the microbial ecology of spent fuel storage ponds at

1

The microbial ecology of spent fuel

storage ponds at Sellafield, UK

A thesis submitted to the University of Manchester for the degree of

Doctor of Philosophy in the Faculty of Science and Engineering

Sharon Lorena Ruiz Lopez

School of Earth and Environmental Sciences

September 2019

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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

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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

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Figure 3.3 Sanger sequencing technique (Zhou and Li 2015) ................................................. 68 Figure 3.4 Overview of NGS sequencing by Illumina technology: a)Library-construction process, b)Cluster generation by bridge amplification and c)Sequencing by synthesis with reversible dye terminators (Mardis 2013) .................................................................................. 70 Figure 3.5 Metagenomics workflow. After extraction, DNA is analysed using paired-ends reads to maximise coverage of the amplicons and the reads and assembled into contigs. ............. 73 Figure 3.6 Metagenomic viral identification pipeline. The workflow describes the main steps for phage identification and gene prediction (Zheng et al. 2019) ............................................. 75 Figure 4.1Diagram of the Fuel Handling Plant. It consists of 3 main ponds and 3 subponds linked by a transfer channel which enables water flow. The sampling points are located at the main ponds 2 and 3; subponds 1 and 2; and the head feeding tank (at the top of the pond) 89 Figure 4.2 QPCR results show the number of copies per mL. A standard curve for QPCR reaction was at concentration ranging from 0.00753 to 7530 nanograms per millilitre to estimate the concentration of DNA in the samples. .................................................................. 96 Figure 4.3 Phylogenetic affiliations (closest known genera) of microorganisms detected in Sellafield indoor pond (INP): a)main ponds, b)subponds and c)feeding tank (FT) using Illumina sequencing with broad specificity primers for prokaryote 16S rRNA. Only the genera that contained more than 1% of the total number of sequences are shown. ................................ 100 Figure 5.1Storage pond systems. Metal and legacy spent fuels from outdoor ponds are transported to the INP for interim storage pending a long term disposal solution available. The INP is divided in 3 main ponds (MP), 3 subponds and a feeding tank area (FT); waters from the INP are recirculated to the FGSMP during purging times. The FGMSP and its Auxiliary pond (Aux) store legacy fuel pond (NDA 2015;ONR 2016). ................................................... 129 Figure 5.2 Microbial distribution at order level targeting the 16S rRNA gene. Only components that represented relative abundance higher than 1.5% are shown ........................................ 136 Figure 5.3 Functional categories associated to Level 1 subsystems (Level 1, KEGG) among the sampling sites and times ..................................................................................................... 138 Figure 5.4 Relative abundance of genes related to respiration processes (level 3 subsystems, KEGG database) ........................................................................................................................ 139 Figure 5.5 Relative abundance of genes related to photosynthesis (level 3 subsystems, KEGG database) .................................................................................................................................... 141 Figure 5.6 Relative abundance of genes related to DNA repair functions at level 3 subsystems (KEGG database) ...................................................................................................................... 142 Figure 5.7 Relative abundance of genes related to stress response (level 3 subsystems, KEGG database) ........................................................................................................................ 143 Figure 6.1Storage pond systems. Metal and legacy spent fuels from outdoor ponds are transported to the INP for interim storage pending a long term disposal solution available. The INP is divided in 3 main ponds (MP), 3 subponds and a feeding tank area (FT); waters from the INP are recirculated to the FGSMP during purging times. The FGMSP and its Auxiliary pond (Aux) store legacy fuel pond (NDA 2015;ONR 2016). ................................................... 188 Figure 6.2 Workflow of the analysis performed on the metagenomes from spent fuel storage ponds .......................................................................................................................................... 191 Figure 6.3 Microbial affiliations at phylum level assigned by Kaiju classifier ........................ 192 Figure 6.4 Relative abundance of viruses based on reads (Kaiju classifier) on the indoor and open storage fuel ponds ............................................................................................................ 193 Figure 6.5 Diversity of phage (categories 1 and 2) on assemblies and prediction of CRISPR on metagenomes ....................................................................................................................... 194 Figure 6.6 Defence system prediction based on CRISPR arrays (repeats-spaces) ............. 197

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List of Tables

Table 2.1. Radioactive wastes classification in the UK (NDA, 2019) ....................................... 24 Table 2.2. Half-life of common radionuclides in Spent Nuclear Fuel (Chu, Ekstrom, & Firestone, 1999; Lee, Plant, Livens, Hyatt, & Buscombe, 2015; Oigawa, 2015) .................... 25 Table 3.1 Examples of metagenomics software tools ............................................................... 73 Table 4.1 Distribution of samples taken for a period of 30 months from different areas within the SNF pond, and analysed using high-throughput (Illumina) DNA microbial profiling. Samples SP01 and SP02 (*) were not sequenced using the Illumina platform but instead were analysed using culturing techniques (with Sanger sequencing of isolated pure cultures). .... 90 Table 4.2 Parameters measured on the indoor alkaline spent fuel storage pond (INP). Data provided by Sellafield Ltd ............................................................................................................ 95 Table 5.1Samples distribution................................................................................................... 129 Table 6.1 Distribution of sample points in the Sellafield complex .......................................... 189 Table 6.2 Taxonomic and functional diversity of good bins (>93% completeness and <1% contamination, detailed description on Appendix Table 1) ..................................................... 195

Abreviations

µg Micrograms (10-6 molar) 16S rRNA 16S Ribosomal Ribonucleic Acid 18S rRNA 18S Ribosomal Ribonucleic Acid AGR Advanced gas-cooled reactor ASM American Society for Microbiology Aux Auxiliary pond Bq Becquerel Bq l-1 Becquerel per litre CONACyT Consejo Nacional de Ciencia y Tecnologia (National Council of

Science and Technology) EMBL European Molecular Biology Laboratory FEMS Federation of European Microbiological Societies FGMSP First Generation Magnox Storage Pond FT Feeding Tank INP Indoor hyper-alkaline pond ISME International Society for Microbial Ecology MP Main ponds (from the INP) NDA Nuclear Decommissioning Authority PCR Polymerase Chain Reaction qPCR Quantitative Polymerase Chain Reaction SEES School of Earth and Environmental Sciences SFP Spent Fuel Pond SP Subponds (from the INP) MAG Metagenome Assembled Genome KEGG Kyoto Encylcopedia of Genes and Genomes KAAS KEGG Automatic Annotation Server

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Thesis Abstract

The use of nuclear energy has been of great importance to the United Kingdom, with

Sellafield being the largest nuclear site used for both power production and more recently

reprocessing activities. This project, via collaboration between the Geomicrobiology Group at

the University of Manchester and Sellafield Limited, aimed to investigate the microbial

ecology of a spent fuel storage hyper-alkaline indoor pond (INP) in Sellafield.

The main pre-reprocessing storage pond at the Sellafield site is the Indoor pond (INP), a

concrete walled indoor pond filled with demineralised water, responsible for receiving, storing

and mechanically processing spent nuclear fuel (SNF) from Magnox and Advanced Gas-

cooled Reactor (AGR) stations from across the UK. Samples were taken from the INP at

different spatial locations and depths, encompassing main ponds (MP), subponds (SP) and

a feeding tank (FT).

The present study intended to identify the microbial communities present in the INP and

associated structures to determine if they were stable during a prolonged operational period.

A more academic focus of the PhD was to understand the metabolic processes that underpin

microbial colonisation and adaptation in the pond. In order to achieve these objectives, first

the microbial communities from the indoor alkaline storage pond (INP) were identified to

create a microbial database consisting of population density and diversity of microorganisms

present. Here traditional culturing approaches were trialled but were considered ineffective

for the specialised “extremophilic” organisms present in the INP. Therefore, the bulk of the

microbial analyses focused on DNA sequencing, focusing initially on amplification and

sequencing of two commonly used genetic marker genes, the 16S rRNA and 18S rRNA

genes that can be used to identify prokaryotic (bacteria and archaea) and eukaryotic (algae

and other higher organisms). Finally, a much wider range of genes were targeted to help

identify key processes that support microbial colonisation, via high-throughput “metagenomic”

sequencing and analyses. Overall, these findings are discussed in relation to microbial

survival in hyper-alkaline, oligotrophic and radioactive extreme environments, and microbial

adaptation over time observed during the thirty months of analysis.

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Organisms identified by 16S and 18S rRNA gene Illumina sequencing were predominantly

Proteobacteria, mainly Alpha and Beta in the feeding tank (FT), main pond (MP) and Subpond

(SP) sample sites. The presence of the alkali tolerant hydrogen-oxidising bacterium

Hydrogenophaga sp. solely in the INP main ponds and subponds suggested the metabolism

of hydrogen is occurring within the INP which could be generated by radiolysis of water.

Metagenomic analysis revealed that genes related to membrane transport, oxidative and

osmotic stress functions were more abundant on the FT possibly due to the presence of Na+

ions. Genes related to DNA metabolism (including DNA repair and defence systems) as well

as genes related to respiration functions (hydrogenases) were more abundant on the MP and

SP which reinforces the proposed microbial utilization of H2 as an energy source.

In order to have a broader picture of the bacterial strategies to cope with extreme

environmental conditions (hyper-alkaline, oligotrophic and radioactive background), few

selected samples from an open-air pond, the First Generation Magnox Pond (FGMSP) and

its auxiliary pond (Aux), were analysed and compared to the indoor system (INP). Results

showed that genes associated to photosynthesis were more abundant on the open-air ponds,

revealing that light exposure was a key energy source that promoted microbial colonisation.

Additionally the final part of this research intended to identify virus-host interactions and

its influence on key metabolic processes. Metagenomic analysis revealed the presence of

phages inserted on bacteria affiliated to order Burkholderiales; surprisingly phages did not

seem to affect metabolic responses and promote activation defence systems (CRISPR).

In conclusion, microbiological and genomic analysis showed that the despite the low nutrient

(oligotrophic) nature of the indoor alkaline pond, coupled with the radioactive inventory, a

stable microbial community is able to survive at relatively low energy levels, using alternative

energy sources, potentially hydrogen, to cope with challenging environmental conditions.

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Declaration

No portion of the work referred to in the thesis has been submitted in support of an

application for another degree or qualification of this or any other university or other

institute of learning.

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Copyright Statement

i. The author of this thesis (including any appendices and/or schedules to this

thesis) owns certain copyright or related rights in it (the “Copyright”) and s/he has

given The University of Manchester certain rights to use such Copyright, including

for administrative purposes

ii. Copies of this thesis, either in full or in extracts and whether in hard or electronic

copy, may be made only in accordance with the Copyright, Designs and Patents

Act 1988 (as amended) and regulations issued under it or, where appropriate,

in accordance with licensing agreements which the University has from time to

time. This page must form part of any such copies made.

iii. The ownership of certain Copyright, patents, designs, trademarks and other

intellectual property (the “Intellectual Property”) and any reproductions of copyright

works in the thesis, for example graphs and tables (“Reproductions”), which may

be described in this thesis, may not be owned by the author and may be owned by

third parties. Such Intellectual Property and Reproductions cannot and must not

be made available for use without the prior written permission of the owner(s) of

the relevant Intellectual Property and/or Reproductions.

iv. Further information on the conditions under which disclosure, publication and

commercialisation of this thesis, the Copyright and any Intellectual Property and/or

Reproductions described in it may take place is available in the University IP Policy

(see http://documents.manchester.ac.uk/DocuInfo.aspx?DocID=24420), in any

relevant Thesis restriction declarations deposited in the University Library, The

University Library’s regulations (see

http://www.library.manchester.ac.uk/about/regulations/)andin The University’s policy

on Presentation of Theses

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Acknowledgments

Throughout the writing of this thesis, I have received a great deal of support and assistance. I

would first like to thank my supervisor, Jon Lloyd, for his invaluable support and assistance in

the formulating of the research topic and methodology in particular.

I would like to acknowledge CONACyT (the National Council for Science of Technology), my

sponsor, for providing me with the funding to develop this project. To Sellafield Ltd for giving

me the opportunity to develop this project; for making the necessary arrangements to facilitate

the handling of samples and for the complementary funding that allowed me to expand the

research to a higher scientific level. I also express my gratitude to Nick Cole for his invaluable

assistance on procuring and processing of samples at the Sellafield site.

I also want to thank my colleagues from the Geomicro Group at the University of Manchester,

especially to Chris Boothman, Lynn Foster and Sophie Nixon; for supporting me greatly and

for being always willing to help me.

Additionally, I would like to thank my strongest inspiration: mi Pa, Kika, Licita and Gina for their

incredible counsel through this journey, for believing in me and for being for me all the time no

matter the distance. To Alfred, for his love and understanding, for supporting me on this

journey, for being my greatest motivation and for encouraging me to fight for my dreams and

never give up. I want to express my gratitude to my greatest inspirational force: my family, my

beautiful Dominica, Gus and Nora, and the rest of the Ruiz family. Special thanks to families

Ruiz Valencia, Martinez Ruiz, Miranda Díaz, Núñez Martínez and Saravia Ruiz for their

outstanding example of resilience, care, love and for the splendid moments we have shared.

To the wonderful family I have met in Manchester: Isabelle, Natali, Sul, Monse, Zainab, Mayra,

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!

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The Author

The Author of this thesis obtained a Bachelor of Engineering Degree in Biochemistry in the

National Polytechnic Institute (IPN, Instituto Politecnico Nacional); later she obtained the

Master’s Degree in Chemical and Biological Sciences at the National School of Biological

Sciences (ENCB) at the same institute (IPN) where she specialized on Biotechnology,

Bioengineering and Bioremediation. She briefly worked on a chemical industry where she was

on charge of the quality assessment sub-division. In 2015 she joined the Geomicrobiology

Group at the University of Manchester where the work of this thesis was undertaken. She has

presented sections of this work on International Conferences and has actively participated in

scientific projects, most of them organised by the University of Manchester.

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Purpose and significance of the investigation

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Chapter 1 Purpose and significance of the investigation

1.1 Project context and relevance

The Sellafield complex, which has played a crucial role in the UK nuclear energy program, is

large (approximately 700 acres), dealing with a complex portfolio of nuclear materials in 170

major nuclear facilities that require careful management (Ltd 2019). The site structure includes

several nuclear fuel storage ponds; some in continual use, while others are undergoing

decommissioning. Recent studies have also suggested that microbial processes have the

potential to disrupt pond operation, resulting in, for example high biomass levels that can

potentially foul equipment, accumulate radioactivity in sludges, limit visibility in pond waters

and impact on the integrity of the stored samples.

Recently it has been possible to identify, using molecular (DNA) techniques, the microbial

communities colonizing radioactive sites, and is has been interesting to find many organisms

being able to adapt to highly radioactive conditions. This work, via a collaboration between the

Geomicrobiology Group at the University of Manchester and Sellafield Limited, aimed to

investigate the microbial ecology and biogeochemical conditions of an indoor pond in

Sellafield, to identify the diversity of microorganisms across the pond complex, using

molecular ecology techniques, to understand the biochemical mechanisms of adaptation to

the pond environment, and the potential impact of microbial processes on the site. The

identification of key organisms within the Sellafield pond complex not only offers the potential

to understand the processes that facilitate colonisation of extremely radioactive environments,

but is also an important first step in formulating appropriate control measures where required.

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1.2 Objectives:

To develop and compare both culture-dependent and DNA-based techniques to help

understand the behavior of microbial communities in radioactive environments,

focusing on a selected indoor alkaline pond (INP) located in Sellafield which is

subjected to alkali dosing,

To apply molecular techniques e.g. Illumina high throughput 16S rRNA gene

sequencing, to study the microbial ecology of the pond system (including sub-ponds

and channels), alongside metagenomics studies to help understand the metabolic

processes under high pH and highly radioactive conditions, including energy sources

and survival strategies.

To apply the DNA-based techniques above to monitor the stability of the microbial

communities in the INP system over a prolonged operational period (approximately 3

years), and to contrast them where possible with microbial communities in other pond

facilities being studied in parallel research programs.

To determine the influence of virus-host interactions on the key microbial components

by metagenomic analysis of spent fuel storage systems.

1.3 Thesis structure

The present thesis is divided in four main chapters formatted as publishable papers:

• Chapter two, Introduction, presents a literature review on topics related with this

project; definitions and history of nuclear power and nuclear fuel cycle and findings to

date of microbial colonisation of spent fuel storage systems.

• Chapter three, methodology describes the fundaments and portrayal of the analyses

performed including classic microbiology, molecular biology techniques and next-

generation sequencing techniques.

• Chapter four, paper one, describes the microbial ecology on the indoor pond, INP,

based on analysis of the 16S rRNA gene. Samples were taken for a period of 30

months, creating a database focused on quantifying the diversity and number of

microbial cells over time, thus giving insight of the metabolic adaptation process at

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play in this challenging environment. Culturing proved challenging but DNA analysis

highlighted the importance of hydrogen as a key electron donor in the indoor pond

system, metabolised by organisms such as the bacterium Hydrogenophaga This

paper is intended to be submitted to Frontiers in Microbiology.

• Chapter five, paper two, shows a comparative analysis of taxonomic and metabolic

patterns of microbiomes from open-air and indoor spent fuel storage ponds,

conducted using a metagenomic approach. Relative abundance of functional genes

revealed that bacteria are able to colonise the pond environments through harnessing

light energy (outdoor pond) or hydrogen (indoor pond) as energy sources. This paper

is intended to be submitted to FEMS Microbiology Ecology

• Chapter six, paper three, presents a metagenomic analysis of phages on the spent

fuel storage systems. Interactions between the virus and host microbial cells represent

a novel research topic, and this chapter aims to identify phages that were associated

with key microbial components, to help predict their potential influence on the

microbial communities within the pond (e.g. defence systems, CRISPR-Cas,

hydrogen metabolism). This paper is intended to be submitted to Environmental

Microbiology.

• Chapter 7, conclusions, summarizes the key findings and provides future suggestions.

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1.4 Paper status and collaborator contributions

Chapter 4 consists of a paper entitled “ Identification of stable hydrogen-driven microbes in

highly radioactive storage facilities in Sellafield, UK”, currently in preparation for Frontiers in

Microbiology

S. Ruiz-Lopez – Principal author performed experimental work and concept development

L. Foster – Technical assistance onsite at Sellafield Ltd

C. Boothman – Technical assistance

N. Cole – Assistance on procuring and processing samples at the Sellafield site and

manuscript review

G. Boshoff – Assistance on procuring and processing samples at the Sellafield site

J. R. Lloyd – Initial concept development, conceptual guidance, extensive manuscript review

Chapter 5 consists on a paper entitled “Comparative metagenomic analyses of taxonomic and

metabolic diversity of microbiomes from spent nuclear fuel storage ponds”, currently in

preparation for FEMS Microbiology Ecology





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. Nixon – Technical assistance, concept development, conceptual guidance and extensive

manuscript review




manuscript review

G. Boshoff - Assistance on procuring and processing samples at the Sellafield site

J. R. Lloyd – Initial concept development, conceptual guidance, extensive manuscript review

19

2

Introduction

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Chapter 2 Introduction

This chapter contains a broad overview of the research, including insights of the history of

nuclear power, the nuclear fuel cycle and description of the Sellafield site in particular

describes the studied ponds. Finally, the chapter presents an overview of the microbial

interactions with radionuclides as well as metabolic responses to specific extreme

environments (hyper-alkaline, radioactive and oligotrophic).

2.1 History of nuclear power

The discovery and application of nuclear power has been one the most significant scientific

achievements of the past century. The beginning of nuclear power can be traced to 1895 in

Germany, when William Roentgen discovered a new kind of energy emitted from an energized

device. Soon, in France in 1896 Becquerel noticed the effects of uranium salts on photographic

plates, and Marie and Pierre Curie studied the phenomenon thoroughly and isolated two new

elements involved in the energy production: Polonium and Radium. This new phenomenon

was called radioactivity (Mahaffey, 2011). During the 20th Century, many events happened

and helped to create a better understanding of radioactivity. In 1902, Ernst Rutherford showed

that radioactivity is a spontaneous event that can produces two kinds of particles from the

nucleus; alpha and beta. Contributions from Frederick Soddy, James Chadwick, Cockcroft and

Walton, Enrico Fermi and Irene Curie allowed further progress in nuclear energy by

discovering several radionuclides and their properties including uranium fission effects (WNA,

2016). Those contributions were set to have two main applications; the production of a source

of constant power and for military purposes (superbombs) due to uncontrolled uranium fission

(Mahaffey, 2011). Figure 2.1 shows a resume of the nuclear energy history.

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Figure 2.1 Brief history of nuclear power, adaptation from (WIN, 2013)

2.2 Nuclear Power

Nuclear power uses the energy released by splitting atoms of certain elements by a process

called nuclear fission. A slow-moving neutron collides with an atom (such as uranium) making

the atom unstable. Then the unstable atom splits into two new separate atoms creating heat

that can be used to boil water to make steam. The steam turns the blades of a steam turbine,

driving generators that produce electricity. A separate structure cools the steam back into

water, that can later be reused to create steam and the cycle goes on (Nuclear Energy Agency,

2003) (Figure 2.2).

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Figure 2.2 Radioactive elements (1) encased in fuel rods are split into smaller elements (2) by

high-energy reactions. These reactions release energy as heat (3) and also generate free particles. In a nuclear reactor, this heat converts water to steam, which turns turbines to

generate electricity (4). At the end of its cycle, the nuclear fuel rods are cooled in pools of water for several years (5), and then may be disposed in dry cask storage (6) (Jennewein & Senft,

2018)

The UK has 15 operational reactors in 8 power stations generating about 21% of its electricity,

and also has 1 major reprocessing plant in Sellafield. However the use of nuclear power to

generate electricity has declined since old plants have been shut down, due to ageing-related

problems that affect safety and performance availability (WNA, 2019b).

Worldwide around 11% of the total electricity is generated by nuclear power reactors and the

need for new generating capacity is clear, not only for the increased demand of electricity in

many countries, but to replace old fossil fuel powered units such as coal-fired power stations

that emit large amounts of carbon dioxide (WNA, 2019b).

2.3 The Nuclear Fuel cycle

The nuclear fuel cycle is defined as a series of processes that involve various activities to

produce electricity from uranium after being processed in nuclear reactors (WNA, 2015). The

nuclear fuel cycle consists of three stages. First, the “front end” that comprises the steps

necessary to prepare nuclear fuel for reactor operation, the “service period” where the fuel is

used and the “back end” that comprises the management of highly radioactive spent nuclear

fuel, whether it is reprocessed or sent to a final storage or disposal (Nuclear Energy Agency,

2003).

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The uranium that is used in the nuclear fuel cycle must be prepared by the steps of mining,

milling, conversion, enrichment and fuel fabrication. After the uranium fuel has been used in

the reactors for about three years, the spent fuel is taken through a series of steps including

storage, reprocessing and recycling before disposal as waste. Fig. 2.3 indicates the key steps

in the Nuclear Fuel Cycle (WNA, 2015).

Figure 2.3 Nuclear fuel cycle (WNA, 2017)

Every step in the nuclear fuel cycle produces wastes, and they can be categorised as low

level, produced at all stages; medium level produced during reactor operation and by

reprocessing; and high level, which contain separated highly-radioactive fission products.

These levels of radioactivity are defined according to the amount of radiation they emit (WNA,

2017).

2.4 Nuclear waste

Radioactive waste management and disposal are among of the biggest problems faced by the

nuclear industries, with significant environmental challenges relating to legacy and future

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)


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)


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


was not measured

(Masurat, Fru, &

Pedersen, 2005)

Spent nuclear fuel (SNF)

Samples were taken

from the wall surface,


(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


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


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).


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).


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).


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


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


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


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


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).


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)


69

(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


(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)


(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).


75

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.


76

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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).


85

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


86

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).


87

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


88

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).


89

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


90

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).


91

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


92

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


93

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.


94

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


95

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).


96

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


97

DNA was extracted from the samples, and 16S and 18S rRNA genes were targeted by PCR

using the methods described previously. However, only 16S rRNA gene amplification products

were detected by gel electrophoresis, and it was therefore concluded that eukaryotic

microorganisms were absent, or were below the level of detection in the INP samples. The

16S rRNA amplicons were then sequencing using the Illumina MiSeq next generation

sequencing platform, and analysed using a bespoke bioinformatics platform which included

comparison to prokaryotic gene sequences deposited in the NCBI databases.

Samples from the main ponds (MP) were consistently dominated by Proteobacteria (70-98%)

and Bacteroidetes (2-21%). Organisms affiliated with the phylum Cyanobacteria were not

detected on the initial samples, but were detected in subsequent times (from October 2017 to

April 2019), although at a relative abundance of less than 3%. Samples from the subponds

(SP) were also dominated by Proteobacteria (80-97%) and Bacteroidetes (3-7%), while the

relative abundance of Cyanobacteria was again low (less than 2%). In addition, other phyla

detected at lower levels in the main ponds included organisms affiliated with the Actinobacteria

(8%, January 2018), Armatimonadates (4%, June 2017 and February 2019) and

Deinococcus-Thermus (2-4% from November 2018 to April 2019). Samples from the

supplying feeding tank (FT) were also dominated by Proteobacteria (70 and 75%),

Bacteroidetes (14 and 19%) and Actinobacteria (1 and 4%). Detailed information is shown in

Supplementary data, Figures 1 and 2.

At the genus level (Figure 4.3), both duplicates from the feeding head tank (HT01 and HT02)

were dominated by close relatives to Curvibacter (~21%, Betaproteobacteria, 1 OTU),

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


98

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.


99

a)

b)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

MP01_Oct16

MP02_Oct16

MP03_June17

MP04_June17

MP05_Oct17

MP06_Oct17

MP07_Jan18

MP08_Jan18

MP09_June18

MP10_June18

MP11_Nov18

MP12_Nov18

MP13_Feb19

MP14_Feb19

MP15_Apr19

MP16_Apr19

Relativeabundance

Synechococcus

Trichococcus

Dietzia

Cyanobium

Alkalilimnicola

Rivibacter

Roseomonas

Polynucleobacter

Methylobacterium

Mongoliitalea

uncultured

Others

Sphingomonas

Silanimonas

Roseococcus

Methylophilus

Porphyrobacter

Hydrogenophaga

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

SP03_Jan18

SP04_Jan18

SP05_June18

SP06_June18

SP07_Nov18

SP08_Nov18

SP09_Feb19

SP10_Feb19

SP11_Apr19

SP12_Apr19

Relativeabundance

CyanobiumRivibacter

FlavobacteriumReyranellauncultured

RoseomonasPseudomonas

MongoliitaleaCaulobacterMeiothermus

SphingomonasOthers

SilanimonasMethylophilusRoseococcus

PorphyrobacterHydrogenophaga


100

c) Figure 4.3 Phylogenetic affiliations (closest known genera) of microorganisms detected in

Sellafield indoor pond (INP): a)main ponds, b)subponds and c)feeding tank (FT) using Illumina sequencing with broad specificity primers for prokaryote 16S rRNA. Only the genera that

contained more than 1% of the total number of sequences are shown.

Cultivation-dependent analysis for determining microbial diversity in the INP

In addition DNA-based analyses, culturing techniques were adopted to characterise the

microbial communities within the INP subponds complex, and to provide axenic cultures

representative of the microbes colonising such an extreme environment for future studies. A

series of dilutions from the INP subponds (samples SP01 and SP02), were spread onto agar

plates containing a range of solidified high pH (11.5) solid media. After 7 days of incubation,

growth was detected exclusively on the undiluted samples (100) from plates containing non-

defined complex media (DL, NA and Zobell media; See Supplementary Table 2). CFU per ml

were determined between 700-1000 per ml for each media and eleven distinct colony

morphologies were noted. Representative single colonies were isolated and identified by

sequencing using the dideoxynucleotide technique. The presence of colonies was not

detected at fully defined media (minimal media M9).

Overall, 4 different genera were identified. Representatives of Algoriphagus genus (isolates

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


101

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


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


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


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.


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


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.


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)


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%


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


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


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


110

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5

Research Paper: Comparative metagenomic analyses

of taxonomic and metabolic diversity of microbiomes

from spent nuclear fuel storage ponds


123

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



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


125

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


126

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,




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


131

minutes (DeLong 1992b). For archaeal 16S rRNA genes the thermal cycle protocol consisted

of an initial denaturation step at 94°C for 4 minutes, melting at 94⁰C for 45 seconds, annealing

at 55°C for 30 seconds, extension at 72oC for 1 minute (for a total of 30 cycles) and a final

extension step at 72⁰C for 5 minutes (DeLong 1992b).

The purity of the amplified PCR products was determined by electrophoresis using a 1% (w/v)

agarose gel in 1X TAE buffer (Tris-acetic acid-EDTA). DNA was stained with SYBER safe

DNA gel stain (Thermofisher), and then viewed under short-wave UV light using a BioRad

Geldoc 2000 system (BioRad, Hemel Hempstead, Herts, UK).

The 16S rRNA gene PCR amplicons was sequenced using the Illumina MiSeq platform

(Illumina, San Diego, CA, USA) targeting the V4 hyper variable region (forward primer, 515F,

5′-GTGYCAGCMGCCGCGGTAA-3′; reverse primer, 806R, 5′-

GGACTACHVGGGTWTCTAAT-3′) for 2 × 250-bp paired-end sequencing (Illumina)

(Caporaso et al. 2011) (Caporaso et al. 2012). PCR amplification was performed using the

Roche FastStart High Fidelity PCR System (Roche Diagnostics Ltd, Burgess Hill, UK) in 50μl

reactions under the following conditions; initial denaturation at 95°C for 2 min, followed by 36

cycles of 95°C for 30 s, 55°C for 30 s, 72°C for 1 min, and a final extension step of 5 min at

72°C. The PCR products were purified and normalised to ~20ng each using the SequalPrep

Normalization Kit (Fisher Scientific, Loughborough, UK). The PCR amplicons from all samples

were pooled in equimolar ratios. The run was performed using a 4pM sample library spiked

with 4pM PhiX to a final concentration of 10% following the method of Schloss and Kozich

(Kozich et al. 2013).

For targeting the V9 eukaryotic 18S rRNA gene sequencing primers 1319F and EukBR were

used for 2 × 250-bp paired-end sequencing under the following conditions, initial denaturation

at 95⁰C for 2 min followed by 36 cycles of 95⁰C for 30 s, 72⁰C for 1 min and final extension of

5 min at 72⁰C (Amaral-Zettler et al. 2009).

Raw sequences were divided into samples by barcodes (up to one mismatch was permitted)

using a sequencing pipeline. Quality control and trimming was performed using Cutadapt

(Martin 2011), FastQC (B.I. 2016), and Sickle (N.A. and J.N. 2011). MiSeq error correction


132

was performed using SPADes (Nurk et al. 2013). Forward and reverse reads were

incorporated into full-length sequences with Pandaseq (Masella et al. 2012). Chimeras were

removed using ChimeraSlayer (Haas et al. 2011), and OTU’s were generated with UPARSE

(Edgar 2013). OTUs were classified by VSEARCH (Edgar 2010) at the 97% similarity level,

and singletons were removed. Rarefaction analysis was conducted using the original detected

OTUs in Qiime (Caporaso et al. 2010a). The taxonomic assignment was performed by the

RDP classifier (Wang et al. 2007). Sequences obtained were compared with the NCBI

GenBank database to find the similar organisms (https://www.ncbi.nlm.nih.gov/genbank/).

18S rRNA gene taxonomic assignment was performed by UCLUST using the Silva119

database (Quast et al. 2013).

Whole genome sequencing was achieved using the Illumina Hiseq2000 platform at Celemics

(Celemics, Inc., Seoul, Korea). Raw sequences were uploaded to the Metagenomics Rapid

Annotation using Subsystems Technology (MG-RAST) (Meyer et al. 2008) online server for

taxonomic and functional annotation under the project name “Spent fuel storage ponds_UoM”,

‘ID 86418’. The RefSeq database (Pruitt et al. 2007) was chosen for taxonomic annotation

and the SEED database (Overbeek et al. 2005) was used for functional annotation. The MG-

RAST default parameters (maximum e-value cutoff of 10-5, minimum % identity cutoff of 60%

and minimum alignment length cutoff as 15bp) were used for annotation of the sequences. All

of the Illumina reads that were shorter than 35 bases or had a median quality score below 20

were removed.

Results

Microbial diversity on the indoor spent fuel storage pond (INP)

Six 16S rRNA gene amplicon libraries were generated from DNA extracted from an indoor

pond collected over the 18-month sampling period focusing on three different areas of the

pond complex. Two samples were taken from the feeding tank (INP_FT, October 2016), two

from the main ponds (INP_MP, October 2017) and two from the subponds (INP_SP, January

2018).


133

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


135

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).


136

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


137

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


138

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:


139

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


140

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).


141

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

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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


142

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


143

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


144

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


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


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


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.


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


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


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.


151

Acknowledgements




and IAA funding.


152


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


153

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

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0.8

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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


154

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


155

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


156

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


157

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


158

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


159

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


160

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


161

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


162

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


163

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


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


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


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


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


168

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


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


170

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


171

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


172

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Microbially Influenced Corrosion on Spent Nuclear Fuel Storage Life. In: Laboratory,

I. N. E. (ed.). Idaho, USA: U. S. Department of Energy.

Zhao, F., Zhang, X., Liang, C., Wu, J., Bao, Q. & Qin, S. (2005). Genome-wide analysis of

restriction-modification system in unicellular and filamentous cyanobacteria. Physiol

Genomics, 24, 181-190.

Zhdanova, N. N., Tugay, T., Dighton, J., Zheltonozhsky, V. & McDermott, P. (2004). Ionizing

radiation attracts soil fungi. Mycological Research, 108(9), 1089-1096.


182

6

Research Paper: Metagenomic analysis of viruses in

spent fuel storage ponds at Sellafield, UK


183

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



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


184

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


185

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


186

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-


187

water activity and quality, there is a continuous “once through” purge flow; pond-water from

the main ponds flows into the transfer channel and enters the recirculation pump chamber

where it is continuously pumped round a closed circulation loop and through a heat exchanger

system, which cools the pond-water before it is recycled into the main ponds. Through the

control feed, purge and re-circulation flow rates, the water depth is maintained at 7±0.05m.

The purge flow can be either from a donor plant or from other hydraulically linked ponds within

the Sellafield complex (e.g. FGMSP). The temperature and pH are controlled at 15⁰C and 11.6

respectively. Samples for analysis were taken from designated sample points in the “Feeding

Tank” of the donor plant, where the alkali-dosed demineralised water used to feed the complex

is stored, and main ponds 2 and 3 of the Fuel Handling Plant.

The FGMSP is the primary storage pond for legacy Magnox spent fuel at site. The pond is

continuously purged with alkaline dosed demineralised water at a pH of 11.4, from an East to

West direction along the length of the pond, and contains an outflow point, where water is

removed from the pond, on the Western wall. There are two further feeds into the pond, the

first enters the pond at a location along the Northern wall and contains alkaline dosed water

(pH ~11.4) from another fuel handling pond facility on site. The auxiliary settling tank (auxiliary

pond) is directly connected to the FGMSP, and if the water levels are sufficiently high, the

auxiliary pond feeds the alkaline legacy pond legacy pond along the South wall.


188

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,




Ltd. Extracted DNA samples free from significant radionuclide contamination were shipped to

the University of Manchester and stored at -20⁰C until use.


189

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.


190

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


191

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


192

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


193

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


194

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


195

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 + + + +


196

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.


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


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


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




and IAA funding.


200


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


201

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


202

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


203

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

- -


204

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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


212

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


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.


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


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.


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


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,


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


L. Foster, J. Lloyd and K. Morris. Microbiology Conference Annual Conference, Edinburgh,

Scotland. 3rd-6th April 2017


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


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

the microbial ecology of spent fuel storage ponds at

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