Mechanisms of Microbial Methane Production from Sub-Bituminous Australian Coal

John Webster

A thesis in fulfilment of the requirements for the degree of

Doctor of Philosophy

School of Biotechnology and Biomolecular Science

Faculty of Science

August 2015

Originality Statement ‘I hereby declare that this submission is my own work and to the best of my knowledge it contains no materials previously published or written by another person, or substantial proportions of material which have been accepted for the award of any other degree or diploma at UNSW or any other educational institution, except where due acknowledgement is made in the thesis. Any contribution made to the research by others, with whom I have worked at UNSW or elsewhere, is explicitly acknowledged in the thesis. I also declare that the intellectual content of this thesis is the product of my own work, except to the extent that assistance from others in the project's design and conception or in style, presentation and linguistic expression is acknowledged.’

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Date ……………………………………………...... Acknowledgements

First and Foremost, I would like to express my gratitude to Torsten Thomas and Mike Manefield for accepting me as their student and for their guidance throughout my PhD program. The opportunity that they provided for me is most appreciated and it looks like it paid off in the end (See following six chapters).

I would also like to thank my mum Susan Webster, dad David Webster and sister Elyssa Webster for sticking by me throughout this trying time. Thank you for your support because without the support and love of my family, this achievement would not mean as much to me. To my grandma Helen who was telling people I was a doctor well before the conferral of my degree, I thank you for your support and excitement also and you may now legitimately tell people I am a doctor.

Thank you to Leena and Malu who were part of the project and provided both an experienced view to academic dynamics and a sympathetic ear when things got rough. The pressure and stresses on

PhD students are immense and its individuals such as you that can make the difference between a good day and a bad day.

I would also like to thank my various lab pals, though numerous, I would like to make sure you are all mentioned as you all played a profound role in my life the last few years. So a big thank you to

Enrique, Alexandra, Shaun, Mary, Rajesh, Jim, Manue, Lucas, Ricardo and Valentina as well as the international friends we made in Chris Johansson, Michael Roggenbuck and Jenny Staudigl. I’d also like to specifically thank David Reynolds for helping keep the lab organised, interesting and MagicTM.

Though we may have wasted quite a bit of time, it was a welcome and well needed reprieve from the daily PhD grind. As well as Ana (not the spider) Esteves for her great sense of humour and for providing the short and feisty demographic our lab was missing. A great friend who I know I can always count on and plus, she’s also pretty smart.

Though a major part of my life was based around university, support from friends outside this environment is also important and a major thank you has to go out to my buddies in crime, Emily

Funnell and Rachel Siskovic, my oldest (by only a day) and dearest friend James Tawadros and also the second love of my life Jared Yan (Platonically of course).

And most importantly I thank the first (sorry Jared) love of my life, my partner and my best friend,

Ashlea Grewar. These years have been trying, but you have always been my constant and my rock.

Without your support and love, I can’t imagine where I would be. I’m so glad we have been able to take this journey together and I couldn’t think of anyone else I would want at my side.

"I don't give a rat's ass whether it's science or magical power. No, I guess if I had to choose, I'd rather put my money on the power of science. Humans who used to only roam around on the ground are able to fly now! And finally, we're about to go into outer space. Science is a "Power" created and developed by humans. And science just might be what saves this planet. I was able to earn my living thanks to science. So to me, there's nothing greater!"

-Cid Highwind

Table of Contents Acknowledgements ...... I Table of Contents ...... III Table of Figures ...... VII Table of Tables ...... X Abstract ...... XI Chapter One: Introduction ...... 1 What is coal? ...... 1 Its Structure: ...... 2 Australian Coal Deposits: ...... 3 Coal as a Fuel: ...... 4 Breakdown of Coal: ...... 5 Uses of Methane: ...... 6 Methanogens: ...... 7 Degradation of Coal Hydrocarbons: ...... 11 Microbial Communities and Methane Production from Coal: ...... 15 Chapter Two: Chemical Characterisation of Soluble Compounds in Australian Coal ...... 18 Introduction: ...... 18 Material and Methods: ...... 27 Coal Sampling Locations: ...... 27 Coal characteristics ...... 29 Coal Preparation for chemical analysis: ...... 29 Small Apolar Chemical Compound Extraction: ...... 29 Gas chromatography Flame Ionisation Detection (GC-FID) of DCM extractions: ...... 30 Alignment and Analysis of GC Chromatogram Peaks: ...... 30 Analysis of extractions with GC-MS: ...... 31 Oxidative chemical and enzymatic treatments to coal: ...... 32 Aqueous Extract Analysis: ...... 32 Results: ...... 33 DCM Extracted Coal Compounds from Lithgow State Coal Mine, Pinedale and Casino Coals: .... 33 Impact of oxidative treatment on DCM soluble coal compounds: ...... 37 Discussion: ...... 42 Comparison of Coals in Terms of their Apolar Coal Fractions: ...... 42

III

Comparison of Oxidative Coal Extracts: ...... 45 Conclusion: ...... 50 Chapter Three: Microbial Community Analysis of a Field Trial...... 52 Introduction: ...... 52 Methods: ...... 56 DNA Preparation and Pyrosequencing: ...... 56 Pre-processing and Quality Filtering of 454 Pyrosequencing Reads: ...... 56 Correcting Microbial Sequence Data Using 16S rRNA Gene Copy Number and qPCR Data to Obtain Cell Counts: ...... 57 Community Analysis: ...... 57 Results: ...... 59 Pyrosequencing OTU Classification and Comparison: ...... 59 Phylogenetic Analysis of Microbial Community and its Members from Field Trial Samples: ...... 63 Network Analysis: ...... 70 Discussion: ...... 75 Potential Functions in the Field Trial: ...... 76 Previous Studies of Coal Communities: ...... 84 Conclusion: ...... 87 Chapter Four: Anaerobic Aromatic Compound Degradation Gene Determination from a Field Trial Site...... 89 Introduction: ...... 89 Methods: ...... 97 Field Trial of a Non-Gassy Coal Seam in the Western Coal Fields of NSW, Australia: ...... 97 Identification via PCR for Genes Involved in Anaerobic Hydrocarbon Degrading Genes from Metagenomic Data: ...... 97 Sequencing of purified DNA: ...... 98 Identification of Sanger Sequenced PCR Amplicons using a Basic Local Alignment Search Tool (BLAST): ...... 99 Metagenomic Sample Selection from the Field Trial and Sequencing: ...... 99 Sequence processing:...... 100 Alignment and Phylogenetic Tree Production of Sanger Sequences: ...... 101 Pathway comparisons: ...... 102 Results: ...... 103 Identification of Anaerobic Hydrocarbon Degrading Genes by PCR: ...... 103 Identification of Amplified Sequences: ...... 105

Assembly of Illumina reads from field trial samples: ...... 106 Identification via PCR for genes involved in Anaerobic Hydrocarbon Degrading from Metagenomic Data: ...... 107 Investigation of Metabolic Pathways in Coal and Coal Mine Associated Groundwater Metagenomes: ...... 119 Discussion: ...... 130

Hydrocarbon Degrading Potential of Nutrient Vs Nutrient + CaO2 Treated Wells: ...... 135 Conclusion: ...... 136 Chapter Five: Metagenomic Analysis of Coal Seam Associated Microbial Communities...... 139 Introduction ...... 139 Methods ...... 142 Sequence processing ...... 142 Genome Binning ...... 143 Genome annotation ...... 143 Pathway comparisons: ...... 144 Network analysis: ...... 144 Results: ...... 145 Fermentation pathways ...... 145 Carbon fixation pathways ...... 147 Methanogenesis ...... 151 Sulphur Metabolism ...... 155 Nitrogen Metabolism ...... 157 Discussion: ...... 162 Fermentation ...... 162 Carbon fixation ...... 164 Methanogenesis ...... 166 Sulphur Metabolism ...... 168 Nitrogen Metabolism ...... 170 Conclusion ...... 172 Chapter 6: Discussion ...... 174 Chemical composition of coal: ...... 174 Model of Coal Degradation: ...... 175 i) Fragmentation of coal: ...... 175 ii) Anaerobic degradation of released hydrocarbons: ...... 178

iii) Fermentation of available organic compounds into methanogenic substrates: ...... 181 iv) Production of Methane from Fermentation Products: ...... 183 Nutrient limitation: ...... 184 Inhibition of methanogenesis: ...... 186 Conclusion and Future Directions: ...... 187 References: ...... 190 Appendix: ...... 204 Appendix 1: Example formatting for importing GC-FID data into T-REX for alignment ...... 204 Appendix 2: Square root transformed resemblance matrix of coal extraction profiles ...... 204 Appendix 3: Field Site Treatments ...... 205 Appendix 4: Field Trial Sampling ...... 205 Appendix 5: Environmental/chemical monitoring of wells ...... 206 Appendix 6: DNA Extraction Protocol ...... 210 Appendix 7: List of PCR conditions for amplification of anaerobic hydrocarbon degrading genes...... 212 Appendix 8: 454 Pyrosequencing Processing Pipeline using MOTHUR ...... 213 Appendix 9: Community Sequencing Summary ...... 217 Appendix 10: Binned Genomes from Metagenomic Data ...... 218 Appendix 11: Fermentation Pathways Examined in Field Trial ...... 218

Table of Figures Figure 1: Production of coal and its various ranks ...... 1

Figure 2: Model of spruce lignin ...... 3 Figure 3: Australian coal deposit basins ...... 4 Figure 4: Methanogenesis and the carbon cycle ...... 7 Figure 5: Pathways of Methanogenesis ...... 9 Figure 6: Syntrophic interactions in the degradation of coal to methane...... 12 Figure 7: Proposed mechanisms of stepwise biodegradation of organic matter in coal ...... 13 Figure 8: Diagram and equation for the flux of hydrogen between two bacterium ...... 14 Figure 9: Map of the Sydney-Gunnedah-Bowen Basin ...... 19 Figure 10: Haenel Conceptual model of coal: Two-component system ...... 21 Figure 11: Stratigraphic diagram of the Illawarra Coal Measures (Top) and Walloon Coal Measures (Bottom) ...... 28 Figure 12: Chromatograms of LSCM, Casino and Pinedale DCM extractions run on GC-FID ...... 34 Figure 13: SIMPER analysis of GC-MS data obtained from DCM extractions of three different coal types showing the top 5 compounds contributing to the dissimilarity between a pair of coal types. Where GC-MS identifications were not found, retention time is shown...... 36 Figure 14: Multidimensional scaling plot of GC-FID peaks of coal extracted with DCM before and after oxidative treatment...... 38 Figure 15: HNMR spectra of hydrogen peroxide extracts of LSCM coal at 1%, 10% and 30% concentrations ...... 40 Figure 16: H-NMR spectra of Peroxidase extracts of LSCM coal at 0.0024 mg/ml, 0.024 mg/ml and 0.24 mg/ml ...... 41 Figure 17: HNMR spectrum of a calcium peroxide extract from LSCM coal at 0.25 mg/ml ...... 41 Figure 18: V5-8 region (top) and V3/4 region (bottom), classification of reads at phylum level from.60 Figure 19: MDS plot of communities sequenced with primers for the V3/4 region...... 62 Figure 20: MDS plot of communities sequenced with primers for the V5-8 region...... 62 Figure 21: Phylogenetic classification for the pyrosequencing analysis with phylum composition per sample ...... 65 Figure 22: Diversity of Archaeal 16S sequences...... 66 Figure 23: Network analysis of correlations with methane and acetate data from the acetate amended well...... 70 Figure 24: Network analysis of correlations with methane and acetate data from the nutrient treated well...... 72 Figure 25: Network analysis of correlations with ammonium data from nutrient treated well ...... 73 Figure 26: Network analysis of correlations with nitrate (left) and nitrite (right) data from nutrient treated well ...... 74 Figure 27: Relative abundance of Aquabacterium and Dechloromonas with ammonia, nitrite and nitrate in the CaO2 + nutrient fed well (top) and nutrient only fed well (bottom)...... 81 Figure 28: Reconstruction of potential metabolic processes occurring in field trial wells ...... 86 Figure 29: Benzoyl-CoA degradation pathway showing the range of compounds that can feed into this pathway...... 90 Figure 30: Diagram of benzoyl CoA pathway ...... 93

VII

Figure 31: Molecular Phylogenetic analysis of bcrC, badD and bzdN by maximum likelihood method ...... 108 Figure 32: Alignment of bcrC and bzdN forward primers with BCR sequences and putative metagenomic BCRs ...... 110 Figure 33: Alignment of bcrC reverse primer with BCR sequences and putative metagenomic BCRs ...... 111 Figure 34: Molecular Phylogenetic Analysis by Maximum Likelihood Method of bamB ...... 112 Figure 35: Molecular Phylogenetic analysis of bamA by Maximum Likelihood method ...... 114 Figure 36: Molecular Phylogenetic analysis of bssA and assA by Maximum Likelihood method ...... 116 Figure 37: Benzoyl-CoA KEGG pathway in metagenomic samples ...... 120 Figure 38: Relative abundance of Benzoyl-CoA degradation genes in the five metagenomic samples...... 121

Figure 39: KEGG pathway of toluene degradation in CaO2 + Nutrient well ...... 124 Figure 40: KEGG pathway of Xylene degradation in nutrient amended well ...... 126 Figure 41: Potential toluene degradation pathways ...... 127 Figure 42: KEGG pathway of Ethylbenzene degradation in nutrient amended well ...... 128 Figure 43: KEGG pathway of Styrene degradation in nutrient amended well...... 129 Figure 44: Relative abundance of KEGG pathways per metagenome ...... 129 Figure 45: Copy Number of Benzoyl-CoA reductase genes determined from metagenomic data. ... 132 Figure 46: ClustalW alignment of bamA-700 forward primer with representative sequences of bamA, Oah, bzdY and badI...... 134 Figure 47: Relative abundance of pathways in the metagenomes that results in the production of various fermentation end-products...... 145 Figure 48: Pearson correlations of R >0.85 with pyruvate to acetate fermentation in the nutrient-only well...... 147 Figure 49: Relative abundance of carbon fixation pathways in metagenomes from LSCM field trial...... 148 Figure 50: Abundance of the key enzyme RubisCO and other genes in the reductive pentose phosphate cycle ...... 149 Figure 51: Average abundance of key and other enzymes in the rTCA cycle ...... 150 Figure 52: Average relative abundance of key and other enzymes in the reductive acetyl-CoA pathway ...... 151 Figure 53: Relative abundance of the three major pathways of methanogenesis in metagenomic samples...... 152 Figure 54: Comparison of methane metabolism pathways of two uncultured Methanosarcina binned genomes ...... 153 Figure 55: A comparison of Assimilatory and Dissimilatory sulphate reduction in five metagenomes from LSCM microbial communities...... 156 Figure 56: Relative abundance of nitrogen metabolism related genes in five metagenomes from LSCM microbial communities...... 158

Figure 57: Overview of nitrogen metabolism pathway gene abundance in CaO2 + nutrient and nutrient only treated wells...... 160 Figure 58: Nitrogen metabolism in Alteromonas bin 45 genome...... 161 Figure 59: Total Methane produced in four of the treatment wells...... 207 Figure 60: Phosphate and ammonia data from the field trial over 12 months...... 208

VIII

Figure 61: Calcium and sulphate data from the field trial over 12 months...... 209 Figure 62: Redox potential of the field trial wells, monitored over 12 months...... 210

Table of Tables

Table 1: Comparison of main emission of pollutants produced by gas turbines and conventional methods of electricity generation ...... 7

Table 2: Table showing various chemical treatments of coal (adapted from Haenel 1992) ...... 22

Table 3: Selected Classes of Compounds Found in all the Investigated Coal Extracts by Assis et al

(2000) ...... 24

Table 4: Ultimate analysis of Coal from the Lithgow State Coal Mine ...... 29

Table 5: Resemblance matrix based on presence/absence transformation showing similarity between three different coal types ...... 35

Table 6: Compounds identified in GC-MS that were present in all three types of coal...... 37

Table 7: Number of sequences and unique sequences after quality filtering...... 61

Table 8: PERMANOVA analysis of microbial communities from V3/4 region sequencing with Monte

Carlo testing...... 63

Table 9: Top 30 most abundant OTU found across samples from the pyrosequencing dataset using the V3/4 region and V5-8 region...... 68

Table 10: Primer Sequences used in Anaerobic Hydrogen Degradation PCR Assays ...... 98

Table 11: AHD genes present in bulk aqueous phase of Acetate, Nutrient + CaO2 and Nutrient only, wells (Ac, CaO2 and An respectively) ...... 104

Table 12: Statistics of metagenomic assemblies...... 106

Table 13: Comparison PCR and Metagenomic results of CaO2 + Nutrient Early and Late time point with Nutrient Early Middle and Late time point in regards to assayed hydrocarbon degrading genes.

...... 118

Abstract

Biogenic methane production from coal necessarily involves a community of microorganisms acting in concert. The large, lignin-like molecules in coal cannot be metabolised by methanogens and other microorganisms are required to catalyse the breakdown of coal to acetate, hydrogen and carbon dioxide. This thesis investigated the microbial processes involved in the nutrient and oxygen-stimulated methane production at the Lithgow State Coal Mine (LSCM) in the Eastern NSW coalfields. Sub-bituminous coal from the LSCM was found to contain long chain aliphatic compounds ranging from C11 to C27 and aromatic compounds, such as methylated naphthalenes, fluorene and phenanthrene.

When treated with oxidative chemicals and enzymes mimicking microbial coal degradation, compounds such as acetate, propionate and formate were produced. Methane production and microbial community changes were studied during a field trial with wells drilled into the

LSCM coal seam. Microbial analysis using the 16S rRNA showed the presence of a diverse range of methanogens, including Methanosarcina, Methanoregula and Methanosaeta, associated with LSCM coal. An increase in Methanosarcina abundance was observed to coincide with the increase in methane production in the nutrient-only treated well, while the calcium peroxide (CaO2) + nutrient well saw a shift in methanogen composition from

Methanosarcina-dominated to Methanoregula-dominated. This may potentially represent a change in substrate utilisation from the methanogenic community in this well where

Methanoregula, using a different substrate, was able to out-compete Methanosarcina. High abundances of Desulfovibrio were also observed as well as a number of bacteria potentially capable of hydrocarbon degradation, such as Dechloromonas, Georgfuchsia and

Bradyrhizobium under anaerobic conditions. A PCR approach for detection of anaerobic

XI hydrocarbon degradation genes showed the presence of a number of genes in the benzoyl-

CoA reduction pathway, which is central to many anaerobic aromatic hydrocarbon degradation processes. Metagenomic analysis of microbial communities in the field trial revealed a number of relevant pathways for the biogasification of coal, including anaerobic hydrocarbon degradation pathways, dissimilatory sulphate and nitrate reduction and all three known pathways of methanogenesis. Six microbial genomes (for two species of

Methanosarcina, two Alteromonas, unclassified Bacteroidetes and unclassified Firmicutes) were binned from the metagenomic data obtained from the field trial and this is the first time genomes have been isolated from a coal associated community. Methanosarcina genomes showed the presence of all three major methanogenic metabolisms as well as the ability to fix nitrogen, an important survival mechanism in conditions of low nutrients. The complete pathway for dissimilatory nitrate reduction and denitrification were also observed in the binned genome for Alteromonas. This work has shown the production of methane from sub-bituminous coal can be stimulated by the addition of nutrients, which activates a set of microorganisms involved in anaerobic hydrocarbon degradation, methanogenesis, sulphate and nitrate reduction and fermentation. This work has provided important insights into the microbial community dynamics and the metabolic processes occurring in-situ, during the biogasification of coal.

XII

Chapter One: Introduction

What is coal?

Coal is a heterogeneous rock that is comprised mainly of organic material. The origin of this organic material is thought to be vegetal, from plant debris, that has undergone a number of physical and chemical changes due to sedimentary pressures and temperatures over a long time period (up to several hundred million years). This process whereby organic plant matter transforms to coal is known as coalification (1, 2). Coalification is thought to have begun with large peat deposits formed by bacterial decomposition of swampy forests. This then became covered with sediments and over time formed various types of coal depending on the degree of coalification (Figure 1). As coalification increases, the percentage of carbon increases significantly and the coal becomes harder. The composition of coal can be reflected in its hydrogen:carbon ratio, which for lignite, the first step in coalification after peat, is ~1 and decreases to less than 0.5 for anthracite (1).

Figure 1: Production of coal and its various ranks Figure taken from Haenel 1992.

Its Structure:

The structure of coal is difficult to elucidate as it is a heterogeneous mixture of inorganic and organic constituents that resists chemical breakdown, except in the presence of strong chemical reactants and severe conditions (3). As coal is of plant origin, examination of organic precursors of the formation of coal can help scientists develop insights into its constituent structural units. Coal is thought to be largely lignin derived (4), and in the absence of a standard structure for coal, lignin provides a starting point for the investigation of coal structure. Dry wood is made up of ~40% cellulose, 35% lignin and 30% hemicellulose.

Unlike cellulose and hemicellulose which are easily degraded by enzymatic action, lignin resists both chemical and biological degradation. The structure of a lignin molecule from a spruce is shown in Figure 2. Haenel 1992, suggests that as sedimentary pressure above the forming coal is increased, it is conceivable that during coalification, monocyclic aromatic compounds that are originally present in the peat may be condensed with the lignin to form polycyclic aromatic units like anthracene, naphthalene and phenanthrene (1).

Figure 2: Model of spruce lignin Figure taken from Freudenberg 1965

A definitive chemical structure of coal cannot be given, nor can it be broken down into monomers, as the chemical structure of coal is dependent on its coal rank (determined by level of coalification). Coal consists of a network of aromatic and hydroaromatic structural units bound together by ether and aliphatic bridges. These structural units can be between

3-5 ring structures and anthracitic coals tend to have a markedly higher average size of these aromatic units than lower ranks of coal (3).

Australian Coal Deposits:

Australia produces coal mainly from the eastern region, in New South Wales, Queensland and Victoria (as seen in Figure 3). Australia is estimated to have a total of 325x109 tonnes of coal and produces an estimated 53x106 tonnes of coal per year (5). The following figure details coal basins around Australia.

Figure 3: Australian coal deposit basins Figure taken from Bibler (1998)

Coal as a Fuel:

Coal is the most abundant fossil fuel resource available and is majorly used in the production of electricity and is estimated to still be in use for the next century (6). At present, coal accounts for around 27% of the global energy production, second only to crude oil (7). Worldwide, coal usage was estimated at 8 billion tonnes per year in 2013, an increase of 2.4% (188 Mt) from the previous year and is expected to grow at a rate of 2.1% per year, until 2019 (8). At 2010 production and consumption levels, it was thought then that worldwide coal reserves would last for another 147 years, as opposed to crude oil and natural gas which were both expected to run out in 41 and 63 years respectively (9). Coal is

4 a major source of many countries’ energy generation, China alone is the biggest coal producer (around 40%) and also its biggest consumer, consuming around 3 billion tonnes in

2009 (10, 11). Coal accounts for 63.4% of total primary energy supply in China, 38.7% in

India, 23.1% in South Korea, 72% in South Africa and 23.8% in the USA (9).

There are three main systems of coal combustion, the fixed bed, suspension fired and fluidised bed. The majority of energy generation today is produced through the use suspension firing systems (12).

Breakdown of Coal:

Coal as mentioned previously is resistant to degradation unless subjected to extreme conditions or strong chemical attacks. In 1982, the first report of microbial growth on low rank coal was made. Two white rot fungi, Polyporus versicolor and Poria monticola, were shown to grow on low rank, lignite coal and produce a black liquid that was different in composition to the coal, but still retained similarities (13). Several fungi have been shown now to solubilise different coal varieties, the resulting products were heterogeneous, polar organic compounds with moderate to high molecular weights and a large degree of aromaticity and oxygen content (14, 15). Pretreating the coal under aerobic conditions enhances microbial breakdown of the coal to smaller compounds (15). Under anaerobic conditions, biogenically produced methane is the end product of degradation in the absence of other inorganic electron acceptors such as sulphate, nitrate and ferric ions (16). It is possible to degrade coal to methane with the right microbial communities. Coal bed methane (CBM) is a type of methane deposit that is associated with coal deposits, where the methane is adsorbed to the surface and within the complex of the coal. These methane

5 reserves may be derived from microbial community activity degrading the complex organic matter within the coal. The rate limiting step in the production of methane from coal deposits is the fragmentation of the lignin derived network of aromatic molecules within the coal (4). Fungi and some bacteria are known to produce ligninases, extracellular enzymes that degrade lignin. Extracellular enzymes from microorganisms have been shown to dissolve up to 40% of the weight of coal (1, 4). This releases a number of products from the coal that can be utilised by other microorganisms, such as methanogens, which convert more simple organic compounds produced from the breakdown of coal, into methane.

Uses of Methane:

Methane is the main constituent of natural gas and may be present in anywhere from ~20% to > 90% of the total gas in natural gas reservoirs (17). Natural gas has a relatively high price and as such, in the past has not had high uses, but with recent developments, efficiency of gas turbines has increased to around 60% efficiency. This with increasing environmental pressures has led to natural gas being used in base load power generation (18). The amount of energy formed from the complete combustion of methane, per gram, is greater than energy produced from the combustion of coal (10, 19). Also, the combustion of natural gas produces less emission pollutants than the combustion of coal for energy generation, summarised in Table 1.

Table 1: Comparison of main emission of pollutants produced by gas turbines and conventional methods of electricity generation

Table taken from Islas (1999)

Methanogens:

Methanogens are defined by their ability to generate methane through methanogenesis.

The production of methane through methanogenesis is energetically poor, producing less than 1 ATP per mol of methane produced, but it is an important process in recycling carbon atoms formed in the decomposition of organic matter, back into the carbon cycle to be reused (Figure 4) (20).

Figure 4: Methanogenesis and the carbon cycle Methanogenesis is an important process used to recycle carbon atoms in compounds produced in decomposition of organic matter, back into the carbon cycle1.

1 Figure courtesy Tim Williams- UNSW (edited)

Although all are able to produce methane, methanogens are a diverse group with rRNA sequences that suggest that there has been very early divergences in archaeal lineages (21).

Methanogens have large habitat diversity; isolates have come from almost every biological system in which anaerobic degradation of organic matter occurs. The majority of methanogens inhabit the mesophilic zone and have optimal temperatures around 35oC (16).

Some of these habitats include freshwater and saltwater sediments, anaerobic waste digesters and intestinal tracts of animals. Isolates have been also found in cold locations such as Antarctic lakes, a freshwater lake in Switzerland, cold marine sediment in Alaska and also from boreal fen, tundra and a polluted pond in Russia (22). Methanogens also inhabit the other extremity of the temperature spectrum and can be found in geothermal springs and both shallow and deep sea hydrothermal vents. (23). There are three main nutritional categories determined for methanogens (Figure 5). The first category consists of hydrogenotrophs which oxidise H2 and reduce CO2, forming methane in the process. Also included in this category are the formatotrophs, which reduce formate to form methane

(24). The second category is that of the methylotrophs which use methyl compounds such as methanol, methylamines, or dimethylsufides (25). The final category is acetoclastic methanogens which form methane using acetate. These three categories are not mutually exclusive and a mixture of nutritional categories may be used by a single methanogen, e.g. hydrogeno-methylotrophs which use H2 to reduce methanol to methane (26).

Figure 5: Pathways of Methanogenesis The three pathways of methanogenesis and their reactions, methylotrophic (orange), hydrogenotrophic (purple) and acetoclastic (green). Biomass can also be produced from acetyl-CoA and is shown in pink. Diagram taken from Webster (2010)(27)

Methanogens also share genes for five unique cofactors; this suggests that this kind of complexity has evolved from one ancestral complement of methanogenesis genes rather than through lateral transfer (21). These cofactors include Methanofuran (MF), tetrahydromethanopterin (H4MPT), coenzyme-M (HS-CoM)(2-mercaptoethanesulfonate), coenzyme B (HS-CoB)(7-mercaptoheptanoylthreoninephosphate) and coenzyme F420 (21,

th 28)(8-hydroxy-f-deazaflavin). Recently, a new (6 ) cofactor involved in methanogenesis, methanophenazine, was found and is a 2-hydroxyphenazine derivative linked via an ether bridge to a pentaisoprenoid side chain (28).

In nature, acetoclastic methanogenesis is responsible for approximately two thirds of biogenically produced methane per year (29). Acetoclastic methanogenesis has also been shown to be responsible for the production of methane from anaerobic bioreactors (30) and

9 also responsible for substantial methane production in an abandoned coal mine (31). In acetoclastic methanogenesis, acetate has to first be activated before it can be transformed into methane. There are two methods methanogens use for this, the first is activating acetate to acetyl-CoA, from here, the methyl group on acetyl-CoA can be transferred to the methanogenic pathway (29). This method of activating acetate involves the addition of a phosphate group to acetate via acetate kinase and then phosphotransacetylase adds CoA.

This pathway has been shown to be used by Methanosarcina spp (32). This method is summed up in the following reaction and requires the energy from the cleavage of a high energy phosphate bond from one molecule of ATP (29, 33, 34).

- 2- CH3COO +ATP  CH3CO2PO3 + ADP

(Reaction with acetate kinase)

2- 2- CH3CO2PO3 + CoA-S-H  HPO4 + CH3CO-S-CoA

(Reaction with phosphotransacetylase)

The second method used to activate acetate requires the breakage of two high energy phosphate bonds and uses the enzyme acetyl-CoA synthetase. This method is used by members of Methanosaeta spp (32-34). The reaction is shown below.

- CH3COO + ATP + CoA-S-H  CH3CO-S-CoA + AMP + PPi

In both these genera, the C-C bond between the methyl and carbonyl groups of the acetate are cleaved by CO dehydrogenase and the C-S bond of the carbonyl and CoA are cleaved by acetyl-CoA synthase. This results in a methyl group that is reduced to methane via the rest of the methanogenesis pathway and a carbonyl group that is oxidised to carbon dioxide, providing electrons for the reduction of the methyl group to methane (33).

Degradation of Coal Hydrocarbons:

To degrade coal and produce methane, a community of syntrophically acting microorganisms is required. The large, lignin-like molecules in coal cannot be metabolised by methanogens and therefore no methane is produced unless another organism is present to catalyse the breakdown of coal to less complex organic compounds (Figure 6) (4). This then provides an appropriate carbon source for methanogens to utilise. Aerobic hydrocarbon degraders have been known for quite a while, though the discovery of anaerobic hydrocarbon degraders is only as recent as the 1980’s. There are a number of anaerobic hydrocarbon degraders such as nitrate, sulphate and iron reducing bacteria, only methanogenesis though requires no exogenous electron acceptor and therefore may continue for long periods of time without any external input (35). As stated previously, methanogens require specific substrates for methanogenesis, namely H2, CO2, acetate, formate or methyl compounds such as methylamine, methylsulphides and methanol (26).

These are in short supply in coal as it is composed of large heterogeneous polyaromatic compounds. Complex organic compounds such as coal are broken down initially by

11 fermenting bacteria into a range of smaller products, some such as CO2, H2 and formate can be utilised directly by methanogens, the rest of the products are reduced organic compounds that have to be oxidised by bacteria such as acetogens, before they can be utilised by methanogens (36).

Figure 6: Syntrophic interactions in the degradation of coal to methane.

Acetogens break down long chain fatty acids and as a result, produce acetate and H2 or formate. These products are then directly used by methanogens in the production of methane (16). Acetogens are also capable of using hydrogen and carbon dioxide as substrates for the production of acetate via homoacetogenesis (4). In methane producing bioreactors, the production of methane has been linked with increases in acetate levels in the reactor fluid (30).

Figure 7: Proposed mechanisms of stepwise biodegradation of organic matter in coal Figure taken from Strapoc et al 2008.

Fermenting bacteria can only ferment at low hydrogen concentrations. This can cause a problem if the end products are not removed from the system, as fermentation metabolic reactions would become thermodynamically unfavourable and stall, resulting in a collapse of the microbial community (35, 36). Hydrogen formed by fermenting bacteria is utilised by methanogens in the production of methane (Figure 7), removing hydrogen from the system and allowing both groups of organisms to continue growing (36). Also, by removing acetate, methanogens prevent an increase in acidity, something that may be detrimental to the function of many microorganisms within the system and their metabolic reactions (37).

Therefore, many of these syntrophic interactions are dependent on the flux of hydrogen between the microorganisms. The flux of hydrogen is dependent on the concentration gradient of hydrogen between the producing and consuming bacterium and the distance between them, as seen by the equation in the Figure 8 (36, 37).

Figure 8: Diagram and equation for the flux of hydrogen between two bacterium

Where A= Surface area, D= Diffusion coefficient, Cp=Concentration at producing bacterium, Cc= Concentration at consuming bacterium and d= interbacterial distance.

Diagram from taken from Stams (1994).

One method of overcoming the problem of hydrogen flux is for the fermenting bacteria to be in close proximity to a methanogen. Pelotomaculum thermopropionicum (Class

Clostridia), a known fermentative syntroph, has been shown to produce flagellin molecules that adhere to Methanosaeta thermophila and Methanothermobacter thermoautotrophicus. The flagellin D (FliD) monomer, when bound to the cell of M. thermoautotrophicus caused an upregulation of over 50 genes, many of these related to methanogenesis and hydrogenases (38). This kind of symbiosis not only ensures close proximity of the organisms, but also directly affects the proteins being produced inside the cells, preparing them for the syntrophic interactions to follow.

Microbial Communities and Methane Production from Coal:

One method of producing methane from a microbial community found within coal is to produce a synthesis gas from the coal, this is known as gasification (not to be confused with biogasification). This gas is formed by an oxidant being injected into a coal seam, generating a mixture of CO2, H2 and CO gases. Microbes, such as methanogens and acetogens, are able to utilise these gases and produce products such as methane and acetate. This method of methane production requires the use of anaerobic bioreactors such as a continuous stir tank, batch reactor or bubble column reactor (39).

An understanding of the composition of microbial communities in coal is essential to understand the processes and organisms involved in the production of methane. A number of previous studies have identified methanogens responsible for CBM. An underwater coal seam microbial community from Northern Japan was enriched and then analysed using archaeal 16S rRNA amplification and found to contain mostly isolates from the

Methanoculleus and Methanolobus genera. These two genera contain methanogens that produce methane via the hydrogenotrophic and methylotrophic pathways respectively (40).

Faiz et al (2006) also stated that biogenic methane production from Eastern Australian coal deposits also occurs through the hydrogenotrophic pathway (16). Methanocorpusculum is another methanogen shown to produce methane hydrogenotrophically in a coal bed in

Illinois (4). Alternatively, acetate production has been shown to be linked with the production of methane in bioreactors (30) and also for methane production in an abandoned coal mine (31). Acetoclastic methanogens have been associated with the production of coal bed methane (40) and as most of the world’s biogenically produced methane is due to acetoclasts (29), the role of acetoclastic methanogenesis should not be

15 ruled out in the production of methane from coal. Wawrik et al (2012) also demonstrated that methanogens, predominantly belonging to Methanosarcinales and Methanomicrobiales were present in production waters from the San Juan Basin in New Mexico, may be involved in all three metabolisms of methanogenesis; acetoclastic, methylotrophic and hydrogenotrophic (41).

Unlike the methanogens, the roles of specific bacteria within the methanogenic communities are not fully understood. From 16S rRNA sequences, a number of attempts have been made to hypothesise on the various roles specific genera of bacteria play in the breakdown of coal. These roles have been based on the metabolism of related organisms to those found in the clones and due to the limited number of clones, only a simplified and hypothetical mechanism for the degradation of coal can be inferred (4). Examples of bacterial groups associated with coal biogasification include Alphaproteobacteria,

Firmicutes, Deltaproteobacteria and Bacteroidetes (4, 7, 42). Wawrik et al (2012) found that methanogenesis was higher in samples that contained a greater richness in Firmicutes and that stress should be placed on investigating bacterial communities responsible for anaerobic coal activation (41).

Much work is needed in determining microbial communities involved in coal biogasification as it seems that they can vary depending on their locale (16, 43, 44). An understanding of the terminal process of methanogenesis is well understood and is not discussed in this thesis. However there still exists a substantial knowledge gap regarding the breakdown of coal, the organic intermediates and the microorganisms responsible for their production.

Knowledge in this area would be effective in determining optimal conditions and

16 communities to be utilised in the efficient biogasification of coal and which methods this should be achieved by.

Chapter Two: Chemical Characterisation of Soluble Compounds in

Australian Coal

Introduction:

Australian coal deposits are primarily found in the eastern portion of the continent, localised in the States of Queensland, New South Wales and Victoria. The total amount of coal held in these various deposits is estimated to be nearly 325 x109 ton (5). In New South

Wales, bituminous coal is contained within several significant deposits. The largest of these deposits is that of the Sydney Basin, which is a large sedimentary basin on the east coast of

Australia and is part of the larger Sydney-Gunnedah-Bowen Basin that extends from Durras

Lake in coastal southern NSW, to just north of Bowen, central Queensland (45, 46), as seen in Figure 9.

Figure 9: Map of the Sydney-Gunnedah-Bowen Basin Taken from “The geological characteristics and history of NSW with a focus on coal seam gas (CSG) resources” (46) Shaded areas represent the more recent Great Australian Basin and Murray Basin, compared to the Permian aged Sydney-Gunnedah-Bowen Basin.

The Sydney Basin is a north-south trending basin that is primarily formed of flat-lying

Permian and Triassic sequences, with the Permian sequences reaching the surface in the western coalfields (46). Sedimentation in the Sydney-Basin has been caused by a number of distinct marine transgression and regression, as well as terrestrial sedimentation events, over its geological history. Due to an uplift event in the mid-Permian period, three major regressive episodes, led to the deposition of Permian terrestrial sediments, producing the most important coal measures in the basin, the Tomago Coal Measures, Illawarra Coal

Measures and Singleton Super Group (46, 47).

Deposition events in the Permian period led to the formation of coal from the dominant floral communities of the time. The major contributing species at the time were two tree species, Glossopteris and Gangamopteris, initially thought to be ferns, but later identified as gymnosperms, a group which includes modern day conifers (48, 49).

Due to its plant origin, coal is an almost non-volatile, insoluble, non-crystalline, extremely complex mixture of organic molecules of varying sizes and structure (1). The process of coalification, whereby plant matter is transformed to peat and eventually coal, is a gradual process which is heavily influenced at a number of stages such as (50):

 Microbial hydrolysis initiated in the surface litter stage of peatification

 Introduction of fungal carbohydrates throughout early peatification

 Biochemical depolymerisation of cellulose in early peatification

 Gradual biotransformation of lignin during peatification (including depolymerisation,

demethylation, demethoxylation and defunctionalisation)

 Additional geochemical transformation in the residual lignin during early

coalification, producing a predominantly aromatic network.

Haenel 1992, also suggests that geochemical transformations, such as overlying sedimentary pressures, may condense monocyclic aromatic compounds with lignin to form polycyclic aromatics (PAHs), such as anthracene, naphthalene and phenanthracene (1).

Due to the fact that the chemical structure of coal appears to be highly dependent on early peatification processes, as well as later geochemical processes, finding a ‘universal coal

20 structure’ is unlikely and coals will have different chemical compositions dependent on their length of the gradual coalification process.

Baset et al (1980) suggests that coal may be visualised as composed of three chemically distinct parts (51):

1. Monomeric compounds, which are soluble in solvents and identifiable by Gas

Chromatography-Mass Spectrometry (GC-MS)

2. Polymeric compounds, which are also soluble in solvents, but are not amenable to

GC-MS

3. Cross-linked polymeric structures which are insoluble in solvents

This structure of coal has also been described by a 2-component model (Figure 10), where small apolar, solvent-soluble compounds (similar to parts 1 and 2 of Baset’s structure seen above) are embedded in a three dimensional cross-linked network of aromatic and aliphatic compounds, which are immobile (part 3) (1).

Figure 10: Haenel Conceptual model of coal: Two-component system

The structure of coal has been difficult to elucidate as it is resistant to breakdown, except by strong chemical reactants and severe physical conditions (3). Examples of this are shown in

Table 2.

Table 2: Table showing various chemical treatments of coal (adapted from Haenel 1992) Chemical treatments of Bituminous Coal

1. Pyrolysis, hydropyrolysis

2. Hydrogenation (hydrocracking) with hydrogen or hydrogen donor solvents

+ 3. Oxidation (HNO3, KMNO4, Na2Cr2O7, H2O2/H , NaIO4/RuCl3)

4. Alkylation and acetylation in the presence of AlCl3

5. Acid-catalysed “depolymerisation”

Ar-(CH2)n-Ar’ + C6H5OH ArH + HOC6H4-(CH2)n-Ar’

6. Reduction with alkali metals, reductive akylation

Investigations into the extractable fractions of coal have shown that a range of compounds are present within the coal matrix and are extracted using a variety of chemical processes.

One of the most prevalent methods of extracting compounds from coal utilises a Soxhlet extraction with an apolar solvent and the analysis of the extraction product with GC-MS to determine compound identities. These extractions in the past, have been carried out using mixtures of benzene and ethanol (51, 52), toluene (53) and dichloromethane (54).

Benzene/ethanol Soxhlett extractions performed by Razvigorova et al (1995) on Russian bituminous coal and Baset et al (1980) on sub-bituminous coal from the Wyodak mine near

Wyoming both showed some similarity in the compounds that were released from their coals with this treatment. These compounds mostly consisted of n-alkanes (C15-C30) n- alkenes, isoprenoids, pentacyclic triterpenoids, methylated naphthalenes and methylated

22 phenanthrene from the Russian coal (52) and n-alkanes (C16-C33), n-alkenes, fatty acids and sesquiterpenes from the coal in Wyoming (51). Both studies also compared these compounds to 500oC pyrolysis products of coal that were soluble in apolar solvent extractions, while Razvigorova et al also tested a 300oC pyrolysis product. Razvigorova et al showed that while there was a difference between the 300oC and 500oC, the 300oC pyrolysate and the ethanol/benzene extraction yielded similar results. Baset et al also showed that their extraction of the 500oC pyrolysate had a different chemical profile to the ethanol/benzene extraction. Razvigorova explained this to be due to the 300oC treatment opening pores in the coal, allowing access to compounds within the coal, that were capable of being extracted in the ethanol/benzene Soxhlet extraction. Additionally, the 500oC pyrolysate contained both the products from the 300oC extraction in addition to decomposition products formed through heat treatment.

Soxhlet extraction has also been shown to release a larger selection of compounds from coal due to its less severe action, as opposed to harsher procedures (53). Assis et al (2000) compared different extraction methods for the isolation of organic compounds from coal.

The differences between Supercritical Fluid Extraction (SFE), Pressurised Fluid Extraction

(PFE), sonication and Soxhlet extraction methods using toluene as a solvent were tested. It was found that each of these methods had different extraction efficiencies, with SFE at

425oC providing the largest yield at 3.32% (w/w). The authors also note that although SFE had the largest yield, it had the worst selectivity for the extraction of some selected classes, such as aromatic hydrocarbons and polycyclic aromatic hydrocarbons. The extreme SFE treatment was thought to cause structural changes in the coal matrix and liberate polar compounds and therefore was richer in these polar compounds, compared to aromatic and

23 polycyclic aromatic hydrocarbons. Less severe treatments, such as Soxhlet extraction, PFE and sonication however resulted in richer fractions of aromatic compounds and polycyclic aromatic hydrocarbons (53). Over the four different extraction types, there were multiple classes of compounds that were found in all investigated coal extracts, shown below in

Table 3

Table 3: Selected Classes of Compounds Found in all the Investigated Coal Extracts by Assis et al (2000)

Classes of compounds appear to be common across the literature in coal compound identification. Aliphatic hydrocarbons are commonly observed, as well as many aromatic compounds such as naphthalene and phenanthrene. However, various species and derivatives of these general compound classes are not necessarily ubiquitous in all coal and even coals of similar ages or ranks appear to have different chemical profiles. Solvent extraction of coal using the Soxhlet method with dichloromethane (DCM) by Zubkova and

Czplicka (2012) with low, medium and high volatile bituminous plasticised coals showed a range of aliphatic and aromatic hydrocarbons such as naphthalene, fluorene, phenanthrene, pyrene and anthracene in the three coal types, though GC-MS showed that many compounds were specific to one coal type (54).

It appears that although there are many classes of both aromatic and aliphatic hydrocarbons found in most coals, the abundance and presence of these may depend highly on their locality and the extent of the coalification process. Extraction of organic compounds from

24 coal using Soxhlet extractions has been the standard method of investigating these compounds (53) and a suitable method to further classify these compounds using GC-MS.

While a number of the compounds previously mentioned are accessible to microorganisms for consumption, some microorganisms exhibit mechanisms in which they are capable of degrading coal. Degradation of coal by two white rot fungi, Poria monticola and Polyporus versicolor, has been previously observed with crushed lignite coal (13). The ability of these two fungi to degrade lignite coal was hypothesised to be due to the fact that both these fungi were wood decaying organisms and that young lignite coal can contain between 35% and 70% lignin (55), one of the main structures found in wood. Many fungal oxidases and peroxidases are responsible for this degradation of lignin. These enzymes have a broad substrate range and are capable of mineralising a plethora of compounds, including hydrocarbons, partially or completely (56). Barborova et al (2006) isolated a manganese peroxidase from the white rot fungus, Irpex lacteus, and were able to demonstrate its capability to degrade four PAHs, phenanthrene, anthracene, fluoranthene and pyrene. This degradation however was only able to occur in the presence of hydrogen peroxide (57).

Hydrogen peroxide has also been used in the past as a pre-treatment of coal in order to introduce oxygen functional groups and break weak covalent bonds (58) and has been previously observed to be produced in some bacteria (59). Addition of hydrogen peroxide during bioremediation releases oxygen that may be utilised for microbial respiration of hydrocarbons, producing oxidised bi-products (60). Mild oxidative treatments of coal such as hydrogen peroxide and the enzymatic action of peroxidase are typical of microbial degradation mechanisms and may be used as a proxy of microbial degradation of coal.

The work presented in this chapter aims to determine the difference and commonalities of soluble chemical components in coal from three geographically similar areas in NSW

Australia. This chapter also aims to identify chemical compounds from coal that have been treated with hydrogen peroxide, peroxidase and calcium peroxide. These extraction methods intend to mimic microbial degradation of coal and provide information as to compounds released from coal that may be viable carbon sources for microbial growth. The results drawn from this chapter also serve as a starting point in which to examine the microbial communities in terms of their metabolisms in later chapters.

Material and Methods:

Coal Sampling Locations:

For this study, coals from three different locations were analysed; from the Lithgow State

Coal Mine (LSCM) and the Pinedale coal mine, both situated in the Late Permian, Sydney

Western Coalfields, and from a mine in Casino, New South Wales, situated on the younger, early Jurassic to late Cretaceous, Clarence-Morton Basin. Both the Pinedale and LSCM coals are sub-bituminous and originate from the Middle Illawarra Coal Measures (Figure 11).

Pinedale coal was mined from the Lisdale seam and the LSCM coal originates from the

Lithgow seam, which are part of the Illawarra Coal Measures. These two coal seams are separated by the Blackman’s Flat Formation, a quartz-sandstone layer caused by a Permian deposition event (61). Casino coal is located further north in NSW in the town of Casino, located on the Clarence-Morton Basin, as opposed to the Sydney-Gunnedah-Bowen that

Pinedale and LSCM coal originate from. The Walloon Coal Measures at this site comprise of high volatile bituminous coal, that dates to the middle Jurassic period (62) and represents a younger coal than the Casino and LSCM coals.

Figure 11: Stratigraphic diagram of the Illawarra Coal Measures (Top) and Walloon Coal Measures (Bottom) These figures show the stratigraphic layers composing the Illawarra coal measures and the Walloon coal measures. The Lithgow coal seam and the Lisdale coal seam that makes up Pinedale coal are separated by the Blackmans Flat formation.

Coal characteristics

Ultimate analysis of coal was performed previously by Hazlin Hazrin Chong via Bureau

Veritas and the results of this analysis are shown in the table below.

Table 4: Ultimate analysis of Coal from the Lithgow State Coal Mine

Analysis Result Percentange Inherent Moisture 2.7 Ash 12.8 Total Sulfur 0.66 Carbon 73.6 Hydrogen 4.83 Nitrogen 1.66 Oxygen 6.45 Phosphorus in 0.02 Coal

Coal Preparation for chemical analysis:

Coal was stored in anaerobic jars under a N2 atmosphere while not in use. Samples were prepared by crushing the coal in an anaerobic chamber with a hammer and mortar and pestle to produce particles approximately smaller than 1 cm.

Small Apolar Chemical Compound Extraction:

Extraction of small apolar compounds from coal was achieved by submerging 100 mg/ml of crushed coal in an apolar solvent, dichloromethane (DCM) (DCM is widely regarded as an apolar solvent, though it is slightly polar and may extract some polar compounds). This mixture was left for 24 hrs so that DCM soluble compounds within the coal would diffuse into the solvent. The extraction mixture was then filtered through a Pasteur pipette with

29 compacted cotton wool to remove coal particulates and stored in 2 ml gas chromatography vials at -20oC. Each extraction was performed in triplicate.

Gas chromatography Flame Ionisation Detection (GC-FID) of DCM extractions:

To determine the presence of apolar compounds with coal DCM extracts, samples were run on a Shimadzu Plus Gas Chromatograph with Flame Ionisation Detector (FID) using an

Agilent J&W HP-5 column (a column for apolar compounds, as such, any polar compounds extracted with DCM will not be observed) with a length of 50 m, diameter 0.32 mm and film

0.25 mm. Injection mode was splitless using He as a carrier gas. An oven temperature of

50oC held for 1 min was used, with an increase of 10oC per minute until a final temperature of 320oC was reached and the held for 10 min.

Alignment and Analysis of GC Chromatogram Peaks:

Retention time and peak area data were aligned with T-Rex (63) T-Rex is a tool designed to analyse and cluster chromatograph peaks based on their retention time. This tool was initially used to analyse DNA fragments that were size-separated by chromatography, but here is applied to analyse peaks that are derived from different chemical molecules based on their retention time in a gas-chromatography. T-Rex was used to cluster slight variations in retention time of GC-FID data, which represented a single compound. Clusters were defined with a threshold of 5 and the option “at most one peak per plot included in each

TRF was used” was used. Clusters were recalculated automatically each time the set of active peaks changed and during Data Matrix/AMMI step three, the Peak Area option was

30 chosen and the data matrix constructed. Though GC-FID is not normally analysed with this tool, by constructing T-Rex input data using GC-FID raw data, the program was able to be used to group GC-FID retention times. Raw input files were structured according to the details listed on the “Upload Data” page. Columns for ‘size’ were replaced with retention time, ‘height’ and ‘area’ were replaced with peak area and ‘data point’ was replaced with an arbitrary number, as this value is not used by T-REX. An example input format is shown in

Appendix 1. The use of an internal standard was not suitable as an unknown, complex mixture of hydrocarbons was expected and compounds of similar chemical structure would be covered by the standard’s signal

The T-Rex processed matrix was imported into Primer-e v6 (64) in tab delimited format. The data was then standardised using the “total option” and transformed using a presence/absence transformation. Resemblance matrices using Bray-Curtis similarity and

Multi-Dimensional Scale (MDS) plots were prepared to show relationships between different samples of coal. A Hierachical cluster analysis using a group average cluster mode was also performed and this was overlaid on the MDS plot to show similarity of treatments.

Analysis of extractions with GC-MS:

Gas-Chromatography Mass Spectrometry (GC-MS) was used to identify compounds extracted from coal. DCM extracts were run on an Agilent HP 5973 MSD GC-MS in EI mode

70eV with a mass range of 50-600 Dalton with a Restek - Rxi®-5Sil MS Columns (fused silica) column. Initial oven temperature was 60oC with a maximum temperature of 320oC and a ramp of 8oC/min to 290oC and held for 8 min and then 20oC/min to 320oC held for 10 min.

Oxidative chemical and enzymatic treatments to coal:

Coal was subjected to the following oxidative treatments: Hydrogen peroxide (Univar) at

1%, 10% and 30% (v/v) concentrations, calcium peroxide at 0.25 mg/ml and peroxidase

(Sigma 250-330 units/mg) at 0.0024 mg/ml, 0.024 mg/ml and 0.24 mg/ml. These solutions were set up in triplicate with 0.1g/ml of coal and left for 24 hrs at room temperature. The supernatant was collected for further experiments; the coal was freeze-dried and extracted with DCM using the method outlined earlier to extract the small apolar compounds.

Aqueous Extract Analysis:

Aqueous extracts were analysed with NMR spectroscopy using a Bruker Avance III HD 600

(600.16 MHz, 1H), with a 5 mm TCI cryoprobe. The spectrometer parameters used were:

9009 Hz spectral width, 3.64 s acquisition time, 1 s recycle delay, 512 scans. The excitation sculpting pulse program was used for water suppression (65). NMR spectra were processed using the Bruker TOPSPIN 3.2 software.

NMR samples were prepared by adding 0.06 mL D2O (0.06 mL) lock solvent to 0.54 mL coal extract. All chemical shifts are stated in ppm (δ) relative to TMSP (δ = 0.00 ppm), referenced to the chemical shift of the water resonance at 4.70 ppm

Results:

DCM Extracted Coal Compounds from Lithgow State Coal Mine, Pinedale and Casino Coals:

Coals from three different locations, LSCM, Pinedale and Casino were extracted with DCM to determine apolar solvent soluble compounds. GC analysis of DCM extracts showed that coals from the Lithgow State Coal Mine, Casino and Pinedale locations have similar profiles, although the abundance of many compounds are different (Figure 12).

Figure 12: Chromatograms of LSCM, Casino and Pinedale DCM extractions run on GC-FID Dichloromethane extractions from the three different coal types (LSCM = pink, Casino = black and Pinedale = blue) were compared to determine differences between their chemical fingerprints. Abundance of minor peaks can be observed to be different, though the majority of the strongly detected peaks appear to be congruent between the three coal types.

Despite these apparent similarities, statistical analysis of aligned chromatograms showed that chemical profiles of LSCM, Casino and Pinedale coals were significantly different (P

<0.05 with Monte Carlo testing) on a presence/absence basis. Rather than abundance, presence absence gives an indication that observed differences are due to compounds being present in one sample and not another, as opposed to being present in all samples, but in varying abundances. Resemblance matrices show that there were large similarities between coal replicates and that the majority of difference was between coal types (Table 5). A resemblance matrix based on abundance data, square root transformed is also available in

Appendix 2.

Table 5: Resemblance matrix based on presence/absence transformation showing similarity between three different coal types Resemblance LSCM Casino Pinedale

LSCM 94.18%

Casino 68.57% 88.84%

Pinedale 77.8% 62.85% 89.17%

A similarity of percentages (SIMPER) analysis which examines the contribution of each compound to average resemblances between coal types, was performed on GC-MS abundance data that were transformed using a square root function, to see the contribution of different compounds to the overall dissimilarity between coal types (Figure 13).

LSCM Vs Casino Group LSCM Group Casino Compound Average Average Abundance % Contribution Variance Abundance to dissimilarity 3-Methylheptadecane 1.98 0 2.69 0.98 Tetradecane 0.98 0.59 2.58 0.95 Pentadecane 1.06 2.13 2.56 1.22 Nonadecane 1.85 2.1 2.52 0.11 Tridecane 0.87 1.99 2.14 0.69

LSCM Vs Pinedale Group LSCM Group Casino Compound Average Average Abundance % Contribution Variance Abundance to dissimilarity 18.46 0 2.15 3.69 2.27 26.15 1.83 0 3.15 0.85 Docosane 1.83 0 3.14 0.85 Retene 1.53 0 2.63 0.65 Tetracosane 1.46 0 2.5 0.53

LSCM Vs Casino Group LSCM Group Casino Compound Average Average Abundance % Contribution Variance Abundance to dissimilarity Docosane 2.34 0 3.43 1.20 Pentadecane 2.93 0.59 3.41 1.78 18.46 0 2.13 3.14 2.24 Nonadecane 0 2.1 3.06 0.64 3-Methylheptadecane 0 1.99 2.9 1.11

Figure 13: SIMPER analysis of GC-MS data obtained from DCM extractions of three different coal types showing the top 5 compounds contributing to the dissimilarity between a pair of coal types. Where GC- MS identifications were not found, retention time is shown.

SIMPER shows that the three different coals were varied in their chemical composition, with many compounds contributing small difference to the overall difference. Interestingly, the differences between the coal types were mainly due to varying abundances of long chain aliphatic compounds, with retene being the only aromatic identified in the LSCM vs Pinedale samples.

The three coal types were also found to share a number of compounds as shown in the following table (Table 6).

Table 6: Compounds identified in GC-MS that were present in all three types of coal. Alkanes Aromatics/Cyclics dodecane 1,7-dimethylnaphthalene tridecane 2,3-dimethylnaphthalene tetradecane 1,4,6-trimethylnaphthalene pentadecane 2,3,5-trimethyl-naphthalene octadecane 1,2,3,4-tetramethylnaphthalene nonadecane 1-methylphenanthrene eicosane 4,5-dimethylphenanthrene heneicosane 1h-cyclopropa[l]phenanthrene,1a,9b-dihydro- 2,6-dimethylundecane 2-methylfluorene 2,6,1-trimethyldodecane 2,3-dimethylfluorene 2-methylhexadecane 3-ethoxyphenylacetone hydroxyoxime 2,6,1,14-tetramethyl-hexadecane 4-carbomethoxy-3-methoxy-4-methyl-2,5- cyclohexadien-1-one 3-methylheneicosane chamazulene 3-methylnonadecane m-cyclohexyltoluene 3-methyloctadecane

Impact of oxidative treatment on DCM soluble coal compounds:

Oxidative treatments were performed to mimic possible mechanisms of microbial action on

DCM soluble compounds within coal. Lithgow State Coal Mine coal was used due to restraints on Casino and Pinedale coal availability. After oxidation by hydrogen peroxide, calcium peroxide or peroxidase, treated coal was dried and DCM soluble compounds extracted to determine the effect the oxidative treatments had on these compounds.

An examination of oxidative treatments, compared to non-treated (water control) sample, in terms of the apolar compounds shows that the addition of any of the treatments affects the chemical profile of the mobile, apolar compounds within the coal (Figure 14). It can be seen that replicates from all treatments appear to cluster within 80% similarity of each other, with the exception of one hydrogen peroxide 1% treatment and one peroxidase treatment. Treatments using calcium peroxide and hydrogen peroxide also cluster within

80% similarity, indicating that the two treatments resulted in a similar chemical profile.

Treatment with peroxidase however, produced a markedly different profile to that of the two peroxide treatments. PERMANOVA statistics with Monte Carlo testing show that all the oxidative-treated coals were statistically significantly different from the water blank

(p=0.001). The peroxidase enzyme treatment also was significantly different from all other treatments (p=0.001). The calcium peroxide and hydrogen peroxide treatments, however were not significantly different from each other (p= 0.946), but together were different from all other treatments, p=0.001. Concentration of peroxidase or hydrogen peroxide did also not appear to have an effect on the chemical profiles.

Figure 14: Multidimensional scaling plot of GC-FID peaks of coal extracted with DCM before and after oxidative treatment. The MDS plot produced shows clearly three separate groupings of the oxidatively treated coal in terms of their extractable apolar compounds. These groupings are the peroxidase treated coal, water control and hydrogen peroxide/calcium peroxide.

The distribution of alkanes and aromatic compounds from the apolar phase extractions were different in each coal type, suggesting a unique chemical profile for coals of different

38 areas and ages. Subsequent oxidative treatment of LSCM coal showed that these compounds were changed due to the action of hydrogen peroxide, calcium peroxide and peroxidase with both peroxides producing a similar chemical profile, but separate to that of the peroxidase treatment (Figure 14). The compounds all represent the small apolar-mobile phase of coal. To get an understanding of water soluble products from oxidatively treated coal, the polar phase of the oxidative treatments were analysed with H-NMR to determine products that may be accessible to a microbial population.

Results from H-NMR show that polar fractions of the hydrogen peroxide, calcium peroxide and peroxidase, all contained compounds and extracted compounds were not oxidised completely to CO2. As seen in the apolar extractions after oxidative treatment, concentration of hydrogen peroxide and peroxidase did not appear to have an effect on the presence of peaks in the NMR, however slight abundance changes which could potentially be a concentration effect are observed.

Peaks relating to formate (8.3ppm), acetate (1.8ppm) and potentially methoxy ester, methoxy groups, tert-butyls and propionate (3.8ppm, 3-3.3ppm,1.8ppm and 2.1/0.8ppm respectively) were observed in the hydrogen peroxide extractions. Formate was observed to increase by approximately double in the 30% treatment, compared to the 1% and 10% treatments. Peaks related to potential propionate and methoxy also appear to increase in abundance in the 30% treatment compared to the 1% treatment, while methoxy ester, acetate and tert-butyl peaks remain the same. Where hydrogen peroxide and calcium peroxide appeared to have similar effects on the apolar compounds within coal, the aqueous phase of the extractions shows that while most peaks are similar, the calcium peroxide extractions appear to lack the methoxy ester and methoxy group peaks that were

39 observed in the hydrogen peroxide extractions. Peroxidase on the other hand, showed a much higher presence of tert-butyl, formate and propionate than any other extraction.

Figure 15: HNMR spectra of hydrogen peroxide extracts of LSCM coal at 1%, 10% and 30% concentrations

Figure 16: H-NMR spectra of Peroxidase extracts of LSCM coal at 0.0024 mg/ml, 0.024 mg/ml and 0.24 mg/ml

Figure 17: HNMR spectrum of a calcium peroxide extract from LSCM coal at 0.25 mg/ml

Discussion:

Comparison of Coals in Terms of their Apolar Coal Fractions:

Apolar solvent extraction of three different coal types showed that the presence of small apolar mobile compounds within coal were statistically significantly different. Although only three coal seams were characterised, their proximity would suggest that even coal of similar geological ages and regions contain varied small apolar soluble compounds. This variation in chemical profile appears to be produced by a large number of smaller differences, as opposed to large abundances of compounds unique to specific coals. A number of reasons for the differences observed in the small apolar compounds of the three coals exist.

Petrographic composition of coal has been shown to depend on the rate and extent of subsidence during a deposition event (66). If the petrographic composition of coal, therefore the distribution of its macerals, is dependent on the rate and extent of subsidence, it is therefore logical that the chemical composition would also be reliant on these events. As these three coal seams are formed in different areas from different geological events, one would expect that these subsidence events could potentially lead to different chemical compositions of the coal. The Casino Coal seam is geographically separated from the Lisdale and Lithgow coal seams and these latter two are also separated by depth (time). It is also conceivable, as the process of coalification is dependent on temperature and pressures over time (2), that coal seams separated by time and geography, would be exposed to different conditions, leading to a difference in coal composition. These differences are reflected in the results obtained from the comparison of small, apolar compounds within the coal and the GC-MS study, that show that each coal is statistically significant in terms of its chemical composition. Small apolar compounds were characterised

42 using GC-MS and were primarily composed of straight chain aliphatic compounds ranging from C11 to C27, including methylated variations as well as PAHs, such as methylated naphthalenes, phenanthrenes and fluorenes.

Alkanes between C8 and C30 have been previously characterised in Australian bituminous coals (49) and alkanes have been suggested to represent up to 0.7% wt% of coal, increasing with coal rank (67). PAHs, such as the methylated naphthalenes, phenanthrenes and fluorenes seen here, are also commonly found in coal and the degree of aromaticity has been linked with the rank of coal, in essence, the length of the coalification process (68).

Using solvent extraction methods with DCM and toluene have identified methylated naphthalenes and anthracene as well as retene, similar to this study, but also other compounds, such as codalene, simonellite, methylated tetrahydronaphthalene, pyrenes and chrysenes that were not found in this study (53, 69). A number of compound classes appear to be found quite frequently in coal worldwide, with polyaromatic hydrocarbons such as retene, naphthalene and anthracene observed in a number of coals, as well as long chain alkanes up to around C30 in length. These classes of compounds are consistent with the results obtained in this study.

Anaerobic microbial degradation of these compounds has been extensively researched, mainly for the role they play in bioremediation of hydrocarbon contaminated sites (70-72).

Compounds such as alkanes and PAHs may be degraded in the presence of terminal electron acceptors such as nitrate, sulphate and iron (73, 74) or may be degraded by the syntrophic interactions of a number of microorganisms, working in concert to thermodynamically drive the degradation of hydrocarbons forward by the removal of H2 (75).

Although alkanes have previously been reported to be aerobically degraded by a range of organisms such as Arthrobacter spp., Acinetobacter spp., Candida spp., Pseudomonas spp.,

Rhodococcus spp., Streptomyces spp. and Bacillus spp. (73), mechanisms for their anaerobic degradation are relatively recently discovered. Two pathways for anaerobic alkane degradation are known. The first involves the addition of fumarate either terminally or sub- terminally to an alkane to form an alkyl succinate which proceeds through -oxidation for further degradation (73). The microbial addition of fumarate to the aliphatic structure is catalysed by the alkyl-succinate synthase (assA) enzyme and has been observed in very few bacteria, namely the sulphate reducer Desulfatibacillum alkenivorans AK-01, and

Betaproteobacteria denitrifying strains HxN1, OcN1 and HdN1 (76-78). These anaerobic alkane degrading microorganisms have been observed to degrade alkanes with carbon skeletons ranging from C13-C18, C6-C8, C8-C12 and C14-C20 respectively (76, 78). The second mechanism of alkane degradation involves the sub-terminal carboxylation and terminal two carbon elimination to produce a fatty acid one carbon shorter than the original alkane. This fatty acid can then be -oxidised and mineralised to CO2 (79). In the model of coal degradation outlined by Strapoc et al (2008), fragmentation of coal and the subsequent release of oil inclusions would present long chain alkanes to a coal associated community

(4). Alkanes, like those outlined in this study (Table 6), would then be suitable substrates for anaerobic alkane degradation via addition of fumarate or carboxylation and further degradation by -oxidation.

Also determined from the apolar solvent extracts were a range of PAHs such as naphthalene, fluoranthene and phenanthrene. In the environment, there appears to be a large potential for natural attenuation in both oxic and anoxic environments (80). Of these

PAHs, naphthalene has the smallest number of aromatic structures and has been studied in depth. Anaerobic degradation of naphthalene occurs via two differing mechanisms (81, 82).

Non-substituted naphthalene may be activated by either carboxylation or methylation followed by fumarate addition at position 2 of the double ring structure, as this is the most electronegative position (82). Activation by both carboxylation and methylation/fumarate addition leads to the production of the 2-napthoyl-CoA intermediate which then proceeds through ring cleavage, cytoskeleton rearrangement and -oxidation to produce acetyl-CoA and CO2 (81, 83). A similar method for phenanthrene degradation is hypothesised as hydrocarbon degrading cultures have been observed to produce phenanthroic acid, analogous to napthoic acid produced during carboxylation of naphthalene, prior to addition of Coenzyme A to produce 2-napthoyl-CoA (84, 85). Table 6 shows that the presence of naphthalene in the apolar extracts was in the form of di- and tri-methylated variations. As methylation of naphthalene is considered an activation step of naphthalene degradation, there is potential that these compounds found within coal may be suitable for fumarate addition and subsequent degradation.

Comparison of Oxidative Coal Extracts:

All the oxidative treatments were significantly different from the water control, suggesting they all have an effect on the chemical composition of the mobile, apolar compounds present in coal. Calcium peroxide and hydrogen peroxide, however, showed similar chemical profiles, as indicated by the MDS plot (Figure 14). Hydrogen peroxide has been studied extensively in terms of its use in degrading toxic and biorefractory organic

45 compounds, such as chlorophenols and substituted benzenes (86). Hydrogen peroxide can be decomposed using a catalyst to form hydroxide radicals, known as Fenton’s Reagent

2+ - 3+ H2O2 + Fe → OH· + OH + Fe

These hydroxyl radicals formed are relatively non-specific and react with alkenes and aromatic compounds (86). Calcium peroxide decomposes in water to produce calcium hydroxide and hydrogen peroxide (87).

CaO2(s) + 2H2O → H2O2 + Ca(OH)2(s)

Both hydrogen peroxide and calcium peroxide treatments showed similar chemical profiles, as seen in Figure 14. Due to the similar chemical profile, these treatments may be oxidising coal through the same mechanisms, potentially through Fenton’s reagents. Concentration did not appear to have any effect on the chemical profiles of coal extracts. Three different concentrations of hydrogen peroxide were used, and none showed any significant difference from each other (P> 0.244). The concentration of calcium peroxide, was the same concentration used in the field trial in Chapter 3. This concentration also yielded a similar chemical profile as hydrogen peroxide. These results suggest that the concentration of hydrogen peroxide and calcium peroxide do not affect the chemical profile and in fact, the mechanism of action is more important in the production of various oxidation products, as the enzyme treatment produced a different chemical profile than the peroxide treatments.

Treatment of coal with hydrogen peroxide has mostly been used for desulfurization, the removal of sulphur compounds from coal (88). Oxidation of coal with hydrogen peroxide has been experimented with in order to produce a more polar substance, capable of dissolution in water (58). Coal extracts with peroxides could potentially be more bioavailable if the

46 resulting products were water soluble. One experiment on producing fluidified coal by oxidation of lignite coal with 30% hydrogen peroxide showed that at varying temperatures,

o CO2 evolution and water soluble organic production could be increased. At 25 C, 2% of carbon from coal could be released as carbon dioxide, while a further 2% was transformed into water soluble organic compounds. At 60oC, however, carbon dioxide production was doubled and 17% of carbon was released as water soluble organic compounds (58). This experiment shows that up to 17% of carbon in low rank-lignite coal may be able to be made available to a microbial community with a hydrogen peroxide pre-treatment. A study by

Jones et al (2013) hypothesised that coal seams in the Powder River Basin, when dewatered, would be exposed to air and this may cause oxidation that may affect the bioavailability of coal to a microbial consortium. To test this, coal was exposed to dilute (1%, 5% and 10%) hydrogen peroxide treatments to mimic coal oxidation in an unsaturated environment and was found to increase bioavailability of organic compounds, primarily bituminous fractions, and increased the methanogenic potential for coalbed methane production (89).

Enzymatic treatment with peroxidase produced a different chemical profile to that of the hydrogen peroxide and calcium peroxide treatments. Peroxidase uses the oxidative potential of hydrogen peroxide to pass onto an electron donor, for example coal. These mechanisms have been widely studied with Horseradish Peroxidase (HRP) and its mechanism of action is representative of the common method of peroxidase oxidation (90).

The interaction of peroxidase with an electron donor is summarised below (91).

3+ Native Peroxidase(Fe ) + H2O2  Compound-I + H2O (1)

* Compound-I + AH2  Compound-II + AH (2)

3+ * Compound-II + AH2  Native Peroxidase(Fe ) + AH + H2O (3)

Initially, the ferriheme of the native peroxidase undergoes a two electron oxidation event, caused by the hydrogen peroxide. This results in an intermediate compound (compound-I) with an oxidation state of +5, an oxyferryl iron and porphyrin π cation radical (reaction 1).

This in turn reacts with an electron donor (AH2) to produce Compound-II (oxidation state +4)

(reaction 2) and a subsequent one electron reduction by a second electron donor AH2, causes the enzyme to return to its native state (reaction 3) (91).

The differences observed in the chemical profiles of the peroxide (calcium and hydrogen) and peroxidase treatments, is likely due to the fundamental differences in the mechanism of action of these compounds in producing radicals that then go on to change the chemical profile of the coal.

Though the concentrations of hydrogen peroxide utilised in these experiments is above what would be produced by microorganisms (92), varying concentrations of hydrogen peroxide produced similar chemical profiles, suggesting that at microbial produced concentrations, similar coal extract chemical profiles may be produced. Both peroxide and peroxidase treatments represent two possible mechanisms a microbial consortium could utilise in order to oxidise coal to produce a more polar, easily consumable, substrate (93).

HNMR results show a similarity in a number of compounds produced in the oxidative extractions. Compounds, such as formate are seen in each of the oxidative extraction methods, while acetate is observed in samples from hydrogen peroxide and calcium peroxide extractions. These results fall in line with those seen in Figure 14, where calcium peroxide and hydrogen peroxide produce similar chemical profiles. This provides more

48 evidence that both these treatments are likely using the same method of action for producing radicals that alter the coal structure and release compounds. The peak related to formate in the HNMR spectra is present in all samples, though a much higher abundance is observed in samples treated with peroxidase. Peroxidase treated coal also showed an increase in tert-butyl and propionate compared to the peroxide treatments. Tert-butyl groups (1,1-dimethylethyl) have previously been seen in fungal extracts of coal in the form of 2-tert-butyl quinolone, 4-tert-butyl quinolone, 2,4-bis(1,1-dimethylethyl)-phenol and 2,6- bis(1,1-dimethylethyl)-2,5-cyclohexadiene (94). These compounds were then tested with a commonly used methanogenic community, WBC-2, for methane production. All samples were shown to produce methane with the methanogenic culture, with most producing methane in amounts similar to previous studies for low and medium methane producing sub-bituminous coals (94). As many white rot fungi are known to produce various peroxidases for the degradation of lignin (95) and white rot fungi have been reported to degrade coal (13), it is not surprising that an increase in compounds previously observed from the fungal biosolubilisation of coal are observed in the peroxidase treatment.

Propionate that was also observed, has been linked to the degradation of benzene (96) as well as an important fermentation product in anaerobic sulphate reduction (97) and a substrate for propionate degrading H2 producing syntrophs in a methanogenic community

(98). As well as propionate, formate and acetate were also present in the oxidative extracts, with each sample, containing at least one of these two compounds. Formate and acetate are both potential methanogenesis substrates and the presence of these three compounds in the oxidative treatments, designed to mimic microbial processes, suggests the potential for their production in a coal associated microbial community. The production of these compounds from coal carbon indicates a mechanism as to which methane may be produced

49 from coal derived carbon. Differing mechanisms for oxidising coal appear to produce various substrates in a coal degrading community that may be utilisable by different fractions of the community. An example of this may be that hydrogenotrophic methanogens could utilise formate, while acetoclastic methanogens utilise acetate. Preferential production of one substrate compared to another, may potentially alter community dynamics, such as the abundance of methanogenic groups, sulphate reducers and various hydrocarbon degraders.

Conclusion:

Coals analysed with apolar solvent extractions were found to contain a range of long chain aliphatic hydrocarbons as well as PAHs such as naphthalene, phenanthrene and fluorene.

The distribution of these compounds over three different Australian coals showed that even coals of similar geographic origin and age displayed statistically different chemical profiles.

These compounds represented small apolar inclusions within the coal that may theoretically be released during coal geopolymer fragmentation according to the model of microbial degradation by Strapoc et al (2008). The compounds characterised through GC-MS experimentation have previously been identified to be capable of being microbially degraded in an anaerobic environment to produce CO2, acetyl CoA and fatty acids, potentially utilisable by a methanogenic community. Oxidative treatments of coal representing potential chemical and enzymatic attacks by bacteria and fungi also yielded compounds of interest to an anaerobic microbial community. Fatty acids such as formate, acetate and propionate were observed in the aqueous fractions. Fatty acids such as these may be direct substrates for various essential anaerobic processes such as methanogenesis and sulphate reduction. Chemical profiles for oxidative extracts by peroxides and

50 peroxidases showed that depending on the method of oxidative attack on coal, different products from coal may be selected for, potentially altering community dynamics. Although treatments such as those carried out in this chapter may be useful in ex-situ oxidation of coal to produce methane, the mechanisms are important to understand as similar oxidation may be possible in-situ. Jones et al (2013) demonstrated that coal seam dewatering led to the oxidation of coal and could lead to increased methane production. These factors are important to consider during the in-situ biostimulation of methanogenesis from coal, as oxidation, though inhibitory to methanogenesis, may lead to higher methane yields through the increase of bioavailable carbon from oxidised coal and may be caused by a variety of means, such as environmental (e.g. dewatering), chemical (e.g. hydrogen peroxide) and biological (e.g. enzymatic). Coal is known to be resistant to chemical and biological breakdown; however, the results shown here indicate that coal has a large variety of compounds accessible through different chemical and biological actions and these may be able to provide carbon to a metabolically active microbial community

Chapter Three: Microbial Community Analysis of a Field Trial

Introduction:

Information on the composition of microbial communities in natural coal seams is important in understanding the processes and organisms involved in the biogenic production of methane from coal. A number of previous studies have identified methanogens being present in samples that produce Coal Bed Methane (CBM). Smith and Pallasser (1996) demonstrated, via stable isotope analysis, that CBM in Australian Permian coal beds was generated by a biological methanogenic process (99). Shimizu et al (2007) produced enrichment cultures from water taken from an underwater coal seam in Northern Japan and found them to contain mostly methanogens from the Methanoculleus and Methanolobus genera. These two genera contain methanogens that produce methane via the hydrogenotrophic and methylotrophic pathways respectively (40). Faiz et al (2006) also stated that biogenic methane production from Eastern Australian coal deposits occurs through the same hydrogenotrophic pathway observed by Shimizu et al (16). As well as the production of methane from the hydrogenotrophic substrates, hydrogen and CO2, acetate consumption has also been shown to be linked with the production of methane in an abandoned coal mine (31). Acetoclastic methanogens have been associated with the production of coal bed methane (40) and as most of the world’s biogenically produced methane is due to acetoclasts (29), the role of acetoclastic methanogenesis is most likely an important process involved in CBM production. These studies show that the presence of methanogens in coal environments appears to be ubiquitous, with all three major mechanisms of methanogenesis being utilised. Coal bed methane and the biogasification of

52 coal are highly dependent on the microbial community’s ability to produce methane from a range of substrates present in the coal environment.

Unlike methanogens, the roles of specific bacteria that are associated with methane- producing coal seams are not fully understood. Microbial communities in coal seams have been studied world-wide, in places such as the Ordos basin in China, coal deposits in

Western Canada and Jharkand, India to name few (42, 100, 101). In Australia, microbial communities have been analysed in coal from the Sydney Basin, Port Phillip Basin, Gippsland

Basin and Surat Basin (44, 102). While these coal-bed associated communities from different regions appear to have similar distribution at the phylum level, with representatives from Proteobacteria, Firmicutes, Bacteroidetes, Acinetobacteria and

Spirichaete, the species found in these sites are different and appear to be site or coal specific (100).

To date, the examination of microbial communities in Australian coal deposits has been assessed using traditional methods, such as culturing and sequencing of 16S rRNA gene PCR clone libraries. Examination of microbial communities associated with coal in Australia has so far, been relatively unexplored with more recent sequencing techniques, such as pyrosequencing, and has not been studied in regards to an in-situ community, changing over time in response to various amendments. Pyrosequencing would allow an in-depth profiling of the microbial community, compared to traditional clone libraries, where the dynamics of many OTU, not just the most abundant, can be followed over time. In-situ biostimulation experiments on coal environment communities have in the past, not been explored and to date, biostimulation experiments have been conducted and analysed in microcosm.

A study by Jones et al (2010) showed that native microbial coal communities, from a sub- bituminous non-productive coal seam in Texas, were able to produce methane when placed in a microcosm with nutrients. The same community was unable to produce methane when nutrients were not offered, indicating a possibility of nutrient starvation that prevented methane production in the original coal seam (75). Green et al (2008) also showed that coal mixed with raw well water from a coal mine well in the Powder River basin was unable to produce methane with coal as a carbon source until other medium components, such as mineral solution, vitamin solution, trace metal solution or acetate were added to the cultures (103). As seen previously, this coal mine was unproductive in terms of natural gas and the study by Green et al (2008) suggests that this community too, was restricted by a lack of nutrients. It is apparent that in non-gassy coal seams, such as the coal seam situated at the Lithgow State Coal Mine, microbial communities may be nutrient limited. Production of methane has previously been restored with the addition of nutrients ex-situ and may potentially play a large role in microbially enhanced coalbed methane generation in an in- situ setting.

To promote the biodegradation of large carbon structures, CaO2 has previously been used in soil environments to provide oxygen for the microbial population capable of aerobic degradation. The degradation of bis-(2-ethylhexyl) phthalate (BEHP) was substantially increased with CaO2 addition to a bioreactor containing BEHP contaminated soil due to the release of oxygen by CaO2 degradation (104). Aerobic hydrocarbon degradation has been shown to be faster and may involve a larger range of compounds than anaerobic hydrocarbon degradation (105). Though methanogenesis is an anaerobic process, an initial

54 aerobic pre-treatment of coal may produce compounds easily degradable for an anaerobic consortium.

A field site at the Lithgow State Coal Mine has been set up with five wells drilled into an underlying coal seam. Wells were drilled ~80 m below ground, in to the top of the Lithgow

Coal Seam. These five wells were treated with combinations of nutrients/no nutrients and calcium peroxide in an attempt to stimulate methane production from the non-producing microbial community (Appendix 2). The coal at this location is non-gassy and classed as sub- bituminous. Work on these wells quantifying methane, acetate and anion/cation data were conducted in conjunction with sequencing of the microbial community and raw data

(Appendix 3) of these quantifications were supplied for analysis with regards to their relation to the microbial community, portrayed in this chapter.

The aim of this chapter is to observe community dynamics in coal seam wells, in relation to various treatments over time, using pyrosequencing technology and determine key members in the biodegradation of coal and production of methane.

Methods:

DNA Preparation and Pyrosequencing:

DNA extracted from coal surfaces and coal associated groundwater samples (Appendix 5) were amplified2 using two 16S rRNA primer pairs that targeted the V3 and V4 regions (519:

5'-CAGCMGCCGCGGTAATWC-3', 926: 5’-CCGYCAATTCCTTTRAGTTT-3’) and the V5, V6, V7 and V8 regions (926: 5'-AAACTYAAAKGAATTGRC-3’, 1392: 5’-ACGGGCGGTGTGTRC-3’).

Samples that targeted the V3-4 regions were sequenced at the Ramaciotti Centre for

Genomics and samples targeting the V5-V8 regions were sequenced at the Hawkesbury

Institute for the Environment. Multiple DNA extractions from the same time points were pooled to produce enough DNA for sequencing. DNA sequencing, were not replicated due to cost restraints and biomass availability.

Pre-processing and Quality Filtering of 454 Pyrosequencing Reads:

Pre-processing and quality filtering of 454 pyrosequencing reads was accomplished with the software MOTHUR (http://www.mothur.org/). The pipeline seen in Appendix 6 was used to process the 454 pyrosequencing data received from both primer pairs. Sequences were initially trimmed of low quality reads and bases and aligned using the Silva database. After alignment, sequences were screened for size and alignment over the variable regions.

Sequences were clustered at 0.1, 0.01 and 0.03 and were classified using the Ribosomal

Database Project (RDP). As different regions were targeted in both sequencing attempts, parameters used vary slightly between processing pipelines. This is expanded on in

2 Amplification of V5-8 region performed by Dr Sabrina Beckmann.

Appendix 6. Pyrosequencing data was subsampled to 700 sequences for the V3/4 region and to 692 sequences for the V5-8 region. Samples that did not reach this subsample threshold were removed from analysis. Samples were sub-sampled to 700 sequences, which was the lowest number of sequences in a set of samples that allowed for statistical comparision of treatment and time-points.

Correcting Microbial Sequence Data Using 16S rRNA Gene Copy Number and qPCR Data to Obtain Cell Counts:

Copy number estimates were obtained by using output FASTA information from MOTHUR at the get.oturep command in the analysis pipeline. This sequence information was uploaded to the Ribosomal RNA Database (rrndb) (106)

(http://rrndb.umms.med.umich.edu/estimate.php) and copy number estimates were determined for the classified reads by searching against the rrnDB. In instances where copy numbers for organisms were unknown, a copy number of one was assumed for the analyses. Pyrosequencing read counts were then corrected according to copy number and a relative abundance of each OTU determined. qPCR to determine total counts of bacterial and archaeal sequences were performed by Dr

Sabrina Beckman. Relative abundance was then multiplied by the cumulative archaeal and bacterial qPCR data to obtain total counts, before continuing with network analysis.

Community Analysis:

Copy number and qPCR adjusted pyrosequencing counts were imported into Primer-E V6 and PERMANOVA, and Multidimensional Scaling plots (MDS) were produced by

57 standardising samples by “total” and transforming the data by square root, using Primer’s inbuilt functions. A resemblance matrix using a Bray-Curtis similarity measure was produced and MDS plots from this using a minimum stress of 0.01 and Kruskal fit scheme 1 with 2500 restarts.

Permutational ANOVA was performed using the PERMANOVA plugin for Primer-E V6. A pairwise test looking at the source of extraction (bulk aqueous, biotrap, basket and original coal surface) using an unrestricted permutation of raw data and Primer-E defaults was performed. Monte Carlo tests were also performed to produce probability distributions as some samples had low numbers of unique permutations.

Copy number and qPCR adjusted OTU counts were imported into CoNet

(http://psbweb05.psb.ugent.be/conet/download.php), a tool for detecting significant non- random patterns of co-occurrence (co-presence and mutual exclusion) in incidence and abundance data. CoNet may be run in command line or as a plugin for Cytoscape

(http://www.cytoscape.org/) and was run as a plugin for cytoscape for this study.

Networks were based off a Pearson correlation of >0.75 with the multigraph option deselected. Nodes of interest were selected and first neighbours of selected nodes were used to visualise networks. All edges between first neighbours not connected to the node of interest were removed to reduce the complexity of networks for viewing and emphasising interactions.

Results:

The importance of community interactions, especially over time, are needed to understand the mechanisms and community dynamics, during the production of methane from coal.

Results portrayed here are a step in trying to elucidate these processes occurring in an in- situ coal associated community over time.

Pyrosequencing OTU Classification and Comparison:

Clustered OTU were classified using the RDP training set version 9 at the phylum level. The following graphs show the distribution of phyla that attributed to >1% of the sequences, for both the V5-8 and V3/4 datasets.

Spirochaetes Gemmatimonadetes V5-8 Region 1% 1% Chlorobi Others Verrucomicrobia 1% 5% 2% Crenarchaeota 2% Planctomycetes 4% Proteobacteria 27% Acidobacteria 7%

Actinobacteria Euryarchaeota 7% 13%

Firmicutes Chloroflexi 13% 7% Bacteroidetes 10%

V3/4Region Other Chlorobi 6% 3% Actinobacteria 12% Acidobacteria 2% Chloroflexi Proteobacteria 3% 51% Firmicutes 15%

Bacteroidetes 8%

Figure 18: V5-8 region (top) and V3/4 region (bottom), classification of reads at phylum level from.

It can be seen that the primers for the V5-8 region pick up a greater percentage of archaea

(phyla Euryarchaeota and Crenarchaeota) than the primers for the V3/4 region. When comparing the quality filtered reads, the dataset produced by the V3/4 region, resulted in more reads and more unique sequences (~42% of all reads). Whereas the data from the V5-

8 region, contained less reads and a smaller percentage of unique sequences (~15% of all reads) as seen in Table 7. This shows that while the V3/4 region set did not sequence archaea, it did provide a greater representation of the bacterial diversity within the community.

For these reasons, both sets of data were used to characterise the community within the field trial.

Table 7: Number of sequences and unique sequences after quality filtering. V region total # of seqs # of unique seqs Percentage Unique 3/4 127436 53514 42% 5-8 86689 15240 17.5%

To compare the microbial communities for both datasets, pyrosequencing data was analysed with Primer-E v6 and permutational ANOVAs (PERMANOVA) showed a statistically significant difference (P<0.05) between the communities extracted from the bulk aqueous phase of the well, from the surfaces of the coal, in the V3/4 region. This difference however was not seen in the V5-8 region primer set data (Figure 19 and Figure 20.)

Figure 19: MDS plot of communities sequenced with primers for the V3/4 region. Plot shows communities extracted and sequenced from the surface of biotrap, basket and original coal as well as communities extracted from the bulk aqueous phase. Communities from different locations where biomass was collected appear to cluster away from each other. Numbers represent the well from which the community was extracted. 1= Acetate, 3= CaO2 + Nutrients, 4= Nutrients, 5= Control.

Figure 20: MDS plot of communities sequenced with primers for the V5-8 region. Plot shows communities extracted and sequenced from the surface of biotrap and basket coal as well as communities extracted from the bulk aqueous phase. Communities from different locations, where biomass was collected do not show the same clustering pattern as observed for communities sequenced with primers for the V3/4 region. Numbers represent the well from which the community was extracted. Samples of original coal surface were below the subsample value and were removed from analysis. 1= Acetate, 3= CaO2 + Nutrients, 4= Nutrients, 5= Control.

Further analysis of the V3/4 region data with PERMANOVA and Monte Carlo testing showed that the surface-associated communities were still quite similar, with all surface communities having P > 0.05, except for the case between the Original Coal and Basket Coal

(P=0.047). While the bulk aqueous phase communities differed significantly from all surface communities (Table 8).

Table 8: PERMANOVA analysis of microbial communities from V3/4 region sequencing with Monte Carlo testing. Groups P P(Monte Carlo) Surface (Basket), Surface (Biotrap) 0.203 0.156 Surface (Basket), Bulk Aqueous 0.006 0.002 Surface (Basket), Surface (original) 0.357 0.047 Surface (Biotrap), Bulk Aqueous 0.005 0.017 Surface (Biotrap), Surface (original) 0.111 0.108 Bulk Aqueous, Surface (original) 0.004 0.003

This kind of differentiation however, was not seen in the communities sequenced for the

V5-8 region.

Phylogenetic Analysis of Microbial Community and its Members from Field Trial Samples:

Phylogenetic classification using the V5-8 region and OTU grouped based on phyla, it can be seen in Figure 21 that community composition at the phylum level shifts greatly over the samples and treatments. In the acetate amended well, a large increase in the relative abundance of Euryarchaeota and concominant decrease of Firmicutes is seen from the 0 month time point to the 3 month time point and this abundance of Euryarchaeota is sustained for the rest of the trial. A similar trend is seen in the first two months of the CaO2

+ nutrient treatment, although the community appears to return to a community profile, similar to the 0 Month time point, by 9 months. The last time point for the CaO2 + nutrient

63 treatment was markedly different from any other treatment or time point with an increase in the abundance of Bacteroidetes and Chloroflexi sequences, with a loss of Euryarchaeota.

The nutrient amended well showed methane production in the last time point of the trial, in this time point it can be seen that there is an increase in the relative abundance of

Euryarchaeota with a decrease in the abundance and diversity of bacterial sequences. This sort of community structure was also evident in a number of the time points from CaO2 + nutrients and acetate amended wells during methane production.

Figure 21: Phylogenetic classification for the pyrosequencing analysis with phylum composition per sample

100%

90% Methermicoccus Methanothermococcus 80% Methanospirillum Methanosphaerula 70% Methanosphaera 60% Methanosarcina Methanosalsum 50% Methanosaeta 40% Methanoregula Methanopyrus 30% Methanoplanus

20% Methanolinea Methanogenium 10% Methanoculleus

0% Methanococcoides Methanocella Methanocalculus Methanobacterium

Methanimicrococcus

Control0Months Control3Months Control6Months Control9Months

Acetate0Months Acetate3Months Acetate6Months Acetate9Months

Acetate12Months

Nutrients 0Months Nutrients 6Months Nutrients Nutrients3Months Nutrients9Months

Nutrients12Months Non-Methanogens

CaO+Nutrients 0Months CaO+Nutrients 3Months CaO+Nutrients 6Months CaO+Nutrients 9Months CaO+Nutrients 12Months

Figure 22: Diversity of Archaeal 16S sequences.

A comparison of the top 30 OTU clustered at a distance 0.03 for both primer sets is shown in

Table 9. No archaea were present in the top 30 OTU targeted in the V3/4 region. The most abundant sequence amplified was a Desulfovibrio sp. sequence. In total, three Desulfovibrio

OTU were clustered at 0.03 and present in the top 30 abundant OTU. Among the top 30

OTU for this region, were a number of genera that have been previously seen in coal environments, such as Propionibacterium, Desulfatibacillum and hydrocarbon dedgraders such as Rhodococcus, Acidovorax and Azospira. In comparison, the top OTU clustered at a

66 distance of 0.03 for the V5-8 region was for the archaeon, Methanosarcina. Twelve of the top 30 OTU from this region belonged to methanogens, compared to zero from the V3/4 region. Two other genera, Methanosaeta and Methanoregula, were present numerous times in the top 30. As well as methanogens, a number of genera, previously implicated in hydrocarbon degradation were seen, such as Dechloromonas, Brachymonas and

Georgfuchsia.

Table 9: Top 30 most abundant OTU found across samples from the pyrosequencing dataset using the V3/4 region and V5-8 region.

V3/4 region V5-8 region Genus Metabolism/source Genus Metabolism/source

Desulfovibrio May transfer H2 to Methanosarcina Acetoclastic other species (107) methanogenesis in coal mines (31) Hydrogenophaga Found in coal before. Aquabacterium Found associated with Hydrogen-Utilising (75) coal (china) Azospira Found associated with Methanoregula Hydrogenotrophic coal (India) (43) methanogen(98)

Desulfovibrio May transfer H2 to Methanosarcina Acetoclastic other species methanogenesis in coal mines Wandonia Strictly aerobic marine Methanosarcina “ “ bacterium (108) Propionibacterium Coal seam in China Treponema Pathogenic bacterium (109) (110) Corynebacterium Hydrocarbon/Tanin Methanosaeta Hydrogenotrophic and degradation(111) acetoclastic methanogenesis (34, 112) Desulfatibacillum Found associated with Dechloromonas Benzene degradation coal (India) (43) linked to nitrate reduction (113)

Desulfurivibrio Utilises acetate and H2. Methanosaeta Hydrogenotrophic and Uses thiosulphate and acetoclastic elemental sulphur as methanogenesis electron acceptor (114) Ignavibacterium Cultured from sulphide Methanoregula Hydrogenotrophic rich, hot spring waters methanogen Clostridium_III Multiple metabolisms, Methanoregula “ “ including acetogenesis(115) Elusimicrobium Elusimicrobia found in Bulleidia Oral cavity bacterium coal mining subsidence (117) areas in china(116) Leptospira Shown to produce Thiobacillus Involved in acid Acetate and H2 drainage in coal mines Sulfuricella Facultatively Simplicispira Nitrate reduction to anaerobic, sulphur nitrite in active sludge oxidising (118) (119) Acidovorax Degrades Ignavibacterium Cultured from sulphide Phenanthrene rich, hot spring waters Ignavibacterium Cultured from sulphide Propionivibrio Produces propionate rich, hot spring waters (120)

Pelotomaculum Coal methane Anaerovorax Strictly anaerobic generation in a putrescine fermenter microcosm, degrades (121) phthalates and benzoates (75) Aquabacterium Found associated with Brachymonas Hydrocarbon coal (china) (109) degradation (122) Aquabacterium “ “ Meniscus Found in anaerobic digester of waste treatment plant (123) Rhodococcus Biphenyl Degrader Limnohabitans Freshwater facultatively anaerobic acetate utilising freshwater bacterium (124) Acetobacterium Formation water in Methanoregula Hydrogenotrophic Sydney basin, (102) methanogen

Dethiobacter Utilises acetate and H2. Methanosarcina Acetoclastic Uses thiosulphate and methanogensis in coal elemental sulphur as mines electron acceptor (114) Desulfobulbus Sulphate and nitrate Ignavibacterium Cultured from sulphide reducing bacterium rich, hot spring waters (125) Desulfosporosinus Found in coal before. Georgfuchsia Aromatic hydrocarbon Hydrogen Utilising degrader (126) (109) Azospira Hydrocarbon Leptospira Pathogenic bacterium degrading with nitrate (127) reduction. Seen in Indian coalbed previously (43) Catellibacterium Seen to degrade Streptacidiphilus Acidophillic bacterium insecticides (128) (129)

Agrococcus Isolated from a coal Desulfovibrio May transfer H2 to mine(130) other species Rubritepida Hot spring bacteria, Methanoregula Hydrogenotrophic oxidises thiosulphate methanogen to sulphate (131) Stenotrophomonas Aromatic hydrocarbon Levilinea Anaerobic non-spore degrader (132) forming filamentous bacteria (133) Starkeya Root nodule nitrogen Methanoregula Hydrogenotrophic fixation(134) methanogen

Network Analysis:

Environmental and chemical data collected during the field trial was correlated with OTU data from pyrosequencing to determine any possible interactions between the microbial community and the environmental factors within the treatment wells. Although correlations do not provide a direct mechanistic link between organisms and environmental parameters, they are frequently used in microbial ecology as a hypothesis-generating tool (135-137).

Figure 23 shows that both methane and acetate were seen in copresence with each other in the acetate amended well. A number of OTU were also seen to correlate with the increase in acetate levels, these OTU were from the three phyla Spirochaetes, Proteobacteria and

Firmicutes.

Figure 23: Network analysis of correlations with methane and acetate data from the acetate amended well. Correlations based on a Pearson coefficient > 0.75 on pyrosequencing data obtained with primers for the V5-8 region. Positive correlations are shown by green connecting lines while negative correlations are shown with red. Node colour is based on phylum classification.

Methane and acetate were also seen to correlate in the CaO2 + nutrient well. In comparison to the acetate amendment community, the CaO2 + nutrients showed that many more OTU

70 were correlated (>50) with the increase of methane and acetate, with representatives from a more diverse selection of phyla.

The nutrient treated well showed a number of positive correlations with various chemical data measurements from the field trial. Methane and acetate were both seen to be positively correlated with 3 OTU, Meniscus sp from the phylum Bacteroidetes,

Desulfosporosinus from the phylum Firmicutes and an unclassified Deltaproteobacteria. This positive correlation, refers to a Pearson coefficient of 0.75 or greater and represents a statistical analysis conducted in Cytoscape with the CoNet plugin. A large number of OTU are seen to positively correlate with methane and of these, five OTU classified as the methanogenic genera Methanosarcina and two as Methanosaeta.

Figure 24: Network analysis of correlations with methane and acetate data from the nutrient treated well. Correlations based on a Pearson coefficient > 0.75 on pyrosequencing data obtained with primers for the V5-8 region. Positive correlations are shown by green connecting lines. Node colour is based on phylum classification.

In the nutrient well, ammonium measurements also produced a number of correlations with other nodes in the network. Sulphate levels were seen to positively correlate with ammonium levels suggesting that the decrease in sulphate may be linked with nutrients, while fifteen OTU were seen to negatively correlate with ammonium (Figure 25). Two methanogens from the genera Methanosaeta were of this 15 as well as a number of

Proteobacteria (dark blue) and Firmicutes (orange).

Figure 25: Network analysis of correlations with ammonium data from nutrient treated well Correlations based on a Pearson coefficient > 0.75 on pyrosequencing data obtained with primers for the V5-8 region. Positive correlations are shown by green connecting lines while negative correlations are shown with red. Node colour is based on phylum classification.

Nitrate and nitrite levels also proved to have positive correlations with OTU from the pyrosequencing results of the nutrient well, whereas in all other samples, these two chemical data nodes failed to produce any significant co-occurrences. Two

Betaroteobacteria OTU, Thiobacillus and Sulfuricella were the only two OTU to be seen to correlate with nitrite levels in the field trial.

Figure 26: Network analysis of correlations with nitrate (left) and nitrite (right) data from nutrient treated well Correlations based on a Pearson coefficient > 0.75 on pyrosequencing data obtained with primers for the V5-8 region. Positive correlations are shown by green connecting lines while negative correlations are shown with red. Node colour is based on phylum classification.

Discussion:

The study of microbial communities in relation to coal seams has been carried out in a range of locations and geographic settings all over the world. Community structure over time and the effect that this may have on the biogasification of coal however is yet to be shown for coal associated microbial communities. The presence of methanogens is important for the production of methane, though other community members such as hydrocarbon degraders, fermenters and sulphate reducers, play crucial roles in the ability of methanogens to produce methane. Not only this, but it is important to understand these interactions over time to elucidate the mechanisms of biogasification. This is the first instance where an in- situ coal associated microbial community has been examined over time and has been done so in relation to various treatments using pyrosequencing technology.

Pyrosequencing with two different 16S primer sets shows that there is a large difference between the results of the two. As seen in Figure 18, the primers targeting the V3/4 region, shows a majority of Proteobacteria as well as a number of other bacterial phyla. This region however, failed to produce archaeal 16S sequences. On the other hand, the V5-8 region targeted by the 926-1392 primers revealed a more diverse community with sequences for

Euryarchaeota, Crenarchaeota and Spirochaetes being seen with more abundance compared to the V3/4 region. MDS plots of these sequences shows a clear pattern in the

V3/4 region, with microbial communities being statistically significantly different between the bulk aqueous phase communities and the surface associated communities (Figure 19).

The V5-8 region data however did not share these same community differences, indicating that these sequencing primers reveal different divisions of the microbial community. The

V5-8 region data which showed the presence of archaea, whereas the V3/4 region did not,

75 indicates that across samples, archaea, which were majorly composed of methanogens

(Figure 22), were not the cause of the statistical difference between samples and that the community dynamics are driven by changes in the abundance of various bacterial species, as opposed to archaea. It is evident from Figure 19 and Table 8 that these microbial communities from various samples are specific to the location from which they were obtained (bulk aqueous phase/surface associated).

Potential Functions in the Field Trial:

While the presence of an organism in the following data does not necessarily indicate that it is currently metabolically active, changes in microbial populations in response to the treatments can be considered as evidence that the organism is involved in that metabolic process in response to the given treatment.

Methane Production:

Methanogens are found in high abundances within the archaeal communities (Figure 22) and also the overall microbial communities (Figure 21). The main representatives of the methanogens in the samples were from the genera Methanosarcina, Methanosaeta and

Methanoregula. Methanosarcina is the only known methanogenic genus capable of all three mechanisms of methanogenesis (138) while Methanosaeta utilises the hydrogenotrophic and acetoclastic pathways and Methanoregula the hydrogenotrophic pathway (34, 101,

112). Coal associated microbial communities have been studied in the past and these three genera belong to the most commonly observed archaeal species in these environments

(101). Methanosarcina was most abundant in the acetate fed well and was most likely utilising this as a substrate for methanogenesis (31). It should be noted that replicates for

76 sequencing seen in Figures 21 and 22 were not possible due to financial and logistical constraints. Though qualitatively it is safe to assume that the genera observed in the sequencing are present, the derivation of quantitative trends from this data would benefit from the additional replicates.

Production of methane in the acetate and nutrient wells appear to coincide with increases in abundance of Methanosarcina. The metabolic diversity of Methanosarcina may allow it to continue methanogenesis under a number of conditions and it has been suggested that membrane bound hydrogenases in Methanosarcina may play a role in energy conservation during hydrogenotrophic methanogenesis, allowing it to survive in conditions of fluctuating sulphate conditions (139). This may explain why methane production was possible when sulphate was still present in the nutrient well, as sulphate reduction is a known inhibitor of methanogenesis.

In the CaO2 + nutrient treated well, a community shift in methanogens from

Methanosarcina dominated to Methanoregula dominated sequences is seen. This shift in community composition does not appear to be reflected in methane production and may represent a switch in substrates utilised for methanogenesis. A study by Hunger et al (2011) showed the consumption of labelled CO2 and formate by a methanogenic fen soil community. Labelled sequences belonging to Methanosarcina were identified to have come from the early utilisation of CO2 for methanogenesis and not from acetate or formate.

Whereas sequences related to Methanoregula boonei appeared later from the utilisation of formate for methanogenesis (24). This could represent in the field trial an early utilisation by

Methanosarcina of CO2 present in the coal/groundwater for methanogenesis followed by a switch to methanogenesis using another substrate, such as formate by Methanoregula.

Propionivibrio which was positively correlated to methane production in this well, has previously been shown to produce propionate (120), a key intermediate in the conversion of complex organic matter to methane. Propionate oxidation usually occurs via the methyl- malonyl-CoA pathway, which can yield acetate, CO2 and H2 or formate and under methanogenic conditions, can be further metabolised to methane (140). Relative abundance of Propionivibrio increases over the timespan of the field trial and the oxidation of propionate could potentially be the source of the secondary methanogenesis substrate, causing the shift in the microbial community.

The control well showed large proportions of the community were composed of methanogens, though no methane production was observed from this well. The most abundant methanogen in this well was Methanosaeta, as visible in (Figure 22). As this coal seam is non-gassy, it was expected that methane production would not be seen. However, this data showing high abundances for methanogens was procured from sequencing with primers 926-139 for the V5-8 region, that had a higher bias for archaea than 519-926 primer pair. As no methane was observed, methanogens portrayed in the sequencing may be in a state of low metabolism and producing methane below detectable limits. The increase in relative abundance of methanogens in the last time point for the control well is due to a drop in bacterial sequencing counts, with only a small increase in archaeal counts and not an increase in metabolic activity of methanogens.

Sulphate Reduction:

Sulphate reducing bacteria (SRB) have been observed in a range of environments, including

Australian coal seams (44, 125). Sulphate reduction and methanogenesis are both terminal anaerobic processes that utilise by-products from other bacterial metabolic activities and

78 mineralises them to CO2 and methane respectively (141). The process of sulphate reduction can prove to be inhibitory to methanogenesis as SRBs can outcompete methanogens for reducing equivalents (16). A number of sulphate reducers were determined to be abundant in both sequencing sets. Primers targeting the V3/4 region amplified sequences belonging to

Desulfovibrio, Desulfotomaculum and Desulfosporosinus genera, while the V5-8 region had sequences amplified from Desulfovibrio and Desulfotomaculum. The well amended with acetate showed a complete depletion of sulphate within the first three months as well as an increase in two highly abundant Desulfovibrio sequences in the pyrosequencing using primers for the V3/4 region. In the 0 month time point, these two Desulfovibrio were seen with a relative abundance of 0.29 and 0.14. Three months later, the most abundant of these increased to a relative abundance of 0.5. The spike in methane production from this well was likely due to the lack of sulphate which allowed for the ability of methanogens to outcompete SRB for reducing equivalents. All three of the nutrient, CaO2 + nutrient and control wells still exhibited a decrease in sulphate concentration, though microbial communities were unable to completely remove sulphate, as was seen in the acetate amended well. Desulfovibrio species and Desulfotomaculum are known to use hydrogen and acetate as electron donors (97, 142, 143) and have a higher affinity for acetate than methanogenic archaea (144). The disappearance of sulphate in the acetate amended well is ostensibly due to presence of acetate and the capacity of the Desulfovibrio and

Desulfotomaculum species observed, to reduce the sulphate present using acetate as an electron donor, therefore allowing methanogens in subsequent time points to flourish. The lack of appropriate reducing equivalents in the other three treatments to remove sulphate, no doubt had a final impact on their methane production.

Nitrogen Cycling:

A number of OTU were seen to negatively correlate with the disappearance of ammonia from the nutrient treated well (Figure 25), indicating an increase in abundance of these OTU as ammonia levels dropped. It is possible that a number of these OTU are utilising the ammonia in the well for incorporation into amino acids and biomass. However, an increase in nitrite and nitrate is also seen in the acetate, and both nutrient fed treatments. The appearance of nitrite and nitrate following the disappearance of ammonia suggests that nitrification of ammonia is occurring. Nitrification of ammonia is mainly performed by members of the genera Nitrosomonas and Nitrobacter (145), neither of which were observed in either pyrosequencing attempt. Two of the most abundant OTU in the nutrient fed wells, Dechloromonas and Aquabacterium, have previously been shown to be capable of nitrate reduction, with the former capable of anaerobically degrading benzene with the reduction of nitrate (113, 146). Aquabacterium’s rise in abundance appears to coincide with the peak production of nitrite and nitrate in the CaO2 + nutrient treatment and with nitrite production in the nutrient only well. Nitrite reduction has so far been unobserved in

Aquabacterium and several studies have shown that it is incapable of utilising nitrite as an electron acceptor (146, 147). Dechloromonas on the other hand has been shown to denitrify nitrate during the degradation of benzene to produce N2 (113) as well as being able to use nitrate and nitrite as electron acceptors in the production of N2 via N2O using a number of carbon compounds as a carbon source, such as acetate (148). A comparison of these two

OTU with ammonia, nitrate and nitrite data is shown in Figure 27

45 0.18 40 0.16

35 0.14 30 0.12 Aquabacterium 25 0.1 Dechloromonas

mg/L 20 0.08 CaO2 + Nutrients NH4 15 0.06 CaO2 + Nutrients NO2 10 0.04 Relative Abundance CaO2 + Nutrients NO3 5 0.02 0 0 0 3 6 9 12 Month

30 0.16 0.14 25 0.12 20

0.1 Aquabacterium

15 0.08 Dechloromonas mg/L 0.06 NH4 mg/L 10 NO2 mg/L 0.04 Relative Abundance 5 NO3 mg/L 0.02 0 0 0 3 6 9 12 Month

Figure 27: Relative abundance of Aquabacterium and Dechloromonas with ammonia, nitrite and nitrate in the CaO2 + nutrient fed well (top) and nutrient only fed well (bottom). Aquabacterium is seen to increase in abundance with the peak production of nitrate in both wells and also with the peak production of nitrite in the CaO2 + nutrient well. A decrease in the abundance of Dechloromonas is seen to accompany the decrease in ammonia in both wells.

This nitrogen data seems to suggest that although some OTU may be utilising ammonia for incorporation into biomass (Figure 25), stoichiometry suggests that a large amount of

81 ammonia is converted to nitrite by an as yet unidentified organism and that this is then potentially converted to nitrate and released to the atmosphere as N2O or N2. Conversion to nitrate by Dechloromonas and N2O/N2 by Dechloromonas, Aquabacterium or a combination of both is a possibility due to the sequences’ presence at these times. However, the changes in the low relative abundance of these OTU over time may indicate larger changes in other

OTU. Nitrification of ammonia and nitrite are both aerobic processes and the higher rate of disappearance of these in the CaO2 + nutrient well, may be attributed to the increased oxygen content provided by the CaO2.

Hydrocarbon Degradation:

A number of OTU found within the pyrosequencing datasets have previously been described as capable of degrading hydrocarbons (Table 9). As mentioned earlier, Dechloromonas has previously been seen to completely mineralise benzene in anaerobic conditions, linked with the reduction of nitrate (113). In anaerobic conditions, an electron acceptor other than oxygen is required to pass reducing equivalents, generated in the degradation of hydrocarbons, to (149). In this case, these reducing equivalents, in the form of NADH, reduce nitrate to nitrite, or nitrate and nitrite to N2 via N2O (113, 150). Another OTU seen in the pyrosequencing study belonged to the genera Georgfuchsia. Geogfuchsia toluolica

(Betaproteobacteria) has been previously shown to utilise nitrate as an electron acceptor

(126). This bacterium grew anaerobically with toluene and was unable to grow with other carbon compounds such as sugars, lactate or acetate. As well as using nitrate, G. toluolica was capable of using MnIV and FeIII as alternative electron acceptors. Present in G. toluolica are genes for the benzylsuccinate synthase enzyme, bssA, involved in the initial activation of toluene and the gene for the ring cleavage enzyme, bamA (126, 151). This indicates that G.

82 toluolica utilises the benzoyl-CoA degradation pathway in order to metabolise aromatic compounds via the central benzoyl-CoA compound. Bradyrhizobium (Alphaproteobacteria) has also been found to contain genes for the benzoyl-CoA degradation pathway (151) and is present in this pyrosequencing study. Although the author of this study notes that

Bradyrhizobium has not been seen previously to degrade aromatic hydrocarbons under anoxic conditions, a previous study by Samir et al (2007) showed that crude oil hydrocarbons could be utilised by root nodule bacteria, including Bradyrhizobium japonicum. Root nodules play a crucial role in the fixation of N2, an anaerobic process, though whether this study was in aerobic or anaerobic conditions was unspecified by the authors (152). The fact that Bradyrhizobium is found in anaerobic root nodules and has been previously shown to be capable of both hydrocarbon degradation and denitrification (153), all provide evidence to the likelihood that this OTU may be involved in anaerobic hydrocarbon degradation in the environment.

A number of OTU from the pyrosequencing dataset have also been previously observed to degrade hydrocarbons under aerobic conditions. These include Rhodanobacter,

Rhodococcus, Arthrobacter and Norcardioides who have been shown to degrade PAH, o- xylene, naphthalene/hexadecane and phenanthrene respectively (154-158). Interestingly, one of these OTU, Arthrobacter, has been shown to be capable of anaerobic growth using nitrate reduction while a second species is capable of a fermentative processes producing lactate, ethanol and acetate (159).

A third option for hydrocarbon degradation is seen in the OTU Desulfotomaculum, which has previously been shown to degrade o-xylene, m-xylene and toluene under strictly anaerobic conditions, linked with sulphate reduction (160). Initiation of degradation was

83 coupled to the production of methylbenzylsuccinate, indicating that fumarate addition was involved in the degradation of these three compounds. As with the hydrocarbon degradation seen with nitrate reduction in G. toluolica, Desulfotomaculum also utilises the benzyl-CoA degradation pathway to degrade aromatic compoundss (151) and reducing equivalents in the form of NADH produced during this degradation and beta oxidation, are used to reduce sulphate to hydrogen sulphide (H2S) (161).

Previous Studies of Coal Communities:

Previous studies of coal have shown that a number of genera are found across many different locations in the world and may be contributing the same hypothesised functions.

The major function studied in these systems is that of methane production. As mentioned earlier, a number of previous studies have found a wide range of methanogens in various coal deposits around the world, that may play a role in producing methane from coal using all three methanogenic metabolisms (42, 44, 103, 109). Here we find that, acetoclastic and hydrogenotrophic methanogens are primarily responsible for methane production, though a range of methanogens in smaller abundances and those with multiple metabolic pathways such as Methanosarcina sp. may also be capable of utilising the methylotrophic pathway.

Though an in-depth characterisation of the microbial communities and their role in methane generation from coal is lacking in the literature, a number of studies have indicated potential roles of genera observed in their coal samples. Guo et al (2012) pyrosequenced the community from the Eastern Ordos Basin in China and found high proportions of

Hydrogenophaga, an OTU found in high abundance in a number of samples found in this study. Hydrogenophaga are typically hydrogen utilising bacteria and are commonly found in methanogenic communities (31, 162). Guo et al explain that their lack of hydrogenotrophic

84 methanogens may be due to competition by Hydrogenophaga who are also utilising the substrates required by the methanogens for hydrogenotrophic methanogenesis. Nitrate reducers, Aqubacterium and Dechloromonas were also seen in pyrosequencing data from the Ordos Basin and may contribute a similar function as seen in this study, for both nitrate reduction and hydrocarbon degradation.

A study of the microbial diversity of Canadian subsurface coal beds notes that a number of the inferred bacterial groups found may have fermentative or hydrogenotrophic metabolisms (42). The majority of 16S rRNA sequences identified belonged to the

Proteobacteria and Firmicutes phyla, a trend seen in a number of coal environments, including this study. Hydrogenophaga was once again seen, along with other hydrogen utilising bacteria such as Acidovorax and Clostridium species, the latter of which was attributed to potentially producing fermentation products, utilisable by methanogens for methanogenesis. Clostridium species were also proposed to be potentially involved in hydrocarbon degradation however, Thauera aromatica was present and may have also been involved in this process.

Midgley et al (2010) show in an Australian coal seam from the Gippsland Basin, Firmicutes,

Proteobacteria and Actinobacteria were again observed to be the predominant phyla in the bacterial community (44). Most of the sequences obtained from their study showed closest

BLAST matches to uncultured bacteria, however one similarity with the study shown here, was a high abundance of Desulfovibrio in both communities. Desulfovibrio sp. are sulphate reducing bacteria and Midgley et al point out that Desulfovibrio species have been observed to produce H2 and may be involved in interspecies hydrogen transfer (44). However, this hydrogen production has only ever been observed under conditions where sulphate was

85 absent and coupled with the fermentation of lactate (107). This syntrophic capability of

Desulfovibrio would therefore only be possible in the amendment fed with acetate, where sulphate levels are seen to disappear completely.

It would appear from literature that many coal environments contain organisms capable of a number of metabolic processes such as methane generation, fermentation, hydrogen utilisation and hydrocarbon degradation. High abundance genera seen amongst the samples and their functions are shown as an overview in Figure 28.

Figure 28: Reconstruction of potential metabolic processes occurring in field trial wells In the reconstruction of possible functions occurring in the field trial, aromatic compoundss would be degraded by nitrate reducing bacteria (blue) such as Georgfuchsia, Aquabacterium and

Dechloromonas species to CO2 while fermenting bacteria (green) would produce fermentation products such as acetate and propionate. Homoacetogens such as Clostridium spp. could produce acetate from CO2 and H2. Acetate and CO2/H2 are then substrates for acetoclastic and hydrogenotrophic methanogens to produce methane from. Sulphate and nitrate disappearance

86 from the wells would be attributed to the sulphate and nitrate reducers in blue and orange respectively.

Conclusion:

This study shows that like previous studies before it, the microbial community of the investigated coal seam shows similarity to other studies around the world. Coal communities are dominated by bacterial species from Proteobacteria and Firmicutes phyla and archaeal species from Euryarchaeota. Here we go into a greater detail, using newer sequencing technologies to elucidate finer community detail than previously examined in

Australian coal seams. An examination of an in-situ coal community over time, in response to various treatments is also reported here and is as far as we know, is the first time that community dynamics in a coal seam has been investigated as such. Methane was produced in all treated wells, with the acetate amended well producing the most methane. This is not surprising and indeed was expected as most of the world’s biogenic methane is due to the action of acetoclastic methanogens (29). The CaO2 + nutrient treated well also produced a comparatively large amount of methane, more so than the nutrient only treated well, suggesting that an initial aerobic treatment of the coal seam leads to a higher methane yield than a standard nutrient treatment alone. Methanogens capable of hydrogenotrophic and acetoclastic methanogenesis were observed in most samples, such as Methanosarcina,

Methanosaeta and Methanoregula, while bacterial communities shifted over time. These bacterial communities were composed of a range of potential metabolic functions, such as nitrate reduction by Aquabacterium, Dechloromonas and Georgfuchsia species, sulphate reduction by Desulfovibrio and Desulfotomaculum and hydrocarbon degradation by the aforementioned Desulfotomaculum, Aquabacterium and Georgfuchsia as well as potential aerobic hydrocarbon degradation by Rhodanobacter, Rhodococcus, Arthrobacter and

Norcadioides spp. In this non-gassy coal seam, methane was successfully produced through the stimulation of a methanogenic community. An understanding of the community dynamics over time is important if biogasification from unproductive coal is ever to be used as an alternative natural gas technology. Several companies (e.g. Luca technologies) are working on microbial stimulation of methane production in coal, however their findings with respect to microbial communities are not publically available due to obvious commercial interests.

Chapter Four: Anaerobic Aromatic Compound Degradation Gene

Determination from a Field Trial Site.

Introduction:

Coal is a highly complex mixture of organic molecules of varying size and structure (1). In

Chapter 2, it was shown that the apolar solvent extractable phase of coal was made up of varying alkyl- and benzyl- organic compounds. Microbial degradation of these kinds of compounds has been well studied in relation to environmental concerns, such as oil spills.

Biodegradation by hydrocarbon-degrading microbial populations is one of the major routes for elimination of these compounds from the environment (163).

Aromatic compounds, such as BTEX (benzene, toluene, ethylbenzene and xylene) compounds and most other aromatic compounds are channelled into the benzoyl-CoA pathway (151, 164, 165) (Figure 29). This pathway is involved in the dearomatisation and hydrolytic ring cleavage of aromatic compounds, leading to the production of one molecule of CO2 and three acetyl-CoA molecules, where they are assimilated into biomass and further oxidised to CO2 (164, 166) or acetate via acetate kinase and phosphotransacetylase (33,

166). Dearomatisation of the aromatic ring is catalysed by two enzyme classes; the ATP- dependent BCR I (benzoyl-CoA reductase) and the ATP-independent BCR II.

Figure 29: Benzoyl-CoA degradation pathway showing the range of compounds that can feed into this pathway. The benzoyl-CoA degradation pathway is central to the degradation of many aromatic compounds. This figure shows many of these compounds as well as highlights a number of assays performed in this project. Figure taken from Kuntze et al (2011).

These two classes of BCRs are both involved in the reductive dearomatisation of Benzoyl-

CoA, via two different mechanisms. The first of these mechanisms is an ATP dependent process, whereby Benzoyl-CoA is reductively dearomatised to 6-oxocyclohex-1-ene-1-

90 carboxyl-CoA in facultative anaerobes by a Class I BCR. This type of BCR can be further separated into the Thauera-, Rhodopseudomonas- and the Azoarcus-type BCRs with the genes bcr, bad and bzd, respectively (151) (Represented in blue/red in Figure 30).

Class II BCR’s are found in obligate anaerobes and are encoded by bamB (151, 167) (Figure

30). Biochemical studies of the mechanism of BCRs in facultative anaerobes made it questionable whether this process would be energetically favourable in strict anaerobes

(168). For every benzoyl-CoA that is catabolised, three acetyl-CoA molecules are produced.

Acetyl-CoA, which is the central intermediate in degradation pathways for fermentative bacteria and acetogens, is used for the production of ATP (33, 166). However, both the activation of benzene with the CoA thioester and the dearomatisation of benzoyl-CoA, require 2 ATP each, resulting in a net decrease in energy (166). Therefore fermentative organisms require an ATP independent mechanism for the dearomatisation of benzoyl-CoA.

To maintain a net increase in ATP, the benzoyl-CoA reductase employed by fermentative bacteria are thought to be ATP independent (164).

Before a number of aromatic hydrocarbons, such as the BTEX compounds, can be degraded, they first have to be activated by the addition of a fumarate group. This is catalysed by the benzyl-succinate synthase (BssA) enzyme. This enzyme is involved in the addition of fumarate to aromatic compounds, such as xylene, toluene, ethylbenzene and cresols. After the addition of fumarate, these compounds are then funnelled into the benzoyl-CoA pathway (151, 166, 169). Under anaerobic conditions, alkanes, much like that of BTEX compounds, may be activated via the addition of the hydrocarbon across the double bond of a fumarate molecule (170). Two genes with similarity to the catalytic subunit of the benzylsuccinate synthase were found in a known alkane degrader, Desulfatibacillum

91 alkenivorans AK-01. These genes assA1 and assA2, were used by D. alkenivorans, a sulphate reducer, to utilise alkanes from C13-C18 (76, 171). This method of hydrocarbon activation, utilising fumarate, is the most widely reported mechanism of hydrocarbon activation and is a currently used biomarker for the identification of anaerobic alkane and aromatic hydrocarbon degradation (76, 170).

Figure 30: Diagram of benzoyl CoA pathway Chemical intermediates in the benzoyl-CoA degradation pathway. Highlighted in blue and red are reactions performed by anaerobic nitrate and nitrite respirers Thauera/Azoarcus, Rhodopseudomons respectively and in green is the pathway utilised for anaerobic fermenting organisms, such as Syntrophus acidotrophicus (149, 164, 166, 168, 172, 173).

Although the benzoyl-CoA degradation pathway and activation of BTEX and alkane hydrocarbons via fumarate addition are common mechanisms for the degradation of these compounds, research into these pathways in terms of coal degradation has been studied very little.

Biogenic formation of methane gas from coal is a potentially significant source for the production of natural gas, a cleaner burning form of energy than the coal used to produce it

(75). For this reason, there is much research into microbial communities that inhabit these environments, such as, but not exclusively, the coalbeds of the US, Canada, Japan, China and

Australia (4, 40, 42, 44, 109). However, molecular identification of degradation pathways used by these communities is absent from their characterisation, resulting in a sizeable knowledge gap of how this valuable reservoir of energy can potentially be produced.

Investigation of intermediate compounds produced during the biodegradation of coal under laboratory conditions revealed that compounds such as n-alkanes, hexadecanoic acid n- octadecanoic acid, -sitosterol, stigmasterol, and phenols were produced by the microbial community, WBC-2 (30). These compounds represent the biodegradation products of the coal geopolymer and potential substrates for hydrocarbon degrading organisms to produce substrates for methanogens, such as acetate.

A study of methanogenesis in biogenic coalbed methane formation in a Chinese coalbed showed the involvement of sulphate and nitrate reducing bacteria in the degradation of coal and the fuelling of methanogens. This study found that a number of 16S PCR clones obtained from wells drilled into a coalbed producing methane belonged to members of

Thauera and Desulfosporosinus sp (98% and 100% identity respectively) (174). Both these

94 genera are involved in hydrocarbon degradation, with Thauera sp known to contain the benzoyl-CoA degradation pathway (175) and Desulfosporosinus sp is known to degrade toluene, potentially utilising benzylsuccinate synthase (176). However, this was not further investigated.

A non-productive coal in Texas was stimulated in microcosm experiments to produce methane by the addition of nutrients and also, separately, bioaugmentation with a mixed methanogenic culture, WBC-2. In the bioaugmented microcosm, the accumulation of various compounds, such as long chain fatty acids, single ring aromatic compounds and long chain alkanes was observed with their eventual degradation and concurrent methane generation (75). Hydrocarbon degradation was observed with the methanogenic community and Pelotomaculum and Geobacter were implicated by the authors as being potentially responsible for this observation. Pelotomaculum has previously been linked with a community capable of phthalate degradation with the production of a benzoate intermediate and acetate end product (177). Geobacter is also known to contain the central benzoyl-CoA degradation pathway in order to utilise aromatic hydrocarbons (178). This study shows again phylogenetic analyses are indicating that coal microbial communities may be utilising the benzoyl-CoA degradation pathway in order to convert coal compounds into methane. However, this has not been studied in detail as of yet.

Genes involved in the benzoyl-CoA degradation pathway are currently being used as biomarkers to investigate environments for anaerobic hydrocarbon degrading potential

(168). Kuntze et al (2011) provided refinement to the bamA targeting PCR assay in order to identify more sequences from two different groups, the GMT (Geobacter, Magnetospirillum and Thauera) and SA (Sulphate reducing, Syntrophus, Azoarcus and Aromatoleum) groups

(151). These new bamA targeting primers along with bcrC, bamB and bzdN were deployed on samples from in situ microcosms that were incubated at two sites, one situated near an old coking plant in the vicinity of a coal mine and the other at an old benzene producing plant. Using specific primers developed previously and refined primers by the group, hydrocarbon degradation genes were able to be identified from both of the aromatic hydrocarbon contaminated sites (151). As well as targeting genes for the benzoyl-CoA degradation pathway, genes involved in the initial activation of some compounds may be targeted in parallel to determine the parent compound, as benzoyl-CoA is central to all aromatic degradation and not solely petroleum derived compounds (179). For this reason, the use of assays for detecting bssA are important in understanding the degradative capabilities of a community.

The aim of this chapter is to determine the presence and diversity of genes involved in anaerobic hydrocarbon degradation (AHD) pathways in samples derived from a methanogenic coal bed community. A number of possible genes from the benzoyl-CoA degradation pathway were screened for as benzoyl-CoA is central to most anaerobic aromatic hydrocarbon degradation. The related gene, assA was also assayed for, to determine if alkane degradation via the addition of fumarate was being utilised by the community.

Methods:

Field Trial of a Non-Gassy Coal Seam in the Western Coal Fields of NSW, Australia:

Five wells were drilled into a non-gassy sub-bituminous coal seam at the abandoned

Lithgow State Coal Mine in the Western Coalfields of NSW, Australia (-33° 27' 36", 150° 9'

36"). These five wells were subjected to 5 different treatments. These treatments are listed in Appendix 2. The wells were monitored for acetate, methane, pH, eH, sulphate, phosphate, nitrate, nitrite, magnesium, calcium and total iron (Fe2+ and Fe3+) levels and 20 L water samples were taken every three months and filtered onto supor polyethersolfone

(PES) filters with a pore size of 0.1 m (Pall Corporation) to be extracted for DNA.

Identification via PCR for Genes Involved in Anaerobic Hydrocarbon Degrading Genes from Metagenomic Data:

DNA was extracted from groundwater and coal samples obtained from the field trial every 3 months, using the protocol outlined in Appendix 5. This DNA was used to determine the presence of various hydrocarbon degrading genes. Samples were selected from the acetate amended well, anaerobic + nutrients well and CaO2 + nutrients well. PCR was performed using 1x concentration of Lucigen Econotaq PLUS GREEN®, 0.8 M of both forward and reverse primers (seen in Table 10), a minimum of 2 ng/l of DNA template and reactions were made up to 20 l using molecular grade water. PCR conditions used for amplification of sequences can be seen in Appendix 7.

PCR products were then run on a 1% (w/v) agarose gel with 1x gel red added into agarose mixture, in TAE buffer 1x for 30 min at 100 V and were viewed using a Biorad Molecular

Imager® Gel Doc™ XR+ System with Image Lab™ Software.

Primers used for PCR amplification of AHD genes are shown in the table below.

Table 10: Primer Sequences used in Anaerobic Hydrogen Degradation PCR Assays Primer Sequence Reference bzdN F 5’-GAGCCGCACATCTTCGGCAT-3’ Kuntze et al. bzdN R 5’-TRTGVRCCGGRTARTCCTTSGTCGG-3’ 2011 (151) bcrC F 5’-CGHATYCCRCGSTCGACCATCG-3’ bcrC R 5’-CGGATCGGCTGCATCTGGCC-3’ bamA-F 5’-CAGTACAAYTCCTACACVACBG-3’ Laban et al. 2010 (180) bamA-700 R 5’-CAKYYSGGGAASAGRTTKG-3’ Kuntze et al. bamA-800 R 5’-TTTTCCTTGTTGVSRTTCC-3’ 2011 (151) bamB F 5’-ATGMGGTAYGSAGARACHGG-3’ Song and bamB R 5’-CCSGCRWRYTTCADYTCCG-3’ Ward. 2005 (167) ass/bss F 5’-TTTGAGTGCATCCGCCAYGGICT-3’ Callaghan et ass/bss R 5’-TCGTCRTTGCCCCATTTIGGIGC-3’ al. 2008 (171)

Sequencing of purified DNA:

Where multiple bands were produced, the band of the correct size was excised from the agarose gel using a QIAquick™ Gel Extraction Kit according to manufacturer’s instructions and extracted DNA eluted with molecular biology grade water.

Purified DNA was sequenced using Sanger sequencing methods at the Ramaciotti Centre for

Genomics at the University of New South Wales, according to their Sequencing Protocol for

ABI 3730 Capillary Sequencer. This was performed using 1 ul BigDye® terminator V3.1 (Life

Technologies) with 20-50 ng PCR product, 3.2 pmol of sequencing primer, 3.5 l of 5x

o sequencing buffer and nuclease free H2O up to 20 l. A thermocylce of 96 C for 10 secs,

50oC for 5 secs, 60oC for 4 mins, repeated for a further 99 cycles was used to exhaust excess primer and nucleotides in the reaction.

Ethanol precipitation using sodium acetate and ethanol was used to clean up sequencing reactions before submission to the Ramaciotti Centre for Genomics.

Identification of Sanger Sequenced PCR Amplicons using a Basic Local Alignment Search Tool (BLAST):

Sanger sequences of AHD PCR amplicons in FASTA file format were searched against the

NCBI gene database using their BLAST tool (http://blast.ncbi.nlm.nih.gov/Blast.cgi) and the

Nucleotide collection (nr/nt) database, optimised for highly similar sequences (megablast) and somewhat similar sequences (blastn). Where possible, sequence results with gene characterisation were used to determine identification of the PCR results. Where characterised genes were not present, BLAST results were used to determine a putative identification.

Metagenomic Sample Selection from the Field Trial and Sequencing:

Samples were selected to compare both wells that were amended with nutrients (aerobic and anaerobic) at an early and late time point, before and after methane production. A fifth sample was also selected as it contained the most diverse AHD genes, according to PCR and

Sanger sequencing results. Samples were chosen from the nutrient + CaO2 (aerobic) well at time points 3 and 9 Months as well as the nutrient-only well (anaerobic) at 0, 3 and 12

Months. Calcium peroxide was present in the first 3 months of the CaO2 + nutrient treatment, and had been removed by the 9 month time point. DNA from these time points were prepared with a Nextera library kit and sequenced in one lane of Illumina HiSeq 2000 at Ramaciotti Centre for Genomics (UNSW, Australia).

Sequence processing:

All raw reads had Illumina Nextera adapter sequences removed using python script

“cutadapt.py” (181) that reads a FASTA or FASTQ file and removes Illumina adapters and writes the changed sequence to standard output. These adapter removed sequences were then quality processed with the python script “qualControl.py”, an in-house program which removes low quality bases (quality score <20, window = 4) from either end of the reads and discards any reads (and their respective paired reads) that contain an “N” or a low quality internal base (quality score < 15). Paired end reads must each be greater than 30 base pairs in order to be included in the output file. All singleton reads are also discarded.

Once sequences were quality trimmed, they were converted from fastq format to fasta format and assembled using the IDBA_UD program (182, 183). Kmers of 40 to 100 were used in increments of 20 to construct assemblies and the assembly with the largest contigs were chosen for further analysis. In each case, this was the 100 kmer assembly. As a note, while IDBA_UD is useful for assembling metagenomic data, the chimera rate may be higher than some other assembly programs as shown by Yang et al 2013 (184). An assembly using reads merged from all samples was also produced. Assemblies were then uploaded to IMG for annotation. (https://img.jgi.doe.gov/cgi-bin/er/main.cgi). IMG-Expert Review is a web

100 based tool that allows users to upload a metagenomic sample for gene identification and annotation. These annotations and a range of publicly available data sets are then available for analysis. Gene prediction is performed first for CRISPRs using the CRT and PILER-CR programs, then non-coding RNA genes, tRNA and rRNA are predicted using tRNAscan and an internally built rRNA prediction model respectively. Finally protein coding genes are identified with a set of four programs, GeneMark, Metagene, Prodigal and FragGeneScan.

Functional annotation associates protein coding genes with Pfams, COGS, KO terms, EC numbers and phylogeny. The metagenome SOP of IMG indicates that model specific trusted cutoffs are used together with an e-value of 0.1 to gather hits, while COG results are found using a cut-off value of 0.01 to filter hits. Assignment of KO, EC and phylogeny annotations are made using similarity searches against a reference database and the top 5 hits against this database are retained for each gene. Phylogeny is determined using the top hit and KO and EC terms use the top 5 hits to genes to determine the annotation, where a hit results in an alignment if there is at least 30% identity and greater than 70% of the query protein sequence or KO gene sequence are covered by the alignment (185).

Alignment and Phylogenetic Tree Production of Sanger Sequences:

Sequences for phylogenetic trees were obtained from results of Sanger sequencing, representative protein sequences from NCBI for AHD genes, KEGG annotated genes from

IMG metagenomes and protein sequences that showed similarity to known NCBI AHD genes in the metagenome (if they weren’t already annotated as such). All open reading frames

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(ORF) from Sanger sequences were translated and aligned with known AHD genes to determine the correct ORF for phylogenetic tree analysis.

Protein sequences were imported into MEGA6 (186) and a ClustalW alignment performed with a pairwise alignment gap opening penalty of 10, gap extension penalty of 0.1 and a multiple alignment gap opening penalty of 10 and gap extension penalty of 0.2 using a

BLOSUM protein weight matrix. Sequences that did not produce any overlapping alignment with the PCR sequences were removed from the alignment. The resulting alignments were used to produce maximum likelihood trees based on the Jones-Taylor-Thornton matrix- based model, with a complete deletion of missing positions and 500 bootstrap replications.

Alignment of nucleotide sequences were performed using a ClustalW alignment program with a pairwise aligment gap opening penalty of 15 and gap extension penalty of 6.66, with a multiple alignment gap opening penalty of 15 and gap extension penalty of 6.66. DNA weight matrix used was IUB and a transition weight of 0.5.

Pathway comparisons:

Relative abundance calculations were performed by comparing KEGG annotations of genes in selected pathways with the total count of KEGG classification for each sample. Total KEGG counts for a pathway were therefore compared to total KEGG classifications per sample to obtain the relative abundance of a pathway in a sample. The average of unique gene KEGG

ID counts in a pathway were compared with the average of all other KEGG counts in a pathway to determine whether genes in the pathway were being used for multiple functions.

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Results:

Identification of Anaerobic Hydrocarbon Degrading Genes by PCR:

To determine genes involved in the anaerobic degradation of hydrocarbons, PCR was conducted to assay a number of genes, known to have anaerobic hydrocarbon degrading potential. Screening was performed on DNA samples extracted from the bulk aqueous phase of the coal associated groundwater (Table 11) and the coal surface. Five time points from the acetate, CaO2 + nutrients and nutrient treatments of the field trial showed that genes involved in hydrocarbon degradation via the benzoyl-CoA pathway were present within the methanogenic coal communities.

The most frequently detected gene of the benzoyl-CoA pathway, was a Class I ATP- dependent benzoyl-CoA reductase, bcrC, involved in the initial dearomatisation of the benzoyl-CoA aromatic structure by Thaeura sp. Another Class I ATP-dependent benzoyl-CoA reductase, bzdN, associated with Azoarcus sp was also detected at three months in the nutrient treated sample. In the same sample, the only occurrence of the ring cleavage gene bamA was observed. All other samples failed to produce any amplifiable sequences for both groups (GMT and SA) of bamA genes. No sequences relating to obligate anaerobic BCRs

(bamB), or benzyl- and alkylsuccinate synthases (assA/bssA) were detected.

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Table 11: AHD genes present in bulk aqueous phase of Acetate, Nutrient + CaO2 and Nutrient only, wells (Ac, CaO2 and An respectively) Time T0 T3 T6 T9 T12 point

Treatment Ac CaO2 An Ac CaO2 An Ac CaO2 An Ac CaO2 An Ac CaO2 An bzdN - - - - - + ------bcrC + - + + - + + - + + - + + + - bamB ------bamA-700 - - - - - + ------bamA-800 ------bssA ------assA ------

A comparison of the bulk aqueous phase PCR results and the coal surface results shows that

the bulk aqueous phase samples have a more complete pathway. Only bcrC genes were

amplified from the coal surface samples in the late time point of the nutrient-only amended

well and the initial sample of the CaO2 + nutrient amended well. In contrast to the surface

associated samples, in the bulk aqueous phase, bcrC was found in all but the last sample of

the nutrient-only fed well and in the last time point of the CaO2 + nutrients. The gene for

bcrC was also observed in the surface-associated DNA of the basket coal in the nutrient-only

amended well.

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Identification of Amplified Sequences: bzdN:

The sequence of the single occurrence of bzdN in T3 of the anaerobic + nutrient amended, bulk aqueous phase sample, was most similar to the Class I BCR gene bzdN from uncultured bacterial clones (88-95% similarity with a query coverage of 100%). The most closely related results with taxonomic identification were to the bzdN genes of several Azoarcus sp

(similarity 80-83%). The Azoarcus sp sequence that showed 80% similarity (AN: AF521665.1) has been characterised as bzdN and involved in the degradation of benzoate (187). bcrC:

Four out of fourteen of the bcrC amplicons provided clean Sanger sequences that could be identified. These sequences belonged to samples from the acetate amended well at time point T3, T6 and T9 and also the nutrient-only amended well at T9 of the bulk aqueous phase. The sequences obtained were most similar (70-75%) to sequences from Thauera sp

(such as T. selenatis, T. chlorobenzoica, T. aromatica), Rhodomicrobium vanielii and

Rhodopseudomonas palustris, all of which contain genes encoding for Class I BCRs (83). bamA:

When searched against the NCBI database, the gene sequence generated from bamA-700 primers from T3 in the nutrient-only well, aligned with a single sequence from an uncultured bacterium, encoding the gene for a putative 6-oxocyclohex-1-ene-carbonyl-CoA hydrolase

(bamA). The same sequence was also searched using the blastn program, to find less similar sequences. This search expectantly yielded many more results, of which, the bamA-700 sequence, was closely related to sequences from Desulfosarcina spp. (79% identity, E=9e-

21, length= 99/125) and Desulfococcus spp (78% identity, E=5e-18, length= 97/125).

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Assembly of Illumina reads from field trial samples:

Samples were assembled using the IDBA_UD program

(http://i.cs.hku.hk/~alse/hkubrg/projects/idba_ud/) with kmers of lengths 40 to 100 in

20mer increments. Assemblies producing the longest contigs and lowest number of contigs, were chosen for analysis, which in all cases were assemblies constructed with 100mer.

Table 12 shows statistics of each sample from their assemblies.

Table 12: Statistics of metagenomic assemblies.

CaO2 + CaO2 + Nutrients Nutrients Nutrients Merged Nutrients Nutrients 0 Months 3 Months 12 Months Assembly 3 Months 9 Months Number of 138667 129588 195777 136889 151881 79248 Contigs Total bp 136764098 152139477 169842751 123315159 161888200 146178472 Shortest 201 200 200 200 200 200 Longest 301806 378569 857039 857039 418349 1036231 Average 986.3 1174 867.5 900.8 1065.9 1844.6 Length Average 53.1 49.7 57.9 56.8 54.6 54.5 GC% Contigs > 59.50% 66.60% 51.20% 54.00% 63.20% 79.30% 1kb Contigs > 43.80% 53.10% 38.30% 42.00% 46.30% 64.50% 2kb Contigs > 15% 25.30% 10.50% 13.70% 15.10% 22% 10kb Contigs > 2.90% 7.00% 2.30% 2.60% 3.30% 6.10% 100kb

A merged assembly of reads from all samples was also produced to obtain longer and more complete contigs. In this assembly, it can be seen in

Table 12 that the longest contig is longer than the longest contig seen in individual samples.

The total number of contigs is also lower, indicating that many of the smaller contigs in the separate samples have assembled together into longer, more complete contigs. When

106 looking at the contigs greater than 1, 2, 10 and 100kb, the merged assembly mostly has a greater percentage of larger contigs in the metagenomes when compared to individual samples.

Identification via PCR for genes involved in Anaerobic Hydrocarbon Degrading from Metagenomic Data:

Figure 31 shows all sequences in the metagenomic study whose sequences were COG classified as bcrC as well as representative sequences from NCBI and Sanger sequencing results from the PCR study performed. The phylogenetic tree in Figure 31 shows that three separate clusters of BCRs; the Thaeura type bcrC, Rhodopseudomonas type badD and

Azoarcus type bzdN, form. Sequences from the PCR assays also appear to cluster with their respective groups. Four separate clusters, phylogenetically related to bcrC also appear and may represent novel benzoyl-CoA reductases that do not have high similarity to known sequences. As the COG annotation for bcrC also includes sequences for badD and hgdB (2- hydroxyglutaryl-CoA dehydratase), representative sequences for hgdB were included into the phylogenetic tree construction to determine if annotated sequences were more closely related to bcrC or hgdB.

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Escherichia coli str.K-12 hgdB

Azoarcus evansii bzdN Azoarcus toluclasticus bzdN Aromatoleum aromaticum EBN1 Anaerobic well T3 bzdN PCR Aromatoleum aromaticum bzdN CaO + Nutrients Late bcrC (10198761) Nutrients Late bcrC (10047853) CaO + Nutrients Early bcrC (10178311) CaO + Nutrients Early bcrC (10429861) Merged Assembly bcrC (100046921) CaO + Nutrients Late bcrC (10118931) CaO + Nutrient Early bcrC (10011914) Nutrient Late bcrC (100039622) CaO + Nutrients Early bcrC (10214881) CaO + Nutrients Late bcrC (10436421) Merged Assembly bcrC (10005415) Nutrients Late bcrC (10010665) CaO + Nutrients Early bcrC (10154232) Rhodopseudmonas palustris CGA009 badD Rhodopseudomonas palustris DX1 badD Rhodopseudomonas palustris TIE1 badD Rhodopseudomonas palustris badD Rhodopseudomonas palustris BisB5 badD Acetate well T6 bcrC PCR Acetate well T9 bcrC PCR Nutrient well T9 bcrC PCR Thaurea aromatica bcrC Rhodomicrobium vannielii bcrC Magnetospirillum magneticum bcrC Acidaminococcus fermentans hgdB Clostridium symbiosum hgdB

Nutrient Late (10315551) Methanocella arvoryzae hgdB CaO + Nutrient Late (10007618) Butyrivibrio proteoclasticus hgdB

Figure 31: Molecular Phylogenetic analysis of bcrC, badD and bzdN by maximum likelihood method The evolutionary history was inferred by using the Maximum Likelihood method based on the JTT matrix- based model (188). The tree with the highest log likelihood (-3560.8885) is shown. The percentage of trees in which the associated taxa clustered together is shown next to the branches. Initial tree(s) for the heuristic search were obtained by applying the Neighbor-Joining method to a matrix of pairwise distances estimated using a JTT model. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. The analysis involved 97 amino acid sequences. All positions containing gaps and missing data were eliminated. There were a total of 40 positions in the final dataset. Evolutionary analyses were conducted in MEGA6 (186). Sequences with • indicate those that have been experimentally characterised (189, 190). Tree branches that contain just sequences from the metagenomic study and no previously characterised benzoyl- CoA reducastes have been collapsed for clarity.

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To determine whether sequences obtained in the metagenomic analysis were capable of being amplified with the benzoyl-CoA reductase primers used, an alignment of the primers used to amplify BCRs, with known and putative BCRs as well as sequences from the metagenomic samples, is shown in Figure 32. This shows three sequences found in the metagenomic samples did not have full sequence lengths to be compared with the primer.

Also, four more of the thirteen metagenomic sequences have 3’ mismatches to the forward primer of bcrC. The remaining six sequences that did show the potential for binding the forward bcrC primer however showed very low degree of similarity for the reverse primer, shown in Figure 33.

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Figure 32: Alignment of bcrC and bzdN forward primers with BCR sequences and putative metagenomic BCRs Alignment of forward primers from the bcrC and bzdN assays with sequences used to make the phylogenetic tree in Figure 31. Conserved sites of 60% or greater are shaded in black.

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Figure 33: Alignment of bcrC reverse primer with BCR sequences and putative metagenomic BCRs Alignment of reverse primer from the bcrC assay with sequences used to make the phylogenetic tree in Figure 31. Conserved sites of 60% or greater are shaded in black.

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PCR assays failed to amplify any bamB sequences from both the coal or coal associated groundwater in the field trial. However, BLAST searches of the metagenomic data reveal sequences that may be related to the Class II BCR, bamB. These sequences are seen in the phylogenetic tree in Figure 34.

Geobacter daltonii FRC-32 Geobacter metallireducens GS-15 Geobacter bemidjiensis Desulfomonile tiedjei CaO + Nutrient Late (10037712) Nutrient Late (10040833) CaO + Nutrient Late (10101221) Syntrophus aciditrophicus Nutrient Late (10233632) CaO + Nutrient Early (10223771) Nutrient Late (10071462) CaO + Nutrient Early (10068322) Desulfobacula toluolica Desulfobacula toluolica Tol2 Nutrient Late (10043153) CaO + Nutrient Early (10017781) Nutrient Late (10059544) Nutrient Mid (10011483) Nutrient Early (10008752) CaO + Nutrient Late (10041145) CaO + Nutrient Early (100004257)

Figure 34: Molecular Phylogenetic Analysis by Maximum Likelihood Method of bamB The evolutionary history was inferred by using the Maximum Likelihood method based on the JTT matrix- based model (188). The tree with the highest log likelihood (-5086.8960) is shown. Initial tree(s) for the heuristic search were obtained by applying the Neighbor-Joining method to a matrix of pairwise distances estimated using a JTT model. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. The analysis involved 21 amino acid sequences. All positions containing gaps and missing data were eliminated. There were a total of 302 positions in the final dataset. Evolutionary analyses were conducted in MEGA6 (186). Three distinct groups of sequences align with putative bamB sequences from Geobacter sp, Syntrophus acitrophicus and Desulfobacula toluolica.

Sequences from the metagenomic data cluster with three groups of putative bamB sequences, Geobacter sp. Syntrophus acitrophicus and Desulfobacula toluolica. All these sequences are from the late time point of both the CaO2 + nutrient and the nutrient only

112 amended wells. Another cluster of similar sequences was observed, but were not shown to be clustering with any previously discovered bamB sequences.

COG annotation searches did not yield any results for bamA and sequences for the phylogenetic tree were obtained by BLAST searching the metagenome using known bamA sequence from Geobacter metallireducens and Desulfobacula toluolica. BLAST searches for the sequence obtained from Sanger sequencing of the PCR product for bamA, produced a number of hits in the metagenome. These sequences were used to produce the phylogenetic tree seen in Figure 35.

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Desulfobacula toluolica Desulfatiglans anilinu Desulfosarcina ovata Desulfosarcina cetonica Desulfococcus multivorans Nutrient well T3 bamA-700 PCR Georgfuchsia toluolica Aromatoleum aromaticum EbN1 Azoarcus sp. CIB Azoarcus toluvorans Azoarcus sp. Desulfomonile tiedji CaO + Nutrient Early (10052361) Nutrient Late (10107311) Merged Samples (10102251) Nutrient Late (10596941) Merged Samples (10019949) CaO + Nutrient Early (10027502) CaO + Nutrient Late (10053921) Nutrient Late (10147102) Nutrient Late (10508691) Thauera chlorobenzoica Geobacter metallireducens Thauera aromatica Geobacter sp. Geobacter metallireducens CaO + Nutrient Early (10293271) Nutrient Late (10120951) Geobacter daltonii Geobacter bemidjiensis Thauera sp. Rhodomicrobium vannielii CaO + Nutrient Early (10585331) Bacillus pseudofirmus Enoyl-CoA hydratase Streptomyces coelicolor Enoyl-CoA hydratase Escherichia coli Enoyl-CoA hydratase

Figure 35: Molecular Phylogenetic analysis of bamA by Maximum Likelihood method The evolutionary history was inferred by using the Maximum Likelihood method based on the JTT matrix- based model (188). The tree with the highest log likelihood (-749.8366) is shown. The percentage of trees in which the associated taxa clustered together is shown next to the branches. Initial tree(s) for the heuristic search were obtained by applying the Neighbor-Joining method to a matrix of pairwise distances estimated using a JTT model. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. The analysis involved 36 amino acid sequences. All positions containing gaps and missing data were eliminated. There were a total of 36 positions in the final dataset. Evolutionary analyses were conducted in MEGA6. Sequences with • indicate those that have been experimentally characterised.

The PCR product of bamA-700, amplified from the nutrient amended well at T3 clustered with a number of sulphate reducing bacteria and closely to Azoarcus and Aromatoleum

114 sequences. Two sequences from the metagenomic study also are closely related to

Geobacter and Thauera 6-oxo-cyclohex-1-ene-carbonyl-CoA hydratase sequences. From the metagenomic data, there also appears to be another cluster of sequences that are related to the SA cluster targeted by bamA-700, but were not identified via PCR and may represent a novel collection of bamA sequences. No sequences in the metagenome were annotated as bamA, though when bamA from T. aromatica was BLASTed against the metagenome, all similar sequences were annotated as enoyl-CoA hydratase. Sequences of enoyl-CoA hydratase were also included into the phylogenetic tree to determine whether the sequences in the metagenomic data were bamA or indeed, enoyl-CoA hydratase. These sequences were closely related to bamA characterised from Thauera aromatica and are likely not enoyl-CoA hydratase.

Assays using PCR for bssA and assA genes all came up negative. BLAST searches of the metagenomic sequences, using known Thauera and Azoarcus sequences revealed sequences with similarity to bssA/assA, but annotated as pyruvate formate lyase. Most of these sequences do not cluster with known bssA or assA genes and are potentially pyruvate formate lyase. Five sequences do however cluster near assA genes. Identification of these sequences is difficult as there are very few characterised assA genes (76).

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Figure 36: Molecular Phylogenetic analysis of bssA and assA by Maximum Likelihood method The evolutionary history was inferred by using the Maximum Likelihood method based on the JTT matrix- based model (188). The tree with the highest log likelihood (-22832.2402) is shown. The percentage of trees in which the associated taxa clustered together is shown next to the branches. Initial tree(s) for the heuristic search were obtained by applying the Neighbor-Joining method to a matrix of pairwise distances estimated using a JTT model. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. The analysis involved 45 amino acid sequences. All positions containing gaps and missing data were eliminated. There were a total of 463 positions in the final dataset. Evolutionary analyses were conducted in MEGA6 (186).

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A comparison of the results obtained from molecular vs bioinformatics techniques is presented as Table 13. It is visible from the table that AHD targeted genes with PCR were not observed in a majority of samples, the exception to this was the middle time point (3 months) of the Nutrient fed only well. All other putative AHD sequences were discovered in the metagenomes of the samples.

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Table 13: Comparison PCR and Metagenomic results of CaO2 + Nutrient Early and Late time point with Nutrient Early Middle and Late time point in regards to assayed hydrocarbon degrading genes.

Time Early Middle Late point

Well # CaO2 An CaO2 An CaO2 An Method PCR Metagenome PCR Metagenome PCR Metagenome PCR Metagenome PCR Metagenome PCR Metagenome bzdN - - - - NA NA + - - - - -

bcrC - + + - NA NA + - - + - +

bamB - - NA NA - - -

bamA - + - - NA NA + - - + - +

bssA - - - - NA NA - - - ? - ?

assA - - - - NA NA ------

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Investigation of Metabolic Pathways in Coal and Coal Mine Associated Groundwater Metagenomes:

Benzoyl-CoA:

The genes involved in the degradation of benzoyl-CoA to acetyl-CoA were examined via

KEGG pathways in the metagenomes, from samples obtained during the field trial at the

Lithgow State Coal Mine. A number of these genes served as PCR targets, including the ATP dependent, Class I BCRs bcrC and bzdN, the ATP independent Class II BCR bamB and the gene responsible for ring cleavage, bamA.

As the benzoyl-CoA degradation pathway is central to degradation of many types of aromatic hydrocarbons, the presence of these genes is indicative to the potential of the community to degrade a range of hydrocarbons. Figure 37 shows the presence of the benzoyl-CoA degradation pathway in the five metagenomic samples.

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Figure 37: Benzoyl-CoA KEGG pathway in metagenomic samples This figure shows the presence of the genes responsible for degrading Benzoyl-CoA to Acetyl-CoA via the Benzoyl-CoA reductase pathway. Genes highlighted in blue indicate that the genes was found across all samples, red > 75% of samples, dark orange > 50% of samples, light orang > 25% of samples and yellow up to 25% of samples. Genes found in samples CaO2 + Nutrients Early and Late, and Nutrients only are indicated by black, grey, dark green, green and cream coloured spots respectively.

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Genes encoding enzymes that catalyse the following reactions were found amongst all samples

1. Cyclohexane-1-carboxyl-CoA  Cyclohex-1-ene-1-carboxyl-CoA

2. 2-Hydroxycyclohexane-1-carboxyl-CoA  2-Ketocyclohexane-1-carboxyl-CoA

3. Glutaryl-CoAAcetyl-CoA

Reactions 1 and 2 are both responsible for the degradation of benzoyl-CoA by the

Rhodopseudomonas palustris-like genes, aliB and badH. Five genes involved in the conversion of glutaryl-CoA to acetyl-CoA, are seen in both anaerobic respiration and fermentative pathways involved in the degradation of benzoyl-CoA.

A comparison of the relative abundance of KEGG classified BCR genes against all KEGG classifications was performed for the 5 metagenomes (Figure 38) to show the abundance of these genes in the communities as an indication of potential anaerobic hydrocarbon degradation.

0.0004

0.00035

0.0003 CaO+Nutrients Early 0.00025 CaO+Nutrients Late 0.0002 Nutrients Early

0.00015 Nutrients Mid Nutrients Late Relative Relative Abundance 0.0001

0.00005

Figure 38: Relative abundance of Benzoyl-CoA degradation genes in the five metagenomic samples.

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A comparison of the five metagenomes in terms of the relative abundance of KEGG classifications of all genes involved in the Benzoyl-CoA degradation pathway, in relation to total number of KEGG classifications.

The presence of KEGG annotated benzoyl-CoA degradation genes is observed in all five metagenomes. Metagenomic samples from the CaO2 + nutrient treated well show a nine- fold decrease in relative abundance of these genes after removal of CaO2, in comparison to the nutrient only treated well which showed an increase of seven times of benzoyl-CoA pathway genes from the early to the late time point.

Mechanisms of Hydrocarbon Degradation Found in Field Trial Metagenomic Sequencing:

Other mechanisms of hydrocarbon degradation were also investigated in the field trial metagenomic sequencing data, these included bisphenol, ethylbenzene, fluorobenzoate, naphthalene, polycyclic aromatic hydrocarbons, styrene, toluene, xylene and benzoate degradation pathways, all of which are capable of funnelling carbon through the benzoyl-

CoA degradation pathway.

Analysis of KEGG pathways from the metagenomes indicates that pathways are present for the degradation of a number of aromatic compounds.

Toluene:

Peripheral pathways for channelling toluene and toluene degradation products into the benzoate and xylene degradation pathways are seen in metagenomes from both the CaO2 + nutrients and the nutrients only amended wells. However, the presence of these genes varied greatly over time. In the CaO2 + nutrient well, genes encoding the degradation of toluene via addition of fumarate using benzoyl-CoA as an intermediary, are only present in the early time point and absent in its entirety apart from the presence of one enzyme,

122 benzoylsuccinyl-CoA thiolase (shown in blue below), from the later time point, showing a potential loss of this function in the coal associated microbial community after the removal of CaO2.

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Figure 39: KEGG pathway of toluene degradation in CaO2 + Nutrient well The pathway for toluene degradation shows that most genes are only found in the early time point (yellow) with a few being found in both early and late time point (blue). All genes in yellow are only found in the early time point for the CaO2 treated well. Almost the complete pathway for the preparation of toluene to be degraded via the Xylene and Benzoate degradation pathways are present.

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This loss of potential toluene degradation however was not seen in the nutrient only treated well, with most functions being largely preserved across the early, mid and late time point.

However, an overall decrease in relative gene abundance between the early and late time point (Figure 44) was still observed for both treatment wells.

Xylene:

Partial degradation pathways from para-xylene (1,4-dimethylbenzene) are present in both wells (Figure 40). Interestingly, a more complete pathway from the degradation of 3- methylcatechol, an end product of toluene degradation and an intermediate in the degradation of ortho-xylene, exists that would give the microbial community in the nutrient amended well the potential to degrade toluene via either 3-Methylcatechol into acetyl-CoA or via benzoyl-CoA to acetyl-CoA across time, while the CaO2 treated well loses this function in its later time point. These two potential pathways are represented in Figure 41.

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Figure 40: KEGG pathway of Xylene degradation in nutrient amended well Genes highlighted in blue indicate that they were observed in all three time point (early, mid, late), while yellow and red indicate they were found in only 1 or 2 time point respectively.

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Figure 41: Potential toluene degradation pathways Potential degradation pathways of Toluene as determined by genes present in the Nutrient amended well. One potential pathway that was targeted via PCR assays, involves the degradation of toluene via the benzoyl- CoA pathway. A second pathway was determined via metagenomic data that shows genes for the degradation of toluene via 3-methylcatechol.

Ethylbenzene:

Unlike toluene and xylene degradation, degradation of ethylbenzene appears to be largely incomplete based on metagenomic annotations (Figure 42). The genes observed in the

KEGG pathway for ethylbenzene degradation appear 12 months after nutrient addition, but only in low counts. Though these do not represent a full complement of ethylbenzene degradation enzymes, the fact that they appear in the last time point may be an indication that this function was being selected for with the treatments. The gene for the enzyme responsible for converting benzoylacetyl-CoA into benzoyl-CoA and acetyl-CoA, fadA, is seen across all samples and remains at a higher count than other genes in the pathway. A look at

127 the relative abundance of KEGG classified ethylbenzene degrading genes, shows a decrease

(Figure 44) in both treatments between the early and late time point, in contrast to the fact that a more complete pathway is present in later time point.

Figure 42: KEGG pathway of Ethylbenzene degradation in nutrient amended well Genes highlighted in blue indicate that they were observed in all three time point (early, mid, late), while yellow and red indicate they were found in only 1 or 2 time point respectively. Two enzymatic steps for the degradation of ethylbenzene to benzoyl-CoA are not present in the later time point, while those that are present, are not present in high abundance.

Styrene:

Styrene degradation capabilities are again seen in the nutrient amended well more so than in the CaO2 + nutrient treated well, with the genes required for the conversion of styrene to phenylacetaldehyde not present in the metagenomes of the CaO2 + nutrient treated well.

Conversion from phenylacetate to homgentisate and further onto tricarboxylic acid (TCA) cycle intermediates also does not appear likely from KEGG classifications of the nutrient amended metagenome. It has been suggested that in anaerobic consortia, that

128 phenylacetate is converted to toluene (191, 192) to be further degraded, unlike in the aerobic pathway where phenylacetate is converted to TCA cycle intermediates (Figure 43)

Figure 43: KEGG pathway of Styrene degradation in nutrient amended well. Genes highlighted in blue indicate that they were observed in all three time point (early, mid, late), while yellow and red indicate they were found in only 1 or 2 time point respectively. Styrene monoxygenase function is seen in only the mid time point, indicating that the potential for an aerobic activation of styrene is possible.

0.0016 0.0014 0.0012 0.001 CaO+Nutrients Early 0.0008 0.0006 CaO+Nutrients Late 0.0004 Nutrients Early

Relative Relative Abundance 0.0002 Nutrients Mid 0 Nutrients Late

Figure 44: Relative abundance of KEGG pathways per metagenome The relative abundance of select hydrocarbon degradation KEGG pathways for the 5 metagenomes based on all KEGG classifications.

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Discussion:

In the previous chapter, chemical analyses of the coal obtained from the same site shows the presence of a range of aromatic and aliphatic hydrocarbons. The presence of a range of aromatic hydrocarbons such as methylated naphthalenes and a phenanthrene suggests that there is opportunity for aromatic hydrocarbon degradation in the microbial community.

Methane from coal is thought to originate through two different mechanisms, one of these mechanisms involves microbial interactions in a coal seam to degrade carbon compounds and produce suitable substrates for methanogenesis (16, 193). The production of methane from coal requires the syntrophic interaction of a range of organisms producing catabolic enzymes that degrade components of coal (44). Initial PCR for AHD genes from field trial samples suggests the presence of AHD genes and the potential for hydrocarbon degradation and therefore catabolic consumption of coal components.

Genes involved in anaerobic aromatic hydrocarbon degradation were detected with PCR primers targeting regions of the benzoyl-CoA reduction pathway, a central intermediate for anaerobic aromatic hydrocarbon degradation (164, 168, 172). A diverse spectrum of aromatic compounds (such as styrene, toluene, ethylbenzene, etc.) may have various upstream anaerobic degradation pathways but still feed into the benzoyl-CoA pathway. For this reason, anaerobic degradation of aromatic compounds is better identified through the benzoyl-CoA pathway (168), as it is a central process and most anaerobic aromatic hydrocarbon degradation, proceeds through this pathway. To date, there have only been four applications of the benzoyl-CoA pathway primers used in this study (151, 194-196). The studies by Kuntze and Staats (151, 195) are both in relation to the presence of toxic BTEX contaminants in natural aquatic and terrestrial environments. The ability to screen

130 contaminated sites for organisms capable of natural attenuation of these compounds is of great significance as well as understanding the metabolic processes involved in the removal of these compounds from the environment.

Screening with PCR found that most samples contained the gene encoding for the Class I

BCR, bcrC (Table 11). This Class I BCR is found at the beginning of the benzoyl-CoA pathway and dearomatises the benzoyl-CoA molecule. Class I BCRs were found in all time points of the acetate amended well, all but one of the nutrient amended well, but only one time point of the nutrient + CaO2 treated well after CaO2 removal. This suggests that the nutrient

+ CaO2 well may have been gaining this function, while the nutrient only well was losing this function over time. This however is in contrast to the results from metagenomic data obtained on both nutrient and CaO2 + nutrient amended wells. As seen in Figure 45, genes encoding benzoyl-CoA reductase decrease in the metagenomes between the early and the late time point of the CaO2 + nutrient, whereas PCR assays suggest that no BCRs were present in the early time point. The nutrient amended well, shows that BCRs increase in relative abundance between 0 and 12 month time points. PCR assays however suggest that this function was being lost, with no BCRs being amplified in the late time point. Alignments however of the bcrC and bzdN primers with sequences of BCRs found in the metagenomes of all samples, show that perhaps the chosen primers do not detect the full diversity of BCR sequences in the community (Figure 32, Figure 33).

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30 Genecount 20

0 Nutrient + Nutrient + Nutrient Early Nutrient Mid Nutrient Late CaO Early CaO Late

Figure 45: Copy Number of Benzoyl-CoA reductase genes determined from metagenomic data. Gene counts for the BCRs over all five metagenomics samples show that in the CaO2 + nutrient treated well, that BCRs decrease after the addition of this treatment. However, in the nutrient only well, BCRs are seen to increase threefold within 12 months.

Nutrient addition led to an increase in the abundance of BCRs, indicating that nutrient limitation may prevent organisms capable of metabolising aromatic hydrocarbons from growing to large concentrations. However, when CaO2 is introduced, a decrease in this hydrocarbon degrading potential is observed in the metagenomic data. The decomposition of CaO2 is used to produce hydrogen peroxide and in turn decomposes to produce oxygen.

This chemistry has been previously used in order to enhance biodegradation of organic compounds, via oxygenation of soils, leading to the prevalence of an aerobic microbial consortia (104). The process of dearomatisation of the benzoyl-CoA compound is an anaerobic one (168) and the presence of oxygen inhibits the growth of these organisms, potentially explaining the observed decrease of organisms containing BCRs seen in the late time point of the nutrient + CaO2 treated well. Although CaO2 had been removed by this late time point, the effect that it had on the microbial community early on may have been

132 lasting, with low numbers of BCR gene counts being observed (Figure 45) by the time that redox potential had dropped to a point suitable for methanogenesis (Figure 62- Appendix 4)

Screening with primers bamA-800 and bamA-700, which targets the 6-oxocyclohex-1-ene-1- carbonyl-CoA hydrolase of the GMT (Geobacter, Magnetospirillum, Thauera) cluster and SA

(sulphate reducing bacteria and Syntrophus, Azoarcus and Aromatoleum) cluster respectively (151), show that only one instance of this gene was found via PCR techniques.

The bamA gene is involved in the ring cleavage of the benzoyl-CoA aromatic structure, further downstream in the benzoyl-CoA pathway, compared to bcrC, bzdN and badD.

Interestingly, genes involved initially in the pathway are present in the form of bcrC and bzdN in the community, but only one occurrence of bamA was observed. This was amplified using bamA-700, indicating that the sequence was most likely that of a sulphate reducer or

Aromatoleum/Azoarcus sp.

Investigations into the benzoyl-CoA pathway shows that two variations of the pathway are utilised to degrade benzoyl-CoA to acetyl-CoA. One potential pathway for ring cleavage of benzoyl-CoA is via conversion of benzoyl-CoA to 6-ketocyclohex-1-ene-1-carbonyl-CoA. This occurs by the Thauera and Azoarcus type BCRs, bcrC and bzdN respectively and then the enzyme bamA cleaves the ring product. Closely related to the Thauera type BCR bcrC, is the

Rhodopseudomonas type BCR, badD. The enzyme encoded by badD however does not produce the same reaction product as the Thauera and Azoarcus type BCRs. As seen in

Figure 30, the end product of the badD catalysed reaction proceeds through a slight variant of the Thaurea and Azoarcus pathway, leading to the production of 2-ketocyclohexane-1- carbonyl-CoA, rather than 6-ketocyclohex-1-ene-1-carbonyl-CoA, the target of bamA. Ring cleavage of 2-ketocyclohexane-1-carbonyl-CoA is catalysed via the enzyme badI as opposed

133 to bamA. Alignment of the forward primer of the bamA assay with known bamA and bamA homologues such as Oah and bzdY in Thauera aromatica and Azoarcus sp. respectively

(Figure 46), show that the region in which the primer binds to these genes is different to that of badI in Rhodopseudomonas.

Figure 46: ClustalW alignment of bamA-700 forward primer with representative sequences of bamA, Oah, bzdY and badI. Alignment of bamA-700 forward primer with bamA, Oah, bzdY and badI sequences. Shaded bases indicate conserved sites at a 90% level.

Screening of the metagenomic data for bamA and its homologues yielded sequences that, although not annotated as such, showed similarity to bamA, as seen in Figure 35. However, none of these sequences were related to the Rhodopseudomonas badI gene, only sequences similar to bamA and oah genes were found. These sequences mostly seemed to belong to the SA cluster targeted by bamA-700. Two sequences, relating to the GMT cluster, were also found in the metagenomes of the CaO2 + Nutrient early sample and Nutrient only late sample. PCR assays targeting this cluster with bamA-800 primers, failed to provide any positive amplification of this gene. This is supported by the fact that no sequences in the metagenomes were annotated as belonging to the Rhodopseudomonas type badI as seen in

Figure 35.

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The fact that only one bamA sequence was able to be identified with PCR, while a number of putative bamA sequences were determined from the metagenome indicates that primers developed for the detection of these genes may not convey the full sense of diversity seen in the tested communities. This observation is important as a number of studies are already using bamA as a biomarker for aromatic hydrocarbon degradation due to its lack of

“variability that is inherent in BCR genes” (168). Though here we show that there is indeed variability in this function, in organisms such as Rhodopseduomonas, who use a variation of this gene, badI, and that there is a 5’ mismatch for the commonly used bamA forward primer with badI (Figure 46), as well as the fact that a number of potential bamA genes are not being identified with the current primer set, as seen in Figure 35. This may prove problematic when trying to determine whether a contaminated site has the potential for bioremediation, or in other studies, such as coal degradation, where the microbial community’s ability for biogasification from coal compounds may be underestimated.

Hydrocarbon Degrading Potential of Nutrient Vs Nutrient + CaO2 Treated Wells:

An overall greater diversity of different hydrocarbon degradation genes were observed in the nutrient amended well compared with the CaO2 treated well. As stated previously, the reason for this might be due to the inhibition of strictly anaerobic processes via the addition of oxygen generating CaO2.

Interestingly, although the pathway for naphthalene degradation has a lower relative abundance than other hydrocarbon degradation pathways as can be seen in Figure 44, one gene that is responsible for the conversion of 1-hydroxymethyl-naphthalene to 1- naphthaldehyde, is present in high abundance. This gene also has the function for alcohol

135 dehydrogenase and aldehyde dehydrogenase, according to KEGG annotations. For this reason, it was removed from the relative abundance of naphthalene degradation in Figure

44, as this function may not have been for naphthalene degradation. Chemical analysis shows that there is a high prevalence of methylated naphthalenes in the mobile non-polar phase of the studied coal. The abundance of the gene responsible for both the alcohol/aldehyde dehydrogenase function and the hydroxymethyl-naphthalene to 1- naphthaldehyde could potentially be involved in the degradation of these chemical compounds by the microbial community. However, the production of 1-hydroxymethyl- naphthalene requires the conversion of 1-methylnaphthalene via a monooxygenase, which was not observed in the KEGG annotations of any of the metagenomes. The KEGG category,

KO:00492, for the conversion of 1-methylnaphthalene by 1-methylnaphthalene hydroxylase is also annotated as COG0654 (197) in the COG database as ‘2-polyprenyl-6-methoxyphenol hydroxylase and related FAD-dependent oxidoreductases’. This COG annotation is seen in both samples and decreases over time. Although not annotated as such in the KEGG pathway annotations, genes with a similar function to 1-methylnapthalene hydroxylase for the conversion of 1-methylnaphthalene are present.

Conclusion:

Metagenomic and PCR assays targeting anaerobic hydrocarbon degrading genes, provide evidence that the pathway for the degradation of the central aromatic degradation product, benzoyl-CoA, is present at one point throughout the field trial, in each field trial well, indicating that native populations in the groundwater/coal seam have the potential to degrade hydrocarbons that may be released from the coal. Peripheral pathways for the input of both toluene and styrene into the benzoyl-CoA degradation pathway appear to be

136 present. With the exception of a few missing genes, the pathways for the complete degradation of toluene and styrene to acetyl-CoA or TCA cycle intermediates are present, representing a potential mechanism for the production of methanogenic substrates from coal compounds.

Sequences involved in the benzoyl-CoA degradation pathway also provide evidence for the organisms in the microbial community that may be responsible for this process. Assay results for PCR targeting benzoyl-CoA reductases indicate firstly that a number of Class I

BCRs found across all three treatments. Class I BCRs are ATP dependent and due to the energy production from acetyl-CoA, the end product of the benzoyl-CoA degradation and beta-oxidation pathways, these organisms are most likely gaining their energy from anaerobic respiration. This type of hydrocarbon degradation is unlikely to produce the substrates required for methanogenesis and biogasification from coal is unlikely to occur.

Reducing equivalents produced by the degradation and beta oxidation of aromatic hydrocarbons are most likely used to reduce other electron acceptors such as nitrate and sulphate. However, methane is seen and may be attributed to fermentative hydrocarbon degraders producing substrates, such as acetate, which may be reduced by acetoclastic methanogens to produce methane. A number of putative Class II ATP independent BCRs, that are utilised by aromatic hydrocarbon fermenters, were also determined from metagenomic data. These sequences were found in the late time point for both CaO2 + nutrients and nutrients only treated wells. An acetoclastic methanogenic community would potentially be able to use substrates produced by these fermenters to produce methane, like that seen in the late time point of both treatment wells.

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Finally, although PCR assays were targeted towards the identification of genes involved in anaerobic degradation of hydrocarbons, a greater diversity of sequences were identified in the metagenomic study. Some of these sequences identified in the metagenomic datasets appeared to have sequence mismatches to the primers used to amplify them in the PCR based experiments. For this reason, the metagenomic approach, appears to give a more accurate reflection of the metabolism of hydrocarbon degradation in the field trial, as opposed to identification with targeted PCR primers. The application of these primers are common in community characterisation in relation to BTEX and oil contaminated sites.

Issues may arise when assays for these anaerobic hydrocarbon degradation genes are unable to identify the full diversity of organisms capable of this metabolism. It is important to understand biodegradation pathways for the treatment of contaminated environments

(196) as for bioremediation of these sites to occur, an understanding of the process is imperative. An understanding of these pathways is also important for stimulating the biogasification from coal, as without these pathways, substrates utilisable by methanogens may not be produced from higher molecular weight compounds, found within the coal.

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Chapter Five: Metagenomic Analysis of Coal Seam Associated Microbial Communities.

Introduction

The biogasification of complex hydrocarbons involves the actions of a number of microbial organisms working in concert to degrade hydrocarbons and produce appropriate substrates for methanogenesis. The production of methane by archaea comes from three major methanogenic pathways; hydrogenotrophic, acetoclastic and methylotrophic, which are the use of CO2 + H2, acetate and methylated substrates, respectively. (32, 36, 139). The abundance of these methanogens though is highly dependent on the substrates available, which will govern the composition of the methanogens in a microbial community. For example, in response to high abundance of acetogenic bacteria, an increased presence of acetoclastic methanogens in the microbial community might be seen, as observed by

Beckmann et al (31) or the abundance of methylotrophic methanogens in response to culture amendments with methanol as observed by Guo et al (174). Methanogenic substrates such as acetate, formate and H2 + CO2 are generated via fermentation processes and have been attributed to fuelling methanogenic processes (198).The processes involved in anaerobic hydrocarbon degradation, fermentation and methane production are complex and the diversity and abundance of different organisms are influenced by initial composition of the microbial community present and the substrates available (199). Knowledge about the presence of certain groups of methanogens may therefore also lead to a deeper understanding of other processes occurring within a microbial community, such as the production of specific substrates.

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Aside from methanogenic substrates, nitrogen is an essential nutrient for microbial growth and it is found in a number of oxidation states and forms (200). The removal of the most oxidised form of nitrogen, nitrate, from an environment is attributed to the biological actions of assimilatory microorganisms or by anaerobic-respiring bacteria, who utilise nitrate as a terminal electron acceptor and produce nitrite or dinitrogen gas as an end- product (201). Due to the critical nature of nitrogen in basic cellular building blocks (e.g. proteins) (200), the ability to produce and utilise biologically relevant forms of nitrogen are crucial for microbial communities. As some coal-associated microbial communities have been observed to be N-limited and the addition of nutrients have led to re-establishment of methane generation (75), investigation into nitrogen metabolism in in-situ communities may help understand limitation of the biogasification process. Production of biologically usable NH4 may occur via a number of different processes. The first is the dissimilatory reduction of nitrate to ammonia (DNRA) with nitrite as an intermediate (201). DNRA is distinct from the denitrification pathway, where nitrate is transformed to dinitrogen gas. A second pathway of ammonia production is the fixation of atmospheric nitrogen. The Earth’s atmosphere contains up to 79% dinitrogen gas (N2), though the availability of this is limited to organisms containing the enzyme nitrogenase, which converts nitrogen into ammonia.

Nitrogenase enzymes are especially important in nitrogen-limited environments by supplying nitrogen for biological activity (202). Nutrient-limited microbial communities, such as coal-associated communities, may benefit from the ability to fix atmospheric nitrogen, which would release growth limitation to lead to the production of methane from coal compounds.

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Nitrate, may also be used as a terminal electron acceptor, rather than a source of N and within a coal-degrading community could be utilised for the oxidation of coal compounds.

Analysis of microbial communities in an Indian coal bed showed the presence of

Dechloromonas, Azonexus and Azospira sp., which are capable of anaerobic degradation of aromatic hydrocarbons using nitrate as a terminal electron acceptor (43). In the Eastern

Ordos Basin in China, the sulphate-reducing bacterium Desulfosporosinus sp. was identified, which had been previously observed to be capable of aromatic hydrocarbon degradation

(174). Dissimilatory sulphate reduction has also been linked with the degradation of hydrocarbons in a number of organisms and is thought to be widespread in nature (81).

Given the observations above, this chapter will describe the use of metagenomic analysis to explore metabolic pathways involved in methane production, fermentation, nitrogen and sulphur metabolism in coal-associated communities. These communities have been show to produce methane in situ in response to a bio-stimulation with nitrogen and phosphor nutrient (see Chapter 3).

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Methods

DNA samples extracted from well water were selected from the CaO2 + nutrient and nutrient-only treated wells representing an early time point (3 month and 0 month samples for CaO2 + nutrients and nutrient-only treatments, respectively) and a late time point (9 month and 12 month samples for CaO2 + nutrients and nutrient-only treatments, respectively). A fifth sample (nutrient-only 3 month) was also selected as it contained genes involved in hydrocarbon degradation, according to PCR and Sanger sequencing results

(Chapter 3). These samples were prepared with a Nextera library kit and sequenced in one lane of Illumina HiSeq 2000 at Ramaciotti Centre for Genomics (UNSW, Australia).

Sequence processing

Nextera adapter sequences were removed from raw Illumina reads using the python script

“cutadapt.py” and resulting reads were then quality processed with the python script

“qualControl.py” to remove low-quality bases (quality score <20, window = 4) from either end of the reads and discard any reads that contained an “N” or a low-quality internal base

(quality score < 15). Paired-end reads smaller than 30 base pairs and singleton reads were also removed. Filtered reads were then assembled using the IDBA_UD program (182, 183) using kmers of 40 to 100 in 20 increments.

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Genome Binning

Reads from all individual samples were mapped back to the merged assembly to create sample-specific coverage from depth files. Depth files were created using Bowtie2

(http://bowtie-bio.sourceforge.net/bowtie2/index.shtml) and samtools

(http://www.htslib.org/doc/samtools-1.1.html) and coverage in the depth file was calculated using the perl script “calc.coverage.in.bam.depth.pl”

(https://github.com/MadsAlbertsen/multi- metagenome/blob/master/misc.scripts/calc.coverage.in.bam.depth.pl). Differential coverage binning and bin validation was performed by A/Prof Torsten Thomas using the method described in Imelfort et al (203) and Albertsen et al (204), respectively.

Genome annotation

Genomes were uploaded to IMG for annotation. (https://img.jgi.doe.gov/cgi- bin/er/main.cgi). IMG-Expert Review is a web-based tool that allows users to upload a metagenomic sample or genome bins for gene identification and annotation. Non-coding

RNA genes, tRNA and rRNA are predicted using tRNAscan and an internally built rRNA prediction model, respectively. Protein-coding genes are identified with a set of four programs, GeneMark, Metagene, Prodigal and FragGeneScan. The functional annotation associates protein-coding genes with Pfams, COGS, KO terms, EC numbers and “phylogeny”.

Assignment of KO, EC and “phylogeny” annotations are made using similarity searches against a reference database and the top 5 hits against this database are retained for each gene. KO terms use the top 5 hits to genes of the similarity search to determine the

143 annotation, where a hit results in an alignment if there is at least 30% identity and greater than 70% of the KO gene sequence are covered by the alignment (185).

Pathway comparisons:

ID counts in a pathway were compared with the average of all other KEGG counts in a pathway to determine whether genes in the pathway were being used for multiple functions.

Network analysis:

Networks were produced in Cytoscape using the CoNet plugin and were based off a Pearson correlation of >0.75 with the multigraph option deselected. Nodes of interest were selected and first neighbours of selected nodes were used to visualise networks. All edges between first neighbours not connected to the node of interest were removed to reduce the complexity of networks for viewing and emphasising interactions.

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Results:

Metagenomes from the Lithgow State Coal Mine were examined for metabolic pathways important for the production of methane from coal compounds. Six non-chimeric genomes binned from the metagenomic data were also examined for these pathways to understand in more detail, which organisms may be performing certain metabolic activities and when.

Fermentation pathways

The ability to ferment a range of compounds was indicated by the presence of relevant genes in all metagenomic samples. In total, 45 fermentation pathways were examined3 and they are listed in Appendix 8. Of the 45 pathways investigated, 17 were complete across all samples. The end-products of these fermentation pathways are acetate, butyrate, succinate, lactate, formate, ethanol and propionate and the relative abundance of pathways producing these end products can be seen in Figure 47.

0.025

0.02

0.015 CaO2 + Nutrients Early CaO2 + Nutrients Late 0.01 Nutrients Early

Relative Relative Abundance 0.005 Nutrients Mid Nutrients Late 0

Figure 47: Relative abundance of pathways in the metagenomes that results in the production of various fermentation end-products.

3 Examination of fermentation pathway was performed by Dr Maria-Luisa Gutierrez-Zamora

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An increase in acetate fermentation genes was seen in both the CaO2 + nutrients and the nutrient-only wells, between the first and the last sampling time points, with a higher abundance observed in the CaO2-treated well. Genes in the pathways for butyrate and lactate fermentation were found to drop in frequency from the initial/mid time points to the last time point for the nutrient-only well. The abundance observed in the late time point in the nutrient-only well for lactate and butyrate were similar to the abundance in the CaO2

+ nutrient well, in which only a slight increase in abundance for pathways producing these two fermentation products was observed. Fermentation pathways resulting in acetate and butyrate were generally more abundant than pathways resulting in other fermentation products. Of these fermentation pathways, a number of pyruvate to acetate fermentation pathways also produce H2 as a by-product, as do the pyruvate to succinate and heterolactic fermentation pathways.

The production of acetate via fermentation processes was seen to be positively correlated with a number of functions and environmental measurements from the nutrient-well of the field trial (Figure 48) The fermentation of acetate from pyruvate was positively correlated with both acetoclastic and hydrogenotrophic methanogenesis pathways. The genes for these two pathways were both observed in the highly abundant Methanosarcina sp. binned from the field trial metagenomes. Acetate fermentation and acetate levels in the well were also positively correlated with dissimilatory sulphate reduction. Sulphate levels were also negatively correlated with acetate and its fermentation, suggesting that dissimilatory sulphate processes may utilise acetate being produced in the well.

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Figure 48: Pearson correlations of R >0.85 with pyruvate to acetate fermentation in the nutrient-only well. Nodes coloured based on various categories; green nodes = measurements of environmental parameters from the field trial, pale yellow nodes = sulphur metabolism, red nodes = carbon assimilation, blue nodes = methanogenic metabolisms, light purple node = nitrogen metabolism, dark yellow = methane oxidation.

Carbon fixation pathways

Three pathways for CO2 fixation were examined in the five metagenomic samples. These pathways included the Arnon-Buchanan Cycle (i.e. reductive tri-carboxylic Acid (rTCA) cycle), the Calvin-Benson-Bassham pathway (CBB) (i.e. reductive pentose pathway) and the Wood-

Ljungdahl pathway (i.e. reductive acetyl-CoA pathway). Of these three, only the Wood-

Ljungdahl pathway was seen to be complete on the gene level. The CBB and rTCA pathways were mostly complete, with each only missing one enzymatic step.

The complete Wood-Ljungdahl pathway in the metagenomes suggests its importance for autotrophic carbon assimiliation in acetogens, where it is used to convert CO2 to acetate

(205). Hydrogenotrophic methanogens also utilise the Wood-Ljungdahl pathway for CO2

147 fixation and form acetyl-CoA from two CO2 molecules (206). An increase in the relative abundance of genes involved in the Wood-Ljungdahl pathway is observed in the late time point of the nutrient-only well, while the CaO2 + nutrient well shows no change over time.

An increase in relative abundance of genes involved in the CBB pathway was also observed in the late time point of the nutrient-only well. This pathway is involved in chemoautotrophic carbon assimilation and an increase in this pathway indicates that organisms with a chemoautotrophic metabolism may become more abundant in the late time point for the nutrient-only well.

0.025

0.020

CaO2 + Nutrients Early 0.015 CaO2 + Nutrients Late Nutrients Early 0.010 Nutrients Mid

Relative Relative Abundance Nutrients Late 0.005

0.000 Calvin-Benson Arnon-Buchanan Wood-Ljungdahl

Figure 49: Relative abundance of carbon fixation pathways in metagenomes from LSCM field trial.

As many genes in the CBB cycle are also used in glycolysis/gluconeogenesis processes, an analysis of the key enzyme unique to this cycle, the ribulose-1,5,-bisphosphate carboxylase/oxygenase (RubisCO- Kegg ID: K01601/K01602), provides a better understanding as to whether the relative abundance of CBB is due to carbon fixation genes

148 or sugar metabolism. In the early to late time points of the CaO2 + nutrient treated well, a decrease in RubisCO abundance is seen, while only a slight decrease is observed in the remainder of the genes of the reductive pentose phosphate pathway. A larger increase in

RubisCO and pentose phosphate cycle genes are observed in the late time point of the nutrient-only well, an indication for both potential carbon fixation via the reductive pentose phosphate and sugar metabolism being present in the community.

0.0014

0.0012

0.001 CaO2 + Nutrients Early 0.0008 CaO2 + Nutrients Late Nutrients Early 0.0006 Nutrients Mid 0.0004 Nutrients Late

0.0002 Average Average Relative Abundance/gene

0 RubisCO Reductive Pentose

Figure 50: Abundance of the key enzyme RubisCO and other genes in the reductive pentose phosphate cycle The average relative abundance of Rubisco (small and large chain- 2 genes) was compared with the average relative abundance of genes in the reductive pentose phosphate pathway (15 genes) to observe trends in the key enzyme of the reductive pentose phosphate pathway compared to the shared genes involved in multiple processes (such as glycolysis).

Much like the reductive pentose phosphate cycle, the reductive tricarboxylic acid (rTCA) also utilises certain enzymes from another process, in this case, the TCA cycle (207). However, the rTCA cycle contains a number of key enzymes that allow it to be identified. These enzymes are fumarate reductase, 2-oxoglutarate ferredoxin oxidoreductase and ATP citrate lyase. Although low abundances of ATP citrate lyase subunit genes were observed, high

149 abundances of the remaining two unique enzymes of the rTCA pathway suggest that carbon fixation via the rTCA may not be occurring throughout the field trial.

0.002

0.0018 0.0016 0.0014

0.0012 CaO2 + Nutrients Early 0.001 CaO2 + Nutrients Late 0.0008 Nutrients Early 0.0006 Nutrients Mid 0.0004 Nutrients Late

Average Average Relative Abundance/gene 0.0002 0 ATP-Citrate 2-oxo-glutarate Fumarate Krebs Cycle lyase ferredoxin reductase genes oxidoreductase

Figure 51: Average abundance of key and other enzymes in the rTCA cycle The average relative abundance of ATP-citrate lyase (alpha and beta subunits), 2-oxo-glutarate ferredoxin oxidoreductase (alpha, beta, gamma and delta subunits) and fumarate reductase (flavoprotein subunit and iron-sulfur subunit) were compared with the average relative abundance of genes in the reductive acetyl-CoA pathway (12 genes) to observe trends in the key enzymes of the reductive acetyl-CoA pathway compared to the shared genes involved in the Krebs cycle.

As seen with the other carbon fixation pathways, the reductive acetyl-CoA pathway for carbon fixation contains two indicator enzymes. The first, CO dehydrogenase, catalyses the reduction of CO2 to CO and the second, acetyl-CoA synthase, produces acetyl-CoA from a methyl group and the carbonyl residue produced in the previous reaction (207). High average abundances were observed in CO dehydrogenase in the early time point for the

CaO2 + nutrient treatment and decreased in abundance in the late time point. A large increase in abundance (~7x) was seen in the last time point in the nutrient-only treatment.

For the acetyl-CoA synthase, similar trends in abundance were observed, though not at the

150 same abundance as either CO dehydrogenase or other genes in the reductive acetyl-CoA pathway. The abundance of these genes in the CaO2 treatment suggests that initially carbon assimilation via the reductive acetyl-CoA pathway was common or abundant in the microbial community, but the capacity was then reduced. In contrast, a large increase in the nutrient-only well suggests that a community capable of carbon assimilation via reduction of

CO2 to acetyl-CoA was selected for.

0.0009

0.0008 0.0007

0.0006 CaO2 + Nutrients Early 0.0005 CaO2 + Nutrients Late 0.0004 Nutrients Early 0.0003 Nutrients Mid 0.0002 Nutrients Late

0.0001 Average Average Relative Abundance/gene 0 CO Dehydrogenase Acetyl-CoA Synthase Reductive Acetyl CoA

Figure 52: Average relative abundance of key and other enzymes in the reductive acetyl-CoA pathway The average relative abundance of CO Dehydrogenase (small, medium and large catalytic subunits) and acetyl-CoA synthase (1 gene) compared with the average relative abundance of genes in the reductive acetyl-CoA pathway (12 genes) to observe trends in the key enzymes of the reductive acetyl-CoA pathway compared to the remaining genes in the pathway.

Methanogenesis In each of the metagenomes, the three pathways for methanogenesis were examined in terms of their relative abundance to determine the prevalence and mechanisms by which methanogenesis occur. Genes for all three pathways were found in all samples analysed,

151 with genes involved in hydrogenotrophic and acetoclastic methanogenesis being more abundant than genes for methlyotrophic pathways (Figure 53).

0.01 200 0.009 180 0.008 160

0.007 140

0.006 120 Acetoclastic 0.005 100 Hydrogenotrophic

0.004 80 CH4 (uM) Methylotrophic

Relative Relative Abundance 0.003 60 Methane 0.002 40 0.001 20 0 0 Nutrient + Nutrient + Nutrient Nutrient Nutrient CaO2 Early CaO2 Late Early Mid Late

Figure 53: Relative abundance of the three major pathways of methanogenesis in metagenomic samples. Cumulative relative abundance of all KEGG classified genes involved in the acetoclastic, hydrogenotrophic and methylotrophic methanogenesis pathways. Concentration of methane measured from these samples is overlayed.

A large increase in the abundance of formate dehydrogenase positively correlated with the relative abundance of hydrogenotrophic methanogenesis in the late sample of the nutrient- only well. The enzyme formate dehydrogenase is used to metabolise formate as an alternative to H2 in some hydrogenotrophic methanogens. The oxidation of formate provides electrons to the F420 cofactor enabling the reduction of CO2 into methane (208,

209). This suggests that potentially the large amount of the methane observed is produced via the reduction of CO2 as opposed to acetoclastic methanogenesis. A higher amount of methane production is seen in the CaO2 + nutrient well compared to the nutrient-only amended well (Figure 53).

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Two methanogen genomes belonging to the genus Methanosarcina (bin 33 and 34) could be binned from the metagenomic data. This allowed for a comparative analysis of their methanogenic pathways (Figure 54).

Figure 54: Comparison of methane metabolism pathways of two uncultured Methanosarcina binned genomes The KEGG pathways for two Methanosarcina genomes. Genes in blue indicate that the gene was found in both genomes. Genes in yellow indicate that they were found in only one genome. Formate dehydrogenase (EC 1.2.1.2) for the conversion of formate into CO2, was only found in Methanosarcina Bin 33, while both genomes contained the genes acetate kinase (EC 2.7.2.1) and phosphoacetyl transferase (EC 2.3.1.8) (AckA/Pta) involved in acetate usage. ACDS (Acetyl-CoA decarbonylase/synthase) and the conversion of N5-Formyl-tetrahydromethanopterin (THMPT) to 5,10-Methenyl-THMPT (EC 3.5.4.27) are both important for methane and biomass production and are likely to be absent due to the incompleteness of the genomes as opposed to being entirely missing.

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Methanosarcina are the only species of methanogens known to possess all three metabolic pathways for the production of methane (138). The two Methanosarcina possess the required genes for the conversion of methylamines into methyl-CoM and may use this produce methane via methylotrophic methanogenesis. Both Methanosarcina however are lacking the enzymes to convert trimethylamine to dimethylamine and dimethylamine to methylamine and instead appear to be able to directly introduce these compounds into the methanogenesis pathway without conversion to one or another.

The enzyme responsible for the first step in the hydrogenotrophic methanogenesis pathway, formyl-methanofuran dehydrogenase, was found in both Methanosarcina genome bins, indicating that hydrogenotrophic methanogenesis can be performed by the organisms.

Formyl-methanofuran synthesis requires CO2 and the methanogenesis cofactor, methanofuran, for the conversion of carbon dioxide into methane (21). Interestingly, only one of the Methanosarcina genomes (bin 33), showed the presence of a formate dehydrogenase, responsible for the production of CO2 from formate. This gene was not found in the second Methanosarcina genome, potentially indicating a difference in metabolisms between the two. Methanosarcina bin 33 was observed to change in abundance throughout the field trial with abundance increasing in the CaO2 treated well and decreasing in the nutrient-only treated well. The second Methanosarcina genome (bin

34) dropped in abundance in both wells. It appears that initially in the wells,

Methanosarcina bin 34 is abundant and over time has become excluded from the microbial community. A shift in the methanogen population in the CaO2 + nutrients well from a

Methanosarcina without formate dehydrogenase capability to one with is seen. Formate may be used as a substrate by some methanogens to produce H2 and CO2 that then feeds

154 into the hydrogenotrophic pathway (210). Inspection of the genome fragment containing formate dehydrogenase in Methanosarcina 33 shows close proximity of the gene to the formyl-methanofuran subunit B gene. A search for formyl-methanofuran subunit B genes in

Methanosarcina 34 showed that none of the resulting genome fragments contained flanking formate dehydrogenase genes. This may indicate that Methanosarcina 34 is indeed missing the gene for the formate dehydrogenase as opposed to the gene being missing due to incompleteness of the genome. Bacterial formate dehydrogenases related to those from the genera Smithella, Geobacter and Desulfovibrio were also observed and may serve to produce CO2 from formate for hydrogenotrophic methanogens lacking their own formate dehydrogenase.

Another possible substrate for methanogenesis is acetate, which is converted to acetyl-CoA and enters the methanogenesis pathway as methyl-tetrahydrosarcinapterin (Figure 54). The enzymes responsible for this in Methanosarcina are acetate kinase (AckA) and phosphoacetyl transferase (Pta), which perform a two-step conversion of acetate into acetyl-CoA using one ATP molecule (211). All the required genes were present in both

Methanosarcina genomes.

Overall this result indicated that Methanosarcina bin 34 would be able to produce methane from H2 + CO2, methylamines/methanol and acetate, while Methanosarcina bin 33 is capable of formatotrophy, hydrogenotrophic, acetoclastic and methylotrophic methanogenesis.

Sulphur Metabolism

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Dissimilatory sulphate reduction occurs when microorganisms use inorganic sulphate as an electron acceptor during the oxidation of carbon substrates. Sulphate becomes reduced to a final product of H2S through intermediates of adenosine 5’-phosphosulphate (APS) and sulphite (125). Evidence for sulphate reduction is seen in the field trial with the disappearance of sulphate from the groundwater inside wells (Appendix 4, Figure 61) The appearance of OTUs known for dissimilatory sulphate reduction, such as Desulfovibrio, was also observed in Chapter 3. Genes for both assimilatory and dissimilatory sulphate reduction are seen in all metagenomic data sets (Figure 55). Genes for assimilatory sulphate reduction are seen to drop from the early to late time points in relative abundance in CaO2 + nutrients and nutrient-only treatments. Genes involved in dissimilatory sulphate reduction in contrast appear to halve in abundance in the CaO2 + nutrient well, while increasing 3x in the nutrient-only treated well, indicating a potential enrichment of sulphate reducers in the nutrient-only well and that CaO2 addition is detrimental to sulphate reduction processes.

0.007

0.006

0.005 CaO2 + Nutrients Early 0.004 CaO2 + Nutrients Late 0.003 Nutrients Early

0.002 Nutrients Mid Relative Relative Abundance 0.001 Nutrients Late 0 Assimilatory Sulfate Reduction Dissimilatory Sulfate Reduction

Figure 55: A comparison of Assimilatory and Dissimilatory sulphate reduction in five metagenomes from LSCM microbial communities.

Of the six non-chimeric binned genomes, only the two Alteromonas showed complete pathways for assimilatory sulphate reduction, and none possessed genes for dissimilatory

156 sulphate reduction. A bin for Desulfovibrio was obtained, though unavoidable contamination with other organisms precluded it from analysis. Methanogens are generally unable to assimilate sulphate and instead utilise sulphide and sulphur (S0) as their sulphur sources. A number of compounds in methanogens, such as Fe-S cluster, cysteine and methionine require sulphur, though how sulphur is incorporated into their structures is still unknown (212). Though the sulphur donor is unknown, the enzyme responsible for the incorporation of sulphur into cysteine, Sep-tRNA:Cys-tRNA synthase, has been previously identified (213) and is present in both binned Methanosarcina genomes.

Nitrogen Metabolism

The genes present in the metagenomes show a variety of nitrogen metabolic processes occurring in the community (Figure 56). Genes for the complete reduction of nitrate to dinitrogen gas are seen across all samples, though the genes for a number of metabolic steps are very low in abundance in some samples (e.g. nitrate uptake in CaO2 + nutrients late and nitrite reduction to NO in both CaO2 samples).

157

Figure 56: Relative abundance of nitrogen metabolism related genes in five metagenomes from LSCM microbial communities.

DNRA reduces nitrate to a more biologically accessible form of nitrogen compared to the production of dinitrogen gas and nitric oxide from the denitrification process (201). The low abundance of nitrification genes in the metagenomes of the coal communities suggests that any ammonia produced by DNRA would potentially be available for the use in biomass production, as opposed to being oxidised back into nitrate via the nitrification pathway such as in the CaO2 oxygen generating treatment. The low abundance of nitrification genes observed is consistent with the pyrosequencing results of Chapter 3, which showed that

Nitrosomonas and Nitrobacter sp., key members of nitrification, were absent from the microbial community. Sequences of amoA genes present in the metagenome were most closely related those from Methylosinus sp. and Norcardioides sp.

Nitrogen fixation genes were observed in high abundance in the nutrient/CaO2-treated well.

For the nutrient-only treatment a large increase in the relative abundance of nitrogenase

158 was observed in the late time point. A similar large increase in the nutrient only-treated well was seen for hydrogenotrophic methanogenesis genes (see above). Correlations of methanogenesis pathway gene abundance with various nitrogen metabolisms showed that there is a high, positive correlation between hydrogenotrophic and acetoclastic methanogenesis genes and nitrogen fixation genes.

Ammonia, being the only added form of nitrogen in the field trial, was seen to slowly decrease from ~1.5 mM to 0 mM in the nutrient-only well from the early to late time points, while in the CaO2/nutrient-treated well, a decrease from ~0.4 mM to 0 mM was seen between the early and late time points (Appendix 4, Figure 60). As ammonia levels decreased in the field trial all nitrogen metabolism processes (DNRA, Assimilatory nitrate reduction, denitrification, nitrogen fixation and nitrification) decreased in the CaO2 + nutrient treatment and all but nitrogen fixation processes, decrease also in the nutrient-only treatment. Assimilatory nitrate reduction is virtually absent from the CaO2 + nutrient well and is seen to drop in the nutrient-only treated well. This may indicate a lack of nitrate in the wells from which nitrogen may be incorporated into cells and this was confirmed by actual measurments (Figure 57). However, a large increase in nitrogen fixation genes is seen that could counteract the lack of assimilatory nitrate and free ammonium to provide biologically available N to the community.

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Figure 57: Overview of nitrogen metabolism pathway gene abundance in CaO2 + nutrient and nutrient only treated wells.

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Out of the six genomes binned, one classified as an Alteromonas sp. contained genes involved in nitrogen cycling. This genome contains the complete set of genes responsible for

DNRA and denitrification, as well as genes for the oxidation of nitrite to nitrate and nitrite to nitric-oxide and also a single gene involved in the assimilatory reduction of nitrite (Figure

58). This genome was also observed to be more abundant in the nutrient-only treated well compared to the CaO2 + nutrient treatment (Appendix 7), likely due to the anaerobic requirements for denitrification.

Figure 58: Nitrogen metabolism in Alteromonas bin 45 genome

Nitrogenase genes are also observed in the Methanosarcina bin 33 and 34. Genes involved in the production of Mo-dependent nitrogenases (Nif) were present in both Methanosarcina genomes, while the presence of an Fe-only nitrogenase (Anf) was seen in only

Methanosarcina 33 genome. The majority of nitrogen fixed in is due to the action of Mo- dependent nitrogenases, however, the presence of Fe-only nitrogenases are important sources of fixed nitrogen in environments that are limited in Mo (214).

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Discussion:

Fermentation

The fermentation of a range of compounds is evident from the genes observed in the metagenomic community. The ability to ferment hydrocarbons, such as coal degradation products, is important for producing intermediates, such as succinate, propionate, acetate,

CO2 and H2 (4), which are substrates or precursors for methanogenesis (42).

The relative abundance of acetate fermentation pathways were seen to increase in both the

CaO2 + nutrient and nutrient-only treatments. Acetate is a direct substrate for methanogenesis and as such, is commonly studied in relation to the biogasification of coal

(4, 31, 103). Acetate was found to be the main pre-cursor of methane in coal and timber cultures obtained from abandoned coal mines in Germany and directed the growth of

Methanosarcina that were able to utilise the acetate produced by acetogens (31). The genetic ability to produce acetate in the microbial community in the LSCM coal seam represents therefore a potential pathway for the biogasification of coal.

Coal has been observed to produce a range of hydrocarbon degradation products as well as fermentation products, such as acetate, propionate and butyrate, when bioaugmented with the methanogenic culture WBC-2 by Jones et al (2010). Disappearance of these fermentation products accompanied a cessation in methane production. The authors concluded that acetoclastic methanogenesis accounted for the majority of methane produced and could be stimulated with either nutrients or bioaugmentation (75). Relative abundance increases of the acetate, propionate and butyrate pathways in the LSCM microbial communities may indicate a shift towards acetate generation and the potential for

162 acetoclastic methanogenesis. The addition of nutrients may have stimulated the LSCM community to produce methane via acetoclastic methanogenesis. Methane results

(Appendix 4, Figure 59), show indeed that methane had increased in response to nutrient treatment and a higher increase was observed in the CaO2 + nutrient treatment.

The relative abundance of fermentation pathways that produce butyrate were seen to be comparatively high in the early time point for the nutrient-only well and decreased in the late time point. Butyrate has been observed to be an important fermentation product involved in anaerobic sulphate reduction processes as well as an intermediate in anaerobic microbial degradation of coal (30, 97). The presence of butyrate as a fermentation product has also been observed in the biodegradation of cellulotic biomass during the production of synthesis gas for biofuel (215). The fermentation of coal compounds to butyrate and its subsequent oxidation to acetate and CO2 + H2 may potentially provide a substrate source for both acetoclastic and hydrogenotrophic methanogens during the field trial. The contribution of the fermentation products butyrate and propionate to methane production in cattle waste has been found to account for 20% of the total methane produced (216) and may represent a significant contribution to the biogasification of coal. The addition of acetate is known to inhibit propionate and butyrate formation and the removal of acetate stimulates growth on these compounds (140). The observed increase in relative abundance of acetate fermentation pathways may thus represent an increase in acetate generation and a subsequent inhibition of butyrate fermentation.

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Carbon fixation

Out of the studied carbon fixation pathways, the only complete pathway observed in the metagenomic sequencing of the microbial community in the LSCM was that of the Wood-

Ljungdahl pathway. The Wood-Ljungdahl pathway of carbon fixation is used to condense two one-carbon compounds into acetyl-CoA and further into acetate (217, 218). Acetate production as mentioned above, is an important process in the production of methane in anaerobic environments (29) and acetogenesis has been observed in a number of coal associated microbial communities, such as in an abandoned coal mine in Germany, a deep underwater coal seam in Japan and in a coal bed in the Powder River Basin in Wyoming (31,

40, 103). In the CaO2 + nutrient treated well, relative gene abundance of carbon fixation via the Wood-Ljungdahl pathway appears to remain stable, while it increased in the nutrient- only treated well. This increase however raises the relative gene abundance of the nutrient- only well to a similar level of that observed in the CaO2 + nutrient treated well. An initial lower relative abundance of the Wood-Ljungdahl pathway and therefore potentially acetogenesis may be a reason as to why methane was not seen in the early time points of the nutrient-only well.

In addition to acetogens, the Wood-Ljungdahl carbon fixation is used by methanogens to assimilate two carbon dioxide molecules and form an acetyl-CoA (206). These two carbon dioxide molecules are assimilated via two different mechanisms, one is reduced to carbon monoxide and the other transformed into a methyl group bound to a carrier and subsequently combined to form acetyl-CoA (219, 220). Archaea also contain incomplete CBB pathways (except for a few examples, where the pathways are complete), and the key enzyme, RubisCO, was found in both Methanosarcina genome bins, although no

164 methanogens have been observed to use it for CO2 fixation (219). Autotrophic carbon fixation in microorganisms via the CBB cycle is seen in Cyanobacteria, some aerobic/facultative anaerobic Proteobacteria and sulphur- and iron-oxidising (219). The abundance of CBB cycle genes in the field trial may be partially due to methanogenic archaea, who contain incomplete CBB cycles and fix CO2 via the Wood-Ljungdahl instead. An increase in the late time point for the nutrient-only treated well and a decrease in the

CaO2/nutrient treated well suggests that CBB carbon fixation is preferentially occurring in anaerobic conditions. The CBB has been implicated in carbon assimilation in anaerobic, ammonium-oxidising (anammox) bacteria (221). However, no genetic evidence for anammox was observed in the metagenomic sequencing of the field trial communities.

Relative abundance of the CBB pathway in the early time point of the nutrient-only well compared to the early time point of the CaO2 shows an initial variation in abundance. This may reflect a change to the microbial community of the nutrient-only well prior to the start of the field trial. Subsequent time points show the nutrient-only well community returning to a composition similar to the early time point of the CaO2 + nutrient treated well.

The third carbon fixation pathway observed in the metagenomes was the rTCA cycle. This pathway synthesises acetyl-CoA using a reversed Krebs cycle, ATP and reducing equivalents to couple two CO2 molecules together (220). The presence of rTCA cycle carbon assimilation has been observed in a diverse range of bacterial groups, but due to the oxygen-sensitivity of some enzymes, is restricted to anaerobic and microaerophilic bacteria (207). Some of these bacterial groups include autotrophic members of the Chlorobiales (such as the GSB),

Aquificales (studied extensively in Hydrogenobacter thermophilus), Nitrospirae (such as the nitrifying Nitrospira sp), Alphaproteobacteria and Epsilonproteobacteria (205, 207, 222,

165

223). It is unlikely though that photoautotrohic bacteria are responsible for the abundance of the rTCA genes in the underground wells. Aquificales and Epsilonproteobacteria are also in low abundance and are unlikely the cause of the potential carbon fixation via rTCA.

Nitrifiers, such as Nitrospira sp, were also not observed in either the pyrosequencing or metagenomic sequencing of the field trial. Previously though, a strictly anaerobic, sulphate- reducing Desulfobacter hydrogenophilus was shown to grow autotrophically with CO2, H2 and sulphate using a rTCA cycle (224). Two Desulfobacter sp (D. curvatus and D. postgatei) were identified in the metagenomic sequence data and it is possibile that they perform carbon assimilation via rTCA. The key enzyme, 2-oxoglutarate ferredoxin oxidoreductase, was also identified in the binned genome of the uncultured Bacteroidetes, though ATP- citrate lyase and fumarate reductase were not observed. In this genome however, 2- oxoglutarate ferredoxin oxidoreductase is likely used in the degradation of glutamylglutamate (225). No ATP-citrate lyase genes were found (Figure 51) and BLAST searches of the genes for fumarate reductase from the metagenome were found to be related to Desulfovibrio, Clostridia and Peptococcacea species and is likely used for anaerobic respiration and not carbon fixation (226, 227).

Methanogenesis

The production of methane gas from coal seams has previously been mostly associated with acetoclastic reactions and the reduction of CO2 (16, 102). The results of this thesis show that hydrogenotrophic and acetoclastic methanogenesis are the most abundant pathways, which could be utilising the products of carbon fixation and fermentation. A large increase in hydrogenotrophic methanogenesis genes is observed in the nutrient only-well between the early and the late time points. This suggests that methanogens in this well were potentially

166 limited by nutrients and that the addition of nutrients as a treatment was sufficient to increase their abundance; a similar observation was made by Green et al (2008) (103).

Though an increase in methanogenic potential was seen in the nutrient-only well, the CaO2

+ nutrient treated well did not see the same increase, this is potentially due to the oxygen generated by CaO2, as methanogens are strict anaerobes and O2 is known to stress methanogens (228).

Genomes from two separate Methanosarcina were obtained from the metagenomic samples. Genomes sizes for Methanosarcina have been shown to range from 4.1 – 5.8 Mb

(229) and the size of the two genomes (Methanosarcina bin 33: 4.63Mb, Methanosarcina bin 34: 4.14 Mb) would indicate that they are mostly complete. The sequence coverage of these two genomes range between 3 and 52 (Appendix 7) over the samples and suggests that these Methanosarcina are two of the most abundant methanogens in the metagenomic data. Pyrosequencing results in Chapter 3 also indicated that Methanosarcina sequences were among the most abundant methanogen sequences. These two genomes, together show the genetic potential for producing methane from CO2 + H2 and formate, via a hydrogenotrophic pathway, from methylamine, dimethylamine, trimethylamine and methanol via a methylotrophic pathway and lastly from acetate via an acetoclastic pathway.

To date, no Methanosarcina sp. however have been identified to grow on formate as a substrate (229, 230). Formate dehydrogenase genes have been previously observed in

Methanosarcina sp., but the organisms were unable to utilise formate as a substrate (230,

231). Formate is likely used by Methanosarcina for biosynthetic processes, for example, as an electron source for other reactions, and not as a methanogenic substrate (231).

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However, the role of formate in Methanosarcina bin 33 cannot be determined without culturing studies.

Sulphur Metabolism

A high abundance of Desulfovibrio were observed in all samples of the field trial in Chapter

3. Desulfovibrio are widely studied in relation to their ability to reduce sulphate (232) and their presence in the field trial indicates dissimilatory reduction of sulphate. Sulphate reduction is an important factor in the biogasification of coal as sulphate reducers can outcompete methanogens for some substrates and this may act as a control on methane production (75). The relative abundance of dissimilatory sulphate reduction genes were observed to decrease by over half between the early and late time points of the CaO2 + nutrients well (Figure 55). However, in the nutrient-only treated well, a large increase in the relative abundance of dissimilatory sulphate reduction genes was observed. Sulphate reducing bacteria (SRB) have reduced activity under lower N:C ratios (233) and their increased abundance in the field trial is likely due to abolishing nutrient limitation through the ammonium treatment. A similar increase in abundance of sulphate reduction genes was not observed in the CaO2 + nutrient treated well and is likely due to the inhibitory effect of oxygen on the strictly anaerobic sulphate reducing process (234).

The presence of SRBs in microbial communities associated with coal seams is not uncommon. For example, bacteria related to the sulphate-reducing Syntrophus were isolated in Japan (40), but their presence was interpreted as providing H2 to hydrogenotrophic methanogens as opposed to their sulphate-reducing metabolism. SRBs, such as Desulfotomaculum, have also been observed in China’s Eastern Ordos Basin coal beds and were implicated in biodegradation of coal components (174). In the study of the

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Ordos Basin, acetogens were also identified and were speculated to produce acetate to fuel sulphate reduction by a number of other sulphate reducers, such as Desulfotomaculum,

Desulfofustis, Desulfomicrobium and Desulfosporosinus. Sulphate reduction was suggested to out-compete acetoclastic methanogenesis for acetate and that this caused the microbial community to produce methane via the methylotrophic pathway so that both sulphate reduction and methanogenesis could occur simultaneously, without a competition for substrates. An investigation into a coal seam in the Gippsland Basin, Australia, also showed the presence of a number of SRB, such as Desulfovibrio, Desulfuromonas and

Desulfomicrobium (44). The high abundance of SRB at this site was explained by a significant amount of sulphur (between 0.5-4%) in this coal deposit due to its marine origin. High sulphate levels were also observed in the Lithgow State Coal Mine site’s groundwater (250-

350 mg/ml) and provide a suitable supply of sulphate for SRB populations.

The decrease in sulphate in the treatment wells indicates that sulphate reduction is occurring (Appendix 4, Figure 61). An increase in abundance of sulphate reduction genes in the metagenome of the nutrient-only well correlates with an increase in abundance of genes from the benzoyl-CoA reduction pathway (R = 0.98). Although no clean genome bin was recovered from the metagenomic assembly that showed a potential for sulphate reduction and hydrocarbon degradation, this correlation suggests that these processes are occurring simultaneously. This could potentially occur in separate organisms or in one SRB using sulphate as an electron acceptor to degrade coal hydrocarbons as suggested by Guo et al (2012) for Desulfotomaculum (174).

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Nitrogen Metabolism

Two pathways are known to exist for DNRA. The first is carried out by fermentative bacteria, while the second is linked to the oxidation of sulphur (201, 235). Conditions in which DNRA would be favoured over other mechanisms of nitrate reduction are poorly understood (236).

However, it is thought that in environments high in labile carbon and low in nitrate, fermentative DNRA is preferentially occurring over denitrification due to the higher efficiency of electron transfer (201). In environments high in sulphides, DNRA using sulphides may be more abundant compared to denitrification and fermentative DNRA processes. Sulphides, such as H2S, produced by sulphate reduction are known to inhibit the final steps of denitrification (235) and may cause nitrate reduction to take place via DNRA.

Sulphate reduction is seen in the field trial and the generated H2S may cause a preferential production of ammonia via DNRA in comparison to the reduction of nitrate to N2.

A study by Enger et al (1987) characterised Alteromonas denitrificans and showed that it was capable of reducing nitrate via nitrite to nitrogen gas (237). The Alteromonas bin 45 genome found in the coal seams also showed genetic support for this metabolism, The presence of this genome was high in the early and mid time points for the nutrient-only well, with data for the late time point unfortunately missing. The lack of Alteromonas in

Chapter 3’s pyrosequencing is likely due to mismatches at the 3’ ends of the reverse primers for both sequencing primer sets. Although Alteromonas species have previously not been described in coal seams, a number of nitrate-reducing bacteria have been found in coal- associated microbial communities. Denitrifiers such as Diaphorobacter Dechloromonas,

Mesorhizobium, Azonexus, Pseudomonas and Thauera were observed in a coal powder and formation water microcosm amended with sodium nitrite (43). Diaphorobacter that was

170 dominant has been previously shown to be capable of hydrocarbon degradation of pyrene via denitrification (238). The Alteromonas genome bin 45 however did not show the presence of any anaerobic hydrocarbon degrading pathways, such as the benzoyl-CoA reduction pathway, that has previously been seen in other Alteromonads, such as

Alteromonas sp SN2 that is known to degrade polycyclic hydrocarbons anaerobically (239).

A comparison between metagenomic samples shows a large increase in nitrogen fixation genes in the late time point for the nutrient-only amended well and as seen in Chapter 3,

Figure 27, nitrogen (NO3, NO2 and NH4) was absent in the late time point. Nitrogen fixation genes were observed in both the Methanosarcina binned genomes. Diazotrophy in

Methanosarcina was initially identified in Methanosarcina barkeri (240) and it has since then been confirmed that diazotrophic growth is utilised by methanogens under NH4 limited conditions (241). Methanosarcina are seen to be in high abundance in Chapter 3 and if other methanogens in the coal community are unable to acquire their own nitrogen source, this would give Methanosarcina a competitive advantage over them, allowing Methanosarcina to outcompete for methanogenic substrates. Positive correlations were also seen between nitrogen fixation and acetoclastic and hydrogenotrophic methanogenesis in the nutrient- only well.

These positive correlations of hydrogenotrophy and acetoclastic methanogenesis with nitrogen fixation indicate that a decrease in ammonium levels may select for methanogens, such as Methanosarcina which was seen to become more abundant in the CaO2 + nutrient treatment. Methanosarcina mazei is one such methanogen which has been shown to upregulate nitrogen fixation genes under nitrogen-limited conditions (242). Nitrogen accounts for ~1.5 wt% of coal and is bound to the organic carbonaceous portion of coal

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(243). Nitrogen sources were observed to be limited in the late time points of both the CaO2

+ nutrients and nutrients-only treatments (Appendix 4, Figure 60 ) and if the bound nitrogen in coal is not easily accessible to the microbial community, this may promote the growth of nitrogen-fixing methanogens.

The lack of nitrification genes in the metagenomic data indicates that added ammonium in nutrient treatments as well as ammonia from ammonia-generating processes, such as DNRA and nitrogen fixation, are likely feeding into microbial biomass as opposed to being converted into nitrate. Due to the fact that the nutrient-only well appears to be anaerobic, with processes such as sulphate reduction and methanogenesis occurring, it would be expected that aerobic processes, such as nitrification pathways, would be absent from the microbial community. Aerobic nitrification processes would then also be unlikely to be observed, even with the addition of CaO2, as the endemic communities were most likely lacking in these processes.

Conclusion

Biogasification of coal to methane depends on a number of important processes, such as the ability to produce methane and the production of substrates to feed methanogenic processes. The biological pathways involved in these processes were observed in the microbial communities of both the CaO2 + nutrients and nutrient-only treatments of the field trial at the Lithgow State Coal Mine. The results portrayed here indicate potential processes involved in biogasification of coal in the LSCM. A higher production of methane

172 was observed in the CaO2 treatment despite the production of oxygen, though an inhibitory effect was observed in the abundance of methanogenesis and nitrogen fixation genes, important to the biogasification process. Inhibition of dissimilatory sulphate reduction genes was also observed due to the CaO2 and may play a role in the increased methane production seen in this well. To supplement these observations, proteomics and transcriptomics would help to further expand an understanding of the processes within the field trial by allowing insights into the expression and regulation of the pathways characterised in this chapter and may be an option for potential future work.

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Chapter 6: Discussion

Chemical composition of coal:

Coal is a heterogeneous rock that is composed mainly of organic material thought to be of plant origin, that has over the course of millions of years solidified through a process known as coalification (2). The model for the structure of coal proposed by Haenel et al (1992) suggests that coal is composed of two major components, a 3-dimensional structure of cross-linked networks of aliphatic and aromatic compounds, which are immobile, and a second, mobile phase composed of small molecules embedded in this immobile matrix (1).

Using this model as a guide and coal from Eastern Australian fields, a method for extracting the mobile phase and analysing the resulting extract was developed to understand the mobile compounds within this coal and to determine the difference between coals from different coal seams.

Coals from the Lithgow, Lisdale and Casino coal seams showed a statistically significant difference in the compound profiles when extracted with dichloromethane (DCM).

Statistical analysis showed that many small changes of abundance for a range of compounds lead to the dissimilarity observed between samples (43-54% dissimilarity). Petrographic composition of coal is dependent on the rate and extent of subsidence during deposition events (66) and differences in the geological histories for these coal locations could foreseeably cause their varying compound profiles. Apolar compounds extracted from these three coals were long chain aliphatic compounds (ranging from C11 to C27) including methylated versions thereof, as well as polyaromatic hydrocarbons (PAHs), such as methylated naphthalene, phenanthrenes and fluorenes. As coal rank increases the abundance and aromaticity of compounds in coal tend to increase also. These compounds

174 have previously been characterised in Australian bituminous coals and other coals from around the world (49, 67).

Model of Coal Degradation:

The proposed model for the degradation of coal is outlined by Strapoc et al (2008) whereby the coal macromolecular structure is i) initially fragmented at oxygen-linked moieties and other functional groups to produce compounds containing oxygen functional groups (such as aromatic compounds and short organic acids) ii) the anaerobic degradation of released aromatic and aliphatic compounds iii) fermentation of available products to H2, CO2 and acetate and iv) the production of methane utilising these fermentation products (4). i) Fragmentation of coal:

The fragmentation of coal was simulated in this thesis through three oxidative chemical treatments, hydrogen peroxide, calcium peroxide and peroxidase. Little experimentation has been conducted on the in-situ effects of oxidative events on coal bed methane production. One study by Jones et al (2012) associated an enhanced potential for biogenic methane in response to coal seam dewatering. Exposure of coal to O2 during dewatering was hypothesised to cause oxidation of structural coal organic compounds and this was mimicked through the action of dilute hydrogen peroxide which increased the bioavailability of coal carbon to the microbial community. Calcium peroxide generates hydrogen peroxide and subsequently oxygen during its decomposition (87) and was used in a field trial to provide oxygen to one of the treatment wells. Hydrogen peroxide has been used in the past as a pre-treatment of coal in order to introduce oxygen functional groups to coal and break weak covalent bonds (58). The production of hydrogen peroxide has also been observed in a

175 number of bacteria (59). Two white rot fungi have previously been seen to enzymatically degrade lignite coal using peroxidase (13). As coal is of plant origin, it is thought to contain high levels of lignin (55) and the broad substrate range of peroxidases allows them to degrade lignin and a range of hydrocarbons (56). Oxidative treatments in this thesis showed an effect on the small apolar compounds of coal. Profiles of small apolar compounds were similar between the hydrogen peroxide and calcium peroxide treatments, which is consistent with the chemical decomposition of calcium peroxide in water to calcium hydroxide and hydrogen peroxide (87). Therefore, the results of these two treatments are likely due to hydrogen peroxide which, in response to a catalyst, produces Fenton’s reagents, a relatively non-specific radical that may react with a number of compounds, such as alkenes and aromatic hydrocarbons (86). The reaction of Fenton’s reagents with a number of hydrocarbons, such as formaldehyde, phenol, naphthalene, fluorene, anthracene and phenanthrene, is known to both completely mineralise these compounds to CO2 or incompletely to polar extractable organic compounds (86, 244, 245).

Enzymatically treated coal with peroxidase produced a different chemical profile in this study to that of the hydrogen peroxide and calcium peroxide treatments. Peroxidases have a reactive ferriheme group and utilise the oxidative potential of hydrogen peroxide to oxidise an electron donor, such as coal (90). A two electron oxidation of the ferriheme group by hydrogen peroxide, produces an unstable intermediate (compound-I) with an oxyferryl iron and a porphyrin  cation radical. An electron donor (AH2) then reduces compound-I with one electron to produce compound-II which accepts a second electron from another electron donor. This returns the peroxidase to its native state and the two electron donors

176 are oxidised to free radicals, which may react with other compounds or decay to non-radical products (90). These reactions are summarised below:

1. Peroxidase + H2O2  Compound-I + H2O (radical) 2. Compound-I + AH2  Compound-II + AH (radical) 3. Compound-II +AH2  Peroxidase + AH

Lignin peroxidase has been shown to oxidise several aromatic compounds producing mainly aromatic compounds with introduced keto- and hydroxyl- functional groups. Carbon-carbon bond cleavage has also been observed, leading to the removal of methyl groups from methylated anthracene (246).

The difference in chemical profiles of the enzymatic vs. peroxide (hydrogen and calcium) treatments is likely due to the fundamental mechanisms of oxidation, through Fenton’s reagents by peroxides and intermediary radical compounds by peroxidases. The differences in these mechanisms of oxidation lead to separate chemical profiles, representing two methods of producing oxidised compounds from coals.

Aqueous solutions produced by oxidative extracts were analysed with H-NMR and show that again hydrogen peroxide and calcium peroxide treatments showed similar profiles.

Importantly, formate, acetate and propionate were detected in the hydrogen peroxide and calcium peroxide treatments, as these may act as substrates for other microbial processes.

Oxidative treatments with peroxidase led to higher amounts of formate and propionate when compared to the peroxide extractions. Early studies of reactions of hydrogen peroxide with fatty acids showed that the major product of these reactions were CO2 (50-80%)(247), and the complete mineralisation of PAHs with Fenton’s reagents (86) indicate that fatty acids produced from the hydrogen peroxide and calcium peroxide treatments may be

177 mineralised to CO2 more readily than in the peroxidase treatments. Formate and acetate are both known substrates for methanogenesis (31) and the production of these compounds from coal may then serve to produce methane in a coal environment. Propionate can be oxidised via the methyl-malonyl-CoA pathway to produce acetate, CO2 and H2 or formate

(31) which may also feed into methanogenic pathways to produce methane. Therefore, treatments, such as peroxidase that produce higher concentrations of fatty acids, such as propionate, formate and acetate, from coal compounds are favourable for simulating the microbial fragmentation of coal for a methane generating community.

ii) Anaerobic degradation of released hydrocarbons:

Hydrocarbons were for a long time believed to require oxygen for their degradation due to their recalcitrant C-H bonds and C-C bonds (83). However, progress in the understanding of anaerobic microbial consortia has revealed that anaerobic hydrocarbon degradation may proceed with the use of external electron acceptors other than oxygen, such as sulphate, nitrate and iron (80, 83). Hydrocarbons released from the coal’s geo-molecular structure, such as those observed in Chapter 2 (e.g. naphthalene, fluorene, phenanthrene and aliphatic compounds), may potentially be degraded in the anaerobic setting of the field trial using the previously mentioned electron acceptors and have, in other experiments, been observed to be degraded in this manner.

The degradation of naphthalene with sulphate as an electron acceptor has been demonstrated with the formation of sulphide and CO2 in groundwater and sediment microcosms from contaminated aquifer and sediment samples (81). A sulphate-reducing

178 deltaproteobacterium was also observed to be able to utilise naphthalene as well as the substituted 2-methylnaphthalene and 2-napthoic acid compounds as sole carbon sources

(248). The degradation of anaerobic compounds requires the initial activation of the substrate, with the most commonly identified mechanism being the addition of the hydrocarbon across the double bond of fumarate (170). This mechanism of fumarate addition has also been recently identified in the sulphate-dependent degradation of long chain alkanes (171). Sulphate reducers, such as Desulfovibrio, Desulfotomaculum and

Desulfosporosinus observed in abundance in the field trial pyrosequencing data (Chapter 3), have been found in other studies to degrade compounds such as PAHs, benzene, phenol and long chain fatty acids (82, 249, 250).

Nitrate has also been linked with the anaerobic degradation of hydrocarbons. Alkanes ranging from C6 to C30 were shown to be degraded to CO2 by three denitrifying strains,

HxN1, OcN1 and HdN1. Two of these strains, HxN1 and OcN1, were observed to degrade

- - alkanes with the addition of NO3 , NO2 and N2O and did so via a fumarate-dependent reaction (251). Degradation of the PAHs naphthalene, phenanthrene and biphenyl were also demonstrated in a nitrate-reducing enrichment, with PAH degradation ceasing upon nitrate depletion (252). Mono-aromatic compounds, such as benzene and ethylbenzene, were reportedly degraded anaerobically by two strains of Dechloromonas using nitrate as an electron acceptor (113) and Georgfuchsia toluolica using both nitrate and iron as electron acceptors (195). OTUs assigned to the genera Dechloromonas and Georgfuchsia were abundant in the field trial and represent possible nitrate-dependent anaerobic hydrocarbon degraders responsible for metabolising released hydrocarbons from coal.

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Many aromatic hydrocarbons, such as BTEX (benzene, toluene, ethylbenzene and xylene) compounds, are converted to the central intermediate benzoyl-CoA where they then pass through the benzoyl-CoA reduction pathway for their degradation (151). This pathway involves the initial dearomatisation and hydrolytic ring cleavage of aromatic compounds, leading to the eventual production of one molecule of CO2 and three acetyl-CoA molecules.

Acetyl-CoA may then be further oxidised via the tricarboxylic acid cycle (164, 166) or converted to acetate via acetate kinase and phosphotransacetylase for energy production

(33, 166). Initial dearomatisation of the aromatic structure is achieved through the enzymatic action of benzyol-CoA reductases (BCRs), which occur in two classes, the ATP- dependent class I BCR and the ATP-independent class II BCR (164, 166). Assays targeting

BCRs from field trial samples showed the presence of class I BCR sequences related to bcrC in Thauera sp and bzdN in Azoarcus sp as well as putative bamB class II BCR sequences related to Geobacter, Syntrophus and Desulfobacula sp. (Chapter 4). Ring cleavage of the benzoyl-CoA aromatic structure is achieved through the enzyme 6-oxocyclohex-1-ene-1- carbonyl-CoA hydrolase (bamA), producing a linear compound, 6-hydroxy-pimeloyl-CoA

(253). The use of bamA as a biomarker has been posited, as unlike BCRs where two different classes exist, the bamA gene does not have the same variability. This allows it to be used to assess the aromatic hydrocarbon degrading potential of a community with one set of biomarker probes (168) and has already begun to be utilised for such a purpose (194, 196,

254). However, modifications to the primer set designed to amplify bamA sequences generated a second set of probes that were together capable of targeting a more diverse selection of bamA genes (151). These new bamA assays were capable of amplifying sequences from Geobacter, Magenetospirillum and Thauera (GMT cluster) as well as the

Gram positive/negative sulphate-reducing bacteria, Syntrophus, Azoarcus and Aromatoleum

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(SA cluster) (151). Rhodopseudomonas, which utilises a different enzyme (badD) in the initial dearomatisation of the benzoyl-CoA aromatic structure, produce intermediates unlike those seen via the Thauera/Azoarcus pathway (Figure 30). This results in the use of a bamA orthologue in Rhodopseudomonas, badI (172). Unlike sequences from the GMT and SA clusters however, Rhodopseudomonas sequences are not identified with bamA primers

(253). When samples from the LSCM field trial wells were analysed for bamA sequences, only one sequence, related to sulphate-reducing bacteria in the SA cluster was found.

However, in the metagenomic data, a novel cluster of bamA related sequences were discovered (Figure 35). The lack of amplification of these genes in addition to the inability for Rhodopseudomonas badI sequences to be identified with current bamA probes indicates that the potential of bamA as a biomarker for anaerobic aromatic degradation needs to be considered when PCR based analyses are used as not all sequences present in a community may be observed. However, metagenomic analysis of the field trial was able to determine the presence of a range of genes involved in the benzoyl-CoA degradation pathway, ethylbenzene activation for benzoyl-CoA reduction and an alternate route for toluene degradation (Figures 40-41), indicating the potential for the microbial community to utilise coal-derived compounds.

iii) Fermentation of available organic compounds into methanogenic substrates:

The degradation of benzoyl-CoA results in three acetyl-CoA molecules that can be used for energy generation and incorporation into biomass (166). Anaerobic respiration in comparison to fermentation results in the production of more ATP from acetyl-CoA molecules using electron-transport phosphorylation (166, 235). Fermentation results in only

181 three ATP being generated by substrate-level phosphorylation of the three acetyl-CoA molecules produced during beta-oxidation of the cleaved ring product, to acetate (33, 166).

This herein becomes problematic for fermenting organisms due to the energy requirements for the production of the benzoyl-CoA thioester and the dearomatisation of the benzene ring, as both steps require the investment of two ATP (83, 166). Therefore, to retain a positive energy flux, fermenting organisms require an ATP-independent mechanism to dearomatise benzoyl-CoA. Class II BCRs, such as the putative bamB observed in the field trial, are ATP-independent and allow fermenting organisms to overcome this problem.

Acetate generating pathways were also observed in the field trial metagenomic data and represent potentially abundant pathways for the production of methane, as acetoclastic methanogenesis is responsible for approximately two thirds of the world’s biologically produced methane (29). Other substrates used during methanogenesis include H2-CO2, methanol, methylamines and formate (103). Complex organic molecules, such as, but not restricted to, previously mentioned benzoyl-CoA, are fermented by a range of organisms to produce reduced organic compounds (e.g. lactate, ethanol, propionate and butyrate).

Acetogenic bacteria may then also oxidize these reduced organic compounds to methanogenic substrates, such as hydrogen, formate and acetate (140). Syntrophic relationships between fermenters and methanogens have been reported numerously as H2 utilising methanogens can create thermodynamically favourable conditions by the removal of the aforementioned fermentation products (36, 140, 255). In addition to acetate fermentation, pathways for the production of propionate and butyrate were also observed in the field trial samples. Relative abundance increases of acetate, propionate and butyrate fermentation pathways in the field trial microbial communities may indicate a shift towards acetate generation and the potential for acetoclastic methanogenesis. Fermentation

182 products of propionate and butyrate have been previously observed to be intermediates in the biodegradation of coal in bioaugmented cultures by Jones et al (2010). Disappearance of these compounds coincided with the cessation of methane in these cultures, indicating the importance of fermentation processes in the generation of methane from coal (75).

iv) Production of Methane from Fermentation Products:

Methanogens produce methane in environments low in inorganic electron acceptors, where the conversion of organic compounds into methanogenic substrates occur via fermentation processes (36). The production of methane is energetically poor, though an important process in terms of the global carbon cycle (33). Three main metabolic pathways exist for the production of methane, these are the hydrogenotrophic, acetoclastic and methylotrophic pathways, utilising H2-CO2, acetate and methanol/methylamines respectively. Though methanogenesis pathways differ in how various substrates are processed into methane (Figure 54), the terminal enzyme complex methyl coenzyme-M reductase (MCR), is unique to and ubiquitous in methanogens (256). This enzyme complex is responsible for the reduction of a methyl group bound to coenzyme-M into methane and defines a cell as a methanogen (21, 256).

Although methanogenesis may occur with a number of substrates, the production of methane gas from coal seams has previously been mostly associated with acetoclastic and hydrogenotrophic reactions (16, 102). The findings in this thesis indicate that in the LSCM field trial, acetoclasty and hydrogenotrophy are also the two dominant methane generating metabolisms. The major contributors to methane generation are thought to be

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Methanosarcina, Methanoregula and Methanosaeta due to their abundance in both the pyrosequencing and metagenomic sequencing results. The most common methanogens detected in coal bed methane reservoirs have previously been reported to include

Methanosarcina, Methanolobus, Methanobacteria, Methanocorposculum, Methanosaeta,

Methanococcus, Methanoculleus and Methanoregula (257).

Methanosarcina is the only known methanogen capable of utilising all three pathways of methanogenesis (138), and the metagenomic bins of two Methanosarcina genomes from the field trial show that both are capable of hydrogenotrophy, acetoclasty and methylotrophy. This diversity in substrate utilisation may allow them to continue methanogenesis during the production of various fermentation products from coal and

Methanosarcina has been previously observed utilising methanol, H2-CO2 and acetate in coal associated communities (40, 103, 174). Acetate utilisation for methanogenesis is restricted to two genera, Methanosarcina and Methanosaeta (103, 258) and like

Methanosarcina, Methanosaeta has also been attributed to acetoclastic methanogenesis in a coal associated microbial community (75).

The generation of methane from coal is thought to be affected by a number of factors, including the bioavailability of carbon, presence of a microbial community that is able to utilise this carbon, as well as environmental conditions to support their growth (such as availability of nutrients and lack of inhibitory compounds) (259).

Nutrient limitation:

The availability of nutrients are commonly observed in coal seams worldwide to be a limiting factor for methane generation (42, 101, 103, 259). The study by Jones et al (2010)

184 showed that native microbial coal communities, from a sub-bituminous, non-productive coal seam in Texas, were able to produce methane when placed in a microcosm with nutrients. Without the addition of nutrients, methane production was not observed (75). A similar observation was made by Green et al (2008) when a coal-derived microbial community were unable to produce methane from coal until minerals, vitamins, trace metals or acetate were added to the cultures (103). The field trial presented in this thesis, was conducted in a non-gassy, sub-bituminous coal seam. Methane generation was achieved in both treatment wells with nutrients added. Methanogenic communities were clearly present at the beginning of the trial; however methane generation required the microbial community’s nutrient limitation to be overcome.

Nitrogen in the form of nitrate and nitrite was absent from the groundwater at the field site and ammonium was present in minimal concentrations. Nitrogen is essential for all living organisms and almost all organic nitrogen on earth originates from nitrogen fixation, a process whereby atmospheric nitrogen is converted into a biologically relevant form (220).

Nitrogen fixation is thought to play an important role for the input of new nitrogen in deep sea methanogenic communities (260) and by extension may be just as important at providing nutrients in a coal community. Upon examination, binned genomes of

Methanosarcina showed the presence of nitrogen fixation genes. Nitrogen fixation was originally observed in Methanosarcina and has been determined to be utilised by methanogens under nutrient limited conditions (241). The ability to acquire their own nitrogen source, could potentially give Methanosarcina a competitive advantage over other methanogens, which would explain their abundance in the field trial.

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A second metabolism for the production of NH4 involves the dissimilatory reduction of nitrate into ammonia. This process may occur via two mechanisms, the first is via fermentative bacteria and the second is linked with the oxidation of sulphur (201, 235). The presence of DNRA pathways in the metagenomic data of the field trial suggests that this process was abundant; however, the conditions in which DNRA occurs and when it is favoured over other mechanisms of nitrate reduction are poorly understood (236). The production of sulphides via dissimilatory sulphate reduction is known to inhibit the final steps of denitrification and may cause the preferential production of ammonia via DNRA using sulphides (235). Results from the field trial indicate that dissimilatory sulphate reduction was occurring at various time points. This may result in DNRA pathways producing

NH4, rather than denifitrifcation to N2. This metabolism would be useful to the nutrient limited community as nitrogen is reused rather than being lost as atmospheric dinitrogen.

Inhibition of methanogenesis:

Methanogenesis is a terminal anaerobic process that utilises low molecular weight products from other bacterial processes and produces methane (141). This however may be interfered with if other microbial processes have a higher affinity for the same substrates used by methanogens, such as in sulphate reduction (141, 261). Sulphate reducers can outcompete methanogens for substrates and this is proposed to control methanogenesis in coal environments (75). Pyrosequencing data from the field trial reveals a number of sulphate reducing bacteria (SRB) such as Desulfovibrio, Desulfatibacillum, Desulfobulbus,

Desulfotomaculum and Desulfosporosinus and metagenomic analysis shows the presence of dissimilatory sulphate reduction genes in high abundances at various times throughout the

186 field trial. Desulfovibrio and Desulfotomaculum are both known to utilise hydrogen and acetate as an electron donor in the dissimilatory reduction of sulphate (97, 142, 143) and the complete removal of sulphate observed in the acetate-amended well is likely due to the presence of acetate.

SRB have been observed in a number of coal communities, including the Chinese Ordos

Basin, Australian Gippsland Basin and Powder River Basin, USA (44, 103, 174). The microbial community of the Chinese Ordos Basin features a methanogenic community dominated by methylotrophic methanogens. Guo et al (2012) suggested that this was due to sulphate reduction processes by Desulfotomaculum, Desulfofustis, Desulfomicrobium and

Desulfosporosinus outcompeting acetoclastic methanogens for acetate, causing the microbial community to produce methane via methylotrophy. Methylotrophic methanogenesis ensured that both sulphate reduction and methanogenic processes could occur simultaneously (174). This observation suggests that sulphate reduction may not only inhibit, but also shape a microbial community, producing methane.

Conclusion and Future Directions:

From this study, it would appear that compounds in coal and their degradation products may be accessible to the microbial community within the wells in the LSCM coal seam.

Pyrosequencing, targeted PCR assays and metagenomic analyses show the presence of organisms capable of anaerobic hydrocarbon degradation as well as a number of the genes involved in this process. Methane was generated with the addition of nutrients to the field trial and appeared mostly to relate to acetoclastic and hydrogenotrophic methanogenesis,

187 potentially by Methanosarcina as well as Methonoregula and Methanosaeta. Binned genomes for Methanosarcina also showed potential for all three metabolisms of methane generation and is the first time coal associated methanogens have had their genomes investigated with such a method. Chemical analyses showed the production of fatty acids through CaO2, hydrogen peroxide and enzymatic treatments of coal. Fermentation pathways that produce similar fatty acids and H2 + CO2 were also observed. These observations could potentially explain the dominance of hydrogenotrophic and acetoclastic methanogenesis pathways in both the nutrient treatments. This previously non-gassy coal seam was stimulated to produce methane with the addition of nutrients, findings that fall in line with previous studies.

The fact that a non-gassy coal seam was able to be stimulated to produce methane in-situ is novel and a step forward in biologically produced methane from unused coal. Though pyrosequencing of coal associated communities in the past has been achieved, the examination of an in-situ community in response to stimulation over time has yet to be explored and the characterisation of such a community with metagenomic techniques is novel. Metagenomic analyses provided detailed information about metabolisms present in the microbial community; a kind of detail that has not previously been observed in an in-situ coal community. However, this method fails to distinguish metabolically active pathways from those present, but not utilised by the coal community. This is apparent in the early time points of the nutrient treated well, where methane production was not observed, though methanogens and methanogenic genes were present. Further research with RNA targeted approaches may help to distinguish with finer detail, which methanogenic pathways are being utilised, whether nitrogen fixation and DNRA are actively occurring and

188 whether or not anaerobic hydrocarbon degradation pathways are being expressed.

Chemical studies presented a range of compounds accessible within the coal of the LSCM and metagenomic analyses revealed potential pathways for the degradation of these compounds, however, which compounds were being degraded, if any, requires further work. Hydrocarbon feeding experiments with inocula from the treatment wells and model compounds were attempted; however, results proved inconclusive. Potential future studies using radio-labelled compounds and RNA-SIP procedures may prove useful in characterising microorganisms capable of hydrocarbon degradation/fermentation, important processes in the biogasification of coal.

Methane is a cleaner burning fuel than coal, and the application of this study presents a method in which environmental concerns, such as carbon emissions from the burning of fuel for energy production may be addressed. An understanding of the microbial processes occurring during the biogasification of coal is crucial if this were to be used as a technology for industrial energy generation.

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Appendix:

Appendix 1: Example formatting for importing GC-FID data into T-REX for alignment

Appendix 2: Square root transformed resemblance matrix of coal extraction profiles

Coal Type Casino Pinedale Lithgow Casino 89.63 Pinedale 59.79 90.37 Lithgow 62.14 81.07 93.00

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Appendix 3: Field Site Treatments Five wells were drilled into the Lithgow State Coal Mine field site and the following four treatments were applied:

 Acetate (20 mM total treatment)

 Calcium Peroxide (0.25mg/ml of groundwater) + Nutrient (1.8 mM ammonium

chloride + 1.9 mM sodium phosphate) addition

 Nutrient (1.8 mM ammonium chloride + 1.9 mM sodium phosphate) addition

 No amendment

Note: Bromide tracers were added to check for the integrity of the well, however it was found that the fifth well was structurally unstable and hence the treatment was found to be not reliable. Therefore this well was excluded from the study

Appendix 4: Field Trial Sampling Samples for sulphate, phosphate, nitrate, nitrite, magnesium, calcium and total iron levels were analysed every three months for Inductively Couple Plasma (ICP) analysis at the Solid

State and Elemental Analysis Facility at UNSW. Acetate, methane, pH, redox potential (Eh) were also sampled and 20 L water samples were taken every three months and filtered onto suporpolyethersolfone (PES) membranes (Pall Corporation) with a pore size of 0.1 m to harvest microorganisms in the coal seam water. Coal was also placed in a PVC ‘basket’ and submerged in the well water to provide a higher coal surface area. Biotraps were set up similar to the basket coal in order to be retrieved for DNA extractions. Coal was removed from the basket and biotrap samples every 3 months and DNA collected from its surface.

Treatments involved nutrient addition of nitrogen and phosphorous to assess whether

205 nutrient starvation was an issue in methane production and hydrocarbon degradation and

CaO2 treatment to provide an aerobic environment to stimulate degradation of coal by aerobic bacteria and fungi. Acetate was also added to one of the wells as methane production from acetate is common from coal seams and this treatment is to provide evidence to the fact that the community is capable of producing methane given the right environment and substrates. In the nutrient poor environment of the coal seam, acetate may also serve to bolster the numbers of members in the microbial community responsible for methane production.

Appendix 5: Environmental/chemical monitoring of wells Methane analysis of samples from each three-month time point shows that over time, methane rises in all treatments, as seen in Figure 59. The highest production of methane was observed in the acetate amendment with a peak of 511 mmol in the well, in comparison to the second highest treatment, the CaO2 + nutrient, which peaked 3 months later at 294 mmol. The control well with no treatments did not produce any methane over the course of 12 months. Methane production was also observed in the final time point for the nutrient only treated well, but did not reach the same levels as the CaO2 + nutrient or

Acetate amended wells.

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600

500

400 Acetate amendment 300 CaO2 + NP

200 NP Methane(mmol) No ammendment 100

0 0 3 6 9 12 Month

Figure 59: Total Methane produced in four of the treatment wells. Methane is observed to rise in all treatments, while the control well produced no methane. Methane production was highest in the acetate amended well, although a dip in methane is seen in the last time point. The other two treatments also saw methane production. CaO2 addition with nutrients produced more methane than nutrients alone.

Phosphate and ammonia were added to the CaO2 + nutrient and nutrient treated wells as a nutrient amendment. These levels were monitored over the period of 12 months in all wells.

Phosphate was only able to be measured in the acetate-amended well and was seen to drop to 0 from 1mM in the first three months. Ammonia was seen to slowly disappear in the acetate, CaO2 + nutrient and nutrients-only wells until it was completely gone in 6, 9 and 12 months respectively (Figure 60).

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1.6 Acetate Well- 1.4 PO4 1.2 Acetate Well- NH4

1 CaO2 + N Well-

0.8 PO4

mM CaO2 + N Well- 0.6 NH4 Nutrient Well- 0.4 PO4 0.2 Nutrient Well- NH4 0 Control Well- 0 3 6 9 12 PO4 Month

Figure 60: Phosphate and ammonia data from the field trial over 12 months. Data from field trial showing levels of nutrients in the wells over the timespan of 12 months. Phosphate was only seen in the acetate amendment in the first time point and not seen in any other time point for any samples. Ammonium was measured in the acetate and both nutrient treatments and was observed to disappear over time.

Calcium and sulphate data were also measured over the course of 12 months to assess the effect of CaO2 on dissolved calcium concentration and to measure sulphate reduction

(Figure 61). Calcium levels were initially higher in the control well, but were gradually decreased, almost reaching a similar level to calcium levels in other wells, which stayed constant throughout the course of the field trial. Sulphate levels decreased in all wells and completely disappeared in the acetate-treated well. However the acetate treatment had a much lower sulphate level to start with.

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400 350 Acetate Well- SO4 300 Acetate Well- Ca 250 CaO2 + N Well- SO4 200

mg/L CaO2 + N Well- Ca 150 Nutrient Well- SO4 100 Nutrient Well- Ca 50 Control Well- SO4 0 Control Well- Ca 0 3 6 9 12 Month

Figure 61: Calcium and sulphate data from the field trial over 12 months. Sulphate was observed to decrease in all samples and completely disappear in the acetate amended well. Calcium was highest in the control well initially and gradually reduced over time, dropping to a level similar to the other wells.

Fluctuations in oxidative redox potential (ORP), monitored with continuous measurements of Eh are useful in tracking bacterial metabolic activities (262) and serve as an indication of concentrations of terminal electron acceptors involved in bacterial respiration in complex environments (263), such as our field trial coal-seam wells (Figure 62). Both nutrient and

CaO2 + nutrient treatments showed an initial high redox potential, which was sustained for 6 months in the CaO2 + nutrient treatment, and displayed a sharp decrease in redox potential in the nutrient-treated well during the first 3 months. Both of these treatments, by 9 months had a lower redox potential than the control well. The acetate amendment also saw a drop in redox potential in comparison to the control well and of the three treatments, showed the lowest redox potential overall.

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0 -50 -100 -150

-200 Acetate

mV -250 CaO2 -300 Nutrient -350 Control -400 -450 0 3 6 9 12 Month

Figure 62: Redox potential of the field trial wells, monitored over 12 months. Redox potential was stable throughout the control well while it was initially higher for the nutrient and CaO2 + nutrient well, which both dropped to ~-350mV after 9 months. The redox potential for acetate stayed consistently low throughout the field trial.

Appendix 6: DNA Extraction Protocol

PES Membranes were sectioned into quarters and one section was used for DNA extraction.

Membrane samples were suspended suspended in 2 ml Tris, NaCl, SDS (TNS) buffer (pH 8) and 7 ml 120 mM NaPO4 buffer. Cells were lysed by bead beating with Lysing Matrix E

(Contains 1.4 ceramic spheres, 0.1 mm silica spheres, and one 4 mm glass bead- MP

Biomedicals, Santa Ana, CA) and vortexing at 6.5 m/s for 3 min. Centrifugation for 4 min at

10°C was performed and contaminants were removed from the aqueous phase with an equal volume of phenol/chloroform/isoamylalcohol (25:24:1) (pH 8) (Sigma-Aldrich). The solution was mixed by inversion, and then centrifuged for 4 min at 10°C. Further purification with equal volumes of chloroform/isoamylalcohol (24:1) was performed after a second mixing and centrifugation, as before. DNA in the aqueous phase was precipitated with 2 volumes of polyethylene glycol (PEG). After incubation overnight at -20°C, the sample was

210 centrifuged for 30 min at room temperature. The nucleic acid pellets were washed with 500

µl 4°C 70% ethanol then centrifuged for 4 min at room temperature at 15,500 x g. The supernatant was removed and the pellet was dried at room temperature before being resuspended in 50 µl molecular biology grade water.

DNA extractions were peroformed by Dr Sabrina Beckman, John Webster and Alison Luk.

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Appendix 7: List of PCR conditions for amplification of anaerobic hydrocarbon degrading genes.

Cycle Gene Primer F Primer R Initial Denaturation Denaturation Annealing Elongation Final Elongation Cycles bzdN bzdNF bzdNR Temp 95 95 70 72 72 35 Time 5:00 0:30 0:30 1:00 10:00 bamA- bamA- 700 SP9 bamA-ASP33 Temp 95 95 52 72 72 35 Time 5:00 0:30 0:30 1:00 10:00 bamA- bamA- 800 SP9 bamA-ASP23 Temp 95 95 54 72 72 35 Time 5:00 0:30 0:30 1:00 10:00 bamA- bamA- 300 SP9 bamA-ASP1 Temp 94 94 59 72 72 30 Time 10:00 0:30 0:45 1:00 10:00 bcrC bcrCF bcrCR Temp 95 95 70 72 72 35 Time 5:00 0:30 0:30 1:00 10:00 bamB bamBF bamBR Temp 94 94 60 72 72 35 Time 5:00 0:30 0:30 1:00 10:00 bssA bssAf bssAr Temp 94 94 52 72 72 30 Time 3:00 0:30 0:30 1:00 5:00 assA assAf assAr Temp 95 94 55 72 72 30 Time 5:00 0:30 0:45 2:00 10:00

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Appendix 8: 454 Pyrosequencing Processing Pipeline using MOTHUR (for this pipeline “Data” is used as a placeholder for filenames and where specific parameters were required, both will be shown using ‘Ram’ or ‘Hwks’ as identifiers)

Sequence information was extracted from .SFF files provided by the sequencing institutes

Mothur> sff.info(sff=data.sff)

This provided both sequence information and base call quality, in terms of a fasta file and quality file respecitively

Sequences were then trimmed, removing barcode and primer sequences using a relevant

‘oligo’ file with the corresponding barcode/primer information. Trimming also removed reads that were below a certain length cutoff and quality cutoff.

Mothur> trim.seqs(fasta=Data.fasta, qfile=Data.qual, oligos=Data.oligo, pdiffs=2, bdiffs=1, maxhomop=8, maxambig=0, qwindowsize=50, qwindowaverage=35, processors=2, minlength=200)

Unique sequences were then determined

Mothur> unique.seqs(fasta=Data.trim.fasta)

And a table of group and name information was produced

Mothur> make.table(name=Data.trim.names, groups=Data.groups)

Sequences were then aligned against the Silva bacterial database to generate a 16S alignment

Mothur> align.seqs(candidate=Data.trim.fasta, template=silva.bacteria.fasta)

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Sequences were then screened to remove reads that did not align in the correct region.

Mothur> screen.seqs(fasta=Ram.trim.unique.align, count=Ram.trim.count_table, start=13129, end=26167)

Mothur> screen.seqs(fasta=Hwks.trim.unique.align, count=Hwks.trim.count_table, minlength=200, start=28467, end=37800)

The correctly aligned sequences were then filtered to ensure an even length across all the reads

Mothur> filter.seqs(fasta=Data.trim.unique.good.align, vertical=T, trump=.)

And ‘unique.seqs’ was run again to help with data processing time

Mothur> unique.seqs(fasta=Data.trim.unique.good.filter.fasta, count=Data.trim.good.count_table)

The ‘pre.cluster’ command was used to minimise the effect of sequencing errors in the generation of OTUs. This command ranks the reads in order of most to least abundance and determines if any of the lower abundant reads are a specified number of differences from the more abundant reads. Sequencing errors can mean that these lower abundant reads can be 1 or 2 base pairs different. Mothur will cluster these together so that spurious OTUs are not generated.

Mothur> pre.cluster(fasta=Data.trim.unique.good.filter.unique.fasta, count=Data.trim.unique.good.filter.count_table, diffs=2)

Chimeras were found and removed using

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Mothur> chimera.uchime(fasta=Data.trim.unique.good.filter.unique.precluster.fasta, count=Data.trim.unique.good.filter.unique.precluster.count_table, processors=2)

Mothur> > remove.seqs(accnos=Data.trim.unique.good.filter.unique.precluster.uchime.accnos, fasta=Data.trim.unique.good.filter.unique.precluster.fasta, count=Data.trim.unique.good.filter.unique.precluster.count_table)

Subsampling was then performed to ensure even counts across samples. Some samples however showed low counts and were removed from further analysis.

Mothur> sub.sample(fasta=Ram.trim.unique.good.filter.unique.precluster.pick.fasta, count=Ram.trim.unique.good.filter.unique.precluster.pick.count_table, size=700, persample=T)

Mothur > sub.sample(fasta=Hwks.trim.unique.good.filter.unique.precluster.pick.fasta, count=Hwks.trim.unique.good.filter.unique.precluster.pick.count_table, persample=T, size=692)

Operational Taxonomic Units were then generated

Mothur> dist.seqs(fasta=data.trim.unique.good.filter.unique.precluster.pick.subsample.fasta, cutoff=0.1, processors=2)

Mothur> cluster(column=Data.trim.unique.good.filter.unique.precluster.pick.subsample.dist, count=Data.trim.unique.good.filter.unique.precluster.pick.subsample.count_table, cutoff=0.08)

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Mothur> get.oturep(fasta=Data.trim.unique.good.filter.unique.precluster.pick.subsample.fasta, list=Data.trim.unique.good.filter.unique.precluster.pick.subsample.an.unique_list.list, column=Data.trim.unique.good.filter.unique.precluster.pick.subsample.dist, count=Data.trim.unique.good.filter.unique.precluster.pick.subsample.count_table)

Mothur> classify.seqs(fasta=

Data.trim.unique.good.filter.unique.precluster.pick.subsample.an.unique_list.0.01.rep.fasta, template=trainset9_032012.rdp.fasta, taxonomy=trainset9_032012.rdp.tax)

And an output was generated to be analysed

Mothur> make.shared(list=

Data.trim.unique.good.filter.unique.precluster.pick.subsample.an.unique_list.list,count=Dat a.trim.unique.good.filter.unique.precluster.pick.subsample.count_table,label=0.03)

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Appendix 9: Community Sequencing Summary

Sample Time point Shannon Ace V5-8 OTU Unclassified Shannon Ace V3/4 OTU Unclassified percentage V3/4 V5-8 count percentage V3/4 count V5-8 V5-8 V3/4 Acetate 0 Months 3.22 184.7 121 3.3 Acetate 3 Months 2.27 229.4 77 2.6 1.5 372.1 86 0.3 Acetate 6 Months 2.47 1020.3 122 11.5 3.1 486.6 132 0.0 Acetate 9 Months 2.55 231.1 88 3.4 2.1 471.0 106 0.1 Acetate 12 Months 2.76 331.7 85 4.7 2.3 305.4 78 0.3 Calcium Peroxide + Nutrients 0 Months 3.02 356.8 104 5.8 1.9 160.2 65 0.1 Calcium Peroxide + Nutrients 3 Months 2.96 247.2 89 7.9 2.0 350.4 81 0.4 Calcium Peroxide + Nutrients 6 Months 4.17 1178.4 235 9.8 Calcium Peroxide + Nutrients 9 Months 3.27 139.6 99 5.1 2.1 164.4 58 0.0 Calcium Peroxide + Nutrients 12 Months 5.46 2026.9 422 12.6 Nutrients 0 Months 5.76 1983.0 489 12.5 2.6 70.6 48 0.1 Nutrients 3 Months 2.91 632.6 138 5.1 2.2 339.1 96 0.6 Nutrients 6 Months 3.28 285.6 107 7.5 2.8 491.0 130 0.3 Nutrients 9 Months 5.04 2859.6 414 15.2 1.7 210.2 74 0.1 Nutrients 12 Months 2.59 321.0 93 2.2 1.8 126.4 79 0.3 No Amendment 0 Months 2.58 225.8 115 5.2 No Amendment 3 Months 2.21 902.9 130 6.9 No Amendment 6 Months 4.37 549.5 206 12.1 No Amendment 9 Months 2.98 602.8 170 10.6

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Appendix 10: Binned Genomes from Metagenomic Data bin Likely Classification length (bp) Coverage Coverage Coverage Coverage id Chimeric CaO2 + CaO2 + Nutrients Nutrients nutrients nutrients early mean late mean early mean late mean 22 False Alteromonas 4505613 2.127 2.5046 536.3873 493.8762 29 False Bacteriodetes 2444756 25.1579 1.1436 0.0012 1.7157 33 False Methanosarcina 4634139 36.1329 52.2892 26.1595 12.9867 34 False Methanosarcina 4145872 14.6149 8.7141 4.7364 2.9741 41 False Firmicutes 2126279 60.1538 32.8927 0.8983 5.0714 45 False Alteromonas 4601870 0.0114 0.0049 122.4528 280.4808

Appendix 11: Fermentation Pathways Examined in Field Trial

Missing enzymes Pathway Complete/ Variants (in all samples) Products of this pathway (MetaCyc nomenclature) Incomplete (EC number) (R,R)-butylene glycol Incomplete 1.1.1.303; 1.1.1.4 R,R-2,3-butanediol fermentation S,S - Butylene glycol Incomplete 1.1.1.304; 1.1.1.76 S,S-2,3-butanediol fermentation Butylene glycol fermentation R,S - Butylene glycol Incomplete 1.1.1.304 R,S-2,3-butanediol fermentation I R, S- Butylene glycol Incomplete 1.1.1.304 R,S-2,3-butanediol fermentation II Acetyl-CoA fermentation to butyrate II None Complete N/A butyrate (from ethanol) Acetylene fermentation None Incomplete 4.2.1.112 ethanol + Acetate 3.7.1.2;1.1.1.259; cyclohexane-1- Benzoate fermentation None Incomplete 4.2.1.150;1.3.8.10; carboxylate + acetate 1.3.8.11 Crotonate fermentation to 4.2.1.150; acetate + cyclohexane-1- acetate and cyclohexane None Incomplete 1.1.1.259; 3.7.1.21; carboxylate carboxylate 1.3.8.10; 1.3.8.11 Complete To butyrate (via (in almost all 4.2.1.- in LSCM3L butyrate hydroxyglutarate) samples) Glutamate fermentation To pyruvate Incomplete 4.2.1.34; 4.1.3.22 acetate +pyruvate

To butyrate II Incomplete 4.2.1.34; 4.1.3.22 H2 + butyrate 4.2.1.34; 4.1.3.22; acetate + propionate + To propionate Incomplete 2.1.3.1 (2/5) ammonium Complete Glycerol fermentation to 1,3- N/A None only in LSCM4 1,3-propanediol propanediol late No EC numbers Glycolate fermentation -- -- acetate + 2 succinate assigned 1.1.1.351 (key ethanol, R-lactate, S- Heterolactic fermentation None Incomplete enzyme missing in lactate all) Hexitol fermentation None Complete N/A S-lactate, acetate,

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ethanol Homolactic fermentation None Complete N/A S-lactate L-alanine fermentation to ammonium + propionate None Incomplete 4.2.1.54; 1.3.1.95 acetate and propionate + acetate Lysine fermentation to acetate + ammonium + None Incomplete 2.8.3.9 acetate and butyrate butyrate succinate, 2-oxoglutarate, Mixed acid fermentation None Complete N/A acetate, ethanol, CO2, H2, R-lactate 2.1.2.4; 3.5.4.8; I Incomplete ammonium, CO , acetate 3.5.4.9 2 Purine fermentation 2.1.2.4; 3.5.4.8; CO , acetate, ammonium, II Incomplete 2 3.5.4.9; 3.5.99.10 glycine To acetate and 2.1.2.4; 3.5.4.8; Incomplete L-alanine + acetate alanine 3.5.4.9; 3.5.99.10 To acetate and Incomplete 1.2.1.- S-lactate + acetate lactate I To acetate and Complete N/A S-lactate + acetate + CO lactate II 2

To acetate I Complete N/A CO2 + acetate To acetate II Incomplete 1.2.1.- CO2 + acetate To acetate III Complete N/A CO2 + acetate To acetate IV Complete N/A formate + acetate

To acetate V Incomplete 1.2.1.- CO2 + acetate + succinate To acetate VI Complete N/A CO2 + succinate To acetate VII Complete N/A CO2 + acetate Complete in LSCM4E and LSCM4M. To acetateVIII Incomplete in 4.1.1.1. CO2 + acetate LSCM4L, LSCM3E and Pyruvate fermentation LSCM3L To acetone Incomplete 2.8.3.9 CO2 + acetone To butyrate Complete N/A CO2 + butyrate To butanol I Incomplete 1.2.1.57 n-butanol To butanol II Incomplete 1.2.1.- n-butanol To ethanol I Complete N/A formate + ethanol

To ethanol II Incomplete 4.1.1.1 (3/5) ethanol + CO2 To ethanol III Complete N/A ethanol + CO2 To lactate Complete N/A S-lactate ß-alanopine, tauropine, To opines Absent all strombine, alanopine, D- octopine Complete in LSCM4L, LSCM3E and To propionate I 2.1.3.1 propionate LSCM3L Incomplete in the rest 1.3.1.95 and To propionate II Incomplete propionate 4.2.1.54 Complete Succinate fermentation to only in None 1.2.1.76 acetate + butyrate butyrate LSCM4L and LSCM3E *Table Generated by Dr Maria-Luisa Gutierrez-Zamora

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