Diversity of Methane and Short Chain Hydrocarbon Degrading Bacteria with an Emphasis on Methane Biofilter Systems

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2018-10-03 Diversity of methane and short chain hydrocarbon degrading bacteria with an emphasis on methane biofilter systems

Khadka, Roshan

Khadka, R. (2018). Diversity of methane and short chain hydrocarbon degrading bacteria with an emphasis on methane biofilter systems (Unpublished doctoral thesis). University of Calgary, Calgary, AB. doi:10.11575/PRISM/33152 http://hdl.handle.net/1880/108810 doctoral thesis

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Diversity of methane and short chain hydrocarbon degrading bacteria with an emphasis on

methane biofilter systems

Roshan Khadka

A THESIS

SUBMITTED TO THE FACULTY OF GRADUATE STUDIES

IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE

DEGREE OF DOCTOR OF PHILOSOPHY

GRADUATE PROGRAM IN BIOLOGICAL SCIENCES

CALGARY, ALBERTA

SEPTEMBER, 2018

Methanotrophs house enzymes capable of methane oxidation, act as a sink for atmospheric methane and play a key role in the global carbon cycle. This study conducted multiple studies on methanotrophs, including: examination of the evolutionary history of copper membrane monooxygenases (CuMMOs), application of methanotrophic communities in protocol design for monitoring methane biofilter systems, and the analyses of single cell genomes containing new CuMMO-encoding genes.

CuMMOs are encoded by three genes, usually in an operon of xmoCAB, and oxidize ammonia, methane, and short chain alkanes and alkenes. To examine the evolutionary history of

CuMMOs, phylogenetic inferences and compositional genome analyses were applied to a set of

66 genomes. Individual phylogeny of all genes xmoA, xmoB, and xmoC closely matched in almost all genomes, indicating this operon evolved as a unit. However in Verrucomicrobia pmoB has a distinct phylogeny from pmoA and pmoC. The gammaproteobacteria AMO (Nitrosococcus spp.), the gammaproteobacterial Pxm, the thaumarcheotal AMO and the NC10 pMMO showed little or no compositional bias in the xmo operon indicating similar compositional biases to its genome. Based on the analysis, possible lateral gene transfer events of xmoCAB genes were predicted. The nitrifying bacterium Nitrosococcus postulated as the donor of pmoCAB to both the alpha- and gammaproteobacterial methanotrophs.

To design a monitoring protocol that would allow a simple, cost effective and accurate estimation of whether a methane biofilter is operating efficiently, microcosms using compost as a biofilter material were tested via growth and starvation experiments for long periods. Analysis of

16S rRNA gene sequences suggested that non-methanotrophic methylotrophic bacteria belonging the family Methylophilaceae showed a rapid response to biofilter methane oxidation

ii activity and may be a good monitoring target. A monitoring system based on these

“methanotroph-associated methylotrophs” is proposed and a ratio of Methylophilaceae to

Methylococcaceae of 0.35 was related to high methane activity and 0.1 to low activity.

Novel copper membrane monooxygenase encoding operons (xmoCAB) were detected while screening metagenomes obtained from oil sands environments. Quantitative PCR assays were developed for detection of xmoCAB genes in methane, ethane and propane enrichment cultures from environmental samples. Single cell genomes were sequenced from the xmoCAB positive sorted cells of a propane enrichment culture. Screening the genomes identified

Polaromonas and Rhodoferax as containing multiple xmoCAB operons. Potential propane oxidation pathways were predicted based on enzymes present in single cell genomes of these two genera.

iii Preface

This research results section of this dissertation consists of one research paper under review and two additional thesis chapters.

Chapter 3 is in review (Frontiers in Microbiology).

Chapter 3: Roshan Khadka, Lindsay Clothier, Lin Wang, Chee Kent Lim, Martin G. Klotz and Peter F. Dunfield (2018). Evolutionary history of copper membrane monooxygenases. Frontiers in Microbiology. (accepted with minor revisions)

Supplementary information from the above paper is included in Appendix A.

An additional co-author research paper that is equally contributed as first author is included as appendix B and is based in part on the results presented in Chapter 5.

Appendix B: Rochman FF, Khadka R, Tamas I, Lopez-Jauregui AA, Malmstrom RR, Dunfield PF, and Verbeke TJ (2018). New copper containing membrane monooxygenases (CuMMOs) encoded by alkane-utilizing betaproteobacteria in oilsands tailings.

The results presented in Chapter 4 are not yet submitted for publication.

Additional co-author research papers that I contributed to during my thesis research time that are not included in this dissertation are:

Sharp, C.E., Smirnova, A.V., Graham, J.M., Stott, M.B., Khadka, R., Moore, T.R., Grasby, S.E., Strack, M., and Dunfield, P.F. (2014). Distribution and diversity of Verrucomicrobia methanotrophs in geothermal and acidic environments. Environ. Microbiol. doi:10.1111/1462- 2920.12454

Alireza Saidi-Mehrabad; K Dimitri Kits; Joong-Jae Kim; Ivica Tamas; Peter Schumann; Roshan Khadka; W Irene C Rijpstra; Jaap Sinninghe Damsté; Peter F Dunfield (2017). Methylomicrobium oleiharenae sp. nov., an aerobic methanotroph isolated from an oil sands tailings pond. International Journal of Systematic and Evolutionary Microbiology (submitted)

In Chapter 3, Lidsay Clothier provided guidance and involved to perform compositional analysis methods to detect possible lateral gene trasfer. In Chapter 5, Fauziah Rochman provided

13 13 13 13 C-alkane ( CH4, C2H6, and C3H8) SIP fraction of MLSB and BML oilsands samples. Single cell sorting, single cell genome sequencing and their assembly and annotation were performed by

iv DOE Joint Genome Institute-Integrated Microbial Genomes and Microbiomes (JGI-IMG).

Screening of single sorted cell samples to confirm copper monooxygenase-encoding genes was done by former summer student Abraham Lopez-Jauregui.

v Acknowledgements

First and foremost, I would like to express my sincere gratitude to my supervisor Dr.

Peter Dunfield. I have had so much fun and gained tons of experience during my time at

University of Calgary due to your support and guidance. You are an amazing mentor.

I would like to express my deepest thanks to my committee members Dr. Gerrit

Voordouw and Dr. Casey Hubert for their feedback and guidance during my thesis. Special thanks to the Department of Biological Sciences, University of Calgary, Mitacs, and Natural

Sciences and Engineering Research Council of Canada (NSERC) for providing financial support.

Thanks to all past and present Dunfield Lab members. To Dr. Angela Smyrnova, thank you for giving your valuable time and providing guidance especially to verrucomicrobia project. Thanks to JoongJae for your valuable thoughts in Biofilter project, Fauziah for sharing your SIP fractions, especial Evan Haupt for those amazing days, you are a great friend and roommate,

Emad, who was always excited to learn about methanotrophs and the stories you share, Ilona,

Gareth, Andrey, Gul for always being supportive.

My special thanks to my father Rudra Dhoj Khadka and mother Binda Khadka for your love, support and encouragement. This work would not be possible without my wife Susanna Kc who was with me all the time and provide me strength, confidence, support, encouragement, and guidance. My sincere thanks and appreciation to all my family members for your encouragement.

To my family….

vii Table of Contents

Abstract ...... ii Preface ...... iv Table of Contents ...... viii List of Tables ...... xi List of Figures and Illustrations ...... xiii List of Symbols, Abbreviations and Nomenclature ...... xix

CHAPTER ONE: INTRODUCTION ...... 1 1.1 Research objectives ...... 1 1.2 Dissertation structure ...... 2

CHAPTER TWO: LITERATURE REVIEW ...... 4 2.1 Methane as a greenhouse gas ...... 4 2.2 Global methane cycle ...... 5 2.3 Aerobic methanotrophic bacteria ...... 7 2.3.1 Methanotrophic proteobacteria ...... 7 2.3.2 Verrucomicrobia ...... 9 2.4 Anaerobic oxidation of methane (AOM) ...... 11 2.5 The aerobic methane-oxidation process ...... 12 2.5.1 Soluble methane monooxygenase (sMMO) ...... 14 2.5.2 Particulate methane monooxygenase (pMMO) ...... 15 2.6 Ammonia oxidation ...... 16 2.6.1 Ammonia monooxygenase (AMO) ...... 17 2.7 Copper membrane monooxygenases (CuMMOs) ...... 18 2.8 Ecology of methanotrophic bacteria ...... 19 2.8.1 Methods used for assessing methane oxidation ...... 22 2.9 Enzyme kinetics of methane monooxygenase ...... 24 2.9.1 Differential expression of pmoCAB operons ...... 26 2.10 Methylotrophic bacteria ...... 28 2.10.1. Methanol oxidation ...... 29 2.11 Connection between methanotrophs and non-methanotrophic methylotrophs 31 2.12 Methane biofilter systems ...... 32 2.12.1 Physical parameters affecting biofiltration of methane ...... 35 2.12.1.1 Nutrients ...... 35 2.12.1.2 Other growth conditions (Oxygen, pH, Temperature) ...... 38 2.13 Conclusions ...... 39

CHAPTER THREE: EVOLUTIONARY HISTORY OF COPPER MEMBRANE MONOOXYGENASES ...... 41 3.1 Abstract ...... 42 3.2 Introduction ...... 43 3.3 Materials and Methods ...... 46 3.3.1 Gene and genome sequences ...... 46 3.3.2 Phylogenetic constructions ...... 47

viii 3.3.3 Compositional evidence of lateral gene transfer (LGT) ...... 47 3.3.4 Specificity of different primer sets ...... 49 3.4 Results ...... 49 3.4.1 Phylogenetic analyses of concatenated XmoCAB ...... 49 3.4.2 Phylogenetic analyses of individual genes ...... 54 3.4.3 Compositional LGT detection ...... 58 3.4.4 Mutation rate analyses ...... 60 3.4.5 Assessing xmoA primer sets ...... 60 3.5 Discussion ...... 61 3.5.1 Verrucomicrobia ...... 63 3.5.2 Lateral gene transfer ...... 65 3.6 Acknowledgements ...... 69

CHAPTER FOUR: DEVELOPING A MONITORING PROTOCOL FOR METHANE BIOFILTER SYSTEM ...... 71 4.1 Abstract ...... 71 4.2 Introduction ...... 72 4.3 Materials and Methods ...... 76 4.3.1 Experimental design ...... 76 4.3.2 Molecular analyses ...... 78 4.3.3 Data analysis ...... 81 4.3.3.1 Multivariate analysis of biofilter communities ...... 81 4.4 Results ...... 82 4.4.1 GC measurements ...... 82 4.4.2 Beta diversity ...... 84 4.4.3 Bacterial composition analysis ...... 86 4.4.3.1 Methanotrophic bacteria in microcosms...... 86 4.4.3.2 Non-methanotrophic methylotrophic bacteria in microcosms ...... 90 4.4.3.3 Methylotroph to methanotrophs ...... 93 3.4.2.5 CCA analysis ...... 94 4.5 Discussion ...... 97 4.6 Conclusions ...... 101

CHAPTER FIVE: ANALYSIS OF COPPER MONOOXYGENASE- ENCODING GENES DETECTED IN METAGENOMES, SINGLE CELL GENOMES AND ENRICHMENT CULTURES FROM OILSANDS ENVIRONMENTS ...... 103 5.1. Introduction ...... 103 5.2 Material and Methods ...... 105 5.2.1 Database development ...... 105 5.2.2 Sampling and enrichments ...... 106 5.2.3 Quantitative PCR primer design ...... 107 5.2.4 16S rRNA sequencing and analysis ...... 110 5.2.5 Single cell sorting ...... 111 5.3 Results and Discussion...... 112 5.3.1 Phylogeny of detected CuMMO xmoCAB operon ...... 112

ix 5.3.2 Enrichment of tailings pond water on short chain hydrocarbons and community analysis ...... 116 5.3.3 Quantitative PCR analysis of enrichment samples ...... 120 5.3.4 Single cell genomics ...... 129 5.4 Conclusions ...... 140

CHAPTER SIX: GENERAL CONCLUSIONS AND FUTURE RESEARCH...... 142 6.1 Research summary ...... 142 6.2 Directions for future research ...... 145

REFERENCES ...... 149

APPENDIX A: SUPPLEMENTARY INFORMATION – PHYLOGENETIC HISTORY OF COPPER MEMBRANE MONOOXYGENASES ...... 180

APPENDIX B: SUPPLEMENTARY INFORMATION – DEVELOPING A MONITORING PROTOCOL FOR A METHANE BIOFILTER SYSTEMS ...... 213

APPENDIX C: SUPPLEMENTARY INFORMATION – ANALYSIS OF COPPER MONOOXYGENASE-ENCODING GENES DETECTED IN METAGENOMES, SINGLE CELL GENOMES AND ENRICHMENT CULTURES FROM OILSANDS ENVIRONMENTS ...... 217

APPENDIX D: SUPPLEMENTARY INFROMATION – ANALYSIS OF COPPER MONOOXYGENASE-ENCODING GENES DETECTED IN METAGENOMES, SINGLE CELL GENOMES AND ENRICHMENT CULTURES FROM OILSANDS ENVIRONMENTS ...... 223

x List of Tables

Table 4.1: Different treatment characteristics used in microcosms ...... 76

Table 5.1: Specific primers for new xmoA lineages detected in oilsands environments, along with amplicon length and PCR cycling conditions...... 109

Table A-1: Estimation of potential LGT in genomes having xmoCAB operons. Columns 2-4 shows G+C compositional bias in the genomes compared to the operons. Column 5 -7 shows KL divergence values between 0 to1. Higher KL values indicate higher compositional bias of the operon compared to the host genome, and a high probability of LGT. Values close to 0 indicate low compositional bias, and a lower chance of LGT. TETRA gives an output of tetra nucleotide frequency or tetranucleotide usage patterns in a DNA sequences (Teeling et al., 2004b), CodonW calculates codon usage frequency in a DNA sequences (Angellotti et al., 2007), and Alien Hunter gives an output of every 2500 base pair sequences which calculates the frequency of every n-mer from two nucleotide to eight nucleotide to reliably find the local composition of a sequence compared with fixed order methods (Vernikos and Parkhill, 2006). BT indicates the KL calculated by Alien Hunter was below a significance threshold (i.e. not significantly greater than 0). Column 8 indicates whether the xmoCAB operon was estimated to be located within a genomic island using IslandViewer (Vernikos and Parkhill, 2006), placement on an island is indicated by “LGT”...... 194

Table A-3: Specificity of group specific primers to target methanotrophs and nitrifiers in previous studies. Number represent specificity to organisms. High number represents low specificity...... 200

Table A-4: XmoC, XmoB and XmoA locus tag id of different organisms taken from NCBI/JGI database. The respective protein or concatenate XmoCAB was used for phylogenetic analysis of CuMMOs gene family...... 204

Table B-1 Summary of spore/cyst formations among the species of methanotrophs. Reference taken from review by Semrau et al., (2010)...... 214

Table B-2 Outline of the protocol used for QIIME analyses of 16S rRNA Illumina output. Each steps function is outlined, and samples details are not presented...... 215

Table B-3 Outline for CCA analysis performed in R platform...... 216

Table B-4 Calculated concentration copies formulae ...... 216

Table C-1: Metagenomes used for the screening of CuMMO-encoding operons...... 217

Table C-3: Calculated concentration copies formulae ...... 219

Table C-4: Summary of xmoCAB gene detected in single sorted cells samples. The assays were run as describe in Table 5.1. The single cell sorted samples was sent in plate 2 by JGI. Well position represent the position of single cell sorted location in a 96 well plate. 220

Table C-5: Taxonomy of single cell sorted samples based on 16S rRNA gene sequence. A total of 93 cells were sorted by JGI...... 221

Table C-6: Analysis of MANOVA and Significance test of qPCR output...... 221

Table C-7: Significance of ANOVA ...... 222

xii List of Figures and Illustrations

Figure 3.1: Phylogenetic tree based on concatenated inferred XmoCAB sequences (minimum 910 amino acids). The tree was constructed using Bayesian analysis employing: A. a gamma site heterogeneity model with 4 gamma categories with a relaxed clock log normal model and B. a Blosum62 substitution model with gamma site heterogeneity model under a strict clock. Node value are based on 10,000,000 iterations, minus a burn- in of 20% of total. The scale bar represents 0.2 changes per amino acid position. Colours indicate coherent functional and taxonomic groups. The protein accession numbers for the operons are given in Table A-4...... 53

Figure 3.2: Phylogenetic trees based on inferred XmoA, B, and C sequences. Trees were constructed using Bayesian analysis (strict molecular clock) employing a Blosum62 substitution model and Gamma site heterogeneity model with 4 gamma categories. Node values are based on 10,000,000 iterations after a burn-in of 20% of total trees. The scale bar represents 0.2 changes per amino acid position. Lineages are coloured and labelled as in Figure 3.1...... 56

Figure 3.3: Phylogenetic tree based on inferred XmoA, B, and C sequences. Trees were constructed using Bayesian analysis (relaxed clock model) employing a Gamma site model with 4 gamma categories. Node values are based on 10,000,000 iterations after a burn-in of 20% of total trees. The scale bar represents 0.08 and 0.1 changes per amino acid position for XmoA and C, and XmoB respectively. Lineages are coloured and labelled as in Figure 3.1...... 57

Figure 3.4: KL divergence measure of xmoCAB versus the entire genome in different taxonomic/functional groups. Values on the x axes represent calculated KL values (0 - 1) by three different analysis: (A) TETRA, (B). CodonW, and (C) AlienHunter. Values close to 1 indicate that the pmoCAB operon likely underwent horizontal transfer into its genome and values close to 0 indicate no LGT...... 59

Figure 3.5: Systematic diagram of predicted LGT events in between taxa or lineages based on phylogeny and compositional analysis (A - C). Box represents taxa and line represents lineages...... 66

Figure 4.1: Incubation of compost for the starvation experiment...... 78

Figure 4.2: Headspace methane (v/v) in different treatments during incubation. A. Methane oxidation by compost in the starvation (aerobic and anaerobic) treatments. Methane starvation was started after 3 weeks of incubation under aerobic or anaerobic conditions. The aerobic condition was generated by replacing the head space of the bottles with air and the anaerobic condition was maintained by flushing with N2 after samples were taken. Starved aerobic, Starved anaerobic and Control are coloured in pink, light green and gray. B. Methane oxidation in compost for the nutrient amended treatment (blue)

xiii and continuous feed treatment (red). Methane was replaced each week for the treatments. Error bar represents ± 1 SEM of 3 replicates...... 83

Figure 4.3: NMDS plot of microbial communities based on 16S rRNA gene sequencing of samples from different treatments and different days of incubation. Black, green and orange circles represent Starved aerobic treatment, Starved anaerobic treatment, and Continuous feed treatment respectively. The number represents the day of incubation in each sample and the letter represents the treatment. The stress value of the ordination is 0.110...... 85

Figure 4.4: Relative abundances of known methanotrophs present in the microcosms of different treatments based on 16S rRNA gene sequencing. A. Total methanotrophs, B. Methylobacter sp., C. Methylomicrobium sp. and D. Methylocaldum sp. Genera with less than 0.5% relative abundance are not shown in the graph. Treatments are Starved aerobic (pink), Starved anaerobic (green), Continuous feed (red) and Control (grey). Error bars represents ± SEM of duplicate experimental vials...... 87

Figure 4.5: Quantitative PCR analysis pmoA genes in the Continuous feed vs Starved aerobic treatments over time. Error bars represents ± 1 SEM of two separate experimental replicates...... 89

Figure 4.6: Relative abundance of Methylophilaceae found in different microcosms. A. Total Methylophilaceae, B. OM3B group, C. Methylotenera sp., D. uncultured group, and E. Methylophilus sp. Relative abundances lower than 0.01% are not presented here. Different treatments are color coded: Starved aerobic (pink), Starved anaerobic (light green), Continuous feed (red), and Control (light gray). Error bar represents ± SEM of duplicate experimental vials...... 91

Figure 4.7: Relative abundance of Methylococcaceae compared to Methylophilaceae in different treatments before and after starvation. Methylococcaceae are in red and Methylophilaceae in blue. Error bar represents ± SEM of experimental duplicates...... 92

Figure 4.8: Ratio of methylotrophs (Methylophilaceae) to methanotrophs (Methylococcaceae) in Starved aerobic and Continuous feed treatment. Red bars represent the ratio for Continuous feed and green bars the ratio for the Starved aerobic treatment. Error bars represents ± 1 SEM of experimental duplicates...... 94

Figure 4.9: CCA ordination plot of samples from different treatments. Only samples after the onset of starvation (21 - 182 days) were analyzed. Bacterial communities that were classified at the genus level at different treatments was used for the analysis. Only selected known methylotrophs and methanotrophs are shown in the CCA plot. Symbols represent genera (labelled) and arrows represent the treatments. Arrow length represents the strength of separation based on the treatment...... 96

xiv replicates. The scale bar represents 0.2 change per amino acid position. TP2, 490, WIP, TP1, Coal, TP6, and HR are seven different metagenome sequences represented with different colours. Text at the bottom-left of the figure represents JGI-IMG metagenome ID and the environment...... 115

Figure 5.2: Depletion of methane, ethane, and propane in MLSB and BML water samples. .... 117

Figure 5.3: Community structures of methane, ethane and propane enrichments of MLSB and BML. Each MLSB and BML sample was enriched in duplicate, and labelled as A and B (e.g., Methane_BML(A) and Methane_BML(B) were two separate methane enrichment). Predominant taxa are identified based on comparison to the SILVA 119 database (Silva_119_rep_set97.fna). Genera below 1% relative abundance were represented as ‘others’...... 119

Figure 5.4: Quantitative PCR analysis of specific xmoA genes in methane, ethane and propane amended MLSB and BML samples: A. TP2 assay, B. 490 assay, C. TP6 assay, D. Coal assay, E. TP1 assay, F. WIP assay and G. HR assay. The x-axis represents MLSB or BML samples enriched with methane, ethane and propane (or unenriched controls). The y-axis represents the number of gene copies per ml of enrichment culture. ANOVA was run to test the differences between methane and propane enrichment gene copies obtained from different assay. The p value represents the significance of ANOVA calculated using Tukey test. Error bars represent ± SEM, based on duplicate qPCR reactions of the same extracts (i.e. technical replicates)...... 124

Figure 5.5: qPCR analysis of xmoA gene copies for the TP2 assay of MLSB tailings water sampled in August, 2015 and enriched with the addition of labelled substrates: A. 12C- methane, B.13C-methane, C. 12C-ethane, D. 13C-ethane, E. 12C-propane, F. 13C-propane. The x-axis represents DNA density (g ml-1) for each fraction. The y-axis dotted line represents the relative DNA concentration and bar represents number of gene copies per fraction. Error bars represents ± SEM of two technical replicates. SIP fractions analyzed in this study were prepared as described by Rochman, 2016...... 127

Figure 5.6: qPCR analysis of xmoA gene copies for 490 assay of MLSB tailings water sampled in August, 2015 with the addition of labelled substrates: A. 12C-methane, B.13C-methane, C. 12C-ethane, D. 13C-ethane, E. 12C-propane, F. 13C-propane. The x- axis represents DNA density (g ml-1) for each fraction. The y-axis dotted line represents the relative DNA concentration and bar represents number of gene copies per fractions. Error bars represents ± SEM of two technical replicates. SIP fractions analyzed in this study were prepared as described by by Rochman, 2016...... 128

Figure 5.7: Maximum-likelihood mmoX-gene based phylogeny of derived amino acid sequences. The tree was constructed using Seaview 4.4.12 employing a GTR model. Node values are based on 100 bootstrap replicates. The scale bar represents 0.2 changes per nucleotide position. mmoX gene detected in the SAGs are indicated in red colour. .... 135

Figure 5.8: Predicted propane oxidation pathway based on gene identified in SAGs...... 138

xv Figure 5.9: Predicted aromatic compounds degradation pathways based on genes detected on genomes. A. Benzene metabolic pathway, B. Toluene metabolic pathway. Enzymes are in blue colour...... 139

Figure A-1: Maximum-likelihood XmoCAB based phylogeny of a Cu-monooxygenase. The tree was constructed using Seaviw 4.4.12 (Gouy et al., 2010) employing an LG model (Le and Gascuel, 2008) (100 iterations). Node values are based on 100 bootstrap replicates. The scale bar represents 0.2 change per amino acid position. Colours indicate coherent functional and taxonomic groups...... 180

Figure A-2: Phylogenetic tree of inferred XmoCAB sequences based on a Neighbor-joining method with Poisson model constructed using Seaview 4.4.12 (Gouy et al., 2010). Node values are based on 100 bootstrap replicates. The scale bar represents 0.2 change per amino acid position. Colours indicate coherent functional and taxonomic groups...... 181

Figure A-3: Phylogenetic tree based on inferred XmoA sequences constructed using Bayesian analysis employing a gamma site heterogeneity model with 4 gamma categories with a relaxed clock log normal model. Node values are Bayesian posterior probabilities based on 10,000,000 iterations, minus a burn-in of 20% of total. The scale bar represents 0.08 changes per amino acid position. Colours indicate coherent functional and taxonomic groups...... 182

Figure A-4: Phylogenetic tree based on inferred XmoA sequences constructed using Maximum-likelihood. The tree was constructed using Seaview 4.4.12 (Gouy et al., 2010), employing an LG model (Le and Gascuel, 2008) (100 iterations). Node values are based on 100 bootstrap replicates. The scale bar represents 0.2 changes per amino acid position. Colours indicate coherent functional and taxonomic groups...... 183

Figure A-5: Phylogenetic tree based on inferred XmoA sequences constructed using a Neighbor-joining method with Poisson model using Seaview 4.4.12 (Gouy et al., 2010). Node values are based on 100 bootstrap replicates. The scale bar represents 0.2 changes per amino acid position. Colours indicate coherent functional and taxonomic groups...... 184

Figure A-6: Phylogenetic tree based on inferred XmoB sequences constructed using Bayesian analysis employing a gamma site heterogeneity model with 4 gamma categories with a relaxed clock log normal model. Node value are Bayesian posterior probabilities based on 10,000,000 iterations, minus a burn-in of 20% of total. The scale bar represents 0.01 changes per amino acid position. Colours indicate coherent functional and taxonomic groups...... 185

Figure A-7: Phylogenetic tree based on inferred XmoB sequences constructed using Maximum-likelihood. The tree was constructed using Seaview 4.4.12 (Gouy et al., 2010), employing an LG model (Le and Gascuel, 2008) (100 iterations). Node values are based on 100 bootstrap replicates. The scale bar represents 0.2 changes per amino acid position. Colours indicate coherent functional and taxonomic groups...... 186

xvi Figure A-8: Phylogenetic tree of inferred XmoB sequences constructed using Neighbor- joining with a Poisson model. The tree was constructed using Seaview 4.4.12 (Gouy et al., 2010). Node values are based on 100 bootstrap replicates. The scale bar represents 0.2 changes per amino acid position. Colours indicate coherent functional and taxonomic groups...... 187

Figure A-9: Phylogenetic tree of inferred XmoC sequences, constructed using Bayesian analysis employing a gamma site heterogeneity model with 4 gamma categories with a relaxed clock log normal model. Node value are Bayesian posterior probabilities based on 10,000,000 iterations, minus a burn-in of 20% of total. The scale bar represents 0.08 changes per amino acid position. Colours indicate coherent functional and taxonomic groups...... 188

Figure A-10: Phylogenetic tree of inferred XmoC sequences, constructed using Maximum- likelihood The tree was constructed using Seaview 4.4.12 (Gouy et al., 2010).employing LG model (Le and Gascuel, 2008) (100 iterations). Node values are based on 100 bootstrap replicates. The scale bar represents 0.2 changes per amino acid position. Colours indicate coherent functional and taxonomic groups...... 189

Figure A-11: Phylogenetic tree of inferred XmoC sequences constructed using Neighbor- joining with a Poisson model in Seaview 4.4.12 (Gouy et al., 2010). Node values are based on 100 bootstrap replicates. The scale bar represents 0.2 changes per amino acid position. Colours indicate coherent functional and taxonomic groups...... 190

Figure A-12: Phylogenetic tree based on inferred XmoA sequences. The tree was constructed using Bayesian analysis employing a gamma site heterogeneity model with 4 gamma categories with a relaxed clock log normal model. Node value are evolutionary rate for selected lineages (indicated as either light green or blue) based on 10,000,000 iterations, minus a burn-in of 20% of total. The light green colour indicate pmoA in operon3 for the Verrucomicrobia genus “Methylacidiphilum”...... 191

Figure A-13: Phylogenetic tree based on inferred XmoB sequences. The tree was constructed using Bayesian analysis employing a Gamma site heterogeneity model with 4 gamma categories with a relaxed clock log normal model. Node value are evolutionary rate for lineage (indicated as either light green or blue) based on 10,000,000 iterations, minus a burn-in of 20% of total. The light green colour indicate pmoB in operon3 for the Verrucomicrobia genus “Methylacidiphilum”...... 192

Figure A-14: Phylogenetic tree based on inferred XmoC sequences. The tree was constructed using Bayesian analysis employing a Gamma site heterogeneity model with 4 gamma categories with a relaxed clock log normal model. Node value are evolutionary rate for lineage (indicated as either light green or blue) based on 10,000,000 iterations, minus a burn-in of 20% of total. The light green colour indicate pmoC in operon3 for the Verrucomicrobia genus “Methylacidiphilum”...... 193

Figure B-1: Neighbor-joining concatenated pmoCAB gene-based phylogeny. The tree was constructed using Seaview 4.4.12 employing Jukes-Cantor distance model. Node values

xvii are based on 100 bootstrap replicates. The scale bar represents 0.05 changes per nucleotide position...... 213

xviii List of Symbols, Abbreviations and Nomenclature

Symbol Definition ANME anaerobic methanotroph AMO ammonia monooxygenase AMS ammonium mineral salts AOB ammonia oxidizing bacteria AOM anaerobic oxidation of methane BML Base Mine Lake BMO butane monooxygenase

C2H6 ethane

C3H8 propane CBB Calvin Benson Bassham

CH4 methane

CO2 carbon dioxide CuMMO copper membrane monooxygenase DGGE denaturing gradient gel electrophoresis EPA Environmental Protection Agency FaDH formaldehyde dehydrogenase FID flame ionization detector GEBA Genomic Encyclopedia of Bacteria and Archaea GHG greenhouse gas GTC guanidine thiocyanate GWP global warming potential

H4F tetrahydrofolate

H4MPT tetrahydromethanopterin HAO hydroxylamine oxidoreductase HMO hydrocarbon monooxygenase HMP Hydrocarbon Metagenome Project HP/HB hydrooxypropionate/hydroxybutyrate KL Kullback Leibler LGT lateral gene transfer LP2A Loop Road Isolate 2A LRT likelihood ratio test MANOVA multivariate analysis of variance MDA multiple displacement amplification MDH methanol dehydrogenase MDM Microbial Dark Matter

xix MGAP Microbial Genome Annotation Pipeline ML maximum likelihood MLSB Mildred Lakes Settling Basin NJ neighbor joining NMDS non-metric multidimensional scaling NMS nitrate mineral salts OSPW Oil Sands Process Affected Water OTU operational taxonomic unit PCR polymerase chain reaction PLFAs phospholipid fatty acids pMMO particulate methane monooxygenase ppmv parts per million by volume PQQ pyrroloquinoline quinone PVC Planctomycetes, Verrucomicrobia, Chlamydiae QIIME Quantitative Insights Into Microbial Ecology qPCR quantitative polymerase reaction QRP quinone reactive protein rpm revolutions per minute RuMP ribulose monophosphate SIP stable isotope probing sMMO soluble methane monooxygenase

Chapter One: Introduction

1.1 Research objectives

Global warming is becoming a greater concern than ever before. Decreasing methane emissions from the environment would decrease the rate of global climate change.

Methanotrophs are of special interest in research because of their importance in the global methane budget. Several studies based on both cultivation and cultivation independent methods have explored the diversity and habitat preference of methanotrophs in different environments

(Knief, 2015).

Methanotrophic bacteria are of great importance in their ability to degrade methane as well as organic pollutants by the key enzyme methane monooxygenase (Hanson and Hanson,

1996; Semrau et al., 2010). Currently there are approximately 60 different cultivated representative species of aerobic methanotrophs that belong to the Gammaproteobacteria and

Alphaproteobacteria (reviewed by Knief, 2015). Detecting methanotrophs based on cultivation independent methods targeting the pmoA gene as a functional marker has advanced the known diversity of methanotrophs.

The copper membrane monooxygenases (CuMMOs) are a diverse enzyme family that oxidize a range of compounds such as methane (particulate methane monooxygenase), ammonia

(ammonia monooxygenase) and short chain alkanes (butane monooxygenase) (Semrau et al.,

2010; Coleman et al., 2011; Sayavedra-Soto et al., 2011; Tavormina et al., 2011). These enzymes are present in members of the phyla Proteobacteria, Verrucomicrobia, Candidate division NC10,

Actinobacteria and Thaumarcheota. Methanotroph genome sequencing projects led by JGI for characterization of new, uncultured methanotrophs have provided the insight of methanotrophs

that exists beyond known taxa and their potential functional roles. Genome projects like

Genomic Encyclopedia of Bacteria and Archaea (GEBA) and Microbial dark matter (MDM) have made further genomes available containing copper membrane monooxygenase genes from known phyla as well as candidate phyla (Rinke et al., 2013; Whitman et al., 2015). CuMMO detection from genomes and metagenomic studies keeps expanding, however the evolutionary history of this enzyme family is not yet fully understood.

Methanotrophs oxidize methane and can be regarded as biofilters decreasing the release of methane to the atmosphere. One of many applications is the use of methanotrophs in biofiltration. Biofilters have potential in removal of methane originating from wastewater treatment plants, landfilling, composting, animal husbandry, natural gas mining and refineries, and abandoned wells (from mining and oil extraction).

The major objectives of this dissertation were to:

1. Understand the evolutionary history of copper membrane monooxygenases.

2. Characterize novel copper membrane monooxygenases detected in metagenomes of

oilsands environments.

3. Develop a monitoring protocol for a methane biofilter system.

1.2 Dissertation structure

The present research was aimed at studying the diversity of copper membrane monooxygenases and their evolutionary history as well as to design an assay for monitoring methane biofilter system. Chapter 2 provides the in-depth overview of methane and

methanotrophs associated with these research areas. General background information, objectives, methods, results and discussion are included in the chapters of this dissertation.

Chapter 3 describes the evolutionary history of copper membrane monooxygenases. This was analyzed using genomes that have copper membrane monooxygenase operon(s), which includes members of the phyla Proteobacteria, Verrucomicrobia, Candidate division NC10,

Actinobacteria and Thaumarchaeota. This study built a copper membrane monooxygenase database that was intended for Chapter 4 to design qPCR probes.

Chapter 4 examines methanotrophs and methylotrophs in compost-based laboratory microcosms. The goal was to design a monitoring protocol for biofilter system which is simple, cost effective and gives an accurate estimate of activity. This was investigated using compost as a biofilter material.

Chapter 5 investigates novel copper membrane monooxygenase-encoding xmoCAB operon(s) detected in oilsands metagenomes and their characterization using single cell genomics. The genome xmoCAB dataset from Chapter 3 was used together with newly detected novel xmoCAB operon(s) to understand their evolutionary relationships. Single cell genomes were analyzed to predict potential metabolic pathways.

Chapter 6 is a summary of all the major findings and general conclusions of this dissertation, and directions for future research.

Chapter Two: Literature Review

2.1 Methane as a greenhouse gas

Global carbon emission has been increasing since the nineteenth century due to industrialization. Since then, emitted greenhouse gas (GHG) particularly from fossil fuel combustion and industry has been contributing about 78% of the increase of total GHG emissions from 1970 to 2011 (IPCC, 2013). Methane and carbon dioxide are the two most important greenhouse gases contributing to their recent increase and help in warming the Earth’s atmosphere. Greenhouse gas emission is usually calculated in carbon dioxide equivalents

(CO2e). It is determined by multiplying the total amount of emissions of a particular gas by the global warming potential (GWP) of that gas. For each greenhouse gas, GWP shows how long it remains in the atmosphere on average and how strongly it absorbs energy. CO2 has been given a

GWP value of 1 for reference. Methane is a more potent greenhouse gas than CO2, and has a

GWP of 34 over 100 years (IPCC, 2013).

Global methane emission is estimated to be 542 to 852 Tg CH4 per year (IPCC, 2013) and globally this has been mostly due to methane produced by anthropogenic activities (Ghosh et al., 2015). The main anthropogenic emissions are related to oil and gas, coal, livestock and landfills. Over the last 110 years oil and gas sector-related methane emission increased from 12 to 78 Tg per year (Ghosh et al., 2015). Based on U.S. Environmental Protection Agency (EPA) estimates, the oil and gas industry contributes nearly 30% of total anthropogenic US methane emission, estimated at roughly 200 Mt CO2e for 2016 (Environmental Protection Agency, 2017).

Landfills are the third largest sector for global anthropogenic methane emission, and account for

500-800 Mt CO2e per year (IPCC, 2013). In Canada, landfills account for 20% of total methane emission which is 19 Mt CO2e (Government of Canada, 2017).

Alberta contains third largest crude oil reserves in the world (Natural Resources Canada,

2017). The oil and gas sector is the largest methane emission source in this province, contributing 25% of the total anthropogenic methane emission (Government of Alberta, 2017).

Other sources include landfills (municipality waste, sanitary, wood waste, and paper industry), waste water treatment plants, and agriculture. The Alberta government has made its greenhouse gas reduction guidelines and regulation standards to reduce methane emission by 45% by 2025 under the "Climate Leadership Plan" (Government of Alberta, 2017).

2.2 Global methane cycle

Natural sources for methane emission include wetlands, oceans, rice fields, gas hydrates, and termites. Wetlands are the largest source of methane that generate between 177 – 284 Tg

-1 -1 CH4 yr , while the rest of the biogenic sources generate between 61 – 200 Tg CH4 yr (IPCC,

2013). In an anoxic environments methane is produced by methanogenesis. Archaeal methanogens are capable of producing methane in the final step of energy yielding anaerobic degradation of organic carbon. Methanogenesis is carried out via three major pathways, reduction of carbon dioxide, fermentation of acetate and dismutation of methanol or methylamines (Ferry, 1992; Lessner, 2009).

Other than natural sources, emission from fossil fuel extraction, rice paddy agriculture, and landfills and other waste-related emissions are the dominant anthropogenic methane sources.

Methane emission due to anthropogenic activities contributes between 50 and 65% of total

methane sources (IPCC, 2013). Rice fields are one of the major methane source and have been intensively studied. Based on cultivation independent studies data, rhizosphere rice cluster I methanogens, Methanosarcinaceae, Methanosaetaceae, Methanobacteriales, Methanocellales, and Methanomicrobiales were dominant in production of methane in rice paddies (Alpana et al.,

2017).

As methane diffuses from subsurface origins towards surface oxic zones, it becomes an energy source for methanotrophs. Microbial methane release into the atmosphere is moderated by two different mechanisms: aerobic and anaerobic methane oxidation. Aerobic methanotrophs oxidize methane and act as a biological filter for mitigating methane emission. Aerobic methanotrophs belongs to the taxa Gammaproteobacteria, Alphaproteobacteria and

Verrucomicrobia.

Anaerobic oxidation of methane (AOM) is an important sink of methane in anoxic marine and freshwater sediments. In anoxic conditions methane is oxidized in a syntrophic consortium of anaerobic methanotrophic archaea (ANME) and sulfate reducing bacteria. On a global scale anaerobic oxidation of methane is estimated to be 70 – 300 Tg per year (Reeburgh,

2007).

If it escapes past the biological methane sink of methanotrophs, unoxidized methane gets released into the atmosphere. The largest sink for methane in the atmosphere is the photochemical oxidation of methane with hydroxyl radicals (OH·). Hydroxyl radicals are extremely reactive and able to oxidize most of the chemicals in the troposphere. This sink is estimated to about 80% of the total sink for methane (Conrad, 2009). Some of the methane in the atmosphere is also oxidized in oxic upland soils. This methane sink is due to methanotrophic

-1 bacteria and is estimated to be between 9 to 47 Tg CH4 yr (Curry, 2007; Ito and Inatomi,

2012).

2.3 Aerobic methanotrophic bacteria

Aerobic methanotrophs are a group of bacteria that use methane as a sole energy source in environments where O2 is readily available (Hanson and Hanson, 1996). They are usually found at oxic-anoxic interfaces of wetland environments and diverse upland soils and use available O2 as the electron acceptor and oxidize methane to CO2. Methanotrophic species are adapted to diverse environmental conditions of temperature, pH, and salinity (Hanson and

Hanson, 1996; Conrad, 2007; Dunfield, 2009; Bowman, 2014).

Aerobic methanotrophs are mostly obligate methane oxidizers that can only use C1 compounds such as methane (or methanol, or methylamines). However, facultative methanotrophs have been isolated and described (Dedysh et al., 2005). Most of the known methanotrophs belong to the phylum Proteobacteria, but non-proteobacterial methanotrophs have been isolated. These methanotrophs belong to a novel division or order within the

Verrucomicrobia (Op den Camp et al., 2009) and to the candidate phylum NC10 (Ettwig et al.,

2009).

2.3.1 Methanotrophic proteobacteria

Proteobacteria methanotrophs are found in classes Alphaproteobacteria and

Gammaproteobacteria. In the Alphaproteobacteia, methanotrophs are classified into the families

Methylocystaceae and Beijerinckiaceae, and include the genera: Methylosinus, Methylocystis,

Methylocella, Methylocapsa and Methyloferula. Similarly, Gammaproteobacteria methanotrophs

are found in the families Methylococcaceae, Methylothermaceae, and Crenotrichaceae and include the genera: Methylomonas, Methylobacter, Methylococcus, Methylomicrobium,

Methylosphaera, Methylocaldum, Methylosarcina, Methylohalobius, Methylosoma,

Methylomarinum, Methylovulum, Methylogaea, Clonothrix, Methylothermus, Methyloglobulus,

Methylomagnum, Methyloparacoccus, Methyloprofundus, and Methylomarninovum. The genus

Crenothrix is methanotrophic but has not been isolated into pure culture yet (Stoecker et al.,

2006).

Based on the carbon assimilation pathways they use, methanotrophs were originally grouped into types I and II. Type I (Gammaproteobacteria) methanotrophs use the ribulose monophosphate (RuMP) pathway for carbon fixation and type II (Alphaproteobacteria) use the serine pathway (Hanson and Hanson, 1996). Other characters like cell morphology, internal membrane structure (perpendicular versus parallel cell envolope), phylogeny

(Alphaproteobacteria vs Gammaproteobacteria), the dominant phospholipid fatty acids (PLFAs;

16C vs 18C), and methane monooxygenase enzyme were used for comparison and classification of methanotrophs into these types (Hanson and Hanson, 1996; Op den Camp et al., 2009;

Chistoserdova, 2011).

Several methanotrophs are not easily categorized into the type I and type II system. For example, verrucomicrobia methanotrophs of the proposed genus "Methylacidiphilum" use the

Calvin-Benson-Bassham (CBB) cycle for carbon fixation (Khadem et al., 2011) and do not fall into the previous “type” system classification. The genera Methylocella and Methyloferula do not possesses the pMMO (Dedysh et al., 2000; Dunfield et al., 2003; Vorobev et al., 2011). These methanotrophs, along with the verrucomicrobia methanotrophs, do not have typical internal membrane structures originally described for type I and type II methanotrophs (Op den Camp et

al., 2009). Signature fatty acids found in type I and type II are missing in some methanotrophs such as in Beijerinckiaceae (the genera Methylocella and Methyloferula) (Dedysh et al., 2000;

Dunfield et al., 2003; Vorobev et al., 2011), Methylothermus thermalis (Tsubota et al., 2005),

Methylocystis heyeri (Dedysh et al., 2007) and methanotrophic Verrucomicrobia (Op den Camp et al., 2009).

Methanotrophs are also categorized as obligate or facultative based on their single or multi carbon compound use. Obligate methanotrophs that use methane as a sole energy source are found in the classes Alphaproteobacteria and Gammaproteobacteria. However facultative methanotrophs like Methylocella silvestris, Methylocapsa aurea, Methylocystis strain SB2 use methane or some multicarbon substrates (e.g., acetate, pyruvate, succinate, malate, acetone, methyl acetate, acetol, glycerol, propionate, gluconate, ethane, ethanol, propanol, propanediol, and propane) (Semrau et al., 2011; Crombie and Murrell, 2014; Dunfield and Dedysh, 2014).

2.3.2 Verrucomicrobia

Verrucomicrobia is a widely distributed phylum with representatives found in a variety of habitats such as in terrestrial, marine, artic ocean, freshwater and hotsprings. Culture independent analyses suggest that this phylum is ubiquitous in soil (Hugenholtz et al., 1998; Bergmann et al.,

2011). This phylum is part of the large Planctomycetes, Verrucomicrobia, Chlamydiae (PVC) complex together with Lentisphaerae and the candidate phyla Poribacteria and OP3 (Schlesner et al., 2006).

There are altogether seven class level groups in the Verrucomicrobia. Out of seven, three classes, Verrucomicrobiae, Spartobacteria and Opitutae have been described (Hedlund, 2010), but a very small fraction of the Verrucomicrobia has as yet been isolated in pure culture. The 9

newly discovered verrucomicrobial methanotrophs probably represent a new class (Op den

Camp et al., 2009). Furthermore, pyrosequencing of 16S rRNA gene amplicons showed that putative methanotrophic Verrucomicrobia were found at a broad range of temperatures from

22.5 ᴼC to 81.6 ᴼC at a pH range of 1.8-5.0 detected in geothermal areas (Sharp et al., 2014b).

Verrucomicrobia methanotrophs have also been detected in 16S rRNA gene pyrosequencing of corroded concrete pipe samples (Pagaling et al., 2014).

The first verrucomicrobial methanotrophs were isolated around 10 years ago from three geographically distant geothermal areas; isolate SolV from a mudpot in a solfatara field in Italy

(Pol et al., 2007), isolate V4 from hot soil in New Zealand (Dunfield et al., 2007), and isolate

Kam1 from an acidic hot spring in Uzon Caldera, Kamchatka, Russia (Islam et al., 2008). They were designated as “Methylacidiphilum fumarolicum” (strain SolV), “Methylacidiphilum infernorum” (strain V4), and “Methylacidiphilum kamachatkense” (strain Kam1) (Op den Camp et al., 2009) based on their 16S rRNA gene sequences, which are >98.4% identical. These members of Verrucomicrobia display an extremely acidophilic phenotype, are moderate thermophiles, lack the intracytoplasmic membranes typical of proteobacterial methanotrophs, and have electron-dense carboxysome-like polyhedral and globular inclusions when observed by electron microscopy. The cells are short rods with a Gram-negative cell wall structure. The extensive intracellular membrane system found in proteobacterial methanotrophs houses the key enzyme, particulate methane monooxygenase (pMMO), which is responsible for the methane oxidation activity, so the enzyme is likely localized differently in verrucomicrobia.

Genome analysis of M. infernorum V4 revealed a lack of several of the genes encoding key enzymes for the ribulose monophosphate pathway, which is involved in the fixation of carbon from CHOH in gammaproteobacterial methanotrophs. The serine pathway for 10

assimilation of carbon in alphaproteobacterial methanotrophs, was also found to be incomplete in strain V4, suggesting that the verrucomicrobial methanotrophs fix carbon via another pathway

13 (Dunfield et al., 2007; Hou et al., 2008). Based on a CO2 incorporation assay, it was shown that strain SolV assimilates carbon from CO2 (Khadem et al., 2011) , and can thus be classified as an

‘autotrophic methanotroph’. The Calvin-Benson-Bassham (CBB) cycle appears to be complete both in strain V4 and SolV, and a high expression level of all genes for CBB in strain SolV was observed (Khadem et al., 2011). Using Stable Isotope Probing (SIP) with labeled CH4 and CO2,

13 it was observed that strain V4 assimilated CO2 (Sharp et al., 2012). This explains why growth of the methanotrophic verrucomicrobia is dependent on CO2 (Op den Camp et al., 2009).

Unlike “Methylacidiphilum” spp., Verrucomicrobia methanotroph isolate LP2A (Loop

Road isolate 2A) is a meso-acidophilic aerobic methanotroph which belongs to a new phylogenetic branch of the Verrucomicrobia subphylum 6 (Sharp et al., 2014b). The closest relative was strain V4 with a 16S rRNA gene 6 sequence identity of only 90.6%. Its optimum growth temperature is at 30 ᴼC and optimum pH at 3.1. Strain LP2A lacks key enzymes for the

RuMP pathway and the serine cycle, but has a complete Calvin-Benson-Bassham cycle for CO2 assimilation. Three more similar isolates of meso-acidophilic verrucomicrobial methanotrophs have been isolated from the Solfatara crater, Italy (van Teeseling et al., 2014). The isolates

(along with LP2A) were proposed to represent a new genus “Methylacidimicrobium”.

2.4 Anaerobic oxidation of methane (AOM)

ANME archaea are divided into 3 groups, ANME-1, ANME-2 and ANME-3 (Orphan et al., 2001; Schleper et al., 2005). During the process of oxidation, different terminal electron acceptors are used such as sulphate, nitrate, nitrite and metals (Cui et al., 2015). Sulphate 11

dependent AOB is mainly found in marine environments where methane is oxidized to give hydrogen (Nauhaus et al., 2002; Nauhaus et al., 2005) or acetate which is consumed by sulfate reducing bacteria (Valentine and Reeburgh, 2008). There is no pure culture representative of

ANME. However, ANME-1 and ANME-2 are found to be associated with sulphate reducing bacteria genus Desulfosarcina and Desulfococcus respectively whereas ANME-3 group are associated with Desulfobulbus (Cui et al., 2015). Recently, it has been shown that AOM happens between ANME-1 and SRB consortia by direct electron transfer via nanowire structures

(Wegener et al., 2015).

Nitrate and nitrite dependent AOM was found in an anoxic microbial consortium

(Raghoebarsing et al., 2006). A new candidate phylum NC10 was found in nitrite rich enrichment culture oxidizing methane without association with ANME (Ettwig et al., 2008).

Later an enriched member of NC10, Candidatus Methylomirabilis oxyfera was found to oxidize methane aerobically by utilizing oxygen generated by anaerobic reduction of nitrite (Ettwig et al., 2010). Genomic data showed Ca. Methylomirabilis oxyfera possess a complete pmoCAB operon required for aerobic oxidation of methane.

Recently, the nitrate dependent AOM archaeon Candidatus Methanoperedens nitroreducens under the ANME-2d group was found capable of oxidising methane (Haroon et al.,

2013).

2.5 The aerobic methane-oxidation process

Methanotrophs oxidize methane aerobically via methane monooxygenases. The use of this enzyme to catalyze the oxidation of methane to methanol is a key process for methanotrophs.

Two different and unrelated methane monooxygenases are found; a membrane-bound or

particulate methane monooxygenase (pMMO, belonging to the copper monooxygenase CuMO family), and a soluble (cytoplasmic) methane monooxygenase (sMMO). pMMO has a narrow substrate range compared to sMMO and oxidize alkanes (methane to pentane), alkenes (e.g., propene and butene) and trichloroethene (Burrows et al., 1984; Smith and Dalton, 1989; Jiang et al., 2010; Miyaji et al., 2011). All the methane-oxidizing bacteria possess pMMO except for the genera Methylocella and Methyloferula (which only possesses sMMO) (Dedysh et al., 2000;

Vorobev et al., 2011). Methanotrophy has been especially well studied in Methylococcus capsulatus (Bath), which contains both pMMO and sMMO (Hakemian and Rosenzweig, 2007;

Balasubramanian et al., 2010; Karlsen et al., 2011; Larsen and Karlsen, 2016).

Other enzymes involved in the successive oxidation steps include methanol dehydrogenase (MDH) that oxidizes methanol to formaldehyde. Formaldehyde is further oxidized by using different pathways in methanotrophs. An enzyme formaldehyde dehydrogenase (FaDH) oxidizes formaldehyde to formate or use multienzyme cofactor-linked

C1 transfer pathways (Chistoserdova, 2011). An alternative to the FaDH enzyme is the use of the tetrahydromethanopterin (H4MPT) pathway. The C1 transfer linked to tetrahydrofolate (H4F) pathway is analogous to (H4MPT) pathway (Chistoserdova et al., 2009). Formate is further oxidized by formate dehydrogenase (FDH) to CO2.

Carbon assimilation occurs at different stages: formaldehyde assimilation via the RuMP pathways, methylene-H4F and CO2 via serine cycle or CO2 via the CBB cycle (Hanson and

Hanson, 1996; Chistoserdova et al., 2009). Gammaproteobacteria methanotrophs assimilate formaldehyde via RuMP pathway. Formaldehyde is added to ribulose-5-monophosphate and via sugar rearrangements produces glyceraldehyde-3-phosphate as a biosynthetic building block.

Two key enzymes hexulose-6-phosphate synthase and hexulose-6-phosphate isomerase are 13

needed for this carbon fixation pathway. Three formaldehyde molecules result in net production of one C3 compound. Alphaproteobacteria methanotrophs assimilate formaldehyde via serine cycle. During the series of reactions, where methylene-H4F and CO2 are assimilated to C3 and

C4 compounds. Two key enzymes, hydroxypyruvate reductase and serine glyoxylate aminotransferase are used to indicate an active serine pathway. The members of the phylum

Verrucomicrobia and Candidate division NC10 use the CBB cycle (Op den Camp et al., 2009;

Rasigraf et al., 2014). The key enzyme ribulose-1,5-bisphosphate carboxylase/oxygenase is used to indicate CBB cycle.

2.5.1 Soluble methane monooxygenase (sMMO)

sMMO is a multicomponent monooxygenase that has been purified from a number of type II methanotrophs and in some type I methanotrophs. It does not contain heme cofactor or any other cofactors found previously in oxygenase chemistry (Lipscomb, 1994). sMMO consists of three components: (1) hydroxylase (MMOH) which contains a non-heme di-iron site and is an oligomer of three different subunits (α,β,γ) which catalyze di-oxygen activation and methane hydroxylation, (2) reductase (MMOR) which contains flavin adenine dinucleotide and [Fe2S2] cofactors and (3) a regulatory protein (MMOB) which is colorless and has no cofactor (Merkx et al., 2001; Sirajuddin and Rosenzweig, 2015).

sMMO contains an MMOH subunit in a α2β2γ2 dimer, and an MMOB anchor at the α2β2 interface. sMMO crystal structure from Methylococcus capsulatus (Bath) shows a 251 kDa

MMOH which houses a non-heme diiron active site in the alpha subunit of each monomer

(Rosenzweig et al., 1993). The function of MMOB binding to the alpha subunit of MMOH is to control methane , oxygen, and proton access to the active site through conformational changes

(Lee et al., 2013). The diiron oxidation state controls the substrate access to the sMMO and accession of the substrate induces conformational change in the sMMO enzyme (Lee et al., 2013;

Wang and Lippard, 2014).

sMMO has a wide substrate range and can oxidize n-alkanes/alkenes, alicyclic hydrocarbons, halogenated aliphatic and aromatic compounds (Colby et al., 1977; Semrau et al.,

2010; Semrau, 2011).

2.5.2 Particulate methane monooxygenase (pMMO)

Particulate methane monooxygenase is an integral membrane protein composed of three different subunits, α, β, and γ, encoded by the pmoB, pmoA and pmoC genes, respectively. The structure of pMMO from Methylococcus capsulatus with its distinct metal center has been determined with a resolution of 2.8Å (Lieberman and Rosenzweig, 2005). This multicomponent enzyme is a trimer of three αβγ protomers (α3β3γ3) (Lieberman and Rosenzweig, 2005;

Hakemian and Rosenzweig, 2007; Balasubramanian et al., 2010). pMMO is a metalloenzyme containing copper, zinc and possibly iron binding sites, but the exact binding and roles of the metals are still unclear. In Methylococcus capsulatus the pMMO di-copper center is located in a solvent-exposed N-terminal domain (spmoBd1) between the highly conserved residues His 33,

His 137 and His 139 within a distance of 2.5-2.7 Å. A mutational analysis showed that the dicopper center was essential for methane oxidation (Balasubramanian et al., 2010).

Recent crystallographic analyses of pMMO from Methylosinus trichosporium OB3b,

Methylocystis sp. strain M, and Methylocystis sp. strain Rockwell were consistent with the overall architecture of pMMO, but differed in the metal binding sites (with the exception of the

conserved di-copper binding site) (Hakemian et al., 2008; Smith et al., 2011; Sirajuddin et al.,

2014). Based on crystal structure and with computational studies, the suggested metal content of pMMO may be a dinuclear Fe site or mono or di or trinuclear Cu sites (Lieberman et al., 2003;

Martinho et al., 2007; Culpepper and Rosenzweig, 2012; Wang et al., 2017). Recently using crystallographic data with quantum-mechanical calculations result showed mononuclear Cu sites can oxidize methane rather than dinuclear Cu site in pMMO (Cao et al., 2018a).

2.6 Ammonia oxidation

Ammonia is released in the decay of organic matter and is a major fertilizer in agricultural practice. Nitrification is the biological process in which ammonia is oxidized into N- oxides facilitated by nitrifying bacteria. Ammonia oxidizers are unique in their ability to convert ammonia to nitrite and are thus important in the global cycling of nitrogen. One of the most studied nitrifying bacteria, Nitrosomonas europaea, is an obligate chemo-lithoautotroph (Engel and Alexander, 1958). Nitrosomonas europaea uses only NH3, CO2 and mineral salts for growth. Nitrifying bacteria that oxidize ammonia are found in genus Nitrosomonas,

Nitrosococcus, and Nitrosospira and nitrifying bacteria that oxidize nitrite are found in

Nitrobacter, Nitrospina, Nitrosococcus, and Nitrospira (Stein and Nicol, 2018). During nitrification, ammonia is oxidized into nitrite by Nitrosomonas and then converted into nitrate by bacteria such as Nitrobacter. Recently, a complete nitrification by a single bacterium Candidatus

Nitrospira inopinata was found (Daims et al., 2015).

Ammonia oxidising bacteria belong to the Gamma- or Betaproteobacteria. Besides

Proteobacteria, there are some archaeal nitrifiers of the phylum Thaumarchaeota (Brochier-

Armanet et al., 2008; Spang et al., 2010). Recently, it has been found that ammonia oxidizing 16

archaea use the hydroxypropionate/hydroxybutyrate (HP/HB) cycle for aerobic CO2 fixation.

This HP/HB cycle makes ammonia oxidizing archaea energy efficient to fix inorganic carbon in the presence of oxygen and allows them to thrive in nutrient-limited environments (Konneke et al., 2014). The HP/HB cycle requires slightly less ATP for the synthesis of cellular precursor metabolites (acetyl-CoA, pyruvate, phosphoenolpyruvate, oxaloacetate, and 2-oxoglutarate) compared to the Calvin-Benson cycle.

2.6.1 Ammonia monooxygenase (AMO)

Like many methanotrophs, ammonia-oxidizing bacteria contain multiple copies of the amoCAB operon. AMO has 3 polypeptides encoded by genes amoA, amoB, and amoC, organized as an amoCAB operon, and is present at as many as 3 copies in a genome (McTavish et al., 1993;

Bergmann and Hooper, 1994; Klotz and Norton, 1998; Sayavedra-Soto et al., 1998). This enzyme catalyzes the first step of ammonia oxidation. One of the most studied ammonia oxidizers, Nitrosomonas europaea also possesses two identical operons (amoCAB1,2) and a divergent copy of the amoC3 gene. This divergent copy amoC3 in Nitrosomonas europaea showed significant role in the recovery from ammonia starvation and heat shock (Berube and

Stahl, 2012). Another important gene involved is hao which encodes hydroxylamine oxidoreductase and catalyzes the oxidation of hydroxylamine to nitrite (Sayavedra-Soto et al.,

1994; Bergmann et al., 2005). AMO is a membrane bound enzyme which is similar to pMMO in methanotrophs. Both have a broad substrate range (Arp et al., 2002; Hakemian and Rosenzweig,

2007).

The activity of ammonia-oxidizing bacteria (AOB) has been reported under varying environmental conditions. The majority of the research done on the oxidation of ammonia and

nitrate involves chemolithotropic Bacteria and Archaea in oxic (soil) environments. During nitrification, the soil pH, ammonia and nitrite are important predictors of nitrogen-oxide formation and accumulation (Venterea and Rolston, 2000). Nitrification in response to temperature has been studied in different soil environments where optimum temperature has been found to be environment specific (Stark and Firestone, 1996). In terrestrial systems, community composition is highly variable and similar ecosystem types did not always have the same community (Fierer et al., 2009).

2.7 Copper membrane monooxygenases (CuMMOs)

Sayavedra-Soto et al., 2011; Coleman et al., 2012; Suzuki et al., 2012). The enzymes are encoded by three genes, as xmoC, xmoA, and xmoB (amo specifically refers to genes encoding

AMO, and pmo specifically refers to genes encoding pMMO).

Due to the difficulty in isolating purified enzymes, minimal structural data is available for this superfamily. pMMO is the best characterized and thoroughly studied enzyme both at the biochemical and genetic level (Hakemian and Rosenzweig, 2007; Balasubramanian et al., 2010;

Culpepper and Rosenzweig, 2012; Ross and Rosenzweig, 2017). pMMOs are encoded in the genomes of Alphaproteobacteria, Gammaproteobacteria, Verrucomicrobia and candidate division NC10 methanotrophs as pmoCAB operons. The ammonia monooxygenase (AMO) is encoded by an amoCAB operon in ammonia oxidizing bacteria and in Thaumarchaeota. Other

CuMMO enzymes are distributed among Gram-positive bacteria belonging to the lineage

Actinobacteria. Nocardioides sp strain CF8 possesses a putative butane monooxygenase (BMO) enzyme (Sayavedra-Soto et al., 2011). A similar hydrocarbon monooxygenase (HMO) enzyme is present in both the Mycobacterium chubuense strain NBB4 (Coleman et al., 2011; Coleman et al., 2012) and a marine isolate which can oxidize up to 5-C hydrocarbons as detected by stable isotope probing (Redmond et al., 2010). Enzymes highly similar to ammonia/methane monooxygenase are also present in the ethylene assimilating bacteria, Haliea species ETY-M and ETY-NAG (Suzuki et al., 2012). This is hypothesized to be a hydrocarbon monooxygenase due to the identification of a CuMMO gene cluster which has homology to both pMMO and

AMO, and the arrangement of the hmoCAB subunits identical with amoCAB and pmoCAB.

Beside bacteria, CuMMOs are also present in ammonia oxidizing archaea as archaeal AMOs

(Walker et al., 2010). However, several Gammaproteobacteria (Tavormina et al., 2011) and a few members of Alphaproteobacteria (Methylocystis rosea SV97 (Wartiainen et al., 2006) and

Methylocystis sp. strain SB2 (Dam et al., 2012), the genes encoding pXMO (encoded by a homologous operon to the pmoCAB encoding pMMO) are uniquely organized in the pxmABC order, instead of the usual pmoCAB. The function and substrate specificity of the pxm operon in methanotrophs is not clear yet, however the transcript level of pxmA increased in

Methylomicrobium album strain BG8 (Kits et al., 2015a) and Methylomonas denitrificans strain

FJG1 (Kits et al., 2015b) under nitrite rich and oxygen limited conditions.

2.8 Ecology of methanotrophic bacteria

Methanotrophs are found in diverse environments such as wetlands, freshwater and sediments, rice fields, upland soils, landfills, oceans and extreme environments. Studies of their diversity, distribution and abundance are focused on both cultivation and cultivation independent molecular methods.

Rice paddies are one of the main global methane sources. Rice paddies have high diversity of methanotrophs including type II methanotrophs like Methylocystis and Methylosinus and type I methanotrophs like Methylobacter, Methylomonas, Methylomicrobium,

Methylocaldum, and Methylococcus. (Gilbert and Frenzel, 1998; Hoffmann et al., 2002). A few isolates were isolated from rice paddies e.g., Methylogaea oryzae and Methylomagnum ishizawai

(Geymonat et al., 2011; Khalifa et al., 2015). Abundance and distribution of methanotrophs were affected by oxygen and nutrient availability, and age of rice plants (Eller and Frenzel, 2001;

Shrestha et al., 2008).

Fresh water bodies are mostly dominated by type I methanotrophs (Costello et al., 2002;

Rahalkar and Schink, 2007). In Lake Washington samples type I was detected as 2 to 4-fold more abundant than type II methanotrophs (Costello et al., 2002). In another study water samples from different depths were dominated by Methylobacter related sequences (Rahalkar and Schink,

2007). Some methanotrophs that have been isolated are Methylobacter whittenburyi Y, and

Methyloglobulus morosus KoM1 (Deutzmann et al., 2014) from lake sediment, Methylosarcina lacus LW14 (Kalyuzhnaya et al., 2005) from sediment of Lake Washington and

Methyloparacoccus murrellii (Hoefman et al., 2014) from pond water.

Atmospheric methane oxidation (i.e. the oxidation of the trace 1.8 ppbv level of methane in the atmosphere) has been detected in many upland soils from artic to temperate to tropic as well as in desert soil (Whalen and Reeburgh, 1990; Mosier et al., 1991). Culture independent techniques have detected pmoA sequences in upland soils that are not present in cultivated methanotroph genomes. The upland soil clusters are phylogenetically clustered into two groups

USCα and USCγ (Knief et al., 2003). USCα are distantly related to the genus Methylocapsa and

USCγ distantly related to type I methanotrophs. The first genome sequence of USCα has been published and 16S rRNA gene sequence only show an identity of 96% to Methylocapsa palsarum NE2 and Methylocapsa aurea KYG (Pratscher et al., 2018).

Recently the USCγ has been detected from an Antarctic mineral cryosol and a draft gene sequence has been published (Edwards et al., 2017). A recent study showed the diversity of

USCγ in previously undetected habitats such as subterranean and volcanic biofilm environments

(Pratscher et al., 2018). Besides USCα and USCγ clusters in upland soil, methanotrophs in the

Alphaproteobacteria belonging to the genera Methylocystis, and Methylosinus and

Gammaproteobacteria belonging to the genera Methylomonas, Methylobacter,

Methylomicrobium, Methylococcus, and Methylocaldum have been detected (Holmes et al.,

1999; Jensen et al., 2000; Bourne et al., 2001; Steinkamp et al., 2001). The active methanotrophs detected in upland soils based on 13C and PLFA analysis were USCα in acidic soils and USCγ in neutral soils (Knief et al., 2003).

Methanotrophs are found in extreme environments such as high temperature (geothermal springs) or low temperature (ocean sediments), at a range of pH and salinity and are classified as thermophiles, acidophiles, alkaliphiles, halophiles and psychrophiles. Aerobic methanotrophs grow from as low as 0 to as high as 72 ⁰C and anaerobic methanotrophs from -1 to 90 ⁰C have been found (Dunfield, 2009). The Gammaproteobacteria methanotroph Methylosphaera sp can grow at 0 ⁰C while Methylothermus spp. have a highest reported growth temperature at 72 ⁰C

(Bowman et al., 1997; Bodrossy et al., 1999). There are many thermophiles that grow between

45 and 60 ⁰C in the genera Methylothermus, Methylococcus, Methylocaldum, and

“Methylacidiphilum” (Trotsenko and Khmelenina, 2002; Op den Camp et al., 2009). Methane

oxidation in hypersaline environments has been detected, such as in hypersaline lakes at 8 – 33% salt (Sokolov and Trotsenko, 1995; Khmelenina et al., 1996; Joye et al., 1999), and Big Soda

Lake at 12.5% salt (Iversen et al., 1987). Halophilic methanotrophs belonging to the genus

Methylomicrobium have been isolated from seawater (Bowman et al., 1993). Methylosphaera hansonii was isolated from Antarctic lakes and reported to require sea water for growth

(Bowman et al., 1997). Methylohalobius crimeensis was isolated from hypersaline lakes and reported to grow in a range of 0.2 up to 2.5 M NaCl concentrations (Heyer et al., 2005).

Methanotrophs found and isolated from soda lakes belong to the genus Methylomicrobium. The haloalkaliphilic methanotrophs Methylomicrobium buryatense and M. alcaliphilum can grow at pH up to 11.0 (Trotsenko Iu and Khelenina, 2002; Trotsenko and Khmelenina, 2002).

Acidophilic methanotrophs include Methylocella silvestris isolated from acidic forest soil, which grows at pH between 4.5 and 7 (Dunfield et al., 2003) and Methylocella palustris and

Methylocapsa acidiphila isolated from acidic peat bogs, which grow at pH between 4.2 and 7.5

(Dedysh, 2002). Methanotrophs belonging to the phylum Verrucomicrobia in the genus

"Methylacidiphilum" were reported to grow at pH 0.8 (Op den Camp et al., 2009).

Verrucomicrobia methanotrophs were detected in a broad range of temperature from 22 to 80 ⁰C and pH from 1.8 to 5.0 in geothermal environments (Sharp et al., 2014b).

2.8.1 Methods used for assessing methane oxidation

During the 1990s, 16S rRNA gene primers were designed for detecting different methanotrophs. These 16S rRNA probes were used to detect methanotrophs in a PCR based assay, often combined with denaturing gradient gel electrophoresis (DGGE) to detect

methanotrophic communities (Tsien et al., 1990; Henckel et al., 1999; Henckel et al., 2000;

Miller et al., 2004). However, the primers were not specific to methanotrophs. The pmoA gene is universal to methane and ammonia oxidizers presently known except Methylocella and

Methyloferula. Therefore different pmoA primer sets have been developed used as molecular biomarkers for methanotroph detection (Kolb et al., 2003; McDonald et al., 2008; Knief, 2015).

pmoA gene PCR assays have been widely used to identify methanotrophic bacteria from various environments like soil (landfill cover soil, forest soil, agricultural soil, rice paddies, tundra soil), sediment (seawater sediment, lake sediment, marine sediment, freshwater sediment), water (pond water, fresh water, ground water, coal mine drainage water), soda lake water, sewage, manure, peat bogs, hydrothermal systems, and geothermal hotsprings (reviewed by

(Knief, 2015)). Quantitative qPCR and amplicon sequencing of pmoA genes have been used to understand the abundance and community structure of methane oxidizing bacteria in studies focusing on ecology, bioremediation, methane biofiltration, landfill, agricultural and rice fields.

Signature phospholipid fatty acids (PLFAs) are specific to Alphaproteobacteria, and

Gammaproteobacteria (Hanson and Hanson, 1996). For example 16C versus 18C found in proteobacterial methanotrophs: major PLFAs such as 16:1ꞷ8c in Methylococcaceae and

Methylocystaceae, 18:1ꞷ7c, Methylococcaceae, Methylocystaceae, and Beijerinckiaceae.

Verrucomicrobia methanotrophs contain distinctive PFLAs (i14:0, a15:0) not found in type I and type II methanotrophs (Op den Camp et al., 2009). Analysis of phospholipid fatty acids specific to methanotrophs was used as an alternative to pmoA gene PCR to detect methanotrophs in different environments (Bowman et al., 1993; Sundh et al., 1995; Dedysh et al., 2007).

2.9 Enzyme kinetics of methane monooxygenase

Methane monooxygenases are multicomponent enzymes. The two forms, pMMO and sMMO are different in their overall structure and substrate selectivity. For an enzyme catalyzed reaction, the maximum rate of reaction and the concentration of substrate both play important roles. The rate of reaction when the enzyme is saturated with substrate is the maximum rate of reaction and defined as Vmax. The actual rate of reaction depends on the concentration of substrate and enzyme affinity to the substrate (Km).

The kinetic properties of the methane oxidizing bacteria in the environment have been investigated under different methane concentrations. Vmax and Km were measured in different oxic soils, either fresh or preincubated under 20% methane mixing ratios (Bender and Conrad,

1992). The measured Km values were 30-51 nM CH4 for fresh soil and 13-470 nM CH4 for 3 weeks preincubated (20% methane) soil. Similarly, high affinity to methane was observed in fresh soils (lower Km) compared to the Km values reported in pure culture strains in a laboratory

(Knief and Dunfield, 2005). In a study by Knief and Dunfield, 2005 calculated Km varied from

2.2 to 10.2 µM for genera Methylocystis, Methylosinus, Methylocaldum and Methylobacter that were isolated from different upland soil.

The atmospheric mixing ratio of methane is only 1.8 parts per million by volume (ppmv)

(IPCC, 2013), and it is assumed that high affinity methane oxidation in upland soil is an adaptation to this atmospheric methane concentration. Most cultured methane oxidizing bacteria do not oxidize at atmospheric methane concentration and are referred to as low affinity methane oxidizers.

Some methanotroph e.g. Methylocystis sp. LR1, Methylocystis sp. DWT and

Methylomicrobium album NCIMB showed oligotrophic growth between 10-100 ppmv methane

(Knief and Dunfield, 2005). Growth observed at low methane concentration is likely related to high affinity for methane. In hydromorphic soils which are often O2 limited, the apparent Km apparent values for methane were measured between 70 and 800 ppmv; these were dominated by type II methanotrophs (Knief et al., 2006). Methanotrophs contain single or multiple pmoCAB operons and the pMMO enzyme specificity to methane is yet to be fully resolved. It is likely that pMMO enzyme have different Km values and activity under different methane mixing ratios. In cultured species, bacterium Methylocystis sp. SC2 has two different pmoCAB operons. One of the two pmoCAB operons showed high affinity for methane, although not as high as observed in upland soils (Baani and Liesack, 2008).

The size of substrate binding sites has been estimated for methane and larger substrates for this enzyme (Miyaji et al., 2011). It has been known that pMMO uses copper to oxidize methane (Balasubramanian et al., 2010; Sirajuddin et al., 2014). The available crystallographic data shows that the active site is located at the dicopper center in PmoB (Balasubramanian et al.,

2010). The copper site contains three histidines and are conserved in all PmoB (as identified in proteobacterial methanotrophs) except from the methanotrophic Verrucomicrobia (Op den Camp et al., 2009). This suggests an inability to bind copper ions and a possible copper-independent catalysis mechanism in Verrucomicrobia. However, the mono copper site showed to bind methane more effectively, can hold water molecules more effectively and the reaction is exothermic than the dicopper site based on quantum-mechanical calculations (Cao et al., 2018a).

The binding of O2 to the dicopper active site in pMMO has been examined

experimentally. An absorbance feature at 345 nm was observed (peak generated from O2 binding at active site) after treatment of recombinant protein fragment corresponding to the soluble region of the M. capsulatus (Bath) pmoB subunit (spmoB) with O2 and H2O2 or pMMO with

H2O2.(Culpepper et al., 2012) This spectroscopic feature was not observed when the dicopper center was disrupted in the spmoB variants (disrupted copper sites of spmoB) which explains O2 binding at the pMMO active site for activity. The di-copper center in PmoB is consistent at same location throughout the available crystal structure of pMMO and therefore the most likely site for activation of the oxygen molecule used in the methane oxidation reaction.

2.9.1 Differential expression of pmoCAB operons

Genome sequences from methane oxidizing bacteria have revealed that pmo operons are often present in multiple copies within individual genomes. Type I methanotrophs (e.g.

Methylomicrobium album BG8), type II (e.g. Methylosinus trichosporium OB3b), and type X

(e.g. Methylococcus capsulatus Bath) have been shown to have duplicate pmoCAB gene clusters of nearly identical sequence (Semrau et al., 1995; Stolyar et al., 1999; Gilbert et al., 2000).

However, type II methanotrophs like Methylocystis strain SC2 have two different pmoA like genes in which pmoA2 exhibited only 73% identity with pmoA1 at the nucleotide level and 83% similarity at the amino acid level (Dunfield et al., 2002). In Verrucomicrobia

"Methylacidiphilum" strains, three pmo operons were detected within each strain. The three orthologous operons differ by as much as 50% at the amino acid level (Op den Camp et al.,

2009). The Gammaproteobacteria methanotrophs in the genera Methylomonas, Methylobacter and Methylomicrobium possesses sequence divergent pxmABC operon where predicted proteins

were 53% identical to the other known pMMO and AMO subunit proteins (Tavormina et al.,

2011).

There have been many studies performed to understand the expression of multiple pmoCAB operons used for oxidation of methane in methanotrophic Proteobacteria and

Verrucomicrobia. The type II methanotroph Methylocystis sp SC2 possesses two pmoCAB1,2 operons, which express two isoenzymes with different methane oxidation kinetics. The pmoCAB2 oxidizes methane at lower mixing ratios with an apparent Km(app) of 0.11 µM CH4

(Baani and Liesack, 2008). In the same organism pmoCAB1 have Km(app) approximately two orders of magnitude higher than pmoCAB2. In type I methanotrophs, Methylococcus capsulatus

(Bath) contains two nearly identical pmoCAB operons. Under optimal copper concentration

(5µM) pmoCAB2 was expressed twice as strongly as pmoCAB1 (Stolyar et al., 2001) indicating the expression was regulated by copper levels.

Expression studies have been carried out in some members of the genus

“Methylacidiphilum”, which have 3 diverse pmoCAB operons. In Methylacidiphilum. kamachatkense strain Kam1, a differential expression of the three pMMO operons has been observed, with the pmoCAB2 operon being 35-fold more strongly transcribed than the other two operons (pmoCAB1 and pmoCAB3) (Erikstad et al., 2012). It could be the pmoCAB2 operon encodes the primary functional pMMO in Verrucomicrobia. Batch and chemostat cultures of another verrucomicrobial methanotroph, Methylacidiphilum fumarolicum strain SolV, showed different expression of the pmoCAB operons under nitrogen fixing and oxygen limiting conditions. Operon pmoCAB2 was highly expressed when cells were grown with excess ammonium and O2 (Khadem et al., 2012), consistent with the results for strain Kam1. When the

cells were under nitrogen fixing conditions and under oxygen-limiting conditions the pmoCAB1 operon was highly expressed and pmoCAB2 was 40 times down regulated compared to the batch culture (Khadem et al., 2012). Under these conditions pmoCAB3 operon expression was similar to the cells grown on batch culture suggesting another growth condition is needed to elucidate its function.

Besides pMMO, oxidation of methane is catalyzed by soluble methane monooxygenase.

The genome of Methylocella silvestris possesses two soluble di-iron center monooxygenase gene clusters. Methylocella silvestris showed differential growth pattern when grown on methane, propane or in combination. The maximum methane oxidation rates of cells grown on methane

(expressing only sMMO) were 2.3-fold higher than cells grown on propane and had an apparent

Km of 53 µM. When the cells grown on propane (expressing sMMO and propane monooxygenase), the maximum propane oxidation rates were 2.4-fold higher than cells grown on methane and had an apparent Km of 19 µM (Crombie and Murrell, 2014).

2.10 Methylotrophic bacteria

Methanotrophs are a subset of methylotrophs, organisms that grow on methyl compounds like methanol. Obligate and facultative non-methane utilizing methylotrophs are found in many taxonomic groups. The classification, distribution and biochemistry of methylotrophic bacteria has been described (Dalton, 1983; Hanson and Hanson, 1996; Chistoserdova et al., 2009;

Chistoserdova, 2011; Chistoserdova and Lidstrom, 2013; Chistoserdova, 2015; Chistoserdova and Kalyuzhnaya, 2018). Beside Proteobacteria, many other phyla of Bacteria, and some archaea (e.g. methanogenic archaea) are methylotrophic (Thauer, 1998; Chistoserdova and

Lidstrom, 2013; McTaggart et al., 2015). This review is mainly focused on aerobic non- methanotrophic methylotrophs that use methyl compounds other than methane. Methylotrophs are either obligate or facultative based on substrate use. Methylotrophs use substrates like methanol, methylated amines or sulfur and halogenated methanes.

Non-methanotrophic methylotrophs are found in the class Alphaproteobacteria, in the families Methylobacteriaceae (Vuilleumier et al., 2009; Marx et al., 2012), Hyphomicrobiaceae

(Brown et al., 2011; Vuilleumier et al., 2011), Bradyrizobiaceae, Acetobacteraceae (Greenberg et al., 2007), Rhodobacteraceae (Li et al., 2011; Siddavattam et al., 2011) and

Xanthobacteraceae (Kappler et al., 2012). In Betaproteobacteria, in the family Methylophilaceae

(Chistoserdova et al., 2007; Giovannoni et al., 2008; Lapidus et al., 2011; Beck et al., 2014;

Kaparullina et al., 2017; Lv et al., 2018), Burkholderiaceae (Kalyuzhnaya et al., 2008a), and

Rhodocyclaceae (Kittichotirat et al., 2011) ; phylum Gammaproteobacteria, in the family

Piscirickettsiaceae (Boden et al., 2011; Han et al., 2011; Shetty et al., 2013). Besides Gram negative bacteria, Gram positive non-methane utilizing methylotrophic bacteria are present in the family Bacillaceae (Heggeset et al., 2012), Pseudonocardiaceae (Grostern and Alvarez-Cohen,

2013), Microbacteriaceae (McTaggart et al., 2015) and Micrococcaceae.(McTaggart et al.,

2015).

2.10.1. Methanol oxidation

Obligate methylotrophs uses methanol as a substrate. It is oxidized into formaldehyde by methanol dehydrogenase enzymes. Methanol dehydrogenases are present in Gram negative bacteria as quinoprotein methanol dehydrogenase (MDH) encoded by genes mxaFI. The enzyme

consists of two different subunits in an α2β2 structure and contain the cofactor pyrroloquinoline quinone (PQQ) (Goodwin and Anthony, 1998). Methylotrophs that have mxaFI genes have been studied in detail (Nunn and Lidstrom, 1986; Smejkalova et al., 2010). MDH is well studied and

X-ray structures have been identified for Methylobacterium extorquens AM1 (Williams et al.,

2005), Hypomicrobium denitrificans (Nojiri et al., 2006), Methylophilus W3A1 (Xia et al., 1996) and Methylophaga aminisulfidivorans MP (Choi et al., 2011; Cao et al., 2018b).

Other methylotrophic methanol dehydrogenases (MDH2) (containing a single subunit encoded by the mxaF gene) are found in Burkholderiaceae and Rhodocyclaceae and have close to 35% amino acid identity with PQQ-methanol dehydrogenases (Kalyuzhnaya et al., 2008a).

MDH2 alcohol dehydrogenases are found in Methyloversatilis universalis FAM5 and

Methylibium petroleiphilum PM1, Burkholderiales strains RZ18-153 and FAM1 (Kalyuzhnaya et al., 2008a) and Burkholderia phymatum STM815 (Chistoserdova et al., 2009).

A gene related to mxaF, called xoxF, is present in many methylotrophs. The protein sequence of XoxF has nearly 50% amino acid identity with MDH or MDH2 (Giovannoni et al.,

2008; Hou et al., 2008; Taubert et al., 2015). Most Gram negative methylotrophs contain this xoxF gene, eg Methylotenera mobilis (Kalyuhznaya et al., 2009; Kalyuzhnaya et al., 2012),

Rhodobacter sphaeroides (Wilson et al., 2008), Methylophilales bacterium HTCC2181

(Giovannoni et al., 2008). XoxF is found in non-methylotrophs, proteobacterial methanotrophs and verrucomicrobial methanotrophs.

However, Gram positive bacteria possesses NAD(P)-binding alcohol dehydrogenases other than PQQ dependent alcohol dehydrogenases (e.g. Amycolatopsis methanolica and

Mycobacterium gastri MB19 (Bystrykh et al., 1993), Bacillus methanolicus.(Brautaset et al.,

2004), and Arthrobacter sp (McTaggart et al., 2015)).

Methylotrophy is well studied based on the enzymes used and their connection to specific metabolic pathways (Chistoserdova, 2011; Chistoserdova and Kalyuzhnaya, 2018). Methanol is oxidised to formaldehyde and further oxidised to CO2. Assimilation of carbon takes place by either the serine cycle, RuMP pathway, or CBB cycle. In Alphaproteobacteria, the serine cycle is used in the family Methylobacteriaceae, Hyphomicrobiaceae, Acetobacteraceae whereas

Rhodobacteraceae and Xanthobacteraceae use the CBB cycle In Betaproteobacteria,

Burkholderiaceae and Rhodocyclaceae use the serine cycle, and Methylophilaceae use the RuMP cycle. In Gammaproteobacteria, Piscirickettsiaceae use the RuMP cycle. In Gram positive nonmethane methylotrophs, Bacillaceae, Pseudonocardiaceae, and Micrococcaceae use the

RuMP cycle. In addition, assimilation of carbon by methanotrophs using serine cycle, or RuMP pathway or CCB cycle is discussed in section 2.5.

2.11 Connection between methanotrophs and non-methanotrophic methylotrophs

Stable Isotope Probing (SIP) experiments with 13C labelled methane have identified that methanotrophic and non-methanotrophic bacteria coexist in many environments (Radajewski et al., 2000; Hutchens et al., 2004; Jensen et al., 2008; Kalyuzhnaya et al., 2008b; Saidi-Mehrabad et al., 2012). SIP experiments with 13C labelled C1 substrates showed the labelled carbon transfer first into methanotrophs (family Methylococcaceae) and then into non-methanotrophic methylotrophs (family Methylophilaceae) species, demonstrating ecological cross-feeding roles in Lake Washington natural microbial communities (Kalyuzhnaya et al., 2008b). In a 13C

methane SIP study of methanotrophic bacteria in a tailings pond, heavy fraction DNA was dominated by Methylocaldum (23.2%) and Methylomonas spp (36.2%), but a large amount of

Methylophilaceae (9.6%) was also detected, likely due to cross feeding via methanol (Saidi-

Mehrabad et al., 2012). Methylotrophs from different families within the Alphaproteobacteia have been isolated such as Xanthobacter, Hyphomicrobium, Methylobacteriaum, Methylopila and Paracoccus) and a comparative genomic study showed their metabolic flexibility in terms of their physiological roles as a autotrophs or facultative autotrophs, or heterotrophs (Beck et al.,

2015). Gene expression studies from cocultures of methanotrophs (genus Methylobacter) and non-methanotrophic methylotrophs (genus Methylotenera) showed methanol is the main source of carbon and energy provided by methanotrophs to non-methanotroph methylotrophs (Krause et al., 2017). Besides methanol, methanotrophs may also leak products like formaldehyde, exopolysaccharides (Wang et al., 2015; Flemming et al., 2016), and organic acids (e.g. formate, acetate, succinate, and lactate) (Kalyuzhnaya et al., 2008b) produced in the process of methane oxidation and thereby feed other bacteria.

2.12 Methane biofilter systems

A conventional practice in the oil and gas industry, and often in landfills, is to flare or vent methane into the atmosphere. Flaring produces toxic gaseous by-products like hydrogen sulphide, dioxide of sulphur, nitrogen dioxide, aromatics etc. and cause severe health effect to humans. One alternate strategy to avoid flaring is to channel gas mixtures to a biofilter where methanotrophs convert methane to CO2. Methanotrophs in a biofilter use methane as a substrate for energy and carbon, converting it to methanol and finally into biomass and CO2. Methane

biofilters could be useful when the concentration is less than 3% (v/v) that is difficult for physical-chemical treatments (La et al., 2018a), the potential areas such as low point methane sources e.g. abandoned wells and gas metering stations or in remote geographical areas.

Biofilters in reducing methane gas emission have been used in different applications such as landfills and industry related waste (Melse and Van der Werf, 2005; Nikiema et al., 2007;

Hettiaratchi et al., 2011; Pratt et al., 2012).

Municipal solid wastes are usually stored in landfills. Biogas is produced from landfills due to aerobic or anaerobic degradation of waste. Biogas is composed primarily of methane and

CO2 however the composition of biogas depends upon the type of waste and age of landfill site.

According to Canada’s Greenhouse Gas Inventory 2015, landfills in Canada account for 20% of total methane emissions. Approximately 30 Mt of CO2e were generated and 19 Mt of CO2e were emitted. Besides methane, other compounds found in biogas are alkanes, alkenes, aromatic and aliphatic hydrocarbons, alcohols, ketones, chlorinated compounds, and terpenes (Allen et al.,

1997; Staley et al., 2006; Scheutz et al., 2008; Font et al., 2011; Ménard et al., 2012).

Proper measurement of CH4 emissions involves labor-intensive methods like flux chambers, but due to the high spatial heterogeneity chambers can deliver very inaccurate results if randomly placed. Gas profile analyses give more rapid flux estimates, but rely on accurate estimation of diffusion coefficients and gas-filled porosity, which are not trivial (Gebert et al.,

2011a). Methods used to estimate CH4 oxidation rates include gas push-pull tests (Henneberger et al., 2012) and measuring CO2/CH4 soil gas ratios to estimate CH4 oxidation efficiency (Gebert et al., 2011b), but both have drawbacks. The first is still labor intensive, the second is only useful

under constant subsurface conditions. It is also unclear of the relationship of these measures to

CH4 efflux.

Biofilters can be placed at low point methane source where energy production from generated methane is not feasible. In methane biofilters a gas stream is passed through a filter bed, usually packed with biological material such as compost where methanotrophs oxidize biogas and degraded products are released through an outlet chamber. Performance of a biofilter is affected by different factors such as nature of packing materials, pH, temperature, aeration, moisture content, and nutrients that are required for methanotrophic growth (Nikiema et al.,

2007; Ménard et al., 2012; Su et al., 2014; La et al., 2018a). These physical factors will affect the growth and survival of methanotrophs inside the filter bed. Selection of filter material is one of the important aspects of designing a biofilter. Porous filter materials are usually chosen because of the ease of gas exchange inside the filter bed.

Compost is extensively used as a filter bed packing material because of its high methane oxidation rates (Stein and Hettiaratchi, 2001; Gebert et al., 2011a; Pawlowska et al., 2011;

Mancebo et al., 2014; Mancebo and Hettiaratchi, 2015). Packing materials that have been used in combination with compost or without compost, include perlite (Plessis et al., 2003; Melse and

Van der Werf, 2005), sand (Berger et al., 2005), wood fibers and peat (Streese and Stegmann,

2003), paper pellets and wood chips (Haubrichs and Widmann, 2006), bark chips (Kettunen et al., 2006), biochar (Syed et al., 2016), stone (Josiane and Michèle, 2009; Ramirez et al., 2012;

Ferdowsi et al., 2016), gravel (Nikiema et al., 2009), and volcanic pumice (Pratt et al., 2012).

Due to the characteristics like larger particle size, high surface area, porosity and moisture retention capacity, use of inactive materials will prevent clogging of the filter bed and enhance

effective gas diffusion (Gebert et al., 2003; Nikiema et al., 2007; Josiane and Michèle, 2009).

Compost in combination with other packing material showed enhanced methane elimination in a range from around 60 to 98 % compared to biofilter without compost (Haubrichs and Widmann,

2006; Nikiema et al., 2007; Pawlowska et al., 2011; Hernandez et al., 2015). The compost will provide nutrients and the hybrid mixture provides efficient gas channeling, structural stability and was found to eliminate more methane by maintaining steady operation. In a hybrid mixture of compost with either biochar or lava rock, compost with biochar (7:1) was efficient in eliminating methane (La et al., 2018c). Methanotrophs of the genus Methylobacter (around 30 % relative abundance) were found to be responsible for methane oxidation.

2.12.1 Physical parameters affecting biofiltration of methane

2.12.1.1 Nutrients

Compost is nutritionally rich and suitable for growth of microorganisms. Methanotrophs require macronutrients such as nitrogen, sulfur, phosphorous, calcium, sodium, magnesium and iron and micronutrients such as manganese, zinc, copper, molybdenum, cobalt and nickel.

Commonly used nutrient media for growth of methanotrophs in a laboratory are Nitrate Mineral

Salts (NMS) and Ammonium Mineral Salts (AMS) (Whittenbury et al., 1970b).

Nitrogen is usually used by microorganisms in an inorganic form as nitrate, ammonium or nitrite ions. Effects of nitrogen on methanotrophs in the rice rhizosphere have been studied.

Nitrogen can either inhibit (Steudler et al., 1989) or stimulate (Bodelier et al., 2000) methane oxidation and both responses are possible in wetlands and upland soils (Bodelier and Laanbroek,

2004).

Aerobic methanotrophs can oxidize ammonia to hydroxylamine by the characteristics of methane monooxygenase enzyme (Dalton, 1977; Campbell et al., 2011). Ammonia oxidizers have hydroxylamine oxidoreductases (HAO) that oxidize hydroxylamine and can drive energy production and cellular growth. However, methanotrophs do not grow from oxidation of ammonia or hydroxylamine but have pathways to deal with hydroxylamine and other toxic nitrosating intermediates (Stein and Klotz, 2011; Stein et al., 2012).

Genome sequences of alphaproteobacterial, gammaproteobacterial, and verrucomicrobial methanotrophs possesses homologous genes that encode hao. The encoding haoAB genes are induced by ammonia in Methylococcus capsulatus Bath and Methylomicrobium album

(Campbell et al., 2011). In verrucomicrobial methanotrophs Methylacidiphilum fumariolicum

SolV showed upregulation of haoA gene and can perform ammonia oxidation producing nitrite

(Mohammadi et al., 2017). Like all bacteria, methanotrophs required nitrogen for growth.

In one study, ammonium sulfate and urea treated rice rhizosphere soil samples showed abundance of type II methanotrophs. However, the community structure was significantly affected by ammonium sulphate (Shrestha et al., 2010). In an in-situ study, type I methanotrophs mostly dominated in urea treated rice field samples (Qiu et al., 2008). In many cases fertilized soil can stimulate methane oxidation (Bodelier and Laanbroek, 2004) and the methanotrophic community shifts with fertilization (Noll et al., 2008).

In an inorganic material filter bed biofilter, addition of ammonium (0.05 to 0.5 gN-

+ NH4 /L) reduced methane oxidation efficiency from 68 to almost 12% (Josiane and Michèle,

+ 2009). A comparison between two ammonium loads, one from 0.01 to 0.025 gN-NH4 /L and

+ other from 0.01 to 0.05 gN-NH4 /L was used to evaluate methane oxidation rate (Veillette et al.,

2012a). The methane oxidation rate was higher with the lower ammonium load. However, N-

+ NO3 addition to the soil biofilter did not show any influence on methane oxidation rate (Park et al., 2002). Inhibitory effect of ammonium was lowered while methane concentration was increased (De Visscher et al., 1999; Cai and Mosier, 2000; Kravchenko, 2002).

The abundance of methanotrophs following phosphorus addition varied in soil and sediments (e.g., rice paddies, agriculture soils, landfills, peat bogs, permafrost soils and forest)

(Veraart et al., 2015). In a permafrost soil gammaproteobacterial methanotrophs abundance was related to phosphorus (Gray et al., 2014). In the same study, methane oxidation was highest for phosphorus rich compost. In rice paddy soil total methanotrophs abundance increased upon phosphorus addition (Zheng et al., 2013). Methane oxidation rate in a landfill cover soil was increased to 26% when a soil mixed with sewage sludge slurry containing 0.1g K2HPO4 per kg of soil was used (Kightley et al., 1995). In the same study, no significant change in methane oxidation was observed when the soil was mixed with same concentration of K2HPO4. Similar results were obtained with phosphorus supply in rice field soils with no affect on methane oxidation rate (Lu et al., 1999).

Nutrients are often added to the filter bed to obtain high methane oxidation rates, these include commercial fertilizers, or NMS or AMS medium used for methanotrophic growth.

Depletion of nutrients results in a decline in methane oxidation rates in different biofilters

(Jugnia et al., 2012; Mei et al., 2015; Karthikeyan et al., 2016). Formation of exopolymeric substances (EPS) is observed at limiting inorganic nitrogen conditions, and induces clogging of the filter bed (Wilshusen et al., 2004a). Clogging is a major issue in biofilters that limits gas diffusion and reduces methane oxidation rates. EPS production and resulting low methane

oxidation rates have been observed in landfill cover soils and biofilters (Chiemchaisri et al.,

2001). Nutrient depleted or starved condition are often associated with EPS formation in bacteria

(Kim and Sorial, 2007; More et al., 2014). EPS formation is associated with the carbon source

(e.g., glucose), low oxygen concentration and high carbon to nitrogen (C/N) ratio (Miqueleto et al., 2010). When the C/N ratio is high, bacteria may produce EPS and store the excess carbon.

EPS may be used as an energy storage compound that is consumed during starvation conditions

(Konopka et al., 2002; Zhang and Bishop, 2003).

Methanotrophs are found to be abundant when methane and nutrients are readily available in methane biofilters. In methane biofilter systems where there is limited or no methane, methanotrophs will enter a starvation phase and may produce spores/cysts or become dormant. Methanotrophs belonging to the genera Methylobacter, Methylococcus,

Methylocaldum, Methylocystis, Methylosinus, Methylocapsa, and Methylocella produce either spores or cysts (except Methylomicrobium) when they are dormant (Whittenbury et al., 1970a;

Semrau et al., 2010).

2.12.1.2 Other growth conditions (Oxygen, pH, Temperature)

Aerobic methanotrophs use O2 for the enzyme methane monooxygenase. Two molecules of O2 are required for oxidizing one molecule of methane to CO2. In methane biofilter systems, air is usually fed from either the bottom or top of the biofilter chamber. In landfill cover soil, oxygen to methane ratios of 2:1 to 30:1 were able to produce 50 to 80 % of methane conversion in different studies (Kightley et al., 1995; Stein and Hettiaratchi, 2001; Berger et al., 2005). High

O2 to methane inlet supply from 50:1 to 400:1 have a high methane conversion percentage (80 -

98 %) (Plessis et al., 2003; Streese and Stegmann, 2003; Nikiema et al., 2009; Pratt et al., 2012;

Ferdowsi et al., 2016). Shortage of O2 may even induce anoxic conditions where methanotrophic activity will be affected. Thus, interest has also focused in feeding air simultaneously from the top and bottom of the biofilter chamber for uniform gas diffusion inside the filter bed (Kettunen et al., 2006).

Methanotrophs can grow at a range of pH. The lowest reported was below pH 1 for

“Methylacidiphilum” sp. and the highest reported was pH 11 for Methylomicrobium sp. (Op den

Camp et al., 2009). Methanotrophs are characterized as thermophilic, mesophilic or psychrophilic based on their optimum growth temperature. The Gammaproteobacteria methanotroph Methylosphaera sp can grown at 0 ⁰C while Methylothermus sp. have highest reported growth at 72 ⁰C. In biofilter systems, methanotrophic growth depends upon the nature of the packing material used in the filter bed, their pH at different time points, temperature based on different seasons, nutrient availability, and methane concentration and its affinity to different methanotrophs.

2.13 Conclusions

Methane is rising in the atmosphere due to anthropogenic activities. Methanotrophs are widely studied in terms of their physiology, biochemistry and molecular biology in order to understand their function and its application for mitigating greenhouse gas accumulation. The known diversity of methanotrophs is expanding due to new cultured representatives and advancement in culture independent techniques. Detection of copper monooxygenase genes in laboratory cultured methanotrophs as well as in enrichment cultures, single cell genomes,

metagenomes, and pmoA based assays is expanding our thoughts about its diversity. The aim of this dissertation is to understand the phylogenetic history of copper monooxygenase gene and to characterize novel methane monooxygenase genes. Furthermore, application of methane biofilters and a protocol for assessing biofilter performance was investigated.

Chapter Three: Evolutionary history of copper membrane monooxygenases

Roshan Khadka1, Lindsay Clothier1, Lin Wang2†, Chee Kent Lim2¥, Martin G. Klotz3,4, and Peter

F. Dunfield1*

1 Department of Biological Sciences, University of Calgary, 2500 University Drive NW, Calgary, Alberta, Canada, T2N 1N4

2 Department of Biological Sciences, University of North Carolina, Charlotte, NC, United States

3 School of Molecular Biosciences, College of Veterinary Medicine, Washington State University, Richland, WA, United States; ORCID: 0000-0002-1783-375X

4 State Key Laboratory of Marine Environmental Science, Institute of Marine Microbes and Ecospheres, College of Ocean and Earth Sciences, Xiamen University, Xiamen, China

* Correspondence: PF Dunfield Tel: 403-220-2469 Fax: 403-289-9311 E-mail: [email protected]

† Present address: Center for Microbial Pathogenesis, Institute for Biomedical Sciences, Georgia State University, Atlanta, GA 30303, United States

¥ Present address: School of Energy & Environment, City University of Hong Kong, Kowloon, Hong Kong, SAR

Status: Frontiers in Microbiology (accepted with minor revision)

3.1 Abstract

Copper membrane monooxygenases (CuMMOs) oxidize ammonia, methane and some short-chain alkanes and alkenes. They are encoded by three genes, usually in an operon of xmoCAB. We aligned xmo operons from 66 microbial genomes, including members of the

Alpha-, Beta-, and Gammaproteobacteria, Verrucomicrobia, Actinobacteria, Thaumarchaeota and the candidate phylum NC10. Phylogenetic and compositional analyses were used to reconstruct the evolutionary history of the enzyme and detect potential lateral gene transfer

(LGT) events. The phylogenetic analyses showed at least 10 clusters corresponding to a combination of substrate specificity and bacterial taxonomy, but with no overriding structuring based on either function or taxonomy alone. Adaptation of the enzyme to preferentially oxidise either ammonia or methane has occurred more than once. Individual phylogenies of all three genes, xmoA, xmoB and xmoC, closely matched, indicating that this operon evolved or was consistently transferred as a unit, with the possible exception of the methane monooxygenase operons in Verrucomicrobia, where the pmoB gene has a distinct phylogeny from pmoA and pmoC. The combination of phylogenetic analyses and compositional genome analyses suggested several key LGT events in the history of the enzyme. For example, the results support the hypothesis that an ancestor of the nitrifying bacterium Nitrosococcus was the donor of methane monooxygenase (pMMO) to both the alphaproteobacterial and gammaproteobacterial methanotrophs, but that before this event the gammaproteobacterial methanotrophs originally possessed another CuMMO (Pxm), which has since been lost in many species.

3.2 Introduction

The copper membrane monooxygenase (CuMMO) enzyme family includes ammonia monooxygenase (AMO), particulate methane monooxygenase (pMMO) and a few short-chain alkane and alkene monooxygenases (Tavormina et al., 2011). AMO and pMMO perform the initial oxygenation steps in the aerobic catabolism of methane (CH4) and ammonia (NH3), which produce toxic intermediates (methanol, formaldehyde, and/or hydroxylamine) that constitute the internal sources of energy and reductant for these microbes (Klotz and Stein, 2008; Klotz and

Stein, 2011; Simon and Klotz, 2013). Organisms with these enzymes therefore play key roles in the global carbon and nitrogen cycles. Substrates other than methane and ammonia are also targeted by some CuMMOs: some Actinobacteria possesses a putative CuMMO that acts as a butane monooxygenase (BMO) (Coleman et al., 2011; Sayavedra-Soto et al., 2011; Coleman et al., 2012), and ethylene-assimilating Gammaproteobacteria belonging to the genus Haliea also possess CuMMOs (Suzuki et al., 2012). Genomics projects are also beginning to find operons encoding CuMMOs of unknown function in bacterial genomes, such as Solimonas aquatica and

Bradyrhizobium sp. ERR11 (Kyrpides et al., 2014; Whitman et al., 2015). The known diversity of the CuMMO enzyme family is therefore expanding. CuMMOs are promiscuous and co- oxidise several structurally similar substrates, although they usually show clear specialization to one particular substrate (Bedard and Knowles, 1989; Semrau et al., 2010). For example, pMMO will oxidise ammonia and some alkanes, but usually with a lower affinity and lower reaction rates than for methane (Bedard and Knowles, 1989; Nyerges and Stein, 2009). This co-oxidation of ammonia and the resulting production of toxic hydroxylamine and nitrite leads to an inhibitory effect of ammonia on methanotrophs (Bodelier and Laanbroek, 2004; Nyerges and Stein, 2009).

CuMMO enzymes are encoded by three genes (Klotz et al., 1997), here referred to collectively as xmoC, xmoA, and xmoB (amo specifically refers to genes encoding AMO, and

pmo specifically refers to genes encoding pMMO). A clear homology of amoAB/pmoAB genes was first shown by Klotz and Norton (1998), demonstrating that AMO and pMMO were evolutionarily related. In bacteria, the xmoAB genes are always clustered with an xmoC, although xmoC genes sometimes occur as singletons in addition to operonal copies (Klotz et al., 1997; Arp et al., 2007; Op den Camp et al., 2009; Dam et al., 2012). In ammonia oxidizing bacteria, AMO is encoded by an amoCAB operon, but in Thaumarchaeota the amoA, amoB, and amoC gene are not always clustered together. A pMMO is encoded in the genomes of nearly all known aerobic methane oxidizing bacteria as a pmoCAB operon. Another ortholog present in some proteobacterial methanotrophs has been dubbed pxmABC, and the encoding operon is uniquely organized in the ABC order, instead of the more common CAB (Tavormina et al., 2011).

Phylogenies of amoA/pmoA match closely to 16S rRNA gene phylogenies, at least to the extent that individual genera, families, classes, and phyla can be clearly identified via comparative sequence analysis (Knief, 2015; Alves and Minh, 2018). Recovery of pmoA and amoA genes from natural samples has therefore been used extensively to identify species of methanotrophs and ammonia oxidisers in diverse environments (Rotthauwe et al., 1997;

McDonald et al., 2008; Tavormina et al., 2013; Dumont, 2014; Knief, 2015). The first published primer set developed to target pmoA, A189f/A682r (Holmes et al., 1995), is still widely used as a broad-spectrum primer set for detecting known methanotrophs and discovering new ones (Knief et al., 2003). However it has become clear via cultivation and genomics studies that these supposed universal primers do not target all xmoA genes in nature, for example universal pmoA primers do not amplify genes from verrucomicrobial methanotrophs (Op den Camp et al., 2009).

Aerobic methanotrophy is a rare trait, limited to a few monophyletic clusters of bacteria within the Alphaproteobacteria, Gammaproteobacteria, Verrucomicrobia and candidate phylum 44

NC10 (Ettwig et al., 2009; Op den Camp et al., 2009). Dissimilatory ammonia oxidation is similarly rare, known in only two clusters of bacteria within the classes Betaproteobacteria and

Gammaproteobacteria, and some Thaumarcheota (Kuypers et al., 2018). This mosaic nature of ammonia and methane-oxidising taxa, which are constrained to a few monophyletic clusters

(based on 16S rRNA gene phylogeny) scattered across several different phyla, indicates that there have been only a few key LGT events of xmo genes. For example, Tamas et al. 2014 suggested that all Alphaproteobacteria methanotrophs arose from a single common methylotrophic ancestor that had obtained pmoCAB via a single lateral gene transfer event from a gammaproteobacterium. Gammaproteobacteria and Verrucomicrobia methanotrophs also appear to be monophyletic based on 16S rRNA genes.

However, some Gammaproteobacteria (Tavormina et al., 2011), Alphaproteobacteria

(Tchawa Yimga et al., 2003; Wartiainen et al., 2006; Vorobev et al., 2014) and Verrucomicrobia

(Dunfield et al., 2007; Op den Camp et al., 2009) methanotrophs have multiple divergent copies of the pmo operon, indicating a more complex history of gene duplication, evolution, and LGT.

Two pmoCAB operons in Methylocystis sp. strain SC2 (59.3 – 65.6 % amino acid sequence identity) (Dunfield et al., 2002) are suspected to have different methane affinities (Baani and

Liesack, 2008). Methylacidiphilum spp. have three phylogenetic distinct pmoCAB operons with different expression patterns (Op den Camp et al., 2009; Erikstad et al., 2012; Khadem et al.,

2012), although the functional differences among the encoded enzymes are not yet known. A pxmABC operon is also found in addition to pmoCAB in some proteobacterial methanotrophs. Its function is not clear yet, but transcript levels of pxmA increased in Methylomicrobium album strain BG8 (Kits et al., 2015a) and Methylomonas denitrificans strain FJG1 (Kits et al., 2015b) under nitrite rich and oxygen limited conditions. In addition to utilizing CuMMOs with different

substrate affinities and preferences, some Gammaproteobacteria and Betaproteobacteria with multiple nearly identical copies of xmoCAB operons employ differential regulation to preferentially express individual copies upon environmental cues (Stolyar et al., 1999; Stolyar et al., 2001; Berube et al., 2007; Berube and Stahl, 2012).

Although phylogenetic constructions using xmoA are extensively used to delineate species (Knief, 2015; Alves and Minh, 2018), the evolutionary history of the genes has not been well elucidated. In this study we assembled a database of xmoCAB operons from microbial genomes, and performed in depth compositional and phylogenetic analyses of the genes and their genomes to better understand their evolutionary history.

3.3 Materials and Methods

3.3.1 Gene and genome sequences

All genes and genomes analyzed in this study were downloaded from the JGI

(http://genome.jgi.doe.gov/), NCBI (http://www.ncbi.nlm.nih.gov/nucleotide) or RAST

(http://rast.theseed.org/FIG/rast.cgi) genomic databases. The database contained 66 genomes, including 4 Thaumarchaeota, 50 Alpha-, Beta-, and Gammaproteobacteria, and 12 non- proteobacterial genomes (Verrucomicrobia, NC10 and Actinobacteria), all of which contained

CuMMO-encoding operons. These genomes were chosen based on published papers and BLAST sequences of JGI/NCBI databases. Our aim was to include only cultured bacteria for which physiological data and complete genomes are available. The analyses therefore did not include metagenomic sequences. Some bacterial genomes containing xmo genes were also ignored if they appeared to be the result of private sequencing projects with no published reports to date.

3.3.2 Phylogenetic constructions

We constructed Bayesian, Maximum likelihood (ML) and Neighbor-Joining (NJ) phylogenies based on derived XmoA, XmoB, XmoC and concatenated XmoCAB protein sequences. The alignment of derived amino acids was done using ClustalW. Seaview version

4.4.12 (Gouy et al., 2010) was used for Neighbor-Joining (with a Poisson evolutionary distance model) and Maximum likelihood (with a Le and Gascuel model) constructions. Bayesian inference methods were calculated with BEAST (Bouckaert et al., 2014), both with strict clock and relaxed clock log normal models. Bayesian analysis employed a Blosum62 substitution model and Gamma site heterogeneity model under a strict clock, or a Gamma site heterogeneity model with 4 gamma categories with a relaxed clock log-normal model. The inferred tree topology was assessed with 100 bootstrap replications for NJ and ML methods, and 10,000,000 iterations for Bayesian phylogenies minus a burn-in of 20% of the total.

3.3.3 Compositional evidence of lateral gene transfer (LGT)

Four programs were used to detect possible LGT of xmo operons based on compositional genome biases: TETRA, Alien Hunter, CodonW and Island Viewer.

FASTA files containing DNA sequences were uploaded into the TETRA program

(Teeling et al., 2004b) (http://www.megx.net/tetra/index.html). Each xmoCAB operon and the genome from the respective organism was uploaded separately. Before running the program, the sequences were extended by their reverse complement, and tetra-nucleotide usage patterns were calculated by the program. The program generated a table consisting of all possible 256 tetranucleotide combinations with their frequencies in each gene or genome.

The CodonW program (Angellotti et al., 2007), requires coding sequences for calculation. Therefore the FeatureExtract 1.2 server

(http://www.cbs.dtu.dk/services/FeatureExtract/) was used to extract the coding sequences of a genome from GenBank (.gbk) files. Then the protein coding genes of each xmoCAB operon and a genome from each organism were uploaded simultaneously into the CodonW database

(http://mobyle.pasteur.fr/cgi-bin/portal.py#forms::CodonW). The sequences of all coding genes

(operons and genomes, separately) were concatenated. The output result contained all the 64 codon bias values. The frequency of each codon was calculated as '(codon value)/64' for each gene or genome separately.

Genomes were uploaded into the Alien Hunter program (Vernikos and Parkhill, 2006) using Linux. The predictions were visualized using Artemis. The program predicts LGT in a moving 2500-bp window, using the Kullback-Leibler (KL) divergence statistic (Kullback and

Leibler, 1951) for combined 2-mers to 8-mers. The loci of the xmoCAB operon(s) in the genomes were used to determine whether these were located within predicted "alien" regions.

For the above three programs, the KL statistic was used to determine whether the compositional biases of the xmo operons differed from those of their respective genomes

(Overbeek et al., 2005; Becq et al., 2010). KL was calculated using tetranucleotide frequencies, codon usage frequencies, and/or kmer frequencies (2-mers to 8-mers in Alien hunter) with the following equation:

푔푒푛푒(𝑖) 퐷 (푔푒푛푒||푔푒푛표푚푒) = ∑ 푙푛 ( ) 푔푒푛푒(𝑖) 퐾퐿 푔푒푛표푚푒(𝑖) 푖

This equation gives numeric values between 0 to1 as an output. Values close to one indicate that

the xmoCAB operon has a much different compositional bias compared to the genome, and likely underwent horizontal transfer into its genome. Values close to zero indicate the operon has similar kmer composition to its genome (no detectable LGT). Alien Hunter selects a significance

KL threshold to determine whether the composition of the operon differs from the genome. For

TETRA and CodonW we simply report and compare KL values of the xmoCAB operon (on average 2730 nucleotides) compared to the composition of the entire genome. Individual genes were not tested as they contained insufficient information to accurately predict k-mer frequencies.

Genomes were also examined via Island Viewer (Bertelli et al., 2017), which combines compositional analyses with examination of mobility genes to identify genomic islands.

3.3.4 Specificity of different primer sets

Several primers that are specific to detect methanotrophs have been developed (Dumont,

2014; Wang et al., 2017). We have analyzed the sensitivity of primers to different groups of methanotrophs (Tables A-2, A-3) that are used in this study. We used the Arb platform to detect the number of mismatches in the existing primer sets (Ludwig et al., 2004).

3.4 Results

3.4.1 Phylogenetic analyses of concatenated XmoCAB

Highly resolved phylogenies of concatenated inferred XmoCAB sequences were constructed via Bayesian inference methods (strict clock vs. relaxed clock log-normal model)

(Figure 3.1), as well as Maximum likelihood (ML) and Neighbor-Joining (NJ) methods

(Supplementary Figures A-1, A-2). We constructed phylogenies of XmoCAB both with and without thaumarcheotal XmoCAB sequences included, since a large number of sequence gaps/insertions when comparing bacterial and thaumarchaeotal sequences reduce the amount of information available for phylogenetic calculations. Results were similar in each case.

The different construction methods largely agreed, with AMO from Thaumarchaeota the most distant group (and a logical outgroup based on total evolutionary distance), and a highly supported primary node separating the AMO of Thaumarchaeota, the BMO of Actinobacteria, and all others. All constructions generally agreed upon at least 10 well-supported monophyletic clusters corresponding to a combination of function (e.g. pMMO vs. AMO) and taxonomic affiliation. These clusters are summarized by the different colours in Figure 3.1. Support values for these taxonomic/functional clusters are very high in all constructions, reaching 100% posterior probabilities in Bayesian analyses. Relationships across these groups was slightly variable in the different constructions. Most notable was a variable placement of the

Verrucomicrobia and NC10. In the Bayesian relaxed clock model and the NJ tree these methanotroph groups were monophyletic with the proteobacterial methanotrophs, in the

Bayesian strict clock model and the ML model they were not.

Most of the groups indicated in Figure 3.1 also represent coherent monophyletic clusters based on 16S rRNA phylogeny, but they are separated by vast numbers of species that do not have xmoCAB genes in their genomes. The most parsimonious scenario for the distribution of xmoCAB is therefore that a single common ancestor of each of the groups indicated received xmoCAB via a single LGT event. Possible instances where recent LGT has disrupted coherent taxonomic groups include: i) the pxmABC of the alphaproteobacterium Methylocystis rosea,

which clusters within a group of Gammaproteobacteria, and ii) several sequences of unknown function (indicated in black) belonging to Alpha- Beta- and Gammaproteobacteria, particularly near the base of Figure 3.1. However, while the sequences indicated in black are included to support tree construction, we prefer not to discuss them in detail because: 1) in most cases the function of the respective Xmo enzymes is unknown; 2) most of the branches are deeply rooted and contain a single sequence, and the exact positioning of such branches is problematic due to plesiomorphic character states, and 3) there are still too few of these sequences to draw solid conclusions. Therefore, we focus our analysis on the better known groups of methane and ammonia oxidisers.

Figure 3.1: Phylogenetic tree based on concatenated inferred XmoCAB sequences (minimum 910 amino acids). The tree was constructed using Bayesian analysis employing: A. a gamma site heterogeneity model with 4 gamma categories with a relaxed clock log normal model and B. a Blosum62 substitution model with gamma site heterogeneity model under a strict clock. Node value are based on 10,000,000 iterations, minus a burn-in of 20% of total. The scale bar represents 0.2 changes per amino acid position. Colours indicate coherent functional and taxonomic groups. The protein accession numbers for the operons are given in Table A-4.

3.4.2 Phylogenetic analyses of individual genes

Phylogenies of individual inferred XmoA, XmoB, and XmoC polypeptides were constructed using Bayesian inference (strict clock vs relaxed clock log normal model),

Maximum-Likelihood, and Neighbor-Joining methods (Supplementary Figures A-4 – A-11).

When included, thaumarchaeotal sequences always formed the most distant group. However removing thaumarchaeotal sequences in some of the individual gene trees showed better support values because the large number of sequence gaps/insertions in the thaumarchaeotal genes greatly reduce the amount of information available for phylogenetic calculations. Only trees without thaumarchaeotal sequences are therefore presented. The Bayesian phylogenies are summarized in Figures 3.2, 3.3 in a manner to stress the similarities of the three individual

XmoA XmoB and XmoC trees. Generally the same taxonomic/functional groups are identified in the individual trees as in the concatenated tree, suggesting that the three genes/polypeptides have mostly parallel evolutionary histories and are evolved or transferred as a coherent operon.

One major inconsistency in the individual trees is the placement of the three homologues found in Verrucomicrobia. In the Bayesian (strict molecular clock model) constructions (Figure

3.2) the XmoC3 and XmoA3 homologues separate out from the respective XmoC1/XmoC2 and

XmoA1/XmoA2 homologues. However, this separation is highly dependent on the construction method and often not well supported (Supplementary Figures A-3, A-4, A-5, and Supplementary

Figures A-9, A-10, and A-11). It is not seen in the Bayesian (relaxed molecular clock model)

(Figure 3.2) where the all three homologues in the Verrucomicrobia are always monophyletic.

Another inconsistency is that for XmoB, the entire cluster of Verrucomicrobia is more ancestral in the tree than it is for XmoA and XmoC (Figure 3.2, 3.3). XmoA and XmoC are

monophyletic with the corresponding homologues in other, proteobacterial methanotrophs, but

XmoB is not. The uniqueness of the XmoB phylogeny compared to XmoA and C is seen in all four methods (Bayesian strict clock, Bayesian relaxed clock, ML and NJ) (Figures 3.2, 3.3,

Supplementary Figures A-6, A-7, and A-8).

3.4.3 Compositional LGT detection

The KL divergences calculated with Alien Hunter, TETRA, and CodonW are summarized in Supplementary Table A-1 and Figure 3.4. Higher values indicate greater compositional bias between the xmoCAB operon and host genome. Alien Hunter found evidence of significant compositional bias in most of the main clusters we identified in Figure 3.1. This supports the hypothesis that each group arose via an ancestral LGT event, and the composition of many xmo operons has not yet normalized to the composition of the host organisms.

The notable exceptions showing little or no compositional bias in the xmo operon were the gammaproteobacterial Pxm cluster, the gammaproteobacterial AMO (Nitrosococcus spp.), the thaumarchaeotal AMO, and the NC10 pMMO. These were below the significance threshold using Alien Hunter (Supplementary Table A-1) and also showed the lowest KL values of all the groups in other compositional analyses (Figure 3.4). These KL values calculated using CodonW and TETRA differ on average from those of Alien Hunter, but generally agree on the groups showing either very high or low compositional bias compared to their genomes (Figure 3.4).

Although the acclimatization rates of foreign DNA to a genome may vary, we speculate that these four groups may represent those that have possessed the operon for the longest evolutionary time, and their ancestors were likely donors of xmo to the other groups.

On the other hand, several clusters of xmoCAB show extremely high compositional bias compared to their host genomes: particularly the pMMO in Gammaproteobacteria and the AMO in Betaproteobacteria, but also to a lesser extent the pMMO in Alphaproteobacteria and the

HMO in Actinobacteria. These may represent more recent LGT acquisitions of xmoCAB. This hypothesis is supported by the analysis using IslandViewer 4, a tool for detection and

visualization of genomic islands (Bertelli et al., 2017), which shows that xmoCAB operons in some betaproteobacterial nitrifiers, actinobacterial butane oxidisers, and gammaproteobacterial methanotrophs can still be detected on genomic islands likely transferred from a foreign genome

(Supplementary Table A-1).

3.4.4 Mutation rate analyses

Based on compositional analysis (Supplementary Table A-1), “Methylacidiphilum” sp. xmoCAB3 operons have high KL values for AlienHunter (0.47), TETRA (0.13), and CodonW

(0.23) compared to the other two operons in the Verrucomicrobia. This suggests that they are compositionally foreign. They are also placed variably in different phylogenies.

Longer branch lengths to the xmoCAB3 genes were also observed in some phylogenies.

Long branch lengths are associated with accelerated mutation rates. In order to test this possibility we used Bayesian inference phylogeny with a relaxed clock log-normal model to estimate the evolutionary rate at each branch in the phylogenetic trees (Bouckaert et al., 2014;

Ho and Duchene, 2014). The evolutionary rate of verrucomicrobial pmoCAB3 genes was higher than the evolutionary rates of the pmoCAB1 and pmoCAB2 operon copies, with rates of 2.66,

2.64 and 3.46 for XmoA, XmoB, and XmoC phylogenies respectively (Supplementary Figures

A-12, A-13 and A-14). Evolutionary rates were mostly below 0.99 for most of the other lineages.

This high rate suggests that verrucomicrobial pmoCAB operon 3 genes are mutating at high rate.

3.4.5 Assessing xmoA primer sets

We tested specificity of different primers that have been used to amplify xmoA genes from environmental DNA extracts. These included several “universal” and group-specific primers for methanotroph pmoA genes. The results are summarized in Supplementary Tables A-2, A-3. The commonly used primer sets for methanotrophs 189f/682r (Holmes et al., 1995) and 189f/661

(Costello and Lidstrom, 1999) both have apparent biases and may miss many species, not only the exotic newly described methanotrophs such as the Verrucomicrobia and NC10, but also some proteobacteria like Methylocapsa. Recently, 189f forward primer in combination with newly

developed reverse primer HD616 was used to amplify CuMMO genes with high coverage (Wang et al., 2017). The HD616 primer is more universal than any other primer tested in our database

(Supplementary Tables A-2, A-3), but the large number of primer degeneracies may result in non-specific amplifications. However, the primer does not cover specific groups like the methanotrophic USCγ group, and thaumarcheotal amoA genes (Wang et al., 2017).

3.5 Discussion

Methane and ammonia oxidizing microorganisms are well studied in different environments because of their importance in biogeochemical cycling of carbon and nitrogen.

Studies based on 16S rRNA genes and functional marker genes like amoA and pmoA have been used to interpret their diversity and abundance in the environment (Knief, 2015). By using an extensive copper monooxygenase-encoding gene database from fully sequenced genomes, we constructed robust CuMMO phylogenies and performed comparative genomics to elucidate the phylogenetic history of the encoding genes.

All phylogenies showed at least 10 distinct groups based on a combination of substrate and taxonomy. Phylogenies of individual XmoA, B and C products also showed similar patterns

(with the exception of the Verrucomicrobia, discussed below). This suggests that the operon has evolved and been transferred primarily as a single unit. The evolutionary history of Xmo is therefore a mixture of functional evolution and a few LGT events of the entire operon, at minimum one event to each distinct group shown in Figure 3.1. Although to some extent

CuMMOs can each oxidise all the relevant substrates (ammonia, methane, short chain alkanes and alkenes), they tend to prefer one substrate, indicating specialization to different substrates

(Bedard and Knowles, 1989). The integration of CuMMOs into catabolic pathways targeting particular substrates has occurred multiple times. For example, in all phylogenies, the AMOs of

Thaumarchaeota, Betaproteobacteria, and Gammaproteobacteria formed highly supported, distinct clusters, indicating that integration of CuMMO enzymes into an ammonia oxidising metabolism occurred multiple times. The most parsimonious scenario based on the Bayesian tree

(ignoring the Xmo’s of unknown function in Figure 3.1, and assuming Pxm is a methane monooxygenase) begins with either a pMMO or AMO ancestor followed by 6 changes of function (Figure 3.5).

Despite the coherent functional/taxonomic groups, there is no overriding structuring of the tree based on either function or taxonomy alone. Taxonomically, for example,

Gammaproteobacteria clusters are scattered about the phylogenetic tree. Functionally, AMOs of

Thaumarchaeota, Betaproteobacteria, and Gammaproteobacteria each form coherent, highly supported clusters, but these three do not together form a monophyletic group, indicating that incorporation of ammonia-specific CuMMO into metabolic pipelines capable of utilizing the high-throughput capacity of CuMMO for producing toxic intermediates for catabolic gain

(Simon and Klotz, 2013) evolved multiple times. Individual pMMO clusters of

Alphaproteobacteria, Verrucomicrobia, NC10, and Gammaproteobacteria methanotrophs can be discerned. These four groups all cluster closely together, however methanotrophy as a function is paraphyletic, with a group of ammonia-oxidisers (Nitrosococcus spp.) and the non- methanotrophic Skermanella nested within the larger group of methanotrophs. The Pxm homologues of pMMO in methanotrophs are also not monophyletic with the known pMMO enzymes. While experiments with M. denitrificans FJG1 suggested that Pxm is an alternate

methane monooxygenase the organism produces particularly under microoxic conditions (Kits et al., 2015b), its function is still not completely clear. Nevertheless, if it indeed acts as a methane monooxygenase, this pattern indicates the adaptation of CuMMOs to catabolizing methane also evolved multiple times.

3.5.1 Verrucomicrobia

The major inconsistencies in different phylogenetic constructions (both in the concatenated trees and in the individual gene trees) were in the placement of the

Verrucomicrobia. Verrucomicrobia methanotrophs contain up to three homologous but distinct pmoCAB operons, with the most divergent of these usually named pmoCAB3 (Op den Camp et al., 2009). Some constructions showed the three operons of Verrucomicrobia to be polyphyletic, with XmoC3 and XmoA3 separate from the corresponding XmoC1/2 and XmoA1/2 (Figures 3.2,

3.3). However, this was most evident in construction methods that are easily violated by variable mutation rates in different lineages (strict-clock Bayesian and NJ trees). When the clock assumption was relaxed in ML or Bayesian relaxed clock models, the three verrucomicrobial homologues were always monophyletic (Figures 3.2, 3.3 and Supplementary Figures A-4, A-7, and A-10). Long branch lengths to the xmoCAB3 genes were observed in most phylogenies, suggesting that an accelerated mutation rate of this third operon, which probably made some phylogenetic constructions inconsistent. A Bayesian estimation of evolutionary rates suggested that the xmoCAB3 is indeed evolving faster than other lineages in the phylogenetic tree. We therefore propose that the three operons in the Verrucomicrobia are monophyletic and have all arisen from lineage-specific duplications, but that operon 3 is under accelerated, relaxed

selection. This is consistent with expression studies suggesting that this operon is only weakly expressed under methanotrophic growth conditions (Erikstad et al., 2012; Khadem et al., 2012;

Anvar et al., 2014).

The other oddity regarding the Verrucomicrobia is that their XmoB gene shows a different phylogeny than XmoA and XmoC (Figures 3.2, 3.3). In XmoA and XmoC trees, all methanotrophs including the Proteobacteria, Verrucomicrobia and NC10 form a single cluster with only the Nitrosococcus AMO group as a nested group. However, in the XmoB trees the methanotrophs are more polyphyletic. The proteobacterial methanotrophs, NC10 and

Nitrosococcus remain monophyletic in the XmoB tree, but the Verrucomicrobia are not monophyletic with them, instead placing nearer the base on the tree. This pattern is consistently seen regardless of the construction method used. It strongly suggests a distinct evolutionary history of XmoB compared to XmoA and XmoC in this phylum. A possible evolutionary scenario to explain this pattern is that the pMMO evolved only once, as indicated by XmoA and

XmoC (and XmoCAB) phylogenies, but that the Verrucomicrobia at one time exchanged their xmoB gene(s) for that from another (unknown) donor organism. Crystal structure of pMMOs from Methylococcus capsulatus, Methylosinus trichosporium OB3b, Methylocystis sp. strain M and Methylocystis Rockwell all possesses a copper binding site at the N-terminus of the PmoB subunit (Sirajuddin and Rosenzweig, 2015). This copper binding site is conserved in all methanotrophs except for the Verrucomicrobia (Op den Camp et al., 2009). It is possible that the

Verrucomicrobia have adopted a more distant PmoB to alter the metal profile of their enzymes

(Culpepper and Rosenzweig, 2012).

3.5.2 Lateral gene transfer

Detecting LGT generally relies on phylogenetic incongruence between different gene trees

(Delwiche and Palmer, 1996; Daubin et al., 2003; Lerat et al., 2003; Beiko et al., 2005; Adato et al., 2015), and on the compositional bias of genes or operons compared to genomes (Garcia-

Vallve et al., 2000; Karlin, 2001; Teeling et al., 2004a). We used both phylogenetic interference and compositional analysis methods to detect possible LGT events. Compositional analysis showed evidence of extensive lateral gene transfers, while phylogenies demonstrated that these were limited to few (minimum 10) total events, followed by diversification of functional/taxonomic groups. From the phylogeny and compositional analysis, we postulated several possible routes of LGT into different taxa and lineages (Figure 3.5). We restrict our speculation to clusters containing many sequences, and therefore prefer not to speculate greatly on the NC10 and Nitrospira, although the history of LGT from or to these groups would also be illuminating.

Figure 3.5: Systematic diagram of predicted LGT events in between taxa or lineages based on phylogeny and compositional analysis (A - C). Box represents taxa and line represents lineages.

I: The Nitrosococcus amoCAB is nested within a large cluster of methanotrophic pmoCABs, but compositional analysis indicates that it is more native to its genomes than the pmoCABs are to the methanotrophs. pmoCAB operons in Alphaproteobacteria and Gammaproteobacteria each show strong evidence of LGT. We therefore propose that the ancestor of the gammaproteobacterial Nitrosococcus group was a methanotroph with a pmoCAB operon. This

Nitrosococcus lineage first transferred its pmoCAB genes to Alphaproteobacteria methanotrophs and then later to the gammaproteobacterial methanotrophs. This fits with recent analyses showing that some gammaproteobacterial nitrifiers: specifically Nitrosococcus wardiae (Wang et al., 2016) and Nitrosococcus halophilus (Campbell et al., 2011), encode a complete methane- oxidation pipeline, including methanol dehydrogenase, a tetrahydromethanopterin-based formaldehyde oxidation pathway, and formate dehydrogenase (Chistoserdova, 2016; Wang et al.,

2016). These findings may indicate that ancestors of extant Nitrosococcus once functioned primarily as methanotrophs. The AMO from Nitrosococcus oceani also does not exhibit substrate preference for ammonia or methane (Lontoh et al., 2000).

This pattern of lateral transfer of genes implicated in ammonia catabolism from ancestors of Nitrosococcus to the ancestors of extant gammaproteobacterial methanotrophs has also been proposed based on an analysis of the gene cluster encoding hydroxylamine dehydrogenase

(Wang et al., 2016). Hydroxylamine dehydrogenase detoxifies and extracts catabolic electrons from hydroxylamine, the product of ammonia oxidation by CuMMO, and is co-expressed in ammonia-catabolic bacteria with the cytochrome c-based conduit that channels the extracted electrons from the periplasm into the Quinone-pool (Klotz and Stein, 2011; Simon and Klotz,

2013). Interestingly, the hydroxylamine detox capacity has been retained only in methanotrophs

that are exposed to fluctuating ammonium concentrations in the environment (Nyerges and Stein,

2009; Nyerges et al., 2010; Campbell et al., 2011; Stein and Klotz, 2011) but lost in those that typically exist in low ammonium environments (Tamas et al., 2014).

II: The “Methylacidiphilum” group possesses two similar (pmoCAB1 and pmoCAB2) and one divergent pmoCAB3 operon. All are monophyletic, but the phylogeny of the pmoB differs from that of pmoC and pmoA. We propose that the ancestor of “Methylacidiphilum” obtained pmoCAB as well as pmoC genes from an ancestral methanotroph (perhaps the NC10 lineage or the Nitrosococcus ancestor), but replaced that original pmoB gene with an xmoB gene from another, unknown source. The three operons were created by two successive lineage specific duplications. High evolutionary rates of the verrucomicrobial pmoCAB3 operon have cause this operon to diverge rapidly, and make phylogenetic constructions inconsistent.

III: In the Gammaproteobacteria methanotrophs, the pxmABC operon is compositionally indistinguishable from the genomes. Their pmoCAB operons, on the other hand, are compositionally very distinct from most genomes. This suggests that these bacteria first possessed the pxmABC operon, and only later obtained the pmoCAB operon via LGT (from the ancestor of Nitrosococcus). It has been speculated that gammaproteobacterial pxm genes were horizontally transferred (Tavormina et al., 2011); however, above analyses support an opposite evolutionary scenario. The Gammaproteobacteria pxmABC operon may have been transferred to some Alphaproteobacteria methanotrophs, and to the Betaproteobacteria nitrifiers.

IV: As previously proposed (Tamas et al., 2014), a methylotrophic ancestor of all alphaproteobacterial methanotrophs in the families Methylocystaceae and Beijerinckiaceae all obtained pMMO via a single LGT event from the Nitrosococcus ancestor. This operon

underwent a duplication and divergence into two lineages in the Methylocystaceae, and was lost in many Beijerinckiaceae. A second transfer event of the pxmABC operon from the

Gammaproteobacteria has also occurred.

We stress that the evolution of a methanotrophic or ammonia-oxidising phenotype is more complicated than the simple acquisition of a CuMMO. The downstream metabolic machinery to detoxify toxic alcohol and aldehyde intermediates such as methanol, formaldehyde, and hydroxylamine, while simultaneously feeding catabolic electrons into the Q-pool, are as critical as the monooxygenase step (Klotz and Stein, 2008; Klotz and Stein, 2011; Simon and

Klotz, 2013). Hence, the acquisition of the genetic basis for a functional CuMMO (xmoCAB) has to be preceded by the existence of genes that encode detoxification and electron extraction modules paired with a respective quinone-reactive protein (QRP) in order to create an efficient electron-flow pipeline that will integrate the CuMMO to the genome. Hence, it has been proposed for example that the original alphaproteobacterium that obtained pmoCAB via LGT must have been a methylotroph, a proposition strongly supported by phylogenetic examination of methylotrophy genes (Tamas et al., 2014). In this study we have concentrated on the critical first step of the process, and elucidated some of the steps in the evolution, transfer, duplication, and adaptation of CuMMOs.

3.6 Acknowledgements

This work was funded via an NSERC (Natural Sciences and Engineering Research

Council of Canada) Discovery Grant (2014-05067). Many of the methanotroph genome sequencing projects that delivered the raw data for this work were a collaborative effort of

OMEGA, a group including: Claudia Knief, Huub Op den Camp, Mike Jetten, Valentina

Khmelenina, Yuri Trotsenko, Colin Murrell, Jeremy Semrau, Alan DiSpirito, Mette Svenning,

Lisa Stein, Yasuyoshi Sakai, Francoise Bringel, Stephane Vuilleumier, and Marina Kalyuzhnaya, along with the Joint Genome Institute. We also thank the many others who have released genomes of ammonia oxidisers and other bacteria containing xmo operons to the public domain.

Chapter Four: Developing a monitoring protocol for methane biofilter system

4.1 Abstract

The major goal of this portion of the project was to develop a simple and cost-effective strategy for monitoring the methane oxidation efficiency of a field methane biofilter via DNA- based detection and quantification of methanotrophic bacteria. We hypothesized that population levels of methanotrophs would be correlated with methanotrophic activity. We tested this via growth and starvation experiments using the compost biofilter material. Unfortunately, most of the methanotrophs in the biofilter were very recalcitrant, their populations did not decline even under long-term starvation of months. Therefore high populations did not necessarily indicate high activity. The genus Methylomicrobium was the best candidate, as it did show some decline during starvation. However, the effect was not marked, and a careful genetic alignment and analysis of methanotroph genome sequences revealed that it was nearly impossible to develop a desired qPCR assay specific for this genus, as it is too closely related to other (more recalcitrant) methanotrophs. However, unexpectedly these experiments suggested that some non- methanotrophic bacteria showed the desired rapid response to biofilter methane-oxidation activity and would be better monitoring candidates. These were bacteria associated with

Methylophilaceae (such as the Methylotenera spp., and the Methyloteneraceae OM43 clade).

These bacteria are themselves incapable of methane oxidation but are often found associated with methanotrophs because they grow on byproducts of methanotrophy like methanol. These bacteria grew rapidly with the methanotrophs in our experiments and died rapidly as soon as

methane was removed from the systems. Therefore, we have proposed a monitoring system based on these "methanotroph-associated methylotrophs".

4.2 Introduction

Global carbon emission has significantly increased since the industrial era began.

Methane is a more potent greenhouse gas than carbon dioxide. The main anthropogenic methane emission sectors include energy, agriculture, and waste. The fossil energy sector produces methane during production, processing, distribution and other industrial processes. Methane production in agriculture is primarily from livestock. Landfills produce methane from the anaerobic decomposition of waste. Total anthropogenic methane emissions range from 542 to

852 Tg per year (IPCC, 2013). Landfills are the third largest source of methane emission (IPCC,

2013). In Canada, the oil and gas sector are the largest source of methane emission. The Alberta government has mandated greenhouse gas reduction guidelines and regulation standards to reduce emission. Under the Climate Leadership Plan, the Alberta government has made a mandate to reduce methane emission by 45% by 2025. To implement such regulations, energy, industry, and waste sectors will need to introduce technologies to reduce emission of methane to the atmosphere. One of these technologies is reliable cost-effective methane biofiltration.

Since the 1990s biofilters have been used to treat toxic air contaminants such as volatile organic compounds (Malhautier et al., 2005). Since then, engineered biofilters have made advancements in terms of design, maintaining physical and chemical parameters to enhance biofilter performance (La et al., 2018a). Methane biofilters have been used widely in cases where methane flow rate is low, methane source might be intermittent, and the nature of gas (e.g., sour

gas) all make flaring difficult. For example in landfill systems combustion can be achieved when the methane mixing ratio reaches higher than 20% (v/v) (Haubrichs and Widmann, 2006), hence a low flow rate of methane and intermittent source is not practical. In Canada venting or flaring is commonly used in the oil and gas sector. In 2016, the volume of flared and vented gas in

Alberta province was 617×106 m3 (2017). Flaring releases toxic compounds (sulphur oxides, nitrogen oxides, and volatile organic compounds) in the atmosphere and the exposure to such compounds may increase risk of respiratory track and cardiopulmonary infection and severe health problems to humans.

Methane biofiltration is a technology where methanotrophs housed inside a biofilter will oxidize methane to CO2 without producing toxic by-products. The application of this technology is to convert and control low volume methane emissions from point sources in the energy and industry sector, agriculture, or landfill waste. In addition, biofilters are cost-effective, mobile and versatile to that it can be used in remote areas as well. Biofilter technology in engineered system has been widely used in landfill waste to control low volume methane emissions (Hilger et al.,

2000; Hettiaratchi et al., 2011; Ménard et al., 2012; Mancebo and Hettiaratchi, 2015). Various packing materials like sand, compost, gravel, wood shavings, lava rocks, and biochar have been used in biofilters (Streese and Stegmann, 2003; Berger et al., 2005; Malhautier et al., 2005;

Nikiema et al., 2009; Pratt et al., 2012; Syed et al., 2016). A promising packing material is compost because of observed high methane oxidation rates and low cost (Stein and Hettiaratchi,

2001; Gebert et al., 2011a; Ménard et al., 2012; Mancebo et al., 2014; Mancebo and Hettiaratchi,

2015). Compost is biologically active organic material, rich in nutrients, and can hold adequate water content required for microorganism growth. However, overall performance of compost

based biofilters decreases over time because compost is susceptible to compaction and biomass accumulation which may create channeling of gases and increase pressure drop (Huete et al.,

2018; La et al., 2018a).

Efficiency of methane oxidation in methane biofilters is affected by different factors such as nutrients, methane, O2, moisture, pH, and temperature. Different methanotrophs have different affinities to methane (Knief et al., 2003; Knief and Dunfield, 2005), and can grow at range of temperature and pH (Hanson and Hanson, 1996; Dunfield, 2009; Op den Camp et al., 2009).

Stability of a biofilter is an important factor for efficient methane oxidation. Production of exopolysaccharide (EPS) or other cell biomass can reduce gas flow, which will lower methane oxidation over time (Chiemchaisri et al., 2001; Wilshusen et al., 2004a). Engineered methane biofilters have been optimized in terms of their design, packing material and other physical factors for high methane oxidation rate (Park et al., 2002; Hettiaratchi et al., 2011; Veillette et al., 2012b; Amodeo et al., 2015; Mancebo and Hettiaratchi, 2015).

Monitoring a methane biofilter and its performance in methane oxidation is a key to many biofilter systems. Methanotrophs can be monitored by quantitative measurement of pmoA genes or specific 16S rRNA genes of methanotrophs (Knief et al., 2003; McDonald et al., 2008;

Sharp et al., 2014a; Knief, 2015). A high abundance of methanotrophic-specific pmoA or 16S rRNA genes is associated with high population sizes, and possibly also high methane oxidation rates. However, with the exception of some genera like Methylomonas and Methylomicrobium, most methanotrophs produce either spores or cysts as resting stages, so many detected cells may be inactive. Studies have shown the association of methanotrophs with other bacteria, particularly non-methanotrophic methylotrophs (Jensen et al., 2008; Redmond et al., 2010; Saidi-

Mehrabad et al., 2012; Beck et al., 2013; Dedysh and Dunfield, 2014; Hernandez et al., 2015;

Oshkin et al., 2015; Krause et al., 2017; Tavormina et al., 2017; Yu et al., 2017). In a metagenomic study cross-feeding between methanotrophs (family Methylococcaceae) and non- methanotrophic methylotrophs (family Methylophilaceae) species showed species response to

C1 substrate (Kalyuzhnaya et al., 2008b). In a DNA-SIP enrichment of tailings water

Methylocaldum (23.2%) were detected together with Methylophilaceae (9.6%) in heavy fraction

DNA where Methylophilaceae possibly used methanol produced by methanotrophs (Saidi-

Mehrabad et al., 2012). Methylomicrobium was responsible for methane assimilation in deep water coral reef sediment samples where Methylophaga, Hyphomicrobium and uncultured group of Gammaproteobacteria were active cross-feeders (Jensen et al., 2008). Similarly, cross-feeding between methanotrophs of the genus Methylobacter and non-methanotrophic methylotrophs of the genus Methylotenera has been reported based on gene expression studies of co-cultures

(Krause et al., 2017). These findings raise the possibility that not only methanotrophs, but also associated methylotrophs could be an indicator for activity in biofilter systems.

In this study we performed growth and starvation experiments of methanotrophs in laboratory microcosms using compost biofilter material. This compost material has been used in some pilot filters either alone or mixed with materials such as biochar and lava rock, and shows an abundance of methanotrophs belonging to the genera Methylobacter, Methylococcus,

Methylocystis and Methylocella (La et al., 2018b; La et al., 2018c). The goal of this study was to design a simple, cost effective and accurate monitoring protocol for biofilter performance based on the presence of particular species.

4.3 Materials and Methods

4.3.1 Experimental design

The goal of this study was to develop a monitoring protocol to determine performance of a methane biofilter system. We supplied methane to microcosms for short and long periods to study methanotrophic community composition and population size changes under methane starved vs methane supply conditions. We hypothesized that methanotrophic populations should decline under methane starved conditions. Monitoring of methanotrophic populations under methane starved vs non, estimate a simple monitoring protocol for biofilter activity.

Different microcosm experiments were performed to investigate bacterial community structure over time under different methane concentrations and to develop a monitoring protocol for the methane biofilter system. Microcosms without methane supply after 3 weeks under aerobic and anaerobic conditions are referred as ‘Starved aerobic’ and ‘Starved anaerobic’ respectively (Table 4.1). Microcosms with continuous methane feed at aerobic conditions were investigated with and without nutrient supply and referred as ‘Nutrient treated’ and Continuous fed’ respectively. The control group represents microcosms with compost at aerobic condition.

Table 4.1: Different treatment characteristics used in microcosms

Treatment CH4 supply (days) Nutrient Air

Nutrient treated (Nt) 185 NMS media +

Starved aerobic (SAe) 21 - +

Starved anaerobic (SAn) 21 - - (after 21 days)

Continuous feed (Cf) 185 - +

Control (C) 0 - +

We used compost as a biofilter material for this study. Compost was taken from East

Calgary Waste Management Facility in Calgary, Alberta in July 2015. The compost had organic content of 0.41 wt/wt%, moisture content (dry wt.) of 0.62 % (wt/wt), water holding capacity of

1.90 (dry wt.) and pH of 7.6 (La et al., 2018c) and has been investigated as a biofilter packing material for commercial biofilters. Properties of compost were determined by Hettiaractchi Lab,

Civil Engineering, University of Calgary.

Each 1-L Duran bottle contained 25 grams of compost (wet wt.) or mixed with nitrate mineral salts (NMS) medium (0.1 ml per gram of compost) (Whittenbury et al., 1970b) (Figure

4.1). Bottles were sealed with butyl rubber stoppers and approximately 10% (v/v) of CH4 and 5% of CO2 added to the headspaces. The bottles were incubated at room temperature and CH4 and

CO2 levels monitored every week until 26 weeks using gas chromatography (8610C, SRI

Instruments), equipped with a HayeSep-D column coupled to a flame ionization detector (FID)

Column T 100 ⁰C, detector T 300 ⁰C and N2 as carrier gas) and a FID-methanizer (packed with a nickel catalyst power). A 0.5% (v/v) methane in an air mixture (Praxair, Danbury, CT, USA) was used as a standard. Each week microcosms were opened and left to stand in a class II safety cabinet for ̴ 2 hours to allow the gases inside the bottle to be replaced with sterile air. Each bottle was then re-sealed with a butyl rubber stopper and CH4 and CO2 re-injected at the same mixing ratios as described above.

After 3 weeks of incubation, some treatments (Continuous feed and Nutrient treated) were continued with constant methane and CO2 replacement and others (Starved aerobic and

Starved anaerobic treatment) were incubated henceforth without any methane or CO2 added into the headspace. Methane starved treatments were incubated either under oxic or anoxic conditions

defined here as starved aerobic treatment and starved anaerobic treatment respectively. For anaerobic treatments, the headspace of each bottle was flushed under a flow of N2 (10 min) for

O2 removal and a syringe needle inserted into a sealed bottle cap to remove headspace N2 while flushing. However micro-aerobic condition is expected after N2 flushing because traces of O2 might be left in the compost. The measurement of O2 concentration was not possible through GC

(HayeSep-D column coupled to a flame ionization detector and N2 as a carrier gas).

All treatments were performed in triplicate. Each week one gram of compost was taken from the bottles for DNA extraction and microbial community analysis.

Figure 4.1: Incubation of compost for the starvation experiment.

4.3.2 Molecular analyses

DNA was extracted from 0.5 g of compost using the FastDNA SPIN Kit for Soil (MP

Biomedicals) following the manufacturer’s instructions with some modification. An additional purification step using 5.5 M guanidine thiocyanate (GTC) was introduced in the washing step

(Knief et al., 2003). The DNA concentration was determined by using a Qubit BR kit (Thermo

Fisher Scientific) and the purity of DNA was determined by using a Nanovalue PlusTM (GE

Healthcare).

PCR was performed to amplify 16S rRNA genes for Illumina sequencing. DNA extracted from samples was used and around 5 ng/µl of input DNA (quantified by Qubit) was PCR amplified based on the KAPA HiFi HotStart Ready Mix protocols (Kapa Biosystems). 16S rRNA gene primer set 341f/785r (Klindworth et al., 2013) was used and cycling conditions were performed with a three step reaction: initial denaturation at 95 ⁰C for 3 min; 25 cycles of 95 ⁰C for 30 s, 55 ⁰C for 30 s and 72 ⁰C for 30 s; and a final elongation of 72 ⁰C for 5 min. Amplified products were verified by an agarose gel run and later PCR products were purified using

AMPure XP beads (Beckman Coulter). Purified PCR products were re-amplified by using

Illumina barcoded amplicon primers containing 16S rRNA gene targeted primers employing a three step reaction: initial denaturation at 95 ⁰C for 3 min; 8 cycles of 95 ⁰C for 30 s, 55 ⁰C for 30 s and 72 ⁰C for 45 s; and a final elongation of 72 ⁰C for 5 min. Amplified PCR products were re- run on a agarose gel and purified as described above. DNA samples were measured using Qubit and then normalized to 4 nM. Normalized samples were pooled and run for quality control with a

BioAnalyzer (Aligent). Samples were then prepared and run through high-throughput sequencing using Illumina Miseq at the University of Calgary.

Raw sequence data reads were analyzed using QIIME (Kuczynski et al., 2012). For quality control, sequences with a phred score of lower than 20 were removed. Then sequences were clustered at a sequence identity of 97%. The most abundant member of each cluster was selected as a representative sequence for that cluster. Taxonomic identities were assigned via

BLAST comparison to the Silva 119 database (Silva_119_rep_set97.fna) and those sequences

that could not be matched with a reference sequence in the Silva database were assigned a de novo OTU. Taxonomy was assigned based on the closest representative detected on the Silva reference database (McDonald et al., 2011). Details of QIIME commands and parameters used is described in Supplementary Table B-2. Processed data were used for understanding bacterial community structure using non-metric multidimensional scaling (NMDS) (Section 3.4.2) and influence of parameters such as nutrients, methane and O2 was estimated using canonical correspondence analysis (CCA) (Section 3.4.2.5).

Quantitative PCR was performed on the DNA extracted from the compost samples using pmoA gene specific primers, A189F / MB661R for total methanotrophs (Kolb et al., 2003). qPCR was performed on the RotorGene Q (Qiagen). A three-step reaction was used for cycling conditions: an initial denaturation of 5 min at 94 ⁰C; 35 cycles of 94 ⁰C for 60 s, 65.5 ⁰C for 45 s and 72 ⁰C for 45 s: and a final elongation step of 72 ⁰C for 10 min. qPCR master mix (12.5 µl) contains 6.5 µl of SYBR Green PCR Master Mix (Qiagen), 2.5 µl of each forward (A189F) and reverse (MB661R) primer and 1µl of sample DNA . Standards were prepared using a serial dilution of a plasmid containing a pmoA gene. A PCR product of pmoA from Methylomicrobium album BG8 was cloned into the pJET vector using a CloneJET PCR Cloning Kit (ThermoFisher

Scientific). The positive clones were verified by sequencing. Concentration of standard was measured using Qubit and the measured DNA amount was converted to target molecules per g of compost (Supplementary Table B-4). Dilutions were adjusted to 109, 107, 105, 103, and 102 target molecules per microliter. Fluorescence data was obtained during the last step of each cycle. The specificity of the qPCR assay was verified by melt curve analysis. Efficiencies of qPCR runs

ranged from 94 - 98% and gene copy numbers were calculated based on obtained standard curves.

4.3.3 Data analysis

4.3.3.1 Multivariate analysis of biofilter communities

Non-metric multidimensional scaling (NMDS) was performed using the metaMDS function in the vegan package of the R platform (Oksanen et al., 2007). This function required dissimilarities as input, where community data from QIIME was used to calculate the similarity indices. The QIIME taxa output data was rarefied to 1000 reads and the level 6 (genus level) taxa table was used as an input table. NMDS does not use the absolute abundance of species in communities but rather their rank orders. NMDS determines observed microbial community dissimilarities nonlinearly onto ordination space. The observed dissimilarities in an ordination space can be evaluated by stress value and R2 values. Further, p value was calculated to determine the significance of the NMDS plot for this analysis by using R platform.

CCA is a multivariate method often used to elucidate the relationship between biological assemblages of species and their environment. CCA was performed by using a community data matrix (assigned taxonomy of samples vs their relative abundance) obtained from QIIME

(rarefied to 1000 reads) to visualize the relationship of individual genus obtained from taxonomy level 6 (genus level) with treatment variables: methane, nutrient as NMS medium, available O2

(Starved aerobic and Starved anaerobic treatment), and incubation time for different microcosms used in this study. Environmental variables were coded with time values based on days of incubation. CCA was performed in the vegan package of the R platform (Oksanen et al., 2007).

ANOVA was performed using anova.cca. The coded data are described in Supplementary Table

B-3.

4.4 Results

4.4.1 GC measurements

Each microcosm was supplied with ~10% CH4 (v/v) and 5% CO2 (v/v) in the headspace of 1-L Duran bottles, with continuous replacement of headspace methane and CO2 to stimulate growth. After 3 weeks of an initial growth phase, the methane replacement was stopped for the starved aerobic and starved anaerobic treatments (Figure 4.2). The continuous feed treatments

(active microcosms), included compost and compost with added nutrients (NMS medium). The initial headspace methane (~10%) dropped to ~3% or lower within a week of incubation (Figure

4.2).

4.4.2 Beta diversity

NMDS was used to observe microbial community dissimilarity, and possibly to identify candidate taxa for monitoring (i.e. taxa characteristic of active versus starved conditions).

Samples that were incubated for a month (numbers < 21 in Figure 4.3) generally all group together, as this initial phase was the same for all treatments (Figure 4.3). Longer time incubation samples are distributed often further away from the cloud of early samples, especially for the starvation treatments. Starved aerobic and starved anaerobic samples for later times (>30 d) are clustered together and continuous feed active treatment samples for later times (>30 d) are distributed far from other two treatments (towards the right in the figure). This suggests microbial communities in the continuous feed active treatment become more different from the starvation treatments at later stages.

4.4.3 Bacterial composition analysis

4.4.3.1 Methanotrophic bacteria in microcosms

Methanotrophs belonging to the family Methylococcaceae were dominant after enrichment with methane (3 weeks) in all treatments (Figure 4.4A). Dominant gammaproteobacterial methanotrophs reached between 20 - 50% relative abundance. Within the

Methylococcaceae, the genus Methylobacter was dominant (20 - 40%) in all treatments representing almost all total methanotrophs (Figure 4.4A, B). Methylomicrobium was the second most dominant methanotroph genus, reaching about 3.5% after 3 weeks of incubation with methane and then dropping slightly to around 2 % after 4 weeks of incubation. The other methanotrophs included Methylocaldum at around 0.6% relative abundance. In a laboratory based biofilter composed of hybrid mixture of biochar, lava rock and compost (compost used in this study), methanotrophs belonging to Methylobacter (in the range of 4.44 – 29.12% relative abundance) were dominant and responsible for methane oxidation (La et al., 2018c). Similarly, compost was used as methanotroph starter material in a biochar and lava rock based biofilter where Methylobacter and Methylomicrobium were dominant (in the range of 1 – 14.90% relative abundance) even when methane oxidation rate decreased (La et al., 2018b). These results provide evidence that Methylobacter and Methylomicrobium were dominant methane oxidizers in compost biofilters and can be detected even when the oxidation rate decreased due to high nitrogen addition. These Methylococcaceae genera (Methylobacter, Methylomicrobium or

Methylocaldum) showed only a very slow decrease of their relative abundance after the onset of starvation (Figure 4.4), in accordance with the ability of many species to form resting stages.

Quantitative PCR was performed to test if an absolute quantification procedure can be used as a monitoring tool for biofilter performance. The number of pmoA gene copies per gram of compost was monitored over 98 days. At day 21, pmoA gene copy number of the continuous feed treatment increased to 1.30 × 107 ± 0.098 which is 36.6 times higher than at day 0 (Figure

4.5). Similarly, the starved aerobic treatment at day 21 was 40.6 times higher than its initial pmoA gene copy number, as the initial methane feeding stage was the same. Methane starvation started from day 21 in the starved aerobic treatment while methane was continuously supplied in continuous feed treatment (Figure 4.2). At day 98, the continuous feed treatment pmoA gene copy number was only 1.2 times higher than starved aerobic treatment. A surprisingly high pmoA gene copy number in the starved aerobic treatment was detected even after there had been no methane in the system for 77 days (Figure 4.5). Overall pmoA gene copy number matched the relative abundance of methanotrophs during incubations.

Figure 4.5: Quantitative PCR analysis pmoA genes in the Continuous feed vs Starved aerobic treatments over time. Error bars represents ± 1 SEM of two separate experimental replicates.

4.4.3.2 Non-methanotrophic methylotrophic bacteria in microcosms

Methanotrophs produce products like methanol that are used by methylotrophs. The methylotrophic group Methylophilaceae (Garrity et al., 2015) showed a positive growth pattern in response to the growth of methanotrophs. A rarefied taxa table (level 6, representing genus) was used for this analysis. Methylophilaceae increased in relative abundance to about 9% of total relative abundance after 3 weeks of methane feeding (Figure 4.6A). At the onset of starvation, the family Methylophilaceae declined far more rapidly than the methanotrophs (Figure 4.7C).

Continuous feed is the only treatment where methane and CO2 are continuously available for 185 days. The relative abundance of total Methylophilaceae in the continuous feed of methane and

CO2 was higher than in the starved treatments (Starved aerobic and Starved anaerobic). In the

Methylophilaceae, the genera Methylotenera and Methylophilus, uncultured Methylophilaceae-

OM3B group, and an uncultured genus identified as Methylophilaceae bacterium_1 all showed high relative abundance compared to other genera (not presented here). The highest relative abundance was found in the Methylophilaceae-OM3B group (̴ 6%) followed by Methylotenera ( ̴

1.5%) , Methylophilaceae bacterium_1 ( ̴ 1%) and Methylophilus.( ̴ 0.1% ) at 3 weeks period

(i.e. immediately before starvation).

The continuous feed microcosm had higher relative abundance compared to the starvation microcosms for all of these groups (Figure 4.6 B, C, D, and Figure 4.7C).

4.4.3.3 Methylotroph to methanotrophs

While the methanotrophs stayed relatively constant, the amount of non-methanotrophic methylotrophs varied greatly in all treatments (Figure 4.7) reaching a peak at 21 d in all treatments.

During starvation methanotroph populations remained in high relative abundance, but methylotrophs did not. This led us to a conclusion that rather than methanotrophs, non- methanotrophic methylotrophic bacteria could be a potential candidate for monitoring a methane biofilter. The ratio of methylotrophs (Methylophilaceae) to methanotrophs (Methylococcaceae) is shown in Figure 4.8. The ratio of methanotrophs to non-methanotrophic methylotrophs was high (̴ 0.35) at 3 weeks in all treatments. The onset of aerobic starvation showed a sharp decline in the ratio to 0.05. The ratio in the continuous feed also dropped slightly but remained high at

0.1 to 0.2. A high ratio of methylotrophs to methanotrophs in the continuous feed treatment compared to the starved aerobic treatment suggests that this could be an indicator of activity.

3.4.2.5 CCA analysis

CCA analysis was performed to understand the influence of external treatments on the microbial composition throughout the period of incubation, and to identify signature species that correlated strongly with these treatments. The CCA ordination plot was obtained (Figure 4.9) and significance of model (p value of 0.001) was calculated using ANOVA for this CCA output.

Based on the ordination plot, some patterns are evident:

1) Nutrient treatment (NMS) was positively related to an abundance of the genus Methylomonas.

2) Other methanotrophs such as Methylobacter and Methylocaldum cluster near the centroid and have little correspondence with the treatments.

3) The genera Methylotenera, Methylophilus, Methylophilaceeae-OM43 clade,

Methylophilaceae-PRDO1a group, and Methylophilaceae bacterium_1 were correlated to active methane feed (either with or without nutrient). They cluster opposite the starvation vectors, indicating they were not predominant in the starvation treatments.

4.5 Discussion

In a biofilter system production of intracellular compounds, exopolymeric substances and biomass by methanotrophs and other bacteria can negatively affect the performance of biofilter.

Production of such products in a compost based biofilter makes it more susceptible to compaction and clogging which may cause gas channeling through the biofilter. The negative impact of gas channeling affects methane oxidation efficiency and short lifespan of biofilter.

Decline in methane oxidation rate was associated to age of biofilters (i.e. excessive biomass) and reported to show significant decline in performance (Wilshusen et al., 2004b; Powelson et al.,

2006). Clogging is also associated with production of biofilm by microorganisms in nutrient deficient conditions. Efficiency of biofilters were found to be high in conditions where the production of biomass and EPS are minimal (Hernández et al., 2015; La et al., 2018a). EPS formation was associated with higher concentration of O2 in a compost biofilter (Wilshusen et al., 2004a). At high O2 concentration, EPS production was 250% of the amount produced at low oxygen concentration. A compost based biofilter decline in methane oxidation rate after 100 days of operation was associated with EPS formation however when compost was mixed after EPS formation high methane oxidation rate was observed (Wilshusen et al., 2004b).

Quantitative PCR probes targeting pmoA genes that encode a subunit of the enzyme involved in methane oxidation have been designed and applied to estimate methanotroph populations in different environments (Kolb et al., 2003; Dumont, 2014; Sharp et al., 2014a;

Knief, 2015). In landfill cover soil and landfill biofilter operated in landfill sites, quantitative data obtained from qPCR of pmoA genes were correlated with methane oxidation rates and methanotrophic activity (Gebert et al., 2008; Henneberger et al., 2012; Su et al., 2014). pmoA

based qPCR together with relative abundances of methanotrophs from 16S rRNA amplicon sequencing have been used to link methane oxidation efficiency in methane biofilters (Gebert et al., 2008; Kim et al., 2013; Farrokhzadeh et al., 2017; La et al., 2018b; La et al., 2018c).

To examine the methanotrophic population in biofilter compost quantitatively, pmoA based qPCR was applied (Figure 4.5). pmoA gene copy number dramatically increased during the initial stages of a methane consumption period. However, a high level of pmoA gene copies was detected even after 60 days after methane feed was stopped (Figure 4.5). This indicates that methanotrophs in the compost do not die immediately when the substrate methane is depleted, but survive, perhaps by entering to a resting state like a spore or cyst. Methanotrophs belonging to the genera Methylobacter, Methylococcus, Methylocaldum, Methylocystis, Methylosinus,

Methylocapsa, and Methylocella produces spores or cysts as resting stages (Table B-1) (Semrau et al., 2010). This may present a problem with using the qPCR technique to monitor biofilter activity, since it will accurately predict the number of methanotrophs and perhaps even the

“potential” methane oxidation rate of the system (if the cells can be revived) but may not predict the present and actual methane oxidation rate. The goal of our monitoring protocol is to determine if a biofilter is actively oxidising methane at any given time.

However, members of the genus Methylomonas rarely form cysts and never spores, and

Methylomicrobium do not form either spores or cysts. This indicates that these two species might be more suitable indicator strains to monitor the methanotrophic activity in a methane biofilter system. Since they do not form resting stages, they are likely to die rapidly in compost that is not actively consuming methane, and therefore their presence could be used as an indication of an active biofilter. Based on 16S rRNA gene amplicons, Methylomicrobium spp. in different

treatments increased their relative abundance to ̴ 3.5% in the first 3 weeks of incubation (Figure

4.4C). Under aerobic starved conditions, the Methylomicrobium relative abundance dropped to ̴

1.5% while in the anaerobic starvation treatment their population fluctuates over time. In continuous feed active treatments, Methylomicrobium relative abundance maintained at ̴ 2.5%, almost twice the aerobic starvation treatment (Figure 4.4C). Thus Methylomicrobium could be an indicator. A pmoCAB gene database was assembled from all available methanotroph genomes to design a quantitative PCR primer to monitor populations of Methylomicrobium. However, the pmoA gene sequence of Methylomicrobium spp. was highly conserved compared to other methanotrophs (some of which are known to form spores, like Methylobacter) and the genus is polyphyletic (Supplementary Figure B-1). It was impossible to design a specific primer set for this genus alone (Supplementary Figure B-1).

Although methanotroph species did not seem to be a good indicator of biofilter activity, other methylotrophs (the Methylophilaceae) declined far more rapidly with the onset of starvation (Figure 4.6, and Figure 4.7). Within the Methylophilaceae the genus Methylotenera and Methylophilaceae-OM3B clade changed drastically in relative abundance. The probable explanation for this is that these methylotrophic bacteria require active methanotrophic activity to be supplied with energy substrates, for example methanol. In microbial communities, cross feeding, competition for nutrients, and cooperation between species exists (Grosskopf and Soyer,

2014; Seth and Taga, 2014; Zelezniak et al., 2015; Yu and Chistoserdova, 2017). Methylotrophs in the class Methylophilaceae and the methanotrophs class Methylococcaceae show a coordinated response to methane in a methane rich environment (Beck et al., 2013). In this study, abundance of the methanotroph genus Methylobacter and the non-methanotrophic methylotrophs

Methylotenera were positively related to methane oxidation under the conditions of variable oxygen and nitrate additions. A gene expression profile study identified the connection between methanotrophs and methylotrophs in a lanthanide-dependent cross feeding experiment, where non-methanotrophic methylotrophs induced the expression of methanol dehydrogenase genes specific to methanotrophs for sharing (Krause et al., 2017). In a metagenomic study of methane supplied microcosms the most dominant genus Methylobacter supplied carbon substrate from methane to diverse bacteria including Methylotenera (Oshkin et al., 2015). In a DNA-SIP experiment with methane of permafrost sample, heavy DNA fractions the genera Methylophilus and Methylotenera were labelled together with Methylobacter (Martineau et al., 2010), possibly due to cross-feeding. Heavy fraction DNA was dominated with Methylocaldum (23.2%) together

13 with Methylophilaceae (9.6%) in oil process water samples incubated with CH4 (Saidi-

Mehrabad et al., 2012). In a DNA stable-isotope probing of Coral reef sediment sample, 13C labelled C1 substrate carbon transfer linked Methylomicrobium and non-methanotrophic methylotrophs (Jensen et al., 2008).

In the Methylophilaceae, Methylotenera was dominant (Figure 4.6) during methane supply whereas in the Methylococcaceae, Methylobacter was dominant (Figure 4.4). A

Methylobacter/Methylotenera partnerships was observed in a metagenomic study of microcosms under different oxygen supply (Yu and Chistoserdova, 2017), suggesting a cooperation of

Methylobacter and Methylotenera under continuous feed (Figure 4.4, 3.6). The high relative abundance of Methylococcaceae/Methylophilaceae in continuous feed (Figure 4.7) versus low abundance in starved aerobic and starved anaerobic condition suggests Methylophilaceae

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depends on products of methane oxidation (e.g., methanol) from Methylococcaceae and follow a

“boom and bust” existence.

The methylotrophic co-feeders appear to be short-lived and decline when methane is removed from the system (Figure 4.7). Therefore, we propose a monitoring system based on the ratio (16S rRNA abundance) of the "methanotroph-associated methylotrophs” to the methanotrophs Methylococcaceae (Figure 4.8). In active biofilters the ratio was maximal at around 0.35 and varied from (0.1-0.35). However, this ratio dropped below 0.05 within one and half month of the onset of starvation and continued to decline over time to values 0.01 with extended starvation. We consider this ratio as a more sensitive test of recent biofilter activity in monitoring of methanotrophs. If the ratio drops below 0.1, a biofilter should be examined more.

4.6 Conclusions

Based on laboratory experiments of methanotrophs incubated under actively growing or starvation conditions, we have developed a monitoring system that should indicate the current methanotrophic activity of a biofilter using compost as a packing material. Methylomicrobium was hypothesized to be the best methanotroph species for monitoring biofilter activity, as it does not form resting stages. However, its relative abundance compared to other methanotrophs and other bacteria in general did not markedly decline during starvation, nor did its absolute abundance based on qPCR, and we concluded that a monitoring protocol based on any methanotroph was unlikely to be successful. This might show potential rates but not actual rates.

In contrast, methanotroph activity can be measured by pmoA transcript levels, targeting rRNA using DNA probe instead of 16S rRNA gene, or in situ quantification and visualization by

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hybridization assays such as by florescence in situ hybridization (FISH). However, some methylotrophic bacteria do decline very rapidly during methane starvation. Methylophilaceae spp (Methylotenera). are a clear example. The best overall indicator species for methane- oxidising activity in the compost are in fact not methanotrophs, but rather short-lived methylotrophs dependent on methanotrophic activity for their sustenance. This monitoring protocol has been dubbed the MOM (methylotroph over methanotrophs) protocol. It is based on high-throughput sequencing of community 16S rRNA genes, followed by assessment of the relative abundance of methylotrophic bacteria like Methylotenera spp. that grow and die quickly in response to methanotrophic activity.

The aim was to develop cheap, versatile and accurate monitoring protocol. The ratio of

Methylophilaceae over Methylococcaceae gives the actual activity and provides high efficiency over the conventional surface gas flux rate measurement practice. There will be certain challenges in compost biofilter such as stability of compost, top surface layer drying, compaction, EPS formation, and gas channeling. Due to compaction and clogging methanotrophs become active only in a certain depth or zone. To overcome these issues a sampling protocol for heterogenous samples is important to accurately assess activity. To validate this protocol testing on field-based samples and other biofilter material is necessary.

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Chapter Five: Analysis of copper monooxygenase-encoding genes detected in metagenomes,

single cell genomes and enrichment cultures from oilsands environments

5.1. Introduction

The copper membrane monooxygenases (CuMMOs) are a diverse enzyme family that oxidize a range of compounds such as ammonia, methane and short-chain alkanes (Semrau et al.,

2010; Coleman et al., 2011; Sayavedra-Soto et al., 2011; Tavormina et al., 2011; Coleman et al.,

2012). Cu-monooxygenase enzymes are found in members of the Alpha-, Beta-, and

Gammaproteobacteria, phylum Verrucomicrobia, candidate division NC10, phylum

Actinobacteria and the archaeal phylum Thaumarchaeota.

Methanotrophs and nitrifiers have been extensively studied in terms of their genome composition and biochemistry of methane and ammonia oxidation. Besides methane or ammonia, CuMMO substrates include alkanes (C2-C4), as reported in Mycobacterium chubuense NBB4, Mycobacterium rhodesiae NBB3, Nocardioides sp CF8, and Smaragdicoccus niigatensis DSM 44881 (Adachi et al., 2007; Coleman et al., 2011; Sayavedra-Soto et al., 2011;

Coleman et al., 2012). Ethylene assimilating bacteria Haliea sp. ETY-M and ETY-NAG were reported to possess CuMMO-encoding genes (Suzuki et al., 2012).

Genomic projects have now detected operons encoding CuMMOs of unknown function in genomes such as Solimonas aquatica, Bradyrhizobium sp ERR11, Hydrogenophaga sp. T4, and Skermanella aerolata KACC 11604 (Weon et al., 2007; Whitman et al., 2015; Chen et al.,

2017). Culture-independent studies such as metagenomic or metatranscriptomic sequencing, as well as SIP experiments have detected CuMMO-encoding operons in different environments

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(Dumont et al., 2013; Tavormina et al., 2013; Li et al., 2014; Knief, 2015). Thus diversity and potential of CuMMOs are expanding beyond the known methane and ammonia oxidisers, indicating possible novel biochemical and physiological roles.

West-In Pit oil sands process-affected water (WIP-OSPW) belongs to Syncrude Canada,

Ltd, and contains a high concentration of polycyclic aromatic compounds. In a metagenomic study of WIP-OSPW, high number of genes belonging to xenobiotic biodegradation and metabolism were detected (Rochman et al., 2017). A novel CuMMO operon was detected in a metagenomic analysis of WIP-OSPW by Fauziah Rochman, 2016. In the study, SIP was

13 13 13 performed without and with labelled CH4, C2H6 and C3H8 substrate using WIP-OSPW and

Mildred Lake Settling Basin process-affected water (MLSB-OSPW) water samples (Appendix

D). A qPCR assay was designed and applied to methane, ethane and propane enrichments as well as SIP fractions to specifically target this novel CuMMO operon detected in a WIP-OSPW metagenomic study. In both 12C and 13C methane enrichments the highest number of gene copy numbers was found in light DNA fractions which shows the organism containing the xmoCAB gene did not assimilate 13C methane. MLSB enrichments with ethane enriched 1.7 times the gene copy number compared to control sample while propane enrichments were heavily enriched to

27.8 times after 6 weeks of incubation showing a presence of possible propane oxidizing

13 13 capability in the organism(s) possessing the operon. In a heavy fraction SIP of C2H6 and C3H8 incubations, both showed high numbers of genes where 13C propane was 58.4 times enriched compared to 13C-ethane. This showed organisms possessing the operon have potential for both propane and ethane assimilation, but propane was more preferred. However the organism(s) responsible for ethane or propane oxidation has not been isolated yet.

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In this study, we detected operons encoding novel CuMMOs in several metagenomes of oilsands environments available in the JGI database. We built phylogenies of these CuMMOs to infer the phylogenetic affiliation to other known CuMMOs operons. We developed seven different qPCR assays to target novel CuMMOs. Enrichment of BML and MLSB water samples was performed using methane, ethane and propane as substrates. We used 16S rRNA gene

Illumina sequencing, qPCR of specific xmoA genes, and single-cell amplified genomes (SAGs) for the analysis.

5.2 Material and Methods

5.2.1 Database development

CuMMO-encoding operons analyzed in this study were obtained from metagenomes under hydrocarbon metagenome project (HMP) available in the JGI portal (Supplementary Table

C-1). Metagenomes were obtained from hydrocarbon resource environments in Alberta, Canada.

Metagenomes were screened for CuMMO-encoding operons using Gene Search function in the

JGI portal and 10 xmoCAB operons were detected. Sequence similarity was analyzed by feeding xmoA protein sequence as query against the NCBI protein sequence database by using blastP

(protein BLAST) function in NCBI (Supplementary Table C-2).

xmoCAB operons detected were aligned with the database used in Chapter 3. The database contains 120 CuMMO-encoding xmoCAB operons from isolated organisms.

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5.2.2 Sampling and enrichments

The Athabasca oilsands reserves located in northern Alberta, Canada are the largest deposit of bitumen or heavy crude oil in Canada. During oil extraction processes mined oilsands consume water and produce water to form tailings pond consisting of sand, clays, residual bitumen, inorganic and organic compounds, and oil sands process-affected water (OSPW)

(Holowenko et al., 2002; Quagraine et al., 2005). Mildred lake settling basin (MLSB) and base mine lake (BML, previously known as West-In-Pit) are both owned by Syncrude Canada, Ltd. and are located at the Athabasca oilsands reserves in northern Alberta, Canada. Large volumes of tailings produced from oil extraction is still deposited into MLSB and represents an active tailings pond. After 2012, depositing tailings water in BML has been stopped and is currently being reclaimed.

MLSB and BML water samples were collected from Syncrude Canada Ltd. in September

2016. Surface water (0 m) samples were used for this study. Surface water of OSPW were previously detected with methanotrophs actively assimilating methane (Saidi-Mehrabad et al.,

2012), high number of genes for xenobiotic biodegradation and metabolism (Rochman et al.,

2017), and xmoCAB operon detected in WIP-OSPW metagenome (Appendix D).

In order to estimate the short chain hydrocarbon oxidation potential of MLSB and BML water samples, the amount of methane, ethane and propane in the headspace of incubation bottles was measured to observe degradation over time. MLSB and BML samples of 200 ml were poured into a 1-L duran bottles (800 ml of headspace). The bottles were sealed with butyl rubber stoppers and capped. Each bottle was supplemented with 10-15% methane (v/v), ethane (v/v) or propane (v/v) respectively and incubated at optimum temperature of 30 ⁰C on a rotary shaker at

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150 revolutions per minute (rpm). Headspace methane, ethane and propane were monitored using gas chromatography (8610C, SRI Instruments) equipped with a HayeSep-D column coupled to a flame ionization detector (FID) (Column T 190 ⁰C, detector T 300 ⁰C and N2 as carrier gas).

After 42 days of incubation, 10 ml of enrichment sample was taken, and cells were collected by centrifugation for 10 min at 10,000 x g. DNA was extracted using the FastDNA

Spin Kit for Soil (MP Biomedicals). Additional purification was performed using 5.5 M guanidine thiocyanate (Knief et al., 2003). The extracted DNA was stored at -20 ⁰C for further analysis.

5.2.3 Quantitative PCR primer design

qPCR primers were designed for targeting 10 clades of novel xmoCABs (Figure 5.1) by using ‘Probe Design’ tool in ARB (Ludwig et al., 2004) (Table 5.1). The TP1 clade represents two and TP6 represents three different xmoCAB operons respectively. Other clades are represented by individual xmoCAB operons. DNA extracted from fresh MLSB and BML samples

(described in section 5.2.2) was used as a positive control to verify the assays. Target gene products were amplified via PCR for all seven assays. Cycling conditions were performed and optimal conditions for each assay were determined as shown in Table 5.1. DNA bands were visualized in all assays in both MLSB and BML samples. PCR products for each assay were verified via xmoA gene Sanger sequencing done at the University of Calgary Genetic Analysis laboratory.

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DNA extracted from ethane and propane enrichment samples were run for qPCR assays.

Each reaction tube contains 12.5 µl volume: 2µl each specific forward and reverse primer, 7.5 µl of Syber green (Qiagen) and 1 µl of DNA. A three-step reaction was used for cycling conditions: an initial denaturation of 5 min at 94 ⁰C; 35 cycles of 94 ⁰C for 60 s, (annealing temperature mentioned in table 5.1) ⁰C for 45 s and 72 ⁰C for 45 s: and a final elongation step of 72 ⁰C for 10 min. The samples were run in a Rotor-Gene Q (Qiagen).

Standards for qPCR were prepared using a serial dilution of purified PCR products of each primer set. The concentration of standard was measured using Qubit and the measured

DNA amount was converted to target molecules per microliter (Supplementary Table C-3).

Dilutions were adjusted to 109, 107, 105, 103, and 102 target molecules per microliter.

Fluorescence data was obtained during the last step of each cycle. The specificity of the qPCR assay was verified by melt curve analysis. Efficiencies of standard curves used to calculate qPCR values ranged from 95-98% and gene copy numbers were calculated based on the obtained standard curve of Ct vs standard concentration.

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Table 5.1: Specific primers for new xmoA lineages detected in oilsands environments, along with amplicon length and PCR cycling conditions.

Primer name Sequence (5’ - 3’) Length (bp) PCR Cycle Conditions*

TP2 Forward: GTC TGG ATT GTC CGC TAC CA 288 Initial denaturation 94 ⁰C, 5 minutes; Denaturation 94 ⁰C, 0:30; Annealing 60 ⁰C, 0:30; Elongation72 ⁰C, 2:00; Elongation stop 72 ⁰C, 10:00 Reverse: GCC TGC TCG AGG GAA TGT G

TP1 Forward: GCC TGG GGC AGC CAC TAC 272 Initial denaturation 94 ⁰C, 5 minutes; Denaturation 94 ⁰C, 0:30; Annealing 64 ⁰C, 0:30; Elongation72 ⁰C, 2:00; Elongation stop 72 ⁰C, 10:00 Reverse: TCG TAC CCC ATC AAA TCG GCC

WIP Forward: GCG TCT GGA TTG TTC GCT ACC A 295 Initial denaturation 94 ⁰C, 5 minutes; Denaturation 94 ⁰C, 0:30; Annealing 64 ⁰C, 0:30; Elongation72 ⁰C, 2:00; Elongation stop 72 ⁰C, 10:00 Reverse: TCA GGC GTG CCC GAG CGG

Coal Forward: ATG GCG TGG GTG AGC CGC 297 Initial denaturation 94 ⁰C, 5 minutes; Denaturation 94 ⁰C, 0:30; Annealing 69 ⁰C, 0:30; Elongation72 ⁰C, 2:00; Elongation stop 72 ⁰C, 10:00 Reverse: CGG CAT GGC GGT GCG GAT

HR Forward: GAT CAG CCG CTA TGT GAA CTT 280 Initial denaturation 94 ⁰C, 5 minutes; Denaturation 94 ⁰C, 0:30; Annealing 66 ⁰C, 0:30; Elongation72 ⁰C, 2:00; Elongation stop 72 ⁰C, 10:00 Reverse: AGT GCG GAT GTA CTG GAA GC

TP6 Forward: CTG GAT AAG CCG CTA TCT 288 Initial denaturation 94 ⁰C, 5 minutes; Denaturation 94 ⁰C, 0:30; Annealing 58 ⁰C, 0:30; Elongation72 ⁰C, 2:00; Elongation stop 72 ⁰C, 10:00 Reverse: ATG GCC GTG CGA ATG TAT

490 Forward: CTG CCA GTG TGC TGC TAT 320 Initial denaturation 94 ⁰C, 5 minutes; Denaturation 94 ⁰C, 0:30; Annealing 66 ⁰C, 0:30; Elongation72 ⁰C, 2:00; Elongation stop 72 ⁰C, 10:00 Reverse: CCC GCT CTA GGG AAT GTG

* Temperature (⁰C) and Duration (minute:seconds), total of 30 cycles

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5.2.4 16S rRNA sequencing and analysis

16S rRNA gene amplicon Illumina sequencing was performed for community analysis.

DNA extracted from enrichment samples was used and around 5 ng/µl (total 20 ml reaction volume) of input DNA was PCR amplified based on the KAPA HiFi HotStart Ready Mix protocols (Kapa Biosystems). 16S rRNA Illumina prime set 341f/785r (Klindworth et al., 2013) was used and cycling conditions were performed with a three step reactions: initial denaturation at 95 ⁰C for 3 min; 25 cycles of 95 ⁰C for 30 s, 55 ⁰C for 30 s and 72 ⁰C for 30 s; and a final elongation of 72 ⁰C for 5 min. Amplified product was verified by an agarose gel run and later

PCR products were purified using AMPure XP beads (Beckman Coulter). Purified PCR products were re-amplified by using Illumina barcoded amplicon primers containing 16S rRNA gene targeted primers employing three step reactions: initial denaturation at 95 ⁰C for 3 min; 8 cycles of 95 ⁰C for 30 s, 55 ⁰C for 30 s and 72 ⁰C for 45 s; and a final elongation of 72 ⁰C for 5 min.

Amplified PCR product was re-run on a agarose gel and purified using method described above.

DNA samples were measured using Qubit BR kit (Thermo Fisher Scientific) and then normalized to 4 nM. Normalized samples were pooled and run for quality control with a

BioAnalyzer (Aligent). Samples were then prepared and run through high-throughput sequencing using Illumina Miseq, University of Calgary.

Raw sequence data reads were analyzed by using QIIME (Kuczynski et al., 2012). For quality control sequences with a phred score of lower than 20 were removed. Sequences were then clustered at a sequence identity of 97%. The most abundant member of each clustered was selected as a representative sequence. Assigned representative sequences were aligned to the

Greengenes core reference alignment and those sequences that could not be matched with

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reference sequence in the Greengenes database were assigned an OTU. Taxonomy was assigned based on the closest representative detected on the Greengenes reference database (McDonald et al., 2011). This allowed the determination of ecological statistics and detection of individual species.

5.2.5 Single cell sorting

Based on the pmoA based qPCR assay, a BML water sample enriched with propane was selected for single cell sorting. This propane-BML(A) water sample (5 ml) was filtered through a sterile 0.2 µm diameter filter. The filter was washed with sterile water and was preserved in a 5% glycerol solution and stored in dry ice for immediate delivery to University of California,

Lawrence Berkeley National Laboratory, DOE - Joint Genome Institute. Single cell sorting was performed by Joint Genome Institute (Rinke et al., 2014). Based on protocol described by Rinke et al., 2014, single cell separation of few picoliters of sample was performed by fluorescence activated cell sorting (FACS) where cells can be identified by their DNA content based on fluorescence and scatter signals. The sorted cells were lysed based on an alkaline lysis method to degrade microbial cell envelopes and release chromosomal DNA. Then whole genomes were amplified using multiple displacement amplification (MDA) method using the Phi29 polymerase. Phylogenetic screening using 16S rRNA genes was used for taxonomic assignment for sorted cells. Universal pyrotag primer set 926wF (5’ - AAACTYAAAKGAATTGRCGG -

3’) and 1392R (5’ - ACGGGCGGTGTGTRC - 3’) were used.

Aliquots of the single cell sorted 96 well plate were sent by JGI for screening of

CuMMO-encoding genes. Based on full sequence 16S rRNA gene the identified sorted cells

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belonged to genus Polaromonas (74 wells), Rhodoferax (15 wells), Methyloversatilis (1well),

Hygrogenophaga (1 well), Xanthobacter (1 well) and Fusibacter (1 well). Screening of single sorted cell samples was performed using PCR assay described above to confirm CuMMO- encoding operon by summer student Abraham Lopez-Jauregui in 2017. A total of 15 single sorted well samples were detected positive with TP2 (13 wells), TP1 (1 well) and WIP (1 well) in the PCR assay (Supplementary Table C-4) and selected for full genome sequencing. The selected cells were used for shortgun sequencing using Illumina NextSeq-HO sequencing technology. Sequence data was assembled using SPAdes v.3.10.1(Bankevich et al., 2012) to produce single-cell amplified genomes (SAG) and annotated using Microbial Genome

Annotation Pipeline (MGAP) (Huntemann et al., 2015). Sequencing of 15 SAGs, their assembly and annotation were done by DOE Joint Genome Institute- Integrated Microbial Genomes and

Microbiomes (JGI-IMG) (Chen et al., 2017).

5.3 Results and Discussion

5.3.1 Phylogeny of detected CuMMO xmoCAB operon

The phylogeny of xmoCAB operons obtained from the oilsands metagenomes were only distantly related to known CuMMOs operons found in genomes of known bacteria (Figure 5.1).

Sequence identity of detected xmoA genes with the known CuMMO-encoding xmoA gene is presented in Supplementary Table C-2. The TP2 clade showed high sequence identity of 92% over the xmoA sequence of Burkholderiales bacterium RIFCSPLOWO2_02_FULL_67_64

(accession no. OGB13554.1) compared to all other clade (Figure 5). Our novel xmoCAB operons are grouped into two distant lineages: one clade contained TP2, 490 and WIP, and another clade

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contained sequences TP1, Coal, TP6 and HR. The clade containing TP2, 490, and WIP is more closely related to bacterium Bradyrhizobium sp. ERR11. Bradyrhizobium sp. ERR11 was isolated from root nodules of a leguminous tree Erythrina brucei, and possesses genes for nodulation, nitrogen fixation and denitrification. Its growth reported on multi carbon sources

(Aserse et al., 2017). Growth on methane, ammonia or alkanes has not been examined.

Bradyrhizobium sp. ERR11 falls under class Alphaproteobacteria, and family

Bradyrhizobiaceae. The other closely related bacteria are Haliea spp. and Cycloclasticus sp..

Haliea sp ETY-M and ETY-NAG were isolated from seawater and reported to assimilate ethylene (Suzuki et al., 2012). Cycloclasticus sp. SCGC AC281-P21 is a single cell genome sequenced by JGI, sampled from oil spill deep water environment. In the genus Cycloclasticus,

Cycloclasticus pugetii, and Cycloclasticus spirillensus are predominantly found in marine environments degrading polycyclic aromatic hydrocarbon including naphthalene, phenanthrene and pyrene (Staley, 2010) and phenanthrene (Chung and King, 2001), respectively. This suggests clades containing TP2, 490 and WIP are closely related to known hydrocarbon degraders and have roles in assimilating ethane or propane.

Other identified xmoCAB operons TP1, Coal, TP6 and HR cluster in a broad group with some pmoCAB operons of Alpha, Beta, and Gammaproteobacteria that have no known function.

The isolates Solimonas aquatica DSM 25927, Burkholderiales bacterium and Hydrogenophaga sp. T4 are distantly related to xmoCAB operons within this monophyletic cluster. Solimonas aquatica was isolated from freshwater spring in Taiwan (Sheu et al., 2011). The closest relatives of Solimonas aquatica based on 16S rRNA gene sequences are Solimonas soli LMG 24014,

Solimonas flava, Solimonas varicoloris, with sequence similarity of 96.7, 96.6 and 96.2%,

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respectively. Solimonas aquatica is metabolically versatile, grows at optimum growth temperature at 25 ⁰C, at optimum pH 7.5 and optimum 0.5% NaCl. However growth on methane or any hydrocarbon has not been reported. The CuMMO-encoding operons were identified through genome sequence data as a part of the Genomic Encyclopedia of Archaeal and Bacterial

Type Strains (Whitman et al., 2015). The Burkholderiales have been reported in assimilation of aromatic hydrocarbons including benzene (Liou et al., 2008; Xie et al., 2011), toluene (Sun et al.,

2010), phenol (Manefield et al., 2005), naphthalene and phenanthrene (Singleton et al., 2005).

The genus Hydrogenophaga that is associated with oxidation of molecular hydrogen including

Hydrogenophaga flava, Hydrogenophaga palleronii, Hydrogenophaga pseudoflava, and

Hydrogenophaga taeniospiralis (Willems et al., 1989). Some studies have reported on the capability of aromatic hydrocarbon degradation by Hydrogenophaga (Aburto and Peimbert,

2011; Yan et al., 2017). Detection of CuMMO-encoding operons in genomes of Solimonas aquatica, Burkholderiales bacterium and Hydrogenophaga sp. T4 suggests these organisms have potential for assimilation or co-oxidation of hydrocarbons.

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Figure 5.1: Phylogenetic tree based on concatenated inferred XmoCAB sequences (generally 910 amino acid sequences). The tree was constructed using Maximum likelihood with Seaview 4.4.12 employing LG model. Node values are based on 100 bootstrap replicates. The scale bar represents 0.2 change per amino acid position. TP2, 490, WIP, TP1, Coal, TP6, and HR are seven different metagenome sequences represented with different colours. The metagenomes were constructed from hydrocarbon resources environment. Text at the bottom-left of the figure represents JGI-IMG metagenome ID and the environment.

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5.3.2 Enrichment of tailings pond water on short chain hydrocarbons and

community analysis

Water samples from BML and MLSB were supplied with methane, ethane, and propane and decrease in headspace mixing ratios of these gases was observed over a period of 42 days

(Figure 5.2). Headspace methane decreased rapidly to below 1% (v/v) within one week and followed a similar pattern when re-gassed. Slower decreases in headspace mixing ratios of ethane and propane were observed throughout the incubation period.

Enrichment on short chain alkanes such as methane, propane and ethane were conducted previously by Fauziah Rochman (Appendix 1) in 2016 from MLSB water. Ethane and propane enriched MLSB water showed increasing of xmoCAB gene copy numbers. Similarly, in the same study, 13C-SIP of ethane and propane enrichments was performed. Density gradient separation showed 13C incubations shift toward high density indicating heavy DNA fraction representing the active 13C assimilating communities. 13C-propane heavy fractions had 58.4 times higher gene copy numbers than the 13C-ethane heavy fraction suggests the organism(s) possessing these operons in MLSB water samples prefer to oxidize propane.

Based on this previous work, further enrichment experiments were performed with two objectives: A. Isolation of an ethane or propane-oxidizing organism that has a unique xmoCAB operon, and B: Screening of pathways and genes associated with short chain hydrocarbon oxidation from the genomes of organisms containing unique xmoCAB operons, via single cell sorting and whole genome sequencing.

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Figure 5.2: Depletion of methane, ethane, and propane in MLSB and BML water samples.

16S rRNA gene amplicon sequencing was performed from the enrichment samples at day

42. Methane enrichment samples showed high relative abundances of known methanotrophs

(Figure 5.3). Crenothrix (90.3%) and Methylobacter (71.8%) were dominant in methane enriched BML(A) and BML(B) samples, respectively. Methanotrophs belonging to

Methylomonas comprised relative abundances of 57.8% in methane-MLSB (A) and 60.2% in methane-MLSB (B). Other methanotrophs enriched were Methylocaldum, Crenothrix, and

Methylobacter between 5- 20%. The 16S rRNA sequences obtained from enrichment samples belonging to Crenothrix, Methylobacter, Methylocaldum all displayed 98% or more identity

(based on NCBI top blast hit) to cultured bacteria. Methane oxidation in oilsands tailings ponds was expected, and it was previously reported that gammaproteobacterial methanotrophs belonging to genus Methylocaldum predominate in surface water (Saidi-Mehrabad et al., 2012).

In the same study, genera Methylocaldum and Methylomonas were shown to be responsible for oxidizing methane based on a 13C-SIP experiment. The other detected genera were

Methylomicrobium, Methylobacter and Crenothrix.

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Ethane and propane enrichments were mostly dominated by Gammaproteobacteria and

Betaproteobacteria. The most dominant genus was the gammaproteobacterial genus Lysobacter

(51% relative abundance) in ethane-BML(A). Betaproteobacteria not identified to the genus level but belonging to the family Commonandaceae (29.5% relative abundance) dominated in ethane-

BML(B) and 20.3% in propane-BML(B) sample. The betaproteobacterial genus Polaromonas was highly enriched in both propane-BML(A) and BML(B) with 84.4% and 28.3% (relative abundance) respectively. Propane-MLSB(A) and MLSB(B) enrichments were dominated by the genus Methyloversatilis with 40.9% and 53.2% (relative abundance) respectively.

Ethane and propane enrichments were mostly dominated by Betaproteobacteria in the family Commonandaceae (genus Polaromonas, Acidovorax, Variovarax, Albidiferax,

Hydrogenophaga and Uncultured genera) (Figure 5.2). One of the highly dominant genera was

Polaromonas (84.4%) in a propane enriched BML(A) sample. The genus Polaromonas is widely distributed in diverse environments (Darcy et al., 2011). Polaromonas species have been identified to be involved in pollutant degradation. The naphthalene degrading bacterium

Polaromonas napthalenivorans CJ2 was isolated from Coal-tar waste (Jeon et al., 2004), a similar environment to tailings ponds where polycyclic aromatic hydrocarbons are found in high concentration. Similarly, genes associated with degradation of n-alkanes have been detected in a genome analysis of Polaromonas sp. strain JS66 (Mattes et al., 2008). These findings support the assumption that the predominant Polaromonas in propane-BML enrichment are likely to be involved in propane metabolism.

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5.3.3 Quantitative PCR analysis of enrichment samples

Quantitative PCR was performed for the methane, ethane and propane enrichments of

MLSB and BML sampled at day 42. The aims of this qPCR assay were to know gene copy number (i.e. growth of the microbes possessing these genes) in response to enrichment and to select samples for single cell sorting as well as for isolation via plating.

Gene copy number of xmoA were detected in BML and MLSB enriched with methane, ethane and propane (Figure 5.4). The level of xmoA gene detected on seven different qPCR assays showed highest gene copy number in TP2 and 490 assays compared to other assays. The highest xmoA gene copies detected for MLSB amended with methane, ethane, and propane were:

Methane 6,500 ± 79 (MLSB(B), WIP assay), Ethane 15,860 ± 57 (MLSB(A), WIP assay),

Propane 204,517 ± 83 (MLSB(A), 490 assay). Similarly the highest xmoA gene copies detected for BML enriched with methane, ethane and propane were: Methane 23,541 ± 99 (BML(B), TP2 assay), Ethane 113,396 ± 23 (BML(B), TP2 assay), and Propane 556,816 ± 22 (BML(A), TP2 assay) respectively.

Based on the results, the organisms targeted by the TP2 and 490 assays appear to increase in the propane enrichments (Figure 5.4 panels A and B). Our results correspond to the previous work on 13C-propane SIP experiments, where the heavy fractions were enriched in xmoA targeted with xmoCAB assay (qPCR assay by Rochman, 2016, Appendix D) and had 58.4 times the xmoA gene copies compared to 13C-ethane heavy fractions (Rochman, 2016). The low number of gene copies in methane and ethane enrichments suggests that propane is more preferred substrate for the microorganisms that carry the xmoCAB operons of the TP and 490 clades (Figure 5.1).

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Although we cannot say anything certain about the role of the CuMMOs these results provide possible evidence of substrate specificity for either ethane or propane rather than methane.

The levels of xmoCAB gene copies for TP6, Coal, TP1, WIP, and HR assays were always at relatively low levels (below 40,000 gene copies per ml) compared to the TP2 or 490 assays. It was expected to get significant levels of xmoA gene in enrichments based on the amplified PCR products in all assays in a fresh BML and MLSB samples. Interestingly all clades enriched in ethane and propane substrate and showed some level of xmoCAB gene compared to methane enrichment and controls. However the low level of xmoA in TP6, Coal, TP1, WIP, and HR could be cross-reactions of different assays. One of the potential risk of cross reactions correlates with the number of mis-matches in the primer sets. However all primer set combinations have high mis-matches to other non-target group (Table 5.2). Cross reactions of assay 490 and TP2 can be expected with low number of mis-matches between them. The assays were verified by comparing the sequences of PCR product and their identity in our database. All sequences showed 100% identity.

The influence of enrichment sample (MLSB versus BML) on gene copy levels (qPCR) in seven different assays was examined using multivariate analysis of variance (MANOVA). The 2- factor MANOVA analysis was performed in R platform that included all seven assay gene copies as dependent variables and enrichment samples (MLSB and BML) as fixed factors

(Supplementary Figure C-6). The significance of the MANOVA model was examined using

Pillai’s statistics. The p-value of 0.002 gave a significance difference among the assays.

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Table 5.2: Primer set used in different PCR assay and their mis-matches with other primer sets. Symbol F, and R represent forward and reverse primer, and B represent more than five mis-matches. TP_1F/R TP_2F/R WIP_F/R Coal_F/R HR_F/R TP6_F/R 490_F/R TP_1F/R 0/0 B/B B/B 4/5 B/B B/5 B/B TP_2F/R B/B 0/0 1/B B/B B/B B/B 0/3 WIP_F/R B/B 1/4 0/0 B/5 B/5 B/5 1/B Coal_F/R 5/B B/B B/B 0/0 B/1 B/2 B/B HR_F/R B/B B/B B/B 5/2 0/0 3/B B/B TP6_F/R 5/B 5/B B/B 4/3 5/3 0/0 5/B 490_F/R B/B 2/2 4/B B/B B/B B/B 0/0

Overall, the highest number of xmoCAB genes were detected on the TP2 assay for propane amended BML(A) sample and therefore this was selected for single cell sorting. We hypothesized that high gene number corresponds to higher cell numbers in enrichment culture which will provide a better chance of getting more cells in single cell sorting.

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Figure 5.4: Quantitative PCR analysis of specific xmoA genes in methane, ethane and propane amended MLSB and BML samples: A. TP2 assay, B. 490 assay, C. TP6 assay, D. Coal assay, E. TP1 assay, F. WIP assay and G. HR assay. The x-axis represents MLSB or BML samples enriched with methane, ethane and propane (or unenriched controls). The y- axis represents the number of gene copies per ml of enrichment culture. ANOVA was run to test the differences between methane and propane enrichment gene copies obtained from different assay. The p value represents the significance of ANOVA calculated using Tukey test. Error bars represent ± SEM, based on duplicate qPCR reactions of the same extracts (i.e. technical replicates).

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In a study by Rochman, one xmoCAB lineage (xmoCAB detected in WIP-OSPW metagenome) was investigated in 13C-methane, 13C-ethane and 13C-propane SIP experiments

(Rochman, 2016). Here, we have run all seven-different qPCR assays for 12C and 13C-methane, ethane and propane SIP fractions performed by Rochman, 2016 in order to see the levels of xmoA genes for clades TP1, TP2, TP6, HR, Coal, 490, and WIP (Figure 5.1, Table 5.1). The TP2 assay for this study and xmoCAB assay by Rochman, 2016 targeted the clade TP2 (Figure 5.1), however the targeting DNA sequence region and primer sets were different (annealing temperature 60 ⁰C (TP assay) versus 56 ⁰C xmoCAB assay). A shift in DNA distribution to heavier values of 1.72 – 1.74 g ml-1 confirmed the incorporation of 13C into microbial DNA. The heavy 13C fractions represent the genomes of 13C-assimilating communities. The level of xmoA genes was higher for TP2 and 490 assays compared to other assays (Figure 5.5, 5.6), confirming the earlier enrichment results (Figure 5.4). For 13C-propane heavy fractions, the maximum level for TP2 and 490 assays were at 5,372,1555 ± 39 and 97053 ± 50 gene copies per fraction respectively (Figure 5.5). In a study by Rochman (2016) xmoA genes from the TP2 clade were detected at higher numbers in both 13C-ethane and propane heavy fractions compared to light fractions. However, we only detected for 13C-propane heavy fractions, possibly due to different

PCR conditions used in the previous study. Based on our result and study from Rochman (2016), there is a possibility that the organism(s) possessing the xmoA genes of the TP2 and 490 clades are actively involved in assimilating propane.

The level of xmoA genes for all other assays were detected below 1000 gene copies ml-1 in heavier fractions and were below at 10,000 gene copies ml-1 in lighter fractions, hence are not presented here. The levels of xmoA gene in ethane and propane enrichment were nearly 45 times

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higher than 13C-ethane and propane SIP xmoA gene levels. The quantification of genes based on qPCR appears to be affected by the detection limit of the assays. The annealing temperature was selected for high detection limit for all assays (sharp DNA bands visualized in agarose gel). For example, PCR products were amplified in range of annealing temperature (58 – 66 ⁰C); sharp

DNA band was visualized in agarose gel and annealing temperature was selected for 64 ⁰C for

TP1 assay. The qPCR assay performed at high detection limits could be associated with low levels of xmoA gene detected in 13C-ethane and propane SIP fractions.

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Figure 5.6: qPCR analysis of xmoA gene copies for 490 assay of MLSB tailings water sampled in August, 2015 with the addition of labelled substrates: A. 12C-methane, B.13C- methane, C. 12C-ethane, D. 13C-ethane, E. 12C-propane, F. 13C-propane. The x-axis represents DNA density (g ml-1) for each fraction. The y-axis dotted line represents the relative DNA concentration and bar represents number of gene copies per fractions. Error bars represents ± SEM of two technical replicates. SIP fractions analyzed in this study were prepared as described by by Rochman, 2016.

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5.3.4 Single cell genomics

In recent years, single cell omics techniques have revolutionized molecular ecology

(Rinke et al., 2013; Gawad et al., 2016; Kodzius and Gojobori, 2016). In this study single cell genomes from a propane enrichment culture provides us the opportunity to identify the bacteria possessing particular genes. Here we analyzed different single cell amplified genomes (SAGs), genes that are present in SAGs and predicted possible pathways for propane metabolism.

A total of 384 single cells were sorted from the enrichment by JGI (Supplementary Table

C-5). SAGs were screened positive for xmoA sequences with TP2, TP1 and WIP assay

(Supplementary Table C-4) and 15 cells were selected for full genome sequencing after confirming sequence identity from Sanger sequencing of PCR products. SAGs were screened for

CuMMO-encoding operons where 9 out of the 15 sequenced and annotated SAGs possessed either pmoC, pmoB or pmoA genes or a combination (Table 5.3). A complete xmoCAB operon was detected in 5 different SAGs: BML02L09, BML02D16, BML02C18, BML02F20, and

BML02G21. A complete soluble monooxygenase-encoding operon was also detected only in

BML02L21. SAGs that were detected with CuMMO-encoding operons were only used for analysis.

In order to see the relatedness between SAGs, pairwise ANI was performed. Based on results, more than 97% identity were found in BML02L21, BML02L09, BML02D16, and

BML02E16 SAGs (Table 5.4). Similarly more than 96% identity was found in BML02I19,

BML02C18, BML02F20, and BML02F14 SAGs (Table 5.4). To assign taxonomy an average

ANI threshold range of 95-96% has been used for species relatedness which corresponds to about 98.65% 16S rRNA gene sequence similarity (Kim et al., 2014).

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16S rRNA genes from the SAGs were blasted against NCBI database and showed best hit above 99% with members of either the genera Polaromonas or Rhodoferax. Average ANI results and 16S rRNA gene similarities confirmed that SAGs BML02L21, BML02L09, BML02D16, and BML02E16 belong to the genus Polaromonas and SAGs BML02C18, and BML02F20 belong to the genus Rhodoferax. Based on the 16S rRNA analysis of the enrichment sample used for cell sorting, propane-BML(A) was predominated by the genus Polaromonas at 84.4%, and

Rhodoferax at 1.2%.

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Table 5.3 xmoC, A, and B and soluble monooxygenase-encoding genes identified in single cell amplified genomes (unscreened genomes). The cells were sorted by JGI from propane-BML(A) enrichment sampled at day42. Genome ID (JGI) SAG Monooxygenase-encoding genes 16S rRNA gene blast particulate soluble best hit (genus) in % 2773857729 BML02L21 pmoCA mmoXYBZDC Polaromonas (99)

2773857733 BML02L09 pmoCAB mmoXYBZ Polaromonas (100) pmoAB 2773857737 BML02D16 pmoCAB mmoYBZDC Polaromonas (100) pmoC 2773857747 BML02E16 pmoC mmoXY Polaromonas (99) pmoC 2773857739 BML02C18 pmoCAB Rhodoferax (100) pmoB 2773857741 BML02F20 pmoCAB Rhodoferax (100)

2773857743 BML02F14 pmoCA *no 16s

2773857717 BML02I19 pmoB *no 16s

2773857749 BML02G21 pmoCAB *no 16s

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Table 5.4 Pairwise average nucleotide identities (ANI) between SAGs. Each SAG contains xmoA, or xmoB and/or xmoC gene sequences. ANI was run on the pairwise ANI tool in the JGI-IMG genome portal. SAG BML02L21 BML02L09 BML02D16 BML02E16 BML02G21 BML02I19 BML02C18 BML02F20 BML02F14 BML02L21 - 99.59 99.98 98.03 99.31 77.43 77.96 76.39 73.65 BML02L09 99.59 - 99.58 99.88 99.43 75.62 76.95 76.3 75.67 BML02D16 99.98 99.58 - 90.34 99.45 78.76 77.67 80.79 74.95 BML02E16 97.59 99.88 90.35 - 97.39 79.77 77.53 76.66 75.01 BML02G21 99.31 99.42 99.44 97.38 - 76.81 77.03 76.27 73.58 BML02I19 77.42 75.61 78.96 79.78 76.76 - 99.46 96.02 99.74 BML02C18 77.95 76.91 77.65 77.53 77.06 99.47 - 98.43 99.26 BML02F20 76.37 76.13 80.78 76.66 76.28 96.23 98.43 - 92.75 BML02F14 73.47 75.7 74.97 74.97 73.61 99.75 99.26 92.77 -

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We analyzed CuMMO-encoding xmoCAB sequences detected from the SAGs. Some of the SAGs possesses multiple operons: BML02L09 (xmoCAB and xmoAB related to genus

Polaromonas), BML02D16 (xmoCAB and orphan pmoC related to Polaromonas) and

BML02C18 (pmoCAB and orphan xmoB related to genus Rhodoferax) (Table 5.2). A derived

XmoCAB based phylogenetic tree was generated in order to see evolutionary relationships of

CuMMOs present in the SAGs and metagenomes (Figure 5.1). xmoCAB operons detected in

SAGs were coloured in red in Figure 5.1. Detected xmoCAB sequences in the SAGs have two distinct lineages: BML02F20, BML02G21, BML02D16, and BML02L09 distantly related to

Haliea sp. or Bradyrhizobium sp., and BML02C18 related to Solimonas aquatica and

Hydrogenophaga sp T4 (Figure 5.1). Out of seven assays developed for detecting clade TP2,

490, WIP, TP1, Coal, TP6 and HR (Figure 5.1), the xmoA sequence detected in BML02F20 was

100% identical to the xmoA primer sequences designed for the TP2 assay. BML02G21 and

BML02D16 are also very similar to the TP2 and 490 clades.

Bradyrhizobium sp. ERR11 is closely related to the TP2 lineage. This bacterium is reported to use multi carbon compounds (Aserse et al., 2017), however oxidation of short chain hydrocarbons (C1 - C5) have not been reported for this organism. The other related organism that possesses CuMMO gene is an ethylene oxidizing Haliea spp. (Suzuki et al., 2012).

An mmoX gene (encoding a subunit of the soluble monooxygenase enzyme family) tree was generated to infer phylogenetic relationship of the detected mmoX gene (Figure 5.7). SAGs

BML02L21, BML02E16 and BML0L09 related to Polaromonas have identical mmoX gene clustered together as a separate lineage and phylogenetically related to Solimonas aquatica,

Thauera butanivorans and Brachymonas petroleovorans. Soluble methane monooxygenase is

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found in methanotrophs and related enzymes are found in some alkane oxidisers. The enzymes are found to co-oxidize alkanes (up to C8), cyclic alkanes, n-alkenes, and aromatic hydrocarbons

(Colby et al., 1977; Burrows et al., 1984; Semrau et al., 2010). Thauera butanivorans reported to oxidize n-alkanes (C2 - C9) (Dubbels et al., 2009) and Brachymonas petroleovorans oxidize n- alkanes (C5 - C10), as well as aromatic compounds (Rouviere and Chen, 2003). The role of soluble diiron monooxygenase in Thauera butanivorans was responsible for terminal hydroxylation during oxidation of aliphatic alkanes producing primary alcohols for downstream catabolism (Dubbels et al., 2007; Cooley et al., 2009). Tailing ponds water are rich in simple to complex hydrocarbons (Quagraine et al., 2005; Saidi-Mehrabad et al., 2012). The mmoX detected in our SAGs could encode an enzyme to degrade some pollutants in BML water.

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Most studies of CuMMOs have focused on methane or ammonia oxidation rather than other short chain hydrocarbons. However, Mycobacterium strains that have CuMMO butane monooxygenases have been shown to oxidize short chain hydrocarbons (C2 -C4) (Coleman et al., 2012). Ethylene assimilating bacteria, Haliea sp ETY-M and ETY-NAG that possesses genes similar to particulate methane monooxygenases and ammonia monooxygenases have been reported to degrade ethane, propylene and propane in presence of ethylene (Suzuki et al., 2012).

Our result shows that CuMMOs enzyme are present in SAGs belonging to species of

Polaromonas and Rhodoferax. The dominance of Polaromonas in 16S rRNA amplicon sequencing of propane enrichments show and Polaromonas related SAGs with CuMMO- encoding operon support the assumptions that Polaromonas are may be involved in propane metabolism.

There is a possibility that xmoCAB operons detected in SAGs related to genus

Polaromonas or Rhodoferax were responsible for initial step of propane oxidation. Therefore we analyzed genes from SAGs and predicted possible propane oxidation pathways. Propane can be oxidized in different pathways: terminal oxidation via 1-propanol, propionaldehyde and propanoic acid (Woods and Murrell, 1989; Shennan, 2006) or subterminal oxidation to 2- propanol producing acetone ,or acetate (Coleman and Perry, 1984; Woods and Murrell, 1989;

Clark and Ensign, 1999; Shennan, 2006). The first stage in aerobic propane metabolism is the oxidation of alkane to alcohol. Several enzymes are known to catalyze for example soluble diiron butane monooxygenase in Thauera butanivorans (Dubbels et al., 2007), di-iron propane monooxygenases in Gordonia sp. strain TY-5(Kotani et al., 2003), and Mycobacterium sp. strain

TY-6 (Kotani et al., 2006). Two distinct monooxygenases were found in Nocardioides sp strain

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CF8: a CuMMO butane monooxygenase responsible for oxidizing butane and an iron alkane monooxygenase for oxidizing C6 or higher alkane (Hamamura et al., 2001). None of the propane monooxygenases related to CuMMO have been reported in detail yet.

A detailed predicted propane oxidation pathway based on genes present in SAGs related to Polaromonas and Rhodoferax is presented in Figure 5.8. SAGs related to Polaromonas (SAGs

BML02L21, BML02L09, BML02D16 and BML02E16) and Rhodoferax (BML02C18, and

BML02F20) were combined respectively for analysis. The first stage in aerobic propane metabolism is the oxidation of propane to 1-propanol or 2-propanol by enzyme propane monooxygenase (Kotani et al., 2006). Predicted 1- propanol related pathway is described in

Appendix D, Figure 5. The propanol dehydrogenase oxidizes 1-propanol to propionaldehyde and further oxidized into propionyl-CoA by successive steps. Propionyl-CoA can take two pathways methylcitrate or citramalate pathway and converted into succinyl-CoA (Suvorova et al., 2012).

Polaromonas SAGs were only identified with 2-propanol degradation pathway (Figure 5.8 II).

This is further oxidized to methyl acetate. Methyl acetate is hydrolyzed to acetate and methanol by esterase (acmB gene) in Gordonia sp. strain TY-5, however an acmB gene was not detected in

SAGs. Acetate is then targeted by acetyl CoA synthase and acetyl-CoA enters to the glyoxylate cycle. The key genes for the glyoxylate cycle, isocitrate lyase and malate synthase for converting isocitrate to glyoxylate to malate are present. The subsequent oxidation of malate by malate dehydrogenase regenerates oxaloacetate. The succinate produced from isocitrate can be used to generate malate or can be used in cell carbon biosynthesis.

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Figure 5.8: Predicted propane oxidation pathway based on enzyme identified in SAGs.

Genome prediction of propane degradation pathway was the main objective however

SAGs were screened for genes associated with other possible hydrocarbon degradation pathways as well. Tailings water are rich in aromatic hydrocarbons hence the potential aromatic hydrocarbon degradation pathways were also analyzed for genes encoding aromatic hydrocarbon degradation (benzene and toluene). Two enzyme systems (dioxygenase and monooxygenase enzymes) are identified for aerobic degradation of benzene and toluene (Jindrová et al., 2002).

Recently aerobic aromatic hydrocarbon degradation pathways have been predicted from the metagenome sequence of tailings water (Rochman et al., 2017). The predicted benzene and toluene degradation pathways are presented in Figure 5.9. Benzene is converted into phenol by phenol 2 monooxygenase. Phenol is converted to catechol, it is then either converted to formate or 2-hydroxymuconate. In a series of reactions 2-hydroxymuconate produces acetyl-CoA and further connects to glyoxylate cycle as described in previously. During toluene degradation, 138

toluene is converted to benzyl alcohol by phenol 2 monooxygenase. Benzyl alcohol is converted to catechol and uses similar pathways described above in benzene metabolic pathway.

Figure 5.9: Predicted aromatic compounds degradation pathways based on genes detected on genomes. A. Benzene metabolic pathway, B. Toluene metabolic pathway. Enzymes are in blue colour. Copper containing membrane monooxygenase-encoding operons that are distantly related to known copper monooxygenase-encoding operons of alpha- and gammaproteobacterial 139

methanotrophs have been found recently. For example butane monooxygenase-encoding genes have been found in actinobacterial genomes, and distantly related copper monooxygenase- encoding genes have been detected in ethylene assimilating Haliea spp., and CuMMO-encoding operons have been found in genomes of Solimonas aquatica and Bradyrhizobium sp ERR11.

Detection of CuMMO-encoding gene sequences in genomes other than known methanotrophs and nitrifiers in the families Methylococcaceae, Methylocystaceae, Beijerinckiaceae and

Nitrosomonadaceae is expanding the diversity of the CuMMO-encoding gene family and may suggest specificity to substrates other than methane and ammonia.

The majority of cultivated methanotrophs are from the Proteobacteria. Cultivation independent techniques like single cell sorting and sequencing of genomes have made possible to detect novel CuMMO sequences found in genomes. Project like Genomic Encyclopedia of

Bacteria and Archaea (GEBA), and Microbial dark matter (MDM) led by JGI have made sequence available from the genomes of known phyla as well as candidate phyla (Rinke et al.,

2013; Whitman et al., 2015). Here we explore the CuMMOs sequence diversity found in metagenomes developed from hydrocarbon resources environments.

5.4 Conclusions

Seven PCR primer pairs were developed in this study to detect novel CuMMO-encoding genes in different oilsands environments. SAGs obtained from propane-BML(A) enrichment culture contains different CuMMO xmoCAB operons in the genomes of BML02l09, BML02D16,

BML02C18, BML02F20, BML02G21 related to either Polaromonas or Rhodoferax. Multiple xmoCAB operons were identified with both Polaromonas and Rhodoferax related SAGs. The

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identified novel xmoCAB operons have expanded the known diversity of CuMMOs. xmoCAB sequences were detected in SAGs belonging to the betaproteobacterial genera Polaromonas and

Rhodoferax have expanded the metabolic potential in this taxonomic groups. This is a novel discovery which has not been reported before. Based on gene sequences identified on SAGs, these organisms possessing xmoCAB operons have a potential for propane, benzene and toluene degradation in tailings pond water. However we are not yet sure of the function of the CuMMOs.

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Chapter Six: General conclusions and future research

This dissertation applied phylogenetic and compositional analysis, 16S rRNA gene

Illumina sequencing, qPCR assays, and single cell genome approaches to investigate diversity and evolutionary history of copper membrane monooxygenases, and methanotrophic community dynamics in biofilter systems. The objectives that directed this research work were: 1)

Understand the evolutionary history of copper membrane monooxygenase, 2) Characterize novel copper membrane monooxygenase-encoding genes detected in metagenomes, and 3) Develop a monitoring protocol for a methane biofilter system.

6.1 Research summary

The description of over 100 new isolates of methanotrophs in 1970 by Whittenbury,

Wilkinson and their colleagues (Whittenbury et al., 1970a; Whittenbury et al., 1970b) shifted our understanding on diversity of methane oxidizing bacteria into a new era. The first description of methane monooxygenase consisting of three subunits in Methylococcus capsulatus (Colby and

Dalton, 1978) provided a path to explore the diversity of methane monooxygenases present in different taxa. The study outlined in Chapter 3 applied large scale phylogenetic and compositional analysis of copper membrane monooxygenases present in 66 genomes belonging to the phyla Proteobacteria, Verrucomicrobia, Candidate division NC10, Actinobacteria and

Thaumarchaeota. The aim was to include only cultured microorganisms for which physiological data and complete genomes are available. To analyze this, a copper membrane monooxygenase database was generated and a phylogenetic history of CuMMO-encoding xmoCAB operons was

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generated applying different phylogenetic inference methods. Compositional analysis of

CuMMO-encoding operons versus genomes was performed to detect evidence of significant compositional bias between the xmoCAB operon and host genome. Based on the phylogenies and compositional analyses findings, possible gene transfer events were detected. Compositional analysis showed evidence of multiple lateral gene transfers and phylogenies demonstrated that these were limited to about 10 events. From phylogenies and compositional analysis possible routes of lateral gene transfer into different taxa and lineages were postulated. The ancestor of gammaproteobacterial Nitrosococcus now containing amoCAB was a methanotroph and this lineage transferred its pmoCAB to proteobacterial methanotrophs in at least two events. The

“Methylacidiphilum” group possesses two similar and one divergent pmoCAB operon. The phylogeny of the pmoB differs from pmoC and pmoA. We propose that the ancestor of

“Methylacidiphilum” also obtained pmoCAB as well as pmoC and pmoA genes from an ancestral methanotroph but replaced pmoB with an xmoB gene from another unknown source. In the gammaproteobacteria methanotrophs, the pxmABC operon appears to be the first one they possessed, they later obtained the pmoCAB operon via LGT from the ancestor of Nitrosococcus.

This study demonstrated the evolutionary history of copper membrane monooxygenases.

Biofiltration has been a recognized approach for controlling atmospheric emission of volatile organic compounds and odors. Methane biofilter is a promising technology for reducing greenhouse gases. In Chapter 4 a monitoring protocol of a compost-based methane biofilter was proposed. We hypothesized that population levels of methanotrophs would be correlated with methanotrophic activity. However, this was not so because most of the methanotrophs form spores or cysts and are very recalcitrant. Different laboratory microcosms were tested via growth

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and starvation experiments for long periods using compost as a biofilter material. Next- generation 16S rRNA gene Illumina sequencing and qPCR of pmoA genes were used to investigate methanotroph populations over time. Quantitative PCR analysis showed high levels of total methanotrophs during the initial stages of methane consumption period however gene copy levels did not change rapidly when methane was removed from the system. Our results based on 16S rRNA gene Illumina sequencing suggested that some non-methanotrophic bacteria showed a more rapid response to biofilter methane oxidation activity and might be considered as better monitoring candidates. The methylotrophic co-feeders were bacteria associated with

Methylophilaceae and grew rapidly with the methanotrophs in our microcosms, and then died rapidly as soon as methane was removed from the system. Within the Methylophilaceae, the genus Methylotenera, uncultured genera in the Methylophilaceae-OM3B group, uncultured genera in the Methylophilaceae bacterium-1 group and the genus Methylophilus showed rapid response to methane starved conditions. Therefore, a monitoring system based on these

“methanotroph-associated methylotrophs” is proposed. In active biofilters the ratio

(Methylophilaceae/Methylococcaceae) was maximal at around 0.35 and the ratio dropped below

0.11 within 1 week of methane starvation This study demonstrated that methylotrophic co- feeders followed a “boom and bust” existence where they grow and die rapidly in response to the availability of methane. If the ratio of Methylophilaceae/Methylococcaceae drops below 0.1, a biofilter should be examined more, via flux measurement.

In recent years, the availability of cultured isolates and genomes from culture independent techniques have expanded. The study in Chapter 5 discusses the diversity of copper membrane monooxygenases detected in various oilsands metagenomes and their

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characterization. Quantitative PCR assays were designed for the novel xmoCAB operon containing microorganisms. Enrichment cultures were grown in methane, ethane and propane as energy sources in water samples from oilsands tailings ponds. High-throughput 16S rRNA gene

Illumina sequencing was performed to understand bacterial communities in enriched samples.

Quantitative PCR assays designed for seven different lineages of xmoCAB detected the presence of the genes in ethane and propane amended systems. One of these seven assays, TP2, detected high levels of xmoCAB genes in a propane enrichment. A total of 15 genomes from single sorted cells from a propane enrichment were sequenced by JGI. Single cell genomes of sorted cells identified two bacteria belonged to the genus Polaromonas and Rhodoferax possessing multiple xmoCAB operon. Phylogenetic analysis showed detected xmoCAB were distantly related to know copper monooxygenases operons found in genomes of known bacteria. Some potential propane oxidation pathways were proposed based on enzymes detected in single cell genomes. This study showed the detection and characterization of novel CuMMO xmoCAB operons and proposed potential propane oxidation pathways. The presence of enzymes provides evidence for propane oxidation however cultured isolates are required to prove its physiological and biochemical properties.

6.2 Directions for future research

Some possibilities for the future research are:

Methanol, formaldehyde and hydroxylamine detoxication pathways: The evolution of a methanotrophic and ammonia oxidizing phenotype is more complicated than the importance of methane monooxygenase enzyme in the first stage of methane oxidation. In Chapter 3 we have

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analyzed evolution, transfer, duplication and adaptation of methane monooxygenase enzyme, however downstream metabolic intermediates such as methanol, formaldehyde, and hydroxylamine are critical as well. For example aerobic nitrifiers catabolize ammonia as their sole energy source using ammonia monooxygenase, hydroxylamine oxidoreductase, and cytochrome c to relay the electrons to the quinone pool (Klotz and Stein, 2011; Simon and Klotz,

2013). In-depth study of existence of genes that encodes detoxification and the electron flow pipeline available in methanotrophs and ammonia oxidizers will provide more information in their evolutionary history.

In Chapter 4, we propose a monitoring system based on “methanotroph-associated methylotrophs”, where methylotroph co-feeders of the Methylophilaceae family are associated with a “boom and bust” existence. This protocol will provide methane oxidation activity and provides high efficiency over the conventional surface gas flux rate measurement practice.

However there are certain issues that needs to address. There will be certain challenges in compost biofilter such as stability of compost, top surface layer drying, compaction, EPS formation, and gas channeling. For example due to compaction and clogging methanotrophs becomes active in a certain depth. During such condition samples analyzed from biofilter other than methanotroph active layer will not provide actual methane oxidation activity. In order to get actual methane oxidation activity and performance of biofilter sampling protocol to get heterogenous sample is essential. However, in many cases taking samples from different layers of biofilter will not be practical (large size biofilter, biofilter installed in remote location, dismantle and reassemble). Methanotrophic community will be different based on environmental factors like temperature and pH variation, and nutrient availability. To address these issues

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testing of this monitoring protocol in mesocosms and field based biofilters with different packing material is necessary to see if this bottle-based compost biofilter protocol is applicable. It is also possible to test active methanotrophs community by using 13C-methane SIP experiments. A combined 13C-methane, 13C-methanol, real-time quantitative PCR (qRT-PCR) targeting methanotrophs and methylotrophs, transcriptomics and gas chromatography approach will show the cross-feeding and co-occurrence patterns between methanotrophs and methylotrophs, as done previously (Krause et al., 2017). This will help to identify active community associated with methane. This study was based on microcosms and 16S rRNA gene relative abundance.

Quantitative PCR targeting methylotrophs can be an option. Methanotroph activity can be measured by pmoA transcript levels, targeting rRNA using DNA probe instead of 16S rRNA gene, or in situ quantification and visualization by hybridization assays such as by florescence in situ hybridization (FISH).

In Chapter 5, we have identified several xmoCAB operons and single cell genomes are available. A metagenome binning will be helpful for verification of SAGs and their completeness. Integration of metagenome and single cell assemblies provide better assembly and improve the completeness of genomes (Mende et al., 2016). RNA sequencing (RNA-seq) in enrichment cultures could be an alternative to single cell genome sequencing that will provide understanding of gene expression. For example, xmoCAB genes, and genes that are found in benzene and toluene degradation, detected on SAGs does not provide the information about their expression levels. Cultivation of propane degrading organisms (Rhodoferax and Polaromonas) possessing xmoCAB would provide more information and confirm its metabolic potential.

Various growth media such as nitrate mineral salts, ammonium mineral salts, mineral salts

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medium M10, can be used. Sterile tailings pond water can be used instead of water for preparing growth medium to reproduce the natural condition of tailings water. CuMMOs are promiscuous and have ability to catalyze the oxidation of different substrates. Other substrates, ethane, ethylene, propylene, n-propanol or iso-propanol. can be used for enrichment.

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APPENDIX A: SUPPLEMENTARY INFORMATION – PHYLOGENETIC HISTORY OF COPPER MEMBRANE MONOOXYGENASES

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Figure A-8: Phylogenetic tree of inferred XmoB sequences constructed using Neighbor- joining with a Poisson model. The tree was constructed using Seaview 4.4.12 (Gouy et al., 2010). Node values are based on 100 bootstrap replicates. The scale bar represents 0.2 changes per amino acid position. Colours indicate coherent functional and taxonomic groups.

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Organism Genome pmoCAB GC (%) Kullback Leibler (KL) divergence Island GC (%) GC (%) Deviation Viewer Alien Hunter CodonW TETRA Betaproteobacteria-nitrifiers Nitrosomonas europaea ATCC 19718 50.7 48 -2.7 0.430 0.135 0.072 - Nitrosomonas eutropha C91 operon1 48.5 47 -1.5 0.281 0.146 0.073 - Nitrosomonas eutropha C91 operon2 48.5 46.9 -1.6 0.341 0.149 0.074 - Nitrosomonas sp. AL212 operon1 48.8 47.6 2.8 0.246 0.212 0.071 - Nitrosomonas sp. AL212 operon2 48.8 47.7 2.9 0.250 0.214 0.071 - Nitrosomonas sp. AL212 operon3 48.8 47.7 2.9 0.269 0.214 0.071 LGT Nitrosomonas sp. Is79A3 operon1 45.5 47.4 2.0 0.279 0.202 0.080 - Nitrosomonas sp. Is79A3 operon2 45.5 47.3 1.9 0.181 0.198 0.080 - Nitrosomonas sp. Is79A3 operon3 45.5 47.4 2.0 0.209 0.201 0.080 LGT Nitrosospira multiformis ATCC 25196 operon1 53.9 58.8 4.9 0.169 0.211 0.084 - Nitrosospira multiformis ATCC 25196 operon2 53.9 58.8 4.9 0.235 0.211 0.085 - Nitrosospira multiformis ATCC 25196 operon3 53.9 58.8 4.9 0.285 0.212 0.084 - Mean 2.0 0.265 0.192 0.077 Gammaproteobacteria-pxm Methylobacter luteus IMV-B-3098 operon1 51.1 43.5 -7.6 BT 0.087 0.035 - Methylobacter marinus A45 operon2 52.6 51.9 -0.7 BT 0.084 0.038 - Methylomonas sp. 11B operon1 51.4 45.3 -6.1 BT 0.070 0.032 -

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Methylomicrobium album BG8 operon1 56.3 48.3 -8.0 BT 0.050 0.035 - Mean -5.6 BT 0.073 0.035 - Alphaproteobacteria-pxm Methylocystis rosea SV97 operon3 62.5 56.9 -5.6 0.184 0.077 0.072 - Verrucomicrobia Methyacidiphilum infernorum V4 operon1 45.5 48.9 3.4 BT 0.078 0.064 LGT Methyacidiphilum infernorum V4 operon2 45.5 48.1 2.6 BT 0.081 0.043 LGT Methyacidiphilum infernorum V4 operon3 45.5 37.9 -7.6 0.467 0.228 0.130 - Verrucomicrobia 3C 60.6 59.8 -0.7 0.206 0.111 0.059 - Verrucomicrobium LP2A operon1 62.7 60.1 -2.6 0.274 0.073 0.063 - Verrucomicrobium LP2A operon2 62.7 60.3 -2.4 0.274 0.073 0.059 - Mean -1.2 0.107 0.070 NC10 Candidatus Methylomirabilis oxyfera 58.6 56.3 -2.3 BT 0.067 0.046 - Beijerinckaceae Methylocapsa acidiphila B2 61.9 60 -1.9 0.273 0.131 0.064 - Methylocystaceae Methylosinus trichosporium OB3b operon1 66 63 -1.7 0.191 0.152 0.085 - Methylosinus trichosporium OB3b operon2 66 63 -1.7 0.163 0.151 0.084 - Methylocystis sp. Rockwell operon1 62.8 61.3 -1.5 0.192 0.202 0.076 - Methylocystis sp. Rockwell operon2 62.8 61.3 -1.5 0.214 0.133 0.075 - Methylocystis sp. SC2 operon1 63.4 61.3 -2.1 0.269 0.101 0.052 - Methylocystis sp. SC2 operon2 63.4 61.7 -1.7 0.189 0.226 0.065 - Methylosinus sp. LW3 operon1 64.7 62.4 -2.3 0.187 0.065 0.050 - Methylosinus sp. LW3 operon2 64.7 63 -1.7 0.148 0.184 0.109 - Methylosinus sp. LW3 operon3 64.7 63 -1.7 0.151 0.184 0.109 - Methylosinus sp. LW4 operon1 64.9 63.1 -1.8 0.199 0.174 0.113 - Methylosinus sp. LW4 operon2 64.9 62.5 -2.4 0.228 0.067 0.052 - Methylosinus sp. LW4 operon3 64.9 63.1 -1.8 0.218 0.174 0.112 - Methylocystis rosea SV97 operon1 62.5 61.2 -1.3 BT 0.219 0.067 - Methylocystis rosea SV97 operon2 62.5 61.2 -1.3 BT 0.219 0.068 - Mean -1.8 0.161 0.080 Gammaproteobacteria-nitrifiers Nitrosococcus oceani C-107 50.3 50.4 0.1 0.198 0.088 0.035 - Nitrosococcus watsoni C-113 50.1 50.5 0.4 0.196 0.094 0.034 - Nitrosococcus halophilus Nc4 51.6 52.4 0.8 BT 0.072 0.055 - 195

Nitrosococcus AFC27 51 50.1 -0.9 BT 0.087 0.048 - Nitrosococcus AFC132 51 49.8 -1.2 BT 0.085 0.042 - Nitrosococcus C27 51 49.4 -1.6 BT 0.085 0.042 - Mean -0.4 0.085 0.043 Gammaproteobacteria Methylococcus capsulatus Bath operon1 63.6 60.7 -2.9 0.194 0.190 0.065 - Methylococcus capsulatus Bath operon2 63.6 60.8 -2.8 0.157 0.196 0.065 - Methylocaldum szegediense O12 57.2 56 -1.2 0.217 0.307 0.072 - Methylococcus capsulatus ATCC 19069 63.51 58.9 -4.6 BT 0.323 0.051 - Methylohalobius crimeensis operon1 58.28 58.1 -0.1 0.145 0.215 0.094 - Methylohalobius crimeensis operon2 58.28 58.1 -0.2 BT 0.215 0.094 - Methylomonas methanica MC09 51.3 44.9 -6.4 0.364 0.562 0.182 - Methylomonas sp. 11B operon2 51.4 45.3 -6.1 0.292 0.525 0.138 LGT Methylosarcina fibrata AML-C10 operon1 54.1 46.9 -7.2 0.318 0.486 0.172 - Methylomicrobium album BG8 operon2 56.3 48.3 -8.0 0.273 0.583 0.203 - Methylosarcina lacus LW14 operon1 54.7 46.8 -7.9 0.283 0.244 0.201 - Methylomicrobium buryatense 5G 48.7 44.4 -4.7 0.462 0.405 0.145 - Methylomicrobium alcaliphilum 48.7 44.1 -4.6 0.414 0.474 0.162 - Methylovulum miyakonense HT12 operon1 50.7 42.1 -8.6 0.296 0.287 0.190 - Methylobacter luteus IMV-B-3098 operon2 51.1 43.5 -7.7 0.324 0.565 0.175 LGT Methylobacter marinus A45 operon1 52.6 43.3 -9.3 0.346 0.638 0.198 - Mean -5.1 0.388 0.137 Thaumarcheota Cenarchaeum symbiosum A 57.4 52.3 -5.1 BT 0.135 0.108 - Candidatus Nitrosoarchaeum limnia SFB1 32.5 38.4 5.9 BT 0.154 0.092 - Candidatus Nitrosoarchaeum koreensis MY1 32.7 38.6 6.0 0.207 0.173 0.094 - Candidatus Nitrosopumilus sp. BD31 32.1 33.5 1.4 BT 0.164 0.040 - Mean 2.3 0.157 0.088 Actinobacteria butane oxidiser Nocardioides sp. CF8 70.03 63.93 -6.1 0.297 0.104 0.093 LGT Mycobacterium chubuense NBB4 68.29 60.90 -7.3 0.290 0.172 0.104 - Mycobacterium rhodesiae NBB3 65.49 64.41 -1.0 0.163 0.057 0.045 LGT Smaragdicoccus niigatensis DSM 44881 64.34 62.93 -1.4 0.214 0.066 0.043 - Nocardioidaceae bacterium Broad-1 69.55 65.66 -3.9 0.330 0.061 0.056 - Mean -3.9 0.259 0.092 0.068

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Table A-2: Commonly used primers for detection of methanotrophs and their specificity to respective methane oxidisers and nitrifiers. Number represents the total miss match of the primer sequence to the organisms DNA sequences. Higher number represents lower specificity. Organism pmoA primers* A189F A682r Mb661 650r Betaproteobacteria-nitrifiers Nitrosomonas europaea ATCC 1 2 # # 19718 Nitrosomonas eutropha C91 1 4 # # Nitrosomonas sp. AL212 1 3 # # Nitrosomonas sp. Is79A3 2 # # # Nitrosospira multiformis ATCC 0 2 # # 25196 Gammaproteobacteria-nitrifiers Nitrosococcus oceani C-107 1 5 3 2 Nitrosococcus watsoni C-113 1 # 3 3 Nitrosococcus halophilus Nc4 1 3 3 4 Nitrosococcus AFC27 1 5 3 2 Nitrosococcus AFC132 3 # # 4 Nitrosococcus C27 3 # # 4 Gammaproteobacteria-pxm Methylobacter luteus IMV-B-3098 # # # # Methylobacter marinus A45 # # # # Methylomonas sp. 11B # # # # Methylomicrobium album BG8 # # # # Alphaproteobacteria-pxm Methylocystis rosea SV97 # # # # Beijerinckaceae 197

Methylocapsa acidiphila B2 1 2 4 # Methylocystaceae Methylosinus trichosporium OB3b 0 2 2 0 Methylocystis sp. SC2 0 2 1 0 Methylosinus sp. LW3 0 2 3 0 Methylosinus sp. LW4 0 2 3 0 Methylocystis rosea SV97 0 2 1 0 Gammaproteobacteria Methylococcus capsulatus Bath 0 2 1 0 Methylocaldum szegediense O12 0 1 1 1 Methylohalobius crimeensis 0 2 1 1 Methylococcus capsulatus Texas 0 2 1 0 Methylomonas methanica MC09 0 3 1 1 Methylomonas sp. 11B 2 1 3 1 2 Methylosarcina fibrata AML-C10 1 3 1 2 Methylomicrobium album BG8 0 4 1 1 Methylosarcina lacus LW14 0 3 1 1 Methylomicrobium buryatense 5G 1 3 1 3 Methylomicrobium alcaliphilum 1 3 1 3 Methylovulum miyakonense HT12 1 3 1 2 Methylobacter luteus IMV-B-3098 1 3 1 2 Methylobacter marinus A45 1 3 1 2 Verrucomicrobia Methyacidiphilum infernorum V4 5 4 # # Verrucomicrobia 3C 3 1 # # Verrucomicrobium LP2A 3 1 # # Methylacidiphilum fumarolicum 2 5 # # SolV 198

NC 10 Candidatus Methylomirabilis 0 # # # oxyfera * Primer (5'- 3') (reference) #: 5 or more miss matches A189f: GGNGACTGGGACTTCTGG Mb661: CCGGMGCAACGTCYTTACC (Holmes et al., 1995) (Costello and Lidstrom, 1999) A682r: GAASGCNGAGAAGAASGC A650r: ACGTCCTTACCGAAGGT (Holmes et al., 1995) (Bourne et al., 2001)

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Table A-3: Specificity of group specific primers to target methanotrophs and nitrifiers in previous studies. Number represent specificity to organisms. High number represents low specificity.

Group specific primers

659R

2IR

244F/ComaA

244F/ComaB

1/amaA

346f

amoA ComaA F326 r643 Forest675R Usca ComaB II223F Mcap630 II646R Mc468R Mb601R V170f/V613B cmo682/cmo568 cmo568 cmo182 HD616 Betaproteobacteria- nitrifiers Nitrosomonas 0/1 3/# # # # # 4/# # # # # # 2/# # # # 1 europaea ATCC 19718 Nitrosomonas eutropha 1/0 3/# # # # # 4/# # # # # # 2/# # # # 1 C91 Nitrosomonas sp. 2/2 4/4 # # # # # # # # # # 3/# # # # 0 AL212 Nitrosomonas sp. 1/2 4/3 # # # # # # # # # # 3/# # # # 1 Is79A3 Nitrosospira 0/0 3/4 # # # # # # # # # # 4/# # # # 1 multiformis ATCC 25196 Gammaproteobacteria- nitrifiers Nitrosococcus oceani # # # 2 # # # # # # # # 3/# # # # 0 C-107 Nitrosococcus watsoni # # # 3 # # # # # # # # 2/# # # # 0 C-113

200

Nitrosococcus # # # 3 # # # 4 3 # # # 3/# 3 # # 0 halophilus Nc4 Nitrosococcus AFC27 # # # 2 # # # # # # # # 3/# # # # 0 Nitrosococcus AFC132 # # # 4 # # # # # # # # 2/# # # # 0 Nitrosococcus C27 # # # 4 # # # # # # # # 2/# # # # 0 Gammaproteobacteria- pxm Methylobacter luteus # # # # # # # # 3 3 # # 2/# # # # 1 IMV-B-3098 Methylobacter marinus # # # # # # # # 2 3 # # 2/# # # # 1 A45 Methylomonas sp. 11B # # # # # # # # 4 4 # # 4/# # # # 1 Methylomicrobium # # # # # # # # 4 2 # # # # # # 1 album BG8 Alphaproteobacteria- pxm Methylocystis rosea # # # # # # # # 2 4 # # # # # # 1 SV97 Beijerinckaceae Methylocapsa # # # 3 3 2 # 4 0 1 # # 2/# # # # 1 acidiphila B2 Methylocystaceae Methylosinus # # 1 2 # # # 0 2 0 # # 4/# # # # 0 trichosporium OB3b Methylocystis sp. SC2 # # 0 3 # # # 0 2 0 # # 3/# # # # 0 Methylosinus sp. LW3 # # 0 2 # # # 0 2 0 4 # # # # # 0 Methylosinus sp. LW4 # # 0 2 # # # 0 3 0 # # 4/# # # # 0 Methylocystis rosea # # 0 3 # # # 0 2 0 # # 3/# # # # 0 SV97 Gammaproteobacteria Methylococcus # # 0 1 # 2 # # # # 0 3 # # # 3 0 capsulatus Bath

201

Methylocaldum # # # 2 # 3 # 3 # # 1 2 # # # 4 0 szegediense O12 Methylohalobius # # # 2 # 2 # 3 # # 2 3 # 4 # # 0 crimeensis Methylococcus # # 0 1 # 2 # # # # 0 3 # # # 3 0 capsulatus Texas Methylomonas # # # 3 # 3 # # # # # 1 3/# # # # 0 methanica MC09 Methylomonas sp. 11B # # # 3 # 4 # # # # # 0 3/# # # # 0 Methylosarcina fibrata # # # 3 # 4 # 4 # # # 0 3/# # # # 0 AML-C10 Methylomicrobium # # # 2 # 3 # 4 # # # 0 3/# # # # 0 album BG8 Methylosarcina lacus # # # 3 # 3 # # # # # 0 3/# # # # 1 LW14 Methylomicrobium # # # 3 # 3 # # # # # 2 2/# # # # 0 buryatense 5G Methylomicrobium # # # 3 # 3 # # # # # 2 2/# # # # 0 alcaliphilum Methylovulum # # # 3 # 3 # # # # # 0 2/# # # # 1 miyakonense HT12 Methylobacter luteus # # # 3 # 3 # # # # # 0 2/# # # # 1 IMV-B-3098 Methylobacter marinus # # # 3 # 3 # # # # # 0 2/# # # # 1 A45 Verrucomicrobia Methyacidiphilum # # # # # # # # # # # # 0/0 # # # 0 infernorum V4 Verrucomicrobia 3C # # # # # # # # 3 # # # 2/2 # # # 1 Verrucomicrobium # # # # # # # # 2 # # # 3/2 # # # 1 LP2A Methylacidiphilum # # # # # # # # # # # # 1/0 # # # 1 fumarolicum SolV NC 10

202

Candidatus # # # # # # # # 4 # 3 # 4/# 0/0 0 0 2 Methylomirabilis oxyfera Primer (5'- 3') (reference) AmoA-1: GGGGTTTCTACTGGTGGT F326: TGGGGYTGGACCTAYTTCC V170f: GGATWGATTGGAAAGATMG (Rotthauwe et al., 1997) (Fjellbirkeland et al., 2001) (Sharp et al., 2012) AmoA-2IR: CCCCTCIGIAAAGCCTTCTTC r643: CCGGCRCRACGTCCTTACC V613B: GCAAARCTYCTCATYGTWCC (Avrahami et al., 2003) (Fjellbirkeland et al., 2001) (Sharp et al., 2012) ComaA-244F: TAYAAYTGGGTSAAYTA II223F: CGTCGTATGTGGCCGAC cmo682: TCGTTCTTYGCCGGRTTT (Pjevac et al., 2017) (Kolb et al., 2003) (Luesken et al., 2011) ComaA-659R: ARATCATSGTGCTRTG II646R: CGTGCCGCGCTCGACCATGYG cmo568: GATGGGGATGGAGTATGTGC (Pjevac et al., 2017) (Kolb et al., 2003) (Luesken et al., 2011) ComaB-244F: TAYTTCTGGACRTTYTA Mcap630: CTCGACGATGCGGAGATATT cmo182: TCACGTTGACGCCGATCC (Pjevac et al., 2017) (Kolb et al., 2003) (Luesken et al., 2011) ComaB-659R: ARATCCARACDGTGTG Mc468R: GCSGTGAACAGGTAGCTGCC Usca-346f: TGGGYGATCCTNGCNC (Pjevac et al., 2017) (Kolb et al., 2003) (Degelmann et al., 2010) Forest675R: CCYACSACATCCTTACCGAA Mb601R: ACRTAGTGGTAACCTTGYAA HD616: AYCWKVCKNAYRTAYTCVGG (Wang et (Kolb et al., 2003) (Kolb et al., 2003) al., 2017)

203

Genome Genome Accession no. PmoC PmoA PmoB

Methylacidiphilum sp. RTK17.1 LN998017 NC58166.1 ANC58167.1 ANC58168.1

operon1

Methylacidiphilum sp. RTK17.1 ANC58166.1 ANC58170.1 ANC58171.1

operon2

Methylacidiphilum sp. RTK17.1 ANC58172.1 ANC58118.1 ANC58119.1

operon3

Methylacidiphilum infernorum V4 NC_010794 Minf_1508 Minf_1507 Minf_1506

operon1

Methylacidiphilum infernorum V4 Minf_1511 Minf_1510 Minf_1509

operon2

Methylacidiphilum infernorum V4 Minf_1591 Minf_1590 Minf_1589

operon3

Methylacidiphilum fumariolicum NZ_LM997411 ABU88150 ABU88148 ABU88149

SolV operon1

Methylacidiphilum fumariolicum ABU88151 ABU88152 ABU88153

SolV operon2

Methylacidiphilum fumariolicum ABU88154 ABU88155 ABU88156

204

SolV operon3

Methylacidiphilum kamchatkense NZ_JQNX00000000 AFC75740 AFC75741 AFC75742

Kam1 operon1

Methylacidiphilum kamchatkense AFC75743 AFC75744 AFC75745

Kam1 operon2

Methylacidiphilum kamchatkense AFC75746 ACK55193 AFC75748

Kam1 operon3

Verrucomicrobium sp. 3C NZ_KB901882 A37ADRAFT_0437 A37ADRAFT_0436 A37ADRAFT_0435

Verrucomicrobium sp. LP2A NZ_JAFS01000000 G346DRAFT_1245 G346DRAFT_1244 G346DRAFT_1243 operon1

Verrucomicrobium sp. LP2A G346DRAFT_1248 G346DRAFT_1247 G346DRAFT_1246 operon2

Bradyrhizobium sp. ERR11 NZ_FMAI01000000 Ga0061098_101192 Ga0061098_101193 Ga0061098_101194

Solimonas aquatica NZ_FOFS00000000 Ga0070002_104143 Ga0070002_104144 Ga0070002_104145

Cycloclasticus sp. SCGC AC281- Ga0055579_00185 Ga0055579_00184 Ga0055579_00183 Gs0032475 P21

Burkholderiales bacterium MERU00000000 Ga0156155_1046 Ga0156155_1046 Ga0156155_1046

Skermanella aerolata KACC 11604 NZ_AVFK01000000 Ga0069205_1047 Ga0069205_1047 Ga0069205_1047

Methylocapsa aurea KN050804 WP_084572853.1 WP_084572913.1 WP_051953405.1

205

Methylocapsa palsarum NE2 NZ_FOSN01000000 Ga0116912_107 Ga0116912_107 Ga0116912_107

Methylocapsa acidiphila B2 NZ_ATYA00000000 MetacDRAFT_3746 MetacDRAFT_3747 MetacDRAFT_3748

USCalpha - CAJ01564.1 CAJ01563.1 CAJ01562.1

Methylomonas sp. FJG1 operon1 NZ_CP014476 JTDD01000035 JTDD01000035 JTDD01000035

Methylomonas sp. FJG1 operon2 JTDD01000139 JTDD01000139 JTDD01000139

Methylomonas sp. LW13 operon1 NZ_JNLB00000000 U737DRAFT_scaffold00008.8 U737DRAFT_scaffold00022.22 U737DRAFT_scaffold00022.22

Methylomonas sp. LW13 operon2 U737DRAFT_scaffold00008.8 U737DRAFT_scaffold00008.8 U737DRAFT_scaffold00008.8

Methylomonas sp. MK1 operon1 AQOV01000000 G006DRAFT_scaffold00001.1 G006DRAFT_scaffold00001.1 G006DRAFT_scaffold00001.1

Methylomonas sp. MK1 operon2 G006DRAFT_scaffold00001.1 G006DRAFT_scaffold00001.1 G006DRAFT_scaffold00001.1

Methylomonas methanica MC09 CP002738 AEF98752 AEF98753 AEF98754

Methylomonas sp. 11b operon1 NZ_KI911557 Meth11bDRAFT_3088 Meth11bDRAFT_3086 Meth11bDRAFT_3087

Methylomonas sp. 11b operon2 Meth11bDRAFT_3297 Meth11bDRAFT_3298 Meth11bDRAFT_3299

Methylomonas koyamae JCM 16701 NZ_BBCK00000000 Ga0128345_10381 Ga0128345_10382 Ga0128345_10383 operon1

Methylomonas koyamae JCM 16701 Ga0128345_10542 Ga0128345_10544 Ga0128345_10543 operon2

Methyloprofundus sedimenti NZ_LPUF01000000 WP_080524231.1 WP_080524391.1 WP_080524230.1 operon1

Methyloprofundus sedimenti OQK15279.1 OQK15452.1 OQK15278.1 operon2

206

Methylogaea oryzae JCM 16910 NZ_BBDL01000000 Ga0128369_1626 Ga0128369_1475 Ga0128369_1475

Methylobacter tundripaludum SV96 JH109152 EGW22255 EGW22253 EGW22254 operon1

Methylobacter tundripaludum SV96 EGW23567 EGW23568 EGW23569 operon2

Methylobacter luteus IMV-B-3098 KE386569 WP_027157618 WP_027157616 WP_027157617 operon1

Methylobacter luteus IMV-B-3098 WP_027159169 WP_027159170 WP_027159171 operon2

Methylobacter marinus A45 NZ_ARVS01000000 WP_020158145 WP_020158144 WP_020158143 operon1

Methylobacter marinus A45 WP_027147300 WP_020159526 WP_020159527 operon2

Methylobacter sp. BBA5.1 operon1 NZ_JQKS01000000 WP_020158145 WP_020158144 WP_020158143

Methylobacter sp. BBA5.1 operon2 WP_036253497 WP_020159526 WP_036253495

Methylococcus capsulatus Bath NC_002977 AAB49820 AAB49821 AAB49822 operon1

Methylococcus capsulatus Bath AAB51064 AAB51065 AAB51066 operon2

Methylocaldum szegediense O-12 NZ_ATXX01000000 WP_026609016 WP_026609851 WP_026609852

207

Methylohalobius crimeensis 10Ki NZ_ATXB01000000 WP_022947314 WP_022947315 WP_022947316 operon1

Methylohalobius crimeensis 10Ki WP_022949196 WP_022947315 WP_022947316 operon2

Methyloglobulus morosus KoM1 NZ_AYLO00000000 MKO_MKO1_c77.74 MKO_MKO1_c77.74 MKO_MKO1_c77.74 operon1

Methyloglobulus morosus KoM1 MKO_MKO1_c123.115 MKO_MKO1_c123.115 MKO_MKO1_c123.115 operon2

Methyloglobulus morosus KoM1 MKO_MKO1_c167.152 MKO_MKO1_c167.152 MKO_MKO1_c167.152 operon3

Methylomarinum vadi IT-4 NZ_JPON01000000 EP25DRAFT_1965 EP25DRAFT_1964 EP25DRAFT_1963

Methylomicrobium agile ATCC JPOJ01000000 CC94DRAFT_0956 CC94DRAFT_0955 CC94DRAFT_0954

35068 operon1

Methylomicrobium agile ATCC CC94DRAFT_2803 CC94DRAFT_2805 CC94DRAFT_2804

35068 operon2

Methylomicrobium album BG8 CM001475 EIC29217 EIC29218 EIC29219 operon 1

Methylomicrobium album BG8 EIC31240 EIC31239 EIC31238 operon 2

Methylomicrobium alcaliphilum NC_016108 CCE22212 CCE22213 CCE22214

208

Methylomicrobium buryatense 5G WP_017840377 WP_017840378 WP_017840379

Methylocystis rosea SV97 operon1 ARCT01000000 WP_026222791 WP_014892304 WP_018408664

Methylocystis rosea SV97 operon2 WP_026222791 WP_014892304 WP_018408664

Methylocystis rosea SV97 operon3 WP_018409559.1 WP_026223175.1 WP_018409560

Methylocystis sp. SB2 operon 1 NZ_AYNA01000000 WP_014889687 WP_014889688 WP_014889689

Methylocystis sp. SB2 operon 2 WP_014890337 WP_014892304 WP_014892305

Methylocystis sp. Rockwell ATCC NZ_KE124774 WP_084678888 WP_036281738 WP_036287217

49242 operon1

Methylocystis sp. Rockwell ATCC WP_084678888 WP_036281738 WP_036287217

49242 operon2

Methylocystis sp. SC2 operon1 NC_018485 CCJ05653 CCJ05654 CCJ05655

Methylocystis sp. SC2 operon2 CCJ08277 CCJ08278 CCJ08279

Methylocystis sp. SC2 operon3 CCJ08984 CCJ08985 CCJ08986

Methylosinus trichosporium OB3b NZ_ADVE02000000 ATQ66668 ATQ66669 ATQ70154 operon1

Methylosinus trichosporium OB3b ATQ68210 ATQ68209 ATQ70325 operon2

Methylosinus sp. LW4 operon1 KB900626 WP_018265151 WP_018265152 WP_043332230

Methylosinus sp. LW4 operon2 WP_018265988 WP_018265987 WP_018265986

Methylosinus sp. LW4 operon3 WP_018265151 WP_018265152 WP_043332230

209

Methylovulum miyakonense HT12 NZ_KB913025 BAJ17640 BAJ17641 BAJ17642

Methylosinus sp. LW3 operon1 NZ_AZUO01000000 WP_024879510 WP_024879511 WP_024879512

Methylosinus sp. LW3 operon2 WP_024880171 WP_018265152 WP_051465414

Methylosinus sp. LW3 operon3 WP_024880171 WP_018265152 WP_051465414

Nitrosococcus oceani ATCC 19707 NC_007484 WP_011330983 WP_011330982 WP_002811287

Nitrosococcus oceani C-27 NZ_JPGN01000000 WP_036526307 WP_011330982 WP_002811287

Nitrosococcus oceani AFC132 NZ_JPFN01000000 WP_036502511 WP_011330982 WP_036502509

Nitrosococcus halophilus Nc4 NC_013960 ADE13855 ADE13856 ADE13857

Nitrosococcus watsoni C-113 NC_014315 WP_013219692 WP_013219693 WP_013219694

Mycobacterium chubuense NBB4 NC_018027 AFM20520 AFM20519 AFM20518

Mycobacterium rhodesiae NBB3 NC_016604 WP_014211367.1 WP_014211362.1 WP_014211290.1

Smaragdicoccus niigatensis DSM NZ_AQXZ01000000 F600DRAFT_00579 F600DRAFT_00580 F600DRAFT_00581

44881

Nocardioides sp. CF8 NZ_CM001852 EON22009 EON22010 EON22011

Nocardioidaceae bacterium Broad-1 GL873260 EGD43188 EGD43187 EGD43186

Candidatus Nitrosoarchaeum limnia AEGP01000000 EGG41086 EGG41084 EGG41085

SFB1

Candidatus Nitrosopumilus salaria NZ_AEXL02000000 EIJ67027 EIJ67028 EIJ67026

BD31

Candidatus Nitrosopumilus NZ_AFPU01000000 AFS81497 AFS81495.1 AFS81498.1

210

koreensis MY1

Cenarchaeum symbiosum A DP000238 ABK77035 ABK77038 ABK77030

Nitrosomonas europaea ATCC NC_004757 WP_011111554 WP_041357108 WP_011111552

19718

Nitrosomonas eutropha C91 NC_008344 WP_011635098 WP_041353839 WP_011635096 operon1

Nitrosomonas eutropha C91 WP_011635098 WP_041353839 WP_011635096 operon2

Nitrosomonas sp. AL212 NC_015222 WP_013646791 WP_013646792 WP_013646793 operon1

Nitrosomonas sp. AL212 WP_013646791 WP_013646792 WP_013646793 operon2

Nitrosomonas sp. AL212 WP_013646791 WP_013646792 WP_013646793 operon3

Nitrosomonas sp. Is79A3 NC_015731 WP_013964653 WP_013964654 WP_013964655 operon1

Nitrosomonas sp. Is79A3 WP_013964653 WP_013964654 WP_013964655 operon2

211

Nitrosomonas sp. Is79A3 WP_013964653 WP_013964654 WP_013964655 operon3

Nitrosospira multiformis ATCC NC_007614 WP_011379539 WP_011380154 WP_011380155

25196 operon1

Nitrosospira multiformis ATCC WP_011379539 WP_011380154 WP_011380155

25196 operon2

Candidatus Nitrospira inopinata NZ_LN885086 WP_062484140 WP_062484767 WP_062484768 sp. ENR4

Haliea sp. ETY-M - BAM38052 BAM38053 BAM38054

Haliea sp. ETY-NAG - BAM38058 BAM38059 BAM38060

Candidatus Methylomirabilis FP565575 CBE69521 CBE69519 CBE69517 oxyfera

212

APPENDIX B: SUPPLEMENTARY INFORMATION – DEVELOPING A MONITORING PROTOCOL FOR A METHANE BIOFILTER SYSTEMS

Figure B-1: Neighbor-joining concatenated pmoCAB gene-based phylogeny. The tree was constructed using Seaview 4.4.12 employing Jukes-Cantor distance model. Node values are based on 100 bootstrap replicates. The scale bar represents 0.05 changes per nucleotide position.

213

Table B-1 Summary of spore/cyst formations among the species of methanotrophs. Reference taken from review by Semrau et al., (2010).

Group Genus Spore Cyst

Type Ia Methylobacter N/A Yes

Methylomonas No Yes

Methylomicrobium No No

Type Ib Methylococcus No Yes

Methylocaldum N/A Yes

Type II Methylocystis No Yes

Methylosinus Yes No

Methylocapsa - Yes

Methylocella Yes N/A

Verrucomicrobia Methylacidiphilum N/A N/A

214

Table B-2 Outline of the protocol used for QIIME analyses of 16S rRNA Illumina output. Each steps function is outlined, and samples details are not presented. 1. multiple_join_paired_ends.py

Joins two pair end sequence from Illumina output using command: qiime_parameters_pairing.txt

2. cp ./*/*_join.fastq

Copies all files ending in _join.fastq to the specified location

3. split_libraries_fastq.py

Performs demultiplexing of fastq sequence data where barcodes and sequences are contained in two separate fastq files using quality filter at Phred >= 19

4. pick_open_reference_otus.py

Performs OUT picking using open_ref_otus_parameters.txt (parameter file) and

Silva_119_rep_set97.fna (reference database) and gives output as final_otu_map_mc2_w_tax.biom (taxonomy table in biom format)

5. biom summarize-table

Provides summary statistics of OTUs as otu_table_mc2_w_tax_summary_stats.txt

6. Choose an e value based on the summary statistics

Samples that were lower than chosen e value will be discarded for further analyses

7. core_diversity_analyses.py

Provides a core set of QIIME diversity analyses based on e value using parameter qiime_parameters_core_statistics.txt

215

Table B-3 Outline for CCA analysis performed in R platform. 1. CCA_Biofilter_Species <- read.csv("D:/Canonical Correspondence analysis/Biofilter_QIIME_L6.csv", header = TRUE, row.names = 1)

2. CCA_Biofilter_Parameter <- read.csv("D:/Canonical Correspondence analysis/Biofilter_Parameter.csv", header = TRUE, row.names = 1)

3. CCA_Biofilter_ <-cca(CCA_Biofilter_Species, CCA_Biofilter_Parameter, data=

CCA_Biofilter_Species)

4. CCA_Biofilter_1 < ordiplot(CCA_Biofilter, scaling =2, display="sites", cex=1.25)

5. CCA_Biofilter_2 < text(CCA_Biofilter_1, display=“species”, cex= 0.9)

6. CCA_Biofilter_3 < anova.cca(CCA_Biofilter, by=”term”)

7. CCA_Biofilter_4 < anova.cca(CCA_Biofilter, by=”axis”)

Table B-4 Calculated concentration copies formulae A. Calculated concentration for enrichment samples 퐴퐴 푥 10 µl ∗ = 퐶표푝𝑖푒푠 푝푒푟 푔 표푓 푐표푚푝표푠푡 0.5 푔 표푓 푐표푚푝표푠푡 ∗∗

AA is calculated concentration (copies) per µl of sample calculated based on qPCR standard curve.

* Each DNA extracted sample was diluted to 10 µl

** The amount of compost used for DNA extraction.

B. Standard Error (SE) 푠 푆퐸푥̅ = √푛 s is the sample standard deviation n is the size of the sample 216

APPENDIX C: SUPPLEMENTARY INFORMATION – ANALYSIS OF COPPER MONOOXYGENASE-ENCODING GENES DETECTED IN METAGENOMES, SINGLE CELL GENOMES AND ENRICHMENT CULTURES FROM OILSANDS ENVIRONMENTS

Table C-1: Metagenomes used for the screening of CuMMO-encoding operons. Metagenome Name IMG Genome Source Habitat Geographic ID Location A Coal bed methane well microbial 3300000052 HMP Hydrocarbon Alberta, Canada communities from Alberta, Canada (CO182: resource coal bed cutting Illumina Assembly) environments B Tailings pond microbial communities from 3300001239 HMP Hydrocarbon Fort McMurray, Northern Alberta - Syncrude Mildred Lake resource Alberta, Canada Settling Basin (WIP- environments PD_SYN_TP_WS_002_003_071511 and isolates PD8, PD9 joint assembly) C Oil sands microbial communities from Horse 3300001393 HMP Hydrocarbon Horse River, River, Alberta, Canada - outcrops collected resource Fort McMurray, from inside the river (H1R: 454 sequencing environments Alberta, Canada assembly) D Tailings pond microbial communities from 2209111015 HMP Hydrocarbon Alberta, Canada Northern Alberta -TP6_2008_2010: resource environments E Wastewater microbial communities from 3300000558 HMP Hydrocarbon Fort McMurray, Syncrude, Ft. McMurray, Alberta - West In resource Alberta Pit SyncrudeMLSB2011 environments (SyncrudeMLSB2011: 454+illumina assembly) F Wastewater microbial communities from 3300002455 HMP Hydrocarbon Wood Buffalo, Syncrude, Ft. McMurray, Alberta - Tailing resource Alberta, Canada Pond Surface TP_surface (WIPaJuly2011 environments illumina assembled contigs) G Wastewater microbial communities from 2236876024 HMP Hydrocarbon Fort McMurray, Syncrude, Ft. McMurray, Alberta - resource Alberta, Canada Methanotrophs enriched from Methane environments stable-isotope-probing experiment (PD5: SOAPDenovo assembly) H Wastewater microbial communities from 3300000054 HMP Hydrocarbon Fort McMurray, Syncrude, Ft. McMurray, Alberta - resource Alberta, Canada Methanotrophs enriched from Methane environments stable-isotope-probing experiment (PD4and5:soapDenovo joint assembly) I Tailings pond microbial communities from 3300001605 HMP Hydrocarbon Fort McMurray, Northern Alberta—Syncrude Mildred Lake resource Alberta, Canada Settling Basin (PDSYNTPWS: 454+illumina environments sequencing assembly)

217

Table C-2: Sequence similarity of CuMMO-encoding xmoA with known sequences. xmoA encoding protein sequence was used as query against the NCBI protein sequence database by using blastP (protein BLAST) function. xmoA Description Query E Identity Accession no. cover value TP2 hypothetical protein A3I64_13755 99% 0 92% OGB13554.1 [Burkholderiales bacterium RIFCSPLOWO2_02_FULL_67_64] WIP hypothetical protein A9Q89_11910 94% 3E-114 67% OUS10042.1 [Gammaproteobacteria bacterium 53_120_T64] TP1 hypothetical protein A9Q89_11910 99% 2E-137 71% OUS10042.1 [Gammaproteobacteria bacterium 53_120_T64] Coal hypothetical protein A3I64_18305 95% 7E-141 81% OGB13019.1 [Burkholderiales bacterium RIFCSPLOWO2_02_FULL_67_64] WIP1 hypothetical protein A3I64_13755 99% 4E-149 74% OGB13554.1 [Burkholderiales bacterium RIFCSPLOWO2_02_FULL_67_64] TP6 Particulate methane monooxygenase beta 99% 2E-151 85% EWS66135.1 subunit [Hydrogenophaga sp. T4] HR hypothetical protein A3I64_18305 92% 2E-78 50% OGB13019.1 [Burkholderiales bacterium RIFCSPLOWO2_02_FULL_67_64] 11600 hypothetical protein A3I64_13755 100% 3E-180 91% OGB13554.1 [Burkholderiales bacterium RIFCSPLOWO2_02_FULL_67_64] 490 Particulate methane monooxygenase beta 99% 3E-145 84% EWS66135.1 subunit [Hydrogenophaga sp. T4] 156 methane monooxygenase/ammonia 100% 7E-172 94% WP_02714884 monooxygenase subunit A [Methylobacter 1.1 tundripaludum]

218

Table C-3: Calculated concentration copies formulae

C. Calculated concentration for enrichment samples 퐴퐴 푥 10 µl ∗ = 퐶표푝𝑖푒푠 푝푒푟 푚푙 표푓 푒푛푟𝑖푐ℎ푚푒푛푡 푠푎푚푝푙푒 2 푚푙 표푓 푒푛푟𝑖푐ℎ푚푒푛푡 ∗∗

AA is calculated concentration (copies) per µl of sample calculated based on qPCR standard curve.

* Each DNA extracted sample was diluted to 10 µl

** The amount of each enrichment sample used for DNA extraction.

D. Standard Error (SE) 푠 푆퐸푥̅ = √푛 s is the sample standard deviation n is the size of the sample

219

Enrichment Substrate Plate Well position PCR assays BML-Propane(A) Propane 2 E16 WIP, TP2 BML-Propane(A) Propane 2 G17 TP2 BML-Propane(A) Propane 2 L21 TP2 BML-Propane(A) Propane 2 L9 TP2 BML-Propane(A) Propane 2 G21 TP2 BML-Propane(A) Propane 2 K7 TP2 BML-Propane(A) Propane 2 D16 TP2 BML-Propane(A) Propane 2 N11 TP2 BML-Propane(A) Propane 2 C18 TP2 BML-Propane(A) Propane 2 F20 TP2 BML-Propane(A) Propane 2 L16 TP2 BML-Propane(A) Propane 2 I19 TP2 BML-Propane(A) Propane 2 E14 TP2 BML-Propane(A) Propane 2 F14 TP2 BML-Propane(A) Propane 2 E17 TP1

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Table C-5: Taxonomy of single cell sorted samples based on 16S rRNA gene sequence. A total of 93 cells were sorted by JGI. Phylum Class Order Family Genus Count Proteobacteria Betaproteobacteria Burkholderiales Comamonadaceae Polaromonas 74/93 Proteobacteria Betaproteobacteria Burkholderiales Comamonadaceae Rhodoferax 15/93 Proteobacteria Betaproteobacteria Rhodocyclales Rhodocyclaceae Methyloversatilis 1/93 Proteobacteria Betaproteobacteria Burkholderiales Comamonadaceae Hydrogenophaga 1/93 Proteobacteria Alphaproteobacteria Rhizobiales Xanthobacteraceae Xanthobacter 1/93 Firmicutes Clostridia Clostridiales Acidaminobacteraceae Fusibacter 1/93

Table C-6: Analysis of MANOVA and Significance test of qPCR output

ros1 <- manova(cbind(TP2, R490, TP1, WIP,TP6, COAL, HR) ~ Sample, data=qPCR.xmo.STATISTICS) output 1 Terms: Sample Residuals resp 1 101570882570 156260324874 resp 2 35915623598 356280569 resp 3 401121780 463375915 resp 4 893155606 138163601 resp 5 601056501 302048173 resp 6 1058158094 391684489 resp 7 553743425 176368147 Deg. of Freedom 7 2 Residual standard errors: 279517.7 13346.92 15221.3 8311.546 12289.19 13994.36 9390.638 Estimated effects may be unbalanced ros3 <- summary.manova(ros1, test = c('Pillai'), tol = 0) output 2 Df Pillai approx F num Df den Df Pr(>F) Sample 7 6.9 19.707 49 14 2.1e-07 *** Residuals 2 Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

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Table C-7: Significance of ANOVA > summary(qPCRanova <-aov(qPCR1$tp1 ~ qPCR1$treatment + qPCR1$enrichment, data = qPCR1))

> TukeyHSD(qPCRanova) `qPCR1$enrichment`

diff lwr upr p adj propane-methane 1.49689080 8.199033e-01 2.173878263 0.0003336

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APPENDIX D: SUPPLEMENTARY INFROMATION – ANALYSIS OF COPPER MONOOXYGENASE-ENCODING GENES DETECTED IN METAGENOMES, SINGLE CELL GENOMES AND ENRICHMENT CULTURES FROM OILSANDS ENVIRONMENTS

New copper containing membrane monooxygenases (CuMMOs) encoded by alkane-utilizing betaproteobacteria in oilsands tailings

Rochman FF1, Khadka R1, Tamas I1,2, Lopez-Jauregui AA1,3, Malmstrom RR4, Woyke T4,

Dunfield PF1*, Verbeke TJ1

1 Department of Biological Sciences, University of Calgary, 2500 University Dr. NW Calgary

AB Canada T2N 1N4

2 Department of Biology and Ecology, Faculty of Sciences, University of Novi Sad, Novi Sad,

Serbia

3 Instituto Tecnologico y de Estudios Superiores de Monterrey, Chihuahua, Mexico

4Department of Energy Joint Genome Institute, Walnut Creek, California 94598, USA.

*Corresponding author

Keywords: Copper containing membrane-bound monooxygenase (CuMMOs); single amplified genomes (SAGs); oil sands; stable-isotope probing; methane monooxygenasel; ammonia monooxygenase

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Abstract:

Enzymes in the copper-containing membrane monooxygenase family (CuMMOs) catalyze the oxidation of methane, ammonia, and some short chain alkanes and alkenes. They are usually encoded by operons of three genes: xmoCAB. We detected a phylogenetically new lineage of xmoCAB in a metagenome constructed from an oilsands tailings pond, which showed only 59% derived amino acid identity to known CuMMO-encoding operons. Stable isotope probing experiments combined with a specific qPCR assay for the new xmoA gene demonstrated that the bacterium possessing this gene was incapable of methane assimilation, but did grow well on propane and to a lesser extent ethane. Single cell genomics of sorted cells from enriched tailings water identified two bacteria possessing close relatives of this operon. Both were betaproteobacteria, one in the genus Rhodoferax and another in the genus Polaromonas. The single cell genomes of these bacteria also identified two additional, previously unknown phylogenetic lineages of xmoCAB, one additional lineage in each genus. Metabolic predictions from the genome analyses verified that neither bacterium was likely to be capable of catabolic methane or ammonia oxidation, but they were capable of higher n-alkane degradation. We propose that these new betaproteobacterial lineages of CuMMO probably represent alkane or alkene monooxygenases, although a conclusive demonstration of their function will require pure cultures. This study widens the known diversity of known CuMMOs, and identifies non- nitrifying betaproteobacteria as possessing such enzymes.

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Introduction

Enzymes in the copper-containing membrane monooxygenase family (CuMMOs) catalyze diverse reactions. Particularly important are CuMMOs that act as ammonia and methane monooxygenases, as these play important roles in global carbon and nitrogen biogeochemical cycles (Hakemian and Rosenzweig, 2007; Canfield et al., 2010; Semrau, 2011; Bodelier and

Steenbergh, 2014). Nitrifying Bacteria and Archaea use ammonia monooxygenase (AMO) to catalyze the oxidation of ammonium to hydroxylamine, while methanotrophic bacteria use particulate methane monooxygenase (pMMO) to convert methane into methanol. Evidence for

CuMMO-mediated metabolism of other compounds like short-chain alkanes and alkenes has begun to emerge in recent years. CuMMOs have been reported to be involved in alkane/alkene oxidation in a number of actinobacterial strains including Mycobacterium chubuense NBB4

(Coleman et al., 2011), Mycobacterium rhodesiae NBB3 (Coleman et al., 2012) and

Nocardiodes sp. CF8 (Sayavedra-Soto et al., 2011). Ethylene-assimilating Haliea spp. within the class Gammaproteobacteria have also been shown to possess CuMMOs, although the role of the enzymes in ethylene oxidation is not firmly established (Suzuki et al., 2012). Enzymes belonging to this superfamily are also known to act on numerous other substrates, particularly hydrocarbons containing methyl and alkyl groups (Semrau, 2011), although in most cases this occurs as incidental co-oxidation with the converted substrate not supporting the growth of the bacteria.

CuMMOs are usually encoded in an operon of three genes in the order CAB. These are by convention named amoCAB (encoding AMO), pmoCAB (encoding pMMO), or other names depending on the substrate-specificity. However, all are homologous and for simplicity we will refer to them collectively as xmoCAB. A tight correlation between the functional role of

CuMMOs and the phylogeny of the encoding genes, particularly amoA and pmoA, has been well 225

established (Purkhold et al., 2000; Knief, 2015). Functionally and taxonomically coherent groups of methane and ammonia oxidisers are distinguishable on the basis of xmoA phylogeny, making these genes excellent biomarkers to identify environmental populations. "Universal" primers targeting these genes are therefore extensively used in ecological studies of methanotroph and nitrifier diversity. A common result of such studies is the detection of divergent sequences of unknown function within the gene family (Knief, 2015). Genome and metagenome studies are also uncovering increasing numbers of new operons encoding divergent CuMMOs. Notable examples include the three pmoCAB operons reported in verrucomicrobial methanotrophs (Op den Camp et al., 2009; Khadem et al., 2012; Anvar et al., 2014) as well as the “pxm-group” identified in some alphaproteobacterial and gammaproteobacterial methanotrophs (Tavormina et al., 2011; Vorobev et al., 2014; Kits et al., 2015). The pxm-gene cluster can further be differentiated by the unusual ABC gene arrangement in the encoding operons (Tavormina et al.,

2011). Other divergent operons have been identified in sequenced genomes, including those of the gammaproteobacterium Solimonas aquatica DSM 25927 (Whitman et al., 2015) and the betabroteobacterium Hydrogenophaga sp. T4 (Genbank accession number: AZSO00000000), but the functional roles of these CuMMOs are not yet explored. Collectively, there is increasing evidence that the diversity of bacteria encoding CuMMO enzymes, and perhaps the substrates they act on may be greater than is currently appreciated.

Petroleum-impacted environments are good habitats to explore for new hydrocarbon monooxygenases such as CuMMOs. In the Athabascan oil sands of Alberta, Canada, industrial oil extraction typically involves a combination of alkali-hot water treatment and addition of chemical diluents (naphtha). The extraction process generates fluid tailings that are stored in open ponds to allow for particle settling, surface water reuse in extraction, and long-term 226

pollutant containment (Foght et al., 2017). These tailings ponds contain high (up to 10 mM) concentrations of ammonia/ammonium (Allen, 2008; Saidi-Mehrabad et al., 2013), along with residual bitumen-derived and naphtha-derived hydrocarbons including C3-C14 alkanes and monoaromatics (benzene, toluene, ethylbenzene, xylene) (Siddique et al., 2007; Siddique et al.,

2015). Some oilsands tailings ponds are strong net emitters of methane and other C2-C10 volatile organic compounds as a result of the anaerobic metabolism of naphtha-derived hydrocarbons

(Siddique et al., 2007; Simpson et al., 2010; Siddique et al., 2015; Small et al., 2015), and aerobic methanotrophs are therefore abundant (Saidi-Mehrabad et al., 2013).

Given the wealth of known substrates for CuMMOs in these tailings ponds, the oxic surface waters may offer a unique environment in which to discover new CuMMOs. Numerous investigations of the microbial communities in oil sands tailings ponds have been undertaken

(Ramos-Padrón et al., 2011; An et al., 2013; Saidi-Mehrabad et al., 2013; Siddique et al., 2015;

Mohamad Shahimin et al., 2016), including metagenomic analyses (Aguilar et al., 2016;

Rochman et al., 2017). Through data mining of these metagenomes, a CuMMO-encoding operon highly divergent from any previously recognized operon was discovered. The objective of this study was to identify the bacterium encoding this sequence and to gain insights into its function prediction.

Results

Sequence discovery & phylogenetic analyses

Analysis of a previously published metagenome (Rochman et al., 2017) identified a scaffold of 4,485 bp that encoded three-gene cluster homologous to known CuMMO encoding operons. Like most known pMMO and AMO encoding operons the genes were organized in the 227

C-A-B order. Phylogenetic analyses (Figure 1) showed that this xmoCAB operon (designated as

WIP xmoCAB1 in the figure) is most closely related to an operon in Solimonas aquatica DSM

25927, a gammaproteobacterium isolated from a freshwater spring in Taiwan (Sheu et al., 2011).

The individual subunits however share only 54%-66% derived amino acid identity with each other (59% overall). The operon also clustered near those of the Gammaproteobacteria Haliea sp. ETY-M and Haliea sp. ETY-NAG. The function of the CuMMOs has not been firmly established in any of these organisms.

Only two other genes were annotated on the genomic scaffold, providing little additional information as to a potential functional role for the xmoCAB cluster or the identity of the microbe encoding the operon. One gene was annotated as encoding a long-chain fatty acid transport protein showing a maximal amino acid identity of 67% to a protein in the alkane-oxidizing betaproteobacterium, Thauera butanivorans (Dubbels et al., 2009). The other encoded a hypothetical protein showing 61% amino acid identity to a protein in the nitrifier Candidatus

‘Nitrospira inopinata’ (Daims et al., 2015).

Stable isotope probing with potential substrates of the new xmoCAB gene cluster

Given the high sequence divergence of the WIP xmoCAB1 relative to sequences from known methanotrophs and nitrifiers (Figure 1), we sought to identify a possible ecological role for the organism possessing this xmoCAB operon. A specific qPCR assay was developed to determine gene abundance in tailings pond water samples enriched with methane, ammonium, ethane, or propane. The number of xmoA gene copies was low (12,849  2,628 gene copies ml-1) in natural tailings water at the onset of the experiment and stayed relatively constant over six weeks of incubation, with perhaps a modest increase after prolonged incubation (Figure 2). No 228

consistent increase above these controls was observed during incubation under ethane, methane, or ammonia. The only clearly stimulatory treatment was propane.

Freshly acquired tailings pond samples were then enriched using isotopically light (12C) or heavy (13C) hydrocarbon substrates. Rapid alkane oxidation was observed using both the 12C and 13C substrates, showing maximal potential oxidation rates of 117, 90, and 63 mol L-1 d-1 for methane, ethane and propane, respectively (Figure S1). In the density gradient-fractionated DNA extracted from the unamended control sample, qPCR analyses of WIP xmoA1 copies showed a maximum gene copy number in a "light" DNA fraction (1.69 g ml-1) close to the peak of total

DNA.

The peak DNA abundance in the methane-incubated samples shifted from a density fraction of 1.69-1.70 g ml-1 for the 12C-incubations to 1.71-1.74 g ml-1 for the 13C-incubations due to incorporation of the heavy-isotope label into methanotrophs. However, a corresponding shift in the fractions with the highest xmoA1 gene copy number did not occur. In both the 13C and

12C methane enrichments, peak xmoA1 copies were observed in light 1.68-1.69 g ml-1 fractions, suggesting that the bacterium encoding the WIP xmoCAB1 did not assimilate methane-derived carbon (Figures 3A, 3B).

In contrast, the density fraction showing the maximum WIP xmoA1 copy numbers did shift after 13C-ethane and 13C-propane enrichment (Figures 3D, 3F). In both cases, xmoA1 gene copy numbers were highest at densities of 1.69-1.70 g ml-1 in the 12C enrichments but shifted to

>1.71 g ml-1 after enrichment with 13C substrates (Figures 3C, 3E). This shift suggests that the bacterium encoding WIP xmoCAB1 is capable of assimilating carbon from ethane and propane.

However, peak xmoA1 copy numbers were three orders of magnitude higher in the propane enrichment, suggesting that this is the preferred substrate. 229

SIP community analyses

16S rRNA amplicons of the unenriched tailings pond sample DNA and its corresponding heavy fraction (i.e. just the heaviest PCR-amplifiable DNA fraction, representing high G+C genomes) showed diverse communities (707-1,098 OTUs detected; Simpson index ≥0.975). The hydrocarbon enriched samples were dominated by fewer taxonomic groups (367-622 OTUs;

Simpson index <0.911). Gammaproteobacteria was the predominant class in the unamended tailings pond sample as well as the methane enrichment (Figure S2). In the methane enrichment, the community was dominated by a single OTU that comprised 52% of the total reads (Figure 4) and is most closely related to the methanotrophs Methyloparacoccus and Methylocaldum (97% sequence identity), which agrees with previous analyses of methanotrophic communities in this environment (Saidi-Mehrabad et al., 2013). Betaproteobacteria were much more abundant in the ethane and propane enrichments, comprising 48% and 77% of the total read sets respectively

(Figure S2). Particularly dominant OTUs belonged to the genus Methyloversatilis (Figure 4).

Relative abundances of OTUs within the genera Hydrogenophaga, Pedomicrobium, Arenimonas,

Acidovorax, Rhodoferax and Oxalicibacterium also increased after propane enrichment.

Screening single-cell genomes for xmoCAB

About 98% of the identified sorted cells from a propane enrichment belonged to the class

Betaproteobacteria and six distinct genera were identified overall (Table S4). Three of the detected genera, Rhodoferax, Hydrogenophaga and Methyloversatilis, had been shown to assimilate propane in earlier SIP enrichments (Figure 4). However, the most abundant genus was

Polaromonas, a genus detected only at low abundance (<0.5%) in the propane SIP experiments, 230

and not enriched compared to the control conditions. The longer enrichment phase and the different field sample used for single sorting may have favoured this genus.

Aliquots of amplified genomic DNA from the sorted wells were screened using the WIP xmoA1-specific PCR assay. Bands of the expected size were observed in multiple SAGs identified as Rhodoferax and Polaromonas, leading to the selection of five SAGs of each genus for genome sequencing (Table S5). In silico DNA-DNA hybridizations (Meier-Kolthoff et al.,

2013) of the draft genomes within each genus were all >70% identical, suggesting each genus was represented by a single species in the sorted plates (Table S3, Table S4). The 16S rRNA gene sequences from the Polaromonas SAGs were identical and showed 98.0% nucleotide identity to Polaromonas naphthalenivorans CJ2, an aromatic hydrocarbon degrading bacterium

(Yagi et al., 2009). For the Rhodoferax genomes, the full length 16S rRNA gene sequences were identical except for a single nucleotide mismatch present in SAG-1 (Table S2) and closely matched (98.5%) Rhodoferax ferrireducens T118 (Finneran et al., 2003). Fully finished genomes for both P. naphthalenivorans CJ2 and R. ferrireducens T118 are available, but neither organism possesses a CuMMO.

The combined Rhodoferax SAGs possessed two divergent xmoCAB operons (Figure 1,

Table S6). One of the operons (e.g. Ga0215897_1072-1074; SAG-3) showed >99.9 % nucleotide identity to the WIP xmoCAB1 operon found in the original oilsands tailings pond metagenome.

However, a second operon (Ga0215886_10644-10646; SAG-1) clustered in a distinct clade

(Figure 1). The Polaromonas SAGs also encoded two divergent operons. Again, one was closely homologous to the WIP xmoCAB1 metagenome operon. The other formed a third new lineage not closely homologous to the second operon in the Rhodoferax SAGs (Figure 1, Table S6). Due to SAG incompleteness, multiple CuMMO-encoding operons were not identified in every SAG. 231

However, in one SAG of each genus (Rhodoferax SAG 1, 2773857740; Polaromonas SAG-2,

2773857734) parts of both operons were present (Table S6), suggesting that multiple CuMMOs existed in single genomes.

The Polaromonas genomes also encoded an orphan xmoC (e.g. Ga0215891_10812). This was identified in three of the five Polaromonas genomes, with flanking genes on the scaffolds confirming that the absence of the expected A- and B-subunits was not due to missing sequence data.

Metabolic potential

While the major goal of sequencing the SAGs was to identify the organisms containing the xmoCAB operons, they were also analysed to indicate any potential for ammonia, methane, or alkane oxidation, in order to support the results of the incubation experiments. Given the partial nature of SAGs these analyses are only supportive, not conclusive. Unscreened SAGs ranged from 20% to 62% complete based on CheckM, with a mean of 41% (Table S5). The 5 SAGs were pooled and collectively analyzed for each genus.

Hydroxylamine oxidoreductase, an enzyme essential for ammonia oxidation (Schmid et al., 2008), was not annotated in any SAG of either genus. Each genus possessed a diversity of genes encoding transporters and metabolic modules for organoheterotrophic growth. Known bacterial nitrifiers have very limited organoheterotrophic capacity (Arp et al., 2007). Therefore the genomes do not indicate a capacity for nitrification.

Key methanotrophic functions are also missing. A complete Calvin Benson Bassham

(CBB) cycle for autotrophic CO2 fixation including the large subunit of ribulose bisphosphate carboxylase was detected in two of the Polaromonas SAGs (Ga0215911_14316; 232

Ga215901_1152) but not in the Rhodoferax SAGs. Polaromonas may therefore be capable of 1-

C fixation via the CBB cycle, but neither a Ribulose monophosphate (RuMP) nor a Serine cycle for fixation of 1-C intermediates of methane oxidation is complete in either organism. Genes encoding the key RuMP enzymes 3-hexulose-6-phosphate synthase and 6-phospho-3- hexuloisomerase were not found, nor were genes for the key Serine cycle enzyme serine glyoxylate aminotransferase. A hydroxypyruvate reductase encoding gene was annotated, but is different from the form other methanotrophs use for the serine cycle (EC 1.1.1.81 instead of

1.1.1.29). There was no clear mxaFI or xoxF-encoded methanol dehydrogenase, which is common in other methanotrophs (Lau et al., 2013; Chu and Lidstrom, 2016). However, there were two other pyrrolquinoline quinone (PQQ)-binding alcohol dehydrogenases encoded in both

Rhodoferax and Polaromonas: a homologue to the single subunit mdh2-type methanol dehydrogenase identified in the methylotroph Methyloversatilis universalis FAM5 (Kalyuzhnaya et al., 2008) (e.g. Ga0215885_1254 – Rhodoferax; Ga0215901_10418– Polaromonas), and a second PQQ-binding alcohol dehydrogenase (e.g. Ga0215904_10692 – Rhodoferax;

Ga0215901_1397– Polaromonas) located just downstream of CuMMO-encoding subunits. The

Polaromonas did encode multiple subunits of a formate dehydrogenase (e.g. Ga0215892_11102 to 11104), but formaldehyde dehydrogenase or a tetrahydromethanopterin-linked pathway to convert formaldehyde to formate were not encoded. In summary, there is little evidence that the

Rhodoferax has the capacity for methanotrophy. The Polaromonas has more 1-C metabolic capacity (including CO2 fixation and formate oxidation), but also does not appear to be a typical methanotroph. Indeed, only a single read in the heavy fraction of the 13C-methane SIP experiments matched Rhodoferax and no reads matched Polaromonas, suggesting these bacteria did not assimilate methane, which is consistent with their lack of methanotrophy genes. 233

However, potential pathways for propane oxidation were evident in Rhodoferax and

Polaromonas. In addition to the two CuMMOs, both genera encode multiple other alkane monooxygenases, including a homologue of soluble methane/propane monooxygenase in the

Polaromonas (Table S7). The oxidation of propane could be initiated by one or several of these enzymes, either terminally forming 1-propanol or sub-terminally forming 2-propanol (Kotani et al., 2006). Multiple catabolic routes have been described for both terminal (Textor et al., 1997) and sub-terminal (Hausinger, 2007) oxidation with potential pathways in Rhodoferax and

Polaromonas depicted in Figure 5. In brief, both organisms can theoretically convert 1-propanol to propionyl-CoA. At this branch point, one possible degradation route includes oxidation via the citramalate cycle where propionyl-CoA is converted into succinyl-CoA (Textor et al., 1997;

Suvorova et al., 2012). In the Rhodoferax strain, the genes encoding a propionyl-CoA carboxylase (e.g. Ga0215895_1164-1165) and a methylmalonyl-CoA mutase (e.g.

Ga0215895_1161) are co-localized in the genome. A similar gene neighbourhood architecture is observed in the Polaromonas genomes. Neither organism had gene homologues encoding known methylmalonyl-CoA epimerases, which catalyze the conversion between 2R-methylmalonyl-

CoA and 2S-methylmalonyl-CoA (Figure 5). Multiple other epimerases are annotated in each genome, however, which may act as functional equivalents.

Another possible pathway includes the conversion of propionyl-CoA (plus oxaloacetate) to pyruvate (plus succinate) via the methylcitrate pathway (Figure 5A). In Polaromonas, genes encoding 2-methylcitrate synthase, 2-methylcitrate dehydratase and 2-methylisocitrate lyase are co-localized in the genome (e.g. Ga0215912_10019-100111). Genes for 2-methylcitrate synthase could not be identified in the Rhodoferax genomes although a citrate synthase encoding gene

234

(e.g. Ga0215903_11011) is located just downstream of annotated 2-methylcitrate dehydratase and 2-methylcitrate lyase encoding genes.

No known 2-propanol degradation pathways were identified in Rhodoferax (Figure 5B).

In Polaromonas, three distinct NAD(P)-dependent alcohol dehydrogenase encoding genes

(ADH) are co-localized alongside an annotated acetone/cyclohexanone monooxygenase (e.g.

Ga0215909_1235). In the actinomycete Gordonia sp. TY-5, where the pathway of 2-propanol oxidation via acetone was first described (Kotani et al., 2007), the acetone monooxygenase is co- localized with a methylacetate hydrolase (an esterase). A homologous methylacetate hydrolase was not identified in the Polaromonas SAGs, but an esterase of unspecified activity is located just upstream of its acetone monooxygenase.

Discussion

Phylogenies of genes encoding CuMMOs can clearly delineate functional and taxonomic groups of bacteria, including ammonium oxidisers (in the phylum Thaumarchaeota, and the proteobacterial classes Gammaproteobacteria, and Betaproteobacteria), methane oxidisers

(phyla Verrucomicrobia and NC10, proteobacterial classes Gammaproteobacteria, and

Alphaproteobacteria), and alkane oxidisers (phylum Actinobacteria) (Purkhold et al., 2000;

Tavormina et al., 2011; Knief, 2015). This phylogenetic structure has served as a useful backbone to establishing community structure-function relationships in numerous ecological surveys investigating methane and ammonium biogeochemical cycles. The objective of this research was to investigate a CuMMO-encoding xmoCAB operon identified in the metagenome of an oilsands tailings pond. This operon was highly divergent from any known functional/taxonomic cluster and could not be assigned to a probable function or taxon. The 235

closest database xmoCAB sequence showed only 59% amino acid (AA) identity of the concatenated sequences and is encoded by the gammaproteobacterium Solimonas aquatica. This offers little insight into a possible functional role for the enzyme. S. aquatica is a metabolically versatile bacterium (Sheu et al., 2011) whose CuMMO-enzymes were identified solely through sequencing of the bacterial genome as part of the Genomic Encyclopedia of Archaeal and

Bacterial Type Strains, Phase-II sequencing initiative (Whitman et al., 2015). A formal genome study or examination of enzyme function for this bacterium it not available. Distantly similar operons (41-42% amino acid identity) are also found in the ethylene-assimilating gammaproteobacterium Haliea spp., although the role of the CuMMO in ethylene oxidation is not proven (Suzuki et al., 2012).

Single-cell genomics positively identified bacteria possessing this operon in our samples as members of the class Betaproteobacteria, in the genera Rhodoferax and Polaromonas.

Unexpectedly, the SAG analyses further revealed the presence of a second divergent CuMMO- encoding operon in Rhodoferax and another in Polaromonas. Collectively, the CuMMOs identified clustered in three distinct clades. One (containing Rhodoferax xmoCAB2) includes a second operon in the gammaproteobacterium S. aquatica, and an operon from the betaproteobacterium Hydrogenophaga T4. Formal genome studies or investigations into the function of these enzymes in these organisms have not been reported. The clustering of the xmoCABs from Betaproteobacteria SAGs closely with some Gammaproteobacteria (Figure 1) is unexpected given the taxonomic coherence of most clusters in the overall xmoCAB phylogeny.

However, the level of divergence between these sequences is similar to divergences observed between other taxonomically coherent clades: e.g. gammaproteobacterial versus alphaproteobacterial methanotrophs (Figure 1). It is therefore likely that these sequences appear 236

to cluster together solely due to the underrepresentation of similar CuMMOs in public domain databases. More genome data may be needed to discern taxonomic patterns.

CuMMOs in Betaproteobacteria have previously only been associated with nitrification

(Tavormina et al., 2011). The low copy numbers of WIP xmoA1 observed under ammonia enrichments (Figure 2), however, suggested that neither the Rhodoferax nor the Polaromonas strains identified here contributed to nitrification. This was further supported by the lack of a hydroxylamine oxidoreductase in any of the SAGs. Methane oxidation also seemed an unlikely function based on SIP and qPCR analyses of methane enrichments (Figure 2,3). The sequenced genomes did indicate some methylotrophic capability, especially for the Polaromonas, which possessed genes encoding methanol and formate oxidation and CO2 fixation, but not formaldehyde oxidation or fixation. Both genera possessed annotated PQQ-dependent alcohol dehydrogenases homologous to known methanol dehydrogenases (mdh2) in other

Burkholderiales (Kalyuzhnaya et al., 2008). Methylotrophic Burkholderiales encoding mdh2 fix

C1-compounds via the serine cycle (Kalyuzhnaya et al., 2008), but this cycle was not predicted in the Rhodoferax and Polaromonas SAGs. Neither organism assimilated detectable 13C from methane (Figure 4). Growth of either bacterium via methanotrophy seems unlikely. However, given the apparent potential for some steps of methylotrophy evident in the SAGs, methane cannot be firmly discounted as a possible substrate for any of the new CuMMOs identified.

Conclusive proof will likely require a experimentation using a cultured isolate, which has not yet been achieved.

Propane enrichment led to strong growth of the xmoA-containing bacteria (Figures 2,3).

To date, CuMMO-enabled alkane/alkene oxygenation has been identified in some Actinomycetes

(Coleman et al., 2011; Sayavedra-Soto et al., 2011; Coleman et al., 2012) and postulated in 237

alkene-oxidizing Gammaproteobacteria (Suzuki et al., 2012). At least seven terminal oxidation

(Textor et al., 1997) and five sub-terminal oxidation (Hausinger, 2007) pathways for propane degradation have been suggested in bacteria. Genomic analyses suggested two plausible pathways for terminal oxidation in both Rhodoferax and Polaromonas (Figure 5A) along with a pathway in Polaromonas for the oxidation of 2-propanol (Figure 5B). These genome predictions, along with the strong enrichment of the Rhodoferax and Polaromonas under propane and the assimilation of propane-derived C demonstrated by SIP studies, show clearly that the bacteria themselves are capable of alkanotrophy. However, they do not together prove that the CuMMOs are key enzymes in propane oxidation. The genomic data suggests that both organisms possess multiple other hydrocarbon monooxygenases that could potentially catalyze the initial oxidation reaction (Table S6).

This study has expanded the known diversity of xmoCAB operons encoding CuMMOs and the taxonomic groups that encode this enzyme to include several new Betaproteobacteria.

Definitive functional roles for any of the encoded CuMMOs could only be inferred, and conclusive evidence will require further experimentation, such as using laboratory cultures.

However, evidence was provided that the metabolisms of these CuMMO-encoding bacteria include alkane oxidation, but not ammonia oxidation. Methane oxidation is a possibility based on genomic inference only but is not supported by any incubation studies. Propane oxidation would represent a novel functionality for CuMMO-encoding Betaproteobacteria.

Materials & Methods

Sample sites and metagenomes

238

Samples from two oilsands tailings ponds near Fort McMurray, Alberta were used in this study. The first is an active tailings pond in use by Syncrude Canada, Ltd. at the time of writing

(Mildred Lake Settling Basin, MLSB). The second tailings pond (West In-Pit, WIP) was used for storage of fluid fine tailings until 2012 and then repurposed as an End-Pit Lake thereafter

(Syncrude, 2012). The locations and chemical compositions of both tailings ponds, and the sampling methods used have been described previously (Saidi-Mehrabad et al., 2013; Rochman et al., 2017). A metagenome (IMG Genome ID: 3300002856) of the WIP pond was generated on

Illumina and Roche 454 platforms and the sequences assembled as described previously

(Rochman et al., 2017). An unusual xmoCAB operon (draft_100068512-draft_100068514) identified in this metagenome is referred to "WIP xmoCAB1" (Figure 1, Table S1).

Bacterial enrichments

Surface water samples (0-10 cm depth) of MLSB sampled in August 2015 were used for enrichments and stable isotope probing (SIP) experiments. Twenty milliliters of surface water were added to 100-ml serum bottles, which were sealed with butyl rubber stoppers. For hydrocarbon enrichments, the headspaces of triplicate capped bottles were augmented with 10% v/v methane, ethane or propane. Ammonium chloride (20 mM) was added to enrich for nitrifying bacteria. The headspace of each bottle was then supplemented with 5% v/v CO2 to support autotrophy or anapleurotic CO2 fixation. Bottles were incubated at 23C with shaking (180 rpm) for the duration of the experiment. Gaseous hydrocarbon consumption and CO2 production were determined using a Varian 450-gas chromatograph (Varian, Walnut Creek, CA) equipped with a thermal conductivity detector at 150 after separation in a 2 mm  0.5 m Hayesep N column and a 2 mm  1.2 m molecular sieve 16X column in series (70). 239

DNA extraction and qPCR-based quantification of the WIP xmoA1 gene

Enriched cultures were centrifuged for 10 min at 10,000  g prior to DNA extraction using the FastDNA Spin Kit for Soil (MP Biomedical, Santa Ana, CA). Eluted DNA was stored at -80C. The WIP xmoA1 gene identified in the metagenome was targeted for quantitative analysis. Specific qPCR primers were designed using the “Probe Design” tool in ARB (Ludwig et al., 2004), using a curated database of pmoA/amoA genes from public domain genomes.

Primers xmoA-f (5’-AAATGACTTCGCTCGCTG) and xmoA-r (5’-

CATGCTGCCACCTTCTTT) were selected, producing a predicted partial xmoA amplicon of

271 bp. These primers targeted a section near the end of the xmoA1 gene that is past the stop codon (absent completely) in many other xmoA genes, increasing the specificity of the assay.

Non-quantitative amplification of the xmoA gene fragment was achieved via three-step PCR including: an initial denaturation at 94C for 5 min; 35 cycles of 94C (30 s), 55C (30 s) and

72C (30 s); followed by a final elongation step at 72C for 10 min. The resulting PCR product was then cloned into vector pJET1.2 provided as part of the CloneJET PCR cloning kit (Thermo

Fisher Scientific, Waltham, MA), transformed into E. coli, recovered via colony PCR and used as a template for the construction of qPCR standards ranging from 102 – 108 gene copies per microliter (Sharp et al., 2014b). qPCR was performed on a Qiagen RotorGene-Q (Qiagen,

Toronto, ON) using SsoAdvanced Universal SYBR green supermix (Bio-Rad, Hercules, CA).

Cycling conditions for the qPCR assay were: 94C for 5 min; 35 cycles of 94C for 60 s, 56C for 45 s and 72C for 45 s; and 72C for 10 min. Samples from enriched cultures were compared

240

against the standards and expressed as gene copies per ml of tailings water or culture. Reaction specificity was assessed via melt curve analysis.

Stable isotope probing (SIP) and community analyses

One-liter bottles containing 150 mL of MLSB sampled in August 2015 were sealed using butyl rubber stoppers and the headspace supplemented with 10% v/v of isotopically light (12C) or heavy (99 mol% 13C, Sigma-Aldrich, Oakville, Canada) versions of methane, ethane or propane

12 (duplicate bottles of each). Five percent (v/v) CO2 was also added to minimize cross-feeding of

13 CO2. Bottles were incubated as described above and gas depletion regularly measured via GC.

Total hydrocarbon oxidation rates were assessed by comparison against controls that contained

150 mL of filter-sterilized tailings water and an identical headspace (Figure S1). To minimize the metabolite cross-feeding, experiments were stopped after ten days when between 21-33% of the supplied hydrocarbon had been consumed. Extracted DNA (FastDNA Spin Kit) was separated via isopycnic ultracentrifugation in cesium chloride and divided into twelve fractions of ~0.4 mL each, as described previously (Sharp et al., 2012). The density of each fraction was measured using an AR200 refractometer (Reichert Technologies, Depew, NY). Recovered DNA was precipitated with polyethylene glycol and glycogen, washed with 70% ethanol, eluted, and quantified using the Quant-iT dsDNA HS assay kit (Invitrogen) (Sharp et al., 2012). Samples from the SIP density fractions, unamended controls, and the initial (t=0) community were investigated via qPCR of xmoA genes as described above, as well as via Illumina sequencing of

16S rRNA gene amplicons.

For amplicon sequencing multiple DNA density fractions of 1.72-1.74 g ml-1 were pooled to form a single representative ‘heavy DNA pool’. These fractions were selected because they were much 241

greater in respective 13C versus 12C incubations under each substrate (methane, ethane, and propane;

Figure 3). Two controls were used: unfractionated DNA from the initial community; and the heaviest

PCR-amplifiable fractions (1.71-1.73 g ml-1) of the unamended samples. The latter control verified that designated "heavy" fractions were not simply GC-rich organisms. Heavy DNA fractions of all the labeled samples were used for 16S rRNA gene amplification and sequencing. Amplification of 16S rRNA genes was carried out as described previously (Sharp et al., 2014a). The resulting PCR products were purified using AMPure XP beads (Beckman Coulter) and re-amplified using Illumina barcoded primers targeting the 341-785 region of 16S rRNA genes (Klindworth et al., 2013). The second-step PCR was run for 8 cycles using the same cycling conditions as described. After additional bead-purification, the PCR products were quantified via Qubit (Thermo Fisher Scientific, Waltham, USA) and sequenced using an

Illumina MiSeq (Illumina, San Diego, CA).

Reads were paired, filtered to exclude samples with quality-scores below 19 and analyzed using QIIME (Caporaso et al., 2010) with parameter settings described previously (Rochman et al., 2017). Taxonomic identities were assigned via BLAST comparison to the Silva database (v.

123) (Glockner et al., 2017). Taxonomic identities of OTUs representing >1% of any relative read-sets were validated through manual BLAST against the NCBI NR database. For calculation of diversity indices, read sets were rarefied to 7,500 reads.

Single-cell genomics

MLSB tailings water sampled in August 2015 was enriched under 10% propane and 5%

CO2 as described above. Propane consumption was routinely monitored using an SRI-8610C gas chromatograph (SRI Instruments, Torrance, CA) containing a HayeSep-D column (190C) coupled to a flame ionization detector (300C) using N2 as the carrier gas. When propane

242

consumption began to slow, bottle headspaces were flushed with air and reconstituted with propane and CO2. After the second-round feeding, 2-ml aliquots of culture were removed and centrifuged at 300  g for 2 min to remove particulate matter present in the tailings water. Cell biomass was recovered via transfer of the supernatant and centrifugation at 6,000  g for 3 mins.

Cell pellets were washed three times in 50% strength PBS, then resuspended in 1 ml of 50 mM

Tris-EDTA buffer (pH 8.0) containing 10% v/v glycerol. The prepared cells were then sorted into 384-well plates and single amplified genomes (SAGs) prepared using methods described previously (Rinke et al., 2013; Rinke et al., 2014). SAGs were screened for 16S rRNA genes with standard protocols, and each well containing an identified 16S rRNA gene was then screened via the specific WIP xmoA1 PCR assay described above. Bands of the correct size were sequenced via the Sanger method to validate amplification of the correct product. Based on these results, ten SAGs positive for both 16S rRNA and WIP xmoA1 were selected for complete genome sequencing on an Illumina NextSeq (Rinke et al., 2014), followed by assembly and annotation using the standard operating procedure of the Joint Genome Institute’s microbial annotation pipeline (Huntemann et al., 2015).

ACKNOWLEDGMENTS

This work was made possible through an NSERC (Natural Sciences and Engineering Research

Council of Canada) Collaborative Research and Development Grant (NSERC grant

CRDPJ478071-14), along with financial assistance from Genome Canada, Genome Alberta,

Genome BC and the Government of Alberta (GC Grant 1203). SAG sequencing and analysis was supported by the U.S. Department of Energy Joint Genome Institute, a DOE Office of 243

Science User Facility supported under Contract No. DE-AC02-05CH11231. We acknowledge the assistance of Syncrude Canada, Ltd. AA Lopez-Jauregui was supported by a Mitacs

Globallink internship award.

244

1 Figure Legends

3 Figure 1. Maximum-likelihood tree of concatenated derived amino acid sequences of CuMMO-

4 encoding genes xmoC, xmoA and xmoB. The tree was constructed using using Seaview 4.4.12

5 (Gouy et al., 2010) employing the LG model. Node values were determined based on 100

6 bootstrap replicates. Preferred substrate or enzyme function, if known, for the CuMMOs are

7 indicated in brackets. Accession numbers for sequences are given in Table S1. For the

8 Rhodoferax and Polaromonas SAGs, the specific genomes encoding the CuMMOs are found in

9 Tables S2-S3. For genomes encoding multiple CuMMOs, numerical identifiers were assigned to

10 unique sequences (e.g. xmoCAB1 or xmoCAB2). The scale bar represents substitutions per site.

11 Branch support values are shown at each node.

13 Figure 2. Abundance of WIP xmoA1 gene copies (per ml of water) during enrichment of

14 oilsands tailings pond water under methane, ethane, propane, ammonium chloride or no added

15 substrate. Error bars indicate 1 SEM of triplicates.

17 Figure 3. Abundance of WIP xmoA1 gene copies (per ml) in SIP enrichments. Samples were

18 enriched using isotopically light (12C) or heavy (13C) methane, ethane and propane or were left

19 unamended. The bar graph indicates the number of xmoA1 gene copies per SIP fraction. Error

20 bars indicate 1 SEM of two separate SIP gradients. The line graph indicates the relative DNA

21 concentration per fraction with the highest quantity detected in any fraction set to 1.

245

23 Figure 4. Genus-level identities of the major OTUs comprising ≥1% of the relative read

24 abundance in SIP-labeling experiments. Community analyses of pooled 13C-labeled SIP fractions

25 are compared to two controls: the ‘heavy’ fraction from an unamended condition, and an

26 unfractionated DNA extract from an unamended sample (see Materials and Methods). Bubble

27 size shows the relative abundance range (%) of a genus in a specific treatment.

29 Figure 5. Possible pathways for terminal (A) or sub-terminal (B) oxidation of propane in the

30 Rhodoferax and Polaromonas SAGs. Both the citramalate (i) and methylcitrate (ii) pathways are

31 shown in panel A.

246

34 Figure 1. Maximum-likelihood phylogenetic tree of concatenated derived amino acid sequences

35 of CuMMO-encoding genes xmoC xmoA and xmoB. The tree was constructed using Seaview

36 4.4.12 (Gouy et al., 2010) employing the LG model. Node values were determined based on 100

37 bootstrap replicates. Preferred substrate or enzyme function, if known, for the CuMMOs are

38 indicated in brackets. Accession numbers for sequences are given in Table S1. For the

39 Rhodoferax and Polaromonas SAGs, the specific genomes encoding the CuMMOs are found in

40 Tables S2-S3. For genomes encoding multiple CuMMOs, numerical identifiers were assigned to

41 unique sequences (e.g. xmoCAB1 or xmoCAB2). The scale bar represents substitutions per site.

42 Branch support values are shown at each node.

247

248

47 Figure 2. Abundance of WIP xmoA1 gene copies (per ml of water) during enrichment of

48 oilsands tailings pond water under methane, ethane, propane, ammonium chloride or no added

49 substrate. Error bars indicate 1 SEM of triplicates.

249

54 Figure 3. Abundance of WIP xmoA1 gene copies (per ml) in SIP enrichments. Samples were

55 enriched using isotopically light (12C) or heavy (13C) methane, ethane and propane or were left

56 unamended. The bar graph indicates the number of xmoA1 gene copies per SIP fraction. Error

57 bars indicate 1 SEM of measurements from two separate SIP gradients. The line graph indicates

58 the relative DNA concentration per fraction with the highest quantity detected in any fraction set

59 to 1.

250

251

61 Figure 4. Genus-level identities of the major OTUs comprisig ≥1% of the relative read

62 abundance in SIP-labeling experiments. Community analyses of pooled 13C-labeled SIP fractions

63 are compatred to two controls: the ‘heavy’ fraction from an unamended condition, and an

64 unfractionated DNA extract from an unamended sample (see Materials and Methods). Bubble

65 size shows the relative abundance range (%) of a genus in a specific treatment.

252

70 Figure 5. Possible pathways for terminal (A) or sub-terminal (B) oxidation of propane in the

71 Rhodoferax and Polaromonas SAGs. Both the citramalate (i) and methylcitrate (ii) pathways are

72 shown in panel A.

253

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