Introduction & Objectives

Microbial Community Characterization of Produced Water and Surrounding Seawater from Oil and Gas Production Platforms in Eastern Canada

Chiu William Yeung

Department of Natural Resource Sciences McGill University (Macdonald Campus) Ste-Anne-de-Bellevue, Quebec August 2010

A thesis submitted to McGill University in partial fulfillment of the requirements of the degree of Ph.D.

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Abstract

Produced water is the largest volume of waste produced during the recovery of oil from offshore oil and gas production platforms, and is discharged directly into the surrounding marine environment. Emerging evidence suggests that offshore produced water discharges impact biota at significant distances from the point of discharge. The objectives of this study were to characterize the marine bacterial and/or archaeal populations in and around the Hibernia, Terra

Nova and Thebaud production platforms, to determine if changes in the bacterial community structure might be related to the influence of the produced water, and to develop molecular methods to monitor the bacterial community structure in seawater and to track the dispersion of produced water. The marine bacterial community structure remained relatively constant (with SAB > 70%) across large distances (up to 50 km away from the platforms) and throughout several years of sampling, suggesting that the produced water discharge did not have a detectable effect on the surrounding seawater. Bacteroidetes, Alpha-proteobacteria, and

Gamma-proteobacteria were the most common and abundant phyla detected in the northwestern Atlantic Ocean. The only effect potentially related to the input of produced water was found on the sediment close to the Thebaud platform, suggesting that any impact might be restricted to a small area at the sediment level adjacent to the discharge.

Firmicutes was the most common and dominant phylum detected in all three produced waters, represented principally by species of Thermoanaerobacter.

Both species-specific detection methods (Q-PCR and nested-PCR) detected

Thermoanaerobacter in surrounding seawater within 1000 m of the production

platforms, but most of the Thermoanaerobacter were found at 100 m from

Hibernia near the bottom of the water column, with only a small amount traveling into the upper water column. The properties of the produced water and existing modeling studies also support this dispersion pattern for produced water.

This study provided the first characterization of the microbial diversity in and around the three major offshore oil and gas production platforms in Eastern

Canada and developed new molecular methods to monitor and track the discharge of produced water.

Résumé

L‟eau de production représente le plus grand volume de déchets produits lors de la récupération du pétrole des plateformes pétrolières côtières, et cette eau est libérée directement dans le milieu marin avoisinant. Certaines évidences suggèrent que ces décharges d‟eau de production auraient un impact sur la vie marine même à une distance considérable du point de décharge. Cette étude avait pour objectifs de caractériser les populations bactériennes et/ou archéennes marines aux abords des plateformes d‟Hibernia, Terra Nova et de Thebaud, de déterminer si des changements dans la structure de la communauté procaryotique peuvent être reliés à l‟influence de l‟eau de production, de développer des outils moléculaires pour le suivi des communautés procaryotique dans l‟eau de mer, et finalement, de retracer la dispersion de l‟eau de production. La structure de la communauté bactérienne marine est demeurée relativement constante (SAB >

70%) sur de longues distances (jusqu‟à 50 km des plateformes) et sur plusieurs années d‟échantillonnage. L‟eau de production n‟aurait donc pas d‟effet détectable sur la structure de la communauté bactérienne environnante. Les

Bacteroidetes, Alpha-proteobacteria et Gamma-proteobacteria représentaient les phyla les plus communs et abondants dans le Nord-Ouest de l‟océan Atlantique.

Le seul effet possiblement relié à l‟introduction de l‟eau de production fut observé dans les sédiments près de la plateforme Thebaud, ce qui suggère que l‟impact pourrait se limiter à l‟étroite région au niveau des sédiments adjacents à la décharge.

Les Firmicutes constituaient le phylum dominant détecté dans les trois eaux de production et étaient représentés principalement par des espèces de

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Thermoanaerobacter. Deux méthodes moléculaires spécifiques à l‟espèce (Q-

PCR et PCR-niché) ont pu détecter Thermoanaerobacter dans l‟eau de mer dans un rayon de 1000 m des plateformes de production, mais la majorité des

Thermoanaerobacter trouvés à 100 m d‟Hibernia se situait au bas de la colonne d‟eau, avec seulement une petite quantité migrant plus haut dans la colonne. Les propriétés de l‟eau de production et les modèles existants supportent également le profil de dispersion pour l‟eau de production.

Cette étude rapporte la première caractérisation de la diversité microbienne aux abords de trois plateformes côtières de production de pétrole et gaz de l‟est du Canada et le développement de nouveaux outils moléculaires pour retracer et suivre la libération de l‟eau de production.

ACKNOWLEDGEMENTS

First I would like to thank Dr. Charles W. Greer for allowing me to study in his lab and for all his support in the projects. I would also like to thank Dr. Ken

Lee for introducing me to the project, for all the support in the project and the freedom to do the work. Dr. Whyte, Dr. Driscoll, Dr. Niven, Dr. Chénier and other members of my committees greatly improved the quality of my work by constantly pushing me to perform better. My NRC lab-mates Dr. Nancy Perrault,

Dr. Louis Jugnia, Dr. Marc Auffret, Terry Bell, Nathalie Fortin, Claude Masson, and Sylvie Sanschagrin were all very helpful in my Ph.D. studies. I would like to thank my COOGER lab-mates Dr. Paul Kepkay, Dr. Zhengkai Li, Dr. Haibo Niu,

Jay Bugden, Lorraine Hamilton, Tom King, Susan Cobanli, Brent Law, Byron

Amirault, and Brian Robinson for all their support in Halifax. I would also like to acknowledge the organizations, NRCan PERD, DFO and NRC, for their financial support.

Finally, none of this would have been possible without the support and encouragement of my parents and my wife. It is to them that I dedicate this thesis.

Thank you!

CONTRIBUTIONS TO KNOWLEDGE

 This study presents the first culture-independent analysis of the bacterial and archaeal communities present in produced water originating from three eastern Canada oil and gas production platforms (Hibernia, Terra Nova and Thebaud).

 This study presents the first culture-independent analysis of the bacterial communities present in the surrounding seawater adjacent to three eastern Canada oil and gas production platforms (Hibernia, Terra Nova and Thebaud).

 This is the first marine bacterial and/or archaeal community analysis study of three ecologically and commercially important areas: the Grand Banks, Sable Island Bank and the Gully (the first Marine Protected Area in the Eastern Canada) in the western Atlantic Ocean.

 The first marine 16S rRNA gene-based taxonomic microarray was developed for rapid detection, identification and profiling of the bacterial diversity in seawater and in produced waters.

 Sensitive Q-PCR and nested-PCR methods were developed to track specific target bacteria associated with produced waters from Hibernia and Terra Nova in the marine environment.

 This study is the first to quantitatively monitor a natural component of the produced water, the bacterium Thermoanaerobacter spp., in the surrounding seawater, allowing determination of the direction and dilution effect of the produced water effluent.

TABLE OF CONTENTS

ABSTRACT ...... i RÉSUMÉ ...... iii ACKNOWLEDGEMENTS ...... v CONTRIBUTIONS TO KNOWLEDGE ...... vi TABLE OF CONTENTS ...... vii LIST OF TABLES ...... xi LIST OF FIGURES ...... xii

CHAPTER 1: Introduction and Literature Review 1.1 Introduction ...... 1 1.2 Study objectives ...... 3 1.3 Study sites ...... 4 1.4 Literature Review ...... 5 1.4.1 Produced water ...... 5 1.4.2 Physical and chemical composition of produced water ...... 6 1.4.3 Fate of produced water discharges ...... 7 1.4.4 Toxicity of produced water ...... 8 1.4.5 Methods for monitoring produced water discharge ...... 9 1.4.6 Molecular techniques for marine microbial community Analysis ...... 12 1.4.7 Marine hydrocarbon degraders ...... 16 1.4.8 Produced water microbial community ...... 17

Connecting text ...... 21 CHAPTER 2: Characterization of the Bacterial Community Structure and the Physicochemical Characteristics in the Produced Water and Seawater from the Hibernia Oil Production Platform Abstract ...... 22 2.1 Introduction ...... 23 2.2 Materials and Methods ...... 28 2.2.1 Sample collection ...... 28 2.2.2 Nutrients analyses ...... 29 2.2.3 Chlorophyll a analysis ...... 30 2.2.4 Total bacterial counts ...... 31 2.2.5 Organic chemical analyses ...... 31 2.2.6 Genomic DNA extraction ...... 34 2.2.7 PCR amplification of 16S rRNA gene for produced water clone libraries ...... 35 2.2.8 PCR amplification of 16S rRNA genes for produced water and seawater DGGE ...... 38 2.2.9 Produced water and seawater Denaturing Gradient Gel Electrophoresis (DGGE) analysis ...... 39 2.2.10 Nucleotide sequence accession numbers ...... 40 2.3 Results ...... 41 2.3.1 Physicochemical and chemical analyses for the produced

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water and seawater ...... 41 2.3.2 Produced water bacterial clone library analysis ...... 41 2.3.3 Archaeal clone library analysis ...... 42 2.3.4 Statistical analysis of clone libraries ...... 43 2.3.5 Produced water bacterial and archaeal DGGE analysis ...... 44 2.3.6 Seawater bacterial DGGE analysis ...... 45 2.4 Discussion ...... 46 2.4.1 Physical and chemical characterization of the Hibernia produced water ...... 46 2.4.2 Comparison of the produced water and surrounding seawater ...... 47 2.4.3 Comparison of physicochemical and biological parameters ..... 49 2.4.4 Produced water bacterial community structure ...... 51 2.4.5 Produced water archaeal community structure ...... 54 2.4.6 Comparison of physicochemical parameters with the bacterial and archaeal community structure ...... 55 2.4.7 Comparison of clone library and DGGE ...... 56 2.4.8 Comparison of produced water and seawater bacterial community structure ...... 56 2.5 Conclusions ...... 57 2.6 Acknowledgements ...... 58

Connecting text ...... 74 CHAPTER 3: Characterization of the Bacterial Community in Terra Nova Seawater and Produced Water Abstract ...... 75 3.1 Introduction ...... 76 3.2 Materials and Methods ...... 78 3.2.1 Sample collection ...... 78 3.2.2 Genomic DNA extraction ...... 79 3.2.3 PCR amplification of the 16S rRNA gene ...... 79 3.2.4 Denaturing Gradient Gel Electrophoresis (DGGE) analysis ...... 79 3.2.5 Constructing “Marine” 16S rRNA gene taxonomic microarray ...... 80 3.2.6 PCR amplification and microarray hybridization ...... 81 3.2.7 Nucleotide sequence accession numbers ...... 82 3.3 Results ...... 83 3.3.1 DGGE analysis for produced water and seawater ...... 83 3.3.2 Produced water bacterial and seawater bacterial phylogentic analysis ...... 83 3.3.3 Microarray analysis of produced water and seawater ...... 84 3.4 Discussion ...... 86 3.4.1 Produced water bacterial diversity ...... 86 3.4.2 Seawater bacterial community analysis ...... 88 3.5 Conclusions ...... 89 3.6 Acknowledgements ...... 89

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Connecting text ...... 95 CHAPTER 4: Analysis of Bacterial Diversity and Metals in Produced Water, Seawater and Sediments From Around an Offshore Oil and Gas Production Platform. Abstract ...... 96 4.1 Introduction ...... 97 4.2 Materials and Methods ...... 98 4.2.1 Sample collection ...... 98 4.2.2 Water sample collection ...... 99 4.2.3 Sediment sample collection and analysis ...... 100 4.2.4 Genomic DNA extraction ...... 102 4.2.5 PCR amplification of the 16S rRNA gene ...... 102 4.2.6 Denaturing Gradient Gel Electrophoresis (DGGE) analysis ... 102 4.2.7 Nucleotide sequence accession numbers ...... 103 4.3 Results ...... 103 4.3.1 Physicochemical analyses of the produced water and the surrounding seawater ...... 103 4.3.2 Produced water metal analysis ...... 103 4.3.3 DGGE analyses ...... 105 4.3.4 Phylogenetic analysis of produced water ...... 106 4.3.5 Seawater and Sediments phylogentic analysis ...... 106 4.4 Discussion ...... 107 4.4.1 Sediment metal monitoring ...... 107 4.4.2 Produced water bacterial diversity ...... 109 4.4.3 Seawater and sediment bacterial DGGE analysis ...... 110 4.4.4 Epsilon-proteobacteria subgroup ...... 111 4.4.5 Relationship between Fe, Mn and Arcobacter ...... 113 4.5 Conclusions ...... 113 4.6 Acknowledgements ...... 114

Connecting text ...... 130 CHAPTER 5: Microbial Community Characterization of The Gully: A Marine Protected Area Abstract ...... 131 5.1 Introduction ...... 132 5.2 Materials and Methods ...... 134 5.2.1 Site description and sample collection ...... 134 5.2.2 DNA extraction ...... 135 5.2.3 PCR amplification of 16S rRNA gene ...... 136 5.2.4 Denaturing Gradient Gel Electrophoresis (DGGE) ...... 137 5.2.5 Sequencing analysis and phylogenetic analyses ...... 138 5.2.6 Nucleotide sequence accession numbers ...... 139 5.3 Results ...... 139 5.3.1 Environmental characteristics of the water column ...... 139 5.3.2 Archaeal and bacterial DGGE analysis ...... 141 5.3.3 Bacterioplankton composition in the Gully ...... 141

5.4 Discussion ...... 143 5.4.1 Comparison of phylogeny and physical characteristics ...... 146 5.5 Acknowledgements ...... 148

Connecting text ...... 154 CHAPTER 6: Nested-PCR and Quantitative-PCR Methods for Rapid and Sensitive Detection of Thermoanaerobacter spp. in seawater Surrounding the Hibernia and Terra Nova Oil Production Platforms Abstract ...... 155 6.1 Introduction ...... 156 6.2 Materials and Methods ...... 158 6.2.1 Sample collection ...... 158 6.2.2 Produced water/seawater dilution samples ...... 159 6.2.3 Genomic DNA extraction ...... 159 6.2.4 PCR amplification of the 16S rRNA gene ...... 160 6.2.5 Denaturing Gradient Gel Electrophoresis (DGGE) analysis ...... 160 6.2.6 Q-PCR and Nested-PCR primer design ...... 160 6.2.7 Q-PCR amplification ...... 161 6.2.8 Nested-PCR amplification ...... 163 6.3 Results and Discussion ...... 164 6.3.1 Nested-PCR result from Terra Nova surrounding seawater ...... 164 6.3.2 DGGE detection limit ...... 165 6.3.3 Species-specific oligonucleotide probe detection methods ...... 166 6.3.4 Q-PCR analysis ...... 167 6.3.5 Testing the Q-PCR method on Hibernia seawater samples .. 168 6.3.6 Testing the nested-PCR method on Hibernia seawater samples ...... 170 6.4 Conclusions ...... 171 6.5 Acknowledgements ...... 171

CHAPTER 7: General Discussion and Conclusions ...... 184

REFERENCES ...... 192

APPENDIX ...... 218

A. Produced water conference paper ...... 219

B. Table of microarray probes ...... 234

LIST OF TABLES

Table 2.1. Physicochemical and chemical characteristics of Hibernia produced water and seawater ...... 59 Table 2.2. Concentration of silicate, phosphate and ammonia in seawater at different distances from the Hibernia platform ...... 60 Table 2.3. Salinity and temperature in seawater at different distances from the Hibernia platform ...... 61 Table 2.4. Total bacterial counts and chlorophyll in seawater at different distances from the Hibernia platform ...... 62 Table 2.5. Bacterial and archaeal clone library analysis ...... 63 Table 4.1. Bottom sediment and seawater sample locations ...... 115 Table 4.2. Thebaud produced water metal analysis ...... 116 Table 6.1. Q-PCR and nested-PCR primers sets used in this study ...... 173 Table 6.2. Surrounding seawater Q-PCR results ...... 174 Table 6.3. Surrounding seawater nested-PCR results ...... 175

LIST OF FIGURES

Figure 1.1. Locations of the major oil and gas production platforms in Eastern Canada ...... 20 Figure 2.1. Phylogenetic relationship of the bacterial 16S rRNA gene sequences obtained from Hibernia produced water clone library ...... 64 Figure 2.2. Phylogenetic relationship of the archaeal 16S rRNA gene sequences obtained from Hibernia produced water clone library ...... 66 Figure 2.3. Rarefaction analysis of the overall, combined bacterial 16S rRNA gene clone library recovered from Hibernia produced water sample ...... 67 Figure 2.4. Hibernia produced water 16S rRNA gene DGGE fingerprint ...... 68 Figure 2.5. Phylogenetic relationship of the bacterial 16S rRNA gene sequences obtained from Hibernia produced water ...... 69 Figure 2.6. Phylogenetic relationship of the archaeal 16S rRNA gene sequences obtained from Hibernia produced water ...... 70 Figure 2.7. DGGE fingerprints cluster analysis from Hibernia seawater and produced water ...... 71 Figure 2.8. Hibernia seawater 16S rRNA gene DGGE fingerprint ...... 72 Figure 2.9. Phylogenetic relationship of the bacterial 16S rRNA gene sequences obtained from Hibernia seawater DGGE ...... 73 Figure 3.1. DGGE fingerprints cluster analysis from Terra Nova produced water and surrounding seawater samples ...... 90 Figure 3.2. Phylogenetic relationship of the 4 bacterial 16S rRNA gene sequences obtained from Terra Nova produced water sample ...... 91 Figure 3.3. Phylogenetic relationship of the bacterial 16S rRNA gene sequences obtained from Terra Nova surrounding seawater samples ...... 92 Figure 3.4. Bacterial phyla detected from the produced water and seawater samples using the marine 16S rRNA gene taxonomic microarray ...... 93 Figure 4.1. Thebaud sampling locations ...... 117 Figure 4.2. Plot of barium concentration vs. aluminum concentration .... 118 Figure 4.3. Plot of iron concentration vs. aluminum concentration ...... 120 Figure 4.4. Plot of manganese concentration vs. aluminum concentration ...... 122 Figure 4.5. Thebaud seawater DGGE ...... 124 Figure 4.6. Phylogenetic relationship of the bacterial 16S rRNA gene sequences obtained from Thebaud produced water sample .. 125 Figure 4.7. Phylogenetic relationship of the bacterial 16S rRNA gene sequences obtained from Thebaud seawater samples ...... 126 Figure 4.8. Thebaud sediment DGGE ...... 127

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Figure 4.9. Phylogenetic relationship of the bacterial 16S rRNA gene sequences obtained from Thebaud sediment samples ...... 128 Figure 5.1. Conductivity, temperature and depth profile ...... 149 Figure 5.2. Cluster analysis of the Gully bacterial and archaeal DGGE banding patterns ...... 150 Figure 5.3. Phylogenetic relationship of the bacterial 16S rRNA gene sequences obtained from the 10 m water sample ...... 151 Figure 5.4. Phylogenetic relationship of the bacterial 16S rRNA gene sequences obtained from the water samples below 500 m ..... 152 Figure 5.5. Phylogenetic relationship of the archaeal 16S rRNA gene sequences obtained from all depth samples ...... 153 Figure 6.1. Terra Nova produced water and surrounding seawater nested-PCR results ...... 176 Figure 6.2. Produced water and seawater dilution series DGGE analysis ...... 177 Figure 6.3. Detection limit of Q-PCR primers (TMF1 and TMR1) using regular PCR ...... 178 Figure 6.4. Q-PCR standard curves ...... 179 Figure 6.5. Correlation between numbers of copies per 5 μL of genomic DNA vs. PW/SW dilution ...... 180 Figure 6.6. DGGE analysis of the 1 m deep surrounding seawater from around the Hibernia production platform ...... 181 Figure 6.7. DGGE analysis of the 50 m deep surrounding seawater from around the Hibernia production platform ...... 182 Figure 6.8. Schematic representation of the transport and dilution of Hibernia produced water ...... 183

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CHAPTER 1

INTRODUCTION AND LITERATURE REVIEW

1.1 INTRODUCTION

Offshore Eastern Canada has long been recognized as one of the world's most hydrocarbon-rich areas, but it is on a much smaller scale than the oil sands in northern Alberta. The steady decline in new fossil fuel resources and skyrocketing prices have prompted companies to invest in offshore oilfields. As a result, the exploitation of offshore oil and gas resources has been seen by Ottawa and the Maritime provinces as an important tool for improving economic development. In light of these economic incentives, it is reasonable to expect that the environmental controls may seem to be applied relatively loosely. In Canada, offshore oil production is regulated through a partnership of government and industry with less stringent enforcement and more self-regulation for environmental responsibility (Lee, personal communication).

With increasing oil demand and consumption, the frequency of petroleum- related incidents could increase. Petroleum-related pollution events have the potential to cause extensive damage to marine habitats as well as to the fishing and tourism industries. Catastrophic oil spills, such as the BP Deepwater Horizon and the Great Barrier Reef oil spills of 2010, Prestige 2002, Erika 1999, Exxon

Valdez 1989 and Odyssey 1988, remind us of the need for a better understanding of the impact of petroleum production, transport and consumption on the surrounding marine ecosystems. Other than occasional large oil spills, produced water is the most common petroleum-related contaminant frequently discharged

into the surrounding marine ecosystems. One of the major environmental contaminants found in produced water is petroleum hydrocarbons. Biodegradation offers one of the best options for the clean-up of large-scale petroleum hydrocarbon contamination from both produced water discharges and oil spills.

However, very little is known about the biological processes involved in the bioremediation of contaminated marine environments. Generally, after direct physical collection of the visible oils, bioremediation, by stimulating the biodegradation of spilled oil, is one of the safest and most effective methods for dealing with marine petroleum hydrocarbon contamination. Some of the important recent advances in microbial ecology have enabled the identification of microbial populations effective in the degradation of hydrocarbons in natural environments. Most of these hydrocarbon degrading microbial populations, however, remain uncultured or not cultivable, so there is little or no understanding of their physiology, functions, and effects on contaminated marine ecosystems.

Although more effort should be made to improve current methods for isolating oil-degrading bacteria, the initial steps still rely on the ability to identify and characterize the types of microorganisms present in natural environments using culture-independent methods.

Methods for monitoring the environmental effects of produced water discharges that do not require culturing are needed. These culture-independent methods would provide a better understanding of microbial community structure and hydrocarbon-degrading potential and enable comparative evaluation of potential effects from produced water discharges on the surrounding marine ecosystems. However, the Canadian offshore oil and gas production platforms and

the surrounding northwestern Atlantic Ocean represent some of the most poorly characterized and unique microbial habitats and have the potential to harbor novel microbial diversity. Also, the concentration and composition of chemicals in produced water vary considerably in different geological formations, so region- specific studies should be carried out to determine the environmental risk from individual oil and gas production platforms.

1.2 STUDY OBJECTIVES

Before making a decision to use alternative disposal methods that have little or no net environmental benefit, a comprehensive study on the potential impact of produced water discharged from Eastern Canada‟s frontier oil and gas reserves is needed. The main research objective in this study were to investigate the phylogenetic diversity of bacterial and/or archaeal communities in the produced waters and the surrounding seawater from Hibernia, Terra Nova and

Thebaud oil and gas production platforms (Fig. 1.1). As an important component of aquatic microbial food webs, bacterioplankton play a significant role in the global cycle of carbon, nitrogen and other elements. Understanding how the composition of the microbial community changes over time and space in a given environment could shed light on the ecological role of microbes in this environment.

The objectives of this study were to: (i) compare the bacterial community structure in the seawater surrounding the platforms and detect changes in the seawater bacterial community structure that might be related to the influence of the produced water, (ii) characterize the natural bacterial and/or archaeal

community structure in the produced water, (iii) develop molecular methods to monitor the bacterial community structure in the seawater and to track the dispersion of produced water.

In this study, we used 1) DGGE and a marine taxonomic microarray to characterize and compare the bacterial community composition in seawater and/or sediment around the production platforms; 2) DGGE and clone libraries to characterize and compare the produced water bacterial and/or archaeal community composition from different production platforms; 3) Q-PCR and nested-PCR to develop tracking methods to monitor the microbial component of the produced water in the seawater and sediment surrounding the production platforms.

1.3 STUDY SITES

Samples from three oil production platforms were analyzed in this study and samples were collected from 2005 to 2008:

1. The Hibernia Production Platform is the largest offshore oil producing

platform in Canada, located 315 km east of St. John's, NL. The reservoir

contains about 1 billion barrels of oil and is producing ~200,000 barrels of

crude oil per day (bpd). Produced water is discharged 40 m below surface.

2. The Terra Nova Production Platform, soon-to-be Canada‟s second major

offshore oilfield, is a Floating Production, Storage and Offloading facility

(FPSO) located 350 km east-southeast of St. John's, NL. It has an

estimated 370 million barrels of recoverable crude oil and a production

capacity of 110,000 bpd. Produced water is discharged at 10 m below the

surface.

3. The Thebaud Production Platform is part of the Sable Offshore Energy

project (Canada's first offshore natural gas project) and is located 230 km

from Halifax, NS. Production of gas averages 500 million cubic feet per

day (cfpd). Produced water is also discharged at 10 m below the surface.

Although the total oil production from Thebaud is much lower than that of

Hibernia and Terra Nova, there are greater environmental concerns due to

the close proximity to the sensitive and valued environment of the Gully

Marine Protected Areas.

1.4 LITERATURE REVIEW

1.4.1 PRODUCED WATER

Produced water is generally the largest volume of waste generated from offshore platforms during oil and gas production (Stephenson 1991). It can constitute as much as 80% of the waste produced from oilfield operations

(McCormack et al. 2001). The amount of produced water generated depends on the characteristics of the particular oil field and has a tendency to increase during the life of each well. In 2003, it was estimated that 667 million metric tons of produced water were discharged offshore throughout the world (OGP 2004;

Garland 2005). Generally, produced water consists of water that has accumulated or is trapped within the petroleum in geologic formations over millions of years

(Collins 1975). This ancient water is called formation water and is as old as the fossil fuel in the reservoir. With increasing production, a large amount of seawater is injected into the formation to replace the oil that has been extracted, thus maintaining the pressure in the well. This injected seawater is mixed with the

formation water during the oil recovery process (Neff 2002) and is generally referred to as produced water.

1.4.2 PHYSICAL AND CHEMICAL COMPOSITION OF PRODUCED

WATER

No two produced waters are alike. The physical and chemical properties of produced water can vary greatly depending on the geochemistry of the petroleum formation, the amount of injected seawater, and the type of process chemicals used. Produced water consists of complex dissolved and dispersed mixtures of various organic and inorganic chemicals specific to the type of petroleum formation and the production system. There are more than 17,000 distinct compounds in petroleum, making it one of the most complex natural chemical mixtures (Marshall and Rogers 2003). Although much of the volume of produced water is simply injected surrounding seawater, the injected seawater is often heated within the formation and released at high temperature (up to 130°C), so the produced water can dissolve a wide variety of contaminants. Neff (2002) summarized the concentration ranges of the common chemicals found in produced waters world-wide. These include heavy metals, radionuclides, inorganic nutrients (ammonia, sulfate, nitrate, etc), organic acids, phenols, unidentified polar compounds, petroleum hydrocarbons and chemical amendments that are used in various phases of production (i.e. emulsifiers, corrosion inhibitors and biocides) (Johnsen et al. 2004; Neff 2002; Somerville et al. 1987). Generally in produced water, petroleum hydrocarbons are the chemicals of greatest environmental concern, so produced water is usually treated to remove

dispersed oil prior to discharge, depending on local environmental regulations and available technology. In offshore Canada, produced water is currently discharged into the surrounding marine environment under strict regulation (Canada‟s revised

Offshore Waste Treatment Guideline 2002; National Energy Board 2002). Similar to the North Sea regulations, only the concentrations of petroleum hydrocarbons in the discharges are controlled, and the regulatory limit for total petroleum hydrocarbons in produced waters discharged offshore in Canada is 40 mg/L

(Ayres and Parker 2001; National Energy Board 2002). Other than the evaluated chemical concentrations, most produced waters are discharged at high temperature and have salinities that are higher than the surrounding seawater

(Ritternhouse et al. 1969; Large 1990; Collins 1975). Therefore, the produced water is a hot contaminated brine with a chemical composition quite different from that of seawater.

1.4.3 FATE OF PRODUCED WATER DISCHARGES

Rapid dilution of produced water with ambient seawater is often believed to be sufficient to mitigate any influence from produced water on the marine environment. A modeling study by Somerville et al. (1987) found that even at a

10,000 m3/day discharge rate, a 100-fold dilution was estimated at 50 m from the platform, and a 2800-fold dilution was estimated at 1000 m from the platform. At a low discharge rate (2000 m3/day), Furuholt (1996) estimated a 1000-fold dilution would be found at 50 m downstream from the discharge point. However, the dilution rate was expected to decrease at greater distances from the discharge point (Terrens and Tait 1993; Strømgren et al. 1995; Brandsma and Smith 1995;

Smith et al. 2004). Generally, the dilution rate is dependent on the discharge rate, ambient current speed, water turbulence, water depth, water column stratification, and differences in density and chemical composition between produced water and the surrounding seawater. In terms of petroleum hydrocarbons, Terrens and Tate

(1996) found that at an 11,000 m3/day discharge rate, just 20 m downstream from the discharge most Benzene, Toluene, Ethylbenzene and Xylene (BTEX) and

Polycyclic Aromatic Hydrocarbons (PAHs) were diluted by 2,000 to 14,900-fold.

These findings suggested that the rapid dilution of the discharge would dilute the contaminant concentration to non-acute toxic levels within a very short distance from the discharge point.

1.4.4 TOXICITY OF PRODUCED WATER

Considering the physical and chemical structure differences, no two produced waters are identical, and these characteristics could also change over the lifetime of the same well. Therefore, a wide variation in toxicity can be found in different produced waters (summary by Patin (1999)). Generally, a moderate to low toxicity is found in most produced waters. The toxicity can be caused by physical and/or chemical factors (i.e. temperature, salinity, nutrients, heavy metals, and petroleum hydrocarbons). Petroleum hydrocarbons are the chemicals of greatest environmental concern in produced water due to their potential for bioaccumulation and toxicity, particularly those that are soluble in water.

Produced water, with its hydrocarbon content alone, is known to be lethal to many organisms contacting the effluent at the discharge pipe. Paine et al. (1992) found that even after short term exposure, hydrocarbons at concentration as low as 1.3

mg/L had lethal effects on fish larvae, and sublethal effects were observed at concentration as low as 0.13 mg/L. Other studies have revealed that a variety of physiologically adverse effects on marine organisms, such as damage to the reproductive and developmental process (Meier et al. 2007; Nicolas 1999), biochemical and hormonal changes (Schwaiger et al. 2002) and formation of

DNA adducts (i.e. genetic deformity) (Aas et al. 2000), were caused by the various petroleum hydrocarbons found in produced water. Other than toxicity from the petroleum hydrocarbons, Pillard et al. (1999) found that the survival rates of a number of marine organisms, like shrimps and minnows, were affected by elevated salinity levels in seawater mixed with produced water. Other than the effects on higher organisms, Gamble et al. (1987) and Stephenson et al. (1994) found that produced water, at concentrations as low as 400-500-fold dilutions, could have an effect on marine planktonic communities (i.e. phytoplankton, zooplankton, bacteria). Most of these effects from produced water are sub-lethal and induced by concentrations higher than the concentrations commonly found in the surrounding seawater around the discharges. Therefore, acute toxic effects on the biological communities in the ambient water column are expected to be minimal and localized to a small area adjacent to the discharge.

1.4.5 METHODS FOR MONITORING PRODUCED WATER

DISCHARGE

The environmental impact of produced water on the natural environment has usually been measured in terms of the chemical composition (Tibbets et al.

1992; Jacobs et al. 1992; Flynn et al. 1995) or by ecotoxicological assessment

(Brendehaug et al. 1992; Neff and Sauer 1995). Monitoring by chemical composition usually entails a direct measurement of the chemicals, unique to the produced water, in the surrounding environment. Ecotoxicological assessments, on the other hand, are conducted by measuring acute toxicity, chronic toxicity, bioaccumulation and sometimes by monitoring biomarkers. Acute toxicity is expressed as the concentration of a toxin that causes harmful effects through short-term exposure. Chronic toxicity is expressed as the concentration that produces harmful effects through long-term or repeated/continuous exposure.

Other than toxicity from produced water, organisms near the produced water discharge might accumulate toxic metals and hydrocarbons from the ambient environment or from their food sources. This bioaccumulation could induce changes in the organisms at the physiological or biochemical levels that have no immediate harmful effects to the organisms, but these changes could be used as biomarkers to monitor the longer-term exposure effects of produced water discharge and as an early warning of possible risk to the exposed organisms

(Forbes et al. 2006).

Considering the dilution factor in the environment, monitoring for components of produced water, like metals, usually indicated that they were diluted to background concentrations in seawater within a few meters of the discharge point, so it was believed that it did not to contribute to ecological risk

(Neff 2002). In terms of ecotoxicological measurements, a number of studies in the North Sea deployed fish (Abrahamson et al. 2008; Børseth and Tollefsen

2004; Hylland et al. 2008) and shellfish (Durell et al. 2006; Hylland et al. 2008;

Johnsen et al. 1998; Neff et al. 2006; Røe Utvik et al. 1999) to monitor the longer

term exposure of produced water in the surrounding environment. The studies indicated that exposure levels were generally low. The exposure level and the bioaccumulation concentrations were generally found to decrease with distance down-current from the discharges (Børseth and Tollefsen 2004; Neff and Burns

1996), suggesting that produced water caused minor environmental impact after discharge. Therefore, with the currently available detection methods, there is no evidence of harmful effects on the marine environment from produced water.

With such low levels of contaminants detected in the surrounding environment, the environmental impact of produced water was also assessed using plume dispersion-transport modeling (Brandsma and Smith 1995; Brandsma et al.

1992; Washburn et al. 1999). The modeling methods from Rivkin et al. (2000) and Khelifa et al. (2003) predicted that the discharge of inorganic nutrients and dissolved organics in the produced water effluent may alter the planktonic community structure.

There is still too much uncertainty about the environmental fate of the long term effects from produced water discharge. Additional research is required for a better understanding of the fate of produced water in the environment, the effects of low-level exposure and chronic toxicity. Therefore, there is a need to develop methods that are sensitive enough to detect the components in produced water at the levels found in the surrounding ecosystem. Since microorganisms are typically the first organisms to respond to changes in their environment at the population level, they may be the key to developing an effective method. With their short generation times and relatively large population densities, they can rapidly respond negatively or positively to change. In light of their sensitivity and

rapid response times, we hypothesized that microbial monitoring using molecular methods could be used to define the extent of impact from the discharge of produced water in the surrounding marine environment.

1.4.6 MOLECULAR TECHNIQUES FOR MARINE MICROBIAL

COMMUNITY ANALYSIS

Marine bacterioplankton play a central role in terms of their biomass and organic matter/nutrient recycling in marine ecosystems (Atlas and Bartha 1993;

Whitman et al. 1998). Although culture-based methods have traditionally been used as the primary tools to study microorganisms in the environment, they do not provide the comprehensive information that is needed to understand the complex structure of microbial communities (Van Hamme et al. 2003). Culture- independent surveys of rRNA are powerful tools for detecting the presence of a high diversity of these microorganisms (Amann et al. 1995). In 1965,

Zuckerkandl and Pauling (1965) revealed that the characteristics of biological macromolecules could be used to infer evolutionary relationships among microbes. Molecular-based characterization was first used in microbial taxonomy in 1977 (Woese and Fox 1977) and since then our understanding of microbial diversity has been greatly improved. The ribosomal RNA (rRNA) gene was chosen as the phylogenetic marker for several reasons related to its role in biology

(suggested in Woese and Fox 1977; Klijn et al. 1991; Ludwig et al. 1998). One of the first seawater 16S rRNA phylogenetic analyses was obtained from the

Sargasso Sea (Giovannoni et al. 1990). The result further revealed that many identified sequences were unrelated to sequences from previously known

cultivated organisms, suggesting that these culture-independent methods could greatly improve our understanding of the microbial diversity in the ocean. The culture-independent methods commonly used to characterize microbial communities were reviewed comprehensively in Spiegelman et al. (2005) who concluded that each method has specific advantages and limitations. Therefore, the selection of the right method would be dependent on the questions to be answered and on the number of samples to be processed within a reasonable period of time. Although 16S rRNA gene clone library analysis is often the best method for obtaining the greatest estimate of diversity and can provide direct sequence information for individuals (DeSantis et al. 2007), analysis of individual clones is a time-consuming, expensive and extremely inefficient approach for comparing a multitude of microbial communities. In terms of comparing microbial assemblages and assessing temporal and spatial changes on a large- scale, PCR-DGGE offers the best compromise and allows higher sample throughput with DNA-based phylogenetic resolution for an entire target community (Casamayor et al., 2000; Casamayor et al. 2002; Felske et al. 1998;

Muyzer et al. 1993; Nubel et al. 1999; Riemann et al. 1999). After separation, each band on the DGGE gel represents one of the major species that comprises

1% or more of the community sample and thus, can be isolated (Muyzer et al.

1993; Muyzer et al. 1998; Muyzer and Smalla 1998). Hence, with the constant input and the large volume of produced water, the changes in the surrounding microbial community, if any, would be expected to be fairly significant.

Therefore, the „1% resolution‟ of the DGGE method might potentially be sufficiently sensitive to monitor changes in the water column. The DGGE method

can handle much larger numbers of samples than clone libraries for comparing microbial assemblages over space and time (Casamayor et al. 2002) and one study revealed that the results from bands in DGGE profiles are comparable to the results from clone libraries (Roling et al. 2001). However, other high-throughput methods, like microarrays, should also be evaluated and compared with DGGE to ensure the accuracy of the microbial community structure analyzed with DGGE.

Using culture-independent methods, Lee and Fuhrman (1991) revealed that the bacterioplankton population differed significantly between different ocean basins. However, at a smaller scale (meters to kilometers), studies found high similarities in bacterioplankton distribution in the horizontal scale in the same and adjacent sea areas (Gonzales and Moran 1997; Murray et al. 1998; Riemann and

Middelboe 2002; Riemann et al. 1999). Monitoring the changes in this kind of highly similar bacterioplankton populations in seawater surrounding the oil production platforms might provide the sensitivity that is needed to pinpoint the impact of produced water in the surrounding seawater.

However, the microbial community analysis approach might still not have the sensitivity to detect the minor changes caused by rapid dilution, so finding other more sensitive molecular methods for monitoring the large regions of microbiota around the production platforms is required to identify the potential impact of produced water. More specific PCR-based methods could be used to improve the reliability of detection in the surrounding environment around the production platforms.

Specific oligonucleotide PCR-based methods have provided a very sensitive and specific tool for detecting very low numbers of bacteria including

viable but non-culturable forms. The use of specific oligonucleotide probes in methods like nested-PCR and quantitative-PCR (Q-PCR) to detect pathogens from various environments have been documented (Kim et al. 2008; Nayak and

Rose 2007; Furet et al. 2009; Waage et al. 1999; Arias et al. 1995). Q-PCR is considered to be an effective tool to monitor microbial species because of its sensitivity and is increasingly being used as the method of choice for quantifying the abundance of microbial taxa in environmental samples (Yu et al. 2006;

Kindaichi et al. 2006; Da Silva and Alvarez 2007). Q-PCR primer sets usually targeting signature regions of the 16S rRNA gene from unique organisms were used to track the signature organisms in their surrounding environment. A study employing Q-PCR to track specific species from point-source pollution in the surrounding environment has recently emerged (Shanks et al. 2008). This approach combines the extreme sensitivity of PCR and the ability to quantitatively process large numbers of samples rapidly and would enable reliable detection of the unique microorganisms from produced water in the surrounding seawater.

Similar to Q-PCR, nested-PCR (Arias et al. 1995) is also an effective and robust tool to detect microbial species, but cannot be used quantitatively. However,

Loffler et al. (2000) and Ritalahti and Loffler (2004) found that the nested-PCR approach could increase the detection limit by up to 2 orders of magnitude for the analysis of the negative samples from PCR, so the nested-PCR method could usually be used to verify negative results from other PCR methods.

The key to the success of using these methods relies on calibrating the method. In terms of calibrating the quantification for both Q-PCR and nested-

PCR, plasmid DNA containing cloned target sequences have been used in a

number of studies to create a standard curve (Galluzzi et al. 2004; Wawrik et al.

2002; Zhu et al. 2005). It is also the only available method for calibrating Q-PCR detection of uncultured taxa (Suzuki et al. 2000). The advantage of calibrating with plasmid DNA is that the exact number of target genes can be calculated by measuring the concentration of a known DNA standard.

1.4.7 MARINE HYDROCARBON DEGRADERS

With the large and continued input of petroleum hydrocarbons from the produced water discharge, microorganisms specializing in using the hydrocarbons could potentially be enriched. Identification of the natural hydrocarbon-degrading populations from the seawater could improve the success for natural attenuation of residual hydrocarbons in the released produced water. Hydrocarbons from crude oil (600,000 tonnes/yr) (National Research Council 2002) enter the sea by anthropogenic input and natural seepage over many, many years, but the fact that the ocean is not currently covered with a vast layer of oil is due to the biodegradative capacity of the ocean. There are more than 75 genera of bacteria in the ocean that are able to grow on hydrocarbons (Prince 2005). Some of these, defined as obligate marine hydrocarbon-degrading bacteria (OHCB), have evolved or acquired the ability to utilize hydrocarbons as the sole carbon and energy sources and play a significant and global role in the natural cleansing of hydrocarbon pollution (Yakimov et al. 2007). Most of the OHCB are from the genera Alcanivorax, Cycloclasticus, Marinobacter, Neptunomonas, Oleiphilus, and Oleispira within the Gamma-proteobacteria. Many other „non-obligate‟ hydrocarbon degraders from the genera Vibrio, Pseudoalteromonas,

Marinomonas, Halomonas, and many more are also found in the marine environment. These are potentially the same bacteria that attenuate residual hydrocarbons from the produced water in the surrounding seawater. Therefore, a better understanding of marine hydrocarbon-degrading microbes would expand our understanding of the fate of produced water in the surrounding seawater.

1.4.8 PRODUCED WATER MICROBIAL COMMUNITY

Other than the surrounding seawater, the microbial community from produced water could also provide the information needed to understand the fate of produced water discharges. Studies of the distribution of microorganisms in produced water from high-temperature oil reservoirs by construction of 16S rRNA gene libraries is so far limited to a few Californian reservoirs (Orphan et al.

2000; Orphan et al. 2003), an Alberta oilfield (Gonzales and Moran 1997), a long term water-flooded reservoir in China (Li et al. 2006; Li et al. 2007), and the

North Sea (Dahle et al. 2008). Produced water contains a wide range of hydrocarbons and nutrients from the petroleum formation and can support a wide variety of microorganisms, including fermentative organisms (Bonch-

Osmolovskaya et al. 2003; Grassia et al. 1996; L‟Haridon et al. 2002; Magot et al.

1997; Miroshnichenko et al. 2001; Ravot et al. 1997; Takahata et al. 2000), methanogens (Mueller and Nielsen 1996; Nazina et al. 1995a; Ni and Boone

1991; Nilsen and Torsvik 1996; Ollivier et al. 1997; Ollivier et al. 1998), manganese and iron reducers (Greene et al. 1997; Nazina et al. 1995b; Slobodkin et al. 1999), acetogens (Davydova-Charakhch‟yan et al. 1992; Hermann et al.

1992), nitrate and sulfate reducers (Cord-Ruwish et al. 1987; Leu et al. 1998;

Tardy-Jacquenod et al. 1996; Voordouw et al. 1991; Voordouw et al. 1992), and aerobic organisms (Dahle et al. 2008) (for reviews see Birkeland 2004; Magot

2005; Magot et al. 2000). However, the detection of these organisms in produced water does not indicate that the organisms were indigenous to the reservoir, i.e. being a natural inhabitant of the reservoir before the onset of any oil exploitation.

Most of the samples from oil reservoirs are, for practical and economical reasons, normally obtained from the well-head or even at a subsequent point in the pipeline system, after the produced water has been transported in pipelines that could extend several kilometers from the reservoir. As an example, there were some mesophilic organisms detected in samples from high temperature oil wells (Magot et al. 1992; Tardy-Jacquenod et al. 1998). The possible sources of contamination by organisms not native to the reservoir are numerous (Magot et al. 2000).

Furthermore, aerobic and anaerobic hydrocarbon degraders have been detected in produced water and oil formations (Grabowski et al. 2005; Li et al.

2007 Magot 2005; Rabus 2005; Rueter et al. 1994). A better understanding of the potential aerobic and anaerobic hydrocarbon-degrading microbes in the produced water will provide some insight on subsequent treatments for produced water. In this study, the microbial community structure from a sample of produced water from each of the platforms was characterized using culture-independent methods.

Attempts should be made to identify any unique microorganisms present in the produced water that could be used as tracers to develop Q-PCR and nested-PCR methods to track the effluent of the produced water discharge in the surrounding ecosystem. There are also needs to identify hydrocarbon degraders that have the

potential for natural attenuation of residual hydrocarbons in the released produced water.

Fig. 1.1. Locations of the major oil and gas production platforms in Eastern Canada.

Connecting text

There is a need to develop sufficiently sensitive methods to detect the effects from produced water discharge in the surrounding ecosystem. This study used the microbial community monitoring methods to define the degree of impact of the discharge of produced water into the surrounding marine environment. The first objective of this study was to characterize the chemical composition and the bacterial and archaeal diversity in the Hibernia produced water. The second objective of this study was to characterize the bacterial community in the seawater surrounding the Hibernia production platforms and to compare the result with those results using the traditional chemical and physicochemical monitoring methods.

CHAPTER 2

Characterization of the Bacterial Community Structure and the Physicochemical Properties of Produced Water and Seawater from the Hibernia Oil Production Platform

C. William Yeung1,2, Ken Lee3, Susan Cobanli3, Tom King3, Jay Bugden3, Lyle G. Whyte2, and Charles W. Greer1.

1National Research Council Canada, Biotechnology Research Institute, 6100 Royalmount Ave. Montreal, Quebec. H4P 2R2. 2Department of Natural Resource Sciences, McGill University, Ste-Anne-de- Bellevue, Quebec. H9X 3V9. 3Fisheries and Oceans Canada, PO Box 1006, Dartmouth, Nova Scotia. B2Y 4A2.

CONTRIBUTIONS OF AUTHORS Dr. Lee chose the sites for analysis. The sample collections were performed by Dr. Greer, Susan Cobanli, Jay Bugden and myself. The chlorophyll a analysis was performed by Jay Bugden and myself. The nutrient analysis and total bacterial counts were coordinated and performed by Susan Cobanli. The organic analysis was coordinated by Tom King. Writing and preparation of the manuscript was performed by me. Drs. Greer, Lee and Whyte critically read and edited the manuscript.

Abstract

The Hibernia production platform is the largest oil producing platform off the east coast of Canada. The produced water, which contains minor amounts of natural organic and inorganic components from the subsurface geological formation and the chemical amendments that aid in oil production, is the major source of contamination from the platform into the ocean. Before making a decision to use any alternative disposal methods that might have little or no net environmental benefit, a comprehensive study on the potential impact of produced water discharged from Eastern Canada‟s frontier oil and gas reserves is needed.

Microorganisms are typically the first organisms to encounter changes in their environment and were used as an alternative monitoring method. The first objective of this study was to characterize the bacterial and archaeal communities and the chemical composition of Hibernia produced water. The second objective was to characterize changes in the indigenous seawater bacterial community structure around the platform and to compare the changes with the chemical and physicochemical changes in the seawater. The DGGE analysis revealed that the seawater bacterial community is relatively stable with a similarity value around

70%, suggesting that the bacterial community structure in seawater around the platform was spatially stable, so the discharge of produced water did not have a detectable effect on the surrounding seawater bacterial community 500 m or more from the Hibernia production platform. Similarly, the chemical and physicochemical analyses also revealed that the discharge of produced water did not have a detectable effect on the surrounding seawater 500 m or more from the

Hibernia production platform. Both results concluded that any effect from the produced water discharge might be restricted to within 500 m from the discharge.

However, unique microorganisms and chemical characteristics were detected in the produced water, but appeared to be below detection limits in the surrounding water. These particular signature microorganisms may be useful as targets to monitor the dispersion of produced water in the surrounding ocean.

2.1 Introduction

Between the central Alberta and the offshore Atlantic deposits, Canada has the world's second-largest oil reserves just behind Saudi Arabia. The Hibernia oil

production platform is the largest offshore oil production platform in Canada and is situated on top of the fifth largest field ever discovered in Canada. The geological formation contains about 1 billion barrels of oil and is currently producing approximately 230,000 barrels of crude oil per day

(http://www.hibernia.ca). Along with the oil production, produced water is the largest volume of waste material discharged into the ocean under Canadian regulations (Canada‟s revised Offshore Waste Treatment Guideline 2002).

Produced water consists of formation water (water trapped with oil and gas in the reservoirs) and seawater injected during the drilling process to maintain reservoir pressure. The chemical compounds present in the produced water are basically the same as those present in the most water-soluble fraction of the crude oil in the reservoir, which includes natural organics (aromatic and aliphatic hydrocarbons, organic acids, phenols), metals and traces of chemicals added during oil production (Røe 1998). A large proportion of these compounds are potentially toxic, genotoxic and carcinogenic to aquatic organisms (Jha 2004; 2008).

Discharge of these chemicals poses a great environmental concern, and certain chemicals like the hydrocarbons pose an even greater environmental concern because of their toxicity, potential for bioaccumulation and persistence in the marine environment (Neff 1987; 2002). A thorough characterization of the

Hibernia produced water and the surrounding marine environment is necessary to estimate the potential environmental impact. At the beginning of Hibernia oil production, Paine et al. (1992) found that even after short term exposure, hydrocarbons at concentrations as low as 1.3 mg/L had lethal effects on fish larvae, and sublethal effects at concentrations as low as 0.13 mg/L. Although the

discharge concentration is generally much lower, the discharge volume is usually high and has a tendency to increase during the life of the well. This large volume of pollutants, even at low concentrations, could still have long term effects, and therefore requires monitoring at the contaminant and biological response levels.

Emerging evidence from the Baltic Sea and the North Sea investigations suggested that oil production activities may have an impact on fish and larvae at even greater distances from the platforms (Stagg and McIntosh 1996; Rybakovas et al. 2009). With current detection methods, however, there is very little evidence of harmful effects in the marine environment from the discharge of the produced water into the ocean. Some studies that used fish (Abrahamson et al. 2008;

Hylland et al. 2008) or shellfish (Hylland et al. 2008) to monitor the long term effects of produced water in the surrounding environment have shown that the exposure levels were generally low and caused minor biological impacts at the deployment locations over the experimental time periods. In order to monitor chronic effects, a better understanding of how the chemicals and the other components in the produced water effluent are transported and diluted in the surrounding marine environments is required. It is also necessary to develop more sensitive methods to monitor the components in produced water at the concentrations that are found in the surrounding ecosystem.

Marine bacterioplankton are central mediators of many oceanic biogeochemical processes with abundances that far exceed other living organisms

(Azam and Malfatti 2007). Since microorganisms are typically the first organisms to encounter and respond to changes (i.e., chemical, thermal, and etc.) in their environment at the population level, close monitoring of this ubiquitous biological

component may provide a sensitive indicator of water quality. The produced water conference paper (Yeung et al. 2010; Appendix A) proposed using molecular biology methods, like DGGE, to survey the 16S rRNA gene from the seawater microbial community structure as a monitoring method to define the extent of produced water discharge around the offshore platform. The results revealed that both bacterial and archaeal community structures were relatively stable within a 50 km region around the production platform, suggesting that any changes to this environment could be an indication of the impact from produced water.

Furthermore, a wide range of unique microorganisms have also been identified by culture-dependent and -independent methods from samples of produced water obtained from geographically distant oil reservoirs throughout the world (Grassia et al. 1996), predominately in the North Sea (Dahle et al. 2008),

California (Orphan et al. 2000; 2003), China (Li et al. 2006; 2007), Siberia

(Bonch-Osmolovskaya et al. 2003; Davydova-Charakhch‟yan et al. 1993) and

Western Canada (Grabowski et al. 2005; Voordouw et al. 1996). These microorganisms have different physiological characteristics and include fermentative microorganisms (Grassia et al. 1996; Van Hamme et al 2003), manganese and iron reducers (Rees et al. 1995; Greene et al. 1997; Slobodkin et al. 1999), acetogens (Davydova-Charakhch‟yan et al. 1993), sulfate reducers

(Nilsen et al. 1996; Rueter et al. 1994; Voordouw et al. 1993), sulfidogens

(L‟Haridon et al. 1995; Magot et al. 2000), methanogens (Nilsen and Torsvik

1996; Mochimaru et al. 2007), aerobic organisms (Orphan et al. 2000) and hydrocarbon-oxidizing bacteria (Nazina et al. 2001) (for summary see Magot et

al. 2000; Birkeland 2004). The identification of these microorganisms has helped not only to improve our understanding of petroleum microbiology, but also to develop new environmental and industrial applications, such as oil spill remediation (Prince et al. 1999) and microbial enhanced oil recovery (Banat et al.

2000; Banat 1995).

Despite all of the environmental and economic interests in the offshore oil and gas reserves in Eastern Canada from both government and industry, relatively limited knowledge is available on the microbial diversity in the marine environment around the production platform and subsurface ecosystems.

Therefore, an in-depth characterization of the microbial community structure in the produced water from this petroleum-rich region is much needed and long overdue. The molecular techniques, in particular, clone library and DGGE analysis of the 16S rRNA genes, have been shown to be effective for a more complete characterization of complex microbial assemblages in environmental samples (Amann et al. 1995).

In this study, the first objective was to characterize the bacterial and/or archaeal communities in the produced water and surrounding seawater using 16S rRNA gene clone libraries, DGGE and sequencing analysis. Another objective was to conduct comprehensive physical and chemical analyses of the seawater around the Hibernia production platform and the Hibernia produced water, and to compare the results from nutrient, petroleum hydrocarbon, and physicochemical analyses to the structure of the bacterial community. The combination of these results might help define the extent of potential impact from the discharge of produced water.

2.2 Materials and methods

2.2.1 Sample collection

Seawater samples were collected in July 2005 from various locations (S0:

500 m South, S1: 1000 m South, S3: 3000 m South, S6: 20 km South, N0: 500 m

North, NW0: 500 m Northwest, W0: 500 m West) around the Hibernia platform using a Seabird Niskin rosette frame (24 X 10 L bottles) containing a Seabird conductivity, temperature, and depth detectors (CTD). Depth profile are 2 m, 10 m, 25 m, 50 m and near-bottom (NB:0.5 m off the bottom) were collected at various locations around the production platform and at a reference seawater location 50 km (R50K) west of the Hibernia production platform. All containers used in the filtration were rinsed three times with the sample water. Surrounding seawater was collected in two different types of containers: acid rinsed 10 L

Nalgene® HDPE jerricans and solvent rinsed 4 L amber glass bottles (for organic chemicals analysis). Samples were stored at 4°C on the research ship until processed. For organic chemical analyses, samples stored in amber glass bottles were used. From the jerrican samples, an aliquot was removed for the measurement of nutrients (silicate, nitrate, nitrite, ammonia, and phosphate), chlorophyll a, total bacterial counts and bacterial community analyses. For microbial analysis, about 4 liters of seawater were immediately filtered through sterile 0.22 μm GSWP (Millipore) filters. Following filtration all filters were transferred to sterile 50 mL Falcon tubes and stored at -20°C until analyzed.

In addition, samples of fresh produced water were kindly provided by the personnel of the Hibernia production platform in 2006. All produced water

samples were collected at the well head at one hour intervals into two different types of containers: acid rinsed 10 L Nalgene® HDPE jerricans and solvent rinsed

4 L amber glass bottles. All containers were rinsed three times with produced water, then filled completely without headspace, sealed and transported to the research ship for further processing. Each sample container was labelled with pertinent information such as, date, location and time of sample collection. After collection, samples were transferred from the rig to the supply vessel. Once onboard the supply vessel, an aliquot was removed for the measurement of an aliquot was removed for the measurement of temperature, pH, salinity, nutrients

(silicate, nitrate, nitrite, ammonia, and phosphate), and bacterial community analyses. Four 4 L samples were stored at 4°C for chemical analysis and a

Nalgene jerrican sample was used immediately for microbial analysis. For microbial analysis, 2 liters of the produced water were immediately filtered through sterile 0.22 μm GSWP (Millipore) filters. Following filtration all filters were transferred to sterile 50 mL Falcon tubes and stored at -20°C until analyzed.

2.2.2 Nutrient analyses

Silicates - The determination of soluble silicates in seawater was based on the

Industrial Method 186-72W (Strickland and Parsons 1973) by reduction of silicomolybdate in acidic acid solution to '„molybdenum blue‟ by ascorbic acid which was read colorimetrically at 660nm. Oxalic acid was introduced to the sample stream before the addition of ascorbic acid to eliminate interference from phosphate.

Nitrate/Nitrite - The determination of nitrate/nitrite in seawater was based on an

Industrial Method 158-71W (Armstrong et al. 1967) by reducing nitrate to nitrite by a copper-cadmium reductor column. The nitrite ion reacted with sulphanilamide under acidic conditions to form a diazo compound. This compound coupled with N-1-naphlylethylenediamine dihydrochloride to form a reddish-purple azo dye, which was read colorimetrically at 550 nm. Nitrite was determined with identical chemistry but omitting the copper-cadmium column from the sample stream.

Ortho Phosphate - The determination of ortho-phosphate was based on Industrial

Method 155-71W (Murphy and Riley 1962) to measure the formation of phosphomolybdenum blue complex, read colorimetrically at 880 nm, produced by the reaction of phosphate with an acidic ammonium molybdate solution containing a small amount of antimony and ascorbic acid. The original method called for combining ammonium molybdate, antimony potassium tartrate and ascorbic acid into one working reagent. In house, the ascorbic acid is introduced into the sample stream separately.

Ammonia - The method for determination of ammonia (Kerouel and Aminot

1997) was based on the reaction of ammonia with ortho-phthaldialdehyde (OPA) and sulfite. The reaction was monitored fluorometrically with excitation at 370 nm and emission at 418-700 nm.

2.2.3 Chlorophyll a analysis

Duplicate 100 mL samples of seawater were collected in bottles, previously rinsed 3 times with the sample seawater, for chlorophyll a analysis.

The samples were filtered through two GF75 filters. The filters were transferred into 90% acetone in scintillation vials and stored at –20°C for at least 24 h prior to analysis. For analysis, the samples were allowed to warm up to room temperature

(for at least 1 h) and analyzed using a Turner Designs Fluorometer. After the initial reading was taken, samples were reanalyzed after the addition of 2 drops of

10 % HCl.

2.2.4 Total bacterial counts

Two mL of seawater sample were fixed in 1% (final concentration) paraformaldehyde for 10 minutes at room temperature in a cryogenic vial. The samples in cryogenic vials were then quick frozen in liquid nitrogen and stored in either liquid nitrogen or -80oC freezer until flow cytometric analysis (Li and

Dickie 2001). The abundance of bacteria was determined by FACSortTM flow cytometry (Becton Dickinson, San Jose, CA, USA) equipped with a 488-nm argon-ion laser (Li et al. 1995). Samples were stained with SYBR Green 1 and analyzed using standard protocols (Marie et al. 1999; Li and Dickie 2001).

Heterotrophic bacterial cells were discriminated from concomitantly-stained phytoplankton and cyanobacteria by listmode gating to eliminate redfluorescing cells. Flow cytometric counts of bacteria were verified using Acridine Orange

Direct Counts (AODC) (Hobbie et al. 1977).

2.2.5 Organic chemical analyses

Polycyclic aromatic hydrocarbons (PAH) and aliphatic hydrocarbons: This method was based on a modified version of EPA Method 8270. Briefly, 1 L of

produced water was spiked with a surrogate standard containing a series of deuterated aliphatic and aromatic hydrocarbons, and extracted with dichloromethane in a separatory funnel (2L). The sample extracts were concentrated on a TurboVap (Zymar) and the concentrated extracts were purified on a silica gel column. The purified extracts were exchanged into isooctane and spiked with internal standards. Purified sample extracts were analyzed using an

Agilent 6890 Gas Chromatograph (GC) coupled to a 5975 Mass Spectrometer

(MS). The column was a Supelco MDN-5s 30m x 250µm x 0.25µm (length x i.d. x film thickness) with a 1 m retention gap of deactivated fused silica. The sample was injected using the cool-on-column using oven track mode with a sample injection volume of 1 µL. Helium was used as a carrier gas with a flow rate of 1.0 mL/min. The oven temperature program was set to hold 85ºC for 2 minutes, followed by a ramp to 280ºC at 4ºC/min which was held for 20 minutes for a total run time of 70.75 minutes. The mass spectrometer was operated in the selected ion monitoring (SIM) mode with specific ions and retention windows applied for each compound. Samples were calibrated against a 7 point calibration curve containing a mixture of aliphatic hydrocarbons as well as parent and alkyl PAHs.

For some of the alkyl PAHs, where standards were not available, the response of the parent PAH was used for quantification.

Alkylated and Nonyl Phenols: Phenols were processed according to a modified version of EPA method 8041. Briefly, 1 L produced water sample was acidified with 6N HCl to a pH <2 and extracted in a separatory funnel with dichloromethane. The solvent was concentrated on a TurboVap and the concentrated extract was exchanged into hexane and spiked with an internal

standard containing deuterated phenol. Sample extracts were analyzed using an

Agilent 6890 Gas Chromatograph (GC) coupled to a 5975 Mass Spectrometer

(MS). The column was a Supelco MDN-5s 30m x 250µm x 0.25µm (length x i.d. x film thickness) with a 1 m retention gap of deactivated fused silica. The sample extract was injected using the cool-on-column mode with a sample injection volume of 1 µL. Helium was used as a carrier gas at a flow rate of 1.0 mL/min.

The oven temperature program was set to hold 55ºC for 2 minutes, followed by a ramp to 100ºC at 10ºC/min which was held for 2 minutes, a ramp to 115ºC at

10ºC/min which was held for 2 minutes, and a ramp to 220ºC at 20ºC/min which was held for 4 minutes for a total run time of 34.75 minutes. The mass spectrometer was operated in the selected ion monitoring (SIM) mode with specific ions and retention windows applied for each compound. Samples were calibrated against a 10 point calibration curve.

BTEX (Benzene, Toluene, Ethylbenzene, and Xylene): All BTEX samples were analyzed within 2 weeks of collection. For the analysis of BTEX, EPA

Method 8240 (purge and trap) was modified by running the GC/MS in selected ion monitoring mode. The instrument consisted of a Teledyne Tekmar Purge and

Trap system coupled to an Agilent 6890 Gas Chromatograph (GC) and a 5973N

Mass Spectrometer (MS). The samples (40 mL purge and trap vials) were placed sequentially on the autosampler. The autosampler dispensed 5 mL and the sample was purged with helium. The volatile components were trapped on a tenex trap and desorbed onto the GC column. Data was collected and results were quantified using a 5 point calibration curve containing the components of interest.

2.2.6 Genomic DNA extraction

Total community DNA from the produced water sample was extracted using the method described by Fortin et al. (1998) with minor modifications. A 4

L produced water sample was filtered through a sterile 0.22 μm GSWP

(Millipore) membrane for DNA extraction. Following filtration, the membrane was transferred to a sterile 50 mL Falcon tube and was stored at -20°C before extraction. Lysis treatment was performed by adding 4.5 mL of sterilized distilled water, 500 μL of 250 mM Tris-HCl (pH 8.0) and 50 mg lysozyme to the Falcon tube containing the filter membrane and incubated for 1 h at 37°C with mixing at low speed. After incubation, 50 μL of Proteinase K (20 mg/mL) was added and incubated for 1 h at the previously described conditions. The lysis treatment was completed with the addition of 500 mL of 20% SDS solution and 30 min of incubation at 85°C with gentle mixing. Membranes were then removed from the tubes. The lysates were treated with one-half volume of 7.5 M ammonium acetate, incubated on ice for 15 min to precipitate proteins and humic acids, and centrifuged for 15 min at 4°C (9,400 x g). The supernatants were transferred to sterilized 50 mL Falcon tubes and treated with one volume of cold (-20°C) 2- propanol. DNA was precipitated overnight at -20°C, after which samples were centrifuged at 4°C for 30 min (9,400 x g). Pellets were washed with 70% cold (-

20°C) ethanol, air dried, and each pellet was resuspended in 150 μL of 10 mM

Tris-HCl, 1 mM EDTA (TE, pH 8.0). DNA concentrations were estimated by agarose gel electrophoresis using 5 μL of purified material against the Lambda

HindIII DNA ladder (Amersham Biosciences, Piscataway, NJ, USA) standard on

a 0.7% agarose gel which was stained with SYBR Safe (Molecular Probes,

Eugene, OR, USA).

2.2.7 PCR amplification of 16S rRNA gene for produced water clone libraries

The extracted DNA from the produced water was used to construct two

16S rRNA gene clone libraries, one bacterial and one archaeal. To construct each library, three PCR replicates per sample were combined to minimize bias.

Bacterial and archaeal 16S rRNA genes were PCR amplified using two sets of primers: F1 (5‟–GAGTTTGATCCTGGCTCAG-3‟) and R13 (5‟–

AGAAAGGAGGTGATCCAGCC-3‟) (Liesack et al. 1991), and A2F (5‟–

TTCCGGTTGATCCYGCCGGA-3‟) (Reysenbach and Pace 1995; Martinez-

Murcia et al. 1995; Jurgens et al. 2000) and A1406R (5‟ –

GACGGGCGGTGTGTRCA- 3‟) (Hansen et al. 1998; Reysenbach and Pace

1995), respectively. Each 50 uL PCR mixture contained 1 μL of the template

DNA, 25 pmol of each oligonucleotide primer, 200 μM of each dNTP, 1 mM

MgCl2, 2.5 units of Taq polymerase (Amersham Biosciences, Piscataway, NJ,

USA) and 1 x Taq polymerase buffer (10 mM Tris-HCl pH 9.0, 50 mM KCl, 1.5 mM MgCl2). Cycle conditions were an initial denaturation temperature of 96°C for 5 min for all reaction mixtures; then 25 cycles at 94°C for 1 min, 60°C for 1 min and 72°C for 2 min for bacterial PCR, and 30 cycles at 94°C for 1 min, 56°C for 1 min and 72°C for 2 min for archaeal PCR. PCR products were loaded onto a

1% agarose gel with SYBR Safe (Molecular Probes, Eugene, OR, USA), using a

1-kb ladder (MBI Fermentas, Amherst, NY, USA) to determine the presence, size and quantity of the PCR products. Triplicate PCR products were combined,

concentrated by ethanol precipitation and resuspended in sterile deionized water.

DNA fragments were cloned using a QIAGEN PCR cloning kit at an insert:vector ratio of 3:1. The ligations were transformed by electroporation into Escherichia coli strain SURE cell (Invitrogen). Transformants were selected on Luria-Bertani medium supplemented with ampicillin (100 mg liter-1), 5-bromo-4-chloro-3- indolylbeta-D-galactopyranoside (X-Gal; 80 mg liter-1), and isopropyl-beta-D- thiogalactopyranoside (IPTG; 50 µM).

A total of 106 white colonies from the bacterial library and 95 white colonies from the archaeal library were randomly picked, transferred onto ampicillin-supplemented LB plates, and then incubated overnight. The 16S rRNA gene fragments from each clone were reamplified using vector primers (pDrive-

F2: 5‟-GTATCGGATCCAGAATTCGTGA-3‟; pDrive-R2 5‟-

GAAGCTTGTCGACGAATTCAGA-3‟). The PCR products were used for clone screening by sequencing the forward bacterial or archaeal primers (i.e. F1 or A2F) at the Université Laval. Forward sequences from each clone were edited with

BioEdit v7.0 (Hall 1999) and were aligned by the MacVector 9.0 software package (Accelrys, Cary, NC, USA). This allowed us to identify groups of clones containing (presumably) the same inserts. All sequences having ≥98% similarity and matching the same GenBank sequence were assigned to the same phylotype.

Depending on the number of representatives per phylotype, one to four representatives of each phylotype were then chosen for having both forward and reverse DNA strands sequenced with their respective primers. The sequences were manually aligned by comparing forward and reverse sequences using

BioEdit. The occurrence of chimeric sequences was determined manually with the

CHECK_CHIMERA function from the Ribosomal Database Project-II

(http://35.8.164.52/cgis/chimera.cgi?su=SSU; Cole et al. 2003) and Bellerophon

(http://foo.maths.uq.edu.au/~huber/bellerophon.pl; Huber et al. 2004). Sequences were compared to those available in GenBank using the NCBI BLASTN algorithm and close relatives of the different phylotypes were tentatively identified. Phylotypes, closely-related representatives and related novel, full and partial 16S rRNA sequences from GenBank, were aligned with the MacVector 9.0 software. Phylogenetic relationships were inferred using the Jukes-Cantor algorithm to estimate evolutionary distances, and the Neighbor-Joining method to construct the phylogenetic tree, with 1000 bootstrap re-sampling of the data. For rarefaction-curve analysis, the rarefaction curves were calculated using Analytic

Rarefaction (http://www.uga.edu/_strata/software/index.html) and constructed using MS Excel. The coverage of the libraries was calculated with the following formula: C = (1 - n1 / N) x 100, where n1 is the number of phylotypes appearing only once in a library and N is the library size (Good 1953). The Shannon index

(H‟) of diversity (Magurran 2004), the Simpson‟s reciprocal index of dominance

(Simpson‟s index of dominance (D) is the inverse of the Simpson‟s reciprocal index of dominance) (Magurran 1988; Magurran 2004; Hayek and Buzas 1996;

Simpson 1949), and the bias-corrected Chao1 estimator of total species richness

(Chao 1984) were determined using EstimateS 8.2

(http://viceroy.eeb.uconn.edu/EstimateS; Colwell 2005). The Shannon evenness index was calculated with the formula E = eH’ / N, where H’ is the Shannon index of diversity and N is the total number of phylotypes (Krebs 1989).

2.2.8 PCR amplification of 16S rRNA genes for produced water and seawater

DGGE

PCR amplification of the 16S rRNA genes was performed using the bacteria-specific forward primer U341F (5‟-CCTACGGGAGGCAGCAG-3‟)

(Muyzer et al. 1993) and the reverse primer U758R (5‟-

CTACCAGGGTATCTAATCC-3‟) (Lee and Malone 1993). These primers, complementary to conserved regions of the 16S rRNA gene were used to amplify a 418-bp fragment corresponding to positions 341 to 758 in the Escherichia coli sequence and covered the variable regions V3 and V4. The bacteria-forward primer used for DGGE incorporated a GC clamp (5‟-

GGCGGGGCGGGGGCACGGGGGGCGCGGCGGGCGGGGCGGGGG-3‟) at the 5‟ end. This GC-clamp stabilizes the melting behavior of the amplified fragments (Sheffield et al. 1989). Archaea 16S rRNA gene regions were PCR amplified using the archaea-specific forward primer ARC344F (5‟-

ACGGGGYGCAGCAGGCGCGA-3‟) with the same GC clamp sequence at the

5‟ end as for the Bacteria forward primer and the reverse primer ARC915R (5‟-

GTGCTCCCCCGGCAATTCCT-3‟) which generates a 572-bp fragment. Each 50 uL PCR mixture contained ~1 ng/μL of the template DNA, 25 pmol of each oligonucleotide primer, 200 μM of each dNTP, 1 mM MgCl2, 2.5 units of Taq polymerase (Amersham Biosciences, Piscataway, NJ, USA) and 1 x Taq polymerase buffer (10 mM Tris-HCl pH 9.0, 50 mM KCl, 1.5 mM MgCl2).

Briefly, after an initial temperature of 96°C for 5 min and thermocycling at 94°C for 1 min, the annealing temperature was set to 65°C (for bacterial PCR) or 60°C

(for archaeal PCR) for 1 min and decreased by 1°C every cycle for 10 cycles, with

a 3 min elongation time at 72°C. Additional cycles (15-20) were performed with annealing temperatures of 55°C for bacteria and 50°C for archaea. PCR products were loaded onto a 1% agarose gel with SYBR Safe (Molecular Probes, Eugene,

OR, USA), using a 100-bp ladder (MBI Fermentas, Amherst, NY, USA) to determine the presence, size and quantity of the PCR products.

2.2.9 Produced water and seawater Denaturing Gradient Gel Electrophoresis

(DGGE) analysis

The 16S rRNA gene products from four to eight PCR reactions were combined for each sample and concentrated by ethanol precipitation for DGGE analysis. About 550 ng of the 16S rRNA gene product from each sample was applied to a lane, and analyzed on 8% polyacryalmide gels containing gradients of

30-70% denaturant (a solution with 7M urea and 40% deionized formamide was considered to be 100% denaturant). DGGE was performed using a DCode

Universal Mutation Detection System (Bio-Rad). Electrophoresis was run at a constant voltage of 80 V for 16 h at 60°C in 1x TAE running buffer. The gels were then stained with VistaGreen (Amersham Biosciences, Piscataway, NJ,

USA), and imaged with the FluoroImager System Model 595 (Molecular

Dynamics, Sunnyvale, CA, USA). The gel images were analyzed with

GelCompar II v4.6 (Applied Maths, Sint-Martens-Latem, Belgium) to generate dendrogram profiles. The genotypes were visually detected based on presence or absence of bands in the different lanes. A band was defined as “detected” if the ratio of its peak height to the total peak height in the profile was >1%. After conversion and normalization of gels, the degrees of similarity of DNA pattern

profiles were computed using the Dice similarity coefficient (Dice, 1945), and dendrogram patterns were clustered by the unweighted pair group method using arithmetic average (UPGMA) groupings with a similarity coefficient (SAB) matrix.

Individual bands from the DGGE gels were excised and eluted with 25 μL of dH2O overnight at 4°C before being re-amplified with the same set of primers without the GC-clamp. One microliter of DNA was re-amplified with the appropriate corresponding bacterial or archaeal primers (without the GC clamp) as follows: an initial denaturation of 5 min at 96°C, followed by 30 cycles of 94°C for 1 min, 60°C (bacterial) or 55°C (archaeal) for 30 sec, and 72°C for 1 min.

PCR products for sequencing were purified using Illustra GFX™ PCR DNA and

Gel Band Purification Kit (GE Healthcare, Backighamshire, UK). Sequencing was performed at the Université Laval Plate-forme d'analyses biomoléculaires using a model ABI Prism 3130XL (Applied Biosystems, Foster City, CA, USA) with their respective primers. Raw sequence data were assembled in BioEdit v7.0

(Hall 1999). The sequences were manually aligned by comparing forward and reverse sequences. The occurrence of chimeric sequences was determined manually with the CHECK_CHIMERA function from the Ribosomal Database

Project-II and Bellerophon. Sequence alignment and phylogenetic analysis were performed as previously described.

2.2.10 Nucleotide sequence accession numbers

The 16S rRNA gene sequences obtained in this study were deposited in the

GenBank database under accession numbers JF789483 to JF789518 and

JF789541 to JF789563.

2.3 Results

2.3.1 Physicochemical and chemical analyses of the produced water and seawater

The Hibernia produced water temperature was around 82°C, with the pH of 7.78, and the salinity was 45.6 ppt. In contrast, the temperature and salinity were much lower in the surrounding seawater (Table 2.1 and 2.3). Other physicochemical characteristics for the produced water and seawater are listed in Table

2.1. The results showed that several chemicals (i.e. silicate, ammonia, and various petroleum hydrocarbons) in the Hibernia produced water occur at concentrations at least hundreds of times greater than those in the surrounding seawater (Table

2.1) and thus could potentially be used as natural tracers to track the discharge of produced water. In particular, the produced water had a variety of petroleum hydrocarbons: 4100 ± 128 μg/L of BTEX, 11.9 ± 1.1 μg/L phenols, 33.4 ± 0.8

μg/L of alkanes, 116.4 ± 17.9 μg/L of PAHs (Table 2.1). However, the seawater chemical analysis revealed that the PAHs, phenols, alkanes, silicate, phosphate, ammonia were all either not detected or at very low concentrations in the surrounding seawater 500 m from the discharge (Table 2.1 and 2.2), suggesting that although they were discharged at high concentration, all these chemicals were well diluted to below detection levels within 500 m of the production platform.

2.3.2 Produced water bacterial clone library analysis

The produced water bacterial clone library was composed of 106 clones that grouped into 23 phylotypes (Fig. 2.1). Most of the phylotypes showed at least a 97% match in GenBank to a known cultured bacterium. The phylotypes could

be divided into three phyla as follows: Proteobacteria (60.4% of the total clones),

Firmicutes (34.9%), and Deferribacteres (4.7%). The majority of the phylotypes

(16 out of 23) belonged to the Proteobacteria which had representatives from the

Gamma-proteobacteria (25.5% of the total clones), Epsilon-proteobacteria

(21.7%), Alpha-proteobacteria (10.4%) and Delta-proteobacteria (2.8%). The most dominant Epsilon-proteobacteria was Arcobacter, which comprised the entire group with 21.7% of the clone library. However, the most dominant phylotype in the bacterial library was Thermoanaerobacter, from the Firmicutes.

Firmicutes was the second largest phylum in the library with 34.9% of the clones.

Within this phylum, Thermoanaerobacter by itself consisted of 25.5% of all the clones in the library. The other phylum was Deferribacteres, represented by

Flexistipes. Overall, ten out of 23 phylotypes (68 clones) were related to anaerobic or microaerophilic bacteria, which consisted of 64.2% of the total number of clones. Within the anaerobe genera, 9 (45 clones) were related to thermophilic genera, which consisted of 43% of the clones. The remaining clones consisted of a diverse number of phylotypes (13 out of 23) with fewer clones

(35.8%), but they were all related to mesophilic aerobic Alpha- and Gamma- proteobacteria.

2.3.3 Produced water archaeal clone library analysis

The archaeal clone library had a lower diversity than the bacterial clone library. The archaeal clone library comprised 95 clones that grouped into only 3 phylotypes (Fig. 2.2). All of the phylotypes showed at least a 99% match to the available known cultured sequences from GenBank. All the phylotypes belonged

to the Euryarchaeota. The phylotypes could be further divided into two classes:

Thermococci and Archaeoglobi. The Thermococci was the most dominant (96.8% of the total clones) and was represented by 2 different phylotypes closely related to species of Thermococcus: T. litoralis and T. alcaliphilus. Only a very small percentage of the clones (3.2%) were very closely related to A. fulgidus from the

Archaeoglobi.

2.3.4 Statistical analysis of clone libraries

The number of clones, phylotypes, and biodiversity indices calculated for the two clone libraries are summarized in Table 2.5. The coverage of both clone libraries was high ranging from 91.5 to ~100%, suggesting that the majority of the microbial diversity was identified in this study. The high bacterial clone library coverage value corresponded to the near-plateau rarefaction curve (Fig. 2.3) and the Chao1 diversity estimation result was 29 (Table 2.5). The Chao1 value estimated, theoretically, that a total of 29 phylotypes could potentially be expected from this environment, of which 23 were identified. For the archaeal clone library, the very high coverage value (~100%) was supported by the rarefaction curves that almost reached a plateau (Fig. 2.3). Furthermore, the total number of phylotypes (3) is equal to the estimation from the Chao1 diversity analysis (Table

2.5).

Comparatively, the diversity of the bacterial clone library was much higher (23 phylotypes) than the diversity of the archaeal library (3 phylotypes).

This is supported by the higher value of the Shannon index (2.45 for bacterial,

0.51 for archaeal) and the higher value of the Simpson‟s reciprocal index (7.77 for

bacterial, 1.38 for archaeal) (Table 2.5). Even with the higher diversity, the bacterial population distribution is more even than the archaeal. The bacterial clone library Shannon evenness index was 0.78 (with 1 being even) compared to the archaeal clone library index value of 0.46. The unevenness of the archaeal population distribution suggested a high species dominance (the Thermococci dominated at 96.8% of the clones), which is also supported by the higher value of the inverse Simpson‟s index of dominance (0.72: with 1 being no diversity and 0 being infinite diversity) (Table 2.5).

2.3.5 Produced water bacterial and archaeal DGGE analysis

The DGGE analysis for the bacterial and archaeal 16S rRNA genes was intended to identify the dominant microbial groups in the composition of the produced water, so only the major bands were excised and re-amplified for sequencing analysis. The bacterial DGGE gel displayed a much higher number of bands than the archaeal DGGE gel (only 2 major bands) (Fig. 2.4). From the bacterial DGGE, a total of 7 DGGE bands were excised and sequenced (Fig. 2.4).

All of the sequences showed at least a 97% match to the available sequences of known cultured bacteria from GenBank. Even with just 7 sequences, they all fall into a diverse number of groups: the most dominant are from Alpha- proteobacteria and Gamma-proteobacteria (both were represented by two out of seven sequences), and one sequence from each of the Epsilon-proteobacteria,

Firmicutes and Thermococci (Archaeal: from bacterial primer mismatches (data not shown)) (Fig. 2.5).

Similar to the clone library results, the archaeal DGGE showed lower diversity than the bacterial DGGE. Only two intense bands were excised and sequenced (Fig. 2.4). One of the sequences was closely related to Thermococcus sp., similar to the one that was identified from the bacterial DGGE, and the other band produced a sequence closely related to Archaeoglobus sp. (Fig. 2.6).

2.3.6 Seawater bacterial DGGE analysis

Seawater bacterial community structure analysis revealed that the community from the surrounding seawater clustered closely by depth with 2 major clusters: one from the surface to 25 m and the other from 50 m to NB (SAB ≈

67) (Fig. 2.7). Much higher SAB values were observed with distance from the production platform, which suggested that, the variation in the bacterial community structure is more likely related to depth, rather than by distance from the discharge (Figs. 2.7 and 2.8). Similar to the chemical analyses, the microbial analysis revealed that the bacterial community structure from the produced water is very different from that of the surrounding seawater (SAB = 38.8, Fig. 2.7), even though the major component of the produced water comes from the injected surrounding seawater, suggesting that the bacterial community structure in the produced water is unique.

The seawater DGGE analysis also revealed that there is a higher bacterial diversity in the seawater (Fig. 2.8) than the produced water (Fig. 2.4). A total of

24 DGGE bands were excised and sequenced from the seawater DGGE analysis.

Most of the sequences were at least a 97% match to the existing sequences from

GenBank and showed the highest similarities to GenBank sequences from

bacteria originating from either the Arctic Sea or other Atlantic regions (Fig. 2.9).

The sequences fall equally into only 2 groups: Proteobacteria and Bacteroidetes

(with 12 sequences each). Within the Proteobacteria, the 12 sequences were further divided into 2 subclasses: Alpha-proteobacteria (7 sequences), and

Gamma-proteobacteria (5 sequences).

Bands Hib05-SW4, 6, 7, 8, 9, 11, 12, 16, 17, and 22 were present only in the bottom water samples (50 m and NB) (Fig. 2.8) suggesting that some species were unique to the lower water column. These sequence differences were more closely related to depth than to distance from the platform, suggesting that the seawater depth was having a more significant effect on the community structure than the distance from the produced water discharge.

2.4 Discussion

2.4.1 Physical and chemical characterization of the Hibernia produced water

Since no two produced waters are the same, for a better understanding of transport and dilution a detailed analysis of the physical and chemical characteristics of the Hibernia produced water was required. Similar to other produced waters from the Northern Atlantic region (like the North Sea detailed in

Tibbetts et al. 1992; Flynn et al. 1996), the Hibernia produced water is discharged at a relatively high temperature (Table 2.1). This high-temperature, complex discharge consists of dissolved and particulate inorganic and organic chemicals.

Salinity of produced waters in general ranges from a few parts per thousand (ppt) to a saturated brine (~300 ppt) and is usually denser than seawater (Rittenhouse et al. 1969; Large 1990; Collins 1975). The salinity of Hibernia produced water is

45.6 parts per thousand (ppt). Hibernia produced water also had noticeably elevated concentrations of silicate, ammonia, phosphate, and various petroleum hydrocarbons (Table 2.1). The Hibernia produced water has higher ammonia and phosphate concentrations (Table 2.1) than those observed in other produced waters (Neff 2002), but lower nitrate concentrations than those found normally in other produced waters from around the world (Neff 2002).

Produced water contains residual petroleum hydrocarbons, which are the organic components of greatest environmental concern. Produced water is generally pre-treated to remove dispersed oil before being discharged to the ocean. After treatment, the remaining petroleum hydrocarbons are mainly low molecular weight aromatic hydrocarbons (like benzene, toluene, ethylbenzene, and xylenes (BTEX)) and small amounts of dissolved saturated hydrocarbons

(alkanes), in addition to polycyclic aromatic hydrocarbons (PAHs) and phenols.

BTEX are often the most abundant hydrocarbons in produced water, ranging from

0.068 to 578 mg/L in produced water world-wide (Neff 2002). Similarly, the highest concentrations of hydrocarbons in Hibernia produced water are BTEX at

4100 μg/L (Table 2.1). The second most abundant hydrocarbons in the Hibernia produced water are PAHs at 137 μg/L (Table 2.1) and are still in the concentration ranges that were found world-wide (Neff 2002). The Hibernia produced water contains relatively low concentrations of alkanes and phenols (~40 μg/L and 12

μg/L, respectively) when compared to the concentrations that have been measured world-wide (Neff 2002).

2.4.2 Comparison of the produced water and surrounding seawater

The Hibernia produced water, although consisting mainly of injected seawater, is physically, chemically and biologically distinct from the surrounding seawater (Table 2.1 and Fig. 2.7). Our results showed that several chemicals (i.e. silicate, ammonia, and various petroleum hydrocarbons) in the Hibernia produced water occur at concentrations at least hundreds times greater than those in the surrounding seawater and thus can potentially be used as natural tracers to track the discharge of produced water. The most abundant hydrocarbons in produced water are the one-ring aromatic hydrocarbons (BTEX). Benzene is the most abundant BTEX in the Hibernia produced water (data not shown), and it is extremely volatile and is usually lost rapidly during the discharge (Terrens and

Tait 1996). Therefore, it is understandable that these volatile compounds would rapidly fall below detection limits in seawater samples outside 500 m from the production platform.

The second most abundant hydrocarbons in the Hibernia produced water were the dissolved PAHs (137 μg/L, Table 2.1), which pose the greatest environmental concern in produced water because of their toxicity, bioaccumulation and persistence (less volatile and degradable) in the marine environment (Neff 1987; 2002). Therefore, PAH concentration is a good parameter for monitoring produced water discharge. As an example, Harman et al.

(2009) assessed the environmental impact of the discharge of produced water by monitoring the dissolved PAHs and alkylphenols (AP) noting that the concentrations of these compounds were several orders of magnitude lower than those reported to give both acute and sub-lethal effects. Similarly, our results found that PAHs, phenols and alkanes were not detected in the surrounding

seawater outside of 500 m (Table 2.1), suggesting that although they were discharged at concentration higher than in the surrounding seawater, PAHs were diluted to below detection levels within 500 m of the production platform.

Furthermore, the values of silicate, ammonia, phosphate, salinity, and temperature in the surrounding seawater varied, but the variations were more likely correlated by the change in depth, rather than the change in the distance from the discharge (Table 2.2 and 2.3). These results suggested that the various chemical components within produced water had shown that the concentrations of individual chemical markers alone could not be used to define the area of impacts accurately. The use of biological factors may provide a better resolution.

2.4.3 Comparison of physicochemical and biological parameters

Batch toxicity studies demonstrated that most treated produced water still has a low to moderate toxicity to higher organisms (fish larvae, Mariani et al.

2004) and to bacteria in the Microtox assay (Hendersen et al. 1999), and thus can still represent a risk to biological communities close to the discharge. The harmful effects could be generated from both physical and chemical factors, such as temperature, salinity, inorganic and organic components.

Temperature could play an important role in changing the composition of the microbial community, the chemical behavior of the petroleum hydrocarbons and the rate of biodegradation. Nedwell (1999) found that temperature controls the nature and extent of microbial hydrocarbon metabolism. Temperature also affects the physicochemical behaviour of hydrocarbons, such as viscosity, diffusion and volatilization, which changes the oil composition, and the

bioavailability of the different components (Northcott and Jones 2000). Choi et al.

(2002) and Chuang et al. (2009) found that the discharge of thermal effluents from a coastal power station lowered microorganism production/abundance, grazing rates, and chlorophyll a concentrations, even though the effluent temperature was only 5 to 10°C higher than the ambient temperature. The

Hibernia produced water is discharging at temperature more than 80°C, which is generally at least 70°C higher than the ambient seawater temperature. This large thermal difference might also have similar effects on bacterial production and abundance, but this effect may be restricted to the zones immediately adjacent to the discharge.

Produced water salinity is also much higher than seawater. High salinity was known to have an effect on microbial cells from disrupted tertiary protein structures and denatured enzymes to cell dehydration (Pollard et al. 1994), and different species having different sensitivities to salt which could alter the microbial community structure. Montgomery et al (2009) found that the overall bacterial production rate decreased as the salinity of a salty wastewater stream increased. Kaartokallio et al. (2005) showed that a change in salinity of 20 ppt could change both the bacterial abundance and community structure in sea ice, suggesting that salinity fluctuation could be an important selective factor in shaping bacterial community structure. The salinity from Hibernia produced water was ~13 ppt higher than the surrounding seawater and could have negative effects on the bacterial community structure, abundance and function adjacent to the platform.

Hibernia produced water is discharging a high concentration of nutrients and petroleum hydrocarbons. Nutrients and hydrocarbons could have both positive and negative effects on the microbial community. Ecosystem models by

Rivkin et al. (2000) and Khelifa et al. (2003) predicted that nutrients (nitrate, phosphate, ammonia, organic acids) and hydrocarbons (as carbon and energy sources) may alter the planktonic community structure in the receiving waters.

The high concentrations of these nutrients and hydrocarbons from the Hibernia discharge were likely to have an effect on shaping the planktonic community structure, abundance and function in the seawater around the platform.

However, the seawater total bacterial counts and chlorophyll a results revealed that some variability was observed in the water column, but the trend suggested that the variability was more likely influence by the depth than by distance from the produced water discharge (Table 2.4).

2.4.4 Produced water bacterial community structure

In this study, molecular analyses were also used to characterize the bacterial and archaeal diversity in Hibernia produced water and seawater around the production platform. First, although much effort has been made to define the microbial diversity of petroleum reservoirs (e.g. Grassia et al. 1996), this study was the first to do so in Canadian offshore oil production areas. The results showed that Hibernia produced water harbors a diverse microbial community. All phylotypes were closely matched to microbes associated with oil reservoirs, produced water, or other similar environments around the world (Figs. 2.1 and

2.2).

The Proteobacteria were the most dominant phylum with over 60% of the total clones and with representatives from four out of five classes: Alpha-,

Gamma-, Delta-, and Epsilon-proteobacteria. Almost all the phylotypes identified within the Alpha- and Gamma-proteobacteria were closely related to previously identified mesophilic aerobic marine genera: Sphingomonas, Sulfitobacter,

Loktanella, Paracoccus, Acidovorax, Pseudoalteromonas, Glaciecola,

Alteromonas, Alcanivorax, Pseudomonas, and Marinobacter (Fig. 2.1). Since the

Hibernia oil reservoir is continuously injected with seawater, large numbers of mesophilic aerobic marine microorganisms could be introduced into the reservoir.

Some of these introduced microorganisms may reside in the cooler portions of the oil production system (like the production piping), which may explain the detection of mesophilic bacteria in the produced water (Orphan et al. 2000). Some studies have shown that mesophilic microorganisms constituted a large fraction of microorganisms detected in the produced water and similar environments (Li et al. 2006; Magot et al. 1992; Orphan et al. 2000; 2003). It is also important to note that within these mesophilic aerobic marine genera, most of them, like the Nazina et al. (2001) findings, were related to genera of known hydrocarbon degraders:

Sphingomonas (Romine et al. 1999), Acidovorax (Meyer et al. 1999),

Pseudoalteromonas (Hedlund and Staley 2006), Alteromonas (Iwabuchi et al.

2002), Alcanivorax (Yakimov et al. 1998), Pseudomonas (Le Petit et al. 1975), and Marinobacter (Gauthier et al. 1992). The sequences not related to any known hydrocarbon degrader isolates were still closely related to sequences from hydrocarbon-degrading microbial communities (e.g. Sulfitobacter (Brakstad and

Lødeng 2005), Paracoccus (Guo et al. 2005), and Glaciecola (Brakstad et al.

2008)). These mesophilic bacteria might have survived in hot formations, and the surviving mesophilic bacteria might have thrived in the cooler portion of the oil production tubing environment, using the various types of hydrocarbons in the produced water (Table 2.1) from the crude oil as energy and/or carbon sources.

Two phylotypes closely related to Delta-proteobacteria were identified, but with less than three clones, which suggests that they might not be the major components of the bacterial community. Also as mentioned previously, the major component of produced water came from the injected surrounding seawater, so it is understandable that Beta-proteobacteria were not found in the produced water, since they appear to be relatively rare in marine bacterial communities (Hahn

2006).

Most interestingly, Epsilon-proteobacteria were found as the second most dominant class within the Proteobacteria with almost a quarter of the total clones

(21.7%) (Fig. 2.1), and it is represented by a single phylotype closely related to

Arcobacter sp. Similar Arcobacter spp. have been found in microbial communities in a high-temperature offshore petroleum reservoir in the North Sea

(Kaster et al. 2009). Kaster et al. (2009) also demonstrated sequences similar to

Arcobacter were sulphur-compound oxidizers growing in acetogenic and fermentative cultures at 55°C as well as in fermentative and SRB media at 70°C.

Recently, Arcobacter spp. were also identified in a petroleum degrading wetland soil from the Shengli Oil Field on the Yellow River Delta (Han et al. 2009) and in the production water in a crude oil gathering and transferring system (Liu et al.

2009) at much lower temperatures. Liu et al. (2009) also revealed that the

Arcobacter was only found in the production water and not in the crude oil. This

result suggests that Arcobacter is not from the crude oil but could become enriched using the components of crude oil in the produced water as a carbon source. Additional studies are needed to investigate the physiological characteristics of Arcobacter spp. in produced water.

Firmicutes was found as the second dominant phylum in the produced water bacterial community. With over a quarter of the total clones (25.5%), they were grouped into a phylotype closely related to Thermoanaerobacter sp. (Fig.

2.1). In the phylogenetic tree, this phylotype formed a cluster adjacent to

Thermoanaerobacter mathranii isolated from a hot spring in Iceland (Larsen et al.

1997) and another Thermoanaerobacter sp. from produced water (Fig. 2.1).

Thermoanaerobacter was commonly identified in other produced water samples, and is known as a thermophilic fermentative and organotrophic sulfur-respiring bacterium. This genus has often been isolated from geographically separated oil reservoirs throughout the world, which suggests that it might be indigenous to the petroleum reservoir (Cayol et al. 1995; Grassia et al. 1996; Magot et al. 2000), although it cannot be ruled out that it originates from the water injected into the formation (Dahle et al. 2008).

2.4.5 Produced water archaeal community structure

Only three phylotypes were identified in the archaeal clone library (Fig.

2.2). The archaeal sequences showed high similarities to the genera

Archaeoglobus (A. fulgidus) and Thermococcus spp.: T. litoralis and T. alcaliphilus. A. fulgidus is a hyperthermophilic sulphidogenic bacterium that was isolated from a marine hydrothermal system (Stetter 1988) and from a North Sea

oil reservoir (Beeder et al. 1994). T. litoralis is a hyperthermophilic sulfate- reducing bacterium first isolated from a submarine thermal springs (Neuner et al.

1990). T. alcaliphilus is also a hyperthermophilic sulfur-respiring bacterium isolated from a submarine thermal system (Keller et al. 1995). Numerous

Thermococcus species have been described from high-temperature oil reservoirs around the world, suggesting the indigenous origin of hyperthermophilic Archaea in the deep subsurface biosphere (Neuner et al. 1990; L‟Haridon et al. 1995;

Grassia et al. 1996; Orphan et al. 2000).

2.4.6 Comparison of physicochemical parameters with the bacterial and archaeal community structure

The Hibernia produced water is hot and salty, but has a neutral pH (Table

2.1). In most thermal environments, oxygen is usually limiting. The results revealed that 64.2% of the total number of clones from the bacterial clone library and 100% of the total number of clones from the archaeal clone library were closely related to known anaerobes (Fig. 2.1 and 2.2). Within these anaerobic genera, almost all of them (as shown in Figs. 1 and 2: 11 phylotypes from both the bacterial and archaeal clone libraries) were related to thermophilic genera that have previously been identified from a number of high-temperature petroleum reservoirs world-wide, suggesting that these microbes may be a common component of geothermally heated subsurface environments (i.e. indigenous bacteria to petroleum reservoirs) and probably play a role in the geochemical trophic web of these ecosystems.

The Hibernia produced water had a high concentration of sulfur compounds (726 ± 2 mg/L, Table 2.1). As mentioned previously, all the detected phylotypes with large numbers of clones belonged to genera related to sulfur- compound utilizing microbes (Arcobacter, Thermoanaerobacter, Themococcus, and Archaeoglobus), suggesting that these phylotypes might play a major role in the sulfur cycle (an important anaerobic process) in the produced water system.

2.4.7 Comparison of clone library and DGGE

Similar to the clone library analyses, the bacterial DGGE showed a much higher diversity than the archaeal DGGE (Fig. 2.4). Although only 7 dominant bands were excised from the bacterial DGGE fingerprints and sequenced for phylogenetic analyses, these 7 bands confirmed the identification of the major phylotypes in the clone libraries (i.e. those related to Thermoanaerobacter,

Arcobacter, Alcanivorax). Similarly, the archaeal fingerprints confirmed the identification of members of the two major archaeal classes, Thermococci and

Archaeoglobi. These results suggested that a DGGE analysis might be sufficient for monitoring the major members in the microbial community in the produced water and seawater in the future.

2.4.8 Comparison of produced water and seawater bacterial community structure

The DGGE results showed a high similarity in bacterial community structure for the water column both horizontally and vertically, suggesting that there is a spatially stable bacterial community in the surrounding seawater

covering a large region (500 m – 50 km from the platform). Therefore, any changes in the bacterial community in the water column could potentially be used as an indicator for the effects from the produced water discharge, and this stable bacterial community in the surrounding seawater could also suggest that any effects from the produced water were restricted to the region immediately adjacent to the platform (within 500 m).

2.5 Conclusions

This work provided the first insight into the bacterial and/or archaeal community in and around the Hibernia offshore oil and gas production platform.

This study characterized both the bacterial and archaeal community structures and chemical compositions of Hibernia produced water and the indigenous bacterial community structure and chemical/physicochemical characteristics in the seawater around the platform. All analyses revealed that the discharge of produced water did not have a detectable effect on the seawater 500 m or more from the Hibernia production platform, suggesting that any effect from the produced water discharge might be restricted to within 500 m from the discharge.

However, the results also revealed that the produced water has a rich microbial diversity and has unique chemical properties, but these specific characteristics appeared to be below detection limits in the surrounding water. Hence, signature microorganisms from produced water could be use as targets to monitor the dispersion of produced water in the surrounding ocean.

2.6 Acknowledgements

The authors thank Jennifer Mason from the Centre of Offshore Oil, Gas and

Energy Research for their analytical and technical support.

Table 2.1. Physicochemical and chemical characteristics of Hibernia produced water and seawater; the first number is the average and the second number is the standard deviation.

Average Surrounding Produced Water Seawater

Temperature ~82°C ~2.62°C pH 7.78 N.A.

Salinity 45.6 ppt ~32.7 ppt

Silicate 852.04 ± 89 μM 2.5 μM

Nitrate 0.34 ± 0.005 μM 1.95 μM

Nitrite 0.34 ± 0.05 μM 0.36 μM

Ammonia 641.49 ± 0.38 μM 5.26 μM

Phosphate 12.08 ± 3.62 μM 0.72 μM

Total Sulfur 726 ± 2 mg/L N.D.

Total BETX 4100 ± 128 μg/L N.D.

Total dissolved PAHs 116.4 ± 17.9 μg/L N.D.

Total Alkanes 33.4 ± 0.8 μg/L N.D.

Total Phenols 11.9 ± 1.1 μg/L N.D.

N.D. - Not Detected N.A. - Not Analyzed

Table 2.2. Concentration of silicate, phosphate and ammonia in seawater at different distances from the Hibernia platform from 3 different depths (2m, 50m and NB); the first number is the average and the second number is the standard deviation.

Silicate (μM) Phosphate (μM) Ammonia (μM) 2m 50m NB 2m 50m NB 2m 50m NB S0 0.47 ± 0.03 0.38 ± 0.03 4.80 ± 0.59 0.37 ± 0.00 0.46 ± 0.04 1.22 ± 0.10 0.31 ± 0.09 0.92 ± 0.08 3.12 ± 0.30 S1 0.41 ± 0.02 0.35 ± 0.02 5.51 ± 0.01 0.45 ± 0.06 0.48 ± 0.01 1.28 ± 0.01 0.46 ± 0.04 0.51 ± 0.04 3.56 ± 0.09 S3 0.40 ± 0.01 0.39 ± 0.16 5.71 ± 0.04 0.32 ± 0.05 0.30 ± 0.10 1.29 ± 0.01 0.60 ± 0.02 0.61 ± 0.13 3.82 ± 0.20 S6 0.70 ± 0.01 0.79 ± 0.01 6.67 ± 0.47 0.32 ± 0.01 0.38 ± 0.01 1.25 ± 0.05 0.42 ± 0.07 0.39 ± 0.12 3.73 ± 0.05 N0 0.37 ± 0.04 0.58 ± 0.01 5.37 ± 0.02 0.38 ± 0.01 0.52 ± 0.01 1.44 ± 0.17 0.26 ± 0.16 0.65 ± 0.13 3.50 ± 0.09 NW0 0.38 ± 0.01 0.34 ± 0.17 5.16 ± 0.13 0.39 ± 0.01 0.39 ± 0.51 1.16 ± 0.00 0.30 ± 0.06 0.58 ± 0.10 3.57 ± 0.34 W0 0.56 ± 0.02 1.02 ± 0.06 5.72 ± 0.04 0.30 ± 0.01 0.51 ± 0.01 1.25 ± 0.05 0.36 ± 0.16 1.09 ± 0.08 3.64 ± 0.12 R50K 0.21 ± 0.02 6.08 ± 0.35 8.02 ± 0.32 0.32 ± 0.01 1.23 ± 0.05 1.37 ± 0.03 0.43 ± 0.017 4.10 ± 0.13 5.14 ± 0.03

Table 2.3. Salinity and temperature in seawater at different distances from the Hibernia platform from 3 different depths (2m, 50m and NB).

Salinity (ppt) Temperature (˚C) 2m 50m NB 2m 50m NB S0 32.391 32.6834 32.9675 7.258 0.843 -0.355 S1 32.3979 32.6584 32.9656 7.328 0.922 -0.344 S3 32.3744 32.6262 32.9766 7.092 1.114 -0.349 S6 32.387 32.6611 32.9676 6.902 0.930 -0.241 N0 32.3496 32.6478 32.939 7.520 0.721 -0.250 NW0 32.3907 32.6875 32.9675 6.774 0.713 -0.331 W0 32.2905 32.6238 32.9216 7.748 0.9831 -0.230 R50K 32.418 32.8259 32.8395 6.761 0.756 0.632

Table 2.4. Total bacterial counts and chlorophyll in seawater at different distances from the Hibernia platform from 3 different depths (2m, 50m and NB).

Total bacterial counts (cells/mL) Chlorophyll (µg/L) 2m 50m NB 2m 50m NB S0 1.07 x 105 3.27 x 105 3.74 x 105 2343 1475 386 S1 1.31 x 105 4.48 x 105 3.96 x 105 3227 1164 447 S3 1.03 x 105 2.73 x 105 3.87 x 105 2919 583 361 S6 0.94 x 105 2.84 x 105 4.70 x 105 1607 963 421 N0 1.23 x 105 4.25 x 105 3.38 x 105 4793 1758 657 NW0 1.12 x 105 3.25 x 105 3.68 x 105 3152 1851 336 W0 1.05 x 105 3.64 x 105 3.34 x 105 4139 1129 803 R50K 0.96 x 105 4.08 x 105 4.48 x 105 5119 684 261

Table 2.5. Bacterial and archaeal clone library analysis

Bacterial Archaeal

Number of clones 106 95

Number of phylotypes 23 3

Percentage coverage 91.5% ~100%

Shannon index 2.45 0.51

Shannon Evenness index 0.78 0.46

Simpson index 7.77 1.38

Inverse Simpson's Index (D) 0.13 0.72

Chao1 29 3

63 Aquifex pyrophilus (M83548)

100 Desulfacinum subterraneum from high temperature oil field Russia (AF385080) HibPWCl-BAC1 (1) 98 96 Denmark oil production water bacterium clone (FN356315) Delta-proteobacteria 100 Ekofisk Oil Reservoir bacterium clone (EU573126) 2.8% HibPWCl-BAC2 (2) Pelobacter sp. from high-temperature North Sea oil-field produced water (DQ647159) 100 HibPWCl-BAC3 (4) Sphingomonas sp. from diesel-contaminated soil (AY646154) 100 Sulfitobacter sp. from Arctic sea ice (EF673288) 99 HibPWCl-BAC4 (4) 100 Loktanella sp. from Arctic seawater (FJ889559) Alpha-proteobacteria 99 100 HibPWCl-BAC5 (1) 10.4% Arctic Ocean bacterium clone (EU919786) HibPWCl-BAC6 (1) 87 Paracoccus sp. from Arctic seawater (FJ889574) HibPWCl-BAC7 (1)

100 Acidovorax sp. from municipal wastewater treatment plant (AM084010) HibPWCl-BAC8 (1)

100 HibPWCl-BAC9 (2) 100 Pseudoalteromonas arctica from Spitsbergen (DQ787199) HibPWCl-BAC10 (2) 100 99 Pseudoalteromonas sp. from Arctic sea ice (EF673281) 100 HibPWCl-BAC11 (2) Glaciecola sp. from coastal Mediterranean seawater (AM990782) 100 88 Pseudoalteromonas atlantica T6c (CP000388) Gamma-proteobacteria 25.5% 100 HibPWCl-BAC12 (4) 94 Alteromonas sp. from Arctic seawater (FJ889595) 100 HibPWCl-BAC13 (12) 95 Alcanivorax sp. - a hydrocarbon-degrading bacterium (AB058675) 100 Pseudomonas stutzeri strain SA1 (DQ059546) HibPWCl-BAC14 (1)

100 Marinobacter sp. from Arctic ocean (DQ665806) HibPWCl-BAC15 (3) 100 HibPWCl-BAC16 (23) 99 Arcobacter sp. from oil field sewage (DQ452483) Epsilon-proteobacteria 98 21.7% Arcobacter sp. from anaerobic wastewater reactor (AY692045) Flexistipes sp. from California high-temperature petroleum reservoir (AF220344) 100 Deferribacteres HibPWCl-BAC17 (5) 4.7% 100 Acetobacterium sp. from Alaska mesothermic production water (EU721786) 100 100 HibPWCl-BAC18 (1) 100 Alkalibacter saccharofermentans from Russia soda lake (AY312403) HibPWCl-BAC19 (1) 100 Thermacetogenium sp. from North Sea oil field produced water (DQ647145) HibPWCl-BAC20 (5) Firmicutes 100 HibPWCl-BAC21 (27) 80 34.9% Thermoanaerobacter mathranii from hot spring in Iceland (Y11279) 74 Thermoanaerobacter sp. from oil-producing water (EF026571)

100 Moorella thermoacetica ATCC 39073 (CP000232) HibPWCl-BAC22 (2) 72 Thermoanaerobacteriaceae clone from deep terrestial subsurface (DQ079638) 100 Thermovirga sp. from high-temperature North Sea oil-field (DQ647110) HibPWCl-BAC23 (1)

0.05

Fig. 2.1. Phylogenetic relationship of the 23 bacterial 16S rRNA gene sequences obtained from Hibernia produced water clone library (HibPWCl). The clones were labeled with HibPWCl- and with Bacteria (BAC) and a number. The tree was inferred by neighbor-joining analysis of sequence from each clone. Aquifex pyrophilus was used as the outgroup. Numbers on the nodes are the bootstrap values based on 1,000 replicates. The scale bar indicates the estimated number of base changes per nucleotide sequence position.

Aquifex pyrophilus (M83548)

Archaeoglobus fulgidus DSM 4304 (AE000782) 100 HibPWCl-ARC1 (3) 91 Archaeoglobi Archaeon clone from Denmark 3.2% 100 high-temperature oil production water (FN356422) Thermococcus litoralis DSM 5474 from deep-sea hydrothermal vents (AY099180) 95 HibPWCl-ARC2 (12) 95 Archaeon clone from Denmark Thermococci high-temperature oil production water (FN356360) 96.8% HibPWCl-ARC3 (80) 96 Thermococcus alcaliphilus DSM 10322 (AB055121)

0.05

Fig. 2.2. Phylogenetic relationship of the 3 archaeal 16S rRNA gene sequences obtained from Hibernia produced water clone library (HibPWCl). The clones were labeled with HibPWCl- and with Archaea (ARC) and a number. The tree was inferred by neighbor-joining analysis of sequence from each clone. Aquifex pyrophilus was used as the outgroup. Numbers on the nodes are the bootstrap values based on 1,000 replicates. The scale bar indicates the estimated number of base changes per nucleotide sequence position.

Bacterial Clone Library 10

Archaeal Clone Library

Number of Phylotypes of Number Phylotypes of Number 5

0 0 10 20 30 40 50 60 70 80 90 100 110

Number of clones

Fig. 2.3. Rarefaction analysis of the overall, combined bacterial 16S rRNA gene clone library recovered from Hibernia produced water sample. The rarefaction curve, plotting the number of observed phylotypes as a function of the number of clones, was computed by ESTIMATES. The error bars indicate the 95% confidence interval.

(A) HibPW (B) HibPW BAC ARC

2 3 4

1 2

Fig. 2.4. Hibernia produced water 16S rRNA gene DGGE fingerprint. (A) Bacterial DGGE fingerprint, bands were labeled from 1-7 (B) Archaeal DGGE fingerprint, bands were labeled from 1-2

Aquifex pyrophilus (M83548)

100 Sphingomonas sp. from diesel-contaminated soil (AY646154) HibPW-BAC5 95 Alpha-proteobacteria Sulfitobacter sp. from Arctic Sea ice (EF673288) 100 HibPW-BAC3

Alteromonas sp. from Arctic seawater (FJ889595) 100

88 HibPW-BAC2 81 Gamma-proteobacteria Alcanivorax sp. from marine hydrocarbon degrading consortia (AB257196) 100 HibPW-BAC4 98 Arcobacter sp. from oil field sewage (DQ452483) 100 Epsilon-proteobacteria HibPW-BAC1 Thermoanaerobacter mathranii from hot spring in Iceland (Y11279) 100 100 HibPW-BAC6 Firmicutes Thermoanaerobacter sp. from oil-producing water (EF026571)

Thermococcus alcaliphilus DSM 10322 100 from high-temperature oil reservoir (AJ298872) Thermococci HibPW-BAC7 0.05 Fig. 2.5. Phylogenetic relationship of the 7 bacterial 16S rRNA gene sequences obtained from Hibernia produced water (HibPW). The bands were labeled with HibPW- and with Bacteria (BAC) and their band numbers from Fig. 4A. The tree was inferred by neighbor-joining analysis of sequence from each clone. Aquifex pyrophilus was used as the outgroup. Numbers on the nodes are the bootstrap values based on 1,000 replicates. The scale bar indicates the estimated number of base changes per nucleotide sequence position.

Aquifex pyrophilus (M83548)

Archaeoglobus fulgidus DSM 4304 (AE000782)

100 HibPW-ARC1 Archaeoglobi

100 Archaeon clone from Denmark high-temperature oil production water (FN356422)

Archaeon clone from Denmark high-temperature oil production water (FN356360) 100 HibPW-ARC2 Thermococci

Thermococcus alcaliphilus DSM 10322 (AB055121)

0.05

Fig. 2.6. Phylogenetic relationship of the 2 archaeal 16S rRNA gene sequences obtained from Hibernia produced water (HibPW). The bands were labeled with HibPW- and with Archaea (ARC) and their band numbers from Fig. 4B. The tree was inferred by neighbor-joining analysis of sequence from each clone. Aquifex pyrophilus was used as the outgroup. Numbers on the nodes are the bootstrap values based on 1,000 replicates. The scale bar indicates the estimated number of base changes per nucleotide sequence position.

80 90 100

70 60

50 40

SAB 98 NW0-NB 97 S3-NB 94.4 W0-NB 90.9 S1-NB 89.1 N0-NB NB 83.1 S6-NB S0-NB R50km-NB 78.2 S1-50m 85.7 R50km-50m 75.8 S0-50m NW0-50m 92.3 79.5 86.7 S3-50m 50 m W0-50m 83 N0-50m 89.5 S6-50m 67.6 N0-2m 93.3 92.9 S3-2m 91 S6-2m 87.3 S1-2m 38.8 82.6 NW0-2m 2 m

78.8 W0-2m R50km-2m 81.1 S0-2m HibPW

Fig. 2.7. Bacterial 16S rRNA gene DGGE fingerprint cluster analysis from Hibernia seawater and produced water.

100 100 100

90 90 90

70 70 70

80 80 80 4 6 8 11 12 16 21

96.2 R50K-NB 89.8 S0-NB 83.7 R50K-50m

S0-50m 79 17 22 67.5 R50K-10m 94.1 R50K-25m 100 R50K-2m

97.1 S0-25m

S0-10m 100 2 14 19

S0-2m 1 3 5 10 13 15 18 20 23 24 Fig. 2.8. Hibernia seawater bacterial 16S rRNA gene DGGE fingerprint. Bands were labeled from 1-24.

72 Aquifex pyrophilus (M83548)

100 Alpha-proteobacterium from North Carolina continental shelf (U70679) Hib05-SW13

100 Alpha-proteobacterium from Boothbay Harbor ME (EF508147) Hib05-SW14

99 Loktanella sp. (FJ889559) 99 Hib05-SW17 96 Alpha-proteobacteria 100 Hib05-SW19

100 Sulfitobacter sp. from Antarctica (AY794100) Hib05-SW23 87 Hib05-SW16 99 Roseobacter sp. from Arctic Ocean (AF353235) Hib05-SW18

100 Gamma-proteobacterium from western Arctic Ocean (DQ184429) Hib05-SW12 86 100 Gamma-proteobacterium from marine sponge (EU350886) Hib05-SW24

Hib05-SW20 100 Gamma-proteobacteria Gamma-proteobacterium from Boothbay Harbor ME (EF202341) 93 Gamma-proteobacterium from Arctic Ocean Laptev Sea (EU544735) 99 Hib05-SW21

100 Gamma-proteobacterium from Arctic Ocean Laptev Sea (EU544851) Hib05-SW22 100 Bacteroidetes bacterium from north water polynya Canadian Arctic (EF486554) 96 Hib05-SW3 Flavobacterium sp. from Sapelo Island GA (FJ745102) 99 100 Hib05-SW5 100 Cytophagales bacterium from Arctic Ocean (AF355051) 100 Lacinutrix sp. (EU581705) Hib05-SW1 99 Bacteroidetes bacterium from Arctic Ocean Laptev Sea (EU544751)

97 Gilvibacter sp. from Atlantic Ocean Guanabara Bay (AM990700) 82 100 Hib05-SW2 Flavobacteriaceae bacterium from northern Bay of Biscay (EU394565) Hib05-SW4 Tenacibaculum sp. from NW Mediterranean Sea surface water (EU253574) 90 Bacteroidetes 98 89 100 Hib05-SW9 Hib05-SW10 99 95 Polaribacter sp. from Arctic Ocean (DQ186949) Polaribacter sp. from UK coastal seawater (AF493675) Hib05-SW7 82 98 Hib05-SW6 80 Arctic Ocean bacterium clone (EU919764) 99 Hib05-SW8 Bacteroidetes bacterium from north water polynya Canadian Arctic (EF486552) 100 88 Cytophaga sp. from eastern Mediterranean (AJ635361) 100 Sphingobacterium sp. from Boothbay Harbor ME (EF508145) Hib05-SW11 100 Bacteroidetes bacterium from NE Mass. coastal water (AY580728) Hib05-SW15

0.05

Fig. 2.9. Phylogenetic relationship of the 24 bacterial 16S rRNA gene sequences obtained from Hibernia seawater DGGE. The bands were labeled with Hib05-SW and a number Fig. 2.8. The tree was inferred by neighbor-joining analysis of sequence from each clone. Aquifex pyrophilus was used as the outgroup. Numbers on the nodes are the bootstrap values based on 1,000 replicates. The scale bar indicates the estimated number of base changes per nucleotide sequence position.

Connecting text

In the previous chapter, the DGGE results and chemical and physicochemical analyses revealed that the discharge of produced water did not have a detectable effect on the seawater 500 m or more from the Hibernia production platform, suggesting that any effect from the produced water discharge might be restricted to the area within 500 m of the production platform. In this chapter, we constructed the first marine-specific 16S rRNA taxonomic microarray and used it as an alternative culture-independent bacterial community monitoring method to compare to the results from the DGGE analysis. Both the DGGE and the microarray were used to characterize the bacterial community structure in the

Terra Nova produced water and the surrounding seawater from within 500 m of the Terra Nova production platform.

CHAPTER 3

Characterization of the Bacterial Community in Terra Nova Seawater and

Produced Water

C. William Yeung1,2, Ken Lee3, Lyle G. Whyte2, and Charles W. Greer1.

CONTRIBUTIONS OF AUTHORS Writing and preparation of the manuscript was performed by me. Drs. Greer, Lee and Whyte critically read and edited the manuscript.

Abstract

The Terra Nova production platform will soon be the second largest oil producing platform off the east coast of Canada. The produced water, which contains minor amounts of natural organic and inorganic components from the subsurface geological formation and the chemical amendments that aid in oil production, is the major source of contamination from the platform into the ocean.

The objective of this study was to characterize the indigenous bacterial community structure in the produced water and in the seawater within 500 m from the Terra Nova production platform using two different culture-independent techniques (DGGE and microarray) to determine whether the release of produced water is impacting the natural ecosystem. Results from both techniques showed that the produced water did not have a detectable effect on the bacterial populations in the surrounding water. Cluster analysis from both microarray and

DGGE results showed high similarity (SAB > 60 and 80) in the bacterial community structure for all seawater samples across a 20 km range. However, both microarray and DGGE results revealed that there were distinct differences in the composition of the bacterial communities in the produced water compared to seawater near the production platform (with SAB only around 30 and 50 respectively), even though the major component of the produced water comes from injected surrounding seawater, indicating that the effect from produced water may be restricted to the region immediately adjacent to the platform. Some specific microorganisms (e.g. Thermoanaerobacter), possibly originating from the geological formation, were identified with both techniques.

3.1 Introduction

The Terra Nova production platform is a relatively new oil producing platform off the eastern coast of Canada. It had been in production since 2002 and is the second largest offshore oil field in eastern Canada with an estimated 440 million barrels of recoverable oil. Similar to Hibernia and other oil and gas production platforms, produced water is the major production waste discharged from the platform into the surrounding water during crude oil recovery. The Terra

Nova produced water consists of formation water (water naturally present in the geological formation) and injected seawater (water used to maintain reservoir pressure). This produced water is a contaminated effluent with high concentrations of metals, nutrients, and petroleum hydrocarbons that could cause potential acute and chronic toxicity to the surrounding marine environment. In the two initial studies from the Hibernia production platform, in Chapter 2 and

Appendix A, we revealed that the discharge of produced water did not induce any detectable changes in the microbial community structure in the surrounding seawater around the production platforms.

In the Hibernia studies, DGGE was the molecular method used to evaluate the water column microbial community and to detect changes that were associated with the input of produced water. A known limitation for DGGE fingerprinting is the lower detection sensitivity: it is limited to detect a bacterial population when it represents less than 1% of the total bacterial community (Muyzer et al. 1993;

Murray et al. 1996). The dilution of produced water effluent is very rapid, so changes in the seawater microbial community could be minimal (similar to the findings from the other studies using various methods (Chapter 1) or could potentially be out of the DGGE detection range. When comparing DGGE and clone libraries, in Chapter 2 we found that DGGE could sufficiently identify the major species in the population identified by clone library method, but results from the clone library on the same sample revealed a much higher diversity and provided a more detailed view on the richness. However, clone libraries are more time-consuming to prepare and analyze. Other high-throughput methods for determining the composition of the marine community would therefore be beneficial to confirm the findings from DGGE analysis.

Another commonly used high-throughput method is detection-type DNA microarrays (Ye et al. 2001). Microarray technology was found to be a suitable high-throughput technique to evaluate the complexity of microbial communities

(Huyghe et al. 2008). The microarrays generally contain oligonucleotides targeting a set of sequences, usually the 16S rRNA gene, because it is highly

conserved between different species of bacteria and archaea. The oligonucleotides probes were designed by in silico prediction using sets of known sequences from the databases and subsequently printed and arrayed onto slides. The probes, ideally, have to be designed to hybridize with similar efficiencies to a target group of sequences. The target DNA or rRNA from the sample is purified and prepared to incorporate a fluorescent label, fragmented, and hybridized to the arrays.

Essentially, once the arrays have been designed and tested, identification can be accomplished with the array within a matter of hours without any prior cultivation or knowledge of the sample under investigation. However, there are many disadvantages of the microarray. The main disadvantage is that since the oligonucleotides were designed by in silico prediction using previously identified sequences from the databases, it has a limitation for discovering new groups that is unique to the environment.

In this study, our goals were to characterize the bacterial community structure in the Terra Nova produced water and surrounding seawater using

DGGE and a “marine” microarray and to compare the results obtained from the

DGGE and the microarray analyses.

3.2 Materials and Methods

3.2.1 Sample collection

A produced water sample was kindly provided by the personnel from the

Terra Nova production platform (46°28′30″N 48°28′46″W) in August 2007.

Seawater samples were collected from a number of locations (TS0: 0 m, TS10: 10 m, TS: 25 m, TS50: 50 m, TS100: 100 m, TS200: 200 m, and TS500: 500 m)

south of the platform at a depth of 10 m using a Niskin bottle attached to a

Seabird conductivity, temperature, depth detector (CTD) deployed by hand from one of the ship‟s launches. A reference seawater was collected with the Seabird

Niskin rosette frame (24 X 10 L bottles) containing a CTD from a location 20 km

(R20K) north of the Terra Nova production platform in 2007. All containers used in the filtration were rinsed three times with the sample water. Four liters of seawater and produced water were immediately filtered through sterile 0.22 μm

GSWP (Millipore) filters. Following filtration all filters were transferred to sterile

50 mL Falcon tubes and were stored at -20°C until analyzed.

3.2.2 Genomic DNA extraction

Total community DNA from the seawater and the produced water were extracted from the filter with an UltraClean® Water DNA isolation kit (Mo Bio

Laboratories, Carlsbad, CA) following the manufacturer‟s protocol. DNA concentration was estimated by agarose gel electrophoresis using 5 μL of purified material against the Lambda HindIII DNA ladder (Amersham Biosciences,

Piscataway, NJ) standard on a 0.7% agarose gel stained with SYBR safe.

3.2.3 PCR amplification of the 16S rRNA gene

The 16S rRNA PCR amplification was performed using the same method as described in Chapter 2.

3.2.4 Denaturing Gradient Gel Electrophoresis (DGGE) analysis

The DGGE analysis was performed using the same method as described in

Chapter 2.

3.2.5 Constructing “Marine” 16S rRNA gene taxonomic microarray

The 16S rRNA gene probe design for the microarray was as follows: 1)

RDP II database search for 16S rRNA genes with ≥ 1200 base pairs of good quality sequence using keywords “marin*”, “sea”, “ocea*”, “pelagi*”, “SAR*” but not “chloroplast”. A total of 28620 sequences were retrieved on February 15th,

2009. 2) Sequence diversity was first examined by cluster analysis using

Blastclust (NCBI, Bethesda, MD) with 98%-similarity resulting in 209 major clusters with at least 3 or more representatives. 3) With this sufficiently diverse set of sequences, the file downloaded from the RDP search was input into ARB

(http://www.arb-home.de/) to build a phylogentic tree, and the set of sequences was further divided into 744 clusters. 4) A 25-mer probe was built for each cluster with 3 or more representatives using the ARB probe design function. 5) The oligonucleotide probe sequences were the reverse and complement. 6) The specificity of the probes was checked using BLAST. 7) The probes were designed to target genus, family, order, class, phylum and sub-phylum. 8) However, a probe could not be designed to all clusters, and some clusters required more than one probe to cover all/most of the representatives. The probes used and their sequences are listed in Appendix B. Oligonucleotides (synthesized by IDT,

Coralville, IA) were printed in triplicate on amino-silane coated glass slides

(Corning, Acton, MA) using a VersArray Chip Writer Pro printer (Bio-Rad,

Hercules, CA). Each slide contained two identical sub-arrays.

3.2.6 PCR amplification and microarray hybridization

Total genomic DNA was amplified with bacterial 16S rRNA gene primers

F1 (5‟–GAGTTTGATCCTGGCTCAG-3‟) and R13 (5‟–

AGAAAGGAGGTGATCCAGCC-3‟) (Liesack et al. 1991) with PCR condition as follows: each 50 μL of PCR mixture contained 1 μL of the template DNA (~1 ng/μL), 25 pmol of each of the forward and reverse primers, 1 mM MgCl2, 200

μM of each dNTP, 2.5 units of Taq polymerase (Amersham Biosciences,

Piscataway, NJ, USA) in Taq polymerase buffer (10 mM Tris-HCl pH 9.0, 50 mM KCl, 1.5 mM MgCl2). PCR conditions were as follows: after an initial temperature of 96°C for 5 min, followed by 10 cycles at 94°C for 1 min, 60°C for

1 min and decreased by 1°C every cycle for 10 cycles, and 72°C for 2 min, with a final extension at 72°C for 10 min. Twenty additional cycles were performed with the lower annealing temperature. After PCR, the concentration of product was estimated on a 1% agarose gel with SYBR Safe stain (Molecular Probes,

Eugene, OR, USA) and a 1kb ladder (MBI Fermentas, Amherst, NY, USA) was used to determine the presence, size and quantity of the PCR products. Three

PCRs were combined to minimize bias. The PCR products were purified and combined using Illustra GFX™ PCR DNA and Gel Band Purification Kit (GE

Healthcare, Baie d‟Urfé, Quebec) and quantified with NanoDrop. The purified

PCR products were then chemically labeled using the Cy-5 Label IT nucleic acid labelling kit (Mirus Bio, Madison, WI) and clean up with PureLink PCR clean kit

(Invitrogen). The percentage of incorporation was checked with NanoDrop and

analysis with the % Incorporation Calculator

(http://www.pangloss.com/seidel/Protocols/percent_inc.html).

Slides were pre-hybridized for 1 h at 37°C with a DIG easy hybridization solution (Roche Applied Science, Laval, QC, Canada) containing 5% BSA.

Samples were then assigned randomly to a sub-array and hybridization was carried out for ~ 20 h at 37°C on a Slide Booster apparatus (Implen, Calabasas,

CA). Slides were washed three times for 5 minutes at 37°C in 0.1X SSC / 0.1%

SDS followed by a single wash for 5 minutes at 37°C in 0.1X SSC. Slides were scanned in a ScanArray GX PLUS Microarray Scanner (Perkin-Elmer, Boston,

MA) at a resolution of 10 µm and images were transferred to ScanArray Express

(Perkin-Elmer) where each spot was identified and quantified. Data was then exported to Excel where spot intensity was background-subtracted. A spot was scored as “present” if the background-subtracted intensity (i.e. intensity divided by the average median intensity (500)) was at least 4. For a taxa to be scored as present, all of its triplicate spots had to be scored as present. The results from the taxonomic microarray were only used in the binary form (presence/absence of taxa). The degrees of similarity between the samples were computed with

DendroUPGMA online software

(http://genomes.urv.cat/UPGMA/index.php?entrada=Example2) using the Dice similarity coefficient (Dice, 1945), and dendrogram patterns were clustered by the unweighted pair group method using arithmetic average (UPGMA) groupings with a similarity coefficient (SAB) matrix.

3.2.7 Nucleotide sequence accession numbers

The 16S rRNA gene sequences obtained in this study were deposited in the GenBank database under accession numbers JF789519 to JF789540.

3.3 Results

3.3.1 DGGE analysis for produced water and seawater

The DGGE analysis revealed that the bacterial community structure in the surrounding seawater was more diverse than the produced water (Fig. 3.1). Only 4 bands were found in the produced water compared to a total of 18 major bands found in the seawater samples. The DGGE dendrogram cluster analysis also revealed that the produced water bacterial community profile was very different from the rest of the surrounding seawater (SAB = 50). The bacterial community profile from the R20K reference seawater also was slightly different from the rest of the seawater samples from closer to the production platform (SAB = 80.4). All the seawater samples from within 500 m of the production platform showed very high similarities to each other (SAB ≥ 95) (Fig. 3.1).

3.3.2 Produced water bacterial and seawater bacterial phylogenetic analysis

One of the advantages of using the bacterial 16S rRNA gene DGGE analysis was to identify the dominant bacterial groups in the produced water and surrounding seawater, so all the major bands were excised and re-amplified for sequencing analysis. From the produced water, the sequences of the 4 DGGE bands were excised and sequenced (Fig. 3.1). The sequencing results revealed that all 4 sequences showed at least a 97% match to the GenBank sequences related to

Thermoanaerobacter spp. from the Firmicutes (Fig. 3.2).

Compared to the higher diversity seawater, a total of 18 DGGE bands were excised and sequenced (Fig. 3.3). Most of the sequences showed at least a

97% match to the available sequences in GenBank with their closest matches were related to Actinobacteria, Proteobacteria or Bacteroidetes sequences from either the Atlantic or Arctic Ocean. Within the Proteobacteria, the Alpha- and

Gamma-proteobacteria comprised 6 and 2 sequences, respectively. The highest diversity was in the Bacteroidetes group with 8 sequences. Only two sequences from the Actinobacteria were identified (Fig. 3.3).

3.3.3 Microarray analysis of produced water and seawater

A total of 621 probes was designed and printed on the “marine” 16S rRNA gene taxonomic microarray. The microarray was used to evaluate the bacterial community structure in the seawater and produced water samples and to compare the results to the DGGE analytical results. Out of the 621 bacterial taxa targeted on the microarray, only 64 were detected in both produced water and seawater.

On average, 31.2 taxa were detected per individual sample, ranging from 17 to 57 taxa. Similar to the result from the DGGE analysis, the produced water had the lowest diversity compared to the diversity found in the seawater. The highest diversity was observed in the R20K reference site seawater (Fig. 3.4). The diversity in the seawater samples from 0 m to 500 m varied very little between 27 to 30 hits (probes detected). Similar to the result from the DGGE cluster analysis, the microarray cluster analysis revealed that the produced water bacterial community structure was very different from the rest of the surrounding seawater

(SAB = 27). The bacterial community structure from R20K was also different from

the other seawater samples closer to the production platform (SAB = 64). All the seawater samples from within 500 m of the production platform revealed very high similarities to each other (SAB ≥ 93) (Fig. 3.4).

There were 17 hits in the produced water microarray with only 7 of them found solely in the produced water (Fig. 3.4). The 7 unique produced water hits were Coxiella, Enterovibrio, Streptococcaceae, Thermoanaerobacter,

Thermosipho, unclassified_Clostridiaceae, and unclassified Clostridiaceae 2.

Similar to the DGGE results, Thermoanaerobacter from the Firmicutes phylum was one of the 7 hits that were found only in the produced water sample, and interestingly 6 of the 7 hits in the produced water were from the Firmicutes.

A total of 10 probes (Bacillaceae 2, Epsilon-proteobacteria, Silicibacter,

Fusobacteriaceae, Flavobacteria, unclassified_Rhizobiaceae 1, Sterolibacterium,

Gp6, Caminicella, and deinococci) had hits in both produced water and seawater.

In contrast, no similar bands or sequences were found in the produced water and seawater samples in the DGGE analysis.

There were 47 hits found only in the seawater, and among them 18 of the hits found only in the seawater samples. These seawater hits were Belliella,

Crenotrichaceae, Curtobacterium, Deferribacteraceae, Devosia 1,

Geodermatophilaceae, Jannaschia, Kordiimonas, Methylophilus, Nocardioides,

Oleispira, Rhodobacteraceae, Roseovarius, Rubritalea, Saprospiraceae,

Thalassobius 2. Within these hits, 6 of them belonged to Alpha-proteobacteria, 3 from Actinobacteria, 2 from Gamma-proteobacteria, 2 from Bacteroidetes, and 1 each from Beta-proteobacteria, Deferribacteres and Verrucomicrobia. Lastly, a higher than expected diversity was found in the R20K sample: 26 of the 57 hits

were only found in this sample, i.e. 31 hits were found in the other samples which were similar to the number of hits in the other seawater samples (27 to 30 hits each). However, it is important to note that even though there were more unique hits found only in the R20K sample, the intensity for all these hits was relatively low (Fig. 3.4).

Overall, Alphaproteobacteria were the most prevalent organisms in the seawater microarray result, accounting for 17 out of 64 (26.6%) taxa detected.

Other phyla that were well represented included Gammaproteobacteria (14%),

Actinobacteria (12.5%), Firmicutes (10.9%), Betaproteobacteria (9.4%) and

Bacteroidetes (6.3%) (Fig. 3.4).

3.4 Discussion

3.4.1 Produced water bacterial diversity

Comparing with the bacterial community analysis results from other produced waters from nearby petroleum producing regions like Hibernia (Chapter

2) and the North Sea (Kaster et al. 2009), the bacterial community in the Terra

Nova produced water was relatively simple with only 4 major bands observed

(Fig. 3.1). The sequences of all the bands were all closely related to

Thermoanaerobacter spp. (Fig. 3.2) that were also found in other produced waters from the nearby regions (Chapter 2; Kaster et al. 2009; Dahle et al. 2008; Nazina et al. 2007), suggesting that Thermoanaerobacter spp. might be a common genus in produced waters from the North Atlantic regions. In terms of phylogenetic analysis, all Thermoanaerobacter spp. phylotypes were closely clustered with other Thermoanaerobacter spp. from produced waters in China and Kamchatka

and closely to Thermoanaerobacter pseudethanolicus species (Fig. 3.1).

Thermoanaerobacter is a thermophilic fermentative and organotrophic sulfur- respiring bacterium that was commonly identified and isolated from geographically separated oil reservoirs and from other produced water samples throughout the world, which suggests that Thermoanaerobacter spp. might likely be indigenous to the petroleum reservoir (Cayol et al. 1995; Grassia et al. 1996;

Magot et al. 2000), although it cannot be ruled out that it originates from the water injected into the formation

Microarray analysis revealed a higher diversity in the Terra Nova produced water than the DGGE analysis. The number of phyla and sub-phyla found in the microarray analysis was more comparable to the diversity found in the Hibernia produced water clone library analysis (Chapter 2). In Chapter 2, we found the highest number of clones came from the Firmicutes and Epsilon- proteobacteria, and the highest phylotypes diversity was found in the Firmicutes,

Gamma- and Alpha-proteobacteria. The Terra Nova produced water microarray result showed that 10 out of the 17 hits on the microarray were from the

Firmicutes, Gamma- and Alpha-proteobacteria. The rest of the 7 hits were spread evenly among 7 other phyla and sub-phyla, including one hit from the Epsilon- proteobacteria, suggesting that similar phylotypes diversity were found in both

Hibernia and Terra Nova produced waters. Although these results were generated using two different methods (clone library and microarray), the same primers set

(F1 and R13) was used (both results would be subjected to the same primer bias), so these similar findings were comparable. It would be important to also construct clone libraries for the Terra Nova produced water for a more thorough

comparison in the future. However, similar to the findings from Chapter 2, comparing the results from the 2 molecular methods (microarray and DGGE) revealed that DGGE method was most likely capable to identify most of the major population in the samples but not the minor population.

3.4.2 Seawater bacterial community analysis

In the seawater, both DGGE and microarray analysis revealed that there was a much higher bacterial diversity than in the produced water. Phyla and sub- phyla like Alpha-, and Gamma-proteobacteria, Bacteroidetes, and Actinobacteria were identified by both analyses. The same Alpha-, Gamma-proteobacteria and

Bacteroidetes were commonly found in seawater around the Hibernia production platform (Chapter 2). The seawater microarray results revealed a much higher bacterial diversity than the DGGE results. A few phyla were only identified using the microarray method (Fig. 3.4), suggesting that DGGE could only be used to identify the major members in the population. Interestingly, one of the phyla found only using the microarray in both produced water and seawater was Beta- proteobacteria, which are considered to be quite rare in marine bacterial communities (Hahn 2006). This result suggested that the microarray, like the clone libraries (Chapter 2), might have the sensitivity required to detect minor populations in the marine community.

Both the DGGE and microarray methods were able to identify the major members in the population, and the results from cluster analysis from both methods were very similar. Both methods found that the bacterial community structure in the produced water was very different from the bacterial community

structure in the surrounding seawater (SAB = 50 and 27). The seawater bacterial community structures were more similar (SAB ≥ 80.4 and 64), in particular, from samples within 500 m of the platform (SAB ≥ 95 and 93). In previous chapters

(Appendix A and Chapter 2), it was found that there was a stable bacterial community in the surrounding seawater 500 m outside of the Hibernia production platform. As was concluded from the results from Hibernia, using different molecular monitoring techniques, the effluent of produced water might not have detectable effects on the bacterial community structure in the seawater column from around the Terra Nova production platform.

3.5 Conclusions

We constructed the first marine-specific 16S rRNA taxonomic microarray.

Both DGGE and microarray methods were used to characterize the bacterial community structure in the surrounding seawater and the produced water from the

Terra Nova production platform. The microarray results agreed with the DGGE results, both suggesting that the seawater bacterial community structure from near the discharge to 20 km from the platform was very similar (with SAB ~ 60 and 80 respectively), indicating that the produced water did not have any detectable effect on the bacterial populations in the surrounding water.

3.6 Acknowledgements

The authors acknowledge Sylvie Sanschagrin, Melanie Arbour, Julie Champagne, Jay Bugden, and Susan Cobanli for their excellent technical assistance. The authors thank the NRCan PERD program for financial support.

100

70 80 90 50 60 1 2 3 4 6 7 9 10 1113 15 17 18 SAB R20K

TS500

100 TS200 80.4 TS100 95 TS50

TS25 50 100 TS10

TS0

5 8 12 14 16 TNPW a b c d Fig. 3.1 Bacterial 16S rRNA gene DGGE fingerprints cluster analysis from Terra Nova produced water sample (TNPW) and surrounding seawater samples from TS0 to R20K. Produced water bands were labeled from a-d and seawater bands were labeled from 1-18.

Aquifex pyrophilus (M83548) Thermoanaerobacter mathranii from hot spring in Iceland (Y11279) 99

Thermoanaerobacter sp. from Hibernia Produced Water

70 Thermoanaerobacter sp. from oil-producing water (EF026571)

Thermoanaerobacter subterraneus from oil reservoirs (AY216596) 91 Thermoanaerobacter uzonensis from hot spring Kamchatka (FJ360438) 91 TNPW-a

Thermoanaerobacter pseudethanolicus ATCC 33223 (CP000924) 98 TNPW-c 97 TNPW-d 95

Thermoanaerobacter sp. from China oil-producing water (EF026572)

0.02 95 TNPW-b

Fig. 3.2. Phylogenetic relationship of the 4 bacterial 16S rRNA gene sequences obtained from Terra Nova produced water DGGE analysis from Fig. 3.1. The bands were labeled with Terra Nova produced water (TNPW) and their band letter from Fig. 3.1. The tree was inferred by neighbor-joining analysis of sequence from each clone. Aquifex pyrophilus was used as the outgroup. Numbers on the nodes are the bootstrap values based on 1,000 replicates. The scale bar indicates the estimated number of base changes per nucleotide sequence position.

Aquifex pyrophilus (M83548)

Roseobacter sp. from Monterey Bay (AY627365) 100 Alpha-proteobacterium from Maine harbor (EF508147)

TNSW-10 100 100 Roseobacter sp. from Norwegian coastal water (EU819142) TNSW-11

Roseobacter sp. from North Atlantic (AF245634) 90 94 TNSW-12 Alpha-proteobacteria

73 Sulfitobacter sp. from Arctic Ocean (FJ889529)

79 TNSW-13

100 Alpha-proteobacterium from Arctic Ocean (AF353233)

72 TNSW-9

100 Alpha-proteobacterium from Mediterranean Sea (DQ436614) TNSW-15 98 100 Gamma-proteobacterium from Arctic Ocean (DQ184456) 100 TNSW-14 Gamma-proteobacteria 100 Gamma-proteobacterium from Mass. coastal water (AY580751) TNSW-16

99 Polaribacter sp. from Arctic Ocean (DQ186943) TNSW-8

TNSW-4

100 Tenacibaculum sp. from Mediterranean Sea (EU253574)

Flavobacteria from Maine Harbor (EF202335)

TNSW-3

100 99 Lacinutrix sp. (EU581705) TNSW-1 85 Bacteroidetes from Arctic Ocean (DQ186960) Bacteroidetes Gilvibacter sp. from Atlantic Ocean (AM990700)

96 TNSW-2

Bacteroidetes from Arctic Ocean (DQ186964) 80 100 Flavobacteria from Maine harbour (EF202336) TNSW-6

Bacteroidetes from Mass. coastal seawater (AY580612)

98 TNSW-5 72 100 Bacteroidetes from Mass. coastal seawater (AY580648) TNSW-7

100 Actinobacterium from Mass. coastal water (AY580347) TNSW-18 100 Actinobacteria TNSW-17

Actinobacterium from Chesapeake Bay (EF471631)

0.05

Fig. 3.3. Phylogenetic relationship of the 18 bacterial 16S rRNA gene sequences obtained from Terra Nova surrounding seawater DGGE analysis from Fig. 3.1. The bands were labeled with Terra Nova seawater (TNSW) and their band number from Fig. 3.1. The tree was inferred by neighbor-joining analysis of sequence from each clone. Aquifex pyrophilus was used as the outgroup. Numbers on the nodes are the bootstrap values based on 1,000 replicates. The scale bar indicates the estimated number of base changes per nucleotide sequence position.

92 20

100 T T SAB T T S S R N T S 1 5 2 P S 5 0 0 0 Probes W 0 0 0 0 K Asticcacaulis Caulobacteraceae Devosia1 Ensifer Jannaschia Kordiimonas Nereida Phyllobacterium Alpha-proteobacteria Rhodobacteraceae Roseovarius Silicibacter Sphingobium Sphingomonadaceae Thalassobius2 unclassified_Rhizobiaceae1 unclassified_Rhodospirillales Alpha-proteobacteria Azoarcus Burkholderiales Comamonadaceae Beta-proteobacteria Incertae sedis 5 Methylophilus Sterolibacterium Detla-proteobacteria Bacteriovoracacea Alteromondaceae Chromatiaceae Coxiella Crenotrichaceae Gamma-proteobacteria Enterovibrio Idiomarinaceae Oleispira Pseudoxanthomonas Vibrionaceae Epsilon-proteobacteria Epsilon-proteobacteria Belliella Flavobacteria Bacteroidetes Saprospiraceae Sphingobacteriaceae Actinomycetales Curtobacterium Geodermatophilaceae Nocardioides Actinobacteria Olsenella Streptomyces Streptomycetaceae unclassified_Micromonosporacea Bacillales Bacillaceae 2 Caminicella Firmicutes Streptococcaceae Thermoanaerobacter unclassified_Clostridiaceae unclassified_Clostridiaceae 2 Thermotogae Thermosipho Deferribacteres Deferribacteraceae Fusobacteria Fusobacteriaceae Gp3 Acidobacteria Gp6 Deinococcus-Thermus Deinococci >50 Nitrospirae Nitrospira 50-35 Rubritalea Verrucomicrobia 35-20 Verrucomicrobiaceae_genera_inc 20-4 Cyanobacteria GpXIII <4 Aquificales Aquificales 93

Fig. 3.4. Bacterial phyla detected from the produced water and seawater samples using the marine 16S rRNA gene taxonomic microarray. Blank squares indicate a background-subtracted intensity higher than 50 and white squares indicate a background-subtracted intensity lower than 4. Grey-scales were used for all the other intensities between these maximina and minima. A total of 64 probes were detected, with 17 from TNPW, 28 from TS0, 27 from TS50, 28 from TS100, 30 from TS500, and 57 from R20K. Cluster analysis of microarray profiles based on hits using unweighted pair group method using arithmetic average (UPGMA) of an SAB matrix.

Connecting text

In the previous chapter, two different microbial community monitoring methods (DGGE and marine microarray) were used. Both analyses revealed that the discharge of produced water did not have a detectable effect on the bacterial community structure in the surrounding seawater within 500 m of the production platform, nor were any produced water-specific species detected in the water column from the surrounding seawater immediately adjacent to the discharge.

These findings suggest that any influence from the discharge of produced water in the water column was minimal or any impact from the produced water might be restricted to the sediment level adjacent to the discharge. However, it is still necessary to determine the transport pattern and dilution factor of the produced water effluent. In this chapter, the bacterial community from the Thebaud surrounding seawater and sediments were analyzed, and the results were compared to the sediment heavy metal analysis to determine the influence from the produced water discharge on the sediments around the production platform.

CHAPTER 4

Analysis of Bacterial Diversity and Metals in Produced Water, Seawater and Sediments From Around an Offshore Oil and Gas Production Platform.

C. William Yeung1,2, Brent A. Law3, Ken Lee3, Lyle G. Whyte2, and Charles W.

Greer1.

CONTRIBUTIONS OF AUTHORS Site selections for analysis were chosen by Dr. Lee and Brent Law. The sample collections were performed by Brent Law and myself. The metal analysis was performed by Brent Law. Writing and preparation of the manuscript was performed by me. Drs. Greer, Lee and Whyte critically read and edited the manuscript.

Abstract

Produced water is one of the largest waste products routinely discharged into the ocean from offshore oil and gas platforms. This study analyzed bacterial communities and metals in the produced water, surrounding seawater, and sediment around the Thebaud platform. The bacterial community within the produced water was different from the seawater (SAB = 13.3), but the discharge had no detectable effect on the bacterial communities in the seawater (SAB > 97).

In contrast, genomic analysis of sediments revealed that the bacterial community from 250m was different (SAB = 70) from other locations further from the discharge, suggesting that the produced water had a detectable effect on the

bacterial community in the sediment closest to the discharge. These near-field sediments contained elevated concentrations of manganese and iron that are associated with the produced water effluent. The results suggested that the discharge of produced water has influenced the bacterial community structure of sediments adjacent to the platform.

4.1 Introduction

Produced water, formation water within the geological formation and liquid chemicals introduced into the process stream to improve safety and enhance recover of oil and gas, is the largest waste product discharged into the surrounding seawater during exploration and production operations. Produced water discharged from offshore oil and gas wells is usually anoxic and has a salinity and temperature that is different from that of the receiving water. It also contains higher concentrations of metals, radionuclides, hydrocarbons, and ammonia compared to the receiving environment. In Canada, only the concentrations of hydrocarbons in the discharges are regulated. The discharges usually rely on dilution to decrease any environmental effects of anoxic conditions, temperature and salinity, or potential toxic effects of metals or other chemicals in the discharge. Although it is generally believed that the acute toxicity of produced water may be reduced by hydrocarbon removal and dilution of the plumes

(Armsworthy et al. 2005), concerns remain over the potential chronic effects of discharges and the bioaccumulation of contaminants (Perez-Casanova et al. 2010).

In order to understand the potential chronic effects of produced water and its zone

of influence, the first step is to understand the transport and dilution of the chemicals in the produced water discharge.

Neff (2002) reviewed produced waters from around the world and found that several metals in produced water could be present at concentrations substantially higher (i.e. 1,000 times or more) than their concentration in the surrounding seawater. The metals most frequently present at elevated concentrations in produced water are barium, iron, manganese, and zinc. (cf. Neff

2002). When discharged into well oxygenated, sulfate rich surface waters, the high concentrations of dissolved iron and manganese can precipitate out as oxides

(Azetsu-Scott et al. 2007). Iron and manganese oxides could settle out of the water column and accumulate in sediments around the discharge (Neff 2002; Lee et al. 2005). Depending on the current regime at the site of concern, these metal oxides could be dispersed over a large area, elevating their concentrations in the sediments above natural background concentrations. Our principal objectives were to examine the metal concentrations in the near-field sediments (< 500 m from point of discharge) of an operational offshore platform and to characterize the bacterial community structure in the surrounding seawater and sediments with the goal to identify whether there was any evidence of effects from produced water discharge on the bacterial community.

4.2 Materials and methods

4.2.1 Sample Collection

All 2002 sediment samples were collected during the DFO Hudson Cruise

(2002-054) on board the CCGS Hudson from September 10 – October 3, 2002 at

stations radiating mostly north, east, west and south from the edge of the 250 m exclusion zone of the Thebaud platform (43° 53.5' N, 60° 12'W) on the Scotian

Shelf off the coast of Nova Scotia, Canada (Fig. 4.1 and Table 4.1). The Thebaud platform is one of the largest gas and light oil production platforms on the eastern sea coast of Canada. It is a gas-gathering center collecting gas from the Thebaud, the North Triumph and Venture wells before the gas is sent through a pipeline to a processing plant in Nova Scotia. All 2003 sediment samples were collected during the DFO Hudson Cruise (2003-059) on board the CCGS Hudson from September

25 – October 15, 2007 at stations similar to 2002. All 2007 samples were collected during the DFO Terra Nova Cruise (2007-036) on board the CCGS

Hudson from August 12 – 24, 2007. Sediment samples were collected at stations similar to 2002 and 2003. All seawater samples were collected at a depth of 10 m from three locations (FRC 1N, FRC NE, FRC E) next to the mouth of the produced water plume (Fig. 4.1 and Table 4.1) and from a reference station located 10 km west of the platform (R10kmW) (Table 4.1).

4.2.2 Water Sample collection

All seawater samples were collected using a shipboard 10 L or 20 L

Niskin bottles equipped with Conductivity, Temperature, and Depth (CTD) to provide temperature and salinity measurements in addition to water samples.

Samples of fresh produced water collected from the discharge process lines on the

Thebaud production platform were transferred at sea to our research vessel for simultaneous processing with our field samples. Samples were collected in two different types of containers: acid rinsed 10 L Nalgene® HDPE jerricans and

solvent rinsed 4 L amber glass bottles. All containers used in the filtration were also rinsed three times with the sample water. After collection, samples were stored at 4°C on the research ship until further processing. From the jerrican samples, an aliquot was removed for pH, salinity measurements and bacterial community analyses. Then, 4 L of seawater and 5 L of the produced water were immediately filtered through sterile 0.22 μm GSWP (Millipore) filters. Following filtration, all filters were transferred to sterile 50 mL Falcon tubes and stored at -

20°C until analyzed. For organic chemical analyses, samples were stored in amber glass bottles. Polycyclic aromatic hydrocarbons (PAH) and aliphatic hydrocarbons (alkanes) were determined using a modified version of EPA

Method 8270. Alkylated and nonyl phenols (phenols) were analyzed using a modified version of EPA method 8041. All BTEX (Benzene, Toluene,

Ethylbenzene, and Xylene) samples were analyzed within 2 weeks of collection using modified EPA Method 8240 (purge and trap).

4.2.3 Sediment sample collection and analysis

Bottom sediment core samples were collected at a grid of stations radiating mostly east from the edge of the 250 m exclusion zone from the

Thebaud platform (Fig. 4.1 and Table 4.1). The station numbers indicate both the direction and the distance from the platform (e.g. TE250, is 250 m east of the platform) (Table 4.1). Sampling consisted of deployment of a Bothner-type Slo-

Corer (Law et al. 2008). The Slo-Corer applied a large driving force (~350 kg) onto a polycarbonate core barrel while hydraulically damping the rate of descent.

This slow rate but large driving force prevented disturbance of the sediment water

100

interface. Once full penetration was achieved, recovery began with the sealing of the core top by an o-ring and flange which prevented the loss of sediment as the barrel was withdrawn. As soon as the core barrel cleared the bottom, a gasketed spade plate covered the bottom of the core, thus sealing both the top and bottom of the core. The supernatant water column was retained with no free surface, thus ensuring that the sediment water interface remained undisturbed even during rough recoveries. Once recovered and on deck the clear core liner was inspected for sample integrity. If any evidence of leakage from the bottom seal or disruption of the sediment-water interface was detected, the sample was discarded.

Collected cores were placed on an extruder and a series of 2 cm-thick sections were taken for the entire length of the core. The outer edges of the core were aseptically cut off and discarded, and only the center of the core was kept for chemical and biological analyses. For microbial analysis, 5 - 10 g of the core samples were transferred to sterile 50 mL Falcon tubes and stored at -20°C until analyzed. For metal analysis, samples were stored at 4°C in polyethylene cups with taped caps.

At the lab, samples were dried at < 60°C, sieved using a 1 mm stainless steel mesh and then sent out for trace metal analysis at the Research Productivity

Council (RPC), Fredericton, N.B. At RPC, samples were digested in a mixture of concentrated nitric and hydrofluoric acids, taken to incipient dryness, and re- dissolved in a mixture of hydrochloric and nitric acids. After dilution to volume with deionized water, the samples were analyzed by Inductively Coupled Plasma-

Emission Spectroscopy (ICP-ES) for higher concentration elements and

Inductively Coupled Plasma-Mass Spectrometry (ICP-MS) for trace elements. To

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ensure quality control, National Research Council (NRC) – marine sediment

Certified Reference Materials, CRM‟s, (MESS-3, HISS-1) were included in the samples sent for analysis. RPC also has an extensive internal quality assurance protocol that included analysis of NIST CRMs 1646a and 2709.

4.2.4 Genomic DNA extraction

Total community DNA from seawater and produced water were extracted from the filter with an UltraClean® Water DNA isolation kit (Mo Bio

Laboratories, Carlsbad, CA) following the manufacturer‟s protocol. Total community DNA from 0-2 cm sediment sample was extracted from 2 g of sediment sample with an UltraClean® Soil DNA isolation kit (Mo Bio

Laboratories, Carlsbad, CA) following the manufacturer‟s protocol. DNA concentration was estimated by electrophoresis on a 0.7% agarose gel using 5 μL of purified material and the Lambda HindIII DNA ladder (Amersham

Biosciences, Piscataway, NJ) standard.

4.2.5 PCR amplification of the 16S rRNA gene

The 16S rRNA gene PCR amplification was performed using the same method as decribed in Chapter 2.

4.2.6 Denaturing Gradient Gel Electrophoresis (DGGE) analysis

The DGGE analysis was performed using the same method as described in

Chapter 2.

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4.2.7 Nucleotide sequence accession numbers

The 16S rRNA gene sequences obtained in this study were deposited in the GenBank database under accession numbers HQ852399 to HQ852438.

4.3 Results

4.3.1 Physicochemical analyses of the produced water and surrounding seawater

The pH of the produced water was 5 and the salinity was 12.3 ppt. The produced water contained a mixture of petroleum hydrocarbon constituents: 0.05

μg/L BTEX, 0.18 μg/L phenols, 0.13 μg/L total alkanes, and 2.15 μg/L PAHs. In contrast, the surrounding seawater was 13.6°C with a salinity of 32 ppt. Due to the limited volume of produced water discharged from the platform and the extent of dilution following discharge, petroleum hydrocarbon concentrations in the sediments (~250 m from the point of discharge) were below detection limits (data not shown).

4.3.2 Produced water metal analysis

The produced water discharged from the Thebaud facility contained a variety of metals at high concentrations, including aluminum, barium, calcium, iron, lithium, magnesium, manganese, potassium, sodium, and strontium (Table

4.2). Some of these metals, like calcium, magnesium, potassium, sodium, and strontium are commonly found in relatively high concentrations in seawater

(Turekian 1968) that may also be injected into the formation to maintain well

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pressure. Therefore, focus was given to metals that were distinctly associated with the formation water (i.e. barium, iron, and manganese).

Geochemical normalization of specific metals to 'conservative' elements, such as lithium (Li) and aluminum (Al), has been used with varying degrees of success to differentiate natural accumulation from anthropogenic inputs, and to reduce the trace metal variability caused by grain size as well as by mineralogy

(Loring 1990; Hirst 1962a; 1962b). While Loring (1990) determined that Li is a good standard for normalization of samples from eastern Canadian estuarine and coastal sediments, Muschenheim et al. (2009) reported that normalization of metal concentrations vs. Li failed to produce a good correlation in samples from the sediments of Sable Island Bank because of the limitations in the Li data (i.e. there are usually too many samples with Li concentrations at or below the method detection limit). They suggested the use of Al as a good alternative since it provided geochemical grain-size normalization for the metals in the Scotian Shelf sediments and does not suffer from the same detection limit problems that were observed in the Li normalization (Muschenheim et al. 2009). In consideration of this previous work, aluminum was used to normalize all the other metal data from the sediment in this analysis. Average background concentrations and 5% confidence intervals, based on normalization to Al, were determined from the sample analysis and regression of over 500 bottom sediment samples collected from the Scotian Shelf (Yeats, unpublished data).

The results found elevated normalized concentrations of Ba, Fe, and Mn in the sediments closest to the platform (Figs. 4.2, 4.3 and 4.4). Barium concentrations showed some temporal differences with concentrations varying

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from background values up to 2500 mg/kg (Fig. 4.2). The difference in concentration generally corresponded to the distance from the platform with the highest values associated with the sample stations at 250 and 500 m to the east of the platform (Fig. 4.2). Iron concentrations exhibited a noticeably temporal increase from 2002 to 2007 at the sample location occupied at 250 and 500 m to the east of the platform with the highest values approaching 15000mg/kg from the samples collected in 2007 with near baseline values from samples in 2002 (Fig.

3). Samples collected at stations in 2003 (i.e. TNW250, TNE250, TSE250,

TW250) and 2007 (i.e. SE312) also showed elevated values for iron when compared to background values. Results suggest that the increase in iron concentration might be correlated to the produced water discharge. Similar to the iron data, the highest manganese concentrations also demonstrated similar temporal and spatial patterns with the highest values from samples closest to the discharge and from the most recent year (Fig. 4.4). This combination of the temporal and spatial trends from barium, iron, and manganese suggest that the accumulation of these metals in the sediment may be related to the cumulative input of the produced water over the years.

4.3.3 DGGE analyses

DGGE analysis revealed that the bacterial community structure from the produced water was very different from the surrounding seawater (SAB = 13.3, Fig.

4.5), even though the major component of the produced water was the injected surrounding seawater. The seawater DGGE results showed that the bacterial community structure was virtually identical (SAB ≥ 97.1, Fig. 4.5) in all the

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samples from the mouth of the produced water plume to 10 km away from the platform, suggesting that the bacterial community structure was spatially very stable in the surrounding seawater.

Similarly, the produced water/sediment DGGE analysis also revealed that the bacterial community structure from the produced water was very different from the sediment (SAB = 26, Fig. 4.8). The sediment DGGE results revealed that there was a high similarity in the bacterial community structure in the sediments which clustered into 2 major groups: one from samples from the 250 m locations and the other from samples beyond 250 m (SAB = 70.2, Fig. 4.8). This suggests that the bacterial community in the sediment from the 250 m locations is detectably different from the bacterial community located further away from the platform.

4.3.4 Phylogenetic analysis of produced water

The other purpose of the bacterial 16S rRNA gene DGGE analysis was intended to identify the dominant bacterial groups in the produced water, seawater and sediment, so all the major bands were excised and re-amplified for sequence analysis. From the produced water DGGE, a total of 4 DGGE bands were excised and sequenced (Figs. 4.5 and 4.8). All of the sequences showed at least a 97% match to GenBank sequences related to Acinetobacter sp. (1 sequence) and

Geobacillus sp. (3 sequences) from the Gamma-proteobacteria and Firmicutes, respectively (Fig. 4.6).

4.3.5 Phylogentic analysis of seawater and sediments

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Both seawater and sediment DGGE displayed a much higher bacterial diversity (i.e. higher number of bands) than the produced water (Figs. 4.5 and

4.8). From the seawater DGGE, a total of 15 DGGE bands were excised and sequenced (Fig. 4.5). Most of the sequences (except TBSW-3 and -10) showed at least a 97% match to sequences in GenBank, and most of the closest matches were related to Proteobacteria and Bacteroidetes from the marine environment

(Fig. 4.7). Within the Proteobacteria, the phylum can be divided into Alpha- and

Gamma-proteobacteria with 4 sequences and 1 sequence, respectively (Fig. 4.7).

The highest diversity was in the Bacteroidetes group with 10 sequences (Fig. 4.7).

The highest bacterial diversity was found in the sediment with a total of 21

DGGE bands excised and sequenced (Fig. 4.8). Most of the sequences showed at least a 97% match to sequences in GenBank from marine sediments or seawater from the Actinobacteria, Proteobacteria and Bacteroidetes groups. Similar to the seawater, most of the sequences (14 out of 21) belonged to Bacteroidetes. Only 2 sequences were related to the Actinobacteria and 5 sequences belonged to the

Proteobacteria including the Delta-, Gamma-, and Epsilon-proteobacteria subgroups. As previously mentioned, there was some uniqueness in the bacterial community in the sediment samples from the 250 m locations, in which the only

Epsilon-proteobacteria were identified (Fig. 4.9).

4.4 Discussion

4.4.1 Sediment metal monitoring

Trace metals, such as barium, iron and manganese, had noticeably higher concentrations in the produced water (Table 4.2) than commonly found in

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seawater (Turekian 1968) and the reference sediment (data not shown). Therefore, monitoring the concentrations of these metals in the surrounding environment could potentially be used as a method to define the discharge and dilution pattern of the produced water constituents.

The highest concentrations of barium, iron and manganese were found mainly in samples collected closest to the production platform in the most recent sampling year (Figs. 4.2, 4.3 and 4.4), suggesting that the higher concentrations of these metals might be linked to increases in annual discharge rates and/or the cumulative discharge of the produced water over time. Evidently, the highest concentrations of trace metals associated with raw produced water were found in the sediments mainly to the east of the platform which is in the mean current drift direction (Hannah et al. 2001). Yeats et al. (in press) argued that the higher concentrations of barium could be the result of sedimentary Ba that originated from drilling muds, however, they also pointed out that drilling mud would not necessarily explain the above background observations of Fe and Mn concentrations that were found in the sediments closest to the production platform. This temporal trend of increased iron and manganese suggests that the accumulation was most likely related to the input from the produced water discharge. The metal results also revealed the potential transport and dilution pattern of the produced water, suggesting that constituents of produced water would travel downward in the water column, settle onto and bury into the sediment near the platform. This result further suggested that any potential biological impact from the effluent of produced water would most likely be at the sediment level close to the production platform.

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4.4.2 Produced water bacterial diversity

Compared to produced waters from other petroleum production regions like Hibernia (Appendix A), Terra Nova (Chapter 3), and the North Sea (Kaster et al., 2009), the bacterial community structure in the produced water recovered from the discharge lines of the Thebaud platform was relatively simple with only

4 major bands detected by DGGE. The sequences from these bands were closely related to Acinetobacter spp. and Geobacillus spp. These genera were not found in other produced waters from nearby regions (Chapter 2; Chapter 3; Kaster et al.

2009; Dahle et al. 2008), suggesting that the bacteria from the Thebaud produced water were unique to this produced water.

Most Acinetobacter isolates are classified as mesophilic, strictly aerobic and non-fermentative bacteria. Only a few Acinetobacter-like sequences or isolates have been identified from petroleum environments, such as produced water from high-temperature reservoirs in oilfields in China (Li et al. 2007;

Nazina et al. 2005), suggesting that finding Acinetobacter spp. in the produced water in this region was not impossible, but uncommon for Atlantic oilfields. One of the explanations for finding Acinetobacter in the Thebaud produced water could be their relationship with hydrocarbons. Isolates of Acinetobacter are known to be able to use hydrocarbons as sole carbon and energy sources (Le Petit et al. 1975), and the Acinetobacter sp. detected in the Thebaud produced water may originate from the cooler portions of the oil production environment or be present in treating agents (e.g. emulsion breakers, corrosion inhibitors, etc) that are pumped down the well.

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In the geothermally heated oil reservoirs, the petroleum hydrocarbons could also create a unique ecological niche for thermophilic, aerobic or facultative anaerobic, hydrocarbon-degrading bacteria (Nazina et al. 2001; 2005).

Geobacillus spp. are known to be physiologically versatile and function in both thermophilic and mesophilic environments as aerobes or facultative anaerobes.

Nazina et al. (2001) isolated five strains of bacteria belonging to Geobacillus from the formation waters of oilfields in Russia, Kazakhstan and China. These strains were all able to degrade a wide range of hydrocarbons, lower alcohols, and organic acids, suggesting that, similar to Acinetobacter spp., the Geobacillus sp. from Thebaud produced water might also be utilizing the hydrocarbons. However, neither Acinetobacter or Geobacillus are strictly anaerobic bacteria that are more commonly found in produced waters (Magot et al. 2000; Birkeland 2004), suggesting that they might not be indigenous to the anoxic petroleum formation.

The presence of these genera in Thebaud produced water suggests that the produced water and the petroleum production system are providing a suitable environment for their survival and/or growth.

4.4.3 Seawater and sediment bacterial DGGE analysis

In Yeung et al. (Appendix A), Chapter 2 and Chapter 3, we found that there was a stable bacterial community in the surrounding seawater around the

Hibernia and Terra Nova production platforms and suggested that this could be an indication that there were no detectable changes on the bacterial community from the produced water. It was also suggested that any effects from the produced water might be restricted to the region immediately adjacent to the platform or at

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the sediment level. The Thebaud seawater DGGE results revealed that the bacterial community structure in the seawater was also very uniform in the region immediately adjacent to the platform and suggested that the discharge of produced water did not have a detectable impact on the seawater bacterial community (Fig.

4.5). The sediment DGGE results showed that there were some differences in the banding patterns in the sediments from samples closest to the platform (250 m)

(Fig. 4.8), possibly related to the proximity to the produced water discharge. The results revealed that sequences from the unique DGGE bands belonged to members from the Epsilon-proteobacteria that are closely related to Sulfurovum and Arcobacter (Fig. 4.9). No other Epsilon-proteobacteria were found in the seawater or other sediment samples (Figs. 4.7 and 4.9), suggesting that these

Epsilon-proteobacteria were unique to these samples and might be related to the influence from the produced water discharge.

4.4.4 Epsilon-proteobacteria subgroup

Sulfurovum spp. have been isolated since 2004 (Inagaki et al. 2004), but very little is known about these bacteria. Sulfurovum is a sulfur-oxidizing and/or symbiotic bacterium and is only isolated from deep-sea hydrothermal vent environments (Tokuda et al. 2008; Inagaki et al. 2004), suggesting that they might originate from environments associated with thermophilic formations.

Members of the genus Arcobacter are better known and are usually classified as nitrate-reducing and sulfide-oxidizing bacteria. Most of the

Arcobacter isolates were identified as potential pathogens associated with humans and livestock (Vandamme et al. 1992), so most of the phylogenetic and

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physiological studies conducted on this genus were pathogen-related studies.

From these studies, Arcobacter spp. were generally found to grow under aerobic and microaerophilic conditions requiring 3–10% oxygen and they could grow over a wider temperature range (15–42°C) (Snelling et al. 2006). Recently,

Arcobacter spp. have been found in natural marine and lake environments, such as a Hawaiian hypersaline lagoon (Donachie et al. 2005), Black Sea sediment

(Thamdrup et al. 2000), the North Sea (Eilers et al. 2000) and Solar Lake (Teske et al. 1996). Donachie et al. (2005) isolated the first obligate halophilic

Arcobacter (later named A. halophilus) from a hypersaline lagoon in the Hawaiian

Islands. Arcobacter spp. have also been isolated from oil fields in Saskatchewan,

Canada (Gevertz et al. 2000), identified in a high-temperature offshore petroleum reservoir in the North Sea (Kaster et al. 2009) and detected in the Hibernia produced water discharge (Chapter 2). Kaster et al. (2009) also demonstrated that sequences similar to Arcobacter, a sulphur-compound oxidizing Epsilon- proteobacterium, were enriched in acetogenic and fermentative media at 55°C as well as fermentative and sulfate reducing media at 70°C. Arcobacter spp. were also found in lower temperature environments from a petroleum degrading wetland soil from the Shengli Oil Field on the Yellow River delta (Han et al.

2009) and from the produced water in a crude oil gathering and transferring system (Liu et al. 2009). Liu et al. (2009) demonstrated that the Arcobacter were only found in the produced water but not in the crude oil, which suggests that

Arcobacter might not originate from the petroleum formation but may become enriched by the hydrocarbons in the produced water. Therefore, finding Epsilon- proteobacteria in the sediment close to the platform, their unique physiological

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characteristics and their relationship to other oil fields suggested that these bacteria could be related to the produced water discharge.

4.4.5 Relationship between Fe, Mn and Arcobacter

Arcobacter spp. have been identified as iron- and/or manganese-reducing bacteria and may be able to utilize the elevated concentrations of iron and manganese that were found in the sediments around Thebaud. Otth et al. (2005) found Arcobacter butzleri from various sources were all resistant to relatively high concentrations of Mo, Mn, Ni, Co, Pb, and Fe. Thamdrup et al. (2000) found that Arcobacter sequences were the only sequences identified from the 16S rRNA gene clone libraries from manganese-reducing sediments from the Black Sea.

Most dissimilatory Mn reducers are also known as dissimilatory Fe-reducing bacteria, using Fe (III) as an electron acceptor (Bowman et al. 1997; Ehrlich 1993;

Laverman et al. 1995). This suggests that Arcobacter spp. could play an important role in both iron and manganese reduction in the Thebaud sediments.

4.5 Conclusions

The results of this study provided the first bacterial community structure characterization of produced water from an offshore platform on the Scotian Shelf of Atlantic Canada, the surrounding seawater, and the surrounding sediment near the Sable Island Bank. The results revealed that the bacterial community profiles in various seawater samples from around the Thebaud offshore oil and gas production platform (within 250 m) were very similar with SAB > 97, suggesting that the produced water did not have a detectable effect on the microbial

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populations in the surrounding water. However, sediment analysis revealed that the bacterial community profiles from 250 m samples were different from other locations further away from the production platform with SAB = 70. Arcobacter was found as a major band on the DGGE only in the 250 m sediment samples, suggesting that the produced water might have some detectable effect on the bacterial populations in sediment adjacent to the discharge. Similarly, sediment metals analysis revealed that higher concentrations of manganese and iron were found in the sediment samples near the platform. The manganese and iron concentrations were also high in the Thebaud produced water. Both microbial and metals analyses suggested that the influence from produced water may be restricted to the region adjacent to the platform at the sediment level. The flow rate and volume of Thebaud produced water is very small compared the other oil and gas production platforms (i.e. Hibernia and Terra Nova) in the region. If the discharge of Thebaud produced water has influenced the microbial community structure and the metal composition in sediments adjacent to the platform, similar effects could be expected in other production platforms with higher flow rates and volumes.

4.6 Acknowledgments

The authors acknowledge Jay Bugden, Susan Cobanli, Tom King, Brian

Robinson, Byron Amirault, Robert Benjamin, and Vanessa Page for their excellent technical assistance. The authors thank the NRCan PERD program for financial support.

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Table 4.1. Bottom sediment and seawater sample station names, collection dates, and locations.

Station Year Latitude Longitude TE250-1 2002 43.8888 -60.1997 TE250-2 2002 43.8859 -60.1996 TE500-1 2002 43.8902 -60.1918 TE500-2 2002 43.8911 -60.1933 TE500-3 2002 43.8908 -60.1930 TNE250 2003 43.8933 -60.1981 TE250 2003 43.8914 -60.1959 TE500 2003 43.8914 -60.1925 TSE250 2003 43.8891 -60.1983 TW250 2003 43.8910 -60.2034 TW500 2003 43.8910 -60.2069 TNW250 2003 43.8933 -60.2018 N280 2007 43.8944 -60.1986 NE325 2007 43.8941 -60.1974 TE250-1 2007 43.8923 -60.1971 TE250-2 2007 43.8923 -60.1968 TE250-3 2007 43.8919 -60.1966 TE500-1 2007 43.8921 -60.1930 TE500-2 2007 43.8886 -60.1871 TE500-3 2007 43.8923 -60.1932 W584 2007 43.8909 -60.2075 TE1000 2007 43.8910 -60.1863 SE312 2007 43.8896 -60.1978 E516 2007 43.8908 -60.1936 FRC 1N 2007 43.8918 -60.2015 FRC NE 2007 43.8925 -60.2014 FRC E 2007 43.8914 -60.2003 R10kmW 2007 43.8890 -60.4312

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Table 4.2. Thebaud produced water metal analysis

Concentration (µg/L) Aluminum 25 Antimony < 2 Arsenic < 50 Barium 58,000 Beryllium < 0.1 Bismuth < 0.5 Boron 2,200 Cadmium < 0.02 Calcium 1,220,000 Chromium < 10 Cobalt < 10 Copper < 10 Iron 10,300 Lanthanum 1 Lead 1.2 Lithium 860 Magnesium 85,200 Manganese 1,270 Molybdenum 0.94 Nickel < 20 Phosphorus 50 Potassium 45,800 Rubidium 140 Selenium < 50 Silicon 1270 Silver < 0.2 Sodium 2,910,000 Strontium 102,000 Sulfur 330 Tellurium < 2 Thallium 5 Thorium < 0.2 Tin < 0.5 Titanium < 1 Uranium 0.006 Vanadium < 5 Zinc 27

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Fig. 4.1. Thebaud sampling locations. Sediment core sampling locations labeled with a red star and their names are represented by: e.g. TE250, T = Thebaud, E= East, 250 = 250 m away from platform. Seawater sampling locations labeled with a green star and their names are represented by only the direction from the platform.

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Fig. 4.2. Plots of trace metal analysis from sediment cores taken in the vicinity of the Thebaud platform. Trace metal data has been plotted against aluminum to remove the effects of grain size. A. Plot of Barium concentration (mg/kg) vs aluminum from samples taken in 2002. Core location names are represented by: (e.g. TE250, T = Thebaud, E= East, 250 = 250 m away from platform, -1, -2 represent core 1 and core 2 from the same location. B. Barium concentration vs. aluminum from cores collected in 2003. C. Cores collected in 2007. Sediment cores were sectioned into 2 cm intervals downcore and samples from each core represent a 2 cm homogenized sample. Solid red line and green dashed lines represent average background concentration of Barium based on aluminum concentration and the 5% confidence intervals, respectively.

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Fig. 4.3. Plots of trace metal analysis from sediment cores taken in the vicinity of the Thebaud platform. Trace metal data has been plotted against aluminum to remove the effects of grain size. A. Plot of Iron concentration (mg/kg) vs aluminum from samples taken in 2002. Core location names are represented by: (e.g. TE250, T = Thebaud, E= East, 250 = 250 m away from platform, -1, -2 represent core 1 and core 2 from the same location. B. Iron concentration vs. aluminum from cores collected in 2003. C. Cores collected in 2007. Sediment cores were sectioned into 2 cm intervals downcore and samples from each core represent a 2 cm homogenized sample. Solid red line and green dashed lines represent average background concentration of Barium based on aluminum concentration and the 5% confidence intervals, respectively.

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Fig. 4.4. Plots of trace metal analysis from sediment cores taken in the vicinity of the Thebaud platform. Trace metal data has been plotted against aluminum to

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remove the effects of grain size. A. Plot of Manganese concentration (mg/kg) vs aluminum from samples taken in 2002. Core location names are represented by: (e.g. TE250, T = Thebaud, E= East, 250 = 250 m away from platform, -1, -2 represent core 1 and core 2 from the same location. B. Manganese concentration vs. aluminum from cores collected in 2003. C. Cores collected in 2007. Sediment cores were sectioned into 2 cm intervals downcore and samples from each core represent a 2 cm homogenized sample. Solid red line and green dashed lines represent average background concentration of Barium based on aluminum concentration and the 5% confidence intervals, respectively.

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100 50 1 3 5 7 9 11 13 15 SAB R10kmW

97.1 FRC E

97.9 FRC NE 13.3

100 FRC 1N

2 4 6 8 10 12 14

TBPW

a b c d

Fig. 4.5. DGGE fingerprint cluster analysis from surrounding seawater samples listed in Table 4.1 and a Thebaud produced water sample (TBPW). Seawater bands were labeled from 1-15 and produced water bands were labeled from a-d.

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Aquifex pyrophilus(M83548) TBPW-a (HQ852399) 100 Gamma-proteobacteria Acinetobacter sp. (EU260179) 100 TBPW-b (HQ852400) 74 TBPW-c (HQ852401) Firmicutes Geobacillussp. from thermophilic fuel cell (EU638544) 100 TBPW-d (HQ852402)

0.05

Fig. 4.6. Phylogenetic relationship of the 4 bacterial 16S rRNA gene sequences obtained from Thebaud produced water DGGE. The bands were labeled with TBPW- and the corresponding letter from Fig. 4.5. The tree was inferred by neighbor-joining analysis of sequence from each clone. Aquifex pyrophilus was used as the outgroup. Numbers on the nodes are the bootstrap values based on 1,000 replicates. The scale bar indicates the estimated number of base changes per nucleotide sequence position.

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Aquifex pyrophilus (M83548) 100 Roseobactersp. from Spain coastal upwelling (EU600645) Sulfitobacter sp.from Sapelo Island GA surface water (FJ744918) 99 TBSW-13 (HQ852415) Silicibacter sp. from Sapelo Island GA surface water (FJ745239) 91 TBSW-14 (HQ852416) Alpha-proteobacteria 99 Rhodobacteraceae from Bay of Biscay seawater (EU394569) 100 100 Roseobacter sp. from Norwegian coastal water (EU819142) TBSW-12 (HQ852414) 84 Alpha-proteobacterium from Boothbay Harbor ME (EF508147) 99 TBSW-11 (HQ852413) 100 Oceanospirillales from Hawaii Ocean (EU361650) 98 TBSW-15 (HQ852417) Gamma-proteobacteria 100 Gamma-proteobacterium bacterium from NE Mass. coastal water (AY580759) 100 Bacteroidetes from NW Mediterranean Sea (DQ436746) TBSW-9 (HQ852411) TBSW-3 (HQ852405) TBSW-10 (HQ852412) 100 Polaribacter sp. from UK coastal seawater (AF493676) 83 100 Tenacibaculum sp. from NW Mediterranean Sea (EU253574) TBSW-4 (HQ852406) 100 Flavobacteria from Spain coastal upwelling (EU600664) TBSW-2 (HQ852404) 100 Flavobacteria from Germany North Sea (AM279200) Bacteroidetes 95 TBSW-5 (HQ852407) 99 Lacinutrix sp. (EU581705) TBSW-1 (HQ852403) 99 88 Bacteroidetes from Arctic Ocean (EU544751) 100 Bacteroidetes from NE Mass. coastal water (AY580627) TBSW-6 (HQ852408)

83 100 Bacteroidetes from NE Mass. coastal water (AY580648) TBSW-7 (HQ852409) 98 Bacteroidetes from Denmark North Sea (DQ839246) 92 TBSW-8 (HQ852410) 0.05

Fig. 4.7. Phylogenetic relationship of the 15 bacterial 16S rRNA gene sequences obtained from Thebaud seawater DGGE. The bands were labeled with TBSW- and the corresponding number from Fig. 4.5. The tree was inferred by neighbor- joining analysis of sequence from each clone. Aquifex pyrophilus was used as the outgroup. Numbers on the nodes are the bootstrap values based on 1,000 replicates. The scale bar indicates the estimated number of base changes per nucleotide sequence position.

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100

80 90

50 60 70 30 40 1 3 4 810 12 15 17 21 SAB 93.3 TE1000 91.4 N280 88.4 W584 77.5 NE325

75.7 SE312 TE500-2 86.7

70.2 TE500-3 90.5 TE500-1 TE250-3

76.5 26 TE250-2 7 9 14 18 19 20 85.7 TE250-1 2 5 6 11 13 16 TBPW a b c d Fig. 4.8. DGGE fingerprint cluster analysis from surrounding sediment samples listed in Table 4.1 and a Thebaud produced water sample. Sediment bands were labeled from 1-21 and produced water bands were labeled from a-d.

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Aquifex pyrophilus (M83548) TBsed-21 (HQ852438) Actinobacterium from tidal flat (EF088495) Actinobacteria TBsed-19 (HQ852436) Sea floor sediment bacterium (AM911554) 100 Gamma-proteobacterium from deep-sea hydrothermal vent (AB468957) Arctic surface sediment bacterium (EU287179) Gamma-proteobacteria 79 100 TBsed-20 (HQ852437) 100 TBsed-18 (HQ852435) Delta-proteobacterium from North Sea sediments (AM040129) Delta-proteobacteria 95 TBsed-17 (HQ852434) Epsilon-proteobacterium from tidal flat sediment (AY304364) 100 Sulfurovum sp. from deep-sea hydrothermal field (AP009179) 100 TBsed-14 (HQ852431) Epsilon-proteobacteria 100 Epsilon-proteobacterium from NE Mass (AY580420) TBsed-13 (HQ852430) 96 Arcobacter sp. (EF092169) 99 Bacteroidetes from marine sediments (DQ351766) 80 TBsed-9 (HQ852426) Tenacibaculum marinus (EU290161) TBsed-11 (HQ852428) 99 Gaetbulibacter sp. (FJ490367) TBsed-10 (HQ852427) 88 TBsed-7 (HQ852424) TBsed-8 (HQ852425) Marine sediment bacterium (FJ223399) Winogradskyella thalassocolafrom Arctic (AY771731) TBsed-4 (HQ852421)

93 Bacteroidetes from Chesapeake Bay (EF471618) 89 Winogradskyella echinorum (EU727254) TBsed-5 (HQ852422) Cytophagales from Arctic Ocean (AF354619) 88 Psychroserpens sp. (AY576714) Bacteroidetes TBsed-3 (HQ852420) Bacteroidetes from Chesapeake Bay (EF471615) Formosa sp. from Ross Sea Antarctica (EU237139) Olleya marilimosa (EU000228)

99 TBsed-2 (HQ852419) Psychroserpens mesophilusfrom marine biofilm (DQ001321) TBsed-1 (HQ852418) Ross Sea sediment bacterium (EU857889) 100 Ulvibacter litoralis (NR_025731) TBsed-6 (HQ852423) Pibocella sp. from Antarctic ice (EF490378) 99 Arctic sea ice associated bacterium (AF468420) 100 TBsed-12 (HQ852429) TBsed-15 (HQ852432) Maribacter polysiphoniae from red algae (AM497875) TBsed-16 (HQ852433) 87 Maribacter sp. from Lagoon sediment (AM501627)

0.05

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Fig. 4.9. Phylogenetic relationship of the 21 bacterial 16S rRNA gene sequences obtained from Thebaud sediment DGGE. The bands were labeled with TBsed- and the corresponding number from Fig. 4.8. The tree was inferred by neighbor- joining analysis of sequence from each clone. Aquifex pyrophilus was used as the outgroup. Numbers on the nodes are the bootstrap values based on 1,000 replicates. The scale bar indicates the estimated number of base changes per nucleotide sequence position.

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Connecting text

The Gully is the first Marine Protected Area in the east coast of Canada.

With its proximity to the Thebaud oil and gas production platform, a number of research and monitoring programs have been initiated by Fisheries and Oceans

Canada (DFO) to increase our understanding of the Gully and to assess the potential impacts of offshore oil and gas development on this ecosystem so that the health and integrity of the Gully ecosystem can be protected. In this chapter,

DGGE and nucleotide sequencing were used to characterize the microbial community structure in the Gully to create a baseline for the future monitoring.

The 16S rRNA gene analysis showed that the bacterioplankton sequences generally clustered into one of nine major lineages commonly found in marine systems, suggesting that probes targeting these common lineages could be used to monitor the bacterial community structure in the seawater.

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CHAPTER 5

Microbial Community Characterization of The Gully: A Marine Protected Area

C.W. Yeung1,2, K. Lee3, L.G. Whyte2, and C.W. Greer1.

Published in: Canadian Journal of Microbiology, May 2010, 56, 421-431

As of 2009, copyright in all articles in NRC Research Press journals remains with the authors. Authors can reuse all or part of their manuscript in other works created by them for non-commercial purposes, provided the original publication in an NRC Research Press journal is acknowledged through a note or citation.

CONTRIBUTIONS OF AUTHORS Writing and preparation of the manuscript was performed by me. Drs. Greer, Lee and Whyte critically read and edited the manuscript.

Abstract

The Gully is the first Fisheries and Oceans Canada Marine Protected Area off the eastern coast of Canada. To ensure success of conservation efforts in this area, it is essential to develop a better understanding of the microbial community composition from the euphotic zone to the deep-sea in this previously unsurveyed environment. Denaturing gradient gel electrophoresis (DGGE) and nucleotide sequencing were used to characterize the microbial community structure. DGGE results showed a clear difference in the microbial community structure between the euphotic zone and the deep-sea water. Cluster analysis showed high similarity

(>85%) for all the samples taken from below 500 m, but lower similarity (49-

72%) when comparing samples from above and below 500 m. Changes in the

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microbial community structure with depth corresponded well with changes in oceanographic physical parameters. Furthermore, 16S rRNA gene analysis showed that the bacterioplankton sequences generally clustered into one of nine major lineages commonly found in marine systems. However, not all of the major lineages were detected at all the different depths. The SAR11 and SAR116 sequences were only present in the surface water and the SAR324 and

Actinobacteria sequences were only present in deep sea water. These findings provide a preliminary characterization of the microbial communities of this unique ecosystem.

5.1 Introduction

The Gully, located at the edge of the continental shelf off Nova Scotia, is the largest submarine canyon in the northwest Atlantic. This deep-sided canyon with depths of more than 2 km provides a wide range of environmental conditions and a varied sediment surface that is home to a highly diverse population of organisms (Rutherford and Breeze 2002, Gordon and Fenton 2002) including cold-water corals and endangered northern bottlenose whales (Whitehead et al.

1996). In 2004 Fisheries and Oceans Canada (DFO) designated the Gully as a

Marine Protected Area (MPA) through regulations under Canada‟s Oceans Act.

With emerging concerns over the impact of commercial fisheries and the proximity of the canyon to oil and gas development on the Scotian Shelf (Gordon and Fenton 2002) research and monitoring programs have been initiated to increase our understanding of the Gully and the potential for human impacts on this ecosystem. DFO recognized that there was a need for baseline studies in order

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to provide a reference point for future marine conservation efforts, so that the health and integrity of the Gully ecosystem could be protected. However, to date, there have been no studies of the basic microbial ecology in this area. Since microorganisms are responsible for biogeochemical reactions and are the foundation of marine food webs, it is important to increase our basic knowledge of the microbial community structures within this ecosystem.

The question of “who is there” remains essential to the understanding of microbial community structure and function. The common understanding, however, is that less than 1% of marine microbes can be cultured (Fuhrman et al.

1992; Rappe and Giovannoni 2003), and the culturing process becomes more difficult with increasing depth (Simonato et al. 2006). Culture-independent surveys of rRNA genes have greatly expanded our knowledge of the phylogenetic diversity of microorganisms. In particular, denaturing gradient gel electrophoresis

(DGGE), a method used to separate PCR-amplified DNA fragments based on nucleotide sequence differences (Muyzer et al. 1993), is an extensively used rRNA gene screening method for fast and semi-quantitative assessment of the diversity of dominant microbial species, and it allows high sample throughput with DNA-based phylogenetic resolution for an entire community. DGGE can also be coupled with the sequencing of separated 16S rDNA fragments to rapidly determine taxonomic information about the dominant members of a microbial community. Because a large number of samples can be processed using DGGE, throughput is often higher than clone libraries for comparing microbial assemblages across space (Casamayor et al. 2002). It is well documented that many of the new microbial groups that have been discovered via these culture-

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independent surveys are involved in important processes within natural microbial assemblages in marine systems (Karl 2002).

Using 16S rRNA gene PCR-DGGE, we compared the community composition and the phylogenetic relationships between communities at a variety of depths from the euphotic zone to the deep sea. This microbial phylogenetic study in the Gully is the first step in the characterization of the microbial community in this previously unsurveyed water column. We hypothesized that the prokaryotic communities experience a high degree of dissimilarity across the vertically stratified water column, and are influenced by the strong selective pressure of the Gully‟s unique abiotic characteristics. The primary objective of this work was to identify the dominant bacterial and archaeal taxa under these abiotic selective pressures. The findings of this research provided a preliminary characterization of the microbial communities of this unique ecosystem.

5.2 Materials and Methods

5.2.1 Site description and sample collection

Four water samples were collected from 1972 m, 1000 m, 500 m, and 10 m at a station located at 43°50‟33”N, 58°54‟10”W during a CCGS Hudson oceanographic expedition in June 2006. All samples were collected using the

Seabird Niskin rosette frame (24-10 L bottles) containing a Seabird conductivity, temperature, and depth (CTD) detector, a Chelsea AquaTracka Mk3 fluorometer, and a Seabird E43 oxygen detector. The fluorometer was set to 430 nm excitation and 685 nm emission wavelengths for phytoplankton chlorophyll a determination.

The fluorometer was calibrated with various concentrations of chlorophyll a

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dissolved in pure water, and the zero offset was determined in the laboratory using purified water from a reverse osmosis/ion exchange column. CTD data were plotted with SigmaPlot v10.0 (Systat Software, San Jose, CA). Acid-washed

Nalgene jerricans were rinsed three times with sample water before sample collection. Each individual water sample was immediately filtered through a sterile 0.22 μm GSWP (Millipore) filter. Five liters of water were filtered for samples from 1000 m and below, but only 4.5 L and 1.75 L were filtered for the

500 m and 10 m samples, respectively. Following filtration, all filters were transferred to sterile 50 mL Falcon tubes and were stored at -20°C until further analyses.

5.2.2 DNA extraction

Total community DNA was prepared by a modified version of the method used by Fortin et al. (1998). Prior to lysis treatment, each 50 mL Falcon tube containing the filter (from the water sample filtering step) was filled with 4.5 mL of sterilized distilled water. A 500 μL aliquot of 250 mM Tris-HCl (pH 8.0) and

50 mg lysozyme were added and the samples were incubated for 1 h at 37°C with mixing at low speed. Fifty microliters of Proteinase K (20 mg/mL) were added to the samples and they were incubated for 1 h at 37°C with mixing at low speed.

The lysis treatment was completed with the addition of 500 mL of 20% SDS solution and 30 min of incubation at 85°C with gentle mixing. The filter was then removed from the 50 mL Falcon tube. The lysates were treated with one-half volume of 7.5 M ammonium acetate, incubated on ice for 15 min to precipitate proteins and humic acids, and centrifuged for 15 min at 4°C (9,400 x g). The

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supernatants were transferred to a sterilized Corex tube and treated with one volume of cold 2-propanol. The DNA was precipitated overnight at -20°C, after which samples were centrifuged at 4°C for 30 min (12,100 x g). Pellets were washed with 70% cold ethanol and air dried, and the DNA was resuspended in

250 μL of Tris-EDTA (TE) (pH 8.0) (10 mM Tris and 1 mM EDTA). DNA concentrations were estimated by agarose gel electrophoresis of 5 μL of purified material against the Lambda HindIII DNA ladder (Amersham Biosciences,

Piscataway, NJ) standard on a 0.7% agarose gel.

5.2.3 PCR amplification of 16S rRNA gene

For PCR amplification of the 16S rRNA gene, the eubacteria-specific forward primer U341F (5‟-CCTACGGGAGGCAGCAG-3‟) (Muyzer et al. 1993) and the reverse primer U758R (5‟-CTACCAGGGTATCTAATCC-3‟) (Fortin et al. 2004) were used. These primers, complementary to conserved regions of 16S rRNA gene, were used to amplify a 418-bp fragment corresponding to positions

341 to 758 in the Escherichia coli sequence and covered the variable regions V3 and V4. The bacteria-forward primer used for DGGE possessed a GC clamp (5‟-

GGCGGGGCGGGGGCACGGGGGGCGCGGCGGGCGGGGCGGGGG-3‟) at the 5‟ end. This GC-clamp stabilizes the melting behavior of the amplified fragments (Sheffield et al. 1989). The 16S rRNA gene from archaea was PCR amplified using the archaea-specific forward primer ARC344F (5‟-

ACGGGGYGCAGCAGGCGCGA-3‟) and the reverse primer ARC915R (5‟-

GTGCTCCCCCGGCAATTCCT-3‟). These primers complementary to conserved regions of 16S rRNA gene were used to amplify a 572-bp fragment corresponding

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to positions 344 to 915 in the Escherichia coli sequence. The Archaea-forward primer used for DGGE possessed another GC clamp (5‟-

CGCCCGCCGCGCCCCGCGCCCGTCCCGCCGCCCCCGCCCG-3‟) at the 5‟ end. Each 50 uL PCR mixture contained 1 μL of the template DNA (undiluted,

10-1 or 10-2), 25 pmol of each oligonucleotide primer, 200 μM of each dNTP, 1 mM MgCl2 and 2.5 units of Taq polymerase (Amersham Biosciences, Piscataway,

NJ) in 1 x Taq polymerase buffer (10 mM Tris-HCl pH 9.0, 50 mM KCl, 1.5 mM

MgCl2). Briefly, after an initial temperature of 96°C for 5 min and thermocycling at 94°C for 1 min, the annealing temperature was set to 65°C (for bacterial PCR) or 60°C (for archaeal PCR) for 1 min and decreased by 1°C every cycle for 10 cycles, and 3 min elongation time at 72°C. Additional cycles (15-20) were performed with annealing temperatures of 55°C for bacteria and 50°C for archaea.

PCR products were loaded onto a 1% agarose gel with SYBR Safe (Molecular

Probes, Eugene, OR, USA), using a 100-bp ladder (MBI Fermentas, Amherst,

NY, USA) to determine the presence, size and quantity of the PCR products.

5.2.4 Denaturing Gradient Gel Electrophoresis (DGGE)

The 16S rRNA gene products from eight individual PCR reactions were combined for each sample and concentrated by ethanol precipitation for DGGE analysis. About 450 ng of the 16S rRNA gene product from each sample was applied to a lane, and analyzed on an 8% polyacryalmide gel containing a gradient of 30-70% denaturant (100% denaturant consisted of a solution with 7M urea and

40% deionized formamide). DGGE was performed using a DCode Universal

Mutation Detection System (Bio-Rad). Electrophoresis was run at a constant

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voltage of 80 V for 16 h at 60°C in 1x TAE running buffer. The gels were then stained with VistaGreen (Amersham Biosciences, Piscataway, NJ), and imaged with the FluoroImager System Model 595 (Molecular Dynamics, Sunnyvale, CA).

The gel images were analyzed using GelCompar II v4.6 (Applied Maths, Sint-

Martens-Latem, Belgium) to generate dendrogram profiles. The genotypes were visually detected based on the presence or absence of bands in each lane. A band was defined as “present” if its peak intensity was at least 3% of the most-intense band in the sample. After conversion and normalization of gels, the degrees of similarity of DNA pattern profiles were computed using the Dice similarity coefficient (Dice 1945), and dendrogram patterns were clustered by the

Unweighted Pair Group Method using Arithmetic average (UPGMA) groupings with a similarity coefficient (SAB) matrix.

5.2.5 Sequencing analysis and phylogenetic analyses

Individual bands from the DGGE gels were excised and eluted with 25 μL of dH2O for 48 hr at 4°C before being re-amplified with the same set of primers without the GC-clamp. One microliter of DNA was re-amplified with the appropriate corresponding eubacteria or archaea primers as follows: an initial denaturation of 5 min at 96°C, followed by 30 cycles of 94°C for 1 min, 60°C

(bacterial) or 55°C (archaeal) for 30 sec, and 72°C for 1 min. PCR products for sequencing were purified using Illustra GFX™ PCR DNA and Gel Band

Purification Kit (GE Healthcare, Piscataway, NJ). Sequencing was performed at the Université Laval Plate-forme d'analyses biomoléculaires using a model ABI

Prism 3130XL (Applied Biosystems, Foster City, CA) with their respective

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primers. Raw sequence data were assembled in BioEdit v7.0 (Hall 1999). The sequences were manually aligned by comparing forward and reverse sequences.

Complete aligned sequences containing the primer regions at both ends were used for further sequencing and phylogenetic analyses. The occurrence of chimeric sequences was determined manually with the CHECK_CHIMERA function from the Ribosomal Database Project-II (http://35.8.164.52/cgis/chimera.cgi?su=SSU;

Cole et al. 2003) and Bellerophon

(http://foo.maths.uq.edu.au/~huber/bellerophon.pl; Huber et al. 2004). Close relatives of the final selection of different sequences (phylotypes) were tentatively identified by NCBI BLASTN search (http://ncbi.nlm.nih.gov/blast/). Sequences were aligned by the MacVector 9.0 software package (Accelrys, Cary, NC) with both closely-related representatives from NCBI BLASTN and as well as novel complete and partial sequences obtained from GenBank. Additional manual alignment was done if necessary. Phylogenetic relationships were constructed with evolutionary distances (Jukes-Cantor distances) and the neighbor-joining method using the MacVector software package. The bootstrap analyses for the phylogenetic trees were calculated by running 1000 replicates for the neighbor- joining data.

5.2.6 Nucleotide sequence accession numbers

The 16S rRNA gene sequences obtained in this study have been deposited in the GenBank database under accession numbers GQ372917 to GQ372965.

5.3 Results

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5.3.1 Environmental characteristics of the water column

The depth profiles for temperature, salinity, oxygen content and chlorophyll a content of the water column are shown in Figure 5.1. All four parameters were very stable below 1000 m but highly variable above 1000 m. The upper water column temperature was highly stratified with two major thermoclines, the first showing a temperature maximum of ~16°C for the 10 m sample and a second temperature maximum of 10°C at 125 m. The first thermocline was present at depths between 10 and 50 m. The temperature declined rapidly to form the first temperature minimum of 6°C (Fig. 5.1B). In addition, a much deeper and smaller thermocline was present at depths between

125 and 500 m, bringing the temperature from 10°C (the second temperature maximum) to a constant lower water column temperature of around 4°C.

Similarly, there were two major haloclines in the upper water column. The first halocline was present at depths between 10 and 100 m. The freshest water was

32.5 ppt around 10 m (Fig. 5.1B), after which salinity increased to ~35 ppt at 100 m. The second major halocline was present between 150 and 250 m, where the salinity decreased to 34.9 ppt and remained uniform to the bottom (Fig. 5.1A).

The chlorophyll a concentration was high at depths above 100 m with a maximum of 0.68 μg/L at 35 m, but declined dramatically below 100 m to near the detection limit (Fig. 5.1B). Dissolved oxygen exhibited values that matched the other parameters. The oxygen maximum of 6 mg/L was present at ~30 m just above the chlorophyll maximum. The oxygen minimum of 3.5 mg/L was present at ~200 m just below the second temperature maximum, after which the oxygen content remained relatively unchanged at around 5.2 mg/L to the bottom (Fig. 5.1A). The

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Niskin rosette frame deployed from the ship enabled the collection of physical- chemical data from its probes in real-time during its down-cast and the recovery of discrete water samples at defined depths during its recovery. Therefore, the samples that were collected represent the two most extreme physical conditions in the vertical profile. The surface sample from the photic zone of the water column experienced warmer temperatures and lower salinity, as compared to the deep- water samples from the aphotic zone of the water column, which exhibited lower temperature, and higher salinity and hydrostatic pressure profiles.

5.3.2 Archaeal and bacterial DGGE analysis

DGGE analysis of bacterial and archaeal 16S rRNA genes was performed to compare the microbial composition of surface-, mid- and deep-water samples, and dendrograms of the DGGE banding patterns were constructed for statistical analysis. The dendrograms for both bacteria and archaea revealed a division into two clusters based on their sampling depths. Cluster analysis of the dendrograms demonstrated lower SAB values between the surface water and 500 m, with 72.7% for bacteria (Fig. 5.2A) and 49.3% for archaea (Fig. 5.2B). The SAB values for water samples below 500 m were much higher, with 90-100% for bacteria (Fig.

5.2A) and 86.8-94.1% for archaea (Fig. 5.2B). Therefore, both bacterial and archaeal DGGE analyses revealed dissimilarities between the community structures of the samples collected from the surface down to 500 m, and high similarities between those collected from below 500 m.

5.3.3 Bacterioplankton composition in the Gully

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Specific phylogenetic information was determined by sequencing of the individual DGGE bands. All bands that migrated to the same position in the gel were first sequenced, then aligned with each other and assigned to an operational taxonomic unit (OTU). If the sequences were identical they were assigned to the same OTU. The bacterial DGGE gel displayed a much higher number of bands than the archaeal DGGE gel. Both bacterial and archaeal banding patterns, except the archaeal surface sample, showed a high microbial diversity. In the bacterial surface sample, a total of 22 DGGE bands out of 26 bands were excised and sequenced. Most of the sequences showed at least a 97% match to the available sequences from GenBank: however, most of them were closely related to uncultured bacteria, but generally clustered well into one of the nine major marine lineages. In the surface sample, most of the sequences were grouped into Alpha- proteobacteria (45.5% of the sequences) and Bacteroidetes (45.5% of the sequences). Only 2 out of 22 sequences were related to Gamma-proteobacteria, which is the only other group in the phylogenetic tree (Fig. 5.3).

In the samples below 500 m, 19 DGGE bands out of a total of 28 bands were excised and sequenced. As in the surface sample, most of the sequences were closely related to uncultured bacteria like the surface sample, but again they clustered well into one of the nine major marine lineages. However, the sequences belonged to more diverse groups. Bacteroidetes was again represented with the highest number of sequences (47%). Only one sequence was identified as Alpha- proteobacteria. All other sequences belonged to Gamma-proteobacteria,

Actinobacteria, Delta-proteobacteria, Gemmatimonadetes, and an unknown group (Fig. 5.4).

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In the archaeal DGGE gel, a total of 8 bands were excised and sequenced.

Many of the faint bands were unique only to the surface water. However, due to the resolution limitation of the gel we were only able to excise and sequence the dominant band (Fig. 5.2B). The sequence was related to an uncultured group II euryarchaea from oxic coastal surface water (Fig. 5.5). The other 7 bands were from below 500 m. Most of them showed a high similarity to available sequences in GenBank from uncultured crenarchaea and euryarchaea (Fig. 5.5). All sequences clustered well within the two common major marine Archaea groups:

Archaea Group I and Archaea Group II.

5.4 Discussion

Numerous studies have looked at the 16S rRNA gene to assess microbial diversity in surface water samples (Giovannoni et al. 1990; DeLong 1992;

Fuhrman et al. 1992) and deep-sea samples (DeLong et al. 1997; Kato et al. 1997;

Nogi et al. 1998). However, only a limited number of studies have documented a complete depth profile. These full-depth profile studies revealed a shift of microbial community composition over the transition from the photic to aphotic zones using various culture-independent methods (Lee and Fuhrman 1991; Karner et al. 2001; DeLong et al. 2006; Celussi et al. 2009). Similarly, our findings showed a clear difference in the microbial community structures between the photic and aphotic zones (Figs. 5.2A and B).

Since each band on the DGGE gel generally represents a major species that comprises at least 1% of the total population in a sample (Muyzer et al.

1993), the high number of bands from the DGGE analysis suggests that the Gully

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water column has a diverse microbial community. Similar to other marine environments, 16S rRNA gene analysis of the Gully samples also showed that the bacterioplankton sequences generally clustered into one of the nine major marine lineages: SAR11, Roseobacter/SAR83, Gamma-proteobacteria/SAR86, SAR116,

Chloroflexi/SAR202, Delta-proteobacteria/SAR324, Chlorobium/SAR406,

Cyanobacteria and Bacteroidetes, that are commonly found in marine systems.

However, not all of the major lineages were found in all different depths. Our results showed that SAR11 and SAR116 sequences (both Alpha-proteobacteria clusters) were only present in the surface sample, and that SAR324 (Delta- proteobacteria) and Actinobacteria sequences were only present below the aphotic zone. Similarly, DeLong et al. (2006) and Sogin et al. (2006) found an increase in the relative abundance of Delta-proteobacteria and Actinobacteria with increasing depth. A recent study also found that Actinobacteria showed a much greater presence in the deep ocean (Jensen and Lauro 2008). Zaballos et al.

(2006) have observed that the increase in the percentage of Delta-proteobacteria with depth often co-occurs with a decrease in Alpha-proteobacteria based on their observations from the Mediterranean and Greenland Seas. Morris et al. (2002) and

Alonso-Sáez et al. (2007) found that the SAR11 clade dominated in the ocean surface. Our sequencing results also revealed that two of the major clades of

Alpha-proteobacteria, SAR11 and SAR116, were only present in the surface sample. The Alpha-proteobacteria decreased from 10 representatives in the surface sample to only 1 representative in the deep-water samples, and this is from the SAR83/Roseobacter group. Generally, Alpha-proteobacteria are most competitive at low ambient nutrient concentrations (Pinhassi and Berman 2003),

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and are better at glucose uptake at low concentration than Gamma-proteobacteria and Bacteroidetes (Alonso and Pernthaler 2006). Glucose is typically the most abundant free neutral aldose in upper water column seawater (Skoog et al. 1999).

Therefore, the higher diversity of Alpha-proteobacteria, present mostly in the upper water column, might be related to the higher glucose concentration typical of the upper water column.

We also found an increase in the number of Gamma-proteobacteria sequences in deep water samples. Most of the piezophilic microorganisms that have been isolated from many regions of the world belong to the genera Colwellia and Shewanella within the Gamma-proteobacteria (DeLong et al. 1997; Kato et al. 1998; Nogi et al. 1998). Zaballos et al. (2006) also found that this increase in the percentage of Gamma-proteobacteria with increased depth is usually associated with a decrease of Alpha-proteobacteria, based on their observations from the Mediterranean and Greenland Seas.

Interestingly, our phylogenetic tree also revealed a high diversity of

Bacteroidetes in both water layers. Usually, Bacteroidetes are the most abundant group of bacteria in coastal pelagic habitats (Cottrell and Kirchman 2000; Eilers et al. 2001). Some findings (summary from Kirchman 2002) suggested that members of this phylum could play an important role in the degradation of complex and polymeric organic matter in marine algae in the euphotic zone and of marine snow in the aphotic zone. Bacteroidetes are commonly found in high abundance during natural and induced phytoplankton blooms and as primary colonizers of marine phytoplankton, further suggests a role in algae-derived metabolite consumption (Riemann et al. 2000; Pinhassi et al. 2004; Grossart et al.

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2005). Therefore, high abundances of the Bacteroidetes in the offshore oligotrophic water suggests a high primary productivity in the Gully from top to bottom. In the deep-water samples, the Bacteroidetes was by far the most diverse group, suggesting that the degradation of complex and polymeric organic matter might be occurring throughout the water column.

The archaeal phylogenetic tree showed that the group II euryarchaeal group was the only major band and the only sequence from the surface water sample. A similar finding to Massana et al. (1997) showed that the group II euryarchaeal group dominated in the surface water. The archaeal phylogenetic tree also showed an increase in the number of sequences related to the group I crenarchaea in deeper samples. Karner et al. (2001) and Massana et al. (1997) found a relative 16S rRNA gene abundance of the crenarchaeal group, which suggests that its members constitute a significant fraction of the prokaryotic biomass in subsurface waters.

5.4.1 Comparison of Phylogeny and Physical Characteristics

The phylogenetic analysis from the different depths corresponded well with the common oceanographic physical parameters in the vertical water column.

Differences in physical parameters resulted in an altered microbial community structure. When the abiotic conditions remained constant, the microbial community structures also remained unchanged. Low temperature, absence of solar radiation, and especially high hydrostatic pressure are the characteristics of this environment (Bartlett 1992), and in general, they are physically relatively uniform across space (Fuhrman et al. 1992). The hydrostatic pressure can be more

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than three orders of magnitude higher at the bottom of the Gully than at the surface, and this drastic changes in pressure was previously found to be the major factor contributing to the change in the microbial community structure (Celussi et al. 2009; Yoshida et al. 2007). However, DGGE results from the deep-water samples showed that the microbial community structure was similar between environments where other abiotic conditions such as temperature, salinity, chlorophyll a content and oxygen content remained nearly constant. This suggests that the hydrostatic pressure does not have as much of an impact on shaping the microbial community structure as the temperature, salinity, chlorophyll a content and oxygen content. Interestingly, in a recent full depth profile study, Celussi et al. (2009) found that in the epipelagic zone light-affected variables (i.e. oxygen content and fluorescence) had a greater influence on the bacterial community structure than the temperature. High variations in temperature, salinity, chlorophyll a content and oxygen content were observed in the epipelagic zone in the Gully (Figure 5.1B). Further investigation focussing on the epipelagic zone is required to complete the full characterization of this valuable Marine Protected

Area.

In general, the aphotic zone is the largest fraction of the ocean, and is populated by a high diversity but low abundance of organisms (Fuhrman et al.

1992; Sogin et al. 2006). It is believed to contain 55% of all the prokaryotes found in all aquatic habitats (Whitman et al. 1998). The findings of this research provide a preliminary characterization of the microbial communities of this valuable

Marine Protected Area and contribute to a better understanding of similar unsurveyed ecosystems.

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5.5 Acknowledgments

The authors acknowledge Marc Auffret, Susan Cobanli, Jay Bugden, and

André Migneault for their excellent technical assistance. The authors thank the

NRCan PERD program for financial support.

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Fig. 5.1. Conductivity, temperature and depth (CTD) profile. Sample collection depths are indicated by solid lines. Temperature (°C), salinity (ppt), oxygen saturation (mg/L), and chlorophyll fluorescence (μg/L) profiles are presented. From surface to 2000 m (A). From surface to 500 m (B).

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49.3 72.7 S AB 50 A SAB B 55 75 60 80 65 70 85 75 90 80 90 86.8 85 94.1 90 95 100 95 100 100

a 1 2

b 3 4 c e d g f 5 i h 6 k j 8 7 2 l 10 9 m n 11 o p 3 q 12 r 13 s t u 14 v 16 15 17 4 18

5 6 1 7 8 19

10 m 500 m 1000 m 1972 m 10 m 500 m 1000 m 1972 m

Fig. 5.2. Cluster analysis of DGGE banding patterns based on band positions using UPGMA of an SAB matrix. (A) Dendrogram for Bacterial DGGE; bands collected for sequencing are marked with letters (a-v) for 10m sample and with numbers (1-19) for ≥ 500m samples. (B) Dendrogram for Archaeal DGGE; bands collected for sequencing are marked with numbers (1-8). The depths of the different samples are marked below the lanes.

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Aquifex pyrophilus (M83548) 100 Sargasso Sea Chloroflexi bacterium (AY534100) SAR202/Chloroflexi Sargasso Sea Chloroflexi bacterium (AY534088) 100 GBS-f (GQ372922) Mediterranean Sea Bacteroidetes bacterium T41_68 (DQ436750) 100 Plymouth UK coastal water Polaribacter sp. SW146 (AF493676) 99 GBS-i (GQ372925) Lacinutrix copepodicola KMM 3967 (AB261015) GBS-a (GQ372917) 81 99 Arctic Ocean Bacteroidetes bacterium SBI04_29 (DQ186960) GBS-d (GQ372920) Bacteroidetes Arctic Ocean Cytophagales Arctic97A-13 (AF354618) 100 GBS-e (GQ372921) Mediterranean Sea Bacteroidetes bacterium (DQ436767) 82 GBS-h (GQ372924) GBS-j (GQ372926) 96 Mass. coastal water Bacteroidetes bacterium (AY580639) 98 GBS-k (GQ372927) 99 GBS-g (GQ372923) Boothbay Harbor ME Flavobacteria MS024-2A (EF202336) 73 GBS-c (GQ372919) 99 Mass. coastal water Bacteroidetes bacterium PI_4b11f (AY580625) 99 South Pacific off Chile SAR406 bacterium (DQ810538) SAR406/Marine Group A North Pacific Ocean SAR406 bacterium (EU361305) Arctic Ocean SAR11 alpha-proteobacterium (DQ186926) GBS-b (GQ372918) 82 SAR11 Mass. coastal water bacterium CBM02G01 (EF395754) 94 Arctic Ocean SAR11 alpha-proteobacterium (DQ186915) Antarctic Roseobacter sp. (EU237137) 73 GBS-p (GQ372932) Boothbay Harbor ME alpha-proteobacterium MS190-2A (EF508147) GBS-o (GQ372931) Thalassobacter oligotrophus CECT 5294T (AJ631302) North Atlantic Roseobacter NAC11-3 (AF245632) GBS-q (GQ372933) SAR83/ 100 Mass. coastal water alpha-proteobacterium PI_RT240 (AY580453) Roseobacter GBS-t (GQ372936) China Sea Roseobacter sp. (EF061449) Alpha-proteobacteria 99 Mass. coastal water alpha-proteobacterium PI_RT264 (AY580469) 88 GBS-s (GQ372935) 75 Red Sea stony coral bacterium BB1S16SI-5 (EF433149) GBS-r (GQ372934) Arctic Ocean alpha-proteobacterium (AF355035) 75 95 North Pacific SAR116 alpha-proteobacterium (EU361492) Nisaea denitrificans DR41_18 (DQ665839) 73 North Carolina continental shelf alpha-proteobacterium OM75 (U70683) 96 GBS-v (GQ372938) GBS-m (GQ372929) SAR116 Arctic Ocean alpha-proteobacterium Arctic96A-18 (AF353233) 91 Mass. coastal water alpha-proteobacterium PI_4f4d (AY580536) 94 GBS-l (GQ372928) Mediterranean Sea SAR116 alpha-proteobacterium (AM748249) 90 Hawaii Ocean SAR86 cluster gamma-proteobacterium (EU361700) 94 Mediterranean Sea SAR86 gamma-proteobacterium (AM748237) Mass. coastal water gamma-proteobacterium (AY580777) 74 GBS-n (GQ372930) SAR86/ North Sea gamma-proteobacterium KTc1119 (AF235120) Gamma-proteobacteria Adriatic Sea gamma-proteobacterium (AM259730) GBS-u (GQ372937) 100 Pacific Ocean gamma-proteobacterium (AY726943) Arctic Ocean gamma-proteobacterium (AF354606) 98 Arctic Ocean delta-proteobacterium Arctic96BD-3 (AF355041) 100 North Atlantic SAR324 bacterium (AF245652) SAR324/ North Pacific SAR324 bacterium (EU361262) Delta-proteobacteria 92 South Pacific SAR324 bacterium (DQ810618) 90 Mediterranean Sea marine actinobacteria (EF683026) Adriatic Sea actinobacterium (AM259924) Actinobacteria Mediterranean Sea sediment actinobacterium (EU374087) 0.05

Fig. 5.3. Phylogenetic relationship of the 23 bacterial 16S rRNA gene sequences obtained from the 10 m water sample. The bands were labeled with Gully Bacteria Surface (GBS) and their band letters from Fig. 2A. The tree was inferred by neighbor-joining analysis of sequence from each clone. Aquifex pyrophilus was used as the outgroup. Numbers on the nodes are the bootstrap values based on 1,000 replicates. The scale bar indicates the estimated number of base changes per nucleotide sequence position.

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Aquifex pyrophilus (M83548) 100 Sargasso Sea Chloroflexi bacterium (AY534100) SAR202/Chloroflexi Sargasso Sea Chloroflexi bacterium (AY534088) 100 Mediterranean Sea Gemmatimonadetes bacterium KM3-173-A8 (EF597690) Gemmatimonadestes GBD17 (GQ372955) (500m, 1000m, 1972m) 100 Ridge flank crustal fluid bacterium FS142-32B-02 (DQ513031) Unknown GBD19 (GQ372957) (500m, 1000m, 1972m) 98 North Atlantic SAR324 bacterium (AF245652) Arctic Ocean delta-proteobacterium Arctic96BD-3 (AF355041) Arctic Ocean delta-proteobacterium Arctic95C-5 (AF355039) SAR324/ GBD18 (GQ372956) (500m, 1000m, 1972m) Delta-proteobacteria North Pacific SAR324 bacterium (EU361262) 95 South Pacific SAR324 bacterium (DQ810618) Adriatic Sea gamma-proteobacterium (AM259730) 100 GBD12 (GQ372950) (500m, 1000m, 1972m) North Pacific Ocean gamma-proteobacterium JL-ETNP-Y24 (AY726890) 83 GBD13 (GQ372951) (1000m, 1972m) Arctic Ocean gamma-proteobacterium (AF354606) 86 San Pedro marine bacterium SPOTSMAY03_890m (DQ009466) SAR86/ Pacific Ocean gamma-proteobacterium (AY726943) 98 Gamma-proteobacteria North Pacific Ocean gamma-proteobacterium JL-ETNP-Z29 (AY726938) GBD11 (GQ372949) (500m, 1000m, 1972m) GBD10 (GQ372948) (500m, 1000m, 1972m) Mytilidae sp. gill symbiont BC 294 (AM503922) 96 Mediterranean Sea SAR86 gamma-proteobacterium (AM748237) Hawaii Ocean SAR86 gamma-proteobacterium (EU361700) 98 Mediterranean Sea SAR116 alpha-proteobacterium (AM748249) SAR116 North Pacific SAR116 alpha-proteobacterium (EU361492) 81 100 Antarctic Roseobacter sp. (EU237137) China Sea Roseobacter sp. (EF061449) SAR83/ 85 Alpha-proteobacteria GBD15 (GQ372953) (500m, 1000m, 1972m) Roseobacter 98 Mediterranean Sea alpha-proteobacterium KM3-47-A6 (EF597705) Arctic Ocean alpha-proteobacterium (AF355035) Arctic Ocean SAR11 alpha-proteobacterium (DQ186926) SAR11 Arctic Ocean SAR11 alpha-proteobacterium (DQ186915) South Pacific off Chile SAR406 bacterium (DQ810538) SAR406/Marine Group A North Pacific Ocean SAR406 bacterium (EU361305) 99 Ridge flank crustal water bacterium FS266-67B-03 (DQ513081) 97 GBD8 (GQ372946) (500m, 1000m, 1972m) 96 GBD6 (GQ372944) (500m, 1000m) GBD9 (GQ372947) (500m, 1000m, 1972m) 100 Tenacibaculum ovolyticum SE1 (AY771741) 87 GBD5 (GQ372943) (1972m) 100 GBD4 (GQ372942) (500m, 1000m, 1972m) Bacteroidetes San Pedro marine bacterium SPOTSFEB02_70m25 (DQ009434) 96 GBD3 (GQ372941) (500m, 1000m, 1972m) 98 West Florida shelf Flavobacterium sp. S03 (EU021292) GBD1 (GQ372939) (500m, 1000m) Olleya marilimosa CAM030 (EF660466) 99 GBD2 (GQ372940) (1972m) Antarctic seafloor Bacteroidetes 131848 (AY922247) 88 GBD7 (GQ372945) (500m, 1000m, 1972m) Mass. coastal water Bacteroidetes bacterium PI_RT311 (AY580639) Adriatic Sea Actinobacterium (AM259924) GBD16 (GQ372954) (500m, 1000m, 1972m) 85 Ridge flank crustal water bacterium FS117-46B-02 (DQ513067) 81 99 North Pacific Ocean bacterium JL-ETNP-S56 (AY726867) Actinobacteria GBD14 (GQ372952) (500m, 1000m) 93 90 Mediterranean Sea sediment Actinobacterium (EU374087) Iamibacter majanohamensis (AB360448) Mediterranean Sea marine Actinobacteria (EF683026) 0.05

Fig. 5.4. Phylogenetic relationship of the 19 bacterial 16S rRNA gene sequences obtained from water samples at and below 500 m. The bands were labeled with Gully Bacteria Deepsea (GBD) and their band numbers from Fig. 2A. The depth from which the band was excised is indicated in the brackets after the accession number. The tree was inferred by neighbor-joining analysis of sequence from each clone. Aquifex pyrophilus was used as the outgroup. Numbers on the nodes are the bootstrap values based on 1,000 replicates. The scale bar indicates the estimated number of base changes per nucleotide sequence position.

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Aquifex pyrophilus (M83548)

93 Crenarchaeote from 2164m bathypelagic sediment (EF069341) 93 GA3 (GQ372960) (500m, 1000m, 1972m) GA2 (GQ372959) (500m, 1000m, 1972m) 85 Crenarchaeota Crenarchaeote from bathypelagic Mediterranean (EF597707) 99 GA4 (GQ372961) (500m, 1000m, 1972m) 100 92 Crenarchaeote from Canadian Arctic water (EF486602) 100 Euryarchaeote from North Pacific subtropical (DQ156472) GA7 (GQ372964) (500m, 1000m, 1972m) 100 GA8 (GQ372965) (500m, 1000m, 1972m) Euryarchaeote from Arctic Ocean (AY288407) GA6 (GQ372963) (500m, 1000m) Euryarchaeota

GA5 (GQ372962) (1000m, 1972m)

Euryarchaeote from deep-sea hydrothermal vent (DQ465919) 87 GA1 (GQ372958) (10m)

0.05 Archaeon bacterium from coastal Arctic ocean (DQ146736)

Fig. 5.5. Phylogenetic relationship of the 8 archaeal 16S rRNA gene sequences obtained from all depth samples. The bands were labeled with Gully Archaea (GA) and their band numbers from Fig. 2B. The depth from which the band was excised is indicated in the brackets after the accession number. The tree was inferred by neighbor-joining analysis of sequence from each clone. Aquifex pyrophilus was used as the outgroup. Numbers on the nodes are the bootstrap values based on 1,000 replicates. The scale bar indicates the estimated number of base changes per nucleotide sequence position.

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Connecting text

In the previous chapters (2, 3, and 4), using bacterial community monitoring methods, we found that the produced water discharge did not have a detectable effect on the bacterial community structure in the seawater surrounding

Hibernia, Terra Nova and Thebaud oil and gas production platforms nor were any produced water specific species found in the surrounding seawater. The only effect on the bacterial community structure potentially related to the input of produced water was found in the sediment 250 m from the Thebaud discharge, suggesting that any influence from the discharge in the water column was minimal, and any impact might be restricted to a small area at the sediment level adjacent to the discharge. In order to pinpoint the transport and dilution of the effluent, it is essential to develop methods that are sensitive enough for the large volumes that need to be examined in these environments. In this chapter, we developed species-specific detection methods, Q-PCR and nested-PCR, to monitor species unique to the produced water (Thermoanaerobacter) in the surrounding seawater.

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CHAPTER 6

Nested-PCR and Quantitative-PCR Methods for Rapid and Sensitive Detection of Thermoanaerobacter spp. in Seawater Surrounding the Hibernia and Terra Nova Oil Production Platforms

C. William Yeung1,2, Ken Lee3, Lyle G. Whyte2, and Charles W. Greer1.

CONTRIBUTIONS OF AUTHORS Writing and preparation of the manuscript was performed by me. Drs. Greer, Lee and Whyte critically read and edited the manuscript.

Abstract

Produced water is the largest waste input regularly discharged into the surrounding marine environment from the offshore oil and gas productions. To pinpoint the transport and dilution of the effluent, it was essential to develop methods that were sensitive enough for the volume and the type of environments.

Primers sets were designed to target and quantify the 16S rRNA gene from

Thermoanaerobacter spp. originated from both Hibernia and Terra Nova produced waters. Nested-PCR and quantitative-PCR (Q-PCR) methods for the detection of Thermoanaerobacter spp. in the surrounding seawater around the

Hibernia and Terra Nova oil production platforms were developed. The Q-PCR assay could detect as low as 8 copies in an environmental sample DNA. Similar levels of sensitivity were observed from both environmental samples and plasmid samples. Our results indicate that these selective primers sets were powerful tools with sensitivity and specificity to detect Thermoanaerobacter spp. in seawater.

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Nested-PCR results revealed that Thermoanaerobacter spp. were presented in seawater 500 m from the Terra Nova production platform. Both Q-PCR and nested-PCR results revealed that Thermoanaerobacter spp. were present in the surrounding seawater 1000 m from the Hibernia produced water discharge. The

Q-PCR result further revealed that the highest number of Thermoanaerobacter spp. (26,700 copies per litre) were found in the bottom seawater (50 m depth) 100 m from the platform, suggesting that majority of the Thermoanaerobacter traveled with the effluent to the deeper part of the water column.

6.1 Introduction

The Hibernia and Terra Nova platforms are the two largest oil producing platforms off the eastern coast of Canada. Similar to the other oil and gas production platforms around the world, produced water is the major source of petroleum contaminated waste discharged from the platform into the ocean during crude oil recovery. Produced waters usually consist of water naturally present in the geological formation and water injected into the oil field to maintain reservoir pressure, which in this case is the surrounding seawater. These effluents are also frequently contaminated with metals, nutrients, and petroleum hydrocarbons that could cause acute and chronic toxicity to the surrounding marine environment.

However, previous chapters (Appendix A, Chapters 2, 3, and 4) revealed that the discharge of produced water did not induce any detectable changes in the microbial community structure in the surrounding seawater adjacent to the discharge from three different offshore production platforms in Eastern Canada

(Hibernia, Terra Nova and Thebaud). The only abnormality that could be related

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to the produced water effluents was found in the bacterial community structure in the sediments close to the Thebaud produced water discharge (Chapter 4). This result suggested that any influence from the produced water would most likely occur in the lower part of the water column or only at the sediment level close to the discharge.

In addition, the produced water microbial community structure analyses found that there were some unique microorganisms from the three different produced waters that were not detected in their respective surrounding seawater, even though the major component of the produced water was injected surrounding seawater (Chapters 2, 3, and 4). These results suggest that identifying these unique microorganisms from produced waters could be useful in developing microbial tracking methods to monitor the transport and dilution of the effluent into the surrounding seawater. In particular, Chapters 2 and 3 found that similar

Thermoanaerobacter spp. were present in both Hibernia and Terra Nova produced waters, suggesting that the Thermoanaerobacter could be used to develop a microbial tracking method for seawater monitoring in these two locations.

Molecular techniques, particularly specific oligonucleotide probes, provide a very sensitive and specific tool for detecting very low numbers of bacteria including viable but non-culturable forms. The use of specific oligonucleotide probes in methods like nested-PCR and quantitative-PCR (Q-

PCR), to detect various pathogens from various environments have been documented (Kim et al. 2008; Nayak and Rose 2007; Furet et al. 2009; Waage et al. 1999; Arias et al. 1995). Q-PCR is considered to be an effective tool to monitor microbial species because of its sensitivity, and most importantly it can

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be used quantitatively (Yu et al. 2006; Kindaichi et al. 2006; Da Silva and

Alvarez 2007). Similar to Q-PCR, nested-PCR is also an effective and robust tool to detect microbial species, but cannot be used quantitatively. However, Loffler et al. (2000) and Ritalahti and Loffler (2004) found that the nested-PCR approach could increase the detection limit by up to 2 orders of magnitude for the analysis of the negative samples from PCR. Therefore, the nested-PCR method could also be used to verify negative results from other PCR methods.

In this study, we designed and employed the 16S rRNA gene-based nested-PCR and Q-PCR to investigate produced water specific-species in the surrounding seawater adjacent to the produced water effluent from the Hibernia and Terra Nova production platforms.

6.2 Materials and Methods

6.2.1 Sample collection

Seawater samples were collected in August 2007 from a number of locations (0 m, 10 m, 25 m, 50 m, 100 m, 200 m, and 500 m) south of the Terra

Nova platform from a depth of 10 m for nested-PCR analysis. Seawater samples for Q-PCR analysis were collected in July 2008 from a number of locations (50 m, 100 m, 200 m, 300 m, 500 m, 1000 m, and 2000 m at 2 depths (1 m and 50 m) from the south of the Hibernia platform. All seawater samples were collected using a Niskin bottle attached to a Seabird conductivity, temperature, depth detector (CTD) which was deployed by hand from one of the ship‟s launches. A reference seawater was collected with the Seabird Niskin rosette frame (24 X 10 L bottles) containing a CTD from a depth of 10 m at a location 20 km (R20k) north

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from the Terra Nova production platform in 2007. Another reference seawater was collected at the same two depths (1m and 50 m) with the Seabird Niskin rosette frame containing a CTD from reference location 50 km (R50k) west of the

Hibernia production platform in 2008. In addition, samples of fresh produced water were kindly provided by the personnel of the Terra Nova production platform in 2007 and the personnel of the Hibernia production platform in 2008.

All containers used in the sampling were rinsed three times with the sample water.

Four liters of seawater or produced water were immediately filtered through sterile 0.22 μm GSWP (Millipore) filters. Following filtration all filters were transferred to sterile 50 mL Falcon tubes and were stored at -20°C until analyzed.

6.2.2 Produced water/seawater dilution samples

In addition, another Hibernia produced water and a Hibernia reference seawater from R50K at 10 m were used to create a dilution series. Triplicate 1 L dilution mixtures of various produced water: seawater ratios were made containing the following concentrations: full produced water, 1:10, 1:20, 1:50,

1:100, 1:250, 1:500, 1:1000, 1:2000, 1:5000, 1:10,000, and a blank (1 L of seawater only). The dilution series were immediately filtered through sterile 0.22

μm GSWP (Millipore) filters. Following filtration all filters were transferred to sterile 50 mL Falcon tubes and were stored at -20°C until analyzed.

6.2.3 Genomic DNA extraction

Total community genomic DNA from all the seawater, the produced water and seawater/produced water mixtures were extracted from the filter with an

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UltraClean® Water DNA isolation kit (Mo Bio Laboratories, Carlsbad, CA) following the manufacturer‟s protocol. The total community genomic DNA was eluted in 3 mL of elution buffer. Duplicate 5 μL samples of the extracts were used to estimate DNA concentrations using the PicoGreen quantification method and analyzed with a Tecan Safire Fluorometer (Tecan US Inc., Durham, NC).

6.2.4 PCR amplification of the 16S rRNA gene

The 16S rRNA PCR amplification was performed using the same method as described in Chapter 2.

6.2.5 Denaturing Gradient Gel Electrophoresis (DGGE) analysis

The DGGE analysis was performed using the same method as described in

Chapter 2.

6.2.6 Q-PCR and Nested-PCR primer design

All primers used in this study (Table 6.1) were designed using PRIMER

3.0 (http://frodo.wi.mit.edu/primer3/; Rozen and Skaletsky 2000) based on the

16S rRNA gene sequences of the clones from the Hibernia produced water

Thermoanaerobacter 16S rRNA gene clone library (Chapter 2), the

Thermoanaerobacter sequences from Terra Nova produced water DGGE

(Chapter 3), and a number of Thermoanaerobacter sequences from GenBank matches. All sequences were first aligned using the MacVector 9.0 software package (Accelrys, Cary, NC, USA) to create a consensus sequence. Primer design was carried out with the PRIMER 3.0 program using the consensus

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sequence. A pair of primers (TMF1 and TMR1) targeting a 201 bp fragment of the 16S rRNA gene from the consensus sequence was designed and used for both

Q-PCR and the second part of the nested-PCR (Table 6.1). Another pair of primers (TMfull-F and TMfull-R) targeting a longer fragment (849 bp) of the 16S rRNA gene from the consensus sequence was designed for the first part of the nested-PCR. The specificity of the primers was tested by submitting the primer sequences to the PROBE MATCH (Ribosomal Database Project II; Maidak et al.

2001) and BLAST programs (http://blast.ncbi.nlm.nih.gov/Blast.cgi; Altschul et al. 1990) before laboratory testing.

6.2.7 Q-PCR amplification

For laboratory testing and calibration curves for testing the Q-PCR primers, plasmid DNA containing cloned target sequences was used. This method had been used in a number of studies to create a standard curve (Galluzzi et al.

2004; Wawrik et al. 2002; Zhu et al. 2005). It is also the only available method for calibrating Q-PCR detection of uncultured taxa (Suzuki et al. 2000). The advantage of calibrating with plasmid DNA is that the exact number of target genes can be calculated, knowing the concentration of the plasmid DNA standard.

A plasmid DNA containing the 201 bp sequence was manufactured from

MiniGenes Custom Gene Synthesis (IDT, Coralville, IA). Annealing temperature for the primer set was first calculated with their ATGC content and tested with a temperature gradient PCR and Q-PCR, where 61°C was found to be the optimum temperature. Primer specificities were first assessed using regular PCR and then

Q-PCR against the plasmid DNA, and finally they were tested against genomic

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DNA from “clean” surrounding seawater with and without plasmid DNA to check for non-specific binding.

The Q-PCR reaction mixture followed the manufacture‟s instructions. In brief, in each sample, a 20 μl reaction mixture consisted of 10 μl of QuantiTect

SYBR Green PCR Master Mix (including HotStarTaq DNA polymerase, 5 mM

MgCl2, dNTP mix, QuantiTect SYBR Green PCR Buffer), 0.4 μl of each (25 μM) forward species-specific primer and reverse universal primer, 0.8 μl of 25 mM

MgCl2, 3.4 μl of RNase-free water and 5 μl template DNA (~0.5 ng/μl). Real-time

PCR assays were conducted using a Rotor-Gene 3000 Real-Timer PCR machine and monitored with the Rotor-Gene 6 software (Corbett Research, San Francisco,

CA). The cycling program consisted of an initial denaturation at 95°C for 15 min, followed by 50 cycles of 95°C for 10 sec, 61°C for 15 sec, and 72°C for 15 sec.

The Q-PCR results were analyzed with Rotor-Gene 6 software.

All Q-PCR experiments were performed with triplicate 10-6 sample dilution from the standard curve as positive controls and a triplicate of negative controls (sterile water) for each run to assess reaction to reaction variability. Three serial dilutions of each test sample were used for Q-PCR amplification to determine the best concentration range. Reaction efficiency ([10(-1/M)] – 1) and amplification value (10(-1/M)), where M = the slope of the standard curve, were calculated as per the Rotor-Gene manual. An ideal amplification value should be close to 2 and the ideal reaction efficiency should be close to 1. The Q-PCR assay also produced a threshold cycle (CT) value, which is the number of cycles at the point where fluorescence rises prominently above background.

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Two disadvantages of using plasmid DNA as a standard are due to extraction and purification efficiency of DNA from environmental samples and the possibility of multiple copies of the 16S rRNA gene in the genome of the targeted bacteria (Acinas et al. 2004). Therefore, a triplicate dilution series containing various ratios of produced water in natural seawater were used to determine the extraction efficiency from high to low concentrations of produced water, to determine the correlation between dilution and gene copy number and to evaluate environmental background.

6.2.8 Nested-PCR amplification

Similar to the optimization of Q-PCR primers, the annealing temperature for the nested-PCR primer sets was calculated using their ATGC content and tested with a temperature gradient PCR, where 57°C was found to be the optimum temperature. Primer specificities were first assessed using the regular PCR against the 16S rRNA gene clones from the library from Chapter 2 and against “clean” surrounding seawater DNA to check for non-specific binding. The first part of

PCR was performed with a 50 μL PCR mixture containing 2.5-5 ng of template

DNA, 25 pmol of each oligonucleotide primer (TMfull-F and TMfull-R), 200 μM of each dNTP, 1 mM MgCl2 and 2.5 units of Taq polymerase (Amersham

Biosciences, Piscataway, NJ, USA) in Taq polymerase buffer (10 mM Tris-HCl pH 9.0, 50 mM KCl, 1.5 mM MgCl2). The PCR was performed with a predenaturation at 96°C for 5 min followed by 25 cycles of denaturation at 94°C for 1 min, annealing at 57°C for 1 min, and extension at 72°C for 2 min. The second part of the PCR was performed with the same PCR mixture but with 5 μL

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of the first PCR reaction as template. The PCR conditions were similar to Q-PCR amplification. Briefly, initial denaturation was at 95°C for 15 min, followed by 35 cycles of 95°C for 10 sec, 61°C for 15 sec, and 72°C for 15 sec.

6.3 Results and Discussion

6.3.1 Nested-PCR result from Terra Nova surrounding seawater

In Chapter 3, both microarray and DGGE analyses found that there was a spatially stable bacterial community in the seawater around the Terra Nova production platform. This finding revealed that the discharge of produced water might not have a detectable influence on the bacterial community structure in the surrounding seawater and bacteria from the produced water community were also not detected in the water column using different community characterization methods. However, in order to fully understand the potential impact from the produced water, it was important to develop a method that is sufficiently sensitive to track the transport and dilution of the effluent. In this study, we first used the species-specific nested-PCR method to re-exam the same seawater samples from

Terra Nova. The nested-PCR revealed that Thermoanaerobacter spp. was detected in the seawater samples from 50 m to 500 m from the platform, but not in the reference seawater nor samples from within 50 m (Fig. 6.1). The sequencing results also revealed that the Thermoanaerobacter sequences from the seawater were identical to the sequence obtained from the Terra Nova produced water (data not shown), which suggested that the Thermoanaerobacter in the seawater was most likely originating from the produced water effluent. Thermoanaerobacter spp. are thermophilic, strictly anaerobic and not known to grow in the marine

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pelagic environment. As an example, however, with another similar thermophilic anaerobic microorganism, Jannasch et al. (1998) found that even though they were generally not known to grow in the oxic environment, they could still survive in this environment. Huber et al. (1990) further revealed that these types of thermophilic anaerobic microorganisms could be found in the seawater 1 km away from the sources traveling along with the plume. This result demonstrated that component of produced water could be detected in the surrounding seawater, at a very low concentration, and suggested that species-specific primers could be used to detect Thermoanaerobacter spp. in the surrounding seawater and to follow the flow of produced water discharges.

6.3.2 DGGE detection limit

The nested-PCR result indicated that bacterial species specific to the produced water were present in the water column in the surrounding seawater.

The inability of DGGE and/or microarray analyses to detect the microbial components in the produced water could be related to the detection limit (Chapter

2, 3, and 4). It is known that the DGGE detection limit is 1% of the total bacterial community (Muyzer et al. 1993; Murray et al. 1996). In terms of the cell number,

Zoetendal et al. (2001) found that the detection limit of PCR-DGGE for the major intestinal bacterial groups is 105 cells/mL fecal sample, and also depends on the

DNA extraction method. From another bacterial seeding experiment, Kan et al.

(2006) revealed that detection thresholds for PCR-DGGE ranged from 2.5 x 103 to

1 x 104 cells/mL, depending on the copy number of rRNA operons in the genome of individual species. In order to determine the DGGE detection limit in this

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system, produced water/seawater dilutions were used for the analysis. The results demonstrated that the Thermoanaerobacter DGGE band was visible to the 1:500 dilution (Fig. 6.2). Since dilution of the produced water effluent is very rapid, this detection limit would be reached within a very short distance from the platform.

Since the number of PCR cycles was the same for PCR amplification for DGGE and microarray analyses (Chapter 3), a similar detection limit would be expected using the microarray method. The results indicated that microbial community detection methods might not have the sensitivity needed to detect the impact from produced water. Specific oligonucleotide probe detection techniques, like nested-

PCR and Q-PCR, would be required to fulfill the need for higher sensitivity.

6.3.3 Species-specific oligonucleotide probe detection methods

In order to determine the detection limit of the species-specific detection methods, the same produced water/seawater dilution series were used. First, a regular PCR was used to determine the sensitivity of the primers. The result showed that with regular PCR using the Q-PCR amplification protocol we were able to detect Thermoanaerobacter in the 1:10,000 dilution (Fig. 6.3), suggesting that this species-specific detection method is more sensitive and could be applied to detect Thermoanaerobacter spp. in environmental samples. However, the presence of Thermoanaerobacter spp. in the seawater samples is not sufficient alone to track the transport and dilution of the produced water effluent. In order to determine the rate of dilution, development of a quantification method was needed.

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6.3.4 Q-PCR analysis

To calibrate the Q-PCR method to the dilution factor of the produced water, we used plasmid DNA containing cloned target sequences. Since the

Thermoanaerobacter sp. from the Hibernia produced water is uncultured, plasmid

DNA was the only method that could be used to produce the standard curve. By optimizing Q-PCR conditions, we obtained a standard curve with a linear range across six 10-fold dilutions of DNA concentrations (Fig. 6.4A). The standard curve was plotted with the threshold cycle (CT) against the copy number of

Thermoanaerobacter plasmid DNA (Fig. 6.4B). The detection limit was 21 copies in our standard curve (Fig. 6.4). The standard curve indicated a good correlation between the amount of template (number of copies) and the amount of product

2 (represented by CT) (r = 0.99901). The linearity of the standard curve and the fact that the PCR operates with constant efficiency confirmed that the assay was well suited to quantitative measurements of Thermoanaerobacter from environmental samples.

However, as mentioned previously, the disadvantages of using plasmid

DNA standards are the uncertainty of the DNA extraction efficiency from environmental samples and the potential presence of multiple copies of the 16S rRNA gene. Therefore, instead of just using plasmid DNA to determine the number of copies of the gene, serial dilutions with various ratios of produced water in natural seawater were used to determine the extraction efficiency from high to low produced water concentrations and to determine the correlation between dilution and number of copies of the gene. A total of 79,585 copies of

Thermoanaerobacter were present in the 5 μL of extracted genomic DNA from

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raw produced water (i.e. 47,750,958 copies in 1 liter of produced water). With this high starting concentration of Thermoanaerobacter copies in the raw produced water, three 10-fold dilutions of the produced water with seawater were made. We obtained a standard curve with a linear range (r2 = 0.9998) across 7 DNA concentrations (Fig. 6.5). The detection limit was about 8 copies in 5 μL of the genomic DNA per Q-PCR reaction (i.e. about 4775 copies per liter), equal to a

1:10,000 dilution (Fig. 6.5). The standard curves indicated a good correlation between the amount of template (number of copies) and the dilution (r2 = 0.9998)

(Fig. 6.5). The linearity of the standard curves and the fact that the PCR operates with constant efficiency between replicates, confirmed that the assay was well suited for quantitative measurements of the dilution of produced water in environmental samples.

6.3.5 Testing the Q-PCR method on Hibernia seawater samples

Before applying the Q-PCR method to the seawater samples, it was important to confirm that the bacterial community structure from the Hibernia

2008 surrounding seawater was still showing the same stable structure that had been found in previous experiments (Chapter 2, 3, and 4). The same DGGE method that was used in the other studies was used to evaluate the bacterial community structure in the 2008 Hibernia seawater samples. The DGGE results

(Figs. 6.6 and 6.7) revealed that similar banding patterns were observed in all the seawater samples and suggested that the bacterial community structure in the surrounding seawater was stable both spatially and temporally.

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Q-PCR was then used to estimate the number of copies of

Thermoanaerobacter in the Hibernia surrounding seawater (Table 6.2). The results revealed that Thermoanaerobacter was detected in almost all the samples from within 500 m, showing that components of produced water were detectable and quantifiable in the surrounding seawater within 500 m of the discharge. The highest concentration of Thermoanaerobacter (26,700 copies/L of seawater) was found in seawater sampled at 100 m from the production platform at a depth of 50 m, suggesting that most of the produced water effluent was transported to that location following discharge. However, the number of copies of

Thermoanaerobacter in raw produced water was 47,750,958 copies/L, but only

26,700 copies/L of Thermoanaerobacter were detected at the 100 m location, suggesting that the dilution factor was very high. A 1600-fold dilution factor (i.e.

26,700 copies/L ÷ 47,750,958 copies/L × 100% = 0.056%) was calculated at the

100 m location from the platform. A modeling study by Somerville et al. (1987) found that even at a 10,000 m3/day discharge rate, a 100-fold dilution was estimated at 50 m from the platform, and a 2800-fold dilution was estimated at

1000 m from the platform. Another modeling study at a low discharge rate (2000 m3/day), Furuholt (1996) estimated a 1000-fold dilution would be found at 50 m downstream from the discharge point. Our finding measured similar dilution factors as those predicted by the modeling studies, suggesting that with this detection method we can fairly accurately measure the predicted dilution factors.

The Q-PCR results also showed that more Thermoanaerobacter were found at 50 m than at 1 m, suggesting that the components of produced water trend to travel downward to the bottom of the water column (Fig. 6.8). In Chapter

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4, we identified some differences in the bacterial community structure in the sediments close to the Thebaud production platform suggesting that any influence from the produced water effluent would most likely be seen in the lower part of the water column or in the sediments close to the discharge. The Q-PCR results supported this observation and provided a quantitative method to monitor the zone of influence of the produced water.

A lower number of copies of Thermoanaerobacter were found in the rest of the seawater samples, from around 200 copies in most of the 1 m depth samples to 12,000 copies in the 50 m deep samples at 50 m from the production platform (Table 6.2). This result suggested that although most of produced water might travel downward to the bottom of the water column, some components are diluted and transported to other parts of the water column. Interestingly, the number of copies detected at a depth of 50 m was lower at 50 m than at 100 m from the platform (Table 6.2). A possible explanation for this is that the relatively high discharge force of the produced water (discharged at a depth of 40 m), may require more distance before it sinks to a depth of 50 m.

6.3.6 Testing the nested-PCR method on Hibernia seawater samples

The nested-PCR method was used to test Hibernia seawater samples for two purposes: to re-evaluate previous negative results and to confirm the positive results from Q-PCR analysis. The nested-PCR result confirmed all the positive amplifications from the Q-PCR analysis, and showed positive amplification from the 1000 m samples at both depths (Table 6.3). No amplification of

Thermoanaerobacter was detected in the 1000 m samples using Q-PCR (Table

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6.2). The results indicated that nested-PCR could provide the needed sensitivity to detect even lower concentrations of Thermoanaerobacter in the surrounding seawater adjacent to the produced water discharge and to confirm the findings from Q-PCR analysis.

6.4 Conclusions

Instead of using bacterial community monitoring methods, Q-PCR and nested-PCR methods were developed to monitor species unique to the produced water (Thermoanaerobacter spp.) in the surrounding seawater. Nested-PCR results revealed that Thermoanaerobacter spp. were present in seawater 500 m from the Terra Nova production platform. Both Q-PCR and nested-PCR results revealed that Thermoanaerobacter spp. were present in the surrounding seawater as far as 1000 m from the Hibernia production platform. The Q-PCR result further found that the highest number of the Thermoanaerobacter spp. (26,700 copies per litre) were found in the bottom seawater (50 m depth) 100 m from the platform, suggesting that majority of the Thermoanaerobacter traveled with the effluent to bottom of the water column. Only about 200 – 300 copies of Thermoanaerobacter spp. per litre were found in the seawater samples from the upper water column, so only a very small amount of Thermoanaerobacter (i.e. produced water) traveled to the upper water column.

6.5 Acknowledgements

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The authors thank Sylvie Sanschagrin, Jay Bugden, Susan Cobanli and Marc

Auffret for their excellent technical assistance. The authors also thank the NRCan PERD program for financial support.

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Table 6.1. Q-PCR and nested-PCR primers sets used in this study.

Starting Amplicon Temp Primer name Primer sequence PCR assay function(s) Location (bp) size (bp) (°C)

TMfull-F TGTAGCGGTGAAATGCGTAG 653 849 57 nested-PCR external primer TMfull-R ACCTTCCGATACGGCTACCT 1501

TMF1 CCGTAGCGAACGCAATAAGT 824 Q-PCR, nested-PCR internal 201 61 TMR1 CTGTGCAGGCTCCTTACCTC 1024 primers

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Table 6.2. Surrounding seawater Q-PCR results. Thermoanaerobacter 16S rRNA gene copies per liter of seawater.

Distance from Platform

50 m 100 m 200 m 300 m 500 m 1000 m 2000 m R50K

1 m 300 258 222 210 198 N.D. N.D. N.D. Sampling Depth 50 m 12,000 26,700 3492 1350 N.D. N.D. N.D. N.D.

N.D. = not detectable.

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Table 6.3. Surrounding seawater nested-PCR results

50 m 100 m 200 m 300 m 500 m 1000 m 2000 m R50K 1 m + + + + + + - -

50 m + + + + + + - -

+ detected - not detected

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1 2 3 4 5 6 7 8 9 10 11 12

)

) )

- -

( ( (

500 m m 500500

TN PW TN

TNPW

R20 km R20R20K

Nested ( Nested

50 m m 5050

Nested ( Nested

0 m m 00 m m 200200

25 m m 2525

100 m m 100100

10 m m 1010

Fig. 6.1. Terra Nova produced water (TN PW) and surrounding seawater nested- PCR results. Lanes: 1 to 8, Terra Nova surrounding seawater from 0 m to R20K; 9, Terra Nova produced water (TN PW); 10, nested-PCR negative control; 11, negative control (sterile distilled water); 12, 100-bp ladder marker.

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1 2 3 4 5 6 7 8 9 10 11 12

Fig. 6.2. Produced water and seawater dilution series DGGE analysis. Lanes: 1, full produced water (Hib PW); 2 to 11, dilution series of produced water in seawater; 12, full seawater (0%). Arrow showed the position of Thermoanaerobacter band on the DGGE.

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1 2 3 4 5 6 7 8 9 10 11 12

Fig. 6.3. Detection limit of Q-PCR primers (TMF1 and TMR1) using regular PCR. Lanes: 1 to 11, dilution series of produced water in seawater; 12, full seawater (0%).

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2 3 4 5 6 7

32 Std Curve: 30 R = 0.99951 28 R2 = 0.99901 26 M = -3.587 24 B = 36.410

Reaction Efficiency = 0.9 CT CT 22 Amplification value = 1.9 20 18 16 14

2.1x101 2.1x102 2.1x103 2.1x104 2.1x105 2.1x106 Concentration (Copies/reaction)

Fig. 6.4. (A). Standard curves (2.1 x 106 to 0 copies/reaction) from the Q-PCR assay. Plasmid DNA was used as the template. Circled numerals: 1, negative control (sterile distilled water); 2 to 7, plasmid DNA serially diluted from 2.1x106 copies/reaction to 2.1x101 copies/reaction. (B). Standard curve for six 10-fold serial dilutions of Thermoanaerobacter plasmid DNA (copy number 21 to 2.1 x 6 10 ). Quantification was performed by determining the threshold cycle (CT) against the calculated copies of plasmid DNA. The straight line, which was calculated by linear regression, shows an r2 of 0.99901; slope (M) of -3.587; and intercept (B) of 36.410.

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10000 9000 8000

7000

6000

5000 y = 82606x

4000 Number of Copies Number of Copies R2 = 0.9998 3000 2000 1000 0 0 0.02 0.04 0.06 0.08 0.1 0.12

PW/SW dilution

Fig. 6.5. Correlation between numbers of copies per 5 μL of genomic DNA vs. PW/SW dilution. The straight line, which was calculated by linear regression, shows an r2 of 0.9998; slope of 82606; and intercept equal to 0 (i.e. no produced water = zero copy).

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HibPW50 m 100 m 200 m 300 m500 m 1000 m2000 m R50K

Fig. 6.6. DGGE analysis of the 1 m deep surrounding seawater from the Hibernia production platform.

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HibPW50 m 100 m 200 m 300 m500 m 1000 m2000 m R50K

Fig. 6.7. DGGE analysis of the 50 m deep surrounding seawater from the Hibernia production platform.

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Fig. 6.8. Schematic representation of the transport and dilution of Hibernia produced water.

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CHAPTER 7

General Discussion and Conclusions

The Canadian offshore petroleum oil and gas reservoirs and the northwestern Atlantic Ocean are poorly characterized unique microbial habitats and have the potential to harbor novel microbial diversity. Since different produced waters from different geological formations could have considerably different chemical concentrations and compositions, regional- and platform- specific studies must be carried out to determine the environmental risk from each produced water discharge. The natural bacterial and/or archaeal populations from in and around the Hibernia, Terra Nova and Thebaud production platforms were characterized in this research. In this chapter, the research results presented in the previous chapters are further discussed as follows: (i) characterization of seawater bacterial diversity, (ii) characterization of produced water bacterial and/or archaeal diversity, (iii) development and application of species-specific monitoring and profiling methods.

This is the first study to characterize the marine bacterial diversity from these ecologically and commercially important areas: the Grand Banks (Chapters

2 and 3), Sable Island Bank (Chapter 4) and the Gully (the first Marine Protected

Area in Eastern Canada) (Chapter 5), around the Hibernia, Terra Nova and

Thebaud oil and gas production platforms. A total of 65 seawater samples covering an area 10 – 50 km around each of the three production platforms were analyzed. The seawater bacterial community analyses from all three different platforms revealed that the produced water discharges did not have detectable effects on the community structure, nor were any produced water specific species

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found in their respective surrounding seawaters. High similarity values (SAB =

~70%) were found when comparing the bacterial community structure in the seawater from the same region regardless of sample depth, and higher similarity values (SAB = 75 – 100) were observed when comparing bacterial community structure from seawater samples from the same depth. These results suggested that there is a stable seawater bacterial community covering a relatively large region (10 – 50 km range) surrounding each production platform. Any detectable changes to this stable bacterial community could be used as an indicator of the effects of the produced water discharges. Although some different species were found in the seawaters from around different platforms, generally similar proportions of the same phyla were found in the seawaters around all the platforms and from the Gully. The DGGE results revealed that most of the sequences from the seawater around all three platforms and from the Gully were related to sequences from either Bacteroidetes or Alpha-proteobacteria. About

80% of the sequences from the Hibernia surrounding seawater, 78% of the sequences from the Terra Nova surrounding seawater, and 93% of the sequences from the Thebaud surrounding seawater belonged to species closely related to either Bacteroidetes or Alpha-proteobacteria. In a high diversity environment like the Gully, 91% of the sequences from the surface water belonged to these two phyla. Similar results were found using the marine microarray on the Terra Nova surrounding seawater samples. Most of the remaining 10 – 20% of the sequences primarily belonged to Gamma-proteobacteria (present in all the surrounding seawaters) and Actinobacteria (only found in Terra Nova and the Gully).

Therefore, Bacteroidetes and Alpha-proteobacteria appeared to be the most

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abundant phyla and Bacteroidetes, Alpha-proteobacteria, and Gamma- proteobacteria appeared to be the most common phyla in the northwestern

Atlantic Ocean.

Both DGGE and microarray analyses identified stable bacterial communities in the seawater surrounding the production platforms, suggesting that in the future both methods could be used to monitor and profile changes in the community structure. Similarly, chemical and physicochemical analyses (i.e. organic chemicals, nutrients, etc.) also revealed that the produced water discharges did not have detectable effects on these characteristics of the seawater surrounding the production platforms. The results from both bacterial community analysis and traditional chemical/physicochemical analyses concluded that the produced water discharge did not have detectable effects on the surrounding seawater ecosystem. However, continued monitoring of both bacterial community and the chemical/physicochemical characteristics in the seawater around the

Canadian offshore oil and gas production platforms would be prudent to ensure that effects are not observed over longer periods of time.

Overall, the only potential effect that was related to the input of produced water was found in the sediment samples close to the Thebaud discharge. These results demonstrated that if there is an effect from produced water on the bacterial community structure in the surrounding environment, the bacterial community analysis would be able to detect these changes. Also, this result further confirmed that the influence of produced water discharge on the water column was minimal and any impact might be restricted to a small area primarily at the sediment level immediately adjacent to the discharge.

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Interestingly, our results did not find any commonly known hydrocarbon- degrading microorganisms in the surrounding seawater around the production platforms, even though there is a constant input of petroleum hydrocarbons from the produced water discharge. However, it is known that hydrocarbon-degrading microorganisms can be present at very low abundance (below detection limits) in marine environments and growth can be stimulated and cause changes in the microbial community structure only when there are sufficient nutrients (petroleum hydrocarbons). The stable bacterial community structures in the seawater suggests that the input of the petroleum hydrocarbons from produced water discharge is very low and well diluted, so that any changes in the bacterial community are not detectable. In the future, it would be useful to confirm these results using more sensitive methods, such as high throughput sequencing analysis and to identify potential hydrocarbon degraders and their functions.

As the chemical composition could vary considerably in different geological formations, the bacterial and archaeal diversity could understandably vary in different produced waters. This is the first study to determine the bacterial and/or archaeal diversity in produced water from all the major Canadian offshore oil and gas production platforms (Hibernia, Terra Nova and Thebaud). The results revealed that there are different bacterial communities inhabiting the different produced waters from similar geological regions. Most members of the produced water bacterial and/or archaeal communities were closely related to cultured mesophilic or thermophilic species. Since the oil reservoirs were continuously injected with seawater, large numbers of mesophilic microorganisms could be introduced into the reservoir from seawater. Some of these introduced

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microorganisms may reside and survive in the cooler portions of the oil production system (like the production piping). In the Hibernia produced water, sequences closely related to known mesophilic bacteria constituted a large fraction (more than 30% of the total number of clones) of the detected microorganisms. Most of these potential mesophilic bacteria were related to genera of known hydrocarbon-degrading bacteria (Chapter 2). These bacteria may have originated from the surrounding seawater and survived in cooler portions of the oil production piping using hydrocarbons in the produced water as energy and/or carbon sources. These hydrocarbon-degrading bacteria might be responsible for the bioremediation of produced water and spilled oil in the marine environment. Identification and characterization of hydrocarbon-degrading microorganisms in produced water is important for developing in situ bioremediation strategies for the future.

In addition to mesophilic bacteria, sequences from species related to known thermophilic bacteria and archaea were the most dominant groups of microbes in the produced waters‟ bacterial and archaeal communities. The results showed that Firmicutes was the most common and dominant phylum in all three produced water bacterial communities. In Hibernia and Terra Nova produced water, Thermoanaerobacter spp. appeared to be the most common and dominant

Firmicutes. For example, over 25% of the clones from the Hibernia produced water bacterial clone library belonged to Thermoanaerobacter spp., and all the sequences obtained from bands from the Terra Nova produced water bacterial

DGGE were related to Thermoanaerobacter spp. Thermoanaerobacter spp. were also detected in the Thebaud produced water but only when using a nested-PCR

188

method. Thermoanaerobacter spp. are known as thermophilic, fermentative, organotrophic sulfur-respiring bacteria and are commonly identified in other produced waters and have been isolated from geographically separated oil reservoirs throughout the world, which suggests that they might be indigenous to petroleum reservoirs. Identification of microbes that may be common to petroleum reservoirs supports the concept that similar microbes could thrive in the deep biosphere around the world. Although there is no direct proof for the origin of these thermophilic bacteria in the petroleum reservoirs, the existence of indigenous thermophilic microorganisms has been widely suggested. Some microbes are commonly found in all produced waters and therefore have the potential to be indigenous to the petroleum reservoirs. The results of this study also suggested some of these interesting produced water specific microbes could be used for the development of tracking methods.

The unique thermophilic microorganisms which were mainly found in the produced waters provided a good target to track the produced water effluent in the surrounding marine environment. Species-specific monitoring methods were developed based on the produced water thermophilic microorganism,

Thermoanaerobacter spp., to pinpoint the transport pattern and dilution of the effluent. Both nested-PCR and Q-PCR methods were developed for the detection of Thermoanaerobacter spp. in the surrounding seawater around the Hibernia and

Terra Nova oil production platforms. The results indicated that the selective primer sets were highly specific, sensitive tools that could detect the targeted bacterium in the surrounding seawater at considerable distances from the discharge point and could also be used to determine the direction of flow of the

189

produced water. Thermoanaerobacter spp. were detected in surface seawater (1 m depth) 1000 m from the platform, but the highest number was found in the deeper seawater (50 m depth) 100 m from the platform. When comparing the highest value to the total number of Thermoanaerobacter spp. in the raw produced water, even with it being the highest value, it represented a 1600-fold dilution factor at the 50 m depth and 100 m from the platform. Compared to traditional chemical and physicochemical methods, however, this is the first time that both the direction and concentration of actual biological components of produced water could be measured in the surrounding seawater. The values from this analysis agreed with the modeling predictions that indicated that the rapid dilution of the produced water discharge in the surrounding seawater could reduce the contaminant concentrations to non-acute toxic levels within a very short distance from the discharge point. This rapid dilution and relatively low concentration of contaminants supported the findings seen with the seawater bacterial community structures and confirmed that minimal amounts of produced water were transported to the upper water column, so there was no detectable effect on the bacterial community. The majority of the Thermoanaerobacter spp. was detected at bottom of the water column, so changes would be expected at the sediment level near the discharge, which agreed with past findings that there may be changes at the sediment level close to the Thebaud produced water discharge.

In conclusion, similar to the findings from the traditional chemical and physicochemical monitoring methods, the bacterial community analysis revealed comparable results and demonstrated that produced water discharges did not have detectable effects on the bacterial community structure in the seawater around the

190

three different oil and gas production platforms. However, using species-specific methods, species found mainly in produced water were also detected in the surrounding seawater and provided a sensitive method to monitor a component of the produced water effluents. The results suggested that the produced water discharge was rapidly diluted within a very short distance from the platform, so there was no detectable effect found in the surrounding seawater. Overall, this study provided the first characterization of the bacterial community structures in and around the three major offshore oil and gas production platforms in Eastern

Canada and developed new methods to monitor and track the discharge of produced water.

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APPENDIX

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Appendix A

Microbial Community Characterization of Produced Water from the Hibernia Oil Production Platform

C.W. Yeung1, and K. Lee2 and C.W. Greer1. 1National Research Council of Canada, 6100 Royalmount Ave., Montreal, QC. H4P 2R2. 2Fisheries and Oceans Canada, PO Box 1006, Dartmouth, NS. B2Y 4A2.

Published in K. Lee and J. Neff (Eds): Produced Water: Environmental Risks and Advances in Mitigation Technologies. Springer Publishers. ISBN: 978-1-4614- 0045-5.

The copyright in Springer publications remains with the author. Author retains, in addition to uses permitted by law, the right to communicate the content of the Contribution to other scientists, to share the Contribution with them in manuscript form, to perform or present the Contribution or to use the content for non- commercial internal and educational purposes, provided the Springer publication is mentioned as the original source of publication in any printed or electronic materials.

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Microbial Community Characterization of Produced Water from the Hibernia Oil Production Platform ...... 219 1.1 Introduction ...... 222 1.2 Materials and Methods ...... 224 1.2.1 Site description and sample collection ...... 224 1.2.2 DNA extraction ...... 224 1.2.3 PCR amplification of the 16S rRNA gene ...... 225 1.2.4 Denaturing Gradient Gel Electrophoresis (DGGE) ...... 226 1.3 Results and Discussion ...... 227 1.4 References ...... 230

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Abstract The Hibernia production platform is the largest oil producing platform off the east coast of Canada. The produced water is the major source of contamination from the platform into the ocean. A comprehensive study on the potential impact of the produced water discharge is needed. Microorganisms can rapidly respond to change, whether negative or positive, and at the population level, are powerful indicators of change in their environment. The objective of this study was to characterize the indigenous microbial community structure, by denaturing gradient gel electrophoresis (DGGE), in the produced water and in seawater around the production platform, and to determine whether the release of produced water is impacting the natural ecosystem. The DGGE results showed that the production water did not have a detectable effect on the bacterial populations in the surrounding water. Cluster analysis showed a >90% similarity for all near surface water (2m) samples, ~86% similarity for all the 50m and near bottom (NB) samples, and ~78% similarity for the whole water column from top to bottom across a 50km range, based on two consecutive yearly sampling events. However, there were distinct differences in the composition of the bacterial communities in the produced water compared to seawater near the production platform (~50% similar), indicating that the effect from produced water may be restricted to the region immediately adjacent to the platform. Specific microorganisms (Thermoanaerobacter for eubacteria and Thermococcus and Archaeoglobus for archaea) were detected as significant components of the produced water. These particular signature microorganisms may be useful as markers to monitor the dispersion of produced water into the surrounding ocean.

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1.1 Introduction

The Hibernia production platform is the largest oil producing platform off the east coast of Canada. The geological formation contains about 1 billion barrels of oil and is currently producing ~200,000 barrels of crude oil per day. The produced water, which contains minor amounts of natural organic (petroleum hydrocarbons, organic acids, alkylphenols) and inorganic (heavy metals, radionuclides) components from the subsurface geologic formation and the chemical amendments that aid in oil production, is the major source of contamination from the platform into the ocean. It is currently discharged into the surrounding marine environment under strict regulation (Canada‟s revised Offshore Waste Treatment Guideline 2002). In general, there is no evidence of harmful effects on the marine environment from produced water due to rapid dilution. However, the uncertainty of long term effects still remains. Therefore, a comprehensive study on the potential impact of produced water discharged from the Hibernia production platform is needed.

A number of studies in the North Sea, that deployed fish (Abrahamson et al. 2008, Hylland et al. 2008) and shellfish (Hylland et al. 2008) to monitor the long term effects of the produced water in the surrounding environment, have shown that the exposure levels were low and caused minor environmental impact at the deployment locations. Therefore, there is a need to develop methods that are sufficiently sensitive to components in produced water at the levels found in the surrounding ecosystem. Microorganisms are typically the first organisms to encounter changes in their environment, and with their short generation times and relatively large population densities, can rapidly respond negatively or positively to change. In light of their sensitivity and rapid response time, we hypothesized that monitoring the microbial community structure may be used to define the extent of impact zones around offshore platforms.

Culture-independent surveys of rRNA genes have greatly expanded knowledge of the microbial community structure and species content. Denaturing gradient gel

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electrophoresis (DGGE), a method to separate PCR-amplified DNA fragments based on their based composition (Muyzer et al. 1993), is an extensively used rRNA gene screening method for fast assessment of the dominant microbial community members‟ diversity and dynamics and allows high sample throughput with DNA-based phylogenetic resolution for an entire target community. Because DGGE can handle larger numbers of samples, throughput is often more extensive than clone libraries for comparing microbial assemblages over space and time (Casamayor et al. 2002). DGGE can also provide a quick semi-quantitative analysis of community diversity. Each band on the DGGE gel represents one of the major species in the community. Using the DGGE method to explore variability of the microbial community structure may provide important data on the potential impact of produced water on indigenous marine microbial populations.

The objectives are to: (i) Identify the impact of produced water discharges on the environment, if any, (ii) Compare microbial community structure changes related to released produced water, (iii) Identify unique microorganisms from the produced water for tracking in the surrounding seawater.

The results of this research will provide insight for determining the acceptable disposal limits for produced water to minimize the environmental impact, while taking into account the need and cost of produced water treatment and/or disposal by re-injection. The present study used DGGE to compare the microbial community composition by depth and distance, from around 500 m to 50 km, around the production platform.

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1.2 Materials and Methods

1.2.1 Site description and sample collection

Seawater samples were collected in July 2005 and June 2006 from a number of locations and depths from the Hibernia platform. All seawater depth profile samples were collected using the Seabird Niskin rosette frame (24 X 10 L bottles) containing a Seabird conductivity, temperature, depth detector (CTD). For 2005, depth profile samples (of 2m, 10m, and Near Bottom (NB - 0.5 m off the bottom)) were collected at a 500 m location (500m) and at a reference seawater location 50 km (R50k) from the Hibernia production platform. Also, two surface water samples (<500mA and <500mB) were collected at locations within the 500 m exclusion zone using only the Seabird CTD. Similarly, for 2006, depth profile samples (2m, 10m, and NB (0.5 m off the bottom)) were collected at two 500 m locations (500mA and 500mB) and at the same reference seawater location 50 km (R50k) from the Hibernia production platform. In addition, a sample of fresh produced water was kindly provided by the personnel of the Hibernia production platform. All containers used in the filtration were rinsed three times with the sample water. About 4 liters of seawater samples and 2 liters of the produced water sample were immediately filtered through sterile 0.22 μm GSWP (Millipore) filters. Following filtration all filters were transferred to sterile 50 mL Falcon tubes and were stored at -20°C until analyzed.

1.2.2 DNA extraction

The recovery of total community DNA was performed using a modified version of Fortin‟s method (Fortin et al. 1998). Prior to lysis treatment, 4.5 mL of sterilized distilled water was added to each 50 mL Falcon tube containing the filter paper. A 500 μL aliquot of 250 mM Tris-HCl (pH 8.0) containing 50 mg lysozyme was added and the samples were incubated for 1 h at 37°C with gentle orbital mixing. Fifty microliters of proteinase K (20 mg/mL) was added to the samples and they were incubated for 1 h at 37°C with gentle orbital mixing. The

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lysis treatment was completed with the addition of 500 mL of 20% SDS and 30 min of incubation at 85°C with gentle inversion mixing every 10 min. The filter paper was then removed from the 50 mL Falcon tube. Supernatants were treated with one-half volume of 7.5 M ammonium acetate, incubated on ice for 15 min to precipitate proteins, and centrifuged for 15 min at 4°C (9,400 x g). The supernatants were transferred to a sterilized 50 mL Falcon tube and treated with one volume of cold 2-propanol. The DNA was precipitated overnight at -20°C. Samples were centrifuged at 4°C for 30 min (9,400 x g). Pellets were washed with 70% cold ethanol and air-dried. The DNA was re-suspended in 250 μL of Tris-EDTA (pH 8.0). DNA concentrations were estimated by running 5 μL of purified material against the Lambda HindIII DNA ladder (Amersham Biosciences, Piscataway, NJ) standard on a 1.4% agarose gel with SYBR Safe (Molecular Probes, Eugene, OR, USA).

1.2.3 PCR amplification of the 16S rRNA gene

For PCR amplification of the 16S rRNA gene, the bacteria-specific forward primer U341F (5‟-CCTACGGGAGGCAGCAG-3‟) and the reverse primer U758R (5‟-CTACCAGGGTATCTAATCC-3‟) were used. These primers, complementary to conserved regions of 16S rRNA gene, were used to amplify a 418-bp fragment corresponding to positions 341 to 758 in the Escherichia coli sequence (Muyzer et al., 1993) and covered the variable regions V3 and V4. The bacteria-forward primer used for DGGE possessed a GC clamp (5‟- GGCGGGGCGGGGGCACGGGGGGCGCGGCGGGCGGGGCGGGGG-3‟) at the 5‟ end. This GC-clamp stabilized the melting behavior of the amplified fragments (Sheffield et al., 1989). The archaea-specific forward primer ARC344F (5‟-ACGGGGYGCAGCAGGCGCGA-3‟) and the reverse primer ARC915R (5‟- GTGCTCCCCCGGCAATTCCT-3‟) were used to amplify archaea DNA. These primers amplified a 572-bp fragment corresponding to positions 344 to 915 in the Escherichia coli sequence. The archaea-forward primer used for DGGE possessed another GC clamp (5‟-

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CGCCCGCCGCGCCCCGCGCCCGTCCCGCCGCCCCCGCCCG-3‟) at the 5‟ end. Each 50 uL PCR mixture contained 1 μL of the template DNA (undiluted, 10-1 or 10-2), 25 pmol of each oligonucleotide primer, 200 μM of each dNTP, 1 mM MgCl2 and 2.5 units of Taq polymerase (Amersham Biosciences, Piscataway, NJ, USA) in 1x Taq polymerase buffer (10 mM Tris-HCl pH 9.0, 50 mM KCl, 1.5 mM MgCl2). Briefly, after an initial temperature of 96°C for 5 min and thermocycling at 94°C for 1 min, the annealing temperature was set to 65°C (for bacterial PCR) or 60°C (for archaeal PCR) and was decreased by 1°C at every cycle for 10 cycles, and 1 min (bacteria) or 3 min (archaea) elongation time at 72°C. Additional cycles (15-20) were performed with annealing temperatures of 55°C for bacterial and 50°C for archaeal. PCR products were loaded onto a 1% agarose gel with SYBR Safe (Molecular Probes, Eugene, OR, USA) with a 100- bp ladder (MBI Fermentas, Amherst, NY, USA) to determine the presence, size and quantity of the PCR products.

1.2.4 Denaturing Gradient Gel Electrophoresis (DGGE)

The 16S rRNA gene PCR products from eight PCR reactions were combined for each sample and concentrated by ethanol precipitation for DGGE analysis. For both eubacteria and archaea DGGEs, about 650 ng of 16S rRNA gene PCR product from each sample was applied to a lane, and was analyzed on 8% polyacryalmide gels containing gradients of 35-65% denaturant (7M urea and 40% deionized formamide were considered to be 100% denaturant). DGGE was done with a DCode Universal Mutation Detection System (Bio-Rad). Electrophoresis was run at a constant voltage of 80 V for 16 h at 60°C in 1x TAE running buffer. The gels were then stained with VistaGreen (Amersham Biosciences, Piscataway, NJ, USA). The gels were imaged with the FluoroImager System Model 595 (Molecular Dynamics, Sunnyvale, CA, USA). The gel images were analyzed with GelCompar II v4.6 (Applied Maths, Sint-Martens-Latem, Belgium) to generate dendrogram profiles. The genotypes were visually detected based on presence or absence of bands in the different lanes. A band was defined

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as “detected” if the ratio of its peak height to the total peak height in the profile was >1%. After conversion and normalization of gels, the degrees of similarity of DNA pattern profiles were computed using the Dice similarity coefficient (Dice, 1945), and dendrogram patterns were clustered by the Unweighted Pair Group Method using Arithmetic average (UPGMA) groupings with a similarity coefficient (SAB) matrix. Also, some of the individual bands from produced water DGGEs were excised, eluted and re-amplified with the same set of primers without the GC-clamp. The PCR products from DGGE band re-amplification were sequenced at the Université Laval Plate-forme d'analyses biomoléculaires using a model ABI Prism 3130XL (Applied Biosystems, Foster City, CA, USA) with their respective primers. The sequences were edited and were tentatively identified using NCBI BLASTN search (http://ncbi.nlm.nih.gov/blast/).

1.3 Results and Discussion

Two eubacterial DGGEs for samples from 2005 and 2006 and one archaeal DGGE for samples from 2005 were analyzed.

From both 2005 and 2006 eubacterial DGGEs, the cluster analyses showed a >90% similarity for all the near surface water (2m) samples from within 500 m to the 50 km reference seawater location, and a high similarity (~86%) for deeper seawater (50m and NB) from the 500 m and 50 km reference locations (Fig. 1 and 2). There was also a high similarity (>78%) of eubacterial community structure for the whole water column from top to bottom (Fig. 1 and 2). However, the composition of the eubacterial community in the produced water was only about 50% similar to the seawater around the production platform (Fig. 1 and 2), even though the major component of the produced water comes from the injected surrounding seawater.

The 2005 archaeal DGGE also revealed a high similarity of archaeal community structure in the surrounding seawater, with 79% for the surface seawater (2m) and

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67.7% for the bottom seawater (50m and NB), from within 500 m to the 50 km reference location (Fig. 3). Like the eubacterial DGGE results, there is a higher similarity (34%) value for archaeal community structure from top to bottom in the water column in comparison to the produced water archaeal community structure (9.4%) (Fig. 3).

The results from both eubacterial and archaeal DGGEs showed a high similarity for the water column both horizontally and vertically, suggesting that there is a fairly stable microbial community in the surrounding seawater. Therefore, any abnormal changes in the microbial community in the water column could potentially be used as an indicator of foreign input. Furthermore, the DGGE pattern from produced water indicated that the microbial community structure was distinctly different from the seawater microbial community structure near the production platform, suggesting that the microbial community structure in the produced water is unique. Thus, the findings of stable microbial communities in the surrounding seawater, may indicate that any effects from the produced water are be restricted to the region immediately adjacent to the platform or on the sediments.

From the eubacterial and archaeal produced water DGGEs, a number of species (Thermoanaerobacter sp. for eubacteria and Thermococcus sp. and Archaeoglobus sp. for archaea) were identified through band excision and sequencing. The anaerobic and thermophilic nature of these species was unique to the produced water and strongly suggests that they originate from the geological formation. Furthermore, these species were detected as significant components of the produced water, but appeared to be below detection limits in the surrounding water outside the 500 m exclusion zone (data not shown). The uniqueness of these produced water specific species may be useful as targets to monitor the dispersion of produced water in the surrounding marine ecosystem. The discovery of these produced water specific species has enabled us to design species-specific 16S rRNA gene primers for the quantitative PCR (Q-PCR) of

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target microorganisms in the marine environment following the discharge of the produced water.

Acknowledgments The authors thank Susan Cobanli, Nathalie Fortin, Sylvie Sanschagrin and Marc Auffret for their excellent technical assistance. This research was supported by Fisheries and Oceans Canada and the National Research Council of Canada.

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Fig. 1. Cluster analysis of Hibernia 2005 surrounding seawater and Hibernia 2006 produced water eubacterial DGGE banding patterns based on band positions using UPGMA of a SAB matrix.

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Fig. 2. Cluster analysis of Hibernia 2006 surrounding seawater and produced water eubacterial DGGE banding patterns based on band positions using UPGMA of a SAB matrix.

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Fig. 3. Cluster analysis of Hibernia 2005 surrounding seawater and Hibernia 2006 produced water archaeal DGGE banding patterns based on band positions using UPGMA of a SAB matrix.

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Appendix B Table of the 16S rRNA gene-targeted bacterial–specific microarray probes developed in this study. Acetivibrio GCGGCGCCCTCAATAAAGTTAAGCT Acetobacterium CAGTATTTGTCCAGCAAGCCGCCTT Acholeplasma ACCTGTCTCCATGGTAACCTCCACT Achromatium TGAGGCTACAAGAGTCCCCCACTTT Achromobacter TCTGCAGGTACCGTCAGTTTCACGG Acidithiobacillus CCACATCATCCACCGCTTGTGCGGG Acidovorax GTCATGGACCCCAGGTATTAACCAG Acinetobacter AGTAGCCTCCTCCTCGCTTAAAGTG Actinomadura GTATCCACCGCAGACCCACGGTTAA Actinomyces CCCATCCCCCACCAGAAAAACCTTT Actinomycetales ACTCAAGTCTGCCCGTATCGCCTGC Aequorivita TGCGCAGTGGCTGCCCATTGTACCT Aerococcus CGCTAACGTCAGAAGTGCAAGCACT Aeromicrobium GCCCTGGACATAAGGGGCATGAAGA Aeromonas TCAAGGACACAGCCTCCAAATCGAC Aestuariibacter GCGCGGGGCTTTCACCTCTAGCTTA Afipia AGGTTCCATCTCTGGTACCGGTCAT Agarivorans ACGACATGCTTTTTGGGGTCCGCTT Agrococcus CGTGAGCTGATCCTTGACCGAAGTT Alcaligenaceae TTTCGCGTAGTTATCCCCCGCTACT Alcaligenes TTTCTTTCCGAACCGCCTACACACG Alcanivorax1 GCGCCACCAAAGTCACTAAGGACCC Alcanivorax2 CTAATCCGACGCGAGCTCATCCATC Algibacter CGGTCGTCATCTGAAGCAAGCTCCA Algoriphagus AGCCGGAGTCTTTCACCACTGACTT Alkalibacterium CATGCGCTATGATCACCTATGCGGT Alkalilimnicola CTAGAGTGCAAGGCCTCCGAAGAGG Alkanindiges CCTGGAATTCTACCTTCCTCTCCCA Allochromatium TGCGCCACTCAGCCCTTAAATGGAC Alpha-proteobacteria CCTCTACACTCGGAATTCCACTCAC Alteromonas CCAACTGTTATCCCCCTCGCAAAGG Alteromondaceae TCGCTACCCACACTTTCGCACATGA Amaricoccus1 GTCTTGGATCCAGCCGAACTGAAAG Amaricoccus2 CCAGCAGTATTAGAGGCAGTTCCAG Amycolatopsis CATCTCTGCCGGTTTCCAGTGCATG Anaerophaga AGCTAATGTCACGCATGCCCACCTT Anoxybacillus TCTCGGTGTTATCCCCGTCTACAGG Antarctobacter CCAGCCTAACTGAAAGCTCCATCTC Aquabacterium CTCCCGGGGTATTAGCCCAGAAGAT Aquicella CGGAAATTCCGCCACCCTCTCATAT Aquificaceae AACCAGACGCTCCACCGGTTGTGCG

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Aquificales TTACGCCCAGTAATTTCGCGCAACG Aquimarina TCCGGTTGAGCCGAAAACTTTCACC Aquimonas CGACACTGATCTCCAAAGTGAGACC Aquitalea GTCATCCCCCAGCGATATTAGCGCT Arcicella GACCAGATTCGGCCGACACCTAGTT Arcobacter CGCCTTCGCAATCGGTATTCCTTCT Arenibacter GTCGCCGGCGGATAAGCAAGCTTAT Asticcacaulis TCACCTCCAACTTGATAACCCGCCT Atopostipes AAGGCTCGCGCCCTCGCAACTCGTT Aurantimonas GTCTCCGGATACCGTCGAGGCATGT Azoarcus GTGTCCTGGCTCCCGAAGGCACTCC Azohydromonas GAGTAGAAACCCACCCAACAACCAG Azonexus ATTAGCCAACGCGATTTCTTCCCGG Bacillaceae 2 CTTCCCGCGCTCGACTTGCATGTAT Bacillales AGCACTAAAGGGCGGAAACCCTCTA Bacillales GGCAGTCACCTTAGAGTGCCCAACT Bacillus a GACTGCGATCCGAACTGAGAACAGA Bacillus c CGTCAGTTACAGACCAAAAAGCCGC Bacillus d CCGCCGCTAACTTCATAAGAGCAAG Bacillus e AAGCCACCTTTCAACCTTCCCCCAT Bacillus f GTCAAGGTACGGACAGTTACTTCCG Bacillus g ACTTCGGCACTACGGGCATCGAAAC Bacillus h CGTCTTACAGGCAGATTACCCACGT Bacillus I TTCCCGGAGTTATCCCCGTCTCAAG Bacillus j TCCCCGAAAGGGGAACGCCCTATCT Bacteriovoracacea TTCCTCACTCACGCGGCATTGCTGC Bacteriovorax CTTCGGCTCTGGTGTTCCTTCGCAT Bacteroides TACACCACGAATTCCGCCCACCTCT Balnearium CGAAGGCGGCAACGACCCTGCCTTT Balneola AAAGCCGTTTACAACCCATAGGGCC Beggiatoa ACAGCGAGTGCTTGCGCACTCTTTT Beijerinckia1 TCACCATTGCTGGTTCGCTCGACTT Beijerinckia2 TCTCTGCCATCCGTCCAGGACATGT Beijerinckiaceae TCTTTCTCCTCTCGGACGTATCCGG Belliella CGTGCCTCAGCGTCAGTTATCGATA Beta-proteobacteria CCACATCATCCACCGCTTGTGCGGG Blastococcus TCTCTGGGAGATTTCCGTGCATGTC Blastomonas TCGCCTCTCCAAGATTCAAGCCATC Bordetella TATTCTGCAGGTACCGTCAGTTGCC Bosea TGCCACTAGCACCGAAGTGCCCGTT Brachybacterium CACCTCACAGTTTCGCAACCCATTG Bradyrhizobiaceae GGTACCGTCATTATCTTCCCGCACA Bradyrhizobium TTGCGAAGGGTCGCCCCTTAGCATC Brevibacillus GCTACACGTGGAATACCGCTTTCCT Brevibacterium ATTCCAGACTCCCCTACTGCACTCT

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Brevundimonas GTAATGAGCCAGTATGTCGCCTTCG Brucella AGCAAATGGTACGTTCCCACGCGTT Brumimicrobium GCCGGTCCTTATTCGTTTAGTACCG Burkholderia AAGCCAATGAAGGCCCGACAACCAG Burkholderiales CAGGCCCAGGGGATTGCCTTCGCCA Butyrivibrio ACTGCTGGCTACTAGGACAAGGGTT Byssovorax CTCAGCCACTTCTGGAGCAGTCAAC Caldithrix GTCGAATCCGCCAACACCTAGTGCC Caloranaerobacter TTTCCCGAGGCTATCCCCCTGCATA Caminibacter TCTCTCTCTTCCCCTGCAAAAGGAG Caminicella CCGTCCGCCGCTAAGCTTTAATCTT Campylobacter CAACTGTTGTCCTCTTGTGTAGGGC Capnocytophaga GACCGTCTTTATAGATTCGCGCCTG Carnobacterium TTGGCCTCGCGACCTTGCGACTCGT Castellaniella ACTCGCCACCAAAGAGCAAGCTCTT Caulobacter CTTCCTCCGGATTAACTCCGGCAGT Caulobacteraceae GGTCCAGGCATGTCAAAAGGTGGTA Cellulomonas AAATCTTTCCACACCCCCACATGCG Cellulophaga TACAGTACCGTCACCAGACTACACG Cellulosimicrobium AAGAGACCACCATCTCTGGTGGCTT Cetobacterium GAGCTGACTTCTCCATCGGCATTCC Chitinibacter GCGTTACTAAGCTCCGAAAAGCCCA Chlamydiales GTCCCAGTGTTGGCGGTCAATCTCT Chlorobaculum1 GCGCAAGGCTTAACCTTGTAAAGGC Chlorobaculum2 TGGAACATAGGCTCATCCTTTGGCG Chlorobiaceae TTGCCACCCCTGTATCACCGCGGCT Chlorobium CCTCGCGGCTTTGCCCTCTGTAGCT Chromatiaceae AATGAGCCCAACGGCTAGTCGACAT Chromohalobacter CAATCCGGACTGAGGCCGGCTTTCT Chryseobacterium CCAACTATCTAATCTTGCGCGTGCC Citricoccus CCGATAAATCTTTCCACCCCCCACC Clavibacter CAGAGCAAGCTCTGACATCACCGTT Cloacibacterium GCCGCTCTCTCATTTCCGAAGAAAC Clostridiaceae 1 CCTCTCCTGCACTCTAGATATCCAG Clostridiaceae 2 GCCTTTCACCCCTGGCTTATTTGGC Clostridium CCCCTCTATGAGGCAGGTTACCCAC Cobetia CCAATCCGGACTGAGGCAAGCTTTA Colwellia AGCTGCGCCACTCACGGATCAAGTC Comamonadaceae CTGAGTCAGTGAAGACCCAACAACC Comamonas ACTACCTAATCTGCCATCAGCCGCT Conexibacter AGTTTCCTCGGGTTGTCCCGGTCAT Coriobacteriaceae CATCCCTCACCGCCGGAGCTTTCCC Corynebacterium TCAAGTTATGCCCGTATCGCCTGCA Coxiella AGTTCTCATCGTTGACGGCGTGGAC Crenotrichaceae CCCGCCTACGCACCCTTTACACCCA

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Croceibacter CAGGCTTTACAGATTCGCTCCTGGT Crocinitomix GCCTGATTTCTCAAGCGGTCACTCT Cupriavidus TGTCCACTTTCCCTTTCGGGCACCT Curtobacterium GACACAGAAACCGTGGAAAGGTCCC Curvibacter1 TTCTTACAGTACCGTCATGAGCCCC Curvibacter2 GGCACGTTCCGATACTTTACTCACC Cyclobacterium CGCACTTTTAGAGGTTGGCTTCACG Cycloclasticus TAGCTGCACCACTAAGCGGAAACCC Dechloromonas GGCACACCCCAATCTCTCAGGGCTT Deferribacter CACCGTTTCCGGCATTGCGCAATAT Deferribacteraceae CCCTGACTTGCATGTGTTAAGCACG Defluvibacter CCGATCCAGCCTAACTGAAGGACAA Defluviicoccus CCGAAGCCCTGCTTCCCGTTCGACT Deinococci GGCCGTAAGGGCCATGCTGACCAGA Deinococcus GTAAGGACCATGCTGACTAGACGTC Delftia GGCCCCCTGTATTAGAAGGAGCTTT Desulfacinum TCGGCCAAGTCGAAACCTAGCCTTT Desulfobacter CCCCGAGCTTTCACACCTGACGGAC Desulfobacula TAGTTAGCCCAACGACGTCGGGTAT Desulfocapsa CACCAGGCTCCTCTAATGAGGCACT Desulfofaba TACCGGGTATTAGCATCGCTTTCGC Desulfofrigus GCAGTGTCTCCAGAGTCCCCGCCAT Desulfohalobium GCTGCTTGCAACAGAGGCAGCCTTT Desulfomicrobium TTAACCCGGGCAGTCTCCACAGAGT Desulfomonile GCCACTTTACTCCACACCCCGAAGG Desulforhabdus GCCACTGATATTCCTCCCGATCTCT Desulforhopalus TGACATAACAGACCACCTACGCGCG Desulfosarcina CGCCACTTTACTTACCCTAGCAAGC Desulfosporosinus GGTAGCATCTTCAGAGGCCACCTTT Desulfotalea CGCAAGCTCCTCCTGATACAATAGC Desulfotignum CTCCACTGGTACCGTCAATGCTATC Desulfotomaculum TCAGGGCCAGGCCAGAGAGCCGCCT Desulfovibrio ATTCATCGTTGCCACGGTAGGCCGT Desulfuromonaceae TCTACGGATTTCACCCCTACACTCG Desulfuromonas GGACCGCGGACTCATCTGATAACAA Desulfuromusa ACCGACCATACCATAAACGGCTGCT Dethiosulfovibrio CTCTACCCCTCCAGCAAGACAGTTT Devosia1 CCAGCCGAACTGAAGCAATCCATCT Devosia2 TTCCTTACGGTCAGCGCACCGGCTT Diaphorobacter TACGGTACCGTCATGACCCCTCTTT Dietzia CTGCCGTCGTCCTGTATATGTCAAG Dinoroseobacter TCGCCCGAAGGCCACATACGGTATT Dokdonella TTCTTTGGGTACCGTCATCCGCACG Donghaeana ACTGACTTATCAGCCCGCCTACGGA Dyadobacter TATCACCATGCGGTGCCCCTGTTTT

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Echinicola CTGTCCAGAGTCCCCAGCTTAACCT Ectothiorhodospira CCTCGCGGCTTGGCAACCCACTGTA Edwardsiella AGCAAGCTTTCTCCCTGCTACCGCT Elizabethkingia GCAGTTCGACAGTTAAGCTGTCGGA Enhydrobacter AGCGCCCTCTTTCGTTAGGCTACCT Ensifer GCGGGCTCATCCTTTCCCGATAAAT Enterobacteriaceae TTATGAGGTCCGCTTGCTCTCGCGA Enterococcus CCATCAGTGACGCAAAAGCGCCTTT Enterovibrio TTCGCTCCACCTCGCGGCCTCGCTG Epsilon-proteobacteria CCACTTGTGCGGGTCCCCGTCTATT Erythrobacter TCTGTCTCCAGAAACCGTCCTAGGA Ethanoligenens CCATCCCTCAGCGGATTACTCCTTT Exiguobacterium AGCCCAACTCATAAGGGGCATGATG Ferrimonas ATCTGAGCGTCAGTCTCTTGCCAGG Filomicrobium AGCCGAAGTTTCCCTCGGTTATCCC Flammeovirga TGAGGAATTCCGCCTACCCATACGT Flavobacteria CTGACGACAACCATGCAGCACCTTG Flavobacterium CAGTATCAATGGCCGTTCCACCGTT Flectobacillus ACGATTTCTCTGTACCACTCACCCC Fluoribacter GCACCTGTTCTAACATGCCCGAAGG Francisella CTCCGTGGTAAACGCCCATTCGTTA Frigoribacterium AGCAAGCTGGAGTTCATCGTTCGAC Fulvimarina TGACCGTCAGAGGATGTCAAGAGCT Fusobacteriaceae ACTCACCCGTCCGCCACCGTACTAT Fusobacteriales GGCACGTATTTAGCCGTCGCTTCTT Gelidibacter GAACGCATGCCCATCTCTCACCCAT Gemmata CTCCCTTTCGGGTAGCAACTAAGGG Gemmatimonas CCGGTGCTTCCTCACCCGGTACCGT Geobacillus CGCTCCTTCGTCCCTCACAACAGAG Geobacter CCGAAGGTCCCCCCCTTTTCCCGCA Geodermatophilaceae GGGCCACCGGCTTCGGGCGTTACCG Gillisia CGTAGAGTACCGTCAAGCTCCGACA Glaciecola ACGACGAGCTTTAAGGGATCCGCTT Gordonia AGTCTCCTGCAAGTCCCCGGCATAA Gp1 TTCTACCCCGGTCCACTACATTTCG Gp10 CTCCCCGATTTCCGGGGCAGTCTCC Gp11 CCACTCCAGTTCAAGCCCAGGTAAG Gp17 TAGTTTCCCGCGCAGTTCCCCAGTT Gp18 GGGACTCAATACCCCCAACACCAAG Gp21 CAGCGTCAGTGTCTGTCCAGGAAGC Gp22 CCTCAGCGTCAGTACCCGTCCAGGT Gp23 GACACCCGCTGCACCTAGTGCCCAT Gp26 CGCATGCTTGAAGCAGAGGCATGCT Gp3 GCACGTAGTTAGCCGCAGCTTCTTC Gp4 CCCGTAGGAGTCTGACCCGTTATTC

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Gp5 CAGTTGCGATCCAGAAAGCCGCCTT Gp6 GTCCAGGATGCCGCCTTCGCCACCG Gp7 CACCGACTTCTAGTGCAACCGACTT Gp9 GCGGAATTAGCCCAGGTTTCCCTGA GpIIa TGCCATGATGGAGTTAAGCTCCACG GpIII CGGAATTAGCCGATGCTGATTCCTC GpXIII CGGCCCGGGTCGATACAGGCCACAC Gramella CACGTTTCCATGGGCTATTCCCCGC Haemophilus ATTCCCAAGGGGCTGCCTTCGCCTT Hahella CGCCACTAAAGTCTCTAAGGACCCC Halanaerobium GTCTGTCGGTTAAGCCGACACCTTT Haliangium AGTCACTCTTTAGGCGGCTGCCTCC Haliscomenobacter GCTTTCACACACAGGCACTCGCATT Halobacillus AAAGTATTACCCGGCATTAGCCCCG Halochromatium GAAGGCACCTGTCATCTCTGACAGT Halolactibacillus GCCCATGTTTCCACGAGTTATCCCA Halomonas AGTGCGCTAGGCACCCAACGGCTGG Halospina TAAGGGATTCGCTGCACCTCGCGGC Halothiobacillus GTGTTGCCGGTATTAGCGACAACCT Halovibrio TCCGCCGCTCGCCAGCCTCCCGAAG Helicobacter CAGTTCCAGTGTGTCCGTTCACCCT Herbaspirillum CGACTAGTTATCCCCCACTCCAGGG Hoeflea CAATGTCTCCACTGTCCGCGACCGG Hongiella CAGTATCAACGGCACTGCTCCGGTT Hydrocarboniphaga GTCCCCGACTTTGCTCTCACGAGTT Hydrogenimonas TCGCCATCGGTATTCCTGGTGATCT Hydrogenivirga GTTCCGCCGTTAAGCGGCGGGCTTT Hydrogenophaga GCACTCCAGCCTTGCAGTCACAAGC Hymenobacter CATCTCTGAGCCGGTCACGCGCATT Hyphomicrobiaceae GCCACCGACAAGCAAGCTTGCCGAC Hyphomicrobium AGCACCTGTGTTCCGACCCATTGCT Hyphomonas TTCCTTAGAGTGCCCACCCAAACGT Idiomarina CCTCAGCGTCAGTCTCTGACCAGGT Idiomarinaceae CTGACCAGGTGGCCGCCTTCGCCAC Incertae Sedis TCAGAAACCGACCAGAGAGTCGCCT Incertae sedis 2 CCGTCCCTTCCTCTCCGGGTACCGT Incertae sedis 5-1 AGCTTCGTTACTGAACAGCAAGCCG Incertae sedis 5-2 GAGAAGAAACCCTCCCAACAACCAG Inquilinus CACCTGTGCGGATGCCAGCCGAACT Isoptericola AGCCTGAGGTTTTCACACCAGACGC Janibacter ATGGGTGCCCCTTTTGGTCGGTATA Jannaschia ATCTAATCAGACGCGGGCCAATCCT Janthinobacterium TACACTTTCTGCGATTAGCTCCCCC Jeotgalibacillus GGGAAAACACTGTCTCCAGTGCGGT Jeotgalicoccus CTGCCCTTTGTAACCTGCCCATTGT

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Kangiella CTTTACACCGAAGTGCACATGCGGT Knoellia GGAGCAAGCTCCCAAGATCACCGTT Kordia AAGAAAAAGGCGTCTCTGCCCCTGT Kordiimonas CAGCCGAACTGATGGCAACTGAAAG Kytococcus AAGCACCCGCTTCACCGTTCGACTT Labrys CTAGCTGTTCCACCAACCTCTTCCG Lactobacillales CGCCTGCGCTCGCTTTACGCCCAAT Lactobacillus GCGTCAGTTACAGACCAGACAGCCG Lactococcus CTCAAAGGCAGATTCCCCACGCGTT Leeuwenhoekiella CTACCTATCGTTGCCTAGGTGTGCC Legionellaceae CGGTGCTGTTTATGGTGGGTAACGT Leifsonia TGCGGCTGAAGCTCGTATCCAGTAT Lentisphaera ACGTCGCTCAATGTCAAACCCGGGT Leptonema GACGCCTGGTCGACAACGTTTAGGG Leptospira TCACCCCCTTCACGAGTTTCACCTT Leptospiraceae TCACTCTTGCGAGCATAGTCCCCAG Leptothrix1 TCTCAAGGGTTGCCCCTCTACCGTT Leptothrix2 CCACATCATCCACCGCTTATGCGGG Leucobacter CGTGGAACAGGCCCTACATCTAGTT Leuconostoc GTGCAAGCACCTTTCGCTGTGCGTT Leucothrix TGGCTTGTCCCCCACTACTGGGTAG Lewinella CTTCACGGAGTCGAGTTCCAGACTC Limnobacter CCCCTAGGGTATTAGCCCAAAGGAT Loktanella CACCTGTCACTCTGTCTCTTACGAG Lutibacter CAGTGGCTGCTCTCTGTCCATACCA Lysobacter AGATTCCGACGTATTCCTCACCCGT Magnetospirillum CATGAGCGTCAATCGACGGCCAGGT Maribacter TTCCATACGCGGTGCGCACCCGTGC Maricaulis TCCCGAACTCAAGACTGGCAGTATC Marichromatium GCAGGGCAGATTCCCACACATTACT Marinibacillus AAGCAAGCTTCTCTAACTCCGCTCG Mariniflexile CCATGGTGTGCCGTTACCACTCCAT Marinilabilia GAGTGCCATGGAGCATTAATCCCCG Marinilactibacillus GCCGAAGCCACCTTTCATCTAAGAG Marinitoga TCGCCTTAGCTTCAGCACGAACGGT Marinobacter GCTGCGCCACTAAGACTTCAAGAGT Marinobacterium GAAGATCCCCCACTTTCCCCCGAAG Marinococcus AATGGCTTTTCTGGATTGGCTCCCC Marinomonas CTGCGCCACTAAGTCATTACAACCC Marinospirillum ATAGCGCAAGCCTAAGCCTGCTTTC Marinovum CGATCTCTCGATGTAGCTGAGGATG Marmoricola CGCTTCGTCTGCGCTGAAAGAGGTT Martelella TGTCCTGGCGTCCCGAAGGAACCCT Massilia CCTCGATCTCTCGTGGCTTCCGTAC Mechercharimyces GCTGATTCCACACCCGAAGGTGCTT

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Mesonia TCGCTTAGCCACTCAGTCTCAATCG Mesorhizobium CCCTGGGTTATTCCGTAGAGCTAGG Methylarcula CAAGTTTCCCTGGGCTATTCCGCAG Methylibium AGAACGGCGCTTTTCTTCCCTGACA Methylobacterium1 CCGATCCTTCGGCAGTAAACCTTTC Methylobacterium2 CTCGCAGTTCCACCAACCTCTACCA Methylohalobius TTCGTCCCTCAGCGTCAGTCTTGGT Methylomicrobium CCCTAGAGTTCCCAGCTTGACCTGT Methylophaga CGTCCGCCACTAATCAGAAACAGCA Methylophilaceae TGTATTACGTGTGAAGCCCTGGCCA Methylophilus TCGGCCGCTCTAATAACGCAAGGTC Methylosinus GAAGCCATCTCTGGCGACCATACTG Methylosphaera GAAGGCAAGCCTGCTCGTTACCGTT Microbacteriaceae GCACCACCTGTTTACGAGTGTCCAA Microbacterium GGAATGGACCCCACAACTAGTTCCC Microbulbifer CTAGCCAGCCAGTTCTGAATGCAGT Micrococcaceae TAGTCTGCCCGTACCCACCGCAGAT Micrococcus TCTCTACGGCGATCGAGAACATGTC Micromonospora ATGCGTTAGCTGCGGCACAGGGAAC Micromonosporaceae AACCGGAGAGGCCCCCCACACCTAG Microscilla CATGCCCATCTCCTACCGTAACCTT Modestobacter GGCACCCCGTATCTCTACGAGATTT Morganella AACCTTGACACCTTCCTCCCGACTG Moritella CCAGTTCTCAAGGAACCAAACTCCG Muricauda TACACCACATGTTCCGGCAACCCCA Mycobacterium TCACGAACAACGCGACAAACCACCT Mycoplasma GCCCCACTTGTAAGAGGCATGATGA Myroides CGCCTTAACGCGCTGGTAACTAACA Myxococcus GGGTCAACTCCCACGACACCTAGTT Nautilia CTACCGAAGTAGCACCCCCGCATCT Nautiliaceae TCCCCACCTTCCTCCCGGTTGCCCG Neptunomonas ACCCTCTTCTGTACTCTAGCTTGGC Nereida GGCATCGCTAGATCAGGGTTTCCCC Nesterenkonia TAGCGACTCCAACTTCACGAAGTCG Nevskia AGCGTCAAAACAGGCCCAGGAGGTT Niastella TGCGTAGCCTGCTGCCTTCGCAATT Nitratifractor AGCACGTGTGTCGCCCCAGCCGTAA Nitratireductor TCAGACATGGGCTCATCCAACTCCG Nitratiruptor ATAGGCCGCAGCCCCATCCCATAGC Nitrincola AACTTCACACCCTTCCTCACCACTG Nitrobacter GTGCTCCATGCTCCGAAGAGAAGGT Nitrosococcus GCATCGCTGCTTGGCCACCCTCTGT Nitrosomonas AGGCATCGGCCACTCCAAAAGCGCA Nitrosospira GCGTTAGCTACGTTACCAAGCCCGT Nitrospina ATGCAGCACCTGCACACGGACTCCG

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Nitrospira AGAGCGCCGCCTTCGCCACCGGCCT Nocardia ACCACAAGGGGGCCTACATCTCTGC Nocardioides CAAGCTGATAGGCCGCGAGCACATC Nocardiopsis CCGGTCCTTATTCCCCACCTACCGT Nonomuraea GTGTGTCCGGGTGATGTCAAACCTT Novosphingobium ATTCCGAAGCAAAGGGCAGATTCCC Oceanicaulis CGATAGGGATGTCAAGCGCAGGTAA Oceanicola TTCCGCAGAGAAGGGTACATTCCCA Oceanimonas TAGCCAACCTCCTTTCCTCACCACT Oceanithermus CCTTGCGGGGTTTTCGTCCCCTGTT Oceanobacillus CAGGCCCATCTGTAAGTGACAGCAA Oceanobacter CTCGCGACTTGGCAACCGTTTGTAC Oceanospirillales GTCAGTGTCAGTCCAGAAGGCCGCC Oceanospirillum CCCCCGCTACCGGGCAGATTCCTAC Ochrobactrum TGAAAGACACATCTCTGTGTCCGCG Octadecabacter TGTCACTGCGTCCCCTAAGGGAACT Oleiphilus TATCCCCCTCTGCTGGGTAGATTCC Oleispira CAGTATCGAGCCAGTCAGTCGCCTT Olleya TCAGCGGAGCAAGCTCCCTGCTACC Olsenella CTCAGCGTCAGTCGTGGCCCAGAAA Opitutus CGAGGTTTGTCTTCCTAGAGTCCCC Orientia CGCAGCTGAAGCAAGCTCCAGCTTT Ornithinimicrobium TTTCCACCCCATACCCATGCAGGTT Oxalobacteraceae CGCGTTAGCTGCGTTACCAAGTCAA Paenibacillaceae TTCGGCACCAAGGGTATCGAAACCC Paludibacter TTCTTCCTTCACGCGACTTGGCTGG Pandoraea CCCCCACTACAAGGTACATTCCGAT Paracoccus GTGTGCAGGTCTCTTACGAGAAAGC Parvibaculum TCCGACTTATCATCGGCAGTCCCTC Parvularcula AACTAGCCCTCATCGTTTACGGCGT Pasteuriaceae Incertae Sedis TCTCTACAGTGGTCAACGGGATGTC Patulibacter GCGGCGTTGCTGCATCACGCTTTCG Pedomicrobium AGCACCTGTGTCCCGACCTATTGCT Pelagibaca CCGCTAAACCCGAAGGTCTCGCTCG Pelobacter ACTGTTTCCAGAAAGTGCGATCGGG Pelomonas ATGAGTCCCGGGTATTAGCCAGAAC Pelotomaculum ACGCGGACCCATCTTAATGCGGATT Peptostreptococcaceae CCGAGGGGGGTAACCCCCGACACCT Peptostreptococcaceae Incerta AAGGGGGTAACCTCCGACAGCTAGT Sedis Persephonella CTGACAGGTGTTTACACCCCGAAGG Persicobacter CTCTCTCATGCGATCGTGCAGTGTT Petrotoga GGTCCCTACCTTTTACTCACCCGTT Phenylobacterium CCCTCAGTTGTTCCGTACCAAAGGG Photobacterium AATCCCACCTGGGCTAATCCTGACG

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Phyllobacteriaceae TTCCGTAGAGCTGGGTAGATTCCCA Phyllobacterium GCACAGCACCTTCGGGTAAAACCAA Phytoplasma AGGTGATCCATCCCCACCTTCCGGT Pigmentiphaga CGCCAGGTATTATCCGGCACCGTTT Pirellula GGCACGAACTTAGCCCACCCTTCCT Planctomyces CGTGCTTCCTCTGAGGCTCTGTCAA Planctomycetaceae AGGATAAGGTTCTTCGCGTAGCCTC Planifilum AAGAGCTATTCGCCCTTCCGCCGTT Planococcaceae ATCCTGCAGTGACAGCCGAAACCGT Planococcus AGCCTCATGAGAGGCCGCAAACTAT Planomicrobium AGCCTTATCTCTAAGGCGGTCAGCG Plantibacter AGCAACTCACCATGCGGTGGAAGCT Plesiocystis AGGGATACCGTCAAGGCCAGCTCTT Polaribacter TCCCTCAGCGTCAGTACATACGTAG Polaromonas GGCTCTTTGCAGAGTCCCCCGCTTT Polynucleobacter TCCCGGACTGTGTTAGAGCCGGTGT Pontibacillus CCGTCAAGGTACCAGCATTTCCTCT Porphyrobacter CATCCTTGCGGAATAGCTGCCCACT Prevotella CCTAGGCCGACCCTCGCGGTCACGG Prevotellaceae GGATAACGCCTGGACCTTCCGTATT Promicromonospora AACCCCATCTCTGGGGCTTTCTCGT Propionibacterium GTCCATCCCCAACCGCCGAAACTTT Propioniferax GTTCTCCCCTACCTTCCTCCAGTCT Propionigenium CTAATGGCACGCAAAGCTCTCCTCT Prosthecochloris CCTGAGCGTCAGTTGTCGACCAGAA Prosthecomicrobium1 CCTTGGTTGTTCCGCACCTTTGGGT Prosthecomicrobium2 GAAGGATACCATCTCTGTTACCGCG Proteus TAACCTTATCACCTTCCTCCCCGCT Pseudidiomarina TGAGATCCGCTCACTATCGCTAGCT Pseudoalteromonas CCGAGGTTCCGAGCTTCTAGTAGAC Pseudobutyrivibrio CTCCCTATGGAAGCCAACACCTAGT Pseudomonadaceae TGCCCTTCCTCCCAACTTAAAGTGC Pseudomonas GCGCCACTAAAAGCTCAAGGCTTCC Pseudonocardia CACGCTCAGAGTTAAGCCCCAAGTT Pseudovibrio ACCACAAGGTAGCTTCCCACGCGTT Pseudoxanthomonas CTACACCAGAAATTCCGCATCCCTC Psychrobacter1 ATTCTAATTCCCGAAGGCACTCCCG Psychrobacter2 TCAAGGGACCCAACGACTAGTAGAC Psychroflexus CCGTCAACAGTTCACACGTGAACCT Psychromonas CCCACGGCATATAGCCACAAACTTC Ralstonia CGGTCCTTATTCTTCCGGTACCGTC Reichenbachiella CGCCTGCCTCAACAATACTCAAGCC Reinekea GTCAGTGTCAGTCCAGAAGGCCGCC Rheinheimera TCGGGCAGCTCCCTATACATTACTC Rhizobiaceae CCGGATACCGTCATTATCTTCTCCG

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Rhizobium CCGAACAGTCAACTGCCCGACGGCT Rhodanobacter CCAACTAGCTAATCGCACATCGGTC Rhodobaca TCCCATTGCTGGGTTAGCGCACCAC Rhodobacter CCTCTCTCGACCTCAAGACTGGGAG Rhodobacteraceae GAACAACGCTAACCCCCTCCGTATT Rhodobium CTGATGAGTATACCCACCAACGGCT Rhodococcus GCGGGCCCATCCTGCACCGATAAAT Rhodoferax TACAGTCACAAATGCAGGTCCCAGG Rhodopseudomonas TCGCAGTTCCACTCACCTCTGCCAT Rhodospira ATCTCTAGAGGCGGTCACGGGATGT Rhodothermus ATGCAGCACCTCACCACCAGCCCTT Rhodovibrio GGCTGCGCCACTGAACAACTAGGTT Rickettsiella TGCTCCCTACACATCACAGCCAGTT Roseivivax AACCCCGTTTCCAGGGCCTATTCCG Roseobacter CACCGCGTCATCCTGTTACGCGATT Roseospira GAATTCCATACCCCTCTCCCGAACT Roseovarius GGCCGATCCTCCTCCGATAAATCTT Rothia GCGAGGGTTGGTCGTATCCGGTATT Rubritalea CCAGTGTTGCTGATCGTCCTCTCAG Rubrobacter ACCCTCGAAGGTCACACGTTTCCGT Ruegeria ACTTGGCATGTGTTAAGCCTGGCCG Ruminococcaceae AAGGGGTCAGACCCCCCACACCTAG Ruminococcus GGTCAGTCCCCCCACACCTAGTAAT Saccharomonospora ACGCCCACGGTTAAGCCGCAGGTTT Saccharophagus CCCCACCTACTAGCTAATCCGACGC Saccharospirillum TCACACCCAACTTAACTTGCCGCCT Salicola AACACTGGCCGGTATTAGCGACCAG Salinibacter TATTCCTGAACTACCGTCGCGCCCT Salinicoccus TTTCTGGTCAGGTACCGTCACCCGA Salinisphaera GACGCCCATCCGAAGATGTCGTTTC Sanguibacter CTACCCATCTCTGAGCAGTTCCGGT Saprospira GGATTATTCGCCGTTTCCAGCGACT Saprospiraceae GTCCGTGTCTCAGTACCCGTGTGGG Serinicoccus CCAGCCACAAAGGGAAACCACATCT Serratia CAAATGCAGTTCCCACGTTAAGCGC Shewanella TCCCACCTAGGTTCATCCAATCGCG Shinella TCTCTGTAATCCGCGACCGACATGT Silicibacter TAACCCGTCCGCCGCTAATCCGAAA Sinococcus CCCTGTTAGAACCGCCCTTGTTCTT Skermanella ATCTCTGGACCCGCGACGCCCATGT Smithella CAATCAACTTTCAGGGTGTGACGGG Sodalis AGATCCCCAGACTTTACTCACCCGT Sphingobacteriaceae CAACTAGCTAATGTCACGCGAGCCC Sphingobacterium TGCGACAGTTAAGCTGCCGTCTTTC Sphingobium ACACCATGTGCCCCGGCAGCTAGTT

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Sphingomonadaceae TGCGTTAGCTGCGCCACCCAAGCTC Sphingomonas1 GAAATCGTCCGGACATGTCAAACGC Sphingomonas2 TCAAGCGATGCAGTCTCAAAGGCAG Sphingopyxis GCGCCACCAAAGCCCTGTGGGCCCT Sphingosinicella GCCTAAAAGGCCCTGACAGCTAGTT Spirochaeta TTGCATGCTTAAAACGCGCCGCCAG Sporolactobacillaceae CGAACGGCACTTGTTCTTCCCTCAT Sporosarcina TAATACGCCGCGGGCCCATCTCGTA Staphylococcus AGCCGTGGCTTTCTGATTAGGTACC Stappia GCCACCAAATAGCATGCTACCTGAC Stella1 CTGTCGAAGCCGCCTACACGCCCTT Stella2 CATAAAGCTTTCCCCCTAGGGGCGT Stenotrophomonas CTAGTCGCCCAGTATCCACTGCAGT Sterolibacterium ACCCGTTCGCCGCTCGCCGCCAGTC Streptococcaceae ATAAATCCGGACAACGCTCGGGACC Streptococcus CCGTCCCTTTCTGGTAAGATACCGT Streptomyces CCCGGCGGTCTCCCGTGAGTCCCCA Streptomycetaceae AATTCCGATCTCCCCTACCGAACTC Subdivision 3_genera CCGTCCCTTCCTCTTTCGCTACTAT Subdivision 5_genera ATCCCAATCACCACCCACACCTTCG Sulfitobacter CGATCTCTCGAGTTAGCACAGGATG Sulfobacillus ATTGCCGCGCCTTTCGGCCCGATAT Sulfuricurvum CAGTATCATCCCAGCAGATCGCCTT Sulfurihydrogenibium CTTTCATACCCGACACACCGCATGT Sulfurimonas ATCATCCTCTCAAACCCGCTACCCG Sulfurivirga TAGCCCCTCCCATAAGGGCCATGAT Sulfurospirillum TAGCAACTAATCACGAGGGTTGCGC Syntrophaceae CCCCCAACACCTAGTGAACATCGTT Syntrophobacter ACTCGCCCAACCGAAATCAGGCTTT Syntrophomonas ATGCGCTCTCGTTAGCTTATGCGGT Syntrophus TTACTCAGGTCCCGAAGGACCCTTT Tatumella GGGTAACGTCAATCAGCCGTGCTAT Tenacibaculum AGACCCCCTACCTATCGTTGCCATG Tepidibacter GGGGTAACCCCCGACACCTAGCACT Terasakiella ACTCGGAATTCCACCACCCCCTTAC Teredinibacter TCAGAATTCCCGAAGGCACCCTCTC Tetragenococcus CTGATGGATAGGTTCCCCACGTGTT Tetrathiobacter TAAGCCCCGAAGCCAACAGCTAGTT Thalassobius1 GGGCCAATCCCTCACCGAAATTCTT Thalassobius2 GTGGGTTGCGCTCGTCGCCGGACTT Thalassolituus GACCAGCTTTGTGGGATTAGCTCCA Thalassospira CGCTTTCGCACCTGAGCGTCAGATC Thauera TAGCTGCGTCACTCAGTGCATTGCT Thermaerobacter AGCCGGGACCTTTCACATCCGACTT Thermincola CAGTCTCAAAGGCAGGCTGGCAGTT

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Thermoanaerobacter CAGTTTCATGTGCATCCCCCGGGTT Thermodesulfatator TTAACCCCCCACACCTAGCACCCAT Thermoleophilum CCAATCCGAACCTAGACCGGCTTTT Thermomonas1 TCCGGTACCGTCAGAACTCCAGGGT Thermomonas2 TTCGCCACAGATGTCCTCCCGATCT Thermosediminibacter CCCAGGCCATAAAGGGCATGATGAT Thermosipho TATTCGTCTCCCACCACAGCGGTTT Thermotoga TATTCTTCCCCCGCCACAGCGGTTT Thermotogaceae CACACCTAGTGGGCATCGTTTACGG Thermovibrio GCACACGGCCTTTCGGCCGCTTTTC Thermus TCTAGCCTGAGCGTATCCCACGCTC Thioalkalicoccus GGCTCATCCCGTAGCAAGAGCATGT Thioalkalivibrio AGGAGCAAGCTCCCGCGTTACCGTT Thiobacillus GGATTTCACTCCCCCAACAACCAGT Thiocapsa AAGCCCTTAAATGGACCCCACGGCT Thioclava1 CCCTTTTGCAAGGTTGGCGCACCGT Thioclava2 CTTCCTCCGACTTTACATCGGCAGT Thiocystis CCACCAAGCCCTTAAATGGACCCGA Thioflavicoccus CATCCTCTAGCAGGAGCTTGCAAGC Thiolamprovum ACTCAGGCCAAAGCCCTCTCGCGTT Thiomicrospira GGGCACCTTTACTCCGTAGAGCATA Thiomonas CTCCATCGACGCAAGGCCTTGCGGT Thiorhodococcus ATCGGACGTGGGCTCATCTAGCAAC Thiorhodovibrio GCACCAAGGCATCTCTGCCTTGTTC Thiothrix GCGGACTCATCCAATAGTGGCCGAA Tistrella TCATCCATCTACGGCCGAAGCCTTT Tolumonas TAGCTTACTCCCCTTCCTCCCCACT Tsukamurella AACCGAATCTCTCCGGCGATCCTCT unclassified_Alcaligenaceae ATTCTGCAGGTACCGTCATCCACAC unclassified_Alteromonadaceae TTTATGGGTTCCGCTCCACCTCACG unclassified_Aquificaceae GGATACCTTAGTGGCACCCGGCATT unclassified_Bacillaceae CGCGTTAACTTACAGCACTAAGGGG unclassified_Beijerinckiaceae TCCCCGCCAAGGCGTATACGGTATT unclassified_Bradyrhizobiaceae GCTCCGAAGAGAAGGCTCCATCTCT unclassified_Chlamydiales CACGGCGTTATTGCTGACACGCCAT unclassified_Clostridiaceae CCGGTTAAGGTGCTGTCAAAGGGAT unclassified_Clostridiaceae 1 CTGCACCATGCGATGCTACAAGCTT unclassified_Clostridiaceae 2 CGTCCGCCGCTACTCTTTATGTAGT unclassified_Coriobacteriaceae AACTCCGACTTCACGGAGGCGAGTT unclassified_Desulfuromonaceae TGGGCCGCGGACTCATCCAACAACG unclassified_Flexibacteraceae CTTCCTCTCTGCTTACGCAGGCAGT unclassified_Fusobacteriaceae CTTAACAAGCCGCCTAGACTCGCTT unclassified_Halanaerobiaceae GCTGGCACGTAGTTGGCCAGGGCTT unclassified_Hyphomicrobiaceae CGGAGCTATTCCGTACCACAGGGAA unclassified_Incertae sedis 2 GCTTGCAAAGCAGAGGCCCCCTTTT

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unclassified_Incertae Sedis XI CCTCTCCTTCACTCTAGCCTTACAG unclassified_Incertae Sedis XV GATGCCCTACGACACCCACACCTAG unclassified_Micrococcaceae GTCCCTCCTTGACCAGAAACCCTTT unclassified_Micromonosporacea CTGCAGGTACCGTCACAAACGCTTC unclassified_Oxalobacteraceae CCCGGGCATTTCACATCTGTCTTAC unclassified_Parachlamydiaceae CCCTCCTACACCTAGTATGCATCGT unclassified_Peptococcaceae 1 CGGCTAGCTCCTTTTCAGGTTACCT unclassified_Peptococcaceae 2 CAGTTTCTATCGCAAGCCAGCGGTT unclassified_Planococcaceae AGCATATCTCTATGCCGGTCAGTGG unclassified_Polyangiaceae TCCACCGTAGTGGGCGTGGTCTTAC unclassified_Prevotellaceae GCTTTCGCTTGGCGGCTTACATTGT unclassified_Rhizobiaceae1 GCCCCCGTCAATTCCTCTCCGTCTT unclassified_Rhizobiaceae2 TCTCCACTGCCCAAACCCCGAATGT unclassified_Rhodocyclaceae TCCACACAGAGTATTAGCCTGTGCG unclassified_Rhodospirillales GGCAACCCCCCGACATCTAGCAGAC unclassified_Rubrobacteraceae CAGTACCGTCCCAGAGAGCTGCCTT unclassified_Sphingobacteriace GCACTGCGATAGTTAAGCTACCGTC unclassified_Syntrophobacteral TTTTACCTCTGCCGCCGAAACGGCT unclassified_Thermoactinomyce CAGTTTCAGGTGCCGCTCTGCGGTT unclassified_Thermotogaceae CTACTGTCCTCAAGCCCAGCAGTTT Ureibacillus CCTTCCTCCGTATTTGTACGGCAGT Variovorax GGCCGCTCCATTCGCGCAAGGTCTT Verrucomicrobiaceae AGCGACAGCCTTGCGGCCACCTTTA Verrucosispora ATGCGCCCGTGAGTGAATATTCGGT Vibrio CCTCTACAGCACTCTAGTTCACCAG Vibrionaceae AAGCCACGGCTCAAGGCCACAACCT Virgibacillus ACCGATTGCAGAGGATGTCAAGACC Vogesella TGCGTTATCCCCCACTACTCGGCAC Williamsia TATCGCCCGCACGCTTGGAGTTAAG Winogradskyella CGTGCGCCACTCGTCAGCAAAAGAG Woodsholea CAATACCAGTCCAGTGTGTCGCCTT Xanthomonadaceae CACCCAACATCCAGTTCGCATCGTT Xanthomonadaceae TTGCAGCCCTCTGTCCCTACCATTG Xanthomonas ATTCAATCGCGCGAAGCCCGAAGGT Xiphinematobacteriaceae CTTCCTCCCCGTTTCACAGGGCAGT Xylella ATCCAATCGCACAAGGCCCTAAGGT Zobellella ACCCCCCTCTCCAAGACTCTAGCCT Zobellia TTAACCGTAAACCCCATGCGGGGAT Zoogloea GCTGCGTTACTCAATGAGTCTCCTC Zooshikella CAGTATTGACCCAGCGAGTCGCCTT Zymobacter TTAGGTCTCAGGCTTTCTTCCCCAC

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