1 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
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1
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
Chapter 4 Identification of stable hydrogen-driven microbes in highly radioactive storage facilities in Sellafield, UK ............................................................................................................. 83
Chapter 5 Comparative metagenomic analyses of taxonomic and metabolic diversity of microbiomes from spent nuclear fuel storage ponds .............................................................. 123
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
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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
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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
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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.
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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.
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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.
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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
for administrative purposes
ii. Copies of this thesis, either in full or in extracts and whether in hard or electronic
copy, may be made only in accordance with the Copyright, Designs and Patents
Act 1988 (as amended) and regulations issued under it or, where appropriate,
in accordance with licensing agreements which the University has from time to
time. This page must form part of any such copies made.
iii. The ownership of certain Copyright, patents, designs, trademarks and other
intellectual property (the “Intellectual Property”) and any reproductions of copyright
works in the thesis, for example graphs and tables (“Reproductions”), which may
be described in this thesis, may not be owned by the author and may be owned by
third parties. Such Intellectual Property and Reproductions cannot and must not
be made available for use without the prior written permission of the owner(s) of
the relevant Intellectual Property and/or Reproductions.
iv. Further information on the conditions under which disclosure, publication and
commercialisation of this thesis, the Copyright and any Intellectual Property and/or
Reproductions described in it may take place is available in the University IP Policy
(see http://documents.manchester.ac.uk/DocuInfo.aspx?DocID=24420), in any
relevant Thesis restriction declarations deposited in the University Library, The
University Library’s regulations (see
http://www.library.manchester.ac.uk/about/regulations/)andin The University’s policy
on Presentation of Theses
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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,
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!
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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.
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1
Purpose and significance of the investigation
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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.
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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
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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.
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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
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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
19
2
Introduction
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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.
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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).
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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).
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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
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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 (ILW)
The major components are 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).
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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
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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
27
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
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
Sharon L. Ruiz Lopez PhD Thesis
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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).
Sharon L. Ruiz Lopez PhD Thesis
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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
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).
Sharon L. Ruiz Lopez PhD Thesis
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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
Sharon L. Ruiz Lopez PhD Thesis
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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).
Sharon L. Ruiz Lopez PhD Thesis
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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
Sharon L. Ruiz Lopez PhD Thesis
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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).
Sharon L. Ruiz Lopez PhD Thesis
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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
Sharon L. Ruiz Lopez PhD Thesis
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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
Sharon L. Ruiz Lopez PhD Thesis
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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
Sharon L. Ruiz Lopez PhD Thesis
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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.
Sharon L. Ruiz Lopez PhD Thesis
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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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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 (average)
pH Temperature
⁰C
Na+ (µg/ml)
TOC
(µg/ml) Phosphates
PO4-2
(g/ml)
Nitrates
NO3-2
(µg/ml)
Beta AC
(Bq/ml)
Feeding tank (FT)
11.6 18 80.6 1< 0.0 0.01 NA
Main ponds (MP)
11.6 20.9 80.3 2.0 0.0 0.01 1,117
Subponds (SP)
11.5 20.7 81.7 2.13 0.0 0.01 1,132
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).
Sharon L. Ruiz Lopez PhD Thesis
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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.
0
50000
100000
150000
200000
250000
300000
350000
400000
450000
500000
FT01_OCt16
FT02_Oct16
MP01_Oct16
MP02_Pct16
MP03_June17
MP04_June17
MP05_Oct17
MP06_Oct17
MP07_Jan18
MP08_Jan18
MP09_June18
MP10_June18
SP03_Jan18
SP04_Jan18
SP05_Jun18
SP06_June18
DNAcopies/m
l
Sharon L. Ruiz Lopez PhD Thesis
97
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),
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.
Sharon L. Ruiz Lopez PhD Thesis
99
a)
b)
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
MP01_Oct16
MP02_Oct16
MP03_June17
MP04_June17
MP05_Oct17
MP06_Oct17
MP07_Jan18
MP08_Jan18
MP09_June18
MP10_June18
MP11_Nov18
MP12_Nov18
MP13_Feb19
MP14_Feb19
MP15_Apr19
MP16_Apr19
Relativeabundance
Synechococcus
Trichococcus
Dietzia
Cyanobium
Alkalilimnicola
Rivibacter
Roseomonas
Polynucleobacter
Methylobacterium
Mongoliitalea
uncultured
Others
Sphingomonas
Silanimonas
Roseococcus
Methylophilus
Porphyrobacter
Hydrogenophaga
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
SP03_Jan18
SP04_Jan18
SP05_June18
SP06_June18
SP07_Nov18
SP08_Nov18
SP09_Feb19
SP10_Feb19
SP11_Apr19
SP12_Apr19
Relativeabundance
CyanobiumRivibacter
FlavobacteriumReyranellauncultured
RoseomonasPseudomonas
MongoliitaleaCaulobacterMeiothermus
SphingomonasOthers
SilanimonasMethylophilusRoseococcus
PorphyrobacterHydrogenophaga
Sharon L. Ruiz Lopez PhD Thesis
100
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
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.
Sharon L. Ruiz Lopez PhD Thesis
105
Supplementary information
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.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
FT01
_Oct
16
FT02
_Oct
16
MP0
1_Oc
t16
MP0
2_Oc
t16
MP0
3_Ju
ne17
MP0
4_Ju
ne17
MP0
5_Oc
t17
MP0
6_Oc
t17
MP0
7_Ja
n18
MP0
8_Ja
n18
MP0
9_Ju
ne18
MP1
0_Ju
ne18
MP1
1_No
v18
MP1
2_No
v18
MP1
3_Fe
b19
MP1
4_Fe
b19
MP1
5_Ap
r19
MP1
6_Ap
r19
SP03
_Jan
18
SP04
_Jan
18
SP05
_Jun
e18
SP06
_Jun
e18
SP07
_Nov
18
SP08
_Nov
18
SP09
_Feb
19
SP10
_Feb
19
SP11
_Apr
19
SP12
_Apr
19
Verrucomicrobia
Thaumarchaeota
Proteobacteria
Planctomycetes
Patescibacteria
Others
Gemmatimonadetes
Firmicutes
Dependentiae
Deinococcus-ThermusCyanobacteria
Chloroflexi
Bacteroidetes
Armatimonadetes
Actinobacteria
Acidobacteria
Main ponds (MP) Subponds (SP) FT
Sharon L. Ruiz Lopez PhD Thesis
106
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.
Sharon L. Ruiz Lopez PhD Thesis
107
Supplementary 2. 3 Description of the media (selective and non-selective) used for microorganisms isolation
Media Classification Composition per litre Final
pH
Concentration Reference
Minimal medium (M9)
Defined
medium
Na2HPO4 42.5 g
KH2PO4 15.0 g
NH4Cl 5.0 g
MnCl2 2.5 g
CuCl2•2H2O 43 mg
ZnCl2 70 mg
CoCl2•6H2O 60 mg
Na2MoO4•2H2O 60
mg
7
10
10%
50%
100%
(Harwood
and Cutting
1990)
Luria Bertani (LB)
Complex
Basal
Tryptone 10 g
Yeast extract 5 g
Sodium Chloride 10 g
7
10
11
10%
50%
100%
(Sezonov et
al. 2007)
Nutrient Agar for
Aquiflexum (NA)
Complex
Basal
KH2PO4 0.3 g
Na2HPO4 0.98 g
MgSO4 0.10 g
NaCl 5 g
Yeast extract 5 g
Peptone 5 g
Agar 15 g
7
10
11
10%
50%
100%
(Misal et al.
2013b)
DL medium
Complex
Selective
medium
NaHCO3 2.5 g
Na2CO3 5.0 g
NH4Cl 0.25 g
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
7
10
11
10%
50%
100%
(Lovley et al.
1984b)
ZoBell
Complex
Selective
medium
NaCl 19.45 g
MgCl2 8.8 g
Na2SO4 3.24 g
CaCl2 1.8 g
C6H5FeO7 0.1 g
Yeast extract 1 g
Peptone 5 g
Mineral mix 10 ml
Agar 15 g
7
10
11
10%
50%
100%
(Brettar et al.
2004)
Sharon L. Ruiz Lopez PhD Thesis
108
Supplementary 2. 4 Different media at a range of concentration and pH values and bacteria identified
Media pH Growth Organisms isolated and similarity
percentage
Similarity
(forward
and
reverse)
NCBI
Taxonomy
ID
Minimal
medium
7
10
11
Growth was not detected at any concentration nor pH range
Zobell 7 Detected at 10%
concentration
Strain S03: Cyclobacteriaceae
bacterium CUG 91308, 93.5%
F: 95%
R: 92%
2483804
10 Growth was not detected
11 Detected at 50%
concentration
Strain S09: Aquiflexum balticum DSM
16537, 93.5%
F: 96%
R:91%
758820
Nutrient Agar
for Aquiflexum
NA
7 Detected at 10%
and 100%
concentration
Strain S01: Algoriphagus sp.
XAY3209,91.5%
Strain S05: Algoriphagus sp.
XAY3209,91%
F: 91%
R: 92%
F: 91%
R: 91%
2007308
2007308
10 Detected at 50%
concentration
Strain S02: Aquiflexum sp. 20021,
91%
F: 94%
R: 88%
1089537
11 Not detected
DL 7 Detected at 50
and 100%
concentration
Strain S08: Aquiflexum sp. BW86-86,
88%
Strain S07: Algoriphagus sp. R-36727,
89.5%
Strain S010: Cyclobacteriaceae
bacterium CUG 91308, 85%%
F: 87%
R:89%
F: 89%
R: 90%
F: 86%
R: 84%
647411
885463
2483804
10 Detected at 50
and 100%
concentration
Strain S011: Cyclobacteriaceae
bacterium CUG 91308, 91%%
Strain S06: Algoriphagus sp. BAL344,
91.5%
F: 94%
R: 88%
F: 93%
R:90%
2483804
1708148
11 Detected at 50
and 100%
concentration
Strain S04: Bacteroidetes sp. BG31,
91.5%
F: 92%
R:91%
1109254
LB 7
10
11
Growth was not detected at any concentration nor pH range
Sharon L. Ruiz Lopez PhD Thesis
109
Supplementary 2. 5 Abundance of microorganisms detected by Sanger sequencing compared with NGS Illumina MiSeq
Sample Organism identified by
Sanger sequencing
Abundance detected
by 16S NGS Illumina
MiSeq
MP05 Aquiflexum sp 0.28% OTU3
MP06 Aquiflexum sp 0.39% OTU3
Sharon L. Ruiz Lopez PhD Thesis
110
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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).
Sharon L. Ruiz Lopez PhD Thesis
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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
Sharon L. Ruiz Lopez PhD Thesis
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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
Sharon L. Ruiz Lopez PhD Thesis
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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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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).
Sharon L. Ruiz Lopez PhD Thesis
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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.
Sharon L. Ruiz Lopez PhD Thesis
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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
Sharon L. Ruiz Lopez PhD Thesis
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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
Sharon L. Ruiz Lopez PhD Thesis
131
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
Sharon L. Ruiz Lopez PhD Thesis
132
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).
Sharon L. Ruiz Lopez PhD Thesis
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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
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
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
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
INP_FT01_Oct16
INP_FT02_Oct16
INP_MP01_Oct17
INP_MP02_Oct17
INP_SP01_Jan18
INP_02_Jan18
OUT_FGMSP_Sept17
OUT_Aux01_May16
OUT_Aux02_Jun17
OUT_Aux03_Sept17
Relativeabundance
Others
Cyanobium
Verrucomicrobium
Spirosoma
Cytophaga
unclassified(derivedfromFlavobacteriales)Ruegeria
Chitinophaga
Roseobacter
Brevundimonas
Methylovorus
Sphingomonas
Algoriphagus
Caulobacter
Roseomonas
Bradyrhizobium
Ralstonia
Synechococcus
Novosphingobium
Xanthomonas
Flavobacterium
Leptothrix
Pseudomonas
Cupriavidus
Methylibium
Delftia
Variovorax
Verminephrobacter
Rhodobacter
Methylobacillus
Polynucleobacter
Methylotenera
Sharon L. Ruiz Lopez PhD Thesis
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a)
b)
Supplementary 5. 6 Microbial distribution of eukaryotic organisms at class level by a)18S rRNA sequencing profile and b)metagenomics sequencing
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
INP_FT01_Oct16
INP_FT02_Oct16
INP_MP01_Oct17
INP_MP02_Oct17
INP_SP01_Jan18
INP_SP02_Jan18
OUT_FGMSP_Sept17
OUT_Aux01_May16
OUT_Aux02_Jun17
OUT_Aux03_Sept17
Relativeabundance
Saccharomycetes
Discosea
Unknownfungalspecies
Bicosoecida
Heterobolosea
Eustigmatophyceae
Dinophyceae
Aphelida
Chrysophyceae
Oligohymenophorea
Apiales
Trebouxiophyceae
Eurotiomycetes
Dothideomycetes
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
INP_FT01_Oct16
INP_FT02_Oct16
INP_MP01_Oct17
INP_MP02_Oct17
INP_SP01_Jan18
INP_02_Jan18
OUT_FGMSP_Sept17
OUT_Aux01_May16
OUT_Aux02_Jun17
OUT_Aux03_Sept17
Relativeabundance(%
)
Bryopsida
Schizosaccharomycetes
Agaricomycetes
Bangiophyceae
Actinopterygii
Liliopsida
Aconoidasida
Chlorophyceae
Dinophyceae
Chromadorea
Prasinophyceae
Sordariomycetes
Eurotiomycetes
Saccharomycetes
Anthozoa
Bacillariophyceae
Mammalia
Insecta
Coscinodiscophyceae
unclassified(derivedfromEukaryota)
Oligohymenophorea
unclassified(derivedfromStreptophyta)Amphibia
Hydrozoa
Sharon L. Ruiz Lopez PhD Thesis
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Supplementary 5. 7 Total sequences annotated using the MGRAST web server
Supplementary 5. 9 Relative abundance of functional genes by subsystems Level 1 (KEGG database)
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
INP_FT01_Oct16
INP_FT02_Oct16
INP_MP01_Oct17
INP_MP02_Oct17
INP_SP01_Jan18
INP_02_Jan18
OUT_FGMSP_Sept17
OUT_Aux01_May16
OUT_Aux02_Jun17
OUT_Aux03_Sept17
Relativeabundance
DormancyandSporulation
SecondaryMetabolism
Photosynthesis
Potassiummetabolism
Ironacquisitionandmetabolism
CellDivisionandCellCycle
SulfurMetabolism
PhosphorusMetabolism
RegulationandCellsignaling
NitrogenMetabolism
Phages,Prophages,Transposableelements,Plasmids
MotilityandChemotaxis
MetabolismofAromaticCompounds
StressResponse
FattyAcids,Lipids,andIsoprenoids
NucleosidesandNucleotides
Virulence,DiseaseandDefense
MembraneTransport
RNAMetabolism
CellWallandCapsule
Respiration
DNAMetabolism
Miscellaneous
Cofactors,Vitamins,ProstheticGroups,Pigments
ProteinMetabolism
AminoAcidsandDerivatives
Carbohydrates
Clustering-basedsubsystems
Sharon L. Ruiz Lopez PhD Thesis
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a)
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
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
INP_FT01_Oct16
INP_FT02_Oct16
INP_MP01_Oct17
INP_MP02_Oct17
INP_SP01_Jan18
INP_02_Jan18
OUT_FGMSP_Sept17
OUT_Aux01_May16
OUT_Aux02_Jun17
OUT_Aux03_Sept17
Relativeabundance(%
)
Rhodobacterales
Alteromonadales
Nitrosomonadales
Cytophagales
Chloroflexales
Acidithiobacillales
Flavobacteriales
Oscillatoriales
Nostocales
Chroococcales
Rhodospirillales
Sphingomonadales
Actinomycetales
Rhodocyclales
Rhizobiales
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
INP_FT01_Oct16
INP_FT02_Oct16
INP_MP01_Oct17
INP_MP02_Oct17
INP_SP01_Jan18
INP_02_Jan18
OUT_FGMSP_Sept17
OUT_Aux01_May16
OUT_Aux02_Jun17
OUT_Aux03_Sept17
Relativeabundance(%
)
Oscillatoriales
Pasteurellales
Rhizobiales
Acidithiobacillales
Aeromonadales
Actinomycetales
Alteromonadales
Aquificales
Archaeoglobales
Bacillales
Bacteroidales
Burkholderiales
Sharon L. Ruiz Lopez PhD Thesis
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a)
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
0
0.05
0.1
0.15
0.2
0.25
INP_FT01_Oct16
INP_FT02_Oct16
INP_MP01_Oct17
INP_MP02_Oct17
INP_SP01_Jan18
INP_SP02_Jan18
OUT_FGMSP_Sept17
OUT_Aux01_May16
OUT_Aux02_Jun17
OUT_Aux03_Sept17
Relativeabundance(%
) Funariales
Eupodiscales
Euglenales
Cyanidiales
Coniferales
Coleochaetales
Chroococcales
Chlorellales
Chlamydomonadales
Brassicales
Bangiales
Anthocerotales
0
0.05
0.1
0.15
0.2
0.25
INP_FT01_Oct16
INP_FT02_Oct16
INP_MP01_Oct17
INP_MP02_Oct17
INP_SP01_Jan18
INP_SP02_Jan18
OUT_FGMSP_Sept17
OUT_Aux01_May16
OUT_Aux02_Jun17
OUT_Aux03_Sept17
Relativeabundance(%
)
Funariales
Oscillatoriales
Peridiniales
Marchantiales
Prochlorales
Caudovirales
Bangiales
Pyrenomonadales
Cyanidiales
Nostocales
Eupodiscales
Chroococcales
Sharon L. Ruiz Lopez PhD Thesis
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a)
b)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
INP_FT01_Oct16
INP_FT02_Oct16
INP_MP01_Oct17
INP_MP02_Oct17
INP_SP01_Jan18
INP_02_Jan18
OUT_FGMSP_Sept17
OUT_Aux02_Jun17
OUT_Aux03_Sept17
OUT_Aux03_Sep17
Relativeabundance(%
)
Chromatiales
Nitrosomonadales
Rhodocyclales
Caulobacterales
Pseudomonadales
Cytophagales
Actinomycetales
Methylophilales
Rhodospirillales
Xanthomonadales
Flavobacteriales
Rhizobiales
Rhodobacterales
Sphingomonadales
Burkholderiales
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
INP_FT01_Oct16
INP_FT02_Oct16
INP_MP01_Oct17
INP_MP02_Oct17
INP_SP01_Jan18
INP_SP02_Jan18
OUT_FGMSP_Sept17
OUT_Aux01_May16
OUT_Aux02_Jun17
OUT_Aux03_Sept17
Relativeabundance(%
)
Caulobacterales
Rhodospirillales
Alteromonadales
Chroococcales
Sphingobacteriales
Flavobacteriales
Actinomycetales
Pseudomonadales
Rhizobiales
Xanthomonadales
Rhodobacterales
Methylophilales
Sphingomonadales
Burkholderiales
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c)
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
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
INP_FT01_Oct16
INP_FT02_Oct16
INP_MP01_Oct17
INP_MP02_Oct17
INP_SP01_Jan18
INP_SP02_Jan18
OUT_FGMSP_Sept17
OUT_Aux01_May16
OUT_Aux02_Jun17
OUT_Aux03_Sept17
Relativeabundance(%
)
Deinococcales
Desulfobacterales
Desulfovibrionales
Enterobacteriales
Desulfuromonadales
Flavobacteriales
Herpetosiphonales
Lactobacillales
Methylococcales
Actinomycetales
Alteromonadales
Bacillales
Bacteroidales
Bifidobacteriales
Burkholderiales
0
0.1
0.2
0.3
0.4
0.5
0.6
INP_FT01_Oct16
INP_FT02_Oct16
INP_MP01_Oct17
INP_MP02_Oct17
INP_SP01_Jan18
INP_SP02_Jan18
OUT_FGMSP_Sept17
OUT_Aux01_May16
OUT_Aux02_Jun17
OUT_Aux03_Sept17
Relativeabundance(%
)
Caulobacterales
Enterobacteriales
Clostridiales
Rhodocyclales
Nitrosomonadales
Chlorobiales
Rhizobiales
Chromatiales
Pasteurellales
Xanthomonadales
unclassified(derivedfromOpitutae)Pseudomonadales
Desulfuromonadales
Alteromonadales
Hydrogenophilales
Burkholderiales
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Supplementary 5. 13 Relative abundance of genes associated to bacterial hemoglobins (stress response, Level 3 subsystems) and its correlation to microbial cells
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-
Sharon L. Ruiz Lopez PhD Thesis
187
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,