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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
2
List of contents
Thesis Abstract .............................................................................................................................. 7
Declaration ..................................................................................................................................... 9
Copyright Statement .................................................................................................................... 10
Acknowledgments ........................................................................................................................ 11
The Author .................................................................................................................................... 12
Chapter 1 Purpose and significance of the investigation .......................................................... 14
1.1 Project context and relevance .......................................................................................... 14
1.2 Objectives: ......................................................................................................................... 15
1.3 Thesis structure ................................................................................................................. 15
1.4 Paper status and collaborator contributions .................................................................... 17
Chapter 2 Introduction ................................................................................................................. 20
2.1 History of nuclear power ................................................................................................... 20
2.2 Nuclear Power ................................................................................................................... 21
2.3 The Nuclear Fuel cycle ..................................................................................................... 22
2.4 Nuclear waste .................................................................................................................... 23
2.5 Sellafield site ...................................................................................................................... 28
2.6 Sellafield spent fuel storage ponds .................................................................................. 29
2.7 Microorganisms in nuclear facilities ................................................................................. 31
2.8 Metabolic responses to extreme environments .............................................................. 42
References ............................................................................................................................... 47
Chapter 3 Methodology ............................................................................................................... 61
3.1 Culturing techniques ......................................................................................................... 61
3.2 Molecular biology techniques ........................................................................................... 62
3.2.1 DNA extraction ........................................................................................................... 63
3.2.2 Polymerase Chain Reaction (PCR) .......................................................................... 64
3.2.3 Real Time PCR (qPCR) ............................................................................................. 65
3.2.4 DNA sequencing: Sanger sequencing ..................................................................... 67
3.2.5 Next-generation DNA Sequencing: Illumina sequencing ........................................ 68
3.2.5 Metagenomics ............................................................................................................ 70
3.4 References......................................................................................................................... 76
Chapter 4 Identification of stable hydrogen-driven microbes in highly radioactive storage facilities in Sellafield, UK ............................................................................................................. 83
Abstract .................................................................................................................................... 83
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Introduction .............................................................................................................................. 84
Materials and Methods ............................................................................................................ 88
Indoor Nuclear Fuel Storage Pond (INP) ........................................................................... 88
Samples ............................................................................................................................... 89
Cultivation independent DNA analyses of microbial communities ....................................... 90
DNA extraction ..................................................................................................................... 90
Polymerase Chain Reaction ............................................................................................... 91
Quantitative Polymerase Chain Reaction (Real-time PCR, QPCR). ............................... 91
Next-generation Sequencing .............................................................................................. 92
Culturing and identification of the pond microorganisms. ................................................. 93
Results ...................................................................................................................................... 94
Identification of microorganisms by next generation DNA sequencing ............................... 96
Cultivation-dependent analysis for determining microbial diversity in the INP.................. 100
Discussion .............................................................................................................................. 101
References ............................................................................................................................. 110
Chapter 5 Comparative metagenomic analyses of taxonomic and metabolic diversity of microbiomes from spent nuclear fuel storage ponds .............................................................. 123
Abstract .................................................................................................................................. 123
Introduction ............................................................................................................................ 124
Materials and methods .......................................................................................................... 127
Samples ............................................................................................................................. 127
Methods.............................................................................................................................. 130
Results .................................................................................................................................... 132
Microbial diversity on the indoor spent fuel storage pond (INP) .................................... 132
Microbial diversity on the legacy First Generation Magnox Storage Pond (FGMSP) .. 134
Microbial diversity on the auxiliary outdoor spent fuel storage pond (Aux) ................... 134
Microbial diversity of eukaryotic organisms ..................................................................... 136
Functional classification ........................................................................................................ 137
Respiration ......................................................................................................................... 139
Photosynthesis .................................................................................................................. 140
DNA metabolism ................................................................................................................ 141
Stress response ................................................................................................................. 143
Discussion .............................................................................................................................. 144
Microbial diversity .............................................................................................................. 144
Adaptation to extreme environments ............................................................................... 146
Acknowledgements ............................................................................................................... 151
Supplementary information ................................................................................................... 152
4
References ............................................................................................................................. 172
Chapter 6 Metagenomic analysis of viruses in spent fuel storage ponds at Sellafield, UK.. 183
Abstract .............................................................................................................................. 183
Introduction ........................................................................................................................ 184
Methods .................................................................................................................................. 186
Samples ............................................................................................................................. 186
Results .................................................................................................................................... 192
Microbial diversity of reads ............................................................................................... 192
Discussion .............................................................................................................................. 197
Acknowledgements ............................................................................................................... 199
Supplementary information ................................................................................................... 200
References ............................................................................................................................. 204
Chapter 7 Conclusions and future work ................................................................................... 211
Conclusions ............................................................................................................................ 211
Future work ............................................................................................................................ 215
Conference presentations and Awards .................................................................................... 218
Awards .................................................................................................................................... 218
Oral Presentations ................................................................................................................. 218
Poster Presentations ............................................................................................................. 219
Outreach ................................................................................................................................. 220
Complementary courses ....................................................................................................... 220
List of Figures
Figure 2.1 Brief history of nuclear power, adaptation from (WIN, 2013) ................................. 21 Figure 2.2 Radioactive elements (1) encased in fuel rods are split into smaller elements (2) by high-energy reactions. These reactions release energy as heat (3) and also generate free particles. In a nuclear reactor, this heat converts water to steam, which turns turbines to generate electricity (4). At the end of its cycle, the nuclear fuel rods are cooled in pools of water for several years (5), and then may be disposed in dry cask storage (6) (Jennewein & Senft, 2018) .................................................................................................................................. 22 Figure 2.3 Nuclear fuel cycle (WNA, 2017)................................................................................ 23 Figure 2.4 During nuclear fission one large atomic nucleus is divided into smaller nuclei. The fission process may produce more neutrons that induce further fissions and so on, an event known as fission chain reaction (GCSE, 2019) ......................................................................... 25 Figure 2.5. The Sellafield site is located in the northwest of England, approximately 15 km to the south of Whiteheaven (Sellafield Ltd., 2019) ....................................................................... 29 Figure 2.6 Mechanisms of radionuclide-microbe interactions (Lloyd & Macaskie, 2000) ...... 42 Figure 3.1 Summary of PCR (NCBI 2014). ................................................................................ 65 Figure 3.2 Illustration of dye SYBER Green binding to a double stranded DNA (Praveen and Koundal 2013) .............................................................................................................................. 66
5
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.
8
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.
9
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.
10
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
11
Acknowledgments
Throughout the writing of this thesis, I have received a great deal of support and assistance. I
would first like to thank my supervisor, Jon Lloyd, for his invaluable support and assistance in
the formulating of the research topic and methodology in particular.
I would like to acknowledge CONACyT (the National Council for Science of Technology), my
sponsor, for providing me with the funding to develop this project. To Sellafield Ltd for giving
me the opportunity to develop this project; for making the necessary arrangements to facilitate
the handling of samples and for the complementary funding that allowed me to expand the
research to a higher scientific level. I also express my gratitude to Nick Cole for his invaluable
assistance on procuring and processing of samples at the Sellafield site.
I also want to thank my colleagues from the Geomicro Group at the University of Manchester,
especially to Chris Boothman, Lynn Foster and Sophie Nixon; for supporting me greatly and
for being always willing to help me.
Additionally, I would like to thank my strongest inspiration: mi Pa, Kika, Licita and Gina for their
incredible counsel through this journey, for believing in me and for being for me all the time no
matter the distance. To Alfred, for his love and understanding, for supporting me on this
journey, for being my greatest motivation and for encouraging me to fight for my dreams and
never give up. I want to express my gratitude to my greatest inspirational force: my family, my
beautiful Dominica, Gus and Nora, and the rest of the Ruiz family. Special thanks to families
Ruiz Valencia, Martinez Ruiz, Miranda Díaz, Núñez Martínez and Saravia Ruiz for their
outstanding example of resilience, care, love and for the splendid moments we have shared.
To the wonderful family I have met in Manchester: Isabelle, Natali, Sul, Monse, Zainab, Mayra,
Roy, Reynol, Ho-kyung, Emma, Farah, Karla, Roberto, Karen, Cecilia, Noel, David, Mario,
Rebeca, Cesar and Valerie; and my lifelong friends: Hugo, Vianey, Sambres, Alberts,
Richards, Luiso, Ivan, Carmen, Anali, Xochitl and Marcia, for their support in deliberating over
our problems and findings; for the good times and the amazing memories we have created.
¡Gracias!
12
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.
13
1
Purpose and significance of the investigation
14
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.
15
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
16
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.
17
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
18
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
20
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.
21
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).
22
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).
23
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
24
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).
25
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
26
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
Russian Federation) (IAEA, 1999; Schneider & Marignac, 2008; WNA, 2019a). Both
categories can be handled by dry or wet storage technologies (NRC, 2019a).
Wet systems imply that the storage is in ponds (or pools) in which spent fuel is kept under
water. Storage ponds are reinforced concrete stainless-steel lined structures built above
ground. Initially ponds were open-air systems but due to the need to control the water quality,
most recent built ponds are now covered (indoor) (IAEA, 1999). In order to avoid corrosion,
ponds are filled with deionized (or demineralized) water and depending on the activity of ion
exchange or purge; a chemical range may be imposed (e.g. sodium nitrite as corrosion
inhibitor) (IAEA, 1982). Pond water both shields the radiation and cools the irradiated fuel
assemblies (Y. Y. Liu, 2015).
Wet and dry storage systems are design to maintain cladding integrity during handling and
exposure to corrosion effects of the storage environmental, and to protect plant operators by
shielding radiological material and also to assure environmental protection by minimising the
release of radioisotopes (NRC, 2019b).
Since it is such a complicated issue to manage, only a few countries such as Finland, China,
France, Germany and Japan have well developed plans to facilitate long-term disposal.
Meanwhile, the UK government is actively engaged in supporting decommissioning of plants
such as Sellafield and identifying a site for geodisposal of legacy and future wastes (NWMO,
2018; WNA, 2018).
Reprocessing of waste is an option to minimise waste production, and with this process
uranium and plutonium are separated and recycled to be re-used in a nuclear reactor.
Countries like France, UK, Russia and Japan are pioneers in the reprocessing stages at
28
different levels. There are several alternative reprocessing technologies, and these are
reviewed elsewhere (WNA, 2018).
2.5 Sellafield site
Sellafield was established in 1941 as a Royal Ordnance Factory for the production of
trinitrotoluene (TNT) for the Second World War effort. The Windscale piles and the Windscale
reprocessing facility were then built to produce plutonium for the UK atomic weapons
programme until nuclear military purposes ceased in 1995 (Gray, Jones, & ASmith, 1995;
Mahaffey, 2011). Nuclear power became commercial on 1953 with the construction of the
Calder Hall nuclear plant at Sellafield in Great Britain and proved to be a highly reliable power
source. The plant operated until 2003 without incident, focusing on electricity generation
(Mahaffey, 2011). Today the Sellafield site, which is located near the village of Seascale on
the coast of the Irish Sea in Cumbria (Figure 2.5), is the most complex industrial site requiring
remediation in Western Europe responsible for nuclear fuel reprocessing and nuclear
decommissioning (Tierney et al., 2016).
Sellafield comprises approximately 700 acres containing more than 2,200 buildings including
170 major nuclear facilities carried out by a 10,000 strong workforce (Ltd, 2019; Sellafield Ltd.,
2011). The site is now home to a wide range on nuclear facilities and operations, which
involves hazard and risk reduction, including the decommissioning of legacy ponds and silos
from old facilities, reprocessing, fuel manufacturing and nuclear waste management. This
includes the treatment of low, intermediate and high level wastes, a unique capability in the
UK (Sellafield Ltd., 2019).
29
Figure 2.5. The Sellafield site is located in the northwest of England, approximately 15 km to the south of Whiteheaven (Sellafield Ltd., 2019)
Sellafield is the only nuclear site in the country able to manage the three forms of radioactive
waste: low, intermediate and high (Sellafield Ltd., 2019).
2.6 Sellafield spent fuel storage ponds
Contrasting to fossil fuels, nuclear fuel can be re-used in a process called reprocessing, that
aims to separate uranium and plutonium from spent fuel.
30
After being used to generate power, the spent fuel is stored on storage ponds under water,
which enables to cool it and remain shielded from emitting radiation (IAEA, 2011). The storage
system in Sellafield consist in the following buildings:
• Magnox Reprocessing plant was constructed during 1950s and its role is to receive
and store irradiated fuel from Magnox reactors and remove the fuel cladding before
the fuel is processed (Sellafield Ltd., 2015, 2017b).
• First Generation Magnox Storage Pond was constructed as an open-air pond which
caused accumulation of waste materials like fuel fragments, fuel cladding, sludges
from corrosion and other debris brought by the wind. The First Generation Magnox
Storage Pond combines used nuclear fuel, sludge, intermediate level waste and pond
water, each of which needs to be safely removed and processed through separate
routes (Sellafield Ltd., 2015, 2017a)
• Fuel Handing Plant is an indoor pond responsible for receiving, storing and
mechanically processing spent nuclear fuel from Magnox and Advanced Gas-cooled
Reactor (AGR) stations from across the UK (Sellafield Ltd., 2015). After a general
inspection, Magnox and AGR flasks are transferred to the FHP using the site rail
system. The fuel is removed from the flasks and then transferred into the storage pond
where it remains for a set period of time until the short-lived fission products decay.
When the storage period is over, the fuel is transferred into the decanner facility, for
Magnox fuel or alternatively the AGR dismantler for AGR fuel. In order to be able to
reprocess the fuel rod its outer cladding is stripped off by using specially designed
remote-control equipment. The cladding is peeled off into small pieces a few
centimetres in length. The remaining waste is made primarily of swarf from fuel
elements that have been processed. After the Magnox fuel cladding is removed, the
uranium metal bar is loaded into a magazine and transferred into a shielded transport
flask and finally taken across the site to the Magnox reprocessing plant (Sellafield Ltd.,
2015)
• The Thermal Oxide Reprocessing Plant (Thorp) at Sellafield reprocesses both UK and
foreign spent fuel. Its construction began on the Thorp Head End and Chemical
Separation plants in 1985 and the first fuel was moved in 1994. The operations
31
performed are divided into three main areas; fuel receipt and storage, the head end
plant operation and the chemical separation of uranium and plutonium. The efficiency
of the Thorp reactors is about 97% after 4 years, and the spent fuel is recycled,
whereas the rest is waste (Sellafield Ltd., 2016).
• To sum up, the options for used fuel are direct disposal to a geological repository,
aqueous reprocessing to remove uranium and plutonium and advanced
electrometallurgical reprocessing which removes uranium, plutonium and minor
actinides (WNA, 2015).
2.7 Microorganisms in nuclear facilities
As mentioned above, storage of spent nuclear fuel requires specific chemical and physical
conditions to avoid contamination of personnel and the environment. Spent fuel storage ponds
are radioactive (due to the nature of the stored material) and often oligotrophic (due to the
demineralized/deionized water) environments that represent challenging habitats for several
forms of life (Rothschild & Mancinelli, 2001).
However, recent publications have shown the presence of microorganisms, mainly bacteria
and algae, living in the ponds, most often found in biofilms attached to the walls of the ponds.
Table 2.3 summarises the research and findings on the microbial ecology and
biogeochemistry of nuclear ponds.
32
Location Summary Sample analysis Organisms found Radionuclides found References
SNF at the Cofretes Nuclear Power (Valencia, Spain) Boiling Water Reactor (BRW)
Biofilm formation analysed by
immersing different
austenitic stainless-
steel coupons, as well
as balls of stainless
steel and titanium
Epifluorescence microscopy and scanning
electron microscopy were
used
Standard culture methods
and sequencing of 16S
rDNA fragments
α-, β- and γ-Proteobacteria, Firmicutes and
Actinobactericeae
Biofilms were able to retain radionuclides,
especially 60Co
(Sarró, García, & Moreno, 2005)
SNF at the Cofretes Nuclear Power (Valencia, Spain) Boiling Water Reactor (BRW)
The microorganisms
attached to the
nuclear pool wall were analysed.
Amplification of
16S rDNA fragments from
the microorganisms by PCR using universal
primers for the domain
Bacteria, and the
Denaturing Gradient Gel
Electrophoresis was used.
β-Proteobacteria,
Actinomycetales and the
Bacillus/Staphylococcus group. The fungus Aspergillus
fumigatus was also found
The radionuclides
found in the water
were 60Co, 137Cs, 134Cs, 54Mn, and 65Zn
(Chicote et al.,
2004)
SNF at the Cofretes Nuclear Power (Valencia, Spain) Boiling Water Reactor (BRW)
Biofilm formation on
three different types of
austenitic stainless
steel
Standard culture
microbiological methods,
microscopy techniques
(epifluorescence
microscopy and scanning electron microscopy SEM)
and molecular biology
α-, β-, and γ-Proteobacteria,
Bacilli and Actinobacteria
Radionuclides were
found trapped in
biofilms in water,
mainly 60Co, 65Zn, 54Mn, 58Co and 95Zr
(Sarró et al., 2003)
33
techniques (PCR and gel
electrophoresis)
SNF at the Cofretes Nuclear Power (Valencia, Spain)
Biofilm
characterisation in two different metallic
materials: stainless
steel and titanium
Standard culture
microbiological methods, microscopy techniques
(epifluorescence
microscopy and scanning
electron microscopy SEM)
and molecular biology
techniques (PCR and gel
electrophoresis)
α-, β-, and γ-Proteobacteria,
Actinobacteria and Firmicutes
Biofilms are able to
retain radionuclides from water, especially 60Co
(Sarró, García,
Moreno, & Montero, 2007)
Pool water of the interim spent fuel storage (JAVYS Inc.), Slovak Republic
Characterization of
bacterial contamination in pool
water
Standard microbiology
methods and sequencing of 16S rDNA
Kocuria palustris, Micrococcus
luteus, Ochrobactrum spp. and Pseudomonas aeruginosa.
Isolated bacteria were
able to accumulate 60Co and 137Cs
(Tišáková et al.,
2013)
Water sample from an external storage pond at Sellafield
Isolated Co2+ and Cs+
resistant bacteria from
water were collected
from a nuclear fuel
storage pond
Standard microbiology
Methods using selective
medium and sequencing of
16S rDNA
Cs+ resistant isolates Serratia
and Yersinia
And Co2+ isolates were closely
related to Curvibacter and
Tardiphaga
Isolated bacteria are
tolerant to high
concentrations of Cs+
and Co2+
(Dekker, Osborne,
& Santini, 2014)
34
Ltd obtained from 5 m below the surface
Samples from the Atomic Energy Research Institute in Budapest
Water samples from
the storage of spent
nuclear fuel
Bacteria were analysed by
atomic force microscopy
Six morphologically different
bacteria were isolated
Sorption of Cd, Co
and Sr by bacteria
(Diósi, Telegdi,
Farkas, Gazsó, &
Bokori, 2003)
Samples from the Rustler Formation at the Waste Isolation Pilot Plant (WIPP), NM, USA; and at the Grimsel test Site (GTS), Switzerland
A couple of
groundwater samples
were studied to analyse the
biosorption of uranium
and Plutonium
DNA standard techniques:
DAPI, DGGE and PCR
When nutrients were added,
Halomonas sp, Acetobacterium
sp from WIPP and Haloanaerobium , Bacillus
subtilis and Pseudomonas
fluorescens from GTS were
responsible for the sorption of
Uranium; Acetobacterium sp
was also involved in the uptake
of Plutonium
The sorption of
Uranium was higher
than observed of 241Plutonium
(Gillow, Dunn,
Francis, Lucero, &
Papenguth, 2000)
Spent nuclear fuel storage basins at
Microbiological studies
were performed to
determine the
Four different types of
metal coupons (chromium-
nickel and aluminium-
After 2-year period microbial
densities of 104to 107cells/ml
were determined in water
Radionuclides content
was not determined
(Santo Domingo,
Berry, Summer, &
Fliermans, 1998)
35
Savannah River Site (SRS)
potential for microbial-
influenced corrosion (MIC)
based alloys) were
submerged on water samples were collected
from the SNF basin and
analysed by X-ray spectra
techniques
samples and on submerged
metal coupons
Spent fuel pool and transfer channel of a nuclear power plant, Rio de Janeiro, Brasil
Samples were taken
on the liner of the
spent fuel pool (SFP)
and the fuel transfer
channel (FTC) of a Nuclear Power Plant
(NPP)
Metagenomics and
metatranscriptomics
Phyla: Proteobacteria,
Actinobacteria, Firmicutes,
Bacteroidetes, Acidobacteria,
Cyanobacteria, Chloroflexi,
Planctomycetes, Deinococcus-Thermus, Verrucomicrobia,
Chlorobi, Chlamydiae,
Euryarchaneota, Ascomycota,
Basidiomycota, Others (2-5%)
Fungus was detected
Samples previously
analysed showed the
content of 51Cr, 58Co, 60Co, and 137Cs
(Silva et al., 2018)
Water filled storage basin for spent nuclear fuel reactor (white flocculent was evident),
Concrete pool, volume
of 13,000m3 water
Temperature 18-26 ⁰C
Deionized water
pH 6.1
Molecular techniques: 454
pyrosequencing and
amplicon analysis
Cell numbers from 4x103 to
4x104 cells/ml
4,000 OTUs
Families: Burkholderiaceae,
Nitrospiraceae, Hyphomicrobiaceae and
Comamonadaceae
Radionuclides were
not measured, instead
bacterial diversity was
associated with
aluminum (oxy) hydroxide complexes
(Bagwell, Noble,
Milliken, Li, &
Kaplan, 2018)
36
Savannah River, Aiken SC, USA
Outdoor spent fuel storage pond at Sellafield, UK
Outdoor pond colonised by a
seasonal bloom of
microorganisms
Molecular biology techniques targeting the
16S and 18S genes.
Fourier transform infrared
(FT-IR) analysis
Actinobacteria, Bacteroidetes, cyanobacteria, Proteobacteria,
Verrumicrobia
Accumulation of 137Cs and 90S was
determined
(MeGraw et al., 2018)
Spent nuclear fuel storage basin in Sweden (CLAB facility)
Temperature reported
between 25-36
degrees
Biofilm formation was
detected
No data about pH or
water treatment
Water quality was
measured with by ion
chromatography
additionally TOC levels
were measured Microscopy (SEM, TEM
and fluorescence) were
used to analyse the
planktonic cells
Culturing and DNA
techniques targeting the
16S gene to identify the
microbial diversity
Planktonic cell populations
ranged between 1.4×103 and
5.2×103 ml−1, correlated with
the system configuration, and
was inversely correlated with total organic carbon (TOC)
levels. Most abundant organism
was genus Meiothermus
Radionuclides content
was not measured
(Masurat, Fru, &
Pedersen, 2005)
Spent nuclear fuel (SNF)
Samples were taken
from the wall surface,
Microbiological studies
(culturing in LB medium),
Cell counts were ~1x103 CFU/ml Radionuclides content
was not measured,
(Karley, Shukla, &
Rao, 2018)
37
pond in Kalpakkam, India
temperature was 37⁰C
and pH was neutral
radio-tolerance of
microorganisms, biofilm quantification, and uptake
of cobalt and nickel were
achieved
Six bacterial species in the SNF
poolwater samples were isolated, which had significant
radio-tolerance (D10val-ue 248
Gy to 2 kGy) and also biofilm-
forming capabilities
instead removal of
heavy metals was tested
Bacteria were isolated from pool water in the Interim Spent Nuclear Fuel Storage Facility in JAVYS, Inc. in Jaslovské Bohunice, Slovak Republic
Bioaccumulation and
biosorption were
tested on previously
isolated bacteria
Bacteria were cultivated
and harvested from a
bioreactero (BIOSTAT A
plus, Sartorius AG,
Germany) Bioaccumulation and
biosorption character-
istics of Mn2+ ions by
both dead and living,
non-growing biomass of
bacteria
Bacteria Kocuria palustris and
Micrococcus luteus, previously
isolated, were tested
Bioaccumulation and
biosorption were
determined using 54Mn
as radioindicator
(Pipíška, Trajteľová,
Horník, & Frišták,
2018)
Bacteria were isolated from storage ponds at the Idaho Nuclear
22 species of bacteria
were cultivated in
nutrient-rich media, to test vessels containing
irradiated cladding
Molecular biology
techniques to identify the
surviving species targeting the 16S gene (LI-COR
Bacteria strains tested showed
the ability to form biofilms on
spent-fuel materials and may have implications on microbial
influenced corrosion (MIC)
Major radionuclides
detected were 137Cs, 90Sr, 90Y and 60Co
(Bruhn, Frank,
Roberto, Pinhero, &
Johnson, 2009)
38
Technology Centre on the IL site (Idaho, USA)
sections and that was
then surrounded by radioactive source
material.
4200 automated
sequencer) Absorbed beta and gamma
dose measurements were
performedusing LiF
thermoluminescent
dosimeters (TLDs)
Spent Nuclear Fuel (SNF) pools in Argentina
Microbiological studies
were performed to
evaluate the risk of microbial-induced
corrosion by microbial
organisms isolated
from the spent fuel
pools
Identification was achieved
targeting the 16S rRNA
gen and coupons corrosion was determined by SEM-
EDX and CFLM analysis
Microbial diversity was
dominated by Bacillus cereus,
followed by Rhizobium,
Leisfonia, Micrococcus and
Pseudomonas
Radionuclides content
was not determined
(Forte Giacobone,
Rodriguez, Burkart,
& Pizarro, 2011)
Sharon L. Ruiz Lopez PhD Thesis
39
Microorganisms can be part of the natural environment in radioactive environments. Although
some environments can be toxic for many organisms, it is common to find diverse microbial
communities in geological nuclear waste disposal sites like the High Activity Disposal
Experimental Site (HADES) in the Boom Clay in Belgium, where at least seven bacterial phyla
have been identified and there is a relationship between the organisms and the organic matter
of the environment (Wouters, Moors, Boven, & Leys, 2013). However, these environments
have been studied in less detail due to the technical problems of working with highly
radioactive regions.
Additionally, it has been reported that some bacteria can survive in high-radiation
contaminated sites such as Chernobyl and Fukushima (Fredrickson et al., 2004; Møller &
Mousseau, 2016; Ruiz-González et al., 2016; Shukla, Parmar, & Saraf, 2017; Srinivasan et
al., 2015; Yazdani et al., 2009; Zavilgelsky, Abilev, Sukhodolets, & Ahmad, 1998); surviving
high radiation doses, although long-term radiation exposure can cause irreversible DNA
damage. In this category bacterial species like Deinococcus radiodurans, Microbacterium
testaceum, Rhodococcus sp., Pseudomonas aeruginosa, Micrococcus luteus, and
Pseudomonas monteilii, Rufibacter, Arthobacter and mutants of Escherichia coli are included
(Battista, 1997; Bruhn et al., 2009; Fredrickson et al., 2004; Srinivasan et al., 2015; Zavilgelsky
et al., 1998); along with algae species such as Cystoseira, Coccomyxa actinabiotis,
Parachlorella sp. binos (Binos) among others (Adam & Garnier-Laplace, 2003; Gabani &
Singh, 2013; Krejci, Finney, Vogt, & Joester, 2011; M. Liu et al., 2014; Peletier, Gieskes, &
Buma, 1996; Ragon, Restoux, Moreira, Møller, & López-García, 2011; Rivasseau et al., 2013;
Shimura et al., 2012).
Specifically at the Sellafield site, microbial populations present in aqueous and biofilm
samples from outdoor and indoor spent fuel storage ponds have been analysed. Common
freshwater Proteobacteria and Cyanobacteria have been the principal bacterial phylogenetic
groups detected, while algal species have also been detected in outdoor highly radioactive
storage ponds (Dekker et al., 2014; Foster, 2018; MeGraw et al., 2018; Newsome, Morris,
Trivedi, Atherton, & Lloyd, 2014; Thorpe, Morris, Boothman, & Lloyd, 2012).
Sharon L. Ruiz Lopez PhD Thesis
40
Microorganisms can play a significant role in the transformations of radionuclides in the
environment by altering their chemical speciation, solubility and sorption properties, causing
an increase or decrease in concentrations, hence affecting their environmental mobility and
bioavailability (Francis, 2012; Newsome, Morris, & Lloyd, 2014).
The biogeochemistry of redox-active radionuclides can be controlled by the microbial
metabolism of the involved organisms. Microbes can reduce and precipitate some priority
radionuclides such as U(VI), Np(V) and Tc(VII) via bioreduction processes. These can be
stimulated by a range of electron donors and can operate at alkali conditions associated with
cementitious intermediate level waste (Rizoulis, Morris, & Lloyd, 2016).
Several microorganisms involved in the biogeochemistry of uranium and the interaction with
actinides have been studied. This comprises the removal of uranium from solution, including
the enzymatic reduction of U(VI) to U(IV), precipitation of U(VI) and the biosorption of U(VI).
Recently, there have been important studies focused on the bioreduction of U(VI) through in
situ and ex situ technologies (Anderson & Lovley, 2002; Choudhary & Sar, 2015; Lloyd &
Renshaw, 2005; Merroun & Selenska-Pobell, 2008).
Microbial interactions with radionuclides are driven by the following mechanisms:
• Biosorption, implies the sequestration of radionuclides to the outer surface or cell
membranes of microorganisms (Ding, Cheng, & Nie, 2019; Gadd, 2009). It occurs by
electrostatic attraction between radionuclide cations and anionic cell wall functional
groups (Xie et al., 2008). Ligands such as carboxyl, amine, hydroxyl, phosphate and
sulfhydryl groups are involved (Ding et al., 2019; Lloyd & Macaskie, 2000; Simonoff,
Sergeant, Poulain, & Pravikoff, 2007).
• Metabolism-dependent bioaccumulation (cell surface sequestration) is defined as
intracellular accumulation of toxic compounds (Gadd, 2009); it occurs as a classical
transport system involving ions (such as Cs+, K+, Sr2+ and Ra2+) in the physiology of
the cells that are exchanged by the toxic metal (often radionuclides) (Shukla et al.,
2017; Simonoff et al., 2007).
Sharon L. Ruiz Lopez PhD Thesis
41
• Bioreduction involves redox reactions that affect solubility of radionuclides by
forming oxides, coprecipitates, ionic and organic or inorganic complexes (Ding et al.,
2019). Microorganisms such as Fe(III)-reducing bacteria G. metallireducens,
Clostridium sp., Desulfovibrio desulfuricans and Desulfovibrio vulgaris are examples
of bacteria able to use radionuclides (e.g. U(VI) and Tc(VII)) as the terminal acceptor
(Francis, 1994; Lloyd & Lovley, 2001; D. R. Lovley & Phillips, 1992). Enzymatic
processes play a role by transforming the toxic metals making them more volatile, or
changing their solubility (Lloyd, 2003). Alternative enzymatic transformations include
bioreduction under anaerobic conditions, biomethylation that produce volatile methyl
derivates and biodegradation of chelating agents which can produce the precipitation
of the radionuclide (Lloyd & Macaskie, 2002; Simonoff et al., 2007).
• Biomineralization by ligands. Represents the process by which microorganisms
provide nucleation sites for the precipitation of radionuclide ions to insoluble minerals
(Lloyd, 2003; Merroun & Selenska-Pobell, 2008; White & Gadd, 1990). Bacterial
species are able to use ligands such as phosphate (observed in E. coli and Serratia
sp.), carbonate (observed in Ralstonia eutropha and Pseudomonas fluorescences)
and sulphide to precipitate metals and provide a way to remove radionuclides from
solution (Newsome, Morris, & Lloyd, 2014; Simonoff et al., 2007).
Figure 2.6 shows the main pathways radionuclides can be altered by bacteria (Lloyd &
Macaskie, 2000).
Sharon L. Ruiz Lopez PhD Thesis
42
Figure 2.6 Mechanisms of radionuclide-microbe interactions (Lloyd & Macaskie, 2000)
2.8 Metabolic responses to extreme environments
In addition to the described microbial interactions with radionuclides, microorganisms are able
to display a broad range of metabolic responses to help them cope with harsh environmental
conditions. It has been widely studied that microorganisms can thrive under broader swaths
of temperature, pH, pressure, radiation, salinity, energy and nutrient limitation (Merino et al.,
2019).
The development of genomic tools has provided insights into the adaptive strategies of
microbes in their natural settings and provides greater understanding on how environments
may impact the evolution of microbial communities (Hemme et al., 2010; Li et al., 2014).
One important environmental parameter that influences the microbial diversity is the pH,
alkalinity and acidity habitats can promote different metabolic responses. Since bacteria must
maintain a neutral cytoplasmic pH for survival, exchange of protons on other ions occurs
through various transporters (Merino et al., 2019). For instance prokaryotic voltage-gate
channels play a crucial role on physiological adaptations to alkaline and hyper alkaline
Sharon L. Ruiz Lopez PhD Thesis
43
environments. Na+/H+ antiporters catalyse accumulation coupled to Na+ efflux to maintain the
internal pH below the external medium (Krulwich, T., 1995); a Na+ channel also provides an
alternative for Na+ re-entry route to maintain the pH homeostasis (Krulwich, 2001); a Na+-
coupled solutes control the required Na+ concentration for antiporter function and specifically
for Bacillus the Na+-translocating Mot channel energizes flagellar rotation required for motility
(Ito et al., 2004).
Additionally, bacteria contain physiological features that help them to obtain nutrients from the
surrounding environment; for instance studies have shown that bacteria can excrete
extracellular polysaccharides, creating a matrix that acts as diffusion barrier that allows
nutrients from the water to reach bacterial cells (Cooksey, 1992; Kulakov, McAlister, Ogden,
Larkin, & O’Hanlon, 2002). Other example is biofilm formation that plays a role for protection
from external stimuli (McFeters, Broadaway, Pyle, & Egozy, 1993). Biofilms are constituted by
several layers that present accumulation of dead cells which can also be used as carbon
source for successive generations of bacteria, a phenomenon called cryptic growth (Kulakov
et al., 2002; Roszak & Colwell, 1987).
On low-nutrient content systems, a variant photosynthetic electron flow has been suggested
(Morel & Price, 2003); findings showed that members of Cyanobacteria may be able to route
electrons derived from the splitting of H2O to the reduction of O2 and H+ in a water-to-water
cycle to satisfy their energetic and nutritive requirements (Grossman, Mackey, & Bailey,
2010).
Exposure to radiation is a key factor that delimits microbial survival. Organisms living in
extreme niches such as radioactive sites have evolved wide range of biochemical and
physiological features to survive to challenging environments (Merino et al., 2019). Radiation
affects cellular biomolecules, including proteins, lipids and nucleic acids directly (e.g. ionizing
particles interact with purine/pyrimidine base) or indirectly (e.g. formation of reactive oxygen
species, ROS, through radiolysis of water) (Jung, Lim, & Bahn, 2017).
It has been studied that radiation can propitiate the radiolysis of water hence the production
of molecular hydrogen, peroxide hydrogen and other radicals (OH•, O2-•) (Libert, Bildstein,
Esnault, Jullien, & Sellier, 2011). In such environments hydrogen can be an important electron
Sharon L. Ruiz Lopez PhD Thesis
44
and energy source for bacterial growth (Galès et al., 2004; Libert et al., 2011; Pedersen,
2000). The cellular respiration process uses oxygen, nitrate or sulphate to break down
nutrients to generate cell’s energy. Since molecular hydrogen can be produced as result of
anaerobic decomposition of organic material, it can be used a substrate for cellular respiration
(Brazelton, Nelson, & Schrenk, 2012). On bacterial metabolism hydrogen respiration can
occur whether through the oxidation of H2 to H+ releasing electrons that are channelled to the
respiratory electron transport chain or as the reduction of H+ to H2 in the terminal reaction of
an anaerobic electron transport system, both reactions are mediated by hydrogenases
enzymes (Vignais, 2004).
Several chemolithoautothrophic microorganisms can oxidize hydrogen, including species
from phyla Proteobacteria (Azotobacter, Escherichia coli), Actinobacteria and Cyanobacteria
(Bothe, Distler, & Eisbrenner, 1978). In hydrogen-metabolic bacteria hydrogenases are
membrane-bound enzymes responsible for the initial oxidation on the inorganic substrate,
hydrogen, and are directly connected to the respiratory chain where the generation of ATP
molecules initiates (Hernsdorf et al., 2017).
Radiation exposure also has a dramatic effect on cellular DNA. Since DNA is a permanent
copy of the cell genome, alterations in its structure are of much greater consequence on other
cell components such as RNAs or proteins (Byrne et al., 2014). Alterations may be effect of
the incorporation of incorrect bases during DNA replication, for exposure to chemicals or
radiation, or can even occur spontaneously. Damaged DNA can block replication or
transcription which leads to mutation and finally affects cell reproduction (Cooper, 2000).
Damages on DNA can lead to alterations in base sequence as result of replication and
recombination that may affect the function of survival of microbial cells. In order to cope with
DNA alterations a number of repair systems have evolved including direct damage reversal,
nucleotide excision repair and recombinational repair; each repair system is specialized in the
repair on certain types of damage (Truglio, Croteau, Van Houten, & Kisker, 2006).
Besides the well-known DNA repair strategies, the clustered regularly interspaced short
palindromic repeats (CRISPR) and accompanying Cas proteins represent a relatively new
studied adaptive immunity microbial feature (Reeks, Naismith, & White, 2013). CRISPR-Cas
Sharon L. Ruiz Lopez PhD Thesis
45
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
46
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
47
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3
Methodology
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Chapter 3 Methodology
The study of microbial ecology grants a better understanding of the microorganisms in their
natural habitat and their interactions with other microorganisms, host microorganisms and with
their physicochemical environment (Relman et al. 2009).
The importance of microbial ecology rests in the fact that microbes are responsible for cycling
nutrients in the environment, creating symbiotic relationships, providing energy (even in
absence of light) and adapting to extreme habitats (Gray and Head 2008). In this chapter
microbial ecology techniques used for this project are explained
3.1 Culturing techniques
The ability to culture microorganisms is important because culture-dependent techniques can
target some of the active components of a microbial community, yielding quantitative data and
model organisms needed for pure culture studies in laboratory experiments.
Culture media provide the chemicals and substrates that fulfill the growth requirements of the
organisms being cultured. Culturing media can be classified on the basis of consistency in
solid medium, semisolid media and liquid (broth) medium) (Acharya 2010;Tiwari et al. 2009).
Based on the basis of composition, culture media can be classified in:
• Synthetic or chemically defined medium; a chemically defined medium prepared from
purified ingredients and therefore the exact composition is known (Madigan et al.
2003).
• Non synthetic or chemically undefined medium; contains at least one component that
is neither purified nor completely characterized nor even completely consistent from
batch to batch (Madigan et al. 2003).
Media can be solid, often referred as plates or liquid (broth). The aims for culturing media are
to identify, isolate, characterize and study physiological microbial characteristics (Tiwari et al.
2009).
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Based in their functional usages, media are classified as:
• General purpose/basic media. Basal media are simple media that supports most non
-fastidious bacteria such as nutrient agar. They are generally used for primary
isolation of microorganisms (Acharya 2010;Tiwari et al. 2009).
• Enriched media. These are rich in nutrients, ideal for most of the organisms. They
often contains blood, haemolysed blood, egg yolk, serum and ascetic fluid as
additional supplement to the basal medium (Tiwari et al. 2009). Examples of enriched
media are blood agar, chocolate agar, etc (Acharya 2010).
• Selective and enrichment media: The medium composition is designed to inhibit
unwanted or contaminating bacteria by adding appropriate chemicals in order to grow
a particular group of organisms (Tiwari et al. 2009). Any agar media can be made
selective by the addition of certain inhibitory agents that do not affect the targeted
organisms (Tiwari et al. 2009), or by including chemicals that selectively support the
growth of target organisms. Examples of selective media are Mannitol Salt Agar and
MacConkey’s Agar (Acharya 2010).
• Differential medium: The purpose of this medium is to support the growth of target
organisms and make them easily recognized on the basis of their colony colour
(Acharya 2010;Madigan et al. 2003;Tiwari et al. 2009). Examples of differential media
include Mannitol salts agar (mannitol fermentation is yellow), Mac Conkey agar
(lactose fermenters are pink colonies), etc (Madigan et al. 2003).
3.2 Molecular biology techniques
Although conventional methods have proved useful for identification and characterization of
microorganisms, those methods present certain limitations on the study of natural or
engineered environments. For instance the proportion of cells which can be cultured is
estimated to be between 0.1 and 10% of the total population, providing insufficient data
concerning the composition of bacterial communities (Ranjard et al. 2000).
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Molecular biology is defined as the study of the molecular basis of composition, structure and
interactions of biological activity including DNA, RNA and protein synthesis (Sanz and
Köchling 2007) (Vitale 2017). Since genomes comprise all the DNA from an organism and
carry all the information needed to specify the structure of every protein produced by cells,
their study represents greater understanding of molecular processes in health and disease
(Rapley 2010).
Molecular techniques developed during the 1980s and 1990s represented a milestone in the
microbial ecology field (Howe 2018) and contributed to develop ambitious projects such as
the Human Genome Project (HGP) (NIH 2018). The continuous development and
improvement of molecular techniques allow us to understand the basic structure of nucleic
acids and to gain appreciation of how this dictates the cellular responses to external stimuli
(Rapley 2010).
Molecular biology techniques have also become powerful analytical tools in biotechnology,
genome mapping, microbial ecology, and medicine and gene therapy. Nowadays molecular
biology techniques are widely used in environmental studies involving DNA extraction,
polymerase chain reaction amplification using universal primers for bacterial genes coding for
16S rRNA and DNA sequencing of targeted genes or whole genomes (Wouters et al. 2013).
The isolation of genomic DNA from microorganisms has become a useful tool to reveal the
genotypic diversity and the change in microbial ecosystems (Mesapogu et al. 2013).
3.2.1 DNA extraction
Deoxyribonucleic acid (DNA) is composed of polymers of four deoxynucleotides (thymine,
cytosine, adenine and guanine). Those nucleotides are composed by a heterocyclic base, a
sugar and a phosphate groups. Replication of DNA is the normal process of doubling the DNA
content of cells prior to cell division. The process of DNA replication involves multiple
enzymatic activities leading to a complement of the parental cell (King 2007).
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The purpose of DNA extraction is to obtain DNA in relatively purified form which can be used
for further investigations such as PCR or sequencing. Most DNA protocols consist of two parts
(Biotech 2009):
1. A technique to lyse the cells gently and solubilize the DNA
2. Enzymatic or chemical methods to remove contaminating proteins, RNA or any other
macromolecules
The first step is lysis, in this step the cell wall is disrupted by mechanical force and a detergent
breaks down the cell membrane. Next step is the precipitation, where the DNA is separated
from the rest of the cell components by addition of salts, solvent and by spinning in a
centrifuge. Then washing occurs by ethanol to remove salts and other water soluble impurities,
and finally the resuspension to clean the DNA in a buffer solution to ensure stability and long
term storage. To confirm the presence of DNA absorbance can be measured. Alternatively,
gel electrophoresis is also used to corroborate the presence and quality of DNA (Biotech 2009)
(Mesapogu et al. 2013).
3.2.2 Polymerase Chain Reaction (PCR)
PCR was developed by Kary Mullis in 1983 and it has been useful in simplifying and
accelerating molecular biology. PCR is an enzymatic reaction that allows amplification of DNA
through a repetitive process. During each cycle of PCR, any DNA that is present in the reaction
is copied. During the process, the amount of DNA doubles during each cycle. Approximately
25 to 30 cycles result in about 106 fold increase in the amount of DNA present. Targeted
amplification of DNA increases the sensitivity of detection of sequences present even in trace
amounts (Dowd and Pepper 2007).
The stages involved in the PCR process begin when the DNA double helix strands are
separated, this process is called denaturation and it is achieved by raising the temperature of
the DNA solution. This causes the hydrogen bonds between the complementary DNA chains
to break, and the two strands to separate (Biotech 2007) (NCBI 2014).
In the next step, the temperature is lowered and the enzyme Taq polymerase joins free DNA
nucleotides together. This nucleotides order is determined by the original DNA strand that is
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being copied. The result is a double stranded DNA molecule that contains one new strand
and an original one (Biotech 2007). Figure 3.1 shows the summary of PCR.
Figure 3.1 Summary of PCR (NCBI 2014).
3.2.3 Real Time PCR (qPCR)
Real time PCR or quantitative PCR is a variation of the standard PCR used to determine the
amount of PCR products in a sample (Frąc et al. 2015).
The quantification of amplified samples is obtained by using fluorescent probes and it is based
in the detection of fluorescence produced by a specific molecule, which increases as the
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reaction proceeds; this increase occurs due to the accumulation of the PCR product during
each cycle of amplification (Praveen and Koundal 2013) (Maddocks and Jenkins 2017). Real
time PCR is the conversion of fluorescent signal, often provided when the molecular dye
SYBR Green binds to the double stranded DNA or the sequence specific probes (Figure 3.2)
from one or more polymerase chain reaction over a range of cycles into a numerical value for
the sample (Shipley 2006) (Jia 2012).
Figure 3.2 Illustration of dye SYBER Green binding to a double stranded DNA (Praveen and
Koundal 2013)
The advantages of real-time PCR include the ability to monitor the PCR reaction progress in
real time, the ability to measure the amount of amplicon at each cycle, then the initial material
can also be quantified, and the amplification and detection occurs in a single tube, avoiding
further manipulations (Fairfax and Slimnia 2010).
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3.2.4 DNA sequencing: Sanger sequencing
DNA sequencing is defined as the process to determine the sequence of nucleotide bases
(Adenine, Thymine, Cytosine and Guanine) in a DNA fragment. Improvement and optimization
of new DNA sequencing methods have contributed to major advances in biological, medical
and biotechnological research (NIH 2015).
In Sanger sequencing (also known as chain termination method), the target DNA is copied
many times, making fragments of different lengths (Sanger and Coulson 1975) (Sanger et al.
1977). Fluorescent chain terminator nucleotides mark the end of the fragments and the
sequence can be determined (Sanger et al. 1977).
Sanger sequencing starts when the DNA sample is mixed with the primer, DNA polymerase
and the DNA nucleotides (dATP, dTTP, dGTP and dCTP) (Zhou and Li 2015). The four dye-
labeled, chain-terminating dideoxy nucleotides are added as well but in smaller concentrations
than the ordinary nucleotides (Scitable 2019).
The mixture is initially heated to denature the DNA and then cooled so the primer binds to the
single stranded template. Once the primer has bound, the temperature raises again to allow
the DNA polymerase to synthetize new DNA starting from the primer. This process will repeat
until a dideoxy nucleotide is added instead of a normal one. The process is repeated until the
cycle is complete meaning that a dideoxy nucleotide will be incorporated at every single
position of the target DNA in at least one reaction (Merck 2019) (Zhou and Li 2015).
When the reaction is finished, the fragments are analysed on a process called capillary gel
electrophoresis where the dyes attached to DNA fragments will be read by a laser (Merck
2019). Figure 3.3 shows the general description of Sanger sequencing technique.
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Figure 3.3 Sanger sequencing technique (Zhou and Li 2015)
Overall, Sanger sequencing gives high-quality for relatively longs stretches of DNA and
represents a useful tool to sequence individual pieces of DNA such as bacterial plasmids or
DNA copied in PCR (Hagemann 2015).
3.2.5 Next-generation DNA Sequencing: Illumina sequencing
Next-generation sequencing (NGS), also known as high-throughput sequencing, is a term that
incorporates modern DNA and RNA sequencing technologies such as Illumina sequencing,
Roche 454 sequencing and Ion Torrent: Proton (PGM) sequencing (EMBL-EBI 2019)
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(Shendure and Ji 2008). Most next-generation sequencing techniques are distinguished for
being highly parallel, micro scale, fast, low-cost and create shorter length (range between 50-
700 nt) (Shendure and Ji 2008).
Illumina sequencing (also named Illumina/Solexa) is a “sequencing-by-synthesis” technology
developed by Shankar Balasubramanian and David Klenerman in 1998. llumina sequencing
method is based on the incorporation of reversible dye terminators that allow the identification
of single bases as they are incorporated into DNA strands (Zhou and Li 2015).
Illumina sequencing works similar to Sanger sequencing, but it uses modified dNTPs
containing a terminator that blocks further polymerisation, therefore solely a single base can
be added by a polymerase enzyme to each growing DNA copy strand (Singh and Kumari
2014). The sequencing reaction occurs simultaneously at different template molecules spread
on a solid surface (Mardis 2013).
The main steps are library preparation, cluster generation, sequencing and data analysis
(Figure 3.4) (Illumina 2013) (Mardis 2013). The process begins when the purified DNA is
chopped up into smaller pieces and certain molecular modifications act as reference points
during amplification, sequencing and analysis. Then, the modified DNA is loaded onto a
specialized chip, composed by hundreds of thousands of oligonucleotides, where
amplification and sequencing are carried out. The oligonucleotides grab the DNA fragments
that have complementary sequences. Once the fragments have attached, about a thousand
copies of each fragment of DNA is made, this step is called cluster generation. Then, primers
and modified nucleotides enter the chip; these have reversible 3’ blockers that force the
primers to add on only one nucleotide at a time as well as fluorescent tags. The fluorescent
wavelength is determined for every spot in the chip. The process continues until the genome
is fully sequenced (Illumina, 2010) (YG 2015) (Singh and Kumari 2014).
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Figure 3.4 Overview of NGS sequencing by Illumina technology: a)Library-construction
process, b)Cluster generation by bridge amplification and c)Sequencing by synthesis with reversible dye terminators (Mardis 2013)
Over the past years massively parallel DNA sequencing technologies have become
extensively available for generating sequence libraries, evolving new data analysis and
developing new experimental design (Shendure and Ji 2008).
3.2.5 Metagenomics
As previously mentioned, genomics reveal a general phylogenetic description including
insights into genetics, physiology and biochemistry of the microbial diversity. Recently
innovative metagenomics tools have been developed in order to facilitate the study of the
physiology and ecology of environmental organisms and their response to external stimuli,
antimicrobial activity, nutrient cycling, gene function and gene transfer within communities
(Handelsman 2004).
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Metagenomics, also known as environmental and community genomics, is defined as the
genomic analysis of microorganisms by direct extraction and cloning of DNA from an
assemblage of microorganisms (Handelsman 2004). Metagenomics provides an unbiased
display of the community structure (species richness and distribution) and the functional
(metabolic) potential (Hugenholtz and Tyson 2008).
As research technique, metagenomics involves a series of tools to examine thousands of
organisms in parallel providing insight into community diversity and function even for
organisms with low abundance (Thomas et al. 2012).
The main steps of the metagenomics workflow are DNA extraction, library preparation,
sequencing, assembly, binning, annotation and statistical analysis (shown in Figure 3.5):
• Sample extraction is the most critical step in metagenomics analysis. The extracted
DNA should be representative of the site of interest and extraction must yield sufficient
amounts of high-quality nucleic acids for subsequent library preparation and
sequencing (Thomas et al. 2012).
• Library preparation: overall the process is standardized to manipulate the DNA
sample by fragmentation, end repair and adaptor ligation, size fractionation and
amplification (Solonenko and Sullivan 2013)
• Sequencing technologies offer a wide variety of read lengths and outputs depending
on the applied technology. For instance Illumina sequencing offers short reads (2x250
or 2x300 bp) but generates high sequencing depth; whereas Oxford Nanopore offers
lower sequencing depth (Solonenko and Sullivan 2013).
• Assembly involves the merging of reads from the same genome into a single
sequence (contigs) and orientation of these into scaffolds (Thomas et al. 2012).
Assembling of shorter reads into contigs occurs by two different routes:
§ Referenced-based assembly uses one or more reference genomes as a map to
create contigs which can represent genomes or part of genomes belonging to
specific species or genus (Oulas et al. 2015).
§ De novo assembly generates assembled contigs using no prior reference to
known genomes, this step requires heavily and sophisticated graph theory
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algorithms such as de-Brujin graphs (Oulas et al. 2015) (Paszkiewicz and
Studholme 2010).
• Binning is the process of grouping reads or contigs into highly similar groups and
assigning them to groups of specific species, subspecies or genera (Ghosh et al.
2019). Binning can use two types of algorithms:
§ Composition-based binning is based on the observation that individual
genomes have a unique distribution of k-mer sequences. The algorithm uses
the conserved species-specific nucleotide composition to group the
sequences into their respective genomes (Oulas et al. 2015).
§ Similarity-or homology-based binning uses alignment algorithms such as
BLAST or profile hidden Markov models (pHMMs) to obtain information about
specific sequences/genes from publically databases (eg NCBI) (Oulas et al.
2015)
• Annotation is the prediction of CDS (coding DNA sequences) followed by functional
assignment using similarity based searches of query sequences against known
functional and/or taxonomic information (Ghosh et al. 2019). A series of steps are
necessary to prepare the reads for annotation including:
o Trimming of low quality reads
o Masking of low-complexity reads
o De-replication step that removes sequences that are not 95% identical
o Screening step to remove reads that are near-exact matches to the genomes
of handful model organisms
o Identification of genes within the reads/assembled contigs (gene calling
process). Genes are labelled as coding DNA sequences (CDSs) and non-
coding RNA genes whereas some tools also predict for regulatory elements
such as clustered regularly interspaced palindromic repeats (CRISPRs)
o Functional assignment to the predicted protein coding genes achieved by
homology-based searches of query sequences against databases containing
known functional and/or taxonomic information
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• Statistical analysis: Several software packages perform statistical analysis of
metagenomic data presenting results on alpha diversity (diversity within the sample)
and beta diversity (diversity across samples), taxonomic composition and
phylogenetic analysis.
Figure 3.5 Metagenomics workflow. After extraction, DNA is analysed using paired-ends reads
to maximise coverage of the amplicons and the reads and assembled into contigs.
Examples of Software packages used for these steps are mentioned in table 3.1.
Table 3.1 Examples of metagenomics software tools Category Tools References
Assembly MEGAHIT
MetaVelvet (de novo)
Omega
metaSPAdes
MetAMOS (referenced-based)
(Li et al. 2015)
(Namiki et al. 2012)
(Haider et al. 2014)
(Nurk et al. 2017)
(Treangen et al. 2013)
Binning CONCOT
MG-RAST (similarity-based)
MEGAN (similarity-based)
MetaCluster (both algorithms)
CARMA (similarity-based)
MetaPhyler (similarity-based)
TETRA (composition-based)
PhyloPythiaS (compositon-based)
(Alneberg et al. 2014)
(Meyer et al. 2008)
(Huson and Weber 2013)
(Wang et al. 2014)
(Krause et al. 2008)
(Liu et al. 2011)
(Teeling et al. 2004)
(Patil et al. 2012)
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Annotation
Trimming FastQC
SolexaQA
Masking step DUST
Screening step Bowtie 2
Gene Calling Prodigal
FragGeneScan
Databases for Functional and Taxonomic Annotation
SILVA
KEGG
SEED
eggNOG
COG/KOG
Display of taxonomic information Prokka
(Huson and Weber 2013)
(Cox et al. 2010)
(Morgulis et al. 2006)
(Langmead and Salzberg
2012)
(Hyatt et al. 2010b)
(Rho et al. 2010)
(Quast et al. 2013)
(Ogata et al. 1999)
(Overbeek et al. 2005)
(Powell et al. 2014)
(Tatusov et al. 2000)
(Seemann 2014)
Annotation
pipelines
MG-RAST
EBI-Metagenomics (MGnify)
IMG/MER
(Meyer et al. 2008)
(Mitchell et al. 2018)
(Chen et al. 2017)
OTU Clustering QIIME
Mothur (Caporaso et al. 2010b)
(Schloss et al. 2009)
Statistical analysis QIIME
MEGAN
Primer-E Package
R programming language:
Vegan
Phyloseq
Bioconductor
(Caporaso et al. 2010b)
(Huson and Weber 2013)
(Clarke and Gorley 2015)
(Oksanen et al. 2007)
(McMurdie and Holmes 2013)
(Gentleman et al. 2004)
Metagenomics has had a dramatic effect on application on different fields such as
bioremediation (Yergeau et al. 2012;Techtmann and Hazen 2016;Paul et al. 2005), industrial
bioproducts (Lorenz and Eck 2005), plant-microbe interactions (Kaul et al. 2016) (Knief 2014)
and human microbiome (Turnbaugh et al. 2007) (Abubucker et al. 2012).
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One of the approaches of metagenomics is their application on the study of viruses.
Metagenomic investigations provide insights to the central role of viruses in microbial evolution
and ecology (Hugenholtz and Tyson 2008). Figure 3.6 shows the common workflow for viral
and phage identification via VirMiner server (Zheng et al. 2019).
Figure 3.6 Metagenomic viral identification pipeline. The workflow describes the main steps for
phage identification and gene prediction (Zheng et al. 2019)
Despite their importance, identification of phages and their interactions with the microbiome
is limited due to the difficulties for virus isolation and purification (Zheng et al. 2019;Roux et
al. 2015b); the lack of a universal marker gene for viruses; the limited available databases;
and the restricted availability of bioinformatics tools, mostly suitable for prokaryotic genome
sequencing data and not designed for metagenomic data (Roux et al. 2015a). Next-
Generation sequencing tools such as metagenomics has created a wider panorama of virus
abundances providing an insight of the host-bacteria interactions and their influence on the
microbial ecology.
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4
Research Paper: Identification of stable hydrogen-
driven microbes in highly radioactive storage facilities
in Sellafield, UK
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Chapter 4 Identification of stable hydrogen-driven microbes in highly radioactive storage facilities in Sellafield, UK
S. Ruiz-Lopez1, L. Foster1, C. Boothman1, N. Cole2, K. Morris1, J.R. Lloyd1
1 School of Earth and Environmental Sciences, University of Manchester Oxford Road, Manchester,
M13 9PL
2 Sellafield Ltd, Hinton House, Birchwood Park Ave, Birchwood, Warrington WA3 6GR
Corresponding author: [email protected]
Abstract
The use of nuclear power has been a significant part of the United Kingdom’s energy portfolio
for more than 60 years, with the Sellafield site being used for power production and more
recently reprocessing and decommissioning of spent nuclear fuel activities. Before being
reprocessed, spent nuclear fuel is stored in water ponds with significant levels of background
radioactivity, and in many cases high alkalinity (to minimise fuel corrosion). Despite these
challenging conditions, the presence of microbial communities has been detected in these
harsh storage environments. To gain further insight into the microbial communities present on
extreme environments, an indoor, hyper-alkaline, oligotrophic and potential radioactive spent
fuel storage pond (INP) located on Sellafield was analysed. Water samples were collected
from sample points within the INP complex, and also the purge water feeding tank (FT) that
supplies water to the pond, and were analysed by 16S and 18S rRNA gene sequencing over
a period of thirty months. Only 16S rRNA genes were successfully amplified, suggesting that
the microbial communities in INP and the feeding tank were dominated by prokaryotes.
Quantitative Polymerase Chain Reaction (QPCR) analysis targeting 16S rRNA genes
suggested that in the order of 104-105 bacterial cells per ml were present in the samples, with
higher loadings, rising with time, in the INP samples versus the feeding tank. Next generation
Illumina MiSeq sequencing was performed to identify the dominant microorganisms at eight
sampling times.16S rDNA sequence analysis suggested that 70% and 97% of the OTUs, from
the FT HT and INP samples respectively, belonged to the phylum Proteobacteria, mainly from
the Alpha and Beta subclasses. The remaining OTUs were assigned primarily to the phyla
Acidobacteria, Bacteroidetes and Cyanobacteria. Greater phylogenetic diversity was
observed in the HT samples; overall the most abundant genera identified were
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Hydrogenophaga, Curvibacter, Porphyrobacter, Rhodoferax, Polaromonas,
Sediminibacterium, Roseococcus and Sphingomonas. The presence of organisms most
closely related to alkaliphilic Hydrogenophaga species, in the INP main ponds and subponds,
suggests the metabolism of hydrogen as an energy source, possibly linked to hydrolysis of
water caused by the stored fuel. Isolation of axenic cultures using a range of minimal and rich
media was also attempted but only relatively minor components (from the genera
Algoriphagus and Aquiflexum) of the pond water communities were obtained. The
identification of organisms revealed that despite the mentioned genera do not represent major
components, the microbial members were able to adapt to a combination of challenging
conditions such as oligotrophy, radioactivity and hyper-alkalinity. The results observed by
culturing techniques emphasise the importance of DNA-based, not culture dependent
techniques, for assessing the microbiome of nuclear facilities.
Introduction
Nuclear power supplies about 11% of the world’s electricity (WNA 2006), and with increasing
global energy demands this seems unlikely to decline. Although considered a “low carbon”
generating energy source, radioactive waste is produced, including spent fuels that need
storage prior to reprocessing and final disposal (Deutch et al. 2009). In the UK, this task is
performed at Sellafield, one of the largest and most complex nuclear sites in Europe. With
over 1400 discrete operations, handling 240 nuclear materials, it is located in Cumbria on the
North West coast of England and has been operated by the Nuclear Decommissioning
Authority (NDA) since 2005 (Baldwin 2003) (WNA 2018a). Calder Hall, located on the site,
was the world’s first commercial nuclear power station, and here energy was generated from
1956 to 2003. The Sellafield site also contains a range of storage ponds built during the 1950s
which were intended to support the production of weapons grade plutonium, and more recently
fuels from the UK’s fleet of nuclear power stations (Reddy et al. 2012) (WNA 2018b). This
legacy of activities have left a complex range of nuclear operations at Sellafield, including the
decommissioning of redundant facilities associated with the site’s early defence work, and
spent fuel management including Magnox and Oxide fuel reprocessing (GOV UK 2018).
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Prior to reprocessing, all irradiated fuel delivered to Sellafield is stored for a period of at least
100 days in water-filled reinforced concrete ponds that allow the decay of short-lived
radioisotopes. During storage, the degree of corrosion experienced by the fuel is monitored
to determine storage life and optimise water chemistry (Shaw 1990). Temperature within the
ponds is controlled by refrigerant chillers to further limit fuel corrosion, while the levels of both
radioactive and no-radioactive ions in the pond waters are controlled by purging cycles of
demineralised water adjusted to pH 11.1-11.6 with the addition of sodium hydroxide (Howden
1987). The main pre-reprocessing storage pond at the Sellafield site is the indoor alkaline
storage pond (INP), a concrete wall pond filled with demineralised water, responsible for
receiving, storing and mechanically processing spent nuclear fuel (SNF) from Magnox and
Advanced Gas-cooled Reactor (AGR) stations from across the UK (Sellafield 2015).
Although Sellafield’s nuclear facilities, including INP, are considered to be oligotrophic with
high background levels of radiation, these conditions do not prevent microbial colonisation
and survival (MeGraw et al. 2018), and the presence of diverse microbial communities may
therefore impact on site operation, fuel stability, and ultimately the biogeochemical fate of any
solubilised radionuclides within the pond waters (Lloyd and Renshaw 2005). There is
emerging understanding that microbial processes can impact on many aspects of site
operations. Microorganisms can play a significant role in the transformations of radionuclides
in the environment by altering their chemical speciation, solubility and sorption properties,
ultimately impacting on their environmental mobility and bioavailability (Francis 2012b)
(Newsome et al. 2014a). For example, the interactions between microbial populations and
soluble radionuclides in groundwater can lead to precipitation reactions (e.g. via U(VI) or
Tc(VII) bioreduction) and subsequent bioremediation (Newsome et al. 2014b). Of particular
note within these pond environments is the fate of 90Sr and 137Cs. Previous studies showed
that seasonal blooms dominated by the alga Haematococcus, have adapted to survive in a
circumneutral pH outdoor spent fuel storage pond at Sellafield, and are able to accumulate
high levels of these radionuclides (MeGraw et al. 2018) (Ashworth et al. 2018).
The accumulation of radionuclides by microbial cells can be driven by a range of process
including biosorption, biomineralization and bioprecipitation (Gadd 2009), although these are
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).
Sharon L. Ruiz Lopez PhD Thesis
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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
Sharon L. Ruiz Lopez PhD Thesis
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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
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DNA was extracted from the samples, and 16S and 18S rRNA genes were targeted by PCR
using the methods described previously. However, only 16S rRNA gene amplification products
were detected by gel electrophoresis, and it was therefore concluded that eukaryotic
microorganisms were absent, or were below the level of detection in the INP samples. The
16S rRNA amplicons were then sequencing using the Illumina MiSeq next generation
sequencing platform, and analysed using a bespoke bioinformatics platform which included
comparison to prokaryotic gene sequences deposited in the NCBI databases.
Samples from the main ponds (MP) were consistently dominated by Proteobacteria (70-98%)
and Bacteroidetes (2-21%). Organisms affiliated with the phylum Cyanobacteria were not
detected on the initial samples, but were detected in subsequent times (from October 2017 to
April 2019), although at a relative abundance of less than 3%. Samples from the subponds
(SP) were also dominated by Proteobacteria (80-97%) and Bacteroidetes (3-7%), while the
relative abundance of Cyanobacteria was again low (less than 2%). In addition, other phyla
detected at lower levels in the main ponds included organisms affiliated with the Actinobacteria
(8%, January 2018), Armatimonadates (4%, June 2017 and February 2019) and
Deinococcus-Thermus (2-4% from November 2018 to April 2019). Samples from the
supplying feeding tank (FT) were also dominated by Proteobacteria (70 and 75%),
Bacteroidetes (14 and 19%) and Actinobacteria (1 and 4%). Detailed information is shown in
Supplementary data, Figures 1 and 2.
At the genus level (Figure 4.3), both duplicates from the feeding head tank (HT01 and HT02)
were dominated by close relatives to Curvibacter (~21%, Betaproteobacteria, 1 OTU),
Rhodoferax (~20%, Betaproteobacteria, 1 OTU), Sediminibacterium (~10%, Bacteroidetes, 2
OTUs), Polaromonas (~6%, Betaproteobacteria, 2 OTUs), Methylotenera (~6%,
Betaproteobacteria, 2 OTUs), Novosphingobium (~3%, Alphaproteobacteria, 2 OTUs),
Flavobacterium (~3%, Bacteroidetes, 2 OTUs), Unidibacterium (~3%, Betaproteobacteria, 2
OTUs) and more than 20% of the total OTUs (26) represented unidentified organisms.
Although the microbial profiles of both samples were very similar, there were relatively minor
differences (Curvibacter, Sediminibacterium, Flavobacterium and Unidibacterium were more
abundant on HT01 whilst Methylotenera, Polaromonas and Novosphingobium, were more
Sharon L. Ruiz Lopez PhD Thesis
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abundant on HT02). Both samples contrasted very strongly with the INP communities
suggesting that the INP pond supported a distinct microbial community.
Microbial distribution was consistent at all sampling times within the main ponds (MP). Overall
at the genus level the microbial diversity was dominated by 1 OTU belonging to genus
Hydrogenophaga (Betaproteobacteria) representing up to 40% of the total population,
followed by Porphyrobacter (~21%, Alphaproteobacteria, 1 OTU), Roseococcus (~9%,
Alphaproteobacteria, 3 OTUs), Silanimonas (~9%, Gammaproteobacteria, 1 OTU),
Sphingomonas (~7%, Alphaproteobacteria, 2 OTUs), and Synechococcus (~1%,
Cyanophyceae, 3 OTUs). The exception was one set of samples taken on January 2018
(MP07 and MP08), where broad microbial diversity was recorded and the abundance of
Hydrogenophaga, and Porphyrobacter dropped to 23% and 7% respectively. Additionally,
representatives of the genera Methylophilus (13%, Betaproteobacteria, 1 OTU) and
Mongoliitalea (9%, Bacteroidetes, 2 OTUs) were exclusively identified on this sampling time.
Unidentified (uncultured) sequences, although detected at all sampling times, represented
more than 2% of the total community in samples MP03 (June 2017, 8%, 24 OTUs), MP07
and MP08 (January 2018, 8% and 10%, 38 OTUs) and MP16 (April 2019, 21%, 47 OTUs).
The microbial profiles of the subponds (SP) were similar to the main ponds (MP), and were
dominated by representatives of the genera Hydrogenophaga (30%, Betaproteobacteria, 1
OTU), Porphyrobacter (23%, Alphaproteobacteria, 1 OTU), Roseococcus (8%,
Alphaproteobacteria, 2 OTUs), Silanimonas (8%, Gammaproteobacteria, 2 OTUs), and
Sphingomonas (2.4%, Alphaproteobacteria, 3 OTUs). Samples SP03 and SP04 (January,
2018) showed few differences with close relatives affiliated to genus Methylophilus (~14%,
Alphaproteobacteria, 1 OTU) detected in these samples only.
Although looking similar at the Phylum level (MP, SP and FT samples dominated by
Proteobacteria), it was clear from the results above that the contrasting microbial communities
differed substantially at the genus level. Data would seem to suggest that the microbial
community compositions in the main ponds, subponds and feeding head tank samples
represent distinct ecosystems, most likely linked to the impacts of the spent fuel on the INP
environment.
Sharon L. Ruiz Lopez PhD Thesis
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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
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c) Figure 4.3 Phylogenetic affiliations (closest known genera) of microorganisms detected in
Sellafield indoor pond (INP): a)main ponds, b)subponds and c)feeding tank (FT) using Illumina sequencing with broad specificity primers for prokaryote 16S rRNA. Only the genera that
contained more than 1% of the total number of sequences are shown.
Cultivation-dependent analysis for determining microbial diversity in the INP
In addition DNA-based analyses, culturing techniques were adopted to characterise the
microbial communities within the INP subponds complex, and to provide axenic cultures
representative of the microbes colonising such an extreme environment for future studies. A
series of dilutions from the INP subponds (samples SP01 and SP02), were spread onto agar
plates containing a range of solidified high pH (11.5) solid media. After 7 days of incubation,
growth was detected exclusively on the undiluted samples (100) from plates containing non-
defined complex media (DL, NA and Zobell media; See Supplementary Table 2). CFU per ml
were determined between 700-1000 per ml for each media and eleven distinct colony
morphologies were noted. Representative single colonies were isolated and identified by
sequencing using the dideoxynucleotide technique. The presence of colonies was not
detected at fully defined media (minimal media M9).
Overall, 4 different genera were identified. Representatives of Algoriphagus genus (isolates
S01, 91.5% similarity; S05, 91% similarity; S06, 91.5% similarity; and S07, 89.5% similarity)
were isolated on DL and NA agars, and produced light pink-coloured, rod-shaped and raised
colonies (1-2 mm diameter). Aquiflexum genus (isolates S02, 91% similarity; S08, 88%
similarity; and S09, 93.5 similarity %) were obtained on the DL and NA agar plates, and
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
FT01_Oct16 FT02_Oct16
Relativeabundance
Unidibacterium
Novosphingobium
Flavobacterium
Polaromonas
Methylotenera
Sediminibacterium
Rhodoferax
Curvibacter
Others
Sharon L. Ruiz Lopez PhD Thesis
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produced red-coloured colonies, that were rod-shaped with raised elevation (2-3 mm
diameter). Strains S03, S10 and S11 were isolated from DL, NA and Zobell plates; were rod-
shaped, translucent and had raised colonies (2-3 mm diameter) and were affiliated to an
Unclassified genus from the family Cyclobacteriaceae (S03, 93.5% similarity; S10, 85%
similarity and S11, 91% similarity). Finally, a close relative to Bacteroidetes (strain S04, 91.5%
similarity) was isolated from the DL plates and produced short round-shaped, bright-orange
raised colonies (1-2 mm diameter). The total eleven isolated strains belong to the phylum
Bacteroidetes. Specific details on similarity and media are shown on supplementary Table 3.
Members belonging to genus Aquiflexum (phylum Bacteroidetes) were previously detected on
the MP and SP samples by DNA-based techniques; however the mentioned genus does not
represent a major component. Genus Aquiflexum was detected exclusively on samples MP05
and MP06 (October 2017) at a relative abundance of 0.28% and 0.39% respectively (see
supplementary Table 4).
Discussion
The present research was focused on characterising the microbial community composition of
a Sellafield INP complex containing main ponds (MP), subponds (SP) and a feeding head
tank (FT) over a period time of 30 months. The results showed that bacteria affiliated with a
range of phylogenetic groups are able to survive and colonize the different areas across the
INP complex.
Microbial diversity on the feeding tank area (FT), an oligotrophic and hyper-alkaline
environment, was dominated by members belonging to the Proteobacteria and Bacteroidetes.
Previous studies showed that oligotrophic conditions do not prevent microbial colonisation and
allow microbial communities to display diverse adaptation mechanisms (Chen et al.
2004;Kawai et al. 2002;Kulakov et al. 2002). Specifically, organisms associated to
Proteobacteria and Bacteroidetes have been identified previously in similar oligotrophic
environments, INP including industrial ultrapure water (Bohus et al. 2010;Gales et al.
2004;Proctor et al. 2015). Microbial colonisation in such environments has been linked to low
Sharon L. Ruiz Lopez PhD Thesis
102
levels of residual organic matter in the system, originating from dead microbial cells that were
unable to adapt to the harsh environments and to biofilm formation on the walls, linked to due
to planktonic cells delivered by water recirculation on the pond areas (Bohus et al. 2010).
Organisms detected in the FT area are reported to support diverse forms of heterotrophic
metabolism, which could occur within the FT. For example members of the genera
Rhodoferax (Finneran et al. 2003) (Risso et al. 2009), Curvibacter and Sediminibacterium (Ma
et al. 2016) (Ding and Yokota 2010) (Qu and Yuan 2008;Kang et al. 2014) (Kim et al. 2013)
are able to oxidize a range of complex organic compounds while Methylotenera can utilise
reduced one-carbon compounds (methylotrophy) such as methanol as energy sources
(Kalyuzhnaya et al. 2012) (Kalyuzhnaya et al. 2006). However, the source of carbon and
energy in the FT remains to be investigated.
Although the INP has a continual feeding input composition, the main ponds (MP) and
subponds (SP) contained stable microbial populations with similar community profiles, which
contrasted with the distinct microbiome of the FT. Key organisms detected in MP and SP
samples included species of Hydrogenophaga, Silanimonas, Porphyrobacter and
Roseococcus.
In addition to the oligotrophic and hyper-alkaline characteristics of the MP and SP areas, these
components of INP complex contain spent fuel creating challenging high background
radioactivity further challenging microbiome development. Despite these adverse conditions,
microbial colonisation of similar spent fuel storage systems has been documented (Gales et
al. 2004;Bruhn et al. 2009;Karley et al. 2018;Santo Domingo et al. 1998), and dominated by
organisms associated to the phyla Proteobacteria (Bagwell et al. 2018;Silva et al.
2018b;Chicote et al. 2004;MeGraw et al. 2018), Firmicutes (Sarro et al. 2005), Actinobacteria
(Sarro et al. 2005), Cyanobacteria (Silva et al. 2018b;MeGraw et al. 2018), Deinococcus-
Thermus (Masurat et al. 2005) and eukaryotic fresh water microalgae (Rivasseau et al.
2016;MeGraw et al. 2018) and Fungi (Silva et al. 2018b;Chicote et al. 2004). Although the
energy sources supporting microbial growth in these systems remains largely
uncharacterised, it is possible that radiolysis could play a direct role in supporting microbial
Sharon L. Ruiz Lopez PhD Thesis
103
growth. The presence of alpha, beta and gamma radiation from the spent fuel can promote
the radiolysis of water, driving to the formation of short-lived, highly oxidising free radical
species, such as -OH and H2O2 (Jonsson et al. 2007) (Shoesmith 2000) and also the
production of H2 (Libert et al. 2011;Brodie et al. 2006) that could be utilised by hydrogen-
oxidizing (Knallgas) bacteria) (Yu 2018). The most abundant organism in the MP and SP
areas in this study were affiliated with the genus Hydrogenophaga (44-48%), which comprise
aerobic, chemoorganotrophic organisms that use hydrogen as an energy source (Willems et
al. 1989) (Kampfer et al. 2005) (Yoon et al. 2008). Members of genus Hydrogenophaga are
present in a variety of natural and engineered (e.g. waste water) environments (Schwartz and
Friedrich 2006) (Yoon et al. 2008) (Lambo and Patel 2006) (Fahy et al. 2008), including hyper
alkaline sites such as Allas Springs, Cyprus where the pH was 11.9, similar to the alkaline
conditions to the INP waters (pH 11.6) (Rizoulis et al. 2014) and serpentinizing springs (pH
11.6, The Cedars, California USA) (Suzuki et al. 2014) . The presence of Hydrogenophaga as
a key microbial component during all the sampling times indicates the metabolism of H2 is
occurring within the pond which is of particular interest since oxidation of hydrogen could also
be potentially linked to the reduction of a range of electron acceptors, including radionuclides
(Lloyd 2003).
It is of interest to note that the other members of the identified microbial community are not
reported to metabolise hydrogen. Porphyrobacter, an aerobic anoxygenic phototroph bacteria
(AAP) has the ability to harvest energy photosynthetically (Yoon et al. 2004) (Liu et al. 2017)
(Hanada et al. 1997); however, is able to grow in the dark using diverse energy sources (Liu
et al. 2017). Roseococcus, an obligately aerobic and chemoorganotrophic, contains
Bacteriochlorophyll a and carotenoid pigments (Yurkov 2015) (Boldareva et al. 2009) and is
also able to grow in the dark (Yurkov et al. 1994). Sphingomonas is metabolically versatile,
can use a wide range of compounds as energy sources (Feng et al. 2014) (Singh et al. 2015)
(Lee et al. 2001) such as polycyclic aromatic hydrocarbons (Leys et al. 2004); and contains
ubiquinone Q-10, a molecule involved in respiratory functions (Niharika et al. 2012) where
hydrogen, abundant on the MP and SP areas, is required. Roseomonas species also contain
ubiquinone Q-10 (Kim et al. 2009) (Wang et al. 2016) and have the ability to grow on biofilms
Sharon L. Ruiz Lopez PhD Thesis
104
to protect from adverse surrounding conditions (Diesendorf et al. 2017) which would be
relevant to the harsh SNP conditions studied here. Microorganisms associated to phylum
Cyanobacteria, a blue-green algae, oxygenic and phototrophic bacteria (Peschek 1999), were
much less abundant (identified as phyla Synecochoccus and Cyanobium) possibly associated
to the restricted exposure to light in the INP, where light levels are kept low.
Finally, agar-based cultivation approaches were tested alongside DNA-based approaches in
this study, and resulted in the isolation of bacteria from the family Cyclobacteriacea, but
proved unsuccessful for targeting organisms that were numerically dominant within the INP
complex. It is interesting to note however, that despite the isolated organisms do not represent
the major components identified by NGS techniques, the findings show that organisms
associated to the genera Algoriphagus and Aquiflexum are able to tolerate wide range of
alkaline conditions and additional challenging conditions such as radioactivity and limited
nutrient sources. In that sense this research represents a potential breakthrough since
organisms affiliated to the identified genera have been previously studied in neutral
environments (ideal pH 7-8) (Alegado et al., 2013; Glaring et al., 2015; Kang,
Weerawongwiwat, Jung, Myung, & Kim, 2013; Misal, Bajoria, Lingojwar, & Gawai, 2013;
Tiago, Chung, & Veríssimo, 2004; Yoon, Lee, & Oh, 2004) and the information about their
population on oligotrophic and radioactive environments is limited.
This observation reinforces the view that cultivation-independent molecular ecology
techniques are crucial first steps in understanding microbiome dynamics in oligotrophic SNPs,
offering the benefits of high-throughput sequencing of DNA that has been purified away from
contaminating radionuclides present in the pond waters. This opens up the way for more
detailed metagenomic analyses which are ongoing in our laboratories.
Acknowledgments
SRL acknowledges financial support from a PhD programme funded by the National Council
of Science and Technology (CONACyT). This work was also supported by funding from
Sellafield Limited and the Royal Society to JRL. LF was supported by an EPSRC CASE PhD
and IAA funding.
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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Wolfram, J. H., Mizia, R. E., Jex, R., Nelson, L. & Garcia, K. M. (1996). The Impact of
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5
Research Paper: Comparative metagenomic analyses
of taxonomic and metabolic diversity of microbiomes
from spent nuclear fuel storage ponds
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Chapter 5 Comparative metagenomic analyses of taxonomic and
metabolic diversity of microbiomes from spent nuclear fuel storage
ponds
S. Ruiz-Lopez1, Nick Cole2, Ho Kyung Song1, Lynn Foster1, Chris Boothman1, Jonathan R.
Lloyd1
1 School of Earth and Environmental Sciences, University of Manchester Oxford Road,
Manchester, M13 9PL
2 Sellafield Ltd, Hinton House, Birchwood Park Ave, Birchwood, Warrington WA3 6GR
Corresponding author: [email protected]
Abstract
Nuclear power is an important energy source that can compensate for carbon emissions from
fossil fuel power plants. However, processing of radioactive waste from nuclear plants is a
significant challenge. The current treatment prior to final geological disposal involves wet
storage of spent fuel in designated ponds, and microbial colonisation of these ponds can
complicate plant operation.
To help identify the key microbes that colonise hydraulically interlinked spent fuel storage
ponds at Sellafield, UK, a series of samples were collected and analysed using next
generation (Illumina) sequencing. Samples were taken from the facility´s indoor hyper-alkaline
pond (INP) (feeding head tank, main and subponds), and also from the open-air First-
Generation Magnox Storage Pond (FGMSP) and its auxiliary pond (Aux). 16S rRNA gene
sequencing revealed that the INP is colonized mainly by Bacteria (99%), affiliated with species
of orders Burkholderiales, Sphingomonadales, Nitrosomonadales, Sphingobacteriales
(including representatives of the genera Curvibacter, Rhodoferax, Sphingomonas and
Roseococcus,) in addition to the hydrogen-oxidising bacterium Hydrogenophaga. In contrast,
the open-air ponds contained species of Hydrogenophaga, Nevskia, and Roseococcus, and
also photosynthetic cyanobacteria (Pseudanabaena).
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Biological function of the microbiomes within the fuel storage ponds was also assessed by
metagenomic sequencing and analyses. The most abundant genes associated with
respiration, stress responses, DNA metabolism, cell wall and capsule synthesis and
photosynthesis were analysed. Genes underpinning hydrogen metabolism were more heavily
represented in the indoor pond samples, whilst photosynthesis genes were more abundant in
the open-air ponds, supporting the hypothesis that hydrogen (from water radiolysis) and light
energy supported ecosystem development in the indoor and outdoor ponds respectively.
These datasets give valuable insight into the microbial communities inhabiting nuclear storage
facilities, the metabolic processes that potentially underpin their colonisation and ultimately
can help inform appropriate microbial growth control strategies.
Introduction
The nuclear fuel cycle has supported a broad range of activities including power generation,
medical applications, defence and research, and through these activities has created a
significant legacy of radioactive waste around the world. The UK and other countries have
developed strategies for the safe long-term management of radioactive waste forms, including
the higher-activity wastes from energy generation, where the final destination will be
geological disposal into the subsurface (NDA 2010).
Prior to reprocessing or final disposal, high level waste (HLW), including nuclear fuel materials,
is stored in water-cooled, stainless steel tanks with thick concrete walls to shield operators
from the high radiation levels (NDA 2010). Spent fuel storage ponds are often filled with
demineralized water and sodium hydroxide is added as corrosion inhibitor, which could also
impact on microbial colonisation (IAEA 1997). However, although base addition has proved
efficient to minimise corrosion of spent fuel , it has not prevented microbial colonisation
(Chicote et al. 2005) (Bohus et al. 2010). Microorganisms detected in spent fuel storage ponds
may include fungi (Basidiomycota and Ascomycota), bacteria associated to Proteobacteria,
Actinobacteria, Firmicutes and Cyanobacteria and even eukaryotic microalgae (Silva et al.
2018a) (MeGraw et al. 2018) (Foster 2018). The presence of microbes in spent fuel ponds
(SFP) is critical to plant operation as microbial growth can cause turbidity in the water, making
fuel inspection and inventory management challenging (Chicote et al. 2004). Microorganisms
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can also interact with the storage racks leading to microbiologically induced corrosion (MIC)
of the stored material (Wolfram et al. 1996b) (Chicote et al. 2004), while the accumulation of
radioactive microbial biomass can pose an addition disposal challenge.
Although the oligotrophic pond conditions imposed, often alongside high pH treatment, are
intended to limit microbial growth, several studies have suggested a variety of metabolisms to
explain the abundance of microorganisms in extreme environments (Sarró et al. 2005) (Santo
Domingo et al. 1998;Rivasseau et al. 2016). Organisms that are adapted to grow optimally at
or near extreme ranges of environmental variables, such as radioactivity or hyper-alkalinity,
are called extremophiles. Extremophiles organisms display a rage of metabolic abilities
coupled with extraordinary physiological capacities to colonize the surrounding environment
such as photosynthesis and the metabolism of alternative energy sources including hydrogen,
methane, sulphur and even iron (Kristjánsson and Hreggvidsson 1995), (Pedersen et al. 2004)
(Joshi et al. 2008), (Nazina et al. 2010), (Liu et al. 2009), (Merroun and Selenska-Pobell 2008),
(Ragon et al. 2011) (Sarró et al. 2005).
Microbial adaptation strategies vary across the environment of study (Rampelotto 2013). For
instance, to cope with hyper-alkaline environments (pH>10), molecular strategies comprise
the activation of both symporter and antiporter systems (Orellana et al. 2018) which allow the
exchange/uptake of Na+ and other solutes into the cells (Rothschild and Mancinelli 2001); and
the physiological high internal buffer capacity maintains the homeostasis and thermodynamic
stability of the cells (Krulwich et al. 1998). Microbial adaptations to radiation include more
genome copies for genome redundancy, efficient machinery for DNA repair (Byrne et al.
2014), a condensed nucleoid that may prevent the dispersion of DNA fragments (Confalonieri
and Sommer 2011), utilization of smaller amino acids that allow the accumulation of Mn2+-
peptide for protecting irradiated cytosolic enzymes from ROS (Sghaier et al. 2013),
accumulation of Mn(II) that facilitates recovery from radiation injury (Daly et al. 2004),
induction of chaperones and active defence against UV-induced oxidative stress (Webb and
DiRuggiero 2013). Deinococcus radiodurans, a widely studied radio-tolerant microorganism,
has adapted to radioactive sites by containing a unique repair mechanism that reassembles
fragmented DNA (Battista 1997). Additionally phenotypic changes to survive in radiation
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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).
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The storage conditions are similar: ponds are filled with demineralized water and in order to
avoid corrosion caustic solution is added to create an alkaline environment (pH approx. 11.6);
therefore, the spent fuel ponds represent extreme oligotrophic, hyper-alkaline and radioactive
environments.
The Indoor Storage Pond (INP) is an indoor pond complex divided into 3 main ponds and 3
subponds linked by a transfer channel that enables water flow. In order to control the pond-
water activity and quality, there is a continuous “once through” purge flow; pond-water from
the main ponds flows into the transfer channel and enters the recirculation pump chamber
where it is continuously pumped round a closed circulation loop and through a heat exchanger
system, which cools the pond-water before it is recycled into the main ponds. Through the
control feed, purge and re-circulation flow rates, the water depth is maintained at 7±0.05m.
The purge flow can be either from a donor plant or from other hydraulically linked ponds within
the Sellafield complex (e.g. FGMSP). The temperature and pH are controlled at 15⁰C and 11.6
respectively. Analysed samples were taken from designated sample points on the “Feeding
Tank” of the donor plant, where the demineralised water used to feed the complex is stored,
and main ponds 2 and 3 of the Fuel Handling Plant.
The FGMSP is the primary storage pond for legacy Magnox spent fuel. The pond is
continuously purged with alkaline dosed demineralised water at a pH of 11.4, from an East to
Westerly direction along the length of the pond, and contains an outflow point, where water is
removed from the pond, on the Western wall. There are two further feeds into the pond, the
first enters the pond at a location along the Northern wall and contains alkaline dosed water
(pH ~11.4) from another fuel handling pond facility on site.
The auxiliary settling tank (auxiliary pond) is directly connected to the legacy pond (FGMSP),
and if the water levels are sufficiently high, the auxiliary pond feeds the alkaline legacy pond
along the South wall.
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Figure 5.1Storage pond systems. Metal and legacy spent fuels from outdoor ponds are transported to the INP for interim storage pending a long term disposal solution available. The INP is divided in 3 main ponds (MP), 3 subponds and a feeding tank area (FT); waters from the
INP are recirculated to the FGSMP during purging times. The FGMSP and its Auxiliary pond (Aux) store legacy fuel pond (NDA 2015;ONR 2016).
A total of 10 samples were taken from different sites from the storage ponds between 2016
and 2018 (Table 5.1). Samples were collected from a depth of 1 m using a hose syringe to
withdraw the water into sterile plastic bottles. In order to avoid any risk of contamination,
samples transferred directly from the pond to the NNL Central Laboratories (National Nuclear
Laboratory, Cumbria UK), where DNA was extracted and the samples where checked for
radioactivity in line with the Environmental Permits and Nuclear Site licences held by Sellafield
Ltd. Extracted DNA samples free from significant radionuclide contamination were shipped
to the University of Manchester and stored at -20⁰C until use.
Table 5.1Samples distribution
Sample Storage pond Conditions Date
INP_FT01 INP, feeding tank area Indoor pond October 2016
INP_FT02 INP, feeding tank area Indoor pond October 2016
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INP_MP01 INP, main pond 2 Indoor pond October 2017
INP_MP02 INP, main pond 3 Indoor pond October 2017
INP_SP01 INP, Subpond 2 Indoor pond January 2018
INP_SP02 INP, Subpond 3 Indoor pond January 2018
FGMSP FGMSP Open-air system September 2017
Aux01 Auxiliary Open-air system May 2016
Aux02 Auxiliary Open-air system June 2017
Aux03 Auxiliary Open-air system September 2017
Methods
Sequencing and sequence processing
DNA extraction was conducted at the Central Laboratories s at NNL on the Sellafield site, from
filtered biomass using a PowerWater DNA Isolation Kit (Mobio Laboratories, Inc., Carlsbad
California, USA). After appropriate radiometric analyses, the DNA was then transported to the
Manchester University laboratories for amplification and analyses.
PCR amplification was performed from the extracted DNA using a Techne Thermocycler
(Cole-Parmer, Staffordshire, UK). Primers used for bacterial 16S rRNA gene amplification
were the broad-specificity 8F forward primer and the reverse primer 1492R (Eden et al.
1991a), while primers used for eukaryote 18S rRNA gene amplification were Euk F forward
primer and the reverse primer Euk R (DeLong 1992b) and primers used for the archaea 16S
rRNA gene amplification were forward primer 21F and reverse primer 958R (DeLong 1992b).
The PCR reaction mixture contained; 5 µl PCR buffer, 4 µl 10 mM dNTP solution (2.5mM each
nucleotide), 1 µl of 25 µM forward primer, 1 µl of 25 µM forward reverse and 0.3 µl Ex Takara
Taq DNA Polymerase, which was made up to a final volume of 50μL with sterile water, and
finally 2µL of sample was added to each tube. The thermal cycling protocol used was as
follows for the bacterial 8F and 1492R primers; initial denaturation at 94°C for 4 minutes,
melting at 94°C for 30 seconds, annealing at 55°C for 30 seconds, extension at 72°C for 1
minute (35 cycles with a final extension at 72°C for 5 minutes, Eden et al., 1991). For
eukaryotic 18S rRNA gene amplification, the temperature cycle was; initial denaturation at
94°C for 2 minutes, melting at 94⁰C for 30 seconds, annealing at 55°C for 1.5 minutes,
extension at 72oC for 1.5 minutes for a total of 30 cycles and final extension at 72⁰C for 5
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minutes (DeLong 1992b). For archaeal 16S rRNA genes the thermal cycle protocol consisted
of an initial denaturation step at 94°C for 4 minutes, melting at 94⁰C for 45 seconds, annealing
at 55°C for 30 seconds, extension at 72oC for 1 minute (for a total of 30 cycles) and a final
extension step at 72⁰C for 5 minutes (DeLong 1992b).
The purity of the amplified PCR products was determined by electrophoresis using a 1% (w/v)
agarose gel in 1X TAE buffer (Tris-acetic acid-EDTA). DNA was stained with SYBER safe
DNA gel stain (Thermofisher), and then viewed under short-wave UV light using a BioRad
Geldoc 2000 system (BioRad, Hemel Hempstead, Herts, UK).
The 16S rRNA gene PCR amplicons was sequenced using the Illumina MiSeq platform
(Illumina, San Diego, CA, USA) targeting the V4 hyper variable region (forward primer, 515F,
5′-GTGYCAGCMGCCGCGGTAA-3′; reverse primer, 806R, 5′-
GGACTACHVGGGTWTCTAAT-3′) for 2 × 250-bp paired-end sequencing (Illumina)
(Caporaso et al. 2011) (Caporaso et al. 2012). PCR amplification was performed using the
Roche FastStart High Fidelity PCR System (Roche Diagnostics Ltd, Burgess Hill, UK) in 50μl
reactions under the following conditions; initial denaturation at 95°C for 2 min, followed by 36
cycles of 95°C for 30 s, 55°C for 30 s, 72°C for 1 min, and a final extension step of 5 min at
72°C. The PCR products were purified and normalised to ~20ng each using the SequalPrep
Normalization Kit (Fisher Scientific, Loughborough, UK). The PCR amplicons from all samples
were pooled in equimolar ratios. The run was performed using a 4pM sample library spiked
with 4pM PhiX to a final concentration of 10% following the method of Schloss and Kozich
(Kozich et al. 2013).
For targeting the V9 eukaryotic 18S rRNA gene sequencing primers 1319F and EukBR were
used for 2 × 250-bp paired-end sequencing under the following conditions, initial denaturation
at 95⁰C for 2 min followed by 36 cycles of 95⁰C for 30 s, 72⁰C for 1 min and final extension of
5 min at 72⁰C (Amaral-Zettler et al. 2009).
Raw sequences were divided into samples by barcodes (up to one mismatch was permitted)
using a sequencing pipeline. Quality control and trimming was performed using Cutadapt
(Martin 2011), FastQC (B.I. 2016), and Sickle (N.A. and J.N. 2011). MiSeq error correction
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was performed using SPADes (Nurk et al. 2013). Forward and reverse reads were
incorporated into full-length sequences with Pandaseq (Masella et al. 2012). Chimeras were
removed using ChimeraSlayer (Haas et al. 2011), and OTU’s were generated with UPARSE
(Edgar 2013). OTUs were classified by VSEARCH (Edgar 2010) at the 97% similarity level,
and singletons were removed. Rarefaction analysis was conducted using the original detected
OTUs in Qiime (Caporaso et al. 2010a). The taxonomic assignment was performed by the
RDP classifier (Wang et al. 2007). Sequences obtained were compared with the NCBI
GenBank database to find the similar organisms (https://www.ncbi.nlm.nih.gov/genbank/).
18S rRNA gene taxonomic assignment was performed by UCLUST using the Silva119
database (Quast et al. 2013).
Whole genome sequencing was achieved using the Illumina Hiseq2000 platform at Celemics
(Celemics, Inc., Seoul, Korea). Raw sequences were uploaded to the Metagenomics Rapid
Annotation using Subsystems Technology (MG-RAST) (Meyer et al. 2008) online server for
taxonomic and functional annotation under the project name “Spent fuel storage ponds_UoM”,
‘ID 86418’. The RefSeq database (Pruitt et al. 2007) was chosen for taxonomic annotation
and the SEED database (Overbeek et al. 2005) was used for functional annotation. The MG-
RAST default parameters (maximum e-value cutoff of 10-5, minimum % identity cutoff of 60%
and minimum alignment length cutoff as 15bp) were used for annotation of the sequences. All
of the Illumina reads that were shorter than 35 bases or had a median quality score below 20
were removed.
Results
Microbial diversity on the indoor spent fuel storage pond (INP)
Six 16S rRNA gene amplicon libraries were generated from DNA extracted from an indoor
pond collected over the 18-month sampling period focusing on three different areas of the
pond complex. Two samples were taken from the feeding tank (INP_FT, October 2016), two
from the main ponds (INP_MP, October 2017) and two from the subponds (INP_SP, January
2018).
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Analysis of PCR amplified 16S rRNA genes showed that the microbial population was
predominantly bacterial. Neither archaeal 16S rRNA or eukaryotic 18S SSU rRNA genes were
amplified by PCR. Averaged samples from the feeding tank (INP_FT, October 2016) were
dominated by Proteobacteria (74%) and Bacteroidetes (16%). The most abundant genera
identified were Curvibacter (21%, 1 OTU), Rhodoferax (19%, 1 OTU), Sediminacterium (10%,
1 OTU), Polaromonas (5%, 1 OTU) and Novoshpingobium (4%, 2 OTUs). Although members
from phylum Cyanobacteria were detected exclusively on the feeding area (INP_FT), their
abundance represented only 1.5% (3 OTUs). Approximately 30% of the OTUs (26) could not
be identified through sequence homology to known organisms.
The microbial communities in the main ponds (INP_MP, October 2017) were dominated by
Proteobacteria (94%) and Bacteroidetes (6%). The most abundant genera identified were
Hydrogenophaga (~40%, 1 OTU), Methylotenera (~21%, 1 OTU), Porphyrobacter (~25%, 1
OTU), Roseococcus (~10%, 2 OTUs) and Silanimonas (~5%, 2 OTUs). Unidentified
organisms represented 0.5% (60 OTUs) and 10.63% (74 OTUs) for the INP_MP01 and
INP_MP02 samples respectively.
Averaged samples from the subponds (INP_SP, January 2018) showed similar microbial
distribution to the main ponds, dominated by representatives of the phyla Proteobacteria
(86%), Bacteroidetes (6%) and Actinobacteria (6%). The most abundant genera identified
were Hydrogenophaga (up to 35%, 1 OTU), Porphyrobacter (30%, 1 OTU), Methylotenera
(18%, 1 OTU), Silanimonas (11%, 2 OTUs) and Polynucleobacter (7%, 3 OTUs). Unidentified
organisms represented 0.95% (74 OTUs) and 8.76% (74 OTUs) for each sample.
Microbial identification by metagenomics using the MG-RAST tools (Meyer et al. 2008)
confirmed that the community was dominated by bacteria (98%). Contrasting to the SSU
amplification targeting the 18S rRNA gene, eukaryotic sequences were identified but
represented less than 2% (Supplementary Figure 5.1) mainly dominated by phyla Chordata,
Cnidaria and Streptophyta (Supplementary Figure 5.2). Despite the contrasting microbial
diversity detected at the genus level from whole genome sequencing, the most abundant
organisms (identified by both approaches) belonged to the same orders.
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Microbial diversity on the legacy First Generation Magnox Storage Pond (FGMSP)
Due to challenges associated with sampling from this higher activity nuclear facility, only one
representative sample was analysed from September 2017. Previous studies on the FGMSP
showed the pond experienced the presence of microbial blooms during which the visibility
within the pond was significantly reduced, hampering pond management procedures (Foster
2018). The sample obtained for this study was obtained at the end of a bloom period; 16S
rRNA gene amplification and sequencing revealed that the microbial community was mainly
dominated by Proteobacteria (83.7%) and Bacteroidetes (15.4%) (Supplementary Fig 5.3a);
dominated by genera Hydrogenophaga, Nevskia, Roseococcus, Belliela, Rhodobacter and
Porphyrobacter. Unidentified organisms represented 1.9% of the total sequences (Foster
2018).
In contrast, whole genome sequencing revealed that the microbial profile was dominated by
Proteobacteria (90%), Bacteroidetes (3.35%), Actinobacteria (2.71%) and Cyanobacteria
(1%). The most abundant genera were Rhodobacter (9.93%, Rhodobacterales), Acidovorax
(5.45%, Burkholderiales), Erythrobacter (4.12%, Sphingomonadales), Polaromonas (3.42%,
Burkholderiales), Pseudomonas (2.58%, Pseudomonadales) and Burkholderia (2.29%,
Burkholderiales) (Supplementary Figure 5.3b). Sequences affiliated to eukaryotic genes
represented 0.9% relative abundance.
Microbial diversity on the auxiliary outdoor spent fuel storage pond (Aux)
Three samples were taken at three different operational times; Aux01 (May 2016), Aux02
(June 2017) and Aux03 (September 2017). The 16S rRNA community profile revealed that
the microbial composition from the sample taken on May 2016 (Aux01) was dominated by
Bacteroidetes (40.1%) and the most abundant genera identified were Algoriphagus (12.5%),
Porphyrobacter (11.6%) and Prosthecobacter (7%). The sample Aux02 (June 2017) contained
a large proportion of unidentified OTUs (32.2%); the remaining OTUs were ascribed to genera
Flavobacterium (18.8%), Verrumicrobia (12%), Limnohabitans (9.7%) and Polynucleobacter
(7.9%). Finally, a sample taken on September 2017 (Aux03) also contained a large proportion
of unidentified OTUs (28.8%); the remaining OTUs were affiliated to Polynucleobacter (15.9%)
and contrasting to the previous auxiliary samples, members affiliated with the phylum
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Cyanobacteria (affiliated to genus Cyanobium and unidentified Cyanobacteria) represented a
major component (up to 25%) exclusively in sample Aux03 (Supplementary Fig 5.5a).
A whole genome sequencing approach identified a community profile that was dominated by
Proteobacteria (58.5%), Bacteroidetes (18.13%), Cyanobacteria (5.8%), Actinobacteria (4%)
and Verrumicrobia (2.43%) (Supplementary Figure 5.3b). The most abundant genera were
Polynucleobacter (6.8%, Burkholderiales), Erythrobacter (4.69%, Sphingomonadales),
Flavobacterium (3.8%, Flavobacteriales), Synechococcus (3%, Chrococcales), Algoriphagus
(2.39%, Cytophagales) and Acidovorax (2.33%, Burkholderiales) (Supplementary Fig 5.5b).
Overall, the use of targeted PCR 16S rRNA amplification and sequencing versus
metagenomic sequencing gave results that were similar for each of the sites at the phylum,
class and order levels, but identification to the genus level differed (see Supplementary
Figures 5.3, 5.4 and 5.5 for more information).
Figure 5.2 shows the microbial distribution at the order level. The most abundant orders were
Burkholderiales, Sphingomonadales, Xanthomonadales, Flavobacteriales and
Nitrosomonadales. Organisms affiliated with photosynthetic Cyanobacteria (Synechococcales
and Croococcales) were identified exclusively with the open pond samples (OUT).
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Figure 5.2 Microbial distribution at order level targeting the 16S rRNA gene. Only components
that represented relative abundance higher than 1.5% are shown
Microbial diversity of eukaryotic organisms Although eukaryotic organisms, including fungi, were not major contributors, their presence
was detected targeting the 18S rRNA gene, was exclusively detected in the open-air ponds
(detailed information shown in Supplementary Figure 5.6a). There was a greater difference
between the Eukaryotic profiles obtained from targeted PCR amplifications and sequencing
versus metagenomic sequencing of DNA from the open-air ponds
Metagenomics sequencing revealed the presence of eukaryotic sequences in the indoor
ponds; however, the relative abundance represented less than 1.5%. The most abundant
class identified by metagenomics on the open-air ponds were associated to
Oligohymenophorea, Saccharomycetes, Euromycetes and Bacillaryiophyceae
(Supplementary Figure 5.6b).
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
Others
Rhizobiales
Acidimicrobidae
Actinomycetales
Caulobacterales
Synechococcales
Planctomucetales
UnclassifiedPlanctomycetes
Unidibacterium
Verrucomicrobiales
UnclassifiedCyanobacteria
Flavobacteriales
Sphingobacteriales
Rhodobacteriales
Rhodospirillales
Sphingomonadales
Nitrosomonadales
UnclassifiedVerrumicrobia
Xanthomonadales
Unidentified
Burkholderiales
Sharon L. Ruiz Lopez PhD Thesis
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Functional classification
To have a better insight into functional diversity metagenomes were uploaded to the MGRAST
server to annotate functional genes. More than 80% of the total reads were annotated
following the standard MGRAST features; detailed information is shown in Supplementary 5.7.
MG-RAST platform was used to correlate specific categories with the corresponding
organisms where functional genes were more abundant. Since this approach to whole
genome sequencing may not show the precise taxonomic description at genus level,
functional genes were correlated with the appropriate organism at the order level.
Relative abundances of sequences were assigned to a subsystem (SEED, level 1). Relative
abundance of functional genes detected within the indoor alkaline pond (INP) and the open-
air ponds, FGMSP and auxiliary (Aux), is shown in Supplementary information (Figure 5.8 and
Figure 5.9). Overall 45% of the identifiable genes were associated with clustering-based
systems, functional coupling evidence but unknown function (~12%), carbohydrates (~10%),
amino acids and derivatives (~10%), protein metabolism and cofactors (~8%) and vitamins,
prosthetic groups and pigments (~6.3%).
The subsystems Approach to Genome Annotation SEED (Overbeek et al. 2005) level 1
functional genes were standardized to create a heatmap (Figure 5.3) in order to identify the
contrasting differences among the sampling sites and times. Functional genes related to
membrane transport, carbohydrates, stress response, potassium metabolism and motility and
chemotaxis were more abundant on the indoor pond and feeding tank area (INP_FT, Oct 16).
Genes related to DNA metabolism, respiration, cell division and cell cycle and regulation and
cell signalling were more abundant on the main indoor pond and related subponds (INP_MP,
Oct 17 and INP_SP, Jan 18). Genes related to nucleosides and nucleotides, metabolism of
aromatic compounds, fatty acids, lipids and isoprenoids, respiration and motility and
chemotaxis were the most relatively abundant on the legacy alkaline open pond FGMSP
(OUT_FGMSP_Sept17). Finally, genes related to photosynthesis, miscellaneous, secondary
metabolisms, dormancy and sporulation, nucleosides and nucleotides, RNA metabolism and
protein metabolism were more abundant in the outdoor auxiliary pond. Although the functional
genes were consistent in the Auxiliary pond through the sampling times, genetic differences
Sharon L. Ruiz Lopez PhD Thesis
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were found relating to phages and transposable elements that were more abundant in sample
Aux01 (May 16), while genes related to nucleosides and nucleotides were more abundant in
sample Aux02 (Jun 17), and genes related to cell wall and capsule were more relatively
abundant on sample Aux03 (Sept 17).
Figure 5.3 Functional categories associated to Level 1 subsystems (Level 1, KEGG) among the sampling sites and times
Comparative analysis revealed contrasting differences in the relative abundance of key genes
related to respiration, DNA metabolism, photosynthesis and stress response; hence specific
functions at level 3 from the KEGG database, and their associations with microorganisms
(SEED database) were further analysed as follows:
Sharon L. Ruiz Lopez PhD Thesis
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Respiration
The relative abundance of genes related to respiration were also analysed using the MGRAST
server (Fig 5.4). The respiratory complex I was a major component on all sampling times and
sites. Specific genes related to hydrogenases and [Ni-Fe]-hydrogenase maturation process
were most highly represented in the main and subpond samples (INP_MP, Oct 17 and
INP_SP, Jan 18) and the legacy FGMSP (Sept 17).
Figure 5.4 Relative abundance of genes related to respiration processes (level 3 subsystems, KEGG database)
Genes related to hydrogenases were mostly detected in the samples from the main and
subponds (INP_MP and INP_SP) and also the FGMSP samples, and were primarily
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_SP02_Jan18
OUT_FGMSP_Sept17
OUT_Aux01_May16
OUT_Aux02_Jun17
OUT_Aux03_Sept17
Relativeabundance(%
)
Respiratory_Complex_I F0F1-type_ATP_synthase
Terminal_cytochrome_C_oxidases Hydrogenases
Formate_hydrogenase Biogenesis_of_c-type_cytochromes
Respiratory_dehydrogenases_1 Ubiquinone_Menaquinone-cytochrome_c_reductase_complexes
Anaerobic_respiratory_reductases Succinate_dehydrogenase
Soluble_cytochromes_and_functionally_related_electron_carriers NiFe_hydrogenase_maturation
Biogenesis_of_cytochrome_c_oxidases Quinone_oxidoreductase_family
Sharon L. Ruiz Lopez PhD Thesis
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associated with organisms from order Burkholderiales (Proteobacteria) (Supplementary
Figure 5.10a). Similar results were observed when analysing genes related to [NiFe]-
hydrogenases maturation process, which were also more abundant in the main and subponds
and FGMSP. Again, these were associated to order Burkholderiales and Rhizobiales
(Proteobacteria) (Supplementary Figure 5.10b). It is interesting to note that the Genus
Hydrogenophaga that featured heavily in these samples (from targeted 16S rRNA gene
amplification and sequencing) is a member of the order Burkholderiales.
Photosynthesis
Relative abundance of genes associated to photosynthesis was mainly identified on the open-
air ponds (FGMSP and Aux) (Fig 5.5). Genes associated to proteorhodpsin, a light dependent
proton pump that is has a key role on the metabolism of aquatic organisms, was the detected
on all the samples including indoor systems. Functional genes associated to Photosystem I
and Photosystem II were exclusively detected on the auxiliary pond (OUT_Aux). Genes
related to photosystem I and photosystem II were mostly associated to the taxonomic orders
Chroococcales (Cyanobacteria) and Eupodiscales (Bacillariophyta, Eukaryota)
(Supplementary Figure 5.11).
Sharon L. Ruiz Lopez PhD Thesis
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Figure 5.5 Relative abundance of genes related to photosynthesis (level 3 subsystems, KEGG
database)
DNA metabolism
Genes related to DNA metabolism were relatively more abundant on the indoor pond (INP)
(Fig 5.6). Level 3 subsystems analysis revealed that functions related to general bacterial DNA
repair mechanisms were consistent at all sampling times and sites. Specific genes associated
to DNA base excision repair, CRISPRs and restriction-modification repair mechanisms were
notably more abundant in the areas where exposure to radioactive spent fuel material would
be continuous (e.g. the main ponds, INP_MP; subponds, INP_SP and OUT_FGMSP).
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
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(%
)
Bacterial_light-harvesting_proteins Phycobilisome
Photosystem_II-type_photosynthetic_reaction_center Photosystem_II
Photosystem_I Proteorhodopsin
Sharon L. Ruiz Lopez PhD Thesis
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Figure 5.6 Relative abundance of genes related to DNA repair functions at level 3 subsystems (KEGG database)
Again, the genes that were associated with bacterial DNA repair were affiliated mainly to the
order Burkholderiales in the feeding tank area (INP_FT). In the main and subponds (INP_MP
and INP_SP) these DNA repair genes, and also DNA base excision repair, CRISPRs and
restriction modification systems, were also affiliated to the order Burkholderiales and also the
Sphingomonadales and Xanthomonadales, Hydrogenophilales, Alteromonadales,
Desulfuromonadales and Pseudomonadales.
Genes related to DNA metabolism were less abundant in the open-air pond samples. Once
again, functional genes related to DNA metabolism that were detected on the FGMSP were
mainly associated with the order Burkholderiales (Supplementary Figure 5.12).
0
1
2
3
4
5
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(%
)
CRISPRs DNA_topoisomerases,_Type_I,_ATP-independentDNA_repair,_bacterial_RecFOR_pathway DNA_repair,_bacterial_UvrD_and_related_helicasesDNA_topoisomerases,_Type_II,_ATP-dependent DNA_repair,_bacterial_MutL-MutS_systemDNA_Repair_Base_Excision Type_I_Restriction-ModificationRestriction-Modification_System DNA_repair,_UvrABC_systemDNA_repair,_bacterial DNA-replication
Sharon L. Ruiz Lopez PhD Thesis
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Stress response
Genes associated to stress response were primarily identified in the feeding tank area
(INP_FT, Oct 16) where the oligotrophic water is purged (Figure 5.7). Functional genes related
to bacterial hemoglobins were more abundant and were mostly associated to organisms from
order Burkholderiales (Supplementary Figure 5.13).
Figure 5.7 Relative abundance of genes related to stress response (level 3 subsystems, KEGG database)
Further differences on relative abundance were found on genes related to motility and
chemotaxis, cell wall and capsule, potassium metabolism and membrane transport, and will
be discussed later in this paper (Supplementary, Figure 5.14 to 5.17).
0
0.5
1
1.5
2
2.5
3
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(%
)
Heat_shock_dnaK_gene_cluster_extended Oxidative_stress
Bacterial_hemoglobins Glutathione:_Biosynthesis_and_gamma-glutamyl_cycle
Protection_from_Reactive_Oxygen_Species Regulation_of_Oxidative_Stress_Response
Glutathione:_Non-redox_reactions Choline_and_Betaine_Uptake_and_Betaine_Biosynthesis
Hfl_operon Periplasmic_Stress_Response
Synthesis_of_osmoregulated_periplasmic_glucans Glutathione:_Redox_cycle
Glutathione-dependent_pathway_of_formaldehyde_detoxification Acid_resistance_mechanisms
Uptake_of_selenate_and_selenite
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Discussion
Microbial diversity The microbial diversity of nuclear regulated sites has been the focus of several studies using
both culture dependent and molecular (DNA-based) tools (REFS). This has included spent
fuel storage ponds, although typically at circumneutral or mildly acidic pH values (pH 4.0 to
8.0) (ADD REFS), contrasting with the contrasting to the hyper-alkaline indoor (INP) and open-
air ponds (FGMSP and Aux) at Sellafield studied here. Physical conditions vary; for instance
pH is not usually considered a relevant parameter hence is not controlled (Chicote et al.
2005;Chicote et al. 2004;Masurat et al. 2005;Santo Domingo et al. 1998;Sarró et al.
2005;Tišáková et al. 2012). On studies where pH was measured it ranged from 4.0 to 8.0
(Bagwell et al. 2018;Bruhn et al. 2009;Chicote et al. 2004;Karley et al. 2018;Silva et al. 2018a);
contrasting to the hyper-alkaline habitat handled on the indoor (INP) and open-air ponds
(OUT) at Sellafield.
The microbial diversity of nuclear regulated sites has been the focus of several studies using
both culture dependent and molecular (DNA-based) tools (REFS). This has included spent
fuel storage ponds, although typically at circumneutral or mildly acidic pH values (pH 4.0 to
8.0) (ADD REFS), contrasting with the contrasting to the hyper-alkaline indoor (INP) and open-
air ponds (FGMSP and Aux) at Sellafield studied here. Physical conditions vary; for instance
pH is not usually considered a relevant parameter hence is not controlled (Chicote et al.
2005;Chicote et al. 2004;Masurat et al. 2005;Santo Domingo et al. 1998;Sarró et al.
2005;Tišáková et al. 2012). On studies where pH was measured it ranged from 4.0 to 8.0
(Bagwell et al. 2018;Bruhn et al. 2009;Chicote et al. 2004;Karley et al. 2018;Silva et al. 2018a);
contrasting to the hyper-alkaline habitat handled on the indoor (INP) and open-air ponds
(OUT) at Sellafield.
The first part of this study focused on the taxonomy of the microorganisms present in the pond
systems. The compositions of the microbial communities in three sampling sites were similar
at the phylum, class and order levels, although there were clear differences when the rRNA
gene data were investigated at the family and genus levels. The differences observed using
both sequencing techniques employed here may derive from experimental parameters
(differences in sampling, amplification or sequencing technologies) or, most likely, the
Sharon L. Ruiz Lopez PhD Thesis
145
classification and binning processes used during targeted 16S rRNA amplification of
metagenomic sequencing and analyses. Furthermore the SSU rRNA sequence datasets are
widely described (more than 1 million sequences, e.g. SILVA) whilst whole genome databases
for comparison and annotation are smaller and are still being developed (e.g. SEED, KEGG)
(Steven et al. 2012). To facilitate comparisons across these datasets, taxonomic data was
appraised at the order level.
Overall organisms affiliated with the order Burkholderiales (Betaproteobacteria) were the most
abundant component in all sampling sites at all times. Members from this order have also
been detected in in spent fuel waste containers (Vazquez-Campos et al. 2017) (Ahn et al.
2019), and are involved in radionuclide immobilization (Dhal et al. 2011). It is interesting
though that in majority of previous studies the lack of nutrients and hyper-alkalinity were not
limiting factors. In this study we noted that organisms from the order Burkholderiales,
specifically members affiliated with the genera Hydrogenophaga, Methylotenera and
Curvibacter, were able to adapt to the extreme environments studied here. Similar behaviour
was observed with organisms affiliated with the order Sphingomonadales and
Sphingobacteriales (of the phylum Bacteroidetes), also previously associated with uranium-
contaminated soils and sediments (Ellis et al. 2003) (Reardon et al. 2004).
More widely, the presence of heterotrophic bacterial groups including members of the
Actinobacteria, Bacteroidetes, Acidobacteria and Proteobacteria have been reported widely
in a wide range of radioactive sites (Chicote et al. 2005;Chicote et al. 2004;Masurat et al.
2005;Santo Domingo et al. 1998;Sarró et al. 2005;Tišáková et al. 2012) (Bagwell et al.
2018;Bruhn et al. 2009;Chicote et al. 2004;Karley et al. 2018;Silva et al. 2018a). In contrast,
cyanobacteria have been typically noted a lower relative abundance in such sites, and similar
results were noted in this study where Cyanobacteria represented less than 2% on most of
the samples, except on open-air pond sample Aux03 (Sept 17) where Cyanobacteria
constituted 24% of the total diversity, presumably in response to the higher light levels in this
environment.
Along with a significant prokaryote population, eukaryotes were detected exclusively on the
open-air ponds (Out_FGMSP and Auxiliary pond). The presence of eukaryotic organisms has
Sharon L. Ruiz Lopez PhD Thesis
146
been detected in other radioactive environments (Jung et al. 2016) (Rivasseau et al. 2013)
(Foster 2018), (MeGraw et al. 2018) and multiple adaptation strategies have been identified.
Microalgae are capable to strongly accumulate radionuclides such as 238U, 137Cs, 110Ag, 60Co,
54Mn, 65Zn and 14C and can also display DNA repair mechanisms (Rivasseau et al. 2013)
(Krejci et al. 2011) (Adam and Garnier-Laplace 2003) (Garnier and Baudin 1989). Members
from family Chrysophyceae were highly abundant in the auxiliary pond, and accumulate
carotenoids and xanthophylls to protect themselves against ionising radiation (Korbee et al.
2012) (Demmig-Adams and Adams 2006). Likewise the production of mycosporine-like amino
acids, play an important role in protection against UV radiation in photosynthetic eukaryotes
present in lichens (Karsten et al. 2005) (Karsten et al. 2007) (Ragon et al. 2011). The fungal
classes Dothideomycetes, Aphelida and Glomeromycetes were found solely on the open
ponds. Members from class Dothideomycetes have been found in highly radioactive sites
such as the old nuclear plant at Chernobyl (Dadachova and Casadevall 2008). Indeed,
Zhdanova et al (Zhdanova et al. 2004) proposed that beta and gamma radiation in the
Chernobyl site promote growth of hyphae on fungal species affiliated to Dothideomycetes.
Microbial diversity identified in this study suggest that challenging environmental conditions
such as low nutrient content, hyper alkalinity and presence of radioactivity does not prevent
colonisation by diverse microbial communities.
Adaptation to extreme environments The second part of this study was to assess the metabolic potential of the pond microbiomes
using a metagenomic approach. Our hypothesis was that the functional components of the
microbial communities would change in response to conditions, for example energy sources,
in the open-air and indoor systems.
Feeding tank (INP_FT) The hyper-alkalinity on the INP_FT area (pH 11.6) may generate a range of cellular
responses. Genes related to membrane transport were more abundant in the INP_FT area,
specifically genes related to ABC transporters for branched-chain amino acids. The leucine,
isoleucine, valine (LIV) ABC branched-chain amino acids transporters (belonging to the polar
amino acid transport family) are believed to be important for alkaliphily, due to the ability to
convert leucine, isoleucine and valine to L-glutamate which is negatively charged at pH values
Sharon L. Ruiz Lopez PhD Thesis
147
higher than it pKa (3.9 or 4.3). The accompanying proton produced could contribute to the
acidification of the bacterial cytoplasm to maintain the internal pH between 8.0 and 8.5
(Takami et al. 2002). These ABC transporters are also thought to be coupled with potassium
metabolism, that helps regulate proton-potassium exchange, which would help maintain
internal cellular homeostasis in this alkali (NaOH) dosed environment (Padan et al. 2005). In
addition genes related to bacterial hemoglobins (Figure 7) were more abundant on the feeding
tank area than the other sampling sites. Bacterial hemoglobins (Hb) belong to the superfamily
of haemoglobin-like proteins and have the ability to reversibly bind oxygen (Hardison 1996).
Although Hb has been mainly found on mammals, recent findings reveal its presence on non-
vertebrates, plants and bacteria (Bollinger et al. 2001). The presence of Hb on bacteria has
been analysed for its potential use in improving cell growth and productivity under oxygen
limitation, and their increased abundance may be an adaptive response to oxygen limitation
within the INP_FT pond, although this requires further investigation.
Indoor alkaline pond: main ponds (INP_MP) and subponds (INP_SP)
The indoor pond INP is fed with demineralized water from the feeding tank that has been pH
is adjusted (see methods section) to 11.6; this with stored spent fuel material result in a unique
oligotrophic, hyper-alkaline and radioactive environment. Standardized data showed that the
most abundant genes were related to biochemical regulation and energy metabolism.
Genes related to respiration were predominant on the indoor pond, and here the use of
hydrogen as an electron donor for metabolism is of particular relevance given the potential for
radiolytic hydrogen production from water in the ponds (Libert et al. 2011). Functional
annotation showed that hydrogenases and [NiFe]-maturation systems were associated with
the INP pond. Hydrogen metabolism is carried on by NiFe-containing hydrogenase that
catalyse the reversible oxidation of molecular hydrogen according to the reaction H2↔ 2H+2e-
and play a crucial role in microbial energy metabolism (Vignais, 2001; Vignais 2004).
Depending on the environment, [NiFe]-hydrogenases have are used to either oxidize H2 as a
source of energy or produce the gas as a means of disposing of excess reducing equivalents.
Sharon L. Ruiz Lopez PhD Thesis
148
Clearly the former process is likely to support hydrogen-oxidising microbial pioneer species in
the indoor pond system. Interestingly, the genes encoding hydrogenases and related [NiFe]-
hydrogenase maturation systems were associated with the microbial order Burkholderiales,
and in this group genera including Hydrogenophaga and Polynucleobacter (Supplementary
Fig 7), have the clear potential for hydrogen-oxidation. This has led to the colonisation of high
pH hydrogen-rich environments including serpentinisation systems (Brazelton et al. 2012)
(Suzuki et al. 2014), and from this study most likely spent nuclear fuel.
In addition to respiration functions, genes related to DNA metabolism were more frequent on
the indoor pond than other sites (Figure 6). Level 3 subsystems (KEGG database) revealed
that genes involved in DNA general repair, DNA repair base-excision, restriction-modification
system and CRISPRs were largely detected on the INP_MP and INP_SP. DNA repair is
considered a key strategy used by microorganisms to survive high radiation fluxes (Pettijohn
and Hanawalt 1964). Radiation affects a wide range of cellular biomolecules, including
proteins, lipids and nucleic acids directly (e.g. ionizing particles interacting with
purine/pyrimidine base) or indirectly (e.g. formation of reactive oxygen species, ROS, through
radiolysis of water) (Jung et al. 2017). However, since DNA is a permanent copy of the cell
genome, alterations in its structure are of potentially greater consequence compared to other
cell components such as RNAs or proteins (Cooper 2000).
Along with well-known DNA repair strategies (e.g. reversal of base damage; restriction-
modification system (RM) and base excision repair (BER)) (Friedberg et al. 2006) (Wilson
1991) (Raleigh and Brooks 1998) (Zhao et al. 2005) (Murray 2000), clustered regularly
interspaced short palindromic repeats (CRISPR) and accompanying Cas proteins represent
a newly identified system of relevance (Reeks et al. 2013). CRISPR-Cas are DNA-encoded,
RNA-mediated defence system that provide sequence-specific recognition, targeting and
degradation of exogenous nucleic acid (Barrangou 2015). Initial insights suggested that the
CRISPR-Cas function was mainly for antiviral defence, however recent studies have revealed
that they also play critical roles beyond immunity, including endogenous transcriptional control
and regulation of bacterial phenotype to help to adapt to environmental stresses (Barrangou
2015) (Sorek et al. 2013). For example, several studies have shown that the CRISPR-Cas
Sharon L. Ruiz Lopez PhD Thesis
149
system genes are induced in bacteria and archaea in response to external abiotic stimuli such
as UV light and ionizing radiation (Gotz et al. 2007) (Sorek et al. 2013) and in response to
internal cellular stress (e.g. from oxidative stress) (Strand et al. 2010) (Sorek et al. 2013).
Clearly the surprising increment of CRISPR-Cas systems in indoor spent nuclear fuel ponds
and could represent an unexpected and novel mechanism supporting colonisation of the
ponds
Outdoor (FGMPS and Auxillary) legacy ponds
The legacy alkaline First Generation Magnox Storage Pond (FGMSP) is an open-air pond
system that periodically accepts waters from the upstream INP, and discharges “purge” waters
to a radionuclide removal plant (SIXEP) prior to release. A key difference between the INP
environments and the FGMSP and the Auxillary pond (which is a closed system and can
overflow to FGMSP) is light availability, which results in both pond systems being prone to
algal blooms. In both of these systems there was a relative enrichment of photosynthetic
genes, although this was most marked in the Auxillary pond, consistent with build up of
eukaryotic photosynthetic organisms in the closed pond system. The single FGMSP sample
that was available for analysis was obtained during a purge period when algal blooms were
not visible in the pond, and hence it is not surprising that the levels of photosystem I and II
genes were very low. This sample shared, with the indoor pond systems, low levels of genes
encoding Proteorhodopsin, a light-driven H+ pump which can be present in a wide range of
microorgansims, including the proteobacteria present in the samples.
Although there were clearly similarities in the prokaryotic communities in the nuclear fuel
ponds, including the persistence of hydrogen-utilising members of the Burkholderiales (and
the detection of hydrogense genes), it was notable that eukaryotes were exclusively
associated with the outdoor ponds, and this included fungal components. This would seem to
imply a richer diversity of heterotrophs linked to primary productivity of ingress of external
organic sources. A more detailed assessment of the complex microbial communities within
this external pond systems should include analyses through microbial bloom events, and be
Sharon L. Ruiz Lopez PhD Thesis
150
linked to biogeochemical changes within the pond. This work is ongoing. Another area
obvious area of interest should be the potential role of the microbes within these pond systems
in mediating the solubility and fate of key radionuclides. For example bioaccumulation of
radionuclides (Cs and 90Sr) has previously been observed in a circumnetral spent fuel storage
pond on the Sellafield site (MeGraw et al. 2018), with immobilisation of 90Sr as an insoluble
carbonate linked to photosynthesis in other studies (Lee et al. 2014). It should be noted that
fungi are also well known to immobiilise a wide range of radionuclides (Gadd, 2016) via a
range of mechanisms, and they could play a role in these process in the outdoor ponds, to
augment prokaryotic radionuclide removal processes (Lloyd, 2002) expected in the indoor
ponds. These observations stress the importance of a systems wide knowledge of the
complex microbial communities present in the pond systems described here.
In summary these studies confirm a role in culture-independent DNA-based studies in
characterising complex microbial communties within highly radioactive microbial
environments. High-throughput sequencing of purified DNA targeting, for example
phylogenetic (16S and 18S rRNA) marker genes, give a good indication of diversity, which
can be further interrogated using metagenomic tools that elucidate potential functionality.
Although extracting DNA from active samples on nuclear regulated sites is challenging, these
proof of concept studies illustrate the potential for multi-omics studies on such unique sites in
the future, targeting in due course RNA and expressed proteins to probe microbial processes
in situ. Our studies have shown that key organisms, and the likely sources or energy that they
use in the pond systems can be identified with current technologies. These data not only
extend our knowledge of the microbial ecology of extreme environments, but will ultimately
prove useful in understanding the impact of microbial processes on nuclear waste materials,
and designing robust control measures that can be adopted to control microbial growth if
required.
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151
Acknowledgements
SRL acknowledges financial support from a PhD programme funded by the National Council
of Science and Technology (CONACyT). This work was also supported by funding from
Sellafield Limited and the Royal Society to JRL. LF was supported by an EPSRC CASE PhD
and IAA funding.
Sharon L. Ruiz Lopez PhD Thesis
152
Supplementary information
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_
June17
OUT_Aux03_
Sept17
Archaea 0.128192 0.139049 0.097097 0.107867 0.148901 0.137643 0.109451 0.242143 0.180112 0.262177
Bacteria 98.29654 98.50063 98.92678 98.82252 98.76924 98.61816 99.03582 93.42333 95.26092 94.97394
Eukaryot
e 1.543045 1.326681 0.95428 1.02287 1.060243 1.231552 0.812098 6.051417 4.425204 4.637322
Others 0.032222 0.033644 0.021847 0.046742 0.021616 0.012648 0.042634 0.283112 0.133767 0.126558
Supplementary 5. 1 Microbial distribution at kingdom level, whole genome sequencing
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_June17
OUT_Aux03_Sept17
Relativeabundance
Others
Eukaryota
Bacteria
Arcahea
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Supplementary 5. 2 Microbial distribution at phylum level filtered by eukaryotic on the indoor
pond INP by whole genome sequencing
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
INP_FT01_Oct16
INP_FT02_Oct16
INP_MP01_Oct17
INP_MP02_Oct17
INP_SP01_Jan18
INP_SP02_Jan18
Relativeabundance(%
)
Others
Apicomplexa
Basidiomycota
Chlorophyta
Arthropoda
Ascomycota
Bacillariophyta
Streptophyta
Cnidaria
unclassified(derivedfromEukaryota)
Chordata
Sharon L. Ruiz Lopez PhD Thesis
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a)
b)
Supplementary 5. 3 Microbial distribution at phylum level a)by 16S rRNA gene and b)by 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_02_Jan18
OUT_FGMSP_Sept17
OUT_Aux01_May16
OUT_Aux02_Jun17
OUT_Aux03_Sept17
Realtiveabundance
Unidentified
Gemmatimonadetes
Acidobacteria
Armatimonadetes
Firmicutes
Planctomycetes
Deinococcus-Thermus
Cyanobacteria
Verrucomicrobia
Others
Actinobacteria
Bacteroidetes
Proteobacteria
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
ElusimicrobiaFibrobacteresCandidatusPoribacteriaDictyoglomiTenericutesChrysiogenetesDeferribacteresSynergistetesFusobacteriaLentisphaeraeThermotogaeNitrospiraeunclassified(derivedfromBacteria)ChlamydiaeAquificaeSpirochaetesGemmatimonadetesDeinococcus-ThermusChloroflexiChlorobiAcidobacteriaPlanctomycetesFirmicutesVerrucomicrobiaCyanobacteriaActinobacteriaBacteroidetesProteobacteria
Sharon L. Ruiz Lopez PhD Thesis
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a)
b)
Supplementary 5. 4 Microbial distribution at order level a)by 16S rRNA gene and b)by whole genome sequencing. Only components that represented more than 1.5% relative abundance
are shown
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
OthersRhizobialesAcidimicrobidaeActinomycetalesCaulobacteralesSynechococcalesPlanctomucetalesUnclassifiedPlanctomycetesUnidibacteriumVerrucomicrobialesUnclassifiedCyanobacteriaFlavobacterialesSphingobacterialesRhodobacterialesRhodospirillalesSphingomonadalesNitrosomonadalesUnclassifiedVerrumicrobiaXanthomonadalesUnidentifiedBurkholderiales
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(%
)
OthersVerrucomicrobialesPlanctomycetalesEnterobacterialesChromatialesAlteromonadalesSphingobacterialesChroococcalesCaulobacteralesRhodocyclalesXanthomonadalesPseudomonadalesActinomycetalesCytophagalesRhodospirillalesFlavobacterialesRhodobacteralesMethylophilalesRhizobialesSphingomonadalesBurkholderiales
Sharon L. Ruiz Lopez PhD Thesis
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a)
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
Fluviimonas
Siphonobacter
Methylophilus
Gemmata
UnclassifiedPlanctomycetes
Unidibacterium
Novosphingobium
Prosthecobacter
UnclassifiedCyanobacteria
Rhodobacter
Mongoliitalea
Polaromonas
Roseococcus
Sediminibacterium
Others
Flavobacterium
Limnohabitans
Polynucleobacter
UnclassifiedVerrumicrobia
Silanimonas
Algoriphagus
Cyanobium
Porphyrobacter
Methylotenera
Belliella
Nevskia
Unidentified
Rhodoferax
Curvibacter
Hydrogenophaga
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b)
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
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Supplementary 5. 7 Total sequences annotated using the MGRAST web server
Sample Number of
sequences
basepairs Average
length
Predictable
feature (%)
Unknown
(%)
Failed QC
(%)
INP_FT01 3,296,982 491,844,282 151 bps 89.47 2.59 7.95
INP_FT02 3,956,936 597,497,185 151 bps 90.68 2.79 6.53
INP_MP01 3,826,923 577,865,373 151 bps 81.18 3.23 15.59
INP_MP02 3,782,419 571,145,269 151 bps 80.64 3.11 16.24
INP_SP01 3,874,693 585,078,643 151 bps 88.22 3.10 8.69
INP_SP02 3,378,803 510,199,253 151 bps 87.80 2.98 9.22
OUT_FGMSP 3,779,537 570,710,087 151 bps 83.31 2.07 14.62
OUT_Aux01 3,086,795 466,106,045 151 bps 86.12 3.59 10.29
OUT_Aux02 3,779,537 570,710,087 151 bps 83.31 2.07 14.62
OUT_Aux03 3,167,140 478,238,140 151 bps 82.11 1.96 15.93
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Supplementary 5. 8 Relative abundance of genes at Level 1 subsytems (KEGG database)
INP_FT01_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
Clustering-
based
subsystems 11.57988 11.40728 12.1312 11.97963 12.11747 11.78454 12.06242 12.75073 12.84559 12.78287
Carbohydrate
s 11.25289 11.19929 9.559558 9.536645 9.103018 9.096713 10.87795 10.78554 10.35405 9.829491
Amino Acids
and
Derivatives 10.37377 10.77546 10.09817 10.07184 10.05988 10.15335 10.22522 9.548668 9.701429 9.323116
Protein
Metabolism 6.907944 6.669388 8.98125 9.038336 9.33315 9.188847 7.804932 9.061273 9.412656 9.734391
Cofactors,
Vitamins,
Prosthetic
Groups,
Pigments 5.453456 5.654813 6.7217 6.207845 6.273371 6.386887 6.593794 6.339342 6.255422 6.685028
Miscellaneou
s 6.456245 6.524691 5.935402 6.144818 6.324909 6.332219 6.245776 6.221555 6.442969 6.850702
DNA
Metabolism 4.176114 3.983526 6.365796 6.356389 5.968745 6.128186 4.883953 4.996441 4.896336 4.977645
Respiration 4.149566 3.989669 4.513845 4.501116 4.643291 4.675309 4.444791 4.803407 4.172296 4.233963
Cell Wall and
Capsule 4.196144 4.102436 4.085014 4.263478 4.411916 4.42554 3.895859 3.889541 4.53769 4.997406
RNA
Metabolism 3.555205 3.55275 4.038005 4.296081 4.419227 4.46069 3.701233 4.25466 4.434356 4.679114
Membrane
Transport 4.311879 4.800213 3.464596 3.272775 3.482834 3.317129 3.674487 3.835671 3.572679 3.210804
Sharon L. Ruiz Lopez PhD Thesis
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Virulence,
Disease and
Defense 4.416466 4.156192 2.666652 2.974054 3.119798 3.248897 3.173316 3.19201 3.6777 3.380889
Nucleosides
and
Nucleotides 3.149423 3.168535 3.087105 3.163838 3.145458 3.169666 3.350588 3.283717 3.519395 3.19563
Fatty Acids,
Lipids, and
Isoprenoids 2.848983 3.252686 2.765499 2.621099 2.549658 2.481644 3.219166 2.714662 2.759505 2.68802
Stress
Response 2.979835 2.793698 2.30592 2.376458 2.238745 2.282986 2.587289 2.319402 2.302024 2.531697
Metabolism
of Aromatic
Compounds 2.475606 2.795153 1.42951 1.273005 1.155632 1.676925 2.421161 1.644531 1.803351 1.287815
Motility and
Chemotaxis 2.620157 2.116533 2.152182 2.260596 2.234067 2.270994 1.641941 0.877097 0.790961 0.632351
Phages,
Prophages,
Transposable
elements,
Plasmids 0.97728 1.083609 1.628694 1.493447 1.430139 1.281016 1.73603 2.317692 1.390156 1.211947
Nitrogen
Metabolism 1.547738 1.533785 1.983462 1.925226 1.815399 1.606791 1.206521 0.87496 0.915664 1.014161
Regulation
and Cell
signaling 1.26147 1.21327 1.525374 1.637477 1.340294 1.397051 1.263436 0.895695 0.968667 1.024571
Phosphorus
Metabolism 1.131279 1.131545 1.294235 1.378596 1.335908 1.161756 1.37655 1.134049 1.136953 1.238765
Sulfur
Metabolism 1.354625 1.261448 0.936343 0.918727 0.977477 1.04018 0.870682 0.773205 0.908635 1.036039
Cell Division
and Cell
Cycle 0.807314 0.8314 0.820099 0.868538 1.004745 0.935806 0.881587 0.938663 1.001846 1.062152
Sharon L. Ruiz Lopez PhD Thesis
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Iron
acquisition
and
metabolism 0.767444 0.738277 0.821235 0.696107 0.668977 0.665857 0.864234 0.961109 0.870394 0.690928
Potassium
metabolism 0.869481 0.844334 0.269555 0.333502 0.469987 0.504169 0.519878 0.452337 0.530869 0.547132
Photosynthes
is 0.119231 0.110018 0.098349 0.083726 0.086702 0.07857 0.180377 0.766792 0.396183 0.689693
Secondary
Metabolism 0.169021 0.208719 0.222618 0.232736 0.212149 0.191379 0.217073 0.26593 0.241253 0.275948
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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
164
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
165
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
166
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
Sharon L. Ruiz Lopez PhD Thesis
167
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
Sharon L. Ruiz Lopez PhD Thesis
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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
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
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(%
)Actinomycetales
Chroococcales
Campylobacterales
Methylophilales
unclassified(derivedfromGammaproteobacteria)
Thiotrichales
Rhodocyclales
Pseudomonadales
Rhizobiales
0
0.5
1
1.5
2
2.5
3
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(%
)
Additional_flagellar_genes_in_Vibrionales
Archaeal_Flagellum
Rhamnolipids_in_Pseudomonas
Control_of_Swarming_in_Vibrio_and_Shewanella_species
Flagellum_in_Campylobacter
Flagellar_motility
Bacterial_Chemotaxis
Flagellum
Sharon L. Ruiz Lopez PhD Thesis
169
Supplementary 5. 14 Relative abundance of genes associated to motility and chemotaxis (level 3, subsytems)
Supplementary 5. 15 Relative abundance of genes associated to cell wall and capsule (level 3, subsystems)
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
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(%
)
Recycling_of_Peptidoglycan_Amino_Acids
LOS_core_oligosaccharide_biosynthesis
Lipid_A-Ara4N_pathway_(_Polymyxin_resistance_)
UDP-N-acetylmuramate_from_Fructose-6-phosphate_Biosynthesis
Alginate_metabolism
Lipopolysaccharide-related_cluster_in_Alphaproteobacteria
dTDP-rhamnose_synthesis
mycolic_acid_synthesis
Rhamnose_containing_glycans
Peptidoglycan_biosynthesis--gjo
Sialic_Acid_Metabolism
Murein_Hydrolases
Lipopolysaccharide_assembly
KDO2-Lipid_A_biosynthesis
Peptidoglycan_Biosynthesis
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Supplementary 5. 16 Relative abundance of genes associated to potassium metabolism (level
3, subsystems)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
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(%
) pH_adaptation_potassium_efflux_system
Hyperosmotic_potassium_uptake
Glutathione-regulated_potassium-efflux_system_and_associated_functions
Potassium_homeostasis
Sharon L. Ruiz Lopez PhD Thesis
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Supplementary 5. 17 Relative abundance of genes associated to membrane transport (level 3,
subsystems)
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(%
)
Type_VI_secretion_systems
Transport_of_Manganese
Twin-arginine_translocation_system
ABC_transporter_alkylphosphonate_(TC_3.A.1.9.1)
Widespread_colonization_island
pVir_Plasmid_of_Campylobacter
ABC_transporter_dipeptide_(TC_3.A.1.5.2)
Multi-subunit_cation_antiporter
Transport_of_Zinc
Tricarboxylate_transport_system
General_Secretion_Pathway
ABC_transporter_oligopeptide_(TC_3.A.1.5.1)
Conjugative_transfer
HtrA_and_Sec_secretion
ABC_transporter_branched-chain_amino_acid_(TC_3.A.1.4.1)
Ton_and_Tol_transport_systems
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6
Research Paper: Metagenomic analysis of viruses in
spent fuel storage ponds at Sellafield, UK
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Chapter 6 Metagenomic analysis of viruses in spent fuel storage ponds
at Sellafield, UK
1S. Ruiz-Lopez, 1S. Nixon, 1L. Foster, 2N. Cole and 1J. Lloyd
1Williamson Research Centre for Molecular Environmental Science, School of Earth and
Environmental Sciences, University of Manchester, Manchester, United Kingdom
2 Sellafield Ltd, Hinton House, Birchwood Park Ave, Birchwood, Warrington WA3 6GR
Corresponding author: [email protected]
Abstract
Development of cultivation-independent methods, including new “omic” techniques have
contributed to a greater understanding of microbial diversity under extreme conditions. Next-
Generation sequencing tools such as metagenomic sequencing and analyses are showing a
wide diversity of viruses, although viral-host interactions remain poorly characterised,
especially in extreme environments such as nuclear storage ponds.
In this study two indoor and outdoor spent fuel storage ponds were analysed. Initial functional
analyses of the recovered metagenome assembled genomes (MAGs) gave valuable insight
to the identification of prokaryotes that colonised the pond, dominated by Proteobacteria, and
predicted the metabolic microbial adaptations to the surrounding environment, where the
metabolism of hydrogen (from radiolysis) represented the main energy source. Further
analyses of the MAGs identified prophages and CRISPR loci within the microbiome. Samples
from the open air ponds (FGMSP and Auxiliary pond) contained the highest amount of phages
(free viral signals) which may allow viral predation to develop.
Identification of CRISPR spacer-repeats arrays predicted the viral immunity response
displayed by these organisms to viral infections, and how these could potentially influence the
structure of the microbial communities and energy flow in the system. Highest abundance of
CRISPR spacer-repeats arrays and prophages (virus integrated to a host) was detected on
the indoor subponds and adjacent pond showing the interaction of host-virus and the CRISPR
defence response is occurring within the microbiome. Overall our findings showed those
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prophages are integrated into major components of the microbial communities, including
Proteobacteria associated with the bacterial order Burkholderiales.
Introduction
Viruses are the most abundant biological entities on the planet plays a key role in driving
microbial evolution and can also influence biogeochemical cycles (Breitbart and Rohwer
2005;Fierer et al. 2007;Parsley et al. 2010;Rodriguez-Brito et al. 2010;Berg Miller et al. 2012).
Viruses of microorganisms (from archaea, bacteria and microbial eukaryotes) can be
responsive to and the source of environmental change (Allen and Abedon 2014), and are
found in wide range of environments including; extreme thermal acidic (Yellowstone National
Park) (Rice et al. 2001), hypersaline (Guixa-Boixareu et al. 1996;Sandaa et al. 2003), alkaline
(Jiang et al. 2004), deserts (Evans and Johansen 2010;Prigent et al. 2005), polar (Maranger
et al. 1994;Borriss et al. 2003;Kepner Jr et al. 2003), deep subsurface sediments (Bird et al.
2001) and extreme thermal environments such as terrestrial hot springs (Rice et al.
2001;Rachel et al. 2002;Prangishvili and Garrett 2005).
Viruses that are parasitic to bacteria, Bacteriophages (phages), can impact on microbial
ecology, leading to dramatic lytic infections or genetic modification by lysogenic disturbances
(Allen and Abedon 2013). In addition, viruses are able to move genetic material between
different hosts and ecosystems (e.g. photosynthetic genes on cyanobacteria and microalgae
(Lindell et al. 2004)) (Rohwer et al. 2009;Lindell et al. 2004) leading to changes in
environmental conditions (Allen and Abedon 2013). Furthermore, viruses play roles in
controlling cellular numbers by facilitating horizontal gene transfer (HGT, the transfer of
genetic material from an organism to another that is not its offspring) (Breitbart and Rohwer
2005;Berg Miller et al. 2012;Aminov 2011) altering bacterial phenotype and selecting phage-
resistant microbes (Breitbart and Rohwer 2005).
However, despite their importance, identification of phages and knowledge of their interactions
with the microbiome is limited due to the challenges associated with virus isolation and
purification (Zheng et al. 2019;Roux et al. 2015b). These include the lack of a universal marker
gene for viruses, the limited available viral databases and the restricted availability of
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bioinformatics tools, which have been mostly developed for prokaryotic genome sequencing
data and not designed for handling metagenomic data (Roux et al. 2015a).
Although phage DNA constitutes 20% of the bacterial genome, most of the functional roles
are not defined (Wang et al. 2010). However, it has been documented that interactions
between bacteria and phages can lead to benefits for the host. For example phages can infect
and protect bacteria against secondary phages, preventing them attaching to the host, a
phenomenon called superinfection exclusion (Obeng et al. 2016). Throughout transduction,
phages can integrate and transfer genes from a previous host, enhancing bacterial
metabolism to improve bacteria survival under challenging environmental conditions (Obeng
et al. 2016) . For instance, stress response in Escherichia coli can be modulated by inserted
phages (Wang et al. 2010). Wang et al (Wang et al. 2010) found that E. coli strains inserted
with phages CPS-53 and CP4-57 were more stable under oxidative, osmotic and acid-stress
conditions, with obvious relevance to the microbiology of nuclear facilities being discussed
here.
More widely, bacteriophages infect bacteria in order to reproduce and usually kill the host cell
when replication is complete (Gasiunas et al. 2014). In response bacteria has evolved multiple
defence mechanisms to interfere with selected phage life cycles, including restriction enzymes
that destroy viral RNA, development of receptors that interfere with virus attachment to the
cell and even by programming cell death (apoptosis) (Gasiunas et al. 2014;Labrie et al. 2010)
(Sturino and Klaenhammer 2006). Most recently the discovery of an adaptive immune system,
known as clustered regularly interspaced short palindromic repeats (CRISPRs), has
revolutionized the study of life sciences. The CRISPR system is a stand-alone adaptive
immune system that targets DNA or RNA, as a way of protecting against viruses and other
mobile genetic elements (Rath et al. 2015;Barrangou 2015;Labrie et al. 2010) (Gasiunas et
al. 2014). It is encoded by one contiguous sequence in the genome known as the CRISPR
locus (Karginov and Hannon 2010). CRISPR loci are constituted by an array of conserved
direct repeats, that are interspersed by non-repetitive spacer sequences typically located
adjacent to a leader sequence and CRISPR-associated genes (Cas) (Sorek et al. 2008).
CRISPR loci are hypervariable sites widely distributed in approximately 50% and 90% of
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sequenced bacterial and archaeal genomes, respectively (Makarova et al. 2011). Analysis of
the CRISPR locus provides crucial information to detect and recover genome sequences from
uncultivated phage, linking phage to their host, providing insight into the impacts of phage on
population and community structure (Andersson and Banfield 2008).
Recent studies have used cultivation-independent DNA sequencing techniques, including
metagenomic sequencing and analyses to characterise the prokaryotic and eukaryotic
components of microbiomes within spent nuclear fuel storage ponds at Sellafield. This study
extends metagenomic analyses to, for the first time; explore host-viral interactions within this
unusual extreme environment.
Methods
Samples
In the present study two spent fuel ponds systems were analysed; (1) an indoor pond (INP)
including its feeding tank area (FT), main ponds (MP), subponds (SP) and adjacent pond (Adj)
and (2) an open-air (OUT) First Generation Magnox Storage pond (FGMSP) and its auxiliary
open-air system (Aux). The presence of microbial blooms has been detected previously in the
FGMSP (Lynne REF here) and Aux ponds, while a stable background population has been
detected in the indoor pond system (chapters X and Y in this thesis).
The pond system is located on the Sellafield nuclear site, Cumbria UK. The INP receives and
stores metal fuel and legacy spent fuel from outdoor ponds (including the FGMSP) for interim
storage pending a long term disposal solution becoming available. The FGMSP receives water
from the INP for a pond purge, which enters the pond at a different location to the main purge
water (Figure 1) (NDA 2015) (ONR 2016). The water supplied to both ponds is similar though,
comprising demineralized water that has been adjusted to pH 11.6 to avoid corrosion of the
stored fuels. The spent fuel ponds represent, therefore, extreme oligotrophic, hyper-alkaline
and radioactive environments.
The Indoor Storage Pond (INP) is an indoor pond complex divided into 3 main ponds and 3
subponds linked by a transfer channel that enables water flow. In order to control the pond-
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water activity and quality, there is a continuous “once through” purge flow; pond-water from
the main ponds flows into the transfer channel and enters the recirculation pump chamber
where it is continuously pumped round a closed circulation loop and through a heat exchanger
system, which cools the pond-water before it is recycled into the main ponds. Through the
control feed, purge and re-circulation flow rates, the water depth is maintained at 7±0.05m.
The purge flow can be either from a donor plant or from other hydraulically linked ponds within
the Sellafield complex (e.g. FGMSP). The temperature and pH are controlled at 15⁰C and 11.6
respectively. Samples for analysis were taken from designated sample points in the “Feeding
Tank” of the donor plant, where the alkali-dosed demineralised water used to feed the complex
is stored, and main ponds 2 and 3 of the Fuel Handling Plant.
The FGMSP is the primary storage pond for legacy Magnox spent fuel at site. The pond is
continuously purged with alkaline dosed demineralised water at a pH of 11.4, from an East to
West direction along the length of the pond, and contains an outflow point, where water is
removed from the pond, on the Western wall. There are two further feeds into the pond, the
first enters the pond at a location along the Northern wall and contains alkaline dosed water
(pH ~11.4) from another fuel handling pond facility on site. The auxiliary settling tank (auxiliary
pond) is directly connected to the FGMSP, and if the water levels are sufficiently high, the
auxiliary pond feeds the alkaline legacy pond legacy pond along the South wall.
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Figure 6.1Storage pond systems. Metal and legacy spent fuels from outdoor ponds are transported to the INP for interim storage pending a long term disposal solution available. The INP is divided in 3 main ponds (MP), 3 subponds and a feeding tank area (FT); waters from the
INP are recirculated to the FGSMP during purging times. The FGMSP and its Auxiliary pond (Aux) store legacy fuel pond (NDA 2015;ONR 2016).
A total of 12 samples were taken from different sites from the storage ponds between 2016
and 2018 (Table 1). Samples were collected from a depth of 1 m using a hose syringe to
withdraw the water into sterile plastic bottles. In order to avoid any risk of contamination,
samples transferred directly from the pond to the NNL Central Laboratories (National Nuclear
Laboratory, Cumbria UK), where DNA was extracted and the samples where checked for
radioactivity in line with the Environmental Permits and Nuclear Site licences held by Sellafield
Ltd. Extracted DNA samples free from significant radionuclide contamination were shipped to
the University of Manchester and stored at -20⁰C until use.
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Table 6.1 Distribution of sample points in the Sellafield complex
Sample Storage pond Conditions Date
A INP, feeding tank area Indoor pond October 2016
B INP, feeding tank area Indoor pond October 2016
C INP, main pond 2 Indoor pond October 2017
D INP, main pond 3 Indoor pond October 2017
E INP, Subpond 2 Indoor pond January 2018
F INP, Subpond 3 Indoor pond January 2018
G INP, adjacent pond Indoor pond April 2017
H INP, adjacent pond Indoor pond April 2017
I Auxiliary pond Open-air system May 2016
J Auxiliary pond Open-air system June 2017
K FGMSP Open-air system September 2017
L Auxiliary pond Open-air system September 2017
Sequencing and sequence processing
DNA extraction was conducted at the Central Laboratories s at NNL on the Sellafield site, from
filtered biomass using a PowerWater DNA Isolation Kit (Mobio Laboratories, Inc., Carlsbad
California, USA). After appropriate radiometric analyses, the DNA was then transported to the
Manchester University laboratories for amplification and preliminary analyses. Metagenomic
sequencing was completed using the Illumina Hiseq2000 platform at Celemics (Celemics, Inc.,
Seoul, Korea).
All sequence reads were processed using the bioinformatic pipeline described in Figure 2.
First, FastQC (Andrews 2010) was used to visualise the quality scores on raw reads. Reads
were processed with Trimmomatic (Bolger et al. 2014) to trim Illumina adaptor sequences and
remove low quality and short reads with ambiguous bases to a quality score of 30 on the
phred33 quality score scale (default parameters). Taxonomic classification of reads was
performed with Kaiju (Menzel et al. 2016) version 1.7.2 using default parameters and viruses,
refseq and progenomes databases.
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Reads were assembled de novo using MEGAHIT (version 1.1.3, default generic parameters)
with the minimum contig length set to 200bp (Li et al. 2015). In order to identify and classify
potential viral sequences, the predicted Megahit contigs were analysed with VirSorter default
parameters (Roux et al. 2015a). All the protein sequences predicted as genes with VirSorter
were used as potential virus amino acid sequences for the following analyses.
Taxonomic identification of viral contigs
To identify the viral and functional gene diversity from each contig, the predicted viral proteins
were compared against the GenBank protein database manually using Blastp. Hits returned
with the specific e-value of 1e-8 and with bit score >60 were considered homologs. Taxonomic
classification for each contig was also done manually with blastn using the NCBI taxonomy ID
for each BLAST hit, and classified to the highest taxonomic level (order or family) based on
the taxonomic information shared by the majority of the genes in each virus contig. The virus
contigs were classified as viral or prophage; categories were assigned based on confidence
determined by VirSorter (categories 1 and 2).
Binning
Assemblies were grouped using the Maxbin 2.0 annotation program and the quality of the bins
was assessed with CheckM (Parks et al. 2015) on pipeline mode SEARCH version 3.2.1
(2018), using a cut-off E value of 1e-5 to identify the best quality bins based on draft quality
(DQ) genomes; >93% completeness and 1<% contamination (detailed binning categories
based on quality score are shown on Supplementary 6.1).
Annotation
Both bins and assemblies were analysed with Prokka (Seemann 2014) to obtain structural
and functional annotation. Prokka pipeline annotates proteins coding genes using Prodigal
(Hyatt et al. 2010a) that identifies the coordinates of candidate genes but does not describe
the putative gene product. Output files were then uploaded to KEGG KASS program on search
program GHOSTX (amino acid query only) using a gene database specific for prokaryotic
organisms a specific set of organisms (gene data sets sce, pfa, eco, pae, bsu, mja, afu, has,
aar, hel, maq, amc, ilo, mac, mmh, mpy, mer, ant, abu, sun, sku, pol, cce, cpe, cac, ckr, hch,
hna, drt, dvu, ade, hal, dar, tbd, gca, gsu, dps, sfu, pde, hma) and the assignment method
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was SBH (single-directional best hit), to obtain structural and functional annotation (Moriya et
al. 2007).
Additionally, annotated bins were analysed with VirSorter (Roux et al. 2015a) to find prophage
signals integrated within them. Bins were also analysed with CRT (Bland et al. 2007) to find
CRISPR arrays and analysed with CRASS (Skennerton et al. 2013) on Geneious R8 (Kearse
et al. 2012) to predict the number and diversity of CRISPR loci based on repeat and spacer
sequences.
Finally, bins were analysed with CAMITAX (Bremges et al. 2019) for taxonomic labelling.
CAMITAX combines genome distance and gene-homology taxonomic assignments with
phylogenetic placement for taxonomic identification.
Figure 6.2 Workflow of the analysis performed on the metagenomes from spent fuel storage ponds
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Results
Microbial diversity of reads
Microbial classification was assigned by Kaiju software. Proteobacteria, Bacteroidetes,
Actinobacteria, Firmicutes, Cyanobacteria, Gemmatimonadetes and Planctomycetes were the
most abundant phyla identified at all sampling sites and times (Figure 6.3). On the indoor
system, Proteobacteria represented more than 90% of the total reads, except for sample G
(adjacent pond) where 72% of the reads were from this group. Proteobacteria was also the
dominant phylum on the open system, representing 65% of the reads in the auxiliary pond (I,
J and L) samples and 92% in the FGMSP (sample K).
Figure 6.3 Microbial affiliations at phylum level assigned by Kaiju classifier
Although viruses did not represent a major component of the sequences, a greater relative
abundance was observed on the open ponds (Figure 6.4).
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
A_Feedingtank
B_feedingtank
C_Mainpond
D_Mainpond
E_Subpond
F_Subpond
G_Adjacentpond
H_Adjacentpond
I_Auxiliary
J_Auxiliary
K_FGMSP
L_Auxiliary
Relativeabundance Viruses
Planctomycetes
Gemmatimonadetes
Cyanobacteria
Firmicutes
Actinobacteria
Bacteroidetes
Proteobacteria
Open air ponds system Indoor ponds system
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Figure 6.4 Relative abundance of viruses based on reads (Kaiju classifier) on the indoor and open
storage fuel ponds
Metagenomes contained sequences in the range of 6.6 to 7.7 million reads. Since sequences
assembled into contigs/scaffolds may not truly represent heterogeneity in the samples, the
initial approach was to predict CRISPR loci on the sequences prior to contig assembly. The
number of CRISPR loci ranged between 1.5 and 4.2 CRISPR per million reads; a greater
number or CRISPR were identified on the indoor system main ponds, subponds and adjacent
pond (samples C to H) as well as on the open air system (I to L). The lowest diversity of
CRISPR systems was identified on samples A and B, from the indoor system feeding tank
area (Figure 6.5). VirSorter was performed to predict free phage detection (outside a host
genome) on assembled metagenomes. Only categories reported by the software (Roux et al.
2015a) as 1 (”most confident” predictions) and 2 (“likely” predictions) were considered reliable
and are included in Figure 6.5. A greater number of phages was detected on the open air
pond samples (samples I to L).
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
A_Feedingtank
B_feedingtank
C_Mainpond
D_Mainpond
E_Subpond
F_Subpond
G_Adjacentpond
H_Adjacentpond
I_Auxiliary
J_Auxiliary
K_FGMSP
L_Auxiliary
Relativeabundance
Indoorponds
Openairponds
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Figure 6.5 Diversity of phage (categories 1 and 2) on assemblies and prediction of CRISPR on
metagenomes
Contigs were grouped into bins and good quality bins are shown on Table 6.2. The taxonomic
classification was assigned according to the percentage of similarity via CAMITAX; taxonomic
identification via blastn was also performed and the results were consistent (Supplementary
6.2). Additionally functional annotation was predicted via KEGG KASS. Four functional
categories were compared in the samples. Hydrogen metabolism, determined by Hox
hydrogenases (involved in H2 oxidation), and implicated in previous chapters as supporting
microbial metabolism in the pond systems, was detected on the majority of the bins (except
on 3 bins from the indoor adjacent pond). Nitrogen fixation, which could support microbial
growth in this oligotrophic environment, was only detected on bins associated with the
adjacent pond and the open air system (samples H to L). Nitrate reduction and sulphur
oxidation (latter determined by Sox system) were consistent on all the samples. Nitrate-dosing
has been proposed in the past as an anticorrosion treatment in nuclear storage ponds, and
0
10
20
30
40
50
60
70
A-Feedingtank
B-Feedingtank
C-Mainponds
D-Mainponds
E-Subponds
F-Subponds
G-Adjacentpond
H-Adjacentpond
I-Auxiliary
J-Auxiliary
K-FGMSP
L-Auxiliary
Num
berofSequences
AssembledSamples
Reads(million)
Phagecategory1
Phagecategory2
CRISPRpermillionreads
Indoor ponds Open-air ponds
Sharon L. Ruiz Lopez PhD Thesis
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the potential for this form of metabolism is therefore of interest. Sulphur oxidation was included
as a contrasting metabolic baseline process.
Table 6.2 Taxonomic and functional diversity of good bins (>93% completeness and <1% contamination, detailed description on Appendix Table 1)
Location Good
quality
bins
Identification via
CAMITAX
Functional annotation (via Prokka and KEGG
KAAS)
Hydrogen
metabolism
(hox
hydrogenase)
Nitrogen
fixation
Nitrate
reduction
Sulphur
oxidation
(Sox
system)
Feeding
tank
A1 Comamonadaceae + - + +
A2 Comamonadaceae + - + -
B1 Comamonadaceae + - + -
B2 Comamonadaceae + - - +
B3 Comamonadaceae + - + +
Main
ponds
C1 Serpentimonas + - + +
C4 Silanimonas lenta + - + -
C5 Porphyrobacter + + - -
D1 Serpentimonas + - + +
D5 Methylophilaceae + - + +
Subponds E1 Methylophilaceae + - + +
E2 Serpentimonas + - + +
E3 Xanthomonadales + - - -
E5 Burkholderiales + - + +
E7 Bacteria + - - -
F1 Methylophilaceae + - - +
F3 Comamonadaceae + - + +
F4 Erythrobacteraceae + + - -
F5 Bacteria + - - -
Adjacent
pond
G1 Serpentimonas + - - +
G2 Acetobacteraceae + + + +
G3 Flavobacteriaceae - - + -
G4 Sphingomonadales + - - +
G8 Actinobacteria - - - -
H1 Serpentimonas + - + +
H2 Acetobacteraceae + + + +
H3 Erythrobacteraceae + + + +
H6 Rhodobacteraceae + + + +
H7 Actinobacteria - - - -
FGMSP K1 Serpentimonas
raichei
+ - + +
K2 Rhodobacteraceae + + - +
K4 Acetobacteraceae + + + +
Sharon L. Ruiz Lopez PhD Thesis
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K7 Cyclobacteriaceae + - + +
Auxiliary
pond
I2 Algoriphagus + + + -
J6 Bacteroidetes + - - -
L2 Synechococcaceae + + + -
VirSorter was used to identify viral sequences integrated into the good quality bins. In contrast
to the contigs counts, only 1 prophage sequence was detected on the feeding tank and
subponds (indoor system, INP), and prophages were not detected on the main ponds. Greater
variation was detected on the adjacent pond, were 10 prophage sequences were identified.
In the outdoor system (OUT) samples only 2 and 3 prophage sequences were identified, in
the auxiliary pond and FGMSP respectively. Reconstruction of CRISPRs (via Crass) showed
the presence of CRISPR arrays the defence system on the samples, but it was not possible
to identify the host organism. Repeats were then extracted from the CRISPR sequences and
used in a blastn search against the good bins to identify the host for the CRISPR arrays.
Likewise, spacers were extracted from the CRISPR arrays and were used in a blastn search
against viral contigs, to identify associations between viruses and CRISPR arrays. The highest
abundance of CRISPR repeats and spacers was observed on the main ponds, subponds and
adjacent pond (indoor system). The number of CRISPR arrays (loci and sapcers) was lower
on the open ponds (OUT) and the heading tank (INP). Bacteria belonging to the order
Burkholderiales were the most common host were CRISPR arrays were identified on indoor
ponds whilst on the auxiliary pond Cyanobacteria was identified to be the host for the unique
prophage identified. Detailed information is shown in Supplementary Table 2.
Sharon L. Ruiz Lopez PhD Thesis
197
Figure 6.6 Defence system prediction based on CRISPR arrays (repeats-spaces)
Discussion
The spent fuel storage ponds are hyper alkaline, oligotrophic and store radioactive material,
leading to challenging conditions for microbial survival. This study expands previous work on
prokaryotic and eukaryotic components of the pond microbiomes, to, for the first time, analyse
viral interactions and defence systems in this unique extreme environment. In addition,
metageomic “bins” were assembled representing key host prokaryotes, helping identify key
metabolic traits in potential pioneer species in the ponds
Samples were collected over a period of 15 months from different areas of the system.
Microbial diversity on the indoor system (INP) was dominated by bacteria, mainly associated
with Proteobacteria, Bacteroidetes and Actinobacteria. Although members of the
Proteobacteria are not often considered extremophiles, there is evidence that members of this
0
20
40
60
80
100
120
140
160
180
200
0
5
10
15
20
25
30
INPFeedingtank
INPmainponds
INPsubponds
INPadjacentpond
OUT_FGMSP
OUT_auxiliarypond01
OUT_auxiliarypond02
CRISPRspaces
Num
berofsequences
Goodqualitybins Prophagedetection CRISPRloci CRISPRspaces
Sharon L. Ruiz Lopez PhD Thesis
198
group can tolerate and populate extreme radioactive conditions (Yu et al. 2015) (Chicote et
al. 2005), hyperalkalinity, e.g. in hot springs (Lau et al. 2009) (Baker et al. 2001) and low levels
of nutrients e.g. ultra-pure water (Bohus et al. 2010;Chicote et al. 2005). Organisms
associated with Proteobacterial genera have also been previously identified on spent fuel
storage ponds (Chicote et al. 2005;Sarró et al. 2005;Silva et al. 2018a;Tišáková et al. 2012)
(MeGraw et al. 2018).
Functional annotation revealed that the majority of the recovered bins corresponded to
organsims that supported hydrogen metabolism, determined by the presence of [NiFe]-
hydrogenase (Hox). Hox hydrogenase catalyses the reversible oxidation of molecular
hydrogen according to the reaction H2↔ 2H+2e- and play a crucial role in microbial energy
metabolism (Vignais et al. 2001;Vignais et al. 2004). Hydrogen metabolism, potentially
produced by water radiolysis, is likely to support hydrogen-oxidising microbial pioneer species
in the pond system. Previous studies on the storage ponds at Sellafield revealed that genus
Hydrogenophaga on the indoor system and Cyanobacteria on open-air ponds represented
major components of the microbial diversity (MeGraw et al. 2018) (Foster et al. 2019a;Ruiz-
Lopez et al. 2019); both organisms are well studied examples of hox hydrogenases containers
(Shafaat et al. 2013;Eckert et al. 2012) (Yoon et al. 2008).
Overall viral abundance represented less than 1% on the samples. Environmental variables,
e.g. temperature, light exposure and salinity, can directly affect virus-host interactions
(Baudoux and Brussaard 2005) (Hardies et al. 2013;Williamson and Paul 2006;Finke et al.
2017;Jia et al. 2010) (Baudoux and Brussaard 2008;Finke et al. 2017). UV radiation is a major
factor for decay rates of cyanobacteria, eukaryotic phytoplankton and viruses of bacteria
(Cottrell and Suttle 1995) (Noble and Fuhrman 1997) (Murray and Jackson 1992). Additionally
nutrient availability has an important effect on virus-host interactions (Chow et al. 2014); for
instance nutrients such as phosphorus and nitrogen are highly demanded for viral replication
(Bratbak et al. 1998;Suttle 2007). Besides environmental conditions it is important to note that
the DNA extraction methods used here included an initial filtration step where most of the free
viruses may have not been retained due to the pore size used (2µm). However, it is clear from
the analyses presented, that the microbiomes of the indoor ponds contained lower levels of
Sharon L. Ruiz Lopez PhD Thesis
199
phage DNA than the outdoor ponds, especially the auxillary pond samples, which are not
exposed to purge cycles. There, establishment of microbial communities in these closed
outdoor systems may therefore help promote viral infection.
CRISPR arrays were, however, detected at similar levels (per million reads) across all the
pond samples, suggesting that they could play a role in helping host organisms adapt to the
extreme environments (alkalinity and radiotoxicity) (Le Romancer et al. 2006) across the pond
complexes. Additionally, phages can modulate the community structure by transferring
genetic material to their host and, in the specific case of the oligotrophic ponds, by promoting
phage-mediated microbial mortality that generates available nutrients for the cells (Breitbart
et al. 2004).
Identification of CRISPR repeats and spacers showed that the most frequent host in the
storage ponds were associated with the order Burkholderiales. The findings complement the
previous studies on this specific environment. Bacterial members belonging to order
Burkholderiales are able to adapt and populate oligotrophic, hyperalkaline, radioactive and
light-limited environments by displaying a set of genomic adaptations and the phage
transduction may be involved in these processes. Specifically evidence has been found of
phage regions and transfer of genomic material in members of the genus Hydrogenophaga
(Burkholderiales) (Gan et al. 2017), previously identified on the INP (Ruiz-Lopez et al. 2019),
and could represent an adaptation mechanism in the storage pond. This clearly warrants
further investigation.
Acknowledgements
SRL acknowledges financial support from a PhD programme funded by the National Council
of Science and Technology (CONACyT). This work was also supported by funding from
Sellafield Limited and the Royal Society to JRL. LF was supported by an EPSRC CASE PhD
and IAA funding.
Sharon L. Ruiz Lopez PhD Thesis
200
Supplementary information
Supplementary 6. 1 Binning categories based on quality score
1
Good bins
Near-complete genome >93% completeness, <5% contamination
2 Medium-quality genome >70% completeness, <10% contamination
3 Partial genome >50% completeness, <15% contamination
4 Low quality genomes Quality lower than 50%
Supplementary 6. 2 Identification of the CRISPR arrays including spacers and repeats.
Location Good
quality
bins
Taxonomy
(Blastn)
CRISPR loci
sequence
CRISPR
spacers
Prophage
detection
sequence
Contig
coverage
Feeding
tank
A1 Burkholderiales - - k141_2694 81.71953351
A2 Comamonadaceae - - k141_10854
k141_7131
59.15403
56.28694
B1 Burkholderiales - - k141_25103
k141_8678
56.48447
58.31636
B2 Burkholderiales - - k141_13766 49.24896
B3 Burkholderiales k141_25927 (5
repeats)
k141_599 (4
repeats)
- k141_23304 40.73938
Main
ponds
C1 Burkholderiales k141_320 (5
repeats)
k141_28213
146
spacers
- 244.8874
-
C4 Unclassified k141_1470 (8
repeats)
k141_4770 (5
repeats)
k141_9346 (2
repeats)
- - 12.96596
13.68435
13.58487
C5 Erythrobacteraceae -
1 spacer -
D1 - 1 spacer -
D5 Betaproteobacteria k141_20391 (1
repeat)
k141_21531 (65
repeats)
k141_3261 (2
repeats)
1 spacer - 37.80389
49.50615
37.76542
36.03357
Sharon L. Ruiz Lopez PhD Thesis
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k141_8909 (2
repeats)
Subponds E1 Serpentimonas k141_13878 (1
repeat)
k141_14166 (2
repeats)
k141_4524 (2
repeats)
k141_8151 (4
repeats)
- - 127.6577
126.1004
120.9153
67.03781
E2 Burkholderiales k141_1850 (105
repeats)
21 CRISPR
spacers
- 124.222
E5 Burkholderiales 2 CRISPR sites
cat1 k141_2226
42 CRISPR
spacers
- 2.710262
E7 Acidimicrobiaceae k141_9961 (2
repeats)
2 CRISPR
Spacers
- 7.952549
2.710262
10.09421
F1 Methylophilaceae k141_14522 (1
repeat)
k141_2257 (1
repeat)
k141_3056 (2
repeats)
k141_6367 (3
repeats)
1 CRISPR
Spacer
- 123.6058
37.50187
125.9339
120.5019
F3 Burkholderiales k141_13460 (4
repeats)
k141_14093 (8
repeats)
k141_716 (8
repeats)
k141_8211 (50
repeats)
1 CRISPR
Spacer
- 62.78257
67.31107
17.38226
62.75976
F4 Sphingomonadales 2 CRISPR sites
k141_12426 (8
repeats)
k141_2140 (3
repeats)
33 CRISPR
spacers
k141_12648 3.361462
15.30374
15.77768
Adjacent
pond
G2 Acetobacteraceae k141_10208 (27
repeats)
k141_11126 (18
repeats)
k141_3075 (6
repeats)
- k141_11388,
k141_1469
62.68504
69.98809
65.62389
59.25636
57.88733
58.52471
Sharon L. Ruiz Lopez PhD Thesis
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k141_6440 (1
repeat)
k141_6612 (1
repeat)
k141_6704 (1
repeat)
k141_8564 (3
repeats)
61.18004
Prophage
62.11882
60.38804
G3 Unclassified k141_11457 (80
repeats)
k141_13891 (10
repeats)
k141_16321 (141
repeats) k141_2271
(4 repeats)
k141_10572 (35
repeats)
k141_16117 (43
repeats)
- - 56.5327
51.74855
56.04868
60.24455
50.71173
46.9203
G4 Sphingomonadales - - k141_18077
k141_9472
k141_2226
k141_10171
Prophage
34.24099
13.88803
2.870175
2.772622
G8 Actinomycetales - 1 CRISPR
Spacer
2.870175
H1 Burkholderiales k141_8000 (115
repeats)
179
CRISPR
spacers
99.2235
H2 Rhodospirillales k141_1975 (115
repeats)
k141_2555 (120
repeats)
k141_4557 (1
repeat)
k141_4938 (6
repeats)
k141_5176 (1
repeat)
k141_5467 (2
repeats)
k141_6598 (4
repeats)
1 CRISPR
Spacer
k141_2468
k141_7244
k141_9770
70.66051
72.22759
66.44355
79.51366
73.75719
72.31211
73.58034
83.10537
74.20821
Prophage
74.23412
76.51427
73.88614
Sharon L. Ruiz Lopez PhD Thesis
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k141_9755 (165
repeats)
k141_9827 (2
repeats)
H3 Sphingomonadales k141_1331 (30
repeats)
k141_1546 (10
repeats)
k141_7147 (8
repeats)
2 CRISPR
Spacers
k141_2550 64.59156
61.6015
24.86944
Prophage
31.24127
H6 Rhodobacteraceae -
1 CRISPR
Spacer
- -
H7 Actinomycetales - 1 CRISPR
Spacer
- -
FGMSP K1 Burkholderiales k141_18101 (95
repeats)
k141_2551 (4
repeats)
k141_28760 (8
repeats)
k141_31558 (70
repeats)
3 CRISPR
Spacers
- 29.11369
86.41386
35.36795
82.09703
K2 Rhodobacteraceae - - k141_819 Prophage
53.12703
K4 Acetobacteraceae k141_10718 (3
repeats)
k141_34443 (2
repeats)
k141_7843 (1
repeat)
- k141_26474
k141_24535
29.11369
27.82314
29.57929
Prophage
29.59703
28.61228
K7 Cytophagales k141_10718 (3
repeats)
k141_34443 (2
repeats)
k141_7843 (1
repeat)
1 CRISPR
Spacer
- 29.11369
27.82314
29.57929
Auxiliary
pond
L2 Cyanobacteria - - k141_100911
k141_84037
Prophage
17.51644
18.34898
J6 Bacteroidetes -
1 CRISPR
Spacer
- -
Sharon L. Ruiz Lopez PhD Thesis
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7
Conclusions and Future work
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Chapter 7 Conclusions and future work
Conclusions
The microbial ecology of spent fuel storage ponds was studied in this thesis. The spent fuel
removed from nuclear power plants is a mixture of radionuclides and waste materials no
longer usable as fuel and management prior to reprocessing or final disposal represents a
significant challenge.
To ensure safe containment during storage, the material is packaged into a solid, stable form
that is kept within high integrity into stainless steel or concrete containers. Afterwards fuel
assemblies are stored in water ponds which provide adequate shielding from radiation. Ponds
are filled with demineralised water and alkali (NaOH) is added to prevent corrosion of the
materials in the pond. The spent fuel ponds therefore represent an extreme environment; the
hyper alkalinity (pH 11.6) combined with a lack of nutrients (oligotrophy) and high background
radioactivity levels create conditions challenging for life. Despite these harsh conditions, the
presence of microbial communities has been detected in spent fuel ponds, including those at
Sellafield.
Microbial colonisation can cause significant challenges during spent fuel pond management.
Excessive microbial growth can cause turbidity in the water, complicating fuel movements and
retrievals, and ultimately increasing the costs of decommissioning. In addition,
microorganisms can interact with the stored materials promoting corrosion of containers, and
also interacting with radionuclides contained on the pond affecting their speciation and
solubility. On the other hand, the identification of microorganisms with the ability to survive in
highly radioactive waters while accumulating radionuclides, could lead to the development of
bioremediation process for contaminated waters.
The global objective of this thesis was to describe the microbial ecology of spent fuel storage
ponds and based on the genomic fingerprints, to predict the mechanisms used to underpin
the colonisation of these extreme environments. The study of microbial adaptation
mechanisms in a range of extreme environments is a rapidly expanding field, however, the
combined challenging conditions dictated by the nature of the site represents a novel area of
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fundamental research, that will also lead a better understanding of the microbiological
processes that occur within the SNF ponds (which could have benefit to the pond operators).
In Chapter 4 the microbial ecology of an indoor storage pond (INP) was analysed. The INP
stores nuclear material and nuclear wastes from different stations and ponds across the UK,
and the light exposure limitation proved to be an important factor for survival of photosynthetic
organisms, which have been identified in outdoor Sellafield ponds, but were not detected in
the indoor ponds. Samples were taken for a period of 30 months from main ponds, subponds
and the feeding tank area where demineralised water is purged. Analysis of the 16S rRNA
gene based sequencing provided a broad overview of the microbial diversity surviving in the
oligotrophic pond environment. Microbial community composition was stable over the period
of time studied, and was dominated by bacterial genera including Hydrogenophaga,
Methylotenera, Silanimonas and Porphyrobacter. Since Hydrogenophaga, a
chemoorganotroph hydrogen-oxidizing organism, was the dominant genus at all sampling
times, the metabolism of H2, potentially formed through water radiolysis, was proposed as a
key energy source for microbial survival and colonisation on the INP. Neither 16S rRNA
archaeal or 18S rRNA eukaryotic genes were detected showing that environmental conditions
may be limiting for those organisms. Additionally, microbial growth was estimated by the
quantification of 16S rRNA copies determined by qPCR. Results showed an increase in
biomass over time. Finally, classic culturing-dependent techniques were tested to isolate
representative microbial components, and proved efficient for isolation of members associated
to bacterial family Cyclobacteriacea, but inadequate for isolation of major microbial
components such as Hydrogenophaga. This chapter provided an initial insight into the
microbial ecology of the INP, an indoor, hyper alkaline, oligotrophic and radioactive
environment and suggested that due to limiting carbon sources, microorganisms may use
alternative energy sources such as H2.
In Chapter 5 the microbial ecology of indoor and open-air storage ponds was analysed and
compared using a metagenomic approach. Due to the difficulty of isolating major microbial
components, metagenomics techniques were applied. Metagenomics is a relatively recently
developed tool to directly access the genetic material of entire communities of organisms,
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leading to the analysis of microbial ecology and evolution by the prediction of metabolic
microbial activities within the studied environment. Samples from the previously studied indoor
pond (INP), including its main ponds, subponds and feeding area, were analysed and
compared with an open system, the First Generation Magnox Storage Pond (FGMSP) and its
auxiliary pond (Aux). Microbial diversity was consistent to the diversity determined by 16S
rRNA gene analysis at phylum, order and family levels, being dominated by Proteobacteria,
Burkholderiales and Comamonadaceae, respectively. Contrasting differences were observed
at the genus level, where Hydrogenophaga (within the Burkholderiales), the most abundant
organism previously identified on the INP was not detected by metagenomics. The depth of
sequence reads and the different reference data bases may have influenced the observed
results. On the open air systems, microbial diversity was also dominated by Proteobacteria
and the presence of photosynthetic organisms belonging to Cyanobacteria was identified.
Metagenomic analyses also provided a better understanding of the microbiological adaptation
processes occurring in the oligotrophic environments studied. When microbial communities
were exposed to challenging conditions associated with the pond environment, evidence for
different survival strategies were collected; the relative abundance of genes related to
respiration, specifically to hydrogenases were increased in the INP. These results support the
hypothesis Burkholderiales that is present and has hydrogenase genes, but its precise
phylogenetic affiliation requires further work. It is probably closely related to Hydrogenophaga
though. However, relative abundance of genes related to respiration were detected at lower
levels in the Aux open air pond, and here genes related to photosynthesis were exclusively
detected, suggesting that sunlight is a key energy source on open systems. Additionally,
functional annotation of genes revealed the abundance of genes related to bacterial stress
response possibly linked by •OH radicals that also formed through radiolysis. Also, genes
related to membrane transport and potassium homeostasis were abundant, suggesting that
Na+ membrane transport systems seem to be a key mechanism used by microorganisms to
keep the osmotic balance correct within the cells in an environment heavily dosed with NaOH.
Finally, the relative abundance of genes related to bacterial defence systems such as
modification-restriction, base excision and the immunity system CRISPR, were increased in
the INP, implying the environmental conditions on the INP seem to be more challenging than
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the open air system (FGMSP and Aux). It is concluded that the 16S rRNA gene based
sequencing provided a broad overview of the microbial diversity surviving in the oligotrophic
pond environment, whilst metagenomics successfully provided a complementary functional
overview of microbial processes in the system.
On Chapter 6 the potential influence of viruses on the storage systems was analysed. Viruses
represent the most abundant entities on Earth and their ability to infect bacteria could
modulate community structure. Viral distribution, prediction of hosts and defence integrated
systems were analysed using a metagenomic approach. Metagenomes were assembled and
grouped into bins to find CRISPR loci as a measure of viral-host associations in each sample,
and the prediction of CRISPR spacers-repeats arrays allowed the measurement of bacterial
defence responses within the community. The majority of the viral hosts were associated with
the order Burkholderiales, which were previously identified as major microbial components.
Most of the CRISPR repeats-spacers were identified in the INP main ponds, subponds and
adjacent pond, whilst CRISPR arrays were poorly represented in the initial feeding tank area.
These results suggest that microbial members populating the radioactive storage ponds
contain adaptation and defence systems that allow them to cope not only with challenging
environmental conditions but also against viral infections. Finally, functional annotation of
metagenomic “bins” corroborated our previous findings; the presence of genes related to
hydrogenases revealed the H2 metabolism may be the main energy source within the ponds.
This thesis provides a taxonomic and functional overview of the spent fuel storage systems at
Sellafield, UK. The INP is a novel site of study and due to its characteristics, and for optimal
long-term management it is crucial to analyse the microbial ecology and possible interactions
with other hydraulically linked ponds such as the FGMSP and Auxiliary. The microbial ecology
of the FGMSP and Aux ponds had been studied previously and in this project the analysis of
microbial distribution and microbial responses was assessed using a metagenomic approach.
Complementary viral distribution and possible interactions of host-phage were identified to
find have a better understanding of the global microbial activities occurring within the storage
ponds.
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The findings on this thesis corroborate the presence of extreme oligotrophic, alkaline and
irradiated ecosystem does not prevent the colonisation of microbial populations due to their
adaptation to the surrounding environment. The adaptation process can occur either by
acclimation to limited nutrient sources (head tank), by metabolizing chemical species e.g.
hydrogen derived from the interaction between the stored material and the neighbouring
ecosystem (main ponds and subponds) or by metabolizing other available energy sources
such as sun light (auxiliary pond) and by water recirculation and material transfer between
ponds (FGMSP and INP). A key observation throughout the time period studied is that distinct
microbial communities exist across the Sellafield estate, and the microbiomes of these
individual ponds are remarkable stable, despite the range of operations taking place there.
Future work
The results described in this thesis show the importance of microbial communities and could
lead to additional topics of research. The microbial diversity analysis of the indoor alkaline
pond, INP, over 30 months revealed that the microbial diversity was stable over the
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operational period. Further analyses over time would be important to see if this baseline
microbial ecology shifts during periods of time when the inventory in the pond is altered
significantly. Also, would be important to track the biomass contents level and to match any
detected changes on linkages to the pond to find any possible alterations in pond water
chemistry.
The majority of the analyses were performed from DNA extracted from samples. It would be
crucial to develop incubation experiments with “fresh” samples obtained directly from the
ponds. Hence to create microcosms to determine the interactions of microbial communities
with storage material (steel and concrete) to analyse and prevent corrosion in situ. It would
also be critical to track interactions of live microbial communities with radionuclides to
determine the microbial influence on radionuclide solubility and (im)mobilization.
From the metagenomic analysis, more bioinformatics analysis can be developed. The
microbial diversity and functional annotation were obtained via the MG-RAST pipeline with
standard parameters. It would be interesting to run the metagenomic analysis de novo,
assembling and recovering genes into MAGs to track specific changes on genes sequences,
and statistically comparing different assembly and annotation tools to have a more accurate
prediction of microbial metabolism occurring within the SNF ponds. A similar scenario is
suggested for taxonomic identification. Over the last 3 years, new, more efficient and reliable
bioinformatics tools have been developed to describe taxonomic diversity; it would be
interesting to compare the available tools such as ANI/AAI (Rodriguez and Kostantinidis 2016)
and CAMITAX (Bremges et al. 2019) to most commonly used RefSeq (O'Leary et al. 2016) to
have a more exact picture of the microbial distribution.
Since culturing dependent techniques proved in adequate for microbial isolation, the recovery
of complete genomes could be an efficient way to identify uncultivable organisms and since
the SNF ponds are recently studied sites, the identification of new species or even new genera
of bacteria could potentially be assessed.
Complementary omics techniques have proved useful for studying microbial ecology and
evolution in other extreme sites, and could prove useful here also. Metagenomics provides a
general perspective of the potential microbial metabolisms in a studied environment Follow-
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up work to analyse the SNF ponds via metatranscriptomics and metaproteomics are
warranted to provide more precise evidence of adaptations that support microbial colonisation
based on genes expression and protein synthesis.
The metavirus (metagenomics of viruses) analysis is also a potential line of further research.
In Chapter 7, the objectives were to identify host-phage interactions and the presence or lack
of immunity defence systems. To date the information about viruses on SNF is non-existent,
which makes this a ground-breaking topic of research and the possibility to identify host-phage
interactions could potentially contribute to obtain a better understanding of the microbial
ecology on the spent fuel storage ponds, and could even help identify new “biocontrol”
strategies for organisms causing problematic blooms in SNPs.
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Conference presentations and Awards
Awards
Best poster presentation: School of Earth and Environmental Sciences Postgraduate
Conference, The University of Manchester, UK. 5th December 2017.
Oral Presentations
• 2019
Metagenomic analysis of an indoor spent fuel storage ponds at Sellafield, UK. S. Ruiz-Lopez,
L. Foster, N. Cole, H. Song, J. Adams and J. Lloyd. Geomicrobiology Research in Progress
Meeting (RiP)., Manchester Metropolitan University, UK. 27th-28th June 2019
Metagenomic analysis of open-air and indoor spent fuel storage ponds at Sellafield, UK. S.
Ruiz-Lopez, L. Foster, N. Cole, H. Song, J. Adams and J. Lloyd. Microbiology Society Annual
Conference, Belfast, UK. 8th-11th April 2019
• 2017
Understanding the microbial productivity in highly radioactive storage facilities. S. Ruiz-Lopez,
L. Foster, K. Morris, N. Cole and J. Lloyd. Geomicrobiology Research in Progress Meeting
(RiP), The University of Manchester, UK. June 2017
Understanding the microbial productivity in highly radioactive storage facilities. S. Ruiz-Lopez,
L. Foster, K. Morris, N. Cole and J. Lloyd. 6th Symposium of CONACyT Fellows in Europe,
European Parliament in Strasbourg, France. 29th-31st March, 2017.
Understanding the microbial productivity in highly radioactive storage facilities. S. Ruiz-Lopez,
L. Foster, K. Morris, N. Cole and J. Lloyd. XV Symposium of Mexican Studies and Students
in the UK, Durham University, UK. 12th-14th July, 2017
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Poster Presentations
• 2019
Metagenomic analysis of open-air and indoor spent fuel storage ponds at Sellafield, UK. S.
Ruiz-Lopez, L. Foster, N. Cole, H. Song, J. Adams and J. Lloyd. Poster presentation at
Topical Day: Aquatic microbiota in or near nuclear facilities: insights, discoveries and
solutions. September 12th, 2019, Brussels, Belgium.
Metagenomic analysis of open-air and indoor spent fuel storage ponds at Sellafield, UK. S.
Ruiz-Lopez, L. Foster, N. Cole, H. Song, J. Adams and J. Lloyd. Poster presentation at the
Federation of European Microbiological Societies Conference, FEMS. 7th-11th July, 2019.
Glasgow, Scotland.
• 2018
Characterisation of Microbial Populations in Highly Radioactive Storage Facilities in Sellafield,
UK. S. Ruiz-Lopez, L. Foster, C. Boothman, N. Cole and J. Lloyd. Microbiology Society
Annual Conference, Birmingham, UK. 9th-12th April, 2018
Characterisation of Microbial Populations in Highly Radioactive Storage Facilities in Sellafield,
UK S. Ruiz-Lopez, L. Foster, C. Boothman, N. Cole and J. Lloyd. The International Society
for Microbial Ecology, ISME, Leipzig, Germany. 12th-17th August,
Characterisation of Microbial Populations in Highly Radioactive Storage Facilities in Sellafield,
UK. S. Ruiz-Lopez, L. Foster, C. Boothman, N. Cole and J. Lloyd. The American Society of
Microbiology (ASM), Atlanta, USA. 6th-11th June, 2018
• 2017
Understanding the microbial productivity in highly radioactive storage facilities. S. Ruiz-Lopez,
L. Foster, J. Lloyd and K. Morris. Microbiology Conference Annual Conference, Edinburgh,
Scotland. 3rd-6th April 2017
Understanding the microbial productivity in highly radioactive storage facilities. S. Ruiz-Lopez,
L. Foster, J. Lloyd and K. Morris. School of Earth and Environmental Sciences
Postgraduate Conference, The University of Manchester, UK. 5th December 2017
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2016
Understanding the microbial productivity in highly radioactive storage facilities. S. Ruiz-Lopez,
K. Morris and J. Lloyd. School of Earth and Environmental Sciences Postgraduate
Conference, The University of Manchester, UK. December 2016
Outreach
§ Organiser at the “Pint of Science” global event, team Planet Earth, editions 2017,
2018 and 2019. Manchester, UK
Complementary courses
§ Metagenomics Bioinformatics Course at the European Bioinformatics Institute,
EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK. July 2018
§ 28th Summer School Bioinformatics for Microbial Ecologists at the University of
Jyvaskyla, Finland. 6th-10th August 2018