MOLECULAR ANALYSIS OF MICROBIAL COMMUNITIES FROM OIL INDUSTRY ENVIRONMENTS
Magdalena K. Sztyler
A thesis submitted in partial fulfilment of the requirements for the award of the degree of Doctor of Philosophy of the University of Portsmouth
Institute of Biomedical and Biomolecular Sciences School of Pharmacy and Biomedical Sciences
May 2014
“Więcej się można nauczyć podróżując...”
Olga A. Jackowska
Rodzicom
AUTHORS’ DECLARATION
Whilst registered as a candidate for the above degree, I have not been registered for any other research award. The results and conclusions embodied in this thesis are the work of the named candidate and have not been submitted for any other academic award.
Magdalena K. Sztyler
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ABSTRACT
The effects of microbiologically influenced corrosion (MIC) can be very expensive to correct, dangerous to workers and its mechanisms are poorly understood. Understanding these processes is important so that they can be monitored and mitigated (Koch et al., 2001). It is now accepted that for the assessment of biocorrosion risks, the most powerful approach is to detect functional genes encoding the enzymes that play an important part in material deterioration (Schadt et al., 2004).
The main aim of this study was to identify the microbial community present in corroded and non-corroded systems, and to detect genes that might be implicated in corrosion processes, particularly iron corrosion, so that a biochip could be designed for risk assessment of oil environments. In this thesis the microbial populations and their actives were assessed using sequencing and hybridisation techniques for three oil field sites, generating information that can help identify MIC risk. The final section of the thesis describes the development and design of functional gene probes, identified from hybridisation studies that might be included in a biochip for risk assessment in oil field environments.
Microbial groups known to be involved in MIC, such as sulphate-reducing procaryota, iron-reducing bacteria, nitrate-reducing bacteria, hydrocarbon- degrading bacteria were detected, according to their 16S rRNA gene sequence, in the water injection system and production pipelines. In addition to these expected groups, sequences for Firmicutes, acetogens and methanogens were detected. Firmicutes, primarily Clostridium species, and Synergistetes sequences pre- dominated the corroded systems. Functional genes involved in biocorrosion, many of which belonged to the groups named above, were detected using the GeoChip, and a list of marker genes that can be utilised for biocorrosion monitoring has been proposed. Oligonucleotide probes for biochip development were either designed or selected from published sources. A quick and inexpensive method for probe evaluation during microarray development is described. A total of 16
II probes, representing 15 genes were tested; all the probes exhibited similar hybridisation behaviour under standard conditions.
The results presented in this thesis were part of an extensive EU project, BIOCOR, involving academic and industrial partners, on fundamental and applied aspects of microbial corrosion in oil field environments, which was funded to generate the knowledge needed to develop monitoring techniques for corrosion. The results presented in this thesis are the final report to the European Commission.
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ACKNOWLEDGEMENTS
There are many people that I would like to thank and without whom this thesis would not be possible. First, I would like to thank my supervisors for giving me the great opportunity to be involved in the BIOCOR ITN project, which allowed me to develop myself, to deepen my scientific knowledge and to meet so many interesting people. Above all, I am sincerely grateful to Dr Julian Mitchell for accepting the task of being my supervisor, for guidance, for friendly and useful discussions during meetings. Without him this thesis would not have been completed. I am most grateful to Dr Sarah Thresh who was always interested, always available and who helped me so much. Thank you for your support, useful suggestions and patience. I would like to acknowledge the University of Oklahoma especially Dr Kathleen E. Duncan, Dr Bradley Stevenson, Dr Athenia L. Oldham, Dr Joseph M. Suflita and Dr Joy D. Van Nostrand for the warm welcome during my stay at the university and for their helpfulness. I am very indebted to my parents Bogusława and Wojciech, sister Marta and my friend Marcin for their encouragement, forbearance and support throughout my education. I really appreciate it. Finally, I wish to thank all of BIOCOR partners, coordinators and fellows for advice, friendship and efforts during the project. This research was supported by funding from the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement n°238579 as a part of extensive EU project under Marie Curie Thematic Network Programme “BIOCOR”. Project website: www.biocor.eu/ip5 (RSP1&2).
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LIST OF ABBREVIATIONS
AMP Adenosine monophosphate AOP Ammonia oxidising prokaryote APB Acid producing bacteria APS Ammonium persulphate APS Adenosine phosphosulfate ATCC American type culture collection ATP Adenosine triphosphate bp Base pairs CCD Charge coupled device cDNA Complementary DNA CGA Community genome array CN Copy number CSLM Confocal laser scanning microscope CT Cycle threshold Cy3 Bis(dithiarsolanyl)-bis(sulfobutyl) Cyanine 3 Cy5 Bis(dithiarsolanyl)-bis(sulfobutyl) Cyanine 5 DAPI 4’,6-diamidino-2-phenylindole dATP Deoxyadenosine triphosphate dCTP Deoxycytidine triphosphate ddNTP Dideoxynucleotide triphosphate DGGE Denaturing gradient gel electrophoresis dGTP Deoxyguanosine triphosphate DNA Deoxyribonucleic acid dNDP Deoxynucleotide diphosphate dNMP Deoxynucleotide monophosphate dNTP Deoxynucleotide triphosphate DSMZ Deutsche Sammlung von Mikroorganismen und Zellkulturen dTTP Deoxythymidine triphosphate EDTA Ethyleno-diamino tetra-acetic acid EOR Enhanced oil recovery
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EPS Extracellular polymeric substance ESS Environmental shotgun sequencing FGA Functional gene array FISH Fluorescence in situ hybridization FRET Fluorescence resonance energy transfer GAB General anaerobe bacteria GDP Gross domestic product HDP Hydrocarbon-degrading prokaryotes Hex Hexachloro-fluorescein HFT High frequency trading IOB Iron oxidizing bacteria IPTG Isopropyl-β-D-thiogalactopyranoside IRB Iron-reducing bacteria kb Kilobase pairs LB medium Luria broth medium ln Natural logarithm LSM Laser scanning microscope MEOR Microbial enhanced oil recovery MIC Microbial influenced corrosion MIS Microbiologically induced souring MOB Metal-oxidising bacteria MRB Metal-reducing bacteria mRNA Messenger RNA MW Molecular weight
NA Avogadro’s number NOB Nitrite-oxidising bacteria NRB Nitrate-reducing bacteria NUB Nitrate-utilizing bacteria OTU Operational taxonomic units PAGE Polyacrylamide gel electrophoresis PCR Polymerase chain reaction pH Hydronium ion concentration; pH=-log[H+] Pi Inorganic phosphate
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PNA Peptide nucleic acid POA Phylogenetic oligonucleotide array POP Primary oil production PPi Pyrophosphate QPCR Quantitative PCR QS Quorum sensing RNA Ribonucleic acid rpm Revolutions per minute rRNA Ribosomal RNA SD Standard deviation SDS Sodium dodecyl-sulphate SE Standard error SiNW Silicon nanowires SNP Single nucleotide polymorphisms SOB Sulfur-oxidizing bacteria sp./spp. Species/Many species SQRT Square root SRB Sulphate-reducing bacteria SRP Sulphate-reducing prokaryote SSC Saline-sodium citrate buffer STC Sample tracking control TEMED N,N,N’,N’-Tetramethylethylenediamine Tm Melting temperature, the temperature at which a DNA double helix dissociates into single strands Tris-HCl Tris (hydroxylmethyl) aminomethane UV Ultra violet light VM Vitamin medium VMI Vitamin medium with iron X-Gal 5-bromo-4-chloro-3-indolyl-β-D- galactopyranoside %(v/v) Percentage volume/volume %(w/v) Percentage mass/volume
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LIST OF FIGURES
Figure 1.1.1: Simplified scheme of oil and gas extraction ...... 3 Figure 1.1.2: Pigging devices ...... 7 Figure 1.2.1: A model representing stages of bacterial biofilm development ...... 11 Figure 1.3.1: Basic corrosion cell under anaerobic conditions ...... 15 Figure 1.3.2: Schematic description of microbially influenced corrosion (MIC) .... 20 Figure 1.4.1: Schematic presentation of the specific pathways and the key functional genes used in this study ...... 28 Figure 1.4.2: Principle of GC clamp operation in DGGE ...... 31 Figure 1.4.3: Principle of pyrosequencing technology ...... 34 Figure 2.1.2: A photograph of examples received field samples provided by the industrial partner ...... 47 Figure 3.1.1: CLSM visualisation of microorganisms in the ID1 field sample (Installation D – production pipeline) ...... 75 Figure 3.1.2: Copy number of 16S rRNA genes per ml of sample for Bacteria based on QPCR analysis ...... 77 Figure 3.1.3: Copy number of 16S rRNA genes per ml of sample for Archaea based on QPCR analysis ...... 78 Figure 3.1.4: DGGE profile demonstrating diversity of archaeal populations in pigging samples from production pipelines (Installation C and D) based on PCR products of archaeal 16S rRNA ...... 80 Figure 3.1.5: DGGE profile demonstrating diversity of bacterial populations in pigging samples from production pipeline and water injection system (Installation A, B and D) based on PCR products of 550 bp of bacterial 16S rRNA...... 82 Figure 3.1.6: DGGE dendragram ...... 83 Figure 3.1.7: A maximum likelihood phylogenetic phylogram (In(L) = 25707.6) showing the relationship between 16S rRNA gene sequences of known taxa and thosed excised from DGGE gels ...... 90 Figure 3.1.8: Pyrosequencing analysis of 16S rRNA gene libraries showing relative abundance (%) of represented phyla (class level for Proteobacteria) in pigging samples collected from Installation A (water injection system IA2 and IA5) and Installation C and D which were production pipelines (IC106, IC206, IC2406 and ID1) ...... 92 Figure 3.1.9: Chart showing the relative abundance (%) of 16S rRNA gene sequences of Deltaproteobacteria class in IA2 and IA5 pigging samples collected from Installation A (water injection system) ...... 96 Figure 3.1.10: Chart showing the relative abundance (%) of 16S rRNA gene sequences of Synergistetes phyla in IA2 and IA5 pigging samples collected from Installation A (water injection system) ...... 98
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Figure 3.1.11: Chart showing the relative abundance (%) of 16S rRNA gene sequences of Firmicutes phyla in IA2 and IA5 pigging samples collected from Installation A (water injection system) ...... 100 Figure 3.1.12: Chart showing the relative abundance (%) of 16S rRNA gene sequences of Firmicutes phyla in IC106, IC1206 and IC2406 pigging samples collected from Installation C (production pipelines) ...... 102 Figure 3.1.13: Chart showing the relative abundance (%) of 16S rRNA gene sequences of Firmicutes phyla in IC106, IC1206 and IC2406 pigging samples collected from Installation C (production pipelines) ...... 104 Figure 3.1.14: Chart showing the relative abundance (%) of 16S rRNA gene sequences of Thermotogae phyla in IC106, IC1206 and IC2406 pigging samples collected from Installation C (production pipelines) ...... 106 Figure 3.1.15: Chart showing the relative abundance (%) of 16S rRNA gene sequences of Gammaproteobacteria phyla in ID1 and pigging samples collected from Installation D (production pipelines) ...... 109 Figure 3.1.16: Chart showing the relative abundance (%) of 16S rRNA gene sequences of Gammaproteobacteria phyla in ID1 and pigging samples collected from Installation D (production pipelines) ...... 109 Figure 3.1.17: Chart showing the relative abundance (%) of 16S rRNA gene sequences of Epsilonproteobacteria phyla in ID1 and pigging samples collected from Installation D (production pipelines) ...... 110 Figure 3.2.1: Abundance of genes detected by GeoChip hybridization ...... 126 Figure 3.2.2: Abundance of genes involved in corrosion based on measurement from the GeoChip hybridization ...... 128 Figure 3.2.3: Abundance of microbial strains in the Installation A based on signal intensity of probes for c-cytochrome detection ...... 137 Figure 3.2.4: Abundance of microbial strains in the Installation C based on signal intensity of probes for c-cytochrome detection ...... 138 Figure 3.2.5: Relative abundances of signals from functional genes representing methanogenesis (mcrA) detected in DNA from Installations A and C using GeoChip...... 139 Figure 3.3.1: Alignment showing the position of the aprAB gene probe to selected sequences ...... 146 Figure 3.3.2: Alignment showing the position of the mcrA gene probe to selected sequences ...... 146 Figure 3.3.3: Alignment showing the position of the napA gene probe to selected sequences ...... 147 Figure 3.3.4: Alignment showing the position of the hydA gene probe to selected sequences ...... 147 Figure 3.3.5: Alignment showing the position of the narG gene probe to selected sequences ...... 149 Figure 3.3.6: Alignment showing the position of the nirS gene probe to selected sequences ...... 150
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Figure 3.3.7: Hybridisation of probes: napA2, aprAB3, DSR4R, Geo1A, assAR, FTHFS1, mcrA1, hydA1, nirS11, nirKF and narG3 to selected target DNA acting as positive (+) and negative controls (-) ...... 155 Figure 3.3.8: Hybridisation of probes: EUB338, ARC915, SHEW227, apsA1P1R10 and Arch5F to selected target DNA acting as positive (+) and negative (-) controls...... 156 Figure 3.3.9: Hybridisation of hydA1 and nirS11 probes to target DNA (cloned probes and genomic DNA) ...... 157 Figure A.1.1: A flow chart main process of the Installation D (and similar Installation C). Pigging samples were collected from production pipelines ...... 165 Figure A.1.2: A flow chart main process of the Installation A. Pigging samples were collected from water injection pipeline A ...... 166
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LIST OF TABLES
Table 1.4.1: Listing of organism implicated in MIC of carbon steel and their selected functional genes ...... 25 Table 2.1.1: Description of samples and their ID from Installation A, C and D provided by industrial partner ...... 46 Table 2.1.2: Sequence of primers used in PCR ...... 53 Table 2.1.3: Type strain used in this study ...... 54 Table 2.1.4: Primers used for Q-PCR amplification of archaeal and bacterial 16S rRNA gene ...... 55 Table 2.1.5: Silver staining protocol ...... 58 Table 2.2.1: Primers used for DNA amplification for the pyrosequencing process for bacteria. Life Sciences 454 Titanium “A” and “B” primers are underlined, N’s designate location of unique barcode and spacer nucleotides are in bold (Stevenson et al., 2011; Hamady et al., 2008) ...... 64 Table 3.1.1: Sequence similarity and taxonomic relationships of bacterial 16S rRNA, bends from DGGE gel. Class affiliation as determined by NCBI ...... 85 Table 3.1.2: Pyrosequencing summary ...... 113 Table 3.1.3: List of microorganisms identified by sequencing of cloned 16S rRNA PCR products ...... 115 Table 3.2.1: PCR detection of functional genes in different pigging samples ...... 118 Table 3.2.2: List of microorganisms identified by sequencing of cloned aprA, narG and mcrA PCR products ...... 124 Table 3.2.3: Relative abundances of signals from functional genes representing sulfur reduction and oxidation pathways detected in DNA from Installations A and C using GeoChip. Oligonucleotide probes correspond to gene sequences from organisms listed in the left column ...... 130 Table 3.3.1: Listing of organism implicated in MIC of carbon steel, their selected functional genes representing metabolic pathways which are likely to impact corrosion reactions and reported mechanism of corrosion ...... 143 Table 3.3.2: Selected marker genes and corresponding probe sequences representing key groups of microorganisms and metabolic pathways implicated in MIC ...... 144 Table 3.3.3: List of designed DNA probes...... 145 Table 3.3.4: Taxonomic report ...... 151
Table 4.1.1: Identified risks in tested installations...... 163
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Table A.3.1: VM and VMI medium composition...... 167
Table A.3.2: Modified Wolfe’s mineral elixir (per litre) ...... 168
Table A.3.3: Vitamin stock solution...... 168
Table A.3.4: Composition of glucose yeast extract medium...... 169
Table A.3.5: Composition of LB medium...... 169
Table A.3.6: Composition of nutrient broth ...... 169
Table A.5.1: Accesion numbers for taxas used for phylogenetic analysis ...... 172
Table A.6.1: Accesion number of aprAB gene sequences used in this study ...... 175
Table A.6.2: Accesion number of mcrA gene sequences used in this study ...... 176
Table A.6.3: Accesion number of napA gene sequences used in this study...... 177
Table A.6.4: Accesion number of narG gene sequences used in this study ...... 178
Table A.6.5: Accesion number of hydA gene sequences used in this study ...... 182 Table A.6.6: Accesion number of nirS gene sequences used in this study ...... 183
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Publications arising from this study
Magdalena K. Sztyler, Julian Oligonucleotide probes for detection of Mitchell, Sarah Thresh, Iwona B. microbial genes implicated in biocorrosion Beech and Joy D. Van Nostrand of carbon steel in oil field environments. Bioelectrochemistry, rejected due to late submission, it will be re-submitted.
Marko Stipaničev, Florin Turcu, Loïc Corrosion of carbon steel by bacteria from Esnault, Omar Rosas, Régine North Sea offshore seawater injection Basseguy, Magdalena K. Sztyler and systems: Laboratory investigations. Iwona B. Beech Bioelectrochemistry, published.
Claudia Cote, Omar Rosas, Corrosion of low carbon steel by Magdalena K. Sztyler, Jemimah microorganisms from the ‘pigging’ Doma, Iwona B. Beech, Régine operations debris in water injection Basseguy pipelines. Bioelectrochemistry, published.
Maria L. Carvalho, Jemimah Doma, The study of marine corrosion of copper Magdalena K. Sztyler, Iwona B. alloys in chlorinated condenser cooling Beech, Pierangela Cristiani circuits: The role of microbiological components. Bioelectrochemistry, published.
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Conferences and BIOCOR network meetings attended
1st Network Meeting Scientific presentation; Universidade Nova de Lisboa, 26-29/04/2010 Portugal
ESOF 2010 Poster presentation: ‘BIOCOR ITN- fostering creative 30/06-2/07/10 collaborations with other scientific fields: Biochip for biocorrosion monitoring’; Euroscience Open Forum, Turyn, Italy
Biofilm 4 Attendance; International conference, University of 01-03/09/10 Southampton, Winchester, UK
2nd Network Meeting Scientific presentation; Det Norske Veritas (DNV), Statoil 08-10/12/2010 and Mi SWACO in Bergen, Norway
3rd Network Meeting Scientific presentation; Université Catholique de Louva in 04-06/05/2011 Louvain-la-Neuve, Belgium
IBBS day Poster presentation: ‘Characterisation of microbial 16/05/2011 population in a production pipeline and water injection system at Norwegian oil field’; Institute of Biomedical and Biomelecular Science. University of Portsmouth, UK
Summer School Attendance; The BIOCOR ITN Summer School 2011, 25-30/ July/ 2011 University of Portsmouth, UK
Eurocorr 2011, Oral presentation: ‘The use of functional genes to 4th Network Meeting characterise bacterial communities implicated in 05-08/09/11 biocorrosion of carbon steel in an oil field environment’; The annual congress of the European federation of corrosion. Stockholm, Sweden
5th Network Meeting Scientific presentation; Ricerca sul Sistema Energetico, 27-29/03/2012 Venice, Italy
Eurocorr 2012, Oral presentation: ‘Oligonucleotide probes for detection of 6th Network Meeting microbial genes implicated in biocorrosion in oil field 10-14/09/12 environments’; The annual congress of the European federation of corrosion. Istanbul, Turkey
RMF 2012 Poster presentation: ‘Detection and characterization of 27-28/11/12 functional and structural microbial genes implicated in biocorrosion in oil field environment’; 18th Reservoir Microbiology Forum 2012, Energy Institute, London, UK
7th Network Meeting Scientific presentation; University of Duisburg-Essen, 14-15/02/2013 Germany
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Eurocorr 2013 Presentation: ‘Pyrosequncing analysis of microbial Final Network Meeting diversity in the North Sea oil field installations’; The 01-05/09/2013 annual congress of the European federation of corrosion. Estoril, Portugal
Eurocorr 2013 A co-author of presentation: Final Network Meeting ‘Copper alloys in chlorinated condenser cooling circuits: 01-05/09/2013 Passivation corrosion and microbiology’; ‘Applied data mining on biocorrosion assessment of water injection pipelines’; ‘Analysis of microbial consortia using denaturing gradient gel electrophoresis and sewuencing from oil production facilities in the North Sea’; The annual congress of the European federation of corrosion. Estoril, Portugal
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Trainings, visits and workshops attended
06-08/09/2010 DNA microarrays practical course given by “MBS- Molecular Biology Service”, Poland
20-23/09/2010 Molecular cloning-construction of bacterial expression systems practical course given by “DNA-Gdańsk”, Poland
06-08/10/2010 DGGE training course given by INGENY, Netherlands
16/02-20/04/2011 Research training given by the University of Oklahoma, Norman USA. Department of Botany and Microbiology, Institute for Energy and the Environment. Supervisor: Prof. Kathleen E. Duncan
16/01-10/02/2012 Secondment at DNV, Bergen Norway, Electrochemical basic training
02-23/08/2012 Secondment at UDE Biofilm Centre (University of Duisburg-Essen, Germany), DGGE for archaeal 16S rRNA, CLSM training. Supervisor: Prof. Wolfgang Sand
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Table of Contents
AUTHORS’ DECLARATION ...... I ABSTRACT ...... II ACKNOWLEDGEMENTS ...... IV LIST OF ABBREVIATIONS...... V LIST OF FIGURES ...... VIII LIST OF TABLES ...... XI Publications arising from this study ...... XIII Conferences and BIOCOR network meetings attended ...... XIV Trainings, visits and workshops attended ...... XVI CHAPTER 1: INTRODUCTION ...... 1 Section 1 Oil Field Environment ...... 2 1.1.1 Background ...... 2 1.1.2 Biofouling ...... 4 1.1.3 Microbiologically-induced souring (MIS) ...... 4 1.1.4 Controlling the microbial growth in oil field ...... 5 1.1.4.1 Chemical treatments ...... 5 1.1.4.2 Nitrate Treatment ...... 6 1.1.4.3 Mechanical and chemical pipeline cleaning...... 6 Section 2 Biofilms ...... 8 1.2.1 Background ...... 8 1.2.2 The biofilm concept ...... 9 1.2.3 Biofilms in the oil industry ...... 12 Section 3 Microbiologically-influenced corrosion (MIC) ...... 14 1.3.1 Metal corrosion ...... 14 1.3.2 Microbiologically-influenced corrosion (MIC) ...... 16 1.3.2.1 Mechanisms of MIC ...... 16 1.3.2.2 Groups of microorganisms implicated in corrosion in oil field environments ...... 17 Section 4 Molecular techniques for the detection and identification of oil field microorganisms ...... 21 1.4.1 Background ...... 21
1.4.2 Polymerase chain reaction (PCR) ...... 23 1.4.2.1 Quantitative PCR (QPCR) ...... 28 1.4.3 Denaturing Gradient Gel Electrophoresis (DGGE) ...... 29 1.4.4 Sequencing technology ...... 32 1.4.5 Molecular hybridisation techniques ...... 35 1.4.5.1 DNA microarray ...... 35 1.4.5.1a Principle of DNA microarray ...... 36 1.4.5.1b Functional gene arrays (FGA) ...... 39 1.4.5.1c Community genome array (CGA) ...... 39 1.4.5.1d Phylogenetic oligonucleotide array (POA) ...... 40 AIMS OF THE RESEARCH ...... 41 CHAPTER 2: MATERIALS AND METHODS ...... 42 Section 1 Detection and characterisation of microorganisms and their functional genes in the oil field samples ...... 43 2.1.1 Description of sampling sites ...... 43 2.1.2 DNA extraction from field samples ...... 47 2.1.2.1 PowerBiofilm® DNA Isolation Kit: field samples...... 47 2.1.2.2 PowerSoil® DNA Isolation Kit: field samples ...... 48 2.1.3 Polymerase chain reaction (PCR) ...... 49 2.1.3.1 PCR for bacterial and archaeal 16S rRNA genes ...... 49 2.1.3.2 PCR detection of SRP (dsrAB, aprA, Archaeoglobus-specific 16S r RNA) ...... 50 2.1.3.3 PCR detection of NRB (nirK) ...... 51 2.1.3.4 PCR detection of NRB (narG, napA) ...... 51 2.1.3.5 PCR detection of methanogenes (mcrA) ...... 52 2.1.3.6 PCR detection of N/SRB (nrfA) ...... 52 2.1.3.7 PCR detection of firmicutes (hydA) ...... 52 2.1.3.8 Positive controls for PCR reactions...... 54 2.1.4 Qualitative analysis of PCR products using agarose gel electrophoresis . 54 2.1.5 Quantification of archaeal and bacterial 16S rRNA gene by Q-PCR ...... 55 2.1.6 Denaturing Gradient Gel Electrophoresis (DGGE) ...... 57 2.1.6.1 Bio-Rad system ...... 57 2.1.6.2 Ingeny system...... 58
2.1.7 Extraction of DNA from cut bends from DGGE gel ...... 58 2.1.8 Phylogenetic analysis ...... 58 2.1.9 Cloning: construction of genomic libraries and sequencing ...... 59 2.1.9.1 PCR products clean-up ...... 60 2.1.9.2 Ligation of plasmid DNA and competent cells transformation ...... 60 2.1.9.3 Purification of plasmid DNA from bacterial cells ...... 61 2.1.9.4 Sequencing of transformed plasmid ...... 62 2.1.10 Microorganisms visualisation by confocal laser scanning microscope (CLSM) ...... 62 Section 2 Detection and characterisation of microorganisms and their functional genes in the oil field samples by advance molecular techniques ...... 63 2.2.1 Pyrosequencing of archaeal and bacterial 16S rRNA gene libraries ...... 63 2.2.1.1 Sample preparation for 454 Pyrosequencing of 16S rRNA genes...... 63 2.2.1.2 Tagging PCR ...... 63 2.2.1.3 Pyrosequencing data analysing ...... 64 2.2.2 GeoChip 4.0 microarray ...... 65 2.2.2.1 DNA labelling ...... 65 2.2.2.2 GeoChip hybridisation ...... 66 2.2.2.3 Microarray scanning and data processing ...... 66 Section 3 Probe designing and testing ...... 67 2.3.1 Probe designing ...... 67 2.3.2 Bacteria and Achaea type strains culture conditions ...... 68 2.3.3 Purification of total DNA from bacterial cultured cells ...... 69 2.3.4 Construction of model DNA for hybridisation by PCR amplifications...... 70 2.3.5 Construction of model DNA for evaluation of hybridisation efficiency .... 70 2.3.6 Hybridisation ...... 71 CHAPTER 3: RESULTS AND DISCUSSION ...... 73 Section 1 Analysis of sampling site using 16S rRNA gene sequences...... 74 3.1.1 Microorganisms visualisation by light microscope and confocal laser scanning microscope (CLSM) in provided field samples...... 74 3.1.2 Extraction and purification of DNA for molecular analysis...... 75 3.1.3 Enumeration of bacteria and archaea using quantitative PCR ...... 76 3.1.4 DGGE analysis of microbial communities in the field samples...... 79 3.1.5 Pyrosequencing analysis of eubacterial diversity in the field samples .... 91
3.1.5.1 16S rRNA diversity of Installation A – the water injection system .... 93 3.1.5.2 16S rRNA diversity of Installation C – production pipeline ...... 101 3.1.5.3 16S rRNA diversity of Installation D – production pipeline ...... 107 3.1.5.4 Comparison of 16S rRNA diversity between the installation sites .. 111 3.1.6 Archaeal community profiling ...... 113 Section 2 Functional analysis of sampling side using marker genes...... 117 3.2.1 Examination of bacteria diversity using PCR amplification of functional genes ...... 117 3.2.2 Examination of microorganism diversity by sequencing of PCR products ...... 121 3.2.3 GeoChip characterization of microbial functional gene diversity in the field samples ...... 125 Section 3 Probe designing and testing ...... 142 3.3.1 Oligonucleotide probe design ...... 142 3.3.2 Optimisation of hybridisation conditions and model DNA construction ...... 152 CHAPTER 4: FINAL CONCLUSIONS...... 158 APPENDICES ...... 164 Appendix 1: Flow charts of the sampling site and organisation of the installations ...... 165 Appendix 2: Growth media ...... 167 Appendix 3: Sequences of constructs used for hybridisation...... 170 Appendix 4: List of buffers used for hybrydisation...... 170 Appendix 5: Phylogenetic analysis...... 170 Appendix 6: Taxonomic report ...... 172 REFERENCES ...... 186
CHAPTER 1: INTRODUCTION
Chapter 1 Introduction
Section 1
Oil Field Environment
1.1.1 Background
Crude oil, known as petroleum, is classified as a fossil fuel. It consists of a mixture of hydrocarbons (mostly alkanes, cycloalkanes and various aromatic hydrocarbons) with non-metallic elements, such as sulfur, oxygen and nitrogen as well as trace elements such as: iron, nickel, copper and vanadium. It is present in reservoirs located in porous rocks with water and natural gas and it is recovered by drilling into the reservoirs, forming a primary oil production (POP) system.
Approximately 30% of the petroleum from a reservoir is recovered by direct extraction using POP methods. The majority of the remaining petroleum is recovered using enhanced oil recovery (EOR) techniques. These methods include water or gas (e.g. C02) injection, which displaces oil, driving it to the oil extraction wells for removal. Because of the high pressures and flow-rates used, the water also tends to penetrate into the oil-bearing strata, eventually emerging mixed with the extracted oil. Such “produced water” is separated from the oil. As a reservoir approaches depletion, the amount of produced water increases forming a significant fraction. The injection of produced water, aquifer and seawater, is extensively used to support and maintain the production of oil and gas (Figure 1.1.1) (Devold, 2006).
2 Chapter 1 Introduction
Figure 1.1.1: Simplified scheme of oil and gas extraction (Comanescu et al., in press).
Bastin et al. (1926) first detected microorganisms in oil field production water. The presence of microorganisms (Bacteria and Archaea) has become increasingly apparent throughout all oil field process systems (Kotlar, 2009). These microbes have roles in the following oil-related problems:
Biofouling and plugging resulting from the formation of slime/biofilms that decreases flow in pipes, Microbially influenced corrosion (MIC) also termed biocorrosion (Subsection 1.3.2), Microbiologically induced souring (MIS), due to microbial hydrogen sulfide
(H2S) production, Reduction in hydrocarbon quality due to biodegradation (Sanders, 2003).
Microbes can enter the oil recovery system at many points, during the drilling, preparation of the well, and through the extraction process. The growth of microbes within the system can lead to biofilm formation, which initiate a series of problems connected to installation maintenance, as well as the oil extraction process. The nature of these problems relate to the type of microbes and their
3 Chapter 1 Introduction activities, which is dependent upon physical and chemical characteristics of the ecosystem (Magot et al., 2000).
1.1.2 Biofouling
Biological fouling is a general term, and an established phenomenon, describing the unwanted accumulation of a microbiological deposit on a surface (Borenstein, 1994). Biofouling deposits encountered in industrial settings usually consist of biofilms, inorganic particles, crystalline precipitates or scale, and the products of corrosion (Bayer, 2006; Gu et al., 2011). Industrial fouling leads to the clogging of filters, accelerated MIC rates, pyrite scale formation, and causes souring by the formation and hydrogen sulfide production, which is hazardous and highly corrosive (Choudhary, 1998).
1.1.3 Microbiologically-induced souring (MIS)
Souring refers to the generation of hydrogen sulfide (H2S) in ‘sweet’ reservoirs by e.g. sulfate-reducing prokariota (SRP) (Subsection 1.3.2.2) and it is a common problem in enhancement oil recovery (EOR) (Hubert and Voordouw 2007). The souring of reservoirs is the result of the activity of these bacteria in the zone where seawater mixes with formation water. In this mixing zone, SRP reduce sulfate from the injection water to sulfide, whilst oxidizing hydrogen and degradable organic electron donors present in the oil reservoir (Hubert and Voordouw 2007). This activity can also lead to the deterioration of iron and its alloys, which is of considerable interest to the oil industry. SRP activity is, therefore, associated with the corrosion of pipelines and process equipment, and the blocking of petroleum formation. In addition to this, sulfide production increases the sulfur content of the crude oil, which decreases its value and increases refining costs resulting in reduced oil recovery due to the precipitation of metal sulfides (Hamilton, 1985; Santana et al., 2012; Sheng et al., 2007).
4 Chapter 1 Introduction
1.1.4 Controlling the microbial growth in oil field
In order to reduce the damaging effects of microbial growth and activity, the oil industry uses a variety of chemicals both organic and inorganic supplemented by mechanical cleaning to treated affected systems (McInerney et al., 1993; Quarini and Shire, 2007).
1.1.4.1 Chemical treatments
A wide range of biocides (bactericides) has been developed to minimise microbial growth for industrial systems. There are two main categories of antimicrobial chemicals: oxidizing (typically inorganic compounds) and non-oxidizing (typically organic compounds). Traditionally, the petroleum industry has used ozone and chlorine dioxide as oxidizing biocides, and non-oxidizing agents such as glutaraldehyde, formaldehyde or quaternary ammonium compounds to control mainly SRP populations. Traditional chemical treatments with biocides often have limited success in large industrial systems. This can be due to decreasing concentration of the active agent with increasing distance through the system, or because only a single application was deployed. A more effective strategy is to dose the biocide continuously or, less expensively, quasi-continuously (periodic treatment).
Unfortunately, there are many reasons why conventional biocide applications frequently fail to eliminate microbes: under-dosing or/and improper dose regime, failure to penetrate into the biofilm, biocide deactivation by reactions with inorganic materials such as metal sulfides, development of bacterial resistance or tolerance to the biocide or chemical incompatibility (e.g. insolubility). These problems need to be considered when setting up a microbial control program (Sanders, 2003; Tapper, 1998). A range of alternative treatments has been proposed to replace the use of biocide in petroleum reservoirs, one of which involves the use of nitrate as a cost effective, efficient and environmentally acceptable way of controlling SRP and remediating hydrogen sulfide (Thorstenson et al., 2002).
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1.1.4.2 Nitrate Treatment
The use of nitrate to promote the activity of nitrate reducing bacteria (NRB) and to suppress biogenic H2S production by SRP was first proposed by Postgate (1952) and Jenneman et al. (1986). Since that time, full-scale field applications of nitrate treatment have been carried out both in oil reservoirs and in pipelines (Stott et al., 2008; Vance and Trasher, 2005). Some of these trials have met with success, however a number of problems have been encountered, such as corrosion due to nitrite or sulfur production, topside biofouling due to increased activity of nitrate reducing bacteria, down-hole plugging by nitrate utilizing bacteria (NUB) and sulfide resurgence after nitrate treatment cessation. It appears that reservoir properties have a dominant effect in determining the degree of reduction in H2S levels that can be achieved when applying nitrate.
1.1.4.3 Mechanical and chemical pipeline cleaning
Pipeline cleaning is often used, in association with chemical treatments, to control microbial populations and improve the life of the pipes by reducing the incidence of pipeline failures as a result of corrosion. Two basic techniques are used to clean pipelines: mechanical and chemical. Mechanical cleaning uses special equipment, called a ‘pig’ which is inserted into the pipe and this can be used to inspect the pipeline as well as to remove deposits (Figure 1.1.2). The advanced chemical cleaning method is a combination of chemical cleaning and mechanical pigs to remove a greater volume of deposits and debris in a short time. This cleaning solution is pushed through a pipeline by pigs and tons of deposits and debris are removed.
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Figure 1.1.2: Pigging devices. 1: Cleaning Pig. 2: Brush Cleaning Pig after removal from a pipeline. 3: Intelligent Pig. 4: Pig schematics (http://www.ppsa- online.com/about-pigs.php; Quarini and Shire, 2007).
There is a wide range of different types of pigs for oil field application. Some are used to clean the inside of the pipeline or to monitor its internal and external condition. Pigs can be fitted with brushes or scraper elements to agitate, dislodge, or abrade deposits adhering to pipeline walls or to dislodge debris that has accumulated in system. This type of pig is called a cleaning or mechanical pig. A device or vehicle that uses a non-destructive testing technique to inspect the wall of a pipe is called an intelligent or smart pig. Optical inspection allows the technician to visually inspect the interior of the pipe in 3-dimensions thus increasing the effectiveness of the cleaning process.
Summarizing, there are three main reasons why the pipeline should be cleaned: (1) improving the line flow efficiency; (2) improve or insure good data on inspection tool runs and (3) to improve the lifespan of the pipes by chemical treatment.
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Section 2
Biofilms
1.2.1 Background
Biofilms are accumulations of reproducing, metabolically active cells embedded in a complex matrix, termed extracellular polymeric substances (EPS) (Costerton et al., 1995; Davey and O’Toole, 2000). This matrix is mainly composed of a polysaccharide biopolymer, proteins, lipids and nucleic acids (Branda et al., 2005; Flemming and Wingender, 2010). The nature of biofilms varies according to the environment; those from industrial systems may contain a significant amount of abiotic particles e.g. silt, stones, sand, mineral crystals and corrosion products trapped in EPS (Donlan, 2002; Sanders and Stunnan, 2005).
Biofilms can be created by a single species of microorganism, although in nature they almost always contain many microbial taxa (including species of both Bacteria and Archaea) that interact with each other and their environment (Flemming and Wingender, 2010). Any industrial biofilm is highly heterogenous and is created by wide range of aerobic, anaerobic, heterotrophic and lithotrophic microorganisms, which are able to grow under an extreme range of conditions of temperature, pressure, pH, salinity, UV radiation and nutrient availability (Magot et al., 2000). Bacteria rapidly adapt to changing conditions and have a remarkable ability to survive in different environments, from organic-rich oil-water emulsions to the relatively adverse seawater injection systems (Sanders and Stunnan, 2005). Part of this ability to survive is related to biofilm formation, where the community benefits by having greater access to nutrients, close association with mutalistic or synergistic partners, and protection against a variety of antimicrobial agents (Costerton and Lappin-Scott, 1989), such as biocides, disinfectants, germicides and antibiotics (Sauer, 2003; Seneviratne et al., 2012). Biofilm resistance to varied conditions has been studied for many different bacterial strains (Fux et al., 2005; Stewart and Costeron, 2001; Walters et al., 2003; Williams and Braun-Howland,
8 Chapter 1 Introduction
2003) and documented for chemically diverse biocides including those commonly used in oil field (Grobe et al., 2000; Laopaiboon et al., 2006; Neidle and Ornston 1987).
The various properties of the biofilm have been identified in recent years. The development of new methodologies in the fields of genetic, biochemical, instrumental and microscopic analyses have altered our understanding of biofilm structure and function including: the factors controlling the change from a planktonic (free-swimming) mode of growth to a sessile mode; competition and metabolic interactions amongst members of the biofilm; the physiological differences between attached and planktonic cells; and the role and nature of cell- cell communication (López et al., 2010).
1.2.2 The biofilm concept
Van Leeuwenhoek first observed microorganisms on tooth surfaces and he can be considered as the discoverer of microbial biofilms. Further advancements in our knowledge of biofilms were made by Claude Zobell, almost 70 years ago (Zobell, 1943). Zobell noted the preference of marine bacteria for growth on several types of immersed surfaces including glass, metal and plastics, and proposed the early concepts for the different stages in biofilm development.
Costerton et al. (1987, 1999) extended biofilm study to freshwater systems and to a variety of microbial ecosystems, including those on the surface of tissues. Following this work, the phenomena of bacterial adhesion to surfaces was recognised as of primary importance. It is now realised that biofilms are the preferred form of growth for the majority of microorganisms (Costerton et al., 1978). More than 99% of all bacteria in natural, clinical, and industrial environments exist as attached (sessile) populations (Prakash et al., 2003).
The development of the biofilm is a continuous, dynamic and complex process (Figure 1.2.1) and it is dependent on a number of factors, such as type of
9 Chapter 1 Introduction microorganism, the surface to which attachment occurs, environmental condition and the expression of appropriate genes (Dunne 2002).
Biofilm formation starts when small amounts of organic material adsorb to the surface and microbes attach to this layer. Initial adhesion between bacteria and surfaces is often non-specific, reversible and is mostly dependent on the nature of the material and the adhering species. The attachment of bacteria cells initiates the expression of biofilm-specific genes, which encode proteins that synthesize intercellular signalling molecules and initiate matrix formation (EPS), making sessile bacteria significantly different in physiology to free-living bacteria (Dunne, 2002). The EPS provides the mechanical stability of biofilms, mediates adhesion to surfaces and forms a cohesive, three-dimensional polymer network that interconnects and transiently immobilizes biofilm cells (Flemming and Wingender, 2010). Further growth of the attached microorganisms results in the generation of a complex architecture containing interstitial voids, channels, pores and a redistribution of bacteria away from the substratum (Sutherland 2001).
Mature biofilm can undergo a dispersive process when nutrient levels are low where flow effects cause aggregates to shear off. This allows bacteria to colonize other regions, forming new microcolonies (Prakash et al., 2003) (Figure 1.2.1).
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Figure 1.2.1: A model representing stages of bacterial biofilm development. The development of biofilm is very complex but generally occurs in five stages: Stage 1: biofilm development is initiated by the attachment of bacteria to a solid surface. Stage 2: attached bacteria (irreversibly) multiply and encase the colonies with an exopolymeric matrix (EPS). At stage 3, the first maturation phase is reached, as indicated by early development of biofilm architecture. At the next step [4] maturation of biofilm results in the generation of complex architecture, channels and pores. At the last 5-dispersion stage-single motile cells disperse from the microcolonies (adapted from Stoodley et al., 2002).
The single-species biofilm is often composed of phenotypically distinct subpopulations. Therefore, the biofilm is not just an accumulation of cells on a surface but is considered to be a fundamentally different condition of microbial growth (Hall-Stoodley et al., 2004). The differentiation in microbial communities depends on the extracellular conditions, like gradients of nutrients, temperature, oxygen or electron acceptors and bacterial waste. Gene expression is, therefore, not uniform within a biofilm (Stewart and Franklin, 2008). Biofilm cells respond to different chemical signals by changing their gene expression and often secrete their own signals into the surrounding environment, which influences other cells in terms of gene activity and mobility. This mechanism of cell-cell communication is termed quorum sensing (QS), and it controls the development of the microbial community and contributes to the survival of the organisms under harsh environmental conditions (Davies et al., 1998; Lazar, 2011). Depending on the biofilm (bacterial community) composition and its activity, the physical and chemical conditions within a close area can be rapidly changed (Costerton et al., 1994). Attachment of any biomass to the inanimate
11 Chapter 1 Introduction surfaces is always the beginning of biodeterioration or microbially-influenced corrosion (MIC) for a wide range of materials (Lewandowski et al., 1997).
1.2.3 Biofilms in the oil industry
A variety of different microorganisms is found within oil production system, from oil reservoirs through to oil-producing wells, transport as well as distribution facilities (Magot et al., 2000 and references therein). In the oil and gas industry, biofilm formation causes numerous problems, including plugging of reservoir, blockage of filters by debris accumulation, corrosion and biodeterioration of materials, and loss of process efficiency, such as flow capacity reduction in pipes. Biofilm development can also lead to the spoilage of oil field products by degradation, e.g. increases in the amounts of suspended solid, changes in bulk fluid composition; and petroleum product souring, e.g. through generation of hydrogen sulfide (Bass and Lappin-Scott, 1997).
Unfortunately, microbiological control efforts focus only on the bacteria present in the bulk water. Planktonic counts, however, do not necessarily correlate with the degree of biofilm present. Systems with low planktonic counts may have a significant biofilm problem and vice versa. Therefore, it is very important to control biofilms and planktonic bacteria simultaneously, as planktonic organisms can be free-floating and may grow, degrade corrosion inhibitors and excrete acids, which depress pH.
To minimise problems associated with biofilm formation, the petroleum industry uses, besides chemical biofilm disruptors (biocides), mechanical scraping (pigging) of the inside surface of pipes (Subsection 1.1.4.3). Regular treatment is therefore required because of biofilm regrowth and this increases the cost. Biofilms are ubiquitous and costly to remove; they also exhibit a recalcitrance to treatment and control, which has been frustrating industrial engineers for decades.
Biofilm can, however, also be utilized productively in industries. For example, microbially enhanced oil recovery (MEOR) where the recovery of oil entrapped in
12 Chapter 1 Introduction porous media is greatly improved (Bass and Lappin-Scott, 1997; Finnerty and Singer, 1983). MEOR has been used as a cost-efficient alternative method for the secondary and tertiary extraction of oil from reservoirs (Lazar et al., 2007).
The biocorrosion study of Videla and Herrera (2009) found that, in some instances, bacteria may slow down corrosion processes by providing a protective layer neutralizing corrosive substances present in environment. Recently, Mahanna et al. (2010), working with Geobacter sulfurreducens, showed that these bacteria exerted two different effects on stainless steel. Immediately after inoculation, G. sulfurreducens cells promoted corrosion, but well-established biofilms seem to protect the metal surface. Understanding the nature, and management, of biofilms is complex, but of fundamental importance for the gas and oil industries.
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Section 3
Microbiologically-influenced corrosion (MIC)
1.3.1 Metal corrosion
Metal corrosion is a naturally occurring chemical or electrochemical reaction between the metal and components of its environment, where the refined form reverts to its natural state such as oxides and hydroxides (Fontana and Greene, 1987; Lee and Newman, 2003), and it is often irreversible. It is a common problem in humid and/or aqueous environments, including many branches of industry e.g. oil and gas, nuclear, construction and food processing (Lee and Newman, 2003; Sooknah et al., 2007; Koch et al., 2001), and it leads to the disintegration of the material into its constitutive atoms or ionic compounds.
The corrosion of metal has serious economic, health, safety and technological consequences. This irreversible process results in destructive effects such as the formation of rust and other corrosion products, and the creation of holes or cracks in aircraft, automobiles, boats, plumbing, and many other items constructed of metal. Numerous attempts have been made to quantify the cost of corrosion and it is considered that the worldwide annual cost is estimated to be 2.2 trillion dollars, which represents 3-4% of the gross domestic product (GDP) of industrialised countries (Hays, 2010). A substantial part of these costs is incurred by the oil and gas industry, where carbon steel is a main material used in pipeline construction. It has been proposed that substantial savings can be achieved by the correct design and adoption of protective measures (Buck et al., 1996; Farthing, 1997, Koch et al., 2001).
Disintegration of metallic material can occur either as chemical or electrochemical corrosion. Chemical corrosion is the reaction that takes place directly between a metal surface and an attacking compound such as acid.
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Electrochemical corrosion occurs when there is an exchange of electrons between an electrolyte and the metal. The overall reaction is composed of two half-cell reactions: oxidation (dissolution) of the metal to metal ions on an anodic site and reduction of a chemical species in contact with the metal surface on the cathodic side in presence of an electrotyle (e.g. seawater or other solution capable of conducting electrical flow) (Figure 1.3.1). This can occur under presence or absence of oxygen. Iron is a base metal, which is usually unstable without protection, and it is easily corroded in aquatic environments, sometimes at higher rates under anaerobic conditions (Lee et al., 2004).
Figure 1.3.1: Basic corrosion cell under anaerobic conditions. (http://octane.nmt.edu/WaterQuality/corrosion/corrosion.aspx).
Although there is one fundamental mechanism of corrosion, the electrochemical cell, there are several corrosion forms, or corrosion types, that can occur e.g. pitting corrosion, stress corrosion, galvanic corrosion, atmospheric corrosion. It is also recognised that corrosion can result from abiotic and biotic processes. The latter is referred to as microbiologically influenced corrosion (MIC), or biocorrosion, and this often occurs as pitting, which is usually more severe than the other types of metal corrosion.
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1.3.2 Microbiologically-influenced corrosion (MIC)
MIC is defined as deterioration (loss) of metallic material caused by the presence and activities of microbial communities, including biofilms (Videla and Herrera, 2005; Sooknah et al., 2007). The first reported MIC case for steel and iron, and the suggested mechanism, was published in 1934, where it was concluded that corrosion occurred as a result of the activity of sulfate-reducing bacteria (SRB) (Von Wolzogen Kuhr and Van der Vlugt, 1934).
The relative contribution of biotic factors to the overall corrosion process is controversial. In the past, it has been estimated that 20% of all corrosion problems experienced in industrial systems can be attributed to MIC (Flemming, 1996), however this figure could be higher. The presence of microorganisms, thriving as biofilms, on surface metals and their alloys can increase the rate of corrosion by 1000-10000 times (Videla, 1996).
To date, the cost of biocorrosion remains incalculable, and this is primarily because little is known about mechanisms of its processes and their control. Importantly, there is no agreed consensus on the number, types, or metabolic capability of the microorganisms that are perceived to be key contributors to carbon steel corrosion. Moreover, reliable, rapid methods for biocorrosion risk assessment and efficacy of MIC controlling and its monitoring are scarce (Angell, 1999; Batista et al., 2000; Pope et al., 1989).
1.3.2.1 Mechanisms of MIC
During the past few years, a variety of MIC mechanisms has been identified depending on the types of microorganisms associated with the corrosion. Emphasis has been placed on metabolic products derived from culprit microbial taxa found in corroding environments. These metabolic markers included exo- enzymes associated with EPS, organic and inorganic acids, as well as nitrites, ammonia and sulfides (Beech and Gaylaryde 1999; Beech and Sunner, 2004).
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Microorganisms influence the corrosion process by several mechanisms that operate simultaneously or consecutively (Gu, 2012; Stott, 2010):
Cathodic depolarization by removal of hydrogen e.g. associated with anaerobic growth of SRP or/and methanogens. Direct chemical action of metabolic products such as organic acids, sulfuric acid or reduced sulfur compounds. Disintegration of protective coatings and/or film by their constituents, thereby reducing the efficiency of it protective functions. Localized electrochemical effects because of changes in the environment composition, e.g.: oxygen concentration or pH.
1.3.2.2 Groups of microorganisms implicated in corrosion in oil field environments
MIC will be initiated and propagate corrosion as a result of the presence and activities of specific types of microorganisms. Prokaryotes implicated in biocorrosion can be categorised into a few main groups, based on the presence of metabolic pathways relevant to corrosion reactions (Beech, 2002; Little and Lee 2007).
Sulfate-reducing prokaryota (SRP) comprise a very diverse group of microorganisms varying widely in their metabolic capabilities. They are strict anaerobes which can obtain energy by oxidizing organic compounds (e.g. organic acids) or molecular hydrogen (H2), while reducing sulfate ( ) to hydrogen sulfide (H2S). The latter is a highly corrosive gas that is soluble in water (Barton et al., 2009; Muyzer and Stams, 2008). Beside sulfate reduction, most of SRB are also able to reduce elementary sulfur and thiosulfate into sulfate and sulfide (Campaignolle et al., 1996).
Often, in oil field reservoirs, SRP and methanogens co-exist (Bryant et al., 1977). The latter produce methane as a metabolic product in anaerobic conditions. Both methanogens and SRP species can be hydrogen scavengers, which could cause
17 Chapter 1 Introduction metal corrosion by removing electrons from the metal surface (cathodic depolarization) (Boopathy and Daniels, 1991; Lee et al., 1996). Furthermore, methanogens, which normally use molecular hydrogen and carbon dioxide to produce methane, use either pure elemental iron (Fe0) or iron in mild steel as a source of electrons in the reduction of CO2 to CH4 (Daniels et al., 1987). Some SRB can use metallic iron as the only source of electron donor as well (Dihn et al., 2004).
Other partners of SRPs in corrosion processes are acid-producing bacteria (APB). APBs are a variety of heterotrophic bacteria that secrete organic (e.g. acetic acid) and inorganic acids, which can become trapped under bacterial biofilms and promote corrosion by the removal of the oxide layer. Sulfur-oxidizing bacteria (SOB) e.g. Thiobacillus sp. oxidize elemental sulfur or sulfide to form sulfuric acid. These APBs will cause the pH to drop significantly from neutral to acidic conditions leading to very rapid attack of steel (Iverson, 1987). Moreover APBs produce nutrients for SRPs and consume oxygen, creating conditions that permit anaerobic SRPs to thrive (Maruthamuthu and Palaniswamy, 2007).
Nitrite-oxidising bacteria (NOB) are a group of APBs that can also catalyze the formation of corrosive nitric acid from ammonia (NH3), which can be provided by many bacteria including nitrate-reducing bacteria (NRB). NRBs can utilize nitrate as a terminal electron acceptor in anoxic respiration to drive the oxidation of organic compounds. A source of nitrogen for microbial ammonia production can be nitrate-based corrosion inhibitors, which are supposed to inhibit the growth of SRP (Subsection 1.1.4.2). A number of different SRP genera are able to use nitrate and nitrite instead of sulfate as a terminal electron acceptor (Moura et al., 1997). This may have consequences in sulfide resurgence after nitrate treatment has ceased.
Some SRBs are able to utilize a wide range of substrates including aliphatic and aromatic hydrocarbons as electron donors. Therefore hydrocarbon-degrading prokaryotes (HDP) may present a significant source of sulfide in oil deposits and oil production plants (Rueter et al., 1994). Where the microbes suspected of
18 Chapter 1 Introduction causing corrosion do not utilize hydrocarbon as energy sources, HDPs can be involved into biomass development by providing metabolic products that can be used by others. This will increase the energy available to the corrosion-causing bacteria, maintaining the corrosion process (Rajasekar et al., 2005).
SRPs and Acetogens can accelerate CO2 corrosion. SRBs can either completely oxidize their organic substrates producing CO2, or perform incomplete metabolism that produce CO2 and acetate as final products (Muyzer and Stams, 2008). Carbon dioxide corrosion is one of the most studied form of corrosion in oil and gas industry. CO2 forms carbonic acid (H2CO3), when dissolved in water. Carbonic acid is known to be corrosive to carbon steel or low alloy steel (Kermani and Morshed, 2003).
Acetogens are obligately anaerobic bacteria, which oxidise H2 by reducing CO2 to acetic acid (Müller et al., 2003). This acidification can lead to enhanced CO2 corrosion of the carbon steel (Okafor and Nesic, 2007).
Acetate can be also used up by methanogens to generate methane and CO2 (Muyzer and Stams, 2008). Acetogenic bacteria also remove H2 enabling the obligate H2 - producing bacteria to continue their activity.
Another group of microorganisms able to form acetate are the fermentative bacteria, which represent many genera (anaerobes or facultative anaerobes). These microbes are able to use organic molecules as both electron donors and acceptors. As well as acetate, the most commonly produced final product, fermentative bacteria can also produce H2S from S-containing organic compounds,
CO2 and formate, as well as convert carbohydrates into lactate, which is a source of nutrients for the other group of bacteria. Furthermore they are able to reduce nitrate, and degrade hydrocarbons (Suflita et al., 2008; Voordouw, 2000).
Iron-reducing bacteria (IRB) can enhance the rate of microbial corrosion (Obuekwe et al., 1981). This group of microorganisms is implicated in the reduction of ferric (Fe3+) to ferrous ion (Fe2+). As ferric ion is insoluble except at very low pH, ferric salts protect the metal surface from further corrosion. Ferrous salts are mostly soluble and, therefore, the reduction of ferric salts results in the
19 Chapter 1 Introduction dissolution of the protective oxide layers. Thus, iron reducers promote corrosion directly (Little et al., 1997; Videla et al., 2008). Participation of metal (iron) oxidising bacteria (MOB) in biocorrosion, has been suggested and investigated (Rajasekar et al., 2005; Xu et al., 2007).
Figure 1.3.2: Schematic description of microbially influenced corrosion (MIC). APB (acid-producing bacteria), SRB (sulfate-reducing bacteria), SOB (sulfur- oxidizing bacteria), MRB (metal-reducing bacteria), MOB (metal-oxidizing bacteria) (adapted from: Kan et al., 2011).
All of the types of microorganisms described above, as well as many other that are less well characterised, usually live together synergistically in colonies attached to the surface of the metal in a biofilm that causes the biocorrosion. Kan and co- workers (2011) put forward a model of bacterial group interaction (Figure 1.3.2), that accounts for some groups but not all. The mechanisms for these other microbial groups involved in MIC are not well understood. The development of knowledge on different bacteria is necessary for a better understanding of the anaerobic corrosion phenomenon.
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Section 4
Molecular techniques for the detection and identification of oil field microorganisms
1.4.1 Background
The importance of microbial activities in oil reservoirs was documented a long time ago, but our knowledge of the diversity of microbial metabolic activity in these ecosystems is still weak. Progress in the field of microbial ecology has been led by the development of molecular techniques to study the structure and function of ecosystems. It is commonly acknowledged that probably less than 1% of eubacteria and archaea have been cultured and characterised (Amann et al., 1995; Wayne et al., 1987). The identification of environmental molecular signals and their analysis provides a framework to improve knowledge and understanding of ecosystems that are difficult to study, such as oil reservoirs.
Until recently, microbial monitoring of oilfield systems was carried out using cultivation methods based on enrichment or direct plating of field samples, such as injection water, production water, pigging debris or biofilms retrieved from test coupons, onto specific nutrient media. Enrichments aim to detect certain groups of bacteria, typically SRP, GAB (General Anaerobic Bacteria) and APB (Acid Producing Bacteria) selectively (Hoffmann et al., 2007; Larsen et al., 2011; Skovhus et al., 2009).
Unfortunately, the cultivation methods have a bias regarding strain isolation and growth resulting in only viable and culturable bacteria being detected (Amann et al., 1995). These methods only give information about the presence of bacteria, giving no details on the corrosion process. Recently, quantitative PCR (QPCR) has been used to monitor the numbers of bacteria in oilfield systems (NACE standard, 2012). Little correlation was observed between bacterial number and the degree of corrosion. This was probably due to the fact that MIC is caused by bacterial
21 Chapter 1 Introduction communities in biofilms, rather than planktonic forms. Knowledge about structure and linkages between microbial diversity to ecosystem functions is very important for understanding environmental processes (He et al., 2007), including the MIC phenomenon. Therefore, new techniques need to be developed that detect and quantify oilfield microbial processes and mechanism.
The drive to develop molecular techniques for microbial ecology began with the realisation that traditional methods of identification and classification were insufficient to detail the exact composition of mixed bacterial communities or microbial diversity (Pace, 1997). Traditional methods of microorganism identification are based on cultivation with morphological and physiological analyses, and these can be inaccurate, cumbersome and time-consuming (Bavykin et al., 2001). Moreover culture methods only recover an estimated 1-10% of the original population, and in some cases fails to detect various organisms in the sample (Maxwell et al., 2004). Indeed, it is generally accepted that as few as 0.3% of bacteria in soil and <0.1% in marine waters are culturable (Amann et al., 1995). Furthermore, Suzuki et al. (1997) found that the genes of bacteria cloned directly from environmental DNA do not correspond to the genes of bacteria cloned directly from the cultured environmental samples. The problems of the traditional methods are exacerbated by the lack of knowledge of the conditions under which bacteria are growing in their natural habitat and by the difficulty in developing growth media reproducing these conditions (Vartoukian et al., 2010).
Characterization and quantification of bacteria and archaea without cultivation represents a major challenge. However, molecular techniques provide a complementary tool for the detection, identification, and quantification of microorganisms present in various environmental including oil fields (Giraffa and Neviani, 2001; Hoffmann et al., 2007; Stevenson et al., 2011; Voordouw et al., 1996).
As techniques of molecular biology are based on nucleic acid technology, they circumvent problems associated with selective cultivation of bacteria and thus allow for characterization of multi-species communities (Amann and Schleifer,
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1995). In addition, cultivation-independent techniques can be applied much faster (few hours to a few days) compared to traditional cultivation based techniques, which can take up to 30 days (Kjellerup et al., 2006). Molecular methods are preferred also because of their rapidity and reliability (Ercolini, 2004).
During years, molecular techniques have revolutionized all field of microbiology. Among culture-independent approaches, the 16S rDNA-based Polymerase Chain Reaction (PCR) has been widely used for studying bacterial communities in a variety of environments.
1.4.2 Polymerase chain reaction (PCR)
PCR was invented in 1983 by Kary B. Mullis and represented a great advance in terms of the speed, specificity and sensitivity of microbiological diagnostic techniques (Mullis, 1990). PCR has made it possible to detect corresponding sequences specific for a particular microorganism in a mixture of nucleic acids of different origin without isolating the target organism (Olsen et al., 1995).
In environmental samples e.g. water or pigging envelopes, DNA is present at relatively low concentrations. Generally, it is necessary to first “amplify” the DNA, i.e. to produce multiple copies of a target DNA present in the original sample. The amplification is usually carried out by a DNA replication reaction using a thermophilic DNA polymerase, Taq Polyermase, which can function at high temperatures and short oligonucleotides named primers (Brown, 2003).
Primers can be designed to amplify DNA regions of different length and sequence structure. The 16S rRNA gene, which is highly conserved between different species of Bacteria and Archaea, can be used to characterise microbial taxa (Tringe and Hugenholtz, 2008). The detection and characterisation of bacterial (Lonergan et al., 1996; Todorova and Costello, 2006) and archaeal (Ochsenreiter et al., 2002; Yan et al., 2006) strains, explored using primers for 16S rRNA, has been successful in different habitats in oil field environments (Voordouw et al., 1996). However, other types of primers, designed specifically to target DNA regions which code
23 Chapter 1 Introduction functional genes can also be employed to verify the presence of specific metabolic pathways.
Specific primers are now available to detect a wide range of bacterial groups in different environments, including those of the oil industry (Lever, 2012). The genes targeted for SRPs, NRBs, AOPs, SOBs, HDPs, methanogens, acetogenic bacteria and fermentative bacteria are given in Table 1.4.1.
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Table 1.4.1: Listing of organism implicated in MIC of carbon steel and their selected functional genes.
Group of Marker Function Reference microorganism gene SRP aprAB encodes the dissimilatory Meyer and Kuever, adenosine-5’-phosphosulfate 2007 reductase dsrAB encodes the dissimilatory Wagner et al., 1998 sulfite reductase [NiFe] encodes the hydrogenase Wawer et al., 1997 hydrogenase NRB narG encodes the nitrate reductase Philippot et al., 2002 Smith et al., 2007, a napA encodes the nitrate reductase Smith et al., 2007, a nirK encodes the nitrite reductase Braker et al., 1998 nirS encodes the nitrite reductase Braker et al., 1998 nrfA encodes the periplasmic Mohan et al., 2004 nitrite reductase Smith et al., 2007, a AOP amoA encodes the ammonia Junier et al., 2010 monooxygenase Okano et al., 2004 Sinigalliano et al., 1995 Stephen et al., 1996 Methanogens mcrA encodes the methyl coenzyme Banning et al., 2005 prokaryotes M reductase Castro et al., 2004 Dhillon et al., 2005 Smith et al., 2007, b Fermentative hydA encodes the hydrogenase Pereyra et al., 2010 bacteria Acetogenic FTHFS encodes the Leaphart and Lovell, Bacteria formyltetrahydrofolate 2001 synthetase Leaphart et al., 2003 Xu et al., 2009 SOB soxB encodes the soxB component Anandham et al., of the periplasmic thiosulfate- 2008 oxidizing sox enzyme complex Meyer et al., 2007 Petri et al., 2001 HDP bssA encodes the benzylsuccinate Callaghan et al., 2010 synthase Winderl et al., 2007 assA encodes the alkylsuccinate Callaghan et al., 2010 synthase Legend: SRP: sulfate-reducing prokaryotes, NRB: nitrate-reducing bacteria, AOP: ammonia oxidising prokaryotes, SOB: sulfur-oxidizing bacteria, HDP: hydrocarbon degrading prokaryotes.
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Figure 1.4.1 shows the functional genes that were selected to indicate the presence of metabolic pathways of interest to MIC. SRPs can be detected using PCR- amplification of the aprAB and dsrAB genes, which encodes the dissimilatory adenosine-5’-phosphosulfate reductase and dissimilatory sulfite reductase respectively (Meyer and Kuever, 2007; Wagner et al., 1998). These enzymes are present exclusively in SRP. While the aprAB gene is highly conserved and offers a precise detection, dsrAB gene is not as reliable and can produce misleading results (Friedrich, 2002).
Nitrate-reducing bacteria (NRBs) can be detected using primers for the nitrate reductase genes, narG and napA (Philippot et al., 2002; Smith et al., 2007, a). The reduction of nitrite to nitric oxide (NO) is catalyzed by two different nitrite reductases, coded for by the nirK and nirS genes, and primers for both these genes can be used to detect NO bacteria (Braker et al., 1998). The nitrification process (oxidation of an ammonia compound) can be detected by amplification of subunit A of the ammonia monooxygenase gene (amoA) (Junier et al., 2010, Rotthauwe et al., 1997). Several PCR primers to amplify amoA have been published (Okano et al., 2004; Sinigalliano et al., 1995; Stephen et al., 1996). Bacteria able to reduce nitrite to ammonia can be detected by amplification of nrfA gene (Mohan et al., 2004), which encodes periplasmic nitrite reductase. The presence and relative importance of nitrite-reduction/oxidation pathways in communities recovered from oil field environments can be used as an additional parameter when assessing the risk of biocorrosion.
Methanogens can be detected using primers for the methyl coenzyme M reductase gene (mcrA). The phylogeny of mcrA follows that of 16S rRNA phylogenies (Luton et al., 2002) allowing identification of methanogens based on mcrA sequences. Primers for the amplification of mcrA gene have been used in different environments raning from hydrothermal sediment (Dhillon et al., 2005) to soil and freshwater sediments (Castro et al., 2004; Banning et al., 2005; Smith et al., 2007, b).
26 Chapter 1 Introduction
The [NiFe] hydrogenase (catalysing molecular hydrogen production) gene is considered a good marker for the metabolic activity of a number of bacterial groups. The sequence differences in this gene allow the design of primers for groups such as the SRBs (Wawer et al., 1997) and femermentative bacteria in a wide range of environments (Pereyra et al., 2010).
Acetogens can be detected via the amplification of formyltetrahydrofolate synthetase (FTHFS) gene, which codes for a key enzyme in the CO2-reductive acetogenesis pathway (Leaphart and Lovell, 2001; Leaphart et al., 2003; Xu et al., 2009).
The soxB gene codes for a component of the periplasmic thiosulfate-oxidising sox enzyme complex, and primers for this gene have been used to detect SOBs (Anandham et al., 2008; Meyer et al., 2007; Petri et al., 2001).
Alkane and aromatic hydrocarbon degrading prokaryotes have been detected using primers for the alylsuccinate synthase (assA) and benzylsuccinate synthase (bssA) genes (Callaghan et al., 2010 and Winderl et al., 2007). So far, an universal functional gene, which could be used for detection, hasn’t been recognised for metal reducing bacteria (MRB).
The most significant advance in PCR- based techniques since its discovery was the development of quantitative PCR (QPCR) by Higuchi et al. (1992).
27 Chapter 1 Introduction
Figure 1.4.1: Schematic presentation of the specific pathways and the key functional genes used in this study. A: sulfate pathway. B: sulfate/nitrate pathway. C: nitrate pathway. D: cathodic depolarization. SRP: sulfate-reducing prokaryotes, NRB: nitrate-reducing bacteria, S/NRB: sulfate/ nitrate -reducing bacteria, SOB: sulfur-oxidizing bacteria. apr-adenosine-5-phosphosulfate reductase gene; dsr- dissimilatory sulfite reductase gene; sox-sulfur oxidation enzyme system gene; nrfA-periplasmic nitrite reductase gene; ccNiR-nitrite reductase gene; narG- nitrate reductase gene; napA-nitrate reductase gene; nirK-cooper containing nitrite reductase gene; nirS-nitrite reductase gene; mcrA-methyl coenzyme-M reductase gene; hydA- hydrogenase.
1.4.2.1 Quantitative PCR (QPCR)
Quantitative (QPCR) or real-time PCR is the detection of the amplification product and its quantification during each cycle of the reaction. This allows the amount of the product to be estimated from tissues or environmental sources (Smith and Osborn, 2009). The sensitivity of this system is based upon the detection of a fluorescent reporter, which can be dyes (e.g. SYBR Green I) (Giglio et al., 2003; Wittwer et al., 1997) or fluorogenic probes (e.g. TaqMan®, FRET probes) (Didenko, 2001; Livak et al., 1995), associated with the product, so that this signal
28 Chapter 1 Introduction increases in direct proportion to the amount of PCR product at each PCR cycle (in real time).
QPCR methodologies have been applied to environmental microbiology and allow detection and quantification of microbial gene copy number in different environments (Okano et al., 2004; Panicker et al., 2004; Skovhus et al., 2004) including oil field (Zhu et al., 2005). The application of QPCR has been already successfully used to quantify functional genes as markers for different bacteria detection such as SRBs (Chin et al., 2008), methanogenes (Zhu et al., 2005) or NRBs (López-Gutiérrez et al., 2004) and many different functional genes in the complex environmental samples (Kolb et al., 2003; Mouser et al., 2009).
In conclusion, different types of PCR are available to detect microbial communities qualitatively and quantitatively. Apart from its ability to detect bacteria notoriously difficult to cultivate, the main advantages of PCR techniques, are high sensitivity and specificity. However, high sensitivity is also the greatest potential weakness of PCR methods due to the risk of cross-contamination with minute amounts of external DNA, which can easily produce false results (Corless et al., 2000 and references therein).
1.4.3 Denaturing Gradient Gel Electrophoresis (DGGE)
Numerous variants of PCR-based techniques are used to profile microbial communities in oil field environments. Many of these are linked to electrophoresis techniques that can be used to separate heterogeneous molecules, one of which is denaturing gradient gel electrophoresis (Brakstad et al., 2008).
PCR amplification of 16S rDNA followed by separation of the PCR products (amplicons) on a denaturing gradient gel, formed with urea and formamide (PCR- DGGE), is a powerful method for the analysis of bacterial communities (Muyzer et al., 1993). Since its development, 16S rDNA-DGGE has been successfully applied to the study of planktonic and biofilm microbial populations in environmental
29 Chapter 1 Introduction samples (Casamayor et al., 2000; Diez et al., 2001; Gillan et al., 2005; McBain et al. 2003; Ohkubo et al., 2006).
As stated in subsection 1.4.2 PCR amplification of 16S rDNA from environmental sources results in the amplification of an heterogeneous mixture of DNA fragments that have the same size but different sequences. Denaturing gradient gel electrophoresis (DGGE) is an electrophoretic method capable of separating such DNA fragments according to their sequence. This separation is based on the decreased mobility of a partially melted, double-stranded DNA molecule in a polyacrylamide gel containing a linear gradient of DNA denaturants (Ercolini, 2004; Fontana et al., 2005).
All PCR reactions for DGGE analysis use a primer pair where one contains a 35-40 G-C rich tail, known as a GC clamp. The introduction of a GC-rich sequence to one end of the product alters its melting characteristics ensuring that denaturation occurs at the opposite end, and that the molecule does not completely separate into its constitutent strands (Wu et al., 1998). Because the fragments have different sequences, the melting profile will be different for each one so that they will migrate at different rates through the gradient (Giraffa and Neviani, 2001). Theoretically this allows the detection of a single base difference in the melted part of the DNA fragment (Figure 1.4.2).
30 Chapter 1 Introduction
Figure 1.4.2: Principle of GC clamp operation in DGGE. (Adapted from http://usph.wordpress.com/2008/06/19/teknik-biologi- molekuler-menginspirasi-revolusi-dalam-diskriminasi-komposisi-komunitas- mikrobial).
The main disadvantage of PCR-based techniques, including DGGE, for the analysis of microorganisms in the oil industry is obtaining DNA samples of sufficient purity for amplification (Marty et al., 2012). Generally, the DNA extraction procedure needs to be optimised for each sample, because the levels of impurity can be different for each sample. Alongside this problem, the number of different bacterial species and their concentrations can influence the detection limit of DGGE by affecting both the efficiency of DNA extraction and the PCR amplification due to the possible competition among templates (Ercolini, 2004).
PCR-DGGE methods were recently employed to obtain unique DGGE patterns that are believed to be indicative of the location of oil. Studies on samples obtained through StatoilHydro drilling operations revealed that specimens extracted from
31 Chapter 1 Introduction exploration cores, formation water from the corresponding reservoir section and from the oil itself, gave basically the same organisms. In contrast, none of these organisms were present in sea water or outcrop core samples from other locations, subjected to the same extraction and analytical procedures (Kotlar, 2009). Since its introduction, DGGE has been used extensively in oil field microbiology not only to gain an insight into microbial community structure but also to obtain identity of community members through direct sequencing of bands (Kan et al., 2006; Shartau et al., 2010).
1.4.4 Sequencing technology
The technologies of DNA sequencing have evolved rapidly in the past 20 years. This technique has the ability to determine the order of the nucleotide bases in an unknown molecule of DNA (Ahmadian et al., 2006) and it is one of the most important approaches for the study of biological systems today (Ronaghi, 2001). In the mid 1970s, technologies for sequence determination of DNA were revolutionized by Maxam-Gilbert and Sanger (Ahmadian et al., 2006). The Sanger DNA sequencing technique is based on DNA synthesis with incorporation of normal dNTPs as well as dideoxynucleotides (ddNTPs) causing a termination of the newly synthesized DNA molecule (Sanger et al., 1977). Although the Sanger method of sequencing was the main technique used for many years, it is currently being supplanted by third generation sequencing techniques, such as pyrosequencing.
Pyrosequencing has been applied to microbial ecology as a new approach that is capable of describing the microbial communities present in environmental samples at high resolution (Roesch et al., 2007; Sogin et al., 2006; Teixeira et al., 2010). This technology is sequencing by synthesis. The four-enzyme DNA sequencing technology (Figure 1.4.3) is based on real time monitoring of DNA synthesis by bioluminescence. Each reaction mixture includes: the Klenow fragment of DNA polymerase I, ATP sulfurylase, luciferase, apyrase, the enzyme substrates adenosine phosphosulfate (APS), d-luciferin and the sequencing template with an annealed primer. The solutions of four nucleotides are added
32 Chapter 1 Introduction sequentially. A successful reaction generates pyrophosphate, which is used to generate ATP via sulfurylase activity and this drives the luciferase activity generating a quantum of light. In an unsuccessful reaction the nucleotides are removed by apyrase activity. A charge coupled device (CCD) camera detects the intensity of the flash of light. The height of each peak (light signal) is proportional to the number of nucleotides incorporated (Ahmadian et al., 2006). Pyrosequencing does not require a cloned library, removing one of the main biases in environmental sampling but it does need a library of separate independent DNA fragments. A pyrosequencing library is created by capturing DNA on beads. Each bead contains an individual single-stranded DNA fragment, which is amplified in isolation and in parallel with the rest of the library. After amplification the DNA is sequenced.
33 Chapter 1 Introduction
Figure 1.4.3: Principle of pyrosequencing technology (Gharizadeh et al., 2001). DNA polymerase incorporates the complementary dNTPs onto the template strand. This incorporation releases pyrophosphate (PPi) stoichiometrically. ATP sulfurylase quantitatively converts PPi to ATP in the presence of adenosine 5´ phosphosulfate (APS). This ATP drives the luciferase-mediated conversion of luciferin to oxyluciferin that generates visible light in amounts that are proportional to the amount of ATP. The unincorporated nucleotides and excess ATP are degraded between base additions by a nucleotide-degrading enzyme such as apyrase. The light is detected by CCD camera and seen as a peak in the raw data output.
The pyrosequencing technique possesses several unique features that give it advantages to other sequencing methods. The absence of the need for cloning, electrophoresis, labelling of primers and nucleotides, direct sequencing of fragments in single runs and read lengths make this technique useful in applications such as SNP genotyping, mutation detection, gene identification and microbial genotyping such as the analysis of oil field samples (Ahmadian et al., 2000; Davis et al., 2012; Garcia et al., 2000; Mardanov et al., 2010; Stevenson et al., 2011).
34 Chapter 1 Introduction
The main disadvantage of pyrosequencing includes the short lengths of individual sequence reads (about 300-500 nucleotides) and the fact that modifications need to be made for sample preparation (single-strand procedures) (Gharizadeh et al., 2006).
Pyrosequencing can be used in conjunction with environmental shotgun sequencing (ESS) to study complex microbial communities (Petrosino et al., 2009; Eisen, 2007) allowing the identification of non-cultivable microorganisms. ESS was first used a few years ago (Venter et al., 2004) and it revealed all of the genes present in environmental samples, rather than just 16S rRNA genes or other targeted genes. This provides information both on which organisms are present and what metabolic processes are possible in the community.
1.4.5 Molecular hybridisation techniques
Next generation sequencing techniques, like pyrosequencing, have helped in the development of metagenomics, where genomic techniques have been applied to the study of microbial communities directly in their natural habitats (Handelsman, 2004). An alternative approach, but not a mutually exclusive one, to sequencing of environmental sequences is to use hybridisation techniques, such as micro- and macroarrays, to monitor either the functional genes in a microbial community or their transcripts.
1.4.5.1 DNA microarray
DNA microarrays (biochips, microchips) are a powerful, cost-effective and high- throughput technology that has been used successfully to analyse the presence/abundance, the diversity and the expression of genes in heterogeneous samples (Chee et al., 1996).
DNA microarray technology was first applied to differentiate expression of 45 Arabidopsis genes by two-colour fluorescence hybridization of probes printed on glass microscope slides (Schena et al., 1995). Since then, microarray technology has
35 Chapter 1 Introduction advanced considerably (He et al., 2011), and it is not uncommon for thousands of microbial genes to be identified simultaneously today. This makes micorarray technology suitable for monitoring microbial community structure and dynamics, as well as for detecting genetic polymorphisms and characterizing microorganisms in environmental samples (Wu et al., 2001; Zhou, 2003).
1.4.5.1a Principle of DNA microarray
The principle of DNA microarray technology is based on the hybridisation of environmental DNA (or RNA) with target genes (probes) immobilised on a solid matrix support. Microarrays contain a large numbers of DNA probes (oligonucleotides) spotted onto a glass microscope slide, silicon or nylon membrane, with each spot representing a single gene (Burgess, 2001; Roh et al., 2010).
36 Chapter 1 Introduction
Figure 1.4.4: Microarray application; principle of operation of the microarray (Sztyler et al., in preparation).
The schematic representation of the use of a microarray is depicted in Figure 1.4.4. The process starts by isolation and purification of DNA from a sample. The environmental DNA is labelled with fluorescent dyes, denatured and applied to the microarray, where it is hybridised to the probes spotted on the matrix. The microarray is washed to remove non-hybridised DNA and, finally, the array is scanned and image analysed. Fluorescent signals reveal the presence of genes in the sample (Roh et al., 2010; Watson et al., 1998). Depending on the type of probes spotted on microarray matrix, information about microorganism presence, their metabolic activity or gene expression can be obtained.
37 Chapter 1 Introduction
Typically, the probes are short lengths of single-stranded DNA or RNA, which are part of the target sequence. To design a probe, which would allow screening of an environmental sample for the presence of particular microorganism, the DNA or mRNA sequence of the gene of interest must be known. The probe can be directly designed based on sequence information from public databases. The target gene may code for an enzyme in particular metabolic pathway; in this case, a positive result indicates the presence of the target gene so that the community has the potential for that type of activity (e.g. sulfate reduction). This kind of probe is known as a functional gene probe (Schadt et al., 2004). Another strategy of probe selection is searching for already published probes. Loy and coworkers established a rRNA-targeted oligonucleotide probe database named 'probeBase' with currently more than 1300 entries (Loy et al., 2003).
The target gene should be unique to a particular microbial species or metabolic pathway. Complementary DNA (cDNA) probes are designed based on mRNA transcripts and allow the detection of metabolically active microorganisms. Alternatively a probe can be designed for specific ribosomal RNA (rRNA) species and these are known as phylogenetic probes. The taxonomic level of detection, which could be at the domain, family, genera or species level, is determined by the precise sequence chosen for the probe. It is important to note, that in environmental microbiology there are still more unknown microorganisms then identified ones, which can cause a problem during probe design. The application of DNA probes to evaluate the presence and diversity of microorganisms in a variety of environmental system is well documented (Diels and Mergeay 1990; Holben et al., 1988; Voordouw et al., 1991). Different studies have used oligonucleotides of various lengths. Longer oligonucleotide probes provided better sensitivity to individual target genes, compare with shorter ones but their specificity is significantly lower (He et al., 2005; Relógio et al., 2002). Based on the type of probe adopted, the microarrays used in ecological studies are divided into three main groups: FGA, CGA, POA (Zhou and Thompson, 2002).
38 Chapter 1 Introduction
1.4.5.1b Functional gene arrays (FGA)
Functional gene arrays (FGA) contain probes that detect enzyme-coded genes that are implicated in biochemical cycling (nitrification, sulfate reduction/oxidation, elements fixation) and bioremediation processes. These types of arrays are used to monitor microbial community activities in the natural environment, and for linking the taxonomy of microbial populations to functional processes. The most important step, which is the biggest challenge, is the construction of probes that identify the best suitable target gene for a given process (Schadt et al., 2004; Zhou and Thompson, 2002; Zhou, 2003). FGA has been successfully applied to the analysis of functional gene diversity in marine sediments (Tiquia et al., 2004; Wu et al., 2001), river sediments (Taroncher-Oldenburg et al., 2003) and soil samples (Wu et al., 2001).
An example of a comprehensive FGA is the GeoChip, for which the latest generation (GeoChip 4) contains ~ 84 000 oligonucleotide (50 mer) probes representing bacterial and archaeal genes from 410 functional families involved in biogeochemical, ecological and environmental processes including nitrogen, carbon, sulfur and phosphorus cycling, as well as metal reduction and resistance (He et al., 2011). The GeoChip has been successfully applied to the analysis of functional microbial communities in soil and water samples (He et al., 2010; Liang et al., 2009; Parnell et al., 2010).
1.4.5.1c Community genome array (CGA)
Community genome array (CGA) is a microarray which contains the whole DNA obtained from microorganisms grown in pure laboratory cultures. This is a tool for the detection and identification of microorganisms in environmental samples (Tiquia et al., 2004; Zhou and Thompson, 2002). CGA hybridization is specific, quantitative and useful for detection and identification of cultivable bacteria from different environments (Wu et al., 2004; Wu et al., 2006).
39 Chapter 1 Introduction
1.4.5.1d Phylogenetic oligonucleotide array (POA)
Phylogenetic oligonucleotide arrays (POA) contain information from rRNA sequences, which is a powerful molecule for studying phylogenetic relationships between organisms in environmental samples (Zhou and Thompson 2002). Oligonucleotide probes based on 16S rRNA sequences are widely used to identify bacterial and archaeal taxa (including SRP) in a variety of environments (Loy et al., 2002; Tiquia et al., 2004). An example of a POA is high-density microarray called the PhyloChip™ (Brodie et al., 2006; DeSantis et al., 2007). DeSantis et al. developed a system to determine the presence and relative abundance of more than 50,000 bacterial and archaeal taxa in diverse environmental samples. PhyloChip™ has been successfully used to assess environmental damages and monitor restoration status after Gulf oil spill in 2010 (Hazen et al., 2010).
The identification of microorganisms and their activities by all of the molecular techniques discussed above provides an approach to study organisms in situ that are difficult or impossible to culture. These tools, therefore, are appropriate to study MIC in oil field systems and potentially provide excellent ways of monitoring corrosion.
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AIMS OF THE RESEARCH
The general objective of the research presented in this thesis was the development/design of DNA microarrays (biochips) to facilitate detection, monitoring and mitigation of microbially-influenced corrosion (MIC) in oil field environments. More specifically, the investigation aimed to identify prokaryotic metabolic processes that drive biocorrosion reactions through detection and, if possible, quantification of bacterial and archaeal genes coding for metabolic pathways identified as pertinent to biocorrosion.
Specific aims of the investigation were as follows:
Testing field samples, provided by the industrial partner from systems with different levels of corrosion to recognise the differences between microbial populations from corroding and non-corroding systems.
Selection, detection and quantification of bacterial and archaeal functional and structural genes in samples differentially associated with carbon steel biocorrosion, obtained from Statoil North Sea oil production facilities.
Identifying existing and designing de-novo oligonucleotide (DNA) probes for conserved regions of selected prokaryotic genes related to specific metabolic pathways known to be implicated in biocorrosion in oil field environment.
Testing and optimising DNA probes for hybridization with DNA of model prokaryotic taxa, representative of being involved in MIC.
CHAPTER 2: MATERIALS AND METHODS
Chapter 2 Materials and Methods
Section 1
Detection and characterisation of microorganisms and their functional genes in the oil field samples
2.1.1 Description of sampling sites
The sample sites were gas and oil fields located in the North Sea (56°N 03°E) and their pipelines, which were approximately 10 km long. A map showing the positions of the installations is presented in Figure 2.1.1. The pig samples were collected by operators into separate sterile glass bottles/vials, capped, then flushed with N2 gas, stored in room temperature and delivered to the laboratory. Samples were collected avoiding any possible contamination. The industrial operators made the decisions about the collection and delivery of samples. After delivery, samples were stored in 4°C prior to analysis. The field samples provided by BIOCOR (industrial partner) are listed in Table 2.1.1. Pigging debris (pigging envelopes) and water samples (liquid samples) were collected in the water injection pipeline A at Installation A (IA1, IA4, IA9, IA2 and IA5), in the production pipeline of Installation C (IC1, IC2, IC3, IC102, IC302, IC802, IC1402, IC106, IC206, IC306, IC606, IC1206, IC2406, IC3P, IC11P) and in the production pipeline of Installation D (ID1) after pigs operation. Photographs of examples of received samples are presented in Figure 2.1.2. Because the industrial operators decided the sampling strategy, pigging debris from different sites were used for the different types of experiment. For this reason, samples analysed at the University of Portsmouth, University of Oklahoma and University of Duisburg-Essen had different nomenclatures.
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Chapter 2 Materials and Methods
Figure 2.1.1: A map showing the position of Installations A, C and D.
Water used to maintain pressure and to facilitate oil recovery at Installation A is a mixture of aquifer water and recycled water from the production pipelines. Corrosion inhibitors and scale inhibitors are added regularly to the production lines. Temperature of the system at the water injection manifold varied between 35 and 55°C. At Installation C and D oil evacuation occurs after the injection of seawater, which maintains pressure in the reservoir and enhances recovery. Before injection, nitrate and an oxygen scavenger are added to the seawater. Corrosion and scale inhibitors are added regularly to the systems. The temperature in the production pipeline varied from 80 to 65°C. The rates of corrosion for those systems are reported, by the compant, to be highest in the production pipeline at Installation C. Corrosion in the water injection pipeline at Installation A is believed to be under control by biocide treatment and
44
Chapter 2 Materials and Methods by pigging cleaning. Installations C and D pump some oil from the same reservoir. Severe corrosion has been reported for Installation C, but not for Installation D, where pigging cleaning and inspection is unnecessary. At Installation C, biocide treatment was applied for the first time at the end of the pigging operation at February 2011 (personal communication with operators). Samples before and after treatment were received for analysis. Flow charts of the sampling site and organisation of the installations were kindly provided by industrial partner and shown in Appendix 1.
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Chapter 2 Materials and Methods
Table 2.1.1: Description of samples and their ID from Installation A, C and D provided by industrial partner.
Sample ID Pig number Date Sample details Installation A, water injection pipeline A IA1 Pig nr 1 (10/12/2010) liquid2 IA4 Pig nr 4 (12/12/2010) liquid2 IA9 Pig nr 9 (13/12/2010) liquid2 IA2 Pig nr 2 (29/10/2011) solid1 IA5 Pig nr 5 (30/10/2011) solid1 Installation C, production pipeline IC1 Pig nr 1 (24/11/2010) liquid2 IC2 Pig nr 2 (24/11/2010) liquid2 IC3 Pig nr 3 (25/11/2010) liquid2 IC102 Pig nr 1 (12/02/2011) liquid1 IC302 Pig nr 3 (13/02/2011) liquid1 IC802 Pig nr 8 (14/02/2011) liquid1 IC1402 Pig nr 14 (15/02/2011) liquid1 IC1061 Pig nr 1 (18/06/2011) solid1 IC2061 Pig nr 2 (18/06/2011) solid1 IC3061 Pig nr 3 (18/06/2011) solid1 IC606 Pig nr 6 (19/06/2011) solid1 IC1206 Pig nr 12 (20/06/2011) solid1 IC2406 Pig nr 24 (20/06/2011) solid1 IC3P Pig nr 3 Provetaking (01/08/2011) solid1 IC11P Pig nr 11 Provetaking (03/08/2011) solid1 Installation D, production pipeline ID1 Pig nr 1 (26/09/2011) solid + oil phase1,2
Legend: Pig number states for order of pigging operation, e.g. Pig nr 1 corresponds to the debris collected after the first pigging operation, Pig nr 4 corresponds to the debris collected after the fourth pigging operation, etc.
1 DNA isolated using PowerSoil® Isolation Kit 2 DNA isolated using PowerBiofilm® Isolation Kit
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Chapter 2 Materials and Methods
Figure 2.1.2: A photograph of examples received field samples provided by the industrial partner (1 and 2: Installation C, 3: Installation D).
2.1.2 DNA extraction from field samples
Total DNA present in field samples was isolated using PowerBiofilm® or PowerSoil® DNA Isolation Kit (Table 2.1.1) as described below.
2.1.2.1 PowerBiofilm® DNA Isolation Kit: field samples
Environmental DNA was extracted from field samples using PowerBiofilm® DNA Isolation Kit (MoBio Laboratories, Inc.) following the manufacturer’s instructions (PowerBiofilm® DNA Isolation Kit Sample, Instruction Manual, p.9-10). All chemicals used for extraction were included in the kit. Solid field sample(0.5 g), or pellet after centrifugation, was added to the Power Bead Tubes and vortexed. The samples were resuspended in 350 µl of BF1 solution (pre-warmed in a water bath at 55°C for 5 min and mixed to dissolve any precipitates) and vortexed. BF2 solution (100 µl) was added, vortexed and samples
47
Chapter 2 Materials and Methods were immediately incubated at 65°C for 5 min. After incubation, tubes were vortexed for 10 min. Samples were centrifuged at 13 000 x g for 1 min (Heraeus Fresco 21, Thermo Scientific, UK) and the supernatant was transferred to sterile 2 ml collection tubes. 200 µl of BF3 solution was added to each tube and vortexed. Samples were incubated at 4°C for 5 min and centrifuged at 13 000 x g for 1 min. The supernatant was transferred to sterile 2 ml collection tubes, 900 µl of BF4 solution were added and vortexed. 650 µl of supernatant was loaded onto spin filter column and centrifuged at 13 000 x g for 1 min and the flow through discarded (this step was repeated 3 times until all supernatant has been loaded). Spin filter columns were placed into sterile 2 ml collection tubes. BF5 solution (650 µl) were added, centrifuged at 13 000 x g for 1 min and the flow through was discarded. 650 µl of BF6 solution were added and centrifuged at 13 000 x g for 1 min and flow through discarded. Samples were centrifuged at 13 000 x g for 2 min. Spin columns were transferred into clean 1.5 ml eppendorf tubes and 100 µl of Buffer BF7 was added directly on the filter membrane. After incubation at room temperature for 1 min DNA was collected by centrifugation at 13 000 x g for 1 min.
2.1.2.2 PowerSoil® DNA Isolation Kit: field samples
Total DNA was extracted from field samples using PowerSoil® DNA Isolation Kit (MoBio Laboratories, Inc.) following the manufacturer’s instructions (PowerSoil® DNA Isolation Kit Sample, Instruction Manual, p.7). All chemicals used for extraction were present in the kit. Pigging sample (0.5 g) was added to the Power Bead Tubes and vortexed. 60 µl of Buffer C1 was added, vortexed for 10 min and centrifuged at 10,000 x g for 30 s. 400-500 µl of supernatant was transferred to fresh 2 ml collection tubes. 250 µl of Buffer C2 was added and vortexed. Samples were incubated at 4°C for 5 min, centrifuged at 10 000 x g for 1 min and 600 µl of supernatant was transferred to new 2 ml collection tubes. Buffer C3 (200 µl) was added and vortexed. After incubation at 4°C for 5 min sample were centrifuged at 10 000 x g for 1 min. The supernatant (750 µl) was transferred to sterile 2 ml collection tubes and 1200 µl of C4 was added and vortexed. The supernatant (650 µl) was pipetted into the spin column, centrifuged at 10 000 x g for 1 min and the flow through was discarded
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Chapter 2 Materials and Methods
(this step was repeated 3 times until all supernatant has been loaded). C5 solution (500 µl) was added, centrifuged at 10 000 x g for 30 s and the flow through was discarded. Additional centrifugation was performed to dry filter membrane. Spin columns were transferred into clean 1.5 ml eppendorf tubes. Finally DNA was eluted with 100 µl of Buffer C6 placed directly on the filter membrane, incubated at room temperature for 1 min and then centrifuged at 10 000 x g for 1 min. The concentration of extracted DNA was determined using a NanoDrop ND-1000 spectrophotometer (Thermo Scientific, USA). All DNA was stored at -20°C for subsequent studies, unless specified differently.
2.1.3 Polymerase chain reaction (PCR)
PCR was carried out using DNA extracted from pigging samples. For all PCR amplifications aliquots of 2-10 ng of DNA was added to a PCR master mixture and the volume adjusted to 25 µl or 50 µl with nuclease-free water. The PCR products were separated by agarose gel electrophoresis. Standard PCR reactions were carried out in the RoboCyler® Gradient 96 (Stratagene, UK). Touch down PCR reactions were carried out in the peqStar 96 Universal Gradient, (PeqLab, UK). All primers used in these studies were purchased from Life Technologies, UK and are listed in the Table 2.1.2. GoTaq® Green Master Mix was purchased from Promega, UK. PCR nucleotide mix was purchased from Roche. Invitrogen PCR Buffer was purchased from Invitrogen Corp. Platinum® Taq DNA Polymerase was purchased from Invitrogen. DreamTaq™ DNA Polymerase and DreamTaq™ Buffer were purchased from Fermentas Life Sciences.
2.1.3.1 PCR for bacterial and archaeal 16S rRNA genes
The V3 variable region of the bacterial 16S rRNA gene was amplified using the primers pair F3+GC and Rev2. Final reactions contained: 1x GoTaq® Green Master Mix and 0.2 µM of each oligonucleotide primers. Reactions for the V3 variable region were initially denatured at 95°C for 5 min; followed by 35 cycles of 94°C for
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1 min, 60°C for 1 min and 70°C for 1 min followed by a final extension step at 72°C for 5 min.
The V3, V4 and V5 region of the bacterial 16S rRNA gene was amplified using the primers pair 341F+GC and 907R. Final reactions contained: 1x GoTaq® Green Master Mix and 0.2 µM of each oligonucleotide primers. Reactions for the V3, V4 and V5 region were initially denatured at 94°C for 4 min; followed by a touch down PCR: 20 cycles of 94°C for 1 min, 63-54°C for 1 min and 72°C for 1 min followed by 15 cycles of 94°C for 1 min, 53°C for 1 min and 72°C for 1 min plus an additional 10-min cycle at 72°C.
Archaeal 16S rRNA genes were amplified using the primers pair Arch333F+GC and 958R. PCR reaction mixture included the following: 1x Invitrogen PCR buffer, 0.5 M of betaine, 3 mM of MgCl2, 0.2 mM of each dNTPs, 0.8 µM of oligonucleotide primers and 1.0 U of Platinum® Taq DNA Polymerase. Reactions for archaeal 16S rRNA genes were initially denatured at 94°C for 4 min; followed by a touch down PCR: 20 cycles of 94°C for 1 min, 63-54°C for 1 min and 72°C for 1 min followed by 15 cycles of 94°C for 1 min, 53°C for 1 min and 72°C for 1 min plus an additional 10-min cycle at 72°C.
2.1.3.2 PCR detection of SRP (dsrAB, aprA, Archaeoglobus-specific 16S r RNA)
The aprA gene was amplified using the primers set P1F9 and P1R10. Reactions contained: 1x GoTaq® Green Master Mix and 0.2 µM of each primer. Reactions for the aprA were initially denatured at 95°C for 2 min; followed by 35 cycles of 95°C for 1 min, 62°C for 1 min and 72°C for 1 min followed by a final extension step at 72°C for 10 min.
The dsrAB genes were amplified using the primers set DSR1FK and DSR4RK. Reactions contained: 1x GoTaq® Green Master Mix and 0.2 µM of each primer. Reactions for the dsrAB were initially denatured at 94°C for 4 min; followed by 30
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Chapter 2 Materials and Methods cycles of 94°C for 1 min, 54°C for 1 min and 72°C for 2 min plus an additional 10- min cycle at 72°C.
Archaeoglobus-specific 16S rRNA amplicons were amplified using the primers set Arch5F and ARCH938R. PCR reaction mixture included the following: 1x DreamTaq™ Buffer, 0.5 M of Betaine, 0.2 mM of each dNTPs, 0.2 µM of primers and 1U of DreamTaq™ DNA Polymerase. Reactions for the Archaeoglobus-specific 16S rRNA gene were initially denatured at 94°C for 4 min; followed by 35 cycles of 94°C for 1 min, 61°C for 1 min and 72°C for 1 min followed by a final extension step at 72°C for 10 min.
2.1.3.3 PCR detection of NRB (nirK)
The nirK genes were amplified using the primers pair nirKR and nirKF. PCR reaction mixture included the following: 1x Invitrogen PCR Buffer, 0.5 M of betaine,
2 mM of MgCl2, 0.2 mM of each dNTPs, 1.6 µM of primers and 1.0 U of Platinum® Taq DNA Polymerase. Reactions for the nirK were initially denatured at 94°C for 4 min; followed by a touch down PCR: 20 cycles of 94°C for 45 s, 63-54°C for 45 s and 72°C for 2 min followed by 15 cycles of 94°C for 45 s, 53°C for 45 s and 72°C for 2 min plus an additional 10-min cycle at 72°C.
2.1.3.4 PCR detection of NRB (narG, napA)
The napA gene was amplified using the primers set napAF1 and napAR1. The narG gene was amplified using the primers set narGF1 and narGR1. Reactions contained:
1x Invitrogen PCR Buffer, 0.5 M of betaine, 2 mM of MgCl2, 0.2 mM of each dNTPs, 0.8 µM of primers napAF1, napAR1 and 1.6 µM of primers narGF1, narGR1 and 1.0 U of Platinum® Taq DNA Polymerase. Reactions for the narG and napA genes were initially denatured at 94°C for 4 min; followed by 35 cycles of 94°C for 1 min, 52°C for 1 min and 72°C for 1 min followed by a final extension step at 72°C for 10 min.
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2.1.3.5 PCR detection of methanogenes (mcrA)
The mcrA gene was amplified using the primers set ME1 and ME2. Reactions contained: 1x DreamTaq™ Buffer, 0.5 M of betaine, 0.4 mM of each dNTPs, 0.6 µM of each primer and 1.0 U of DreamTaq™ DNA Polymerase. Reactions for the mcrA gene were initially denatured at 94°C for 4 min; followed by 30 cycles of 94°C for 1 min, 51°C for 1 min and 72°C for 1min plus an additional 10-min cycle at 72°C.
2.1.3.6 PCR detection of N/SRB (nrfA)
The primers nrfAF1 and nrfA7R1 were used for the amplification of the nrfA gene. Final reactions contained: 1x Invitrogen PCR Buffer, 0.5 M of betaine, 3 mM of
MgCl2, 0.4 mM of each dNTPs, 1.0 µM of primers and 1.0 U of Platinum® Taq DNA Polymerase. Reactions for the nrfA gene were initially denatured at 95°C for 5 min; followed by a touch down PCR: 30 cycles of 94°C for 1 min, 60-45°C for 1 min and 72°C for 90 s followed by 30 cycles of 94°C for 30 s, 45°C for 30 s and 72°C for 1 min plus an additional 10-min cycle at 72°C.
2.1.3.7 PCR detection of firmicutes (hydA)
The hydA genes were amplified using the primers pair hydA1290F and hydA1538R. Reactions contained: 1x Invitrogen Buffer, 0.5 M of betaine, 3 mM of
MgCl2, 0.2 mM of each dNTPs, 0.8 µM of primers and 1.0 U of Platinum® Taq DNA Polymerase. Reactions for the hydA gene were initially denatured at 95°C for 3 min; followed by 35 cycles of 95°C for 40 s, 59°C for 30 s and 72°C for 30 s plus an additional 7-min cycle at 72°C.
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Table 2.1.2: Sequence of primers used in PCR.
Primer Sequences of + and – Primers Gene Target Reference Designate (nucleotide) Bacteria 341F+GC 5’-CGCCCGCCGCGCGCGGGCGGGGCGGGGGCA CGGGGGGCCTACGGGAGGCAGCAG-3’ Bacterial Muyzer et 907R 5’-CCGTCAATTCMTTTGAGTTT-3’ 16S rRNA al., 1998 5’- F3+GC CGCCCGCCGCGCGCGGCGGGCGGGGCGGGGGCA Bacterial Muyzer et CGGGGGGCCTACGGGAGGCAGCAG-3’ 16S rRNA al., 1993 Rev2 5’ATTACCGCGGCTGCTGG3’ P1F9 5’-CCAGGGCCTGTCCGCCATCAATAC-3’ Zinkevich aprA and Beech, P1R10 5’-CCGGGCCGTAACCGTCCTTGAA-3’ 2000 DSR1F 5’-AC[C/G]CACTGGAAGCACG-3’ Klein et al., dsrAB DSR4R 5’-GTGTAGCAGTTACCGCA-3’ 2001 nirKF 5’-TCATGGTCCTGCCGCGYGACGG-3’ Qiu et al., nirK nirKR 5’-GAACTTGCCGGTNGCCCAGAC-3’ 2004 narG1F 5’-ACICAYGGIGTIAACTGYAC-3’ narG Alcantara- narG1R 5’-TCGSMRTACCAGTCRTARAA-3’ Hernandez napAF1 5’-CTGGACIATGGGYTTIAACCA-3’ napA et al., 2009 napAR1 5’-CCTTCYTTYTCIACCCACAT-3’ hydA1290F 5’-GGTGGAGTTATGGAAGCWGCHHT-3’ Pereyra et hydA hydA1538R 5’-CATCCACCWGGRCAHGCCAT-3’ al., 2010 nrfAF1 5’-GCNTGYTGGWSNTGYAA-3’ Mohan et nrfA nrfA7R1 5’-TWNGGCATRTGRCARTC-3’ al., 2004 Archaea 5’-CGCCCGCCGCGCGCGGCGGGCGGGGC Arch Reysenbach GGGGCGACGCGGGGTCCAGGCCCTACGGG- Archaeal 333F+GC and Pace, 3’ 16S rRNA 1995 958R 5’-YCCGGCGTTGAMTCCAATT-3’ Archaeoglobus- Arch5F 5`-TATCCGG CTGGGACTAAGC-3` Duncan specific 16S r unpublished Arch938R 5`-TARGGTCTTCAGCCCGACCT-3` RNA ME1 5’-GCMATGCARATHGGWATGTC-3’ (Hales et al., mcrA ME2 5’-TCATKGCRTAGTTDGGRTAGT-3' 1996) Legend: R=G/A, Y=T/C, W=A/T, I = Inosine.
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2.1.3.8 Positive controls for PCR reactions.
Genomic DNA, extracted from laboratory type strains (Table 2.1.3), were used as positive controls for all PCR reactions. Details of microbial cultivation are given in subsection 2.3.2 and media composition in Appendix 2.
Table 2.1.3: Type strain used in this study. Target Genes Type Strain Bacteria Bacterial 16S rRNA Escherichia coli * Desulfovibrio vulgaris (strain Hildenborough, NCIB nrfA 8303) aprA and dsrAB Desulfovibrio spp.* hydA Clostridium pasterianum DSM 525 nirK Alcaligenes faecalis subs. parafaecalis DSM 13975 napA E.coli competent cell from TOPO cloning kit narG Pseudomonas stutzeri DSM 10701 Archaea Archaeal 16S rRNA, Methanococcus jannaschii DSM 2661 mcrA Archaeoglobus-specific Archaeoglobus fulgidus DSM 4304 16S rRNA * Strains optained from University of Portsmouth strains collections.
2.1.4 Qualitative analysis of PCR products using agarose gel electrophoresis
All the amplified PCR products (10 l) were analysed on either 0.9 % (w/v) or 1% (w/v) agarose gel made by dissolving the appropriate amount of agarose (Sigma Chemical Ltd, UK or Genopure LE agarose; BioExpress) in either 1xTAE (20 mM
Tris acetate, 10 mM sodium acetate, 0.5 mM Na2-EDTA) or 1xTBE (89 mM Tris base, 89 mM boric acid, 2 mM Na2-EDTA) buffer. DNA molecular weight markers (GeneRulerTM 1 kb DNA ladder, Thermo Scientific) or GeneRuler DNA mass ladder (Fermentas Life Sciences) were used for size determination of the PCR products. The gels were run in a horizontal electrophoresis chamber (Horizontal gel electrophoresis unit, Scie-Plas, UK) or Mini horizontal electrophoresis system (Mini-Sub Cell GT System, Bio-Rad, USA). A voltage of 50 V was applied for the first
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10 min and 140 V for the remaining 40 min or a voltage of 95 V was applied for 15 min for smaller gel. The gels was stained with the SYBR Safe DNA gel stain (Invitrogen) (6 l of stain added into 80 ml of agarose gel or 3 l of stain added into 40 ml of agarose gel) and was photographed under UV light using Alpha Innotech Gel Documentation System (Alpha Innotech Corporation, USA) and Digital Camera (Olympus C-4000 Zoom) or under UV light using the NucleoCam Digital Image Documentation System (NucleoTech).
2.1.5 Quantification of archaeal and bacterial 16S rRNA gene by Q-PCR
The number of archaeal and bacterial 16S rRNA gene copies in samples was estimated using Q-PCR (quantitative real-time polymerase chain reaction). The reaction contained the following concentrations of reactions: 1x SYBR Green PCR master Mix (Applied Biosystems/Life Technologies Corp., Carlsbad, CA), 0.5 M of Betaine, 500 nM Arc8F and 1 µM Arc344R primer for archaea, 250 nM of Bac8F and 125 nM of Bac338R primer for bacteria (Table 2.1.4) and 6 µl for archaea and 5 µl of a 1:10 dilution of template DNA for bacteria in a total volume of 30 µl. Real- time thermal cycling was performed in an Applied Biosystems StepOnePlus Real- Time PCR Systems (Life Technologies Corp., Carlsbad, CA).
Table 2.1.4: Primers used for Q-PCR amplification of archaeal and bacterial 16S rRNA gene.
Primer Sequences of + and – Primers Reference Designate (nucleotide) Archaela 16S rRNA Kormas et Arc8F 5’- TCCGGTTGATCCTGCC -3’ al., 2003; Raskin et Arc344R 5’- TCGCGCCTGCTGCICCCCGT -3’ al., 1994 Bacterial 16S rRNA Bac8F 5’- AGAGTTTGATCCTGGCTCAG -3’ Kormas et al., 2003; Bac338R 5’- TGCTGCCTCCCGTAGGAGT -3’ Turnbaugh et al., 2009
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Standards for calibration curves were prepared by constructing plasmid containing either bacterial or archaeal 16S rRNA gene from type strains (Bacillus licheniformis ATCC 14580 and Methanococcus jannaschii ATCC 43067). The 16S rRNA genes were PCR amplified, as described above, and cloned into pCR 2.1- TOPO vector according to the manufacture’s instructions (Invitrogen). Clones were recovered after transformation in E.coli TOP10 cells. Recombinant plasmid DNA purification was performed as described in subsection 2.1.9.3. Calibration curves were produced using 10, 1, 0.1, 0.01, 0.001 ng of each standard.
Each run contained negative control (no template control), to monitor for possible contamination, and one positive control comprising DNA from Bacillus licheniformis ATCC 14580 (at 1:100 dilution) and DNA of Methanococcus jannaschii ATCC 43067 (at 1:10 dilution). Reactions were initially denatured at 95°C for 10 min, followed by 40 cycles of 96°C for 30 s, 55°C for 45 s and 72°C for 45 s for bacteria. For archaea, the thermal profile was 95°C for 10 min, followed by 40 cycles of 94°C for 30 s, 55°C for 45 s and 72°C for 45 s.
For each QPCR, a dilution series of control DNA was run in duplicate with triplicate reactions of unknown samples. Data acquisition and analyses were performed using StepOne Software v2.1 and Microsoft Excel. Copy number of 16S rRNA genes for environmental DNA was estimated from the calibration curve, which was a graph of the copy number for standards (X-axis) versus CT value (Y-axis). Estimated copy number (CN) for standards (10, 1, 0.1, 0.01, 0.001 ng of standard) were converted using the equation (1) (adapted from Smith et al., 2006)
(1)
where C is the concentration [ng/µl] of standard, NA is the Avogadro’s number (6,02 x 1023 mol-1), MW is the molecular weight (660 x 109 ng/mol), and lg is the length of the standard - total plasmid size, (5339 bp for bacteria and 5345 bp for archaea)
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Equation nr 2 was used to get the estimated copy number for samples (2)
(2) where, y is the CT value, x is estimated copy number, ln= natural logarithm, a = slope and b = intercept
Finally correction for dilution factor, µl samples for QPCR and total volume of original sample were calculated. A final plot, 16S rRNA copy numbers per 1 ml of sample was generated for the triplicate average. Standard deviation (SD) and standard error (SE) were calculated using:
(3) where, SQRT means the square root and n, number of samples.
2.1.6 Denaturing Gradient Gel Electrophoresis (DGGE)
2.1.6.1 Bio-Rad system
The products of 30 µl PCR-amplified archaeal 16S rRNA genes (Subsection 2.1.3.1) were separated by DGGE using a D-Code™ 16/16–cm Gel Apparatus (Bio-Rad, Hercules, CA), at a constant voltage of 65 V for 16 h and a constant temperature of 60°C, after an initial 5 min. at 135V. The standard gradient was formed of 6% polyacrylamide in 0.5xTAE buffer with between 30% and 80% denaturant (7 M urea and 40% formamide defined as 100% denaturant). After electrophoresis, the gel was stained with standard Silver staining (details in Table 2.1.5) and a picture was taken with a digital camera (Nikon D500).
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Table 2.1.5: Silver staining protocol.
Step Time Reagents Fix 20 min 10% acetic acid Rinse 2 min water Stain 30 min 0.1% AgNO3, 0.15% HCOH Wash 20 s water Develop 2-5 min 3% Na2CO3, 0.15% HCOH, 0.0002% Na2S2O3·5H2O Stop 5 min 10% acetic acid
2.1.6.2 Ingeny system
An Ingeny DGGE apparatus (Ingeny International BV, The Netherlands) was used for sequence-specific separation of PCR products (bacterial 16S rRNA) (Subsection 2.1.3.1). 30 l of each PCR product was applied to 9% (w/v) or 6% (w/v) polyacrylamide gels in 0.5x TAE buffer. The electrophoresis was performed at 60°C using the gel containing 30% to 90% urea-formamide denaturing gradient (100% corresponded to 5.6 M urea and 40% (v/v) formamide) increasing in the direction of electrophoresis. The gel was subjected to electrophoresis for 10 min at 200 V and for 20 hours at 90 V. Following electrophoresis the gel was carefully removed from the tank, placed in a plastic container, stained with 15 l of SYBR Safe DNA gel stain (Sigma, UK) in 750 ml of double distilled water for at least 15 min in a dark room and rinsed thoroughly with distilled water. The gels were photographed under UV light using Alpha Innotech Gel Documentation System (Alpha Innotech Corporation, USA).
2.1.7 Extraction of DNA from cut bends from DGGE gel
All visible bands were cut from the DGGE gel using a sterile scalpel blade, crushed with a pipette tip and transferred into a sterile 1.5 ml microcentrifuge tube containing 30 µl of ultra pure water to extract the DNA, incubated overnight at 4°C, followed by 1 hour incubation at 37°C. Samples were then centrifuged at 13 000 x g for 1 min (Heraeus Fresco 21, Thermo Scientific, UK) and 5 µl aliquots of the supernatants were used for PCR re-amplification as described above (Subsection
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2.1.3.1) (adapted from: Machado et al., 2007). PCR products were purified using the NucleoSpin® Extract II PCR purification kit (Macherey-Nagel, UK) and sent to the GATC Biotech UK (Cambridge, UK) DNA sequencing service.
2.1.8 Phylogenetic analysis
Sequences were aligned using Clustal Omega (Sievers et al., 2011) as implemented in SeaView ver4.0 (Gouy et al., 2010). Phylogenetic trees were constructed using the maximum likelihood optimal criteria implemented in PhyML 3.0 (Guindon et al., 2010), which is part of SeaView ver4.0. The generalized time reversible (GTR) model was the substitution model employed using a rate category of 4. Trees were constructed using BioNJ method (Gascuel, 1997) with NNI tree searching operations. Support for nodes in the produced trees was estimated with a bootstrap analysis, implemented in PhyML 3.0. A bootstrap search of 100 repetitions was used in this analysis.
The DGGE gel was scored for the presence and absence of bands for each sample examined. A comparative table was constructed with band presence score as 1, and absence scored as 0. This table was used to construct a matrix from which genetic distance was calculated according to the algoritm of Nei and Li (1979) as implemented in PAUP* 4.0 (Swofford 2003). From the genetic distance a UPGMA tree was constructed.
2.1.9 Cloning: construction of genomic libraries and sequencing
The DNAs extracted from field samples were used as the PCR templates for cloning procedure. Archaeal, Archaeoglobus-specific, Bacterial 16S rRNA (550bp) aprA, narG and mcrA genes were amplified according to the conditions described in subsection 2.1.3.
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2.1.9.1 PCR products clean-up
PCR products were cleaned-up using NucleoSpin® Extract II (Macherey-Nagel) following the manufacturer’s instructions (PCR clean-up; Gel extraction User Manual, 05/2010 p.13-14, 20). 1 volume of sample and 2 volume of Buffer NT were mixed. Sample was loaded into NucleoSpin® Extract II Column, centrifuged at 11 000 x g for 1 min and flow through was discarded. 700 µl of Buffer NT3 was added to the NucleoSpin® Extract II Column, centrifuged at 11 000 x g for 1 min and flow through was discarded. 250 µl of Buffer NT3 was added to the NucleoSpin® Extract II Column, centrifuged at 11 000 x g for 2 min to remove Buffer NT3 and flow through was discarded. Finally, the PCR product was eluted by placing NucleoSpin® Extract II Column in a clean 1.5 ml eppendorf tube after adding the 25 µl PCR Buffer NE on the membrane, incubated at room temperature for 1 min and then centrifuged at 11 000 x g for 1 min.
2.1.9.2 Ligation of plasmid DNA and competent cells transformation
The ligation reaction (final volume: 10 µl) was set up using pGEM®-T Easy vector (Promega, UK) according to standard methods (Sambrook et al., 1989). 50 ng (1 µl) of vector was used with the molar ratio of vector to insert (PCR product) of 1:3 as recommended by the manufacturer (technical manual no. 042). 1-3 µl of ligation mix, 2X rapid ligation buffer (5 µl) and T4 DNA ligase (1 µl) and the appropriate volume of water were used for subsequent transformation.
The ligation reactions were mixed and incubated for 1 h at room temperature, then centrifuged and 2 µl added to sterile 14 ml tube (Round-Bottom Falcon tube Becton Dickinson) and placed on ice. 50 µl of JM109 high efficiency competent cells (Messing et al., 1981) were added to ligated mixture and incubated for 20 min.
Heat-shock was performed by placing sample in a water bath at 42°C for 50 s and immediately returned to the ice for 2 min. SOC medium (950 µl) (Sigma, UK) was
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Chapter 2 Materials and Methods added to the transformed cells and incubated for 1.5 h at 37°C with shaking (~150 rpm). 100 µl of each transformation were plated onto LB/Ampicillin/IPTG/X-Gal plates in duplicate. The solid LB medium was supplemented with 100 µg ampicillin ml-1 , 100 µg X-Gal ml-1 and 0.5 mM IPTG. Plates were incubated overnight at 37°C and after at 4°C overnight for better blue/white screening. The white colonies were picked from plates and grown overnight in 5 ml of LB medium (Fisher, UK) (Sambrook et al., 1989) at 37°C with vigorous aeration (~200 rpm).
2.1.9.3 Purification of plasmid DNA from bacterial cells
White bacterial colonies were selected and grown overnight at 37°C in 4 ml Luria Broth (Fisher) supplemented with ampicillin (final concentration 100 µg/ml; Fisher) with vigorous shaking. Sterile polypropylene universal tubes were used for colony incubation. Recombinant plasmid DNA purification was performed using liquid cultures of the positive E. coli colonies obtained from the transformation (described above) with NucleoSpin® Plasmid QuickPure (Macherey-Nagel) following the manufacturer’s instructions (Plasmid DNA Purification User Manual, 07/2010 p.18-19). 3 ml of E.coli LB culture harvested in eppendorf tubes (1.5 ml) by centrifugation at 11 000 x g for 1 min (Heraeus Fresco 21, Thermo Scientific, UK) were resuspended in 250 μl Buffer A1. 250 µl of Buffer A2 was added to the sample, mixed thoroughly by inverting the tube 6-8 times and incubated at room temperature for 5 min. Buffer A3 (300 µl) was added, mixed thoroughly by inverting the tube 6-8 times and centrifuged at 11 000 x g for 5 min. NucleoSpin Plasmid QuickPure Columns were placed into clean 2 ml collection tubes, 750 µl of supernatant was loaded onto spin column and centrifuged at 11 000 x g for 1 min and flow through was discarded (this step was repeated until all supernatant has been loaded). Buffer AQ (450 µl) was added and centrifuged at 11 000 x g for 3 min. Finally the plasmid DNA was eluted by placing NucleoSpin Plasmid QuickPure Column in a sterile 1.5 ml eppendorf tube after adding the 25 µl PCR grade H2O directly onto the membrane, incubated at room temperature for 1 min and then centrifuge at 11 000 x g for 1 min.
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Insertion of the PCR product of interest into the plasmid was verified by PCR and by agarose gel electrophoresis analysis (Subsection 2.1.4).
2.1.9.4 Sequencing of transformed plasmid
Plasmids of interest were sent to the GATC Biotech UK DNA sequencing service. The DNA fragments were identified by sequencing using T7 vector primers and homology searches in BLAST. Sequencher (Gene Codes Corpo.) was used to trim vector regions from the cloned sequences and to examine each cloned sequence for the presence of universally conserved regions (e.g. primer regions). For all libraries, initial sequence homology assignments were made following BLASTN searches.
2.1.10 Microorganisms visualisation by confocal laser scanning microscope (CLSM)
Approximately 10 g of ID1 sample was vortexed overnight in a 25 ml universal tube at room temperature with 10 ml of distilled water. The supernatant (1 ml) was filtrated using a vacuum pump and autoclaved polycarbonate filter (GTTB, Ø 2.5 cm, 0.22 μm pore size, Millipore®) which was stained with 5 µl of 0.01% (v/v) DAPI in dark room for 10 min. The membrane was washed twice with deionised water and dried.
Ready membrane was placed on a microscopic slide, covered by Citifluor® AF-2 antifading glycerol solution and visualized using LSM 510 Carl Zeiss® Jena laser scanning microscope. The objective used for the imaging was Carl Zeiss® EC Plan- Neofluar 100x/1.30 Oil Pol. Scanning was performed in multichannel mode. DAPI was excited at the wavelengths of 364 nm and 351 nm. For observation of surface reflection wavelength of 633 nm was applied. The main dichroic mirrors HFT UV (375) and HFT UV/488/568/633, band pass filter BP 385-470 and high pass filter LP 560 were used. The software used to acquire and process the images was LSM 510 Release 3.2 (Zeiss®).
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Section 2
Detection and characterisation of microorganisms and their functional genes in the oil field samples by advance molecular techniques
2.2.1 Pyrosequencing of bacterial 16S rRNA gene libraries
2.2.1.1 Sample preparation for 454 Pyrosequencing of 16S rRNA genes.
Bacterial 16S rRNA gene (spanning the V1-V2 region) libraries were generated for DNA extracted from pigging envelopes (Subsection 2.1.5) from Installations A (samples: IA2 and IA5), C (samples: IC106, IC1206 and IC2406) and D (sample: ID1).
PCR reactions for Bacterial 16S rRNA gene, contained: 1x Invitrogen PCR Buffer,
0.5 M of betaine, 2 mM of MgCl2, 0.2 mM of each dNTPs, 0.25 µM of primers Bac8F, 0.125 µM of Bac338R (Table 2.1.4) and 1.0 U Platinum® Taq DNA Polymerase.
Reactions were initially denatured at 96°C for 3 min; followed by 30 cycles of 96°C for 30 s, 55°C for 45 s and 72°C for 45 s followed by a final extension at 72°C for 10 min. PCR products were separated in a 1.0% (w/v) agarose gel stained with SYBR Safe DNA gel stain (Invitrogen) (Subsection 2.1.4) and viewed under UV.
2.2.1.2 Tagging PCR
The conditions for “tagging PCR” targeting bacterial 16S rRNA were described previously (Subsection 2.1.5), except that the number of cycles, was reduced to 6. Reactions contained: 1x DreamTaq™ Buffer, 0.2 mM of each dNTPs, 0.25 µM of primers TiA-8nt-8F and TiB-338R (Table 2.2.1), and 1U of DreamTaq™ DNA Polymerase.
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Table 2.2.1: Primers used for DNA amplification for the pyrosequencing process for bacteria. Life Sciences 454 Titanium “A” and “B” primers are underlined, N’s designate location of unique barcode and spacer nucleotides are in bold (Stevenson et al., 2011; Hamady et al., 2008).
Primer Sequences of + and – Primers Gene Designate (nucleotide) Target 5’- CCA TCT CAT CCC TGC GTG TCT CCG ACT CAG TiA-8nt-8F NNN NNN NNC AAG AGT TTG ATC CTG GCT CAG -3’ Bacterial 5’- CCT ATC CCC TGT GTG CCT TGG CAG TCT CAG CAT 16S rRNA TiB-338R GCT GCC TCC CGT AGG AGT -3’
All tagged PCR products were purified and concentrated with Amicon Ultra-0.5 Centrifugal Filter Devices (Millipore) according to the manufacturer’s instructions. PCR products were sequenced on a 454 GS-FLX platform following the 454 Roche recommended procedures. The sequencing was done at the Advanced Center for Genome Technology at the University of Oklahoma.
2.2.1.3 Pyrosequencing data analysing
Files uploaded from the University of Oklahoma Advanced Center for Genome Technology (ACGT) in .SFF format were extracted and denoised using the QIIME analysis package version 1.6 (Caporaso et al., 2010, a). Denoising is required to reduce the error rate inherent within 454 pyrosequencing chemistry, while at the same time reducing the rate of chimerism or artificially created taxa (Reeder and Knight, 2010). The resultant output was further checked for chimeras using the USEARCH algorithm (Edgar, 2010). This process checks sequences against a known chimera-checked database and a de-novo process to search for chimeras within the dataset and removes them. Finally, taxa were aligned using the PyNAST aligner (Caporaso et al., 2010, b) and classified to genus level using the RDP aligner (Wang et al., 2007) with the SILVA small ribosomal subunit database (Quast et al., 2013). Beta diversity was carried out using the suggested mean sampling depth within the QIIME package. The analysis of pyrosequencing results was done by Dr Athenia L. Oldham from Department of Microbiology and Plant Biology, University of Oklahoma.
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The samples used for pyrosequencing were unique, so that there were no replicate sequences to make variation comparisons within the samples from the different sites.
2.2.2 GeoChip 4.0 microarray
DNA extracted (Subsection 2.1.2) from bacteria contained in field samples from Installation A (sample: IA1, IA4, IA9) and C (sample: IC1, IC2, IC3) was processed in the Institute for Environmental Genomics (IEG) at the University of Oklahoma, as described previously (He et al., 2011; Wu et al., 2008).
2.2.2.1 DNA labelling
The concentration of DNA for the labeling procedure was determined with PicoGreen® dsDNA quantitation kit performed on BMG LABTECH POLARstar OPTIMA Microplate Reader. At least 1 µg of DNA was labeled with fluorescent dye (0.5 nM of CyDye – Cy3; GE Healthcare) using random octamer primers (20 µl; 2.5x (750 µg/ml) Invitrogen, BioPrime DNA Labelling kit) and dNTP mix (0.25 mM; dTTP 0.125 mM) mixed with the Klenow fragment (80 U; Invitrogen BioPrime Labelling kit). Samples were incubated at 99.9°C for 5 min (Thermo cycler, Applied biosystems, GeneAmp PCR system 9700) then chilled immediately on an ice. The mastermix was added to the mixture with DNA, and the samples were incubated at 37°C for 6 hours and then at 95°C for 3 min. The labelled DNA was purified with QIAquick PCR purification Kit (Qiagen, Handbook p. 19-20) as specified by the manufacturer’s instructions and dehydrated (Savant ISS110 SpeedVac® Concentrators) for hybridization on Geochip 4.0. The pellets were re-hydrated with 2.68 µl of different sample tracking controls (STC). Each STC is a Cy3-labelled 48 mer oligonucleotide. A unique STC is added to each sample before hybridisation to a multiplex array to confirm sample identity. Samples were incubated at 50°C for 5 min in water bath.
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2.2.2.2 GeoChip hybridisation
The labelled DNA was re-suspended in 7.12 µl of hybridization solution (prepared in a dark room) containing 60 µl of 40% formamide, 37.5 µl of 25% SSC, 1.5 µl of 1% SDS, 2.4 µl of aligment oligo Cy3, 2.4 µl of aligment oligo Cy5 and 3 µl of Cy 5 universal standard. The mix was denatured at 95°C for 5 min and DNA were hybridized to slides at least for 16 hours at 42°C.
Hybridizations were performed with a MAUI Hybridization System, BioMicro® Systems according to the manufacturer’s recommended method. After hybridisation 3 washes were performed in washing solutions from NimbleGen Wash Buffer Kit (Roche). Slides were dried at 45°C for 2 min using NimbleGen Microarray Dryer and stored in the dark.
2.2.2.3 Microarray scanning and data processing
Hybridized array was imaged with a microarray scanner (NimbleGen MS 200 Microarray Scanner, Roche) and analysed using the Microarray Data Manager – software designed at IEG (http://ieg.ou.edu). Microarray analysis software was used to quantify the signal intensity of each spot. Spots are evaluated based on signal-to-noise ratio (SNR: SNR = (signal mean – background mean)/ background standard deviation). The signal intensities are then normalized and stored in a database for further analysis.
The normalized hybridisation data for individual functional gene sequences were then reorganized based on functional genes, such as nirS, aprAB (Zhou et al., 2008). The samples used for GeoChip were unique, so that there were no replicate sequences to make variation comparisons within the samples from the different sites.
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Section 3
Probe designing and testing
2.3.1 Probe designing
Target groups of organisms and metabolic pathways implicated in biocorrosion were specified based on PCR, Cloning, GeoChip, Pyrosequencing results (Chapter 3) and literature review. The NCBI database (http://www.ncbi.nlm.nih.gov) was used to derive sequences for probe design. The partial nucleotide and protein sequences for each functional and structural gene were downloaded and saved in FASTA format, initially aligned using the Clustal-X 2.1 program (Larkin et al., 2007), before manually adjusting in the BioEdit program (Sequence Alignment Editor) (Hall, 2011). Protein sequence was converted to DNA sequence with on- line reverse translation of protein to DNA tool (http://www.staff.ncl.ac.uk/n.j.morris/lectures/parts/tools/revscript.html). Based on final alignments, conservated parts of each gene were chosen and used for probe designing using the Primer3 program (v. 0.4.0) (http://frodo.wi.mit.edu/primer3/) (S. Rozen, and H. J. Skaletsky). The length of each probe was established ranging from 17 to 25mer and melting temperatures (Tm) of approximately 60°C. The GC content was calculated and only probes with a GC content between 40 and 60% were selected for further analysis. The possibility of the probes creating hairpins was predicted using the Integrated DNA Technologies SciTools OligoAnalyzer 3.1 (http://eu.idtdna.com/analyzer/applications/oligoanalyzer/default.aspx). All designed probes were verified against the GeneBank nucleic acid database for specificity using the BLAST program (Altschul et al., 1990). Persentage of specificity (POS) were calculated using:
(4)
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Chapter 2 Materials and Methods where, X means the total number of hits and Y, number of hits (of interest) for particular gene.
Published probes and an established rRNA-targeted oligonucleotide probe database named 'probeBase' (Loy et al., 2003) were also used for probe selection.
2.3.2 Bacteria and Achaea type strains culture conditions
DNA for the following prokaryotic strains was used in the hybridisation procedure:
Methanococcus jannaschii DSM 2661, Archaeoglobus fulgidus DSM 4304, Shewanella oneidensis MR-1, Shewanella profunda DSM 15900, Desulfovibrio alaskensis NCIMB 13491, Desulfovibrio indonensiensis NCIMB 13468, Desulfovibrio vulgaris (strain Hildenborough, NCIB 8303), Desulfovibrio vietnamensis DSMZ 10520, Desulfovibrio desulfuricans ATTC 27774, Clostridium pasterianum DSM 525, Alcaligenes faecalis subs. parafaecalis DSM 13975 and Escherichia coli (BP) purchased from German Collection of Microorganisms and Cell Cultures (DSMZ) or obtained from the University of Portsmouth, School of Pharmacy and Biomedical Sciences bacterial culture collection.
SRP strains were cultivated anaerobically in Vitamin Medium (VM medium) (Zinkevich et al., 1996) at 37°C for 3 days (Desulfovibrio sp.) or at 80°C for 7 days (A. fulgidus). S. profunda was cultivated anaerobically in Vitamin Medium with iron (VMI medium) at 37°C for 7 days, S. oneidensis and E. coli were cultivated aerobically in Lysogeny Broth (LB) medium (adapted from Bertani, 1951) (Fisher) at 37°C for 24h. C. pasterianum was cultivated anaerobically in glucose yeast extract medium at 37°C for 1-2 days. A. faecalis was cultivated aerobically in Nutrient Agar 37°C at 37°C for 1-2 days. Additionally Pseudomonas stutzeri (DSM 10701), which DNA was used as a positive control for PCR reaction, was incubated at 30°C in Nutrient Broth in 24h.
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In all experiments 10% (v/v) of inocula were used, except of E. coli where 1% (v/v). The composition of VM, VMI, glucose yeast extract medium, LB medium nutrient broth and nutrient agar is shown in Appendix 2.
DNA of M. jannaschii was obtained from the culture collection of DSMZ.
2.3.3 Purification of total DNA from bacterial cultured cells
Chromosomal DNA was extracted from cultured cells using QIAGEN DNeasy Blood and Tissue Kit (QIAGEN, Germany) following the manufacturer’s instructions (DNeasy Blood and Tissue Handbook 07/2006 p.25-27). The bacterial cells harvested in eppendorf tubes (1.5 ml) by centrifugation at 13 000 x g for 5 min (Heraeus Fresco 21, Thermo Scientific, UK) were resuspended in 180 μl enzymatic lysis mixture. 2 mg of lysozyme (20 mg/ml)(Sigma-Aldrich) was added and incubated at 37°C for 30 min. 25 µl of Proteinase K (10 mg/mL) and 200 µl of Buffer AL were added, mixed by vortexing and incubated at 56°C for 30 min. 200 µl of 100% ethanol was added to the sample, mixed thoroughly by vortexing and the mixture was placed into the DNeasy Mini Spin column (placed in a 2 ml collection tubes) and centrifuged at 6 000 x g for 1 min, the flow through was discarded and the DNeasy Mini Spin column was placed in a new collection tube. 500 µl of Buffer AW1 was added to the column, which was then centrifuge at 6 000 x g for 1 min, the flow through was discarded and the DNeasy Mini Spin column placed in a new collection tube. 500 µl of Buffer AW2 was added to the column, which was then centrifuged at 13 000 x g for 3 min and the flow through was discarded. Finally the DNA was eluted by placing DNeasy Mini Spin column in a clean 1.5 ml eppendorf tubes after adding the 200 µl Buffer AE directly on the DNeasy membrane, incubated at room temperature for 1 min and then centrifuged at 6 000 x g for 1 min.
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2.3.4 Construction of model DNA for hybridisation by PCR amplifications
Probes ARC915, ApsP1R10, Arch5F, and Shew227 for DNA hybridisation procedures were prepared by PCR amplification of the appropriate fragments.
The aprA gene of A. fulgidus and Desulfovibrio sp. was amplified from genomic DNA using the primer pair P1F9 and P1R10 as described by Zinkevich and Beech (2000) (Subsection 2.1.3.2).
The Archaeal 16S rRNA gene was amplified from M. jannaschii and A. fulgidus DNA was amplified using the archaeal- specific primers Arch333F and 958R as reported by Reysenbach and Pace, (1995) (Subsection 2.1.3.1).
The Archaeoglobus-specific 16S rRNA gene was amplified using the primers Arch5F and ARCH938R (Duncan unpublished) (Subsection 2.1.3.2).
The Shewanella 16S rRNA gene (1048 bp) was amplified from S. oneidensis and S. profunda DNA using the primer pair She211F (5’-CGCGATTGGATGAACCTAG-3’) and She1259R (5’-GGCTTTGCAACCCTCTGTA-3’) as described previously (Todorova and Costello, 2006). Final reactions contained: 1x GoTaq® Green Master Mix and 0.4 µM of each primer. Reaction were initially denatured at 94°C for 1.5 min; followed by 24 cycles of 92°C for 1 min, 53°C for 1.5 min, 72°C for 1 min with a final extention at 72°C for 5 min.
2.3.5 Construction of model DNA for evaluation of hybridisation efficiency
Model DNA for hybridisation were prepared by cloning designated oligonucleotides into a standard pGEM-T Easy Vector in groups of 3 (cloning procedure described in subsection 2.1.9). Constructs FGH (containing: FTHFS1,
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Geo1A and hydA1 probes), m1G3S11 (containing: mcrA1, narG3 and nirS11 probes), aRKnA2 (containing: assAR, nirKF and napA2 probes) and D4Ra3d1 (containing: Dsr4R, aprAB3 and dsrAB1 probes). The sequences of tested constructs are shown in Appendix 3.
Oligonucleotide inserts were ordered as the single stranded DNA correspondent to sense and antisense sequence of interest. All designed oligonucleotide inserts were commercially synthesized by Invitrogen. The concentration of oligonucleotide sequence was adjusted to 100 µM by resuspension in appropriate volume of oligonucleotide hybridisation buffer. For duplex preparation 20 µl of each oligonucleotides (sense and antisense) were mixed and incubated for 10 min in 95°C and left overnight in cooling thermoblock.
Plasmids with inserts were sent to the GATC Biotech UK DNA sequencing service. The DNA fragments were identified by sequencing using T7 vector primers.
2.3.6 Hybridisation
All selected oligonucleotides, commercially synthesized by Invitrogen were labeled with hexachloro-fluorescein (Hex) fluorescent dye (absorbance maximum=535nm, emission maximum=556). The concentration of oligonucleotide probes was adjusted to 1 μg/μl by resuspension in appropriate volume of resuspension buffer.
Optimization of hybridization conditions was carried out as described below. The appropriate amount (100-400 µg) of DNA was denatured in 95°C for 15 min in a thermocycler, quenched on ice and transferred to a positively charged nylon membrane (Amersham Hybond N+) cut into strips. DNA was fixed to the membrane by UV cross-linking (UV Stratalinker 2400, Stratagene) for 30 seconds at 1200 μjoules X 100. Any unbound DNA molecules were removed by two washes in 15 ml of wash buffer for 2 min at room temperature. The strips were dried and stored at room temperature, ready for use.
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Prepared membranes were placed in a sterile tube and incubated in 10 ml of pre- hybridization solution at 55°C for 2 hours. The membranes were transferred to 10 ml of hybridization solution, and the appropriate probe added. The mixture was incubated overnight at the melting temperature (Tm) of the probe used. The strips were removed from the tubes and washed in 25 ml of low-stringency buffer followed by 25 ml of high-stringency buffer at the appropriate melting temperature for 20 min (Daly et al., 2000; Anthony et al., 2000). The hybridisation signal was detected by measuring the fluorescence intensity at the emission wavelength of Hex (λ=538 nm) with a fluorescence image analyzer (FUJIFILM FLA- 5000). All buffers used for hybrydyzation are listed in Appendix 4.
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Chapter 3 Results and Discussion
Section 1
Analysis of sampling site using 16S rRNA gene sequences.
The 16S rRNA gene is widely used for microbial identification and phylogeny construction. Molecular techniques including CLSM, (Q)PCR, DGGE and sequencing were used to detect and quantify eubacteria and archaea in samples obtained from North Sea oil production and water injection facilities to recognise the difference in microbial populations between corroding and non-corroding systems.
3.1.1 Microorganisms visualisation by light microscope and confocal laser scanning microscope (CLSM) in provided field samples.
Microscopy techniques were routinely used to examine oil field samples (liquid or sludge) (NACE standard, 2012). In this study the light microscope and CLSM were used to visualise microorganisms in the samples provided. Light microscope studies were used to confirm the presence of microorganisms for each system (Installation A, C and D) before examination by the confocal microscope (CLSM). Cocci, spiral and rod–shaped microorganisms were observed in all samples. Samples stained with DAPI were subjected to CLSM analysis. Acceptable results were obtained only for samples from Installation D – production pipelines (Figure 3.1.1). DAPI staining reveals presence of both dead and live microorganisms. Rod– shaped microorganisms were pre-dominant, characteristic of Clostridia, SRPs and Synergistetes, and this was confirmed by sequencing 16S rRNA genes. Filamentous organisms were not observed. The size of the cells was estimated to be between 0.5-2 µm.
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Figure 3.1.1: CLSM visualisation of microorganisms in the ID1 pigging sample (Installation D – production pipeline); DAPI staining. Red Bar: 2 µm.
3.1.2 Extraction and purification of DNA for molecular analysis.
The most important step for the molecular analysis of microorganisms is the extraction and purification of nucleic acids. Total DNA, from environmental samples, was successfully extracted from the field samples that comprised biofilms from the three different installations. Total genomic DNA from pure cultures was also extracted using several techniques.
DNA from type strains was extracted using standard QIAGEN DNeasy Blood and Tissue Kit. DNA concentration and purity was evaluated by NanoDrop spectrophotometry. The concentration varies between 10- 400 ng/µl depend on microbial strain.
PowerBiofilm® or PowerSoil® (MoBio) DNA Isolation Kit were used to extract DNA from field samples, obtaining concentrations between 5.5 and 93.5 ng/µl.
In general, environmental samples contain inhibitors that prevent further DNA manipulation (Wilson 1997). MoBio Isolation Kits are frequently used for DNA extraction from environmental samples, including those recovered from oil reservoirs (Ferrando and Tarlera, 2009; Stevenson et al., 2011), as it contains
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Chapter 3 Results and Discussion metal ions removal system. All environmental samples were “bead beated”, which caused mechanical damage of microbial cells, even those with tough cell walls like Gram-positive Firmicutes (Oldham et al., 2012).
All extracted DNA was subjected to 16S rRNA gene-PCR using universal primers to confirm that the DNA was relatively pure (PCR inhibitor free) before they were examined further.
The DNA extraction protocols for oil reservoirs samples still require significant effort to optimize. Furthermore, obtaining adequate amounts of DNA can be hindered by the low biomass or limited quantity of samples available.
Generally commercial kits yield high-quality nucleic acids but are time consuming. Nowadays, automated systems (e.g. Maxwell® 16 System (Promega) and QuickGene-Mini80 (Fujifilm)) are proposed as good alternative for different environment study, including oil field (Oldham et al., 2012). The main advantages include time saving and bypassing shifts in bacteria community during the transportation of samples. The quality of extracted DNA is comparable with manual kits. As DNA does not reflect the metabolic activity of present microorganism and may come from dead and dormant cells, development of protocols for RNA extraction from crude samples is needed. The DNA/RNA extraction protocol has already been suggested for this type of sample (Marty et al., 2012).
3.1.3 Enumeration of bacteria and archaea using quantitative PCR
Quantitative PCR (QPCR) is routinely employed to monitor the numbers of microorganisms in oilfield systems (NACE standard, 2012). In this study, QPCR was used to estimate the bacterial and archaeal abundance in two systems: Installation A (IA1, IA4, IA9) and C (IC1, IC2, IC3) using available in this time pigging debris. The sample from Installation D was not available at this time. To ensure that a broad range of templates was accessible, samples representing
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Chapter 3 Results and Discussion individual pigging operations in each installation were combined. In this molecular determination, the number of bacterial and archaeal 16S rRNA gene copies was estimated per volume (ml) of sample.
The number of bacterial 16S rRNA gene copies was calculated to be 9.15 x 108 copys in water injection system (Installation A) and 4.78 x 108 copys in rapidly deteriorating production pipeline (Installation C) (Figure 3.1.2). Archaeal 16S rRNA genes were detected in both samples in numbers 7.51 x 106 and 8.06 x 106 copy respectively, which indicated much lower quantity then bacterial 16S rRNA gene copies (Figure 3.1.3).
Bacterial 16S rRNA copy numbers per ml of samples 1.20E+09
1.00E+09
8.00E+08
6.00E+08
4.00E+08
2.00E+08
0.00E+00 Installation A Installation C
Figure 3.1.2: Copy number of 16S rRNA genes per ml of sample for Bacteria based on QPCR analysis. Error bars indicate standard error of means (n=3). DNA extracted from pigging samples from Installation C (production pipeline) and Installation A (water injection system).
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Archaeal 16S rRNA copy number per ml of samples 9.00E+06 8.00E+06 7.00E+06 6.00E+06 5.00E+06 4.00E+06 3.00E+06 2.00E+06 1.00E+06 0.00E+00 Installation A Installation C Figure 3.1.3: Copy number of 16S rRNA genes per ml of sample for Archaea based on QPCR analysis. Error bars indicate standard error of means (n=3). DNA extracted from pigging samples from Installation C (production pipeline) and Installation A (water injection system).
The pre-dominance of bacterial 16S rRNA genes compared to archaeal 16S rRNA genes obtained in this study is in agreement with the results of Stivenson et al. (2011) where similar oil field samples were examined. The greater bacterial 16S rRNA copy number in Installation A can be associated with its temperature (55°C compared to 80°C) that is favoured by a wider range of microorganisms. Both sites A and C, however, show little correlation between the degree of corrosion and 16S rRNA gene copy number. This lack of correlation is perhaps not surprising since MIC is very complex phenomena caused by a community of microorganisms. It is likely that the activity of a particular group of bacteria and archaea is more important. Bryant et al., (1991) showed that in intensely corroded systems, the number of SRB’s cells was low but they exhibited greater hydrogenase activity, comparing to non-corroding oil pipeline.
It is also important to note, that DNA recovered from a particular environment may also originate from dead or dormant cells. The direct survey of biofilms with QPCR methods may in fact be additionally biased towards those microorganisms
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Chapter 3 Results and Discussion that possess higher 16S rRNA gene copy numbers (Klappenbach et al., 2001), which could explain higher number of 16S rRNA gene. Furthermore, quantitative results do not give complete information about functional activity of microflora when only structural genes are targeted. Therefore, QPCR of marker genes has been used to monitor functional microbial group in examined habitats (NACE standard, 2012).
3.1.4 DGGE analysis of microbial communities in the field samples.
DGGE has been used extensively in oil field microbiology to analyse microbial community structure (Shartau et al., 2010). Each band on a DGGE pattern is representative of specific microbial group and the number of distinctive bands is indicative of microbial diversity. Environmental DNA extracted from the pigging samples was used to analyse the archaeal (Installation C: IC106, IC206, IC306, IC606, IC1206, IC11P and Installation D: ID1) and bacterial (Installation A: IA1, IA4, IA9, IA2; Installation C: IC102, IC802, IC206, IC606, IC3P and Installation D: ID1) communities via denaturing gradient gel electrophoresis (DGGE) of PCR-amplified 16S rRNA gene fragments (625 bp and 550bp respectively). Different pigging debris samples were analysed using DGGE for bacterial and archaeal at different times because of an inconsistency in sample provision by the industrial operators. DGGE analysis revealed the presence of diverse archaea and bacterial populations in biofilms present in the water injection system and production pipelines. Figure 3.1.4 shows the archeal DGGE banding profiles for samples from Installation C and D. Installation C samples had 4 major bands whereas Installation D had 5. The bands displayed similar motilities indicating that the sites had a similar diversity. The DGGE patterns for all samples from Installation C were identical, indicating a stable archaea consortium. Similar banding profiles were present in the Installation D, with the exception of an additional band. This probably reflects the presence of a different taxon in this system at that time, although the additional band may be a result of using a degenerate reverse primer for PCR. To confirm presence of a different species in ID1 sample, this band would have to be
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Chapter 3 Results and Discussion sequenced. The presence of archaea was confirmed by PCR amplification of the 16S rRNA fragment in all systems. DGGE profile was not performed for Installation A, which was not available at this time.
1 2 3 4 5 6 7
Figure 3.1.4: DGGE profile demonstrating diversity of archaeal populations in pigging samples from production pipelines (Installation C and D) based on PCR products of archaeal 16S rRNA. Lane 1: IC606, Lane 2: IC206, Lane 3: ID1, Lane 4: IC106, Lane 5: IC1206, Lane 6: IC306, Lane 7: IC11P. An arrow indicates the additional band found in Installation D.
Bacterial 16S rRNA fragment profiles revealed the greatest biodiversity in tested systems then archaeal. The total number of different bands visible in DGGE patterns in Figure 3.1.5 indicates the presence of at least 20 different bacterial OTUs in Installation A, 13 OTUs in Installation C and 12 OTUs in Installation D. This indicates that the greatest bacterial diversity was found in Installation A. This was subsequently confirmed by pyrosequencing (Subsection 3.1.5) and GeoChip analysis (Subsection 3.2.3). Differences in the bacterial profiles were observed between the pigging samples from the water injection system and production pipelines (Figure 3.1.6).
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Samples from the different systems showed individual bacterial profiles with some common bands. At least 5 common bands can be noticed between Installation A and D, 6 between Installations C and D and at least 5 between Installation A and C.
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-
Lane Lane 14
production production
IC802,
from from
:
acterial 16S rRNA.
13
-
of of b
Lane Lane 12
IC102,
:
11
-
Lane 10
: : ID1,
based based on PCR products of 550 bp
9
-
Lane Lane 7
IA2,
:
6
-
IC606.
19:
Lane Lane 4
-
IA9,
:
Lane 18 Lane
demonstrating diversity of bacterial populations in pigging samples
Lane Lane 3
IC206, IC206,
:
IA4,
:
17
-
DGGE DGGE profile
water water injection system (Installation A, B and D)
Lane Lane 2
Lane 16 Lane
3.1.5:
: : IA1,
line line and
IC3P,
ure
:
pipe Lane 1 15 Fig
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Chapter 3 Results and Discussion
IA1
IA9
IA2
IA4
ID1
IC102
IC802
IC3P
IC206
IC606
Figure 3.1.6: DGGE dendragram. Bands from each sample were coded and a UMPGA tree was produced using the distance algorithm described by Nei and Li (1979) and executed in PAUP* 4.0.
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Chapter 3 Results and Discussion
Some profiles were produced from samples taken at different times (Figure 3.1.5). For Installation A samples IA1, 4, 9 were taken in 2010 while sample IA2 was recovered in 2011. This showed that there was diversity variation within the samples taken at the same time as well as temporal variation. Differences between different pigging samples from different sites can be also observed. It should be also pointed that the DGGE samples had different consistencies (e. g. samples IA1 was liquid and IA2 was solid). The nature of these samples could have influenced the DNA extraction efficiency and this could have affected the DGGE profiles, creating the observed differences. In addition different DNA extraction kits were used for particular pigging debris and the efficiency of extraction was unknown for these samples (Table 2.1.1).
As mentioned before, DGGE can be used not only to gain a microbial community structure but also to obtain identity of community members through direct sequencing of bands (Kan et al., 2006; Shartau et al., 2010). All unique bands (54) form bacterial profiling were excised from gel as described in subsection 2.1.7 and sequenced, however, useful sequences were only retrieved from 29 bands, and the results of BLAST homology searches of Genbank are given in the Table 3.1.1. The main reasons of the failure to obtain useful sequences from some bands may be due to the presence of multiple different target molecules. (This could be a result of cross-contamination of DNA during band cutting procedure, the presence of more than one sequence type in the band, or unsuccessful band separation in the gel).
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Table 3.1.1: Sequence similarity and taxonomic relationships of bacterial 16S rRNA, bends from DGGE gel. Class affiliation as determined by NCBI.
DGGE Accession Simil Most similar Class Environment of band number arity sequences origin number (%) Installation A 1 NR_026439.1 95 Desulfobacter Deltaproteobacte Sediments of halotolerans DSM ria Great Salt Lake 11383 2 X77837.1 97 Clostridium Clostridia Marine and halophilum DSM 5387 hypersaline sediments 3 CP003096.1 94 Thermovirga lienii Synergistia North Sea DSM 17291 4 JQ687110.1 97 Marinifilum sp. KYW unclassified Seawater in 585 Bacteroidetes Gangyang Bay 5 AY281344.1 97 Desulfovibrio sp. Deltaproteobacte Seafloor drill AND1 ria cuttings 6 NR_041793.1 100 Dethiosulfovibrio Synergistia Sulfur mats in russensis strain WS different saline 100; DSM 12537 environments 7 DQ647138.1 99 Uncultured Clostridia Produced water Caminicella sp. clone from a high- TCB261x temperature North Sea oil- field 8 DQ647130.1 99 Uncultured Clostridia Produced water Caminicella sp. clone from a high- TCB207x temperature North Sea oil- field 9 DQ647111.1 100 Uncultured Synergistia Produced water Thermovirga sp. clone from a high- TCB168x temperature North Sea oil- field 10 GU180045.1 99 Uncultured Synergistia Low- Synergistetes temperature bacterium clone petroleum D010011F15 reservoirs 11 DQ647122.1 98 Uncultured Synergistia Produced water Thermovirga sp. clone from a high- TCB8y temperature North Sea oil- field Installation D 12 AY281344.1 98 Desulfovibrio sp. Deltaproteobacte Seafloor drill AND1 ria cuttings 13 AY281344.1 99 Desulfovibrio sp. Deltaproteobacte Seafloor drill AND1 ria cuttings 14 HE601764.1 99 Thermoanaerobacter Clostridia Hot spring brockii subsp. brockii 15 JF754964.1 98 Thermoanaerobacter Clostridia Thermal, pseudethanolicus volcanic strain 22C environments
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16 GU179927.1 98 Uncultured Clostridia Low- Firmicutes bacterium temperature clone D021041C04 petroleum reservoirs 17 JF754961.1 99 Thermoanaerobacter Clostridia Thermal, pseudethanolicus volcanic strain 3LC environments 18 NR_029034.1 100 Dethiosulfovibrio Synergistia Sulfur mats in acidaminovorans different saline strain sr15 environments Installation C 19 CP000924.1 100 Thermoanaerobacter Clostridia Thermal, pseudethanolicus volcanic ATCC 33223 environments 20 FM876224.1 97 Thermosipho sp. LD- Thermotogae Produced water 2008 from a high- temperature North Sea oil- field 21 NR_041484.1 94 Desulfothermus Deltaproteobacte Deep-sea okinawensis strain ria hydrothermal TFISO9 field 22 AB546250.1 99 Desulfovibrio Deltaproteobacte Oil facilities capillatus strain ria Mic4c01 23 AY800103.1 100 Spirochaeta sp. MET-E Spirochaetia Oil field in Congo 24 DQ647138.1 99 Uncultured Clostridia Produced water Caminicella sp. clone from a high- TCB261x temperature North Sea oil- field 25 DQ647138.1 98 Uncultured Clostridia Produced water Caminicella sp. clone from a high- TCB261x temperature North Sea oil- field 26 NR_043912.1 99 Thermosipho Thermotogae Produced water africanus Ob7 from a high- temperature North Sea oil- field 27 NR_044583.1 98 Kosmotoga olearia Thermotogae Oil production TBF 19.5.1 fluid 28 DQ647144.1 99 Uncultured Clostridia Produced water Clostridiaceae from a high- bacterium clone temperature TCB199x North Sea oil- field 29 NR_041793.1 98 Dethiosulfovibrio Synergistia Sulfur mats in russensis strain WS different saline 100; DSM 12537 environments
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In all of the examined samples, six bacteria phyla were present: Proteobacteria, Firmicutes, Synergistetes, Termotogae, Spirochaetes and Bacteroidetes. The presence of Proteobacteria, Firmicutes, Synergistetes, Bacteroidetes and Termotogae representatives were confirmed by pyrosequencing. This study revealed the presence of anaerobic and facultative aerobic, thermophilic and mesophilic microorganisms in the communities. Presence of some bacterial phyla detected in this study has been shown previously in similar sample types (Ren et al., 2011; Shartau et al., 2010).
The class Clostridia was the pre-dominant group of bacteria detected in this survey. Sequences belonging to the classes of Deltaproteobacteria and Synergistia were mostly sulfidogenic bacteria. The presence of Clostridia in Installation D has not been confirmed by pyrosequensing. The presence of these sequence types can be indicative that corrosion associated with H2S, acetate and CO2 production might be occurring.
DGGE band number 27 is present in all samples from Installation C but not in sites A and D. Sequencing results revealed that this band represented presence of Kosmotoga olearia. As this band is absent from Installation A and D, it may be considered as an essential organism in corrosion development. The major fermentation products of K. olearia are hydrogen, carbon dioxide and corrosive acetic acid (Dipippo et al., 2009).
Based on the sequence homology search results for the DGGE bands, it is difficult to conclude much about the differences between corroding and non-corroding installations because the same groups of bacteria were present in all of the systems.
54 unique bands were retrieved from the DGGE gels. From these 29 sequences were obtained that were suitable for phylogenetic analysis. A data matrix of 90 operational taxonomic units (OTUs) was constructed with 16S rRNA sequences from Aquifex aeolicus as the outgroup (Figure 3.1.7, Appendix 5). An alignment for this data set was constructed using Clustal O and modified manually using either
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Chapter 3 Results and Discussion
BioEdit or Se-Al. The alignment was incorporated into SeaView and PhyML executed using a GTR model with bootstrap analysis. The maximum likelihood analysis generated a tree with a In(L) value of 25707.6. The tree identified the position of the sequences from the DGGE bands within well supported clades. DGGE bands 5, 12 and 13 form a clade with sequences from Desulfovibrio at a support of 99%, and DGGE band 22 joined this clade with a support of 100%. This indicated that the DGGE sequences 5, 12 and 13 were closely related but distinct from other Desulfovibrio sequences in the database. DGGE band 21 produced a sequence that formed a clade with a sequence from Desulfothermus okinawensis with 99% support and together these formed a clade with the previous sequences at 82% support.
DGGE bands 7, 25, 2, 28 and 24 formed a clade with weak support (72%) which was strengthened by the addition of DGGE band 8 (99%). These sequences formed a clade with unidentified Caminicella sequences at a support of 100%. These DGGE band sequences probably represent those from uncharacterised bacteria belonging to the Caminicella group.
5 DGGE band sequences (16, 19, 17, 15 and 14) formed a clade within the Thermoanaerobacter group with 100% support. DGGE bands 16 and 19 formed a clade with a sequence from T. pseudothanolieus, while DGGE band 17 formed a clade with T. brockii. These DGGE sequences probably reflect previously uncharacterised bacteria within this genus.
Three DGGE band sequences (6, 29 and 18) formed a clade with sequences from Dethiosulfovibrio acidaminovorans at 100% support. The position of the branch lengths suggest that these sequences may belong to this species, but are uncharacteriased members of this genus.
Two DGGE band sequences (9 and 11) formed a clade with ones from uncultured Synergistetes; however these together formed a clade with sequences from Thermovigra lienii at 100% support suggesting that these DGGE sequences might represent members of the Thermovigra genus. Other DGGE band sequences (10
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Chapter 3 Results and Discussion and 3) also matched uncultured Synergistetes sequences at 100% bootstrap support but they did not form a clade with other known bacteria. The identity of these sequences (3, and 10) remains unknown. DGGE band 26 formed a strongly supported (100%) clade with sequences from Thermosipho species, with DGGE band 20 joining this clade indicating that these two DGGE sequences might belong to this genus. Finally, DGGE band 27 formed a strongly supported clade with sequences from Thermococcoides and Kosmotoga species, but basal to these known species. The identity of this DGGE band remains questionable.
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Figure 3.1.7: A maximum likelihood phylogenetic phylogram (In(L) = 25707.6) showing the relationship between 16S rRNA gene sequences of known taxa and thosed excised from DGGE gels.
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Compared with other techniques, DGGE may be a cheaper alternative to analyse microbial population diversity and monitor changes over the time. The main disadvantage of this technique is low sensitivity and that it is time consuming. Incorrect conclusions can be drawn using this technique because of contamination during PCR, or in the cutting out of bands from the gel, or because of lack of band resolution when separating similar sequences.
3.1.5 Pyrosequencing analysis of eubacterial diversity in the field samples
454 pyrosequencing was used to create 16S rRNA gene sequence libraries for the different installations. This technique provides a relatively quick way to generate molecular diversity estimates for environmental samples. Libraries of 16S rRNA gene sequences were constructed from those available at the time; original pigging samples (IA2, IA5, IC106, IC1206, IC2406 and ID1) from the three different installations, and the overall/general results for those samples are depicted in Figure 3.1.8. The samples used for pyrosequensing were unique, so that there were no replicate sequences to make variation comparisons within the samples from the different sites.
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100%
90%
80%
70% Bacteroidetes
Alphaproteobacteria 60% Deltaproteobacteria Epsilonproteobacteria 50% Gammaproteobacteria Synergistetes 40% Thermotogae Relative abundance Relative Firmicutes 30% Other
20%
10%
0% IA2 IA5 IC106 IC1206 IC2406 ID1 Sample ID IA2 IA5 IC106 IC1206 IC2406 ID1 No. of reads 1714 592 2834 697 1732 2947
Figure 3.1.8: Pyrosequencing analysis of 16S rRNA gene libraries showing relative abundance (%) of represented phyla (class level for Proteobacteria) in pigging samples collected from Installation A (water injection system IA2 and IA5) and Installation C and D which were production pipelines (IC106, IC1206, IC2406 and ID1). Numbers of sequence reads for each sample are presented below figure.
The 16S rRNA gene diversity for each installation is distinct, and different between installations (compare the profiles for the water injection system (IA2 and IA5) with those for the production pipelines (IC106, IC1206 and IC2406)). Differences do exist within samples from the same installation but the predominant taxa were similar. The most striking difference in diversity was found between samples from production pipeline ID with those from the IC production pipeline. This pipeline (ID) has not suffered any corrosion, while that of the IC pipeline is heavily corroded. The water injection system (samples IA2 and IA5) exhibited some corrosion but not as heavy as that observed in the IC production pipeline.
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Five main bacteria phyla were detected in the samples examined: Bacteroidetes, Proteobacteria, Synergistetes, Termotogae and Firmicutes. Within the Proteobacteria four classes (alpha, gamma, delta, and epsilon,) were represented. The relative abundance of these taxa varied between installations with members of the Deltaproteobacteria being present in all samples. The pre-dominant groups present in the non-corroded pipeline (sample ID1) were members of the Gama- proteobacteria, Deltaproteobacteria and Epsilonproteobacteria. The corroded pipeline (samples IC) and water injection system (samples IA) had members of the Firmicutes as the pre-dominant group. The other pre-dominant groups were different for these two installations. The heavily corroded pipeline (IC) had members of the Bacteroidetes and Thermotogae pre-dominating, whereas the water injection system (IA) had members of the Synergistetes and a greater abundance of Deltaproteobacteria.
In general, microflora isolated from oil reservoirs mainly contains anaerobic and facultative aerobic microorganisms. Anaerobic bacteria are found within the Bacteroidetes, Synergistetes and Thermotogae. Anaerobic and facultative anaerobes are present within the Firmicutes (Clostridia, Bacilli) and Proteobacteria. All detected phyla in the DNA recovered from pigging samples are often associated with the marine environment and have been detected before in samples from similar environments (Duncan et al., 2009; Grabowski et al., 2005; Suflita et al., 2012). The following sections deal with the molecular diversity of each installation in greater detail.
3.1.5.1 16S rRNA diversity of Installation A – the water injection system
The pyrosequencing results for the water injection system (Installation A) indicated that there was an abundance of members from the Deltaproteobacteria (25% of whole bacteria population in IA2 and 32.5% in IA5 respectively), the Synergistetes (~27.5% and 28.5% for samples IA2 and IA5 respectively) and Firmicutes (~39% and 29% respectively) (Figure 3.1.8).
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Sequences belonging to the class of Deltaproteobacteria were mostly affiliated with sulfidogenic bacteria. As mentioned before (Subsection 3.2.2) SRP are one of the main contributors to MIC due to a combination of two processes: production of corrosive hydrogen sulfide and cathodic depolarization. Comparing the number of SRB sequences retrieved with others detected in this study, it was only in the IA5 sample where this bacterial group had the greatest proportional share of 16S rRNA sequences. Although a similar level was observed for sample IA2, the Deltaproteobacteria was not the pre-dominant group. Zhu et al. (2005) showed that SRBs do not always constitute the majority of the population in samples from oil field environment. In their study on gas pipelines they observed that sequences of methanogenes were more abundant than those for SRBs using culture-independent molecular techniques, and those dramatic changes in the composition of microbial population can occur after field samples were inoculated into liquid growth media. The preferential growth of SRB in enrichments can lead to false conclusions regarding the contribution of these bacteria to biocorrosion. It is important to note that community profiles from growth medium is not necessarily representative of the constituents of the original field sample, and that some bacteria, present even in small amounts in the original sample, can be enriched in the culture process (Romero et al., 2005).
Sequences representative of members from the genus Desulfococcus were the most abundant (average 55%) class recovered from samples IA2 and IA5, followed by those from Desulfacinum (average 23.5%) (Figure 3.1.9). Members of Desulfococcus are capable of utilizing alkanes as growth substrates, reducing them to CO2 and are classified as alkane-degrading, sulfate-reducing bacterium (Aeckersberg et al., 1991; Aeckersberg et al., 1998). Duncan et al. (2009) demonstrated that the anaerobic hydrocarbon degradation, such as alkane reduction, is an important factor in microbial influenced corrosion in oil fields.
Other sequences detected representing the Deltaproteobacteria included Desulfobulbus, Desulfovibrio, Desulfocella and Pelobacter (Figure 3.1.9), but as these averaged between ~3%-8% of the total they were considered to be relatively minor members of the SRB community. The genus Pelobacter comprises members
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Chapter 3 Results and Discussion that are fermentative microorganisms (Schink, 2001), with some species (e.g. Pelobacter carbinolicus) categorised as iron and sulfur-reducing anaerobic organism. Pelobacter carbinolicus utilises iron and S0 as electron acceptors and ethanol or H2 as electron donors (Lovley et al., 1995). This and related species are commonly found in marine and freshwater debris, also sewage sludge, and they can form extensive portions of the anaerobic microbial community. Although these organisms are primarily mesophilic, the presence of thermophillic, anaerobic iron- reducing and sulfate-reducing organisms in petroleum reservoirs has been reported (Duncan et al., 2009; Slobodkin et al., 1999).
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100%
90%
80% Pelobacter
70% Desulfobulbus 60% Desulfovibrio 50% Desulfacinum 40% Desulfococcus
Relative abundance Relative 30% Desulfocella
20%
10%
0% IA2 IA5
Figure 3.1.9: Chart showing the relative abundance (%) of 16S rRNA gene sequences of Deltaproteobacteria class in IA2 and IA5 pigging samples collected from Installation A (water injection system).
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Bacterial species, other than Deltaproteobacteria, capable of producing H2S are found within the phylum Synergistetes. The Synergistetes are Gram-negative rods or vibrioid cells that inhabit the majority of anaerobic environments, including soil, wastewater treatment plants and oil wells (Bhandari and Gupta, 2012; Jumas-Bilak et al., 2009). In samples from Installation A 87.5% of the Synergistetes’ sequences were representative of the genus Anaerobaculum (Figure 3.1.10). Anaerobaculum species are moderately thermophilic organisms, which grow at temperatures ranging from 28 to 60°C with an optimum close to 55°C, and they are able to reduce thiosulfate, sulfur and L-cysetine to hydrogen sulfide. Strains of these species have been previously isolated from production water of a petroleum reservoir (Maune and Tanner, 2012; Rees et al., 1997).
Other detected sequences from the Synergistetes included representatives from the genus Thermovirga, comprising 12.5% of the total recovered. Thermovirga lienii is the type species for this genus and it has been isolated from oil-well production water obtained from an oil reservoir in the North Sea. The optimum growth temperature of Thermovirga lienii is 58°C. It has a fermentative type of metabolism but it can also reduce cysteine and elemental sulfur to sulfide (Dahle and Birkeland, 2006). This species was detected at the 94% sequence homology level in samples IA1, IA4, IA9 and IA2 (Subsection 3.1.4).
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100%
95%
90% Thermovirga
85% Anaerobaculum Relative abundance Relative
80%
75% IA2 IA5
Figure 3.1.10: Chart showing the relative abundance (%) of 16S rRNA gene sequences of Synergistetes phyla in IA2 and IA5 pigging samples collected from Installation A (water injection system).
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The phylum Firmicutes was one of the most dominant groups of bacteria detected in this survey at sites where pipeline corrosion had occurred. Members of this bacterial group can be endospore-forming anaerobic bacteria that are widely distributed in nature, and these bacteria are known to be able to survive for extended periods of time under adverse environmental conditions (Onyenwoke et al., 2004). They have, therefore, the capability to survive in systems that have been treated with biocides.
In Installation A (Figure 3.1.11) all of the Firmicutes sequences detected were primarily associated with the family Clostridiaceae within the order of Clostridiales. Camnicella (88% sequence homology) was the predominant genus found in these samples. Species of this genus are thermophilic microorganisms with an optimum 55-60°C, and the fermentation products they can produce include H2S, acetate, CO2 and H2, amongst others (Alain et al., 2002).
A few sequences (7.5% of the overall total for all samples) were identified as belonging to the genus Acetobacterium within the Eubacteriaceae of the Clostridiales. The name of this genus originated from the observation that these bacteria are acetogens, predominantly making acetic acid as a by-product of anaerobic metabolism. The optimum temperature for the growth of known species is 20-30°C (Balch et al., 1977; Simankova et al., 2000), and thermophilic isolates of Acetobacterium are not yet known. Their detection in this system needs to be confirmed because they could have been contaminants, introduced during oil field operations. Nevertheless, their presence opens up the possibility that thermophilic varieties of this genus might exist.
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100%
90%
80%
70%
60% Caminicella
50% Acetobacterium Other 40%
Relative abundance 30%
20%
10%
0% IA2 IA5
Figure 3.1.11: Chart showing the relative abundance (%) of 16S rRNA gene sequences of Firmicutes phyla in IA2 and IA5 pigging samples collected from Installation A (water injection system). Other: unidentified environmental sequences.
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3.1.5.2 16S rRNA diversity of Installation C – production pipeline
The greatest proportion of sequences retrieved from this Installation (samples IC106, IC1206 and IC2406), which was the worst corroded, were identified as belonging to the Firmicutes. These sequences represented 79% of those recovered from all of the samples (Figure 3.1.12). The predominant genus represented by these sequences was Caminicella (68% of those recovered for all samples), which was also the predominant sequence type obtained from Installation A. The abundance of Caminicella-like sequences did vary considerably between samples from 96% of the total for sample IC106 to 39% for sample IC2406 (Figure 3.1.12).
In all samples sequence signals matching species of Thermoanaerobacter were detected representing 18% of all Fermicute sequences recovered. Members of this genus are thermophilic anaerobes and they are capable of reducing thiosulfate and sulfur to H2S. Acetate, CO2 and H2 are their main end products of fermentation (Xue et al., 2001). Some Thermoanaerobacter species are able to reduce thiosulfate to elemental sulfur without sulfide formation (Lee et al., 1993; Lee et al., 2007). The presence of sequences from Thermoanaerobacter pseudethanolicus (Onyenwoke et al., 2007), identified at 100% sequence homology, was detected in samples IC802, IC3P, IC206, and IC606 (Subsection 3.1.4).
The other Firmicute sequences detected in Installation A were those of Acetobacterium species. These sequences were detected at Installation C but only in IC106 samples and they represented less than 1% of Firmicutes population. This suggests that they were not a prominent species for this site.
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100%
90%
80%
70% Thermoanaerobacter 60% Caminicella 50% Acetobacterium 40% Other Relative abundance Relative 30%
20%
10%
0% IC106 IC1206 IC2406
Figure 3.1.12: Chart showing the relative abundance (%) of 16S rRNA gene sequences of Firmicutes phyla in IC106, IC1206 and IC2406 pigging samples collected from Installation C (production pipelines). Other: unidentified environmental sequences.
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Sequences representative of Deltaproteobacteria were detected at Installation C. These formed 8% of sequences recovered for all samples, with sequences from the genus Desulfothermus being present in 88.5% of sequences recovered from all the samples (Figure 3.1.13). The type species for this genus is Desulfothermus naphthae (Kuever et al., 2006) and the genus contains one other member, Desulfothermus okinawensis (Nunoura et al., 2007). Both of these species are motile, slightly curved rods, which utilize sulfate and thiosulfate but not sulfite as electron acceptors. They are able to grow at temperatures between 35 and 60°C with an optimum 50-55°C for D. okinawensis and 55-60°C for D. naphthae. In addition, D. naphthae can utilize alkanes (C6–C14) and short- to long-chain fatty acids (C4–C18).
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100%
90%
80%
70%
60% Desulfothermus 50% Other 40%
Relative abundance Relative 30%
20%
10%
0% IC106 IC1206 IC2406
Figure 3.1.13: Chart showing the relative abundance (%) of 16S rRNA gene sequences of Firmicutes phyla in IC106, IC1206 and IC2406 pigging samples collected from Installation C (production pipelines). Other: unidentified environmental sequences.
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A minor class of sequences was recovered from samples IC106, IC802, IC3P, IC606, IC1206 and IC2406 representing 2.6% of the whole bacterial community. These sequences had the greatest homology similarity to members of the phylum Thermotogae. This phylum is composed of Gram-negative, anaerobic, mostly thermophilic and hyperthermophilic bacteria isolated from various environments, such as deep sea hydrothermal vents, hot springs, oil reservoirs and waste digesters (Huber and Hannig, 2006). Samples IC106, IC1206 and IC2406 contained on average 62% of Thermotogae sequences matching those within the genus Thermosipho genus and 32.6% matching those of the genus Kosmotoga (Figure 3.1.14).
Some members of Thermosipho are able to produce H2S (Huber et al., 1989; Takai et al., 2000) and realise acetate, H2 and CO2 as by-products during glucose fermentation (Podosokorskaya et al., 2011). The presence of sequences matching those of Thermosipho africanus, with 99% homology similarity, was detected (Subsection 3.1.4) in samples IC802, IC3P, IC206, and IC606.
The type species of Kosmotoga is K. olearia (Dipippo et al., 2009), which was isolated from oil production fluid of a North Sea petroleum reservoir. It has a wide temperature range of 20-80°C for growth, and its major fermentation products are hydrogen, CO2 and acetic acid when it is grown on maltose. Two other species belong to this genus, K. aernicorallina (Nunoura et al., 2010) and K. shengliensis (Feng et al., 2010). Both of these are thermophilic bacteria and some strains have been reported to have the capacity to reduce sulfur (Nunoura et al., 2010). Sequences with 99% homology matches to K. olearia were detected in samples IC802, IC3P, IC206 and IC606 (Subsection 3.1.4).
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120%
100%
80% Thermosipho 60% Kosmotoga Other
Relative abundance Relative 40%
20%
0% IC106 IC1206 IC2406
Figure 3.1.14: Chart showing the relative abundance (%) of 16S rRNA gene sequences of Thermotogae phyla in IC106, IC1206 and IC2406 pigging samples collected from Installation C (production pipelines). Other: unidentified environmental sequences.
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3.1.5.3 16S rRNA diversity of Installation D – production pipeline
The Installation D community profile was significantly different to that of samples from Installations A and C (Figure 3.1.8). Sequences recovered from site D samples belonged predominantly to the Proteobacteria, in particular the Gammaproteobacteria, Deltaproteobacteria, and Epsilonproteobacteria (Figure 3.1.15-3.1.17). Within the Gammaproteobacteria (Figure 3.1.16) the sequences were identified solely as Pseudomonas species, and these sequences represented 75% of the whole bacterial population. This genus is large and contains members that have a wide variety of capabilities. Some Pseudomonas species (P. nitroreducens) are categorized as a NRB, because they are able to reduce nitrate to nitrite and nitrite to ammonia or nitrogen (Samuelsson, 1985; Sias et al., 1980; Su et al., 2012). Other Pseudomonas species (P. pachastrellae, P. xanthomarina – Romanenko et al., 2005) are present in the marine environment. All respire aerobically, although some can respire anaerobically when using nitrate or other alternative electron acceptors. Many Pseudomonas species (P. iners, P. olevorans) are capable of oil degradation, some (P. taeanensis, Lee et al., 2010) are found in the marine environment.
Approximately 20% of the sequences retrieved from the sample were identified as beloning to the Deltaproteobacteria (Figure 3.1.15), with 77% and 15% of those sequences identified as being members of the genera Desulfotignum and Desulfovibrio respectively. The genus Desulfotignum comprises three members, one of which, D. toluenicum, was isolated from an oil reservoir model and is capable of toluene degradation (Ommedal and Torsvik, 2007). Desulfovibrio is a larger genus with some 73 members, many of which were isolated from oil field environments. This list includes D. bastinii (Magot et al., 2004), D. capillatus (Miranda-Tello et al., 2013), D. gabonensis (Tardy-Jacquenod et al., 1996), D. gracilis (Magot et al., 2004), D. longus (Magot et al., 1992), D. vietnamensis (Dang et al., 1996), and D. alaskensis (Feio et al., 2004). The sequences retrieved from sample ID1 did not match those from any of these species but exhibited 98-99% sequence homology to an unnamed species in the database. All species of Desulfovibrio are capable of iron (III) reduction at rates comparable to other iron (III) reducers (Lovley et al., 1993).
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Approximately 4% of the Deltaproteobacterial sequences detected showed sequence homology with species of Geoalkalibacter, a genus with two species G. ferrihydriticus (type species, Zavarzina et al., 2006) and G. subterraneus (Greene et al., 2009). Both of these species are capable of iron (III) reduction but G. subterraneus was isolated from a petroleum reservoir.
Of the remaining sequences retrieved from Installation D, approximately 5% showed homology matches to those from the Epsilonproteobacteria (Figure 3.1.17) and less than 1% to the Alphaproteobacteria. The presence of Epsilonproteobacteria in oil field environments has been increasingly recognized in recent years (Grabowski et al., 2005). The sequences retrieved at site D exhibited homology matches at the 99% level with those from species of Sulfurospirillum, a genus of seven well characterized species that have spiral, facultative anaerobic cells capable of reducing sulfur. Of these species S. cavolei (Kodama et al., 2007) was isolated from an underground crude oil storage cavity and it is capable of respiratory growth with different electron acceptors including elemental sulfur, sulfite and thiosulfate.
Nitrate-reducing Sulfurospirillum spp. have been shown to successfully inhibit growth of SRBs and thereby to control oil reservoir souring (Hubert and Voordouw, 2007). If SRBs present in this installation are active, it is possible that they are suppressed by Sulfurospirillum, which could explain why no corrosion problems were observed at this installation site.
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100%
80% Geoalkalibacter 60% Desulfovibrio Desulfotignum
40% Other Relative abundance Relative
20%
0% ID1
Figure 3.1.15: Chart showing the relative abundance (%) of 16S rRNA gene sequences of Deltaproteobacteria phyla in ID1 and pigging samples collected from Installation D (production pipelines). Other: unidentified environmental sequences.
100% 90% 80% 70% 60% Pseudomonas 50% 40%
Relative abundance Relative 30% 20% 10% 0% ID1
Figure 3.1.16: Chart showing the relative abundance (%) of 16S rRNA gene sequences of Gammaproteobacteria phyla in ID1 and pigging samples collected from Installation D (production pipelines).
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100% 90% 80%
70% 60% Sulfurospirillum 50% Other 40%
Relative abundance Relative 30% 20% 10% 0% ID1
Figure 3.1.17: Chart showing the relative abundance (%) of 16S rRNA gene sequences of Epsilonproteobacteria phyla in ID1 and pigging samples collected from Installation D (production pipelines). Other: unidentified environmental sequences.
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3.1.5.4 Comparison of 16S rRNA diversity between the installation sites
In the present survey, the most significant differences in microflora composition were detected between Installation C and D (Table 3.1.2). Both systems were production pipeline, with similar physical and chemical conditions, and both fields were fed, or at least partly, from the same reservoir. Scale and corrosion inhibitors were regularly added to both systems and seawater for injection was nitrate treated. Athough severe corrosion was reported at Installation C, none was recorded for Installation D. Installation A was located far away from the production pipelines, and it, therefore, had no connection with systems C and D. The operating temperature was lower at site A and nitrate was not use in this system, although the system was regularly treated with biocides.
The greatest 16S rRNA diversity was exhibited at Installation A site, which was the water injection system where corrosion processes were controlled. The smallest diversity level was recorded at Installation D, the site lacking corrosion.
In the systems suffering corrosion (Installations A and C) the pre-dominant molecular species belonged to thermophilic Firmicutes. In contrast, at Installation D, the site where there was no corrosion, Firmicute sequences represented less than 1% of those recovered, and the microflora was dominated by molecular species identified as belonging to Pseudomonas species. Furthermore, the SRB sequences identified were linked to mesophilic organisms, with most of the Desulfovibrio species identified being capable of reducing nitrate. Therefore, it is plausible that they are probably not reducing sulfate in this system.
The Pseudomonas species detected at site D may have a corrosion-protecting characteristic in this system because as nitrate-reducing bacteria they reduce the injected nitrate to non-corroding forms. Furthermore Pseudomonas species are known as slime forming bacteria which, in this case, may act as a protecting film. However, experimental evidence is required to support this idea.
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The greater diversities observed at Installations A and C presumably reflect the corrosion status of the systems. It has been previously shown that, sulfide accumulation, resulting from microbial metabolic activities, represents an important biocorrosion mechanism for carbon steel in anaerobic seawater environments (Cord-Ruwisch et al., 1987; Cord-Ruwisch, 1995; Hamilton, 1998).
The microbial communities in these samples appear to be capable of H2S production and probably are responsible for the corrosion problems. The main corrosion product found in the pigging debris from Installation A and C was ferrous sulfide (FeS). In addition, the smell of hydrogen sulfide was routinely recognised by the staff during pigging operations (personal communication with operators). H2S accelerates iron corrosion by acting as a source of bound protons and precipitation of iron ions as FeS corrosion product according to Fe + H2S → FeS
+ H2.
16S rRNA sequences recovered from Installation A indicated that the SRB-related ones belonged to mesophilic organisms. Their activity at high temperature (50- 55°C) in this system is unclear and requires experimental evidence. Corrosion in this system, under these circumstances, would probably be caused primarily by the non-SRBs hydrogen sulfide producers, such as those species belonging to the Firmicutes and Synergistetes.
The sequences retrieved from Installation C samples exhibited a reduced diversity level compared to those from site A, with thermophilic sulfidogenic Firmicutes (Archaeoglobus and Thermoanaerobacter) present. Because Firmicutes were the most abundant group of bacteria population, it can be assumed that they are significant contributors to the deterioration processes observed in this system.
Injection of nitrate was not so successful at controlling the production of biogenic sulfide in Installation C, presumably because of the difference in the microbial population. It is, therefore, important to monitor the treatment to ensure that the microbial population does not change from non-corroding one to an aggressive one.
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16S rRNA diversity studies can only give a broad outline to the microbial diversity at a structural level. Inferences drawn about the activities of these communities based on this information should be interpreted with care. Ribosomal RNA sequences are conserved and provide a guide at higher taxonomic levels only. Organisms that share similar or identical 16S rRNA sequences might have different metabolic activities, while bacteria that have dissimilar sequences might share metabolic capabilities. In the present study SRB, NRB, acetate, H2S and H2 producers, MRB and HDB, were all detected, and it is clear that the SRB group were not necessarily the main group responsible for corrosion (Table 3.1.2).
Table 3.1.2: Pyrosequencing summary.
Installation ID Detected bacteria Installation A Firmicutes: Caminicella, Acetobacterium Synergistetes: Anaerobaculum, Thermovirga Deltaproteobacteria: Desulfococcus, Desulfacinum, Desulfobulbus, Desulfocella, Pelobacter, Desulfobulbus Installation C Firmicutes: Caminicella, Thermoanaerobacter, Acetobacterium Deltaproteobacteria: Desulfothermus Thermotogae: Thermosipho, Kosmotoga Installation D Gammaproteobacteria: Pseudomonas Deltaproteobacteria: Desulfotignum, Desulfovibrio, Geoalkalibacter Epsilonproteobacteria: Sulfurospirillum
3.1.6 Archaeal community profiling
The presence of archaea was confirmed in all samples examined by archaeal 16S rRNA gene PCR amplification. Libraries of archaeal 16S rRNA gene sequences were constructed from DNA extracted from the original pigging samples (IA2, IC3P, IC11P, IC206, IC306, IC606, ID1) for three Installations (A, C and D). Non
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Chapter 3 Results and Discussion meaningful (not matching anything) sequences were received for Installation D (ID1).
In addition Archaeoglobus-specific 16S rRNA primers were used to create gene libraries from Installation A (IA1, IA4, IA9) and Installation C (IC1, IC2, IC3) DNA samples, which were sequenced specifically to assess the diversity of sulfate- reducing archaea (SRA) in both pipelines. The identities of these amplified sequences are shown in Table 3.1.3.
All samples had sequences that matched those from Methanomicrobia, Methanococci and Methanobacteria class of archaea, all of which are able to produce methane. Archaeoglobus fulgidus appears to be the dominant species of SRA in both systems. The presence of Archaeoglobus spp. in Installation D was confirmed by PCR only. Methanogenic and SRA microorganisms are consider to be involved in corrosion, and both have been detected previously in oil field environment (Duncan et al., 2009; Uchiyama et al., 2010).
The presence of Methanothermococcus sp. in Installation C was confirmed by PCR amplification of mcrA gene from extracted DNA and its subsequent sequencing (Subsections 3.2.2). These sequences were similar (Table 3.1.3) to those of M. thermolithotrophicus. Species of Methanothermococcus are thermophilic or hyperthermophilic organisms that are able to produce methane from CO2 and hydrogen, and they are widespread in oil production systems where they may promote MIC (Al-Saleh et al., 2011; Daniels et al., 1987).
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Table 3.1.3: List of microorganisms identified by sequencing of cloned 16S rRNA PCR products.
Accession Simil Score ID number arity Most similar sequences Class of (%) clone (%) Installation A IA2 NR_028148.1 99 Methanocalculus strain Methanomicrobia 60% MHT-1 IA2 NR_043960.1 100 Methermicoccus Methanomicrobia 30% shengliensis strain ZC-1 IA2 HM994866.1 99 Uncultured Methanomicrobia 10% Methermicoccus sp. clone A1Fa04 IA:149 AE000782.1 99 Archaeoglobus fulgidus Archaeoglobi 100% DSM 4304 Installation C IC3P NR_044720.1 99 Methanothermococcus Methanococci 50% thermolithotrophicus IC3P AB260045.1 100 Methanothermococcus sp. Methanococci 25% Ep55 IC3P AB260042.1 99 Methanothermococcus sp. Methanococci 25% Mc70 IC11P NR_044720.1 99 Methanothermococcus Methanococci 67% thermolithotrophicus IC11P AB260042.1 99 Methanothermococcus sp. Methanococci 33% Mc70 IC206 HE654005.1 99 Methanobacterium Methanobacteria 25% thermaggregans IC206 EU073827.2 100 Uncultured Methanobacteria 25% Methanothermobacter sp. clone ARCA-3F IC206 AB260045.1 96 Methanothermococcus sp. Methanococci 50% Ep55 IC306 HE654005.1 99 Methanobacterium Methanobacteria 50% thermaggregans IC306 GU129126.1 100 Methanobacteriaceae Methanobacteria 25% archaeon 37aM IC306 AB260042.1 99 Methanothermococcus sp. Methanococci 25% Mc70 IC606 AB260042.1 100 Methanothermococcus sp. Methanococci 50% Mc70 IC606 EU073827.2 100 Uncultured Methanobacteria 25% Methanothermobacter sp. clone ARCA-3F IC606 EU573144.1 100 Archaeon enrichment unclassified 25% culture clone EA29.6 IC1-3 AE000782.1 99 Archaeoglobus fulgidus Archaeoglobi 100% DSM 4304
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The activity of the detected groups of archaea in the systems examined is likely because the environment is at the optimum growth temperature for these organisms. When the ratio of copy number of 16S RNA of archaea:bacteria (for Installation A 1:122 and for Installation C 1:59) is compared (Subsection 3.1.3), it can be concluded that bacteria, not archaea, are pre-dominant in the systems and likely to be the main culprits of MIC. It should be also noticed that the high copy number of bacteria can be overestimated because some bacteria in the system might not be metabolically active. In this case, archaea can be equally responsible for corrosion problems. Therefore, monitoring of bacteria and archaea activity is essential for MIC control.
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Section 2 Functional analysis of sampling side using marker genes.
The presence of functional genes can give an indication of the metabolic ability of microbial population (Zhou et al., 2010). The detection of functional genes associated with carbon steel biocorrosion provides a way to determine which microbes within the population are capable of causing deterioration. In this chapter the functional genes present in populations from non-corroded and corroded environments will be detected using PCR while the GeoChip and sequencing will identify functional genes and their taxa for samples from corroded systems. The functional genes targeted in this study include: aprAB-adenosine-5- phosphosulfate reductase gene, dsrAB-dissimilatory sulfite reductase gene, sox- sulfur oxidation enzyme system gene, nrfA-periplasmic nitrite reductase gene, NiR-nitrite reductase gene, narG-nitrate reductase gene, napA-nitrate reductase gene, nirK-cooper containing nitrite reductase gene, nirS-nitrite reductase gene, amoA-ammonia monooxygenase subunit A gene, mcrA-methyl coenzyme-M reductase gene, FTHFS-formate-etrahydrofolate ligase, assA-alkylsuccinate synthase, c-cytochrome- c-type cytochromes and hydrogenase. All of these genes code for proteins/enzymes involved in processes that could cause corrosion.
3.2.1 Examination of bacteria diversity using PCR amplification of functional genes.
Eight genes were targeted for PCR amplification from DNA samples extracted from Installation A, C and D. The successful, or not, PCR amplifications for genes aprA, dsrAB, nirK, narG, napA, hydA, nrfA and mcrA genes are shown in Table 3.2.1.
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Table 3.2.1: PCR detection of functional genes in different pigging samples.
Gene dsrAB aprA hydA nrfA napA narG nirK mcrA Installation Installation A + + + + + + - + Installation C + + + + + - - + Installation D + + + + n/a n/a - + Legend: (+) – presence of PCR product, (–) – absence of PCR product, ‘n/a’- PCR reaction not performed.
PCR analysis showed the presence of SRP in all systems, as detected by the presence of the aprA and dsrAB genes. The correct sized products (658bp and ~1.9kb respectively) were obtained using primers designed to amplify aprA and dsrAB genes, which are involved in dissimilatory sulfate reduction pathway. Detection of those genes involved in hydrogen sulfide production indicates that the sulfate and sulfite reduction pathways were present at sites suffering corrosion (Installations A and C) but also at non-corroding Installation D. Presence of SRP in analyzed samples was confirmed by sequencing analysis (Subsections 3.1.4, 3.1.5 and 3.2.2) and by GeoChip hybridisation (Subsection 3.2.3). It should be pointed out that the aprA gene can be also be used as a marker for the detection of sulfur- oxidizing bacteria (Meyer and Kuever, 2007).
Successful PCR amplifications were also obtained for hydA and mcrA genes in all samples. Microorganisms possessing hydrogenase include SRPs and Firmicutes, amongst many others, and this enzyme is involved in hydrogen metabolism. The primers used to amplify the hydA gene are reported to be specific to Clostridium species (Pereyra et al., 2010), although experiments were not performed to validate this. The hydrogenase genes can service as a marker for SRBs (Wawer et al., 1997), hovewer the gene needs to be sequenced from a wider variety of taxa to be fully useful.
The amplification of mcrA genes in all systems tested indicates the possibility that microbial methanogenesis is present. In system A and C the presence of mcrA gene was confirmed by GeoChip hybridisation (Subsection 3.2.3). In Installation D the
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Chapter 3 Results and Discussion presence of archaeal 16S rRNA gene was confirmed by PCR and this diversity was examined by DGGE sequencing (Subsection 3.1.4).
Genes involved in the nitrogen cycle include narG, napA, nirK, and nrfA. Nitrate reducing bacteria contain the genes for nitrate reductase (narG and napA) and these were amplified together in Installation A pipelines only. In system C only the napA gene was amplified. The narG and napA genes encode a membrane-bound and periplasmic nitrate reductase respectively, and both catalyse dissimilatory nitrate reduction. The narG gene is present in Proteobacteria, Firmicutes, Actinobacteria and archaea, whereas the napA gene is present only in Proteobacteria. Philippot and Højberg 1999, Richardson et al., 2001 and Bru et al., 2007 have suggested that the roles of these different enzymes might be linked. The inability to amplify the narG gene from Installation C samples was not expected, when the 16S rRNA gene diversity for these samples is considered (Subsection 3.1.5) and it might reflect a failure of the PCR reaction rather than a genuine absence of the gene. Results from the GeoChip hybridisation experiments (Subsection 3.2.3) support this conclusion as the presence of both genes in Installations A and C was detected. Samples from Installation D were not amplified for the narG and napA genes, however 16S rRNA sequences representing members of Pseudomonas were detected after pyrosequencing gene libraries (Subsection 3.1.5), suggesting that bacteria capable of nitrate reduction were present at this site.
The reduction of nitrite to nitric oxide (NO) is catalyzed by nitrite reductases, coded for by the nirK gene. This gene was not detected after PCR amplification of DNA from all of the samples. This result would appear to be a failure in the PCR reaction, rather than an absence of the gene, because genes nirK and nirS were detected after hybridisation using the GeoChip system (Subsection 3.2.3).
Bacteria able to reduce nitrite to ammonia can be detected by amplification of nrfA gene (Mohan et al., 2004), which encodes a periplasmic nitrite reductase. This gene was amplified from all of the DNA samples for each site. The presence of the nrfA gene may indicate that the SRB populations present in the biofilms are also capable
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Chapter 3 Results and Discussion of nitrate-reduction. If true, this could have serious implications for corrosion in the system. If, for instance, nitrate treatment was implemented then, discontinued, sulfate reduction might be encouraged. The nitrate treatment could preferentially select SRB capable of both sulfate and nitrate reduction. Upon the removal of nitrate from such system, SRB population would revert to sulfate respirations and
H2S production, thus posing a corrosion risk.
All primers used in this study successfully amplified DNA from the test samples, exept the primers for the nirK gene. Selected primers amplified target genes with positive control DNA. Different primers for the nirK gene could have been used but this was not investigated further. The chosen primers covered genes that coded for proteins implicated in potential corrosion pathways, futhermore they were all published primers, used extensively for similar studies.
Successful amplification of functional genes using DNA as a template does not necessary reveal the activity of particular pathways in the microbial populations. Therefore, to confirm a metabolic activity of a tested biofilm, it is necessary to sequence the PCR product, for a larger number of genes (Section 3.2.2).
It is difficult to find meaningful differences between corroding and non-corroding systems based only on PCR results, as PCR is not a quantitative technique. Furthermore, it does not give information about metabolic activity, and so it cannot be used as the sole technique for biocorrosion risk assessment. Nevertheless, this cheap and fast method may be successfully used to determine biological ability of examined system by analysing the sequences of the genes amplified.
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3.2.2 Examination of microorganisms diversity by sequencing of PCR products
Selected PCR products of aprA, narG, mcrA genes from different pigging samples were cloned and sequenced in order to identify the taxonomic group to which the gene belonged, and to estimate the molecular diversity of three oil field installations. BLAST homology searches of the amplified sequences were performed using the NCBI database. The results are summarized in Table 3.2.2. In total 28 clones were analysed; 22 from Installation A and 6 from Installation C. The taxonomic origin of the three gene sequences were traced for those originating from Installation A, whereas only those sequences amplified for the mcrA gene were traced for samples from Installation C.
All of the aprA sequences amplified from samples IA1, IA4 and IA9 matched that of an uncultured SRP at homology levels ranging from 95-99%. The presence of this uncultured sulfate-reducing bacterial sequence was 10 out of 10 aprA sequences in sample IA1 and 9 out of 10 in samples IA4 and IA9.
The members of NRB populations were identified according to homology matches to narG gene sequences. These PCR fragments showed matches to sequences from three species at varying levels of homology: with matches of 98-99% homology to sequences from Desulfovibrio alaskensis in sample IA1 and IA4, 84% homology matches to those from Pelobacter carbinolicus in sample IA1 and IA9, and 81% homology matches to those from Paracoccus denitrificansin in sample IA4.
Desulfovibrio alaskensis (detected 3 of 9 sequences in sample IA1 and 1 of 6 sequences in sample IA4) is an SRB within the Delsufovibrionaceae of the Desulfovibrionales, and a number of species within this group have demonstrated the ability to metabolise nitrate compounds.
Homology matches for the narG sequences to species similar to Pelobacter carbinolicus were obtained (4 out of 9 sequences in sample IA1 and 1 out of 6
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Chapter 3 Results and Discussion sequences in sample IA9). The low percentage of homology (84%) for these matches indicated that they probably belong to species that are either novel or known but uncharacterised for the narG gene. P. carbinolicus is a sulfur-reducing anaerobic bacteria within the Desulfuromonadales. This order contains families that have members able to reduce sulfur, such as the Delsulfuromonadaceae which contains the genus Desulfuromonas, and reduce iron, such as species of Geobacter within the Geobacteraceae. The presence of this bacterium was confirmed by the pyrosequencing study (Subsection 3.1.5).
The last organism identified by matching narG sequences was Paracoccus denitrificans, which is a coccoid, denitrifying (nitrate-reducing) bacterium. This sequence was detected in sample IA4 (1 of 6 sequence) but with 81% of similarity match to P. denitrificans, suggesting that it may be from either a novel organism or one that is known but not characterised for this gene. Paracoccus dentrificans is a member of the Rhodobacteraceae within the Rhodobacteriales of the Alpha- Proteobacteria. This family contains chemoorganotrophs and photoheterotrophs. Members of the Alphaproteobacteria were detected at Installation A in the pyrosequencing study (Subsection 3.1.5), but they were a minor component of the 16S rRNA gene diversity. The membrane bound nitrate reductase, coded for by the narG gene, is present within many genera of bacteria including Firmicutes, which was one of the pre- dominant groups detected at Installation A in the pyrosequencing study (Subsection 3.1.5). It is feasible that members of this group could be part of the NRB population. Their absence from this limited study might be real, but it could also be due to a primer failure to amplify the gene from these organisms.
The archaeal 16S rRNA gene diversity study (Subsection 3.1.6) indicated that Installations A and C had methanogenic populations, with the pre-dominant species identified as belonging to the genus Methanothermococcus. This functional group was targeted at Installations A and C by amplifying the mcrA gene from extracted environmental DNA. One half (6 of 12 sequences) of the sequences analysed from the mcrA clone library for Installation C (sample IC2) were matched to those of species belonging to genus Methanothermococcus. Bacteria of this genus
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are coccoid methanogens using H2 + CO2 and formate as substrates (Huber et al., 1982). The environmental sequences matched those from Methanothermococcus thermolithotrophicus with 100% homology and Methanothermococcus okinawesis with 80-85% homology. The remaining environmental sequences amplified showed matches to those of other species including Methanolobus profundi (in sample IA1), Methanolubus tindarius, (in sample IA1 and IA9) Methanolobus sp. (4 of 13 sequences in sample IA1), Methanosarcina barkeri (1 of 12 sequences in sample IA9), all of which are known methane producers. In addition to these matches, mcrA sequences from Archaeoglobus fulgidus (99% homology), a known SRP, were detected in Installation C samples, confirming their presence in the 16S rRNA library for IC3 sample. The genome of A. fulgidus has been sequenced, and it has a high level of sequence similarity to methanogens (Klenk et al 1997).
In addition to matches to known, well-characterized species, the mcrA and 16S rRNA environmental sequences also gave homology matches to a range of sequences from uncultured bacteria and archaea. These matches represent a group of sequences known only from environmental sources. They may represent novel organisms or known species that have not been characterized for these genes. The two different gene libraries showed little overlap in sequence identity for these signals. The principal taxonomic groups matched for these mcrA sequences from Installation C included those from Methanococci and Methanobacteria classes of the Eukaryarchaeota, whereas those from the 16S rRNA library showed matches to sequences from Methanomicrobia class only. The inability to obtain identity matches better than this is a reflection on the content of the database. Obviously, sequence identity matches at greater taxonomic levels can only be obtained if the database contains sequences from a wider range of type species representative of the taxonomic groups. Nevertheless, the homology matches for environmental sequences obtained in this study indicate that the diversity of the methanogenic community is greater than can be identified using current databases.
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Table 3.2.2: List of microorganisms identified by sequencing of cloned aprA, narG and mcrA PCR products.
ID Name Accession Similarity Most similar sequences Class of number (%) gene Installation A IA1 aprA EF052934.1 96-99 Uncultured sulfate-reducing Deltaproteobacteria bacteria narG CP000112.1 98 Desulfovibrio alaskensis Deltaproteobacteria CP000142.2 84 Pelobacter carbinolicus Deltaproteobacteria FJ209622.1 97 Uncultured bacterium unclassified mcrA GU447204.1 99 Uncultured archaeon unclassified EU681946.1 87 AB703629.1 97 Methanolobus profundi Methanomicrobia JF973600.1 93-94 Uncultured Methanolobus sp. Methanomicrobia U22244.1 99 Methanolubus tindarius Methanomicrobia IA4 aprA EF052934.1 95-99 Uncultured sulfate-reducing Deltaproteobacteria bacteria narG CP000490.1 81 Paracoccus denitrificans Alphaproteobacteria CP000112.1 99 Desulfovibrio alaskensis Deltaproteobacteria EU125496.1 82 Uncultured bacterium unclassified FJ209622.1 85 IA9 aprA EF052996.1 99 Uncultured sulfate-reducing Deltaproteobacteria bacteria narG CP000142.2 84 Pelobacter carbinolicus Deltaproteobacteria EU125496.1 91 Uncultured bacterium unclassified EU495838.1 84 mcrA AF414048.1 100 Methanothermococcus Methanococci thermolithotrophicus CP000099.1 100 Methanosarcina barkeri Methanomicrobia U22244.1 99 Methanolobus tindarius Methanomicrobia HM005029.1 100 Uncultured archaeon unclassified Installation C IC2 mcrA AB353226.1 98-99 Methanothermococcus Methanococci thermolithotrophicus AE000782.1 99 Archaeoglobus fulgidus DSM Archaeoglobi 4304 AY354033.1 80-85 Methanothermococcus Methanococci okinawensis JX878364.1 97 Methanothermobacter Methanobacteria thermautotrophicus GU447225.1 91 Uncultured archaeon unclassified GU357471.1 98
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The use of PCR to amplify functional genes from the environment and their subsequent sequencing provides a method to estimate the molecular diversity of populations responsible for the processes influenced by the targeted gene products. In this limited study, the analysis of environmental sequences confirmed the presence of bacteria and archaea capable of reducing sulfate and producing methane at Installations A and C, however, because the sampling was inconsistent between and within sites, little can be concluded about the variation existing between sites. Installations A and C suffer from biocorrosion and have populations rich in Deltaproteobacteria and Firmicutes, as determined by 16S rRNA analysis. This functional analysis for sulfate and nitrate reducing bacteria reveals a pre- dominance of Deltaproteobacteria, with no Firmicute sequences detected. This absence is likely to be due to a failure of the primers used to amplify the targeted genes from these organisms, and it highlights a problem with this type of diversity estimate. No PCR primer set is without bias to some degree and this will be refelcted in the diversity estimate.
3.2.3 GeoChip characterization of microbial functional gene diversity in the field samples
The application of microarray-based technology in microbial ecology offers an alternative approach to assessing the functional diversity of an environment without using PCR. In this study a comprehensive 50-mer array has been used to detect, identify and characterize the microbial population of field samples from Installations A and C. To ensure that a broad range of templates was available for hybridisation, DNA samples representing individual pigging operations from each installation (Installation A: IA1, IA4, IA9 and Installation C: IC1, IC2, IC3) were combined. A total of over 11,500 different functional genes were detected in both DNA samples, of which 9,000 were associated with Installation A. Approximately 735 genes detected in DNA from Installation A and 430 genes in DNA from Installation C were considered to be pertinent to MIC (Figure 3.2.1). The samples used for GeoChip were unique, so that there were no replicate sequences to make variation comparisons within the samples from the different sites.
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Abundance of genes 12000
10000
8000
Total number of genes of 6000 interest Total number of detected genes
4000 Total signal signal Total intensity
2000
0 Installation A Installation C
Figure 3.2.1: Abundance of genes detected by GeoChip hybridization. The y-axis represents the number of genes derived from microorganisms from water injection system (Installation A) and production pipeline (Installation C). Genes of interest: aprAB-adenosine-5-phosphosulfate reductase gene; dsrAB-dissimilatory sulfite reductase gene; sox-sulfur oxidation enzyme system gene; nrfA-periplasmic nitrite reductase gene; NiR-nitrite reductase gene; narG-nitrate reductase gene; napA-nitrate reductase gene; nirK-cooper containing nitrite reductase gene; nirS- nitrite reductase gene; amoA-ammonia monooxygenase subunit A gene; mcrA- methyl coenzyme-M reductase gene; FTHFS-formate-etrahydrofolate ligase; assA- alkylsuccinate synthase; c-cytochrome- c-type cytochromes; hydrogenase.
The general hybridisation results indicated that there was a greater microbial diversity in the water injection system, where corrosion is controlled, than in the rapidly deteriorating production pipeline. Lower population diversity in highly corroded systems, compared with less corroded ones, has been noted previously (Beech, unpublished data). It has been speculated that the biofilm community on severely corroding surfaces is relatively stable, creating a unique co-operative metabolism involving cycling of key elements, namely S, Fe, C and N (Mitchell et al., 2008). This observation concerning microbial diversity is not in agreement with the pyrosequencing study, where the lowest molecular diversity was detected in Installation D (uncorroded pipeline).
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A histogram showing the relative signal strengths for functional genes, such as aprAB, dsrAB, sox, nrfA, NiR, narG, napA, nirK, nirS, amoA, c-type cytochrome, hydrogenase, mcrA, assA, and FTHFS, pertinent to MIC is presented in Figure 3.2.2. For each gene analysed, with the exception of the FTHFS gene, stronger signals were detected in the DNA sample from water injection system. The highest hybridisation signal in this installation was seen for c-type cytochromes and dsrAB genes.
Cytochrome c possesses a wide range of properties and function in a large number of redox reactions, including Fe reduction. Hence, it can serve as an indicator of the latter process. The dsrAB gene codes for the alpha and beta sub-units of an enzyme that controls the six-electron reduction of sulfite to sulfide and is an indicator of biological sulfite, but not sulfate, reduction. The abundance of this gene in oilfield environments is often associated not only with the presence of SRP but also with that of spore forming, CO2 producing thiosulfate and organic sulfur-reducing bacteria representing the phylum Firmicutes.
Among electron donors available for sulfate reduction and methanogenesis, H2- utilizing sulfate reduction yields the greater free energy and energy flux. This suggests that CO2-utilizing methanogens and acetogens will have a lower population density than those of sulfate reducers. This pattern was observed in both installations by comparing signal intensity from aprAB genes, representing SRPs, versus mcrA and FTHFS genes, representing methanogenic and acetogenic populations, respectively.
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- -
- -
5
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axis axis
type type
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-
nrfA nirK
c
mcrA
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adenosine
-
cytochrome
aprAB
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c
hybridization. hybridization. The y
nitrate reductase gene;
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napA
sulfur sulfur oxidation enzyme system gene;
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alkylsuccinate synthase;
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sox
ammonia ammonia monooxygenase subunit A gene;
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assA
amoA
nitrate reductase gene;
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narG
etrahydrofolate ligase;
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corrosion corrosion based on measurement from the GeoChip
nitrite reductase gene;
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formate
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dissimilatory dissimilatory sulfite reductase gene;
nitrite nitrite reductase gene;
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NiR
dsrAB
.
M reductase gene;
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Abundance of genes involved in
Figure Figure 3.2.2: represents the signal intensity of phosphosulfate reductase genes gene; derived from microorganisms periplasmic nitrite reductase from gene; the Installation A cooper and containing nitrite C. reductase gene; methyl coenzyme hydrogenase cytochromes;
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The varying abundance of MIC-pertinent functional genes between the installations could reflect differences in corrosion mechanisms, particularly if some signals were absent. Only one gene, assA, was absent in Installation C. This gene codes for argininosuccinate synthetase involved in aromatic hydrocarbon degradation. Otherwise, all genes potentially involved in MIC were present in both installations. Further identification of corrosion pathways requires a detailed transcriptomic study of these genes. Although further classification of the corrosion mechanism is not possible, the origin of the gene probe can provide information on the microbial strains and genera likely to be present in oilfield systems.
The DNA recovered from Installation C produced high intensity signals for aprAB and dsrAB gene sequences, representative of thermophilic sulfidogenic prokaryotes of the genera Archaeoglobus and Thermoanaerobacter, with a smaller signal for these genes from Thermodesulforhabdus and Thermoproteus genera. Signals for this gene from thermophilic microorganisms, such Archaeoglobus and Thermodesulfobacterium, were also detected in DNA from Installation A but with a lower signal (Table 3.2.3). The pre-dominant Archae genus, according to signal intensities for aprA and dsrAB probes, was Archaeoglobus, within the Euryacrchaeota, in both installations.
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Table 3.2.3: Relative abundances of signals from functional genes representing sulfur reduction and oxidation pathways detected in DNA from Installations A and C using GeoChip. Oligonucleotide probes correspond to gene sequences from organisms listed in the left column.
Taxonomic Groups Functional gene and its relative signal abundance Genus aprAB aprAB dsrAB dsrAB sox sox Bacteria Inst. A Inst. C Inst. A Inst. C Inst. A Inst. C Alphaproteobacteria Rhizobiales Bradyrhizobiaceae Bradyrhizobium spp. 630 1263 Nitrobacter spp. 4565 1351 Rhodopseudomonas spp. 5641 Methylobacteriaceae Methylobacterium spp. 17172 3166 Xanthobacteraceae Xanthobacter spp. 899 582 Rhodobacterales Rhodobacterales spp. 54308 Rhodobacteraceae Dinoroseobacter spp. 978 554 Paracoccus spp. 5660 1932 Phaeobacter spp. 617 Pseudovibrio spp. 489 Rhodovulum spp. 1171 Ruegeria spp. 3466 1255 Sulfitobacter spp. 2513 Magnetococcales Magnetococcaceae Magnetococcus spp. 2497 2522 Rhododspirillales Rhodospirillaceae Magnetospirillum spp. 719
Taxonomic Groups Functional gene and its relative signal abundance Genus aprAB aprAB dsrAB dsrAB sox sox Bacteria Inst. A Inst. C Inst. A Inst. C Inst. A Inst. C Betaproteobacteria Burkholderiales Burkholderiaceae Cupriavidus spp. 2180 Oxalobacteraceae Herminiimonas spp. 1251 Comamonadaceae Polaromonas spp. 655 544 Incertae sedis Methylibium spp. 1321 Gallionellales Gallionellaceae Sideroxydans spp. 563 Hydrogenophilales Hydrogenophilaceae Thiobacillus spp. 851 805 889
Gammaproteobacteria Acidithiobacillales Acidithiobacillaceae Acidithiobacillus spp. 1536 1833 Chromatiales Ectothiorhodospiraceae Alkalilimnicola spp. 1541 2617 Halorhodospira spp. 826 Thioalkalivibrio spp. 780 938 Chromatiaceae Allochromatium spp. 603 6086 1231 Thiorhodovibrio spp. 633 Rhabdochromatium spp. 980 Thiotrichales Thiotrichaceae Leucothrix spp. 1408 1628
Taxonomic Groups Functional gene and its relative signal abundance Genus aprAB aprAB dsrAB dsrAB sox sox Bacteria Inst. A Inst. C Inst. A Inst. C Inst. A Inst. C Deltaproteobacteria Desulfobacterales Desulfobacteraceae Desulfobacter spp. 3394 8785 Desulfococcus spp. 20500 614 Desulfofrigus spp. 1110 Desulfofaba spp. 621 1188 Desulfobulbaceae Desulfofustis spp. 2473 1291 Desulfospira spp. 4903 754 Desulfotignum spp. 1173 Desulfocaldus spp. 1625 465 Desulfovibrionales Desulfohalobiaceae Desulfohalobium spp. 3365 1359 546 Desulfonatronovibrio spp. 486 Desulfonauticus spp. 18135 11874 Desulfomicrobiaceae Desulfomicrobium spp. 681 Desulfovibrionaceae Desulfovibrio spp. 15631 607 26735 2898 Synthrophobacterales Syntrophobacteraceae Desulfacinum spp. 808 4211 Desulfoglaeba spp. 1181 Syntrophobacter spp. 3895 1446 4929 Thermodesulfobacterium spp. 606 Myxococcales Myxococcaceae Anaeromyxobacter spp. 6209
Taxonomic Groups Functional gene and its relative signal abundance Genus aprAB aprAB dsrAB dsrAB sox sox Bacteria Inst. A Inst. C Inst. A Inst. C Inst. A Inst. C Firmicutes; Clostridia Clostridiales Clostridiaceae Clostridium spp. 1037 Peptococcaceae Desulfotomaculum spp. 1665 893 Ruminococcaceae Ruminococcus spp. 1191 Halanaerobiales Halanaerobiaceae Halothermothrix spp. 1029 890 Thermoanaerobacterales Thermoanaerobacteraceae Thermoanaerobacter spp. 20336
Bacteroidetes/Chlorobi Chlorobia; Chlorobiales Chlorobiaceae Chlorobaculum spp. 1033 Prosthecochloris spp. 641 Chlorobium/Pelodictyon group Chlorobium spp. 681 562 1637 2463 Pelodictyon spp. 919 1921
Deinococcus-Thermus Thermales Thermaceae Meiothermus spp. 800 Thermus spp. 640 639 Fibrobacteres/Acidbacteria Fibrobacteraceae Fibrobacter spp. 797 552
Taxonomic Groups Functional gene and its relative signal abundance Genus aprAB aprAB dsrAB dsrAB sox sox Bacteria Inst. A Inst. C Inst. A Inst. C Inst. A Inst. C Thermodesulfobacteria Thermodesulfobacteriales Thermodesulfobacteriaceae Thermodesulforhabdus spp. 514
Aquificeae Aquificales Hydrogenothermaceae Sulfurihydrogenibium spp. 1106 707
Archaea Euryachaeota; Archaeoglobi Archaeoglobales Archaeoglobaceae Archaeoglobus spp. 802 28370 82196 Euryachaeota: Methanomicrobia Methanomicrobiales Methanomicrobiaceae Methanoculleus spp. 584 Crenarchaeota: Thermoprotei Thermoproteaceae Pyrobaculum spp. 2100 1110 6414 6132 Thermoproteus spp. 1118
Uncult. bacterium (dominant) 19589 6486 23809 8809 774 Other uncult. Bacteria 8376 3875 Uncult. sulfate-red. bact. 139994 55860 Other uncult. sulfate-red. bact. 3297 2033 Other 5779 2429 8376 3875 1190 1775
Chapter 3 Results and Discusion
The taxonomic groups detected according to signal intensity for the aprAB, dsrAB and sox probes is shown in Table 3.2.3. A range of taxa belonging to the Alpha- Proteobacter was detected within both installations. The majority of these taxa, with the exception of Magnetococcus species, showed signals for the sox probe. The greatest signal intensity for this gene was for Methylobacterium species in Installation A.
A greater range of taxa within the Betaproteobacteria was detected in Installation A. These taxa also gave signals for the sox probe, with the exception of Thiobacillus species. This group is known as sulfate oxidizing organisms, although their oxidizing mechanism does not necessarily make use of the sox gene products (Petri et al., 2001). Sulfate oxidizing organisms were also detected within the Gama- Proteobacteria. Members of the Acidithiobacillus, Allochromatium and Leucothrix gave signal responses for the sox probe in both installations. The main order within the Gammaproteobacteria detected for the aprAB and dsrAB probes was the Chromatiales. These are the purple sulfur bacteria, capable of photosynthesis using hydrogen sulfide (product of the sulfate reducing bacteria) as the reducing agent. All of these organisms are capable of generating sulfuric acid as part of their metabolism.
Some of the greatest aprAB and dsrAB signal intensities were obtained for members of the Deltaproteobacteria. Installation A samples exhibited aprAB probe signals of great intensity for members of Desulfococcus, Desulfonauticus and Desulfovibrio, while the greatest dsrAB probe signal intensity was for Desulfovibrio species. A greater diversity of species within this class was detected for Installation A although they were present in Installation C samples, where the greatest aprAB signal was for members of Desulfonauticus. The signal intensities for the dsrAB probe were relative equal for the taxa detected, except that for Desulfovibrio.
Surprisingly, the species diversity for these sulfur-metabolizing probes amongst the Firmicutes was less for Installation C, with a strong dsrAB probe signal for members of Thermoanaerobacter only and a weaker aprAB probe signal for
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Halothermothrix species. Installation A samples showed signals for more taxa within this class, but not those for Thermoanaerobacter. Signals for Clostridium, Desulfotomaculum and Ruminococcus species were detected for the dsrAB probe, whereas Desulfotomaculum and Halothermothrix species were detected for the aprAB probe.
The main bacterial genera detected with probes targeting c-type cytochromes were identified as belonging to Geobacter (Mehanna et al., 2009), Pseudomonas (Obuekwe et al., 1981) and Shewanella (Royer et al., 2003). These organisms form a diverse group of metal-reducing bacteria (MRB) able to influence corrosion by facilitating electron transfer to and from a metallic surface. Gene sequences representing these genera were detected in both installations, however, with different relative abundances (Figure 3.2.2). DNA from Installation A had higher level of sequences from SRP (44%) than from IRB (Figure 3.2.3), while the opposite was seen in Installation C (Figure 3.2.4), where genes representative of SRP comprised only 5% of the total pool of genes while those from IRB exceeded 80%.
Species of the genus Geobacter have been reported to carry out direct electron extraction from a metal surface through outer membrane c-type cytochromes which, in complex ways, resulted in severe corrosion (Mehanna et al., 2009). Apart from pathways involving cytochromes, bacteria of the genus Geobacter (Reguera et al., 2005) and Shewanella (El-Naggar et al., 2010; Gorby et al., 2006) are able to exchange electrons between cells through extracellular appendages, such as pili, also termed nanowires. It is postulated that modified pili facilitate transfer of electrons from the cell to extracellular electron acceptors, e.g. Fe(III) oxides, and participate in long-range electron transfer across thick biofilm layers (Malvankar et al., 2012).
Marine Pseudomonas are known iron oxidizers and producers of extracellular polymeric substances (EPS) which contributes to the mechanical stability of biofilms, adhesion to surfaces and the formation of a cohesive, three-dimensional
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Chapter 3 Results and Discusion polymer network that interconnects and transiently immobilizes cells (Flemming and Wingender, 2010). Most of marine Pseudomonas species can degrade hydrocarbons and, in the absence of oxygen, utilize nitrate as terminal electron acceptor. Installation C contained nitrate-treated seawater, therefore supplying electron acceptor, which could promote the anoxic growth of Pseudomonads.
Anaeromyxobacter sp. others 6% 1%
Rhodobacter sp. 3% Shewanella sp. 12%
Pseudomonas sp. 17% Desulfovibrio sp. 44%
Geobacter sp. 17%
Installation A
Figure 3.2.3: Abundance of microbial strains in the Installation A based on signal intensity of probes for c-cytochrome detection. Other: unidentified environmental sequences.
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Anaeromyxobacter others sp. 1% 4% Desulfovibrio sp. 5%
Shewanella sp. 28%
Geobacter sp. 34%
Rhodobacter sp. 7% Pseudomonas sp. 21%
Installation C
Figure 3.2.4: Abundance of microbial strains in the Installation C based on signal intensity of probes for c-cytochrome detection. Other: unidentified environmental sequences.
SRBs belonging to the Desulfovibrio genus are a well characterised group of anaerobic sulfidogenic bacteria considered to be main contributors to corrosion and souring of oil production systems (Kloeke et al., 1995). As stated earlier, key enzymatic pathways governing hydrogen sulfide production are sulfate and sulfite reduction. The former is controlled through aprAB genes while the later involves dsrAB genes (Wagner et al., 1998; Zadvorny et al., 2006). The signal from genes coding for the sulfate reduction pathway in Desulfovibrio spp., although relatively high, was not the dominant one in Installation A and rather minor in Installation C.
Sulfate-reducing prokaryotes and methanogens are known syntrophs and co-exist in biofilms exhibiting co-operative metabolism (Raskin et al., 1996). Both have high requirement for Fe ions and are hydrogen scavengers, which have direct impact on steel corrosion (Daniels et al., 1987; Zadvorny et al., 2006).
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Methanogenic archaea use molecular hydrogen and carbon dioxide to produce methane with either pure elemental iron (Fe0) or iron in carbon steel supplying electrons in the reduction of CO2 to CH4 (Daniels et al., 1987). Relatively low abundance of signals from mcrA gene were detected in field DNA samples compared to those from other functional genes, irrespective of the installation. Likewise, the diversity of methanogenic populations was low (Figure 3.2.5). The ratios of aprAB and dsrAB v.s. mcrA genes were markedly lower for Installation A (17) than for C (42). The true implications of differences in these ratio for corrosion are yet to be determined.
Abundance of mcrA genes 8000
7000
6000
5000
4000
3000
Total signal signal Total intensity 2000
1000
0
Installation A
Installation C
Figure 3.2.5: Relative abundances of signals from functional genes representing methanogenesis (mcrA) detected in DNA from Installations A and C using GeoChip. Other: unidentified environmental sequences.
One of the treatments used in oil recovery systems to control SRP activity is the addition of nitrate to injection water (Al-Humam et al., 2010). In Installation C, seawater is dosed with nitrate prior to its injection into the pipeline. This can cause problems associated with the production of corrosive nitrite due to an imbalance between nitrate and nitrite reduction. A greater signal from narG gene,
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Chapter 3 Results and Discusion which is a marker for nitrate reduction, compared to that from the nirK and nirS genes characteristic of nitrite reduction pathways, was observed in Installation C. In both systems, napA, narG, nirK, and nirS gene sequences were mainly affiliated with uncultured bacteria.
Ammonia produced by nitrite reducing bacteria and accumulated in the system, can be converted to corrosive acids (nitric or nitrous acid) by ammonia-oxidizing microorganisms (AOM) and enhance corrosion rate by pH decrease. AOM are a diverse microbial group present in marine habitats and can be detected by subunit A of ammonia monooxygenase (amoA) gene (Junier et al., 2010). Ammonia monooxygenase (AMO) catalyzes the oxidation of NH3 to NH2OH (Arp et al., 2002). Signal of amoA in both installations was relatively high (Figure 3.2.2). Sequences similar to those of: Corynebacterium spp., Citreicella sp., Desulfovibrio spp., Edwardsiella spp., Methylobacterium spp., Mycobacterium spp., Nitrosospira sp., Oceanicola granulosus, Paracoccus denitrificans, Pseudomonas spp., Rhodopseudomonas palustris, Roseobacter sp., Roseovarius spp., Octadecabacter antarcticus, uncultured crenarchaeote, uncultured ammonia-oxidizing archaeon and uncultured ammonia-oxidizing bacterium were abundant in the analyzed samples.
A number of different SRP genera are able to use nitrate instead of sulfate as a terminal electron acceptor (Moura et al., 1997). SRBs which are able to reduce nitrite, as a second step of dissimilatory nitrate reduction to ammonia, can be detected using probes for NiR and nrfA genes. These markers were abundant in Installation A, with the sequence of nrfA probes from the sulfurogenic bacteria Desulfovibrio and the genera Shewanella producing the greatest signal. Various phylogenetic groups of sulfur-oxidizing bacteria (SOB) that convert sulfur, sulfide and/or thiosulfate to sulfate can be detected by sox gene. Sulfate produced by SOB can be reduced by SRP or transformed into highly corrosive sulfuric acid. Higher intensity of signals for the sox genes was detected in Installation A (Table 3.2.3). The ratio of signal intensity between sox genes and joint apr and dsr genes in this installation was 2.8 suggesting cooperative metabolism between SOB and
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SRP populations, as proposed by Kan and co-workers (2011) in a model of bacterial group interactions. Remarkably, in the Installation C this ratio was 11.7 which indicates the overwhelming presence of signals from the genes controling sulfur reduction pathways relative to genes involved in sulfur oxidation. The implication of the large value of this ratio could translate into higher accumulation of sulfides, which are very corrosive species, in Installation C relative to Installation A.
Analysis of microbial communities in the production pipelines and water injection system revealed significant differences between the two installations. The higher relative abundance of genes present in Installation A might reflect an on-going, albeit controlled, corrosion process at that site, whereas the reduced diversity observed at Installation C, where corrosion appears difficult to manage, is dominated by genes representing sulfidogenic thermophilic prokaryotes and characterised by a markedly high ratio between genes governing sulfur reduction versus oxidation. Moreover, the majority (over 80%) of cytochrome-coding genes in Installation C represented taxa belonging to iron-reducers, while gene sequences in DNA from Installation A were characteristic of sulfate reducing bacteria (44%). Clearly, additional investigations addressing differences in sulfur, nitrogen, iron and, possibly hydrogen cycling between the two installations could provide valuable insights into the corrosion process. The working hypothesis could be offered that in oilfield environments the system suffering severe MIC attack experiences imbalance in “metabolic states” of microbial populations, seen as large differences between the ratios of relative abundance of functional genes coding for certain key redox pathways. The efficacy of biogeochemical cycling of elements such as Fe, S, N and possibly H would be adversely affected in such system.
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Section 3
Probe designing and testing
The need to develop a microarray chip specifically for biocorrosion in oil field environments arises because current techniques lack the specificity to be useful. The GeoChip provided the greatest information set, but it contained groups of other probes that were not appropriate for microbial oil corrosion. Furthermore, in a custom microarray chip the number of probes for specific functions could be increased to cater for a greater range of taxa. In this chapter the design and hybridisation conditions for new sets of probes are described that can be incorporated into a biochip with existing ones. All probes were tested and optimised for hybridization with DNA from model prokaryotic taxa that were representative of MIC culprits using a range of approaches.
3.3.1 Oligonucleotide probe design
A list of the genes representing metabolic pathways that are likely to be implicated in corrosion reactions and mechanisms is shown in Table 3.3.1. The identification of these genes has resulted from previous studies (Table 1.4.1) and the analysis of molecular data from corroded and non-corroded pipelines presented in this thesis.
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Table 3.3.1: Listing of organism implicated in MIC of carbon steel, with their selected functional genes representing metabolic pathways which are likely to impact corrosion reactions and reported mechanism of corrosion.
Group of Marker gene(s) Metabolic activity contributing to microorganism corrosion
SRP (sulfate-reducing aprA, dsrAB, 16S H2S production from S-containing prokaryotes) rRNA inorganic compounds, Fe reduction, CO2 (Archeoglobus) corrosion nitrate reduction, degradation of hydrocarbons
NRB (nitrate/nitrite- narG, napA /nirK, nitrite production/nitrite reduction, reducing bacteria) nirS organic acid production, Fe reduction/oxidation (M)IRB iron-reducing 16S rRNA dissolution of the protective oxide bacteria (Geobacter, layers, Shewanella, nitrate and nitrite reduction, H2S Pseudomonas spp.) production, organic acid production, secretion of exopolymeric material
Fermentative bacteria hydrogenase H2S production from S-containing (Clostridia spp.) (hydA) inorganic and organic compounds, production of organic acids, reduction of nitrate, possible degradation of hydrocarbons, production of acetate
SOB (sulfur-oxidizing soxB lowering pH (H2SO4) production prokaryotes)
Acetogenic Bacteria FTHFS production of acetate
HDM (hydrocarbon- assA, bssA CO2 and acid production, biofilm degrading stabilisation microorganisms)
Methanogens mcrA syntrophy with SRP, acting as electron prokaryotes acceptor for SRP in the absence of sulfate
A number of these genes already had PCR primers designed for their amplification, however, these primers can be used as hybridization probes. Additional probes were included in the analysis from those published. Loy and coworkers established an rRNA-targeted oligonucleotide probe database named 'probeBase' with currently more than 1,300 entries (Loy et al., 2007) and 16S rRNA and other probes were selected from this database (Table 3.3.2).
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Table 3.3.2: Selected marker genes and corresponding probe sequences representing key groups of microorganisms and metabolic pathways implicated in MIC. Group of Gene Probe Probe sequence Tm References microorg name (°C) anism
Bacteria 16S rRNA EUB338 GCTGCCTCCCGTAGGAGT 59.4 Amann et al., 1990 Archaea 16S rRNA ARC915 GTGCTCCCCCGCCAATTCC 62.9 Raskin et al., T 1994 MRB 16S rRNA SHEW2 AGCTAATCCCACCTAGGT 55.8 Huggett et al., 27 WCATC 2008 MRB 16S rRNA Geo1A CTCACGCACTTCGGGACCA 60.5 Demanèche et G al., 2008 NRB nirK nirKF TCATGGTCCTGCCGCGYGA 67.5 Qiu et al., CGG 2004 Hydrocarb assA/assB assAR TCGTCRTTGCCCCATTTIG 65.2 Callaghan et ons GIGC al., 2010 degraders
Acetogens FTHFS FTHFS1 TTCACTGGTGATTTCCAT 55.7 Salmassi and GCC Leadbetter, 2003 SRB aprA apsA1P CCGGGCCGTAACCGTCCTT 63.9 Zinkevich and 1R10 GAA Beech, 2000 SRB dsrAB DSR4R GTGTAGCAGTTACCGCA 52.0 Wagner et al., 1998 SRP 16S rRNA Arch5F TATCCGGCTGGGACTAA 56.5 Duncan GC unpublished Legend: R=G/A, Y=T/C, W=A/T, I = Inosine.
In order to broaden the spectrum of functional genes pertinent to MIC, additional probes were designed (Table 3.3.3). These six probes were chosen from a range of other sequences that were checked for their Tm values, sequence divergence and taxon specificity.
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Table 3.3.3: List of designed DNA probes.
Group of Gene Probe Probe sequence Tm microorganism name (°C) Methanogenes mcrA mcrA1 GGCATCAAGTTCGGACACTT 60.12 Firmicutes hydA hydA1 TCACCACAACAAATATTTGGTACTG 60.08 NRB nirS nirS11 TCCAACAAGATCGCCGTAGT 60.66 NRB narG narG3 ATTCTACGCCCATACCGACC 61.09 NRB napA napA2 TGTGGGTCGAAAAAGAAGGA 60.61 SRB aprAB aprAB3 CGTTGGCAGATCATGATTCA 60.63
The design of probes suitable for screening environmental samples started with the DNA or mRNA sequence of the gene of interest. Sequences for the genes mcrA, hydA, nirS, narG, napA2, and aprAB were obtained from the NCBI database for a range of taxa. Alignments were constructed for each gene, which comprised 28 sequences for mcrA, 12 entries for hydA, 115 for nirS, 148 for narG, 37 for napA and 39 for aprAB. Wherever possible, type species were included in the alignments. The alignments allowed the identification of conserved sequence blocks that could potentially act as a probe. In some instances the chosen probe was heavily conserved for all taxa, i.e. the aprAB probe (Figure 3.3.1). In other cases, napA (Figure 3.3.3) for instance, the probe was conserved for some taxa only, so that it was unlikely to detect all possible organisms. Other probes are, therefore, needed to detect all napA genes.
From the alignments several groups of sequences were identified for each gene, based on homology similarity and specificity to metabolic pathways and particular species groups.
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520 * 540 * 560 * 580 aprAB : ------CGTTGGCAGATCATGATTCA------: 20 EF442914.1 : CGGTCCGTACCGGCAAGTGGCAGATCATGATCAACGGCGAGTCCTACAAGCGGGTCGTTGCCGAAGCCGCCAA : 305 EF442882.1 : CAGTTCGCTCCGGTCGCTGGCAGATGATGATCAACGGTGAAGCATACAAATGCATCGTTGCTGAAGCTGCTAA : 536 EF641918.1 : CTGTTCGTTCCGGTAAATGGCAGATCATGATCAACGGCGAATCCTACAAGTGGATCGTTGCTGAAGCTGCGAA : 518 EF442878.1 : CTGTTCGTTCTGGCCGTTGGCAGATCATGATTAATGGAGAGTCTTATAAGGTTATCGTGGCTGAGGCTGCTAA : 527 EF442899.1 : CTGTCCGCTCCGGCCGCTGGCAGATCATGATCAACGGTGAGTCCTACAAGGTCATCGTTGCTGAAGCCGCCAA : 533 EF442898.1 : CGGTGCGCTCCGGCCGTTGGCAGATCATGATCAACGGTGAGTCCTACAAGTGCATCGTGGCCGAGGCCGCCAA : 527 EF442889.1 : GCGTTCGTTCCGGCCGCTGGCAGATGATGATCAACGGTGAATCCTACAAGTGTATCGTTGCTGAAGCTGCTAA : 533 EF442943.1 : ACGTGCATGAAGGCCGCTGGCAGCTCATGATCAACGGTGAGTCCTACAAGATTCTCATCGCTGAAGCCGCCAA : 440 EF641936.1 : AG---CGCGAGGGTCGCTGGCAGATCATGATCCACGGTGAGTCCTATAAGCCGATCGTCGCCGAGGCGGCAAA : 437 EF442944.1 : ACGTGCATGAAGGCCGCTGGCAGCTCATGATCAACGGTGAGTCCTATAAGATTCTCATCGCGGAAGCCGCCAA : 440 EF442948.1 : ATGTCCACGAAGGTCGTTGGCAGCTCATGATCAACGGCGAATCCTACAAAGTTATCGTTGCCGAAGCGGCGAA : 440 EF442951.1 : ATGTGCATGAAGGCCGCTGGCAGATCATGATCAATGGTGAATCCTACAAGGTCATCGTTGCAGAAGCAGCCAA : 440 EF442968.1 : ACGTCCATGAAGGCCGCTGGCAGATCATGATCAACGGTGAGTCCTACAAGATCATCGTGGCTGAAGCGGCTAA : 440 EF442935.1 : CGGTTCGTACCGGTAAATGGCAGATCATGATCAACGGCGAGTCCTACAAGTGCGTCGTTGCCGAGCCGGCCAA : 515 EF442925.1 : CAGTTCGTACAGGTAAATGGCAGATCATGATTAACGGCGAATCTTACAAAAGAATCGTTGCAGAAGCTGCTAA : 518 EF641925.1 : CGGTGCGTTCCGGTAAGTGGCAGATCATGATCAACGGCGAGTCCTACAAGTGGATCGTCGCCGAAGCCGCCAA : 500 EF641953.1 : AA---CGTGAAGGCCGTTGGCAGATCATGATTCACGGTGAATCCTACAAGCCGATTGTTGCCGAAGCCGCCAA : 437 EF641947.1 : AG---CGCGAAGGTCGCTGGCAGATCATGATTCATGGTGAATCCTACAAGCCGATTGTCGCCGAAGCGGCCAA : 437 EF442939.1 : CGGTACGTACCGGTAAATGGCAGATCATGATCAACGGCGAGTCCTACAAGTGCATCGTCGCCGAGCCGGCCAA : 515 EF442946.1 : TCGTTCGCGAGGGTCGTTGGCAGATCATGATCAACGGCGAGTCCTACAAGGTGATCGTGGCCGAGGCCGCGAA : 440 EF442884.1 : CGGTTAACTCCGGCCGTTGGCAGATCATGATCAACGGTGAGTCCTACAAGTGCATCGTGGCAGAAGCTGCCAA : 536 EF442891.1 : CCGTGCGTTCCGGCCGCTGGCAGATCATGATCAACGGTGAGTCCTACAAGGTCATCGTTGCTGAAGCCGCCAA : 533 EF641957.1 : AG---CGTGAAGGGCGATGGCAGATCATGATTCACGGTGAATCCTATAAGCCTATCGTGGCGGAAGCGGCGAA : 437 EF442960.1 : ACGTTCACGAAGGCCGCTGGCAGATCATGATTAACGGTGAGTCTTACAAGATCATCGTTGCCGAAGCTGCGAA : 440 gt c gg TGGCAGaTcATGAT aAcgg ga tcctacaag atcgt gc ga gc gc aa
Figure 3.3.1: Alignment showing the position of the aprAB gene probe to selected sequences. (Accession numbers and corresponding organism listed in the Appendix 6).
* 160 * 180 * 200 * 220 mcrA : ------GGCATCAAGTTCGGACACTT------: 20 AB300466.1 : AGCCCGGTGGCATCAAGTTCGGACACTTTGCCGACATGGTCCAGACGGACAGGAAGTACCCGAACGACCCCGCA : 222 AY625598.1 : AGCCCGGTGGTATCAAGTTCGGTAACTTTGCCGACATGATCCAGACCGACAGGAAGTACCCGAACGACCCCGCC : 145 AF313868.1 : AACCCGGCGGTCTTTCGTTCGGCTTCTTGTCTGATATCATTCAGACGAGCCGGAAGTACCCCGACGACCCCGTC : 202 EU296536.1 : AACCAGGTGGTATCAAATTCGGTAACTTCTGTGATATGATCCAGGCAGACCGCAAGTATCCAAACGACCCCGCA : 222 EU681942.1 : AGCCAGGTGGTATCAAGTTCGGTCTCTTCTCGGATATCGTCCAGACTGACCGCAAGTACCCGAAGGACCCGTGT : 222 EU302012.1 : AGCCTGGCGGAATGCCACTGGGTGTCTGTGACGACTGTACACGATCTCCTGCACTGTTCCCGAATGACCCGATC : 141 DQ767845.1 : AACCAGGTGGAATTCCATTTGGTTTCTTAGCTGACATTTGTCAGTCTTCAAGAGTTAACCCTGACGACCCAGCC : 222 AJ489775.1 : AGCCCGGTGGTCTGTCATTCGGGTTCATGGCCGACATCTGTCAGAAAGACAGAATCAAGCCCGACGACCCAGTA : 202 AY625601.1 : AGCCCGGTGGTATCAAGTTCGGTCTGTTCTCTGATATCATTCAGGGCAACCGGAAGTACCCGAAGGACCCCGCC : 132 AY760633.1 : AACCGGGCGGTGTATCATTCGGACATCTCGCAGATATTATTCAAACAAGCCGTATAAAATCCGATGATCCTGCA : 201 AM407730.1 : AGCCTGGTGGATTGTCCTTCGGTCACATGGCAGATATCATTCAGACAAGCCGTGTAGACAAGAAAGACCCTGCA : 177 EU302061.1 : AGCCCGGTGGCATCAAGTTCGGTCTCTTTGGAGATATCATTCAGGCAGACCGCAAGCACCCCAATGACCCTGCC : 135 EU681936.1 : AGCCCGGAGGCATCAAGTTCGGACACTTCGCCGATATGATCCAGGCAGACCGCAAGTACCCGCATGACCCGGCC : 222 AF313867.1 : AGCCCGGTGGATTACCGTTCGGCTATCTGGCCGACATGGTGCAGACCAACCGCAAGTACCCGGACGACCCCGTA : 202 AY625597.1 : AGCCCGGTGGTATCAAGTTCGGTCACTTTGCCGACATGATACAGGCCGACAGAAAGTACCCGAACGACCCCGCA : 169 EU301854.1 : AGCCCGGTGGTATCAAGTTCGGTGTCTTCTCCGATATCATTCAGGCAAACCGCAAGTACCCGAACGACCCCGCA : 141 a cc gg GG T tTcGG c t ga at cag c c g a cc a gaccc g
Figure 3.3.2: Alignment showing the position of the mcrA gene probe to selected sequences. (Accession numbers and corresponding organism listed in the Appendix 6).
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40 * 460 * 480 * 500 * napA : ------TGTGGGTCGAAAAAGAAGGA--- : 20 EU495672.1 : --ATCCAACAGTCACCACTTTGGGCGCCGATCTGATCTTGCCGGTAGCGATGTGGGTCGAAAAAGAAGGA--- : 493 EU495651.1 : TCATCCCACCGAAACCACTAAATATGCCGATGTGGTGCTTCCCGCGGCGATGTGGGTCGAAAAAGAAGGA--- : 487 EU495702.1 : CCATCCCACAGAAACCACAAAATATGCTGATGTTGTTTTGCCGGCAGCTATGTGGGTCGAAAAGGAAGGA--- : 486 EU495770.1 : CCACCCTACCGAAACCACCCGCTTTGCCGATGTATTACTGCCTGCGGCCATGTGGGTCGAAAAAGAAGGA--- : 486 EU495664.1 : --ATCCGACGGTCACCGCGCAGGCCGCCGACTTGGTGTTACCGAGCGCGATGTGGGTCGAAAAAGAAGGA--- : 493 EU495733.1 : --ATCCTACGGTAACCGCGCTGGCGGCCGATTTAATCTTGCCAAGCGCTATGTGGGTCGAAAAAGAA------: 489 EU495764.1 : CCATCCCACAGAAACCACCCGCTACGCGGATGTCGTACTTCCGGCGGCCATGTGGGTCGAAAAAGAAGGA--- : 486 EU495687.1 : --ATCCTACAGTGACGGCTCTCGCCGCCGATCTGGTACTTCCCGCAGCGATGTGGGTCGAAAAGGAAGGA--- : 493 EU495671.1 : --ATCCGACCGTGACGGCCCAGGCCGCAGACCTGGTGCTGCCGTCGGCGATGTGGGTCGAAAAAGAAGGA--- : 493 EU495686.1 : --ATCCGACCGTAACGGCCCAGGCCGCGGACCTCGTTCTCCCCTCAGCCATGTGGGTCGAAAAAGAAGGA--- : 493 EF645075.1 : GCGCGCGCTCGCCGAGCTCTACGCGGACCCGAAGACCAAAGTCGTCTCTTACTGGACCATGGGCTTTAACC-- : 487 EF645083.1 : GGACCGGCTGGCCGAGTTGTACGCCGATCCGAAGATCAAGGTGACGTCATTCTGGACGATGGGCTTTAATCA- : 488 EF645069.1 : GAAACATCTGGCCGAACTCTACGCCGACCCGAAGGTCAAAGTGATGTCGCTGTGGACGATGGGCTTTAACC-- : 487 EF645070.1 : GAAACAACTGGCGGAGCTCTACGCCGATCCGAAGGTGCGAGTCACTTCGTTCTGGACGATGGGCTTTAACC-- : 487 EF645123.1 : GCGGCAGCTGGCCGAGCTCTACGCCGATCCCGCGGTCAAGGTCATGTCGCTGTGGACGATGGGCTTTAATC-- : 487 EF645106.1 : TCAGGAGCTCGCCGGACTCTATGCCGATCCGAAGCGCAAGGTGATGTCGCTGTGGACGATGGGCTTTAATC-- : 488 EF644698.1 : GCACCCGATCCTTTGGTCACGTGTTTCTGACAGAAAACTCTCCAACCCTGACAGAGTGAGGATCGTTAACCTT : 465 EF644722.1 : GCACCCAATTCTCTGGTCACGTGTTTCTGACAGAAAGCTCTCCAATCCTGGTAAGGTCAAGGTCGTCAATATC : 466 EF644704.1 : GCACCCGATCCTATGGTCAAGGGTTTCGGATAGAAAACTCTCAAATCCGGACAGAGTAAAGGTCGTAAATATT : 466 AY093618.2 : GCACCCGATTTTATGGTCACGTGTGAGTGATAGAAAACTTACCTCACCTGATCGTGTCAAAATCGTTAACCTC : 466 EF644710.1 : GCACCCAATTCTATGGTCAAGATGTACAGATAGAAAACTAAGTGACCCAAAAAGAGTTAAAGTTGTAAGTATC : 466 EF644815.1 : GCACCCGATCCTTTGGTCAAGATGTACAGATAGAAAACTAAGTGATCCAAATAATGTAAAAGTTGTAAGTATC : 466 EF683089.1 : GCACCCGATTCTTTGGTCTCGTGTAACTGACAGAAAACTAAGTGATCCAGATCGTGTAAAAGTTGTCAATATT : 466 EF644715.1 : GCACCCTATTTTATGGTCTCGTGTTACAGATAGAAAACTATCAAATCCTGATAAAGTAAAAGTTATCAGTATT : 466 a c c g
Figure 3.3.3: Alignment showing the position of the napA gene probe to selected sequences. (Accession numbers and corresponding organism listed in the Appendix 6).
* 960 * 980 * 1000 * 1020 hydA : ---TC------ACCACAACAAATATTTGGTACTG------: 25 JF720844.1 : ACCTTTCATCTGCAAAATCTCCTCAACAAATATTTGGTACTGCATCAAAAACTTATTATCCTTCAATATCAGG : 443 AB016820.1 : ATCTTTCATCAGCTAAATCACCACAACAAATATTTGGTGCAGCATCAAAAACTTATTATCCACAAGTTGCTGA : 1022 GQ180215.1 : ATTTATCTTCAGCTAAATCACCACAACAAATATTTGGAGCAGCAAGTAAAACATATTACCCTACAGTGGAAGG : 771 GQ180214.1 : ATTTATCTTCAGCTAAATCACCACAACAAATATTTGGAGCAGCAAGTAAAACATATTACCCTACAGTGGAAGG : 771 DQ342014.1 : ------TCACCACAACAAATATTTGGTGCAGCAAGTAAAACATATTTCCCTACAGTGGAAGG : 56 DQ342015.1 : ------TCACCACAACAAATATTTGGTGCAGCAAGTAAAACATATTACCCTACAGTGGAAGG : 56 DQ342018.1 : ------TCACCACAACAAATATTTGGTGCTGCAACTAAAACTTACTATCCTTCTATAACTGG : 56 tcaCCaCAACAAATATTTGGtgC Gca aaaac tatta cct ca t gg
Figure 3.3.4: Alignment showing the position of the hydA gene probe to selected sequences. (Accession numbers and corresponding organism listed in the Appendix 6).
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60 * 1280 * 1300 * 1320 * narG : ------ATTCTACGCCCATACCGACC------: 20 FJ556723.1 : G-GCCGCCCACCGCGCCAG---CAGAACTCAACGTCGGCCTTCTACGCCCATACCGACCAGTGGCGTTACGAGA : 118 FJ556713.1 : G-GTCGTCCGCCCCGCCAG---CAGAACTCGACCTCAGCCTTCTACGCCCATACCGACCAGTGGCGCTATGAGA : 118 AY209094.1 : A-CCCGTCCGCCGAGGCAG---ATGAACGGCACCAGCTTCTTCTACGCCCATACTGACCAGTGGCGCTATGAAT : 136 AY325571.1 : G-GCCGCCCTCCGCGCCAG---CAGAACTCGACGTCGGCATTCTACGCCCATACCGACCAGTGGCGGTACGAGA : 136 AY113817.1 : A-GCCGCCCACCACGGCAG---CAGAATGCGACCTCCTATTTCTACGCCCATAGCGATCAATGGCGTTACGAGA : 136 AY552351.1 : A-ACCGTCCACCGCGACAG---CAGAACTCGACCTCGTTCTTCTACGCCCACACCGACCAATGGCGCTACGAGA : 136 AM419310.1 : G-CGCGTCCGCCGCGTCAG---CAGAACGCGACGTCGTTCTGGTACGCCCATACCGACCAGTGGCGCTACGAGA : 136 EU352346.1 : G-CGCGTCCGCCGCGCCAG---CAGAACTCGACATCGTTCTGGTACGCCCACACCGATCAGTGGCGCTACGAGA : 119 DQ177664.1 : A-CCCGTCCAGCGCGCCAG---ATGAACGGCACCAGCTTCTTCTACGCCCACACCGATCAGTGGCGCTACGAGT : 136 AM419336.1 : G-GACGGCCGCCGCGGCAG---ATGAACTCAACGTCGGCGTTCTACGCCCACACCGATCAGTGGCGCTACGAGA : 136 EU714832.1 : G-TTCGCCCGCCGCGCCAG---CAGAACAGCACGAGCTTCTTCTACGCCCACACCGACCAGTGGCGCTACGAGA : 118 AY209084.1 : C-TGCGTCCGCCGCGCCAG---ATGAACAGCACCAGCTTCTTCTACGCCCACACCGACCAGTGGCGCTACGAAA : 136 AY209146.1 : A-CCCGACCGGTGCGGCAG---ATGAACGGTACCAGCTTCTTTTACGCCCACACCGACCAGTGGCGCTACGAGA : 136 DQ481076.1 : A-ACCGCCCGCCGCGGCAG---CAGAATTCTACCTCGTTCTTCTACGCCCACACCGACCAGTGGCGCTACGAGA : 136 EU052899.1 : C-ACCGGCCGCCCCGGCAG---ATGAATTCCACCTCTTTTTTCTATGCCCATACCAGCCAGTGGCGGTATGAAA : 136 AY113742.1 : T-CGCGTCCACCGCGCCAG---ATGGCGGGCACCTCCTACTGGTATGCCCACACCGACCAGTGGCGCTACGACG : 136 EU053003.1 : G-GCCGACCGCCACGACAG---ATGAATTCGACGTCCTTTTTCTATGCCCACACCGACCAATGGCGCTACGAGA : 136 DQ010714.1 : A-GCCGTCCGCCGCGGCAG---ATGAACTCGACGTCGTTCTTCTATGCGCATACCGACCAGTGGCGCTACGAAA : 136 FJ147534.1 : A-TTCGCCCGCCGCGGCAG---CAGAATTCCACCAGCTTCTTCTATGCGCACACCGATCAATGGCGCTACGAAA : 119 AY552373.1 : A-ACCGACCGCCCCGACAC---ATGAACTCGACCTCCTTCTTCTACGCGCATACCGACCAATGGCGCTACGAGA : 136 AY113758.1 : A-TACGCCCGCCGCGCCAG---ATGAACTCGACGAGCTTTTTCTACGCGCAAACCGATCAATGGCGTTACGAGA : 136 AY552399.1 : G-GCCGGCCGCCCCGCCAG---CAGAACTCCACCTCGTTCTTCTACGCGCATTCCGACCAGTGGCGCTACGAAG : 136 AY955171.1 : A-GCCGGCCGCCGCGCCAG---CAGAACTCGACATCGTTCTTTTACGCGCACACCGATCAATGGCGTTACGAAA : 136 DQ481065.1 : G-GCCGTCCGCCGCGCCAG---ATGAACTCGACGAGCTTCTGGTACGCGCATACGGATCAATGGCGCTACGAGA : 136 EU714880.1 : A-ATCGTCCGTCGCGCCAG---ATGAACTCGACTTCGTTCTTCTATGCGCACACCAGCCAGTGGCGCCACGAGA : 118 FN430478.1 : G-TGCCCGCCACCCGCCTG---CAGAATGCTCCGAGCTGGCACTATGTCCATACGGATCAGTGGCGCTACGAAA : 136 EU052915.1 : T-ATCCGCGCTTCCCAGTC---TGCCAAACACCGAGCTGGCACTACGTGCATCCCGACCAGTGGCGGTACGAGA : 136 AY453360.1 : T-CACGGCCGCCGCGTACC---ATGATCGGCACGGCGTACTGGTACATGCATACCGATCAGTGGCGTTTCGACG : 136 AY453355.1 : A-TCAAACCGTCACGCCTG---CAGAACACGCCGAGTTTCTGGTACATGCATTCCCACCAGTGGCGCTACGACC : 136 DQ481075.1 : T-CGCGCCCCCCGCGAACC---ATGACCGGCACGGCGTACTGGTACATGCACACCGACCAGTGGCGAACCGACG : 136 AY325544.1 : T-CGCGGCCGCCTCGCACC---ATGATCGGCACCGCGTACTGGTACATGCATACCGGTCAGTGGCGCTTCGACG : 136 AY955161.1 : T-CACGGCCGCCGCGTACC---ATGATCGGCACCGCGTACTGGTACATGCATACCGATCAGTGGCGCTTTGATG : 136 DQ481115.1 : A-CCATGGCATCGCGGCTG---CAGAACACCCCGAGCTTCTGGTATATGCATTCCGACCAGTGGCGCTACGACC : 136 AY209054.1 : G-TCCGGCCACCGCGCCAG---ATGATCCAGACCGCGTACTGGTACATGCACACCGACCAGTACCGCTACGACC : 136 AM408545.1 : G-GTGGACCGCCGCGCCTC---CAGAACGCCCCGTCGTTCCACTACATGCACTCCGACCAATGGCGCTACGACA : 136 DQ481191.1 : G-CCCGGCCGCCACGGACC---ATGATCGGCACCGGCTACTGGTACATGCATACGGATCAGTGGCGCCAGGACG : 136 DQ248883.1 : T-CGCGGCCTCCCCGGACC---ATGATCGGCACCGCGTACTGGTACATGCATACCGATCAGTGGCGCTTCGACG : 136 EU714887.1 : T-CGCGCCCACCGCGCACG---ATGATCGGGACCGCGTACTGGTACATGCACACCGACCAGTGGCGCACCGACG : 118 DQ481133.1 : A-TGCCCGCCGTGCGGCTG---CAAAACGCACCGAGCTGGCACTACATGCATACCGACCAGTGGCGGTACGAGC : 136 AY453364.1 : T-CCCGGCCGCCGCGGACC---ATGATCGGCACCGCGTACTGGTACATGCATACCGATCAGTGGCGGTTCGATG : 136 FJ147548.1 : G-CGGGGCAGCCGCGGCTA---CAGAACGGGCCGTCGTGGCACTACATGCATTCAGACCAATGGCGCTATGACG : 119 FJ147538.1 : T-TTCCGGCATCCCGTTTG---CAGAACGCGCCGAGCTGGCACTACATCCACACCGATCAGTGGCGCTATGAAA : 119 EU052922.1 : A-AGCGTCCGCCCCGGCAG---ATGAACAGCACCCTCGTTTTTTACAACCACACCAGTCAATGGCGTTATGAAA : 136 DQ177673.1 : A-ACCGTCCGATGCGCCAG---ATGAACGGTACCTCGTTCTTCTACAACCACACCAGCCAGTGGCGCCATGAAA : 136 AY113782.1 : G-TCCGACCGCCGCGTCAG---ATGATCGGCACCGCCTACTGGTACACCCACACCGACCAATGGCGGTACGACG : 136 DQ481181.1 : G-TCAGGCCGCCGCGTCAG---ATGATCGGCACCGCCTACTGGTACACCCACTCCGACCAGTGGCGCTATGACG : 136 DQ233272.1 : A-TCAGGCCGCCGCGGCAG---ATGATCGGCACCGCGTACTGGTACACCCATACCGACCAGTGGCGTTATGACG : 136 DQ233286.1 : G-TGCGGCCGCCGCGACAG---ATGATCGGCACCGCCTACTGGTACACCCATACCGACCAATGGCGGTATGACG : 136 AY209044.1 : T-ACCCCCCCTCCCGCCTT---CAAAACAGCCCCAGCTGGCACTACGTCCATTCCGGACAGTGGCGCTATGAGA : 136 AY113826.1 : A-GCCGGCCGGTCCGGCAG---ATGAACGGCACCAGCTTCTTCTACGCTCACACCGATCAATGGCGTTACGAAA : 136 EU052865.1 : C-ACCGCCCGCCCCGGCAC---ATGAATTCGACCTCGTTCTGGTACATTCACAGCAGCCAGTGGCGGCATGAAA : 136 AY955190.1 : C-AGCGTCCGACCCGGCAC---ATGACGGGCACCTCGTTCTTCTACTTGCACACGGACCAGTGGCGCTACGAGA : 136 AY955195.1 : C-AGCGGCCGACCCGTCAC---ATGACCGGCACTTCCTTCTTTTACTTACATACGGACCAGTGGCGGTATGAGA : 136 AM408509.1 : C-ATCGGCCGCCGCGGCAG---ATGAACTCGACTTCATTTTTCTACGCACACACGGATCAGTGGCGTTACGAAC : 136 AM419356.1 : T-CGCGTCCGCCGCGGCAG---ATGGCGGGCACCTCCTACTGGTACGCACACACCGGCCAGTGGCGTTACGACG : 136 AM408501.1 : T-ATCCACCGTCGCGGCTG---CAGAATGCGCCCAGCTGGCACTACGTCCACTCAGATCAATGGCGTTATGAGA : 136 EU052893.1 : T-TCGGCCCGTCACGGCTG---CAGAACGCTCCAAGCTGGCACTACGTGCATACAGATCAGTGGCGTTACGAAA : 136 FJ556609.1 : T-TCGCCCCCTCGCGGCAG---CAGAATGCGCCGAGCTGGCACTATGTCAACGCCGACCAGTGGCGGTACGAGC : 118 EF645049.1 : A-CCCGTCCGCCTAGGCAG---ATGAACGGCACCAGCTTTTTCTACGCTCATACCGACCAATGGCGTTACGAAT : 136 AM412332.1 : A-GCCGGCCGCCGAGGCAG---ATGAACGGCACCAGCTTCTTCTATGCACACACCGACCAGTGGCGTTATGAAT : 124 EU052847.1 : T-ATCCGCCATCGCGATTG---CAGAATGCGCCAAGCTGGCACTACGTGCATACGGATCAATGGCGATACGAAA : 136 DQ010748.1 : A-GCCGTCCGCCGCGGCAG---ATGAACTCGACGTCGTTCTTCTATGCGCACACCAACCAGTGGCGCTACGAGA : 136 EU053010.1 : C-ACCGCCCGCCCCGGAAC---ATGAACTCCACCTCGTTCTTCTACATCCACTCCAGCCAGTGGCGCTACAAAA : 136 AY209049.1 : T-ATCCGCCCTCACGACAG---CAGAACGCGCCGTCCTGGCACTACGTTCACAGCGACCAGTGGCGTTACGAGC : 136 AY209042.1 : G-CACGGCCACCGCGCTGG---CAGAACTCGACTTCGTTCTTCTATGCACACACCGACCAGTGGCGCTATGAAA : 136 EU052917.1 : T-CACGCCCACCACGGCAA---ATGATCGGCACCGCGTTCTGGTACGTGAACACCGGCCAGTGGCGCTACGACG : 136 AM408520.1 : A-GCAAACCGGCAAGGTTC---CAGAACGCTCCTTCCTACCACTACGTCCATAGCGATCAGTGGCGGTACGAAC : 136 FJ556600.1 : G-TGAAGCCGTCACGGCTG---CAGAACGCGCCGAACTTCTGGTATCTGCACTCGGACCAGTGGCGCTACGACC : 118 AM419256.1 : C-ACGGGCCTTCGCGGCTG---CAGAACTCGCCGAGTTGGCACTACGTCCACAGCGATCAATGGCGCTACGAGC : 136 AY113745.1 : C-TTCCGGCGCCCCGGCTGGTGCAGAACTCACCGAGCTGGCACTACGTCCATAGCGACCAATGGCGCTACGAAG : 139 AM408546.1 : T-TCCCCGCGGTGCGCCTG---CAGAACACGCCGAGCTGGCACTACATCAACAGCGACCAGTGGCGCTACGAGA : 136 FN430485.1 : G-TGCGTCCCACCCGTCAC---CAGGCCACGACGCCCTTCTGGTACCTGGCCACCGACCAGTGGCGCTACGAAC : 136 DQ481149.1 : G-TGCGTCCGCCGCGCCAC---ATGAACGGCACGTCGTTCTTCTATGCGCACAGCGACCAGTGGCGCTACGAGA : 136 FN430474.1 : T-TCCCAGCCTCTCGCTTG---CAGAACGCACCGAGCTGGCACTACATCCACACGGACCAGTGGCGCTACGAGA : 136 AY113797.1 : A-GTCGTCCGGTACGGCAA---ATGAACGGAACGAGCTTCTTCTACGCGCACACCGACCAGTGGCGGTACGAAA : 136 FJ556750.1 : G-GCCGGCCGCCCCGCCAG---GCAAACTCGACCTCCGCGTTCTACGCTCACACCGATCAATGGCGCTACGAGA : 118 AM408529.1 : T-ACCCACCGTCGCGTGTC---CAGAACGCGCCAAGTTGGCACTATGTGCACACCGATCAATGGCGTTATGAAA : 136 AM419254.1 : C-AGCGTCCGCCGCGTCAC---ATGAACTCGACCTCGTACTGGTACTTCCACACCGACCAGTGGCGCTACGAGA : 136 AM408503.1 : T-ACGGGGCTTCGCGTCTG---CAGAACGCACCCAGCTGGCACTACGTCAACTGCGACCAGTGGCGCTACGAAC : 136 AY113790.1 : A-ACCGGCCGCCGCGCCAC---ATGAATTCGACGTCATTCTTCTACGCGCATACCGACCAGTGGCGTTACGACC : 136 FJ556608.1 : C-AGCCCGCGGTAAGGCTG---CAAAACGCTCCCAGTTGGCACTATGTCCATACCGATCAATGGCGCTATGAGA : 118 AY113824.1 : C-AGCGCCCGCCGCGCCAG---ATGATCGGCACCGCGTTCTGGTACACCCACACCGACCAGTGGCGCTACGACG : 136 DQ233258.1 : G-TCCGCCCGCCACGAAAC---ATGATCCAGACGGCGTACTGGTATCTGCACAGCAACCAGTTCCGCCATGACC : 136
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DQ233267.1 : A-ACCGTCCGCCGCGCAAC---ATGATCCAGACCGCCTACTGGTACCTGCACACCAACCAGTTCCGCTACGACC : 136 FJ147515.1 : G-GCCGCCCTCCGCGGCAG---ATGAACTCGACGAGCTTCTTCTACGCGCACACCGATCAGTGGCGCTACGAGA : 119 DQ481153.1 : C-TGAAGCCGCAACGGCTG---CAAAACGGCACTTCGTACTTTTATTTCCATACCGATCAGTTCCATTACGAGC : 136 DQ233278.1 : T-CACGCCCGCCACGCAAC---ATGATCCAGACCGCCTACTGGTACCTCCACAGCAACCAGCTCCGCTATGACC : 136 DQ481175.1 : T-CGCGACCACCGCGCCAG---GCGCAGGGGACGACCGCCTACTACGCGGCTTCGGATCAGTGGCGCTACGACA : 136 EU714835.1 : C-AGCGCCCGCCGCGTCAG---ATGCAGGGCACCGTCTTCTGGTACCTGGCCACTGACCAATGGCGCTACGATC : 118 DQ233269.1 : G-CCCGCCCGACCCGCCAC---ATGGCCGGCACCACCCTCTGGTACTTGGCGACCGACCAGTGGCGCTACGAGA : 136 AY955177.1 : A-CCCGCCCGCCCCGCCAC---ATGGCCGCGACGCCGTTCTGGTATCTGGCCTCCGACCAGTGGCGCTACGAAG : 136 FN401451.1 : C-AGCGCCCCGCGCGGCAG---ATGATCTCCACCGGGTGGTCATACGTTTCCACCAGTCAGTGGCGTTACGACG : 136 DQ481059.1 : G-TGCGGCCTCCCCGCCAG---CAGGCGAGCACTCCTTTCTTCTATCTGGCCACCGACCAGTGGCGCTACGAAG : 136 FN430459.1 : A-CCAGACCACCGCGGCAT---CAGTCGAGCACCCCGTTCTTCTACCTCGCGACCGACCAGTGGCGCTACGAGC : 136 FN554843.1 : G-TGCGGCCGCCCCGCCAG---CAGGCCGCAACCCCGTACTGGTACCTCGCCTCCGACCAGTTTCGCTATGAGG : 85 FN430477.1 : A-CTCGACCGCCGCGCCAG---CAGGCGGGCACACCGTTCTTCTACCTGGCGACCGAGCAGTGGCGCTATGAGC : 101 AY955172.1 : A-CCCGTCCGCCCCGCCAC---ATGGCCGGGACGCCGTTCTGGTACCTGGCCAGCGACCAATGGCGCTACGAAG : 136 AY113743.1 : G-TCCGCCCTCCCCGTCAG---CACATCGGCACCGCCTTCTGGTATCTGGCCAGCGATCAGTGGCGCTATGACG : 136 DQ233273.1 : A-ACCGGCCGCCGCGCAAC---ATGATCCAGACGGCGTTCTGGTACTTGCACACCAACCAGTTCCGCTACGACC : 136 DQ481055.1 : A-GCCGGCCGGGGCGTCAG---ATGATCCAGACGGCCTACTGGTACCTGCACACGGACCAGTTCCGCTACGACC : 136 AY325538.1 : A-ACCGTCCGCCCCGCAAC---ATGATCCAGACAGCCTTCTGGTACCTGCACACCGACCAGTTCCGCTACGACC : 136 AY113806.1 : A-ACCGGCCGCCCCGCAAC---ATGATCCAGACCGCCTACTGGTACCTGCACACCAACCAGTTCCGGTATGACC : 136 AM419350.1 : A-ACCGTCCGCCGCGACAG---ATGATCGGCACCGCGTTCTGGTACCTGGCCACCGACCAGTGGCGCTACGACG : 76 DQ177679.1 : A-GGCGCCCGCCGCGGCAG---ATGATCCACACGGCCTACTGGTATCTCCACACCGACCAGTTCCGCTACGACA : 136 DQ177686.1 : G-ACCGCCCGCCCCGGCAC---ATGATCCAGACCGCCTTCTGGTACCTCCACACGGACCAGTTCCGCTACGACC : 136 DQ233260.1 : A-GCCGCCCGCCACGCAAC---ATGATCCAGACGGCTTTCTGGTACCTGCACACCAACCAGTTCCGGTACGACC : 136 DQ481161.1 : G-TGCGCCCGCCCCGCCAG---ATGATCAGCACCGCATACTGGTATCTCGCCACCGACCAGTGGCGCTACGACG : 136 EU352348.1 : G-GGCGTCCGCCGCGGCAG---ATGAACTCCACCTCGTTCTGGTACGCGCACACCGATCAGTGGCGCTACGAAA : 119 AY325524.1 : C-ATCCCGCGTCGCGCCTC---CAGAACGCGCCGTCGTGGCATTACGTCCACACCGATCAGTGGCGCTACGAGC : 136 FJ147550.1 : G-GCCGCCCTCCGCGGCAG---ATGAACTCGACGAGCTTCTTCTACGCGCACACCGATCAGTGGCGCTACGAGA : 119 cg cc c cg ga c c t t TA ca accga Ca tggcg ta ga
Figure 3.3.5: Alignment showing the position of the narG gene probe to selected sequences. (Accession numbers and corresponding organism listed in the Appendix 6).
* 1200 * 1220 * 1240 * 12 narS11 : ------TCCAACAAGATCGCCGTAGT------: 20 DQ072192.1 : --AGCGGC--CAA---CCAG-TCCAACAAGATTGCAGTGGTCGACTCAAAGGAACGCGAAATTGTGGCCCTGGT : 337 DQ159510.1 : --AGCGGC--CAA---CAAG-TCCAACAAGATCGCAGTGGTCGACTCCAAGGATCGTGATCTGGAAGCCCTGGT : 341 DQ072196.1 : --CGCAGC--CAA---CAAG-TCCAACAAGGTCGCTGTAGTTGACTCGAAGTTGCAGAAGTTGGTCGCACTGGT : 337 AJ440478.1 : --CGCGGC--CAA---CCAG-TCCAACAAGGTGGCCGTCGTCGACTCACTGGAACAGAAGCTCACCGCTCTGAT : 337 AB162305.1 : --GGCGGC--CAA---TGCG-TCCAACAAGATCGCTGCGGTGGACCTGAAGACCGGCAAGCTGGCTGCCTTGGT : 293 DQ337895.1 : --GGCGGC--AAA---TGCA-TCCAACAAGGTCGCGGCCGTGGACACCAAGACCGGCAAGCTGGCCGCGCTGGT : 201 AB164097.1 : --TGCTGC--GAA---TGCC-TCCAACAAGGTCGCCGTGGTTGACACCAAGGACGGCAAGCTGGCCGCACTCGT : 352 AY195929.1 : --GGCGGC--GAA---CAAA-TCCAATCAGGTGGCAGTCGTGGACTCTCGGGACCAGAAGCTCGCCGCCTTGGT : 239 AB162292.1 : --CGCTGC--CAA---CGCT-TCCAACAAGATCGCCGTGATCGACACCAAGGAAGACAAGCTGGCTGCTACCGT : 296 AF548984.1 : --CGCGGC--CAA---CAAA-TCCAATAAGATCGCGGTGATCGATTCCAGGGAACAAAAGATGATTGCGCTGAC : 239 DQ767855.1 : --GGCTGC--CAA---CCAG-TCCAACAAGATCGCCGTAGTCGACGCGCTGGAAGGCAAGCTGGAAGCGCTGGT : 260 DQ118278.1 : --GGCGGT--GAA---CGCG-AGCAACAAAATTGCGGCGGTGGATACCAAAATTGATAAACTGACCACCCTGAT : 335 AY583418.1 : --GGCCGC--GAA---CCAG-TCCAACCAGATCGCCGTGGTCGACGCGAAGCTGGACAAGCTCGAGGCGATCGT : 200 AB456871.1 : --TGCAGC--GAA---TCAG-TCCAACAAGATCGCCGTGGTCGACGCAAAGGAAGGCAAGCTAACGGCGCTGGT : 156 AB185911.1 : --CGCCGC--CAA---CGCT-TCCAACAAGGTGGCGGTGGTGGATACCCAGGAAGGCAAACTGGCTGCTCTGGT : 298 DQ232388.1 : --CGCGGC--CAA---CGCC-TCCAACAAGGTGGCGGTTGTCGATACCAAGGAAGGCAAGCTGGCGGCGCTGGT : 320 DQ182151.1 : --GGCCGC--CAA---TCAG-TCCAACAAGATCGCAGTCATCGATTCGAAGACGCAGAAGTTGATCAAGCTGAT : 200 DQ159507.1 : --GGCGGC--CAA---CCAG-TCCAACAAGCTGGCGGTCATCGACTCCAGGGAGCGCAAGCTGGTCGCGTTGCC : 341 AB162277.1 : --CGCGGC--GAA---TGCC-TCGAACAAGATCGCCGTGGTCGACACCAAGGAAGACAAGCTCGCGGCGCTGAT : 296 DQ337849.1 : --GGCGGC--CAA---CGCT-TCGAACAAGATCGCTGTCGTCGATACCAAAGAGGACAAACTGGCTGCTTTGAT : 200 AB377849.1 : --GGCGGC--GAA---CCAG-TCGAACAAGATCGCCGTGGTCGATGTGAGAGAGGGCAAGCTGGTGAAGCTCGT : 180 GQ328115.1 : --GGCGGC--CAA---CCAG-TCGAACAAGATCGCGGTCGTGGACGCCCGCCGCGGCAAGCTCGAGGCCATCAT : 194 DQ337879.1 : --GGCGGC--CAA---TGCG-TCCAACAAGGTCGCTGCGGTCGATCTCAAGACCGGCACGCTGGCGGCCCTGAT : 200 AY078269.1 : --CGCCGC--CAA---CGCG-TCGAACAAGGTCGCCGCGGTGGACACCAAGACCGGCAAGCTCGCGGGCCTGAT : 352 AB164144.1 : --CGCCGC--CAA---CGCC-TCGAACAAGGTCGCGGTCGTCGACACCAAGGAAGGCAAGCTCGCCGCCCTCGT : 352 GQ328110.1 : --CGCGGC--CAA---CGCT-TCGAACAAGGTCGCCGTCGTCGACACCAAGGAGACCAACGCGGAGGCGCTGAT : 203 AY583409.1 : --GGCCGC--GAA---CGCG-TCGAACAAGGTCGCCGCGGTCGACACCAAGACCGGCAAACTGGCGGGCCTGAT : 200 DQ337916.1 : --CGCGGC--CAA---TGCC-TCGAACAAGATCGCGGCGGTGGACCTCAAGACCGGCAAACTGGCGGCATTGGT : 200 AB162260.1 : --CGCGGC--CAA---TCAG-TCGAACAAGATCGCCGTGGTCGACTCCAAGGAACGCAAGATGGTCGATCTGGT : 296 AB278644.1 : --CGCTGC--CAA---CCAG-TCAAACAAGATCGCTGTGGTCGATGCCAAGGAAGACAAGCTGGTCAAGCTGGT : 305 DQ337831.1 : --GGCAGC--CAA---CAAG-TCAAACCAGATTGCGGTGATCGATTCGAAAGATCGACGACTGGTCAAGCTGGT : 200 AY195932.1 : --CGCGGC--GAA---CAAA-TCAAACAAGATCGCCGTCGTCGATTCCAAAGATCGCAAGCTGACGGCATTGAT : 239 AB162296.1 : --CGCGGC--GAA---TGCC-TCGGACAAGATCGCGGTGATCGATACCAAGGAAGGCAAGCTGGCCGCGGAAAT : 296 DQ159612.1 : --CGCGGC--CAA---CCAG-TCGGACAAGATCGCCGTCGTTGATTCGAGGGAGCGCAAACTGGTGGCTCTGGC : 341 FJ799350.1 : --TGCCGC--GAA---CAAG-TCGGACAAGGTCGCGATAGTGGATTCAAAAGAACGTAAGCTGGTAACCCTTGT : 203 DQ159487.1 : --CGCGGC--CAA---CAAG-TCGGACAAGATCGCCGTGGTCGATTCGAAGGAGCGCAAGCTGACTGCGATGAT : 341 DQ159530.1 : --CGCAGC--CAA---CCAG-TCGGACAAGATCGCGGTCGTTGATTCACGCAATCGCAATCTCGCCGCGCTGGT : 341 AJ811473.1 : --TGCCGC--GAA---CAAG-TCGGACAAGGTCGCGATAGAGGATTCAAAAGAACGTAAGCTGGTAACCCTTGT : 337 DQ303101.1 : --TGCGGC--CAA---TGCC-TCGGACAAGGTCGCCGCCGTCGACACCAAGACCGGCAAGCTGGCCGCGCTGAT : 260 DQ088665.1 : --CGCGGC--GAA---CAAC-TCGGACAAGGTAGTGGTGATCGATTCGAAGGATCGCAAGCTGGCAGCCATCGT : 200 AB162285.1 : --GGCGGC--GAA---CCAG-TCGGACAAGATTGCGGTCATCGACCCCAAGGAGCGCAAGACCACGGCATTGAT : 296 DQ159594.1 : --GGCTGC--AAA---CCAG-TCGGACAAGGTCGCGATCGTCGATTCGCGCGACCGCGAGCTCGAGGCGCTGGT : 341 AJ224912.1 : --GGCCGC--CAA---CGCC-TCGGACAAAGTCGCGGTGGTCGATACCAAGGAAGGCAAGCTGGCCGCGCTGGT : 312 DQ159478.1 : --CGCAGC--CAA---CAAG-TCGGACAAGATTGCCGTGGTCGATTCGAAAGCGCGCAAGCTGACCGCGCTGAT : 341 DQ159533.1 : --GGCAGC--GAA---CCAG-TCGGACAAGATCGCCGTGGTCGACTCCCGCGACCGTGATCTCGAAGCATTGGT : 341
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AB208101.1 : --CGCGGC--CAA---CGCC-TCGCACAAGATCGCGGTGGTCGATACCAAGGAAGGCAAGCTCGCGGCGCTGAT : 296 AB377752.1 : --CGCCGC--CAA---CCAG-TCGCGCAAGATCGCGGTGGTGGATGCGAAGGACGACAAGCTCGCCGCGCTGAT : 180 DQ337875.1 : --CGCGGC--CAA---TGAG-TCTGACAAGATCGCTGTAGTCGACTCCAAGGAACGCGAGCTGGTAGAGCTTGT : 200 AY336904.1 : --CGCGGC--AAA---CCAG-TCTGACAAGATTGCGGTGGTAGATGCCAAGGACAGGGAGATCGAGGCCCTCGT : 355 DQ159561.1 : --GGCCGC--GAA---CAAG-TCTGACAAGGTCGCGGTGGTCGACTCCAAGGACCGCAGTCTCGCCGCGCTGGT : 341 DQ159492.1 : --CGCCGC--GAA---TCAA-TCTGACAAAATTGCAGTCGTCGATTCGCGCGACCGCAACCGCGAGGCGCTCGT : 341 DQ072232.1 : --GGCTGC--CAA---TCAG-TCTGACAAGATCGCTATTGTTGATTCACGCGATCGCGAACTCGAAGCGCTGGT : 337 AY195937.1 : --GGCCGC--CAA---CCAA-TCCGACAAGATCGCGGTGGTCGATTCGAAGGAACAGAAGATGGTGGCCCTCGT : 242 DQ159563.1 : --CGCGGC--CAA---TCAG-TCCGACAAAATCGCGATAGTCGACTCTCGCGATCGCAAGCTGGCGGCACTGGT : 341 DQ159503.1 : --CGCGGC--GAA---CCAG-TCCGACAAGATCGCGGTCGTCGACTCGAAAGACCGATCACTGGCGGCGCTCGT : 341 DQ159584.1 : --GGCCGC--GAA---CCAG-TCCGACAAGATCGCGGTAATCGATTCACGAGACCGTGAGCTCGAGGCCCTGGT : 341 DQ337819.1 : --GGCCGC--AAA---TAAT-TCCGACAAGATCGCGGTGATCGATTCCAAGGAGGGCAAGCTGGTCAATCTGGT : 200 AB162273.1 : --GGCAGC--AAA---CCAG-CGCGGCAAGATCGCGGTGGTGGACACCAAGGAAGACAAGCTGGCGGCACTGGT : 296 DQ159611.1 : --GGCGGC--CAA---TGCC-CGCGACAAGGTGGCGGTGGTCGACACCAAGGAAAACAAGCTGGTGGCGATCTT : 341 DQ159645.1 : --GGCGGC--CAA---CGCC-CGCGACAAGGTGGCCGTGGTTGACACCAAGGACAACAAGCTCGTCGCGCTCAT : 341 gc gc aa tc acaAg T Gc gt gt ga c aa ct g gc t t
Figure 3.3.6: Alignment showing the position of the nirS gene probe to selected sequences. (Accession numbers and corresponding organism listed in the Appendix 6).
All of the designed probes were initially analysed using BLAST sequence homology searches to check that they were specific to their gene and to gauge the range of taxa that could be detected (Table 3.3.4). All of the chosen probes were specific to their gene and showed similar homologies to a range of taxa. Probes with high sequence similarity were selected for subsequent testing by hybridization with model DNA after the Tm values of each probe were determined. It was important that each of the probes had near identical Tm value, so that they had similar behaviors under standard hybridization conditions. The newly designed probes were chosen from those that had a Tm value of approximately 60°C. These values were calculated using an on-line tool, OligoAnalyzer 3.1 or Primer 3. Initially the Tm was calculated for a low salt concentration, however, the Tm values changed marginally when the salt concentration was increased to 50 mM. These values were close to those estimated for the other probes taken from the published literature (Table 3.3.2).
The length of oligonucleotides is also important, in terms of specificity and sensitivity. Longer probes (40-70) are less specific but more sensitive. However, shorter oligonucleotides (20 mer) can differentiate a single mismatch in sequences when the probe is used in targeted hybridisations (Relógio et al., 2002). The length of probes used in this study varies between 17 and 25. All of the probes designed for MIC array were selected to display identical hybridisation behaviour so that test sequences could be processed simultaneously.
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Table 3.3.4: Taxonomic report.
Probe Name % specificity Organisms detected 96% for Methanomicrobiales (Methanolinea, mcrA1 archaea Methanoregula) 2.6% for Alphaproteobacteria (Rhizobiales, bacteria Rhodobacterales, Firmicutes, Actinobacteria) Firmicutes (Clostridiaceae, Peptococcaceae, hydA1 45% for Ruminococcaceae) bacteria Bacilli (Staphylococcaceae, Bacillaceae, Aerococcaceae, Streptococcaceae, Leuconostocaceae, Enterococcaceae) Mollicutes (Spiroplasmataceae, Entomoplasmataceae, Mycoplasmataceae, Acholeplasmataceae) Proteobacteria (Piscirickettsiaceae, Oceanospirillaceae, Enterobacteriaceae, unclassified Betaproteobacteria) Flavobacteriaceae Cyanobacteria Cyanobacteria nirS11 91% for Betaproteobacteria (Rhodocyclaceae, bacteria Neisseriaceae, Oxalobacteraceae, Hydrogenophilaceae, Burkholderiaceae) Gammaproteobacteria (Pseudomonadaceae, Xanthomonadaceae) Alphaproteobacteria (Caulobacteraceae, Cyclobacteriaceae, Rhizobiaceae, Desulfovibrionaceae, Synergistetes, Acidobacteriaceae, Actinobacteridae) Alphaproteobacteria (Acetobacteraceae, narG3 93% for Beijerinckiaceae, Rhizobiaceae, bacteria Xanthobacteraceae, Caulobacteraceae, Rickettsieae, Rhodobacteraceae) Gammaproteobacteria (Enterobacteriaceae, Oceanospirillaceae, Pasteurellaceae, Alteromonadaceae, Xanthomonadaceae, Vibrionaceae) Betaproteobacteria (Comamonadaceae, Burkholderiaceae, Alcaligenaceae, Neisseriaceae, Desulfovibrionaceae) Firmicutes (Clostridiaceae, Eubacteriaceae) Bacilli (Thermoanaerobacterales, Streptococcaceae) Actinomycetales Chroococcales Bacteroidetes Acidobacteria
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Betaproteobacteria (Comamonadaceae, napA2 58.5% for Alcaligenaceae, Burkholderiaceae, Neisseriaceae) bacteria Gammaproteobacteria (Enterobacteriaceae, 0.6% for Shewanellaceae, Alteromonadaceae, archaea Methylococcaceae, Vibrionaceae, Oceanospirillaceae, Pasteurellaceae, Ectothiorhodospiraceae) Alphaproteobacteria (Rhodospirillaceae, Rhodobacteraceae) Cyanobacteria (Prochlorococcaceae, Oscillatoriophycideae, Rivulariaceae, Nostocaceae) Firmicutes (Clostridium, Streptococcaceae, Bacillaceae) Bacteroidetes (Cyclobacteriaceae, Cytophagaceae, Flavobacteriaceae, Spirochaetaceae, Micrococcineae, Corynebacterineae, Acidobacteriaceae, Parachlamydiaceae, Rhodothermaceae, Sphingobacteriaceae, Saprospiraceae, Bacteroidaceae, Prevotellaceae, Cryomorphaceae) Euryarchaeota (Methanomicrobia, Halobacteria, Crenarchaeota) Gammaproteobacteria (Ectothiorhodospiraceae, aprAB3 72% for Chromatiaceae, Halothiobacillaceae, Vibrionaceae, bacteria Psychromonadaceae, Enterobacteriaceae) 0.8% for Betaproteobacteria (Gallionellaceae, archaea Comamonadaceae, Hydrogenophilaceae, Burkholderiaceae) Deltaproteobacteria (Desulfovibrionaceae, Syntrophobacteraceae, Geobacteraceae) Alphaproteobacteria (Rhizobiaceae, Sphingomonadaceae, Rhodobacteraceae) Firmicutes (Bacilli, Thermodesulfobacteriaceae, Cyanobacteria, Actinobacteridae, Bacteroidetes)
3.3.2 Optimisation of hybridisation conditions and model DNA construction
All of the probes, newly designed and published ones, were tested for their hybridisation properties using target DNA immobilised on a nylon membrane. Initially the probe was the molecule immobilised, but this failed to hybridise to the target DNA, presumably because the oligonucleotide was too short. Longer probes
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Chapter 3 Results and Discusion were synthesized (up to 100 nucleotides) but they also failed to hybridise (data not shown). The probes should be in excess of 100 nucleotides in length to achieve successful hybridisation with target DNA (Kevin et al., 2001). Different hybridisation parameters were evaluated to optimise conditions that gave a positive signal with target DNA and which eliminated non-specific binding of probes. Overnight hybridisations were performed at 55°C for all of the selected probes with three different targets: genomic DNA, PCR amplicons, and cloned probe sequences.
Nylon membrane, although commonly used as an inexpensive medium for DNA immobilisation, has its limitations, which include the low availability of target sequence for hybridisation. Therefore, some of the genes of interest were PCR amplified prior to hybridisation. Assessing the hybridisation conditions of multiple genes is time-consuming. In order to simplify this process multiple copies of several probes were cloned as one. The probes were cloned into the pGEM®-T Easy Vector in groups of 3 and these served as the target DNA. Constructs FGH (containing: FTHFS1, Geo1A and hydA1 probes), m1G3S11 (containing: mcrA1, narG3 and nirS11 probes), aRKnA2 (containing: assAR, nirKF and napA2 probes) and D4Ra3d1 (containing: Dsr4R, aprAB3 and dsrAB1 probes) were prepared, but a dsrAB1 probe was not tested in this study (Appendix 4). The principal advantage of this technique was that it reduced both the cost and time when processing a large number of probes. Moreover, a range of probes can be cloned in any order into the vector for simultaneous testing. This procedure also simplified estimating the minimum concentration of target DNA needed to obtain a meaningful signal.
All of the probes produced hybridisation signals under the same hybridisation conditions and subsequent stringency washes (Figures 3.3.7 and 3.3.8). The lowest quantity of target DNA bound to the nylon membrane was 30 ng, while the highest was 420 ng.
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Figure 3.3.7: Hybridisation of probes: napA2, aprAB3, DSR4R, Geo1A, assAR, FTHFS1, mcrA1, hydA1, nirS11, nirKF and narG3 to selected target DNA acting as positive (+) and negative controls (-). The target DNA is (4,6,8) construct FGH, in (7,9,11) construct m1G3S11, in (1,5,10) aRKnA2 and in (2,3) construct D4Ra3d1. 1: probe napA2 hybridised to aRKnA2 construct (+) and empty plasmid pGEM®-T Easy Vector (-), 2: probe aprAB3 hybridised to D4Ra3d1 construct (+) and empty plasmid pGEM®-T Easy Vector (-), 3: probe DSR4R hybridised to D4Ra3d1 construct (+) and empty plasmid pGEM®-T Easy Vector (-), 4: probe Geo1A hybridised to FGH construct (+) and empty plasmid pGEM®-T Easy Vector (-), 5: probe assAR hybridised to aRKnA2 construct (+) and empty plasmid pGEM®-T Easy Vector (-), 6: probe FTHFS1 hybridised to FGH construct (+) and empty plasmid pGEM®-T Easy Vector (-), 7: probe mcrA1 hybridised to m1G3S11 construct (+) and empty plasmid pGEM®-T Easy Vector (-), 8: probe hydA1 hybridised to FGH construct (+) and empty plasmid pGEM®-T Easy Vector (-), 9: probe nirS11 hybridised to m1G3S11 construct (+) and empty plasmid pGEM®-T Easy Vector (-), 10: probe nirKF hybridised to aRKnA2 construct (+) and empty plasmid pGEM®-T Easy Vector (-) and 11: probe narG3 hybridised to m1G3S11 construct (+) and empty plasmid pGEM®-T Easy Vector (-). Pictures: 1, 2, 7-9 and 11 showing hybridisation of probes designed in this study.
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Figure 3.3.8: Hybridisation of probes: EUB338, ARC915, SHEW227, apsA1P1R10 and Arch5F to selected target DNA acting as positive (+) and negative (-) controls. The target DNA is (1) E. coli genomic DNA while in (2-5) the target DNA was PCR products. 1: probe EUB338 hybridised to E.coli DNA (+) and A. fulgidus DNA (-), 2: probe ARC915 hybridised to 16S rRNA PCR products (+) and E.coli DNA (-), 3: probe SHEW227 hybridised to 16S rRNA PCR products (+) and plasmid pGEM®-T Easy Vector (-), 4: probe apsA1P1R10 hybridised to aprA PCR products (+) and plasmid pGEM®-T Easy Vector (-), 5: probe Arch5F hybridised to16S rRNA PCR products (+) and C. pasterianum DNA (-).
Each designed probe was tested with non-sense DNA to confirm its specificity. DNA extracted from C. pasterianum, E. coli, D. vietnamiensis, A. faecalis and S. profunda were used. None of the probes showed hybridisation with non- target DNA (Figure 3.3.9).
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Figure 3.3.9: Hybridisation of hydA1 and nirS11 probes to target DNA (cloned probes and genomic DNA). In both cases the negative controls were 1: E. coli, 2: S. profunda, 3: A. faecalis, and 4: D. vietnamensis. Filter A contained the cloned construct FGH containing probe hydA1 (+), and filter B contained the cloned construct m1G3S11containing probe: nirS11. Filter A was hybridised with the hydA probe while probe nirS was used for filter B.
In future work the hybridization behaviour of oligonuleotide probes immobilized onto a solid surface should be tested, as the dynamic of the reaction is expected to be different.
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CHAPTER 4: FINAL CONCLUSIONS
Chapter 4 Final Conclusions
The microbial corrosion of oil field pipelines, tanks, storage units and associated equipment is an expensive and still poorly understood process, which increases the risk of hazardous material release into the environment. Diagnosing and detecting biocorrosion needs a combination of metallurgical and chemical methods, as well as molecular analyses. In recent years the molecular approach has become popular because it offers the chance to identify specific microbial populations and their metabolic pathway without time consuming cultivation.
In the past most MIC research has focused on microbial communities developed on metal coupons after short cultivation period. Recently, a few studies have investigated the populations present on corroded areas in oil field and gas pipeline directly (Duncan et al., 2009, Stevenson et al., 2011) and this study builds upon this work. No such investigation, involving molecular technique, focusing on the detection of metabolic pathways in oil field samples, has been previously reported and this type of samples was not studied before extensively.
The concept of biofilm-influenced corrosion is introduced in this thesis and molecular methods to investigate the involvement of microorganisms in deterioration of carbon steel in an oil field environment have been employed. Biofilms and water field samples provided by industrial partner were analysed using molecular biology techniques, such as PCR, DGGE, microarray (GeoChip) and (pyro)sequencing, to detect the presence of microorganisms and/or microbial metabolic pathways implicated in biocorrosion of iron and its alloys (Summary in Table 4.1.1).
16S rRNA and functional bacterial and archaeal genes were targeted to give a comprehensive picture of microbial populations in the tested installations. It was also important to use parallel independent techniques because biocorrosion involw very complex phenomena and new approaches are needed to understand this process.
When all installations samples are considered, microorganisms belonging to SRP, NRB, MRB, HDB, firmicutes, acetogens and methanogens groups were identified, all
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Chapter 4 Final Conclusions of which have been documented as organisms responsible for corrosion in oil field and gas environments (Subsection 3.2.2). Although all of the organisms predicted to be involved in MIC were present, the microbial profiles of each sample differed. Nevertheless, clear diversity patterns were associated with samples from corroded and non-corroded systems.
16S rRNA sequences belonging to Firmicutes and Synergistetes phyla were detected from samples recovered from installations suffering corrosion, and these can be regarded as principal candidates that may accelerate the corrosion processes. This conclusion disagrees with the widespread belief that SRBs are the main group of bacteria causing corrosion. This idea is still current among scientists and people from industrial sectors. The monitoring of bacterial groups capable of hydrogen sulfide production, other than just sulfate reducing bacteria, would be an important consideration if biocorrosion mechanisms and persistence is to be understood.
Sequence analysis also revealed the presence of archaea in the examined systems, which suggests that these organisms should be monitored as well as bacteria, particularly if the temperature of the environment is relatively high (80-85°C). Furthermore, the participation of A. fuldigus and methanogens, detected at Installations A and C, in the corrosion process should be confirmed for other sites by pyrosequencing of archaeal 16S rRNA gene so that the corroding and non- corroding systems can be compared.
The pyrosequencing and sequencing of 16S rRNA genes also revealed the presence of thermophilic microorganisms in systems where the temperature was 50-55°C. The activity of those organisms in this temperature remains enigmatic. It is important to verify the activity of these organisms in corrosion at lower temperatures so that they be included in any scheme to monitor their presence in oil pipelines and treatment works.
To analyse the biodiversity of microorganisms by characterising functional genes implicated in biocorrosion in oil-field environments the GeoChip was used. The
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Chapter 4 Final Conclusions functional genes detected correlated with corrosion (aprAB, dsrAB, sox, nrfA, NiR, narG, napA, nirK, nirS, amoA, mcrA, FTHFS, assA, c-cytochrome and hydrogenase). The general hybridisation results indicated that there was a greater microbial diversity in Installation A, where corrosion is controlled, than in the rapidly deteriorating Installation C. Futhermore, the ratio of signal intensity between sox genes and joint apr and dsr genes indicate higher accumulation of sulfides, which are very corrosive species, in Installation C relative to Installation A.
Regular monitoring of oil and water samples for corrosion processes is probably best achieved by using microarrays. To this end, and to further understand the complexity of MIC, an objective of this project was to design a microarray system for biocorrosion risk assessment and monitoring in the oil-field systems by identifying important target genes and designing hybridisation probes for them. It was proposed, therefore, to compile a list of marker genes, which can be utilised for biocorrosion monitoring and design oligonucleotide probes for them that could be used for biochip development. As part of this process a quick and inexpensive method for probe evaluation during microarray development was developed and used. Biochips can provide a unique picture about the metabolic processes occurring in any tested system, but it is important to note that there are limitations when working at the DNA/RNA level in that it can only provide information about the possibility of metabolic processes rather than on-going reactions.
A total of 16 probes, representing 15 genes, was designed and tested; all the probes exhibited similar hybridisation behaviour under standard conditions. More probes are needed for a biochip. Probes targeting several other gene families, the sox genes for instance, which are important in corrosion, are needed. It is also important to design more oligo probes for the genes already considered. These probes should reflect the phylogenetic diversity of the genes to allow identification at this level. All of the probes should be immobilised onto microarray slides (solid surface) and tested against model DNA, DNA extracted from the laboratory cultures, and DNA recovered directly from field samples. Finally, associating hybridisation signals with different levels of corrosion needs to be done in order to assess risk levels.
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DNA was used extensively in this study. Another step in project development would be to focus on improving a protocol for quick mRNA extraction from pigging samples, which would help to provide information, not only about microorganisms presence, but also occurring process in the system. Based on extracted RNA, cDNA could be synthesised and analysed on the microarray.
In terms of industrial applications of the microarray, the development of a simple, quick and more sensitive method of DNA detection would be useful, without resorting to visual labelling techniques which are expensive and time-consuming to perform. Recently a direct, sensitive and label-free electrical detection of DNA method has been developed by detecting the resistance change of SiNWs before and after PNA-DNA hybridisation (Zhang et al., 2008). Silicon nanowire (SiNW) sensor arrays with immobilized peptide nucleic acid (PNA) are capable of recognising the label-free complementary target DNA with a detection limit to the femtomolar levels. This method is highly sensitive and specific, making it attractive for the detection of small sequence variations (Wei et al., 2010). Label-free electrochemical recognition of DNA hybridization using scanning electrochemical microscopy has also been report as an approach for visualizing the status of surface-bound DNA probes (Turcu et al., 2004).
These and others innovative technologies create new opportunities and approaches for fast, accurate and sensitive monitoring of microbial presence and their activity. Molecular techniques together with metallurgical and chemical assays can in future predict corrosion processes, thereby reducing the cost of oil and gas production and minimize the danger of environmental pollution.
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Table 4.1.1: Identified risks in tested installations. Installa Type of Identified risks Identified microorganisms tion ID installation Installa Water -Presence of NRB Pyrosequencing: Desulfococcus, tion A injection -Presence of fermicutes Desulfacinum, Desulfobulbus, system -Presence of metanogenes Desulfocella, Pelobacter, -Possible presence of Desulfobulbus MRB Anaerobaculum, Thermovirga
-Presence of H2S Caminicella, Acetobacterium - Growing temperature in the system Sequencing: Clostridium, A. fuldigus, Methanomicrobia Installa Production -Presence of metanogenes Pyrosequencing: Caminicella, tion C pipelines -Lack (?) of nirK gene Thermoanaerobacter, presence Acetobacterium, -Possible presence of Desulfothermus, Thermosipho,
N/SRB (nrfA gene Kosmotoga
detected) -Possibility of high Sequencing: Caminicella,
accumulation of sulphide Thermosipho, -Possible presence of Thermoanaerobacter, MRB Clostridiaceae, A. fuldigus,
-Presence of H2S Methanococci, Methanobacteria Installa Production -Presence of metanogenes Pyrosequencing: Pseudomonas, pipelines tion D -Lack (?) of nirK gene Desulfotignum, Desulfovibrio, presence Geoalkalibacter -Presence of SRB Sulfurospirillum -Possible presence of N/SRB (nrfA gene Sequencing: detected) Thermoanaerobacter
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APPENDICES
Appendices
Appendix 1: Flow charts of the sampling site and organisation of the installations
The flow charts were provided by the industrial partner.
Figure A.1.1: A flow chart main process of the Installation D (and similar Installation C). Pigging samples were collected from production pipelines.
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Figure A.1.2: A flow chart main process of the Installation A. Pigging samples were collected from water injection pipeline A.
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Appendix 2: Growth media Growth media composition used in this study are described below.
Vitamin Medium and Vitamin Medium with iron (VM and VMI medium)
Medium was prepared as follows (composition per litre) (Table A.3.1).
Table A.3.1: VM and VMI medium composition. S/N Compound g/litre 1 Potassium dihydrogen phosphate (KH2PO4) 0.5 g 2 Ammonium chloride (NH4Cl) 1.0 g 3 Sodium sulphate (Na2SO4) 4.5 g 4 Calcium chloride (CaCl2∙2 H2O) 0.04 g 5 MgSO4∙7 H2O 0.06 g 6 FeSO4∙7 H2O 0.5 g* 7 Sodium citrate (Na3C6H5O7∙2 H2O) 0.3 g 8 NaCl 25.0 g 9 Casamino acids 2.0 g 10 Tryptone 2.0 g 11 Sodium lactate (NaC3H5O3) 6.0 g *For VMI medium 0.004 g of FeSO4∙7 H2O
Medium pH was adjusted to 7.5 by adding NaOH. Sterile trace elements (1 ml stock solution) (Table A.3.2) and 1 ml sterile vitamin stock solution (Table A.3.3) were added. Culture 10 ml vials were filled with a culture medium, purged with an O2 free-N2 flux to achieve anaerobiosis, cover vial with a stopper and crimp with aluminium and autoclaved for 30 min at 121°C.
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Table A.3.2: Modified Wolfe’s mineral elixir (per litre).
Compound g/litre C6H9NO6 1.5 g MgSO4∙7 H2O 3.0 g MnSO4∙ H2O 0.5 g NaCl 1.0 g FeSO4∙7 H2O 0.1 g CoSO4∙7 H2O 0.1 g NiCl2∙6 H2O 0.1 g CuCl2∙2 H2O 0.1 g ZnSO4∙7 H2O 0.1 g CuSO4∙5 H2O 0.01 g AlK(SO4) ∙12 H2O 0.01 g H3BO3 0.01 g NaMoO4∙2 H2O 0.01 g Na2SeO4 0.001 g pH adjusted to 6,5 to 7.
Table A.3.3: Vitamin stock solution. Vitamin Quantity Vitamin C, ascorbic acid 10 g Vitamin B1, thiamine 60 mg Vitamin B2, riboflavine 20 mg Vitamin B12, cobalamin 5 mg Vitamin B3, nicotinic acid 50 mg Vitamin B5, D-pentathonic acid 60 mg Vitamin B6, pyridoxime 61 mg Vitamin H, D-biotin 1 mg
The vitamins were dissolved in 100 ml of dd water.
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Glucose yeast extract medium
Medium was prepared as follows (composition per litre) (Table A.3.4).
Table A.3.4: Composition of glucose yeast extract medium. S/N Compound g/litre 1 Glucose 20 g 2 Yeast extract 10 g 3 CaCO3 20 g
Culture 10 ml vials were filled with a culture medium, purged with an O2 free-N2 flux to achieve anaerobiosis, cover vial with a stopper and crimp with aluminium and autoclaved for 30 min at 121°C.
LB medium
Medium was prepared as follows (composition per litre) (Table A.3.5).
Table A.3.5: Composition of LB medium. S/N Compound g/litre 1 Tryptone 10 g 2 Yeast extract 5 g 3 NaCl 10 g
Nutrient broth/Nutrient agar
Medium was prepared as follows (composition per litre) (Table A.3.6).
Table A.3.6: Composition of nutrient broth. S/N Compound g/litre 1 Peptone 5 g 2 Yeast extract 2 g 3 NaCl 5 g
Medium pH was adjusted to 7. Universal tubes (50 ml) were filled with a culture medium and autoclaved for 30 min at 121°C. For nutrient agar: agar 15g/L.
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Appendix 3: Sequences of constructs used for hybridisation.
Sequence of construct FGH (containing: FTHFS1, Geo1A and hydA1 probes): 5’- TTCACTGGTGATTTCCATGCCCTCACGCACTTCGGGACCAGTCACCACAACAAATATTTGGTACT GA-3’
Sequence of construct m1G3S11 (containing: mcrA1, narG3 and nirS11 probes):
5’-GGCATCAAGTTCGGACACTTTCCAACAAGATCGCCGTAGTATTCTACGCCCATACCGACCA-3’
Sequence of construct aRKnA2 (containing: assAR, nirKF and napA2 probes): 5’- TCGTCRTTGCCCCATTTNGGNGCTCATGGTCCTGCCGCGYGACGGTGTGGGTCGAAAAAGAAGG AA-3’
Sequence of construct D4Ra3d1 (containing: Dsr4R, aprAB3 and dsrAB1 probes):
5’-GTGTAGCAGTTACCGCACGTTGGCAGATCATGATTCAGYGAGTGGKCCTGCTAYGAA-3’
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Appendix 4: List of buffers used for hybrydisation
1xTAE buffer 20 mM Tris acetate, 10 mM sodium acetate, 0.5 mM Na2- EDTA 1xTBE buffer 89 mM Tris base, 89 mM boric acid, 2 mM Na2-EDTA enzymatic lysis buffer 20 mM Tris-HCl; 2 mM sodium EDTA; 1.2% Triton X-100 [pH 8.0] oligonucleotide 10mM Tris-HCl (pH=8), 50 mM NaCl, 1 mM EDTA hybridisation buffer resuspension buffer 1 mM Tris-HCl, 0.01 mM EDTA wash buffer 0.5xSSC, 0.1% SDS pre-hybridization 5xSSC, 0.1% (w/v) N-lauroyl sarcosine, 0.02% (w/v) SDS, solution 1% (w/v) blocking reagent (Boehringer Mannheim) hybridization solution 5xSSC, 0.1% (w/v) N-lauroyl sarcosine, 0.02% (w/v) SDS low-stringency buffer 0.5xSSC, 0.1% SDS high-stringency buffer 0.1xSSC, 0.1% SDS
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Appendix 5: Phylogenetic analysis
Table A.5.1: Accesion numbers for taxas used for phylogenetic analysis.
Accession Strain number AY281344.1 Desulfovibrio sp. AND116S ribosomal RNA gene, partial sequence AY281345.1 Desulfovibrio sp. ANP316S ribosomal RNA gene, partial sequence GU074016.1 Desulfovibrio caledoniensis strain DC 16S ribosomal RNA gene, partial sequence AB546253.1 Desulfovibrio dechloracetivorans gene for 16S rRNA, partial sequence, strain: Mic42c03 CP003220.1 Desulfovibrio desulfuricans ND132, complete genome AB546250.1 Desulfovibrio capillatus gene for 16S rRNA, partial sequence, strain: Mic4c01 NR_041484.1 Desulfothermus okinawensis strain TFISO9 16S ribosomal RNA gene, partial sequence NR_029177.1 Desulfobacter vibrioformis strain B54 16S ribosomal RNA gene, complete sequence NR_026439.1 Desulfobacter halotolerans strain DSM 11383 16S ribosomal RNA gene, partial sequence NR_042142.1 Desulfobacter latus strain DSM 3381 16S ribosomal RNA gene, complete sequence DQ057079.1 Desulfobacter psychrotolerans 16S ribosomal RNA gene, partial sequence NR_041851.1 Desulfobacter curvatus strain DSM 3379 16S ribosomal RNA gene, partial sequence EF442983.1 Desulfobacterium zeppelinii 16S ribosomal RNA gene, partial sequence NR_025348.1 Desulfobacterium anilini strain Ani1 16S ribosomal RNA gene, partial sequence AY274450.1 Desulfobacterium corrodens 16S ribosomal RNA gene, partial sequence DQ647138.1 Uncultured Caminicella sp. clone TCB261 16S ribosomal RNA gene, partial sequence DQ647130.1 Uncultured Caminicella sp. clone TCB207x 16S ribosomal RNA gene, partial sequence NR_025485.1 Caminicella sporogenes strain AM1114 16S ribosomal RNA gene, partial sequence X77837.1 Clostridium halophilum 16S rRNA gene, strain DSM 5387 X78071.1 C. acetobutylicum DSM 1731 16S rRNA NR_028898.1 Clostridium acidisoli strain CK74 16S ribosomal RNA gene, partial sequence NR_026179.1 Clostridium acetireducens strain strain 30A 16S ribosomal
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RNA gene, partial sequence JF754964.1 Thermoanaerobacter pseudethanolicus strain 22C 16S ribosomal RNA gene, partial sequence HE601764.1 Thermoanaerobacter brockii subsp. Brockii partial 16S rRNA gene, type strain DSM 1457T, clone 4 DQ128183.1 Thermoanaerobacter ethanolicus isolate 2 clone CCSD_DF2450_M1_68 16S ribosomal RNA gene, partial sequence L09164.1 Thermoanaerobacter pseudethanolicus ATCC 33223 16S ribosomal RNA gene, complete sequence GU179927.1 Uncultured Firmicutes bacterium clone D021041C04 16S ribosomal RNA gene, partial sequence NR_029034.1 Dethiosulfovibrio acidaminovorans strain sr15 16S ribosomal RNA gene, partial sequence NR_025081.1 Dethiosulfovibrio russensis strain sr12 16S ribosomal RNA gene, partial sequence NR_041793.1 Dethiosulfovibrio russensis strain WS 100 16S ribosomal RNA gene, partial sequence NR_027582.1 Dethiosulfovibrio peptidovorans strain G4207 16S ribosomal RNA gene, partial sequence NR_044215.1 Jonquetella anthropi strain ADV126 16S ribosomal RNA gene, partial sequence JN809776.1 Jonquetella sp. BV3C21 16S ribosomal RNA gene, partial sequence NR_044215.1 Jonquetella anthropi DSM 22815 strain ADV 126 16S ribosomal RNA gene, partial sequence AY278615.1 Synergistes genomo sp. C1 16S ribosomal RNA gene, partial sequence NR_108538.1 Fretibacterium fastidiosum strain SGP1 16S ribosomal RNA gene, partial sequence NR_024925.1 Aminobacterium mobile strain ILE-3 16S ribosomal RNA gene, partial sequence CP001997.1 Aminobacterium colombiense DSM 12261, complete genome NR_109636.1 Cloacibacillus sp. CL-84 16S ribosomal RNA gene, partial sequence CU463952.1 Cloacibacillus evryensis gen. nov. sp. nov. 16S ribosomal RNA gene partial sequence NR_044616.1 Synergistes jonesii strain 78-1 16S ribosomal RNA gene, complete sequence AB299558.1 Candidatus Tammella caduceiae gene for 16S rRNA, partial sequence, clone: Cc3-105 AF071414.1 Thermoanaerovibrio acidaminovorans 16S ribosomal RNA gene, partial sequence NR_043061.1 Aminiphilus circumscriptus strain ILE-2 16S ribosomal RNA gene, partial sequence DQ647111.1 Uncultured Thermovirga sp. clone TCB168x 16S ribosomal RNA gene, partial sequence GU180042.1 Uncultured Synergistetes bacterium clone D009011D13 16S
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ribosomal RNA gene, partial sequence CP003096.1 Thermovirga lienii DSM 17291, complete genome HM041946.1 Uncultured Synergistetes bacterium clone NRB29 16S ribosomal RNA gene, partial sequence EU276415.1 Anaerobaculum thermoterrnum strain YWT-2 16S ribosomal RNA gene, partial sequence NR_036784.1 Anaerobaculum thermoterrnum strain RWcit 16S ribosomal RNA gene, complete sequence AJ243189.1 Anaerobaculum mobile 16S rRNA gene, type strain NGA NR_043912.1 Thermosipho africanus Ob7 strain Ob7 16S ribosomal RNA gene, partial sequence FM876224.1 Thermosipho sp. LD-2008 partial 16S rRNA gene, strain 3 AB257289.1 Thermosipho globiformans gene for 16S rRNA, partial sequence EU276414.2 Thermococcoides shengliensis strain 2SM-2 16S ribosomal RNA gene, partial sequence NR_044583.1 Kosmotoga olearia TBF 19.5.1 16S ribosomal RNA gene, complete sequence NR_075056.1 Aquifex aeolicus strain VF5 16S ribosomal RNA gene, complete sequence
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Appendix 6: Taxonomic report
Table A.6.1: Accesion number of aprAB gene sequences used in this study. Accession Strain number EF442914.1 Desulfococcus oleovorans Hxd3 strain DSM 6200 AprB (aprB) and AprA (aprA) genes, partial cds EF442882.1 Desulfovibrio acrylicus strain DSM 10141 AprB (aprB) and AprA (aprA) genes, partial cds EF641918.1 Thiothrix sp. 12730 AprB (aprB) and AprA (aprA) genes, partial cds EF442878.1 Thermodesulfobacterium commune DSM 2178 AprB (aprB) and AprA (aprA) genes, partial cds EF442899.1 Desulfomicrobium baculatum DSM 4028 AprB (aprB) and AprA (aprA) genes, partial cds EF442898.1 Desulfocaldus sp. Hobo AprB (aprB) and AprA (aprA) genes, partial cds EF442889.1 Desulfovibrio sp. HRS-La4 AprB (aprB) and AprA (aprA) genes, partial cds EF442943.1 Sulfate-reducing bacterium DSM 15769 AprB (aprB) and AprA (aprA) genes, partial cds EF641936.1 Thermochromatium tepidum strain DSM 3771 AprB (aprB) and AprA (aprA) genes, partial cds EF442944.1 Sulfate-reducing bacterium DSM 14454 AprB (aprB) and AprA (aprA) genes, partial cds EF442948.1 Desulfomonile tiedjei DSM 6799 AprB (aprB) and AprA (aprA) genes, partial cds EF442951.1 Desulforhabdus sp. DDT AprB (aprB) and AprA (aprA) genes, partial cds EF442968.1 Desulfotomaculum sp. DSM 8775 AprB (aprB) and AprA (aprA) genes, partial cds EF442935.1 Desulfobulbus propionicus DSM 2032 AprB (aprB) and AprA (aprA) genes, partial cds EF442925.1 Desulfonema limicola str. Jadebusen strain DSM 2076 AprB (aprB) and AprA (aprA) genes, partial cds EF641925.1 Endosymbiont of Inanidrilus makropetalos AprB (aprB) and AprA (aprA) genes, partial cds EF641953.1 Sulfur-oxidizing bacterium AII2 AprB (aprB) and AprA (aprA) genes, partial cds EF641947.1 Rhabdochromatium marinum strain DSM 5261 AprB (aprB) and AprA (aprA) genes, partial cds EF442939.1 Desulfofustis glycolicus strain DSM 9705 AprB (aprB) and AprA (aprA) genes, partial cds EF442946.1 Desulfarculus baarsii DSM 2075 AprB (aprB) and AprA (aprA) genes, partial cds EF442884.1 Desulfovibrio ferrophilus strain DSM 15579 AprB (aprB) and
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Appendices
AprA (aprA) genes, partial cds EF442891.1 Desulfovibrio sp. JD160 AprB (aprB) and AprA (aprA) genes, partial cds EF641957.1 Endosymbiont of Inanidrilus exumae clone 37 AprB (aprB) and AprA (aprA) genes, partial cds EF442960.1 Desulfotomaculum luciae strain DSM 12396 AprB (aprB) and AprA (aprA) genes, partial cds
Table A.6.2: Accesion number of mcrA gene sequences used in this study. Accession Strain number AB300466.1 Methanolinea tarda mcrA gene for methyl-coenzyme M reductase, partial cds AY625598.1 Uncultured Methanomicrobiales archaeon clone Beu4ME-28 methyl-coenzyme M reductase alpha subunit (mcrA) gene, partial cds AF313868.1 Uncultured methanogen RS-ME33 methyl-coenzyme M reductase subunit A (mcrA) gene, partial cds EU296536.1 Methanosphaerula palustris strain E1-9c McrA (mcrA) gene, partial cds EU681942.1 Uncultured archaeon clone TopMcrA54 methyl-coenzyme M reductase subunit A (mcrA) gene, partial cds EU302012.1 Uncultured bacterium clone D09AUGControl methyl co- enzyme A reductase (mcrA) gene, partial cds DQ767845.1 Uncultured organism clone eSm3 methyl-coenzyme M reductase alpha subunit (mcrA) gene, partial cds AJ489775.1 Uncultured methanogenic archaeon partial mcrA gene for methyl-coenzyme M reductase subunit A, clone FenO-MCR AY625601.1 Uncultured Methanomicrobiales archaeon clone Beu4ME-51 methyl-coenzyme M reductase alpha subunit (mcrA) gene, partial cds AY760633.1 Uncultured archaeon clone LCM1404-22 methyl coenzyme-M reductase (mcrA) gene, partial cds AM407730.1 Uncultured archaeon partial mcrA gene for methyl coenzyme M reductase subunit alpha, clone HMMVBeg-ME94 EU302061.1 Uncultured bacterium clone AUG10DEXE05 methyl co- enzyme A reductase (mcrA) gene, partial cds EU681936.1 Uncultured archaeon clone TopMcrA4 methyl-coenzyme M reductase subunit A (mcrA) gene, partial cds AF313867.1 Uncultured methanogen RS-ME32 methyl-coenzyme M reductase subunit A (mcrA) gene, partial cds AY625597.1 Uncultured Methanomicrobiales archaeon clone Beu4ME-18 methyl-coenzyme M reductase alpha subunit (mcrA) gene, partial cds EU301854.1 Uncultured bacterium clone APRCONTROLA09 methyl co- enzyme A reductase (mcrA) gene, partial cds
176
Appendices
Table A.6.3: Accesion number of napA gene sequences used in this study. Accession Strain number EU495672.1 Uncultured bacterium clone T1-F12 NapA (napA) gene, partial cds EU495651.1 Uncultured bacterium clone T1-E02 NapA (napA) gene, partial cds EU495702.1 Uncultured bacterium clone T3-A09 catalytic subunit of periplasmic nitrate reductase (napA) gene, partial cds EU495770.1 Uncultured bacterium clone T3-H10 catalytic subunit of periplasmic nitrate reductase (napA) gene, partial cds EU495664.1 Uncultured bacterium clone T1-F04 NapA (napA) gene, partial cds EU495733.1 Uncultured bacterium clone T3-D10 catalytic subunit of periplasmic nitrate reductase (napA) gene, partial cds EU495764.1 Uncultured bacterium clone T3-G10 catalytic subunit of periplasmic nitrate reductase (napA) gene, partial cds EU495687.1 Uncultured bacterium clone T1-H04 NapA (napA) gene, partial cds EU495671.1 Uncultured bacterium clone T1-F11 NapA (napA) gene, partial cds EU495686.1 Uncultured bacterium clone T1-H03 NapA (napA) gene, partial cds EF645075.1 Uncultured bacterium clone A30P54 putative periplasmic nitrate reductase (napA) gene, partial cds EF645083.1 Uncultured bacterium clone A30G17 putative periplasmic nitrate reductase (napA) gene, partial cds EF645069.1 Uncultured bacterium clone A30G35 putative periplasmic nitrate reductase (napA) gene, partial cds EF645070.1 Uncultured bacterium clone A30B15 putative periplasmic nitrate reductase (napA) gene, partial cds EF645123.1 Uncultured bacterium clone A30C20 putative periplasmic nitrate reductase (napA) gene, partial cds EF645106.1 Uncultured bacterium clone A30P16 putative periplasmic nitrate reductase (napA) gene, partial cds EF644698.1 Uncultured prokaryote clone EPR_AP21 periplasmic nitrate reductase (napA) gene, partial cds EF644722.1 Uncultured prokaryote clone EPR_EW42 periplasmic nitrate reductase (napA) gene, partial cds EF644704.1 Uncultured prokaryote clone EPR_AP4 periplasmic nitrate reductase (napA) gene, partial cds AY093618.2 Sulfurospirillum barnesii SES-3 periplasmic nitrate reductase (napA) gene, complete cds EF644710.1 Uncultured prokaryote clone EPR_EW1 periplasmic nitrate reductase (napA) gene, partial cds EF644815.1 Enrichment culture clone MAR8 periplasmic nitrate reductase (napA) gene, partial cds
177
Appendices
EF683089.1 Sulfurimonas paralvinella periplasmic nitrate reductase (napA) gene, partial cds EF644715.1 Uncultured prokaryote clone EPR_EW23 periplasmic nitrate reductase (napA) gene, partial cds
Table A.6.4: Accesion number of narG gene sequences used in this study. Accession Strain number FJ556723.1 Uncultured bacterium clone MaRi_58 dissimilatory membrane-bound nitrate reductase (narG) gene, partial cds FJ556713.1 Uncultured bacterium clone MaRi_48 dissimilatory membrane-bound nitrate reductase (narG) gene, partial cds AY209094.1 Uncultured bacterium clone S40 putative dissimilatory membrane-bound nitrate reductase (narG) gene, partial cds AY325571.1 Uncultured bacterium clone GG36 putative dissimilatory membrane-bound nitrate reductase (narG) gene, partial cds AY113817.1 Uncultured bacterium clone F53 putative dissimilatory membrane-bound nitrate reductase (narG) gene, partial cds AY552351.1 Unidentified bacterium clone NCRG53 putative dissimilatory membrane-bound nitrate reductase (narG) gene, partial cds AM419310.1 Uncultured bacterium partial narG gene for nitrate reductase, clone TF9 EU352346.1 Uncultured bacterium clone LC7 membrane-bound nitrate reductase (narG) gene, partial cds DQ177664.1 Uncultured bacterium clone 479 putative membrane-bound nitrate reductase (narG) gene, partial cds AM419336.1 Uncultured bacterium partial narG gene for nitrate reductase, clone TF89 EU714832.1 Uncultured bacterium clone HLK39 dissimilatory membrane- bound nitrate reductase (narG) gene, partial cds AY209084.1 Uncultured bacterium clone V28 putative dissimilatory membrane-bound nitrate reductase (narG) gene, partial cds AY209146.1 Uncultured bacterium clone R135 putative dissimilatory membrane-bound nitrate reductase (narG) gene, partial cds DQ481076.1 Uncultured bacterium clone UT-075_14 membrane bound nitrate reductase (narG) gene, partial cds EU052899.1 Uncultured bacterium clone DMG2-250 dissimilatory membrane-bound nitrate reductase (narG) gene, partial cds AY113742.1 Uncultured bacterium clone F60 putative dissimilatory membrane-bound nitrate reductase (narG) gene, partial cds EU053003.1 Uncultured bacterium clone DMG3-795 dissimilatory membrane-bound nitrate reductase (narG) mRNA, partial cds DQ010714.1 Unidentified bacterium clone 6g3 membrane bound nitrate reductase (narG) gene, partial cds FJ147534.1 Uncultured bacterium clone RI29 membrane-bound nitrate reductase (narG) gene, partial cds
178
Appendices
AY552373.1 Unidentified bacterium clone NCSD28 putative dissimilatory membrane-bound nitrate reductase (narG) gene, partial cds AY113758.1 Uncultured bacterium clone B61 putative dissimilatory membrane-bound nitrate reductase (narG) gene, partial cds AY552399.1 Unidentified bacterium clone NCGB50 putative dissimilatory membrane-bound nitrate reductase (narG) gene, partial cds AY955171.1 Uncultured bacterium clone GRAMD8 putative dissimilatory membrane-bound nitrate reductase (narG) gene, partial cds DQ481065.1 Uncultured bacterium clone UT-250_37 membrane bound nitrate reductase (narG) gene, partial cds EU714880.1 Uncultured bacterium clone HLK101 dissimilatory membrane-bound nitrate reductase (narG) gene, partial cds FN430478.1 Uncultured bacterium partial narG gene for nitrate reductase alpha subunit, clone I19_77 EU052915.1 Uncultured bacterium clone DMG2-306 dissimilatory membrane-bound nitrate reductase (narG) gene, partial cds AY453360.1 Uncultured bacterium clone F26 dissimilatory membrane- bound nitrate reductase (narG) gene, partial cds AY453355.1 Uncultured bacterium clone C25 dissimilatory membrane- bound nitrate reductase (narG) gene, partial cds DQ481075.1 Uncultured bacterium clone UT-075_11 membrane bound nitrate reductase (narG) gene, partial cds AY325544.1 Uncultured bacterium clone GA69 putative dissimilatory membrane-bound nitrate reductase (narG) gene, partial cds AY955161.1 Uncultured bacterium clone GRAMB28 putative dissimilatory membrane-bound nitrate reductase (narG) gene, partial cds DQ481115.1 Uncultured bacterium clone RT-250_16 membrane bound nitrate reductase (narG) gene, partial cds AY209054.1 Uncultured bacterium clone V36 putative dissimilatory membrane-bound nitrate reductase (narG) gene, partial cds AM408545.1 Uncultured bacterium partial narG gene for membrane bound nitrate reductase, clone narHJ44 DQ481191.1 Uncultured bacterium clone LT-CLY_36 membrane bound nitrate reductase (narG) gene, partial cds DQ248883.1 Uncultured bacterium clone 6-6 dissimilatory nitrate- reductase (narG) gene, partial cds EU714887.1 Uncultured bacterium clone HLK116 dissimilatory membrane-bound nitrate reductase (narG) gene, partial cds DQ481133.1 Uncultured bacterium clone RT-075_33 membrane bound nitrate reductase (narG) gene, partial cds AY453364.1 Uncultured bacterium clone C34 dissimilatory membrane- bound nitrate reductase (narG) gene, partial cds FJ147548.1 Uncultured bacterium clone RI43 membrane-bound nitrate reductase (narG) gene, partial cds FJ147538.1 Uncultured bacterium clone RI33 membrane-bound nitrate reductase (narG) gene, partial cds EU052922.1 Uncultured bacterium clone DMG2-320 dissimilatory membrane-bound nitrate reductase (narG) gene, partial cds
179
Appendices
DQ177673.1 Uncultured bacterium clone 3256 putative membrane-bound nitrate reductase (narG) gene, partial cds AY113782.1 Uncultured bacterium clone B52 putative dissimilatory membrane-bound nitrate reductase (narG) gene, partial cds DQ481181.1 Uncultured bacterium clone LT-CLY_04 membrane bound nitrate reductase (narG) gene, partial cds DQ233272.1 Uncultured bacterium clone 1_9 dissimilatory nitrate- reductase (narG) gene, partial cds DQ233286.1 Uncultured bacterium clone 1_50 dissimilatory nitrate- reductase (narG) gene, partial cds AY209044.1 Uncultured bacterium clone V232 putative dissimilatory membrane-bound nitrate reductase (narG) gene, partial cds AY113826.1 Uncultured bacterium clone D60 putative dissimilatory membrane-bound nitrate reductase (narG) gene, partial cds EU052865.1 Uncultured bacterium clone DMG1-476 dissimilatory membrane-bound nitrate reductase (narG) gene, partial cds AY955190.1 Uncultured bacterium clone GRAMO7 putative dissimilatory membrane-bound nitrate reductase (narG) gene, partial cds AY955195.1 Uncultured bacterium clone GRAMO37 putative dissimilatory membrane-bound nitrate reductase (narG) gene, partial cds AM408509.1 Uncultured bacterium partial narG gene for membrane bound nitrate reductase, clone narBAu49 AM419356.1 Rhodococcus sp. D2-4 partial narG gene for nitrate reductase, clone D2-4 AM408501.1 Uncultured bacterium partial narG gene for membrane bound nitrate reductase, clone narBAu8 EU052893.1 Uncultured bacterium clone DMG2-241 dissimilatory membrane-bound nitrate reductase (narG) gene, partial cds FJ556609.1 Uncultured bacterium clone LuRi_12 dissimilatory membrane-bound nitrate reductase (narG) gene, partial cds EF645049.1 Uncultured bacterium clone G30G70 putative membrane bound nitrate reductase (narG) gene, partial cds AM412332.1 Uncultured bacterium partial narG gene for dissimilatory membrane-bound nitrate reductase, clone JG35+U4-AG- narG22 EU052847.1 Uncultured bacterium clone DMG1-411 dissimilatory membrane-bound nitrate reductase (narG) gene, partial cds DQ010748.1 Unidentified bacterium clone 6g50 membrane bound nitrate reductase (narG) gene, partial cds EU053010.1 Uncultured bacterium clone DMG3-809 dissimilatory membrane-bound nitrate reductase (narG) mRNA, partial cds AY209049.1 Uncultured bacterium clone R57 putative dissimilatory membrane-bound nitrate reductase (narG) gene, partial cds AY209042.1 Uncultured bacterium clone V125 putative dissimilatory membrane-bound nitrate reductase (narG) gene, partial cds EU052917.1 Uncultured bacterium clone DMG2-311 dissimilatory membrane-bound nitrate reductase (narG) gene, partial cds AM408520.1 Uncultured bacterium partial narG gene for membrane
180
Appendices
bound nitrate reductase, clone narAJ26 FJ556600.1 Uncultured bacterium clone LuRi_3 dissimilatory membrane- bound nitrate reductase (narG) gene, partial cds AM419256.1 Uncultured bacterium partial narG gene for nitrate reductase, clone Dbd6 AY113745.1 Uncultured bacterium clone F105 putative dissimilatory membrane-bound nitrate reductase (narG) gene, partial cds AM408546.1 Uncultured bacterium partial narG gene for membrane bound nitrate reductase, clone narHJ53 FN430485.1 Uncultured bacterium partial narG gene for nitrate reductase alpha subunit, clone K21_86 DQ481149.1 Uncultured bacterium clone LT-600_09 membrane bound nitrate reductase (narG) gene, partial cds FN430474.1 Uncultured bacterium partial narG gene for nitrate reductase alpha subunit, clone A19_73 AY113797.1 Uncultured bacterium clone F62 putative dissimilatory membrane-bound nitrate reductase (narG) gene, partial cds FJ556750.1 Uncultured bacterium clone S_22 dissimilatory membrane- bound nitrate reductase (narG) gene, partial cds AM408529.1 Uncultured bacterium partial narG gene for membrane bound nitrate reductase, clone narAJ51 AM419254.1 Uncultured bacterium partial narG gene for nitrate reductase, clone DBc23 AM408503.1 Uncultured bacterium partial narG gene for membrane bound nitrate reductase, clone narBAu13 AY113790.1 Uncultured bacterium clone A55 putative dissimilatory membrane-bound nitrate reductase (narG) gene, partial cds FJ556608.1 Uncultured bacterium clone LuRi_11 dissimilatory membrane-bound nitrate reductase (narG) gene, partial cds AY113824.1 Uncultured bacterium clone D950 putative dissimilatory membrane-bound nitrate reductase (narG) gene, partial cds DQ233258.1 Uncultured bacterium clone 1_12 dissimilatory nitrate- reductase (narG) gene, partial cds DQ233267.1 Uncultured bacterium clone 2_60 dissimilatory nitrate- reductase (narG) gene, partial cds FJ147515.1 Uncultured bacterium clone RI10 membrane-bound nitrate reductase (narG) gene, partial cds DQ481153.1 Uncultured bacterium clone LT-600_20 membrane bound nitrate reductase (narG) gene, partial cds DQ233278.1 Uncultured bacterium clone 4_12 dissimilatory nitrate- reductase (narG) gene, partial cds DQ481175.1 Uncultured bacterium clone LT-075_34 membrane bound nitrate reductase (narG) gene, partial cds EU714835.1 Uncultured bacterium clone HLK45 dissimilatory membrane- bound nitrate reductase (narG) gene, partial cds DQ233269.1 Uncultured bacterium clone 3_41 dissimilatory nitrate- reductase (narG) gene, partial cds AY955177.1 Uncultured bacterium clone GRAMD33 putative dissimilatory
181
Appendices
membrane-bound nitrate reductase (narG) gene, partial cds FN401451.1 Uncultured bacterium partial narG gene for dissimilatory membrane-bound nitrate reductase, clone Pn_18 DQ481059.1 Uncultured bacterium clone UT-250_16 membrane bound nitrate reductase (narG) gene, partial cds FN430459.1 Uncultured bacterium partial narG gene for nitrate reductase alpha subunit, clone I13_53 FN554843.1 Uncultured bacterium partial narG gene for putative dissimilatory membrane-bound nitrate reductase, clone CT0- narG_40 FN430477.1 Uncultured bacterium partial narG gene for nitrate reductase alpha subunit, clone G19_76 AY955172.1 Uncultured bacterium clone GRAMD10 putative dissimilatory membrane-bound nitrate reductase (narG) gene, partial cds AY113743.1 Uncultured bacterium clone A54 putative dissimilatory membrane-bound nitrate reductase (narG) gene, partial cds DQ233273.1 Uncultured bacterium clone 2_32 dissimilatory nitrate- reductase (narG) gene, partial cds DQ481055.1 Uncultured bacterium clone UT-250_03 membrane bound nitrate reductase (narG) gene, partial cds AY325538.1 Uncultured bacterium clone GA13 putative dissimilatory membrane-bound nitrate reductase (narG) gene, partial cds AY113806.1 Uncultured bacterium clone E84 putative dissimilatory membrane-bound nitrate reductase (narG) gene, partial cds AM419350.1 Microbacterium sp. D1-15 partial narG gene for nitrate reductase, clone D1-15 DQ177679.1 Uncultured bacterium clone 3220 putative membrane-bound nitrate reductase (narG) gene, partial cds DQ177686.1 Uncultured bacterium clone 306 putative membrane-bound nitrate reductase (narG) gene, partial cds DQ233260.1 Uncultured bacterium clone 1_61 dissimilatory nitrate- reductase (narG) gene, partial cds DQ481161.1 Uncultured bacterium clone LT-250_22 membrane bound nitrate reductase (narG) gene, partial cds EU352348.1 Uncultured bacterium clone LC9 membrane-bound nitrate reductase (narG) gene, partial cds AY325524.1 Uncultured bacterium clone GC61 putative dissimilatory membrane-bound nitrate reductase (narG) gene, partial cds FJ147550.1 Uncultured bacterium clone RI45 membrane-bound nitrate reductase (narG) gene, partial cds
182
Appendices
Table A.6.5: Accesion number of hydA gene sequences used in this study. Accession Strain number JF720844.1 Clostridium felsineum strain DSM794 FeFe-hydrogenase (hydA) gene, partial cds AB016820.1 Clostridium perfringens hydA gene for hydrogenase, complete cds GQ180215.1 Clostridium sp. IBUN 13A hydrogenase I (hydA) gene, partial cds GQ180214.1 Clostridium sp. IBUN 158B hydrogenase I (hydA) gene, partial cds DQ342014.1 Uncultured Clostridium sp. clone HydA-6 hydrogenase (HydA) gene, partial cds DQ342015.1 Uncultured Clostridium sp. clone HydA-7 hydrogenase (HydA) gene, partial cds DQ342018.1 Uncultured Clostridium sp. clone HydA-24 hydrogenase (HydA) gene, partial cds
Table A.6.6: Accesion number of nirS gene sequences used in this study. Accession Strain number DQ072192.1 Uncultured bacterium clone M60-121 putative dissimilatory nitrite reductase (nirS) gene, partial cds DQ159510.1 Uncultured bacterium clone hbA_6C putative dissimilatory nitrite reductase (nirS) gene, partial cds DQ072196.1 Uncultured bacterium clone M60-131 putative dissimilatory nitrite reductase (nirS) gene, partial cds AJ440478.1 Uncultured bacterium partial mRNA for nitrite reductase (nirS gene), clone ANIS-80 AB162305.1 Uncultured bacterium nirS gene for nitrite reductase, partial cds, clone:S-Z49 DQ337895.1 Uncultured bacterium clone S12m_nirS-21 NirS (nirS) gene, partial cds AB164097.1 Uncultured bacterium nirS gene for nitrite reductase, clone: NR1- 712S32 AY195929.1 Uncultured bacterium clone m318b04 nitrite reductase (nirS) gene, partial cds AB162292.1 Uncultured bacterium nirS gene for nitrite reductase, partial cds, clone:S-Z36 AF548984.1 Uncultured organism clone G04-15-178 nitrite reductase (nirS) gene, partial cds DQ767855.1 Uncultured organism clone eJ4 cd1 nitrite reductase (nirS) gene, partial cds DQ118278.1 Uncultured organism clone F42 cytochrome cd1 nitrate reductase (nirS) gene, partial cds AY583418.1 Uncultured bacterium clone DGGE band AS5D nitrite reductase (nirS) gene, partial cds AB456871.1 Uncultured bacterium nirS gene for cytochrome cd1 dissimilatory nitrite reductase, partial cds, clone: W6S-06
183
Appendices
AB185911.1 Uncultured bacterium nirS gene for nitrite reductase, partial cds, clone: 28-49 OTU 10 DQ232388.1 Uncultured bacterium clone CYCU-0224 NirS (nirS) gene, partial cds DQ182151.1 Uncultured bacterium clone SRF23 putative nitrite reductase (nirS) gene, partial cds DQ159507.1 Uncultured bacterium clone hbA_5F putative dissimilatory nitrite reductase (nirS) gene, partial cds AB162277.1 Uncultured bacterium nirS gene for nitrite reductase, partial cds, clone:S-Z21 DQ337849.1 Uncultured bacterium clone Psedi_nirS-05 NirS (nirS) gene, partial cds AB377849.1 Uncultured bacterium nirS gene for nitrite reductase, partial cds, clone: 518S54 GQ328115.1 Uncultured bacterium clone HnS64 nitrite reductase (nirS) gene, partial cds DQ337879.1 Uncultured bacterium clone S1m_nirS-14 NirS (nirS) gene, partial cds AY078269.1 Azoarcus evansii putative dissimilatory nitrite reductase (nirS) gene, partial cds AB164144.1 Uncultured bacterium nirS gene for nitrite reductase, clone: NR2- 87S27 GQ328110.1 Uncultured bacterium clone HnS59 nitrite reductase (nirS) gene, partial cds AY583409.1 Uncultured bacterium clone DGGE band AS3D nitrite reductase (nirS) gene, partial cds DQ337916.1 Uncultured bacterium clone S14m_nirS-35 NirS (nirS) gene, partial cds AB162260.1 Uncultured bacterium nirS gene for nitrite reductase, partial cds, clone:S-Z4 AB278644.1 Uncultured bacterium nirS gene for nitrite reductase, partial cds, clone: HSC5 DQ337831.1 Uncultured bacterium clone Bsedi_nirS-37 NirS (nirS) gene, partial cds AY195932.1 Uncultured bacterium clone m318a80 nitrite reductase (nirS) gene, partial cds AB162296.1 Uncultured bacterium nirS gene for nitrite reductase, partial cds, clone:S-Z40 DQ159612.1 Uncultured bacterium clone hbD_5B putative dissimilatory nitrite reductase (nirS) gene, partial cds FJ799350.1 Uncultured bacterium clone ns7b5 cd1-containing nitrite reductase (nirS) mRNA, partial cds DQ159487.1 Uncultured bacterium clone hbA_2H putative dissimilatory nitrite reductase (nirS) gene, partial cds DQ159530.1 Uncultured bacterium clone hbB_3D putative dissimilatory nitrite reductase (nirS) gene, partial cds AJ811473.1 Uncultured bacterium partial nirS gene for putative dissimilatory nitrite reductase, clone AC50-116
184
Appendices
DQ303101.1 Uncultured organism clone aT3 cd1 nitrite reductase (nirS) gene, partial cds DQ088665.1 Pseudomonas sp. C10-2 nitrite reductase (nirS) gene, partial cds AB162285.1 Uncultured bacterium nirS gene for nitrite reductase, partial cds, clone:S-Z29 DQ159594.1 Uncultured bacterium clone hbD_1D putative dissimilatory nitrite reductase (nirS) gene, partial cds AJ224912.1 Azospirillum brasilense Sp7 DSM 1690 nirS gene, partial DQ159478.1 Uncultured bacterium clone hbA_1F putative dissimilatory nitrite reductase (nirS) gene, partial cds DQ159533.1 Uncultured bacterium clone hbB_4B putative dissimilatory nitrite reductase (nirS) gene, partial cds AB208101.1 Uncultured bacterium nirS gene for nitrite reductase, partial cds, clone:Ba10 AB377752.1 Uncultured bacterium nirS gene for nitrite reductase, partial cds, clone: TAS002 DQ337875.1 Uncultured bacterium clone S1m_nirS-09 NirS (nirS) gene, partial cds AY336904.1 Uncultured bacterium clone V483-4E putative dissimilatory nitrite reductase (nirS) gene, partial cds DQ159561.1 Uncultured bacterium clone hbC_10B putative dissimilatory nitrite reductase (nirS) gene, partial cds DQ159492.1 Uncultured bacterium clone hbA_3F putative dissimilatory nitrite reductase (nirS) gene, partial cds DQ072232.1 Uncultured bacterium clone M60-137 putative dissimilatory nitrite reductase (nirS) gene, partial cds AY195937.1 Uncultured bacterium clone m312a93 nitrite reductase (nirS) gene, partial cds DQ159563.1 Uncultured bacterium clone hbC_11B putative dissimilatory nitrite reductase (nirS) gene, partial cds DQ159503.1 Uncultured bacterium clone hbA_5B putative dissimilatory nitrite reductase (nirS) gene, partial cds DQ159584.1 Uncultured bacterium clone hbC_9B putative dissimilatory nitrite reductase (nirS) gene, partial cds DQ337819.1 Uncultured bacterium clone Bsedi_nirS-16 NirS (nirS) gene, partial cds AB162273.1 Uncultured bacterium nirS gene for nitrite reductase, partial cds, clone:S-Z17 DQ159611.1 Uncultured bacterium clone hbD_5A putative dissimilatory nitrite reductase (nirS) gene, partial cds DQ159645.1 Uncultured bacterium clone hbE_3G putative dissimilatory nitrite reductase (nirS) gene, partial cds
185
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