed. However, the presence of conserved regions and featured motif of the

47 translated peptide sequences suggest that they are putative pct even though some of them might be from non-syntrophic acetogens.

A profile hidden Markov Model has been developed for the fhs gene, which encodes formyltetrahydrofolate synthetase (FTHFS), and this model together with the homoacetogens similarity score (HSc) method (Lovell and Leaphart, 2005) was used to distinguish the fhs genes from known homoacetogens and non-homoacetogens

(Henderson et al., 2010). In the present study, a similar approach was attempted, but the number of known pct sequences from syntrophic bacteria was too small. Nevertheless, given the limited knowledge on syntrophic acetogens, some of the new pct genes found in the present study might be recovered from novel syntrophic acetogens, while the other pct genes might be carried by facultative syntrophic or non-syntrophic acetogens, which are more versatile and competitive than obligate syntrophic acetogens (Zellner and

Neudörfer, 1995).

The pct genes found in the present study were grouped into seven clusters, each of which appeared to be associated with a different group of bacteria. However, none of these bacterial groups is a monophyletic assemblage, and multiple copies of pct gene carried by several characterized bacteria (e.g. P. thermopropionicum) were each placed into different groups (Fig. 3.1 and Fig. 3.2). When the Pct sequence-based tree and the

16S rRNA sequence-based tree were compared, it is also clear that previous events of horizontal pct transfers had occurred among different bacteria. This is exemplified by the dissimilarity (<63% amino acid sequence identity) and distinct association of the three pct copies of P. thermopropionicum SI. Nevertheless, some of the Pct clusters (e.g. 48

Clusters I and V) might be involved in syntrophic propionate oxidation, while the other clusters (e.g. II, III, IV, and VII) were probably involved in metabolism other than syntrophic oxidation of propionate, such as non-syntrophic oxidation and production.

Indeed, stable isotope probing (SIP) using 13C-labeled propionate and butyrate showed that members of Clostridium might oxidize propionate and butyrate (Chauhan and Ogram,

2006; Hatamoto et al., 2008). As more pct sequences are determined from propionate- oxidizing syntrophs, the PCR primers can be refined or new primers can be designed to allow for more specific detection of pct genes involved in different types of propionate metabolism.

The digesters sampled in this study appeared to differ in diversity and distribution of pct genes. In digesters S, N, and B, which were fed with readily fermentable sugars

(digesters S and B) or starch (digester N), pct genes related to known syntrophic acetogens were well presented, whereas the two digesters of the TPAD system fed with dairy manure had different pct genes. The qPCR results also showed different diversity and abundance of different clusters of pct genes among these digesters. These results suggest that feedstock can have a great impact on diversity and populations of syntrophic acetogens. Although no data were available, the TPAD system may have lower concentrations of VFA, including propionate, than the other three digesters due to slow hydrolysis and fermentation of the major substrates (mostly cellulose) present in dairy manure (other factors such as organic loading rate and VTS might also have significant effects on VFA concentration). Differences in concentration of propionate, and possibly other VFA, may be a major factor that affects syntrophic acetogens in digesters. Future

49 studies are needed to identify and understand the major factors (e.g. feedstock composition, VFA profiles, and sulfate) that can affect the diversity and populations of different pct-carrying bacteria.

It should be noted that the granular and the liquid fractions of the same digesters

(for digester S, N, and B) also differed to some extent in diversity and distribution of pct genes (Table 3.2 and Fig. 3.5). However, the difference between the two fractions of the same digester was relatively smaller, except for digester S that is an UASB system with well-developed anaerobic granules. Both digesters N and B were SBF digesters with complete mixing, and digester N was inoculated using the sludge from digester S. It was hypothesized that the granular and the liquid fractions of the UASB digester S had different syntrophic acetogens. Our results suggested that syntrophic acetogenesis was more likely associated with the granular microbial biomass fraction in the UASB digesters. Within the SBF digesters N and B, the mixing and change in the feedstocks

(starch for digester N and whey for digester B) might have changed the diversity and distribution of syntrophic acetogens. A similar pattern was noted in a previous study that analyzed bacterial diversity using 16S rRNA gene clone libraries (Nelson et al., 2012).

These results indicate that both digester design and operation can also affect syntrophic acetogens. Future studies using different digester designs/operations on the same feedstock and the same digester/operation on different feedstocks will help to further elucidate the major factors that shaping the diversity and populations dynamics of syntrophic acetogens.

In conclusion, the two-step approach allowed the identification of new clusters of pct genes, while the cluster-specific primers allowed quantification of different clusters of pct genes in different digesters. Although future studies using functional approaches, such as SIP and microautoradiography (MAR)-FISH, are needed to confirm the function and hosts of these pct genes, the present study expanded the diversity of pct genes carried by syntrophic and non-syntrophic propionate-oxidizing bacteria. The qPCR assays provided a quantitative method to investigate the distribution and population dynamics of bacteria involved in propionate metabolism, including the important yet poorly understood and challenging syntrophic propionate-oxidizing acetogenesis. This is also the first study that has examined and compared the abundance of pct genes in different digesters fed different feedstocks. Future studies that couple quantitative analysis of pct genes and chemical parameters of digesters will shine into the ecology of syntrophic acetogens involved in propionate metabolism in anaerobic digesters.

Table 3.1 Primers designed in this study.

Primer Sequencea (5‟- 3‟) Target (approximate amplicon size) Tm (oC) Cluster pct-F CGCCATGGAGAAGCGGTTYYTNGARAC General pct (1300 bp) 62 - pct-R GGCGAACACGGCCCKYTCNGTNAC pct-c1F CCAGAACGACGGCAARTCC Syntrophobacter fumaroxidans MPOB pct 63 I pct-c1R CACGAACGTCTTCAAWCCCAC (229 bp) pct-c2F GCAACCCTGACGCCATAATC Alkaliphilus sp.-related pct (283 bp) 63 II pct-c2R CCCACGAACTTCTTGACCCTG pct-c3F GCCGAACGGATTGACTCTTTAC TPAD Alkaliphilus sp.-related pct (204 bp) 65 III pct-c3R TCGCAGAGCAGATGAGCCA pct-c4F CATCGGCACCATYACCCACG Clostridium sp.-related pct (378 bp) 65 IV

2 pct-c4R TTGTCCTCAATAGAGCCTACCACG pct-c5F CCGCCGCCAARAACAAC Syntrophus sp.-related pct (289 bp) 58 V pct-c5R CCGAGGTTGAGGGTGGAATC pct-c6F TTCGGCATSCGCAARTTCA Tepidanaerobacter sp.-related pct (221 bp) 59 VI pct-c6R ATGCTCATGATGCAKTCGGG pct-c7F CCCTGACCGTCCGTAAAGTTATC Clostridium sp. D5-related pct (326 bp) 63 VII pct-c7R GCGGGTTGCGTTGACATTG

a Degenerate bases follow the IUPAC code.

Table 3.2 BLASTx results of putative pct sequences obtained from the anaerobic digesters.

Similarity No. of Clones BLASTx match Gene ID Microorganisms (%) SG SL NG NL BG BL TPAD-M TPAD-T Syntrophobacter fumaroxidans 2 propionate CoA-transferase 116700414 94-100 15 24 32 MPOB 9 propionate CoA-transferase 85858288 Syntrophus aciditrophicus SB 70-72 8 4 5 3 14

propionate CoA-transferase 315304305 Listeria ivanovii FSL F6-596 61 1

CoA transferase 332799765 Tepidanaerobacter sp. Re1 58-61 10 1 12 6

CoA transferase 150392214 Alkaliphilus metalliredigens QYMF 56-64a 18 6 5 4

CoA transferase 317470440 Anaerostipes sp. 3_2_56FAA 60-64 1 2

acetyl-CoA-transferase 118444712 Clostridium novyi NT 64-65 17 17 subunit propionate CoA-transferase Clostridium botulinum B str. Eklund 187933088 64 6 1 5 17B

Clostridium botulinum E3 str. Alaska propionate CoA-transferase 188588447 65-66 5 4 E43 propionate CoA-transferase 323693897 Clostridium symbiosum WAL-14673 62-63 1 1

propionate CoA-transferase 325263193 Clostridium sp. D5 80-81 4 7

Non-CoA transferase 1 6 2 40 30 8 7

4 Total 45 46 45 43 44 47 43 4

a The Similarity in the samples SG and SL is 63 to 64%; while it is 56 to 57% in the samples TPAD-M and TPAD-T.

Fig. 3.1 Comparison of tree topology between 16S rRNA gene sequences and Pct amino acid sequences. Marked classifications to class level were shown as capital letters as follows: A. Clostridia; B. Bacilli; C. δ-Proteobacteria; D. α-Proteobacteria; E. γ- Proteobacteria; F. β-Proteobacteria; G. Fusobacteria; H. Actinobacteria; I. Thermoprotei.

Fig. 3.2 Phylogenetic tree of deduced amino acid sequences from the cloned pct gene with nearest neighbor reference sequences. Accession number is included after each reference sequence. Numbers of sequences are shown after the collapsed clade for each cluster. 55

Fig. 3.3 Alignment of one representative from each of the seven clusters of putative Pct identified in this study and the characterized Pct from bacterial isolates to show the conserved regions of propionate-CoA transferase. Sfum_Pct, Syntrophobacter fumaroxidans Pct (ABK19602.1); Pthe_Pct1, Pelotomaculum thermopropionicum Pct (YP_001212321.1); Pthe_Pct2, P. thermopropionicum Pct (YP_001212091.1); P. thermopropionicum Pct3 (YP_001211062.1); Cpro_Pct, Clostridium Propionicum Pct (CAB77207.1); Reut_Pct, Ralstonia eutropha H16 Pct (YP_727165.1). Dark shades with asterisks indicate complete conservation; grey shades with colons indicate residues conserved with strongly similar property; light grey shades indicate residues conserved with weakly similar property. The catalytic glutamate in the EXGXXG region and the conserved sequences motif GXGGF for the oxyanion hole are marked by boxes.

Fig. 3.4 Principal coordinate analysis of putative pct from different AD samples using UniFrac program.

log gene copies/ g metagenomic DNA metagenomic g copies/ loggene

Fig. 3.5 The abundance (logs of gene copies per microgram of metagenomic DNA) of each cluster of putative pct genes in different AD samples quantified by qPCR. A, Cluster I (related to Syntrophobacter); B, Cluster II (related to Alkaliphilus-like bacteria); C. Cluster III (related to Alkaliphilus-like bacteria and only found in TPAD samples); D. Cluster IV (related to Clostridium-like bacteria); E. Cluster V (related to Syntrophus-like bacteria); F, Cluster VI (related to Tepidanaerobacter-like bacteria); G, Cluster VII (related to Clostridium sp. D5-like bacteria). Different letters designate significant difference (P<0.05).

CHAPTER 4 Development of a whole-cell bacterial biosensor for the detection and measurement of propionate

4.1 Abstract

Anaerobic digestion is a biological technology that converts biomass wastes into biogas, achieving both waste treatment and energy production. Early detection and monitoring of propionate concentration in digesters allows for interventions to prevent irreversible costly breakdown. In an attempt to develop rapid method of measuring propionate concentration and bioavailability, we constructed a whole-cell bacterial biosensor specific for detection and quantification of bioavailable propionate. Both a plasmid-based and a chromosome-based biosensors were constructed and their performance was compared. The biosensors were constructed by transcriptional fusion of the regulatory gene (prpR) gene and the promoter of the prp operon (PprpB) of

Escherichia coli W3110 with the reporter gene cassette luxCDABE. The chromosome- based biosensor was constructed using homologous recombination. The plasmid-based biosensor emitted stronger luminescence than its chromosome-based counterpart, and thus the former was further characterized and tested. The biosensor responded specifically to propionate. The luminescence signal increased linearly from 1 to 10 mM propionate. The utility of the biosensor was evaluated using samples collected from

59 anaerobic digesters. The whole-cell bacterial biosensor developed in this study provides an alternative method for real-time propionate detection and measurement in digesters.

4.2 Introduction

Anaerobic digestion (AD) is a complex microbiological process that degrades organic compounds into biogas of primarily carbon dioxide (CO2) and methane (CH4). It is widely used in achieving both pollution control and bioenergy biogas production.

Process stability is becoming increasingly important as the primary purpose of AD is shifting from waste treatment to energy production because constant and reliable energy supply is critical for core business operations, such as food processing and manufacturing. Besides, it can be time consuming and costly to restart up a failed large- scale digester.

Many factors influence the stability of the anaerobic digester (Chen et al., 2008), but organic loading rate is one of the most important operational factors. To maximize volumetric biogas yield, digesters are fed as much feedstock as possible. Such aggressive feeding often causes accumulation of volatile fatty acids (VFAs, including acetate, butyrate, propionate, isobutyrate, and valerate) as the intermediates of acidogenesis step of AD. The accumulation of VFAs, especially propionate, can lead to upset or even total failure of AD process because methanogens, which are the direct producers of CH4 in biogas, can be inhibited by propionate even at neutral pH. Therefore, it is desirable to monitor concentration of VFAs, particularly propionate, and detect accumulation of

VFAs before it reaches inhibitory level so that corrective intervention can be executed to avoid digester upset or failure.

Traditionally, samples are collected from digesters and brought to laboratories, and VFAs concentrations are analyzed offline using instrument, primarily gas chromatography (GC) or high-performance liquid chromatography (HPLC) (Seghezzo et al., 1998). Such chemical analysis can precisely and accurately determine the concentrations of individual VFAs, but the procedures are time consuming and require extensive sample preparation. More importantly, results are not available until many hours later, and thus it does not allow real-time monitoring of VFA accumulation in digesters. Therefore, an alternative detection and monitoring method was sought after.

A number of studies have attempted to develop online systems for measurement of VFAs over the last two decades (Slater et al., 1990; Ryhiner et al., 1993; Zumbusch et al., 1994; Pind et al., 2003; Boe et al., 2007; Ward et al., 2011). These systems are mostly based on GC, HPLC, or Near Infrared Spectroscopy (NIRS). The GC- and HPLC-based systems can precisely measure individual VFA concentrations, but most of them required extensive sample purification involving filtration, which is prone to membrane clogging and fouling. Besides, these chromatography-based systems require extensive modification to the GC or HPLC units. NIRS require no sample preparation, but the error of prediction was too large to accurately measure VFA concentrations (Ward et al.,

2011). Furthermore, these systems cannot assess bioavailability of propionate to the microorganisms.

The concept of bacterial biosensor has drawn large attention due to its advantage of rapid detection, low cost, and providing bioavailability information of the analyte. A whole-cell bacterial biosensor contains a genetic construct that is mainly composed of two biological components: a sensing element that recognizes a chemical or physical change a reporting system that produces a measurable signal in response to the sensed environmental change. Cell-based biosensors can be classified according to the response of their sensing element, e.g., change of metabolisms of cells or alteration of expression in genetically modified organisms. Genetically engineered cell-based sensing systems can elicit a response to an analyte by coupling the sensing element to a reporter gene (or gene cassette) through gene fusion, which upon expression produces a readily measurable signal (Ron, 2007). The specificity of the sensing elements for the analyte confers selectivity to the system, while the reporter system determines the sensitivity and detection limits (Daunert et al., 2000). Biosensors has been successfully constructed and applied in many fields for detection and measurement of bioavailability of heavy metals

(Shetty et al., 2003; Liao et al., 2006; Peca et al., 2008; Amaro et al., 2011; Joe et al.,

2012) and organic compounds (Leedjärv et al., 2006; Li et al., 2008; Behzadian et al.,

2011; de Las Heras and de Lorenzo, 2012). Furthermore, the MicroTox® luminescent bacterial toxicity assay (LBTA) developed by the company Beckman is current available to determine the general toxicity of wastewater and it has been accepted as a standard method by many countries including China (GB/T 15441-1995), France (DIN 38412-

1990), and USA (ASTM method D5660-1995).

The prp operon is responsible for the metabolism of propionate in Escherichia coli and other bacteria species (Textor et al., 1997; Brock et al., 2001, 2002). The prpBCDE genes encode several enzymes that are involved in the metabolism of propionate, and this genes are regulated by the RNA polymerase associated with the σ54 factor (RNAP·σ54) holoenzyme and PrpR, an NtrC-like protein. In the presence of propionate, 2-methylcitrate (2-MC) (the metabolite of prpCE from propionate) combines with PrpR, and the 2-MC-PrpR complex activates the promoter of prp operon (PprpB) and turns on the expression of the propionate-degrading enzymes (Horswill and Escalante-

Semerena, 1997; Textor et al., 1997; Palacios and Escalante-Semerena, 2000). In the present study, we developed a whole-cell bacterial biosensor for detection and measurement of propionate concentration by fusing the activator gene (prpR) and the promoter (PprpB) of the prp operon with the luxCDABE gene cassette operon. The propionate-responsive genetic construct was transformed into E. coli W3110 and maintained epigenetically on a plasmid or integrated into the chromosome. The propionate biosensor was characterized with respect to specificity, linear range of detection, and its utility was tested on samples collected from anaerobic digesters. This is the first propionate biosensor that may be further instrumented for future application.

4.3 Materials and Methods

Construction of the plasmid-based bacterial biosensor

The region that spans the activator gene prpR and the promoter PprpB of the prp operon of E. coli W3110 was amplified using the primers prpR-F and prpB-R (Table 1)

63 and the genomic DNA of E. coli W3110. The PCR reaction each (50 µl) contains 1X

PCR buffer, 1.75 mM MgCl2, 670 ng/µl bovine serum albumin, 0.2 mM dNTPs, 0.5 µM each primer, 100 ng genomic DNA, and 1.25 U of Platinum Taq DNA polymerase

(Invitrogen, Carlsbad, CA). The PCR cycling conditions were as follows: initial denaturation at 95 °C for 5 min; followed by 35 cycles of denaturation at 95 °C for 30 s, annealing at 55 °C for 30 s and extension at 72 °C for 2 min. The PCR was ended with a final extension step at 72 °C for 10 min. The PCR product was evaluated using agarose gel (0.8%) electrophoresis, and the band at the expected size (approximately 2 kb) was excised and purified with a QIAquick gel extraction kit (Qiagen, Valencia, CA). The

DNA fragment was verified by sequencing using the Sanger technology.

The plasmid pSB401 (Winson et al., 1998), which contains the luxCDABE reporter gene cassette and a tetracycline-resistant gene, was used as the construct backbone. Both the PCR fragment and pSB401 were digested with the restriction enzyme

EcoRI. After purification using a Qiaquick Gel Extraction Kit, the EcoRI-digested PCR fragment and EcoRI-digested pSB401 were ligated with T4 DNA ligase. The recombinant construct pSB401-prpR-PprpB was transformed into competent E. coli

W3110 (Sambrook and Russell, 2001).

Construction of the chromosome-based biosensor

Since the pSB401-based construct has the cutting site for EcoRI but not for other common endonucleases, we subcloned the prpR-PprpB-luxCDABE cassette into plasmid pBSKS, which contains multiple cloning site (Stratagene, La Jolla, CA). The prpR-PprpB- luxCDABE cassette was amplified from the pSB401-based construct using the primers 64 prp-63F and end-R (Table 4.1) using high fidelity Platinum pfx DNA polymerase

(Invitrogen, Carlsbad, CA, USA). The PCR was cycled as follows: initial denaturation at

95 °C for 5 min; followed by 35 cycles of denaturation at 95 °C for 30 s, annealing at 53

°C for 30 s, and extension at 68°C for 8.5 min. The PCR was ended with a final extension step at 68 °C for 10 min. The amplified fragment and pBSKS were was separately double digested with BamHI and NotI. After purification using a Qiaquick Gel Extraction Kit, the double digested PCR fragment was cloned between the BamHI and NotI sites of pBSKS to generate pBSKS-prpR-PprpB-lux.

The chromosome-based biosensor was constructed using homologous recombination technique and a suicidal plasmid (pKAS32) to replace the prp operon on the chromosome of E. coli W3110 with the luxCDABE cassette. First, a region (560 bp) of the prpE of E. coli W3100 was amplified using primers prpE-F and prpE-R (Table

4.1). The PCR was performed essentially as described earlier in this section, except the primer annealing at 59 °C and the primer extension for 45 s. The amplified fragment was double-digested with BamHI and KpnI, purified and was cloned into pBSKS-prpR-PprpB- luxCDABE to generate pBSKS-prpR-PprpB-luxCDABE-prpE. Subsequently, the prpR-

PprpB-luxCDABE-prpE fragment was cut out from pBSKS-prpR-PprpB-luxCDABE-prpE with NotI and KpnI, and then inserted into the NotI-KpnI site of the suicidal plasmid pKAS32 (Skorupski and Taylor, 1996). The recombinant pKAS32 was transformed into

One Shot® PIR1 Chemical Competent E. coli cells (Invitrogen) according to the manufacturer‟s protocol.

To introduce a positive selection marker for integration of the luxCDABE operon into the chromosome of E. coli W3110 through homologous recombination, a kanamycin-resistant gene (kan gene) was introduced into the plasmid pKAS32-prpR-

PprpB-luxCDABE-prpE. The kan gene was amplified from the plasmid pCR4.0 from

Invitrogen (Carlsbad, CA, USA) using the primer set kmr-F and kmr-R (Table 1). The

PCR was performed essentially as described earlier, except the annealing temperature at

56 °C and the primer extension for 1 min. The amplified fragment was cloned into the

BamHI site on the plasmid pKAS32-prpR-PprpB-luxCDABE-prpE and transformed into

E. coli BW20767 to generate plasmid pKAS32-prpR-PprpB-luxCDABE-kan-prpE. The plasmid pKAS32-prpR-PprpB-luxCDABE-kan-prpE was linearized by digestion with SacI and then transformed into E. coli W3110 using chemical competent method. The transformants were selected on LB agar plates containing 50 µg/ml kanamycin.

Bacterial growth and dose-response test

The bacterial biosensors, which harbored either the plasmid pSB401-prpR-PprpB- luxCDABE or the luxCDABE cassette integrated into the chromosome under the control of prpR-PprpB, were grown in Luria-Broth (LB) containing either 20 µg/ml tetracycline

(for plasmid-based biosensors) or 50 µg/ml kanamycin (for chromosome-based biosensors) at 37 oC with vigorous shaking. For dose-response test, overnight culture was diluted 50-fold with fresh LB containing appropriate antibiotic and was incubated at 37

°C at 200 rpm until the optical density at 590 nm (OD590) reached 0.5 at OD590. One ml of such culture was aliquoted into 1.5 ml tubes, to which sodium propionate were added to reach a series of final concentrations from 0.25 to 100 mM. After mixing, 200 µl culture

66 of the biosensor dosed with different concentrations of sodium propionate was transferred to a 96-well plate in triplicate, and the plate was placed in a Wallac 1420 Multilabel

Counter (PerKin Elmers, Waltham, MA) that maintained an internal incubation temperature at 25 oC without shaking. After incubation for preset length, the relative luminescence intensity (RLI) and OD590 were measured after horizontal shaking for 10 s using the Wallac 1420 Multilabel Counter.

Specificity test

The specificity of the propionate biosensors was evaluated using all the major

VFAs that are found in anaerobic digesters, including formic acid, acetic acid, propionic acid, butyric acid, and valeric acid at a final concentration of 5 mM. The cultivation, induction, and acquisition of luminescent signal were done as described for the dose- response test.

Utility testing using anaerobic digester samples

The plasmid-based biosensor was evaluated for its utility because it showed stronger response and tolerance to high propionate concentration (data not shown).

Samples were collected from the following three types of anaerobic digesters operated at mesophilic temperature: a full-scale upflow anaerobic sludge blanket (UASB) fed a wastewater containing readily digestible carbohydrates (referred to as sample “S”), a full- scale mixed plug-flow loop reactor (MPFLR) fed with dairy manure (referred to as sample G), and a continuously stirred tans reactor (CSTR) digester of a temperature- phased anaerobic digester (TPAD) fed a mixture of dairy manure and dairy whey

(referred to as sample T). Sample S contained anaerobic granules and rather colorless liquid, whereas samples G and T were slurry containing high solid content and brown liquid. The pH of the samples S, G, and T were 6.5, 8, and 8, respectively. To eliminate the potential physical interference from the solid content, all the samples were first centrifuged, and the supernatant was then filtered through 0.45 µm filter. To examine potential inhibition of the samples to the biosensor, a series of two-fold dilutions (2-, 4- and 8-fold) of each sample was prepared for each sample. Furthermore, a series of known concentrations of sodium propionate were spiked into the original sample and the sample dilutions. In an attempt to decolorize samples G and T, activated carbon was added to these two samples at a final concentration of 2.5%. After vigorously shaken for 30 s by hand, the samples were left at room temperature for 10 min and then centrifuged at 16000 xg for 5 min. The supernatant was used for the test.

The growth of the biosensor, induction, and acquisition of luminescent signal were done as described above for the dose-response test after 1-hour exposure. For sample G and T, to correct for the OD590 reading contributed by the color of the samples during the exposure test, the OD590 reading of the biosensor culture dosed with corresponding propionate concentration was used in the RLU calculation instead of using the OD590 of the biosensor cultured dosed with the sample.

Data analysis

The experiments were conducted at least three times for error analyses. The raw data was normalized by using relative luminescence unit (RLU), which was calculated by dividing the RLI by OD590 of each sample. The data were then presented as induction ratio (IR), which was defined as ratio of the RLU between the induced and non-induced biosensor cells. Student‟s t-test analysis was used to analyze the results, and statistical significance was declared at p < 0.05.

4.4 Results

Construction of the plasmid-based and chromosome-based bacterial biosensors

Both a plasmid-based and a chromosome-based biosensor were constructed and their performance was compared. The performance of the plasmid-based biosensor, which contains the pSB401-prpR-PprpB recombinant construct (shown as Fig. 4.1), and the chromosome-based one, which contains the chromosomal integration of the lux reporter cassette under the control of the regulatory gene prpR and the promoter PprpB, were compared to determine which one is more suitable for propionate detection. The plasmid-based biosensor produced strong response, in terms of luminescence signal, upon exposure to propionate, but the chromosome-based biosensor only had limited response to propionate (data not shown). Thus, only the plasmid-based biosensor was further tested.

Characterization of the biosensor

The plasmid-based biosensor (referred to biosensor hereafter) responded positively when exposed to 0.25 to 25 mM propionate in both a dose-dependent and an exposure duration-dependent manners (Fig. 4.2). At 50 mM or above propionate, the luminescence emission increased with exposure duration but did not increase in a dose- dependent manner. A significant luminescence emission (p<0.05) was observed after 1 hr induction at 1.0 mM propionate compared to the no-propionate control. After 1.5 hr induction, 0.25 mM propionate also resulted in significant (p<0.05) luminescence emission. The induction ratio increased with increased induction time and peaked at two hours of exposure to propionate at concentration more than 8 mM. For propionate concentrations at and below 4 mM, the induction ratio continued to increase during the three hours induction. Slow increase in background luminescence was also observed for the no-inducer control.

Specificity test

To examine if the biosensor also responds the common VFAs found in anaerobic digesters other than propionate, the commonly observed VFAs in anaerobic digester including formic acid, acetic acid, butyric acid, and valeric acid were individually (5 mM each) tested on the biosensor. As shown in Fig. 4.3, the biosensor started to respond to propionate, but not other VFAs in as short as 30 min of exposure. Propionate resulted in increased IR over the 2 hr induction, but the other VFAs did not. These results demonstrated that the biosensor is specific to propionate.

Utility testing using anaerobic digester samples

The biosensor was tested for its utility on samples collected from three different anaerobic digesters (Fig. 4.4). For all the samples, lower biosensor responses were observed with undiluted samples and at low dilutions of each sample, suggesting inhibition of the biosensor or quenching of the emitted luminescence. For the UASB sample S (Fig. 4.4A), although colorless, lower biosensor response was observed for the undiluted and the 2-fold diluted sample than for the propionate standard, but comparable responses were achieved when the UASB samples were diluted 4 or 8 folds. For the samples collected from the MPFLR and CSTR (Fig. 4.4B and 4.4C), both of which were dark brown in color, biosensor responses were only detected when each sample was diluted 8-fold.

4.5 Discussion

The whole-cell bacterial biosensor for the detection and measurement of propionate was successfully developed in this study by controlling the expression of luxCDABE under the control of the regulatory components (the PprpB promoter and prpR) of the prp operon of E. coli K12 substrain W3110. When the recombinant pSB401 carrying the propionate-responsive genetic construct was transformed into E. coli DH5α cells, significant luminescence emission was observed and detected in response to propionate. Additionally, the biosensor only responded to propionate but not to the other major VFAs found in anaerobic digesters. These results suggest that the PprpB and prpR only respond to propionate and they can be used to construct biosensor specific for

71 propionate. Furthermore, the biosensor exhibited a linear dose-response relationship when propionate concentrations ranged from 1-10 mM. This range spans the propionate concentration ever reported in all anaerobic digesters, and thus the biosensor can be potentially used in monitoring propionate concentrations in anaerobic digesters.

An attempt was made to construct a chromosome-based biosensor to eliminate the need for selective antibiotics for plasmid maintenance in the plasmid-based biosensor.

Almost the entire prpBCDE operon of E. coli W3110 was successfully replaced with the luxCDABE reporter gene cassette using homologous recombination, but the resultant biosensor did not respond to propionate. It was realized that prpCE is needed to metabolize propionate into 2-methycitrate (2-MC), which combines with PrpR to turn on the PprpB promoter (Horswill and Escalante-Semerena, 1997; Tsang et al., 1998; Horswill and Escalante-Semerena, 1999; Palacios and Escalante-Semerena, 2000, 2004). We also confirmed that the reporter gene could not be expressed in other E. coli strains (eg.

DH10B or JM109) that do not possess the prp operon (data not shown). Although studies also showed that 2-MC could be generated from other pathways such as the condensation of propionyl-CoA and oxaloacetate via the catalyzation by enzyme GltA (Horswill et al.,

2001), the results from the present study supported the study (Horswill and Escalante-

Semerena, 1999) that showed that base-level expression of prpCE is essential for the full activation of the prp operon. Other factors that influence the expression of prpBCDE were also reported. Cells with mutation on cyclic AMP (cAMP), cAMP-receptor protein

(CRP), or the CRP binding site of the prpBCDE operon exhibited decreased transcription of the prpBCDE (Lee et al., 2005; Park et al., 2012). It was also found in E. coli that the

72 repressor AscG of the beta-glucoside utilization operon can inhibit the expression of prp operon because of the competition for cAMP-CRP binding site located in the spacer region of prpR and prpB (Ishida et al., 2009). It will be interesting to investigate the expression of the recombinant construct pSB401-prpR-PprpB in an ascG mutant strain.

The sensitivity of the biosensor might increase in such host cells. Of course, the prpCE genes need to be present and functional also.

When tested on the digester samples, the biosensor did not respond to propionate unless the samples were diluted. Such interference could possibly be explained by the following reasons. First, the growth and/or activity of bacterial cells might have been directly inhibited by some of the substances present in the sample. Second, the expression

(transcription and/or translation) of the lux operon might have been inhibited. Third, the luminescence was emitted in response to propionate, but might have been quenched, at least for the samples collected from the MPFLR and the CSTR digesters, by the dark color of the samples. Indeed, inhibition of luminescence assays has been reported by sample color, inorganic chemicals, pH, and turbidity in other studies (Hirmann et al.,

2007; Girotti et al., 2008). Based demonstrated in the present study, proper dilution can attenuate the interference from digester samples. However, in this scenario, propionate concentrations in samples need to be high enough to induce biosensor response after dilution. Alternatively, more sensitive luminescence detection instrument may be used.

Finally, because luminescence emission depends on active metabolism of the host cells, the assay conditions, such as medium composition and incubation temperature inside the luminescence reader, may be optimized to increase biosensor response.

To our knowledge, only one biological assay has been reported that can specifically detect propionate (Rajashekhara et al., 2006). The assay was based on colorimetric quantification of H2O2 that is generated from a serial reactions carried out by the enzyme propionate CoA transferase (Pct) and short-chain acyl-CoA oxidase

(SCAOx). Although the detection limit was low (1 µM) when exposed to pure propionate, no test was reported on actual samples. Because it is an ex situ colorimetric assay, it is likely difficult to construct a biosensor based on this assay. Additionally, no information on propionate bioavailability will be generated by this assay.

In conclusion, a whole-cell bacterial biosensor for specific detection and measurement of propionate was successfully constructed. This propionate biosensor can potentially serve as an alternative and supplementary tool for detection of propionate in anaerobic digesters. With continued advancement in surface chemistry and transduction systems, the biosensor can be immobilized onto a membrane and integrated into an electronic device, which may eventually be used in online monitoring of propionate concentrations and enable automated control of anaerobic digester operation to avoid costly deterioration of digester operation.

Table 4.1 PCR primers used in this study.

Primer name Primer sequence (5‟à3‟) Endonuclease cutting site (bolded) prpR-F GCC GAA TTC CTG ACA ATA AGA TCC EcoRI prpB-R GCC GAA TTC GAT TTT CTT TAG TCA G EcoRI Prp-63F CCG AGC GGC CGC AAA CAT CTC CGA TGC GTA AG NotI End-R CCC GGA TCC ATC AAC TAT CAA ACGC BamHI prpE-F ACG GAT CCG AGA AAC AGC CAG AG BamHI prpE-R GCC TTT AGG TTT GCC GGT CGT G - kmr-F TCA GGA TCC ACT GGG CGG TTT TAT GGA C BamHI kmr-R AAT GGA TCC GCA TCA GGA AAT TGT AAG BamHI

a All the primers were designed using Primer Premier 5 (Premier Biosoft International, Palo Alto, CA).

Fig. 4.1 Schematic diagram of the biosensor plasmid. Abbreviations: tetr, gene encoding tetracycline resistance; p15A ori, origin of replication; luxCDABE, genes of bacterial luciferase operon.

35 0 0.25 30 0.5 1 25 2 4 20 8 15 15 25

50 Induction ratio Induction 10 75 100 5

0 0 0.5 1 1.5 2 2.5 3 Induction time (hr)

Fig. 4.2 Responses of the plasmid-based biosensor to propionate at different concentration (mM) and after different exposure duration. Induction ratio (IR) was defined as the ratio of relative luminescence unit RLU between the ratio of the RLU between the induced and non-induced biosensor cells.

25 Control 20 Formic acid

Acetic acid 15 Propionic acid Butyratic acid

10 Valeric acid Induction ratio Induction 5

0 0 0.5 1 1.5 2 Time (hr)

Fig. 4.3 Responses of the plasmid-based biosensor to the individual VFAs tested (5 mM each).

Fig. 4.4 Responses of the biosensors to increased propionate concentrations spiked into samples collected from a UASB (A), an MPFLR (B), and a CSTR (C). The induction time was 1 hr. Standard deviation was included for the standard curve, which was generated from solutions of pure propionate. The standard deviations among triplicates of the samples tested were less than 14%.

CHAPTER 5 Spatial and Temporal Variations of Microbial Community in a Mixed Plug-Flow Loop Reactor (MPFLR) Fed with Dairy Manure

5.1 Abstract

Mixed plug-flow loop reactor (MPFLR) has been widely adopted by the US dairy farms to convert cattle manure to biogas. However, the microbiome in MPFLR digesters remains unexplored. In this study, the microbiome in a MPFLR digester operated on a mega-dairy farm was examined thrice over a two-month period. Within 23 days of retention time, 55 to 70% of total manure solid was digested. Except for a few minor volatile fatty acids (VFA), total VFA concentration and pH remained similar along the course of the digester and over time. Metagenomic analysis showed that although with some temporal variations, the bacterial community was rather stable spatially in the digester. The methanogenic community was also stable both spatially and temporally in the digester. Among methanogens, genus Methanosaeta dominated in the digester. qPCR analysis and metagenomic analysis yielded different relative abundance of individual genera of methanogens, especially for Methanobacterium, which was predominant based on qPCR analysis but not detected by metagenomics. Collectively, the results showed that small microbial and chemical gradients existed within the digester, and the digestion

80 process occurred similarly throughout the MPFLR digester. The findings of this study may help improve operation and design of this type of manure digesters.

5.2 Introduction

Anaerobic digestion (AD) is a microbiological technology that converts organic compounds into biogas. This is an attractive technology for both waste treatment and energy production. It was mainly used for wastewater treatment in the past several decades. However, because of the need for alternative energy, in the past several years

AD has been implemented to primarily produce biogas as renewable energy from various waste biomass including manure produced from concentrated animal feeding operations

(CAFOs), particularly dairy farm operation (Yu et al., 2010). In fact, more anaerobic digesters are being operated on dairy farms than on other animal farms for biogas production because the large quantities of dairy manure produced and ease to collect and pump dairy manure from dairy barns to digesters (http://www.epa.gov/agstar/ projects/). Indeed, AD has been the primary technologies used by dairy farms, mostly small ones, to produce biogas from dairy cattle manure in developing countries such as

Costa Rica, China, and India (Hobson, 1990; Lansing et al., 2008). In the US, implementation of anaerobic digesters on dairy farms, mostly large mega dairy farms, has been largely promoted by the AgStar program of EPA (http://www.epa.gov/agstar/).

Among the several designs of anaerobic digesters implemented on the US dairy farms, the mixed plug-flow loop reactor (MPFLR), which is designed and implemented by DVO, Inc. (Chilton, WI), is the most common AD technology. According to a recent 81 survey conducted in May 2013 by USEPA, 65 out of the 167 anaerobic digesters operated on the US dairy farms use the MPFLR design (http://www.epa.gov/agstar/ downloads/digesters_dairy.xlsx). The popularity of MPFLR digesters stemmed from its simplicity in construction and maintenance and its reliability in operation. An MPFLR system is basically a U-shaped tank digester (Fig. 5.1A). Influent enters an MPFLR digester at one end, flows forward and loops back as a “plug”, and finally exits from the other end (Yu et al., 2010). The digester content is continuously mixed by biogas in the direction perpendicular to the plug-flow of the reactor (Fig. 5.1B). A portion of the effluent is typically recycled to inoculate the influent to improve AD.

Microorganisms are the driving force for the whole transformation process in anaerobic digesters, and the microbiome therein has been investigated extensively to understand the microbiology underpinning the AD process and to help optimize digester operation, especially in constantly stirred tank reactor (CSTR) and upflow anaerobic sludge blanket (UASB) digesters. However, only few studies have investigated the microbiome in anaerobic plug-flow digester (Smith and Oerther, 2006; Goberna et al.,

2009), and no study has been reported that investigated the microbiome (both bacteria and archaea) in any digester that uses the MPFLR design. The objectives of this study were to characterize the microbiome and assess its spatial and temporal variation in a full-scale mesophilic MPFLR digester operated on a mega-dairy farm in Wisconsin.

Results of chemical and metagenomic analyses revealed a relatively stable microbiome in the MPFLR digester that underwent some temporal variations, corroborating the stable operation of this type of anaerobic digester.

5.3 Materials and Methods

Sample collection

The MPFLR digester has been operated stably at 37 °C with a retention time

(both hydraulic and solid) of 23 days since 2007 on a mega dairy farm located in

Wisconsin. This farm has 4,000 dairy cows fed a typical corn silage-based total mixed ration (TMR). The dairy manure slurry is continuously pumped into the digester from a manure pit. The biogas (about 60% CH4 and 40% CO2) produced is used to power a

1,200 kW gas-fired combined heat and power (CHP) system. The influent (I), three locations along the course of the digester (T1, T2, and T3. These are thermal probe locations. Fig. 5.1A), and the effluent (E) were sampled three times on August 3

(designated as sample S1), September 15 (S2), and September 30 (S3) in 2011. The samples were frozen immediately and shipped to our laboratory overnight. Samples were kept at –80 °C until the following analysis.

PMA treatment and DNA extraction

To exclude DNA from non-viable microorganisms in the samples from being analyzed, all the samples were treated with propidium monoazide (PMA) as described previously (Nocker et al., 2007) with slight modification. Briefly, 0.5 g of each sludge sample was centrifuged at 10,000 xg at 4 °C for 5 min to separate the microbial biomass from the supernatant. The biomass fraction was resuspended in 1 ml TE buffer. To each microbial biomass sample, 2.5 µl of PMA (20 mM) (Biotium Inc., Hayward, CA) was added to achieve a final concentration at 50 µM. Then the samples were incubated in the

83 dark for 5 min, followed by light activation by exposing to a 600-Watt halogen light at a distance of 20 cm for 5 min. Samples were left on ice on a rocker to avoid excessive heating during light activation. After the PMA treatment, total metagenomic DNA was extracted from each sample using the repeated bead beating plus column purification

(RBB+C) method (Yu and Morrison, 2004b). The integrity of the extracted DNA was evaluated using agarose gel (1.0%) electrophoresis, while the concentrations were quantified using a NanoDrop 1000 spectrophotometer (Thermo Scientific, Wilmington,

DE).

PCR-DGGE

The bacterial community and the archaeal community were examined using PCR-

DGGE using domain-specific primers (Table 1) as described previously (Yu and

Morrison, 2004a; Yu et al., 2008). Briefly, the V3 region of the 16S rRNA gene was amplified from 100 ng of each metagenomic DNA sample using the primer set GC-

357f/519r for bacteria and 344f/GC-519r for archaea. Bovine serum albumin (BSA) was included (670 ng/µL) in all the PCR to attenuate potential inhibition to the PCR reaction.

A touch-down thermal program (61 °C with 0.5 °C/cycle decrement for 10 cycles followed by 25 cycles at 56 °C for primer annealing) was used to maximize specificity.

The PCR was ended with a final extension step at 72 °C for 30 min to eliminate the artifactual double DGGE bands created from possible heteroduplex (Jense et al., 2004).

Size and quality of the PCR products were verified using agarose gel (1.2%) electrophoresis before running DGGE using a PhorU system (Ingeny, Leiden, NL). The gels were stained with CYBR Green dye I (Invitrogen, Carlsbad, CA) and visualized

84 using a Kodak Gel Logic 200 imaging system (Eastman Kodak Company, Rochester,

NY).

16S rRNA gene pyrosequencing

The V1-V3 hypervariable region (about 500 bp) of 16S rRNA gene was amplified from each DNA sample using fusion primers ArcF-A and ArcR-B (for archaea) or BactF-

A and BactR-B (for bacteria) (Table 1). One hundred ng of each genomic DNA was used in each PCR reaction with the following thermal program: initial denaturation at 95 °C for 5 min, followed by 35 cycles of denaturing at 94 °C for 30 s, annealing at 54 °C for

30 s, and extension at 72 °C for 1 min. The PCR cycles were ended with a final extension at 72 °C for 10 min. Quality of the PCR products were examined using agarose gel

(1.0%) electrophoresis. The bands of the expected size (approximately 550 bp) were excised and the DNA amplicons were purified using a Qiaquick gel extraction kit

(Qiagen, Valencia, CA). The purified amplicon libraries were pooled at equimolar ratio to a final concentration of 20 ng DNA /µl for both bacteria and archaea, and the two pools were then combined at a 9:1 molar ratio and sequenced using a Roche GS FLX

Titanium system at the Plant-Microbe Genomics Facility at The Ohio State University.

Analysis of sequences

The sequence reads generated from pyrosequencing the 16S rRNA gene amplicons were processed using the QIIME bioinformatics analysis package, version

1.5.1 (Caporaso et al., 2010). The sequences were filtered and trimmed with default settings. The flowgrams were denoised using Denoiser (Reeder and Knight, 2010), and then the retained sequences were aligned with the Greengenes core set (DeSantis et al., 85

2006) using PyNAST (Caporaso et al., 2010). Probable chimeric sequences were identified by Chimera Slayer (Haas et al., 2011) and removed. uClust (Edgar, 2010) was used to cluster the sequences into species-level operational taxonomic unit (OTUs) at

0.04 and 0.03 distance for bacteria and archaea, respectively (Kim et al., 2011a). Lane mask was applied to the representative sequences from each OTU, and an approximately maximum-likelihood tree was constructed using FastTree (Price et al., 2010). Each OTU representative sequence was assigned to a taxon using Classifier of the RDP database

(Wang et al., 2007). Because variation in number of sequences among samples can significantly influence comparative analysis of microbial communities (Gihring et al.,

2012), rarefaction was performed to normalize the uneven numbers of sequences among samples before diversity analysis. Unweighted distance matrices were generated based on the phylogenetic tree using UniFrac (Lozupone and Knight, 2005), and the diversity of bacterial community among the samples was compared using principal coordinate analysis (PCoA) implemented in QIIME. The distribution of major bacterial OTUs was visualized using heatmap generated using the software GAP (http://gap.stat.sinica.edu.tw/

Software/GAP/). Pearson‟s correlation coefficients were calculated among the samples and among the OTUs to examine the similarity of the profiling. Hierarchical clustering trees were generated using the rank-two ellipse seriation method (Chen, 2002; Wu et al.,

2010) to sort the samples and OTUs.

Quantification of methanogens

Quantitative real-time PCR (qPCR) was used to quantify the major genera of methanogens commonly found in anaerobic digesters using genus-specific primers (Table

1). Individual sample-derived qPCR standards were prepared as described previously (Yu et al., 2005). Briefly, the genomic DNA extracted from all the samples were pooled together and used as template in PCR reaction using each genus-specific primer set. The

PCR was performed as follows: denaturation at 95 °C for 5 min, followed by 35 cycles of denaturation at 95 °C for 30 s, annealing at the respective temperature for each primer set listed in Table 1 for 30 s, and extension at 72 °C for 30 s. The PCR reaction was finished with a final extension at 72 °C for 10 min. The PCR amplicons, which were derived from all the members of the target genus rather than a single strain, were verified by agarose gel (1.2%) electrophoresis and then purified using a Qiaquick Gel Extraction Kit (Qiagen,

Valencia, CA). The purified amplicon was quantified using a NanoDrop 1000 spectrophotometer (Thermo Scientific, Wilmington, DE) and used as the qPCR standards.

TaqMan-based and SYBR green-based qPCR assays including melting curve analysis were performed on a Mx3000p real-time PCR system (Stratagene, La Jolla, CA) as described previously (Nelson, 2011; Wang et al., 2012) using serial (1:10) dilution

(102 - 108 copies/reaction) of each respective qPCR standard (R2 > 0.99 for all standard curves). TaqMan-based qPCR was performed using two-step amplification: initiation at

95 °C for 5 min, followed by 40 cycles of 95 °C for 15 s and 60 °C for 1 min with the fluorescence signal detected at the end of the 60 °C primer annealing/extension step. For the SYBR green-based qPCR, the thermal program was set as: initiation at 95 °C for 5 min, followed by 40 cycles of 95 °C for 30 s, 60 °C for 30 s, 72 °C for 40 s, and 86 °C for

16 s. Dissociation curve was generated using 95 °C for 1 min, 60 °C for 30 s, and 95 °C

87 for 30 s. Fluorescence signal was collected at the end of the 72 °C and 86 °C steps (end point) of the 40 cycles and at the ramping period from 60 °C to 95 °C (all point) of the dissociation curve step. Baseline and threshold were calculated with Mx 3000p software using the fluorescence acquired at the end of 86 °C for 16 s, whereas the primer dimers were completely denatured and would not contribute to the fluorescence (Yu et al., 2005).

Samples with a Ct value below that of the no-template control were considered as below the detection limit.

Chemical analysis

Volatile fatty acid (VFA) concentrations of the digester samples were measured using gas chromatography (HP 5890 series, Agilent Technologies, Santa Clara, CA) fitted with a flame ionization detector (FID) and a Chromosorb W AW packed-glass column (Supelco, Sigma-Aldrich, St. Louis, MO) as described previously (Zhou et al.,

2011). The pH, contents of total solid (TS), and volatile solid (VS) were also measured as described previously (AOAC, 1990).

Statistical analysis

The quantitative PCR data were first log10 transformed to improve normality and analyzed using the GLM Procedure of SAS 9.2 (SAS Institute, Cary, NC, USA). The

Newman-Keuls test was used as the post-hoc test, with significant difference declared at p ≤ 0.05.

Data availability

The QIIME demultiplexed files for each sample are available individually for download from NCBI Short Read Archive (SRA) database under the accession number

SRP028717.

5.4 Results

Chemical characteristics of the samples

The pH values of all the samples were very similar, at about 8.0. The influent samples, except sample S1I, had a much higher TS content than the digester samples

(Table 2). Inside the digester, TS content varied along the course of the digester, but not consistently decreased. A similar trend was observed for VS content. The TS and VS contents were much lower in the effluent sample than in the influent samples (except for sample S1I). Removal of TS or VS could not be calculated because of the temporal variations in TS and VS content and unknowing recirculation rate of the effluent. No information was available on the yields of biogas or methane. The biogas composition was analyzed during the period when the digester samples were collected for this study.

The result showed that the biogas contained about 60% methane and 40% CO2, and this ratio remained relatively stable over time. The electric output of the CHP system that was fueled by the biogas from the MPFLR digester ranged from 537 to 605 kWh over the sampling period.

Volatile fatty acids are intermediates and important indicators of stability of the

AD, and thus VFA concentrations in the samples were analyzed. The total VFA was

89 relatively higher in all the influent samples (37 - 55 mM), but decreased to approximately

7 to 13 mM in the digester samples and the effluents (Table 5.2). Acetic acid, the major

VFA, had a concentration of 19 to 32 mM in the influents and approximately 10 mM in all the other samples except sample S1T1. The other VFA had much lower concentrations, falling below 1 mM except in the influent samples and valeric acid in samples S1I. Overall, both total VFA and individual VFA did not exhibit a large gradient in concentration along the course of the digester.

DGGE Profiles of Bacterial and Archaeal Communities

Duplicated DGGE analysis consistently showed that except for the influent samples, the DGGE profiles for both archaea and bacteria were very similar along the course of the digester and for the effluents (Fig. 5.2). For archaea (Fig. 5.2A), no significantly difference in DGGE banding patterns was observed among all the samples, except sample S3I that showed different intensity for most of the DGGE bands from the other samples. For bacteria (Fig. 5.2B), the influent samples had different DGGE banding patterns than the samples collected inside the digester and from the effluents, and the banding patterns were similar among all the samples collected along the course of the digester and the effluents. These results suggested that the bacterial and archaeal communities were quite stable along the course of the digester.

Phylogenetic Diversity and Composition of Archaeal Communities

In total 38,578 quality-checked archaeal 16S rRNA gene sequences were obtained, with an average of 2,572 sequences per sample. Collectively, 22 species-level

OTUs were found, including five singletons. Beside three OTUs that could not be 90 classified and one OTU that was assigned to the class Thermoprotei in the phylum

Crenarchaeota, all the remaining 18 OTUs were classified to the phylum Euryarchaeota.

Most of the Euryarchaeota sequences were classified to the order Methanomicrobiales.

The largest OTU (OTU #5) contained 97.1% of the total archaeal sequences and was assigned to the genus Methanosaeta, while the second largest OTU (OTU #17) contained

2.2% of the total archaeal sequences and was assigned to the genus

Methanocorpusculum. Genera Methanoculleus, Methanospirillum, and Methanosarcina were also found but each was represented by less than 30 sequences. In sample S3I, 99% of the archaeal sequences were clustered into one OTU (OTU #17) that was classified to genus Methanocorpusculum. In the rest of the samples, about 98% of the archaeal sequences were clustered into OTU #5 classified to genus Methanosaeta. No sequence was assigned to genus Methanobacterium.

Phylogenetic Diversity and Composition of Bacterial Communities

Quality-checked 16S rRNA gene sequences amounted 74,470 in total, with an average of 4,065 sequences per sample. These sequences were clustered into 3,468 species-level OTUs, of which 2,332 were singleton OTUs. About 21% (15,878 sequences) of the total bacterial sequences were classified as unclassified bacteria at phylum level, which were clustered into 941 OTUs with 747 of them being singletons.

Collectively, the bacterial community in the samples contained 15 phyla, including candidate phyla OD1, OP10, OP11, SR1, and TM7. The composition of major bacterial phyla with the abundance greater than 1% was shown in Fig. 5.3. Eight of the major phyla (Fig. 5.3) accounted for 99.4% of the total sequences obtained. Consistent with the

DGGE result, except the influents, the bacterial diversity was similar at the different locations of the digester and the effluents, with Firmicutes (33-53%) and Bacteroidetes

(15-24%) being the predominant phyla. A lower diversity was observed in the influent samples, especially in sample S3I in which Firmicutes alone accounted for 82% of total bacterial sequences, than in the other samples. Actinobacteria and Proteobacteria were more predominant in the influents than in the digesters except S3I that had no detectable

Proteobacteria. The proportion of unclassified bacteria in the samples notably increased over the sampling period, 10 ± 2% for S1, 22 ± 4% for S2, and 30 ± 3% for S3.

The goodness of fit test for multinomial distribution with equal probability was used to test if statistically-different distribution pattern was present on taxa with uneven distribution (each represented by >20% of each taxon in at least one sample) and the corresponding phyla they belonged to. Although distribution of major bacterial phyla was similar (p > 0.05) among the samples (except phyla Actinobacteria, Proteobacteria, and

Chloroflexi), different distributions at lower taxonomic levels were observed (p ≤ 0.05), especially in the influent samples (as shown in Table 3). In sample S3I, the phylum

Actinobacteria was mainly composed of the genus Bifidobacterium and the family

Coriobacteriaceae, while in the other samples, that phylum was mainly composed of the order Actinomycetales. In addition, sequences classified to Bifidobacterium, a beneficial lactic acid producing genus commonly found in intestinal track, was almost exclusively observed in the three influents: 57% in S3I, 25% in S2I, and 6% in S1I, and less than 5% each in the other samples. This observation suggested that the environmental condition in the MPFLR digester did not favor bifidobacterial presence in cow manure. Furthermore,

92 in the phylum Firmicutes, the order Lactobacillales was mostly represented by sequences from the influent samples S1I and S2I, while the genera Enterococcus and Streptococcus were mainly found in the S1 samples. However, the order Clostridiales were found more predominant in sample S3I, except for the genus Sporacetigenium, a fermenting bacterium commonly seen in anaerobic digester, that was found more abundant in sample

S1T2. Except for sample S3I, all the samples contained sequences classified to the family

Synergistaceae and the phylum Chloroflexi, and most of the Chloroflexi sequences were further assigned to the family Anaerolineaceae.

The OTUs (197 in total) that were each represented by at least 50 sequences in at least one sample were used to further compare the bacterial communities among the samples. The phylogenetic tree built based on these major OTUs shared similar topology with the tree generated from all the OTUs, suggesting that these “major” OTUs can represent the structures of the bacterial community of all the samples. The occurrence of the OTUs was compared among all the samples using heatmap, sample-sample correlation, OTU-OTU correlation, and hierarchical clustering tree of OTUs (Fig. 5.4).

Ranked abundance of OTUs in each samples (Fig. 5.4A) and sample-sample correlation

(Fig. 5.4B) clearly showed that the bacterial community structure of sample S3I was distinctly different from that of the other samples. The bacterial community of S2I was also different, though to a lesser extent, from that of the other samples.

Based on OTU-OTU correlation, the major OTUs were grouped into 4 groups

(Fig. 5.4C). Group I contained 30 OTUs that were found in the influent samples, especially predominant in S3I, but barely observed in the digester samples or the effluent 93 samples. Twenty six of these OTUs were assigned to the class Clostridia, with 14 and 7 assigned to the families Ruminococcaceae and Lachnospiraceae, respectively. Of the remaining four OTUs, one was assigned to genus Bifidobacterium, and the remaining three could only be assigned to the phylum Bacteroidetes. Group II were composed of 47

OTUs that were mostly observed in S1I and S2I, with few were seen in the other samples.

This group of OTUs were mainly classified to the orders Clostridiales, Actinomycetales,

Lactobacillales, and phylum Proteobacteria. Group III contained only three ubiquitous

OTUs that were observed in all the samples: OTU #881 (represented by 3,147 sequences) was assigned to only the family Peptostreptococcaceae; OTU #2228 (1,913 sequences) was assigned to the genus Sporacetigenium of Peptostreptococcaceae; and OTU #2636

(58 sequences) was assigned to the genus Desulfobulbus. Group IV were composed of

OTUs that were observed in all samples except S3I. The largest OTU was represented by

8,710 sequences and could not be assigned to any known bacterial phylum. This OTU was found mostly in all the digester samples and the effluent collected on September 30,

2011 (sample S3E). The other OTUs of group IV were assigned mostly to Bacteroidetes,

Proteiniphilum, Anaerolineaceae, Erysipelotrichaceae, Clostridiales, Ruminococcaceae, and Synergistaceae.

Principal coordinate analysis

The diversity of bacterial communities was compared using principal coordinate analysis (PCoA) on the sequence data (Fig. 5.5). Collectively, PC1 and PC2 explained

69.2% of the total variation. All the influents were separately spotted on the PCoA plot without grouping with each other or with any of the other samples along either PC1 or

PC2. Samples collected on the same day grouped together, and the three sampling dates were separated both along PC1, which explained 43.9% of the variation, and along PC2, which explained 25.3% of the variation. Sample S2T1 separated itself from the other S2 samples but stayed close to the S1 samples on the PCoA plot. The results showed different bacterial communities in the influents than in the digester samples, and smaller spatial than temporal variations in bacterial community in the MPFLR digester.

Quantification of major methanogens

Among the five methanogen genera quantified, Methanosaeta was the most abundant in all the samples, reaching approximately 8 x 107 copies of 16S rRNA gene per

µg metagenomic DNA, except in sample S3I, in which genus Methanobacterium dominated with an abundance of 108 copies of 16S rRNA gene per µg metagenomic DNA

(Figs. 5.6A and 5.6B). Methanobacterium, which was not represented by any sequence

(see the pyrosequencing results), was the second most predominant genus in most of the samples, reach at least 107 copies of 16S rRNA gene per µg metagenomic DNA in all the samples (Fig. 5.6B). Methanoculleus was found at about 105 in all the samples except sample S3I (Fig. 5.6C). Both the genera Methanosarcina and Methanocorpusculum were detected at about 105 16S rRNA gene copies/µg in all the samples, with the exception in sample S3I, where Methanosarcina was not detected (Figs. 5.6D and 5.6E). Compared with the other samples, sample S3I had more Methanobacterium and

Methanocorpusculum but less Methanosaeta and Methanoculleus.

5.5 Discussion

Anaerobic digestion is the most suitable technology to manage livestock manure, especially dairy cattle manure, to reduce greenhouse gas emission, harness bioenergy, and decrease pathogens. MPFLR digester is the most common technology implemented on dairy farms in the US. Unlike in CSTR digesters in which the conditions are homogeneous, the plug-flow operation creates a gradient of conditions along the course

MPFLR digesters. To our knowledge, this is the first study that investigated both the archaeal and the bacterial composition inside a MPFLR.

To minimize “contamination” with dead microbes from preceding locations of the process, all the samples were subjected to PMA pretreatment, which has been shown effective in excluding DNA from non-viable cells from PCR-based analysis. In the present study, we used DGGE profiling of both archaeal and bacterial communities to assess the effect of PMA pre-treatment on the samples, but no obvious change in DGGE pattern was noted (data not shown). This observation is consistent with the results reported previously that PMA treatment did not substantially affect the DGGE pattern of environmental samples (Nocker et al., 2007). Although a later study reported that changes of abundance of certain bacterial groups was observed based on pyrosequencing results of 16S rRNA gene amplicons of environmental water samples (Nocker et al.,

2010), it is not certain if the PMA treatment excluded the dead microbes from being analyzed in the present study because the TS contents of the samples we tested were at least ten times higher than the effective range (TS content under 2000 mg/liter) of PMA

96 treatment on biosolid samples (Taskin et al., 2011). It remains a technical challenge to completely exclude dead microbes carried down from preceding locations from being analyzed in plug-flow digesters and other systems.

The reverse primer for the genus Methanocorpusculum published previously

(Goberna et al., 2010) can anneal to the 16S rRNA genes of the four type strains in this genus but not that of the non-type strains, which represented most of the

Methanocorpusculum sequences found in the pyrosequencing reads in the present study.

Therefore, we modified the reverse primer based on the Methanocorpusculum sequences archived in the RDP database and the Methanocorpusculum sequences obtained in this study. When paired with the forward primer Mcp193F published by Goberna et al.

(2010), the new reverse primer allowed specific but inclusive amplification of 16S rRNA genes of both type and noon-type strains of genus Methanocorpusculum, as verified by cloning and sequencing of amplicons produced from this primer pair (data not shown).

This primer pair can be used to quantify the genus Methanocorpusculum in future studies.

In the present study, Methanosaeta was found to be the dominant genus in all the samples except sample S3I, which was dominated by other methanogens

(Methanocorpusculum as determined by pyrosequencing and Methanobacterium by qPCR). As an acetoclastic methanogen genus with greater affinity for its substrate than other methanogens (Yu et al., 2010), the dominance of Methanosaeta is consistent with the low acetate concentration found in all the digester samples. The low acetate concentration also explained the low abundance of Methanosarcina. This explanation is corroborated by the predominance of Methanosarcina in digesters with high acetate 97 concentration (Zinder, 1993; de Vrieze et al., 2012), including in a plug-flow digester fed a mixture of crop and liquid cow manure where acetate was at high concentrations (Lerm et al., 2012). In a recent study (St-Pierre and Wright, 2013), Methanosarcina was found to be the dominant methanogens (>98% of total methanogen sequences) in two MPFLR digesters implemented on dairy farms in Vermont that co-digest dairy manure and waste from ice cream factory or cheese whey. Although acetate concentrations were not reported in that study, the feeding of readily digestible ice cream waste and cheese whey probably increased the acetate concentrations in those two MPFLR digesters.

Nevertheless, methanogen compositions varied considerably in digesters with different designs and feedstocks (Nettmann et al., 2010; Zhu et al., 2011; St-Pierre and Wright,

2013). Investigation into the relationship between population dynamics and chemical data and environmental factors will help further understand the performance and stability of anaerobic digesters.

Although the feedstock of the MPFLR digester was from the same dairy farm, the three influent samples had different archaeal populations, with samples S1I and S2I containing Methanosaeta as the major methanogen genus while sample S3I containing

Methanocorpusculum as the major genus. Unlike samples S1I and S2I, which were collected after cow manure was mixed with the recycled effluents of the MPFLR digester, sample S3I was collected, unintendedly, from the manure pits before mixing with the recycled digester effluent. A recent study has shown that Methanocorpusculum was predominant in cow manure (Yamamoto et al., 2011). Thus, the predominance of

Methanosaeta in samples S1I and S2I might be attributed to the effluent that was

98 recycled back to the digester, whereas the predominance of Methanocorpusculum in sample S3I was probably explained by the lack of recycled effluent from the digester.

Compared to the distinct bacterial community found in sample S3I, the bacterial community in the two influent samples S1I and S2I was more similar to that found in the digesters samples. As mentioned above, this could also be explained by the recycled portion of the digester effluents. The OTUs of bacteria mainly found in sample S3I were classified to the families Ruminococcaceae and Lachnospiraceae, and the genera

Osillibacter. These OTUs were likely present in the cow manure. Some of the OTUs

(e.g., OTUs #1131 and #1836) were found much more abundant in the influents than in the digester samples. These OTUs were assigned to the families Ruminococcaceae and

Lachnospiraceae and have also been found predominant in bovine feces (Dowd et al.,

2008; Rudi et al., 2012), suggesting that they were probably fecal bacteria and they could not proliferate in the digester. The family Peptostreptococcaceae contains several known fermenting bacteria in the genera Anaerosphaera, Peptostreptococcus, and

Sporacetigenium that have been isolated from anaerobic digesters and swine manure

(Chen et al., 2006; Peu et al., 2006; Ueki et al., 2009). Members of Sporacetigenium can produce acetate and ethanol as the main end products, and the type strain of

Sporacetigenium mesophilum was isolated from an anaerobic digester (Chen et al., 2006).

The ubiquitous occurrence of the OTU belonging to Peptostreptococcaceae (OTU #881) and Sporacetigenium (OTU #2228) might reflect their fitness in the MPFLR digester and potentially important contribution to acidogenesis therein.

The total retention time inside of the MPFLR digester was 23 days, and the three sampling locations (T1, T2, and T3) were separated in retention time by at least 5 days.

Despite temporal variations in both the archaeal and the bacterial communities among the three sampling times, the spatial variations in either community were limited. These limited variation in microbial communities concurred with the similarity in the chemical parameters (e.g., pH, TS, VS, and VFA) at these three locations.

It was hypothesized that the rather similar chemical conditions and microbial communities along the course of the MPFLR digester was due to two primary reasons.

First, dairy manure contains little readily digestible substances (e.g., starch and pectin, which have been digested and absorbed by the cows) but large amounts of fecal microbial cells and undigested recalcitrant cellulose, hemicellulose and lignin. As a result, the digestion processes, particularly hydrolysis and subsequent acidogenesis, occur slowly as digester content moves through the digester. Second, the MPFLR digesters are not separated into multiple compartments, and the biogas-driven mixing may also create longitudinal mixing besides mixing perpendicular to the plug flow as the digester content slowly flows through the digester.

Because this was the first bacterial study on any MPFLR digester, there was no study for comparison. However, the above premises seems to be supported by the study of Roy et al. (2009) who observed distinctly different archaeal and bacterial communities in the different compartments of a plug-flow digester fed with swine manure, which typically contains little recalcitrant cellulose or lignin, and operated with a hydraulic retention time of 60 days. Furthermore, it was reported that a gradient of methanogenic

100 activity at different locations was observed in a laboratory-scale plug-flow digester fed with pineapple pulp and peel, which contain readily biodegradable carbohydrates and proteins (Namsree et al., 2012). Future studies using tracers and microbial activity-based analysis on samples collected along the course of the MPFLR digesters can help test the above hypotheses. Collectively, the results of the present study provided chemical and microbial evidence for the stable operation of MPFLR digesters. The results also suggest that further increase in hydraulic retention time may also increase biogas yield per unit of dairy manure fed.

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Table 5.1 Primers used in this study.

Ta Amplicon Analysis Primers/probes Sequences (5' - 3') Target Reference (⁰C) size (bp) 344F ACGGGYGCAGCAGGCGCGA Archaea 56 191 Yu et al., 2008 GC-519Ra ATTACCGCGGCKGCTG DGGE GC-357Fa CCTACGGGAGGCAGCAG Bacteria 56 194 Yu and Morrison, 2004a 519R ATTACCGCGGCKGCTG ArcF-Ab WCYGGTTGATCCYGCCRG Archaea 56 534 Nelson, 2011 ArcR-Bc YGGTRTTACCGCGGCGGCT Pyrosequencing BactF-Ab AKRGTTYGATYNTGGCTCAG Bacteria 56 532 Nelson, 2011 BactR-Bc GTNTBACCGCDGCTGCTG Mbt-202F CGCCTAAGGATGGATC

Mbt-341Taqd FAM-CGCGAAACCTCCGCAATGC-BHQ Methanobacterium 60 148 Nelson, 2011 Mbt-399R TAAGAGTGGCACTTGGGK

Mcu-934F AGGAATTGGCGGGGGAGCAC Franke-Whittle et al., 2009; Mcu-1023Taqd Cy5-GAATGATTGCCGGGCTGAAGACTC-BHQ Methanoculleus 60 309

10 Shigematsu et al., 2003 Mcu-1200R CCGGATAATTCGGGGCATGCTG

2 qPCR

MB1b CGGTTTGGTCAGTCCTCCGG SAR761Taqd HEX-ACCAGAACGGGTTCGACGGTGAGG-BHQ Methanosarcina 60 271 Shigematsu et al., 2003 SAR835R AGACACGGTCGCGCCATGCCT MS1b CCGGCCGGATAAGTCTCTTGA SAE761Taqd FAM-ACCAGAACGGACCTGACGGCAAGG-BHQ Methanosaeta 60 272 Shigematsu et al., 2003 SAE835R GACAACGGTCGCACCGTGGCC Mcp193F TCCTCGAAAGATCCGTC Goberna et al., 2010 Methanocorpusculum 60 314 Mcp491R GCCYTGCCCTTTCTTCAC This study

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Table 5.2 Concentrations of volatile fatty acid (VFA) and content of total solid (TS) and volatile solid (VS).

VFA concentration (mM) Sampling Sample TS VS Total VFA time location (%) (%) Acetic Propionic Isobutyric Butyric Isovaleric Valeric (mM) Acid Acid Acid Acid Acid Acid I 3.2 2.4 32.55 7.63 0.49 4.37 2.33 7.65 55.01 T1 3.3 2.5 2.96 0.60 0.00 0.47 0.27 2.84 7.14 8/3/2011 T2 3.3 2.4 8.47 0.92 0.00 0.34 0.19 1.93 11.85 T3 2.6 2.0 10.28 0.74 0.00 0.29 0.08 1.34 12.74 E 2.8 2.1 10.65 0.30 0.00 0.28 0.00 1.26 12.49 I 7.4 6.1 18.36 9.88 1.00 3.60 1.27 2.52 36.63

103 T1 2.9 2.1 9.53 0.53 0.00 0.35 0.10 0.89 11.40

9/15/2011 T2 3.7 2.8 9.12 0.27 0.00 0.24 0.00 0.68 10.31 T3 3.1 2.2 9.61 1.69 0.00 0.24 0.00 0.64 12.18 E 3.3 2.4 10.93 0.31 0.00 0.31 0.08 0.76 12.39 I 13.7 11.6 29.22 5.73 0.31 2.37 0.62 1.09 39.35 T1 4.1 3.0 8.86 0.84 0.06 0.52 0.11 0.58 10.97 9/30/2011 T2 4.0 2.9 11.87 0.55 0.00 0.36 0.08 0.53 13.38 T3 2.7 1.8 10.16 0.31 0.00 0.24 0.00 0.47 11.17 E 3.9 2.8 9.04 0.30 0.00 0.29 0.00 0.39 10.01

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Table 5.3 Distribution of the taxa with notable difference in relative abundance (%) among samples.

Sampling date 8/3/2011 9/15/2011 9/30/2011 Taxon p-val I T1 T2 T3 E I T1 T2 T3 E I T1 T2 T3 E

Actinobacteria .011 15 6 4 7 8 14 6 8 2 2 6 6 6 3 8 f_Ilumatobacter <.001 12 12 10 3 10 21 5 8 1 1 2 2 2 8

g_Dietzia <.001 11 3 3 6 6 24 12 1 1 9 3 7 12

g_Demequina <.001 32 2 2 6 7 19 6 5 1 2 7 7 2

g_Nocardioides <.001 17 2 3 2 8 31 3 10 5 7 8 3

g_Bifidobacterium <.001 6 1 25 1 57 5 2 2

g_Olsenella <.001 2 1 1 2 4 7 5 1 2 61 10 1 4

f_Coriobacteriaceae <.001 5 5 10 2 5 54 3 5 2 8

Bacteroidetes .367 6 8 4 10 10 5 6 9 4 3 3 8 9 5 11 o_Bacteroidales <.001 3 2 2 2 3 5 3 6 3 2 22 10 12 6 18 f_Flavobacteriaceae <.001 14 5 3 6 6 33 3 4 2 1 3 8 4 9

f_Cytophagaceae <.001 24 5 3 4 6 26 8 7 2 1 4 4 1 6

f_Sphingobacteriaceae <.001 27 6 1 2 9 41 1 2 1 1 3 1 3

Chloroflexi .005 7 10 10 11 13 4 6 10 2 2 4 6 4 9

g_Chloroflexus <.001 44 5 2 2 3 14 6 3 3 5 3 8 3

c_Thermomicrobia <.001 21 9 5 4 7 20 5 4 1 1 4 7 3 8 Firmicutes .505 7 6 6 8 8 7 4 8 3 2 11 7 8 4 10 g_Facklamia <.001 28 2 1 2 1 56 2 2 1 2 2 2 1 1 f_Aerococcaceae <.001 42 1 1 2 41 4 1 1 1 2 2

f_Carnobacteriaceae <.001 30 2 1 2 58 2 1 2 1 1 1

g_Enterococcus <.001 25 21 7 16 15 14 1 1 1 1

o_Lactobacillales <.001 28 3 3 2 2 40 3 2 1 2 3 4 2 4

g_Streptococcus <.001 14 22 9 20 14 1 1 1 2 5 2 8

g_Anaerovorax <.001 7 4 2 6 4 7 3 8 5 2 28 8 7 5 4 f_Lachnospiraceae <.001 9 4 2 7 9 7 4 5 2 2 27 6 7 3 6 g_Roseburia <.001 12 1 1 2 14 1 4 1 55 6 2 1 1

g_Sporacetigenium <.001 7 5 21 7 12 5 7 5 3 3 3 5 6 3 7 g_Oscillibacter <.001 8 1 1 1 1 21 1 1 55 3 3 2 2

f_Ruminococcaceae <.001 7 6 2 6 7 10 3 8 3 3 24 7 6 3 6 Planctomycetes .336 12 6 6 7 9 7 4 6 4 5 1 10 9 6 9 g_Planctomyces <.001 23 4 8 10 1 23 5 1 1 7 5 4 5

Proteobacteria <.001 18 7 3 7 8 23 4 6 2 2 5 6 2 6 g_Labrenzia <.001 23 2 1 8 8 24 7 8 3 2 5 4 3 4 g_Paracoccus <.001 13 7 2 14 7 23 5 5 2 4 4 6 2 6 g_Porphyrobacter <.001 17 9 3 9 8 27 5 4 2 1 5 5 2 5 f_Alcaligenaceae <.001 21 8 7 5 11 19 1 2 2 2 1 8 7 4

g_Hydrogenophaga <.001 16 2 1 9 7 20 1 7 3 2 5 9 4 12 g_Desulfobulbus <.001 10 5 25 15 2 2 3 2 2 12 8 3 12

c_Gammaproteobacteria <.001 25 7 2 6 12 18 8 3 1 2 5 6 1 5 f_Pseudomonadaceae <.001 16 4 4 4 11 23 1 4 1 3 10 1 4 11 g_Luteimonas <.001 11 9 2 7 10 28 6 5 4 2 3 3 2 10 f_Xanthomonadaceae <.001 28 7 1 5 7 25 5 4 2 1 4 4 3 4 g_Pseudoxanthomonas <.001 22 3 2 6 3 46 2 3 2 4 2 7

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Fig. 5.1 Scheme of mixed plug-flow loop reactor (MPFLR). A) Aerial view and sampling locations. B) cross-section view showing mixing perpendicular to the plug flow. I, influent; T1, T2, and T3, thermal probe ports along the plug-flow course of the digester.

105

106

Fig. 5.3 Major bacterial phyla (represented by > 1% total bacterial sequences) in each sample. Sample designations were the same as for Fig. 2.

107

108

Fig. 5.4 Heatmap of the Top 197 OTUs with most counts (> 50 reads) in the MPFLR samples. 108

Fig. 5.5 PCoA plot of the bacterial community in the MPFLR samples based on the pyrosequencing analysis. Samples were collected on August 3 (black), September 1 (grey), and September 30 (open) in 2011. Triangles, influents (I); circles, T1 location (T1) of the digester; squares, T2 location (T2); and diamonds, T3 location (T3); inverted triangles, the effluents (E).

109

Fig. 5.6 Quantification of five commonly observed genera of methanogens in AD using qPCR. A) Mst, Methanosaeta; B) Mbt, Methanobacterium; C) Mcu, Methanoculleus; D) Msc, Mehtanosarcina; and E) Mcp, Methanocorpusculum. Different lower letters designate significant difference (p < 0.05). Labels of X-axis were stated as that in Fig 1.

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CHAPTER 6 Comparison of the Microbial Communities in Solid-State Anaerobic Digesters (SS- ADs) Operated at Mesophilic and Thermophilic Temperatures

6.1 Abstract

Most of the biogas plants in operation are designed for and fed with liquid feedstock. Solid-state anaerobic digestion (SS-AD), however, is getting more attention owing to its lower operation cost, higher volumetric biogas yield, and easier handling of digestate compared to liquid AD (L-AD). Numerous studies have focused on the microorganisms that driving L-AD process, but our knowledge on the microbiome in SS-

AD systems is very limited. In this study, the composition and temporal successions of microbiome in two batch SS-AD systems, one each operated at mesophilic or thermophilic temperatures were investigated and compared using Illumina sequencing of

16S rRNA gene amplicons. The results of principal coordinate analysis (PCoA) and diversity index analysis showed that a distinct microbiome was formed at each of the two operation temperatures, with a greater microbial diversity and evenness observed during the mesophilic SS-AD than during the thermophilic SS-AD. For bacteria, Firmicutes accounted for about 60% and 82% of the total bacteria in the mesophilic and thermophilic digesters, respectively. For archaea, Methanothermobacter dominated in the thermophilic

SS-AD, while Methanoculleus was the most predominant genus in the mesophilic SS-

AD. More interestingly, the bacterial diversity data suggest that syntrophic acetate 111 oxidation coupled with hydrogenotrophic methanogenesis may be an important pathway for biogas production from acetate in the SS-AD. Canonical correspondence analysis

(CCA) showed that temperature was the most significant environmental factor in shaping the microbiomes in the SS-AD. Thermotogae showed strong positive correlation with temperature, while Fibrobacteres, Lentisphaerae, Spirochaetes, and Tenericutes were positively correlated with daily biogas yield. Our finding may help improve our understanding of the microbiome in SS-AD systems and their improvement in design and operation.

6.2 Introduction

Anaerobic digestion (AD) has gained increasing attention in recent years because it is a technology that enables both pollution reduction and bioenergy (as biogas) production. Based on total solid (TS) content of influent feedstock, AD can be divided into two major types: liquid-state AD (L-AD) and solid-state AD (SS-AD) (Guendouz et al., 2010). Wastes that contain low TS content, such as animal manure, food-processing wastewater, and other high-strength wastewater with TS content below 15%, are generally subjected to L-AD. On the other hand, for feedstocks that have high solid content (>15% total solid), such as crop residues, organic fraction of municipal solid wastes (OFMSW), and food wastes with low water content, SS-AD is advantageous and preferred because it eliminates the need to dilute the feedstocks to fluid slurry and produces low-moisture digestate, which is much easier to handle. Besides, SS-AD requires smaller reactor volumes per unit mass of feedstock, less energy for heating and

112 mixing, and produces less digestate (Li et al., 2011). Therefore, although most of the AD systems in use are L-AD systems, the above advantages recently spearheaded research and implementation of SS-AD systems, particularly in Europe, to digest feedstock of high solid content, such as crop residues and OFMSW.

The microbiome in L-AD systems, particularly that in constant stirred tank reactors (CSTR) and upper-flow anaerobic sludge blanket (UASB) reactors, has been extensively investigated, and much of the microbiological knowledge on AD was derived from such studies (Gerardi, 2003; O‟Flaherty et al., 2006; Narihiro and Sekiguchi, 2007;

Amani et al.,, 2010). Conceptually, L-AD process has been divided into four stages: hydrolysis of polymeric feedstock components, acidogenesis from the hydrolysis products, syntrophic acetogenesis from fatty acids that contain more than two carbons, and methanogenesis. In addition to the direct methanogenesis pathway by acetoclastic methanogenic archaea, acetate can be converted to methane through a co-metabolic pathway by acetate-oxidizing bacteria and hydrogenotrophic methanogens under certain conditions (Schnurer and Nordberg, 2008; Shigematsu et al., 2004; Hattori, 2008). Each of the above stages is carried out by a guild of diverse microorganisms, and a successful and stable operation depends on a dynamic balance of each niche of microbes with different functions.

Unlike the microbiome in L-AD processes, the microbiome in SS-AD has not been well studied. The overall metabolic processes in SS-AD is probably very similar to those in L-AD, but the unique physical and chemical conditions, such as low water activity, limited mixing and restricted mass transfer and microbial cell translocation, in 113

SS-AD probably select unique microbiomes. A few studies have examined the microbiomes in SS-AD processes using DGGE (Shi et al., 2013), FISH (Montero et al.,

2008, 2010; Zahedi et al., 2013), clone libraries of 16S rRNA genes (Tang et al., 2011),

454 pyrosequencing of 16S rRNA gene amplicons of archaea only (Cho et al., 2013), and shotgun 454 pyrosequencing of metagenomic DNA (Li et al., 1013). These studies analyzed the microbiome of dry or SS-AD digesters fed OFMSW or food wastes and did not identify detailed information on microbiome composition or diversity in the digesters.

Thus, the major guilds and taxa of bacteria and methanogens involved in and important to

SS-AD processes remain to be determined. The objective of the present study was to elucidate and compare the dynamic microbial compositions in two lab-scale SS-AD systems fed corn stover and operated at mesophilic and thermophilic temperatures. The correlation of microbial populations with environmental factors and SS-AD performance was also investigated.

6.3 Materials and Methods

Sample information, metagenomic DNA extraction, and 16S rRNA gene sequencing

Digestate samples were collected from lab-scale batch SS-AD reactors fed corn stover as the feedstock and operated at mesophilic or thermophilic temperatures in a previous study (Shi et al., 2013). Briefly, dewatered effluent from a mesophilic anaerobic digester treating municipal sludge and food waste was separated, and one aliquot was pre-incubated anaerobically at 36 oC for one week while the other was pre-incubated at

55 oC for two weeks. Each of the temperature-adapted effluent samples was mixed with

114 ground corn stover (<9 mm particles) at a 1:2 ratio (dray matter) and placed in reactors (1 liter working volume each, final TS about 20%). The digesters were maintained at mesophilic (36 oC) or thermophilic (55 oC) temperatures. Incubation of duplicate digesters at each operation temperature was terminated at days 4, 8, 12, and 38, and the entire digestate content was removed from each digester and mixed thoroughly using a hand-held homogenizer prior to sample collection for metagenomic analysis and chemical analysis (Shi et al., 2013).

Total metagenomic DNA was extracted from 0.5 g of each digestate sample using the repeated bead beating plus column purification (RBB+C) method (Yu and Morrison,

2004b). Following confirmation of DNA quality using agarose gel (0.8%) electrophoresis and quantification using a NanoDrop 1000 spectrophotometer (Thermo Scientific,

Wilmington, DE), the DNA had been subjected to DGGE analysis (Shi et al., 2013). In the present study, the microbiome of the SS-AD samples was further analyzed by sequencing the V4 hyper-variable region of prokaryotic 16S genes (Caporaso et al.,

2011). Briefly, the V4 region of 16S rRNA genes were amplified using the prokaryotic universal primers set 515F and 806R that included the Illumina flowcell adapter sequences. The 806R primer also contained a unique barcode sequence (12 nucleotides) for each of the sample for multiplexing. The amplicon libraries were sequenced using an

Illumina MiSeq platform using a 2 x 250 bp paired end protocol.

Sequencing data processing and analysis

The Illumina sequencing reads were first processed for demultiplexing, primer sequences trimming, and assembly from the pair-ended sequences using the software 115

MiSeq Reporter v2.0 (Illumina Inc., San Diego, CA), FastX Trimmer

(http://hannonlab.cshl.edu/fastx_toolkit/), and SeqPrep (https://github.com/jstjohn/

SeqPrep) prior to further data processing using the shell scripts recently developed by

Nelson et al. (2013) used for the bioinformatics software package QIIME v 1.5 (Caporaso et al. 2010). Briefly, the assembled sequences were clustered at 97% similarity against the V4-V5 region of the Greengenes reference OTU alignment using ulust (Edger, 2010) as recommended by Werner et al. (Werner et al., 2012). For the sequences that failed to cluster with the reference sequences, de novo OTU clustering was performed. Chimeric check was performed on the representative sequences picked from each OTU using

ChimeraSlayer (Haas et al., 2011). The non-chimeric sequences were then combined and taxonomically classified using the RDP Classifier (Wang et al., 2007). An approximate maximum-likelihood phylogenetic tree was generated using FastTree (Price et al., 2010).

The OTUs that contain less than 0.005% of the total bacterial sequences or less than 0.5% of the total archaeal sequences were filtered out and discarded prior to further diversity analysis.

To compare the microbiomes among the samples, alpha diversity and beta diversity were calculated using the software tools implanted in QIIME. Rarefaction was performed and then Shannon-Wiener diversity index (H), Simpson diversity index (D), and phylogenetic distance were calculated as the indicators for alpha diversity. For beta diversity calculation, a distance matrix of all the samples was each generated using the

OTU-based Bray-Curtis and binary-Ochiai methods and the phylogenetic tree-based

Unifrac method. Ordination was then performed using principal coordinate analysis

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(PCoA). Canonical corresponding analysis (CCA) was performed using the Vegan

Community Ecology package of R (http://cran.r-project.org/web/packages/vegan/) to elucidate correlation between microbial groups and the reactor performance in terms of biogas production, total solid (TS) and volatile solid (VS) removal, and cellulose and xylan degradation. Relative abundance of the detected phyla and genera was used in the

CCA analysis.

The abundance (represented by number of sequences) of the OTUs that could be classified to known genera was log transformed for normalization. The distributions of these OTUs were visualized using a heatmap and clustering method implemented in the software GAP (http://gap.stat.sinica.edu.tw/Software/GAP/). Pearson‟s correlation coefficients among the samples were calculated to examine the similarity of the profiling.

Hierarchical clustering trees were generated using the rank-two ellipse seriation method

(Chen, 2002; Wu et al., 2010).

Data availability

The demultiplexed dataset obtained in this study are available on NCBI Sequence Read

Archive (SRA) database under the accession number (to be added).

6.4 Results

Summary of sequence data

The Illumina sequencing process was found to generate low levels of system- generated noises, such as carryover contamination between different sequencing runs and

117 index misassignment (Nelson et al., 2013). To minimize overestimate of diversity by such noises, we filtered out the OTUs that were represented by less than 0.005% (60 sequences each) of the total bacterial sequences as recommended previously (Bokulich et al., 2013) and 0.5% (25 sequences each) of the total archaeal sequences. From the five digestate samples each taken from the mesophilic and the thermophilic SS-AD reactors over a period of 38 days, 1.07 million quality-checked sequences were obtained from the

10 samples after the „noise OTUs‟ being filtered out, with the numbers of sequences in each sample ranging from 44,774 to 188,268 (107,472 sequences per sample on average).

About 99.6% of the total sequences were assigned to bacteria, while the remaining 0.4% of the sequences was assigned to archaea. All of the archaeal sequences and about 99% of the bacterial sequences could be classified to existing phyla. In total, 1,052 bacterial and

19 archaeal species-level OTUs were found at 97% sequence similarity.

Comparison of community diversity and similarity

The microbiomes in the two SS-AD digesters over the course of the AD process were compared with respect to Shannon diversity index, Simpson index, and phylogenetic distance (Table 6.1). Overall, nearly all the mesophilic SS-AD samples showed higher values of these diversity measurements than the thermophilic SS-AD samples, indicating a more diverse microbiome underpinning the mesophilic SS-AD process compared to the thermophilic SS-AD process. In addition, as indicated by the evenness index, the microbial populations in the mesophilic SS-AD process had evener distribution than those in the thermophilic SS-AD process. During the first four days of mesophilic SS-AD, there were apparent increase in observed OTU richness, Shannon

118 diversity index, phylogenetic distance, and evenness, while the thermophilic SS-AD process showed decrease in these three diversity indices together with evenness from day

Principal coordinate analysis (PCoA) was conducted to examine the community similarity among all the samples collected from both the mesophilic and the thermophilic

SS-AD processes over time. Four different methods of distance matrix calculation, namely Bray-curtis, Ochiai, weighted Unifrac, and unweighted Unifrac, were used for

PCoA, but they generated very similar distributions of the samples on the PCoA plots

(data not shown); thus only the PCoA plot generated using weighted Unifrac was presented (Fig. 6.1). The samples were well separated based on SS-AD temperature along

PC1 that explained about 55% of total variation. The day 0 samples were plotted distant along PC2 (19% of total variation) from the rest of the samples collected at the later sampling dates for both the mesophilic and the thermophilic SS-AD processes. The samples collected from each SS-AD process also showed small but noticeable separation along PC2, particularly for the thermophilic SS-AD samples.

Archaea

After the data processing and filtration of the OTUs represented by less than 25 sequences each, 4,671 sequences classified in the domain Archaea were obtained, and they were further clustered into 19 OTUs at 97% sequence similarity. Accounting for

88% of the archaeal sequences, 13 OTUs were classified to known genera, while the rest six OTUs could only be classified to the families Methanospirillaceae, WSA2 (in the order Methanobacteriales), and WCHD3-02 (in the class Thermoplasmata) (Table 6.2). 119

A distinct archaeal community was observed in the mesophilic than in the thermophilic SS-AD processes. Specifically, Methanothermobacter dominated the thermophilic SS-AD process (representing 66-93% total archaeal sequences of the thermophilic SS-AD) except at day 0 when Methanosarcina, Methanosaeta,

Methanosphaera and one OTU classified to the family Methanospirillaceae were found as the main archaeal groups and no Methanothermobacter was detected. However, compared to that of the thermophilic SS-AD, the archaeal community was found to contain more diverse and evenly distributed populations in the mesophilic SS-AD, except at day 0 when one Methanosarcina OTU (#367815) dominated (accounting for 83% of total archaeal sequences of the mesophilic SS-AD). Intriguingly, this Methanosarcina

OTU became a minor member from day 4 when another Methanosarcina OTU

(#1129087) and a Methanoculleus OTU (#840393) increased in relative abundance. In addition, from day 4, the mesophilic SS-AD methanogen community remained diverse and evenly composed by the genera Methanobacterium, Methanosphaera,

Methanospirillaceae, Methanosarcina, and the family WCHD3-02. At the expense of abundance other archaeal genera, Methanoculleus considerably increased its relative abundance from days 12 to 38.

Overall, acetoclastic methanogens as a guild (both Methanosarcina and

Methanosaeta) only accounted for a relatively small portion of the total archaeal community, and this guild was less predominant in the thermophilic SS-AD process (0.7-

7.4% of total archaeal sequences of the thermophilic SS-AD) than in the mesophilic SS-

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AD process (14-31% of total archaeal sequences of the mesophilic SS-AD) from days 4 to 38.

Bacteria

Twelve major phyla with relative abundance greater than 1% in at least one sample were found (Fig. 6.2). Among these phyla, the percentage of bacteria that could not be assigned to any known phyla ranged from 0 to 2% across samples. Differences in their relative abundance were observed between the mesophilic and the thermophilic SS-

AD processes (Fig. 6.2). Being the most predominant phylum, Firmicutes sequences accounted for more than 60% of total bacterial sequences among the mesophilic SS-AD samples and more than 80% among the thermophilic SS-AD samples except the thermophilic SS-AD sample at day 0. Bacteroidetes was the second largest phylum in the mesophilic SS-AD (13% at day 0 and around 20% at the later sampling days), but among the thermophilic SS-AD samples it only represented 6% of total bacteria at day 0 and about 1% at the later sampling days. Although the phyla Proteobacteria, OP9,

Synergistetes, Chloroflexi, and Actinobacteria were found in both SS-AD processes, several phyla showed temperature-dependent occurrence. Specifically, Thermotogae increased from being undetected at day 0 to about 6% of the total bacteria at day 38 in the thermophilic SS-AD process. On the contrary, the relative abundance of Spirochaetes,

Lentisphaerae, and Verrucomicrobia grew gradually over the course of the mesophilic

SS-AD process, with Spirochaetes increasing in relative abundance from 0% to 8% and

Lentisphaerae and Verrucomicrobia increasing from 0.3% to 1% of total bacteria at day

38.

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After filtering out of the OTUs represented by less than 0.005% (60 Sequences each) of total bacterial sequences, 1,052 species-level OTUs (defined at 3% sequence dissimilarity) were assigned to the domain Bacteria. Fourteen OTUs were predominant and each was represented by more than 1% total bacterial sequences. These 14 OTUs together accounted for 55.3% of the total bacterial sequences. The largest four OTUs were all classified in the class Clostridia and accounted for about one-third of the total bacterial sequences. The most predominant OTU (#222005) (accounted for 12% of total bacterial sequences) could not be classified to any existing genus or family and only to the candidate order SHA-98; while the second most predominant OTU (#554328) (10.8% of total bacterial sequences) could only be assigned to the order Natranaerobiales. Both of these two OTUs were observed in both the mesophilic and the thermophilic digesters, but they were about twice as predominant in the mesophilic digester as in the thermophilic digester. The third most abundant OTU (#109644, 7.2% of the total bacterial sequences) was assigned to the order Clostridiales, and the majority of this OTU was obtained from the mesophilic digester. On the contrary, the fourth largest OTU

(#251039, 5.3% of the total bacterial sequences) was assigned to the family

Halanaerobiaceae, which contains known cellulolytic bacteria (Simankova et al., 1993).

This OTU was mostly observed in the thermophilic digester. The remaining ten major

OTUs had a relative abundance ranging 1-3% of the total bacterial sequences, and they could be divided into three groups based on their distribution in the SS-ADs. The first group included three OTUs assigned to the order Bacteroidales (with one further assigned to the family Marinilabiaceae and another to family Porphyromonadaceae) and

122 two OTUs assigned to the genus Treponema in the phylum Spirochaetes. This group of

OTUs was mostly observed in the mesophilic digester. The second group included two

OTUs with one assigned to the family Haloplasmataceae and the other to OPB54 in the phylum Firmicutes and they were mostly observed in the thermophilic digester. The third group was found in both digesters and included three OTUs with one each assigned to the candidate family TIBD11 (in the candidate phylum OP9), the family Ruminococcaceae, and the genus Anaerobaculum (in the phylum Synergistetes).

Distribution of OTUs assigned to known genera

To further understand the SS-AD process in each digester, the OTUs that were classified to known genera were further analyzed. In the present study, 259 OTUs (about

24% of the total bacterial OTUs that represented about 17% of the total bacterial sequences) with varying relative abundance were able to be classified to existing genera

(Table S1). These OTUs were clustered into seven groups based on their distribution pattern and relative abundance in the samples (Fig. 6.3). Group I contained 27 OTUs that were all found abundant in the thermophilic SS-AD. Most of the OTUs in this group were classified to the class Clostridia. Interestingly, these OTUs exhibited temporal shifts over the course of the thermophilic SS-AD. Specifically, three of these OTUs assigned to the genus Tepidmicrobium, which contains species with both cellulolytic and xylanolytic abilities (Niu et al., 2009; Phitsuwan et al., 2010) or protein and amino acid degradation abilities (Slobodkin et al., 2006), were found more abundant at day 0. However, at days 4 and 8, OTUs assigned to the genera Clostridium, Dethiobacter, Tepidanaerobacter, all of which contain acetate-oxidizing homoacetogens, along with other OTUs became

123 abundant. From days 8 to 16, OTUs belonging to the genus Thermacetogenium, which is a known acetate-utilizing genus producing hydrogen (Hattori et al., 2000), were found more predominant than the OTUs in other genera. The OTUs representing the candidate genus S1 in the family Thermotagaceae, which contains known acetate oxidizers in the genus Thermotoga, gradually increased in relative abundance over the course of the SS-

AD, and S1 became the most predominant genus at day 38 in the thermophilic SS-AD.

Group II contained 74 OTUs that were ubiquitous in all but the first sample (day

0) in the mesophilic SS-AD. However, these OTUs were more predominant in the samples collected at the early stage of the SS-AD processes (day 4 for the mesophilic SS-

AD and day 0 for the thermophilic SS-AD). The OTUs in group II were diverse and were assigned to genera that are known to utilize a variety of substrates, such as sugar, peptides, and amino acids. Some OTUs were classified to the genera Anaerobaculum and

Sporanaerobacter, both of which contain species capable of utilizing peptides and amino acids in addition to sugars (Hernandez-Eugenio et al., 2002; Maune and Tanner, 2012).

The genera Pyramidobacter and vadinCA02 in the phylum Synergistetes, which also contain amino acid degrading bacteria (Vartoukian et al., 2007), were represented by six and four predominant OTUs, respectively. Five OTUs in this group were assigned to the genus Syntrophomonas. Interestingly, these OTUs showed co-occurrence with hydrogenotrophic methanogens Methanobacterium and Methanosphaera. Besides, some

OTUs were assigned to the genus Rhodobacter, a genus containing species with versatile metabolic capacities, and to the candidate genus T78 (in the phylum Chloroflexi) with potential ability to degrade carbohydrates (Sekiguchi et al., 2001; Ariesyady et al., 2007).

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The ubiquity of these OTUs in this group might be contributed to their versatile function in the system.

Groups III, IV, and VI most likely contained mesophilic species because they were barely observed in the thermophilic SS-AD. Group III contained 80 OTUs and were mostly observed in the mesophilic SS-AD starting from day 4. Among the group III

OTUs, 30 were classified to Clostridium and two to Anaerostipes, a genus containing butyrate-producing bacteria. These OTUs were found predominant especially at days 4 and 8. Another six predominant OTUs each were assigned to the genera Erysipelothrix and Treponema, both of which contain known homoacetogens (Graber and Breznak,

2004). Two predominant OTUs were classified to Sedimentibacter, a genus containing amino acid- and pyruvate-utilizing bacteria. In contrast to group III, group IV OTUs were more abundant at day 0 of the mesophilic SS-AD, but they faded gradually thereafter. In group IV, nine OTUs were assigned to the genus Clostridium and ten large OTUs were assigned to the genus Syntrophomonas. The OTUs in group V were assigned to

Amaricoccus, a genus closely related to Rhodobacter but with uncertain metabolism, and they were found predominant at day 0 in the mesophilic SS-AD.

The OTUs in groups VI and VII were only observed in the samples collected at day 0 and went almost undetectable after day 4 in both digesters. Most of the OTUs in these two groups were assigned to the phylum Proteobacteria. These OTUs might be outcompeted by other guilds in the system or could not survive well under SS-AD conditions.

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Correlation between microbial groups and the environmental factors and SS-AD performance

The physicochemical conditions and performance data of the digesters were collected and reported previously (Shi et al., 2013). To better understand the SS-AD processes, we examined plausible relationship between microbial populations and abiotic environmental variables, including operation temperature, pH, acetate and propionate concentrations, and digester performance, including daily biogas yield, daily fiber reduction, and daily total solid destruction, using canonical correspondence analysis

(CCA). Considering that the SS-AD process is most likely driven by predominant bacteria, we performed the CCA analysis using the 221 major OTUs that was each represented by at least 0.05% of the total sequences, the genera represented by these major OTUs, and phyla (Fig. 6.4). At any of these three taxonomic levels examined, the results of CCA analysis showed that the environmental variables we included in the analysis explained more than 72% of the variations of relative abundance, suggesting that the environmental variables well explained the distribution of the major taxa.

Furthermore, CCA1 and CCA2 together explained more than 69% of the constrained inertia in all the CCA analyses, which indicates that these two coordinates are adequate to represent the CCA results.

Temperature was found the most influential factor (p < 0.05 in Montel test) on the distribution of the detected phyla, major OTUs, and genera. Operation temperature and acetic acid concentration was positively correlated, but they were correlated inversely with the pH. Daily fiber (cellulose and xylan) degradation, daily total solid removal, and

126 daily biogas yield pointed in the same general direction, which implies that they were positively correlated in both the SS-AD processes. At phylum level (Fig. 6.4A),

Thermotogae showed strong positive correlation with temperature, while Fibrobacteres,

Lentisphaerae, Spirochaetes, and Tenericutes were positively correlated with daily biogas yield. At genus level (Fig. 6.4B), Tepidanaerobacter and Thermoacetogenium, both of which contain known acetate-oxidizing bacteria, and Methanothermobacter were positively correlated with temperature. Halanaerobiaceae and Haloplasmataceae, both containing known fermenting bacteria, were also highly correlated positively with temperature and positively correlated with acetic acid concentration. Class Clostridia and genus Methanobrevibacter were found correlated positively with daily biogas yield. At

OTUs level (Fig. 6.4C), a group of OTUs showed strong positive correlation with acetic acid concentration. This group of OTUs included two OTUs assigned to the family

Haloplasmataceae, two OTUs assigned to the family Halanaerobiaceae, two OTUs assigned to the order Clostridia, one OTU assigned to the family Clostridiaceae, one

OTU assigned to the genus Clostridium, one OTU assigned to the candidate family

ML1228 in the phylum Firmicutes, and one OTU assigned to the candidate order SHA in the phylum Firmicutes. This group of OTUs might be involved in either acetate production or oxidation.

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6.5 Discussion

Anaerobic digestion as a technology has been evolving rapidly in the past 10 years from a conventional waste treatment technology to an intensive bioenergy production technology. Cost-effective, efficient, and reliable AD technologies remain the

„holy grail‟ of the AD industry, and SS-AD is the outcome of such pursuit. This study is one of the few studies that investigated the microbiome in SS-AD and examined correlation between microbial groups and the environmental factors and AD performance. In our study, the same seed and feedstocks were used in the two SS-AD processes, and yet a distinct microbiome was developed in each digester, supporting that the principle “Everything is everywhere, but, the environment selects” (Baas-Becking,

1934) operates in SS-AD processes. Consistent with the finding from other studies on L-

ADs (Levén et al., 2007; Pycke et al., 2011), the microbiome was found more diverse in the mesophilic SS-AD than in the thermophilic SS-AD in the present study.

Differences in physicochemical conditions and operation exist between SS-AD and L-AD processes. In the literature, only one study was found that reported similar methane yield from SS-AD and L-AD of the same feedstock (Brown et al., 2012), but no study has been reported that compare the microbiomes of SS-AD and L-AD on the same feedstock. A recent study examined the bacterial and archaeal communities in a mesophilic L-AD fed slurry of corn stover using clone libraries (Qiao et al., 2013).

Comparison between that study and our study revealed some characteristics of the microbiome in the mesophilic SS-AD. At phylum level, the mesophilic SS-AD microbiome had a greater proportion of Firmicutes (more than 60% vs. 48.3%),

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Bacteroidetes (about 20% vs. less than 8%), similar proportion of Proteobacteria (7.2% vs. 7.2%), but smaller proportion of Chloroflexi (0.5% vs. 20.1%), and Actinobacteria

(about 1% vs. 9.1%) than the mesophilic L-AD. Comparison at below phylum level was difficult because of limited information reported in that study. However, no syntrophic acetate oxidizing bacteria was found in that study, whereas several genera containing known homoacetogens were found in the present study. With respect to methanogens, the

L-AD and the mesophilic SS-AD had similar ratio of hydrogenotrophic methanogens and acetoclastic methanogens, but the SS-AD from days 8 to 12 had a greater proportion of

Methanosarcina than the L-AD. Among common hydrogenotrophic methanogens,

Methanosphaerula (29%), Methanospirillum (19%), and Methanobacterium (13%) were the most dominant genera in the L-AD, while in the SS-AD, Methanoculleus was the most predominant followed by Methanobacterium, Methanospirillaceae, and

Methanosphaera. It should be pointed out that besides the physical state of digester matrix (liquid vs. solid), other operational factors, such as mixing, seeding, feeding

(continuous vs. batch), and organic loading rate, could have also affected the microbiome in these two types of SS-SD of corn stover. However, it is not possible to operate an L-

AD and a SS-AD on the same type of feedstock with identical parameters. Therefore, the above differences in microbiome between L-AD and SS-AD may reflect some of the real-world differences in microbiome of L-Ad and SS-AD digesters fed corn stover.

Hydrogenotrophic methanogens were found to be more predominant than acetoclastic methanogens in the thermophilic AD (Hori et al., 2006; Briones et al., 2007;

Sasaki et al., 2011; Tang et al., 2011). In the present study, acetoclastic methanogens

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(i.e., Methanosarcina and Methanosaeta) were detected at very low abundance in the thermophilic SS-AD except at day 0. In the meantime, many genera containing known homoacetogens, which can oxidizes acetate to CO2 and H2 in syntrophy with hydrogenotrophic methanogens (Hattori, 2008), were found in the thermophilic SS-AD.

These two findings supports that the non-acetoclastic oxidative pathway coupling syntrophic acetate oxidation and hydrogenotrophic methanogenesis might be important for biogas production in the thermophilic SS-AD. Such an alternative pathway for biogas production has been reported in SS-AD fed waste paper (Tang et al., 2011) and L-AD fed artificial garbage (Sasaki et al., 2011), but this is the first study suggesting its occurrence in SS-AD of corn stover. As the most predominant methanogen genus in the thermophilic

SS-AD (except at day 0), Methanothermobacter was the main producer of biogas therein.

The predominance of Methanothermobacter increased over time and such increase might be due to its greater affinity for hydrogen than other hydrogenotrophic methanogens

(Zinder, 1993). In the mesophilic SS-AD, Methanosarcina was the dominant methanogen genus in the early stage, but it declined rapidly as hydrogenotrophic methanogens, particularly the genus Methanoculleus, grew in predominance. Several studies have observed such a dynamic succession in L-AD of solid feedstock operated at thermophilic conditions (Liu et al., 2009; Krakat et al., 2010; Sasaki et al., 2011). This archaeal population succession might be attributable to low acetate concentration and high hydrogen affinity of Methanoculleus (Hori et al., 2006; Goberna et al., 2009; Sasaki et al., 2011). This premise is consistent with the low acetate concentration in the mesophilic

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SS-AD (Shi et al., 2013) and the higher abundance of Methanosarcina in a mesophilic

SS-AD fed readily digestible food waste (Cho et al., 2013).

Firmicutes was found more predominant in the thermophilic SS-AD than in the mesophilic SS-AD (about 60% vs. 80% of total bacteria). Dominance of this phylum

(about 90% of total bacteria) was also found in a thermophilic digester treating waste paper (Tang et al., 2011). On the other hand, Bacteroidetes had a greater proportion in the mesophilic SS-AD than in the thermophilic SS-AD, which is consistent with the finding in a recent study comparing microbial compositions in 21 different digesters operated at either mesophilic or thermophilic temperatures irrespective the types of feedstock

(Sundberg et al., 2013).

The bacterial candidate phylum OP9 was represented by a small proportion (1 to

5% total bacterial sequences) in all the digester samples of both digesters. This phylum was first discovered in the hot springs in the Yellowstone National Park (Hugenholtz et al., 1998), and ever since it has been found in other anaerobic environments, such as geothermal areas, wastewater treatment plants, and anaerobic digesters (Rivière et al.,

2009; Tang et al., 2011; Vick et al., 2010). The genome of an isolate in this phylum and metagenomic contigs assigned to this phylum from an anaerobic cellulolytic microbiome suggested capability of hydrolyzing cellulose and hemicellulose and fermenting sugars via glycolysis to hydrogen, acetate and ethanol (Dodsworth et al., 2013). Such cellulolytic and hemicellulolytic capabilities might help explain the presence of the phylum OP9 in our SS-AD of corn stover. Further research is needed to confirm their

131 metabolic capability and to what extent this new phylum contributes to fiber degradation in SS-AD.

Multivariate analyses have been widely used in community ecology of plants, fish, and bacteria to help elucidate the relationship between abundance of organisms and the habitat environments (see review by Ramette, 2007). In the present study, the relationship between the presence and abundance of microbes and some important operational factors/performance indicators in the SS-AD processes, such as temperature, pH, acetate concentration, degradation of feedstock (solid content), and biogas production was shown with CCA. Temperature was found the most influential factor shaping the microbiome. Some microbial groups and guild were highly correlated with acetate concentrations (such as the phylum Synergistetes, families Haloplasmataceae and

Halanaerobiaceae), which might indicate their role in acetate production in SS-ADs. On the other hand, the presence of some microbial groups that were associated with the biogas yield (the genus Methanobrevibacter and the class Clostridia) might contribute to the better performance of this SS-AD. Although the causality between microbes and other factors or indicators could not be confirmed at this stage, the correlations between microbes and environmental variables and performance measurements can still guide future studies and improvement of design and operation of SS-AD. More researches need to be done to confirm the correlations and establish the causality. Targeted quantitative analysis of the microbial groups that have correlations with environmental or performance measurements will help establish possible causality and eventually help optimize SS-AD process.

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Table 6.1 Alpha diversity analysis on the SS-AD samples.

Operation Sampling Observed Simpson Shannon Phylogenetic Evenness temperature time (day) OTUs (D) (H) distance (E ) (°C) H 0 586 0.95 6.10 54.20 0.96 4 822 0.96 6.64 75.34 0.99 36 8 846 0.96 6.46 76.28 0.96 12 839 0.96 6.41 75.45 0.95 38 808 0.96 6.34 73.66 0.95 55 0 685 0.92 5.93 67.15 0.91 4 670 0.93 5.71 64.43 0.88 8 667 0.93 5.68 63.75 0.87 12 560 0.91 5.06 57.29 0.80 13 0.86

3 38 592 0.93 5.48 57.55

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Table 6.2 Abundance of methanogens (shown as percentage in each sample) in the SS-ADs.

Mesophilic Thermophilic #OTU ID Taxonomy assignment 0 day 4 day 8 day 12 day 38day 0 day 4 day 8 day 12 day 38 day N38466 g__Methanobacterium 5.1 5.0 1.9 3.3 2.0 5.6 1.9 1.5 1.5 1.1 551498 g__Methanobacterium 1.1 3.8 7.7 6.5 9.1 7.0 4.3 0.6 0.3 101553 g__Methanobacterium 1.7 5.2 2.1 1.1 0.8 7.4 3.7 1.6 0.9 1.2 167356 g__Methanobrevibacter 3.4 1.2 1.0 0.4 0.3 0.9 1.2 0.5 589886 g__Methanosphaera 13.1 7.5 5.8 1.5 17.7 6.2 1.6 2.6 0.5 227 g__Methanothermobacter 0.7 66.7 83.8 91.7 93.4 27098 f__WSA2 1.8 0.2 0.7 0.2 2.3 3.7 1.3 0.2 4027691 g__Methanoculleus 1.5 0.7 3.0 1.4 0.6 0.8

13 108784 g__Methanoculleus 1.9 6.0 0.7 1.4

840393 g__Methanoculleus 6.6 25.8 27.6 53.4 1.9 1.2 0.5 0.2 263121 f__Methanospirillaceae 1.1 13.3 7.1 8.4 2.6 17.2 1.2 1.3 0.4 0.3 584426 g__Methanosaeta 1.7 13.1 11.3 9.5 2.6 17.2 4.9 1.0 1.7 0.5 62890 g__Methanomethylovorans 6.6 2.1 3.3 367815 g__Methanosarcina 83.0 0.2 0.7 0.2 1.4 1129087 g__Methanosarcina 1.1 12.7 20.0 19.3 11.4 16.3 2.5 0.3 0.2 N52289 f__WCHD3-02 1.1 9.7 7.7 9.8 8.8 136107 f__WCHD3-02 4.0 2.1 1.5 0.5 282634 f__WCHD3-02 0.6 2.4 1.0 1.8 1.6 570725 f__WCHD3-02 1.4 1.0 2.2 2.0 0.5 0.6

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Table 6.3 Relative Abundance (shown as the percentage in the individual sample) of the major OTUs (containing > 1% of the a total sequences) in the SS-AD system.

Mesophilic Thermophilic Lineage 0 day 4 day 8 day 12 day 38 day 0 day 4 day 8 day 12 day 38 day Firmicutes; Clostridia; SHA-98 10.7 11.3 8.1 9.6 10.0 15.0 11.1 11.1 11.2 8.7 Firmicutes; Clostridia; Natranaerobiales; ML1228J-1 - 15.4 7.5 8.7 8.0 21.1 10.2 8.1 7.4 2.0 Firmicutes; Clostridia; Clostridiales 17.3 2.0 13.4 11.1 8.5 0.1 0.1 - - - Firmicutes; Clostridia; Halanaerobiales; Halanaerobiaceae ------18.1 19.3 24.0 7.9 Bacteroidetes; Bacteroidia; Bacteroidales; Marinilabiaceae - - 7.9 6.9 4.2 - - - - - OP9; OPB46; OPB72; TIBD11 2.2 2.5 1.0 1.9 3.2 2.4 1.5 2.5 3.2 4.0 Bacteroidetes; Bacteroidia; Bacteroidales 0.1 0.7 1.2 2.5 7.0 2.9 0.6 0.6 0.3 0.2

13 Firmicutes; Clostridia; Clostridiales; Ruminococcaceae - 2.1 1.2 1.5 1.4 7.1 2.7 2.0 2.1 1.3

5 Firmicutes; Bacilli; Haloplasmatales; Haloplasmataceae ------8.3 7.9 3.9 2.9

Bacteroidetes; Bacteroidia; Bacteroidales; Porphyromonadaceae 2.2 2.4 1.8 2.2 2.1 1.3 0.6 0.7 0.3 0.2 Firmicutes; Clostridia; OPB54 ------1.2 4.7 21.3 Spirochaetes; Spirochaetes; Spirochaetales; Spirochaetaceae; Treponema 0.2 0.5 1.9 2.8 4.0 - - - - - Spirochaetes; Spirochaetes; Spirochaetales; Spirochaetaceae; Treponema 0.2 0.8 1.8 2.4 3.5 - - - - - Synergistetes; Synergistia; Synergistales; Anaerobaculaceae; Anaerobaculum - 1.3 0.5 1.0 2.0 1.8 0.7 0.6 0.6 0.9 a "-" indicates abundance < 0.05% in the sample.

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Fig. 6.1 Principal coordinate analysis (PCoA) of the microbial community in the mesophilic (open diamond) and thermophilic (dark diamond) SS-ADs using weighted Unifrac distance matrix. Sampling day was indicated next to the symbols.

136

Fig 6.2 Major phyla (abundance > 1% in at least one sample) observed in the SS-ADs.

137

Fig. 6.3 Distribution of the OTUs that could be identified to the genus level in the SS-ADs.

138

Fig 6.4 Results of canonical correspondence analysis on A) phyla level, B) abundant OTUs (containing reads more than 0.05% of total reads) clustered to genus level, and C) abundant OTUs. Dots in the figures represented individual taxons/OTUs. Dots with blue color indicated the abundance of the taxon/OTU was ten times more in mesophilic than in the thermophilic SS-AD; dots with red color indicated the abundance of the taxon/OTU was ten times more in the thermophilic than in the mesophilic SS-AD; while the grey dots were dots without abundance differences as big as blue and red dots. The list of the blue, grey and red spots was included in Table S2. Arrows represented the direction of increase of the environmental variables with pH represented pH, Temp represented temperature, dYbiogas represented daily biogas yield, dFiber represented daily fiber (cellulose and xylan) reduction rate, dTS represented daily TS reduction rate, and Acetic represented acetic acid concentration. The length of the arrows indicated the degree of correlation with the ordination axes. Corresponding phyla in A): 1. Euryarchaeota, 2. unclassified bacteria, 3. Actinobacteria, 4. Armatimonadetes, 5. Bacteroidetes, 6. Chloroflexi, 7. Fibrobacteres, 8. Firmicutes, 9. Lentisphaerae, 10. Candidate division NKB19, 11. Candidate division OD1, 12. Candidate division OP9, 13. Planctomycetes, 14. Proteobacteria, 15. Spirochaetes, 16. Synergistetes, 17. Candidate division TM6, 18. Tenericutes, 19. Thermotogae, 20. Verrucomicrobia, and 21. Candidate division WS1.

(Continue) 139

Fig 6.4 Continue

140

CHAPTER 7 Comparison of Microbiota in the Temperature-phased Anaerobic Digestion (TPAD) System Before and After Feedstock Overloading

7.1 Abstract

Temperature-phased anaerobic digestion (TPAD) systems have gained increasing attention in the anaerobic digestion field owing to its advantages of ease to modulate and adjust the digester operation conditions to optimize the growth of different microbial guilds and enhance the overall digester performance. However, research on the impact of organic overloading to microbial composition of TPAD system was limited. In this study, we monitored the digester performance and investigated the composition of the microbiota in both the thermophilic and the mesophilic digester of a lab-scale TPAD system digesting dairy manure and waste whey before and after organic overloading. The thermophilic digester started to deteriorate after the organic loading rate (OLR) reached

60.4 g COD/l/day, with a decrease in pH, accumulation of volatile fatty acids (VFA), decrease in biogas production therein. In the meantime, biogas production increased in the second stage mesophilic digester. Community profiling using both denaturant gradient gel electrophoresis (DGGE) and Illumina sequencing of amplicons of 16S rRNA genes showed that the microbial composition was affected by the organic overloading and revealed successions from a stable community structure to another distinct one in each digester. 141

The phyla Bacteroidetes and Firmicutes were found dominant in both digesters before and after the organic overloading, while Thermotogae was one of the predominant phyla before organic overloadingin both digesters, but it was replaced by Proteobacteria in the thermophilic digester and diminished to almost undetected in the mesophilic digester 25 days after the overloading event implemented. In the thermophilic digester, the dominance of Mathanoculleus decreased as that of Methanobrevibacter and the

Thermogymnomonas (in the Thermoplasmata phylum) increased after the organic overloading. In the mesophilic digester, Methanosarcina, Methanobacterium, and

Methanobrevibacter were the most predominant genera, but Methanosarcina became the only dominant genus after the organic overloading impact. Canonical correspondence analysis revealed that temperature and pH was most influential environmental factors that explain the variations of the microbial community in this TPAD system.

7.2 Introduction

Anaerobic digestion (AD) has been a biotechnology widely used to treat wastewater and municipal sludge for many decades. In recent years, AD attracted additional attention because the biogas produced thereby can be used as renewable energy. The microbiological process of AD can be conceptually divided into four major steps: hydrolysis, acidogenesis, acetogenesis, and methanogenesis. Different guilds, or functional groups, of microorganisms mediate each of these steps. An efficient and stable operation of an AD process requires balanced functions among the different guilds of microorganisms, especially between acidogens and methanogens, the latter of which is

142 particularly fastidious and susceptible to different factors, such as low pH, high concentration of ammonia and volatile fatty acids (Chen et al., 2008). Failure of the AD systems due to un-balanced microbial growth and metabolism, especially acidogenesis and methanogenesis, has been repeatedly reported (Yan et al.; 1993; Batstone et al.,

2002; Banks et al., 2008; Ghanimeh et al., 2013). Thus, great efforts have been put into the design and operation of AD systems to stabilize AD process by balancing acidogenesis and methanogenesis.

Temperature-phased anaerobic digestion (TPAD) is an AD process involves both a thermophilic phase and a mesophilic phase in sequence (Lv et al., 2010). In a TPAD system, the hydrolysis and acidogenesis stages are largely separated from the acetogenesis and methanogenesis stages physically in two digesters. Such a separation enables optimizing the environmental conditions to support the metabolism of different guilds of microorganisms in two separate digesters. The thermophilic phase of a TPAD process can be operated at either acidic or neutral pH, while the mesophilic phase is operated at near neutral pH (Lv, 2012). If the thermophilic phase is acidic (referred to as

AT-TPAD) (Lv et al., 2013b), hydrolysis of polymeric substances (e.g., cellulose, hemicellulose, protein, and lipids) and subsequence fermentation of resultant monomers or oligomers (or acidogenesis) are mainly achieved in thermophilic digester, while methanogenesis and syntrophic acetogenesis mainly happen in the mesophilic digester. If the thermophilic phase is maintained at near neutral pH (referred to as NT-TPAD), all the four steps of the AD process happen in both the thermophilic and the mesophilic digesters. In either case, the mesophilic digester has more permissive conditions than the

143 thermophilic digester, and the mesophilic digester is less likely affected by operational variations, such as organic overloading.

Organic loading rate (OLR) is one of the most important operational factors that affect the stability AD systems. In the pursuit of increased volumetric biogas yield

(volume of biogas produced per unit of digester per day) of a digester, organic loading tends to be aggressive. Depending on the digestibility and composition of the feedstocks, such organic overloading can have a great impact on the microbiome in the digester and the stability of the AD process. Several studies have examined the microbial structures in anaerobic digesters in response to organic overloading (McMahon et al., 2004; Tale et al.,

2011; Chen et al., 2012; Lerm et al., 2012). Although these studies covered a number of designs of digesters, ranging from bench-top scale to full-scale, all of them focused on single-stage digesters, and no study has been reported that investigated how the microbiome in a TPAD system responds to organic overloading. In the present study, we monitored the performance of a TPAD system digesting dairy manure and waste whey and investigated the composition of the microbiota in both the thermophilic and the mesophilic digester of the TPAD system before and after intentional organic overloading.

Furthermore, canonical correspondence analysis was also performed to identify plausible correlation between microbial populations and the environmental variables/digester performance in each of the two digesters. The knowledge learned may help understand and improve stability of TPAD systems.

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7.3 Materials and Methods

TPAD setup, operation, and sample collection

The TPAD system was set up by using two BioFlo 3000 fermentation units (New

Brunswick Scientific Co. Inc., NJ) hooked together in sequence. Each unit has a CSTR reactor with a working volume of 5 liters. The temperature for the first reactor (the thermophilic digester) was maintained at 50 °C, while that of the second reactor (the mesophilic digester) was set at 35 °C. The mixing propeller for both digesters was set at

50 rpm. The thermophilic and the mesophilic digester were respectively seeded with the digestate of a thermophilic and a mesophilic digesters that had been operated in batch mode (Lv et al., 2013a). In the first two weeks of acclamation period, the thermophilic reactor was fed once daily with 100 ml of whey (20%, w/v) and 100 ml dairy manure slurry with a TS content of 5%. The daily feeding resulted in an OLR of 16.16 g COD plus 50 g manure TS per litter per day. Before feeding the above mixture to the thermophilic reactor, 200 ml of the mesophilic reactor content was discharged, and 200 ml was pumped directly from the thermophilic to the mesophilic reactors. Samples were collected from the effluent of each reactor for chemical and microbial analysis (see below). This resulted in a hydraulic retention time HRT (also solid retention time, SRT) of 25 days in each reactor. The biogas production from each digester was measured continuously using a gas flow meter. After the initial two weeks of the system startup, the volume of the daily feeding was increased to 500 ml consisting of 250 ml each of dairy manure slurry (5% TS) and whey. The OLR of the whey was also increased in a stepwise manner: from day 15 to day 39, 32.3 g COD/L/d; from day 40 to day 69, 48.5 g

145

COD/L/d; and from day 70 to the end of the study (day 167), 60.4 g COD/L/d (Fig.

7.2A). The feeding and sampling were done the same as described for the startup period.

Samples were collected periodically from both digesters throughout the whole experimental period and kept at –80 °C before further chemical. Samples taken at days

30, 44, 58, 72, 79, 93, 98, 121, 135, 154, 169 were subjected to microbial analysis.

Chemical analysis

The pH values and daily gas production was continuously monitored and recorded. The methane content of the biogas was determined using gas chromatograph

(HP 5890 Series, Agilent Technologies, USA) equipped with a HP-PLOT Q capillary column and a thermal conductivity detector, while concentrations of individual VFAs in the samples were also analyzed using a gas chromatograph (HP 5890 series, Agilent

Technologies) fitted with a flame ionization detector and a Chromosorb W AW packed glass column (Supelco, USA) (Zhou et al., 2011). Total solid content and total volatile solid content were determined followed the standard methods (American Public Health

Association, 2005). COD was determined using a DR2500 spectrophotometer (Hach,

Loveland, CO) followed the Method 8000 in the water analysis handbook (Hach,

Loveland, CO).

DNA extraction, PCR-DGGE, and Illumina sequencing

Total metagenomic DNA was extracted from 1.5 to 2.1 g of each of the digester samples taken on selected days (as indicated in Figure 7.1) using the repeated bead beating plus column purification (RBB+C) method (Yu and Morrison, 2004b). The integrity of the extracted DNA was evaluated using agarose gel (0.8%) electrophoresis, 146 while the concentrations were quantified using a NanoDrop 1000 spectrophotometer

(Thermo Scientific, Wilmington, DE).

The bacterial community and the archaeal community were profiled using PCR-

DGGE using domain-specific primers as described previously (Yu and Morrison, 2004a;

Yu et al., 2008). Briefly, the V3 hypervariable region of the 16S rRNA gene was amplified from 50 ng of each metagenomic DNA sample using the primer set GC-

357f/519r for bacteria and 344f/GC-519r for archaea. Bovine serum albumin (BSA) was included (670 ng/µL) in all the PCR to attenuate potential inhibition to the PCR reaction.

A touch-down thermal program (61 °C with 0.5 °C/cycle decrement for 10 cycles followed by 25 cycles at 56 °C for primer annealing) was used to maximize specificity.

The PCR was ended with a final extension step at 72 °C for 30 min to eliminate artifactual double DGGE bands created from possible heteroduplex (Jense et al., 2004).

Size and quality of the PCR products were verified using agarose gel (1.0%) electrophoresis before running DGGE using a PhorU system (Ingeny, Leiden, NL). The gels were stained with CYBR Green dye I (Invitrogen, Carlsbad, CA) and visualized using a Kodak Gel Logic 200 imaging system (Eastman Kodak Company, Rochester,

NY).

Preparation of amplicon libraries and subsequent Illumina sequencing were conducted as described previously (Caporaso et al., 2011). Briefly, the V4 hypervariable region of prokaryote 16S gene was amplified using primers 515F and 806R, a set of prokaryotic universal primers, with different sample indices for each sample. The

147 amplicon libraries were sequenced using a 2 x 250 bp paired end protocol on an Illumina

MiSeq system.

Illumina sequencing data processing and analysis

The sequencing data were first processed for demultiplexing, trimming of primer sequences, and assembly of pair-ended reads into single sequences using the software

MiSeq Reporter v 2.0 (Illumina Inc., San Diego, CA), FastX Trimmer

(http://hannonlab.cshl.edu/fastx_toolkit/), and SeqPrep (https://github.com/jstjohn/

SeqPrep) prior to further analysis using the shell scripts developed recently (Nelson et al.,

2013) for the use in the bioinformatics software package QIIME v 1.5 (Caporaso et al.,

2010). Briefly, sequences were clustered at 97% similarity against the V4-V5 region of the Greengenes reference OTU alignment using ulust (Edger, 2010) as recommended by

Werner et al. (2012). For those sequences that failed to cluster with the reference sequences, a de novo OTU clustering was performed. Chimeric check was performed on the representative sequences picked from each OTU using ChimeraSlayer (Haas et al.,

2011). The non-chimeric sequences were combined and then taxonomic assignment was made using the RDP Classifier (Wang et al., 2007). An approximately maximum- likelihood phylogenetic tree was generated using FastTree (Price et al., 2010). OTUs that were each represented by less than 0.005% of the total bacterial sequences for bacteria or by less than 0.25% of the total archaeal sequences for archaea were further filtered out and discarded before performing further diversity analysis.

To compare the diversity of the microbial community in the samples, alpha diversity and beta diversity were calculated using the software tools implanted in QIIME. 148

Rarefaction was performed before Shannon and Simpson diversity indices along with the phylogenetic distance were calculated as the indicators for alpha diversity. For beta diversity, distance matrix of the samples was generated using the OTU based Bray-Curtis and binary-Ochiai methods, and the phylogenetic tree-based Unifrac method. The ordination was then performed using principal coordinate analysis (PCoA). Canonical correspondence analysis (CCA) was performed using the Vegan Community Ecology package for R language (http://cran.r-project.org/web/packages/vegan/) to elucidate the correlation between microbial populations and operational parameters/performance of the

TPAD digesters, including temperature, pH, biogas production, total solid (TS) content, effluent COD, and the concentration of acetic acid, propionic acid, and butyric acid. The

CCA was performed with the phyla identified, the OTUs that were each represented by more than 0.1% of total sequences, and the OTUs that were assigned to known genera.

The distributions of the OTUs that could be classified to genera were visualized using heatmap and clustering method implemented in the software GAP

(http://gap.stat.sinica.edu.tw/Software/GAP/). The abundance (number of sequences) of these OTUs was first log transformed for normalizing before generation of the heatmap.

Pearson‟s correlation coefficients were calculated to examine the community similarity among the samples. Hierarchical clustering trees were generated using the rank-two ellipse seriation method (Chen, 2002; Wu et al., 2010) to sort the samples.

Data availablity

The demultiplexed sequence dataset obtained in this study are available on NCBI

Sequence Read Archive (SRA) database under the accession number (to be added). 149

7.4 Results

Digester performance

A lab-scale TPAD system was set up to examine the effect of organic overloading on digester performance and microbial community structure. The OLR was increased in a stepwise manner by increasing the amount of whey in the influent at days 15, 40, and 69

(Fig. 7.2A). The performance-related data including pH, total solid content, individual

VFA concentrations, and biogas production of the TPAD system was summarized in Fig.

7.2.

The first increase in OLR from 16.2 to 32.3 g COD/l/d starting at day 15 and the second increase in OLR from 32.3 to 48.5 COD/l/d starting at day 40 to day 69 of the operation did not significantly affect the pH of either digester (Fig 7.1C and 7.1F). The total VFA concentration of the thermophilic digester stayed less than 8 mM with acetic acid having the highest concentration until after the second increase of OLR when the total VFA concentration increased to 20 (± 2) mM between days 51 and 63, during which propionic acid had higher concentrations than any other VFA during this period (Fig.

7.2B). In the mesophilic digester, the total VFA concentration remained below 6 mM even the OLR was increased twice, and acetic acid and propionic acid were the major

VFA species (Fig. 7.2F). The COD values in the effluent of the thermophilic digester and the mesophilic digester remained largely unaffected, at 10,000 and 3,000 mg/L respectively, from days 0 to 69 (Fig. 7.2E and 7.2I). During this time period, much more biogas was produced in the thermophilic digester than in the mesophilic digester (Fig.

150

7.2D and 7.2H). These results indicated that the TPAD system can tolerate an OLR of 5% manure solid plus about 48 g COD/l/d as whey during co-digestion.

The third increase in OLR from 48.5 to 60.4 g COD/l/d from day 69 onward dramatically affect the chemical measurements of the thermophilic digester (Fig. 7.2).

Specifically, the pH decreased from 7.7 to 5.1 over about 10 days and stayed slowly decreased to about 4.3 by the end of the operation (Fig. 7.2C), while all the VFAs increased in concentration (Fig. 7.2B). Acetic acid concentration increased to 54 mM and remained at that level until the end of the operation, while concentrations of the other

VFA increased but then gradually decreased. The third increase in OLR caused rapid decrease in biogas production in the thermophilic digester and within about a week, and then biogas production was barely measurable in the thermophilic digester. The COD concentration of the effluent of the thermophilic digester showed an opposite trend as the biogas production (Fig. 7.2E). These deteriorations in digester performance clearly indicate that the third OLR increase constituted organic overloading (referred to as organic overloading thereafter). These results also implied acidogens continued fermentation in the thermophilic digester, but both acetoclastic methanogens and hydrogenotrophic methanogens were inhibited. The lack of biogas production suggests both hydrogen and carbon dioxide that were generated from acidogenesis was converted to other metabolites. The similar COD values in the effluent and the influent of the thermophilic digester is consistent with the above surmise.

Although the organic overloading induced deterioration of the first stage thermophilic digester, it only had mild impacts on the second stage mesophilic digester. 151

The concentration of all the three VFAs increased about one week after the overloading started, but that of propionate and butyrate decreased to levels comparable to those before the overloading, except a sharp increase in propionate concentration towards the end of the operation (Fig. 7.2F). However, the concentration of acetic acid fluctuated and remained much higher than that seen before the overloading. The pH fluctuated, but remained above pH 7.0 (Fig. 7.2G). The COD values in the effluent of the mesophilic digester increased, but to limited levels (Fig. 7.2I), suggesting much of the COD fed from the thermophilic digester was removed in the mesophilic digester. Biogas production in the mesophilic digester increased after the overloading and peaked around day 97, reaching a volumetric biogas yield of about 4. However, biogas production started to decrease after day 97 (Fig. 7.2H). Coinciding with the decrease in biogas production, the pH dropped about 0.5 unit and foaming started around one month after the beginning of the overloading. A 50% decrease in OLR to the mesophilic digester and concomitant increase of HRT to 20 days for last 64 days did not remove the foaming.

Profiling of bacterial and archaeal communities by PCR-DGGE

The DGGE profiling provided a snapshot of the stability and successions of both the bacterial and archaeal communities over the course of the TPAD operation (Fig. 7. 1).

The DGGE profiles clearly showed that the thermophilic and mesophilic digesters had distinct populations of bacteria and the archaea, and the organic overloading events induced successions of several archaeal and bacterial populations. For the archaea in the thermophilic digester, two major bands became weaker and disappeared eventually after the organic overloading, while a few bands intensified. Similar trends were also observed

152 for the mesophilic samples though not based on the same DGGE bands. The banding profiles of the mesophilic digester samples could be roughly divided into three distinct groups: before the overloading, when biogas production increased after the overloading, and thereafter when biogas production fell to pre-overloading level. Same as the archaea community, differences were also observed in the bacterial community between the thermophilic and the mesophilic digesters, and considerable successions occurred after the organic overloading in both the thermophilic and the mesophilic digesters (Fig. 7.2I).

The organic overloading also increased the number of DGGE bands for both digesters.

Summary of the Illumina sequencing data

After quality checking and removing of likely artificial sequences (those sequences of OTUs that were each represented by less than 0.005% total bacterial and

0.25% total archaeal sequences), 1.74 million sequences were obtained and about 98% of them were assigned to known phyla of domain Bacteria. Twenty bacteria phyla were represented, with the Firmicutes (36.3% of total bacterial sequences) and Bacteroidetes

(30.7%) being the most predominant followed by Thermotogae (19.5%), Proteobacteria

(5.8%), Synergistetes (3.0%), and Spirochaetes (1.3%). Other phyla were each represented by less than 1% of total bacterial sequences, which included, Actinobacteria,

Armatimonadetes, Chloroflexi, Fibrobacteres, Fusobacteria, Lentisphaerae,

Planctomycetes, Tenericutes, Verrucomicrobia, and candidate phyla NKB19, OP9, and

WPS-2. The archaeal sequences accounted for about 1% of the total sequences and all of them were assigned to the phylum Euryarchaeota. In total, 966 species-level OTUs

(clustered at 97% sequence similarity) were obtained.

153

To compare the microbial community in each digester at different stage of the operation period, the beta diversity of all the samples calculated using unweighted

Unifrac method and visualized using principal coordinate analysis (PCoA). PC1, which explained 46.8% total variation, separated the mesophilic digester samples from the thermophilic digester samples except the thermophilic digester samples collected before the organic overloading (Fig. 7.3). On the other hand, the samples collected before and after the organic overloading from both digesters were separated along PC2, which explained about 33% of total variation, with the largest separation being seen among the thermophilic digester samples. The samples clustered into four groups based on digester and the organic overloading. Close examination of these groups showed that the microbial community in each digester was relatively stable before the overloading, but underwent considerable successions after the overloading. The community successions were much more dramatic and lasted longer in the thermophilic digester than in the mesophilic digester when overloading started. The community successions were also delayed in the mesophilic digester. In addition, noticeable increases in community diversity and evenness were observed about 24 days after the organic overloading in the thermophilic digester, while in the mesophilic digester no obvious change in species richness or diversity was observed (Table 7.1). Overall, the microbial community in the mesophilic digester was much less impacted by the organic overloading than that in the thermophilic digester.

154

Bacterial community composition at phylum level

Ten bacterial phyla were each represented by more than 1% total bacterial sequences in at least one sample, and they together accounted for 99.3% of the bacterial sequences observed in the TPAD system (Fig. 7.4). From system start up to ten days after the overloading started, the microbial community in the thermophilic digester was quite stable at phylum level, with Thermotogae being the most predominant (represented by about 50% of the bacterial sequences) followed by Firmicutes (20 ± 4%), Bacteroidetes

(21 ± 4%), and Synergistetes (8 ± 3%). At days 93 and 98 (25 to 30 days after the overloading started), the relative abundance of Thermotogae decreased, while that of

Firmicutes increased, both by about one third of total bacteria. During these days (from days 93 to 98), Bacteroidetes relative abundance stayed rather stable, but Proteobacteria and Actinobacteria started to appear and reached a relative abundance about 5% and 3% respectively by day 98. Another two small phyla, Tenericutes and Spirochaetes, also increased their relative abundance, reaching about 0.7% of total bacteria. On the opposite,

Synergistetes population decreased to almost undetected in this period. Starting from day

120 of the system operation, relative abundance of Firmicutes continued to increase from

39% to 62% by the end of the operation, while Thermotogae, the most dominant phylum before the organic overloading became a minor phylum accounting for less than 1% of total bacteria. Proteobacteria and Bacteroidetes together represented 35 -55% of the bacterial population after day 120. Their relative abundance seemed inversely correlated.

Actinobacteria, Tenericutes, Spirochaetes, and Fusobacteria were also found in at low relative abundance (0.4-2%) when the thermophilic was subjected to organic overloading.

155

In the mesophilic digester, relative abundance of the bacterial phyla also stayed relatively stable in the first 79 days (Fig. 7.4), even if the overloading started on day 69 of the operation. Transitions of bacterial phyla were observed at days 93 and 98, and a new stable phylum distribution was established from day 120 to the end of the operation.

Before and during the first 10 days of organic overloading, Bacteroidetes (36 ± 4%),

Firmicutes (32 ± 5%), and Thermotogae (22 ± 6%) were the three most abundant phyla.

During this period, Synergistetes (1.4-4%), Spirochaetes (1.7-4.3%) and

Verrucomicrobia (1-2.5%) were also detected at greater proportion in the mesophilic digester than in the thermophilic digester. At days 93 and 98, Bacteroidetes, Firmicutes, and Tenericutes were found slightly increased in relative abundance compared to the earlier days (42.9%, 46.3%, and 2.3%, respectively), while other phyla declined in relative abundance, especially Thermotogae. After day 120, Bacteroidetes and Firmicutes became the two predominant phyla, which together represented 94% of the bacterial community. Although other phyla with relative abundance above 1% were observed

(Spirochaetes and Synergistetes), their relative abundance stayed low, only 1.1-2.4% of the bacteria sequences obtained.

Bacterial communities at OTU level

The relative abundance of OTUs was visualized via heatmap using the software

GAP (Fig. 7.5). Consistent with the PCoA analysis, the grouping of samples based on

Pearson‟s coefficients showed that clustering of microbial community was influenced by operation temperature and changes in other chemical factors induced by organic overloading. The successions of the bacterial communities in both digesters in response

156 to the organic overloading could be revealed by the hierarchal clustering of the samples.

Based on the distribution pattern, OTUs were clustered into six groups. It was obvious that some OTUs were more temperature-sensitive, and they only appeared in either the thermophilic (groups I and II) or the mesophilic digester (group IV, V, and VI). However, some OTUs were more related to other factors that might be caused by the overloading and these OTUs (group III) were more ubiquitous at both operation temperatures.

Group III contained 216 OTUs, which were mostly detected before the bacterial communities in both digesters were shocked by the overloading (before day 79). In this group, some of the OTUs were predominant, including 12 OTUs (collectively accounting for 20.4% of total bacterial sequences) assigned to the genus Petrotoga (in the family

Thermotogaceae), 11 OTUs (6.4% of total bacterial sequences) classified to the family

Porphyromonadaceae, 20 OTUs (3.0% of total bacterial sequences) assigned to the candidate order MBA08 (in the class Clostridia), and 7 OTUs (2.4% of total bacterial sequences) classified to Anaerobaculum. Some of the OTUs were assigned to genera containing known homoacetogens, such as Tepidimicrobium, Tepidanaerobacter, and

Thermacetogenium. In addition, some OTUs were assigned to Clostridium and

Ruminococcus, both of which contain many fermenting cellulolytic bacterial species, and

Syntrophomonas, which hosts known butyrate-producing bacteria.

Noticeable changes in community composition were observed starting from day

93, which was 25 days after the overloading to the system started. In the thermophilic digester, the bacterial community was mainly composed of 315 OTUs clustered in group

II. These OTUs were mostly observed in the thermophilic digester when it was 157 overloaded. These OTUs were assigned to Bifidobacterium, Lactobacillus, Acetobacter,

Bacteroides, the order Bacteroidales, and the family Porphyromonadaceae.

Erysipelothrix and Treponema, both contain known homoacetogens, were also well presented by this group of OTUs. In addition to the OTUs clustered in group II, several

OTUs classified to the phylum Proteobacteria was found in sample taken at day 135 in the thermophilic digester (group I). Group I OTUs were almost only detected in this sample, including one OTU each assigned to Providencia, Achromobacter,

Alcaligenaceae, and Acetobacteraceae. The OTU assigned to Acetobacteraceae was even more abundant on day 169.

In addition to the OTUs clustered in group III, OTUs in group IV and V were also components of the bacterial community in the mesophilic digester before the bacterial community was changed by the overloading. However, group IV was found only before the overloading, while group V resided in the mesophilic digester both before and after the overloading. Fifty-six OTUs were clustered in group IV, with the largest OTU classified to the order Bacteroidales (1.2% of the total bacterial sequences). OTUs classified to the family Acholeplasmataceae (in the phylum Tenericutes), phylum

Spirochaetes, and the class Clostridia were found relative abundant in this group. Group

V contained 191 OTUs, and 72 of them (representing 14% of the total bacterial sequences) were classified to the order Bacteroidales, with 8, 14, and 1 of them being further classified to the families Bacteroidaceae, Porphyromonadaceae, and

Marinilabiaceae, respectively. Eighty percent of the OTUs in group V classified to the phylum Firmicutes were assigned to the order Clostridiales (represented 9% of the total

158 bacterial sequences), and many OTUs can be further classified to genera: Sedimentibacter

(12 OTUs), Clostridium (27 OTUs), Pelotomaculum (2 OTUs), and Syntrophomonas (5

OTUs). The former two genera contain fermenting bacteria and bacteria that degrade amino acid and peptides, while the latter two genera contain known syntrophic propionate- and butyrate-oxidizing bacteria. Five OTUs assigned to the phylum

Verrucomicrobia and one OTU assigned to the phylum Spirochaetes showed higher relative abundance in the sample before day 79, while two OTUs assigned to the genus

Aminobacterium was found more after day 93 in the mesophilic digester.

Similar as the group V OTUs, most of the OTUs clustered in group VI were also components of the bacterial community in the mesophilic digester after the organic overload. The OTUs in group VI were assigned to order Bacteroidales (3.4% of the total bacterial sequences); to the genera Trichococcus, Enterococcus, and Streptococcus in the order Lactobacillales; to the families Clostridiaceae, Ruminococcaceae; and to the genera Clostridium, Tepidanaerobacter, Thermacetogenium, Sphaerochaeta; and to the class Mollicute (in the phylum Tenericutes).

Archaeal community

Eighteen OTUs clustered at 97% sequence similarity were obtained from the

Illumina sequencing, and all these OTUs were assigned to six genera and one family in the phylum Euryarchaeota. Methanoculleus was most abundant genus and it was represented by two OTUs that together accounted for 32.2% of the archaeal sequences.

The second largest genus was Methanobrevibacter, which contained four OTUs and represented 29.2% of the archaeal sequences. Three OTUs were classified to the genus 159

Methanosarcina and accounted for 24.9% of total archaeal sequences. Other genera were each represented by a few small OTUs: 2 OTUs representing Methanobacterium (7.1% of total archaeal sequences), 2 OTUs representing Methanosphaera (1.4% of total archaeal sequences), and 1 OTUs representing Methanocorpusculum (0.5% of total archaeal sequences). Additionally, 4 OTUs represented Thermogymnomonas (in the class

Thermoplasmata) and accounted for 4.8% of total archaeal sequences.

The relative abundance of archaeal genera differed between the two digesters and was affected by the organic overloading (Table 7.2). In the first stage thermophilic digester, Methanosarcina, Methanoculleus, followed by Methanobrevibacter, were found to be the major genera representing 95% of the total archaeal community. However,

Methanoculleus expended its relative abundance to 86.2% as the operation proceeded until day 72 (sampling time t4 in Table 7.2), while the relative abundance of

Methanosarcina decreased to only 8.8%. Ten days after the start of organic overloading

(t5 in Table 7.2), though with a decreased relative abundance (40.2%), Methanoculleus was still the most predominant genus found in the thermophilic digester. The

Methanobrevibacter population doubled to 27.5% and it was the second most predominant genus. As the third largest genus, Methanobacterium accounted for 18.8% of total archaea. In the later period of the operation after the organic overloading,

Methanobrevibacter became the most dominant group, followed by Thermogymnomonas, whereas Methanosarcina went almost undetected.

In the mesophilic digester, Methanobacterium and Methanobrevibacter were more abundant than they were in the thermophilic digester and represented 8 to 25% of 160 the archaeal population, in the first 72 days of operation. Two OTUs classified as

Methanosarcina together expended their relative abundance from 54.1% to 67.3% of the archaeal community. From day 79 to day 134 of the operation (m5 to m9 in Table 7.2), one of the Methanosarcina OTUs decreased, but the other continued to increase.

Together they represented 49-68% of the archaeal population. In this period,

Methanobacterium represented 10.7-27.9%, while Methanobrevibacter accounted for

3.8-5.9% of the archaeal population. After day 153, although Methanoculleus remained predominant (22.2% of total archaea), Methanosarcina became the most predominant genus throughout the rest of the operation, reaching a relative abundance of 82.9% of total archaea by day 169.

Canonical correspondence analysis

To investigate the correlation between the digester performance/conditions and the microbial taxa in the TPAD system, canonical correspondence analysis was performed. Forward selection determined that temperature, pH, acetic acid, propionic acid, butyric acid, TS content, effluent COD, and biogas yield were the most important factors that explained the composition of microbial community in this TPAD system, and these factors were then included in the CCA. Separate CCA was performed at phylum level using the phyla detected, at OTU level using the major OTUs that each was represented by more than 0.1% of total sequences, and at genus level using the known genera that were represented by the major OTUs (Fig. 7.6). At any of these three levels, the environmental variables included in the CCA analysis (i.e., temperature, pH, biogas yield, VFA concentrations, effluent COD, and TS) explained 57-79% of the variations of

161 relative abundance, suggesting that these environmental variables primarily affected the distribution of the taxa. Furthermore, CCA1 and CCA2 together explained 73-94% of the constrained inertia at any of the three taxonomic levels, which indicates that these two coordinates are adequate to represent the CCA results.

The clustering of samples on the CCA plots was consistent with the clustering on the PCA plot and further attributed the variation of the microbial community to the environmental factors we measured. Temperature and pH was the two most influential factors that explain the relative abundance of the microbial taxa. Samples from digesters operated at different temperatures were separated along the temperature gradient. The environmental factors TS, effluent COD, acetic acid, and propionic acid were highly correlated with each other. At phylum level, Actinobacteria, Fusobacteria, and

Proteobacteria were found highly correlated positively with these factors mentioned above (phyla marked 3, 9, and 14 in Fig. 7.6A). Phyla Synergistetes and Thermotogae were highly correlated positively with the biogas production (phyla marked 16 and 18).

The major OTUs were grouped in to three groups on the CCA plot. One group was positively correlated with biogas production and it contained five OTUs classified to

Anaerobaculum, genus Petrotoga (in the phylum Thermotogae), Bacteroidales, candidate

MBA08 (in the class Clostridia), and Porphyromonadaceae. Another group was highly correlated positively with acetic acid, TS content, and effluent COD, and it included

OTUs classified to a number of genera including Lactobacillus, Acetobacter,

Bifidobacterium, and Erysipelothrix. The third group was found positively correlated

162 with pH but inversely correlated with temperature and VFA (top left on Fig 7.6B and 7.

6C).

7.5 Discussion

Co-digestion of livestock manure, which is rich in nutrients (nitrogen and phosphorus) but contains little readily digestible carbohydrates, and food waste or food- processing waste that have opposite compositional feature of livestock manure is an attractive strategy approach to boost energy yield from anaerobic digesters. However, organic overloading with increased proportion of the food/food-processing waste can cause digester failure. Such failure has been documented and attributed to inhibition of methanogens by low pH and accumulation of VFA, particularly propionate (reviewed by

Chen et al., 2008).

The advancement of metagenomics empowered by NGS provided opportunities to examine how organic overloading affects the entire microbial community in detail. In this study, we investigated both the archaeal and the bacterial communities in a TPAD system co-digesting dairy manure and whey and their successions in response to organic overload using both DGGE profiling and Illumina sequencing. The community composition in the first stage thermophilic digester was apparently different from that in the second stage mesophilic digester of the TPAD system, and both microbial communities, especially the one inside the thermophilic digester, were impacted by the organic overloading. Correlations between some of the performance/environmental factors and some of the identified microbial taxa were revealed through CCA analysis,

163 potentially helping understand functions of different microbial guilds involving in the anaerobic digestion process and improve stability and performance of AD system.

The thermophilic digester deteriorated rapidly right after the organic overloading and was unable to recover as the overloading continued. On the other hand, the mesophilic digester increased biogas production considerably for more than 6 weeks after the overloading. Such increased performance by the mesophilic digester was apparently attributed to the increased COD output from the thermophilic digester before the mesophilic digester was also stressed by the overloading. Our observation was in agreement with the study that reported that biogas was produced mainly in the first stage thermophilic digester of TPAD systems during normal feeding, but the mesophilic digester became the major producer of biogas during organic overloading (Zuo et al.,

2013). These results also corroborated protection of the mesophilic digester from shocking loading by the first stage thermophilic digester. In addition, higher community diversity observed in the mesophilic community may also explain its tolerance to the shocking of organic overload.

Our results showed the thermophilic and mesophilic digesters in the TPAD system harbor different populations. For example, the phyla Verrucomicrobia and

Spirochaetes were barely detected in the thermophilic digester but were mostly observed in the mesophilic digester before the organic overloading. Studies have shown most of the members in the phylum Verrucomicrobia are mesophilic (Sangwan et al., 2004) and are present ubiquitously in soil habitats (Bergmann et al., 2011). Although sequences assigned to this phylum has also been observed in a mesophilic full-scale CSTR treating 164 corn straw (Qiao et al., 2013), however, its ecological relevance in the environment and its role in the AD system remains to be determined. Compared to Verrucomicrobia,

Spirochaetes was often observed in AD systems fed with different substrates (Delbes et al., 2000; Chouari et al., 2005; Lee et al., 2013). Its function in the AD system was suggested to be a sugar fermenter as its relative abundance increased along with the increase of glucose loading to a mesophilic lab-scale batch reactor (Delbes et al., 2000), while a recent study studied the Spirochaetes population in 7 full-scale AD via qPCR and pyrosequencing suggested Spirochaetes might involve in syntrophic acetate oxidation

(Lee et al., 2013). In addition, they also observed the distribution of Spirochaetes was largely affected by operation temperature of the ADs (Lee et al., 2013). However, although it might play an important role before the organic overload in the anaerobic digester, further investigation is needed to elucidate its real function. Because the effluent of the thermophilic digester served as the influent of the mesophilic digester, bacteria and methanogens of the thermophilic digester were transferred to the mesophilic digester.

Some of these carried-over bacteria, such as the OTUs assigned to the families

Thermotogaceae and Porphyromonadaceae, and the class Clostridia (group III in Fig.

7.5) could survive in the mesophilic digester. This finding supported the result from a previous study that microorganisms from first stage thermophilic digester survived and probably even involved in the metabolism in the second stage mesophilic digester in a

TPAD system (Vandenburgh and Ellis, 2002). Consistent with previous findings (Levén et al., 2007; Pycke et al., 2011; Sundberg et al., 2013), we observed that the diversity of the microbial community in the mesophilic digester was higher than it was in the

165 thermophilic digester. The phyla Bacteroidetes and Firmicutes were found predominant in both digesters before and after the overloading, while Thermotogae was predominant before the overloading but was replaced by Proteobacteria in the thermophilic digester and diminished to almost undetected in the mesophilic digester after the overloading. The predominance of these three phyla observed during the stable operation in both the digesters in the present study was consistent with the observation of a previous study on the microbial composition in both a mesophilic and a thermophilic CSTR anaerobic digesters treating organic household waste (Levén et al., 2007). The predominance of these three phyla may be explained by the ability of many of their members to utilize a broad range of substrates in the anaerobic digesters. With respect to Thermotogae, its ability to thrive at high temperature (Huber and Hanning, 2005) and to adapt to mesophilic temperature, at least members of Thermotogales (Nesbø et al., 2006), might be another reason contributing to its predominance in both digesters in the TPAD system observed in the present study. It should also be noted that all the Thermotogae sequences obtained in the present study was classified to the genus Petrotoga, which contains thermophilic xylanolytic species (Miranda-Tello et al., 2004). This group of bacteria may play important role in anaerobic digesters, especially in thermophilic digesters.

Considerable succession of bacterial community was observed after the organic overloading to the TPAD system. Before overloading, genus Petrotoga (in the

Thermotogaceae family) and Anaerobaculum (in the phylum Synergistetes) predominated and both genera contain species that can ferment peptides (Menes and Muxí, 2002;

Maune and Tanner, 2012). These two genera were likely important in the TPAD system

166 at normal OLR. The appearance of lactic acid producer, Lactobacillus and

Bifidobacterium, and acetic acid producer, Acetobacter, after the organic overloading might be owing to their ability to live in acidic environment and utilize the lactose of the influent. The co-occurrence of homoacetogens Thermoacetogenium and hydrogenotrophic methanogens in the thermophilic digester before the organic overloading might imply the importance of methane production through syntrophic acetate oxidation coupled with hydrogenotrophic methanogenesis.

Succession of archaeal community in both digesters after the overloading was clearly demonstrated by the DGGE data and the Illumina sequencing results, which directly indicates that OLR had a great impact on the community composition of methanogens. In the first 72 days of stable operation, both hydrogenotrophic

Methanoculleus and acetoclastic Methanosarcina were abundant in the thermophilic digester, with the former increasing and the later decreasing over time. This observation suggests that the biogas production in the thermophilic digester during this period was probably through the hydrogenotrophic methanogenic pathway, which is consistent with the results from previous researches that hydrogenotrophic methanogenesis is the preferred pathway in thermophilic digesters (Karakashev et al., 2005; Hori et al., 2006;

Sasaki et al., 2011; Sundberg et al., 2013). After the organic overloading, hydrogenotrophic methanogen Methanobrevibacter and another group assigned to an uncultured clone (WCHD3-02) in the genus Thermogymnomonas in the class

Thermoplasmata (accounting for 62-75% and 18-30% of the archaeal population, respectively) became the dominant methanogens in the thermophilic digester.

167

Methanobrevibacter is the most predominant genus of methanogens in the rumen and hind gut of mammalian animals (Kim et al., 2011b), but it has not been widely reported in anaerobic digester. Most species of this genus are acid tolerant and one species of the genus Methanobrevibacter has been isolated from a sour acidogenic anaerobic digester

(Savant et al., 2002). The Methanobrevibacter found in the thermophilic digester after the overloading might be represented by thermotolerant species of this genus. However, the minimal biogas production from the thermophilic digester after the overloading suggests that these methanogens might not actively producing methane. Thermogymnomonas has a cultured representative and no member of the class Thermoplasmata are methanogenic, though thermophilic and acid-tolerant. Thus, although its predominance can be explained by the acidic thermophilic conditions in the thermophilic digester after the overloading, this group‟s function in the thermophilic digester remains unclear. The lack of active biogas production is consistent with previous researches that low pH induced by organic overloading inhibits methanogenic activity (Alkaya and Demirer, 2011; Zuo et al., 2013).

In the mesophilic digester, Methanosarcina increased predominance while

Methanobacterium and Methanobrevibacter decreased in predominance shortly after the organic overloading. Such a shift in these genera can be explained by the increase in acetic acid influx from the thermophilic digester caused by the organic overloading. Such an explanation is consistent with previous reports that high acetic acid environment in anaerobic digester favored the propagation of Methanosarcina (McMahon et al., 2004;

Hori et al., 2006; Blume et al., 2010; Steinberg and Regan, 2011). However,

Methanosaeta, an obligate acetoclastic methanogens, was hardly detected in our study,

168 though it was the most predominant methanogens in the mesophilic digester of a TPAD system fed diary manure only (Lv et al., 2013b). The large population of Methanosarcina may be attributed to its high growth rate at high acetate concentration (Conklin et al.,

2006). It should be noted that although the Methanosarcina population was expending after the overloading with concomitant increase in biogas production and decrease in acetic acid in the mesophilic digester, the biogas production peaked and then decreased gradually. The persistent organic overloading might have changed the conditions in the mesophilic digester that Methanosarcina gradually lost metabolic activity. Therefore, although abundance information provides some insight into the function, confirmation of their actual activity will need further investigation using functional analysis such as the transcriptomics or methanogenic activity assay.

Taken together, the information of microbial communities in the thermophilic and the mesophilic digester and the dynamic successions of these communities induced by the organic overloading provided new insight on the composition and stability of microbial communities in the digesters. The correlation revealed between some of the microbial populations and performance/environmental factors can help further studies on this type of TPAD systems and improve efficiency and stability of TPAD systems.

169

Table 7.1 Alpha diversity analysis on the TPAD samples.

Overloading Sampling Observed Shannon Simpson Phylogenetic Shannon's Compartment event Day OTUs (H) (D) distance Equitability (EH) 30 357 3.46 0.74 43.13 0.59 Before 44 385 3.53 0.74 46.99 0.59 58 348 3.06 0.69 42.04 0.52 72 398 3.39 0.74 49.05 0.57 First stage- 79 549 3.28 0.72 62.17 0.52 thermophilic 93 697 5.37 0.87 74.17 0.82 98 679 5.37 0.89 72.23 0.82 After 121 502 5.13 0.90 58.12 0.82 135 406 4.67 0.88 46.98 0.78 154 668 5.61 0.92 68.96 0.86

0 169 456 4.88 0.92 50.73 0.80 30 549 5.85 0.94 57.52 0.93

Before 44 589 6.09 0.95 61.65 0.95 58 585 5.62 0.92 61.84 0.88 72 583 4.92 0.87 62.12 0.77 79 602 5.77 0.93 63.31 0.90 Second stage- 93 519 5.84 0.96 57.50 0.93 mesophilic 98 497 5.57 0.94 55.54 0.90 After 121 448 5.06 0.91 50.72 0.83 135 473 5.50 0.93 53.84 0.89 154 471 5.48 0.93 53.29 0.89 169 472 5.58 0.94 52.42 0.91

170

Table 7.2 Archaea relative abundance (shown in percentage of the sample) in the TPAD system.

Thermophilic 1st stage Mesophilic 2nd stage Taxonomic OTU ID assignment t1 t2 t3 t4 t5 t6 t7 t8 t9 t10 t11 m1 m2 m3 m4 m5 m6 m7 m8 m9 m10 m11 551498 Methanobacterium 0.3 0.1 1.0 0.4 1.7 2.1 2.7 5.0 14.7 2.2 6.9 3.1 N101286 Methanobacterium 2.4 6.5 1.7 1.0 18.8 5.2 2.8 0.3 14.2 19.3 15.0 11.1 27.2 10.7 22.5 8.8 2.2 1.2 3.1 842598 Methanobrevibacter 7.5 3.0 2.6 1.7 16.5 37.0 36.1 34.0 25.4 6.8 34.5 14.6 16.9 6.3 10.2 5.7 4.8 3.8 5.9 2.6 167356 Methanobrevibacter 0.3 0.1 0.6 6.7 1.5 0.5 0.6 3.1 153168 Methanobrevibacter 4.4 2.0 0.7 0.7 10.6 38.1 38.9 39.0 47.8 6.5 21.0 8.8 7.6 1.8 3.2 4.6 2.4 1.1 470690 Methanobrevibacter 0.4 0.5 0.8 0.4 4.5 1.1 0.2 589886 Methanosphaera 0.3 0.2 0.1 0.4 1.4 1.2 1.9 2.4 1.2 0.5 1.1 150477 Methanosphaera 1.4 0.5 0.1 0.8 0.6 2.2 1.7 0.8 1.1 1.7 0.3 0.5 2.3 1.2

17 212362 Methanocorpusculum 1.6 0.5 7.1 1.8 2.5 0.4

1 108784 Methanoculleus 30.2 59.5 79.6 84.6 34.9 0.6 0.3 0.3 0.8 1.0 4.8 4.2 0.4

840393 Methanoculleus 1.0 5.7 1.3 1.6 6.3 10.6 12.2 6.2 45.0 5.9 0.4 0.9 1.2 1.9 4.8 1.3 22.2 5.2 3.1 4027690 Methanosarcina 3.7 2.0 0.9 0.2 0.4 0.9 0.9 19.2 12.8 19.8 15.3 20.3 46.4 50.0 67.6 46.7 81.5 81.9 592689 Methanosarcina 47.1 19.9 10.9 8.6 7.8 0.4 0.2 0.3 0.8 34.9 33.8 46.7 52.0 33.7 20.2 15.0 2.2 1.2 1.0 N63383 Methanosarcina 0.3 0.3 0.4 0.7 0.5 1.1 1.2 1.3 2.9 4.4 1.7 843408 WCHD3-02 0.3 0.3 0.8 4.5 3.3 3.4 0.4 136107 WCHD3-02 0.5 0.3 8.3 17.9 29.6 22.7 7.8 1.2 1.0 570725 WCHD3-02 1.4 0.7 2.0 1.4 1.6 1.1 1.1 0.3 0.7 0.6 1.2 1.1 282634 WCHD3-02 0.8 2.5 2.0 1.2 4.4 1.7 0.4 1.4 0.6 0.7 7.1 1.3 8.9 0.6 1.0

171

Fig. 7.1 DGGE profiles of archaea (A) and bacteria (B) in the two digesters of the TPAD system. The samples were arranged in a time order. The arrows indicated the beginning of the third organic overloading. DNA samples Nta, Nma, Ntb, and Nmb were not sequenced as others.

172

Fig. 7.2 Organic loading plan (A), influent of the second stage mesophilic digester (which was also the effluent of the first stage thermophilic digester) (E), the performance of the first stage thermophilic (B, C, D) and second stage mesophilic (F, G, H) digesters, and the effluent of the second stage mesophilic digester (I) in the TPAD system. B and F, VFA concentration; C and G, pH; D and H, biogas production. The arrows on the top with corresponding sample ID indicated the samples that were selected for microbial analysis.

173

Fig. 7.3 Principal coordinate analysis of the microbial community in the thermophilic (diamond) and mesophilic (circle) stage of the TPAD system before (open) and after (filled) the overloading event using unweighted Unifrac method. See Fig. 7.1 for the time of sample collection. The arrows indicate the transition following the organic overloading.

174

Fig. 7.4 Major bacterial phyla (abundance > 1% in at least one sample) in the TPAD system. Samples in the figure were arranged in a time order. The arrows on the top indicated the starting time point of the overloading to the system.

175

Fig. 7.5 Distribution of OTUs in the TPAD system. G1-G6, groups of OTUs. 176

Fig. 7.6 Results of constrained correspondence analysis on A) phyla level, B) abundant OTUs (containing reads more than 0.1% of total reads), and C) OTUs classifed to genus level. Dots in the figures represented individual samples. Triangles repsernted individual taxons. Arrows represented the direction of increase of the environmental variables with Temp represented temperature, biogas represented daily biogas yield, TS represented TS content, Eff_COD represented effluent COD. The length of the arrows indicated the degree of correlation with the ordination axes. Corresponding phyla in A): 1. Euryarchaeota, 2. unclassified bacteria, 3. Actinobacteria, 4. Armatimonadetes, 5. Bacteroidetes, 6. Chloroflexi, 7. Fibrobacteres, 8. Firmicutes, 9. Fusobacteria, 10. Lentisphaerae, 11. Candidate division NKB19, 12. Candidate division OP9, 13. Planctomycetes, 14. Proteobacteria, 15. Spirochaetes, 16. Synergistetes, 17. Tenericutes, 18. Thermotogae, 19. Verrucomicrobia, and 20. Candidate division WPS-2.

(Continue)

177

Fig 7.6 Continue

178

CHAPTER 8 General Discussion

As anaerobic digestion has great potential to become a major dual-purpose technology to both achieve pollution control and produce bioenergy supply, maximization of its treatment efficiency and gas production is always a goal to pursue.

Because microorganisms are the driving force of this biochemical process, a better understanding of the composition and dynamics of microbial communities in anaerobic digesters is needed for improved efficiency and stability of AD processes. To help advance our knowledge on AD processes, I conducted a series of studies on the microbial communities in both lab-scale and commercial-scale digesters. Collectively, the results from this series of studies advanced our understanding of the microbial community that underpins the AD process and elucidated possible roles that some bacterial and methanogens play in anaerobic digesters.

As discussed in chapter 3, syntrophic oxidation of propionate is important to stability of AD of processes. Our study on diversity and distribution of pct gene is the first study of this kind. The finding of different clusters of pct genes illustrated that different bacteria may be important to propionate degradation in digesters. The finding that some of the pct gene clusters (such as clusters I, V, and VI) are related to but not very similar to the pct genes of known syntrophic acetogens suggests the presence of new 179 bacteria that may be involved in syntrophic oxidation of propionate in digesters. Some of the pct gene clusters (such as clusters II, III, IV, and VII) were found to be related to bacteria (such as Clostridium spp.) that are not known syntrophic acetogens. Although this study could not prove if these bacteria are propionate producers or degraders, our results can guide future studies on these bacteria using ecophysiological approaches, such as stable isotope probing (SIP) and microautoradiography (MAR)-FISH. In addition, the pct cluster-specific qPCR assays we developed are also useful quantitative tools to investigate the distribution and population dynamics of bacteria involved in propionate metabolism, including the important yet poorly understood and challenging syntrophic propionate-oxidizing acetogenesis. Furthermore, studies that couple quantitative analysis of pct gene clusters and chemical parameters of digesters will shine new light on the ecology of syntrophic acetogens involved in propionate metabolism in anaerobic digesters.

Online measurement of VFA concentrations is highly desired and sought after to have enough time to intervene before a digester fails. By putting the luxCDABE operon under the control of the PprpB promoter, we successfully developed a propionate biosensor (chapter 4). Its specific response to propionate in a dose-dependent manner is promising as a potential alternative tool for real-time detection of propionate in anaerobic digesters. Although it cannot be used with digester samples without dilution, future research can improve its tolerance to inhibitory or interfering substances present in digester samples. With continued advancement in surface chemistry, luminescence detection, and transduction systems, the biosensor can be immobilized onto a membrane 180 and integrated into an electronic device, which may eventually be used in online monitoring of propionate concentrations and enable automated control of anaerobic digester operation to avoid costly deterioration of digester operation.

Dairy manure is one of the single largest feedstocks, especially the manure from mega dairy farms, most suitable for anaerobic digestion. Although CSTR is the oldest type of digesters implemented on dairy farms, more mixed plug-flow loop reactors

(MPFLR) than other types of digesters have been implemented on large dairy farm in the

US in recent years because of its advantages (chapter 5). Our study on the spatial and temporal patterns of microbial communities in MPFLR is the first study ever reported on the microbiome in this type of new digesters. The temporally stable and rather spatially homogeneous distribution of both bacteria and archaea in this digester provided microbiological evidence for its stable performance and need for a long hydraulic retention time. Because biogas is used to mix the digester content, it is not possible to determine biogas production along the course of the digester. Future studies need to determine the populations of different guilds, including cellulolytic bacteria, proteolytic bacteria, acetoclastic methanogens and hydrogenotrophic methanogens.

Solid-state AD is widely used in Europe to digest solid feedstocks, ranging from crop residues to organic fraction of municipal solid wastes. In the US, SS-AD started to receive increasing attention. Unlike the microbiome in liquid digesters that have been studied for many decades, the microbial communities in SS-AD have only been investigated in a couple of studies. The study described in chapter 6 is the first study on

181 the microbiomes in integrated anaerobic digesters (iAD, a type of SS-AD) operated at either mesophilic or thermophilic temperatures. Distinct structure of microbiome with unique populations was revealed in each of the two digesters. The microbial information is helpful to understand the SS-AD processes and the differences in performance between the two digesters. Although the information we obtained in this does not allow us to make any assessment, it can be concluded that the differences in the microbiomes between the two digesters are probably a consequence of both selection and adaptive diversification of the microbes initially present in the inoculum because both digesters were „seeded‟ with the same digested sludge and fed the same feedstock (corn stover). Further studies in this type of digesters will help improve SS-AD design and operation and advance knowledge on microbial ecology in this type of new habitat.

As AD being looked upon a renewable energy source, aggressive feeding of feedstock often results in organic overloading, which can cause upset and total failure of digester. In the study described in chapter 7, we had the opportunity to investigate the community composition and population dynamics in a TPAD system before and after the organic overload to the system. The community successions and the changes of digester performance induced by organic overloading demonstrated that influence of OLR on microbial community and the digester performance. With the power of multivariate analysis we applied in our studies, potential correlation between the microbial community and specific microbial groups and the environmental factors of the AD system was revealed (chapter 6 and 7). Although the causality between microbes and other factors or indicators could not be confirmed at this stage, the correlations between microbes and 182 environmental variables and performance measurements can still guide future studies and improvement of design and operation of AD. More researches need to be done to confirm the correlations and establish the causality. Targeted quantitative analysis of the microbial groups that have correlations with environmental or performance measurements will help establish possible causality and eventually help optimize AD process.

Taken together, the integrated studies generated new information and knowledge on the microbiomes and populations in a number of digesters with different designs, fed a wide range of feedstocks, and operated differently. The information and the new analytical and propionate sensor may be used in future studies and help the design and optimization of AD. Moving forward, future studies should also include functional studies of microbiomes. Functional metagenomics, metatranscriptomics, metaproteomics, and metabolomics are powerful tools that can facilitate or enable functional studies.

183

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