The microbial ecology of spent fuel
storage ponds at Sellafield, UK
A thesis submitted to the University of Manchester for the degree of
Doctor of Philosophy in the Faculty of Science and Engineering
Sharon Lorena Ruiz Lopez
School of Earth and Environmental Sciences
September 2019
1 List of contents
Thesis Abstract ...... 7 Declaration ...... 9 Copyright Statement ...... 10 Acknowledgments ...... 11 The Author ...... 12 Chapter 1 Purpose and significance of the investigation ...... 14 1.1 Project context and relevance ...... 14 1.2 Objectives: ...... 15 1.3 Thesis structure ...... 15 1.4 Paper status and collaborator contributions ...... 17 Chapter 2 Introduction ...... 20 2.1 History of nuclear power ...... 20 2.2 Nuclear Power ...... 21 2.3 The Nuclear Fuel cycle ...... 22 2.4 Nuclear waste ...... 23 2.5 Sellafield site ...... 28 2.6 Sellafield spent fuel storage ponds ...... 29 2.7 Microorganisms in nuclear facilities ...... 31 2.8 Metabolic responses to extreme environments ...... 42
References ...... 47
Chapter 3 Methodology ...... 61 3.1 Culturing techniques ...... 61 3.2 Molecular biology techniques ...... 62 3.2.1 DNA extraction ...... 63 3.2.2 Polymerase Chain Reaction (PCR) ...... 64 3.2.3 Real Time PCR (qPCR) ...... 65 3.2.4 DNA sequencing: Sanger sequencing ...... 67 3.2.5 Next-generation DNA Sequencing: Illumina sequencing ...... 68 3.2.5 Metagenomics ...... 70 3.4 References...... 76 Chapter 4 Identification of stable hydrogen-driven microbes in highly radioactive storage facilities in Sellafield, UK ...... 83 Abstract ...... 83
2 Introduction ...... 84 Materials and Methods ...... 88 Indoor Nuclear Fuel Storage Pond (INP) ...... 88 Samples ...... 89 Cultivation independent DNA analyses of microbial communities ...... 90 DNA extraction ...... 90 Polymerase Chain Reaction ...... 91 Quantitative Polymerase Chain Reaction (Real-time PCR, QPCR)...... 91 Next-generation Sequencing ...... 92 Culturing and identification of the pond microorganisms...... 93 Results ...... 94 Identification of microorganisms by next generation DNA sequencing ...... 96 Cultivation-dependent analysis for determining microbial diversity in the INP...... 100 Discussion ...... 101 References ...... 110 Chapter 5 Comparative metagenomic analyses of taxonomic and metabolic diversity of microbiomes from spent nuclear fuel storage ponds ...... 123 Abstract ...... 123 Introduction ...... 124 Materials and methods ...... 127 Samples ...... 127 Methods...... 130 Results ...... 132 Microbial diversity on the indoor spent fuel storage pond (INP) ...... 132 Microbial diversity on the legacy First Generation Magnox Storage Pond (FGMSP) .. 134 Microbial diversity on the auxiliary outdoor spent fuel storage pond (Aux) ...... 134 Microbial diversity of eukaryotic organisms ...... 136 Functional classification ...... 137 Respiration ...... 139 Photosynthesis ...... 140 DNA metabolism ...... 141 Stress response ...... 143 Discussion ...... 144 Microbial diversity ...... 144 Adaptation to extreme environments ...... 146 Acknowledgements ...... 151 Supplementary information ...... 152
3 References ...... 172 Chapter 6 Metagenomic analysis of viruses in spent fuel storage ponds at Sellafield, UK.. 183 Abstract ...... 183 Introduction ...... 184 Methods ...... 186 Samples ...... 186 Results ...... 192 Microbial diversity of reads ...... 192 Discussion ...... 197 Acknowledgements ...... 199 Supplementary information ...... 200 References ...... 204 Chapter 7 Conclusions and future work ...... 211 Conclusions ...... 211 Future work ...... 215 Conference presentations and Awards ...... 218 Awards ...... 218 Oral Presentations ...... 218 Poster Presentations ...... 219 Outreach ...... 220 Complementary courses ...... 220
List of Figures
Figure 2.1 Brief history of nuclear power, adaptation from (WIN, 2013) ...... 21 Figure 2.2 Radioactive elements (1) encased in fuel rods are split into smaller elements (2) by high-energy reactions. These reactions release energy as heat (3) and also generate free particles. In a nuclear reactor, this heat converts water to steam, which turns turbines to generate electricity (4). At the end of its cycle, the nuclear fuel rods are cooled in pools of water for several years (5), and then may be disposed in dry cask storage (6) (Jennewein & Senft, 2018) ...... 22 Figure 2.3 Nuclear fuel cycle (WNA, 2017)...... 23 Figure 2.4 During nuclear fission one large atomic nucleus is divided into smaller nuclei. The fission process may produce more neutrons that induce further fissions and so on, an event known as fission chain reaction (GCSE, 2019) ...... 25 Figure 2.5. The Sellafield site is located in the northwest of England, approximately 15 km to the south of Whiteheaven (Sellafield Ltd., 2019) ...... 29 Figure 2.6 Mechanisms of radionuclide-microbe interactions (Lloyd & Macaskie, 2000) ...... 42 Figure 3.1 Summary of PCR (NCBI 2014)...... 65 Figure 3.2 Illustration of dye SYBER Green binding to a double stranded DNA (Praveen and Koundal 2013) ...... 66
4 Figure 3.3 Sanger sequencing technique (Zhou and Li 2015) ...... 68 Figure 3.4 Overview of NGS sequencing by Illumina technology: a)Library-construction process, b)Cluster generation by bridge amplification and c)Sequencing by synthesis with reversible dye terminators (Mardis 2013) ...... 70 Figure 3.5 Metagenomics workflow. After extraction, DNA is analysed using paired-ends reads to maximise coverage of the amplicons and the reads and assembled into contigs...... 73 Figure 3.6 Metagenomic viral identification pipeline. The workflow describes the main steps for phage identification and gene prediction (Zheng et al. 2019) ...... 75 Figure 4.1Diagram of the Fuel Handling Plant. It consists of 3 main ponds and 3 subponds linked by a transfer channel which enables water flow. The sampling points are located at the main ponds 2 and 3; subponds 1 and 2; and the head feeding tank (at the top of the pond) 89 Figure 4.2 QPCR results show the number of copies per mL. A standard curve for QPCR reaction was at concentration ranging from 0.00753 to 7530 nanograms per millilitre to estimate the concentration of DNA in the samples...... 96 Figure 4.3 Phylogenetic affiliations (closest known genera) of microorganisms detected in Sellafield indoor pond (INP): a)main ponds, b)subponds and c)feeding tank (FT) using Illumina sequencing with broad specificity primers for prokaryote 16S rRNA. Only the genera that contained more than 1% of the total number of sequences are shown...... 100 Figure 5.1Storage pond systems. Metal and legacy spent fuels from outdoor ponds are transported to the INP for interim storage pending a long term disposal solution available. The INP is divided in 3 main ponds (MP), 3 subponds and a feeding tank area (FT); waters from the INP are recirculated to the FGSMP during purging times. The FGMSP and its Auxiliary pond (Aux) store legacy fuel pond (NDA 2015;ONR 2016)...... 129 Figure 5.2 Microbial distribution at order level targeting the 16S rRNA gene. Only components that represented relative abundance higher than 1.5% are shown ...... 136 Figure 5.3 Functional categories associated to Level 1 subsystems (Level 1, KEGG) among the sampling sites and times ...... 138 Figure 5.4 Relative abundance of genes related to respiration processes (level 3 subsystems, KEGG database) ...... 139 Figure 5.5 Relative abundance of genes related to photosynthesis (level 3 subsystems, KEGG database) ...... 141 Figure 5.6 Relative abundance of genes related to DNA repair functions at level 3 subsystems (KEGG database) ...... 142 Figure 5.7 Relative abundance of genes related to stress response (level 3 subsystems, KEGG database) ...... 143 Figure 6.1Storage pond systems. Metal and legacy spent fuels from outdoor ponds are transported to the INP for interim storage pending a long term disposal solution available. The INP is divided in 3 main ponds (MP), 3 subponds and a feeding tank area (FT); waters from the INP are recirculated to the FGSMP during purging times. The FGMSP and its Auxiliary pond (Aux) store legacy fuel pond (NDA 2015;ONR 2016)...... 188 Figure 6.2 Workflow of the analysis performed on the metagenomes from spent fuel storage ponds ...... 191 Figure 6.3 Microbial affiliations at phylum level assigned by Kaiju classifier ...... 192 Figure 6.4 Relative abundance of viruses based on reads (Kaiju classifier) on the indoor and open storage fuel ponds ...... 193 Figure 6.5 Diversity of phage (categories 1 and 2) on assemblies and prediction of CRISPR on metagenomes ...... 194 Figure 6.6 Defence system prediction based on CRISPR arrays (repeats-spaces) ...... 197
5 List of Tables
Table 2.1. Radioactive wastes classification in the UK (NDA, 2019) ...... 24 Table 2.2. Half-life of common radionuclides in Spent Nuclear Fuel (Chu, Ekstrom, & Firestone, 1999; Lee, Plant, Livens, Hyatt, & Buscombe, 2015; Oigawa, 2015) ...... 25 Table 3.1 Examples of metagenomics software tools ...... 73 Table 4.1 Distribution of samples taken for a period of 30 months from different areas within the SNF pond, and analysed using high-throughput (Illumina) DNA microbial profiling. Samples SP01 and SP02 (*) were not sequenced using the Illumina platform but instead were analysed using culturing techniques (with Sanger sequencing of isolated pure cultures). .... 90 Table 4.2 Parameters measured on the indoor alkaline spent fuel storage pond (INP). Data provided by Sellafield Ltd ...... 95 Table 5.1Samples distribution...... 129 Table 6.1 Distribution of sample points in the Sellafield complex ...... 189 Table 6.2 Taxonomic and functional diversity of good bins (>93% completeness and <1% contamination, detailed description on Appendix Table 1) ...... 195
Abreviations
µg Micrograms (10-6 molar) 16S rRNA 16S Ribosomal Ribonucleic Acid 18S rRNA 18S Ribosomal Ribonucleic Acid AGR Advanced gas-cooled reactor ASM American Society for Microbiology Aux Auxiliary pond Bq Becquerel Bq l-1 Becquerel per litre CONACyT Consejo Nacional de Ciencia y Tecnologia (National Council of Science and Technology) EMBL European Molecular Biology Laboratory FEMS Federation of European Microbiological Societies FGMSP First Generation Magnox Storage Pond FT Feeding Tank INP Indoor hyper-alkaline pond ISME International Society for Microbial Ecology MP Main ponds (from the INP) NDA Nuclear Decommissioning Authority PCR Polymerase Chain Reaction qPCR Quantitative Polymerase Chain Reaction SEES School of Earth and Environmental Sciences SFP Spent Fuel Pond SP Subponds (from the INP) MAG Metagenome Assembled Genome KEGG Kyoto Encylcopedia of Genes and Genomes KAAS KEGG Automatic Annotation Server
6 Thesis Abstract
The use of nuclear energy has been of great importance to the United Kingdom, with
Sellafield being the largest nuclear site used for both power production and more recently reprocessing activities. This project, via collaboration between the Geomicrobiology Group at the University of Manchester and Sellafield Limited, aimed to investigate the microbial ecology of a spent fuel storage hyper-alkaline indoor pond (INP) in Sellafield.
The main pre-reprocessing storage pond at the Sellafield site is the Indoor pond (INP), a concrete walled indoor pond filled with demineralised water, responsible for receiving, storing and mechanically processing spent nuclear fuel (SNF) from Magnox and Advanced Gas- cooled Reactor (AGR) stations from across the UK. Samples were taken from the INP at different spatial locations and depths, encompassing main ponds (MP), subponds (SP) and a feeding tank (FT).
The present study intended to identify the microbial communities present in the INP and associated structures to determine if they were stable during a prolonged operational period.
A more academic focus of the PhD was to understand the metabolic processes that underpin microbial colonisation and adaptation in the pond. In order to achieve these objectives, first the microbial communities from the indoor alkaline storage pond (INP) were identified to create a microbial database consisting of population density and diversity of microorganisms present. Here traditional culturing approaches were trialled but were considered ineffective for the specialised “extremophilic” organisms present in the INP. Therefore, the bulk of the microbial analyses focused on DNA sequencing, focusing initially on amplification and sequencing of two commonly used genetic marker genes, the 16S rRNA and 18S rRNA genes that can be used to identify prokaryotic (bacteria and archaea) and eukaryotic (algae and other higher organisms). Finally, a much wider range of genes were targeted to help identify key processes that support microbial colonisation, via high-throughput “metagenomic” sequencing and analyses. Overall, these findings are discussed in relation to microbial survival in hyper-alkaline, oligotrophic and radioactive extreme environments, and microbial adaptation over time observed during the thirty months of analysis.
7 Organisms identified by 16S and 18S rRNA gene Illumina sequencing were predominantly
Proteobacteria, mainly Alpha and Beta in the feeding tank (FT), main pond (MP) and Subpond
(SP) sample sites. The presence of the alkali tolerant hydrogen-oxidising bacterium
Hydrogenophaga sp. solely in the INP main ponds and subponds suggested the metabolism of hydrogen is occurring within the INP which could be generated by radiolysis of water.
Metagenomic analysis revealed that genes related to membrane transport, oxidative and osmotic stress functions were more abundant on the FT possibly due to the presence of Na+ ions. Genes related to DNA metabolism (including DNA repair and defence systems) as well as genes related to respiration functions (hydrogenases) were more abundant on the MP and
SP which reinforces the proposed microbial utilization of H2 as an energy source.
In order to have a broader picture of the bacterial strategies to cope with extreme environmental conditions (hyper-alkaline, oligotrophic and radioactive background), few selected samples from an open-air pond, the First Generation Magnox Pond (FGMSP) and its auxiliary pond (Aux), were analysed and compared to the indoor system (INP). Results showed that genes associated to photosynthesis were more abundant on the open-air ponds, revealing that light exposure was a key energy source that promoted microbial colonisation.
Additionally the final part of this research intended to identify virus-host interactions and its influence on key metabolic processes. Metagenomic analysis revealed the presence of phages inserted on bacteria affiliated to order Burkholderiales; surprisingly phages did not seem to affect metabolic responses and promote activation defence systems (CRISPR).
In conclusion, microbiological and genomic analysis showed that the despite the low nutrient
(oligotrophic) nature of the indoor alkaline pond, coupled with the radioactive inventory, a stable microbial community is able to survive at relatively low energy levels, using alternative energy sources, potentially hydrogen, to cope with challenging environmental conditions.
8 Declaration
No portion of the work referred to in the thesis has been submitted in support of an application for another degree or qualification of this or any other university or other institute of learning.
9 Copyright Statement
i. The author of this thesis (including any appendices and/or schedules to this
thesis) owns certain copyright or related rights in it (the “Copyright”) and s/he has
given The University of Manchester certain rights to use such Copyright, including
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10 Acknowledgments
Throughout the writing of this thesis, I have received a great deal of support and assistance. I would first like to thank my supervisor, Jon Lloyd, for his invaluable support and assistance in the formulating of the research topic and methodology in particular.
I would like to acknowledge CONACyT (the National Council for Science of Technology), my sponsor, for providing me with the funding to develop this project. To Sellafield Ltd for giving me the opportunity to develop this project; for making the necessary arrangements to facilitate the handling of samples and for the complementary funding that allowed me to expand the research to a higher scientific level. I also express my gratitude to Nick Cole for his invaluable assistance on procuring and processing of samples at the Sellafield site.
I also want to thank my colleagues from the Geomicro Group at the University of Manchester, especially to Chris Boothman, Lynn Foster and Sophie Nixon; for supporting me greatly and for being always willing to help me.
Additionally, I would like to thank my strongest inspiration: mi Pa, Kika, Licita and Gina for their incredible counsel through this journey, for believing in me and for being for me all the time no matter the distance. To Alfred, for his love and understanding, for supporting me on this journey, for being my greatest motivation and for encouraging me to fight for my dreams and never give up. I want to express my gratitude to my greatest inspirational force: my family, my beautiful Dominica, Gus and Nora, and the rest of the Ruiz family. Special thanks to families
Ruiz Valencia, Martinez Ruiz, Miranda Díaz, Núñez Martínez and Saravia Ruiz for their outstanding example of resilience, care, love and for the splendid moments we have shared.
To the wonderful family I have met in Manchester: Isabelle, Natali, Sul, Monse, Zainab, Mayra,
Roy, Reynol, Ho-kyung, Emma, Farah, Karla, Roberto, Karen, Cecilia, Noel, David, Mario,
Rebeca, Cesar and Valerie; and my lifelong friends: Hugo, Vianey, Sambres, Alberts,
Richards, Luiso, Ivan, Carmen, Anali, Xochitl and Marcia, for their support in deliberating over our problems and findings; for the good times and the amazing memories we have created.
¡Gracias!
11 The Author
The Author of this thesis obtained a Bachelor of Engineering Degree in Biochemistry in the
National Polytechnic Institute (IPN, Instituto Politecnico Nacional); later she obtained the
Master’s Degree in Chemical and Biological Sciences at the National School of Biological
Sciences (ENCB) at the same institute (IPN) where she specialized on Biotechnology,
Bioengineering and Bioremediation. She briefly worked on a chemical industry where she was on charge of the quality assessment sub-division. In 2015 she joined the Geomicrobiology
Group at the University of Manchester where the work of this thesis was undertaken. She has presented sections of this work on International Conferences and has actively participated in scientific projects, most of them organised by the University of Manchester.
12 1
Purpose and significance of the investigation
13 Chapter 1 Purpose and significance of the investigation
1.1 Project context and relevance
The Sellafield complex, which has played a crucial role in the UK nuclear energy program, is large (approximately 700 acres), dealing with a complex portfolio of nuclear materials in 170 major nuclear facilities that require careful management (Ltd 2019). The site structure includes several nuclear fuel storage ponds; some in continual use, while others are undergoing decommissioning. Recent studies have also suggested that microbial processes have the potential to disrupt pond operation, resulting in, for example high biomass levels that can potentially foul equipment, accumulate radioactivity in sludges, limit visibility in pond waters and impact on the integrity of the stored samples.
Recently it has been possible to identify, using molecular (DNA) techniques, the microbial communities colonizing radioactive sites, and is has been interesting to find many organisms being able to adapt to highly radioactive conditions. This work, via a collaboration between the
Geomicrobiology Group at the University of Manchester and Sellafield Limited, aimed to investigate the microbial ecology and biogeochemical conditions of an indoor pond in
Sellafield, to identify the diversity of microorganisms across the pond complex, using molecular ecology techniques, to understand the biochemical mechanisms of adaptation to the pond environment, and the potential impact of microbial processes on the site. The identification of key organisms within the Sellafield pond complex not only offers the potential to understand the processes that facilitate colonisation of extremely radioactive environments, but is also an important first step in formulating appropriate control measures where required.
14 1.2 Objectives:
To develop and compare both culture-dependent and DNA-based techniques to help
understand the behavior of microbial communities in radioactive environments,
focusing on a selected indoor alkaline pond (INP) located in Sellafield which is
subjected to alkali dosing,
To apply molecular techniques e.g. Illumina high throughput 16S rRNA gene
sequencing, to study the microbial ecology of the pond system (including sub-ponds
and channels), alongside metagenomics studies to help understand the metabolic
processes under high pH and highly radioactive conditions, including energy sources
and survival strategies.
To apply the DNA-based techniques above to monitor the stability of the microbial
communities in the INP system over a prolonged operational period (approximately 3
years), and to contrast them where possible with microbial communities in other pond
facilities being studied in parallel research programs.
To determine the influence of virus-host interactions on the key microbial components
by metagenomic analysis of spent fuel storage systems.
1.3 Thesis structure
The present thesis is divided in four main chapters formatted as publishable papers:
• Chapter two, Introduction, presents a literature review on topics related with this
project; definitions and history of nuclear power and nuclear fuel cycle and findings to
date of microbial colonisation of spent fuel storage systems.
• Chapter three, methodology describes the fundaments and portrayal of the analyses
performed including classic microbiology, molecular biology techniques and next-
generation sequencing techniques.
• Chapter four, paper one, describes the microbial ecology on the indoor pond, INP,
based on analysis of the 16S rRNA gene. Samples were taken for a period of 30
months, creating a database focused on quantifying the diversity and number of
microbial cells over time, thus giving insight of the metabolic adaptation process at
15 play in this challenging environment. Culturing proved challenging but DNA analysis
highlighted the importance of hydrogen as a key electron donor in the indoor pond
system, metabolised by organisms such as the bacterium Hydrogenophaga This
paper is intended to be submitted to Frontiers in Microbiology.
• Chapter five, paper two, shows a comparative analysis of taxonomic and metabolic
patterns of microbiomes from open-air and indoor spent fuel storage ponds,
conducted using a metagenomic approach. Relative abundance of functional genes
revealed that bacteria are able to colonise the pond environments through harnessing
light energy (outdoor pond) or hydrogen (indoor pond) as energy sources. This paper
is intended to be submitted to FEMS Microbiology Ecology
• Chapter six, paper three, presents a metagenomic analysis of phages on the spent
fuel storage systems. Interactions between the virus and host microbial cells represent
a novel research topic, and this chapter aims to identify phages that were associated
with key microbial components, to help predict their potential influence on the
microbial communities within the pond (e.g. defence systems, CRISPR-Cas,
hydrogen metabolism). This paper is intended to be submitted to Environmental
Microbiology.
• Chapter 7, conclusions, summarizes the key findings and provides future suggestions.
16 1.4 Paper status and collaborator contributions
Chapter 4 consists of a paper entitled “ Identification of stable hydrogen-driven microbes in highly radioactive storage facilities in Sellafield, UK”, currently in preparation for Frontiers in
Microbiology
S. Ruiz-Lopez – Principal author performed experimental work and concept development
L. Foster – Technical assistance onsite at Sellafield Ltd
C. Boothman – Technical assistance
N. Cole – Assistance on procuring and processing samples at the Sellafield site and manuscript review
G. Boshoff – Assistance on procuring and processing samples at the Sellafield site
J. R. Lloyd – Initial concept development, conceptual guidance, extensive manuscript review
Chapter 5 consists on a paper entitled “Comparative metagenomic analyses of taxonomic and metabolic diversity of microbiomes from spent nuclear fuel storage ponds”, currently in preparation for FEMS Microbiology Ecology
S. Ruiz-Lopez – Principal author performed experimental work and concept development
L. Foster – Technical assistance onsite at Sellafield Ltd
C. Boothman – Technical assistance
N. Cole – Assistance on procuring and processing samples at the Sellafield site and manuscript review
G. Boshoff - Assistance on procuring and processing samples at the Sellafield site
H. Song – Concept development, conceptual guidance, and manuscript review
17 J. Adams – Assistance with obtaining whole genome sequencing
J. R. Lloyd – Initial concept development, extensive manuscript review
Chapter 6 consists on a paper entitled “Metagenomic analysis of viruses in spent fuel storage ponds at Sellafield, UK”, currently on preparation for Environmental microbiology
S. Ruiz-Lopez – Principal author performed experimental work and concept development
S. Nixon – Technical assistance, concept development, conceptual guidance and extensive manuscript review
L. Foster – Technical assistance onsite at Sellafield Ltd
C. Boothman – Technical assistance
N. Cole – Assistance on procuring and processing samples at the Sellafield site and manuscript review
G. Boshoff - Assistance on procuring and processing samples at the Sellafield site
J. R. Lloyd – Initial concept development, conceptual guidance, extensive manuscript review
18 2
Introduction
19 Chapter 2 Introduction
This chapter contains a broad overview of the research, including insights of the history of nuclear power, the nuclear fuel cycle and description of the Sellafield site in particular describes the studied ponds. Finally, the chapter presents an overview of the microbial interactions with radionuclides as well as metabolic responses to specific extreme environments (hyper-alkaline, radioactive and oligotrophic).
2.1 History of nuclear power
The discovery and application of nuclear power has been one the most significant scientific achievements of the past century. The beginning of nuclear power can be traced to 1895 in
Germany, when William Roentgen discovered a new kind of energy emitted from an energized device. Soon, in France in 1896 Becquerel noticed the effects of uranium salts on photographic plates, and Marie and Pierre Curie studied the phenomenon thoroughly and isolated two new elements involved in the energy production: Polonium and Radium. This new phenomenon was called radioactivity (Mahaffey, 2011). During the 20th Century, many events happened and helped to create a better understanding of radioactivity. In 1902, Ernst Rutherford showed that radioactivity is a spontaneous event that can produces two kinds of particles from the nucleus; alpha and beta. Contributions from Frederick Soddy, James Chadwick, Cockcroft and
Walton, Enrico Fermi and Irene Curie allowed further progress in nuclear energy by discovering several radionuclides and their properties including uranium fission effects (WNA,
2016). Those contributions were set to have two main applications; the production of a source of constant power and for military purposes (superbombs) due to uncontrolled uranium fission
(Mahaffey, 2011). Figure 2.1 shows a resume of the nuclear energy history.
20
Figure 2.1 Brief history of nuclear power, adaptation from (WIN, 2013)
2.2 Nuclear Power
Nuclear power uses the energy released by splitting atoms of certain elements by a process called nuclear fission. A slow-moving neutron collides with an atom (such as uranium) making the atom unstable. Then the unstable atom splits into two new separate atoms creating heat that can be used to boil water to make steam. The steam turns the blades of a steam turbine, driving generators that produce electricity. A separate structure cools the steam back into water, that can later be reused to create steam and the cycle goes on (Nuclear Energy Agency,
2003) (Figure 2.2).
21
Figure 2.2 Radioactive elements (1) encased in fuel rods are split into smaller elements (2) by high-energy reactions. These reactions release energy as heat (3) and also generate free particles. In a nuclear reactor, this heat converts water to steam, which turns turbines to generate electricity (4). At the end of its cycle, the nuclear fuel rods are cooled in pools of water for several years (5), and then may be disposed in dry cask storage (6) (Jennewein & Senft, 2018)
The UK has 15 operational reactors in 8 power stations generating about 21% of its electricity, and also has 1 major reprocessing plant in Sellafield. However the use of nuclear power to generate electricity has declined since old plants have been shut down, due to ageing-related problems that affect safety and performance availability (WNA, 2019b).
Worldwide around 11% of the total electricity is generated by nuclear power reactors and the need for new generating capacity is clear, not only for the increased demand of electricity in many countries, but to replace old fossil fuel powered units such as coal-fired power stations that emit large amounts of carbon dioxide (WNA, 2019b).
2.3 The Nuclear Fuel cycle
The nuclear fuel cycle is defined as a series of processes that involve various activities to produce electricity from uranium after being processed in nuclear reactors (WNA, 2015). The nuclear fuel cycle consists of three stages. First, the “front end” that comprises the steps necessary to prepare nuclear fuel for reactor operation, the “service period” where the fuel is used and the “back end” that comprises the management of highly radioactive spent nuclear fuel, whether it is reprocessed or sent to a final storage or disposal (Nuclear Energy Agency,
2003).
22
The uranium that is used in the nuclear fuel cycle must be prepared by the steps of mining, milling, conversion, enrichment and fuel fabrication. After the uranium fuel has been used in the reactors for about three years, the spent fuel is taken through a series of steps including storage, reprocessing and recycling before disposal as waste. Fig. 2.3 indicates the key steps in the Nuclear Fuel Cycle (WNA, 2015).
Figure 2.3 Nuclear fuel cycle (WNA, 2017)
Every step in the nuclear fuel cycle produces wastes, and they can be categorised as low level, produced at all stages; medium level produced during reactor operation and by reprocessing; and high level, which contain separated highly-radioactive fission products.
These levels of radioactivity are defined according to the amount of radiation they emit (WNA,
2017).
2.4 Nuclear waste
Radioactive waste management and disposal are among of the biggest problems faced by the nuclear industries, with significant environmental challenges relating to legacy and future
23 wastes. According to the UK Radioactive Waste Inventory, radioactive wastes are classified based on the type and quantity of radioactivity they contain, and how much heat is produced.
Table 2.1 summarizes the main radioactive wastes classes.
Table 2.1. Radioactive wastes classification in the UK (NDA, 2019)
High activity wastes High waste level (HLW) Produced as by-product from reprocessing spent fuel from nuclear reactors, represents less than 1% Intermediate level waste The major components are (ILW) nuclear reactor components, graphite from reactor cores and sludges from the treatment of radioactive liquid effluents, represents about 6% Low level wastes Low level waste (LLW) Includes waste from operation and decommissioning of nuclear facilities such as scrap metal, paper and plastics. It represents about 93% Very low-level waste (VLLW) The major components are building rubble, soil and steel items.
One of the biggest challenges of nuclear power production includes the long-term storage and disposal of the dangerously radioactive products resulting from nuclear fission. The fission of uranium results in the production of two new lesser nuclei that would normally have more neutrons (Figure 2.4). In order to reach the natural equilibrium, the new elements must decay radioactively; the time to achieve it varies on the species from microseconds to thousands of years (Mahaffey, 2011).
24
Figure 2.4 During nuclear fission one large atomic nucleus is divided into smaller nuclei. The fission process may produce more neutrons that induce further fissions and so on, an event known as fission chain reaction (GCSE, 2019)
Two defined processes occur during uranium fission. First, fission produces isotopes
Cesium137 and Strontium90, called “fission products”; those isotopes are responsible for most of the heat and penetrating radiation in high-level waste. Afterwards, few uranium atoms capture free neutrons produced during fission from heavier elements such as plutonium.
Heavier elements, also known as transuranic elements, produce less energy and heat than fission products; however those elements take longer to decay, accounting for most remaining high-level waste (NRC, 2019a). Most of the radioactive waste products decay within a short period of time, even hours or minutes (Table 2.2).
Table 2.2. Half-life of common radionuclides in Spent Nuclear Fuel (Chu, Ekstrom, & Firestone, 1999; Lee, Plant, Livens, Hyatt, & Buscombe, 2015; Oigawa, 2015)
Nuclide Half-life Fission Products Short-lived fission products Sr-90 28.8 years Zr-95 65 days Sn-121 43.9 years I-131 8.02 days Kr-85 10.76 years Cs-137 30.1 years
25 Pm-147 2.6 years Ce-141 33 days Ce-144 285 days Zr-95 65 days Sr-89 51 days Long-lived fission products Tc-99 2.12x105 years I-129 1.57x107 years C-14 5,730 years Ba-140 12.72 days Sn-126 2.3x105 years Se-79 3.27x105 years Zr-93 1.53x106 years Cs-135 2.3x106 years Pd-107 6.5x106 years Se-79 3.27x105 years Pu-238 87.7 years Pu-239 24,400 years Transuranic elements (TRU) Pu-240 6,580 years Pu-241 13.2 years Pu-242 3.79x105 years Np-237 2.14x106 years Np-239 2.35 days Minor actinides (MA) Am-241 458 years Am-242 141 years Am-243 7,950 years Cm-242 163 days Cm-243 32 years Cm-244 17.6 years Cm-245 9,300 years Cm-246 5,500 years
The management of spent nuclear fuel (SNF) and nuclear wastes requires a proper strategy to ensure safety and permanent disposal of radioactive material from power generation or
26 defence uses. Most common strategies include permanent disposal to a geological repository, nuclear fuel reprocessing or interim storage (Sanders & Sanders, 2016).
Typical management of spent nuclear fuel includes two categories. First is the interim storage at the reactor site which may involve secondary connected ponds. The second is storage off site at an independent location at specialized reprocessing sites (e.g. plants Marcoule and La
Hague in France, the UK and the Zheleznogorsk MCC Centre and the SCC Seversk sites at
Russian Federation) (IAEA, 1999; Schneider & Marignac, 2008; WNA, 2019a). Both categories can be handled by dry or wet storage technologies (NRC, 2019a).
Wet systems imply that the storage is in ponds (or pools) in which spent fuel is kept under water. Storage ponds are reinforced concrete stainless-steel lined structures built above ground. Initially ponds were open-air systems but due to the need to control the water quality, most recent built ponds are now covered (indoor) (IAEA, 1999). In order to avoid corrosion, ponds are filled with deionized (or demineralized) water and depending on the activity of ion exchange or purge; a chemical range may be imposed (e.g. sodium nitrite as corrosion inhibitor) (IAEA, 1982). Pond water both shields the radiation and cools the irradiated fuel assemblies (Y. Y. Liu, 2015).
Wet and dry storage systems are design to maintain cladding integrity during handling and exposure to corrosion effects of the storage environmental, and to protect plant operators by shielding radiological material and also to assure environmental protection by minimising the release of radioisotopes (NRC, 2019b).
Since it is such a complicated issue to manage, only a few countries such as Finland, China,
France, Germany and Japan have well developed plans to facilitate long-term disposal.
Meanwhile, the UK government is actively engaged in supporting decommissioning of plants such as Sellafield and identifying a site for geodisposal of legacy and future wastes (NWMO,
2018; WNA, 2018).
Reprocessing of waste is an option to minimise waste production, and with this process uranium and plutonium are separated and recycled to be re-used in a nuclear reactor.
Countries like France, UK, Russia and Japan are pioneers in the reprocessing stages at
27 different levels. There are several alternative reprocessing technologies, and these are reviewed elsewhere (WNA, 2018).
2.5 Sellafield site
Sellafield was established in 1941 as a Royal Ordnance Factory for the production of trinitrotoluene (TNT) for the Second World War effort. The Windscale piles and the Windscale reprocessing facility were then built to produce plutonium for the UK atomic weapons programme until nuclear military purposes ceased in 1995 (Gray, Jones, & ASmith, 1995;
Mahaffey, 2011). Nuclear power became commercial on 1953 with the construction of the
Calder Hall nuclear plant at Sellafield in Great Britain and proved to be a highly reliable power source. The plant operated until 2003 without incident, focusing on electricity generation
(Mahaffey, 2011). Today the Sellafield site, which is located near the village of Seascale on the coast of the Irish Sea in Cumbria (Figure 2.5), is the most complex industrial site requiring remediation in Western Europe responsible for nuclear fuel reprocessing and nuclear decommissioning (Tierney et al., 2016).
Sellafield comprises approximately 700 acres containing more than 2,200 buildings including
170 major nuclear facilities carried out by a 10,000 strong workforce (Ltd, 2019; Sellafield Ltd.,
2011). The site is now home to a wide range on nuclear facilities and operations, which involves hazard and risk reduction, including the decommissioning of legacy ponds and silos from old facilities, reprocessing, fuel manufacturing and nuclear waste management. This includes the treatment of low, intermediate and high level wastes, a unique capability in the
UK (Sellafield Ltd., 2019).
28
Figure 2.5. The Sellafield site is located in the northwest of England, approximately 15 km to the south of Whiteheaven (Sellafield Ltd., 2019)
Sellafield is the only nuclear site in the country able to manage the three forms of radioactive waste: low, intermediate and high (Sellafield Ltd., 2019).
2.6 Sellafield spent fuel storage ponds
Contrasting to fossil fuels, nuclear fuel can be re-used in a process called reprocessing, that aims to separate uranium and plutonium from spent fuel.
29 After being used to generate power, the spent fuel is stored on storage ponds under water, which enables to cool it and remain shielded from emitting radiation (IAEA, 2011). The storage system in Sellafield consist in the following buildings:
• Magnox Reprocessing plant was constructed during 1950s and its role is to receive
and store irradiated fuel from Magnox reactors and remove the fuel cladding before
the fuel is processed (Sellafield Ltd., 2015, 2017b).
• First Generation Magnox Storage Pond was constructed as an open-air pond which
caused accumulation of waste materials like fuel fragments, fuel cladding, sludges
from corrosion and other debris brought by the wind. The First Generation Magnox
Storage Pond combines used nuclear fuel, sludge, intermediate level waste and pond
water, each of which needs to be safely removed and processed through separate
routes (Sellafield Ltd., 2015, 2017a)
• Fuel Handing Plant is an indoor pond responsible for receiving, storing and
mechanically processing spent nuclear fuel from Magnox and Advanced Gas-cooled
Reactor (AGR) stations from across the UK (Sellafield Ltd., 2015). After a general
inspection, Magnox and AGR flasks are transferred to the FHP using the site rail
system. The fuel is removed from the flasks and then transferred into the storage pond
where it remains for a set period of time until the short-lived fission products decay.
When the storage period is over, the fuel is transferred into the decanner facility, for
Magnox fuel or alternatively the AGR dismantler for AGR fuel. In order to be able to
reprocess the fuel rod its outer cladding is stripped off by using specially designed
remote-control equipment. The cladding is peeled off into small pieces a few
centimetres in length. The remaining waste is made primarily of swarf from fuel
elements that have been processed. After the Magnox fuel cladding is removed, the
uranium metal bar is loaded into a magazine and transferred into a shielded transport
flask and finally taken across the site to the Magnox reprocessing plant (Sellafield Ltd.,
2015)
• The Thermal Oxide Reprocessing Plant (Thorp) at Sellafield reprocesses both UK and
foreign spent fuel. Its construction began on the Thorp Head End and Chemical
Separation plants in 1985 and the first fuel was moved in 1994. The operations
30 performed are divided into three main areas; fuel receipt and storage, the head end
plant operation and the chemical separation of uranium and plutonium. The efficiency
of the Thorp reactors is about 97% after 4 years, and the spent fuel is recycled,
whereas the rest is waste (Sellafield Ltd., 2016).
• To sum up, the options for used fuel are direct disposal to a geological repository,
aqueous reprocessing to remove uranium and plutonium and advanced
electrometallurgical reprocessing which removes uranium, plutonium and minor
actinides (WNA, 2015).
2.7 Microorganisms in nuclear facilities
As mentioned above, storage of spent nuclear fuel requires specific chemical and physical conditions to avoid contamination of personnel and the environment. Spent fuel storage ponds are radioactive (due to the nature of the stored material) and often oligotrophic (due to the demineralized/deionized water) environments that represent challenging habitats for several forms of life (Rothschild & Mancinelli, 2001).
However, recent publications have shown the presence of microorganisms, mainly bacteria and algae, living in the ponds, most often found in biofilms attached to the walls of the ponds.
Table 2.3 summarises the research and findings on the microbial ecology and biogeochemistry of nuclear ponds.
31 Location Summary Sample analysis Organisms found Radionuclides found References SNF at the Biofilm formation Epifluorescence α-, β- and γ-Proteobacteria, Biofilms were able to (Sarró, García, & Cofretes analysed by microscopy and scanning Firmicutes and retain radionuclides, Moreno, 2005) Nuclear Power immersing different electron microscopy were Actinobactericeae especially 60Co (Valencia, austenitic stainless- used Spain) steel coupons, as well Standard culture methods Boiling Water as balls of stainless and sequencing of 16S Reactor (BRW) steel and titanium rDNA fragments SNF at the The microorganisms Amplification of β-Proteobacteria, The radionuclides (Chicote et al., Cofretes attached to the 16S rDNA fragments from Actinomycetales and the found in the water 2004) Nuclear Power nuclear pool wall were the microorganisms Bacillus/Staphylococcus group. were 60Co, 137Cs, (Valencia, analysed. by PCR using universal The fungus Aspergillus 134Cs, 54Mn, and 65Zn Spain) primers for the domain fumigatus was also found Boiling Water Bacteria, and the Reactor (BRW) Denaturing Gradient Gel Electrophoresis was used. SNF at the Biofilm formation on Standard culture α-, β-, and γ-Proteobacteria, Radionuclides were (Sarró et al., 2003) Cofretes three different types of microbiological methods, Bacilli and Actinobacteria found trapped in Nuclear Power austenitic stainless microscopy techniques biofilms in water, (Valencia, steel (epifluorescence mainly 60Co, 65Zn, Spain) microscopy and scanning 54Mn, 58Co and 95Zr Boiling Water electron microscopy SEM) Reactor (BRW) and molecular biology
32 techniques (PCR and gel electrophoresis) SNF at the Biofilm Standard culture α-, β-, and γ-Proteobacteria, Biofilms are able to (Sarró, García, Cofretes characterisation in two microbiological methods, Actinobacteria and Firmicutes retain radionuclides Moreno, & Montero, Nuclear Power different metallic microscopy techniques from water, especially 2007) (Valencia, materials: stainless (epifluorescence 60Co Spain) steel and titanium microscopy and scanning electron microscopy SEM) and molecular biology techniques (PCR and gel electrophoresis) Pool water of Characterization of Standard microbiology Kocuria palustris, Micrococcus Isolated bacteria were (Tišáková et al., the interim bacterial methods and sequencing luteus, Ochrobactrum spp. and able to accumulate 2013) spent contamination in pool of 16S rDNA Pseudomonas aeruginosa. 60Co and 137Cs fuel storage water (JAVYS Inc.), Slovak Republic Water sample Isolated Co2+ and Cs+ Standard microbiology Cs+ resistant isolates Serratia Isolated bacteria are (Dekker, Osborne, from an resistant bacteria from Methods using selective and Yersinia tolerant to high & Santini, 2014) external water were collected medium and sequencing of And Co2+ isolates were closely concentrations of Cs+ storage pond from a nuclear fuel 16S rDNA related to Curvibacter and and Co2+ at Sellafield storage pond Tardiphaga
33 Ltd obtained from 5 m below the surface Samples from Water samples from Bacteria were analysed by Six morphologically different Sorption of Cd, Co (Diósi, Telegdi, the Atomic the storage of spent atomic force microscopy bacteria were isolated and Sr by bacteria Farkas, Gazsó, & Energy nuclear fuel Bokori, 2003) Research Institute in Budapest Samples from A couple of DNA standard techniques: When nutrients were added, The sorption of (Gillow, Dunn, the Rustler groundwater samples DAPI, DGGE and PCR Halomonas sp, Acetobacterium Uranium was higher Francis, Lucero, & Formation at were studied to sp from WIPP and than observed of Papenguth, 2000) the Waste analyse the Haloanaerobium , Bacillus 241Plutonium Isolation Pilot biosorption of uranium subtilis and Pseudomonas Plant (WIPP), and Plutonium fluorescens from GTS were NM, USA; and responsible for the sorption of at the Grimsel Uranium; Acetobacterium sp test Site (GTS), was also involved in the uptake Switzerland of Plutonium Spent nuclear Microbiological studies Four different types of After 2-year period microbial Radionuclides content (Santo Domingo, fuel storage were performed to metal coupons (chromium- densities of 104to 107cells/ml was not determined Berry, Summer, & basins at determine the nickel and aluminium- were determined in water Fliermans, 1998)
34 Savannah potential for microbial- based alloys) were samples and on submerged River Site influenced corrosion submerged on water metal coupons (SRS) (MIC) samples were collected from the SNF basin and analysed by X-ray spectra techniques Spent fuel pool Samples were taken Metagenomics and Phyla: Proteobacteria, Samples previously (Silva et al., 2018) and transfer on the liner of the metatranscriptomics Actinobacteria, Firmicutes, analysed showed the channel of a spent fuel pool (SFP) Bacteroidetes, Acidobacteria, content of 51Cr, 58Co, nuclear power and the fuel transfer Cyanobacteria, Chloroflexi, 60Co, and 137Cs plant, Rio de channel (FTC) of a Planctomycetes, Deinococcus- Janeiro, Brasil Nuclear Power Plant Thermus, Verrucomicrobia, (NPP) Chlorobi, Chlamydiae, Euryarchaneota, Ascomycota, Basidiomycota, Others (2-5%) Fungus was detected Water filled Concrete pool, volume Molecular techniques: 454 Cell numbers from 4x103 to Radionuclides were (Bagwell, Noble, storage basin of 13,000m3 water pyrosequencing and 4x104 cells/ml not measured, instead Milliken, Li, & for spent Temperature 18-26 ⁰C amplicon analysis 4,000 OTUs bacterial diversity was Kaplan, 2018) nuclear fuel Deionized water Families: Burkholderiaceae, associated with reactor (white pH 6.1 Nitrospiraceae, aluminum (oxy) flocculent was Hyphomicrobiaceae and hydroxide complexes evident), Comamonadaceae
35 Savannah River, Aiken SC, USA Outdoor spent Outdoor pond Molecular biology Actinobacteria, Bacteroidetes, Accumulation of 137Cs (MeGraw et al., fuel storage colonised by a techniques targeting the cyanobacteria, Proteobacteria, and 90S was 2018) pond at seasonal bloom of 16S and 18S genes. Verrumicrobia determined Sellafield, UK microorganisms Fourier transform infrared (FT-IR) analysis Spent nuclear Temperature reported Water quality was Planktonic cell populations Radionuclides content (Masurat, Fru, & fuel storage between 25-36 measured with by ion ranged between 1.4×103 and was not measured Pedersen, 2005) basin in degrees chromatography 5.2×103 ml−1, correlated with Sweden (CLAB Biofilm formation was additionally TOC levels the system configuration, and facility) detected were measured was inversely correlated with Microscopy (SEM, TEM total organic carbon (TOC) No data about pH or and fluorescence) were levels. Most abundant organism water treatment used to analyse the was genus Meiothermus planktonic cells Culturing and DNA techniques targeting the 16S gene to identify the microbial diversity Spent nuclear Samples were taken Microbiological studies Cell counts were ~1x103 CFU/ml Radionuclides content (Karley, Shukla, & fuel (SNF) from the wall surface, (culturing in LB medium), was not measured, Rao, 2018)
36 pond in temperature was 37⁰C radio-tolerance of Six bacterial species in the SNF instead removal of Kalpakkam, and pH was neutral microorganisms, biofilm poolwater samples were heavy metals was India quantification, and uptake isolated, which had significant tested of cobalt and nickel were radio-tolerance (D10val-ue 248 achieved Gy to 2 kGy) and also biofilm- forming capabilities Bacteria were Bioaccumulation and Bacteria were cultivated Bacteria Kocuria palustris and Bioaccumulation and (Pipíška, Trajteľová, isolated from biosorption were and harvested from a Micrococcus luteus, previously biosorption were Horník, & Frišták, pool water in tested on previously bioreactero (BIOSTAT A isolated, were tested determined using 54Mn 2018) the Interim isolated bacteria plus, Sartorius AG, as radioindicator Spent Nuclear Germany) Fuel Storage Bioaccumulation and Facility in biosorption character- JAVYS, Inc. in istics of Mn2+ ions by Jaslovské both dead and living, Bohunice, non-growing biomass of Slovak bacteria Republic Bacteria were 22 species of bacteria Molecular biology Bacteria strains tested showed Major radionuclides (Bruhn, Frank, isolated from were cultivated in techniques to identify the the ability to form biofilms on detected were 137Cs, Roberto, Pinhero, & storage ponds nutrient-rich media, to surviving species targeting spent-fuel materials and may 90Sr, 90Y and 60Co Johnson, 2009) at the Idaho test vessels containing the 16S gene (LI-COR have implications on microbial Nuclear irradiated cladding influenced corrosion (MIC)
37 Technology sections and that was 4200 automated Centre on the then surrounded by sequencer) IL site (Idaho, radioactive source Absorbed beta and gamma USA) material. dose measurements were performedusing LiF thermoluminescent dosimeters (TLDs)
Spent Nuclear Microbiological studies Identification was achieved Microbial diversity was Radionuclides content (Forte Giacobone, Fuel (SNF) were performed to targeting the 16S rRNA dominated by Bacillus cereus, was not determined Rodriguez, Burkart, pools in evaluate the risk of gen and coupons corrosion followed by Rhizobium, & Pizarro, 2011) Argentina microbial-induced was determined by SEM- Leisfonia, Micrococcus and corrosion by microbial EDX and CFLM analysis Pseudomonas organisms isolated from the spent fuel pools
38 Sharon L. Ruiz Lopez PhD Thesis
Microorganisms can be part of the natural environment in radioactive environments. Although some environments can be toxic for many organisms, it is common to find diverse microbial communities in geological nuclear waste disposal sites like the High Activity Disposal
Experimental Site (HADES) in the Boom Clay in Belgium, where at least seven bacterial phyla have been identified and there is a relationship between the organisms and the organic matter of the environment (Wouters, Moors, Boven, & Leys, 2013). However, these environments have been studied in less detail due to the technical problems of working with highly radioactive regions.
Additionally, it has been reported that some bacteria can survive in high-radiation contaminated sites such as Chernobyl and Fukushima (Fredrickson et al., 2004; Møller &
Mousseau, 2016; Ruiz-González et al., 2016; Shukla, Parmar, & Saraf, 2017; Srinivasan et al., 2015; Yazdani et al., 2009; Zavilgelsky, Abilev, Sukhodolets, & Ahmad, 1998); surviving high radiation doses, although long-term radiation exposure can cause irreversible DNA damage. In this category bacterial species like Deinococcus radiodurans, Microbacterium testaceum, Rhodococcus sp., Pseudomonas aeruginosa, Micrococcus luteus, and
Pseudomonas monteilii, Rufibacter, Arthobacter and mutants of Escherichia coli are included
(Battista, 1997; Bruhn et al., 2009; Fredrickson et al., 2004; Srinivasan et al., 2015; Zavilgelsky et al., 1998); along with algae species such as Cystoseira, Coccomyxa actinabiotis,
Parachlorella sp. binos (Binos) among others (Adam & Garnier-Laplace, 2003; Gabani &
Singh, 2013; Krejci, Finney, Vogt, & Joester, 2011; M. Liu et al., 2014; Peletier, Gieskes, &
Buma, 1996; Ragon, Restoux, Moreira, Møller, & López-García, 2011; Rivasseau et al., 2013;
Shimura et al., 2012).
Specifically at the Sellafield site, microbial populations present in aqueous and biofilm samples from outdoor and indoor spent fuel storage ponds have been analysed. Common freshwater Proteobacteria and Cyanobacteria have been the principal bacterial phylogenetic groups detected, while algal species have also been detected in outdoor highly radioactive storage ponds (Dekker et al., 2014; Foster, 2018; MeGraw et al., 2018; Newsome, Morris,
Trivedi, Atherton, & Lloyd, 2014; Thorpe, Morris, Boothman, & Lloyd, 2012).
39
Sharon L. Ruiz Lopez PhD Thesis
Microorganisms can play a significant role in the transformations of radionuclides in the environment by altering their chemical speciation, solubility and sorption properties, causing an increase or decrease in concentrations, hence affecting their environmental mobility and bioavailability (Francis, 2012; Newsome, Morris, & Lloyd, 2014).
The biogeochemistry of redox-active radionuclides can be controlled by the microbial metabolism of the involved organisms. Microbes can reduce and precipitate some priority radionuclides such as U(VI), Np(V) and Tc(VII) via bioreduction processes. These can be stimulated by a range of electron donors and can operate at alkali conditions associated with cementitious intermediate level waste (Rizoulis, Morris, & Lloyd, 2016).
Several microorganisms involved in the biogeochemistry of uranium and the interaction with actinides have been studied. This comprises the removal of uranium from solution, including the enzymatic reduction of U(VI) to U(IV), precipitation of U(VI) and the biosorption of U(VI).
Recently, there have been important studies focused on the bioreduction of U(VI) through in situ and ex situ technologies (Anderson & Lovley, 2002; Choudhary & Sar, 2015; Lloyd &
Renshaw, 2005; Merroun & Selenska-Pobell, 2008).
Microbial interactions with radionuclides are driven by the following mechanisms:
• Biosorption, implies the sequestration of radionuclides to the outer surface or cell
membranes of microorganisms (Ding, Cheng, & Nie, 2019; Gadd, 2009). It occurs by
electrostatic attraction between radionuclide cations and anionic cell wall functional
groups (Xie et al., 2008). Ligands such as carboxyl, amine, hydroxyl, phosphate and
sulfhydryl groups are involved (Ding et al., 2019; Lloyd & Macaskie, 2000; Simonoff,
Sergeant, Poulain, & Pravikoff, 2007).
• Metabolism-dependent bioaccumulation (cell surface sequestration) is defined as
intracellular accumulation of toxic compounds (Gadd, 2009); it occurs as a classical
transport system involving ions (such as Cs+, K+, Sr2+ and Ra2+) in the physiology of
the cells that are exchanged by the toxic metal (often radionuclides) (Shukla et al.,
2017; Simonoff et al., 2007).
40
Sharon L. Ruiz Lopez PhD Thesis
• Bioreduction involves redox reactions that affect solubility of radionuclides by
forming oxides, coprecipitates, ionic and organic or inorganic complexes (Ding et al.,
2019). Microorganisms such as Fe(III)-reducing bacteria G. metallireducens,
Clostridium sp., Desulfovibrio desulfuricans and Desulfovibrio vulgaris are examples
of bacteria able to use radionuclides (e.g. U(VI) and Tc(VII)) as the terminal acceptor
(Francis, 1994; Lloyd & Lovley, 2001; D. R. Lovley & Phillips, 1992). Enzymatic
processes play a role by transforming the toxic metals making them more volatile, or
changing their solubility (Lloyd, 2003). Alternative enzymatic transformations include
bioreduction under anaerobic conditions, biomethylation that produce volatile methyl
derivates and biodegradation of chelating agents which can produce the precipitation
of the radionuclide (Lloyd & Macaskie, 2002; Simonoff et al., 2007).
• Biomineralization by ligands. Represents the process by which microorganisms
provide nucleation sites for the precipitation of radionuclide ions to insoluble minerals
(Lloyd, 2003; Merroun & Selenska-Pobell, 2008; White & Gadd, 1990). Bacterial
species are able to use ligands such as phosphate (observed in E. coli and Serratia
sp.), carbonate (observed in Ralstonia eutropha and Pseudomonas fluorescences)
and sulphide to precipitate metals and provide a way to remove radionuclides from
solution (Newsome, Morris, & Lloyd, 2014; Simonoff et al., 2007).
Figure 2.6 shows the main pathways radionuclides can be altered by bacteria (Lloyd &
Macaskie, 2000).
41
Sharon L. Ruiz Lopez PhD Thesis
Figure 2.6 Mechanisms of radionuclide-microbe interactions (Lloyd & Macaskie, 2000)
2.8 Metabolic responses to extreme environments
In addition to the described microbial interactions with radionuclides, microorganisms are able to display a broad range of metabolic responses to help them cope with harsh environmental conditions. It has been widely studied that microorganisms can thrive under broader swaths of temperature, pH, pressure, radiation, salinity, energy and nutrient limitation (Merino et al.,
2019).
The development of genomic tools has provided insights into the adaptive strategies of microbes in their natural settings and provides greater understanding on how environments may impact the evolution of microbial communities (Hemme et al., 2010; Li et al., 2014).
One important environmental parameter that influences the microbial diversity is the pH, alkalinity and acidity habitats can promote different metabolic responses. Since bacteria must maintain a neutral cytoplasmic pH for survival, exchange of protons on other ions occurs through various transporters (Merino et al., 2019). For instance prokaryotic voltage-gate channels play a crucial role on physiological adaptations to alkaline and hyper alkaline
42
Sharon L. Ruiz Lopez PhD Thesis environments. Na+/H+ antiporters catalyse accumulation coupled to Na+ efflux to maintain the internal pH below the external medium (Krulwich, T., 1995); a Na+ channel also provides an alternative for Na+ re-entry route to maintain the pH homeostasis (Krulwich, 2001); a Na+- coupled solutes control the required Na+ concentration for antiporter function and specifically for Bacillus the Na+-translocating Mot channel energizes flagellar rotation required for motility
(Ito et al., 2004).
Additionally, bacteria contain physiological features that help them to obtain nutrients from the surrounding environment; for instance studies have shown that bacteria can excrete extracellular polysaccharides, creating a matrix that acts as diffusion barrier that allows nutrients from the water to reach bacterial cells (Cooksey, 1992; Kulakov, McAlister, Ogden,
Larkin, & O’Hanlon, 2002). Other example is biofilm formation that plays a role for protection from external stimuli (McFeters, Broadaway, Pyle, & Egozy, 1993). Biofilms are constituted by several layers that present accumulation of dead cells which can also be used as carbon source for successive generations of bacteria, a phenomenon called cryptic growth (Kulakov et al., 2002; Roszak & Colwell, 1987).
On low-nutrient content systems, a variant photosynthetic electron flow has been suggested
(Morel & Price, 2003); findings showed that members of Cyanobacteria may be able to route
+ electrons derived from the splitting of H2O to the reduction of O2 and H in a water-to-water cycle to satisfy their energetic and nutritive requirements (Grossman, Mackey, & Bailey,
2010).
Exposure to radiation is a key factor that delimits microbial survival. Organisms living in extreme niches such as radioactive sites have evolved wide range of biochemical and physiological features to survive to challenging environments (Merino et al., 2019). Radiation affects cellular biomolecules, including proteins, lipids and nucleic acids directly (e.g. ionizing particles interact with purine/pyrimidine base) or indirectly (e.g. formation of reactive oxygen species, ROS, through radiolysis of water) (Jung, Lim, & Bahn, 2017).
It has been studied that radiation can propitiate the radiolysis of water hence the production
- of molecular hydrogen, peroxide hydrogen and other radicals (OH•, O2 •) (Libert, Bildstein,
Esnault, Jullien, & Sellier, 2011). In such environments hydrogen can be an important electron
43
Sharon L. Ruiz Lopez PhD Thesis and energy source for bacterial growth (Galès et al., 2004; Libert et al., 2011; Pedersen,
2000). The cellular respiration process uses oxygen, nitrate or sulphate to break down nutrients to generate cell’s energy. Since molecular hydrogen can be produced as result of anaerobic decomposition of organic material, it can be used a substrate for cellular respiration
(Brazelton, Nelson, & Schrenk, 2012). On bacterial metabolism hydrogen respiration can
+ occur whether through the oxidation of H2 to H releasing electrons that are channelled to the
+ respiratory electron transport chain or as the reduction of H to H2 in the terminal reaction of an anaerobic electron transport system, both reactions are mediated by hydrogenases enzymes (Vignais, 2004).
Several chemolithoautothrophic microorganisms can oxidize hydrogen, including species from phyla Proteobacteria (Azotobacter, Escherichia coli), Actinobacteria and Cyanobacteria
(Bothe, Distler, & Eisbrenner, 1978). In hydrogen-metabolic bacteria hydrogenases are membrane-bound enzymes responsible for the initial oxidation on the inorganic substrate, hydrogen, and are directly connected to the respiratory chain where the generation of ATP molecules initiates (Hernsdorf et al., 2017).
Radiation exposure also has a dramatic effect on cellular DNA. Since DNA is a permanent copy of the cell genome, alterations in its structure are of much greater consequence on other cell components such as RNAs or proteins (Byrne et al., 2014). Alterations may be effect of the incorporation of incorrect bases during DNA replication, for exposure to chemicals or radiation, or can even occur spontaneously. Damaged DNA can block replication or transcription which leads to mutation and finally affects cell reproduction (Cooper, 2000).
Damages on DNA can lead to alterations in base sequence as result of replication and recombination that may affect the function of survival of microbial cells. In order to cope with
DNA alterations a number of repair systems have evolved including direct damage reversal, nucleotide excision repair and recombinational repair; each repair system is specialized in the repair on certain types of damage (Truglio, Croteau, Van Houten, & Kisker, 2006).
Besides the well-known DNA repair strategies, the clustered regularly interspaced short palindromic repeats (CRISPR) and accompanying Cas proteins represent a relatively new studied adaptive immunity microbial feature (Reeks, Naismith, & White, 2013). CRISPR-Cas
44
Sharon L. Ruiz Lopez PhD Thesis are DNA-encoded, RNA-mediated defence system that provide sequence-specific recognition, targeting and degradation of exogenous nucleic acid (Barrangou, 2015). Initial insights suggested that the CRISPR-Cas function was mainly for antiviral defence; however recent studies have revealed that it also plays critical roles beyond immunity such as endogenous transcriptional control and regulation of bacterial phenotypes to help to adapt to the surrounding environment (Barrangou, 2015; Sorek, Lawrence, & Wiedenheft, 2013).
Although the details of immune response are unclear, several studies have shown that the
CRISPR-Cas system genes are induced in bacterial and archaeal organisms in response to external abiotic stimuli such as UV light and ionizing radiation (Götz et al., 2007; Sorek et al.,
2013) and in response to internal cellular stress (e.g. oxidative stress) (Sorek et al., 2013;
Strand et al., 2010). The presence of CRISPRs has been noted even on non-stress conditions, which implies the system is able to provide a rapid response and consequently defence against genetic alterations (Hale et al., 2012; Juranek et al., 2012).
Studies have shown that defence and repair mechanisms CRISPRs, RMs and BER are widely distributed on members affiliated to phyla Proteobacteria, Actinobacteria, Bacteroidetes and less abundant on Cyanobacteria. The presence of repair and defence mechanisms represents an evolutionary long-standing adaptation process microbial cells developed to cope with foreign DNA and endogenous alterations caused by external factors (Horn et al., 2016).
Development of omic tools has provided new insights into microbial interactions with the environment and also has contributed to understand effect of parasites on microbial communities: virus. Viruses are the most abundant biological entities on the planet and have shown to be a driving factor of microbial evolution and can influence biogeochemical cycles
(Berg Miller et al., 2012; Breitbart & Rohwer, 2005; Fierer et al., 2007; Parsley et al., 2010;
Rodriguez-Brito et al., 2010).
Viruses that parasite bacteria, Bacteriophages (phages), can impact the microbial ecology; phages can lead to dramatic lytic infections or genetic modification by lysogenic disturbances
(Allen and Abedon 2013). In addition, viruses are able to move genetic material between different hosts and ecosystems (e.g. photosynthetic genes on cyanobacteria and microalgae
(Lindell et al., 2004; Rohwer, Prangishvili, & Lindell, 2009) leading to changes in abiotic
45
Sharon L. Ruiz Lopez PhD Thesis conditions (Allen & Abedon, 2013). Furthermore, viruses play roles in controlling the cellular numbers by facilitating horizontal gene transfer (HGT, the transfer of genetic material from an organism to another that is not its offspring) (Aminov, 2011; Berg Miller et al., 2012; Breitbart
& Rohwer, 2005) altering the bacterial phenotypes and by selecting phage-resistant microbes
(Breitbart & Rohwer, 2005).
The analysis of high-abundance phage could play important roles in infecting bacteria and modulating microbial community dynamics (Rohwer et al., 2009).
46
Sharon L. Ruiz Lopez PhD Thesis
References Adam, C., & Garnier-Laplace, J. (2003). Bioaccumulation of silver-110m, cobalt-60, cesium- 137, and manganese-54 by the freshwater algae Scenedesmus obliquus and Cyclotella meneghiana and by suspended matter collected during a summer bloom event. Limnology and Oceanography, 48(6), 2303–2313. https://doi.org/10.4319/lo.2003.48.6.2303
Allen, H. K., & Abedon, S. T. (2013). That’s disturbing! An exploration of the bacteriophage biology of change. Frontiers in Microbiology, 4(295).
Aminov, R. I. (2011). Horizontal gene exchange in environmental microbiota. Frontiers in Microbiology, 2(JULY), 1–19. https://doi.org/10.3389/fmicb.2011.00158
Anderson, R. T., & Lovley, D. R. (2002). Chapter 7 Microbial redox interactions with uranium: an environmental perspective. In Radioactivity in the Environment (Vol. 2, pp. 205–223). https://doi.org/10.1016/S1569-4860(02)80036-5
Bagwell, C. E., Noble, P. A., Milliken, C. E., Li, D., & Kaplan, D. I. (2018). Amplicon sequencing
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Thorpe, C. L., Morris, K., Boothman, C., & Lloyd, J. R. (2012). Alkaline Fe(III) reduction by a novel alkali-tolerant Serratia sp. isolated from surface sediments close to Sellafield nuclear facility, UK. FEMS Microbiology Letters, 327(2), 87–92. https://doi.org/10.1111/j.1574-6968.2011.02455.x
Tierney, K. M., Muir, G. K. P., Cook, G. T., MacKinnon, G., Howe, J. A., Heymans, J. J., & Xu, S. (2016). Accumulation of Sellafield-derived radiocarbon (14C) in Irish Sea and West
of Scotland intertidal shells and sediments. Journal of Environmental Radioactivity, 151, 321–327. https://doi.org/10.1016/j.jenvrad.2015.10.029
Tišáková, L., Pipíška, M., Godány, A., Horník, M., Vidová, B., & Augustín, J. (2013).
Bioaccumulation of 137Cs and 60Co by bacteria isolated from spent nuclear fuel pools. Journal of Radioanalytical and Nuclear Chemistry, 295(1), 737–748. https://doi.org/10.1007/s10967-012-1932-6
Truglio, J. J., Croteau, D. L., Van Houten, B., & Kisker, C. (2006). Prokaryotic Nucleotide Excision Repair: The UvrABC System. Chemical Reviews, 106(2), 233–252.
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Vignais, P. M. (2004). Hydrogen Respiration. In D. Zannoni (Ed.), Respiration in Archaea and Bacteria. Diversity of Prokaryotic Respiratory Systems (pp. 279–295).
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WNA. (2016). History of Nuclear Energy. Retrieved April 2, 2016, from http://www.world- nuclear.org/information-library/current-and-future-generation/outline-history-of-nuclear-
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energy.aspx
WNA, W. N. A. (2015). The Nuclear Fuel Cycle. Retrieved December 8, 2015, from https://www.world-nuclear.org/information-library/nuclear-fuel- cycle/introduction/nuclear-fuel-cycle-overview.aspx
WNA, W. N. A. (2017). The Nuclear Fuel Cycle. Retrieved March 12, 2018, from https://www.world-nuclear.org/information-library/nuclear-fuel- cycle/introduction/nuclear-fuel-cycle-overview.aspx
WNA, W. N. A. (2018). Radioactive Waste Management. Retrieved May 25, 2019, from http://www.world-nuclear.org/information-library/nuclear-fuel-cycle/nuclear- wastes/radioactive-waste-
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Methodology
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Chapter 3 Methodology
The study of microbial ecology grants a better understanding of the microorganisms in their natural habitat and their interactions with other microorganisms, host microorganisms and with their physicochemical environment (Relman et al. 2009).
The importance of microbial ecology rests in the fact that microbes are responsible for cycling nutrients in the environment, creating symbiotic relationships, providing energy (even in absence of light) and adapting to extreme habitats (Gray and Head 2008). In this chapter microbial ecology techniques used for this project are explained
3.1 Culturing techniques
The ability to culture microorganisms is important because culture-dependent techniques can target some of the active components of a microbial community, yielding quantitative data and model organisms needed for pure culture studies in laboratory experiments.
Culture media provide the chemicals and substrates that fulfill the growth requirements of the organisms being cultured. Culturing media can be classified on the basis of consistency in solid medium, semisolid media and liquid (broth) medium) (Acharya 2010;Tiwari et al. 2009).
Based on the basis of composition, culture media can be classified in:
• Synthetic or chemically defined medium; a chemically defined medium prepared from
purified ingredients and therefore the exact composition is known (Madigan et al.
2003).
• Non synthetic or chemically undefined medium; contains at least one component that
is neither purified nor completely characterized nor even completely consistent from
batch to batch (Madigan et al. 2003).
Media can be solid, often referred as plates or liquid (broth). The aims for culturing media are to identify, isolate, characterize and study physiological microbial characteristics (Tiwari et al.
2009).
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Based in their functional usages, media are classified as:
• General purpose/basic media. Basal media are simple media that supports most non
-fastidious bacteria such as nutrient agar. They are generally used for primary
isolation of microorganisms (Acharya 2010;Tiwari et al. 2009).
• Enriched media. These are rich in nutrients, ideal for most of the organisms. They
often contains blood, haemolysed blood, egg yolk, serum and ascetic fluid as
additional supplement to the basal medium (Tiwari et al. 2009). Examples of enriched
media are blood agar, chocolate agar, etc (Acharya 2010).
• Selective and enrichment media: The medium composition is designed to inhibit
unwanted or contaminating bacteria by adding appropriate chemicals in order to grow
a particular group of organisms (Tiwari et al. 2009). Any agar media can be made
selective by the addition of certain inhibitory agents that do not affect the targeted
organisms (Tiwari et al. 2009), or by including chemicals that selectively support the
growth of target organisms. Examples of selective media are Mannitol Salt Agar and
MacConkey’s Agar (Acharya 2010).
• Differential medium: The purpose of this medium is to support the growth of target
organisms and make them easily recognized on the basis of their colony colour
(Acharya 2010;Madigan et al. 2003;Tiwari et al. 2009). Examples of differential media
include Mannitol salts agar (mannitol fermentation is yellow), Mac Conkey agar
(lactose fermenters are pink colonies), etc (Madigan et al. 2003).
3.2 Molecular biology techniques
Although conventional methods have proved useful for identification and characterization of microorganisms, those methods present certain limitations on the study of natural or engineered environments. For instance the proportion of cells which can be cultured is estimated to be between 0.1 and 10% of the total population, providing insufficient data concerning the composition of bacterial communities (Ranjard et al. 2000).
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Molecular biology is defined as the study of the molecular basis of composition, structure and interactions of biological activity including DNA, RNA and protein synthesis (Sanz and
Köchling 2007) (Vitale 2017). Since genomes comprise all the DNA from an organism and carry all the information needed to specify the structure of every protein produced by cells, their study represents greater understanding of molecular processes in health and disease
(Rapley 2010).
Molecular techniques developed during the 1980s and 1990s represented a milestone in the microbial ecology field (Howe 2018) and contributed to develop ambitious projects such as the Human Genome Project (HGP) (NIH 2018). The continuous development and improvement of molecular techniques allow us to understand the basic structure of nucleic acids and to gain appreciation of how this dictates the cellular responses to external stimuli
(Rapley 2010).
Molecular biology techniques have also become powerful analytical tools in biotechnology, genome mapping, microbial ecology, and medicine and gene therapy. Nowadays molecular biology techniques are widely used in environmental studies involving DNA extraction, polymerase chain reaction amplification using universal primers for bacterial genes coding for
16S rRNA and DNA sequencing of targeted genes or whole genomes (Wouters et al. 2013).
The isolation of genomic DNA from microorganisms has become a useful tool to reveal the genotypic diversity and the change in microbial ecosystems (Mesapogu et al. 2013).
3.2.1 DNA extraction
Deoxyribonucleic acid (DNA) is composed of polymers of four deoxynucleotides (thymine, cytosine, adenine and guanine). Those nucleotides are composed by a heterocyclic base, a sugar and a phosphate groups. Replication of DNA is the normal process of doubling the DNA content of cells prior to cell division. The process of DNA replication involves multiple enzymatic activities leading to a complement of the parental cell (King 2007).
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The purpose of DNA extraction is to obtain DNA in relatively purified form which can be used for further investigations such as PCR or sequencing. Most DNA protocols consist of two parts
(Biotech 2009):
1. A technique to lyse the cells gently and solubilize the DNA
2. Enzymatic or chemical methods to remove contaminating proteins, RNA or any other
macromolecules
The first step is lysis, in this step the cell wall is disrupted by mechanical force and a detergent breaks down the cell membrane. Next step is the precipitation, where the DNA is separated from the rest of the cell components by addition of salts, solvent and by spinning in a centrifuge. Then washing occurs by ethanol to remove salts and other water soluble impurities, and finally the resuspension to clean the DNA in a buffer solution to ensure stability and long term storage. To confirm the presence of DNA absorbance can be measured. Alternatively, gel electrophoresis is also used to corroborate the presence and quality of DNA (Biotech 2009)
(Mesapogu et al. 2013).
3.2.2 Polymerase Chain Reaction (PCR)
PCR was developed by Kary Mullis in 1983 and it has been useful in simplifying and accelerating molecular biology. PCR is an enzymatic reaction that allows amplification of DNA through a repetitive process. During each cycle of PCR, any DNA that is present in the reaction is copied. During the process, the amount of DNA doubles during each cycle. Approximately
25 to 30 cycles result in about 106 fold increase in the amount of DNA present. Targeted amplification of DNA increases the sensitivity of detection of sequences present even in trace amounts (Dowd and Pepper 2007).
The stages involved in the PCR process begin when the DNA double helix strands are separated, this process is called denaturation and it is achieved by raising the temperature of the DNA solution. This causes the hydrogen bonds between the complementary DNA chains to break, and the two strands to separate (Biotech 2007) (NCBI 2014).
In the next step, the temperature is lowered and the enzyme Taq polymerase joins free DNA nucleotides together. This nucleotides order is determined by the original DNA strand that is
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Sharon L. Ruiz Lopez PhD Thesis being copied. The result is a double stranded DNA molecule that contains one new strand and an original one (Biotech 2007). Figure 3.1 shows the summary of PCR.
Figure 3.1 Summary of PCR (NCBI 2014).
3.2.3 Real Time PCR (qPCR)
Real time PCR or quantitative PCR is a variation of the standard PCR used to determine the amount of PCR products in a sample (Frąc et al. 2015).
The quantification of amplified samples is obtained by using fluorescent probes and it is based in the detection of fluorescence produced by a specific molecule, which increases as the
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Sharon L. Ruiz Lopez PhD Thesis reaction proceeds; this increase occurs due to the accumulation of the PCR product during each cycle of amplification (Praveen and Koundal 2013) (Maddocks and Jenkins 2017). Real time PCR is the conversion of fluorescent signal, often provided when the molecular dye
SYBR Green binds to the double stranded DNA or the sequence specific probes (Figure 3.2) from one or more polymerase chain reaction over a range of cycles into a numerical value for the sample (Shipley 2006) (Jia 2012).
Figure 3.2 Illustration of dye SYBER Green binding to a double stranded DNA (Praveen and Koundal 2013)
The advantages of real-time PCR include the ability to monitor the PCR reaction progress in real time, the ability to measure the amount of amplicon at each cycle, then the initial material can also be quantified, and the amplification and detection occurs in a single tube, avoiding further manipulations (Fairfax and Slimnia 2010).
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3.2.4 DNA sequencing: Sanger sequencing
DNA sequencing is defined as the process to determine the sequence of nucleotide bases
(Adenine, Thymine, Cytosine and Guanine) in a DNA fragment. Improvement and optimization of new DNA sequencing methods have contributed to major advances in biological, medical and biotechnological research (NIH 2015).
In Sanger sequencing (also known as chain termination method), the target DNA is copied many times, making fragments of different lengths (Sanger and Coulson 1975) (Sanger et al.
1977). Fluorescent chain terminator nucleotides mark the end of the fragments and the sequence can be determined (Sanger et al. 1977).
Sanger sequencing starts when the DNA sample is mixed with the primer, DNA polymerase and the DNA nucleotides (dATP, dTTP, dGTP and dCTP) (Zhou and Li 2015). The four dye- labeled, chain-terminating dideoxy nucleotides are added as well but in smaller concentrations than the ordinary nucleotides (Scitable 2019).
The mixture is initially heated to denature the DNA and then cooled so the primer binds to the single stranded template. Once the primer has bound, the temperature raises again to allow the DNA polymerase to synthetize new DNA starting from the primer. This process will repeat until a dideoxy nucleotide is added instead of a normal one. The process is repeated until the cycle is complete meaning that a dideoxy nucleotide will be incorporated at every single position of the target DNA in at least one reaction (Merck 2019) (Zhou and Li 2015).
When the reaction is finished, the fragments are analysed on a process called capillary gel electrophoresis where the dyes attached to DNA fragments will be read by a laser (Merck
2019). Figure 3.3 shows the general description of Sanger sequencing technique.
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Figure 3.3 Sanger sequencing technique (Zhou and Li 2015)
Overall, Sanger sequencing gives high-quality for relatively longs stretches of DNA and represents a useful tool to sequence individual pieces of DNA such as bacterial plasmids or
DNA copied in PCR (Hagemann 2015).
3.2.5 Next-generation DNA Sequencing: Illumina sequencing
Next-generation sequencing (NGS), also known as high-throughput sequencing, is a term that incorporates modern DNA and RNA sequencing technologies such as Illumina sequencing,
Roche 454 sequencing and Ion Torrent: Proton (PGM) sequencing (EMBL-EBI 2019)
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(Shendure and Ji 2008). Most next-generation sequencing techniques are distinguished for being highly parallel, micro scale, fast, low-cost and create shorter length (range between 50-
700 nt) (Shendure and Ji 2008).
Illumina sequencing (also named Illumina/Solexa) is a “sequencing-by-synthesis” technology developed by Shankar Balasubramanian and David Klenerman in 1998. llumina sequencing method is based on the incorporation of reversible dye terminators that allow the identification of single bases as they are incorporated into DNA strands (Zhou and Li 2015).
Illumina sequencing works similar to Sanger sequencing, but it uses modified dNTPs containing a terminator that blocks further polymerisation, therefore solely a single base can be added by a polymerase enzyme to each growing DNA copy strand (Singh and Kumari
2014). The sequencing reaction occurs simultaneously at different template molecules spread on a solid surface (Mardis 2013).
The main steps are library preparation, cluster generation, sequencing and data analysis
(Figure 3.4) (Illumina 2013) (Mardis 2013). The process begins when the purified DNA is chopped up into smaller pieces and certain molecular modifications act as reference points during amplification, sequencing and analysis. Then, the modified DNA is loaded onto a specialized chip, composed by hundreds of thousands of oligonucleotides, where amplification and sequencing are carried out. The oligonucleotides grab the DNA fragments that have complementary sequences. Once the fragments have attached, about a thousand copies of each fragment of DNA is made, this step is called cluster generation. Then, primers and modified nucleotides enter the chip; these have reversible 3’ blockers that force the primers to add on only one nucleotide at a time as well as fluorescent tags. The fluorescent wavelength is determined for every spot in the chip. The process continues until the genome is fully sequenced (Illumina, 2010) (YG 2015) (Singh and Kumari 2014).
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Figure 3.4 Overview of NGS sequencing by Illumina technology: a)Library-construction process, b)Cluster generation by bridge amplification and c)Sequencing by synthesis with reversible dye terminators (Mardis 2013)
Over the past years massively parallel DNA sequencing technologies have become extensively available for generating sequence libraries, evolving new data analysis and developing new experimental design (Shendure and Ji 2008).
3.2.5 Metagenomics
As previously mentioned, genomics reveal a general phylogenetic description including insights into genetics, physiology and biochemistry of the microbial diversity. Recently innovative metagenomics tools have been developed in order to facilitate the study of the physiology and ecology of environmental organisms and their response to external stimuli, antimicrobial activity, nutrient cycling, gene function and gene transfer within communities
(Handelsman 2004).
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Metagenomics, also known as environmental and community genomics, is defined as the genomic analysis of microorganisms by direct extraction and cloning of DNA from an assemblage of microorganisms (Handelsman 2004). Metagenomics provides an unbiased display of the community structure (species richness and distribution) and the functional
(metabolic) potential (Hugenholtz and Tyson 2008).
As research technique, metagenomics involves a series of tools to examine thousands of organisms in parallel providing insight into community diversity and function even for organisms with low abundance (Thomas et al. 2012).
The main steps of the metagenomics workflow are DNA extraction, library preparation, sequencing, assembly, binning, annotation and statistical analysis (shown in Figure 3.5):
• Sample extraction is the most critical step in metagenomics analysis. The extracted
DNA should be representative of the site of interest and extraction must yield sufficient
amounts of high-quality nucleic acids for subsequent library preparation and
sequencing (Thomas et al. 2012).
• Library preparation: overall the process is standardized to manipulate the DNA
sample by fragmentation, end repair and adaptor ligation, size fractionation and
amplification (Solonenko and Sullivan 2013)
• Sequencing technologies offer a wide variety of read lengths and outputs depending
on the applied technology. For instance Illumina sequencing offers short reads (2x250
or 2x300 bp) but generates high sequencing depth; whereas Oxford Nanopore offers
lower sequencing depth (Solonenko and Sullivan 2013).
• Assembly involves the merging of reads from the same genome into a single
sequence (contigs) and orientation of these into scaffolds (Thomas et al. 2012).
Assembling of shorter reads into contigs occurs by two different routes:
§ Referenced-based assembly uses one or more reference genomes as a map to
create contigs which can represent genomes or part of genomes belonging to
specific species or genus (Oulas et al. 2015).
§ De novo assembly generates assembled contigs using no prior reference to
known genomes, this step requires heavily and sophisticated graph theory
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algorithms such as de-Brujin graphs (Oulas et al. 2015) (Paszkiewicz and
Studholme 2010).
• Binning is the process of grouping reads or contigs into highly similar groups and
assigning them to groups of specific species, subspecies or genera (Ghosh et al.
2019). Binning can use two types of algorithms:
§ Composition-based binning is based on the observation that individual
genomes have a unique distribution of k-mer sequences. The algorithm uses
the conserved species-specific nucleotide composition to group the
sequences into their respective genomes (Oulas et al. 2015).
§ Similarity-or homology-based binning uses alignment algorithms such as
BLAST or profile hidden Markov models (pHMMs) to obtain information about
specific sequences/genes from publically databases (eg NCBI) (Oulas et al.
2015)
• Annotation is the prediction of CDS (coding DNA sequences) followed by functional
assignment using similarity based searches of query sequences against known
functional and/or taxonomic information (Ghosh et al. 2019). A series of steps are
necessary to prepare the reads for annotation including:
o Trimming of low quality reads
o Masking of low-complexity reads
o De-replication step that removes sequences that are not 95% identical
o Screening step to remove reads that are near-exact matches to the genomes
of handful model organisms
o Identification of genes within the reads/assembled contigs (gene calling
process). Genes are labelled as coding DNA sequences (CDSs) and non-
coding RNA genes whereas some tools also predict for regulatory elements
such as clustered regularly interspaced palindromic repeats (CRISPRs)
o Functional assignment to the predicted protein coding genes achieved by
homology-based searches of query sequences against databases containing
known functional and/or taxonomic information
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• Statistical analysis: Several software packages perform statistical analysis of
metagenomic data presenting results on alpha diversity (diversity within the sample)
and beta diversity (diversity across samples), taxonomic composition and
phylogenetic analysis.
Figure 3.5 Metagenomics workflow. After extraction, DNA is analysed using paired-ends reads to maximise coverage of the amplicons and the reads and assembled into contigs.
Examples of Software packages used for these steps are mentioned in table 3.1.
Table 3.1 Examples of metagenomics software tools Category Tools References Assembly MEGAHIT (Li et al. 2015) MetaVelvet (de novo) (Namiki et al. 2012) Omega (Haider et al. 2014) metaSPAdes (Nurk et al. 2017) MetAMOS (referenced-based) (Treangen et al. 2013) Binning CONCOT (Alneberg et al. 2014) MG-RAST (similarity-based) (Meyer et al. 2008) MEGAN (similarity-based) (Huson and Weber 2013) MetaCluster (both algorithms) (Wang et al. 2014) CARMA (similarity-based) (Krause et al. 2008) MetaPhyler (similarity-based) (Liu et al. 2011) TETRA (composition-based) (Teeling et al. 2004) PhyloPythiaS (compositon-based) (Patil et al. 2012)
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Annotation Trimming FastQC (Huson and Weber 2013) SolexaQA (Cox et al. 2010) Masking step DUST (Morgulis et al. 2006) Screening step Bowtie 2 (Langmead and Salzberg Gene Calling 2012) Prodigal FragGeneScan (Hyatt et al. 2010b) Databases for Functional and (Rho et al. 2010) Taxonomic Annotation SILVA KEGG (Quast et al. 2013) SEED (Ogata et al. 1999) eggNOG (Overbeek et al. 2005) COG/KOG (Powell et al. 2014) Display of taxonomic (Tatusov et al. 2000) information Prokka (Seemann 2014) Annotation MG-RAST (Meyer et al. 2008) pipelines EBI-Metagenomics (MGnify) (Mitchell et al. 2018) IMG/MER (Chen et al. 2017)
OTU Clustering QIIME (Caporaso et al. 2010b) Mothur (Schloss et al. 2009) Statistical analysis QIIME (Caporaso et al. 2010b) MEGAN (Huson and Weber 2013) Primer-E Package (Clarke and Gorley 2015) R programming language: Vegan (Oksanen et al. 2007) Phyloseq (McMurdie and Holmes 2013) Bioconductor (Gentleman et al. 2004)
Metagenomics has had a dramatic effect on application on different fields such as bioremediation (Yergeau et al. 2012;Techtmann and Hazen 2016;Paul et al. 2005), industrial bioproducts (Lorenz and Eck 2005), plant-microbe interactions (Kaul et al. 2016) (Knief 2014) and human microbiome (Turnbaugh et al. 2007) (Abubucker et al. 2012).
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One of the approaches of metagenomics is their application on the study of viruses.
Metagenomic investigations provide insights to the central role of viruses in microbial evolution and ecology (Hugenholtz and Tyson 2008). Figure 3.6 shows the common workflow for viral and phage identification via VirMiner server (Zheng et al. 2019).
Figure 3.6 Metagenomic viral identification pipeline. The workflow describes the main steps for phage identification and gene prediction (Zheng et al. 2019)
Despite their importance, identification of phages and their interactions with the microbiome is limited due to the difficulties for virus isolation and purification (Zheng et al. 2019;Roux et al. 2015b); the lack of a universal marker gene for viruses; the limited available databases; and the restricted availability of bioinformatics tools, mostly suitable for prokaryotic genome sequencing data and not designed for metagenomic data (Roux et al. 2015a). Next-
Generation sequencing tools such as metagenomics has created a wider panorama of virus abundances providing an insight of the host-bacteria interactions and their influence on the microbial ecology.
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3.4 References
Abubucker, S., Segata, N., Goll, J., Schubert, A. M., Izard, J., Cantarel, B. L., Rodriguez- Mueller, B., Zucker, J., Thiagarajan, M., Henrissat, B., White, O., Kelley, S. T., Methe, B., Schloss, P. D., Gevers, D., Mitreva, M. & Huttenhower, C. (2012). Metabolic reconstruction for metagenomic data and its application to the human microbiome. PLoS Comput Biol, 8(6), e1002358. Alneberg, J., Bjarnason, B. S., de Bruijn, I., Schirmer, M., Quick, J., Ijaz, U. Z., Lahti, L., Loman, N. J., Andersson, A. F. & Quince, C. (2014). Binning metagenomic contigs by coverage and composition. Nat Methods, 11(11), 1144-6. Biotech. (2007). Polymerase Chain Reaction (PCR) [Online]. New Zealand. Available: http://biotechlearn.org.nz/themes/dna_lab/polymerase_chain_reaction_pcr [Accessed 21st June 2016]. Biotech. (2009). DNA Extraction [Online]. New Zealand Available: http://biotechlearn.org.nz/themes/dna_lab/dna_extraction#wrapper [Accessed 20th June 2016]. Caporaso, J. G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F. D., Costello, E. K., Fierer, N., Peña, A. G., Goodrich, J. K., Gordon, J. I., Huttley, G. A., Kelley, S. T., Knights, D., Koening, J. E., Ley, R. E., Lozupone, C. A., McDonald, D., Muegge, B. D., Pirrung, M., Reeder, J., Sevinsky, J. R., Turnbaugh, P. J., Walters, W. A., Widmann, J., Yatsunenko, T., Zaneveld, J. & Knight, R. (2010). QIIME allows analysis of high-throughput community sequencing data. Nature Methods, 7, 335-336. Chen, I. A., Markowitz, V. M., Chu, K., Palaniappan, K., Szeto, E., Pillay, M., Ratner, A., Huang, J., Andersen, E., Huntemann, M., Varghese, N., Hadjithomas, M., Tennessen, K., Nielsen, T., Ivanova, N. N. & Kyrpides, N. C. (2017). IMG/M: integrated genome and metagenome comparative data analysis system. Nucleic Acids Res, 45(D1), D507-D516. Clarke, K. R. & Gorley, R. N. (2015). Getting started with PRIMER v7. Devon, United Kingdom: PRIMER-E Plymouth. Cox, M. P., Peterson, D. A. & Biggs, P. J. (2010). SolexaQA: At-a-glance quality assessment of Illumina second-generation sequencing data. BMC Bioinformatics, 11, 485. Dowd, S. E. & Pepper, I. L. (2007). PCR: Agricultural and Environmental Applications for Soil Microbes. In: Press, A. (ed.) Manual of Environmental Microbiology. USA. EMBL-EBI. (2019). What is Next-Generation DNA sequencing? [Online]. Hinxton, Cambridgeshire, UK: EMBL-EBI, Wellcome Genome Campus. Available: https://www.ebi.ac.uk/training/online/course/ebi-next-generation-sequencing- practical-course/what-you-will-learn/what-next-generation-dna- [Accessed]. Fairfax, M. R. & Slimnia, H. (2010). Quantitative PCR: An Introduction. In: Nakamura, R. M., Kiechle, F., Grody, W. & Strom, C. (eds.) Molecular Diagnostics. Academic Press. Gentleman, R. C., Carey, V. J., Bates, D. M., Bolstad, B., Dettling, M., Dudoit, S., Ellis, B., Gautier, L., Ge, Y., Gentry, J., Hornik, K., Hothorn, T., Huber, W., Iacus, S., Irizarry,
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S., Glass, E. M., Goesmann, A., Hanson, A., Iwata-Reuyl, D., Jensen, R., Jamshidi, N., Krause, L., Kubal, M., Larsen, N., Linke, B., McHardy, A. C., Meyer, F., Neuweger, H., Olsen, G., Olson, R., Osterman, A., Portnoy, V., Pusch, G. D., Rodionov, D. A., Ruckert, C., Steiner, J., Stevens, R., Thiele, I., Vassieva, O., Ye, Y., Zagnitko, O. & Vonstein, V. (2005). The subsystems approach to genome annotation and its use in the project to annotate 1000 genomes. Nucleic Acids Res, 33(17), 5691-702. Paszkiewicz, K. & Studholme, D. J. (2010). De novo assembly of short sequence reads. Brief Bioinform, 11(5), 457-72. Patil, K. R., Roune, L. & McHardy, A. C. (2012). The PhyloPythiaS web server for taxonomic assignment of metagenome sequences. PLoS One, 7(6), e38581. Paul, D., Pandey, G., Pandey, J. & Jain, R. K. (2005). Accessing microbial diversity for bioremediation and environmental restoration. Trends Biotechnol, 23(3), 135-42. Powell, S., Forslund, K., Szklarczyk, D., Trachana, K., Roth, A., Huerta-Cepas, J., Gabaldon, T., Rattei, T., Creevey, C., Kuhn, M., Jensen, L. J., von Mering, C. & Bork, P. (2014). eggNOG v4.0: nested orthology inference across 3686 organisms. Nucleic Acids Res, 42(Database issue), D231-9. Praveen, S. & Koundal, V. (2013). Fluorescent-Based Detection, Quantitation, and Expression of Viral Gene by qRT-PCR. In: Kumar, A. D., Das, S. & Sukumar, M. (eds.) Analyzing microbes. Manual of Molecular Biology Techniques. Berlin. Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., Peplies, J. & Glockner, F. O. (2013). The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res, 41(Database issue), D590-6. Ranjard, L., Poly, F. & Nazaret, S. (2000). Monitoring complex bacterial communities using culture-independent molecular techniques: application to soil environment. Res Microbiol, 151, 167-177. Rapley, R. (2010). Molecular biology, bioinformatics and basic techniques. In: Wilson, K. & Walker, J. M. (eds.) Principles and Techniques of Biochemistry and Molecular Biology. 7th Edition ed. Cambridge, UK: Cambridge University Press. Relman, D. A., Hamburg, M. A., Choffnes, E. R. & Mack, A. (2009). Microbial evolution and co-adaptation. A tirbute to the life and scientific legacies of Joshua Lederberg. Forum on Microbial Threats. Washinton, DC, USA: The National Academies Press. Rho, M., Tang, H. & Ye, Y. (2010). FragGeneScan: predicting genes in short and error-prone reads. Nucleic Acids Res, 38(20), e191. Sanger, F. & Coulson, A. R. (1975). A Rapid Method for Determining Sequences in DNA by Primed Synthesis with DNA Polymerase. J. Mol. Biol., 94, 441-448. Sanger, F., Nicklen, S. & Coulson, A. R. (1977). DNA sequencing with chain-terminating inhibitors. Proc Natl Acad Sci U S A, 74(12), 5463-5467. Sanz, J. L. & Köchling, T. (2007). Molecular biology techniques used in wastewater treatment: An overview. Process Biochemistry, 42(2), 119-133.
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Schloss, P. D., Westcott, S. L., Ryabin, T., Hall, J. R., Hartmann, M., Hollister, E. B., Lesniewski, R. A., Oakley, B. B., Parks, D. H., Robinson, C. J., Sahl, J. W., Stres, B., Thallinger, G. G., Van Horn, D. J. & Weber, C. F. (2009). Introducing mothur: open- source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol, 75(23), 7537-41. Scitable. (2019). DNA Sequencing [Online]. Available: https://www.nature.com/scitable/definition/dna-sequencing-205 [Accessed 12/07/2019]. Seemann, T. (2014). Prokka: rapid prokaryotic genome annotation. Bioinformatics, 30(14), 2068-9. Shendure, J. & Ji, H. (2008). Next-generation DNA sequencing. Nat Biotechnol, 26(10), 1135- 45. Shipley, G. L. (2006). An introduction to real-time PCR. In: Tevfik, D. M. (ed.) Real-time PCR. Singh, S. & Kumari, A. (2014). DNA Sequencing: Methods and Applications. In: Ravi, I., Baunthiyal, M. & Saxena, J. (eds.) Advances in Biotechnology. India: Springer. Solonenko, S. A. & Sullivan, M. B. (2013). Preparation of metagenomic libraries from naturally occurring marine viruses. Methods Enzymol, 531, 143-65. Tatusov, R. L., Galperin, M. Y., Natale, D. A. & Koonin, E. V. (2000). The COG database: a tool for genome-scale analysis of protein functions and evolution. Nucleic Acids Research, 28(1), 33-36. Teeling, H., Waldmann, J., Lombardot, T., Bauer, M. & Glockner, F. O. (2004). TETRA: a web- service and a stand-alone program for the analysis and comparison of tetranucleotide usage patterns in DNA sequences. BMC Bioinformatics, 5, 163. Thomas, T., Gilbert, J. & Meyer, F. (2012). Metagenomics - a guide from sampling to data analysis. Microb Inform Exp, 2(1), 3. Tiwari, R. P., Hoondal, G. S. & Tewari, R. (2009). Laboratory tecniques in Microbiology and Biotechnology Abishek Publications. Treangen, T. J., Koren, S., Sommer, D. D., Liu, B., Astrovskaya, I., Ondov, B., Darling, A. E., Phillippy, A. M. & Pop, M. (2013). MetAMOS: a modular and open source metagenomic assembly and analysis pipeline. Genome Biology, 14(R2). Turnbaugh, P. J., Ley, R. E., Hamady, M., Fraser-Liggett, C. M., Knight, R. & Gordon, J. I. (2007). The human microbiome project. Nature, 449(7164), 804-10. Vitale, I. (2017). Molecular Biology. Wang, Y., Ming, H. C., Yiu, S. M. & Chin, F. Y. L. (2014). MetaCluster-TA: taxonomic annotation formetagenomic data based on assembly-assistedbinning. BMC Genomics, 15(Suppl 1), 512. Wouters, K., Moors, H., Boven, P. & Leys, N. (2013). Evidence and characteristics of a diverse and metabolically active microbial community in deep subsurface clay borehole water. FEMS Microbiol Ecol, 86(3), 458-73.
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Yergeau, E., Sanschagrin, S., Beaumier, D. & Greer, C. W. (2012). Metagenomic analysis of the bioremediation of diesel-contaminated Canadian high arctic soils. PLoS One, 7(1), e30058. YG, Y. (2015). What is the Illumina method of DNA Sequencing? [Online]. Available: https://www.yourgenome.org/facts/what-is-the-illumina-method-of-dna-sequencing [Accessed 23/07/2019]. Zheng, T., Li, J., Ni, Y., Kang, K., Misiakou, M. A., Imamovic, L., Chow, B. K. C., Rode, A. A., Bytzer, P., Sommer, M. & Panagiotou, G. (2019). Mining, analyzing, and integrating viral signals from metagenomic data. Microbiome, 7(1), 42. Zhou, X. & Li, Y. (2015). Techniques for oral microbiology. In: Zhou, X. & Li, Y. (eds.) Techniques for Oral Microbiology, From Healthy Microflora to Disease.
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Research Paper: Identification of stable hydrogen- driven microbes in highly radioactive storage facilities in Sellafield, UK
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Chapter 4 Identification of stable hydrogen-driven microbes in highly radioactive storage facilities in Sellafield, UK
S. Ruiz-Lopez1, L. Foster1, C. Boothman1, N. Cole2, K. Morris1, J.R. Lloyd1
1 School of Earth and Environmental Sciences, University of Manchester Oxford Road, Manchester,
M13 9PL
2 Sellafield Ltd, Hinton House, Birchwood Park Ave, Birchwood, Warrington WA3 6GR
Corresponding author: [email protected]
Abstract
The use of nuclear power has been a significant part of the United Kingdom’s energy portfolio for more than 60 years, with the Sellafield site being used for power production and more recently reprocessing and decommissioning of spent nuclear fuel activities. Before being reprocessed, spent nuclear fuel is stored in water ponds with significant levels of background radioactivity, and in many cases high alkalinity (to minimise fuel corrosion). Despite these challenging conditions, the presence of microbial communities has been detected in these harsh storage environments. To gain further insight into the microbial communities present on extreme environments, an indoor, hyper-alkaline, oligotrophic and potential radioactive spent fuel storage pond (INP) located on Sellafield was analysed. Water samples were collected from sample points within the INP complex, and also the purge water feeding tank (FT) that supplies water to the pond, and were analysed by 16S and 18S rRNA gene sequencing over a period of thirty months. Only 16S rRNA genes were successfully amplified, suggesting that the microbial communities in INP and the feeding tank were dominated by prokaryotes.
Quantitative Polymerase Chain Reaction (QPCR) analysis targeting 16S rRNA genes suggested that in the order of 104-105 bacterial cells per ml were present in the samples, with higher loadings, rising with time, in the INP samples versus the feeding tank. Next generation
Illumina MiSeq sequencing was performed to identify the dominant microorganisms at eight sampling times.16S rDNA sequence analysis suggested that 70% and 97% of the OTUs, from the FT HT and INP samples respectively, belonged to the phylum Proteobacteria, mainly from the Alpha and Beta subclasses. The remaining OTUs were assigned primarily to the phyla
Acidobacteria, Bacteroidetes and Cyanobacteria. Greater phylogenetic diversity was observed in the HT samples; overall the most abundant genera identified were
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Hydrogenophaga, Curvibacter, Porphyrobacter, Rhodoferax, Polaromonas,
Sediminibacterium, Roseococcus and Sphingomonas. The presence of organisms most closely related to alkaliphilic Hydrogenophaga species, in the INP main ponds and subponds, suggests the metabolism of hydrogen as an energy source, possibly linked to hydrolysis of water caused by the stored fuel. Isolation of axenic cultures using a range of minimal and rich media was also attempted but only relatively minor components (from the genera
Algoriphagus and Aquiflexum) of the pond water communities were obtained. The identification of organisms revealed that despite the mentioned genera do not represent major components, the microbial members were able to adapt to a combination of challenging conditions such as oligotrophy, radioactivity and hyper-alkalinity. The results observed by culturing techniques emphasise the importance of DNA-based, not culture dependent techniques, for assessing the microbiome of nuclear facilities.
Introduction
Nuclear power supplies about 11% of the world’s electricity (WNA 2006), and with increasing global energy demands this seems unlikely to decline. Although considered a “low carbon” generating energy source, radioactive waste is produced, including spent fuels that need storage prior to reprocessing and final disposal (Deutch et al. 2009). In the UK, this task is performed at Sellafield, one of the largest and most complex nuclear sites in Europe. With over 1400 discrete operations, handling 240 nuclear materials, it is located in Cumbria on the
North West coast of England and has been operated by the Nuclear Decommissioning
Authority (NDA) since 2005 (Baldwin 2003) (WNA 2018a). Calder Hall, located on the site, was the world’s first commercial nuclear power station, and here energy was generated from
1956 to 2003. The Sellafield site also contains a range of storage ponds built during the 1950s which were intended to support the production of weapons grade plutonium, and more recently fuels from the UK’s fleet of nuclear power stations (Reddy et al. 2012) (WNA 2018b). This legacy of activities have left a complex range of nuclear operations at Sellafield, including the decommissioning of redundant facilities associated with the site’s early defence work, and spent fuel management including Magnox and Oxide fuel reprocessing (GOV UK 2018).
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Prior to reprocessing, all irradiated fuel delivered to Sellafield is stored for a period of at least
100 days in water-filled reinforced concrete ponds that allow the decay of short-lived radioisotopes. During storage, the degree of corrosion experienced by the fuel is monitored to determine storage life and optimise water chemistry (Shaw 1990). Temperature within the ponds is controlled by refrigerant chillers to further limit fuel corrosion, while the levels of both radioactive and no-radioactive ions in the pond waters are controlled by purging cycles of demineralised water adjusted to pH 11.1-11.6 with the addition of sodium hydroxide (Howden
1987). The main pre-reprocessing storage pond at the Sellafield site is the indoor alkaline storage pond (INP), a concrete wall pond filled with demineralised water, responsible for receiving, storing and mechanically processing spent nuclear fuel (SNF) from Magnox and
Advanced Gas-cooled Reactor (AGR) stations from across the UK (Sellafield 2015).
Although Sellafield’s nuclear facilities, including INP, are considered to be oligotrophic with high background levels of radiation, these conditions do not prevent microbial colonisation and survival (MeGraw et al. 2018), and the presence of diverse microbial communities may therefore impact on site operation, fuel stability, and ultimately the biogeochemical fate of any solubilised radionuclides within the pond waters (Lloyd and Renshaw 2005). There is emerging understanding that microbial processes can impact on many aspects of site operations. Microorganisms can play a significant role in the transformations of radionuclides in the environment by altering their chemical speciation, solubility and sorption properties, ultimately impacting on their environmental mobility and bioavailability (Francis 2012b)
(Newsome et al. 2014a). For example, the interactions between microbial populations and soluble radionuclides in groundwater can lead to precipitation reactions (e.g. via U(VI) or
Tc(VII) bioreduction) and subsequent bioremediation (Newsome et al. 2014b). Of particular note within these pond environments is the fate of 90Sr and 137Cs. Previous studies showed that seasonal blooms dominated by the alga Haematococcus, have adapted to survive in a circumneutral pH outdoor spent fuel storage pond at Sellafield, and are able to accumulate high levels of these radionuclides (MeGraw et al. 2018) (Ashworth et al. 2018).
The accumulation of radionuclides by microbial cells can be driven by a range of process including biosorption, biomineralization and bioprecipitation (Gadd 2009), although these are
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Sharon L. Ruiz Lopez PhD Thesis poorly defined in nuclear storage ponds. Biosorption is species-specific and is affected by the chemistry and the pH of the solution, the physiological state of the cells, the cell wall architecture, and the presence of extracellular polymeric substances (EPS) (Merroun et al.
2006;Comte et al. 2008). The EPS is especially important, being mainly composed of polysaccharides, proteins, humic substances, uronic acids, nucleic acids and lipids
(Wingender et al. 1999), and containing ionisable functional groups that represent potential binding sites for the sequestration of metal ions (Brown and Lester 1982) (Lawson et al. 1984).
Biosorption of divalent cations such Sr2+ is well known (White and Gadd 1990) (Liu et al. 2014)
(Gadd 2009), and would be favoured in high pH pond systems (Ghorbanzadeh and Tajer
Mohammad 2009), while monovalent cations such as Cs+ would sorb less strongly (Andres et al. 2001), although can bioaccumulate in biomass being transported into microbial cells, such as Rhodococcus, via potassium transport systems (Tomioka et al. 1992) (Avery 1995a) (Avery
1995b). Recent work on another high pH outside storage system at Sellafield has identified the cyanobacterium Pseudanabaena catenate as the dominant photosynthetic microorganism present, and its EPS exudates can impact on 90Sr sorption-desorption behaviour at alkaline environmental conditions under pondwater conditions (Ashworth et al. 2018) (MeGraw et al.
2018) .
Biomineralization reaction can also be linked to radionuclide fate (reviewed by (Lloyd and
Macaskie 2000)), due to local redox changes e.g. bioreduction of actinides or key fission products (Lloyd 2003), localized alkalinisation at the cell surface (Van Roy et al. 1997) or the accumulation of microbially-generated ligands e.g. phosphate, sulphide, oxalate or carbonate
(Lloyd and Macaskie 2002) (Boswell et al. 2001) (Macaskie et al. 1992) (White et al. 1998).
For the latter, induced or mediated carbonate mineralization (MICP) (Braissant et al. 2002), can affect the mobility and sequestration of radionuclides in the near surface environment
(Ferris et al. 1994;Reeder et al. 2001) and has been studied widely due to its importance in the remediation on contaminated Sr systems (Mortensen et al. 2011). A variety of microorganisms are able to drive MICP via urea hydrolysis (Fujita et al. 2004) (Bhaduri et al.
2016) (Achal et al. 2012) or via photosynthetic processes (Ferris et al. 1994;Lee et al. 2014)
(Dittrich et al. 2003) (Zhu and Dittrich 2016).
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Finally microorganisms can affect the physical chemistry of the water-fuel interactions, leading to microbial-influenced corrosion (MIC) and hence fuel material degradation and radionuclide release (Rajala et al. 2017;Shaw 1990;Springell et al. 2014) The proliferation of microorganisms (together with the accumulation of sludge as a result of corrosion in spent fuel ponds) can also adversely impact on pond visibility, increasing the costs of fuel storage, hampering decommissioning operations and also increasing the exposure time to personnel
(Wolfram et al. 1996a) (Jackson et al. 2014).
Recent publications have shown the presence of wide diversity of microorganisms living in
SNF ponds, mainly bacteria and algae (Chicote et al. 2005;Chicote et al. 2004;Karley et al.
2018;Pipíška et al. 2018;Sarró et al. 2005;Tišáková et al. 2012) (Sellafield-Ltd 2010). The observed adaptation mechanisms include biofilm formation (Santo Domingo et al. 1998)
(Sarró et al. 2005) (Bruhn et al. 2009), and interactions with radionuclides via biosorption
(Adam and Garnier-Laplace 2003;Ghorbanzadeh and Tajer Mohammad 2009;Tomioka et al.
1992) (Dekker et al. 2014) and bioprecipitation (Bagwell et al. 2018) (Achal et al. 2012;Bhaduri et al. 2016;Dittrich et al. 2003;Ferris et al. 1994;Zhu and Dittrich 2016). To date, most published work on the Sellafield site has been on legacy outdoor pond systems (MeGraw et al. 2018) (Foster 2018) which are open to external energy sources (including daylight, supporting photosynthetic primary colonisers). Indoor pond systems, with lower light intensities, and reduced inputs from atmospheric deposition, have not been studied in such detail.
The aim of this study is to characterize microbial communities of the indoor storage pond at indoor alkaline spent fuel storage pond (INP) to help understand the microbial ecology of this facility, and the principle forms of metabolism that underpin colonisation. An additional goal was to provide baseline microbial community data, so that the impact of receiving new fuels and stored wasted material during upcoming site-wide decommissioning activities can be assessed. The findings of this 30-month survey are discussed in relation to microbial survival to extreme environments (including potential energy sources) and how the extant microbiomes may potentially impact on pond management. The presence of microorganisms in water samples was studied using molecular (DNA) techniques including quantification of
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Sharon L. Ruiz Lopez PhD Thesis microbial biomass density by quantiative PCR (QPCR) and community profiling by Illumina high throughput 16S rRNA gene sequencing. Microbial communities in the feeding tank supplying the pond system were identified and compared to those in the main pond containing spent fuel, to determine which organisms were uniquely adapted to the extreme pond chemistry (e.g. high pH) and high background radiation levels. Throughout the sampling campaign, the presence of hydrogen-oxidising bacteria (affiliated with the Genus
Hydrogenophaga) in the INP, was consistent with the existence of hydrogen-oxidising ecosystem, potentially linked to radiolysis in the fuel storage pond.
Materials and Methods
Indoor Nuclear Fuel Storage Pond (INP)
The INP is an indoor pond complex divided into 3 main ponds and 3 subponds linked by a transfer channel that enables water flow (see Figure 4.1 for schematic of the pond system).
In order to control the pond-water activity and quality, there is a continuous “once through” purge flow; pond-water from the main ponds flows into the transfer channel and enters the recirculation pump chamber where it is continuously pumped round a closed circulation loop and through a heat exchanger system, which cools the pond-water before it is recycled into the main ponds. Through the control feed, purge and re-circulation flow rates, the water depth is maintained at 7±0.05m. The purge flow can be either from a donor plant or from other hydraulically linked ponds within the Sellafield complex. The temperature and pH are controlled at 15⁰C and 11.6 respectively. Analysed samples were taken from designated sample points on the “Feeding Tank (FT)” of the donor plant, where the demineralised water used to feed the complex is stored, from main ponds 2 and 3 (MP) and subponds 1 and 2 (SP) of the indoor alkaline spent fuel storage pond (INP).
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Figure 4.1Diagram of the Fuel Handling Plant. It consists of 3 main ponds and 3 subponds linked by a transfer channel which enables water flow. The sampling points are located at the main ponds 2 and 3; subponds 1 and 2; and the head feeding tank (at the top of the pond)
Samples
Analysis of the indoor spent fuel storage pond (INP) was performed for a period of 30 months
(October 2016 to April 2019); detailed dates and sampling points are shown in Table 4.1.
Water samples from the feeding tank were considered non-active and were shipped directly to the University of Manchester in October 2016 and stored in the dark at 10°C. Water samples from the main ponds 2 and 3 and subponds 1 and 2 were considered radioactive, hence appropriate handling procedures were required. The protocols for these samples were developed and applied under Command & Control regimes by Sellafield Ltd and NNL, with samples transferred directly from the pond to the NNL Central Laboratories (National Nuclear
Laboratory, Cumbria UK), where DNA was extracted and the samples where checked for radioactivity in line with the Environmental Permits and Nuclear Site licences held by Sellafield
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Ltd. Extracted DNA samples free from significant radionuclide contamination were shipped to the University of Manchester and stored at 4⁰C until use.
In addition to microbial profiling via DNA analyses, a complementary “cultivation-dependent” approach was also adopted to help further characterise the pond microbial community composition. Two low-volume samples (approx 5 ml) from the subponds 1 and 2 (shown in
Figure 4.1) were analysed by classic culturing techniques (see below). The subponds are more radioactive than the main ponds, but the temperature and pH values are maintained at the same values as the main ponds, 21⁰C and 11.6 respectively.
Table 4.1 Distribution of samples taken for a period of 30 months from different areas within the SNF pond, and analysed using high-throughput (Illumina) DNA microbial profiling. Samples SP01 and SP02 (*) were not sequenced using the Illumina platform but instead were analysed using culturing techniques (with Sanger sequencing of isolated pure cultures).
Sampling point Date Feeding tank FT01, FT02 October 2016 Main ponds MP01, MP02 October 2016 MP03, MP04 June 2017 MP05, MP06 October 2017 MP07, MP08 January 2018 MP09, MP10 June 2018 MP11, MP12 November 2018 MP13, MP14 February 2019 MP15, MP16 April 2019 Subponds SP01*, SP02* January 2017 SP03, SP04 January 2018 SP05, SP06 June 2018 SP07, SP08 November 2018 SP09, SP10 February 2019 SP11, SP12 April 2019
Cultivation independent DNA analyses of microbial communities
DNA extraction. DNA extraction was conducted in either the Molecular Ecology Lab at the
University of Manchester or the Central Laboratories s at NNL, from filtered biomass using a
PowerWater DNA Isolation Kit (Mobio Laboratories, Inc., Carlsbad California, USA).
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Polymerase Chain Reaction. PCR amplification was performed from the extracted DNA using a Techne Thermocycler (Cole-Parmer, Staffordshire, UK). Primers used for bacterial
16S rRNA gene amplification were the broad-specificity 8F forward primer and the reverse primer 1492R (Eden et al. 1991b), while primers used for eukaryote 18S rRNA gene amplification were Euk F forward primer and the reverse primer Euk R (DeLong 1992a) and primers used for the archaeal 16S rRNA gene amplification were forward primer 21F and reverse primer 958R (DeLong 1992a). The PCR reaction mixtures contained; 5 µl PCR buffer,
4 µl 10 mM dNTP solution (2.5mM each nucleotide), 1 µl of 25 µM forward primer, 1 µl of 25
µM forward reverse and 0.3 µl Ex Takara Taq DNA Polymerase, which was made up to a final volume of 50μL with sterile water, and finally 2µL of sample was added to each tube. The thermal cycling protocol used was as follows for the bacterial 8F and 1492R primers; initial denaturation at 94°C for 4 minutes, melting at 94°C for 30 seconds, annealing at 55°C for 30 seconds, extension at 72°C for 1 minute (35 cycles with a final extension at 72°C for 5 minutes,
Eden et al., 1991). For eukaryotic 18S rRNA gene amplification, the temperature cycle was; initial denaturation at 94°C for 2 minutes, melting at 94⁰C for 30 seconds, annealing at 55°C for 1.5 minutes, extension at 72oC for 1.5 minutes for a total of 30 cycles and final extension at 72⁰C for 5 minutes (DeLong, 1992). For archaeal 16S rRNA genes the thermal cycle protocol consisted of an initial denaturation step at 94°C for 4 minutes, melting at 94⁰C for 45 seconds, annealing at 55°C for 30 seconds, extension at 72oC for 1 minute (for a total of 30 cycles) and a final extension step at 72⁰C for 5 minutes (DeLong 1992a).
The purity of the amplified PCR products was determined by electrophoresis using a 1% (w/v) agarose gel in 1X TAE buffer (Tris-acetic acid-EDTA). DNA was stained with SYBER safe
DNA gel stain (Thermofisher), and then viewed under short-wave UV light using a BioRad
Geldoc 2000 system (BioRad, Hemel Hempstead, Herts, UK).
Quantitative Polymerase Chain Reaction (Real-time PCR, QPCR). Quantitative PCR of the prokaryotic 16S rRNA gene was performed by using Brilliant II Syber Green qPCR Master
Mix and the MX3000P qPCR System (Agilent Genomics, Headquarters, Santa Clara, CA,
United States). The qPCR master mix contained 0.4µL 8F forward primer 25µM (Turner et al.
1999), 0.4µL 519R (Turner et al. 1999) reverse primer 25µM, 0.4µL of 1 in 5 diluted Rox
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Sharon L. Ruiz Lopez PhD Thesis reference dye, 12.5µL of 2x qPCR Syber green master mix and Roche PCR Grade water to make up a final volume of 23µL. Finally 2µL of sample was added. A standard curve from serial dilutions of template DNA was constructed to verify the presence of a single gene- specific peak and the absence of primer dimer. The cycling conditions consisted of one cycle of denaturation at 94⁰C for 10 min, followed by 35 three-segment cycles of amplification (94⁰C for 30 seconds, 50⁰C for 30 seconds and 72⁰C for 45 seconds) where fluorescence was automatically measured during the PCR amplification, and one three-segment cycle of product melting (94⁰C for 10 min, 50⁰C for 30 seconds and 94⁰C for 30 seconds). Gene quantification was achieved by determining the threshold cycle (Ct) of the unknown samples, and of a range of known bacterial 16S rRNA gene standards. The baseline adjustment method for the
Mx3000 (Agilent) software was used to determine the Ct in each reaction. All samples were amplified in triplicate, and the mean was used for further analysis. In order to quantify the concentration of target genes, the absolute quantification by the standard-curve (SC) method was used (Brankatschk et al. 2012). To determine the abundance of cells per ml of sample, the total number of 16S rRNA genes determined by QPCR was adjusted to the approximated number of 16S rRNA copy numbers reported for members of the Protebacteria; specifically for classes α and β the average number of copies is reported to be 4 (Vetrovsky and Baldrian
2013).
Next-generation Sequencing. Sequencing of 16S rRNA gene PCR amplicons was conducted using the Illumina MiSeq platform (Illumina, San Diego, CA, USA) targeting the V4 hyper variable region (forward primer, 515F, 5′-GTGYCAGCMGCCGCGGTAA-3′; reverse primer, 806R, 5′-GGACTACHVGGGTWTCTAAT-3′) for 2 × 250-bp paired-end sequencing
(Illumina) (Caporaso et al. 2011) (Caporaso et al. 2012). PCR amplification was performed using the Roche FastStart High Fidelity PCR System (Roche Diagnostics Ltd, Burgess Hill,
UK) in 50μl reactions under the following conditions; initial denaturation at 95°C for 2 min, followed by 36 cycles of 95°C for 30 s, 55°C for 30 s, 72°C for 1 min, and a final extension step of 5 min at 72°C. The PCR products were purified and normalised to ~20ng each using the SequalPrep Normalization Kit (Fisher Scientific, Loughborough, UK). The PCR amplicons from all samples were pooled in equimolar ratios. The run was performed using a 4pM sample
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Sharon L. Ruiz Lopez PhD Thesis library spiked with 4pM PhiX to a final concentration of 10% following the method of Schloss and Kozich (Kozich et al. 2013).
Raw sequences were divided into samples by barcodes (up to one mismatch was permitted) using a sequencing pipeline. Quality control and trimming was performed using Cutadapt
(Martin 2011), FastQC (B.I. 2016), and Sickle (N.A. and J.N. 2011). MiSeq error correction was performed using SPADes (Nurk et al. 2013). Forward and reverse reads were incorporated into full-length sequences with Pandaseq (Masella et al. 2012). Chimeras were removed using ChimeraSlayer (Haas et al. 2011), and OTU’s were generated with UPARSE
(Edgar 2013). OTUs were classified by Usearch (Edgar 2010) at the 97% similarity level, and singletons were removed. Rarefaction analysis was conducted using the original detected
OTUs in Qiime (Caporaso et al. 2010a). The taxonomic assignment was performed by the
RDP classifier (Wang et al. 2007). Sequences obtained were compared with the NCBI
GenBank database to find the similar organisms (https://www.ncbi.nlm.nih.gov/genbank/).
Culturing and identification of the pond microorganisms.
A complementary culture-dependent approach was also used to help characterise the microorganisms present. To facilitate this, a series of 10-fold dilution water samples from the subponds 1 and 2 were plated onto fresh solid media. A range of complex or semi-defined solid media were used (see SI Table 2) including LB (Sezonov et al. 2007) and NA (Misal et al. 2013a) and DL (Lovley et al. 1984a) at a range of pH values from 7-11. The marine medium of Zobell as also selected for use for isolation of Alpha and Gammaproteobacteria that had been detected in the pond using cultivation-independent DNA sequencing (Brettar et al. 2004)
(Joint et al. 2010)). Finally the fully-defined minimal medium M9 (Neidhardt et al. 1974) was also used at a range of concentrations (100, 75 and 50% dilutions; see supplementary Table
1 for details) at pH 7, 9 or 11. The M9 medium contained no added carbon, selecting for autrophic oligotrophs.
The isolated colonies were then transferred to fresh liquid media and grown aerobically for 48 hours, DNA extracted from the cell pellet using the PowerWater DNA Isolation Kit as mentioned previously, and the 16S rRNA genes of the isolates sequenced.
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The 16S rRNA gene sequences of the isolates were determined by the chain termination sequencing method to facilitate phylogenetic analyses of the pure cultures (Slatko et al. 2001).
PCR amplification was performed from the extracted DNA using a Techne Thermocycler
(Cole-Parmer, Staffordshire, UK). Two PCR mixtures were prepared (one for each primer) and contained 3.5 µl 5X PCR buffer, 0.15 µl of 25 µM primer, and 1 µl Terminator BigDye
(Thermo Fisher Scientific, Waltham, MA, USA), which was made up to a final volume of 15 μL with sterile water, and finally 1 µL of DNA sample was added to each tube. The thermal cycling protocol used was adapted for the primers as follows; initial denaturation at 96°C for 6 minutes, melting at 94°C for 40 seconds, annealing at 55°C for 15 seconds, extension at 60°C for 3 minutes; 30 cycles, and a final extension at 60°C for 5 minutes (Lorenz 2012). The resulting
PCR products were purified using the GlycoBlue coprecipitant protocol AM9516 (Thermo
Fisher Scientific, Waltham, MA, USA) and the resulting pellets were then sequenced. An ABI
Prism BigDye Terminator Cycle Sequencing Kit was used in combination with an ABI Prism
3730XL Capillary DNA Analyzer (Applied Biosystems, Warrington, UK). The primers 8F and
1592R were used for initial amplification and sequencing: 8F 5’ -AGA GTT TGATCC TGG
CTC AG-3’, and 1492R 5’ –TAC GGY TAC CTT GTTACG ACT T-3’ (Lane et al. 1986).
Sequences (typically 950 base pairs in length) were analysed against the NCBI (U.S.) database using BLAST program packages and matched to known 16S rRNA gene sequences
(Islam et al. 2004).
Results
The aim of this study was to characterize the microbial populations living under the harsh high pH and high background radiation conditions within an indoor spent fuel storage pond (INP) at the Sellafield complex. To facilitate this work, a range of pond samples were collected over a 30-month period from the main ponds (MP) and subponds (SP). The microbial populations were analysed using high throughput 16S and 18S rRNA gene sequencing, and complementary culturing techniques. Background data on the alkaline purge waters from the feeding tank (FT) supplied to the pond complex were also analysed, to help identify key organisms exclusively associated with the areas of the pond holding spent fuel. Water
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Sharon L. Ruiz Lopez PhD Thesis analysis of the indoor alkaline spent fuel storage pond (INP) confirmed a high pH oligotrophic environment; the feeding water was demineralised, the pH was adjusted by the addition of
NaOH, and chillers maintained the temperature. Table 4.2 summarizes the physical conditions and water chemistry measured in the sampling areas of the INP.
Table 4.2 Parameters measured on the indoor alkaline spent fuel storage pond (INP). Data provided by Sellafield Ltd Parameter pH Temperature Na+ TOC Phosphates Nitrates Beta (average) ⁰C (µg/ml) (µg/ml) PO4-2 NO3-2 AC (g/ml) (µg/ml) (Bq/ml) Feeding 11.6 18 80.6 1< 0.0 0.01 NA tank (FT) Main 11.6 20.9 80.3 2.0 0.0 0.01 1,117 ponds (MP) Subponds 11.5 20.7 81.7 2.13 0.0 0.01 1,132 (SP)
To assess the abundance of microbial populations, Real Time PCR (QPCR), was used as estimation for the biomass formation over time on representative samples. Extracted DNA could amplify 16S only, while 18S was undetectable. Numbers were low in the FT and SP while MP ranged from 250,000 to 470,000 DNA copies (Figure 4.2), peaking in MP05 and
MP06 (October, 2017) and in MP09 (June2018).
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Sharon L. Ruiz Lopez PhD Thesis
500000
450000
400000
350000
300000
250000
DNA copies/ml 200000
150000
100000
50000
0
FT01_OCt16FT02_Oct16 MP02_Pct16 MP07_Jan18MP08_Jan18 SP03_Jan18SP04_Jan18SP05_Jun18 MP01_Oct16 MP03_June17MP04_June17MP05_Oct17MP06_Oct17 MP09_June18MP10_June18 SP06_June18
Figure 4.2 QPCR results show the number of copies per mL. A standard curve for QPCR reaction was at concentration ranging from 0.00753 to 7530 nanograms per millilitre to estimate the concentration of DNA in the samples.
Identification of microorganisms by next generation DNA sequencing
The first series of samples from this 30-month sampling campaign were taken from two sampling points within the INP (main ponds, MP and subponds, SP) in October 2016 (MP01 and MP02), followed by series of samples taken during January 2017 (SP01 and SP02), June
2017 (MP03 and MP04), October 2017 (MP05 and MP06), January 2018 (MP07 and MP08;
SP03 and SP04), June 2018 (MP09 and MP10; SP05 and SP06), November 2018 (MP11 and
MP12; SP07 and SP08), February 2019 (MP13 and MP14; SP09 and SP10), with a final series of samples taken during April 2019 (MP15 and MP16; SP11 and SP12). Samples HT01 and
HT02 were also taken from a feeding head tank supplying the pond complex with demineralised water adjusted to pH 11.6 in October 2016, to help identify organisms present in the background waters, and hence (by comparison) help identify the organisms that were exclusively present in the INP main and sub-ponds.
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DNA was extracted from the samples, and 16S and 18S rRNA genes were targeted by PCR using the methods described previously. However, only 16S rRNA gene amplification products were detected by gel electrophoresis, and it was therefore concluded that eukaryotic microorganisms were absent, or were below the level of detection in the INP samples. The
16S rRNA amplicons were then sequencing using the Illumina MiSeq next generation sequencing platform, and analysed using a bespoke bioinformatics platform which included comparison to prokaryotic gene sequences deposited in the NCBI databases.
Samples from the main ponds (MP) were consistently dominated by Proteobacteria (70-98%) and Bacteroidetes (2-21%). Organisms affiliated with the phylum Cyanobacteria were not detected on the initial samples, but were detected in subsequent times (from October 2017 to
April 2019), although at a relative abundance of less than 3%. Samples from the subponds
(SP) were also dominated by Proteobacteria (80-97%) and Bacteroidetes (3-7%), while the relative abundance of Cyanobacteria was again low (less than 2%). In addition, other phyla detected at lower levels in the main ponds included organisms affiliated with the Actinobacteria
(8%, January 2018), Armatimonadates (4%, June 2017 and February 2019) and
Deinococcus-Thermus (2-4% from November 2018 to April 2019). Samples from the supplying feeding tank (FT) were also dominated by Proteobacteria (70 and 75%),
Bacteroidetes (14 and 19%) and Actinobacteria (1 and 4%). Detailed information is shown in
Supplementary data, Figures 1 and 2.
At the genus level (Figure 4.3), both duplicates from the feeding head tank (HT01 and HT02) were dominated by close relatives to Curvibacter (~21%, Betaproteobacteria, 1 OTU),
Rhodoferax (~20%, Betaproteobacteria, 1 OTU), Sediminibacterium (~10%, Bacteroidetes, 2
OTUs), Polaromonas (~6%, Betaproteobacteria, 2 OTUs), Methylotenera (~6%,
Betaproteobacteria, 2 OTUs), Novosphingobium (~3%, Alphaproteobacteria, 2 OTUs),
Flavobacterium (~3%, Bacteroidetes, 2 OTUs), Unidibacterium (~3%, Betaproteobacteria, 2
OTUs) and more than 20% of the total OTUs (26) represented unidentified organisms.
Although the microbial profiles of both samples were very similar, there were relatively minor differences (Curvibacter, Sediminibacterium, Flavobacterium and Unidibacterium were more abundant on HT01 whilst Methylotenera, Polaromonas and Novosphingobium, were more
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Sharon L. Ruiz Lopez PhD Thesis abundant on HT02). Both samples contrasted very strongly with the INP communities suggesting that the INP pond supported a distinct microbial community.
Microbial distribution was consistent at all sampling times within the main ponds (MP). Overall at the genus level the microbial diversity was dominated by 1 OTU belonging to genus
Hydrogenophaga (Betaproteobacteria) representing up to 40% of the total population, followed by Porphyrobacter (~21%, Alphaproteobacteria, 1 OTU), Roseococcus (~9%,
Alphaproteobacteria, 3 OTUs), Silanimonas (~9%, Gammaproteobacteria, 1 OTU),
Sphingomonas (~7%, Alphaproteobacteria, 2 OTUs), and Synechococcus (~1%,
Cyanophyceae, 3 OTUs). The exception was one set of samples taken on January 2018
(MP07 and MP08), where broad microbial diversity was recorded and the abundance of
Hydrogenophaga, and Porphyrobacter dropped to 23% and 7% respectively. Additionally, representatives of the genera Methylophilus (13%, Betaproteobacteria, 1 OTU) and
Mongoliitalea (9%, Bacteroidetes, 2 OTUs) were exclusively identified on this sampling time.
Unidentified (uncultured) sequences, although detected at all sampling times, represented more than 2% of the total community in samples MP03 (June 2017, 8%, 24 OTUs), MP07 and MP08 (January 2018, 8% and 10%, 38 OTUs) and MP16 (April 2019, 21%, 47 OTUs).
The microbial profiles of the subponds (SP) were similar to the main ponds (MP), and were dominated by representatives of the genera Hydrogenophaga (30%, Betaproteobacteria, 1
OTU), Porphyrobacter (23%, Alphaproteobacteria, 1 OTU), Roseococcus (8%,
Alphaproteobacteria, 2 OTUs), Silanimonas (8%, Gammaproteobacteria, 2 OTUs), and
Sphingomonas (2.4%, Alphaproteobacteria, 3 OTUs). Samples SP03 and SP04 (January,
2018) showed few differences with close relatives affiliated to genus Methylophilus (~14%,
Alphaproteobacteria, 1 OTU) detected in these samples only.
Although looking similar at the Phylum level (MP, SP and FT samples dominated by
Proteobacteria), it was clear from the results above that the contrasting microbial communities differed substantially at the genus level. Data would seem to suggest that the microbial community compositions in the main ponds, subponds and feeding head tank samples represent distinct ecosystems, most likely linked to the impacts of the spent fuel on the INP environment.
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Sharon L. Ruiz Lopez PhD Thesis a)
100% Synechococcus
Trichococcus 90% Dietzia
80% Cyanobium
Alkalilimnicola 70% Rivibacter
60% Roseomonas
50% Polynucleobacter Methylobacterium 40% Mongoliitalea Relative abundance 30% uncultured Others 20% Sphingomonas
10% Silanimonas
Roseococcus 0% Methylophilus
Porphyrobacter
MP07_Jan18MP08_Jan18 MP01_Oct16MP02_Oct16MP03_June17MP04_June17MP05_Oct17MP06_Oct17 MP09_June18MP10_June18MP11_Nov18MP12_Nov18MP13_Feb19MP14_Feb19MP15_Apr19MP16_Apr19 Hydrogenophaga
b)
100% Cyanobium Rivibacter 90% Flavobacterium 80% Reyranella 70% uncultured Roseomonas 60% Pseudomonas 50% Mongoliitalea 40% Caulobacter Meiothermus Relative abundance 30% Sphingomonas 20% Others 10% Silanimonas
0% Methylophilus Roseococcus Porphyrobacter
SP03_Jan18SP04_Jan18 SP05_June18SP06_June18SP07_Nov18SP08_Nov18SP09_Feb19SP10_Feb19SP11_Apr19SP12_Apr19 Hydrogenophaga
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Sharon L. Ruiz Lopez PhD Thesis
100% Unidibacterium 90% Novosphingobium 80% Flavobacterium 70% 60% Polaromonas 50% Methylotenera
40% Sediminibacterium 30% Relative abundance Rhodoferax
20% Curvibacter 10% Others 0% FT01_Oct16 FT02_Oct16 c) Figure 4.3 Phylogenetic affiliations (closest known genera) of microorganisms detected in Sellafield indoor pond (INP): a)main ponds, b)subponds and c)feeding tank (FT) using Illumina sequencing with broad specificity primers for prokaryote 16S rRNA. Only the genera that contained more than 1% of the total number of sequences are shown.
Cultivation-dependent analysis for determining microbial diversity in the INP
In addition DNA-based analyses, culturing techniques were adopted to characterise the microbial communities within the INP subponds complex, and to provide axenic cultures representative of the microbes colonising such an extreme environment for future studies. A series of dilutions from the INP subponds (samples SP01 and SP02), were spread onto agar plates containing a range of solidified high pH (11.5) solid media. After 7 days of incubation, growth was detected exclusively on the undiluted samples (100) from plates containing non- defined complex media (DL, NA and Zobell media; See Supplementary Table 2). CFU per ml were determined between 700-1000 per ml for each media and eleven distinct colony morphologies were noted. Representative single colonies were isolated and identified by sequencing using the dideoxynucleotide technique. The presence of colonies was not detected at fully defined media (minimal media M9).
Overall, 4 different genera were identified. Representatives of Algoriphagus genus (isolates
S01, 91.5% similarity; S05, 91% similarity; S06, 91.5% similarity; and S07, 89.5% similarity) were isolated on DL and NA agars, and produced light pink-coloured, rod-shaped and raised colonies (1-2 mm diameter). Aquiflexum genus (isolates S02, 91% similarity; S08, 88% similarity; and S09, 93.5 similarity %) were obtained on the DL and NA agar plates, and
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Sharon L. Ruiz Lopez PhD Thesis produced red-coloured colonies, that were rod-shaped with raised elevation (2-3 mm diameter). Strains S03, S10 and S11 were isolated from DL, NA and Zobell plates; were rod- shaped, translucent and had raised colonies (2-3 mm diameter) and were affiliated to an
Unclassified genus from the family Cyclobacteriaceae (S03, 93.5% similarity; S10, 85% similarity and S11, 91% similarity). Finally, a close relative to Bacteroidetes (strain S04, 91.5% similarity) was isolated from the DL plates and produced short round-shaped, bright-orange raised colonies (1-2 mm diameter). The total eleven isolated strains belong to the phylum
Bacteroidetes. Specific details on similarity and media are shown on supplementary Table 3.
Members belonging to genus Aquiflexum (phylum Bacteroidetes) were previously detected on the MP and SP samples by DNA-based techniques; however the mentioned genus does not represent a major component. Genus Aquiflexum was detected exclusively on samples MP05 and MP06 (October 2017) at a relative abundance of 0.28% and 0.39% respectively (see supplementary Table 4).
Discussion
The present research was focused on characterising the microbial community composition of a Sellafield INP complex containing main ponds (MP), subponds (SP) and a feeding head tank (FT) over a period time of 30 months. The results showed that bacteria affiliated with a range of phylogenetic groups are able to survive and colonize the different areas across the
INP complex.
Microbial diversity on the feeding tank area (FT), an oligotrophic and hyper-alkaline environment, was dominated by members belonging to the Proteobacteria and Bacteroidetes.
Previous studies showed that oligotrophic conditions do not prevent microbial colonisation and allow microbial communities to display diverse adaptation mechanisms (Chen et al.
2004;Kawai et al. 2002;Kulakov et al. 2002). Specifically, organisms associated to
Proteobacteria and Bacteroidetes have been identified previously in similar oligotrophic environments, INP including industrial ultrapure water (Bohus et al. 2010;Gales et al.
2004;Proctor et al. 2015). Microbial colonisation in such environments has been linked to low
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Sharon L. Ruiz Lopez PhD Thesis levels of residual organic matter in the system, originating from dead microbial cells that were unable to adapt to the harsh environments and to biofilm formation on the walls, linked to due to planktonic cells delivered by water recirculation on the pond areas (Bohus et al. 2010).
Organisms detected in the FT area are reported to support diverse forms of heterotrophic metabolism, which could occur within the FT. For example members of the genera
Rhodoferax (Finneran et al. 2003) (Risso et al. 2009), Curvibacter and Sediminibacterium (Ma et al. 2016) (Ding and Yokota 2010) (Qu and Yuan 2008;Kang et al. 2014) (Kim et al. 2013) are able to oxidize a range of complex organic compounds while Methylotenera can utilise reduced one-carbon compounds (methylotrophy) such as methanol as energy sources
(Kalyuzhnaya et al. 2012) (Kalyuzhnaya et al. 2006). However, the source of carbon and energy in the FT remains to be investigated.
Although the INP has a continual feeding input composition, the main ponds (MP) and subponds (SP) contained stable microbial populations with similar community profiles, which contrasted with the distinct microbiome of the FT. Key organisms detected in MP and SP samples included species of Hydrogenophaga, Silanimonas, Porphyrobacter and
Roseococcus.
In addition to the oligotrophic and hyper-alkaline characteristics of the MP and SP areas, these components of INP complex contain spent fuel creating challenging high background radioactivity further challenging microbiome development. Despite these adverse conditions, microbial colonisation of similar spent fuel storage systems has been documented (Gales et al. 2004;Bruhn et al. 2009;Karley et al. 2018;Santo Domingo et al. 1998), and dominated by organisms associated to the phyla Proteobacteria (Bagwell et al. 2018;Silva et al.
2018b;Chicote et al. 2004;MeGraw et al. 2018), Firmicutes (Sarro et al. 2005), Actinobacteria
(Sarro et al. 2005), Cyanobacteria (Silva et al. 2018b;MeGraw et al. 2018), Deinococcus-
Thermus (Masurat et al. 2005) and eukaryotic fresh water microalgae (Rivasseau et al.
2016;MeGraw et al. 2018) and Fungi (Silva et al. 2018b;Chicote et al. 2004). Although the energy sources supporting microbial growth in these systems remains largely uncharacterised, it is possible that radiolysis could play a direct role in supporting microbial
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Sharon L. Ruiz Lopez PhD Thesis growth. The presence of alpha, beta and gamma radiation from the spent fuel can promote the radiolysis of water, driving to the formation of short-lived, highly oxidising free radical
- species, such as OH and H2O2 (Jonsson et al. 2007) (Shoesmith 2000) and also the production of H2 (Libert et al. 2011;Brodie et al. 2006) that could be utilised by hydrogen- oxidizing (Knallgas) bacteria) (Yu 2018). The most abundant organism in the MP and SP areas in this study were affiliated with the genus Hydrogenophaga (44-48%), which comprise aerobic, chemoorganotrophic organisms that use hydrogen as an energy source (Willems et al. 1989) (Kampfer et al. 2005) (Yoon et al. 2008). Members of genus Hydrogenophaga are present in a variety of natural and engineered (e.g. waste water) environments (Schwartz and
Friedrich 2006) (Yoon et al. 2008) (Lambo and Patel 2006) (Fahy et al. 2008), including hyper alkaline sites such as Allas Springs, Cyprus where the pH was 11.9, similar to the alkaline conditions to the INP waters (pH 11.6) (Rizoulis et al. 2014) and serpentinizing springs (pH
11.6, The Cedars, California USA) (Suzuki et al. 2014) . The presence of Hydrogenophaga as a key microbial component during all the sampling times indicates the metabolism of H2 is occurring within the pond which is of particular interest since oxidation of hydrogen could also be potentially linked to the reduction of a range of electron acceptors, including radionuclides
(Lloyd 2003).
It is of interest to note that the other members of the identified microbial community are not reported to metabolise hydrogen. Porphyrobacter, an aerobic anoxygenic phototroph bacteria
(AAP) has the ability to harvest energy photosynthetically (Yoon et al. 2004) (Liu et al. 2017)
(Hanada et al. 1997); however, is able to grow in the dark using diverse energy sources (Liu et al. 2017). Roseococcus, an obligately aerobic and chemoorganotrophic, contains
Bacteriochlorophyll a and carotenoid pigments (Yurkov 2015) (Boldareva et al. 2009) and is also able to grow in the dark (Yurkov et al. 1994). Sphingomonas is metabolically versatile, can use a wide range of compounds as energy sources (Feng et al. 2014) (Singh et al. 2015)
(Lee et al. 2001) such as polycyclic aromatic hydrocarbons (Leys et al. 2004); and contains ubiquinone Q-10, a molecule involved in respiratory functions (Niharika et al. 2012) where hydrogen, abundant on the MP and SP areas, is required. Roseomonas species also contain ubiquinone Q-10 (Kim et al. 2009) (Wang et al. 2016) and have the ability to grow on biofilms
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Sharon L. Ruiz Lopez PhD Thesis to protect from adverse surrounding conditions (Diesendorf et al. 2017) which would be relevant to the harsh SNP conditions studied here. Microorganisms associated to phylum
Cyanobacteria, a blue-green algae, oxygenic and phototrophic bacteria (Peschek 1999), were much less abundant (identified as phyla Synecochoccus and Cyanobium) possibly associated to the restricted exposure to light in the INP, where light levels are kept low.
Finally, agar-based cultivation approaches were tested alongside DNA-based approaches in this study, and resulted in the isolation of bacteria from the family Cyclobacteriacea, but proved unsuccessful for targeting organisms that were numerically dominant within the INP complex. It is interesting to note however, that despite the isolated organisms do not represent the major components identified by NGS techniques, the findings show that organisms associated to the genera Algoriphagus and Aquiflexum are able to tolerate wide range of alkaline conditions and additional challenging conditions such as radioactivity and limited nutrient sources. In that sense this research represents a potential breakthrough since organisms affiliated to the identified genera have been previously studied in neutral environments (ideal pH 7-8) (Alegado et al., 2013; Glaring et al., 2015; Kang,
Weerawongwiwat, Jung, Myung, & Kim, 2013; Misal, Bajoria, Lingojwar, & Gawai, 2013;
Tiago, Chung, & Veríssimo, 2004; Yoon, Lee, & Oh, 2004) and the information about their population on oligotrophic and radioactive environments is limited.
This observation reinforces the view that cultivation-independent molecular ecology techniques are crucial first steps in understanding microbiome dynamics in oligotrophic SNPs, offering the benefits of high-throughput sequencing of DNA that has been purified away from contaminating radionuclides present in the pond waters. This opens up the way for more detailed metagenomic analyses which are ongoing in our laboratories.
Acknowledgments
SRL acknowledges financial support from a PhD programme funded by the National Council of Science and Technology (CONACyT). This work was also supported by funding from
Sellafield Limited and the Royal Society to JRL. LF was supported by an EPSRC CASE PhD and IAA funding.
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Supplementary information
FT Main ponds (MP) Subponds (SP) Verrucomicrobia 100% Thaumarchaeota 90% Proteobacteria
80% Planctomycetes
Patescibacteria 70% Others 60% Gemmatimonadetes
50% Firmicutes
Dependentiae 40% Deinococcus- Thermus 30% Cyanobacteria
20% Chloroflexi
Bacteroidetes 10% Armatimonadetes 0% Actinobacteria
Acidobacteria SP03_Jan18 SP04_Jan18 FT01_Oct16 FT02_Oct16 SP11_Apr19 SP12_Apr19 SP09_Feb19 SP10_Feb19 SP07_Nov18 SP08_Nov18 MP07_Jan18 MP08_Jan18 MP01_Oct16 MP02_Oct16 MP05_Oct17 MP06_Oct17 MP15_Apr19 MP16_Apr19 MP13_Feb19 MP14_Feb19 SP05_June18 SP06_June18 MP11_Nov18 MP12_Nov18 MP03_June17 MP04_June17 MP09_June18 MP10_June18
Supplementary 2. 1 Phylogenetic affiliations (closest known phyla) of microorganisms detected in Sellafield indoor pond (INP): feeding tank (FT), main ponds (MP) and subponds (SP) using Illumina sequencing with broad specificity primers for prokaryote 16S rRNA. Only the genera that contained more than 1% of the total number of sequences are shown.
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Supplementary 2. 2 Molecular Phylogenetic analysis by Maximum Likelihood method. The evolutionary history was inferred by using the Maximum Likelihood method based on the Tamura-Nei model (Tamura et al. 2004). The percentage of trees in which the associated taxa clustered together is shown next to the branches. Initial tree(s) for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using the Maximum Composite Likelihood (MCL) approach, and then selecting the topology with superior log likelihood value. The analysis involved 59 nucleotide sequences. All positions containing gaps and missing data were eliminated. There were a total of 194 positions in the final dataset. Evolutionary analyses were conducted in MEGA7 (Kumar et al. 2016). Bootstrap values (percentages) are given at the nodes.
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Supplementary 2. 3 Description of the media (selective and non-selective) used for microorganisms isolation
Media Classification Composition per litre Final Concentration Reference pH
Minimal Defined Na2HPO4 42.5 g 7 10% (Harwood
medium (M9) medium KH2PO4 15.0 g 10 50% and Cutting
NH4Cl 5.0 g 100% 1990)
MnCl2 2.5 g
CuCl2•2H2O 43 mg
ZnCl2 70 mg
CoCl2•6H2O 60 mg
Na2MoO4•2H2O 60 mg Luria Bertani Complex Tryptone 10 g 7 10% (Sezonov et (LB) Basal Yeast extract 5 g 10 50% al. 2007) Sodium Chloride 10 g 11 100%
Nutrient Agar Complex KH2PO4 0.3 g 7 10% (Misal et al.
for Basal Na2HPO4 0.98 g 10 50% 2013b)
Aquiflexum MgSO4 0.10 g 11 100% (NA) NaCl 5 g Yeast extract 5 g Peptone 5 g Agar 15 g
DL medium Complex NaHCO3 2.5 g 7 10% (Lovley et al.
Selective Na2CO3 5.0 g 10 50% 1984b)
medium NH4Cl 0.25 g 11 100%
Na2H2PO4 0.6 g KCl 0.1 g Vitamin mix 10 ml Mineral mix 10 ml Yeast extract 3.0 g Peptone 4.0 g Agar 10.0 g ZoBell Complex NaCl 19.45 g 7 10% (Brettar et al.
Selective MgCl2 8.8 g 10 50% 2004)
medium Na2SO4 3.24 g 11 100%
CaCl2 1.8 g
C6H5FeO7 0.1 g Yeast extract 1 g Peptone 5 g Mineral mix 10 ml Agar 15 g
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Supplementary 2. 4 Different media at a range of concentration and pH values and bacteria identified
Media pH Growth Organisms isolated and similarity Similarity NCBI percentage (forward Taxonomy and ID reverse) Minimal 7 Growth was not detected at any concentration nor pH range medium 10 11 Zobell 7 Detected at 10% Strain S03: Cyclobacteriaceae F: 95% 2483804 concentration bacterium CUG 91308, 93.5% R: 92% 10 Growth was not detected 11 Detected at 50% Strain S09: Aquiflexum balticum DSM F: 96% 758820 concentration 16537, 93.5% R:91% Nutrient Agar 7 Detected at 10% Strain S01: Algoriphagus sp. F: 91% 2007308 for Aquiflexum and 100% XAY3209,91.5% R: 92% NA concentration Strain S05: Algoriphagus sp. XAY3209,91% F: 91% 2007308 R: 91% 10 Detected at 50% Strain S02: Aquiflexum sp. 20021, F: 94% 1089537 concentration 91% R: 88% 11 Not detected DL 7 Detected at 50 Strain S08: Aquiflexum sp. BW86-86, F: 87% 647411 and 100% 88% R:89% concentration Strain S07: Algoriphagus sp. R-36727, F: 89% 885463 89.5% R: 90% Strain S010: Cyclobacteriaceae 2483804 bacterium CUG 91308, 85%% F: 86% R: 84% 10 Detected at 50 Strain S011: Cyclobacteriaceae F: 94% 2483804 and 100% bacterium CUG 91308, 91%% R: 88% concentration Strain S06: Algoriphagus sp. BAL344, 1708148 91.5% F: 93% R:90% 11 Detected at 50 Strain S04: Bacteroidetes sp. BG31, F: 92% 1109254 and 100% 91.5% R:91% concentration LB 7 10 Growth was not detected at any concentration nor pH range 11
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Supplementary 2. 5 Abundance of microorganisms detected by Sanger sequencing compared with NGS Illumina MiSeq
Sample Organism identified by Abundance detected
Sanger sequencing by 16S NGS Illumina
MiSeq
MP05 Aquiflexum sp 0.28% OTU3
MP06 Aquiflexum sp 0.39% OTU3
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Martin, M. (2011). Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal, 17(1), 10-12. Masella, A. P., Bartram, A. K., Truszkowski, J. M., Brown, D. G. & Neufeld, J. D. (2012). PANDAseq: paired-end assembler for illumina sequences. BMC Bioinformatics, 13, 31. Masurat, P., Fru, E. C. & Pedersen, K. (2005). Identification of Meiothermus as the dominant genus in a storage system for spent nuclear fuel. J Appl Microbiol, 98(3), 727-40. MeGraw, V. E., Brown, A. R., Boothman, C., Goodacre, R., Morris, K., Sigee, D., Anderson, L. & Lloyd, J. R. (2018). A Novel Adaptation Mechanism Underpinning Algal Colonization of a Nuclear Fuel Storage Pond. MBio, 9(3). Merroun, M. L., Nedelkova, M., Rossberg, A., Henning, C. & Selenska-Pobell, S. (2006). Interaction mechanisms of uranium with bacterial strains isolated from extreme habitats. Radiochim., 94, 723-729. Misal, S. A., Bajoria, V. D., Lingojwar, D. P. & Gawai, K. R. (2013a). Purification and characterization of nitroreductase from red alkaliphilic bacterium Aquiflexum sp. DL6. Applied Biochemistry and Microbiology, 49(3), 227-232. Mortensen, B. M., Haber, M. J., DeJong, J. T., Caslake, L. F. & Nelson, D. C. (2011). Effects of environmental factors on microbial induced calcium carbonate precipitation. J Appl Microbiol, 111(2), 338-49. N.A., J. & J.N., F. (2011). Sickle: A sliding-window, adaptive, quality-based trimming tool for FastQ files (Version 1.33) [Software] [Online]. Available: https://github.com/najoshi/sickle [Accessed]. Neidhardt, F. C., Bloch, P. L. & Smith, D. F. (1974). Culture Medium for Enterobacteria. Journal of Bacteriology 119(3), 736-747. Newsome, L., Morris, K. & Lloyd, J. R. (2014a). The biogeochemistry and bioremediation of uranium and other priority radionuclides. Chemical Geology, 363, 164-184. Newsome, L., Morris, K., Trivedi, D., Atherton, N. & Lloyd, J. R. (2014b). Microbial reduction of uranium(VI) in sediments of different lithologies collected from Sellafield. Applied Geochemistry, 51, 55-64. Niharika, N., Jindal, S., Kaur, J. & Lal, R. (2012). Sphingomonas indica sp. nov., isolated from hexachlorocyclohexane (HCH)-contaminated soil. Int J Syst Evol Microbiol, 62(Pt 12), 2997-3002. Nurk, S., Bankevich, A., Antipov, D., Gurevich, A., Korobeynikov, A., Lapidus, A., Prjibelsky, A., Pyshkin, A., Sirotkin, A., Sirotkin, Y., Stepanauskas, R., McLean, J., Lasken, R., Clingenpeel, S. R., Woyke, T., Tesler, G., Alekseyev, M. A. & Pevzner, P. (2013). Assembling Genomes and Mini-metagenomes from highly chimeric reads. Research in Computational Molecular Biology: 17th Annual International Conferecne, RECOMB 2013, Beijing China, April 2013.
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Peschek, G. A. (1999). Photosynthesis and Respiration of Cyanobacteria. In: Peschek, G. A., Loffelhardt, W. & Schmetterer, G. (eds.) The Phototrophic Prokaryotes. Boston, MA: Springer. Pipíška, M., Trajteľová, Z., Horník, M. & Frišták, V. (2018). Evaluation of Mn bioaccumulation and biosorption by bacteria isolated from spent nuclear fuel pools using 54Mn as a radioindicator. Radiochimica Acta, 106(3), 217-228. Proctor, C. R., Edwards, M. A. & Pruden, A. (2015). Microbial composition of purified waters and implications for regrowth control in municipal water systems. Environmental Science: Water Research & Technology, 1(6), 882-892. Qu, J. H. & Yuan, H. L. (2008). Sediminibacterium salmoneum gen. nov., sp. nov., a member of the phylum Bacteroidetes isolated from sediment of a eutrophic reservoir. Int J Syst Evol Microbiol, 58(Pt 9), 2191-4. Rajala, P., Bomberg, M., Vepsalainen, M. & Carpen, L. (2017). Microbial fouling and corrosion of carbon steel in deep anoxic alkaline groundwater. Biofouling, 33(2), 195-209. Reddy, S. F., Monk, S. D., Nye, D. W., Colling, B. R. & Stanley, S. J. (2012). Proposal to characterise legacy Sellafield ponds using SONAR and RadLine. Appl Radiat Isot, 70(7), 1162-5. Reeder, J. R., Nugent, M., Tait, C. D., Morris, D. E., Heald, S., Beck, K. M., Hess, W. P. & Lanzirotti, A. (2001). Coprecipitation of Uranium (VI) with calcite: XAFS, micro XAS, and luminescence characterization. Geochim. Cosmochim. Acta, 65, 3491-3503. Risso, C., Sun, J., Zhuang, K., Mahadevan, R., DeBoy, R., Ismail, W., Shrivastava, S., Huot, H., Kothari, S., Daugherty, S., Bui, O., Schilling, C. H., Lovley, D. R. & Methe, B. A. (2009). Genome-scale comparison and constraint-based metabolic reconstruction of the facultative anaerobic Fe(III)-reducer Rhodoferax ferrireducens. BMC Genomics, 10, 447. Rivasseau, C., Farhi, E., Compagnon, E., de Gouvion Saint Cyr, D., van Lis, R., Falconet, D., Kuntz, M., Atteia, A. & Coute, A. (2016). Coccomyxa actinabiotis sp. nov. (Trebouxiophyceae, Chlorophyta), a new green microalga living in the spent fuel cooling pool of a nuclear reactor. J Phycol, 52(5), 689-703. Rizoulis, A., Milodowski, A. E., Morris, K. & Lloyd, J. R. (2014). Bacterial Diversity in the Hyperalkaline Allas Springs (Cyprus), a Natural Analogue for Cementitious Radioactive Waste Repository. Geomicrobiology Journal, 33(2), 73-84. Santo Domingo, J. W., Berry, C. J., Summer, M. & Fliermans, C. B. (1998). Microbiology of spent nuclear fuel storage basins. Current Microbiology, 37, 387-394. Sarro, M. I., Garcia, A. M. & Moreno, D. A. (2005). Biofilm formation in spent nuclear fuel pools and bioremediation of radioactive water. International Microbiology, 8, 223-230. Sarró, M. I., Garcia, A. M. & Moreno, D. A. (2005). Biofilm formation in spent nuclear fuel pools and bioremediation of radioactive water. International Microbiology, 8, 223-230. Schwartz, E. & Friedrich, B. (2006). The H2-Metabolizing Prokaryotes. 496-563.
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Sellafield-Ltd (2010). Groundwater Annual Report 2010. In: Decommissioning, S. R. (ed.). Nuclear Decommissioning Authority. Sellafield, N. (2015). Spent Fuel Management: Enablers [Online]. Available: https://webarchive.nationalarchives.gov.uk/20170712124354/http://www.sellafieldsit es.com/solution/spent-fuel-management/magnox-reprocessing/enablers/ [Accessed 12/July 2015]. Sezonov, G., Joseleau-Petit, D. & D'Ari, R. (2007). Escherichia coli physiology in Luria-Bertani broth. J Bacteriol, 189(23), 8746-9. Shaw, R. D. (1990). Corrosion prevention and control at Sellafield nuclear fuel reprocessing plant. British Corrosion Journal, 25(2), 97-107. Shoesmith, D. W. (2000). Fuel corrosion processes under waste disposal conditions. Journal of Nuclear Materials 282, 1-31. Silva, R., de Almeida, D. M., Cabral, B. C. A., Dias, V. H. G., Mello, I., Urmenyi, T. P., Woerner, A. E., Neto, R. S. M., Budowle, B. & Nassar, C. A. G. (2018). Microbial enrichment and gene functional categories revealed on the walls of a spent fuel pool of a nuclear power plant. PLoS One, 13(10), e0205228. Singh, P., Kim, Y. J., Hoang, V. A., Farh Mel, A. & Yang, D. C. (2015). Sphingomonas panacis sp. nov., isolated from rhizosphere of rusty ginseng. Antonie Van Leeuwenhoek, 108(3), 711-20. Slatko, B. E., Albright, L. M., Tabor, S. & Ju, J. (2001). DNA Sequencing by the Dideoxy Method. In: Brand, W. (ed.) Current protocols in Molecular Biology. Springell, R., Rennie, S., Costelle, L., Darnbrough, J., Stitt, C., Cocklin, E., Lucas, C., Burrows, R., Sims, H., Wermeille, D., Rawle, J., Nicklin, C., Nuttall, W., Scott, T. & Lander, G. (2014). Water corrosion of spent nuclear fuel: radiolysis driven dissolution at the
UO2/water interface. Faraday Discussions, 180(301). Suzuki, S., Kuenen, J. G., Schipper, K., van der Velde, S., Ishii, S., Wu, A., Sorokin, D. Y., Tenney, A., Meng, X., Morrill, P. L., Kamagata, Y., Muyzer, G. & Nealson, K. H. (2014). Physiological and genomic features of highly alkaliphilic hydrogen-utilizing Betaproteobacteria from a continental serpentinizing site. Nat Commun, 5, 3900. Tamura, K., Nei, M. & Kumar, S. (2004). Prospects for inferring very large phylogenies using the neighbor-joining method. Proceedings of the National Academy of Sciences of the United States of America, 101, 11030-11035
Tiago, I., Chung, A. P., & Veríssimo, A. (2004). Bacterial diversity in a nonsaline alkaline environment: Heterotrophic aerobic populations. Applied and Environmental Microbiology, 70(12), 7378–7387. https://doi.org/10.1128/AEM.70.12.7378-7387.2004
Tišáková, L., Pipíška, M., Godány, A., Horník, M., Vidová, B. & Augustín, J. (2012). Bioaccumulation of 137Cs and 60Co by bacteria isolated from spent nuclear fuel pools. Journal of Radioanalytical and Nuclear Chemistry, 295(1), 737-748.
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Tomioka, N., Uchiyama, H. & Yagi, O. (1992). Isolation and Characterization of Cesium- Accumulating Bacteria. APPLIED AND ENVIRONMENTAL MICROBIOLOGY, 58(3), 1019-1023. Turner, S., Pryer, K. M., Maio, V. P. W. & Palmer, J. D. (1999). Investigating deep phylogenetic relationships among Cyanobacteria and Plastids by small subunit rRNA Sequence analysis. J. Eukaryot. Microbiol., 46(4), 327-338. Van Roy, S., Peys, K., Dresselaers, T. & Diels, L. (1997). The use of Alcaligenes eutrophus biofilm in a membrane bioreactor for heavy metal recovery. Res. Microbiol., 148, 526- 528. Vetrovsky, T. & Baldrian, P. (2013). The variability of the 16S rRNA gene in bacterial genomes and its consequences for bacterial community analyses. PLoS One, 8(2), e57923. Wang, C., Deng, S., Liu, X., Yao, L., Shi, C., Jiang, J., Kwon, S. W., He, J. & Li, J. (2016). Roseomonas eburnea sp. nov., isolated from activated sludge. Int J Syst Evol Microbiol, 66(1), 385-90. Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. (2007). Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol, 73(16), 5261-7. White, C. & Gadd, G. M. (1990). Biosorption of Radionuclides by Fungal Biomass. J. Chem. Tech. Biotechnol., 49, 331-343. White, C., Sharman, A. K. & Gadd, G. M. (1998). An integrated microbial process for the bioremediation of soil contaminated with toxic metals Nature Biotechnology, 16(572- 575). Willems, A., Busse, J., Goor, M., Pot, B., Falsen, E., Jantzen, E., Hoste, B., Gillis, M., Kersters, K., Auling, G. & De Ley, J. (1989). Hydrogenophaga, a New Genus of Hydrogen- Oxidizing Bacteria That Includes Hydrogenophaga flava comb. nov. (Formerly Pseudomonas flava), Hydrogenophaga palleronii (Formerly Pseudomonas palleronii), Hydrogenophaga pseudoflava (Formerly Pseudomonas pseudoflava and “Pseudomonas carboxydoflava ”), and Hydrogenophaga taeniospiralis (Formerly Pseudomonas taeniospiralis). INTERNATIONAL JOURNAL OF SYSTEMATIC BACTERIOLOGY, 39(3), 319-333. Wingender, J., Thomas, R. N. & Flemming, H. C. (1999). Microbial Extracellular Polymeric. Substances Characterization, Structure and Function Berlin, Germany. WNA, W. N. A. (2006). World Nuclear Performance Report 2016. London, UK. WNA, W. N. A. (2018a). Nuclear Power in the United Kingdom [Online]. Available: http://www.world-nuclear.org/information-library/country-profiles/countries-t-z/united- kingdom.aspx [Accessed]. WNA, W. N. A. (2018b). Radioactive Waste Management [Online]. London, UK. Available: http://www.world-nuclear.org/information-library/nuclear-fuel-cycle/nuclear- wastes/radioactive-waste-management.aspx [Accessed].
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Wolfram, J. H., Mizia, R. E., Jex, R., Nelson, L. & Garcia, K. M. (1996). The Impact of Microbially Influenced Corrosion on Spent Nuclear Fuel and Storage Life. In: Laboratory, I. N. E. (ed.). USA: U.S. Department of Energy. Yoon, J. H., Lee, M. H. & Oh, T. K. (2004). Porphyrobacter donghaensis sp. nov., isolated from sea water of the East Sea in Korea. Int J Syst Evol Microbiol, 54(Pt 6), 2231-5. Yoon, K. S., Tsukada, N., Sakai, Y., Ishii, M., Igarashi, Y. & Nishihara, H. (2008). Isolation and characterization of a new facultatively autotrophic hydrogen-oxidizing Betaproteobacterium, Hydrogenophaga sp. AH-24. FEMS Microbiol Lett, 278(1), 94- 100. Yu, J. (2018). Fixation of carbon dioxide by a hydrogen-oxidizing bacterium for value-added products. World J Microbiol Biotechnol, 34(7), 89. Yurkov, V., Stackebrandt, E., Holmes, A., Fuerst, J. A., Hugenholtz, P., Golecki, J., Gad'on, N., Gorlenko, V. M., Kompantseva, E. I. & Drews, G. (1994). Phylogenetic positions of novel aerobic, bacteriochlorophyll a-containing bacteria and description of Roseococcus thiosulfatophilus gen. nov., sp. nov., Erythromicrobium ramosum gen. nov., sp. nov., and Erythrobacter litoralis sp. nov. Int J Syst Bacteriol, 44(3), 427-34. Yurkov, V. V. (2015). Roseococcus. Bergey's Manual of Systematics of Archaea and Bacteria. Zhu, T. & Dittrich, M. (2016). Carbonate Precipitation through Microbial Activities in Natural Environment, and Their Potential in Biotechnology: A Review. Front Bioeng Biotechnol, 4, 4.
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Research Paper: Comparative metagenomic analyses of taxonomic and metabolic diversity of microbiomes from spent nuclear fuel storage ponds
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Chapter 5 Comparative metagenomic analyses of taxonomic and
metabolic diversity of microbiomes from spent nuclear fuel storage
ponds
S. Ruiz-Lopez1, Nick Cole2, Ho Kyung Song1, Lynn Foster1, Chris Boothman1, Jonathan R.
Lloyd1
1 School of Earth and Environmental Sciences, University of Manchester Oxford Road,
Manchester, M13 9PL
2 Sellafield Ltd, Hinton House, Birchwood Park Ave, Birchwood, Warrington WA3 6GR
Corresponding author: [email protected]
Abstract
Nuclear power is an important energy source that can compensate for carbon emissions from fossil fuel power plants. However, processing of radioactive waste from nuclear plants is a significant challenge. The current treatment prior to final geological disposal involves wet storage of spent fuel in designated ponds, and microbial colonisation of these ponds can complicate plant operation.
To help identify the key microbes that colonise hydraulically interlinked spent fuel storage ponds at Sellafield, UK, a series of samples were collected and analysed using next generation (Illumina) sequencing. Samples were taken from the facility´s indoor hyper-alkaline pond (INP) (feeding head tank, main and subponds), and also from the open-air First-
Generation Magnox Storage Pond (FGMSP) and its auxiliary pond (Aux). 16S rRNA gene sequencing revealed that the INP is colonized mainly by Bacteria (99%), affiliated with species of orders Burkholderiales, Sphingomonadales, Nitrosomonadales, Sphingobacteriales
(including representatives of the genera Curvibacter, Rhodoferax, Sphingomonas and
Roseococcus,) in addition to the hydrogen-oxidising bacterium Hydrogenophaga. In contrast, the open-air ponds contained species of Hydrogenophaga, Nevskia, and Roseococcus, and also photosynthetic cyanobacteria (Pseudanabaena).
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Biological function of the microbiomes within the fuel storage ponds was also assessed by metagenomic sequencing and analyses. The most abundant genes associated with respiration, stress responses, DNA metabolism, cell wall and capsule synthesis and photosynthesis were analysed. Genes underpinning hydrogen metabolism were more heavily represented in the indoor pond samples, whilst photosynthesis genes were more abundant in the open-air ponds, supporting the hypothesis that hydrogen (from water radiolysis) and light energy supported ecosystem development in the indoor and outdoor ponds respectively.
These datasets give valuable insight into the microbial communities inhabiting nuclear storage facilities, the metabolic processes that potentially underpin their colonisation and ultimately can help inform appropriate microbial growth control strategies.
Introduction
The nuclear fuel cycle has supported a broad range of activities including power generation, medical applications, defence and research, and through these activities has created a significant legacy of radioactive waste around the world. The UK and other countries have developed strategies for the safe long-term management of radioactive waste forms, including the higher-activity wastes from energy generation, where the final destination will be geological disposal into the subsurface (NDA 2010).
Prior to reprocessing or final disposal, high level waste (HLW), including nuclear fuel materials, is stored in water-cooled, stainless steel tanks with thick concrete walls to shield operators from the high radiation levels (NDA 2010). Spent fuel storage ponds are often filled with demineralized water and sodium hydroxide is added as corrosion inhibitor, which could also impact on microbial colonisation (IAEA 1997). However, although base addition has proved efficient to minimise corrosion of spent fuel , it has not prevented microbial colonisation
(Chicote et al. 2005) (Bohus et al. 2010). Microorganisms detected in spent fuel storage ponds may include fungi (Basidiomycota and Ascomycota), bacteria associated to Proteobacteria,
Actinobacteria, Firmicutes and Cyanobacteria and even eukaryotic microalgae (Silva et al.
2018a) (MeGraw et al. 2018) (Foster 2018). The presence of microbes in spent fuel ponds
(SFP) is critical to plant operation as microbial growth can cause turbidity in the water, making fuel inspection and inventory management challenging (Chicote et al. 2004). Microorganisms
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Sharon L. Ruiz Lopez PhD Thesis can also interact with the storage racks leading to microbiologically induced corrosion (MIC) of the stored material (Wolfram et al. 1996b) (Chicote et al. 2004), while the accumulation of radioactive microbial biomass can pose an addition disposal challenge.
Although the oligotrophic pond conditions imposed, often alongside high pH treatment, are intended to limit microbial growth, several studies have suggested a variety of metabolisms to explain the abundance of microorganisms in extreme environments (Sarró et al. 2005) (Santo
Domingo et al. 1998;Rivasseau et al. 2016). Organisms that are adapted to grow optimally at or near extreme ranges of environmental variables, such as radioactivity or hyper-alkalinity, are called extremophiles. Extremophiles organisms display a rage of metabolic abilities coupled with extraordinary physiological capacities to colonize the surrounding environment such as photosynthesis and the metabolism of alternative energy sources including hydrogen, methane, sulphur and even iron (Kristjánsson and Hreggvidsson 1995), (Pedersen et al. 2004)
(Joshi et al. 2008), (Nazina et al. 2010), (Liu et al. 2009), (Merroun and Selenska-Pobell 2008),
(Ragon et al. 2011) (Sarró et al. 2005).
Microbial adaptation strategies vary across the environment of study (Rampelotto 2013). For instance, to cope with hyper-alkaline environments (pH>10), molecular strategies comprise the activation of both symporter and antiporter systems (Orellana et al. 2018) which allow the exchange/uptake of Na+ and other solutes into the cells (Rothschild and Mancinelli 2001); and the physiological high internal buffer capacity maintains the homeostasis and thermodynamic stability of the cells (Krulwich et al. 1998). Microbial adaptations to radiation include more genome copies for genome redundancy, efficient machinery for DNA repair (Byrne et al.
2014), a condensed nucleoid that may prevent the dispersion of DNA fragments (Confalonieri and Sommer 2011), utilization of smaller amino acids that allow the accumulation of Mn2+- peptide for protecting irradiated cytosolic enzymes from ROS (Sghaier et al. 2013), accumulation of Mn(II) that facilitates recovery from radiation injury (Daly et al. 2004), induction of chaperones and active defence against UV-induced oxidative stress (Webb and
DiRuggiero 2013). Deinococcus radiodurans, a widely studied radio-tolerant microorganism, has adapted to radioactive sites by containing a unique repair mechanism that reassembles fragmented DNA (Battista 1997). Additionally phenotypic changes to survive in radiation
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Sharon L. Ruiz Lopez PhD Thesis environments include the production of pigments (Mojib et al. 2013) (MeGraw et al. 2018)
(Asker et al. 2007) and the production of polysaccharides (Foster 2018).
Radiation, in particular UV and gamma rays, can impact directly on microbial populations and indirectly via formation of secondary metabolites by the interaction of radiation in the containing medium (Merino et al. 2019). The storage of irradiated material can promote the
- production of molecular hydrogen, hydrogen peroxide and other radicals (OH•, O2 •) by radiolysis of water or embedding matrices (Libert et al. 2011). In such environments hydrogen can be an important electron and energy source for bacterial growth (Libert et al. 2011) (Gales et al. 2004) (Pedersen 2000). Molecular hydrogen has demonstrated to be an essential energy source for several microorganisms including strains of Proteobacteria on basins containing irradiated waste material (Gales et al. 2004) (Pedersen 1999) (Pedersen et al. 2004)
(Pedersen 1997). Alternatively on oligotrophic open-light systems, variant photosynthetic electron flow has been suggested (Morel and Price 2003); findings showed that bacteria associated to Cyanobacteria may be able to route electrons derived from the splitting of H2O
+ to the reduction of O2 and H in a water-to-water cycle to satisfy their energetic and nutritive requirements (Grossman et al. 2010).
Furthermore microorganisms display mechanisms to interact with radionuclides present on nuclear waste materials leading to changes in radionuclide solubility via bioreduction, biosorption and biomineralization reactions (Bruhn et al. 2009) (Shukla et al. 2017) (Cheng et al. 2009) (Lloyd and Macaskie 2002) (Newsome et al. 2014b) (Tišáková et al. 2012).
A key challenge in studying the microbial ecology of extremely radioactive environments such as SFPs is the difficulty in collecting and processing samples from tightly regulated, highly radioactive nuclear facilities. However, the development of cultivation-independent techniques, including metagenomic analyses (Solden et al. 2016), has the potential to open up these challenging environments for study. For example, recent studies (MeGraw et al.
2018) (Foster 2018) have shown that DNA can be extracted and separated from highly active radionuclides in controlled laboratories on a nuclear site, and then sequenced and analysed in non-active facilities elsewhere, facilitating detailed microbiome characterisation. To date,
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Sharon L. Ruiz Lopez PhD Thesis however, such studies have focused on high throughput 16S and 18S rRNA gene sequencing, and have not made use the latest advances in metagenomic sequencing.
In this study the microbial communities present in three distinct but hydraulically linked storage ponds characterised using a combination of 16S rRNA gene and whole genome shotgun sequencing. Results from the 16S rRNA gene sequencing provided a more accurate picture of the taxonomic composition than the SEED-based whole genome sequencing approach
(Steven et al. 2012). However, information on the functional potential of the microbiomes in the ponds was limited using the SSU rRNA approaches, and the functional potential was more comprehensively understood by metagenomics and together, SSU rRNA and metagenomics approaches were able to provide a wide and more complete insight of the microbial adaptations such as the potential energy sources used by the microbial communities in situ, the metabolic/defense adaptive mechanisms occurring within radioactive, hyper-alkaline and oligotrophic environments and the key differences between the microbial systems in the contrasting open-air and indoor storage ponds.
Materials and methods
Samples
In the present study three spent fuel ponds were analysed; an indoor pond (INP) and its feeding tank area (FT); and an open-air first Generation Magnox Storage pond (FGMSP) and its auxiliary open-air system (Aux). The presence of microbial blooms has previously detected on the FGMSP and Aux pond; whilst on the indoor pond (INP), their presence has not been detected (Foster et al. 2019a;MeGraw et al. 2018).
The pond system is located in Sellafield, Cumbria UK. The INP receives and stores metal fuel and legacy spent fuel from outdoor ponds (including the FGMSP) for interim storage pending a long term disposal solution available. The FGMSP receives water from the INP for the pond purge which enters the pond at a different location to the main purge water (Figure 5.1) (NDA
2015) (ONR 2016).
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The storage conditions are similar: ponds are filled with demineralized water and in order to avoid corrosion caustic solution is added to create an alkaline environment (pH approx. 11.6); therefore, the spent fuel ponds represent extreme oligotrophic, hyper-alkaline and radioactive environments.
The Indoor Storage Pond (INP) is an indoor pond complex divided into 3 main ponds and 3 subponds linked by a transfer channel that enables water flow. In order to control the pond- water activity and quality, there is a continuous “once through” purge flow; pond-water from the main ponds flows into the transfer channel and enters the recirculation pump chamber where it is continuously pumped round a closed circulation loop and through a heat exchanger system, which cools the pond-water before it is recycled into the main ponds. Through the control feed, purge and re-circulation flow rates, the water depth is maintained at 7±0.05m.
The purge flow can be either from a donor plant or from other hydraulically linked ponds within the Sellafield complex (e.g. FGMSP). The temperature and pH are controlled at 15⁰C and 11.6 respectively. Analysed samples were taken from designated sample points on the “Feeding
Tank” of the donor plant, where the demineralised water used to feed the complex is stored, and main ponds 2 and 3 of the Fuel Handling Plant.
The FGMSP is the primary storage pond for legacy Magnox spent fuel. The pond is continuously purged with alkaline dosed demineralised water at a pH of 11.4, from an East to
Westerly direction along the length of the pond, and contains an outflow point, where water is removed from the pond, on the Western wall. There are two further feeds into the pond, the first enters the pond at a location along the Northern wall and contains alkaline dosed water
(pH ~11.4) from another fuel handling pond facility on site.
The auxiliary settling tank (auxiliary pond) is directly connected to the legacy pond (FGMSP), and if the water levels are sufficiently high, the auxiliary pond feeds the alkaline legacy pond along the South wall.
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Figure 5.1Storage pond systems. Metal and legacy spent fuels from outdoor ponds are transported to the INP for interim storage pending a long term disposal solution available. The INP is divided in 3 main ponds (MP), 3 subponds and a feeding tank area (FT); waters from the INP are recirculated to the FGSMP during purging times. The FGMSP and its Auxiliary pond (Aux) store legacy fuel pond (NDA 2015;ONR 2016).
A total of 10 samples were taken from different sites from the storage ponds between 2016 and 2018 (Table 5.1). Samples were collected from a depth of 1 m using a hose syringe to withdraw the water into sterile plastic bottles. In order to avoid any risk of contamination, samples transferred directly from the pond to the NNL Central Laboratories (National Nuclear
Laboratory, Cumbria UK), where DNA was extracted and the samples where checked for radioactivity in line with the Environmental Permits and Nuclear Site licences held by Sellafield
Ltd. Extracted DNA samples free from significant radionuclide contamination were shipped to the University of Manchester and stored at -20⁰C until use.
Table 5.1Samples distribution
Sample Storage pond Conditions Date INP_FT01 INP, feeding tank area Indoor pond October 2016 INP_FT02 INP, feeding tank area Indoor pond October 2016
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INP_MP01 INP, main pond 2 Indoor pond October 2017 INP_MP02 INP, main pond 3 Indoor pond October 2017 INP_SP01 INP, Subpond 2 Indoor pond January 2018 INP_SP02 INP, Subpond 3 Indoor pond January 2018 FGMSP FGMSP Open-air system September 2017 Aux01 Auxiliary Open-air system May 2016 Aux02 Auxiliary Open-air system June 2017 Aux03 Auxiliary Open-air system September 2017
Methods
Sequencing and sequence processing
DNA extraction was conducted at the Central Laboratories s at NNL on the Sellafield site, from filtered biomass using a PowerWater DNA Isolation Kit (Mobio Laboratories, Inc., Carlsbad
California, USA). After appropriate radiometric analyses, the DNA was then transported to the
Manchester University laboratories for amplification and analyses.
PCR amplification was performed from the extracted DNA using a Techne Thermocycler
(Cole-Parmer, Staffordshire, UK). Primers used for bacterial 16S rRNA gene amplification were the broad-specificity 8F forward primer and the reverse primer 1492R (Eden et al.
1991a), while primers used for eukaryote 18S rRNA gene amplification were Euk F forward primer and the reverse primer Euk R (DeLong 1992b) and primers used for the archaea 16S rRNA gene amplification were forward primer 21F and reverse primer 958R (DeLong 1992b).
The PCR reaction mixture contained; 5 µl PCR buffer, 4 µl 10 mM dNTP solution (2.5mM each nucleotide), 1 µl of 25 µM forward primer, 1 µl of 25 µM forward reverse and 0.3 µl Ex Takara
Taq DNA Polymerase, which was made up to a final volume of 50μL with sterile water, and finally 2µL of sample was added to each tube. The thermal cycling protocol used was as follows for the bacterial 8F and 1492R primers; initial denaturation at 94°C for 4 minutes, melting at 94°C for 30 seconds, annealing at 55°C for 30 seconds, extension at 72°C for 1 minute (35 cycles with a final extension at 72°C for 5 minutes, Eden et al., 1991). For eukaryotic 18S rRNA gene amplification, the temperature cycle was; initial denaturation at
94°C for 2 minutes, melting at 94⁰C for 30 seconds, annealing at 55°C for 1.5 minutes, extension at 72oC for 1.5 minutes for a total of 30 cycles and final extension at 72⁰C for 5
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Sharon L. Ruiz Lopez PhD Thesis minutes (DeLong 1992b). For archaeal 16S rRNA genes the thermal cycle protocol consisted of an initial denaturation step at 94°C for 4 minutes, melting at 94⁰C for 45 seconds, annealing at 55°C for 30 seconds, extension at 72oC for 1 minute (for a total of 30 cycles) and a final extension step at 72⁰C for 5 minutes (DeLong 1992b).
The purity of the amplified PCR products was determined by electrophoresis using a 1% (w/v) agarose gel in 1X TAE buffer (Tris-acetic acid-EDTA). DNA was stained with SYBER safe
DNA gel stain (Thermofisher), and then viewed under short-wave UV light using a BioRad
Geldoc 2000 system (BioRad, Hemel Hempstead, Herts, UK).
The 16S rRNA gene PCR amplicons was sequenced using the Illumina MiSeq platform
(Illumina, San Diego, CA, USA) targeting the V4 hyper variable region (forward primer, 515F,
5′-GTGYCAGCMGCCGCGGTAA-3′; reverse primer, 806R, 5′-
GGACTACHVGGGTWTCTAAT-3′) for 2 × 250-bp paired-end sequencing (Illumina)
(Caporaso et al. 2011) (Caporaso et al. 2012). PCR amplification was performed using the
Roche FastStart High Fidelity PCR System (Roche Diagnostics Ltd, Burgess Hill, UK) in 50μl reactions under the following conditions; initial denaturation at 95°C for 2 min, followed by 36 cycles of 95°C for 30 s, 55°C for 30 s, 72°C for 1 min, and a final extension step of 5 min at
72°C. The PCR products were purified and normalised to ~20ng each using the SequalPrep
Normalization Kit (Fisher Scientific, Loughborough, UK). The PCR amplicons from all samples were pooled in equimolar ratios. The run was performed using a 4pM sample library spiked with 4pM PhiX to a final concentration of 10% following the method of Schloss and Kozich
(Kozich et al. 2013).
For targeting the V9 eukaryotic 18S rRNA gene sequencing primers 1319F and EukBR were used for 2 × 250-bp paired-end sequencing under the following conditions, initial denaturation at 95⁰C for 2 min followed by 36 cycles of 95⁰C for 30 s, 72⁰C for 1 min and final extension of
5 min at 72⁰C (Amaral-Zettler et al. 2009).
Raw sequences were divided into samples by barcodes (up to one mismatch was permitted) using a sequencing pipeline. Quality control and trimming was performed using Cutadapt
(Martin 2011), FastQC (B.I. 2016), and Sickle (N.A. and J.N. 2011). MiSeq error correction
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Sharon L. Ruiz Lopez PhD Thesis was performed using SPADes (Nurk et al. 2013). Forward and reverse reads were incorporated into full-length sequences with Pandaseq (Masella et al. 2012). Chimeras were removed using ChimeraSlayer (Haas et al. 2011), and OTU’s were generated with UPARSE
(Edgar 2013). OTUs were classified by VSEARCH (Edgar 2010) at the 97% similarity level, and singletons were removed. Rarefaction analysis was conducted using the original detected
OTUs in Qiime (Caporaso et al. 2010a). The taxonomic assignment was performed by the
RDP classifier (Wang et al. 2007). Sequences obtained were compared with the NCBI
GenBank database to find the similar organisms (https://www.ncbi.nlm.nih.gov/genbank/).
18S rRNA gene taxonomic assignment was performed by UCLUST using the Silva119 database (Quast et al. 2013).
Whole genome sequencing was achieved using the Illumina Hiseq2000 platform at Celemics
(Celemics, Inc., Seoul, Korea). Raw sequences were uploaded to the Metagenomics Rapid
Annotation using Subsystems Technology (MG-RAST) (Meyer et al. 2008) online server for taxonomic and functional annotation under the project name “Spent fuel storage ponds_UoM”,
‘ID 86418’. The RefSeq database (Pruitt et al. 2007) was chosen for taxonomic annotation and the SEED database (Overbeek et al. 2005) was used for functional annotation. The MG-
RAST default parameters (maximum e-value cutoff of 10-5, minimum % identity cutoff of 60% and minimum alignment length cutoff as 15bp) were used for annotation of the sequences. All of the Illumina reads that were shorter than 35 bases or had a median quality score below 20 were removed.
Results
Microbial diversity on the indoor spent fuel storage pond (INP)
Six 16S rRNA gene amplicon libraries were generated from DNA extracted from an indoor pond collected over the 18-month sampling period focusing on three different areas of the pond complex. Two samples were taken from the feeding tank (INP_FT, October 2016), two from the main ponds (INP_MP, October 2017) and two from the subponds (INP_SP, January
2018).
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Analysis of PCR amplified 16S rRNA genes showed that the microbial population was predominantly bacterial. Neither archaeal 16S rRNA or eukaryotic 18S SSU rRNA genes were amplified by PCR. Averaged samples from the feeding tank (INP_FT, October 2016) were dominated by Proteobacteria (74%) and Bacteroidetes (16%). The most abundant genera identified were Curvibacter (21%, 1 OTU), Rhodoferax (19%, 1 OTU), Sediminacterium (10%,
1 OTU), Polaromonas (5%, 1 OTU) and Novoshpingobium (4%, 2 OTUs). Although members from phylum Cyanobacteria were detected exclusively on the feeding area (INP_FT), their abundance represented only 1.5% (3 OTUs). Approximately 30% of the OTUs (26) could not be identified through sequence homology to known organisms.
The microbial communities in the main ponds (INP_MP, October 2017) were dominated by
Proteobacteria (94%) and Bacteroidetes (6%). The most abundant genera identified were
Hydrogenophaga (~40%, 1 OTU), Methylotenera (~21%, 1 OTU), Porphyrobacter (~25%, 1
OTU), Roseococcus (~10%, 2 OTUs) and Silanimonas (~5%, 2 OTUs). Unidentified organisms represented 0.5% (60 OTUs) and 10.63% (74 OTUs) for the INP_MP01 and
INP_MP02 samples respectively.
Averaged samples from the subponds (INP_SP, January 2018) showed similar microbial distribution to the main ponds, dominated by representatives of the phyla Proteobacteria
(86%), Bacteroidetes (6%) and Actinobacteria (6%). The most abundant genera identified were Hydrogenophaga (up to 35%, 1 OTU), Porphyrobacter (30%, 1 OTU), Methylotenera
(18%, 1 OTU), Silanimonas (11%, 2 OTUs) and Polynucleobacter (7%, 3 OTUs). Unidentified organisms represented 0.95% (74 OTUs) and 8.76% (74 OTUs) for each sample.
Microbial identification by metagenomics using the MG-RAST tools (Meyer et al. 2008) confirmed that the community was dominated by bacteria (98%). Contrasting to the SSU amplification targeting the 18S rRNA gene, eukaryotic sequences were identified but represented less than 2% (Supplementary Figure 5.1) mainly dominated by phyla Chordata,
Cnidaria and Streptophyta (Supplementary Figure 5.2). Despite the contrasting microbial diversity detected at the genus level from whole genome sequencing, the most abundant organisms (identified by both approaches) belonged to the same orders.
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Microbial diversity on the legacy First Generation Magnox Storage Pond (FGMSP)
Due to challenges associated with sampling from this higher activity nuclear facility, only one representative sample was analysed from September 2017. Previous studies on the FGMSP showed the pond experienced the presence of microbial blooms during which the visibility within the pond was significantly reduced, hampering pond management procedures (Foster
2018). The sample obtained for this study was obtained at the end of a bloom period; 16S rRNA gene amplification and sequencing revealed that the microbial community was mainly dominated by Proteobacteria (83.7%) and Bacteroidetes (15.4%) (Supplementary Fig 5.3a); dominated by genera Hydrogenophaga, Nevskia, Roseococcus, Belliela, Rhodobacter and
Porphyrobacter. Unidentified organisms represented 1.9% of the total sequences (Foster
2018).
In contrast, whole genome sequencing revealed that the microbial profile was dominated by
Proteobacteria (90%), Bacteroidetes (3.35%), Actinobacteria (2.71%) and Cyanobacteria
(1%). The most abundant genera were Rhodobacter (9.93%, Rhodobacterales), Acidovorax
(5.45%, Burkholderiales), Erythrobacter (4.12%, Sphingomonadales), Polaromonas (3.42%,
Burkholderiales), Pseudomonas (2.58%, Pseudomonadales) and Burkholderia (2.29%,
Burkholderiales) (Supplementary Figure 5.3b). Sequences affiliated to eukaryotic genes represented 0.9% relative abundance.
Microbial diversity on the auxiliary outdoor spent fuel storage pond (Aux)
Three samples were taken at three different operational times; Aux01 (May 2016), Aux02
(June 2017) and Aux03 (September 2017). The 16S rRNA community profile revealed that the microbial composition from the sample taken on May 2016 (Aux01) was dominated by
Bacteroidetes (40.1%) and the most abundant genera identified were Algoriphagus (12.5%),
Porphyrobacter (11.6%) and Prosthecobacter (7%). The sample Aux02 (June 2017) contained a large proportion of unidentified OTUs (32.2%); the remaining OTUs were ascribed to genera
Flavobacterium (18.8%), Verrumicrobia (12%), Limnohabitans (9.7%) and Polynucleobacter
(7.9%). Finally, a sample taken on September 2017 (Aux03) also contained a large proportion of unidentified OTUs (28.8%); the remaining OTUs were affiliated to Polynucleobacter (15.9%) and contrasting to the previous auxiliary samples, members affiliated with the phylum
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Cyanobacteria (affiliated to genus Cyanobium and unidentified Cyanobacteria) represented a major component (up to 25%) exclusively in sample Aux03 (Supplementary Fig 5.5a).
A whole genome sequencing approach identified a community profile that was dominated by
Proteobacteria (58.5%), Bacteroidetes (18.13%), Cyanobacteria (5.8%), Actinobacteria (4%) and Verrumicrobia (2.43%) (Supplementary Figure 5.3b). The most abundant genera were
Polynucleobacter (6.8%, Burkholderiales), Erythrobacter (4.69%, Sphingomonadales),
Flavobacterium (3.8%, Flavobacteriales), Synechococcus (3%, Chrococcales), Algoriphagus
(2.39%, Cytophagales) and Acidovorax (2.33%, Burkholderiales) (Supplementary Fig 5.5b).
Overall, the use of targeted PCR 16S rRNA amplification and sequencing versus metagenomic sequencing gave results that were similar for each of the sites at the phylum, class and order levels, but identification to the genus level differed (see Supplementary
Figures 5.3, 5.4 and 5.5 for more information).
Figure 5.2 shows the microbial distribution at the order level. The most abundant orders were
Burkholderiales, Sphingomonadales, Xanthomonadales, Flavobacteriales and
Nitrosomonadales. Organisms affiliated with photosynthetic Cyanobacteria (Synechococcales and Croococcales) were identified exclusively with the open pond samples (OUT).
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100% Others Rhizobiales 90% Acidimicrobidae 80% Actinomycetales Caulobacterales 70% Synechococcales
60% Planctomucetales Unclassified Planctomycetes 50% Unidibacterium Verrucomicrobiales 40% Unclassified Cyanobacteria Relative abundance 30% Flavobacteriales Sphingobacteriales 20% Rhodobacteriales
10% Rhodospirillales Sphingomonadales 0% Nitrosomonadales Unclassified Verrumicrobia Xanthomonadales Unidentified INP_FT01_Oct16INP_FT02_Oct16INP_MP01_Oct17INP_MP02_Oct17INP_SP01_Jan18INP_SP02_Jan18 OUT_Aux02_Jun17 OUT_FGMSP_Sept17OUT_Aux01_May16OUT_Aux03_Sept17 Burkholderiales
Figure 5.2 Microbial distribution at order level targeting the 16S rRNA gene. Only components that represented relative abundance higher than 1.5% are shown
Microbial diversity of eukaryotic organisms Although eukaryotic organisms, including fungi, were not major contributors, their presence was detected targeting the 18S rRNA gene, was exclusively detected in the open-air ponds
(detailed information shown in Supplementary Figure 5.6a). There was a greater difference between the Eukaryotic profiles obtained from targeted PCR amplifications and sequencing versus metagenomic sequencing of DNA from the open-air ponds
Metagenomics sequencing revealed the presence of eukaryotic sequences in the indoor ponds; however, the relative abundance represented less than 1.5%. The most abundant class identified by metagenomics on the open-air ponds were associated to
Oligohymenophorea, Saccharomycetes, Euromycetes and Bacillaryiophyceae
(Supplementary Figure 5.6b).
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Functional classification
To have a better insight into functional diversity metagenomes were uploaded to the MGRAST server to annotate functional genes. More than 80% of the total reads were annotated following the standard MGRAST features; detailed information is shown in Supplementary 5.7.
MG-RAST platform was used to correlate specific categories with the corresponding organisms where functional genes were more abundant. Since this approach to whole genome sequencing may not show the precise taxonomic description at genus level, functional genes were correlated with the appropriate organism at the order level.
Relative abundances of sequences were assigned to a subsystem (SEED, level 1). Relative abundance of functional genes detected within the indoor alkaline pond (INP) and the open- air ponds, FGMSP and auxiliary (Aux), is shown in Supplementary information (Figure 5.8 and
Figure 5.9). Overall 45% of the identifiable genes were associated with clustering-based systems, functional coupling evidence but unknown function (~12%), carbohydrates (~10%), amino acids and derivatives (~10%), protein metabolism and cofactors (~8%) and vitamins, prosthetic groups and pigments (~6.3%).
The subsystems Approach to Genome Annotation SEED (Overbeek et al. 2005) level 1 functional genes were standardized to create a heatmap (Figure 5.3) in order to identify the contrasting differences among the sampling sites and times. Functional genes related to membrane transport, carbohydrates, stress response, potassium metabolism and motility and chemotaxis were more abundant on the indoor pond and feeding tank area (INP_FT, Oct 16).
Genes related to DNA metabolism, respiration, cell division and cell cycle and regulation and cell signalling were more abundant on the main indoor pond and related subponds (INP_MP,
Oct 17 and INP_SP, Jan 18). Genes related to nucleosides and nucleotides, metabolism of aromatic compounds, fatty acids, lipids and isoprenoids, respiration and motility and chemotaxis were the most relatively abundant on the legacy alkaline open pond FGMSP
(OUT_FGMSP_Sept17). Finally, genes related to photosynthesis, miscellaneous, secondary metabolisms, dormancy and sporulation, nucleosides and nucleotides, RNA metabolism and protein metabolism were more abundant in the outdoor auxiliary pond. Although the functional genes were consistent in the Auxiliary pond through the sampling times, genetic differences
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Sharon L. Ruiz Lopez PhD Thesis were found relating to phages and transposable elements that were more abundant in sample
Aux01 (May 16), while genes related to nucleosides and nucleotides were more abundant in sample Aux02 (Jun 17), and genes related to cell wall and capsule were more relatively abundant on sample Aux03 (Sept 17).
Figure 5.3 Functional categories associated to Level 1 subsystems (Level 1, KEGG) among the sampling sites and times
Comparative analysis revealed contrasting differences in the relative abundance of key genes related to respiration, DNA metabolism, photosynthesis and stress response; hence specific functions at level 3 from the KEGG database, and their associations with microorganisms
(SEED database) were further analysed as follows:
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Respiration
The relative abundance of genes related to respiration were also analysed using the MGRAST server (Fig 5.4). The respiratory complex I was a major component on all sampling times and sites. Specific genes related to hydrogenases and [Ni-Fe]-hydrogenase maturation process were most highly represented in the main and subpond samples (INP_MP, Oct 17 and
INP_SP, Jan 18) and the legacy FGMSP (Sept 17).
5
4.5
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2.5
2 Relative abundance (%)
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0.5
0
INP_FT01_Oct16INP_FT02_Oct16INP_MP01_Oct17INP_MP02_Oct17INP_SP01_Jan18INP_SP02_Jan18 OUT_Aux02_Jun17 OUT_FGMSP_Sept17OUT_Aux01_May16 OUT_Aux03_Sept17
Respiratory_Complex_I F0F1-type_ATP_synthase
Terminal_cytochrome_C_oxidases Hydrogenases
Formate_hydrogenase Biogenesis_of_c-type_cytochromes
Respiratory_dehydrogenases_1 Ubiquinone_Menaquinone-cytochrome_c_reductase_complexes
Anaerobic_respiratory_reductases Succinate_dehydrogenase
Soluble_cytochromes_and_functionally_related_electron_carriers NiFe_hydrogenase_maturation
Biogenesis_of_cytochrome_c_oxidases Quinone_oxidoreductase_family
Figure 5.4 Relative abundance of genes related to respiration processes (level 3 subsystems, KEGG database)
Genes related to hydrogenases were mostly detected in the samples from the main and subponds (INP_MP and INP_SP) and also the FGMSP samples, and were primarily
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Sharon L. Ruiz Lopez PhD Thesis associated with organisms from order Burkholderiales (Proteobacteria) (Supplementary
Figure 5.10a). Similar results were observed when analysing genes related to [NiFe]- hydrogenases maturation process, which were also more abundant in the main and subponds and FGMSP. Again, these were associated to order Burkholderiales and Rhizobiales
(Proteobacteria) (Supplementary Figure 5.10b). It is interesting to note that the Genus
Hydrogenophaga that featured heavily in these samples (from targeted 16S rRNA gene amplification and sequencing) is a member of the order Burkholderiales.
Photosynthesis
Relative abundance of genes associated to photosynthesis was mainly identified on the open- air ponds (FGMSP and Aux) (Fig 5.5). Genes associated to proteorhodpsin, a light dependent proton pump that is has a key role on the metabolism of aquatic organisms, was the detected on all the samples including indoor systems. Functional genes associated to Photosystem I and Photosystem II were exclusively detected on the auxiliary pond (OUT_Aux). Genes related to photosystem I and photosystem II were mostly associated to the taxonomic orders
Chroococcales (Cyanobacteria) and Eupodiscales (Bacillariophyta, Eukaryota)
(Supplementary Figure 5.11).
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0.9
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INP_FT01_Oct16INP_FT02_Oct16INP_MP01_Oct17INP_MP02_Oct17INP_SP01_Jan18INP_SP02_Jan18 OUT_Aux02_Jun17 OUT_FGMSP_Sept17OUT_Aux01_May16 OUT_Aux03_Sept17
Bacterial_light-harvesting_proteins Phycobilisome
Photosystem_II-type_photosynthetic_reaction_center Photosystem_II
Photosystem_I Proteorhodopsin
Figure 5.5 Relative abundance of genes related to photosynthesis (level 3 subsystems, KEGG database)
DNA metabolism
Genes related to DNA metabolism were relatively more abundant on the indoor pond (INP)
(Fig 5.6). Level 3 subsystems analysis revealed that functions related to general bacterial DNA repair mechanisms were consistent at all sampling times and sites. Specific genes associated to DNA base excision repair, CRISPRs and restriction-modification repair mechanisms were notably more abundant in the areas where exposure to radioactive spent fuel material would be continuous (e.g. the main ponds, INP_MP; subponds, INP_SP and OUT_FGMSP).
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6
5
4
3
2 Relative abundance (%)
1
0
INP_FT01_Oct16INP_FT02_Oct16INP_MP01_Oct17INP_MP02_Oct17INP_SP01_Jan18INP_SP02_Jan18 OUT_Aux02_Jun17 OUT_FGMSP_Sept17OUT_Aux01_May16 OUT_Aux03_Sept17
CRISPRs DNA_topoisomerases,_Type_I,_ATP-independent DNA_repair,_bacterial_RecFOR_pathway DNA_repair,_bacterial_UvrD_and_related_helicases DNA_topoisomerases,_Type_II,_ATP-dependent DNA_repair,_bacterial_MutL-MutS_system DNA_Repair_Base_Excision Type_I_Restriction-Modification Restriction-Modification_System DNA_repair,_UvrABC_system DNA_repair,_bacterial DNA-replication
Figure 5.6 Relative abundance of genes related to DNA repair functions at level 3 subsystems (KEGG database)
Again, the genes that were associated with bacterial DNA repair were affiliated mainly to the order Burkholderiales in the feeding tank area (INP_FT). In the main and subponds (INP_MP and INP_SP) these DNA repair genes, and also DNA base excision repair, CRISPRs and restriction modification systems, were also affiliated to the order Burkholderiales and also the
Sphingomonadales and Xanthomonadales, Hydrogenophilales, Alteromonadales,
Desulfuromonadales and Pseudomonadales.
Genes related to DNA metabolism were less abundant in the open-air pond samples. Once again, functional genes related to DNA metabolism that were detected on the FGMSP were mainly associated with the order Burkholderiales (Supplementary Figure 5.12).
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Stress response
Genes associated to stress response were primarily identified in the feeding tank area
(INP_FT, Oct 16) where the oligotrophic water is purged (Figure 5.7). Functional genes related to bacterial hemoglobins were more abundant and were mostly associated to organisms from order Burkholderiales (Supplementary Figure 5.13).
3
2.5
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1.5 Relative abundance (%)
1
0.5
0
INP_FT01_Oct16 INP_FT02_Oct16 INP_MP01_Oct17 INP_MP02_Oct17 INP_SP01_Jan18 INP_SP02_Jan18 OUT_Aux02_Jun17 OUT_FGMSP_Sept17OUT_Aux01_May16 OUT_Aux03_Sept17
Heat_shock_dnaK_gene_cluster_extended Oxidative_stress Bacterial_hemoglobins Glutathione:_Biosynthesis_and_gamma-glutamyl_cycle Protection_from_Reactive_Oxygen_Species Regulation_of_Oxidative_Stress_Response Glutathione:_Non-redox_reactions Choline_and_Betaine_Uptake_and_Betaine_Biosynthesis Hfl_operon Periplasmic_Stress_Response Synthesis_of_osmoregulated_periplasmic_glucans Glutathione:_Redox_cycle Glutathione-dependent_pathway_of_formaldehyde_detoxification Acid_resistance_mechanisms Uptake_of_selenate_and_selenite
Figure 5.7 Relative abundance of genes related to stress response (level 3 subsystems, KEGG database)
Further differences on relative abundance were found on genes related to motility and chemotaxis, cell wall and capsule, potassium metabolism and membrane transport, and will be discussed later in this paper (Supplementary, Figure 5.14 to 5.17).
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Discussion
Microbial diversity The microbial diversity of nuclear regulated sites has been the focus of several studies using both culture dependent and molecular (DNA-based) tools (REFS). This has included spent fuel storage ponds, although typically at circumneutral or mildly acidic pH values (pH 4.0 to
8.0) (ADD REFS), contrasting with the contrasting to the hyper-alkaline indoor (INP) and open- air ponds (FGMSP and Aux) at Sellafield studied here. Physical conditions vary; for instance pH is not usually considered a relevant parameter hence is not controlled (Chicote et al.
2005;Chicote et al. 2004;Masurat et al. 2005;Santo Domingo et al. 1998;Sarró et al.
2005;Tišáková et al. 2012). On studies where pH was measured it ranged from 4.0 to 8.0
(Bagwell et al. 2018;Bruhn et al. 2009;Chicote et al. 2004;Karley et al. 2018;Silva et al. 2018a); contrasting to the hyper-alkaline habitat handled on the indoor (INP) and open-air ponds
(OUT) at Sellafield.
The microbial diversity of nuclear regulated sites has been the focus of several studies using both culture dependent and molecular (DNA-based) tools (REFS). This has included spent fuel storage ponds, although typically at circumneutral or mildly acidic pH values (pH 4.0 to
8.0) (ADD REFS), contrasting with the contrasting to the hyper-alkaline indoor (INP) and open- air ponds (FGMSP and Aux) at Sellafield studied here. Physical conditions vary; for instance pH is not usually considered a relevant parameter hence is not controlled (Chicote et al.
2005;Chicote et al. 2004;Masurat et al. 2005;Santo Domingo et al. 1998;Sarró et al.
2005;Tišáková et al. 2012). On studies where pH was measured it ranged from 4.0 to 8.0
(Bagwell et al. 2018;Bruhn et al. 2009;Chicote et al. 2004;Karley et al. 2018;Silva et al. 2018a); contrasting to the hyper-alkaline habitat handled on the indoor (INP) and open-air ponds
(OUT) at Sellafield.
The first part of this study focused on the taxonomy of the microorganisms present in the pond systems. The compositions of the microbial communities in three sampling sites were similar at the phylum, class and order levels, although there were clear differences when the rRNA gene data were investigated at the family and genus levels. The differences observed using both sequencing techniques employed here may derive from experimental parameters
(differences in sampling, amplification or sequencing technologies) or, most likely, the
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Sharon L. Ruiz Lopez PhD Thesis classification and binning processes used during targeted 16S rRNA amplification of metagenomic sequencing and analyses. Furthermore the SSU rRNA sequence datasets are widely described (more than 1 million sequences, e.g. SILVA) whilst whole genome databases for comparison and annotation are smaller and are still being developed (e.g. SEED, KEGG)
(Steven et al. 2012). To facilitate comparisons across these datasets, taxonomic data was appraised at the order level.
Overall organisms affiliated with the order Burkholderiales (Betaproteobacteria) were the most abundant component in all sampling sites at all times. Members from this order have also been detected in in spent fuel waste containers (Vazquez-Campos et al. 2017) (Ahn et al.
2019), and are involved in radionuclide immobilization (Dhal et al. 2011). It is interesting though that in majority of previous studies the lack of nutrients and hyper-alkalinity were not limiting factors. In this study we noted that organisms from the order Burkholderiales, specifically members affiliated with the genera Hydrogenophaga, Methylotenera and
Curvibacter, were able to adapt to the extreme environments studied here. Similar behaviour was observed with organisms affiliated with the order Sphingomonadales and
Sphingobacteriales (of the phylum Bacteroidetes), also previously associated with uranium- contaminated soils and sediments (Ellis et al. 2003) (Reardon et al. 2004).
More widely, the presence of heterotrophic bacterial groups including members of the
Actinobacteria, Bacteroidetes, Acidobacteria and Proteobacteria have been reported widely in a wide range of radioactive sites (Chicote et al. 2005;Chicote et al. 2004;Masurat et al.
2005;Santo Domingo et al. 1998;Sarró et al. 2005;Tišáková et al. 2012) (Bagwell et al.
2018;Bruhn et al. 2009;Chicote et al. 2004;Karley et al. 2018;Silva et al. 2018a). In contrast, cyanobacteria have been typically noted a lower relative abundance in such sites, and similar results were noted in this study where Cyanobacteria represented less than 2% on most of the samples, except on open-air pond sample Aux03 (Sept 17) where Cyanobacteria constituted 24% of the total diversity, presumably in response to the higher light levels in this environment.
Along with a significant prokaryote population, eukaryotes were detected exclusively on the open-air ponds (Out_FGMSP and Auxiliary pond). The presence of eukaryotic organisms has
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Sharon L. Ruiz Lopez PhD Thesis been detected in other radioactive environments (Jung et al. 2016) (Rivasseau et al. 2013)
(Foster 2018), (MeGraw et al. 2018) and multiple adaptation strategies have been identified.
Microalgae are capable to strongly accumulate radionuclides such as 238U, 137Cs, 110Ag, 60Co,
54Mn, 65Zn and 14C and can also display DNA repair mechanisms (Rivasseau et al. 2013)
(Krejci et al. 2011) (Adam and Garnier-Laplace 2003) (Garnier and Baudin 1989). Members from family Chrysophyceae were highly abundant in the auxiliary pond, and accumulate carotenoids and xanthophylls to protect themselves against ionising radiation (Korbee et al.
2012) (Demmig-Adams and Adams 2006). Likewise the production of mycosporine-like amino acids, play an important role in protection against UV radiation in photosynthetic eukaryotes present in lichens (Karsten et al. 2005) (Karsten et al. 2007) (Ragon et al. 2011). The fungal classes Dothideomycetes, Aphelida and Glomeromycetes were found solely on the open ponds. Members from class Dothideomycetes have been found in highly radioactive sites such as the old nuclear plant at Chernobyl (Dadachova and Casadevall 2008). Indeed,
Zhdanova et al (Zhdanova et al. 2004) proposed that beta and gamma radiation in the
Chernobyl site promote growth of hyphae on fungal species affiliated to Dothideomycetes.
Microbial diversity identified in this study suggest that challenging environmental conditions such as low nutrient content, hyper alkalinity and presence of radioactivity does not prevent colonisation by diverse microbial communities.
Adaptation to extreme environments The second part of this study was to assess the metabolic potential of the pond microbiomes using a metagenomic approach. Our hypothesis was that the functional components of the microbial communities would change in response to conditions, for example energy sources, in the open-air and indoor systems.
Feeding tank (INP_FT) The hyper-alkalinity on the INP_FT area (pH 11.6) may generate a range of cellular responses. Genes related to membrane transport were more abundant in the INP_FT area, specifically genes related to ABC transporters for branched-chain amino acids. The leucine, isoleucine, valine (LIV) ABC branched-chain amino acids transporters (belonging to the polar amino acid transport family) are believed to be important for alkaliphily, due to the ability to convert leucine, isoleucine and valine to L-glutamate which is negatively charged at pH values
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higher than it pKa (3.9 or 4.3). The accompanying proton produced could contribute to the acidification of the bacterial cytoplasm to maintain the internal pH between 8.0 and 8.5
(Takami et al. 2002). These ABC transporters are also thought to be coupled with potassium metabolism, that helps regulate proton-potassium exchange, which would help maintain internal cellular homeostasis in this alkali (NaOH) dosed environment (Padan et al. 2005). In addition genes related to bacterial hemoglobins (Figure 7) were more abundant on the feeding tank area than the other sampling sites. Bacterial hemoglobins (Hb) belong to the superfamily of haemoglobin-like proteins and have the ability to reversibly bind oxygen (Hardison 1996).
Although Hb has been mainly found on mammals, recent findings reveal its presence on non- vertebrates, plants and bacteria (Bollinger et al. 2001). The presence of Hb on bacteria has been analysed for its potential use in improving cell growth and productivity under oxygen limitation, and their increased abundance may be an adaptive response to oxygen limitation within the INP_FT pond, although this requires further investigation.
Indoor alkaline pond: main ponds (INP_MP) and subponds (INP_SP)
The indoor pond INP is fed with demineralized water from the feeding tank that has been pH is adjusted (see methods section) to 11.6; this with stored spent fuel material result in a unique oligotrophic, hyper-alkaline and radioactive environment. Standardized data showed that the most abundant genes were related to biochemical regulation and energy metabolism.
Genes related to respiration were predominant on the indoor pond, and here the use of hydrogen as an electron donor for metabolism is of particular relevance given the potential for radiolytic hydrogen production from water in the ponds (Libert et al. 2011). Functional annotation showed that hydrogenases and [NiFe]-maturation systems were associated with the INP pond. Hydrogen metabolism is carried on by NiFe-containing hydrogenase that
- catalyse the reversible oxidation of molecular hydrogen according to the reaction H2↔ 2H+2e and play a crucial role in microbial energy metabolism (Vignais, 2001; Vignais 2004).
Depending on the environment, [NiFe]-hydrogenases have are used to either oxidize H2 as a source of energy or produce the gas as a means of disposing of excess reducing equivalents.
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Clearly the former process is likely to support hydrogen-oxidising microbial pioneer species in the indoor pond system. Interestingly, the genes encoding hydrogenases and related [NiFe]- hydrogenase maturation systems were associated with the microbial order Burkholderiales, and in this group genera including Hydrogenophaga and Polynucleobacter (Supplementary
Fig 7), have the clear potential for hydrogen-oxidation. This has led to the colonisation of high pH hydrogen-rich environments including serpentinisation systems (Brazelton et al. 2012)
(Suzuki et al. 2014), and from this study most likely spent nuclear fuel.
In addition to respiration functions, genes related to DNA metabolism were more frequent on the indoor pond than other sites (Figure 6). Level 3 subsystems (KEGG database) revealed that genes involved in DNA general repair, DNA repair base-excision, restriction-modification system and CRISPRs were largely detected on the INP_MP and INP_SP. DNA repair is considered a key strategy used by microorganisms to survive high radiation fluxes (Pettijohn and Hanawalt 1964). Radiation affects a wide range of cellular biomolecules, including proteins, lipids and nucleic acids directly (e.g. ionizing particles interacting with purine/pyrimidine base) or indirectly (e.g. formation of reactive oxygen species, ROS, through radiolysis of water) (Jung et al. 2017). However, since DNA is a permanent copy of the cell genome, alterations in its structure are of potentially greater consequence compared to other cell components such as RNAs or proteins (Cooper 2000).
Along with well-known DNA repair strategies (e.g. reversal of base damage; restriction- modification system (RM) and base excision repair (BER)) (Friedberg et al. 2006) (Wilson
1991) (Raleigh and Brooks 1998) (Zhao et al. 2005) (Murray 2000), clustered regularly interspaced short palindromic repeats (CRISPR) and accompanying Cas proteins represent a newly identified system of relevance (Reeks et al. 2013). CRISPR-Cas are DNA-encoded,
RNA-mediated defence system that provide sequence-specific recognition, targeting and degradation of exogenous nucleic acid (Barrangou 2015). Initial insights suggested that the
CRISPR-Cas function was mainly for antiviral defence, however recent studies have revealed that they also play critical roles beyond immunity, including endogenous transcriptional control and regulation of bacterial phenotype to help to adapt to environmental stresses (Barrangou
2015) (Sorek et al. 2013). For example, several studies have shown that the CRISPR-Cas
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Sharon L. Ruiz Lopez PhD Thesis system genes are induced in bacteria and archaea in response to external abiotic stimuli such as UV light and ionizing radiation (Gotz et al. 2007) (Sorek et al. 2013) and in response to internal cellular stress (e.g. from oxidative stress) (Strand et al. 2010) (Sorek et al. 2013).
Clearly the surprising increment of CRISPR-Cas systems in indoor spent nuclear fuel ponds and could represent an unexpected and novel mechanism supporting colonisation of the ponds
Outdoor (FGMPS and Auxillary) legacy ponds
The legacy alkaline First Generation Magnox Storage Pond (FGMSP) is an open-air pond system that periodically accepts waters from the upstream INP, and discharges “purge” waters to a radionuclide removal plant (SIXEP) prior to release. A key difference between the INP environments and the FGMSP and the Auxillary pond (which is a closed system and can overflow to FGMSP) is light availability, which results in both pond systems being prone to algal blooms. In both of these systems there was a relative enrichment of photosynthetic genes, although this was most marked in the Auxillary pond, consistent with build up of eukaryotic photosynthetic organisms in the closed pond system. The single FGMSP sample that was available for analysis was obtained during a purge period when algal blooms were not visible in the pond, and hence it is not surprising that the levels of photosystem I and II genes were very low. This sample shared, with the indoor pond systems, low levels of genes encoding Proteorhodopsin, a light-driven H+ pump which can be present in a wide range of microorgansims, including the proteobacteria present in the samples.
Although there were clearly similarities in the prokaryotic communities in the nuclear fuel ponds, including the persistence of hydrogen-utilising members of the Burkholderiales (and the detection of hydrogense genes), it was notable that eukaryotes were exclusively associated with the outdoor ponds, and this included fungal components. This would seem to imply a richer diversity of heterotrophs linked to primary productivity of ingress of external organic sources. A more detailed assessment of the complex microbial communities within this external pond systems should include analyses through microbial bloom events, and be
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Sharon L. Ruiz Lopez PhD Thesis linked to biogeochemical changes within the pond. This work is ongoing. Another area obvious area of interest should be the potential role of the microbes within these pond systems in mediating the solubility and fate of key radionuclides. For example bioaccumulation of radionuclides (Cs and 90Sr) has previously been observed in a circumnetral spent fuel storage pond on the Sellafield site (MeGraw et al. 2018), with immobilisation of 90Sr as an insoluble carbonate linked to photosynthesis in other studies (Lee et al. 2014). It should be noted that fungi are also well known to immobiilise a wide range of radionuclides (Gadd, 2016) via a range of mechanisms, and they could play a role in these process in the outdoor ponds, to augment prokaryotic radionuclide removal processes (Lloyd, 2002) expected in the indoor ponds. These observations stress the importance of a systems wide knowledge of the complex microbial communities present in the pond systems described here.
In summary these studies confirm a role in culture-independent DNA-based studies in characterising complex microbial communties within highly radioactive microbial environments. High-throughput sequencing of purified DNA targeting, for example phylogenetic (16S and 18S rRNA) marker genes, give a good indication of diversity, which can be further interrogated using metagenomic tools that elucidate potential functionality.
Although extracting DNA from active samples on nuclear regulated sites is challenging, these proof of concept studies illustrate the potential for multi-omics studies on such unique sites in the future, targeting in due course RNA and expressed proteins to probe microbial processes in situ. Our studies have shown that key organisms, and the likely sources or energy that they use in the pond systems can be identified with current technologies. These data not only extend our knowledge of the microbial ecology of extreme environments, but will ultimately prove useful in understanding the impact of microbial processes on nuclear waste materials, and designing robust control measures that can be adopted to control microbial growth if required.
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Acknowledgements
SRL acknowledges financial support from a PhD programme funded by the National Council of Science and Technology (CONACyT). This work was also supported by funding from
Sellafield Limited and the Royal Society to JRL. LF was supported by an EPSRC CASE PhD and IAA funding.
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Supplementary information 100
90
80
70
60
50 Others 40 Eukaryota Relative abundance 30 Bacteria Arcahea 20
10
0
INP_FT01_Oct16INP_FT02_Oct16INP_MP01_Oct17INP_MP02_Oct17INP_SP01_Jan18INP_SP02_Jan18 OUT_FGMSP_Sept17OUT_Aux01_May16OUT_Aux02_June17OUT_Aux03_Sept17
INP_FT01 INP_FT02_ INP_MP01_ INP_MP02_ INP_SP01_ INP_SP02_ OUT_FGMSP_ OUT_Aux01 OUT_Aux02_ OUT_Aux03_
_Oct16 Oct16 Oct17 Oct17 Jan18 Jan18 Sept17 _May16 June17 Sept17
Archaea 0.128192 0.139049 0.097097 0.107867 0.148901 0.137643 0.109451 0.242143 0.180112 0.262177
Bacteria 98.29654 98.50063 98.92678 98.82252 98.76924 98.61816 99.03582 93.42333 95.26092 94.97394
Eukaryot
e 1.543045 1.326681 0.95428 1.02287 1.060243 1.231552 0.812098 6.051417 4.425204 4.637322
Others 0.032222 0.033644 0.021847 0.046742 0.021616 0.012648 0.042634 0.283112 0.133767 0.126558 Supplementary 5. 1 Microbial distribution at kingdom level, whole genome sequencing
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1.8
Others 1.6
Apicomplexa 1.4 Basidiomycota 1.2 Chlorophyta 1 Arthropoda 0.8 Ascomycota
0.6 Relative abundance (%) Bacillariophyta
0.4 Streptophyta
0.2 Cnidaria
0 unclassified (derived from Eukaryota) Chordata
INP_FT01_Oct16INP_FT02_Oct16INP_MP01_Oct17INP_MP02_Oct17INP_SP01_Jan18INP_SP02_Jan18
Supplementary 5. 2 Microbial distribution at phylum level filtered by eukaryotic on the indoor pond INP by whole genome sequencing
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100%
90% Unidentified 80% Gemmatimonadetes 70% Acidobacteria 60% Armatimonadetes 50% Firmicutes
40% Planctomycetes Deinococcus-Thermus
Realtive abundance 30% Cyanobacteria 20% Verrucomicrobia 10% Others 0% Actinobacteria Bacteroidetes Proteobacteria
INP_02_Jan18 INP_FT01_Oct16INP_FT02_Oct16INP_MP01_Oct17INP_MP02_Oct17INP_SP01_Jan18 OUT_Aux02_Jun17 OUT_FGMSP_Sept17OUT_Aux01_May16OUT_Aux03_Sept17
b)
Elusimicrobia 100% Fibrobacteres Candidatus Poribacteria 90% Dictyoglomi Tenericutes 80% Chrysiogenetes Deferribacteres 70% Synergistetes Fusobacteria 60% Lentisphaerae Thermotogae 50% Nitrospirae unclassified (derived from Bacteria) Relative abundance 40% Chlamydiae Aquificae 30% Spirochaetes Gemmatimonadetes 20% Deinococcus-Thermus Chloroflexi 10% Chlorobi Acidobacteria 0% Planctomycetes Firmicutes Verrucomicrobia Cyanobacteria INP_02_Jan18 Actinobacteria INP_FT01_Oct16INP_FT02_Oct16INP_MP01_Oct17INP_MP02_Oct17INP_SP01_Jan18 Bacteroidetes OUT_Aux01_May16OUT_Aux02_Jun17 OUT_FGMSP_Sept17 OUT_Aux03_Sept17 Proteobacteria
Supplementary 5. 3 Microbial distribution at phylum level a)by 16S rRNA gene and b)by metagenomics sequencing
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100% Others Rhizobiales 90% Acidimicrobidae 80% Actinomycetales Caulobacterales 70% Synechococcales 60% Planctomucetales 50% Unclassified Planctomycetes Unidibacterium 40% Verrucomicrobiales 30% Unclassified Cyanobacteria Relative abundance Flavobacteriales 20% Sphingobacteriales 10% Rhodobacteriales Rhodospirillales 0% Sphingomonadales Nitrosomonadales Unclassified Verrumicrobia Xanthomonadales INP_02_Jan18 Unidentified INP_FT01_Oct16INP_FT02_Oct16INP_MP01_Oct17INP_MP02_Oct17INP_SP01_Jan18 OUT_Aux02_Jun17 OUT_FGMSP_Sept17OUT_Aux01_May16OUT_Aux03_Sept17 Burkholderiales
b)
100 Others Verrucomicrobiales 90 Planctomycetales Enterobacteriales 80 Chromatiales 70 Alteromonadales Sphingobacteriales 60 Chroococcales Caulobacterales 50 Rhodocyclales 40 Xanthomonadales Pseudomonadales
Relative abundance (%) 30 Actinomycetales Cytophagales 20 Rhodospirillales 10 Flavobacteriales Rhodobacterales 0 Methylophilales Rhizobiales Sphingomonadales INP_02_Jan18 Burkholderiales INP_FT01_Oct16INP_FT02_Oct16INP_MP01_Oct17INP_MP02_Oct17INP_SP01_Jan18 OUT_Aux02_Jun17 OUT_FGMSP_Sept17OUT_Aux01_May16OUT_Aux03_Sept17
Supplementary 5. 4 Microbial distribution at order level a)by 16S rRNA gene and b)by whole genome sequencing. Only components that represented more than 1.5% relative abundance are shown
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100% Fluviimonas Siphonobacter Methylophilus 90% Gemmata Unclassified Planctomycetes
80% Unidibacterium Novosphingobium Prosthecobacter 70% Unclassified Cyanobacteria Rhodobacter
60% Mongoliitalea Polaromonas Roseococcus 50% Sediminibacterium Others
Relative abundance 40% Flavobacterium Limnohabitans Polynucleobacter 30% Unclassified Verrumicrobia Silanimonas Algoriphagus 20% Cyanobium Porphyrobacter 10% Methylotenera Belliella Nevskia 0% Unidentified Rhodoferax
INP_02_Jan18 Curvibacter INP_FT01_Oct16INP_FT02_Oct16INP_MP01_Oct17INP_MP02_Oct17INP_SP01_Jan18 OUT_Aux02_Jun17 Hydrogenophaga OUT_FGMSP_Sept17OUT_Aux01_May16 OUT_Aux03_Sept17
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Others 100% Cyanobium
Verrucomicrobium
90% Spirosoma Cytophaga
unclassified (derived from Flavobacteriales) 80% Ruegeria
Chitinophaga
Roseobacter 70% Brevundimonas
Methylovorus
Sphingomonas 60% Algoriphagus
Caulobacter
50% Roseomonas Bradyrhizobium
Relative abundance Ralstonia
40% Synechococcus
Novosphingobium
Xanthomonas 30% Flavobacterium
Leptothrix
Pseudomonas 20% Cupriavidus
Methylibium
10% Delftia
Variovorax
Verminephrobacter
0% Rhodobacter
Methylobacillus
Polynucleobacter INP_02_Jan18 INP_FT01_Oct16INP_FT02_Oct16INP_MP01_Oct17INP_MP02_Oct17INP_SP01_Jan18 OUT_Aux02_Jun17 OUT_FGMSP_Sept17OUT_Aux01_May16OUT_Aux03_Sept17 Methylotenera
Supplementary 5. 5 Microbial distribution at genus level a) by 16S rRNA gene and b)by whole genome sequencing. Only components that represented more than 1.5% relative abundance are shown
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100% Saccharomycetes 90% Discosea 80% Unknown fungal species 70% Bicosoecida
60% Heterobolosea
50% Eustigmatophyceae
40% Dinophyceae Aphelida 30%
Relative abundance Chrysophyceae 20% Oligohymenophorea 10% Apiales 0% Trebouxiophyceae
Eurotiomycetes
Dothideomycetes INP_FT01_Oct16INP_FT02_Oct16INP_MP01_Oct17INP_MP02_Oct17INP_SP01_Jan18INP_SP02_Jan18 OUT_Aux02_Jun17 OUT_FGMSP_Sept17OUT_Aux01_May16 OUT_Aux03_Sept17 b)
5 Bryopsida Schizosaccharomycetes
4.5 Agaricomycetes Bangiophyceae
4 Actinopterygii Liliopsida
3.5 Aconoidasida Chlorophyceae
3 Dinophyceae Chromadorea
2.5 Prasinophyceae
Sordariomycetes
2 Eurotiomycetes Relative abundance (%) Saccharomycetes
1.5 Anthozoa
Bacillariophyceae
1 Mammalia
Insecta
0.5 Coscinodiscophyceae
unclassified (derived from Eukaryota)
0 Oligohymenophorea
unclassified (derived from Streptophyta) Amphibia INP_02_Jan18 INP_FT01_Oct16INP_FT02_Oct16INP_MP01_Oct17INP_MP02_Oct17INP_SP01_Jan18 OUT_Aux02_Jun17 Hydrozoa OUT_FGMSP_Sept17OUT_Aux01_May16 OUT_Aux03_Sept17
Supplementary 5. 6 Microbial distribution of eukaryotic organisms at class level by a)18S rRNA sequencing profile and b)metagenomics sequencing
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Supplementary 5. 7 Total sequences annotated using the MGRAST web server
Sample Number of basepairs Average Predictable Unknown Failed QC
sequences length feature (%) (%) (%)
INP_FT01 3,296,982 491,844,282 151 bps 89.47 2.59 7.95
INP_FT02 3,956,936 597,497,185 151 bps 90.68 2.79 6.53
INP_MP01 3,826,923 577,865,373 151 bps 81.18 3.23 15.59
INP_MP02 3,782,419 571,145,269 151 bps 80.64 3.11 16.24
INP_SP01 3,874,693 585,078,643 151 bps 88.22 3.10 8.69
INP_SP02 3,378,803 510,199,253 151 bps 87.80 2.98 9.22
OUT_FGMSP 3,779,537 570,710,087 151 bps 83.31 2.07 14.62
OUT_Aux01 3,086,795 466,106,045 151 bps 86.12 3.59 10.29
OUT_Aux02 3,779,537 570,710,087 151 bps 83.31 2.07 14.62
OUT_Aux03 3,167,140 478,238,140 151 bps 82.11 1.96 15.93
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Supplementary 5. 8 Relative abundance of genes at Level 1 subsytems (KEGG database)
INP_FT01_Oct INP_FT02_Oct INP_MP01_Oct INP_MP02_Oct INP_SP01_Jan INP_02_Jan OUT_FGMSP_Sept OUT_Aux01_May OUT_Aux02_Jun OUT_Aux03_Sept 16 16 17 17 18 18 17 16 17 17 Clustering- based subsystems 11.57988 11.40728 12.1312 11.97963 12.11747 11.78454 12.06242 12.75073 12.84559 12.78287 Carbohydrate s 11.25289 11.19929 9.559558 9.536645 9.103018 9.096713 10.87795 10.78554 10.35405 9.829491 Amino Acids and Derivatives 10.37377 10.77546 10.09817 10.07184 10.05988 10.15335 10.22522 9.548668 9.701429 9.323116 Protein Metabolism 6.907944 6.669388 8.98125 9.038336 9.33315 9.188847 7.804932 9.061273 9.412656 9.734391 Cofactors, Vitamins, Prosthetic Groups, Pigments 5.453456 5.654813 6.7217 6.207845 6.273371 6.386887 6.593794 6.339342 6.255422 6.685028 Miscellaneou s 6.456245 6.524691 5.935402 6.144818 6.324909 6.332219 6.245776 6.221555 6.442969 6.850702 DNA Metabolism 4.176114 3.983526 6.365796 6.356389 5.968745 6.128186 4.883953 4.996441 4.896336 4.977645 Respiration 4.149566 3.989669 4.513845 4.501116 4.643291 4.675309 4.444791 4.803407 4.172296 4.233963 Cell Wall and Capsule 4.196144 4.102436 4.085014 4.263478 4.411916 4.42554 3.895859 3.889541 4.53769 4.997406 RNA Metabolism 3.555205 3.55275 4.038005 4.296081 4.419227 4.46069 3.701233 4.25466 4.434356 4.679114 Membrane Transport 4.311879 4.800213 3.464596 3.272775 3.482834 3.317129 3.674487 3.835671 3.572679 3.210804
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Virulence, Disease and Defense 4.416466 4.156192 2.666652 2.974054 3.119798 3.248897 3.173316 3.19201 3.6777 3.380889 Nucleosides and Nucleotides 3.149423 3.168535 3.087105 3.163838 3.145458 3.169666 3.350588 3.283717 3.519395 3.19563 Fatty Acids, Lipids, and Isoprenoids 2.848983 3.252686 2.765499 2.621099 2.549658 2.481644 3.219166 2.714662 2.759505 2.68802 Stress Response 2.979835 2.793698 2.30592 2.376458 2.238745 2.282986 2.587289 2.319402 2.302024 2.531697 Metabolism of Aromatic Compounds 2.475606 2.795153 1.42951 1.273005 1.155632 1.676925 2.421161 1.644531 1.803351 1.287815 Motility and Chemotaxis 2.620157 2.116533 2.152182 2.260596 2.234067 2.270994 1.641941 0.877097 0.790961 0.632351 Phages, Prophages, Transposable elements, Plasmids 0.97728 1.083609 1.628694 1.493447 1.430139 1.281016 1.73603 2.317692 1.390156 1.211947 Nitrogen Metabolism 1.547738 1.533785 1.983462 1.925226 1.815399 1.606791 1.206521 0.87496 0.915664 1.014161 Regulation and Cell signaling 1.26147 1.21327 1.525374 1.637477 1.340294 1.397051 1.263436 0.895695 0.968667 1.024571 Phosphorus Metabolism 1.131279 1.131545 1.294235 1.378596 1.335908 1.161756 1.37655 1.134049 1.136953 1.238765 Sulfur Metabolism 1.354625 1.261448 0.936343 0.918727 0.977477 1.04018 0.870682 0.773205 0.908635 1.036039 Cell Division and Cell Cycle 0.807314 0.8314 0.820099 0.868538 1.004745 0.935806 0.881587 0.938663 1.001846 1.062152
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Iron acquisition and metabolism 0.767444 0.738277 0.821235 0.696107 0.668977 0.665857 0.864234 0.961109 0.870394 0.690928 Potassium metabolism 0.869481 0.844334 0.269555 0.333502 0.469987 0.504169 0.519878 0.452337 0.530869 0.547132 Photosynthes is 0.119231 0.110018 0.098349 0.083726 0.086702 0.07857 0.180377 0.766792 0.396183 0.689693 Secondary Metabolism 0.169021 0.208719 0.222618 0.232736 0.212149 0.191379 0.217073 0.26593 0.241253 0.275948
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100% Dormancy and Sporulation
Secondary Metabolism
Photosynthesis 90% Potassium metabolism
Iron acquisition and metabolism
80% Cell Division and Cell Cycle
Sulfur Metabolism
Phosphorus Metabolism 70% Regulation and Cell signaling
Nitrogen Metabolism 60% Phages, Prophages, Transposable elements, Plasmids Motility and Chemotaxis
50% Metabolism of Aromatic Compounds
Stress Response
Relative abundance Fatty Acids, Lipids, and Isoprenoids 40% Nucleosides and Nucleotides
Virulence, Disease and Defense
30% Membrane Transport
RNA Metabolism
Cell Wall and Capsule 20% Respiration
DNA Metabolism 10% Miscellaneous
Cofactors, Vitamins, Prosthetic Groups, Pigments 0% Protein Metabolism
Amino Acids and Derivatives
Carbohydrates INP_02_Jan18 INP_FT01_Oct16INP_FT02_Oct16INP_MP01_Oct17INP_MP02_Oct17INP_SP01_Jan18 OUT_Aux02_Jun17 Clustering-based subsystems OUT_FGMSP_Sept17OUT_Aux01_May16OUT_Aux03_Sept17
Supplementary 5. 9 Relative abundance of functional genes by subsystems Level 1 (KEGG database)
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0.8 Rhodobacterales 0.7 Alteromonadales 0.6 Nitrosomonadales Cytophagales 0.5 Chloroflexales 0.4 Acidithiobacillales Flavobacteriales 0.3 Oscillatoriales
Relative abundance (%) 0.2 Nostocales Chroococcales 0.1 Rhodospirillales 0 Sphingomonadales Actinomycetales Rhodocyclales INP_02_Jan18 Rhizobiales INP_FT01_Oct16INP_FT02_Oct16INP_MP01_Oct17INP_MP02_Oct17INP_SP01_Jan18 OUT_Aux02_Jun17 OUT_FGMSP_Sept17OUT_Aux01_May16OUT_Aux03_Sept17 a)
0.2 Oscillatoriales 0.18 Pasteurellales 0.16 Rhizobiales 0.14 0.12 Acidithiobacillales 0.1 Aeromonadales 0.08 Actinomycetales
0.06 Alteromonadales Relative abundance (%) 0.04 Aquificales 0.02 Archaeoglobales 0 Bacillales
Bacteroidales
INP_02_Jan18 Burkholderiales INP_FT01_Oct16INP_FT02_Oct16INP_MP01_Oct17INP_MP02_Oct17INP_SP01_Jan18 OUT_Aux02_Jun17 OUT_FGMSP_Sept17OUT_Aux01_May16OUT_Aux03_Sept17 b) Supplementary 5. 10 Relative abundance of genes related to enzymes hydrogenases (a) and [NiFe]-hydrogenases maturation process (b) and their affiliations to microbial cells at order level
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0.25
0.2 Funariales Eupodiscales Euglenales 0.15 Cyanidiales Coniferales 0.1 Coleochaetales
Relative abundance (%) Chroococcales 0.05 Chlorellales Chlamydomonadales 0 Brassicales Bangiales Anthocerotales
INP_FT01_Oct16INP_FT02_Oct16INP_MP01_Oct17INP_MP02_Oct17INP_SP01_Jan18INP_SP02_Jan18 OUT_Aux02_Jun17 OUT_FGMSP_Sept17OUT_Aux01_May16OUT_Aux03_Sept17 a)
0.25
0.2 Funariales Oscillatoriales Peridiniales 0.15 Marchantiales Prochlorales 0.1 Caudovirales Bangiales Relative abundance (%) 0.05 Pyrenomonadales Cyanidiales Nostocales 0 Eupodiscales Chroococcales
INP_FT01_Oct16INP_FT02_Oct16INP_MP01_Oct17INP_MP02_Oct17INP_SP01_Jan18INP_SP02_Jan18 OUT_Aux02_Jun17 OUT_FGMSP_Sept17OUT_Aux01_May16OUT_Aux03_Sept17 b)
Supplementary 5. 11 Relative abundance of genes related to Photosystem I (a) and to Photosystem II (b); and their affiliation to microbial cells at order level
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0.7
0.6 Chromatiales Nitrosomonadales
0.5 Rhodocyclales Caulobacterales 0.4 Pseudomonadales Cytophagales 0.3 Actinomycetales Methylophilales
Relative abundance (%) 0.2 Rhodospirillales Xanthomonadales 0.1 Flavobacteriales Rhizobiales 0 Rhodobacterales Sphingomonadales Burkholderiales INP_02_Jan18 INP_FT01_Oct16INP_FT02_Oct16INP_MP01_Oct17INP_MP02_Oct17INP_SP01_Jan18 OUT_Aux02_Jun17OUT_Aux03_Sep17 OUT_FGMSP_Sept17OUT_Aux03_Sept17 a)
0.35
0.3 Caulobacterales Rhodospirillales 0.25 Alteromonadales Chroococcales 0.2 Sphingobacteriales Flavobacteriales 0.15 Actinomycetales 0.1 Pseudomonadales Relative abundance (%) Rhizobiales 0.05 Xanthomonadales Rhodobacterales 0 Methylophilales Sphingomonadales Burkholderiales
INP_FT01_Oct16INP_FT02_Oct16INP_MP01_Oct17INP_MP02_Oct17INP_SP01_Jan18INP_SP02_Jan18 OUT_Aux02_Jun17 OUT_FGMSP_Sept17OUT_Aux01_May16OUT_Aux03_Sept17 b)
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0.2 Deinococcales 0.18 Desulfobacterales 0.16 Desulfovibrionales 0.14 Enterobacteriales 0.12 Desulfuromonadales 0.1 Flavobacteriales 0.08 Herpetosiphonales Lactobacillales 0.06
Relative abundance (%) Methylococcales 0.04 Actinomycetales 0.02 Alteromonadales 0 Bacillales Bacteroidales Bifidobacteriales Burkholderiales INP_FT01_Oct16INP_FT02_Oct16INP_MP01_Oct17INP_MP02_Oct17INP_SP01_Jan18INP_SP02_Jan18 OUT_Aux02_Jun17 OUT_FGMSP_Sept17OUT_Aux01_May16OUT_Aux03_Sept17 c)
0.6 Caulobacterales
Enterobacteriales
0.5 Clostridiales
Rhodocyclales
0.4 Nitrosomonadales
Chlorobiales
0.3 Rhizobiales
Chromatiales
0.2 Pasteurellales Relative abundance (%) Xanthomonadales
0.1 unclassified (derived from Opitutae) Pseudomonadales
0 Desulfuromonadales
Alteromonadales
Hydrogenophilales
INP_FT01_Oct16INP_FT02_Oct16INP_MP01_Oct17INP_MP02_Oct17INP_SP01_Jan18INP_SP02_Jan18 OUT_Aux02_Jun17 Burkholderiales OUT_FGMSP_Sept17OUT_Aux01_May16OUT_Aux03_Sept17 d)
Supplementary 5. 12 Relative abundance to genes associated to DNA metabolism (level 3, KEGG database) and its correlation with bacterial cells: a) Bacterial DNA repair, b) Base excision repair, c) CRISPRs and d) Restriction-modification systems
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0.4 Actinomycetales 0.35
0.3 Chroococcales
0.25 Campylobacterales
0.2 Methylophilales 0.15 unclassified (derived from 0.1 Gammaproteobacteria) Relative abundance (%) Thiotrichales 0.05
0 Rhodocyclales
Pseudomonadales
INP_FT01_Oct16INP_FT02_Oct16INP_MP01_Oct17INP_MP02_Oct17INP_SP01_Jan18INP_SP02_Jan18 Rhizobiales OUT_Aux02_Jun17 OUT_FGMSP_Sept17OUT_Aux01_May16OUT_Aux03_Sept17
Supplementary 5. 13 Relative abundance of genes associated to bacterial hemoglobins (stress response, Level 3 subsystems) and its correlation to microbial cells
3 Additional_flagellar_genes_in_Vib rionales 2.5 Archaeal_Flagellum
2 Rhamnolipids_in_Pseudomonas
1.5 Control_of_Swarming_in_Vibrio_ and_Shewanella_species
1 Flagellum_in_Campylobacter Relative abundance (%) 0.5 Flagellar_motility
0 Bacterial_Chemotaxis
Flagellum
INP_FT01_Oct16INP_FT02_Oct16INP_MP01_Oct17INP_MP02_Oct17INP_SP01_Jan18INP_SP02_Jan18 OUT_Aux02_Jun17 OUT_FGMSP_Sept17OUT_Aux01_May16OUT_Aux03_Sept17
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Supplementary 5. 14 Relative abundance of genes associated to motility and chemotaxis (level 3, subsytems)
Recycling_of_Peptidoglycan_Amino_Acids 4.5
LOS_core_oligosaccharide_biosynthesis 4 Lipid_A- Ara4N_pathway_(_Polymyxin_resistance_) 3.5 UDP-N-acetylmuramate_from_Fructose-6- phosphate_Biosynthesis
3 Alginate_metabolism
Lipopolysaccharide- 2.5 related_cluster_in_Alphaproteobacteria dTDP-rhamnose_synthesis
2 mycolic_acid_synthesis
Relative abundance (%) 1.5 Rhamnose_containing_glycans
Peptidoglycan_biosynthesis--gjo 1 Sialic_Acid_Metabolism
0.5 Murein_Hydrolases
0 Lipopolysaccharide_assembly
KDO2-Lipid_A_biosynthesis
Peptidoglycan_Biosynthesis INP_FT01_Oct16INP_FT02_Oct16INP_MP01_Oct17INP_MP02_Oct17INP_SP01_Jan18INP_SP02_Jan18 OUT_Aux02_Jun17 OUT_FGMSP_Sept17OUT_Aux01_May16OUT_Aux03_Sept17
Supplementary 5. 15 Relative abundance of genes associated to cell wall and capsule (level 3, subsystems)
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1 0.9 0.8 pH_adaptation_potassium_effl ux_system 0.7 0.6 0.5 Hyperosmotic_potassium_upt ake 0.4 0.3
Relative abundance (%) 0.2 Glutathione- 0.1 regulated_potassium- efflux_system_and_associated 0 _functions
Potassium_homeostasis
INP_FT01_Oct16INP_FT02_Oct16INP_MP01_Oct17INP_MP02_Oct17INP_SP01_Jan18INP_SP02_Jan18 OUT_Aux02_Jun17 OUT_FGMSP_Sept17OUT_Aux01_May16OUT_Aux03_Sept17
Supplementary 5. 16 Relative abundance of genes associated to potassium metabolism (level 3, subsystems)
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5 Type_VI_secretion_systems
Transport_of_Manganese 4.5
Twin- 4 arginine_translocation_system ABC_transporter_alkylphosphona te_(TC_3.A.1.9.1) 3.5 Widespread_colonization_island
3 pVir_Plasmid_of_Campylobacter
ABC_transporter_dipeptide_(TC_ 2.5 3.A.1.5.2) Multi-subunit_cation_antiporter 2 Transport_of_Zinc Relative abundance (%)
1.5 Tricarboxylate_transport_system
General_Secretion_Pathway 1
ABC_transporter_oligopeptide_(T 0.5 C_3.A.1.5.1) Conjugative_transfer
0 HtrA_and_Sec_secretion
ABC_transporter_branched- chain_amino_acid_(TC_3.A.1.4.1) INP_02_Jan18 INP_FT01_Oct16INP_FT02_Oct16INP_MP01_Oct17INP_MP02_Oct17INP_SP01_Jan18 OUT_Aux02_Jun17 Ton_and_Tol_transport_systems OUT_FGMSP_Sept17OUT_Aux01_May16OUT_Aux03_Sept17
Supplementary 5. 17 Relative abundance of genes associated to membrane transport (level 3, subsystems)
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Mojib, N., Farhoomand, A., Andersen, D. T. & Bej, A. K. (2013). UV and cold tolerance of a pigment-producing Antarctic Janthinobacterium sp. Ant5-2. Extremophiles, 17(3), 367-78. Morel, F. M. M. & Price, N. M. (2003). The Biogeochemical Cycles of Trace Metals in the Oceans. Science, 300(5621), 944-947. Murray, N. E. (2000). Type I Restriction Systems: Sophisticated molecular machines (a legacy of Bertani and Weigle). Microbiology and Molecular Biology Reviews, 64(2), 412-434. N.A., J. & J.N., F. (2011). Sickle: A sliding-window, adaptive, quality-based trimming tool for FastQ files (Version 1.33) [Software] [Online]. Available: https://github.com/najoshi/sickle [Accessed]. Nazina, T. N., Luk'yanova, E. A., Zakharova, E. V., Konstantinova, L. I., Kalmykov, S. N., Poltaraus, A. B. & Zubkov, A. A. (2010). Microorganisms in a Disposal Site for Liquid Radioactive Wastes and Their Influence on Radionuclides. Geomicrobiology Journal, 27(5), 473-486. NDA, N. D. A. (2010). Radioactive Wastes in the UK: A Summary of the 2010 Inventory. In: Change, D. o. E. a. C. (ed.). Cumbria, UK. NDA, N. D. A. (2015). Programmes and Major Projects Report: Sellafield [Online]. Available: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attach ment_data/file/452091/Programmes_and_Major_Projects_Report_-_Sellafield_- _March_2015__data_as_at_December_2014_.pdf [Accessed 28/08/2019]. Newsome, L., Morris, K., Trivedi, D., Atherton, N. & Lloyd, J. R. (2014). Microbial reduction of uranium(VI) in sediments of different lithologies collected from Sellafield. Applied Geochemistry, 51, 55-64. Nurk, S., Bankevich, A., Antipov, D., Gurevich, A., Korobeynikov, A., Lapidus, A., Prjibelsky, A., Pyshkin, A., Sirotkin, A., Sirotkin, Y., Stepanauskas, R., McLean, J., Lasken, R., Clingenpeel, S. R., Woyke, T., Tesler, G., Alekseyev, M. A. & Pevzner, P. (2013). Assembling Genomes and Mini-metagenomes from highly chimeric reads. Research in Computational Molecular Biology: 17th Annual International Conferecne, RECOMB 2013, Beijing China, April 2013. ONR, O. f. N. R. (2016). Pile Fuel Storage Pond Decommissioning - Metal Fuel Stream
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Ruckert, C., Steiner, J., Stevens, R., Thiele, I., Vassieva, O., Ye, Y., Zagnitko, O. & Vonstein, V. (2005). The subsystems approach to genome annotation and its use in the project to annotate 1000 genomes. Nucleic Acids Res, 33(17), 5691-702. Padan, E., Bibi, E., Ito, M. & Krulwich, T. A. (2005). Alkaline pH homeostasis in bacteria: new insights. Biochim Biophys Acta, 1717(2), 67-88. Pedersen, K. (1997). Investigations of subterranean microorganisms and their importance for performance assessment of radioactive waste disposal. Results and conclusions achieved during the period 1995 to 1997. Goteborg University, Institute of cell and molecular biology, Department of General and Marine Microbiology. Pedersen, K. (1999). Subterranean microorganisms and radioactive waste disposal in Sweden. Engineering Geology, 52, 163-176. Pedersen, K. (2000). Microbial processes in radioactive waste disposal. In: Fuel, S. N. (ed.). Goteborg University: Department of Cell and molecular biology, Microbiology. Pedersen, K., Nilsson, E., Arlinger, J., Hallbeck, L. & O'Neill, A. (2004). Distribution, diversity and activity of microorganisms in the hyper-alkaline spring waters of Maqarin in Jordan. Extremophiles, 8(2), 151-64. Pettijohn, D. & Hanawalt, P. (1964). Evidence for Repair-replication of Ultraviolet Damaged DNA in Bacteria. J. Mol. Biol., 9, 395-410. Pruitt, K. D., Tatusova, T. & Maglott, D. R. (2007). NCBI reference sequences (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res, 35(Database issue), D61-5. Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., Peplies, J. & Glockner, F. O. (2013). The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res, 41(Database issue), D590-6. Ragon, M., Restoux, G., Moreira, D., Moller, A. P. & Lopez-Garcia, P. (2011). Sunlight- exposed biofilm microbial communities are naturally resistant to chernobyl ionizing- radiation levels. PLoS One, 6(7), e21764. Raleigh, E. A. & Brooks, J. E. (1998). Restriction Modification systems: Where they are and what they do. In: De Bruijn, F. J., Lupski, J. R. & Wienstock, G. M. (eds.) Bacterial Genomes. Boston, MA: Springer. Rampelotto, P. H. (2013). Extremophiles and extreme environments. Life (Basel), 3(3), 482- 5. Reardon, C. L., Cummings, D. E., Petzke, L. M., Kinsall, B. L., Watson, D. B., Peyton, B. M. & Geesey, G. G. (2004). Composition and diversity of microbial communities recovered from surrogate minerals incubated in an acidic uranium-contaminated aquifer. Appl Environ Microbiol, 70(10), 6037-46. Reeks, J., Naismith, J. H. & White, M. F. (2013). CRISPR interference: a structural perspective. Biochem J, 453(2), 155-66. Rivasseau, C., Farhi, E., Atteia, A., Couté, A., Gromova, M., de Gouvion Saint Cyr, D., Boisson, A.-M., Féret, A.-S., Compagnon, E. & Bligny, R. (2013). An extremely
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radioresistant green eukaryote for radionuclide bio-decontamination in the nuclear industry. Energy & Environmental Science, 6(4), 1230. Rivasseau, C., Farhi, E., Compagnon, E., de Gouvion Saint Cyr, D., van Lis, R., Falconet, D., Kuntz, M., Atteia, A. & Coute, A. (2016). Coccomyxa actinabiotis sp. nov. (Trebouxiophyceae, Chlorophyta), a new green microalga living in the spent fuel cooling pool of a nuclear reactor. J Phycol, 52(5), 689-703. Rothschild, L. J. & Mancinelli, R. L. (2001). Life in Extreme Environments. Nature, 409, 1092- 1101. Saito, H. & Kobayashi, H. (2003). Bacterial responses to alkaline stress. Science Progress, 86(4), 271-282. Santo Domingo, J. W., Berry, C. J., Summer, M. & Fliermans, C. B. (1998). Microbiology of spent nuclear fuel storage basins. Current Microbiology, 37, 387-394. Sarró, M. I., Garcia, A. M. & Moreno, D. A. (2005). Biofilm formation in spent nuclear fuel pools and bioremediation of radioactive water. International Microbiology, 8, 223-230. Sghaier, H., Thorvaldsen, S. & Saied, N. M. (2013). There are more small amino acids and fewer aromatic rings in proteins of ionizing radiation-resistant bacteria. Annals of Microbiology, 63(4), 1483-1491. Shukla, A., Parmar, P. & Saraf, M. (2017). Radiation, radionuclides and bacteria: An in- perspective review. J Environ Radioact, 180, 27-35. Silva, R., Almeida, D. M., Cabral, B. C. A., Dias, V. H. G., Mello, I., Urmenyi, T. P., Woerner, A. E., Neto, R. S. M., Budowle, B. & Nassar, C. A. G. (2018). Microbial enrichment and gene functional categories revealed on the walls of a spent fuel pool of a nuclear power plant. PLoS One, 13(10), e0205228. Solden, L., Lloyd, K. & Wrighton, K. (2016). The bright side of microbial dark matter: lessons learned from the uncultivated majority. Curr Opin Microbiol, 31, 217-226. Sorek, R., Lawrence, C. M. & Wiedenheft, B. (2013). CRISPR-mediated adaptive immune systems in bacteria and archaea. Annu Rev Biochem, 82, 237-66. Steven, B., Gallegos-Graves, L. V., Starkenburg, S. R., Chain, P. S. & Kuske, C. R. (2012). Targeted and shotgun metagenomic approaches provide different descriptions of dryland soil microbial communities in a manipulated field study. Environ Microbiol Rep, 4(2), 248-56. Strand, K. R., Sun, C., Li, T., Jenney, F. E., Jr., Schut, G. J. & Adams, M. W. (2010). Oxidative stress protection and the repair response to hydrogen peroxide in the hyperthermophilic archaeon Pyrococcus furiosus and in related species. Arch Microbiol, 192(6), 447-59. Suzuki, S., Kuenen, J. G., Schipper, K., van der Velde, S., Ishii, S., Wu, A., Sorokin, D. Y., Tenney, A., Meng, X., Morrill, P. L., Kamagata, Y., Muyzer, G. & Nealson, K. H. (2014). Physiological and genomic features of highly alkaliphilic hydrogen-utilizing Betaproteobacteria from a continental serpentinizing site. Nat Commun, 5, 3900.
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Takami, H., Takaki, Y. & Uchiyama, I. (2002). Genome sequence ofOceanobacillus iheyensisisolated from the Iheya Ridge and its unexpectedadaptive capabilities to extreme environments. Oxford University Press, 30(18), 3927-3935. Tišáková, L., Pipíška, M., Godány, A., Horník, M., Vidová, B. & Augustín, J. (2012). Bioaccumulation of 137Cs and 60Co by bacteria isolated from spent nuclear fuel pools. Journal of Radioanalytical and Nuclear Chemistry, 295(1), 737-748. Vazquez-Campos, X., Kinsela, A. S., Bligh, M. W., Harrison, J. J., Payne, T. E. & Waite, T. D. (2017). Response of Microbial Community Function to Fluctuating Geochemical Conditions within a Legacy Radioactive Waste Trench Environment. Appl Environ Microbiol, 83(17). Vermaas, W. F. J. (2001). Photosynthesis and Respiration in Cyanobacteria. Encyclopedia of life sciences, els. Vignais, P. M., Bilboud, B. & Meyer, J. (2001). Classification of hydrogenases. FEMS Microbiology Reviews, 25, 455-501. Vignais, P. M., Willison, J. C. & Colbeau, A. (2004). Chapter 11: Hydrogen respiration. In: Zannoni, D. (ed.) Respiration in Archaea and Bacteria. Advances in photosynthesis and respiration. Springer. Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. (2007). Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol, 73(16), 5261-7. Webb, K. M. & DiRuggiero, J. (2013). Radiation Resistance in extremophiles: fending off multiple attacks. In: Seckbach, J., Oren, A. & Stan-Lotter, H. (eds.) Polyextremophiles: Life Under Multiple Forms of Stress. Dordrecht: Springer. Wilson, G. G. (1991). Restriction and modification systems. Annu Rev Genet, 25, 585-627. Wolfram, J. H., Mizia, R. E., Jex, R., Nelson, L. & Garcia, K. M. (1996). The Impact of Microbially Influenced Corrosion on Spent Nuclear Fuel Storage Life. In: Laboratory, I. N. E. (ed.). Idaho, USA: U. S. Department of Energy. Zhao, F., Zhang, X., Liang, C., Wu, J., Bao, Q. & Qin, S. (2005). Genome-wide analysis of restriction-modification system in unicellular and filamentous cyanobacteria. Physiol Genomics, 24, 181-190. Zhdanova, N. N., Tugay, T., Dighton, J., Zheltonozhsky, V. & McDermott, P. (2004). Ionizing radiation attracts soil fungi. Mycological Research, 108(9), 1089-1096.
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Research Paper: Metagenomic analysis of viruses in spent fuel storage ponds at Sellafield, UK
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Chapter 6 Metagenomic analysis of viruses in spent fuel storage ponds
at Sellafield, UK
1S. Ruiz-Lopez, 1S. Nixon, 1L. Foster, 2N. Cole and 1J. Lloyd
1Williamson Research Centre for Molecular Environmental Science, School of Earth and
Environmental Sciences, University of Manchester, Manchester, United Kingdom
2 Sellafield Ltd, Hinton House, Birchwood Park Ave, Birchwood, Warrington WA3 6GR
Corresponding author: [email protected]
Abstract
Development of cultivation-independent methods, including new “omic” techniques have contributed to a greater understanding of microbial diversity under extreme conditions. Next-
Generation sequencing tools such as metagenomic sequencing and analyses are showing a wide diversity of viruses, although viral-host interactions remain poorly characterised, especially in extreme environments such as nuclear storage ponds.
In this study two indoor and outdoor spent fuel storage ponds were analysed. Initial functional analyses of the recovered metagenome assembled genomes (MAGs) gave valuable insight to the identification of prokaryotes that colonised the pond, dominated by Proteobacteria, and predicted the metabolic microbial adaptations to the surrounding environment, where the metabolism of hydrogen (from radiolysis) represented the main energy source. Further analyses of the MAGs identified prophages and CRISPR loci within the microbiome. Samples from the open air ponds (FGMSP and Auxiliary pond) contained the highest amount of phages
(free viral signals) which may allow viral predation to develop.
Identification of CRISPR spacer-repeats arrays predicted the viral immunity response displayed by these organisms to viral infections, and how these could potentially influence the structure of the microbial communities and energy flow in the system. Highest abundance of
CRISPR spacer-repeats arrays and prophages (virus integrated to a host) was detected on the indoor subponds and adjacent pond showing the interaction of host-virus and the CRISPR defence response is occurring within the microbiome. Overall our findings showed those
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Sharon L. Ruiz Lopez PhD Thesis prophages are integrated into major components of the microbial communities, including
Proteobacteria associated with the bacterial order Burkholderiales.
Introduction
Viruses are the most abundant biological entities on the planet plays a key role in driving microbial evolution and can also influence biogeochemical cycles (Breitbart and Rohwer
2005;Fierer et al. 2007;Parsley et al. 2010;Rodriguez-Brito et al. 2010;Berg Miller et al. 2012).
Viruses of microorganisms (from archaea, bacteria and microbial eukaryotes) can be responsive to and the source of environmental change (Allen and Abedon 2014), and are found in wide range of environments including; extreme thermal acidic (Yellowstone National
Park) (Rice et al. 2001), hypersaline (Guixa-Boixareu et al. 1996;Sandaa et al. 2003), alkaline
(Jiang et al. 2004), deserts (Evans and Johansen 2010;Prigent et al. 2005), polar (Maranger et al. 1994;Borriss et al. 2003;Kepner Jr et al. 2003), deep subsurface sediments (Bird et al.
2001) and extreme thermal environments such as terrestrial hot springs (Rice et al.
2001;Rachel et al. 2002;Prangishvili and Garrett 2005).
Viruses that are parasitic to bacteria, Bacteriophages (phages), can impact on microbial ecology, leading to dramatic lytic infections or genetic modification by lysogenic disturbances
(Allen and Abedon 2013). In addition, viruses are able to move genetic material between different hosts and ecosystems (e.g. photosynthetic genes on cyanobacteria and microalgae
(Lindell et al. 2004)) (Rohwer et al. 2009;Lindell et al. 2004) leading to changes in environmental conditions (Allen and Abedon 2013). Furthermore, viruses play roles in controlling cellular numbers by facilitating horizontal gene transfer (HGT, the transfer of genetic material from an organism to another that is not its offspring) (Breitbart and Rohwer
2005;Berg Miller et al. 2012;Aminov 2011) altering bacterial phenotype and selecting phage- resistant microbes (Breitbart and Rohwer 2005).
However, despite their importance, identification of phages and knowledge of their interactions with the microbiome is limited due to the challenges associated with virus isolation and purification (Zheng et al. 2019;Roux et al. 2015b). These include the lack of a universal marker gene for viruses, the limited available viral databases and the restricted availability of
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Sharon L. Ruiz Lopez PhD Thesis bioinformatics tools, which have been mostly developed for prokaryotic genome sequencing data and not designed for handling metagenomic data (Roux et al. 2015a).
Although phage DNA constitutes 20% of the bacterial genome, most of the functional roles are not defined (Wang et al. 2010). However, it has been documented that interactions between bacteria and phages can lead to benefits for the host. For example phages can infect and protect bacteria against secondary phages, preventing them attaching to the host, a phenomenon called superinfection exclusion (Obeng et al. 2016). Throughout transduction, phages can integrate and transfer genes from a previous host, enhancing bacterial metabolism to improve bacteria survival under challenging environmental conditions (Obeng et al. 2016) . For instance, stress response in Escherichia coli can be modulated by inserted phages (Wang et al. 2010). Wang et al (Wang et al. 2010) found that E. coli strains inserted with phages CPS-53 and CP4-57 were more stable under oxidative, osmotic and acid-stress conditions, with obvious relevance to the microbiology of nuclear facilities being discussed here.
More widely, bacteriophages infect bacteria in order to reproduce and usually kill the host cell when replication is complete (Gasiunas et al. 2014). In response bacteria has evolved multiple defence mechanisms to interfere with selected phage life cycles, including restriction enzymes that destroy viral RNA, development of receptors that interfere with virus attachment to the cell and even by programming cell death (apoptosis) (Gasiunas et al. 2014;Labrie et al. 2010)
(Sturino and Klaenhammer 2006). Most recently the discovery of an adaptive immune system, known as clustered regularly interspaced short palindromic repeats (CRISPRs), has revolutionized the study of life sciences. The CRISPR system is a stand-alone adaptive immune system that targets DNA or RNA, as a way of protecting against viruses and other mobile genetic elements (Rath et al. 2015;Barrangou 2015;Labrie et al. 2010) (Gasiunas et al. 2014). It is encoded by one contiguous sequence in the genome known as the CRISPR locus (Karginov and Hannon 2010). CRISPR loci are constituted by an array of conserved direct repeats, that are interspersed by non-repetitive spacer sequences typically located adjacent to a leader sequence and CRISPR-associated genes (Cas) (Sorek et al. 2008).
CRISPR loci are hypervariable sites widely distributed in approximately 50% and 90% of
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Sharon L. Ruiz Lopez PhD Thesis sequenced bacterial and archaeal genomes, respectively (Makarova et al. 2011). Analysis of the CRISPR locus provides crucial information to detect and recover genome sequences from uncultivated phage, linking phage to their host, providing insight into the impacts of phage on population and community structure (Andersson and Banfield 2008).
Recent studies have used cultivation-independent DNA sequencing techniques, including metagenomic sequencing and analyses to characterise the prokaryotic and eukaryotic components of microbiomes within spent nuclear fuel storage ponds at Sellafield. This study extends metagenomic analyses to, for the first time; explore host-viral interactions within this unusual extreme environment.
Methods
Samples
In the present study two spent fuel ponds systems were analysed; (1) an indoor pond (INP) including its feeding tank area (FT), main ponds (MP), subponds (SP) and adjacent pond (Adj) and (2) an open-air (OUT) First Generation Magnox Storage pond (FGMSP) and its auxiliary open-air system (Aux). The presence of microbial blooms has been detected previously in the
FGMSP (Lynne REF here) and Aux ponds, while a stable background population has been detected in the indoor pond system (chapters X and Y in this thesis).
The pond system is located on the Sellafield nuclear site, Cumbria UK. The INP receives and stores metal fuel and legacy spent fuel from outdoor ponds (including the FGMSP) for interim storage pending a long term disposal solution becoming available. The FGMSP receives water from the INP for a pond purge, which enters the pond at a different location to the main purge water (Figure 1) (NDA 2015) (ONR 2016). The water supplied to both ponds is similar though, comprising demineralized water that has been adjusted to pH 11.6 to avoid corrosion of the stored fuels. The spent fuel ponds represent, therefore, extreme oligotrophic, hyper-alkaline and radioactive environments.
The Indoor Storage Pond (INP) is an indoor pond complex divided into 3 main ponds and 3 subponds linked by a transfer channel that enables water flow. In order to control the pond-
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Sharon L. Ruiz Lopez PhD Thesis water activity and quality, there is a continuous “once through” purge flow; pond-water from the main ponds flows into the transfer channel and enters the recirculation pump chamber where it is continuously pumped round a closed circulation loop and through a heat exchanger system, which cools the pond-water before it is recycled into the main ponds. Through the control feed, purge and re-circulation flow rates, the water depth is maintained at 7±0.05m.
The purge flow can be either from a donor plant or from other hydraulically linked ponds within the Sellafield complex (e.g. FGMSP). The temperature and pH are controlled at 15⁰C and 11.6 respectively. Samples for analysis were taken from designated sample points in the “Feeding
Tank” of the donor plant, where the alkali-dosed demineralised water used to feed the complex is stored, and main ponds 2 and 3 of the Fuel Handling Plant.
The FGMSP is the primary storage pond for legacy Magnox spent fuel at site. The pond is continuously purged with alkaline dosed demineralised water at a pH of 11.4, from an East to
West direction along the length of the pond, and contains an outflow point, where water is removed from the pond, on the Western wall. There are two further feeds into the pond, the first enters the pond at a location along the Northern wall and contains alkaline dosed water
(pH ~11.4) from another fuel handling pond facility on site. The auxiliary settling tank (auxiliary pond) is directly connected to the FGMSP, and if the water levels are sufficiently high, the auxiliary pond feeds the alkaline legacy pond legacy pond along the South wall.
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Figure 6.1Storage pond systems. Metal and legacy spent fuels from outdoor ponds are transported to the INP for interim storage pending a long term disposal solution available. The INP is divided in 3 main ponds (MP), 3 subponds and a feeding tank area (FT); waters from the INP are recirculated to the FGSMP during purging times. The FGMSP and its Auxiliary pond (Aux) store legacy fuel pond (NDA 2015;ONR 2016).
A total of 12 samples were taken from different sites from the storage ponds between 2016 and 2018 (Table 1). Samples were collected from a depth of 1 m using a hose syringe to withdraw the water into sterile plastic bottles. In order to avoid any risk of contamination, samples transferred directly from the pond to the NNL Central Laboratories (National Nuclear
Laboratory, Cumbria UK), where DNA was extracted and the samples where checked for radioactivity in line with the Environmental Permits and Nuclear Site licences held by Sellafield
Ltd. Extracted DNA samples free from significant radionuclide contamination were shipped to the University of Manchester and stored at -20⁰C until use.
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Table 6.1 Distribution of sample points in the Sellafield complex
Sample Storage pond Conditions Date
A INP, feeding tank area Indoor pond October 2016
B INP, feeding tank area Indoor pond October 2016
C INP, main pond 2 Indoor pond October 2017
D INP, main pond 3 Indoor pond October 2017
E INP, Subpond 2 Indoor pond January 2018
F INP, Subpond 3 Indoor pond January 2018
G INP, adjacent pond Indoor pond April 2017
H INP, adjacent pond Indoor pond April 2017
I Auxiliary pond Open-air system May 2016
J Auxiliary pond Open-air system June 2017
K FGMSP Open-air system September 2017
L Auxiliary pond Open-air system September 2017
Sequencing and sequence processing
DNA extraction was conducted at the Central Laboratories s at NNL on the Sellafield site, from filtered biomass using a PowerWater DNA Isolation Kit (Mobio Laboratories, Inc., Carlsbad
California, USA). After appropriate radiometric analyses, the DNA was then transported to the
Manchester University laboratories for amplification and preliminary analyses. Metagenomic sequencing was completed using the Illumina Hiseq2000 platform at Celemics (Celemics, Inc.,
Seoul, Korea).
All sequence reads were processed using the bioinformatic pipeline described in Figure 2.
First, FastQC (Andrews 2010) was used to visualise the quality scores on raw reads. Reads were processed with Trimmomatic (Bolger et al. 2014) to trim Illumina adaptor sequences and remove low quality and short reads with ambiguous bases to a quality score of 30 on the phred33 quality score scale (default parameters). Taxonomic classification of reads was performed with Kaiju (Menzel et al. 2016) version 1.7.2 using default parameters and viruses, refseq and progenomes databases.
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Reads were assembled de novo using MEGAHIT (version 1.1.3, default generic parameters) with the minimum contig length set to 200bp (Li et al. 2015). In order to identify and classify potential viral sequences, the predicted Megahit contigs were analysed with VirSorter default parameters (Roux et al. 2015a). All the protein sequences predicted as genes with VirSorter were used as potential virus amino acid sequences for the following analyses.
Taxonomic identification of viral contigs
To identify the viral and functional gene diversity from each contig, the predicted viral proteins were compared against the GenBank protein database manually using Blastp. Hits returned with the specific e-value of 1e-8 and with bit score >60 were considered homologs. Taxonomic classification for each contig was also done manually with blastn using the NCBI taxonomy ID for each BLAST hit, and classified to the highest taxonomic level (order or family) based on the taxonomic information shared by the majority of the genes in each virus contig. The virus contigs were classified as viral or prophage; categories were assigned based on confidence determined by VirSorter (categories 1 and 2).
Binning
Assemblies were grouped using the Maxbin 2.0 annotation program and the quality of the bins was assessed with CheckM (Parks et al. 2015) on pipeline mode SEARCH version 3.2.1
(2018), using a cut-off E value of 1e-5 to identify the best quality bins based on draft quality
(DQ) genomes; >93% completeness and 1<% contamination (detailed binning categories based on quality score are shown on Supplementary 6.1).
Annotation
Both bins and assemblies were analysed with Prokka (Seemann 2014) to obtain structural and functional annotation. Prokka pipeline annotates proteins coding genes using Prodigal
(Hyatt et al. 2010a) that identifies the coordinates of candidate genes but does not describe the putative gene product. Output files were then uploaded to KEGG KASS program on search program GHOSTX (amino acid query only) using a gene database specific for prokaryotic organisms a specific set of organisms (gene data sets sce, pfa, eco, pae, bsu, mja, afu, has, aar, hel, maq, amc, ilo, mac, mmh, mpy, mer, ant, abu, sun, sku, pol, cce, cpe, cac, ckr, hch, hna, drt, dvu, ade, hal, dar, tbd, gca, gsu, dps, sfu, pde, hma) and the assignment method
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Sharon L. Ruiz Lopez PhD Thesis was SBH (single-directional best hit), to obtain structural and functional annotation (Moriya et al. 2007).
Additionally, annotated bins were analysed with VirSorter (Roux et al. 2015a) to find prophage signals integrated within them. Bins were also analysed with CRT (Bland et al. 2007) to find
CRISPR arrays and analysed with CRASS (Skennerton et al. 2013) on Geneious R8 (Kearse et al. 2012) to predict the number and diversity of CRISPR loci based on repeat and spacer sequences.
Finally, bins were analysed with CAMITAX (Bremges et al. 2019) for taxonomic labelling.
CAMITAX combines genome distance and gene-homology taxonomic assignments with phylogenetic placement for taxonomic identification.
Figure 6.2 Workflow of the analysis performed on the metagenomes from spent fuel storage ponds
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Results
Microbial diversity of reads
Microbial classification was assigned by Kaiju software. Proteobacteria, Bacteroidetes,
Actinobacteria, Firmicutes, Cyanobacteria, Gemmatimonadetes and Planctomycetes were the most abundant phyla identified at all sampling sites and times (Figure 6.3). On the indoor system, Proteobacteria represented more than 90% of the total reads, except for sample G
(adjacent pond) where 72% of the reads were from this group. Proteobacteria was also the dominant phylum on the open system, representing 65% of the reads in the auxiliary pond (I,
J and L) samples and 92% in the FGMSP (sample K).
Indoor ponds system Open air ponds system 100% 90% 80% 70% Viruses 60% Planctomycetes 50% Gemmatimonadetes 40% Cyanobacteria 30% Firmicutes Relative abundance 20% Actinobacteria 10% Bacteroidetes 0% Proteobacteria
K_FGMSP E_SubpondF_Subpond I_AuxiliaryJ_Auxiliary L_Auxiliary C_Main pondD_Main pond A_Feeding tankB_feeding tank G_Adjacent pondH_Adjacent pond
Figure 6.3 Microbial affiliations at phylum level assigned by Kaiju classifier
Although viruses did not represent a major component of the sequences, a greater relative abundance was observed on the open ponds (Figure 6.4).
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0.5 0.45 0.4 0.35 0.3 0.25
0.2 Indoor ponds 0.15 Relative abundance Open air ponds 0.1 0.05 0
K_FGMSP E_SubpondF_Subpond I_AuxiliaryJ_Auxiliary L_Auxiliary C_Main pondD_Main pond A_Feeding tankB_feeding tank G_Adjacent pondH_Adjacent pond
Figure 6.4 Relative abundance of viruses based on reads (Kaiju classifier) on the indoor and open storage fuel ponds
Metagenomes contained sequences in the range of 6.6 to 7.7 million reads. Since sequences assembled into contigs/scaffolds may not truly represent heterogeneity in the samples, the initial approach was to predict CRISPR loci on the sequences prior to contig assembly. The number of CRISPR loci ranged between 1.5 and 4.2 CRISPR per million reads; a greater number or CRISPR were identified on the indoor system main ponds, subponds and adjacent pond (samples C to H) as well as on the open air system (I to L). The lowest diversity of
CRISPR systems was identified on samples A and B, from the indoor system feeding tank area (Figure 6.5). VirSorter was performed to predict free phage detection (outside a host genome) on assembled metagenomes. Only categories reported by the software (Roux et al.
2015a) as 1 (”most confident” predictions) and 2 (“likely” predictions) were considered reliable and are included in Figure 6.5. A greater number of phages was detected on the open air pond samples (samples I to L).
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Indoor ponds Open-air ponds 70
60
50
40 Reads (million) 30 Phage category 1 20 Phage category 2 CRISPR per million reads 10 Number of Sequences 0
K-FGMSP I-AuxiliaryJ-Auxiliary E-SubpondsF-Subponds L-Auxiliary C-Main pondsD-Main ponds A-Feeding tankB-Feeding tank G-Adjacent pondH-Adjacent pond
Assembled Samples
Figure 6.5 Diversity of phage (categories 1 and 2) on assemblies and prediction of CRISPR on metagenomes
Contigs were grouped into bins and good quality bins are shown on Table 6.2. The taxonomic classification was assigned according to the percentage of similarity via CAMITAX; taxonomic identification via blastn was also performed and the results were consistent (Supplementary
6.2). Additionally functional annotation was predicted via KEGG KASS. Four functional categories were compared in the samples. Hydrogen metabolism, determined by Hox hydrogenases (involved in H2 oxidation), and implicated in previous chapters as supporting microbial metabolism in the pond systems, was detected on the majority of the bins (except on 3 bins from the indoor adjacent pond). Nitrogen fixation, which could support microbial growth in this oligotrophic environment, was only detected on bins associated with the adjacent pond and the open air system (samples H to L). Nitrate reduction and sulphur oxidation (latter determined by Sox system) were consistent on all the samples. Nitrate-dosing has been proposed in the past as an anticorrosion treatment in nuclear storage ponds, and
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Sharon L. Ruiz Lopez PhD Thesis the potential for this form of metabolism is therefore of interest. Sulphur oxidation was included as a contrasting metabolic baseline process.
Table 6.2 Taxonomic and functional diversity of good bins (>93% completeness and <1% contamination, detailed description on Appendix Table 1)
Location Good Identification via Functional annotation (via Prokka and KEGG quality CAMITAX KAAS) bins Hydrogen Nitrogen Nitrate Sulphur metabolism fixation reduction oxidation (hox (Sox hydrogenase) system) Feeding A1 Comamonadaceae + - + + tank A2 Comamonadaceae + - + - B1 Comamonadaceae + - + - B2 Comamonadaceae + - - + B3 Comamonadaceae + - + + Main C1 Serpentimonas + - + + ponds C4 Silanimonas lenta + - + - C5 Porphyrobacter + + - - D1 Serpentimonas + - + + D5 Methylophilaceae + - + + Subponds E1 Methylophilaceae + - + + E2 Serpentimonas + - + + E3 Xanthomonadales + - - - E5 Burkholderiales + - + + E7 Bacteria + - - - F1 Methylophilaceae + - - + F3 Comamonadaceae + - + + F4 Erythrobacteraceae + + - - F5 Bacteria + - - - Adjacent G1 Serpentimonas + - - + pond G2 Acetobacteraceae + + + + G3 Flavobacteriaceae - - + - G4 Sphingomonadales + - - + G8 Actinobacteria - - - - H1 Serpentimonas + - + + H2 Acetobacteraceae + + + + H3 Erythrobacteraceae + + + + H6 Rhodobacteraceae + + + + H7 Actinobacteria - - - - FGMSP K1 Serpentimonas + - + + raichei K2 Rhodobacteraceae + + - + K4 Acetobacteraceae + + + +
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K7 Cyclobacteriaceae + - + + Auxiliary I2 Algoriphagus + + + - pond J6 Bacteroidetes + - - - L2 Synechococcaceae + + + -
VirSorter was used to identify viral sequences integrated into the good quality bins. In contrast to the contigs counts, only 1 prophage sequence was detected on the feeding tank and subponds (indoor system, INP), and prophages were not detected on the main ponds. Greater variation was detected on the adjacent pond, were 10 prophage sequences were identified.
In the outdoor system (OUT) samples only 2 and 3 prophage sequences were identified, in the auxiliary pond and FGMSP respectively. Reconstruction of CRISPRs (via Crass) showed the presence of CRISPR arrays the defence system on the samples, but it was not possible to identify the host organism. Repeats were then extracted from the CRISPR sequences and used in a blastn search against the good bins to identify the host for the CRISPR arrays.
Likewise, spacers were extracted from the CRISPR arrays and were used in a blastn search against viral contigs, to identify associations between viruses and CRISPR arrays. The highest abundance of CRISPR repeats and spacers was observed on the main ponds, subponds and adjacent pond (indoor system). The number of CRISPR arrays (loci and sapcers) was lower on the open ponds (OUT) and the heading tank (INP). Bacteria belonging to the order
Burkholderiales were the most common host were CRISPR arrays were identified on indoor ponds whilst on the auxiliary pond Cyanobacteria was identified to be the host for the unique prophage identified. Detailed information is shown in Supplementary Table 2.
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30 200
180 25 160
140 20 120
15 100
80
10 CRISPR spaces 60 Number of sequences 40 5 20
0 0
OUT_FGMSP INP subponds INP main ponds INP Feeding tank INP adjacent pond OUT_auxiliary pond 01OUT_auxiliary pond 02
Good quality bins Prophage detection CRISPR loci CRISPR spaces
Figure 6.6 Defence system prediction based on CRISPR arrays (repeats-spaces)
Discussion
The spent fuel storage ponds are hyper alkaline, oligotrophic and store radioactive material, leading to challenging conditions for microbial survival. This study expands previous work on prokaryotic and eukaryotic components of the pond microbiomes, to, for the first time, analyse viral interactions and defence systems in this unique extreme environment. In addition, metageomic “bins” were assembled representing key host prokaryotes, helping identify key metabolic traits in potential pioneer species in the ponds
Samples were collected over a period of 15 months from different areas of the system.
Microbial diversity on the indoor system (INP) was dominated by bacteria, mainly associated with Proteobacteria, Bacteroidetes and Actinobacteria. Although members of the
Proteobacteria are not often considered extremophiles, there is evidence that members of this
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Sharon L. Ruiz Lopez PhD Thesis group can tolerate and populate extreme radioactive conditions (Yu et al. 2015) (Chicote et al. 2005), hyperalkalinity, e.g. in hot springs (Lau et al. 2009) (Baker et al. 2001) and low levels of nutrients e.g. ultra-pure water (Bohus et al. 2010;Chicote et al. 2005). Organisms associated with Proteobacterial genera have also been previously identified on spent fuel storage ponds (Chicote et al. 2005;Sarró et al. 2005;Silva et al. 2018a;Tišáková et al. 2012)
(MeGraw et al. 2018).
Functional annotation revealed that the majority of the recovered bins corresponded to organsims that supported hydrogen metabolism, determined by the presence of [NiFe]- hydrogenase (Hox). Hox hydrogenase catalyses the reversible oxidation of molecular hydrogen according to the reaction H2↔ 2H+2e- and play a crucial role in microbial energy metabolism (Vignais et al. 2001;Vignais et al. 2004). Hydrogen metabolism, potentially produced by water radiolysis, is likely to support hydrogen-oxidising microbial pioneer species in the pond system. Previous studies on the storage ponds at Sellafield revealed that genus
Hydrogenophaga on the indoor system and Cyanobacteria on open-air ponds represented major components of the microbial diversity (MeGraw et al. 2018) (Foster et al. 2019a;Ruiz-
Lopez et al. 2019); both organisms are well studied examples of hox hydrogenases containers
(Shafaat et al. 2013;Eckert et al. 2012) (Yoon et al. 2008).
Overall viral abundance represented less than 1% on the samples. Environmental variables, e.g. temperature, light exposure and salinity, can directly affect virus-host interactions
(Baudoux and Brussaard 2005) (Hardies et al. 2013;Williamson and Paul 2006;Finke et al.
2017;Jia et al. 2010) (Baudoux and Brussaard 2008;Finke et al. 2017). UV radiation is a major factor for decay rates of cyanobacteria, eukaryotic phytoplankton and viruses of bacteria
(Cottrell and Suttle 1995) (Noble and Fuhrman 1997) (Murray and Jackson 1992). Additionally nutrient availability has an important effect on virus-host interactions (Chow et al. 2014); for instance nutrients such as phosphorus and nitrogen are highly demanded for viral replication
(Bratbak et al. 1998;Suttle 2007). Besides environmental conditions it is important to note that the DNA extraction methods used here included an initial filtration step where most of the free viruses may have not been retained due to the pore size used (2µm). However, it is clear from the analyses presented, that the microbiomes of the indoor ponds contained lower levels of
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Sharon L. Ruiz Lopez PhD Thesis phage DNA than the outdoor ponds, especially the auxillary pond samples, which are not exposed to purge cycles. There, establishment of microbial communities in these closed outdoor systems may therefore help promote viral infection.
CRISPR arrays were, however, detected at similar levels (per million reads) across all the pond samples, suggesting that they could play a role in helping host organisms adapt to the extreme environments (alkalinity and radiotoxicity) (Le Romancer et al. 2006) across the pond complexes. Additionally, phages can modulate the community structure by transferring genetic material to their host and, in the specific case of the oligotrophic ponds, by promoting phage-mediated microbial mortality that generates available nutrients for the cells (Breitbart et al. 2004).
Identification of CRISPR repeats and spacers showed that the most frequent host in the storage ponds were associated with the order Burkholderiales. The findings complement the previous studies on this specific environment. Bacterial members belonging to order
Burkholderiales are able to adapt and populate oligotrophic, hyperalkaline, radioactive and light-limited environments by displaying a set of genomic adaptations and the phage transduction may be involved in these processes. Specifically evidence has been found of phage regions and transfer of genomic material in members of the genus Hydrogenophaga
(Burkholderiales) (Gan et al. 2017), previously identified on the INP (Ruiz-Lopez et al. 2019), and could represent an adaptation mechanism in the storage pond. This clearly warrants further investigation.
Acknowledgements
SRL acknowledges financial support from a PhD programme funded by the National Council of Science and Technology (CONACyT). This work was also supported by funding from
Sellafield Limited and the Royal Society to JRL. LF was supported by an EPSRC CASE PhD and IAA funding.
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Supplementary information
Supplementary 6. 1 Binning categories based on quality score
1 Near-complete genome >93% completeness, <5% contamination
Good bins
2 Medium-quality genome >70% completeness, <10% contamination
3 Partial genome >50% completeness, <15% contamination
4 Low quality genomes Quality lower than 50%
Supplementary 6. 2 Identification of the CRISPR arrays including spacers and repeats.
Location Good Taxonomy CRISPR loci CRISPR Prophage Contig quality (Blastn) sequence spacers detection coverage bins sequence Feeding A1 Burkholderiales - - k141_2694 81.71953351
tank A2 Comamonadaceae - - k141_10854 59.15403 k141_7131 56.28694 B1 Burkholderiales - - k141_25103 56.48447 k141_8678 58.31636 B2 Burkholderiales - - k141_13766 49.24896 B3 Burkholderiales k141_25927 (5 - k141_23304 40.73938 repeats) k141_599 (4 repeats) Main C1 Burkholderiales k141_320 (5 146 - 244.8874 ponds repeats) spacers - k141_28213 C4 Unclassified k141_1470 (8 - - 12.96596 repeats) 13.68435 k141_4770 (5 13.58487 repeats) k141_9346 (2 repeats) C5 Erythrobacteraceae - 1 spacer -
D1 - 1 spacer - D5 Betaproteobacteria k141_20391 (1 1 spacer - 37.80389 repeat) 49.50615 k141_21531 (65 37.76542 repeats) 36.03357 k141_3261 (2 repeats)
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k141_8909 (2 repeats) Subponds E1 Serpentimonas k141_13878 (1 - - 127.6577 repeat) 126.1004 k141_14166 (2 120.9153 repeats) 67.03781 k141_4524 (2 repeats) k141_8151 (4 repeats) E2 Burkholderiales k141_1850 (105 21 CRISPR - 124.222 repeats) spacers E5 Burkholderiales 2 CRISPR sites 42 CRISPR - 2.710262 cat1 k141_2226 spacers E7 Acidimicrobiaceae k141_9961 (2 2 CRISPR - 7.952549 repeats) Spacers 2.710262 10.09421 F1 Methylophilaceae k141_14522 (1 1 CRISPR - 123.6058 repeat) Spacer 37.50187 k141_2257 (1 125.9339 repeat) 120.5019 k141_3056 (2 repeats) k141_6367 (3 repeats) F3 Burkholderiales k141_13460 (4 1 CRISPR - 62.78257 repeats) Spacer 67.31107 k141_14093 (8 17.38226 repeats) 62.75976 k141_716 (8 repeats) k141_8211 (50 repeats) F4 Sphingomonadales 2 CRISPR sites k141_12648 3.361462 k141_12426 (8 33 CRISPR 15.30374 repeats) spacers 15.77768 k141_2140 (3 repeats) Adjacent G2 Acetobacteraceae k141_10208 (27 - k141_11388, 62.68504 pond repeats) k141_1469 69.98809 k141_11126 (18 65.62389 repeats) 59.25636 k141_3075 (6 57.88733 repeats) 58.52471
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k141_6440 (1 61.18004 repeat) Prophage k141_6612 (1 62.11882 repeat) 60.38804 k141_6704 (1 repeat) k141_8564 (3 repeats) G3 Unclassified k141_11457 (80 - - 56.5327 repeats) 51.74855 k141_13891 (10 56.04868 repeats) 60.24455 k141_16321 (141 50.71173 repeats) k141_2271 46.9203 (4 repeats) k141_10572 (35 repeats) k141_16117 (43 repeats) G4 Sphingomonadales - - k141_18077 Prophage k141_9472 34.24099 k141_2226 13.88803 k141_10171 2.870175 2.772622 G8 Actinomycetales - 1 CRISPR 2.870175 Spacer H1 Burkholderiales k141_8000 (115 179 99.2235 repeats) CRISPR spacers H2 Rhodospirillales k141_1975 (115 1 CRISPR k141_2468 70.66051 repeats) Spacer k141_7244 72.22759 k141_2555 (120 k141_9770 66.44355 repeats) 79.51366 k141_4557 (1 73.75719 repeat) 72.31211 k141_4938 (6 73.58034 repeats) 83.10537 k141_5176 (1 74.20821 repeat) Prophage k141_5467 (2 74.23412 repeats) 76.51427 k141_6598 (4 73.88614 repeats)
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k141_9755 (165 repeats) k141_9827 (2 repeats) H3 Sphingomonadales k141_1331 (30 2 CRISPR k141_2550 64.59156 repeats) Spacers 61.6015 k141_1546 (10 24.86944 repeats) Prophage k141_7147 (8 31.24127 repeats) H6 Rhodobacteraceae - 1 CRISPR - - Spacer H7 Actinomycetales - 1 CRISPR - - Spacer
FGMSP K1 Burkholderiales k141_18101 (95 3 CRISPR - 29.11369 repeats) Spacers 86.41386 k141_2551 (4 35.36795 repeats) 82.09703 k141_28760 (8 repeats) k141_31558 (70 repeats) K2 Rhodobacteraceae - - k141_819 Prophage 53.12703 K4 Acetobacteraceae k141_10718 (3 - k141_26474 29.11369 repeats) k141_24535 27.82314 k141_34443 (2 29.57929 repeats) Prophage k141_7843 (1 29.59703 repeat) 28.61228 K7 Cytophagales k141_10718 (3 1 CRISPR - 29.11369 repeats) Spacer 27.82314 k141_34443 (2 29.57929 repeats) k141_7843 (1 repeat) Auxiliary L2 Cyanobacteria - - k141_100911 Prophage pond k141_84037 17.51644 18.34898 J6 Bacteroidetes - 1 CRISPR - - Spacer
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References
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Rodriguez-Mueller, J., Rodriguez-Valera, F., Salamon, P., Srinagesh, S., Thingstad, T. F., Tran, T., Thurber, R. V., Willner, D., Youle, M. & Rohwer, F. (2010). Viral and microbial community dynamics in four aquatic environments. ISME J, 4(6), 739-51. Rohwer, F., Prangishvili, D. & Lindell, D. (2009). Roles of viruses in the environment. Environ Microbiol, 11(11), 2771-4. Roux, S., Enault, F., Hurwitz, B. L. & Sullivan, M. B. (2015a). VirSorter: mining viral signal from microbial genomic data. PeerJ, 3, e985. Roux, S., Hallam, S. J., Woyke, T. & Sullivan, M. B. (2015b). Viral dark matter and virus–host interactions resolved from publicly available microbial genomes. Ecology, genetics and genomics, 4. Ruiz-Lopez, S., Foster, L., Cole, N., Adams, J., Song, H. & Lloyd, J. R. (2019). Metagenomic analysis of open-air and indoor spent fuel storage ponds at Sellafield, UK [Online]. Available: https://www.microbiologyresearch.org/content/journal/acmi/10.1099/acmi.ac2019.po 0284 [Accessed]. Sandaa, R. A., Foss Skjoldal, E. & Bratbak, G. (2003). Virioplankton community structure along a salinity gradient in a solar saltern. Extremophiles, 7(5), 347-51. Seemann, T. (2014). Prokka: rapid prokaryotic genome annotation. Bioinformatics, 30(14), 2068-9. Shafaat, H. S., Rudiger, O., Ogata, H. & Lubitz, W. (2013). [NiFe] hydrogenases: a common active site for hydrogen metabolism under diverse conditions. Biochim Biophys Acta, 1827(8-9), 986-1002. Silva, R., Almeida, D. M., Cabral, B. C. A., Dias, V. H. G., Mello, I., Urmenyi, T. P., Woerner, A. E., Neto, R. S. M., Budowle, B. & Nassar, C. A. G. (2018a). Microbial enrichment and gene functional categories revealed on the walls of a spent fuel pool of a nuclear power plant. PLoS One, 13(10), e0205228. Skennerton, C. T., Imelfort, M. & Tyson, G. W. (2013). Crass: identification and reconstruction of CRISPR from unassembled metagenomic data. Nucleic Acids Res, 41(10), e105. Sorek, R., Kunin, V. & Hugenholtz, P. (2008). CRISPR — a widespread system that provides acquired resistance against phages in bacteria and archaea. Nature Reviews Microbiology, 6, 181-186. Sorek, R., Lawrence, C. M. & Wiedenheft, B. (2013). CRISPR-mediated adaptive immune systems in bacteria and archaea. Annu Rev Biochem, 82, 237-66. Vignais, P. M., Bilboud, B. & Meyer, J. (2001). Classification of hydrogenases. FEMS Microbiology Reviews, 25, 455-501. Vignais, P. M., Willison, J. C. & Colbeau, A. (2004). Chapter 11: Hydrogen respiration. In: Zannoni, D. (ed.) Respiration in Archaea and Bacteria. Advances in photosynthesis and respiration. Springer.
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Wang, X., Kim, Y., Ma, Q., Hong, S. H., Pokusaeva, K., Sturino, J. M. & Wood, T. K. (2010). Cryptic prophages help bacteria cope with adverse environments. Nat Commun, 1, 147. Williamson, S. J. & Paul, J. H. (2006). Environmental factors that influence the transition from lysogenic to lytic existence in the phiHSIC/Listonella pelagia marine phage-host system. Microb Ecol, 52(2), 217-25. Yoon, K. S., Tsukada, N., Sakai, Y., Ishii, M., Igarashi, Y. & Nishihara, H. (2008). Isolation and characterization of a new facultatively autotrophic hydrogen-oxidizing Betaproteobacterium, Hydrogenophaga sp. AH-24. FEMS Microbiol Lett, 278(1), 94- 100. Yu, L. Z., Luo, X. S., Liu, M. & Huang, Q. (2015). Diversity of ionizing radiation-resistant bacteria obtained from the Taklimakan Desert. J Basic Microbiol, 55(1), 135-40. Zheng, T., Li, J., Ni, Y., Kang, K., Misiakou, M. A., Imamovic, L., Chow, B. K. C., Rode, A. A., Bytzer, P., Sommer, M. & Panagiotou, G. (2019). Mining, analyzing, and integrating viral signals from metagenomic data. Microbiome, 7(1), 42.
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Conclusions and Future work
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Chapter 7 Conclusions and future work
Conclusions
The microbial ecology of spent fuel storage ponds was studied in this thesis. The spent fuel removed from nuclear power plants is a mixture of radionuclides and waste materials no longer usable as fuel and management prior to reprocessing or final disposal represents a significant challenge.
To ensure safe containment during storage, the material is packaged into a solid, stable form that is kept within high integrity into stainless steel or concrete containers. Afterwards fuel assemblies are stored in water ponds which provide adequate shielding from radiation. Ponds are filled with demineralised water and alkali (NaOH) is added to prevent corrosion of the materials in the pond. The spent fuel ponds therefore represent an extreme environment; the hyper alkalinity (pH 11.6) combined with a lack of nutrients (oligotrophy) and high background radioactivity levels create conditions challenging for life. Despite these harsh conditions, the presence of microbial communities has been detected in spent fuel ponds, including those at
Sellafield.
Microbial colonisation can cause significant challenges during spent fuel pond management.
Excessive microbial growth can cause turbidity in the water, complicating fuel movements and retrievals, and ultimately increasing the costs of decommissioning. In addition, microorganisms can interact with the stored materials promoting corrosion of containers, and also interacting with radionuclides contained on the pond affecting their speciation and solubility. On the other hand, the identification of microorganisms with the ability to survive in highly radioactive waters while accumulating radionuclides, could lead to the development of bioremediation process for contaminated waters.
The global objective of this thesis was to describe the microbial ecology of spent fuel storage ponds and based on the genomic fingerprints, to predict the mechanisms used to underpin the colonisation of these extreme environments. The study of microbial adaptation mechanisms in a range of extreme environments is a rapidly expanding field, however, the combined challenging conditions dictated by the nature of the site represents a novel area of
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Sharon L. Ruiz Lopez PhD Thesis fundamental research, that will also lead a better understanding of the microbiological processes that occur within the SNF ponds (which could have benefit to the pond operators).
In Chapter 4 the microbial ecology of an indoor storage pond (INP) was analysed. The INP stores nuclear material and nuclear wastes from different stations and ponds across the UK, and the light exposure limitation proved to be an important factor for survival of photosynthetic organisms, which have been identified in outdoor Sellafield ponds, but were not detected in the indoor ponds. Samples were taken for a period of 30 months from main ponds, subponds and the feeding tank area where demineralised water is purged. Analysis of the 16S rRNA gene based sequencing provided a broad overview of the microbial diversity surviving in the oligotrophic pond environment. Microbial community composition was stable over the period of time studied, and was dominated by bacterial genera including Hydrogenophaga,
Methylotenera, Silanimonas and Porphyrobacter. Since Hydrogenophaga, a chemoorganotroph hydrogen-oxidizing organism, was the dominant genus at all sampling times, the metabolism of H2, potentially formed through water radiolysis, was proposed as a key energy source for microbial survival and colonisation on the INP. Neither 16S rRNA archaeal or 18S rRNA eukaryotic genes were detected showing that environmental conditions may be limiting for those organisms. Additionally, microbial growth was estimated by the quantification of 16S rRNA copies determined by qPCR. Results showed an increase in biomass over time. Finally, classic culturing-dependent techniques were tested to isolate representative microbial components, and proved efficient for isolation of members associated to bacterial family Cyclobacteriacea, but inadequate for isolation of major microbial components such as Hydrogenophaga. This chapter provided an initial insight into the microbial ecology of the INP, an indoor, hyper alkaline, oligotrophic and radioactive environment and suggested that due to limiting carbon sources, microorganisms may use alternative energy sources such as H2.
In Chapter 5 the microbial ecology of indoor and open-air storage ponds was analysed and compared using a metagenomic approach. Due to the difficulty of isolating major microbial components, metagenomics techniques were applied. Metagenomics is a relatively recently developed tool to directly access the genetic material of entire communities of organisms,
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Sharon L. Ruiz Lopez PhD Thesis leading to the analysis of microbial ecology and evolution by the prediction of metabolic microbial activities within the studied environment. Samples from the previously studied indoor pond (INP), including its main ponds, subponds and feeding area, were analysed and compared with an open system, the First Generation Magnox Storage Pond (FGMSP) and its auxiliary pond (Aux). Microbial diversity was consistent to the diversity determined by 16S rRNA gene analysis at phylum, order and family levels, being dominated by Proteobacteria,
Burkholderiales and Comamonadaceae, respectively. Contrasting differences were observed at the genus level, where Hydrogenophaga (within the Burkholderiales), the most abundant organism previously identified on the INP was not detected by metagenomics. The depth of sequence reads and the different reference data bases may have influenced the observed results. On the open air systems, microbial diversity was also dominated by Proteobacteria and the presence of photosynthetic organisms belonging to Cyanobacteria was identified.
Metagenomic analyses also provided a better understanding of the microbiological adaptation processes occurring in the oligotrophic environments studied. When microbial communities were exposed to challenging conditions associated with the pond environment, evidence for different survival strategies were collected; the relative abundance of genes related to respiration, specifically to hydrogenases were increased in the INP. These results support the hypothesis Burkholderiales that is present and has hydrogenase genes, but its precise phylogenetic affiliation requires further work. It is probably closely related to Hydrogenophaga though. However, relative abundance of genes related to respiration were detected at lower levels in the Aux open air pond, and here genes related to photosynthesis were exclusively detected, suggesting that sunlight is a key energy source on open systems. Additionally, functional annotation of genes revealed the abundance of genes related to bacterial stress response possibly linked by •OH radicals that also formed through radiolysis. Also, genes related to membrane transport and potassium homeostasis were abundant, suggesting that
Na+ membrane transport systems seem to be a key mechanism used by microorganisms to keep the osmotic balance correct within the cells in an environment heavily dosed with NaOH.
Finally, the relative abundance of genes related to bacterial defence systems such as modification-restriction, base excision and the immunity system CRISPR, were increased in the INP, implying the environmental conditions on the INP seem to be more challenging than
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Sharon L. Ruiz Lopez PhD Thesis the open air system (FGMSP and Aux). It is concluded that the 16S rRNA gene based sequencing provided a broad overview of the microbial diversity surviving in the oligotrophic pond environment, whilst metagenomics successfully provided a complementary functional overview of microbial processes in the system.
On Chapter 6 the potential influence of viruses on the storage systems was analysed. Viruses represent the most abundant entities on Earth and their ability to infect bacteria could modulate community structure. Viral distribution, prediction of hosts and defence integrated systems were analysed using a metagenomic approach. Metagenomes were assembled and grouped into bins to find CRISPR loci as a measure of viral-host associations in each sample, and the prediction of CRISPR spacers-repeats arrays allowed the measurement of bacterial defence responses within the community. The majority of the viral hosts were associated with the order Burkholderiales, which were previously identified as major microbial components.
Most of the CRISPR repeats-spacers were identified in the INP main ponds, subponds and adjacent pond, whilst CRISPR arrays were poorly represented in the initial feeding tank area.
These results suggest that microbial members populating the radioactive storage ponds contain adaptation and defence systems that allow them to cope not only with challenging environmental conditions but also against viral infections. Finally, functional annotation of metagenomic “bins” corroborated our previous findings; the presence of genes related to hydrogenases revealed the H2 metabolism may be the main energy source within the ponds.
This thesis provides a taxonomic and functional overview of the spent fuel storage systems at
Sellafield, UK. The INP is a novel site of study and due to its characteristics, and for optimal long-term management it is crucial to analyse the microbial ecology and possible interactions with other hydraulically linked ponds such as the FGMSP and Auxiliary. The microbial ecology of the FGMSP and Aux ponds had been studied previously and in this project the analysis of microbial distribution and microbial responses was assessed using a metagenomic approach.
Complementary viral distribution and possible interactions of host-phage were identified to find have a better understanding of the global microbial activities occurring within the storage ponds.
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The findings on this thesis corroborate the presence of extreme oligotrophic, alkaline and irradiated ecosystem does not prevent the colonisation of microbial populations due to their adaptation to the surrounding environment. The adaptation process can occur either by acclimation to limited nutrient sources (head tank), by metabolizing chemical species e.g. hydrogen derived from the interaction between the stored material and the neighbouring ecosystem (main ponds and subponds) or by metabolizing other available energy sources such as sun light (auxiliary pond) and by water recirculation and material transfer between ponds (FGMSP and INP). A key observation throughout the time period studied is that distinct microbial communities exist across the Sellafield estate, and the microbiomes of these individual ponds are remarkable stable, despite the range of operations taking place there.
Future work
The results described in this thesis show the importance of microbial communities and could lead to additional topics of research. The microbial diversity analysis of the indoor alkaline pond, INP, over 30 months revealed that the microbial diversity was stable over the
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Sharon L. Ruiz Lopez PhD Thesis operational period. Further analyses over time would be important to see if this baseline microbial ecology shifts during periods of time when the inventory in the pond is altered significantly. Also, would be important to track the biomass contents level and to match any detected changes on linkages to the pond to find any possible alterations in pond water chemistry.
The majority of the analyses were performed from DNA extracted from samples. It would be crucial to develop incubation experiments with “fresh” samples obtained directly from the ponds. Hence to create microcosms to determine the interactions of microbial communities with storage material (steel and concrete) to analyse and prevent corrosion in situ. It would also be critical to track interactions of live microbial communities with radionuclides to determine the microbial influence on radionuclide solubility and (im)mobilization.
From the metagenomic analysis, more bioinformatics analysis can be developed. The microbial diversity and functional annotation were obtained via the MG-RAST pipeline with standard parameters. It would be interesting to run the metagenomic analysis de novo, assembling and recovering genes into MAGs to track specific changes on genes sequences, and statistically comparing different assembly and annotation tools to have a more accurate prediction of microbial metabolism occurring within the SNF ponds. A similar scenario is suggested for taxonomic identification. Over the last 3 years, new, more efficient and reliable bioinformatics tools have been developed to describe taxonomic diversity; it would be interesting to compare the available tools such as ANI/AAI (Rodriguez and Kostantinidis 2016) and CAMITAX (Bremges et al. 2019) to most commonly used RefSeq (O'Leary et al. 2016) to have a more exact picture of the microbial distribution.
Since culturing dependent techniques proved in adequate for microbial isolation, the recovery of complete genomes could be an efficient way to identify uncultivable organisms and since the SNF ponds are recently studied sites, the identification of new species or even new genera of bacteria could potentially be assessed.
Complementary omics techniques have proved useful for studying microbial ecology and evolution in other extreme sites, and could prove useful here also. Metagenomics provides a general perspective of the potential microbial metabolisms in a studied environment Follow-
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Sharon L. Ruiz Lopez PhD Thesis up work to analyse the SNF ponds via metatranscriptomics and metaproteomics are warranted to provide more precise evidence of adaptations that support microbial colonisation based on genes expression and protein synthesis.
The metavirus (metagenomics of viruses) analysis is also a potential line of further research.
In Chapter 7, the objectives were to identify host-phage interactions and the presence or lack of immunity defence systems. To date the information about viruses on SNF is non-existent, which makes this a ground-breaking topic of research and the possibility to identify host-phage interactions could potentially contribute to obtain a better understanding of the microbial ecology on the spent fuel storage ponds, and could even help identify new “biocontrol” strategies for organisms causing problematic blooms in SNPs.
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Conference presentations and Awards
Awards Best poster presentation: School of Earth and Environmental Sciences Postgraduate
Conference, The University of Manchester, UK. 5th December 2017.
Oral Presentations
• 2019
Metagenomic analysis of an indoor spent fuel storage ponds at Sellafield, UK. S. Ruiz-Lopez,
L. Foster, N. Cole, H. Song, J. Adams and J. Lloyd. Geomicrobiology Research in Progress
Meeting (RiP)., Manchester Metropolitan University, UK. 27th-28th June 2019
Metagenomic analysis of open-air and indoor spent fuel storage ponds at Sellafield, UK. S.
Ruiz-Lopez, L. Foster, N. Cole, H. Song, J. Adams and J. Lloyd. Microbiology Society Annual
Conference, Belfast, UK. 8th-11th April 2019
• 2017
Understanding the microbial productivity in highly radioactive storage facilities. S. Ruiz-Lopez,
L. Foster, K. Morris, N. Cole and J. Lloyd. Geomicrobiology Research in Progress Meeting
(RiP), The University of Manchester, UK. June 2017
Understanding the microbial productivity in highly radioactive storage facilities. S. Ruiz-Lopez,
L. Foster, K. Morris, N. Cole and J. Lloyd. 6th Symposium of CONACyT Fellows in Europe,
European Parliament in Strasbourg, France. 29th-31st March, 2017.
Understanding the microbial productivity in highly radioactive storage facilities. S. Ruiz-Lopez,
L. Foster, K. Morris, N. Cole and J. Lloyd. XV Symposium of Mexican Studies and Students in the UK, Durham University, UK. 12th-14th July, 2017
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Poster Presentations • 2019
Metagenomic analysis of open-air and indoor spent fuel storage ponds at Sellafield, UK. S.
Ruiz-Lopez, L. Foster, N. Cole, H. Song, J. Adams and J. Lloyd. Poster presentation at
Topical Day: Aquatic microbiota in or near nuclear facilities: insights, discoveries and solutions. September 12th, 2019, Brussels, Belgium.
Metagenomic analysis of open-air and indoor spent fuel storage ponds at Sellafield, UK. S.
Ruiz-Lopez, L. Foster, N. Cole, H. Song, J. Adams and J. Lloyd. Poster presentation at the
Federation of European Microbiological Societies Conference, FEMS. 7th-11th July, 2019.
Glasgow, Scotland.
• 2018
Characterisation of Microbial Populations in Highly Radioactive Storage Facilities in Sellafield,
UK. S. Ruiz-Lopez, L. Foster, C. Boothman, N. Cole and J. Lloyd. Microbiology Society
Annual Conference, Birmingham, UK. 9th-12th April, 2018
Characterisation of Microbial Populations in Highly Radioactive Storage Facilities in Sellafield,
UK S. Ruiz-Lopez, L. Foster, C. Boothman, N. Cole and J. Lloyd. The International Society for Microbial Ecology, ISME, Leipzig, Germany. 12th-17th August,
Characterisation of Microbial Populations in Highly Radioactive Storage Facilities in Sellafield,
UK. S. Ruiz-Lopez, L. Foster, C. Boothman, N. Cole and J. Lloyd. The American Society of
Microbiology (ASM), Atlanta, USA. 6th-11th June, 2018
• 2017
Understanding the microbial productivity in highly radioactive storage facilities. S. Ruiz-Lopez,
L. Foster, J. Lloyd and K. Morris. Microbiology Conference Annual Conference, Edinburgh,
Scotland. 3rd-6th April 2017
Understanding the microbial productivity in highly radioactive storage facilities. S. Ruiz-Lopez,
L. Foster, J. Lloyd and K. Morris. School of Earth and Environmental Sciences
Postgraduate Conference, The University of Manchester, UK. 5th December 2017
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2016
Understanding the microbial productivity in highly radioactive storage facilities. S. Ruiz-Lopez,
K. Morris and J. Lloyd. School of Earth and Environmental Sciences Postgraduate
Conference, The University of Manchester, UK. December 2016
Outreach § Organiser at the “Pint of Science” global event, team Planet Earth, editions 2017,
2018 and 2019. Manchester, UK
Complementary courses § Metagenomics Bioinformatics Course at the European Bioinformatics Institute,
EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK. July 2018
§ 28th Summer School Bioinformatics for Microbial Ecologists at the University of
Jyvaskyla, Finland. 6th-10th August 2018
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