introduction 2.1 Direct analysis: method development and optimization 2.1.1 Comparison of two 16S rDNA regions metagenetics sequencing to identify microbial diversity in an aquatic environment complemented with microbial harvesting and DNA extraction optimization (article in preparation)

First of all I want to thank the members of the jury, Pr. CONFALONIERI, Dr. CHAPON, Pr. DUPORT, Pr. SCHNEIDER, and Dr. MONSIEURS, for taking the time to evaluate my PhD thesis and for coming to Grenoble to assist to my presentation.

I would like to thank the Laboratoire de Physiologie Cellulaire & Végétale (LPCV), led Dr. Eric MARECHAL, as well as the Plants, Stress & Metals team, led by Drs. Jacques BOURGUIGNON and Stéphane RAVANEL, to have welcomed me for the past three years.

I want to thank the Dr. Corinne RIVASSEAU and Dr. Jean ARMENGAUD for the opportunity they gave me. I would like to thank them for their supervision, advices, and help during this PhD.

Thank you to the Li2D's team at CEA Marcoule for their welcome in their lab. I would like to personally thank Gérard STEINMETZ for his help and contribution to the metaproteomics work performed during this three years. Thank you Gérard for always being in a good mood and never left out of jokes; as well as your serious during our conversations about society. Without your help, part of my work would not be there today. I also want to thank Karim HAYOUN and Béatrice ALPHA-BAZIN for the work done during Karim's internship.

I also thank Mohamed MYSARA, Valérie VAN EESBEECK, and Pieter MONSIEURS from the SCK•CEN for their help with the analysis of the metagenetics results via the OCToPUS software and their advices.

A special thanks for Dr. Claude ALBAN. Your help during these three years was precious, from start to end. Thank you for taking the time to help me in molecular biology and taking the time to help correcting my thesis.

I also want to thank the rest of the Plants, Stress & Metals Team: Sylvie for her microbiological experience, Jacqueline for her bibliographical skills, Anne-Marie for algae culture, Nelson for his friendship and advices in microscopy and cell coloration, Benoit for his kindness, and Manon for her spontaneous help at the end of a day without whom I would not have finished the experiment before 20:30 pm. I want to personally thank Eulalie for her work during her internship and allowed me to write my thesis while dosing the different fractions for the accumulation study. Thank you Pascal MARTINEZ for correcting my thesis formulation and his advice in presentation.

I also wish to thank everyone who helped me from far: Alberto with his phylogeny expertise, Younes with his constructive critics about any presentation I had to give, Christian for being my "Molecular Biology Expert" when I had a question and Claude was not available, and everyone in the PCV lab. METAGENOMICS RESULTS AFTER A V4V5 SEQUENCING AND A 0.016% CUT-OFF OF ALL METHODS. THE PHYLA (OR CLASS FOR

Bibliographical introduction 1.1 Origin of life and diversity of microorganisms

Living organisms can be divided into three domains: Bacteria, Archaea and Eucarya (all Eukaryotes). This classification was first introduced in 1977 by Woese and Fox after the phylogenetic analysis of ribosomal RNA sequences of a large number of organisms [START_REF] Woese | Phylogenetic structure of the prokaryotic domain: The primary kingdoms[END_REF]. Bacteria and Archaea are prokaryotes, meaning that they do not possess a nucleus, unlike Eukaryotes, which possess a nucleus. All animals and plants, including fungi, yeasts and micro-algae, belong to the Eucarya domain. The simplified tree of life obtained with the current available genomics data is presented in Figure 1.1.

Figure 1.1: Tree of life (non-exhaustive) (adapted from [2])

In 1998, Whitman et al. estimated the population of prokaryotes on Earth as being around 4 to 6.10 30 cells [START_REF] Whitman | Prokaryotes: The unseen majority[END_REF], the Prokaryotes kingdom representing most of life on Earth.

The Earth was formed around 4.5 billion years. The first confirmed evidence of life has been evaluated to be 3.5 billion years old thanks to a microfossil, during the Archean eon [START_REF] Dodd | Evidence for early life in Earth's oldest hydrothermal vent precipitates[END_REF][START_REF] Bell | Potentially biogenic carbon preserved in a 4.1 billion-year-old zircon[END_REF]. However some indirect evidence could place life earlier on Earth, between 3.8 and 4.1 billion years ago [START_REF] Dodd | Evidence for early life in Earth's oldest hydrothermal vent precipitates[END_REF][START_REF] Bell | Potentially biogenic carbon preserved in a 4.1 billion-year-old zircon[END_REF][START_REF] Mojzsis | Evidence for life on Earth before 3,800 million years ago[END_REF].

When life first appeared on Earth, the conditions were extremes, when compared to the current ones. Indeed, the first organisms were anaerobic since no free oxygen was present in the atmosphere. Moreover, the ozone layer was inexistent due to the absence of free oxygen, hence the UV rays received from the young Sun were stronger than today and not filtered by the ozone layer. The Earth's surface temperatures may also have been as high as 70°C in the Archean eon [START_REF] Knauth | Origin and diagenesis of cherts: an isotopic perspective. Isotopic signatures and sedimentary records[END_REF]. However, liquid water was likely found beneath the surface in hydrothermal systems [START_REF] Lunine | Physical conditions on the early Earth[END_REF]. Finally the natural radioactivity present on Earth was higher than today due to radioactive decay (cf. 1.4.2). Free oxygen in the atmosphere appeared millions of years after Microorganisms can colonize almost every location on Earth, from animal's gut to the bottom of the ocean and the atmosphere. Microorganisms can also be found in unexpected environment and exhibit resistance

properties to what humans consider as extreme conditions. Those microorganisms can be extremophiles.

Extremophiles

According to Rothschild and Mancinelli, "an organism that thrives in an extreme environment is an extremophile; in more than one extreme it is a polyextremophile" [START_REF] Rothschild | Life in extreme environments[END_REF]. An extremophile can thus survive physical and biochemical conditions where humans, most animals, and plants cannot survive.

Some microorganisms can subsist and even flourish in extreme conditions for which multicellular organisms cannot survive. Extremophile organisms such as bacteria and archaea have indeed been found in hydrothermal vent, acidic or alkaline media, extremely low or high temperature, high pressure, etc. Some of them cannot grow without those extreme conditions [START_REF] Pikuta | Microbial extremophiles at the limits of life[END_REF]. Because they have the ability to occupy every potential environment microorganisms are the most diverse and abundant cellular life forms on Earth [START_REF] Pikuta | Microbial extremophiles at the limits of life[END_REF][START_REF] Rinke | Insights into the phylogeny and coding potential of microbial dark matter[END_REF][START_REF] Kay | Microorganisms in subterranean acidic waters within Europe's deepest metal mine[END_REF][START_REF] Jolivet | Thermococcus gammatolerans sp. nov., a hyperthermophilic archaeon from a deep-sea hydrothermal vent that resists ionizing radiation[END_REF].

Knowing that life can be found in almost every place and conditions, it is not a surprise to find radioresistant microorganisms. One bacterium, identified in 1956 by Arthur W. Anderson, has been proven capable to resist high radiation level, up to 20 kGy (cf. 1.4.1). This unusual bacterium is Deinococcus radiodurans

Common environmental stress and microorganism responses

Throughout evolution, extremophiles have also developed mechanisms to be able to live within the constraints of their environment. For example, microorganisms can be found in the hot springs of Yellowstone. These springs are not only extremely hot (up to 93°C) but also acidic (pH between 2 and 3). As an example, Thermus aquaticus was discovered in one of Yellowstone's hot spring in 1978 by Brock [START_REF] Brock | Thermophilic Microorganisms and Life at High Temperatures[END_REF].

Chapter I -Bibliographical introduction 20

This bacterium had a great influence on biotechnology since its DNA polymerase (called Taq polymerase)

was later used for PCRs (polymerase chain reaction) to amplify DNA fragments.

To survive the different environmental conditions, whether favorable or unfavorable, microorganisms have evolved and developed different metabolic ways to survive. Some of them can be the activation of a certain living form, such as biofilms or spores. Others can be the activation of pathways in order to counter the negative effects of the stresses on the cell and its components, such as oxidation stress for aerobic organisms and the different cell defenses that exist to block its negative impact. The most common environmental stresses can be a deficiency of nutrients, desiccation, presence of antibiotics, predator and food competitor, etc.

Biofilms

In unfavorable environments, microorganisms, as a mean to survive, can form biofilms, either composed by the same species or by a consortia of different species, genus or even kingdom, e.g. bacteria with other bacteria, fungi, micro-algae… This morphological state protects them, since they behave as a complex multicellular organism and communicate with each bacterium, fungal or algal cell through quorum sensing mechanisms. This phenomenon not only preserves them and gives them higher chances of survival but also allows them to concentrate nutrients taken from the medium [START_REF] Costerton | Microbial Biofilms[END_REF]. Stewart and Costerton reviewed in 2001 the ability of biofilms to protect the microorganisms against antibiotics. Some bacteria that did not present any antibiotic resistance genes could survive antibiotics when in biofilms [START_REF] Stewart | Antibiotic resistance of bacteria in biofilms[END_REF]. It is now widely accepted that biofilms are predominant in aquatic ecosystems [START_REF] Leclerc | Relationships between common water bacteria and pathogens in drinking-water. Heterotrophic Plate Counts and Drinking-water Safety[END_REF][START_REF] Geesey | Sessile bacteria: An important component of the microbial population in small mountain streams[END_REF][START_REF] Costerton | Biofilms, the Customized Microniche[END_REF][START_REF] Costerton | Bacterial biofilms in nature and disease[END_REF].

The formation of biofilms in an aqueous environment relies on several steps. First of all bacterial cells come into contact with a solid surface and can attached in a reversible way. They can then grow and proliferate, leading to an irreversible attachment due to the secretion of exopolysaccharide (EPS). This leads to the formation of a thin layer of biofilm, which will then mature and give a complex structure [START_REF] Bozorg | Impact of biofilm on bacterial transport and deposition in porous media[END_REF]. The biofilm formation is eased by the presence of EPS. Once a mature biofilm is formed, it can further enhance bacterial deposition. This explains why, once a biofilm has been observed, it is extremely difficult to eliminate: if only one cell is present, the biofilm can grow again. It is thus necessary to either eliminate the entire biofilm with drastic methods or to regularly clean the surface on which the biofilm has grown [START_REF] Bozorg | Impact of biofilm on bacterial transport and deposition in porous media[END_REF].

The need to eliminate biofilms can be explained by several reasons. It can contaminate drinkable water pipes and provoke diseases like in 1998 and 1999 where several cases of legionnaires' disease, due to Legionella species, were reported in Paris. Moreover, the presence of a biofilm can damage the material it is attached to and cause corrosion.

In nuclear installations, the main danger the biofilms could provoke is their ability to induce corrosion. The degradation process of the biofilm's support is presented in Figure 1.3. At first, a thick biofilm is formed and an anaerobic zone is created next to the colonized surface. The microorganisms then develop colonies and complex microbiota, catching ions and creating localized chemical and physical gradients at the metal surface. The metal can then dissolves, leading to corrosion, or, as it called when caused by microorganisms, Microbial Influenced Corrosion (MIC) [START_REF] Borenstein | Microbiologically Influenced Corrosion Handbook[END_REF]. [START_REF] Borenstein | Microbiologically Influenced Corrosion Handbook[END_REF]). The cells first attached to the surface and a thin anaerobic biofilm is formed. This biofilm grows and matures until it changes the physical and chemical conditions of the surface. These changes can lead to the corrosion of the surface.

The formation of biofilms can thus protect the microorganisms but can also have negative influences on their environment, such as propagating diseases or provoke corrosion in a human made environment.

However the response to an environmental stress can not only be the growth under a biofilm form but also the ability to either form spores (Gram positive bacteria) or enter a viable but non-culturable state.

Sporulation and Viable but Non-Culturable Bacteria (VBNC)

Besides biofilm formation, microorganisms have developed other ways to resist environmental stresses.

Indeed most bacteria can slow their metabolism down. This state, called the viable but non-culturable state, possesses the metabolic activity needed to survive but is extremely low [START_REF] Oliver | The viable but nonculturable state and cellular resuscitation[END_REF][START_REF] Oliver | The Viable but Nonculturable State in Bacteria[END_REF][START_REF] Oliver | Recent findings on the viable but nonculturable state in pathogenic bacteria[END_REF]. Some gram positive bacteria can sporulate, leading to spore(s) formation. It is well known that spores are more resistant to stresses than the vegetative form [START_REF] Murrell | The Heat Resistance of Bacterial Spores at Various Water Activities[END_REF][START_REF] Gould | Heat resistance of bacterial endospores and concept of an expanded osmoregulatory cortex[END_REF][START_REF] Gould | Recent Advances in the Understanding of Resistance and Dormancy in Bacterial Spores[END_REF].

Sporulation

Only Gram positive bacteria can sporulate. Usually bacteria sporulate when the environmental conditions are unfavorable to growth or life. Indeed bacteria can form spore in nutriment starvation or to prevent desiccation. But spores are also a resistant microbial form to ionizing radiation or antibiotics. Moreover, it has been proved that bacterial spores can survive in space, amongst the cosmic radiation and the vacuum [START_REF] Horneck | Long-Term Survival of Bacterial Spores in Space[END_REF][START_REF] Horneck | Resistance of bacterial endospores to outer space for planetary protection purposes--experiment PROTECT of the EXPOSE-E mission[END_REF]. Usually, one bacterial cell produces one spore. However depending on the conditions some bacteria can produce two or more spores [START_REF] Hutchison | Sporulation in Bacteria: Beyond the Standard Model[END_REF]. The main different steps of spore formation in bacteria are schematized in Figure 1.4. During its maturation in the cell, a spore is called "forespore".

Chapter I -Bibliographical introduction [START_REF] Leggett | Bacterial spore structures and their protective role in biocide resistance[END_REF]). A vegetative cell will divide asymmetrically. One cell will lead to the formation of the spore while the mother cell will go under a programmed cell death to release the mature spore.

When the conditions become unfavorable to the bacterium, regulation factors and different genes involved in sporulation are activated. This activation leads to an asymmetric cell division as shown in Figure 1.4. The smaller part becomes the forespore while the bigger one will be considered as the mother cell. Once the spore is mature enough, the mother cell experiences a programmed cell death and lysis. This cell death leads to the release of the mature spore [START_REF] Hutchison | Sporulation in Bacteria: Beyond the Standard Model[END_REF]. Once the conditions are proper enough, the spore can germinate and transform into a new vegetative cell. However, while sporulation can be an effective way to survive environmental stresses, not all bacteria have the ability to form spore.

Viable but Non-Culturable Bacteria (VBNC)

The VBNC state is empirically defined as a state in which cells are viable but not able to divide on nonselective growth medium, leading thus to a lack of visible growth. The first published description of such a state was in 1982 by Xu et al. in which they observed the ability of Escherichia coli and Vibrio cholerae to undergo a dormant-like state in response to starvation [START_REF] Xu | Survival and viability of nonculturable Escherichia coli and Vibrio cholerae in the estuarine and marine environment[END_REF]. The VBNC response differs from other bacterial responses to stress. Moreover cells can remain VBNC for a long period of time, up to several years, meaning that this state is not a temporary response. These characteristics explain why the VBNC state is thought to be a long-term survival mechanism initiated in response to environmental stresses. Those stresses can include starvation but also UV light, extreme temperatures, toxic molecules, radiation, etc. [START_REF] Trevors | Bacterial gene expression at low temperatures[END_REF]. One inconvenient of the VBNC state is that, in order to be able to grow the bacteria, the optimum conditions must be found. It has been estimated that, in water, such as the oceans, lakes, and rivers, bacteria are mainly present under a VBNC state and represent between 90 to 99.9% of the bacterial cells [START_REF] Stokell | Viable but Nonculturable Bacteria[END_REF]. This means that only 0.1 to 10% of aquatic and environmental bacteria are culturable with the classic laboratory methods. Using different direct analysis methods, such as metagenomics (cf. 1.6.2.2), it is possible to identify new microbial species, genera, families or phyla even for unculturable bacteria. Tara Expedition is currently analyzing the oceans around the globe to highlight some of the diversity present in the oceans.

During this PhD thesis, the isolated microorganisms were subjected to three main environmental stresses such as starvation, ionizing radiation and radionuclides. The survivability of isolated bacteria were tested in presence of ionizing radiation and selected radionuclides. 1.4 Unusual environmental stresses: ionizing stress, radiation and radionuclides 1.4

.1 Ionizing stress and radiation

Ionizing radiation is a radiation that carries enough energy to damage the matter and can liberate electrons from atoms or molecules, thereby ionizing them. These radiations thus impact the living organisms. Figure 1.5 represents the different radiations that exist in the environment. Radiations are electromagnetic waves, consisting of oscillating electric and magnetic fields traveling through space. Depending on the frequency (in Hz) of the wave, the radiation can be visible (visible light) or invisible to the human eye and ionizing or not.

Gamma ray, X-ray, and the higher ultraviolet part of the electromagnetic spectrum are ionizing radiations.

The lower ultraviolet part of the electromagnetic spectrum, and also the lower part of the spectrum below UV, including visible light (along with nearly all types of laser light), infrared, microwaves, and radio waves are all considered non-ionizing radiations. The boundary between ionizing and non-ionizing electromagnetic radiation that occurs in the ultraviolet is not sharply defined, since different molecules and atoms ionize at different energies. Conventional definition places the boundary at a photon energy between 10 eV and 33 eV in the ultraviolet. The electronvolt (eV) represents the energy carried by an electron or a photon for example.

Part of radioactivity belongs to the higher frequencies of ionizing radiation. When an atom is unstable, it disintegrates to reach a more stable state (this state can be achieved after one or several disintegrations).

During this transition, energy and ionizing radiation are emitted. This process is called "radioactive decay". Some ionizing radiation occurs from natural sources, due to cosmic and terrestrial radiation. For example, the Sun emits UV rays that reach the Earth and are not entirely stopped by the ozone layer. Each year, rays are received on Earth from cosmic rays.

Several units can be used to evaluate radioactivity and are explained in Scheme 1.1: -Becquerel (Bq): it measures the number of disintegrations of atoms during one second. It thus represents the energy released by the atoms; -Gray (Gy): it assesses the energy received and thus absorbed by a material (organic and inorganic). It can be seen as how much energy you received from the energy that was send; -Sievert (Sv): it estimates the effect of the radiation on the organism.

Scheme 1.1: Schematic view of the different units used in radioactivity (IRSN)

Effect of ionizing radiation on living cells

Ionizing radiation can provoke the formation of Reactive Oxygen Species (ROS), which alters molecules and thus cells.

Ionizing radiations have two effects on DNA. It can either create direct double-strand breaks (around 20% of the damages in eukaryotes) or lead to the formation of ROS due to water radiolysis (around 80% of the damages in eukaryotes) [START_REF] Sutherland | Clustered DNA damages induced in isolated DNA and in human cells by low doses of ionizing radiation[END_REF][START_REF] Gulston | Clustered DNA damage induced by γ radiation in human fibroblasts (HF19), hamster (V79-4) cells and plasmid DNA is revealed as Fpg and Nth sensitive sites[END_REF][START_REF] Ghosal | How radiation kills cells: survival of Deinococcus radiodurans and Shewanella oneidensis under oxidative stress[END_REF]. Water radiolysis leads to the creation of several molecules such as free radicals (Scheme 1.2) that can interact with cell molecules and damage proteins, lipids and DNA. [START_REF] Azzam | Ionizing radiation-induced metabolic oxidative stress and prolonged cell injury[END_REF]). Ionizing radiation can interact with a water molecule. This water molecule will then be modified and lead to the formation of superoxides, free radicals and molecules highly reactive to oxygen.

Scheme 1.2: Water radiolysis and molecules formed (adapted from

Once ROS are formed, they induce negative effects on molecules, and on the structure and functionality of living organisms. ROS can indeed impact proteins and lipids, which themselves can damage DNA due to the by-products of lipid peroxidation [START_REF] Marnett | Lipid peroxidation-DNA damage by malondialdehyde[END_REF] or after exposure of histones proteins to free radicals [START_REF] Luxford | Histone H1-and other protein-and amino acid-hydroperoxides can give rise to free radicals which oxidize DNA[END_REF][START_REF] Luxford | Radicals Derived from Histone Hydroperoxides Damage Nucleobases in RNA and DNA[END_REF][START_REF] Davies | Protein oxidation and peroxidation[END_REF].

Moreover when enzymes used to repair DNA are damaged they cannot properly function. 1.4.1.2 Reactive oxygen species (ROS) 1.4.1.2

.1 Nature and origins

Reactive oxygen species (ROS) are chemically reactive chemical species containing oxygen. ROS are molecules that are more reactive than O 2 . These molecules are naturally produced by the aerobic metabolism [START_REF] Decker | Oxygen and the Evolution of Life[END_REF]. However, external stresses can also be responsible of production of ROS in cells, such as high temperature, desiccation, UV, irradiation, etc. They include superoxides, oxygen radicals but also nonradical derivatives of O2, such as H2O2, as detailed in Table 1 [START_REF] Halliwell | Free Radicals in Biology & Medicine[END_REF] The ROS are mainly due to the presence of free oxygen in the atmosphere. During Earth early age, free oxygen was present in less than 0.1% in the atmosphere: the first cells were confronted from low to inexistent levels of oxidative stress. Then, the anaerobic microorganisms present had to evolve to cope with the oxidation induced by the rising levels of oxygen on Earth. Microorganisms had three possibilities to cope with the presence of high quantity of free oxygen: i) death, ii) life in anaerobic environments or iii) implementation of antioxidant defenses and aerobic metabolism. Most of living organisms have been able

to survive by the use of pathways to repair the damages caused by aerobic respiration and environmental oxidative stress.

Consequences on the cell

A significant part of the initial damage done to the cells by ionizing radiation is due to OH • which leads to ROS. ROS formation can damage three main biological molecules: the proteins, the lipids and the nucleic acids. ROS induce several damages, including lipid peroxidation, protein oxidation, oxidative alterations to DNA, and inactivate enzymes. Lipids are the main targets of ROS in the cell. Lipid peroxidation starts a chain reaction and form several radicals. For example, if only one lipid becomes a lipid peroxide, it can create as many peroxides as it possesses carbon chains. Lipid peroxidation can not only decrease the membrane fluidity but can also enhance the release of internal H2O into the external medium as well as injuring membrane proteins. Moreover in some bacteria, the DNA is close to or attached to the membrane which means that it can be damaged during lipid peroxidation. Other deleterious effects of ionizing radiation and OH • are their interactions with proteins. OH • can indeed oxidize proteins and decrease their activities or even inactivate them. Proteins are essential to cell metabolism and damages repair. When proteins are inactivated or when their activity decreases they can damage the cells by malfunctioning and thus also not be able to repair damages. Furthermore, OH • can also interact directly with DNA by inducing single-and double-strand breaks, modifying nucleotide bases, and damaging the deoxyribose. The deleterious effects of ionizing radiation are enhanced by the presence of O2 [START_REF] Halliwell | Free Radicals in Biology & Medicine[END_REF].

Defenses against ROS

To be able to survive despite the presence of ROS, cells have developed several ways to fight the ROS and their negative effects. Cells have active mechanisms such as DNA repair but can also use passive mechanisms. The high condensation of the genome, as well as the histone-like protein HU associated with bacterial nucleoid, are involved in cell survival after -radiation [START_REF] Slade | Oxidative stress resistance in Deinococcus radiodurans[END_REF][START_REF] Toueille | A comparative proteomic approach to better define Deinococcus nucleoid specificities[END_REF]. It has also been noticed that a high

[Fe]/[Mn] ratio is often involved with radioresistance [START_REF] Latour | Manganese, the stress reliever[END_REF]. Moreover some cells can possess antioxidant molecules. Active and passive mechanisms have been identified in radiation resistance. 1.4.1.2.3.1 Antioxidants Antioxidant molecules are known to prevent the oxidative damages of molecules on the cells. Halliwell and Gutteridge have defined in 2015 an antioxidant molecule as "any substance that delays, prevents, or removes oxidative damage to a target molecule" [START_REF] Halliwell | Free Radicals in Biology & Medicine[END_REF]. The presence of ROS and oxidative stress activates genes coding for molecules involved in ROS scavenging and proteins involved in cell protection.

Some families of antioxidants have been shown to help microorganisms to grow in ionizing radiating environments, such as in space or in the facility of the reactor of Chernobyl [START_REF] Gomoiu | Fungal Spores Viability on the International Space Station[END_REF][START_REF] Aguileta | Lower prevalence but similar fitness in a parasitic fungus at higher radiation levels near Chernobyl[END_REF][START_REF] Zhdanova | Fungi from Chernobyl: mycobiota of the inner regions of the containment structures of the damaged nuclear reactor[END_REF]. Such class of molecules is present in all aerobic organisms to counter the negative effects of oxidation during the aerobic metabolism. Some of these molecules are pigments. One well-known antioxidant pigment is melanin. It can be observed on different domains of life: Eukarya (in almost every animals and fungi) and in some bacteria.

It is admitted that melanin can protect against UV, solar or -radiation as well as metal toxicity due to, for instance, its ability to scavenge free radicals [START_REF] Pacelli | Melanin is effective in protecting fast and slow growing fungi from various types of ionizing radiation[END_REF][START_REF] Dadachova | Ionizing radiation: how fungi cope, adapt, and exploit with the help of melanin[END_REF][START_REF] Butler | Fungal melanins: a review[END_REF]. Melanised fungi can be found in extreme environments, including Arctic and Antarctic [START_REF] Prabagaran | Marinomonas ushuaiensis sp. nov., isolated from coastal sea water in Ushuaia, Argentina, sub-Antarctica[END_REF][START_REF] Selbmann | Fungi at the edge of life: cryptoendolithic black fungi from Antarctic desert[END_REF], and, more surprisingly, the walls of the damaged reactor of Chernobyl, where they are exposed to a high constant radiation field [START_REF] Dadachova | Ionizing radiation: how fungi cope, adapt, and exploit with the help of melanin[END_REF]. Melanised fungi can also be found in another astonishing place where radiation levels are higher than in classical environment:

the International Space Station (ISS) [START_REF] Alekhova | Monitoring of Microbial Degraders in Manned Space Station[END_REF][START_REF] Nosanchuk | The contribution of melanin to microbial pathogenesis[END_REF]. Melanins are highly effective scavengers of free radicals [START_REF] Sichel | In vitro scavenger activity of some flavonoids and melanins against O2[END_REF] and have electron transfer properties [START_REF] Gan | Electron transfer properties of melanin[END_REF]. It has also been shown and reviewed that the degree of radioresistance of an organism is associated with the quantity of produced melanin [START_REF] Nosanchuk | The contribution of melanin to microbial pathogenesis[END_REF].

Another family of pigments involved in the response to oxidative stress are the carotenoids. They represent organic pigments that are produced by plants, algae, as well as several bacteria and fungi. Carotenoids are lipophilic and have the ability to protect some bacteria to UV radiation and ionizing radiation [START_REF] Ordenes-Aenishanslins | Pigments from UV-resistant Antarctic bacteria as photosensitizers in Dye Sensitized Solar Cells[END_REF][START_REF] Tian | Evaluation of the antioxidant effects of carotenoids from Deinococcus radiodurans through targeted mutagenesis, chemiluminescence, and DNA damage analyses[END_REF][START_REF] Tian | Effects of carotenoids from Deinococcus radiodurans on protein oxidation[END_REF]. For example, Deinococcus radiodurans, the most known radioresistant organism, contains high levels of carotenoids. Several carotenoids, such as sarcinaxanthin have been proved to have an anti-oxidative activity [START_REF] Osawa | Characterization and Antioxidative Activities of Rare C50 Carotenoids-Sarcinaxanthin, Sarcinaxanthin Monoglucoside, and Sarcinaxanthin Diglucoside-Obtained from Micrococcus yunnanensis[END_REF]. It has been shown that, amongst the carotenoids, deinoxanthin, found in D. radiodurans, exhibited the highest scavenging activity, followed by lycopene, lutein, zeaxanthin and, finally, b-carotene [63] [66, 67]. The three pigments described above are not the only antioxidant pigments present in microorganisms.

Other pigments, belonging to different families of molecule, such as mycosporine or scytonemin, as well as several other molecules, play a role in the defense against oxidative stress [START_REF] Gao | Microbial ultraviolet sunscreens[END_REF][START_REF] Rastogi | Cyanobacterial Sunscreen Scytonemin: Role in Photoprotection and Biomedical Research[END_REF].

Other molecules such as glutathione are also involved in the response of oxidative stress. Glutathione is present in high concentration in several living cells, including microorganisms. It has however not yet been identified in strictly anaerobic microorganisms. It exists under different form: oxidized (GSSG) or reduced (GSH). In cells, most of the glutathione is under the reduced form [START_REF] Halliwell | Free Radicals in Biology & Medicine[END_REF][START_REF] Penninckx | Metabolism and Functions of Glutathione in Micro-organisms[END_REF]. Reduced glutathione has been proved to be a radioprotective molecule [START_REF] Morse | Cellular glutathione is a key to the oxygen effect in radiation damage[END_REF] by scavenging OH • radical and by being involved in the following reaction: RO2 • + GSH → RO2H + GS • , where GS • is an oxidized molecule [START_REF] Halliwell | Free Radicals in Biology & Medicine[END_REF] Glutathione can be also involved in DNA repair after single and double strand breaks due to ionizing radiation [START_REF] Chatterjee | Reduced glutathione: a radioprotector or a modulator of DNA-repair activity?[END_REF]. It was indeed shown that GSH was involved in the non-homologous end-joining DNA repair pathway when joining both ends [START_REF] Dutta | Interaction of radiation-and bleomycin-induced lesions and influence of glutathione level on the interaction[END_REF]. Moreover, GSH and glutathione S-transferase were shown to be involved in metal stress defenses, such as uranium, copper or cadmium response mechanisms, and even some radionuclides counteraction by chelating the metals [START_REF] Halliwell | Free Radicals in Biology & Medicine[END_REF][START_REF] Penninckx | Metabolism and Functions of Glutathione in Micro-organisms[END_REF][START_REF] Hu | Whole-genome transcriptional analysis of heavy metal stresses in Caulobacter crescentus[END_REF][START_REF] Obeid | Mechanism of Attenuation of Uranyl Toxicity by Glutathione in Lactococcus lactis[END_REF][START_REF] Preveral | Metal(loid)s and radionuclides cytotoxicity in Saccharomyces cerevisiae. Role of YCF1, glutathione and effect of buthionine sulfoximine[END_REF].

One known class of enzymes involved in ROS scavenging is the superoxide dismutase (SOD). This enzyme can react and remove the different ROS present, such as H2O2, O2 •-, OH • , etc [START_REF] Halliwell | Free Radicals in Biology & Medicine[END_REF]. SODs are present in eukaryotes as well as prokaryotes and archaea [START_REF] Miller | Superoxide dismutases: ancient enzymes and new insights[END_REF]. In animal cells, it has been shown that the overexpression of Manganese SODs (MnSODs) leads to a higher radiation resistance [START_REF] Maier | Radioprotective gene therapy[END_REF]. Moreover, MnSOD is present in D. radiodurans and has been proved to be involved in the radioresistance mechanism implemented by the bacterium [START_REF] Markillie | Targeted Mutagenesis by Duplication Insertion in the Radioresistant Bacterium Deinococcus radiodurans: Radiation Sensitivities of Catalase (katA) and Superoxide Dismutase (sodA) Mutants[END_REF][START_REF] Abreu | The kinetic mechanism of manganese-containing superoxide dismutase from Deinococcus radiodurans: a specialized enzyme for the elimination of high superoxide concentrations[END_REF]. Some anaerobic microorganisms possess SODs to help them survive temporary O2 exposure [START_REF] Halliwell | Free Radicals in Biology & Medicine[END_REF]. 1.4.1.2.3.2 The role of Manganese

Antioxidant enzymes protect biomolecules from ROS-mediated damages. This represent the first line of protection against oxidative stress. The second line is manganese. Indeed, a high [Mn]/[Fe] ratio can be observed in resistant organisms. For example, a ratio of 0.24 was observed in D. radiodurans while it dropped to 0.0072 in E. coli [START_REF] Daly | Accumulation of Mn(II) in Deinococcus radiodurans Facilitates Gamma-Radiation Resistance[END_REF]. A high [Mn]/[Fe] ratio increases the capacity to prevent the formation of iron-dependent ROS through the Fenton reaction (Scheme 1.3). It is also thought that Mn 2+ ions do not catalyze the formation of OH • from H2O2, thus preventing the formation of this ROS [START_REF] Halliwell | Free Radicals in Biology & Medicine[END_REF][START_REF] Sharma | Responses of Mn2+ speciation in Deinococcus radiodurans and Escherichia coli to gamma-radiation by advanced paramagnetic resonance methods[END_REF].Furthermore, intracellular manganese can also be protective by scavenging ROS. This high ratio does not directly prevent DNA double-strand breaks but it can mimic the activities of antioxidant enzymes such as superoxide dismutases and catalases [START_REF] Confalonieri | Bacterial and archaeal resistance to ionizing radiation[END_REF].

Scheme 1.3: Reaction of H2O2 with Mn 2+ and Fe 2+ [START_REF] Latour | Manganese, the stress reliever[END_REF] Finally, some enzymes involved in ROS scavenging contain manganese or are manganese-dependent. For example, one known SODs in bacteria is MnSOD, which is involved in ROS scavenging. It has been shown

that D. radiodurans uses the manganese properties to be more tolerant to ionizing radiation [START_REF] Daly | Accumulation of Mn(II) in Deinococcus radiodurans Facilitates Gamma-Radiation Resistance[END_REF][START_REF] Daly | Protein Oxidation Implicated as the Primary Determinant of Bacterial Radioresistance[END_REF]. 1.4.1.3 DNA protection and repair 1.4.1.3.1 Physical protection of DNA One of the different characteristics of D. radiodurans that allows its survival to high radiation levels is its highly compacted DNA [START_REF] Slade | Oxidative stress resistance in Deinococcus radiodurans[END_REF][START_REF] Toueille | A comparative proteomic approach to better define Deinococcus nucleoid specificities[END_REF] which could limit the dispersion of radiation-induced DNA fragments. It was also suggested that D. radiodurans and D. deserti, two highly resistant bacteria, contained a low diversity of nucleoid associated proteins, with the histone-like HU protein and DNA-gyrase being the main proteins involved in their nucleoids [START_REF] Toueille | A comparative proteomic approach to better define Deinococcus nucleoid specificities[END_REF]. The HU protein is involved in supercoiling of DNA. The shape of genomic DNA from D. radiodurans was determined in absence of ionizing stress and was observed as a ring-like (toroidal) shape in the stationary-state while it was dispersed in some cells during the growing state, allowing access for the replicative enzymes [START_REF] Levin-Zaidman | Ringlike structure of the Deinococcus radiodurans genome: a key to radioresistance?[END_REF]. Furthermore, D. radiodurans grows as compartmentalized cells, each compartment having its own genomic DNA in multicopy. After an irradiation at 15 kGy, this peculiar DNA shape was maintained for 1 hour after exposure in culture. However, few hours after irradiation, the DNA toroidal shape changed and the DNA presented an open S-like structure. Then the DNA started spreading between the compartments through a hole in the membrane. Three hours after exposure, the spread DNA started coalescing between each other [START_REF] Levin-Zaidman | Ringlike structure of the Deinococcus radiodurans genome: a key to radioresistance?[END_REF]. It was suggested that this feature is involved in the radioresistance of D. radiodurans. It was also determined that the DNA from D. radiodurans contains high amounts of Mn 2+ [START_REF] Lebowitz | The In Vivo Association of Manganese with the Chromosome of Micrococcus Radiodurans[END_REF]. The nucleoid morphologies of different bacterial species, either radioresistant or radiosensitive, has been determined. It was shown that the radioresistant species possessed a highly condensed genome while the DNA from the radiosensitive species was uniformly distributed in the cells [START_REF] Zimmerman | A ring-like nucleoid is not necessary for radioresistance in the Deinococcaceae[END_REF]. The authors of this study suggested that no obvious relationship between the ring-like shape observed by Lebowitz et al. and the radioresistance property of bacteria exists. The condensation of the DNA, rather than its shape, may play a role in radioresistance.

Finally, it was also suggested that D. radiodurans DNA could be attached to the membrane. This peculiar structure could act as a support to ensure the correct restitution of the genome. Indeed, DNA from unirradiated D. radiodurans cells was found to be associated with the membrane complex, while, after X-ray irradiation, the damaged DNA was dissociated from the membranes. After incubation in proper conditions, the DNA was re-associated to the membrane and reformed the DNA-membrane complex [START_REF] Burrell | DNA-membrane association and the repair of double breaks in X-irradiated Micrococcus radiodurans[END_REF].

The shape of DNA only helps preventing DSBs while DNA systems have been implemented by the cell to repair the damaged DNA. The four main pathways used to repair double strand breaks in DNA (adapted from [START_REF] Confalonieri | Bacterial and archaeal resistance to ionizing radiation[END_REF]). Homologous Recombination (HR) is the main pathway in DNA double-strand break repair in bacteria. It uses an intact homologous DNA to restore the correct DNA sequence at damaged sites. Single-Strand Annealing (SSA) and Synthesis-Dependent Strand Annealing (SDSA) are the main DNA repair pathways found in D. radiodurans: the ends of the DSB are resected to produce a long 3 'ended single stranded DNA (ssDNA). Non-Homologous End-Joining (NHEJ) is the main pathway in DNA double-strand break repair in eukaryotes. This pathway is based on the binding of double-strand DNA carrying strand breaks nucleoids. Proteins ligates the end of the break together.

As presented in Figure 1.6 four known pathways involved to repair DSBs in DNA are particularly common and used. These four pathways are the homologous recombination (HR), the single-strand annealing (SSA), the synthesis-dependent strand annealing (SDSA), and the non-homologous end-joining (NHEJ).

Homologous recombination

The main pathway in DNA double-strand break repair in bacteria and Saccharomyces cerevisiae is the homologous recombination (HR). It uses an intact homologous DNA to restore the correct DNA sequence at damaged sites (Fig. 1.6). Two proteins are involved in this pathway: the RecA protein and the RecBCD enzyme [START_REF] Cox | Problems and Paradigms: Relating biochemistry to biology: How the recombinational repair function of RecA protein is manifested in its molecular properties[END_REF]. The RecA protein is a recombinase and binds strongly to ssDNA in long clusters in order to form a nucleoprotein filament. It can hold a single strand and a double strand together, joining the DSB to the intact homologous DNA fragment [START_REF] Lesterlin | RecA bundles mediate homology pairing between distant sisters during DNA break repair[END_REF][START_REF] Amundsen | Intersubunit signaling in RecBCD enzyme, a complex protein machine regulated by Chi hot spots[END_REF]. The basal level of RecA in D. radiodurans has been estimated to be around 11000 proteins per cell [START_REF] Bonacossa De Almeida | Quantification of RecA protein in Deinococcus radiodurans reveals involvement of RecA, but not LexA, in its regulation[END_REF]. After a 15 kGy irradiation, the gene expression of recA is 8 fold induced [START_REF] Liu | Transcriptome dynamics of Deinococcus radiodurans recovering from ionizing radiation[END_REF]. In Deinococcus deserti the levels of RecA were 4.5 fold induced 22 hours after a 3 kGy irradiation [START_REF] Dedieu | Major soluble proteome changes in Deinococcus deserti over the earliest stages following gamma-ray irradiation[END_REF].

Single-strand annealing and synthesis-dependent strand annealing

These two DNA repair pathways are present in D. radiodurans [START_REF] Zahradka | Reassembly of shattered chromosomes in Deinococcus radiodurans[END_REF]. Single-strand annealing (SSA) can occur when a DSB is made between two repeated sequences oriented in the same direction in the genome. The DNA repair through this pathway is schematized in Figure 1.6. In this pathway, the ends of the DSB are resected to produce a long 3'-ended single stranded DNA (ssDNA). The complementary strands can thus directly rejoin, through the annealing of complementary single-stranded [START_REF] Haber | Partners and pathways: repairing a double-strand break[END_REF]. The DNA overhangs are degraded so the ligation can occur. This process does not preserve the whole genome since the flanking regions of the break are excised.

Synthesis-dependent strand annealing (SDSA) is a variant of SSA. As in SSA, the ends of the DSB are resected to create a ssDNA. A protein then binds to the DNA and form a nucleoprotein filament. A heteroduplex DNA with a homologous DNA is formed, creating a D-loop. This overlap is followed by DNA synthesis beyond the original break site using the homologous DNA sequence as a template, which restores the missing sequence information at the break point unlike SSA [START_REF] Helleday | DNA double-strand break repair: from mechanistic understanding to cancer treatment[END_REF].

Non-homologous end-joining

The non-homologous end-joining (NHEJ) is the main pathway in DNA double-strand break repair in eukaryotes. It has recently been identified and characterized in bacteria. This pathway is based on the binding of double-strand DNA carrying strand breaks nucleoids, as exposed in Figure 1.6 [START_REF] Confalonieri | Bacterial and archaeal resistance to ionizing radiation[END_REF]. Indeed, after a DSB, several proteins can be involved in order to ligate the two ends of the break. Nucleases excise the ends to make them compatible. A ligase then joins both ends. This pathway can lead to alterations of the sequence at the break location since it does not need a homologous DNA template.

Base excision repair and Nucleotide excision repair

The base excision repair is a mechanism used to repair deaminated, alkylated and oxidized bases. It is highly conserved in all three kingdoms and is initiated by DNA glycolases. The base excision repair pathway can be used by organisms when DNA was damaged by oxidation, deamination and alkylation. The first step of this pathway is the removal of the damaged base with a DNA glycolase. The remaining sugar is then excised with an AP-endonuclease and a 5'-phosphodiesterase. A new nucleotide is then added to repair the damaged site with a DNA polymerase and the enzymatic reaction is over after a DNA ligase has ligated the new nucleotide to the DNA [START_REF] Seeberg | The base excision repair pathway[END_REF]. It has been found that D. radiodurans possess more DNA glycolases than most of the bacteria [START_REF] Timmins | A Decade of Biochemical and Structural Studies of the DNA Repair Machinery of Deinococcus radiodurans: Major Findings, Functional and Mechanistic Insight and Challenges[END_REF]. Some pathways, such as Nucleotide Excision Repair (NER) has been shown to have no incidence in radioresistance in D. radiodurans [100]. In the NER pathway the damaged nucleotide is detected by the UvrA protein which binds the UvrABC complex to the DNA. The damaged nucleotide and several surrounding nucleotides are uncoiled by the UvrAB complex before being incised by the UvrBC complex [101]. The incision complex is then removed by a DNA helicase. The gap created is filled by the action of DNA polymerase. In the final step a DNA ligase seals the newly completed repair DNA [102].

Other pathways

Other not often used DNA repair pathways exist in microorganisms. Some of them were recently discovered and are not very well described, such as micro-homology-mediated recombination. Other DNA repair pathway may exist but have not yet been discovered.

Radionuclides

Apart from ionizing radiation and aerobic respiration, other uncommon environments can lead to oxidative stresses such as metallic stress.

Radionuclides description

A radionuclide is an atom with an excess of nuclear energy due to its atomic mass number. This excess of energy makes it unstable: the atom can emit gamma radiation to release the energy, transfer it to one of its electron to release it as a conversion electron. The atom can create and emit a new particle, alpha or beta, from the nucleus. These processes are called radioactive decay and are considered as ionizing radiation. This decay can produce either a stable atom or another unstable atom. If an unstable atom is created, the radioactive decay continues until it reaches a stable nuclide. The duration of the decay is estimated through the half-life, i.e. the time it takes to decompose half of the atoms. The shorter the halftime is, the strongest the radioactivity. For example, 24 Na has a half-time of 15 hours and is highly radioactive and dangerous, while 238 U has an estimated half-life of 4.5 billion years. This uranium isotope is thus less dangerous in terms of radioactivity than this sodium isotope. If naturally only time can stabilize radionuclides, a new method is being developed to faster stabilize radionuclides. This method is called transmutation. Its principle relies on the transformation of long half-life radionuclides (up to hundreds of thousand years) in stable elements or with shorter half-life. To do so a neutron must be absorbed by the radionuclide's nucleus. According to SCK•CEN and CEA transmutation is however still in development and cannot nowadays be used industrially. Finally, not all radionuclides are targeted with this technic [103].

Natural radionuclides

Radionuclides naturally occur on Earth and can be classified in three categories. Natural radionuclides can have extraterrestrial origins as well as an Earth's crust origin. The first category, the primordial radionuclides, are nuclides that existed in their current form at least since the Earth's formation, meaning that their half-lives are long enough to still be present today. To be considered as a primordial radionuclide, the half-life must be greater than 100 million year. 235 U and 238 U are primordial radionuclides [104].

The second category, called secondary radionuclides, are isotopes derived from the decay of primordial radionuclides. For example, 222 Rn and 210 Po are decay products: the radon 222 is naturally formed during 238 U decay chain while the polonium 210 is formed during 222 Rn decay chain. 210 Po decays in stable lead ( 206 Pb). The last category contains the cosmogenic isotopes, such as 14 C or 24 Na for example. They are created when a high-energy cosmic ray interacts with the nucleus of an atom. Cosmogenic radionuclides are formed on Earth with the interaction of the cosmic ray and with an atom present in the atmosphere or on the ground, such as soil or rocks. They can also be produced in extraterrestrial materials, such as asteroids. Some locations on Earth are more radioactive than others due to the presence of natural radionuclides in the soils, such as the states of Minas Gerais and Goias as well as the state Espírito Santo in Brazil near the Atlantic Ocean [105]. In the Kerala state, India, high levels of 238 U, 232 Th and 40 K can naturally be found [106].

Man-made radionuclides

Currently humans use nuclear fission to produce energy and more specifically electricity. During this process, radionuclides are formed and energy is liberated. These radionuclides can be by-products of nuclear fission. For research purpose or medicine some radionuclides are produced to be commercialized.

Several diagnostic methods depend on radioactivity. The combination of radioactive tracers with imaging devices allows the doctors to study flows taking place in the body. A molecule, chosen to target the observed organ, is tagged with a short-life radionuclide (mainly 99m Tc). Aside from diagnosis and imaging radionuclides can also be used in radiotherapy. This treatment is used for some cancers, such as bone cancer or thyroid cancer, 131 I being used for the latter. Several radionuclides linked to molecules, such as antibodies, are currently under clinical trial to treat cancers [107]. For brain tumors a knife containing 60 Co can be used as an external radiation source on the cancerous cells to destroy them [108].

In a nuclear reactor, the fuels (most commonly 235 U and 239 Pu) are activated by a neutron to start a nuclear chain reaction. For uranium the neutron collides with an atom of 235 U which will then be activated and will decompose itself in neutrons and fission products. The fission products can also be unstable while the neutrons will activate other atoms of uranium. In a nuclear reactor, the chain reaction is controlled, meaning that neutrons absorbers are installed to absorb the majority of the neutrons. It will thus not activate too much uranium atoms at the same time and exponentially increase the released energy (Fig.

1 .7). To be able to protect the workers of nuclear installations, several measures exist. First of all, the fuels are stocked in a pool during a working cycle, the water acting as a shield against the radiation, and the heat emitted by the chain reaction and the decay of radionuclides. Once the fuel is too old to produce enough energy, at the end of a cycle, it is removed and placed in a spent nuclear fuel pool (SNFP) to allow the decay of radionuclides so that no ionizing radiation are emitted. When a sodium-potassium alloy is used as a coolant in nuclear reactors, 24 Na is created, meaning that the coolant is itself radioactive. However, since its half-life is around 15 hours, when the reactor is in shut down, no radioactivity in the coolant can be detected after a few days. Cobalt 60 is naturally produced in nuclear facilities and can be found in nuclear effluents. It is a neutronic activation product from materials from nuclear reactors.

Chapter I -Bibliographical introduction 34 In France, there are currently 58 nuclear reactors, distributed in 19 facilities, to produce electricity.

Moreover 8 nuclear reactors used for research are also present in France, 7 of them are present on a CEA center. All these installations produce radioactive wastes that are treated accordingly to their nature. In

France, the radioactive wastes can be classified and treated accordingly to their half-life duration and their activity [109][110][111]. Short-life wastes contain all radionuclides with a half-life smaller than 31 years while those present in long-life wastes have a half-life greater than 31 years (cf. Appendix 1).

Several new treatments are being studied, such as chemical and physical treatments (e.g. the separationtransmutation process) or the use of biotechnology to concentrate radioactivity (e.g. the use of Coccomyxa actinabiotis [112], or the identification of organisms able to decontaminate radionuclides [113][114][115]). It is interesting to notice that, in France, electricity production generates 62% of the wastes, while the 28% left are produced by research, medicine or the army.

Radionuclides release in nature

Human activities may lead to environmental release of radionuclides, either because of nuclear waste treatment, mining or because of the use of fertilizers. Indeed uranium has a high affinity with phosphate, added in the soils as fertilizers. From 1972 to 2013 the addition of phosphate as fertilizers went from 31 to 8 kg/ha of agricultural soils. Naturally, there is only 0.1 to 3.7 kg/ha of phosphate in agricultural soils (Ministère de la Transition Ecologique et Solidaire). The uranium concentration in commonly used Brazilian fertilizers is between 5.17 and 54.3 mg of U/kg [116]. This value can reach 700 mg of U/kg of fertilizer for some fertilizers (personal manufacturer's information). In Europe, the mean value of uranium in soil is around 2 mg of U/kg of soil (IRSN) [117]. Mining activities can also be a source of radionuclide

Fission products

Stops the chain reaction contamination. Depending on the mining process some uranium can leaked in the environment. For example acid mine drainage is formed by oxidation of metal sulfides present in the ore by acidophiles, such as Leptospirillum [START_REF] Kay | Microorganisms in subterranean acidic waters within Europe's deepest metal mine[END_REF]118]. Acidic mine water may contain heavy metals and radionuclides, such as uranium [119]. Finally, another source of massive release of radionuclides is nuclear accidents and nuclear weapons assays. After the Chernobyl and Fukushima accidents, the area around the facilities were forbidden because of the large amount of radioactivity and radionuclides present in the soils and waters [120]. For Chernobyl this area measures 30 km radius from the power plant while the one implemented in Fukushima measures 20 km. The radionuclide pollution occurring after a nuclear accident can travel through hundreds to thousands of kilometers (Fig. 1.8). After the Chernobyl accident, radionuclides, mainly 137 Cs, arrived in

France and are still present 30 years later in some area, such as in Vercors, in the French Alps [121].

Radionuclide resistance in microorganisms

All these radionuclide contaminations can affect life and be lethal. However, some organisms are more resistant to radionuclides than others. Even though several organisms can resist and accumulate radionuclides, such as plants [122][123][124][125], this paragraph will only treat the resistance of microorganisms.

Radionuclides, beside the negative effects due to radiation emission, can also be toxic to the cell because of their chemical form as most of them are metal isotopes. At high concentration metals are toxic to organisms. Some metals are essential for organisms and can be enzymatic co-factors, such as cobalt, copper or zinc. However at levels higher than needed by the cells these metals can be toxic for the organism. To regulate the levels of the essential metals, membrane transports are used [126]. It was shown that metal resistance is often associated with antibiotic resistance [127,128], and metal resistance is often carried by a plasmid [129]. The resistance to essential metal is usually carried by the chromosome while the resistance to non-essential metal is often present on plasmids [130]. Nonessential metals have a greater affinity to thiol-containing groups and oxygen sites than essential metals. The resulting alterations in the conformational structure of nucleic acids and proteins are toxic for the cells. The osmotic balance is also changed, leading to potential lethal effects [126].

Chapter I -Bibliographical introduction 36

Microorganisms which can resist radionuclides have developed several ways of survival. One of them is linked to the oxidative stress produced by the radionuclides, for which several pathways exist to counter ROS lethal effects, mainly the antioxidant mechanisms such as glutathione or SODs (cf. 1.4.1.1). It has also been noticed that the interactions between bacteria and radionuclides are similar to the ones with heavy metals [131].

Some microorganisms can be resistant to radionuclides by preventing them to enter the intracellular environment. The permeability of the cell membrane can decrease due to a gene mutation preventing the production of a porin in Eschericha coli for example [132,133]. Some bacteria can naturally produce an exopolysaccharide coating which can possess attachment sites for metal cations but also bioabsorb metal ions and prevent them from interacting with the cell [126,134,135]. Some Klebsiella aerogenes strains exhibited a resistance twice greater to cadmium when the exopolysaccharide was present [135].

Radionuclides can also be sorbed to the cell membrane component. The cell does not need to be alive to bioadsorb but there is a quick saturation as well as desorption due to cation present in the medium and environment [131].

Some bacteria can bioaccumulate radionuclides. The active accumulation requires energy and can be dependent or independent from the metabolism. These accumulations can occur because of the presence of a transporter or if the radionuclide has an effect on the permeability of the membrane [131]. Metals that are non-essential for the cell usually borrow normal nutrient transport systems to penetrate in the cell but are rapidly exported [126]. Once the metal has entered the cell several mechanisms can be implemented.

First of all, the cell can use transporters to export the nuclide back to the extracellular environment. For example, in the Microbacterium A9 strain uranium was found to be adsorbed by carboxyl and phosphoryl groups present on the cell membrane [136]. In the same strain, the presence of a metabolism-dependent transporter has also been described but was not identified. It is however known that ATPase dependent transporters can be used by bacteria to export metallic anions and cations into the extracellular environment [137,138]. In the last two mechanisms, the cell accumulates the nuclide before exporting the metal back into the extracellular environment. Bacteria exporting the nuclide back to the extracellular environment cannot be used for a bioaccumulation process. Secondly, the cell can also perform an intracellular sequestration of the atom through protein bindings (e.g. metallothionein, GSH, cysteine-rich protein) to prevent the exposure of essential cellular components once the metal has entered the cell.

Some bacteria also possess enzymes to partially detoxify the nuclide by modifying its chemical form. Some enzymes can indeed reduce the nuclide [126].The reduction of uranium or cobalt is implemented by different enzymes. As an example, the cytochrome c3 in Desulfovibrio vulgaris was proved to reduce uranium [139]. It has been shown that some iron-reducing bacteria can reduce cobalt, such as Shewanella alga or Geobacter sulfurreducens. Some bacteria, such as Shewanella oneidensis, are able to preserve energy for anaerobic growth via the reduction of uranium [140]. The number of proteins involved in the phosphate metabolism of the Microbacterium A9 strain was increased in the presence of uranium [141].

Decontamination of radionuclides

Today's treatments to decontaminate radionuclides from the environment are expensive and tedious. To decontaminate soils, different methods exist and can be categorized in three classes: outside of the site, on the site and in situ. For the first two classes the soil needs to be excavated before treatment. The soil can be treated differently but all methods are either expensive or harmful for the environment [142].

Phytoremediation can also be used. However, while it is less expensive and less damaging to the environment than excavation and soil washing, its efficiency was shown to be much lower (a maximum of 24% for phytoremediation in low contaminated soils when soil excavation presents a 100% efficiency) [143].

Moreover phytoremediation for a contaminated site has been estimated to take 5 to 20 years meaning that the land will be unusable during this period [144]. More recently the use of metal resistant bacteria able to produce biosurfactants have been studied to increase phytoremediation efficiency [145]. Biosurfactants can form complexes with metals and increase their bioavailability for plants.

Several microorganisms have been studied for their ability to bioreduce, bioaccumulate and/or biosorb radionuclides. Among them, bacteria, algae and fungi have been identified [113,[146][147][148][149][150][151][152]. The advantages of radionuclides accumulating microorganisms can be numerous. They can in fact concentrate the pollution inside their cells and thus reduce the volume of wastes to store. Microorganisms able to change the toxicity and solubility of radionuclides can be used in the environment, either to immobilize radionuclides or to accelerate their removal [115]. Several studies have been performed in situ with the natural microbiota present in contaminated soils. It was noticed that the addition of a surfactant was necessary to maintain uranium concentration at low levels in groundwater (under 0.1 µM) [153][154][155]. It was also noticed that the main genus found in the tested environments was Geobacter [156]. Moreover the implementation of a biodecontamination process for contaminated environments, such as nuclear effluents, is possible and has already been studied at laboratory scale and plant scale for the micro-algae Coccomyxa actinabiotis [112].

Such process should be highly efficient on large volume of waste with low concentration of radionuclides.

It should also be cheaper than the existing processes and more "eco-friendly" since it does not need any chemicals. Furthermore, the use of a green technology for the nuclear industry could be well accepted by the public opinion. Microorganisms that have shown to possess the greater accumulation of a specific radionuclide can be used in biotechnologies to decontaminate nuclear effluent and be used in bioremediation. Even though several microorganisms have been identified as potential candidate for bioremediation process, large-scale experiments for industrial use have yet to be implemented.

Radioresistant organisms

The ability of life to counter any threat, whether it is biological or environmental, has led to organisms resistant to different stresses. Since 1956, it is known that organisms can be highly radioresistant with the discovery of Deinococcus radiodurans by Anderson et al. [START_REF] Anderson | Studies on a radio-resistant micrococcus. I. Isolation, morphology, cultural characteristics, and resistance to gamma radiation[END_REF]. Several radioresistant organisms have been discovered in natural environments and in man-made radioactive environments, such as nuclear facilities.

Radioresistant organisms found in natural environments

The ability of some bacteria to be radioresistant has been well investigated using D. radiodurans as a biological model. It has been observed that the capacity to withstand high levels of radiation is not part of an evolutionary mechanism due to the presence of high radiation but is the result of a secondary consequence associated to desiccation and oxidation resistance [157,158]. There is indeed naturally no such high level of radioactivity in the world as the one found in nuclear facilities (50 mGy/year nonetheless can be observed in the south-west of India and in a region of Brazil, while radioactivity found in the rest of the world is around 2.4 and 3.6 mGy/year). There are however regions in the world with very low bioavailable water level and high UV radiation which can cause oxidation, such as the Sahara and Atacama deserts, Antarctica, Arctic and other locations. It is in those natural environments that radioresistant microorganisms have mainly been found.

In order to survive these harsh conditions microorganisms have developed ways to counter the lethal effects of desiccation and oxidation. Some microorganisms present in dry and bright environments can yet resist to stresses different from desiccation and oxidation. They can in fact exhibit radioresistance properties thanks to a complex metabolism developed in order to survive the oxidative stress [157,159].

Radioresistance characteristics have been found in all three kingdoms, including multicellular organisms.

However, most radioresistant organisms are microorganisms, mainly bacteria but also some fungi, archaea, and algae. Some radioresistant organisms from different kingdoms are described below.

Tardigrade

One exceptional family of animals that belongs to the Tardigrada phylum, based on their morphology and genome, can resist several extreme conditions. Tardigrades (Fig. However, the eggs produced after a 2k Gy irradiation did not hatch [161]. The genome of two tardigrade species, Hypsibius dujardini and Ramazzottius varieornatus, have recently been sequenced [162]. The authors observed the loss of genes involved the ROS producing pathways. Furthermore, they found several genes involved in ROS defenses, such as genes coding for SODs, genes coding for proteins involved in desiccation tolerance, or trehalose, a sugar known to protect cells against desiccation. Trehalose was only found in R. varieornatus. In the genome of R. varieornatus, genes encoding proteins considered to protect DNA have been identified, such as the Damage suppressor protein (Dsup) which is believed to prevent DSBs [163,164]. A gene coding for an homologous protein was not identified in H. dujardini genome [162].

Tardigrades are thus animals able to resist extreme conditions. However, amongst prokaryotes, those abilities are not so uncommon. Deinococcus radiodurans (Fig. 1.10) was the first radioresistant organism discovered so its ability to survive radiation has been extensively studied and several reviews have been published [START_REF] Ghosal | How radiation kills cells: survival of Deinococcus radiodurans and Shewanella oneidensis under oxidative stress[END_REF][START_REF] Slade | Oxidative stress resistance in Deinococcus radiodurans[END_REF][START_REF] Timmins | A Decade of Biochemical and Structural Studies of the DNA Repair Machinery of Deinococcus radiodurans: Major Findings, Functional and Mechanistic Insight and Challenges[END_REF]157,159,166,167]. Most of the mechanisms found to be involved in radioresistance were identified using D. radiodurans, such as high levels of Mn 2+ or several recA gene copies for example. The ability to survive radiation can be measured with the D10, indicating the dose needed to eliminate 90% of the living population.

Chroococcidiopsis species

Another genus, belonging to the Cyanobacteria phylum, possesses radioresistant species. Several species from this genus have been isolated from the Negev desert in Israel. The Chroococcidiopsis genus (Fig. 1.11) was studied for its ability to survive oxidative stress and its potential ability to survive in space and under

Martian conditions [171,172].

Thermococcus gammatolerans

An archaeon, Thermococcus gammatolerans, was isolated from samples of an active chimney recovered from a hydrothermal site in Guaymas Basin after a 30 kGy irradiation [START_REF] Jolivet | Thermococcus gammatolerans sp. nov., a hyperthermophilic archaeon from a deep-sea hydrothermal vent that resists ionizing radiation[END_REF]. This strain presented an unaffected growth after a 3 kGy irradiation. T. gammatolerans exhibited an enhanced number of oxidized nucleosides after a 5 kGy irradiation. However the number of oxidized nucleosides decreased one hour after the irradiation, indicating the presence of an efficient DNA repair system. Two putative enzymes involved in BER were identified by next-generation proteomics and transcriptomics. These two enzymes were present before the irradiation and their transcription was up-regulated after a 5 kGy irradiation (2 to 3 fold) [174]. The ability of T. gammatolerans to resist radiation was proved to be growth phaseindependent, unlike D. radiodurans [175].

A brief comparison of the gamma-radiation resistance of some microorganisms found in nature and in spent nuclear fuel pools can be found in Table 1.2. Escherichia coli is present as a radiosensitive strain. Several organisms exhibit radioresistance properties due to their ability to survive extreme conditions, such as desiccation and high light intensity. However some organisms have been isolated from environments presenting high radiation levels that are man-made, presenting thus radiation levels never found elsewhere on Earth. The organisms living there have been selected for their ability to survive radiation and evolved accordingly.

To avoid overheating and protect staff, nuclear fuels are submerged in water. The water present in this pool is a deionized water. Due to the presence of high levels of radiation, high quantity of dissolved radionuclides and the lack of nutrients, people have thought for a long time that no life could be found in such environment. Different microorganisms were found living in the cooling water of nuclear reactors. Their presence was made possible by their ability to form biofilms and their resistance to ionizing stress. Different microorganisms, such as bacteria, fungi and a micro-alga, have already been found in several Nuclear Power Plants (NPPs) and Spent Nuclear Fuel Pools (SNFPs) around the world [176,180,181]. The first report of life present in these environments was in 1997, at the Savannah River Site, a nuclear facility in the USA [182].

Furthermore, Microbial Influenced Corrosion (MIC) has been previously described from a water sample of a SNFP. The water was sampled aseptically and enriched with liquid medium in the presence of metal coupons. After several months of incubation, the coupons were recovered and the biofilm removed.

Corrosion was observed on the metal due to MIC. Figure 1.12 highlights the presence of corrosion with gaps measuring less than 2 µm with a depth around 100 nm. This experiment highlighted the presence of microorganisms in the water of this nuclear pool and their ability to do MIC [183]. In different countries, including Hungary and in particular in Spain, studies of the microorganisms present in the water of nuclear facilities have been performed and several bacteria have been isolated through biofilm formation. In the SNFP of Cofrentes in Spain, 56 different bacteria species have been reported to be present. Researchers recovered biofilms from the pool and cultivated them. Biofilms can absorb radionuclides and hence become highly radioactive: it should thus be handle with caution [180,184].

Among the bacteria isolated from the SNFP of Cofrentes, a majority of Proteobacteria were identified (cf.

Appendix 2). A fungi identified as Aspergillus fumigatus was also identified in this pool [181]. Bacteria identified as Kocuria palustris, Micrococcus luteus, and an Ochrobactrum sp., isolated from an interim spent fuel storage pool, were able to accumulate cesium and cobalt [152]. The microorganisms that have already been found in SNFPs and NPPs are bacteria, a few fungi and one micro-algae living in biofilms. They are listed in Appendix 2. The isolated microorganisms from SNFPs were mainly Gram negative bacteria belonging to the Proteobacteria phylum. Among the few Gram positive bacteria identified, the majority belonged to the Bacillus genus (Firmicutes phylum). Some bacteria were found to belong to the Actinobacteria phylum. In these studies there were no indication whether the Gram positive bacteria were present under a vegetative or a spore form in the water [152,177,181,[184][185][186][187][188][189][190][191][192]. More recently, the use of a direct identification approach consisting on sequencing the amplicon of the 16S rDNA of the microbiota was used (cf. 1.6). Different bacterial phyla were identified in a nuclear storage basin, including Acidobacteria, Actinobacteria, Planctomycetes, Proteobacteria, Nitrospirae and Deinoccous-Thermus [193].

Aside from bacteria, several eukaryotes have been found in SNFPs. For example, a micro-algae has been found in a spent nuclear fuel in France. This organism, called Coccomyxa actinabiotis (Fig. 1.13), has been proved to withstand radiation of 20 kGy [176], capacity only found hitherto in prokaryotes, in particular D.

radiodurans. It was also observed that it could accumulate silver, carbon -14, cobalt, cesium, and uranium [112, 176, 194, 195]. A process using this algae was developed at a laboratory scale and displayed encouraging results regarding the use of biotechnology to decontaminate nuclear effluent [112]. C.

actinabiotis indeed exhibited radioactive silver decontamination abilities similar to the ion-exchange resins currently used in nuclear facilities [176]. Several fungi have been isolated near the damaged reactor of Chernobyl [START_REF] Zhdanova | Fungi from Chernobyl: mycobiota of the inner regions of the containment structures of the damaged nuclear reactor[END_REF] and presented what the authors called "radiotropism" [196]. The irradiated fungi grew towards the direction of the radioactive sources and it was the first and only time such phenomenon was observed. The fungi Cladosporium sphaerospermum and Penicillium hirsutum were the main fungi identified in the walls of the damaged nuclear reactor of Chernobyl [START_REF] Zhdanova | Fungi from Chernobyl: mycobiota of the inner regions of the containment structures of the damaged nuclear reactor[END_REF]. It was found that the irradiation of fungal spores led to a slower growth and a decrease metabolism [197].

In order to achieve a complete picture of the different microorganisms present in those highly unfavorable environments, direct identification methods should be used, such as metagenomics, already used for Tara expedition for example [198] and metaproteomics.

1.6 Microorganisms analysis in samples 1.6.1 After a cultivation step -morphological and biochemical identification

Before the apparition of sequencing, microbiologists used different tests to identify and classify the microorganisms. These methods needed the cultivation and isolation of organisms to obtain pure culture.

First of all, the morphology of the cell and the colony was determined, as well as a gram coloration was also performed. Secondly, the biochemical profile of the bacterium was determined using several tests. These tests are still being used to describe a new microbial species.

Genetics rises and metagenomics

When organisms are closely related, they share most of their DNA sequence. It is largely accepted that a similarity of 98.65% or more in the 16S rDNA sequence indicates that the organisms belong to the same species [199,200]. This number drops to an identity between 70 (in 1987) to 95-96% for the entire genome depending on the threshold used [199,201]. However, the gray area around the limits of the taxonomic DNA identity caused some issues concerning some species. Indeed, a same species could have had, or still have, different names and be categorized between two different species, particularly when sequencing started to be used and the error rate was quite high [202,203].

Ribosomal DNA sequencing

The sequencing of the 16S rDNA (prokaryotes) and 18S rDNA or Internal Transcribed Spacer (ITS), a gene located between the small-and the large-subunits of the 18S rDNA (eukaryotes), can be performed to identify microorganisms. This method allows the identification of an organism at a deep taxonomical level.

16S and 18S rRNA and ITS genes are used to determine the taxonomy of an organism [204,205]. This DNA region is similarly organized between eukaryotes and prokaryotes, as seen in Figure 1.14.

Figure 1.14: Comparison between the ribosomal genes in bacteria and eukaryotes [206]

The advantages of these genes are their conserved and hypervariable regions. The conserved regions allow the design of primer used to amplify and sequence this gene. The hypervariable regions, though conserved throughout the evolution between closely related members, allow the identification of the organism. Figure [207,208]. This pair amplifies all nine variable regions of the 16S rDNA. The organization of this gene is similar between all bacteria and its architecture varies slightly between bacteria and archaea. However even though the sequence is smaller in archaea than in bacteria it still possesses nine variable regions that permit the identification of the microorganism (Fig. 1.16).

The variable regions can also be seen when the 16S rDNA sequences of several bacteria and archaea are compared between each other and plot into one figure (Fig. 1.16). It indeed highlights the variable regions (in red) and the conserved ones (in blue). The score of each nucleotide position was determined and, according to the obtained similarity of the nucleotide present in one specific position for all the tested sequences, a color was attributed. The redder the more different the nucleotide present in the specific position between sequences is. According to Figure 1.16, and for the bacteria tested, some regions are more different within another than the rest. Indeed V1, V2, V3 and V6 appear highly different, followed by V4, allowing thus to have a deeper identification of the organism. The difference of variability between the regions can be explained by the organization of the 16S rRNA gene (Fig. 1.17). The variable regions are in fact more accessible to mutations or transcription errors because the heap in these areas are less important than in the conserved regions.

Bacteria Archaea Because most of the microorganisms cannot be observed after culture, scientists have only recently become aware of their presence through shotgun sequencing of conserved marker genes like 16S or 18S rRNA for prokaryotes and eukaryotes respectively. The use of sequencing, particularly the metagenomic sequencing of one specific region of DNA, enables the discovery of new organisms. The metagenomic analysis is therefore a powerful tool to reference and inventory all bacterial species in water and other environments.

In the last 10 years the most commonly used next generation sequencing platform for amplicon sequencing has been the Roche 454. Many bioinformatics pipelines and denoising algorithms have been developed for Roche 454 but also for other sequencing technologies, making metagenomics a robust approach to investigate diversity.

Metagenetics

Metagenetics principle

To analyze the population of a microbiota and identify the present microorganisms, the sequencing of the amplicons of a metagenome from chosen regions of the 16S rDNA can be performed. While no specific term exists for this method [211], the term "metagenetics" will be used all along this thesis to talk about 16S or 18S rDNA amplicon sequencing. Metagenomics and metagenetics differ slightly. Metagenomics is the shotgun sequencing of a metagenome while metagenetics is the sequencing of a specific gene (e.g. 16S rDNA) in a metagenome.

Different methods can be used to sequence the 16S rRNA gene. One of the most commonly used is the MiSeq sequencing platform from Illumina. However, this method is better suited for small fragments, i.e.

sequencing between the regions V1 and V3 or V3 and V4 or either V4 alone. This method is not recommended if the sequence of the whole genome needs to be known. Illumina has also developed HiSeq sequencing, which can sequence smaller fragments than the MiSeq technology [212]. The evolution of sequencing methods and apparition of new technologies provided several generations of sequencing platforms.

To be able to sequence and analyze metagenetics results, several steps are needed. Once the microbial sample has been recovered and the DNA extracted, the targeted region is being amplified by PCR. The resulting amplicons are then sequenced according to the used technology. The results obtained after sequencing need to be analyzed using bioinformatics (Fig. 1.18). Softwares are used to eliminate any PCR and sequencing errors as well as chimeras but also enable the identification of a specific sequence. To do so, an algorithm gathers similar sequences in Operational Taxonomic Units (OTUs). The determined OTUs are then compared to genetic databases, such as NCBI. Different softwares exist to treat the results obtained by metagenetics, such as mothur, OCToPUS, 16S Classifier, or MG-RAST. Mothur was developed by Schloss et al. in 2009 [213]. This software uses algorithms previously developed to identify microorganisms. To treat the raw data obtained after the metagenetic sequencing using mothur, the elimination of chimeras and false sequences must be done. These steps can be performed on mothur but the control command must be written. To do so several algorithms were developed and mothur uses them The advantage of metagenetics on direct samples relies on the fact that VBNC can be identify from environmental samples after a direct DNA extraction and sequencing. It is also an extremely sensitive method since the material needed for this method is in the order of nanograms thanks to the sensitivity of the existing technologies. Thanks to metagenetics, several species and genera but also phyla have been discovered [217][218][219]. Until the unknown organisms can be isolated and studied in more details, their name begins with the term "Candidatus", e.g. "Candidatus Burkholderia verschuerenii" or "Candidate phylum WS1". For example, the "Candidate Division OP10" was separated into two candidate divisions (WS1 and FBP) and the Armatimonadates phylum after the isolation and identification of the type strain Armatimonas rosea YO-36 T [220, 221]. Some of these microorganisms can be well described with an almost complete genome thanks to metagenomics.

Two characteristics can define third-generation sequencing: first of all, no PCR is needed before sequencing which leads to shorter DNA preparation time for sequencing and secondly, the signal is captured in real time [222]. Several sequencing technologies exist and the main ones are described in Table 1.3 which sums up their main advantages and disadvantages. This comparison can provide information on which method should be used according to the exigence on the analysis and the granted budget. The Illumina MiSeq platform features the biggest output for lowest reagent cost, the SOLiD system presents the highest accuracy, and the PacBio system has the longest read lengths. MinIon seems the most promising system because of its price, speed and its ability to sequence extremely long fragments. Since this technique is still in development it will be considered as truly promising if its accuracy increases [223].

Platform

Sanger method

Sequencing by Oligo Ligation Detection

Oxford Nanopore Technologies

Chapter I -Bibliographical introduction 50

The increasing throughput of sequencing technology was followed by the development of different bioinformatics tools to treat and analyze the large set of genome data. Thanks to metagenomics, scientific knowledge on microbiota from different samples (environmental, human…) has drastically increased over the last decade.

Example of microbiota analysis using metagenetics

Metagenomics has been made well-known thanks to the Sorcerer Expedition [228,229], and then the Tara Expedition. In the later project, the microbiota present in 68 oceanic locations at three different depths (0 to 1 km depth) was established [230]. They highlighted the fact that more circa 50% of the bacterial diversity in the oceans were composed of Proteobacteria at all three depths, mainly a-and -Proteobacteria.

Temperature and dissolved oxygen were shown to influence the microbial diversity and evenness [198].

MOCAT and MetaGeneMark were used to assemble the sequences and determine gene predictions. In 2016 the impact of uranium contamination in river was assessed [231]. While the diversity at the phylum taxonomic level was not impacted by the uranium pollution, the evenness was greater in the unpolluted sample. In the uranium polluted sample Proteobacteria indeed dominated the diversity (47.5%) while in the unpolluted sample Bacteroidetes was the main phylum (27.7%). At the species level the unpolluted sample presented a higher diversity. This study pointed out the change occurring in the microbial community in presence of uranium as well as the presence in higher quantity of rare species in the polluted sample. The microbiota present in this uranium polluted river contained mainly Gram negative bacteria compared to the unpolluted sample which contained more Gram positive bacteria. These results also displayed the power of metagenetics to easily compare samples [231].

In 2018 the first metagenetic inventory of a spent nuclear fuel pool (at the Savanah Rive Site) was published [193]. This study was meant to highlight the need to follow the microbiota present in SNFP to anticipate any changes the water storage condition. White precipitant samples from 13 different locations and depths were recovered though filtration. These samples were sequenced and analyzed. The mothur pipeline was used to analyze the sequencing data. After analysis, the samples were mainly composed of Proteobacteria.

The main identified families were Burkholderiaceae, Hyphomicrobiaceae and Comamonadaceae. At the genus identification level, the main identified genera were Nitrospira and Pedomicrobium. In this study the use of V4V5 regions lessened the number of sequences belonging to the Nitrospirae and Actinobacteria phyla compared to the V1V2 sequencing (12.8% to 0.5% and 2.5% to 0.3% respectively). This study confirmed the empirical tendency of the presence in SNFPs of a majority of non-spore forming bacteria [185]. The SNFP from the Savanah River Site did not seem to contain any Firmicutes unlike the SNFP of Cofrentes [181,[184][185][186][187].

1.6.3 Characterization using metaproteomics 1.6.3.1 Principle Metaproteomics is the study of all proteins contained in samples recovered directly from environmental sources. This recent methodology can highlight the microbial mechanisms involved in an ecosystem but also may help to identify organisms. For this, all the proteins are extracted and peptides are generated by proteolysis by trypsin. Then, the peptides are resolved by reverse phase chromatography and analyzed by tandem mass spectrometry. Thanks to bioinformatics and new tandem mass spectrometry technologies, it is now possible to determine the origins of described peptides from a sample composed of a consortium of organisms [232]. The use of tandem mass spectrometers (MS/MS) revolutionized proteomics, allowing to obtain more precise and faster results than the 2D-PAGE gel previously used [233]. Current technology can identify proteins of populations that comprise at least 1% of the community and for which closely related genomic sequences are available [234].

To analyze a sample via metaproteomics, the cellular pellet is lysed and the proteins are recovered. Once the proteins are extracted from the sample, they are hydrolyzed into peptides which are then analyzed through a nanoLC MS/MS. The MS/MS spectra are then interpreted against a protein sequence database representative of the sample. The organisms from which the peptides and thus the proteins came from can be identified on this basis.

The advantages of metaproteomics not only include the identification of organisms but also highlights the mechanisms implemented in the analyzed sample and community. Furthermore, metaproteomics can be a superior approach to metagenetics for reasons such as the proportion of a specific species, which cannot be assessed using a genetic approach. The number of 16S rRNA gene copies depends indeed of the species

[235] but also of the incubation conditions and growth rate which can influence the proportion of identified bacteria. The biases produced by the DNA extraction method, the PCR and the sequencing can also influence the final proportion of one specific gene [236]. These factors can thus influence the overall proportion of a given OTU in the sample. At the present stage, metaproteomics is not considered as a quantitative method. Indeed, the exhaustiveness of the databases used for the interpretation influences the results obtained as well as the number of identified specific peptides of a peculiar taxonomic level. Bias can also occur during protein extraction. Metaproteomics, metagenomics and metagenetics are thus highly complementary, but difficult to perform on the same sample.

Example of microbiota identification using metaproteomics

Several protein extraction protocols can exist and should be adapted to the type of the sample to be analyzed. However protein extraction and peptide purification protocols are relatively similar: they use a cell lysis method such as bead-beating or sonication and then, proteins are hydrolyzed into peptides with trypsin [237]. Nanoscale liquid chromatography coupled online to a tandem mass spectrometry (nanoLC-MS/MS) is the current technique used to analyze complex peptide mixtures. The chromatography separates the molecules along their hydrophobicity on a reverse phase column. After the analysis of their molecular weight, the peptides are fragmented into small pieces and the molecular weight of these fragments is established by mass spectrometry. For correct fragmentation, the goal is to isolate the intended precursor peptide ion from the others, a step generally done by a quadrupole mass filter. Once the MS/MS spectrum acquired and then interpreted into a Peptide-to-Spectrum Match (PSM), the amino-acid sequence identified is used to decipher the nature of the original protein it came from. To do so, the MASCOT software is a possible tool which principle is the comparison of the experimental MS/MS spectrum with the most probable MS/MS spectra theoretically drawn from all the possible peptide sequences from the protein database [238][239][240][241]. The lack of information concerning environmental organisms in the public protein databases and the lack of proper annotation [242] can be a major obstacle for using metaproteomics in environmental samples.

Chapter I -Bibliographical introduction 52

The combined use of metagenomics and metaproteomics can be useful to identify microorganisms present in a community as well as their interactions within each other and the environment. For example in an acid mine drainage in Richmond, the dominant biofilm bacteria belonged to the Leptospirillum group II (metagenetics results). Using metaproteomics and a dedicated protein sequence database 48% of the predicted proteins from Leptospirillum goup II were identified. Many proteins involved in protein refolding and response to oxidative stress were identified [243,244]. The biofilms present in the acid mine drainage from the Richmond mine were highly studied in terms of metagenomics and metaproteomics due to their low diversity and abundant biomass [245]. No cultivation step were thus needed and the interactions observed were the ones actually happening in situ.

The biological diversity of six different aquatic environments has been studied using metaproteomics. The authors confirmed their results with the ones obtained using metagenetics. The identification between both methods were comparable, indicating that metaproteomics could be used as an alternative to metagenetics [246]. The microbial assessment of the Chesapeake Bay was also performed without any cultivation step [246]. However, in both studies, the water volume sample to be analyze was comprised between 10 and 20 L. This project was of great interest for the CEA for several reasons. With the knowledge of the microbial community in SNFPs, microbiological controls can be performed to prevent the apparition of corrosion and any chemical changes in the water due to microbiological activity. This will ensure the safety and integrity of nuclear storage and installation. A second reason concerned a more fundamental area in radiobiology and radionuclides toxicity in microorganisms and thus highlighting the mechanisms and genes implemented to resist these stresses. The last reason was the potential use of some microorganisms in biotechnology process for decontaminating metal pollution, in the same spirit as it was demonstrated using Coccomyxa actinabiotis.

The INFERNUS project involved 5 teams. Team 1 is Team Plant, Stress and Metals from the Cell & Plant Physiology Laboratory in CEA Grenoble. This team studies on heavy metals and their impact on vegetal cells and organisms. They have discovered the Coccomyxa actinabiotis microalgae and developed biotechnological process with this radiotolerant organism. Team 2 is the Technological Innovations for Detection and Diagnosis Laboratory (Li2D) from the CEA Marcoule. This team is expert in proteomics, bioinformatics and high-resolution mass spectrometry techniques, and has already studied several radiotolerant microorganisms (D. deserti, T. gammatolerans among others). Team 3 is the Bioenergetics and Proteins Engineering Laboratory in Marseille (CNRS). They work on eukaryotes (mainly algae) and can reconstruct an intact genome from shotgun metagenomics results. Team 4 comprises the two studied nuclear installations in CEA Saclay and brought expertise for sampling. The last team, Team 5, is the Microbiology group from the SCK•CEN (Belgium). They have developed an expertise in metagenomics analysis of environmental sample as well as several analysis pipelines. Team 1 and Team 2 have been working together since 2012 for the characterization of Coccomyxa actinabiotis and the mechanisms it implemented after an irradiation and the addition of radionuclides.

In the framework of this ambitious project, my PhD thesis had for objectives to find radioresistant microorganisms that can also accumulate radionuclides. The only easily accessible environments where radiation and radionuclides can be found in high quantity, for CEA personal, were spent nuclear fuel pools and the cooling pools of nuclear reactors. The organisms living there may have developed radioresistance mechanisms and a tolerance for radionuclides. By doing a complete inventory of the microbiota present in those environments using direct methods, microorganisms can be detected and latter isolated and studied.

The sampling and analysis condition were first determined and optimized (cf. Chapter II). Several samplings were carried out in two different pools. One pool is used to store radioactive cobalt sources while the other is the cooling pool of a uranium reactor core. In the second pool one sampling was performed during the reactor last working cycle and one sampling was carried out a year after its definitive shutdown. Water samples and biofilms were collected in both pools and analyzed using i) metagenetics and the OCToPUS software (developed by the SCK•CEN) and ii) metaproteomics, more specifically phylopeptidomics (a very innovative method patented by the Li2D and currently still under development) (cf. Chapter III). The identification of microorganisms through metaproteomics is currently based on the fact that some peptides are specific to a distinct species or strain and, thus, by analyzing the peptides from a consortium of microorganisms, it is possible to determine which organisms were present in a sample. However, since metaproteomics is still developing, several factors are limiting this method [242]. Because metaproteomics may use genomics and metagenomics data, it is important to be careful to the presence of possible contaminants in the metagenomics data before interpreting the proteomics results [242]. To identify microorganisms, informatic softwares have been developed and are currently used to avoid false-positive (Fig. 1.19). This method was developed and patented by the Li2D. Another advantage is that it can identify and analyze prokaryotes as well as eukaryotes on the same run, using the same databases at the same price. According to LGC Genomics, a sequencing company, wanting to identify both prokaryotes and eukaryotes is possible. In that aim one pair of primer that can amplify regions of the 16S rDNA plus a pair to amplify regions of the 18S rDNA must be used as well as a pair to amplify the ITS (for fungi [247]). This is not only experimentally heavier but also more expansive, and unfortunately a method not quantitative. In comparison, phylopeptidomics has been proved to be a relatively good quantitative method.

Chapter I -Bibliographical introduction 54 In this latter case, unannotated nucleic acid molecules are translated into open reading frames (ORFs), either along the six or three possible frames. Further proteogenomics approaches may be used to improve the protein sequence database. Tandem mass spectrometry data, acquired on peptides from a given organism or microbiome, are assigned against the most appropriate protein sequence database, giving peptide-to-spectrum matches. These are then interpreted in order to infer which proteins are present and to quantify these items.

The goal was to be able to isolate those microorganisms after identifying the diversity present in these waters. Several microorganisms were isolated using different culture methods and media. The isolated bacteria, fungi and one micro-alga were identified after the sequencing of the 16S rDNA, the 18S rDNA and the ITS for the bacteria, alga, and fungi respectively. The characterization of the isolated bacteria in terms of radioresistance and radionuclides accumulation had also been performed. First their ability to resist radiation was tested. Radionuclides emit radiation in addition to the metallic stress induced by the atom.

The accumulation of uranium and cobalt (not radioactive) was also assessed (cf. Chapter IV). Uranium accumulation is of great interest in terms of environmental pollution while cobalt accumulation is interesting to decontaminate nuclear effluents. In the second pool dissolved radioactive cobalt emitted circa 1.3x10 5 Bq/m 3 and cobalt particles were found in recovered biofilms. The biofilms with the cobalt particles emitted up to 80 Bq per biofilm. Cobalt is thus a potential water contaminant of nuclear installations. Three cobalt accumulating bacterial strains were isolated from a Slovak SNFP [152] while several uranium accumulating bacteria were found in various environments. After the Fukushima accident uranium was released in the environment and was shown to be associated with cesium in microparticles at a 4 km distance from the melted reactor. The use of uranium and cesium accumulating bacteria could be used in this situation [249].

The bacteria that exhibited great radionuclides accumulation performances could be studied to highlight the mechanisms implemented to resist radiations and accumulate radionuclides. In the longer term these microorganisms may be used to decontaminate nuclear effluents and for bioremediation.

Description of the pools

Pool 1

Pool 1 is located at CEA -Saclay, near Paris. It is an irradiating pool, used to store radioactive cobalt sources.

The sources are used to perform irradiation experiments on different materials. This pool does not contain any radionuclides, making thus an excellent choice to develop and implement protocols of analyzing the microorganisms present in its water. The water used to fill the pool is de-mineralized.

In 2015, the purification system filtered the water every day for circa 10 minutes. The characteristics and composition of the water of this pool on July 2015 are presented in Table 1.4. Several ions (Cl -, Na + , K + , Ca 2+ , and Mg 2+ ) can be used as nutrients by microbial cells.

Table 1.4: Characteristics and composition of the water of Pool 1 in July 2015

The dose received by the nearest spot on the wall (less than 20 cm) while radioactive sources are present is around 100 Gy/h. In the middle of the sources, the dose is around 5000 Gy/h. Figures 1.20 and 1.21 illustrate the pool and the position of the sources when present. Natural light is present on this pool due to the presence of windows and the northern wall can be highlighted several hours of a day. In July 2016, the purification system of the pool has been changed, leading to a decrease in the concentration of ions. The new system also increased the filtration flow. The physico-chemistry parameters of the pool were changed, leading to a probable change in the composition of the microbiota present in the water. The water composition, after the implementation of the new filtration system, is presented in Table 1.5. A decrease of ions concentration was observed between 2015 and July 2016. The Cl -concentration was similar between 2015 and July 2016 (2.7 and 2.2 mg/L respectively) while the other detected ions in 2015

were not detected in 2016. NO3 -was present in 2016 (5.5 mg/L) when none was detected in 2015.

Table 1.5: Characteristics and composition of the water of Pool 1 in July 2016 after the purification system change

The pool is deeply cleaned annually in December. Several sampling campaigns were performed during this study. The first one, in November 2015, occurred before the cleaning of the pool. The second one, in June 2016, occurred just before the change of the purification system. Only 6 months had happened between the previous cleaning and the sampling. The last sampling, finally, occurred in March 2017, after the purification system had been changed. The different samplings were performed on several walls and in the water at different depths and are detailed in III.4.

Pool 2

This pool, located at CEA -Saclay, used to be a nuclear reactor until its last run in December 2015. Its main purpose was to produce radionuclides for medical goals. The power created by this nuclear reactor was

- <1,00E+01 - - - - Inc (%) 3 9 - - - 23 - 16 - - - - - - Measuring T°C 23,2 23,2 - - - - - - - - - - - - 57
radionuclides. Due to radioactivity the recovered water has to be treated on the site. Small volume of water was cultivating in several liquid media and agar plate. DNA was extracted on site to eliminate radionuclides.

A chemistry laboratory, located on the site, was used to treat the samples, complemented with an electric Bunsen burner to have a sterility environment around the samples. To concentrate microorganisms, a centrifuge and a vacuum pump, with sterile 0.20 µm filters were also used. The samples, once treated, and controlled to allow an exit from the restricted area, were treated at the LPCV in Grenoble (DNA and protein extractions) or Li2D in Marcoule (liquid kinetic).

The tritium activity is 2.49x10 8 Bq/m 3 (both during operation and at shutdown) while the -emissions were 2.6x10 9 Bq/m 3 when operating and 3.25x10 6 Bq/m 3 when shutdown. The composition of the water, both when operating and stationed is presented in Table 1.6. 24 Na represents more than 99% of the activity of the water during operation. However its period is quite short: in around 15 hours, half of the isotope has been disintegrated. The radioactivity contained in this pool consists of b-and -emissions. This pool is 11 meters depth and the nuclear reactor is located at 9.5 meters depth. A schematic view of the reactor is presented in Figure 1.22. There are also a footbridge, some fuel bundles, and other objects that had to stay in the water until they are safe to be placed into the nearby canals until their radioactivity decayed enough to be safely removed.

Artificial light is present all day on this pool. During a working cycle, the pool is at 38°C under the heavy water layer and at 30±1°C at the bottom. The chimney possess its own water system which circulates less (both during operation and at shutdown) than in any other pool's location. In operation (2015) pH was 7.0 while pH was 5.4 in 2017. Whatever the operating mode, the pool water is continuously filtered at a 500 m 3 /h flow rate, and is purified on ion exchange resins at 30 m 3 /h, meaning a turnover of the water of this 536 m 3 pool in 18 h. In this pool, the selection pressure is quite high. There are indeed very few nutrients, the presence of radionuclides in the water and high levels of radiation.

Operating Shutdown

Two campaigns were performed in this pool. One in 2015 during the last working cycle and one in 2017, more than a year after its definitive shutdown.

Chapter II:

Methods development and optimization

Methods development and optimization

In this chapter, several methods used for this thesis were developed and optimized. As seen in the introduction (sections 1. 6.2 and 1.6.3), different methods exist to analyze a microbial community. The most recent ones, direct metagenetics and direct metaproteomics, have been used during this thesis.

First of all, metagenetics results should be properly analyzed and trusted. In several metagenetics studies several phyla were present in very few quantity, smaller than 0.01% or even less [231,251,252]. In one study, to be sure to eliminate false-positive, only taxa with an abundance greater than 2% were presented [253]. However when setting a cut-off as high as 2%, the risk of removing true positives, i.e. interesting sequences obtained from the sample, is important. The determination of a cut-off through the sequencing and analysis of a standard is highly recommended to eliminate false-positive. However the risk of eliminating organisms actually present can be high. The cut-off should be low enough to select no falsepositive but keep sequences from organisms present in low concentration. In the last decade, progresses have been made in metagenetics, not only in terms of technology's improvement but also in terms of algorithms and softwares used to analyze the results. To determine the sequencing an analysis errors a DNA standard was used. A V4 and V4V5 regions sequencing were performed on this standard.

Metaproteomics is an approach used at first to highlight the interactions existing in a microbial community after the identification of the community members with metagenetics [234,[243][244][245][254][255][256]. Nowadays it can be used to identify the organisms present in a sample, a concept named proteotyping [246,[257][258][259][260].

Here, for the first time we have applied a new approach dubbed phylopeptidomics to identify the community members of biofilms and liquid cultures. In this chapter, we present in details this approach and a case study.

Last, we investigated whether cultivation in liquid media modified the composition of the sampled microflora, taking into account that long-term culture usually resulted in poorer diversity. For this, we monitored by metaproteomics the microbial composition of a series of liquid cultures.

During this PhD, two pools were studied, one being a pool used to stock 60 Co radioactive sources to do irradiation experiments on materials (Pool 1) while the other pool is the one used to cool 235 U radioactive sources (Pool 2). The latter (Pool 2) used to produce radionuclides for medicine (cf. Chapter III.1 for detailed description). Briefly, three campaigns were performed in Pool 1 (in 2015Pool 1 (in , 2016Pool 1 (in , and 2017) ) and two in Pool 2 (in 2015 during the last working cycle and in 2017, more than a year after its definitive shutdown).

Introduction

Since the birth of metagenetics and the rise of NGS (next generation sequencing), the analysis of microbial communities took a new start and highlighted the presence of new taxa. It has been estimated that between 0.1 to 10% of microorganisms present in water cannot be culturable [START_REF] Stokell | Viable but Nonculturable Bacteria[END_REF]. The use of a direct analysis approach should allow the identification of unknown organisms. Thanks to metagenetics a whole new microbial world was discovered in environmental samples [219,228,229,261,262]. Metagenetics sequencing can use one or several hypervariable regions present on the 16S rDNA. There is however no consensus on which region(s) should be sued to identify bacteria [263][264][265]. The choice of the region(s) can depend on several factors: the homology between different species on a peculiar region, the ability to amplify a chosen region, as well as the length of the fragment to be amplified and sequenced. In a study the sequencing of the V4 region gave the highest diversity compared to the other tested regions (V2, V3, V6, V6V7, V8, and V9) [266]. A previous study analyzed the differences between a V4 or a V4V5 from a mock community [267]. However these mocks were chosen to assess the accuracy of OCToPUS a novel pipeline.

Despite NGS advantages the read length of sequencing is not long enough to sequence the 1500 pb constituting the whole 16S rDNA gene. New long read NGS methods are being developed such as PacBio and MinIon but their price and their error rate are not optimal enough today to be used for a metagenetic approach [268,269]. ). The bacterial number is thus higher in feces and less biological material is needed. Several liters of water would be necessary to obtain as much bacteria as 1 gram of feces.

The aim of this study was to assess the impact of i) which 16S rDNA region primers (V4 or V4V5) performed best, using a microbial community DNA standard as control, ii) how cells were harvested from the water samples (either by filtration or by centrifugation), and iii) how DNA was extracted.

Material and Methods

Sampling

DNA extraction

The DNA was extracted with two different methods. The DNA from the microbial communities of half the filters was extracted with DNeasy® PowerWater® kit from Qiagen (Germany) according to the manufacturer's instructions. The centrifuged water and the other half of the filters were extracted using a phenol-chloroform method, which was adapted from . The liquid sample was centrifuged 20 minutes at 20000 g at room temperature and the supernatant discarded. One milliliter of lysis buffer (100 mM Tris pH 8; 100 mM EDTA pH 8; 100 mM NaCl; 1% polyvinylpyrrolidone; 2% SDS) was added and the pellet resuspend. The mixture was then transferred to a glass bead (0.1 mm) tube from Mobio. The tube was disrupted three times at 30 Hz for 60 seconds with 30 seconds break between each run with a disruptor MM 300 from Retsch/Qiagen. The tube was then centrifuged 5 minutes at 20000 g at room temperature and the supernatant removed and transferred into a new tube. One milliliter of phenolchloroform-isoamyl alcohol (25:24:1) was added. The sample was then vortexed and vigorously mixed by inverting the tube. The supernatant was recovered and transferred into a new tube after a centrifugation step of 1 minute at 17000 g at 4°C. Seven hundred microliters of chloroform were added to the sample. The sample was then vortexed and vigorously mixed by inverting the tube. The supernatant was recovered after a centrifugation step of 1 minute at 17000 g at 4°C. The supernatant was divided into two tubes (around 450 µl in each) and 45 µl of sodium acetate 3 M and 500 µL of isopropyl alcohol were added into each tube.

The samples were then mixed by inverting the tubes and put overnight at -20°C. The tubes were centrifuged 30 minutes at 17000 g at 4°C and the supernatant removed. Two steps of washing were performed by adding 750 µL of 75% ethanol and a centrifugation step of 5 minutes at 17000 g at 4°C. The supernatant was removed and the pellet dried with a heating block at 55°C. Sixty microliters of sterile milliQ water were added to dissolve the pellet. To have a more pure DNA and remove any trace of alcohol, the sample was put on a 0.05 µm filter disposed on sterile water for 15 minutes before being transferred into a new tube.

The different methods were called method 1, method 2, and method 3 for centrifugation followed by phenol-chloroform-isoamyl alcohol DNA extraction, filtration followed by phenol-chloroform-isoamyl alcohol DNA extraction, and filtration followed by the DNeasy® PowerWater® kit respectively.

The association of centrifugation and phenol-chloroform-isoamyl alcohol extraction was called method 1.

Methods 2 and 3 correspond to filtration followed by a phenol-chloroform-isoamyl alcohol extraction or the PowerWater® extraction respectively. To test the quality of the sequencing and determine the number of false-positive, 10 µL of a ZymoBIOMICS™ Microbial Community DNA Standard (10 ng/µL) was sent for sequencing (100 ng sent to be sequenced).

Sequencing

The sequencing of genomic DNA on 16S rDNA was performed by LGC Genomics, UK. The sequencing was conducted as followed. The PCRs included about 1-10 ng of DNA extract (total volume 1 μL), 15 pmol of each forward primer and reverse primer (in 20 μL volume of 1 x MyTaq buffer containing 1.5 units MyTaq DNA polymerase (Bioline) and 2 μL of BioStabII PCR Enhancer (Sigma) and additionally 0.2 μl of DNase (Artic Zymes) for the 16S PCR. For each sample, the forward and reverse primers had the same 10-nt barcode sequence. Each PCRs were carried out for 20 cycles, dependent on the primer pair, using the following parameters: 1 min 96°C pre-denaturation; 96°C for 15 s, 50°C for 30 s, 70°C for 90 s and primers without inline barcodes have been used (341F/1061R). For the second round 1 μL PCR product from the first round was used and the PCR conditions were the same as before. For this reaction barcoded primers have been added to amplify the V4V5 regions (515YF-926R: 5'-GTGYCAGCMGCCGCGGTAA-3' and 5'-CCGYCAATTYMTTTRAGTTT-3' respectively). The primers used to amplify V4 region were 515F/806R (5'-GTGCCAGCMGCCGCGGTAA-3' and 5'-GGACTACHVGGGTWTCTAAT-3' respectively).

DNA concentration of amplicons of interest was determined by gel electrophoresis. About 20 ng amplicon DNA of each sample were pooled for up to 48 samples carrying different barcodes. The amplicon pools were purified with one volume AMPure XP beads (Agencourt) to remove primer dimer and other small mispriming products, followed by an additional purification on MiniElute columns (Qiagen). About 100 ng of each purified amplicon pool DNA was used to construct Illumina libraries using the Ovation Rapid DR

Multiplex . Illumina libraries were pooled and size selected by preparative gel electrophoresis. Sequencing was done on an Illumina MiSeq using V3 Chemistry (Illumina).

Chapter II -Methods development and optimization 64

Data analysis

The raw data obtained after sequencing were analyzed using the OCToPUS software [210]. This software eliminated any singleton sequences, chimeras, clustered according to a threshold similar sequences in OTUs, and assigned each OTU to a taxonomical level using the RDP database (Ribosomal Database Project).

Results

Determination of 16S rDNA regions sequencing

Determination of a threshold with V4 and V4V5 amplification using ZymoBIOMICS™ Microbial Community

DNA Standard

The DNA sequenced by LGC Genomics presented little differences with its theoretical composition after a sequencing of the V4 and V4V5 regions of the 16S rDNA in its composition and proportion. Moreover some sequences, belonging to absent bacteria in the mock were identified after sequencing (Table 2.1, Fig.

2.1 & 2.
2). The theoretical proportion of each OTU sequences in the ZymoBIOMICS TM DNA standard was similar to the one found after the sequencing of the V4V5 regions of the 16S rDNA (Fig. 2.1, Table 2.1). The presence after sequencing of absent sequences in the sample was also noticed. They were however present in low quantity, representing less than 0.03% of the sequences (Fig. 2.1). Fourteen bacterial sequences could not be clustered with a known OTU. Among these sequences several OTUs were identified as "Bacteria_unclassified". All "Bacteria_unclassified" OTUs were regrouped under the name of "Bacteria_unclassified" in Table 2.1. Among the false-positive sequences identified after sequencing and analysis using OCToPUS, the maximum of sequences clustered in a single OTU was 4 (data not shown). The number of "Bacteria_unclassified" represented around 0.015% of the sample. This mock highlighted the detection of false-positives. With the number of unclassified bacterial sequences a 0.016% threshold could be chosen to analyze data from microbial samples obtained after a V4V5 Illumina MiSeq sequencing and an OCToPUS analysis (the number of the false-positive OTU presenting the higher number of sequences with a 0.001% margin). The identification quality after a V4 16S rDNA sequencing was assessed. Differences could be observed concerning the proportion of each bacteria in the mock (Fig. 2.2). Moreover, the detection of false-positive was also noticed and in a bigger proportion than after a V4V5 regions sequencing.

After the V4 sequencing and analysis, the identification of Lactobacillus was lower than expected (7.4%) while the proportion of Bacillus was greater (21.5%), as Staphylococcus (18.8%) (Fig. 2.2). Theoretically the proportion of Lactobacillus was 18.8% and the one of Bacillus was 15.7% while the proportion of Staphylococcus was 13.3%. It appeared that the excess of sequences identified as Bacillus and Staphylococcus could correspond in reality to the missing sequences observed for Lactobacillus. These three genera all belong to the Bacillales order and are thus phylogenetically close. A 5.8% and 5.5% differences (for Bacillus and Staphylococcus respectively) can indeed be observed between a V4 sequencing and the theoretical composition of the DNA standard. The sum of both differences is 11.3%, which is similar to the 11.4% missing sequences in Lactobacillus. Different OTUs, not present in the sample, were also identified. However, they represent less than 0.1% of the sample and sequences (Table 2.1). The number of sequences of the first OTU not present in the sample was 33, out of 133319 sequences (0.025%). This OTU was identified as "Enterobacteriaceae_unclassified" (Table 2.1). The proportion of false-positive represented 0.025% of the sample after a V4 sequencing analysis. The cut-off, with a margin, would be settle at 0.026%. Determination of taxonomical level identification and sequencing quality for both V4 and V4V5 sequencing Differences could be observed between a V4 and a V4V5 sequencing with the theoretical composition of the DNA standard. These differences were greater with a V4 sequencing than a V4V5 sequencing (Table 2.2). The under-identification of Lactobacillus after a V4 sequencing can be highlighted by the observed standard deviation (more than 8). Similarly the over-identification of Bacillus and Staphylococcus were also observed (4.1 and 3.9 respectively). The standard deviation with the theoretical DNA standard was closer to 0 with the V4V5 sequencing compared to the V4 sequencing. A V4V5 sequencing appeared thus to be more precise in OTU attribution than a V4 sequencing. As expected, the taxonomical identification with OCToPUS was deeper after a V4V5 sequencing than after a V4 sequencing. The information loss between the family and genus taxonomical levels was indeed smaller for V4V5 (9.9%) than for V4 (12.1%). Some loss was also observed between the class and the order level after the analysis and identification of the V4 sequencing (Table 2.3).

Table 2.3: Number of sequences identified at a specific taxonomic level for the V4V5 and the V4 sequencing and percentage of loss between each taxonomical level

The quality of sequencing and analysis were also compared. Using only a V4 metagenetic sequencing, the number of errors and unattributed sequences was lower than using a V4V5 sequencing (Fig. 2.3). If 66% of the reads were kept and analyzed after a V4 sequencing, only 52% were kept after a V4V5 sequencing. Furthermore the sequencing error rate determined by OCToPUS was lower for the V4 sequencing (0.01%), even though both error rates were extremely low (Table 2.4), confirming the sequencing quality performed by LGC Genomics and the analysis of OCToPUS. The sequencing quality was thus greater after a V4 sequencing, which can be explained by the size of the amplicon (291 bp for V4 vs. 411 bp for V4V5). With 291 bp it is possible to have overlapping sequences after sequencing, allowing a better obtained sequence quality. With 411 bp only the end of the sequences can overlap. The sequences' end obtained after sequencing is of lower quality than the beginning [277].

Despite better sequencing quality, V4 sequencing does not offer a satisfying taxonomical identification level compared to V4V5 sequencing. It also presented a greater threshold (0.025%) than V4V5 sequencing (0.016%) concerning the identification of sequences that were not present in the initial sample.

Comparison of different methods for microbial harvesting and DNA extraction

Different samplings were performed at three different locations (A: bottom of the pool, B: above the chimney, and C: inside the chimney) in the pool. Three different methods were tested: centrifugation of the water followed by DNA extraction with phenol-chloroform-isoamyl alcohol (method 1), filtration of the water followed by DNA extraction with phenol-chloroform-isoamyl alcohol (method 2), and filtration of the water followed by DNA extraction with DNeasy® PowerWater® kit (method 3). The objective was to test different harvesting and extraction methods to obtain as much diversity present in water as possible.

After sequencing and OCToPUS analysis, 100000 ± 5000 sequences were obtained using method 3. Only 3696 sequences were obtained with method 1 at location C after analysis. Nonetheless 84000 ± 15000 sequences were obtained with method 1 at locations A and B. About 64500 ± 355000 sequences were obtained using method 2 after OCToPUS analysis (Table 2.5). For the OTUs common to the different methods, the proportion was determined using the mean of the values obtained for each method. Some differences could be observed between the three methods.

However the main OTUs were all common. A few OTUs were present at only one location and in a very weak proportion (Fig. 2.4). It can be observed that few differences were observed between method 3 and methods 1&2 at locations A and B (only 2 and 7 genera only detected with method 3, respectively). The opposite was observed for the sample collected at location C. 20 genera were only detected using method 3 and only 3 were common to all three methods at location C. When comparing methods 1 and 3 or 2 and 3, no genera were common to both methods (except for the 3 genera common to all three methods). This observation could be confirmed by the ANOVA analysis (Table 2.6). It can be observed that at both locations A and B, methods 1 and 2 were different within each other. Methods 2 and 3 were not significantly different.

However at location C, methods 1 and 2 were similar while method 3 was significantly different to methods 1 and 2 (Table 2.6). Table 2.6 should however be considered carefully since no replicates were performed.

Discussion

The sequencing error rate observed on the mock sample for the V4 metagenetic sequencing was lower than the error rate on the V4V5 sequencing (0.01% vs. 0.02% respectively). However both can be considered as good and similar sequencing error rates. The high number of eliminated reads (Fig. 2.3) can be explained by a poor DNA quality and/or quantity sent to be sequenced (cf. Appendix 3). The quality of the V4 sequencing was sensitively higher due to the length of the region. V4 is indeed smaller than V4V5, allowing a bigger read depth for one DNA amplicon.

The identification of the OTUs after a V4 sequencing was not as precise as after a V4V5 sequencing. The attribution of some closely related organisms can indeed have been mixed. For example, the excess of sequences identified as Bacillus and Staphylococcus could correspond in reality to the missing sequences observed for Lactobacillus. These three genera all belong to the Bacillales order and are thus phylogenetically close. It has been previously shown that closely related bacteria cannot be solely identified using V4 sequencing, such as the Enterobacteriaceae and Peptostreptococcaceae family [279]. Moreover the number of false-positive was higher in the V4 sequencing of the mock sample compared to the V4V5 sequencing. Considering the higher number of false-positive and the lack of depth for the taxonomical identification after a V4 sequencing, this study confirms that V4V5 regions of the 16S rDNA sequencing is recommended over the sequencing of only V4 region.

The methods of microbial harvesting and filtration as well as which DNA extraction protocol was used are complementary. However when looking at the reads number, method 1 presented less sequences, perhaps due to a lack of microorganisms: during the centrifugation, the cell density was so low that not all microorganisms could have been pelleted. It was shown that at two different locations, methods 1 and 2

were significantly different while method 3 was similar to method 2. An important discrepancy between centrifugation (method 1) and filtration (methods 2 and 3) could be observed at location C (between 67000 and 97000 sequences differences with methods 2 and 3 respectively). In this location method 3 and was significantly different to methods 1 and 2, but methods 1 and 2 were similar. The differences observed between the three methods depending on their sampling's location could be explained by the low number of sequences (3696) obtained with method 1 at location C. The second lowest sequence number was 28977 (obtained using method 2 at location C) while the greater sequence number was 127027 (obtained using method 3 at location A) (Table 2 .5). This low number of analyzed sequences could have influenced the observed diversity which would have caused the lack of differences between methods 1 and 2 at location C while being extremely different to method 3 at the same location (95759 obtained sequences).

Conclusion

Metagenetics has been widely used [198, 229-231, 266, 272, 280-282]. There are however biases introduced during the several steps of DNA extraction and sequencing. To overcome these biases the use of a threshold should be used to eliminate any false-positive in the analysis. Eliminating false-positives appears to be critical to analyze microbiota present in extreme environments. In a nuclear reactor cooling pool the identification of different microbial genera can be of great interest and results should be trusted.

By identifying a sequence artefact in such environment could lead researches in the wrong direction. The attribution at the genus level could also be biased by the length of the sequence and the close phylogenetic relationship between several genera. In this study the sequencing of the V4V5 regions was recommended over the sequencing of only the V4 region. Even though V4 presented a better quality of read it also lacked of taxonomical identification. The taxonomical identification level presented a better depth after a V4V5 sequencing. Moreover the number of false-positive was higher after a V4 sequencing compared to the one obtained after a V4V5 sequencing. As a result to this study a threshold of 0.016% is suggested when using an Illumina MiSeq metagenetics sequencing of the V4V5 regions of the 16S rDNA followed with an OCToPUS analysis.

Both centrifugation and filtration can be used to harvest the microorganisms. However filtration provided more sequence number than centrifugation. Filtration was also faster and easier to implement than centrifugation, especially for large volumes. The authors recommend using both methods to treat liquid samples. It is however suggested to favor filtration if only one method is to be used. Even though more diversity was detected using method 3, it provided a lot of false-positive sequences and the quantity of DNA obtained after DNA extraction was lower than after a phenol-chloroform-isoamyl alcohol (Appendix 3). The phenol-chloroform-isoamyl alcohol DNA extraction has to be performed in a secure environment and under a fumehood. For this reasons this protocol, even if the DNA is extracted in higher quantity, is not to be favored in constrained environment. Depending on the sampling and DNA extraction condition the above recommendation could be optimized to recover as much organisms and DNA as possible.

Introduction

Some microorganisms can subsist and even flourish in extreme conditions in which most multicellular organisms, like animals or humans, cannot survive. Bacteria, fungi, archaea, and micro-algae have indeed been found in acidic or alkaline media, in environments with extremely low or high temperature, high pressure, or high salt concentration [START_REF] Kay | Microorganisms in subterranean acidic waters within Europe's deepest metal mine[END_REF][START_REF] Jolivet | Thermococcus gammatolerans sp. nov., a hyperthermophilic archaeon from a deep-sea hydrothermal vent that resists ionizing radiation[END_REF][START_REF] Zhdanova | Fungi from Chernobyl: mycobiota of the inner regions of the containment structures of the damaged nuclear reactor[END_REF]168,176,283]. Some of these organisms are known to withstand high radiation doses. Deinococcus radiodurans was the first radioresistant organism described in 1956 [START_REF] Anderson | Studies on a radio-resistant micrococcus. I. Isolation, morphology, cultural characteristics, and resistance to gamma radiation[END_REF]. It is able to withstand ionizing radiation doses as high as 20 kGy with a LD50 of 6 to 10 kGy [START_REF] Daly | Accumulation of Mn(II) in Deinococcus radiodurans Facilitates Gamma-Radiation Resistance[END_REF],

that is 1000 to 2000 times the human LD 50 . Since then, many radioresistant microorganisms have been reported, such as Deinococcus deserti isolated from the Sahara desert, the archaea Thermococcus gammatolerans isolated from a deep-sea hydrothermal vent, the green micro-algae Coccomyxa actinabiotis isolated from a pool used to store spent nuclear fuels and several fungi isolated particularly from the Chernobyl damaged nuclear reactor [START_REF] Jolivet | Thermococcus gammatolerans sp. nov., a hyperthermophilic archaeon from a deep-sea hydrothermal vent that resists ionizing radiation[END_REF][START_REF] Zhdanova | Fungi from Chernobyl: mycobiota of the inner regions of the containment structures of the damaged nuclear reactor[END_REF]168,176]. While numerous studies have been focussed on deciphering the molecular mechanisms of these model organisms explaining their remarkable tolerance properties, in situ environmental studies are relatively scarce. A variety of bacteria belonging to the genera Bacillus, Burkholderia, Micrococcus, Pseudomonas, Ralstonia or Staphylococcus have been identified in a

French storage pool and in the cooling pool of a Spanish nuclear power plant [184,185,189].

In nature, many microorganisms can form biofilms as a mean to colonize a specific environment and facilitate their long-term fate. For this, different microorganisms stick generally onto a surface, embedded within a self-produced matrix of extracellular polymeric substances, and may further colonize their surroundings. This specific growth pattern is indeed more efficient than the planktonic form, because protective against external conditions and their associated changes. Inside a biofilm, the pH can be appropriately modified, and nutrients and organic material may be more easily recycled, creating more favourable conditions for microorganism survival and growth [START_REF] Costerton | Microbial Biofilms[END_REF]284]. The advantages obtained by growing as biofilms can explain the fact that in aquatic environment less than 0.1% of the bacteria are under planktonic form [START_REF] Costerton | Microbial Biofilms[END_REF]. As a consequence, microbial biofilms have been detected in very inhospitable places such as used nuclear fuel pools [176,181].

Assessing the microbial diversity of aquatic environments is usually done by extraction of the DNA content from the sample, followed by 16 or 18S rRNA gene amplification and sequencing [211,285]. This approach has been challenged because the number of copies of the 16S rRNA gene per chromosome and the number of copies of the chromosomes in bacteria may be different depending on the strains and their physiological state [235]. Moreover, the DNA extraction may be not rigorously identical depending on the microorganisms present and the type of biofilms to analyse. Last, the amplification of 16S rRNA gene from archaea or bacteria, and the amplification of 18S rRNA gene from eukaryotes are done with different sets of primers. Moreover, within the same taxonomical group the neighbouring sequences of the 16/18S rRNA may differ influencing the amplification rate. Thus, for all these reasons the amplification may not be scrupulously identical depending on the strains and phyla present in the sample. Alternatives for taxonomy purpose may rely on the cataloguing of genomic sequences (metagenomics) or protein sequences (proteotyping). While metagenomics gives a glimpse on the potential of the microorganisms present in a sample, metaproteomics is devoted to the global study of proteins present in a complex sample comprising numerous different organisms for better understanding the pathways with key roles and the physiology of the main players, and study the interactions of a microbial community with its environment [286].

Metaproteomics has been used to get new insights into biofilms [START_REF] Anderson | Studies on a radio-resistant micrococcus. I. Isolation, morphology, cultural characteristics, and resistance to gamma radiation[END_REF][START_REF] Brock | Thermophilic Microorganisms and Life at High Temperatures[END_REF][START_REF] Costerton | Microbial Biofilms[END_REF]. Several studies have been performed on acid mine drainage biofilms in the USA where metaproteomics coupled with metagenomics showed the power of combining both methods to study closely related microorganisms within their environment. The genotype of the main phyla and the location of the sample were shown to influence the proportion or the presence of a bacteria belonging to the Leptospirillum genus, the dominant organisms in the biofilm [244]. Proteomics combined with genomics provided direct insights into the functioning of the organisms constituting such biofilms in their natural environment, where they have to challenge extreme acidity and metal toxicity. These investigations also provided insights into the functioning of these organisms as consortia, highlighting how essential functions such as nitrogen fixation and biofilm polymer production are partitioned among members of the community. Last, one highly abundant protein of initially unknown function was found to be an iron-oxidizing cytochrome, a key enzyme for energy production in these biofilms [244]. Comparative proteogenomics of these communities revealed how genomic variation in closely related bacteria could contribute to their ecological divergence [287]. Metaproteomics highlighted diverse post-translational modifications specific to the biofilm growth stage [288]. These microbial communities were further grown in bioreactors and metaproteomics revealed metabolic characteristics such as species-specific hydrogen isotope fractionation in proteins [289], nitrogen flows through the consortia [290], and change in the proteome of individual organisms within the biofilm resulting from an increase in temperature [291]. Other studies on environmental biofilm communities have addressed marine biofilms [239,292] and wastewater treatment biofilms [293,294]. Taxonomical classification and functional potential of microbial communities harvested from ship hulls have been obtained [292], and the activities of key microorganisms responsible for organic pollutant degradation in anaerobic wastewater treatment biofilms have been investigated [294]. The most common strategy for conducting a metaproteomics study is first to collect metagenomics data on the same sample in order to obtain a protein sequence database specific of the sample. For this, either a systematic six frame translation of the reads or contigs may be performed or their tentative annotation, leading to an inflated proteogenomics protein sequence database. Then, MS/MS spectra recorded from the peptides generated by proteolysis of the extracted proteins are assigned based on this metagenomics information [292,294].

However, for precious samples obtained in low amount, or for a quicker analysis, obtaining this metagenomics information may be far too complex.

In this work, we propose a new proteotyping approach consisting in metaproteomics data acquisition and interpretation against a generalist database. Then, the assigned peptides are analysed as a whole dataset in terms of taxonomical information. This method is applied for the first time to decipher the composition of biofilms harvested from a pool used to store radioactive sources in a nuclear facility. A procedure for recovering few milligrams of biological material from this hostile environment and compatible to proteotyping of mixtures of microorganisms is also proposed.

Materials and Methods

Installation studied

The biofilm samples were recovered in a 5.5 m deep pool used to store radioactive sources and perform irradiation experiments in CEA -Saclay, France. The pool contained reverse osmosis-purified water supplemented with tap water. Its conductivity, pH and temperature were 127 µS/cm, pH 7.8, and 26°C, respectively. The analysis of the major anions and cations revealed the presence of chloride, sodium, potassium, calcium, and magnesium at respective mean concentrations of 2.2 mg/L, 5.9 mg/L, 5.8 mg/L, 1.6 mg/L, 41 mg/L, and 1.7 mg/L. Nitrates, phosphates, and sulphates were below the quantification limit of 5 mg/L, 0.2 mg/L, and 10 mg/L, respectively. No radionuclides were detected. The water was filtered daily on resins for 1 hr. When irradiation sources were present in the pool, the dose rate on the wall on which the biofilms were collected reached 100 Gy/h.

Biofilm collection

Biofilms were sampled using sterile Spontex® sponges (Spontex-Mappa). For each sampling, a sponge was fixed with duct tape on a metal pole and was then used to scrub the wall under water. After scrubbing a surface of about 25 cm 2 , the pole was slowly pulled up, the duct tape was removed, and the sponge was introduced into a sterile hermetic bag for transport. Once in the laboratory, each sponge containing the biofilm was then introduced inside a 1 L sterile flask with 200 mL of sterile water. To obtain the biofilm, the sponge was stirred with a sterile spatula and the flask was shaken. The water was recovered and centrifuged for 20 min at 15,000 g at room temperature. The entire supernatant but 1 mL was discarded and the pellet was dissolved in this mL volume. For the metaproteomics analysis, a volume of 750 µL of the suspension was stored at -80°C prior to metaproteomics analysis. The rest was preserved in Glycerol Strain Buffer (65% glycerol v/v, 0.1 M MgSO4, 0.025 M Tris-HCl pH 8), flash-frozen in liquid nitrogen, and stored at -80°C.

Protein extraction and trypsin proteolysis

After thawing the sample tube on ice, cells and cellular debris were precipitated by adding a volume of trichloroacetic acid solution prepared at 50% in water to obtain a final concentration in acid of 10%. The sample was then centrifuged for 10 min at 16,000 g. The resulting pellet was dissolved in a volume of 250 µL of LDS 1X Laemmli buffer (Invitrogen) consisting in 106 mM Tris/HCl, 141 mM Tris base, 2% lithium dodecyl sulfate, 10% glycerol, 0.51 mM EDTA, 0.22 mM SERVA Blue G250, 0.175 mM phenol red, buffered to pH 8.5 and supplemented with 2.5% beta-mercaptoethanol. The sample was heated at 99°C for 5 min followed by a treatment for 5 min in an ultrasonic bath. The sample was introduced into a 2 mL Precellys tube (Bertin Technologies) containing 200 mg of glass beads. The samples were subjected to 3 cycles of grinding at 6,500 rpm for 20 sec followed by 30 sec rest by means of a Precellys grinder (Bertin technologies). After grinding, cellular debris were centrifuged for 1 min at 16,000g. The supernatant was heated for 10 min at 99°C. Then, the sample was loaded onto a 4-12% gradient 12-well NuPAGE gel (Invitrogen). The proteins were subjected to electrophoresis for 5 min. After Coomassie Blue Safe staining (Invitrogen), the polyacrylamide band corresponding to the whole proteome was sliced. The polyacrylamide band was then destained with ultra-pure water, reduced with dithiothreitol, and treated with iodoacetamide, before proteolysis with Trypsin Gold Mass Spectrometry Grade (Promega) in the presence of 0.01% ProteaseMAX surfactant (Promega), as previously described by Hartmann et al. [295].

NanoLC-MS/MS analysis

The extracted peptides (10 μL) were analyzed with an ESI-Q Exactive HF mass spectrometer (Thermo)

equipped with an ultra-high field Orbitrap analyser and coupled to an Ultimate 3000 176 RSL Nano LC System (ThermoFisher). This analysis was performed in data-dependent mode, i.e. fragmentation and MS/MS spectra acquisition was done on specific peptide ions identified by their molecular masses divided by their electric charge. Peptides were injected onto a reverse phase Acclaim PepMap 100 C18 column (3 µm, 100 Å, 75 µm id x 500 mm) and resolved at a flow rate of 0.2 µL/min with a 60 min gradient of CH3CN in presence of 0.1% formic acid. The gradient during MS/MS acquisition consisted in 50 min from 2 to 20% CH3CN, followed by 10 min from 20 to 32% CH3CN. A quick wash of the column was done with an additional gradient from 32 to 72% CH3CN in 1 min, before column equilibration with 2% CH3CN. The Q-Exactive HF instrument was operated with specific parameters with a Top20 strategy: full scan mass spectra were acquired from m/z 350 to 1,500 m/z with an Automatic Gain Control Target and a resolution set at 3x10 6 ions and 60,000, respectively, for the MS scan, and 1x10 5 ions and 15,000, respectively, for the MS/MS scan.

MS/MS scan acquisition was initiated when the threshold intensity of 170,000 was reached. The maximum IT was 60 msec and the isolation window for the quadrupole was 1.6 m/z. Only ions with potential charge states of 2+ and 3+ were selected for MS/MS with a dynamic exclusion of 10 sec.

Proteotyping MS/MS data interpretation

For the proteotyping interpretation of MS/MS spectra, the database used was the National Center for

Results

Selection of the nuclear facility and biofilm sampling

We proposed a direct sampling of biofilm from the walls of a pool used to store radioactive sources. In such peculiar environment, it appears difficult to introduce experimental support and let biofilms grow for several weeks or months. The pool chosen for this case study did not contain any radionuclides, letting a direct manipulation of collected biofilms for full characterization with omics tools. The facility selected is used to carry out irradiation experiments with cobalt irradiation sources either positioned in a concrete blockhouse or in the pool next to the bottom. During storage, sources are maintained under water. In the pool, they are positioned at specific locations near the bottom northern wall. The pool is supplied with reverse osmosis-purified water supplemented with tap water, and its water is oligotrophic and slightly alkaline. It contains very few nutrients, the main macronutrients such as nitrate, phosphate, and sulphate being undetectable. The water surface is in contact with the air. Most investigations performed on biofilms in aquatic environment were carried out either by direct sampling at the air/water interface or after removal of the biofilm support from the water and scraping the biofilm with a spatula [239,296,297]. In this study however, the support could not be taken out of the water, nor could a diver be sent to collect the biofilms. Here, we proposed to detach directly underwater the biofilm from the wall with a sponge rather than with a spatula in order to recover more easily the sample and avoid the sample loss when brought back to the surface. We choose to use a Spontex abrasive sponge, a cellulose sponge equipped with a scour abrasive pad made of synthetic fibers chemically bonded together with a resin. Sampling at 4 m below the surface water necessitated to use a sponge fixed onto a pole with duct tape. This specific sponge enabled an easy manipulation several meters under water as the sponge fibers imprisonned part of the biofilm, allowing its recovery. After scrubbing the wall, the sponge was recovered and some biofilm material could be directly observed by means of a slight yellowish color. After tape removal, the sponge was stored inside a sterile hermetic plastic bag. Once in the analytical laboratory, the sponge was subjected to vigorous shaking with sterile water in order to detach the biological material from the sponge fibers. After sponge removal, the resulting liquid was centrifuged for pelleting the biological material. The supernatant was removed and the biological pellet was stored at -80°C until metaproteomics analysis.

Figure 2.5: Strategy implemented to collect the biofilms and for their direct analysis using metaproteomics

Proteotyping the microbial composition of biofilm by phylopeptidomics

Microbial cells were lyzed by bead beating in presence of Laemmli buffer. The resulting proteins were loaded for a short migration onto a SDS-PAGE gel. As shown in Figure 2.5, the denatured polypeptides were fragmented into peptides with trypsin and the resulting peptides were characterized by high resolution tandem mass spectrometry. Due to scarce biological material recovered from the sponge, only one analytical run could be performed per sample. For proteotyping the microbial composition of biofilm, we applied the phylopeptidomics methodology which allows accurate quantification of the relative ratios of mixtures of microorganisms (Pible, Armengaud, et al., submitted). Briefly, the MS/MS spectra were interpreted against NCBInr, a generalist database comprising annotated proteomes from several tens of thousands of reference organisms. After this metaproteomics interpretation of mass spectrometry data, a MS/MS spectrum may correspond to a peptide sequence found in the database. If this peptide sequence is Identifying the microorganisms present in Biofilm A as case study Table 2.8 reports the numbers of TSMs and specific peptides for Biofilm A at the different possible taxonomical ranks. A total of 1,168 TSMs and 724 unique taxon-specific peptide sequences were exclusive of the Bacteria superkindgdom. Among these, the very large majority are exclusive of only one phylum:

Proteobacteria with 1,136 TSMs and 580 unique taxon-specific peptide sequences. We noted that when focusing our attention from the superkingdom to the phylum taxonomical rank the decrease in terms of unique taxon-specific peptide sequences (-20%) is sharper than the decrease in terms of TSMs (-3%). This discrepancy arises from the peptide sequences belonging to highly conserved parts of housekeeping proteins systematically found in bacteria. While these peptides will not be considered as specific of the respectively. Their respective quantities may be roughly assessed by their phylopeptidome signal (TSMs): 80% (898), 11% (122) and 9% [START_REF] Haber | Partners and pathways: repairing a double-strand break[END_REF]. The proportion of each genus would be significantly different if based on the number of taxon-specific specific peptides: 87% (340), 8% [START_REF] Leggett | Bacterial spore structures and their protective role in biocide resistance[END_REF], and 4% [START_REF] Costerton | Microbial Biofilms[END_REF]. Indeed, the number of taxon-specific specific peptides may differ significantly due to the phylogenetic relationships between the organisms present in the sample. Therefore, it is not a reliable parameter for quantifying the ratio of organisms.

While the decrease of the number of specific peptides is rather sharp along the taxonomical ranks considered (from 724 at the Superkingdom taxonomical rank till 389 at the Genus taxonomical rank), the TSMs parameter is rather stable (from 1,168 till 1,111, respectively). Thus, no other main contributor to the biofilm could explain the proteomic signal such as an unsequenced branch of life of bacteria. The identification of organisms at a lower taxonomical rank than the genus rank would require more starting material and a more comprehensive database for the three identified genera, as the biofilm may comprised several closely related species that have been not yet genome sequence and reported in the NCBInr database. However, the main most-closely related species identified by means of their specific peptides Sphingomonas and Caulobacter, with 497 and 7 taxon-specific peptides, respectively. Therefore, Biofilm C stands out from the others as no trace of the presence of Acidovorax could be detected in this biofilm while large amount of material was available.

Figure 2.6 shows the ratio of microorganisms established for the four sampled biofilms based on the TSMs criterion at two taxonomical ranks, Genus and Order. In the four biofilms, Sphingomonas was the main genus contributor. Sphingomonas and Caulobacter were systematically found. Remarkably, Acidovorax was another genus detectable in Biofilms A and D, as well as faintly perceived in Biofilm B (only formerly identified by three taxon-specific peptides) but explaining a Burkholderiales MS/MS-certified signal (nine taxon-specific peptides). The Biofilm C signal was differing from the others due to the absence of Betaproteobacteria phylopeptidomics signal. Some slight discrepancies in terms of relative ratio of each genus are noted between Genus and Order taxonomical ranks (Fig. 2.6). However, because the Order taxonomical rank benefit of more genome sequence microorganisms in the database and therefore protein sequences, its reliability in terms of quantitation should be better than the Genus taxonomical rank.

Discussion

Sampling of minute amounts of precious biofilm samples from difficult environments such as a pool used to store nuclear elements does not give many analytical options if taxonomical characterization and estimation of the different biomass contributions are the objectives. Extracting the DNA present and analysing the taxonomy either by 16S/18S rRNA amplification and sequencing, or metagenomics, is a first option. The reliability of this approach has been challenged as bulk DNA extraction and rRNA PCR amplification is a problematic methodology because their yields can be species-dependent [242,299].

Furthermore, the use of only rRNA information tends to be insufficient for current taxonomy purpose as a relatively high percentage (at least five per cent) of 16S RNA sequence records were estimated to contain substantial anomalies [300]. Here, we explored another option relying on peptide extraction and identification by high resolution tandem mass spectrometry. The wealth of information obtained in terms of taxonomy appears large because peptide sequence encoded with 20 possible different amino acids is highly condensed (while nucleic acids are encoded with 4 nucleotides), and thus discriminative. Here, a total of 724 bacteria-specific peptides were identified for Biofilm A with the equivalent of 0.6 mg of starting biological material. With an average length of 10 amino acids per peptide, these peptides are totalling 7,240 amino acid positions. Because two amino acids (leucine and isoleucine) are indistinguishable by tandem mass spectrometry, the theoretical combinations of all amino acids lead to an impressive number of combinations (19 7240 ), which can be theoretically obtained by sequencing the equivalent of a nucleic acid sequence totalling 42 trillions of nucleotide positions. However, because of Life history starting from a common ancestor, the molecular relationships between organisms and the constraints of protein folding for a stable structure and function, the sequences possibilities are not as huge. Consequently, the differences between nucleic acid sequencing and peptide sequencing may be not as tremendous.

Here, we have shown that phylopeptidomics can help to establish the composition of microorganisms present in biofilms and estimate their biomass contributions directly through their peptide signal.

Ratio of microorganisms (%)

developed by , as the peptidome signal is assigned to taxonomical information while metaproteomics relies on protein inference and then assignation to taxonomical information of the detected proteins. By bypassing the protein inference problem, phylopeptidomics gives a more direct and unbiaised view of the taxonomical units present in the sample, and thus a better estimation of their biomass contributions.

The limits of the phylopeptidomics approach may be discussed here. First, the current lack of the NCBInr database to comprehensively represent the biodiversity on Earth is trivial. The method may not be applicable for identifying new microorganisms from branches of Life not yet genome sequenced, or for accurately give the most exact taxonomical rank (species, subspecies, or strains ranks). Currently, the generous efforts done by the scientific community in terms of full genome sequencing of pathogens, emergent pathogens, and environmental samples help to fill this gap. Consequently, the phylopeptidomics method should be more powerful and efficient with further database updates. A second caveat is the current limit of detection of tandem mass spectrometers and the impossibility to amplify the peptide signal like with nucleic acid PCR. However, we have shown here that an amount of approximately 0.18 mg of starting biological material was sufficient to give the three main microbial components of Biofilm B with a Q-Exactive HF tandem mass spectrometer, and their respective biomass contributions. For sure, this current threshold may be lowered with novel tandem mass spectrometers as suppliers tend to increase regularly the sensitivity and speed acquisition of their instruments. These limits have to be balanced with the analytical speed and cost achieved by the phylopeptidomics methodology as only 60 min of tandem mass measurements were needed per sample in the present study.

For sampling directly biofilms from aquatic environments without introducing any specific support, we proposed the use of a Spontex® sponge that, as shown here, is able to scrub the biofilm and imprison part of the biofilm, allowing its recovery. The low amount of material obtained even at 4 m depth in the pool was sufficient for its analysis by means of phylopeptidomics.

The three genera observed in these samples, Sphingomonas, Caulobacter, and Acidovorax, are all environmental bacteria. The genus Sphingomonas is known as largely ubiquitous. It has been found in different environments, such as marine and fresh water environments, soils, and deep subsurfaces [304,305]. Noteworthy, bacteria belonging to the Sphingomonas genus have already been identified in spent nuclear fuel pools from the power plant of Cofrentes, Valencia (Spain) [185] and the Idaho Nuclear Technology and Engineering Center, USA [188]. Some Sphingomonas are known to be multi-resistant but are not considered as extremophiles per se. The strain AntH11 [306], isolated from the Antarctic Dry Valleys, encompasses in its genome numerous stress response genes (116 in total), an enzymatic arsenal for facing the extreme environmental conditions encountered in Antarctica, i.e. hyper aridity, UV radiation and cold temperatures [306]. Another Sphingomonas strain exhibited a high resistance to UV radiation with a 10% survival at a 1500 J.m -2 UV dose [307]. The genus Caulobacter is also ubiquitous and can be found in aquatic environments, freshwater sediment and soils [308,309] and even associated with plants [310]. One species of Caulobacter has been proven to grow with 200 µM of uranium at pH 7 [311]. Bacteria belonging to the Acidovorax genus have been isolated from lawn soil, water, commensal flora, the water of an activated sludge plant, and has also been described as a phytopathogenic agent [312][313][314]. Some Acidovorax strains can use the oxidation of H2 as an energy source [315]. H2 can be formed by water radiolysis. To our knowledge, this is the first time the genus Acidovorax has been described in an environment bathed with ionizing radiation.

Conclusions

In this work, we have shown that the microbial composition of biofilms could be directly analysed using metaproteomics. An original methodology has been developed for the remote sampling of biofilms, as well as for the data treatment of the MS/MS spectra obtained after protein extraction, a procedure that we named phylopeptidomics. The strategy developed here can be used to identify microorganisms present in any kind of samples and decipher their respective biomass contributions.

Proteotyping the fraction of microbiomal community from a nuclear pool able to grow in liquid cultures

Abstract

To study the diversity present in an aquatic environment and observe its evolution in liquid medium throughout time, a kinetic can be performed in diluted rich liquid media. By increasing the number of media and dilution some diversity can be observed, however the use of two or three media and two different dilutions could be enough to identify the main genera present in the water of constraint environments. In this study the water of two nuclear pools have been studied over a liquid kinetic. This study allowed the optimization of kinetic's conditions. The kinetics highlighted the presence of a majority of Proteobacteria in both pools after culture. This study present the first description after culture of a water sample from a working nuclear reactor. This study highlighted the presence of a great diversity present in nuclear pools although the samples that could be used were in low amounts. The study of two different pools, including one pool with a change in the living condition, brought to light the differences in microbiota.

Introduction

Microbial biotechnology relies heavily on isolation of novel microorganisms with specific characteristics and their characterization for the most appropriate use as biocatalysts [316]. Many biotechnology-relevant microorganisms have been isolated from extreme environments. However, it is known that only a relatively low number (0.1 to 10%) of the microorganisms present in complex environmental samples can be culturable in normal laboratory conditions [START_REF] Stokell | Viable but Nonculturable Bacteria[END_REF]. To increase the chances to obtain more isolates from complex environmental samples, new strategies relying on the screening of numerous media and growth conditions have been recently proposed [270,317]. Remarkably, culturomics was shown to be more efficient than metagenomics to comprehensively understand the gut microbiota of a patient [318].

Another question regarding isolation of microorganisms is how to propagate the initial sample. Today, the sample is directly used for culturomics screening after its dilution. However, a complementary strategy is to propagate the sample to obtain a larger quantity of the consortium of microorganisms, either before screening and isolation, or their characterization as a consortium in terms of biotechnology application.

Maintaining the diversity of such a consortium is far to be trivial but will be of interest to understand the interactions between the key players of the consortium. The molecular tools to analyze the diversity of a consortium are metagenetics (16S/18S rRNA amplification and sequencing), metagenomics, and proteotyping by mass spectrometry. Whole-Cell MALDI-TOF mass spectrometry has revolutionized the clinical microbiology laboratory as in some minutes a microbial isolate can be identified in terms of species [319]. However, this approach is still not really well adapted to environmental microbiology because of the lack of comprehensiveness of the MALDI-TOF databases for environmental species. Proteogenomics definition of biomarkers was shown to be of interest for screening new isolates by whole-cell MALDI-TOF [320,321] but its usage still remains scarce. Recently, proteotyping by tandem mass spectrometry has shown to be efficient to analyze more complex samples (Pible, Armengaud, et al., submitted). We have developed this latter approach and recently proposed phylopeptidomics as a powerful tool to identify the microorganisms present in a complex sample and assess their respective biomass contributions (Pible, Armengaud, et al., submitted). Phylopeptidomics can give within a few hours an extensive picture of the microbial consortium and thus can be implemented in novel strategies for microbial isolation.

Chapter II -Methods development and optimization 88 Here, we proposed to investigate by means of phylopeptidomics whether cultivation in rich but diluted liquid media of microbial communities sampled from two pools of nuclear facilities affect their diversity.

For this, we sampled different microbial communities and cultivate them in various conditions. We followed their evolution along time and found that a rather stable community can be maintained for various days and months.

Material and Methods

Description of pools and sampling

The water from two pools located in the CEA -Saclay center was sampled in this study. Pool 1 is used to stored 60 Co radioactive sources to perform irradiation experiments on materials. This pool does not contain any radionuclides.

In 2015, the purification system filtered the water every day for circa 10 minutes. The characteristics and composition of the water of this pool on July 2015 are presented in Table 2.9. Several ions (Cl -, Na + , K + , Ca 2+ , and Mg 2+ ) can be used as nutrients by microbial cells.

Table 2.9: Characteristics and composition of the water of Pool 1 in July 2015

Pool 2 is used to cool 235 U radioactive sources. This reactor used to produce radionuclides for medicine before its definitive shutdown in December 2015. While in operation, the pH 7.0 pool's demineralized water (conductivity 0.5 µS/cm), maintained at 35°C, contained the β-emitter 3 H (2.49x10 8 Bq/m 3 ) and the emitter radionuclides 24 Na (2.2x10 9 Bq/m 3 ), 7 Be (4.0x10 7 Bq/m 3 ), 27 Mg, 41 Ar, 56 Mn, 99 mTc, 122 Sb, 187 W, 188 Re, 197 mHg yielding a total  activity of 2.6 10 9 Bq/m 3 . During the reactor shutdown pH 5.4 pool's demineralized water (conductivity 0.5 µS/cm) contained the β-emitter 3 H (2.49x10 8 Bq/m 3 ) and the -emitters 51 Cr (1.6x10 6 Bq/m 3 ), 186 Re (3.7x10 5 Bq/m 3 ), 124 Sb (2.8x10 5 Bq/m 3 ), 58 Co (2.5x10 5 Bq/m 3 ), 99m Tc (2.5x10 5 Bq/m 3 ), 122 Sb (1.7x10 5 Bq/m 3 ), 99 Mo (1.7x10 5 Bq/m 3 ), 60 Co (1.3x10 5 Bq/m 3 ), and 54 Mn (3.6x10 4 Bq/m 3 ), yielding a total  activity of 3.25x10 6 Bq/m 3 .

To sample water from Pool 1, a 1.5 L sampling bottle (Wildco, USA) was used to sample 6 L of water. The recovered water was transferred in sterile bottles (PETG Media Bottle Sterile, VWR). For Pool 2, a 2 L water volume was collected using a permanently installed pipe extensively purged before sampling. The water was centrifuged at 15,000 g for 20 min at room temperature for both pools. The supernatant was discarded and the microbial pellet resuspended in 1 mL of supernatant. From this milliliter, 50 µL were used as starters for liquid and solid cultures. The water from Pool 1 was sampled in November 2015 in five different locations, one in the middle of the pool, and four near the walls after rubbing the walls to resuspend any possible biofilm present. Sample A was collected near the northern wall at a 4 m depth while sample B was recovered at the near the same wall but at a 1 m depth. Sample C was collected near the bottom of the western wall (at a 4 m depth) and sample D was recovered near the same wall at a 1 m depth. Finally sample E was collected in the middle of the pool at a 1 m depth (Fig. 2.7). 2.2.2.2.1 Preliminary experiments in Pools 1 and2 The 1.0 mL concentrate obtained after centrifuging 6L of water was estimated to contain at least 12x10 6 cells per mL. Among the mL recovered only 50 µL were meant to be cultivated in liquid media. The rest was used for metagenetics sequencing, solid cultures and -80°C preservation in Glycerol Strain Buffer (GSB) (65% glycerol v/v, 0.1 M MgSO4, 0.025 M Tris-HCl pH 8) (cf. Chapter III.4). The 50 µL of liquid sample were divided between different liquid media (4 µL per liquid culture for Pool 1 and 6 µL for Pool 2). For Pool 1, the sampling in 2015 was cultivated in Luria Bertani broth (LB) diluted six times (1/6) and in LB diluted twelve times (1/12). For Pool 2, in 2015, the water was cultivated in Brain Heart Infusion (BHI) diluted 10 times (Sigma, Germany), LB diluted twice (Roth, Germany), Tryptic Soy Broth (TSB) diluted twice (Sigma), and Nutrient Broth (NB) diluted twice (Sigma). Dilutions were performed with sterile milliQ water.

LOQ - 2,00E+01 4,00E+00 2,00E+00 1,00E-01 5,00E-01 9,40E-02 5,00E+00 2,00E-01 1,00E+02 5,00E+00 1,80E-01 5,00E+00 4,30E-01 Pool 1 8 1,27E+02 <4,0E+00 <2,0E+00 - 2,70E+00 - - - - 5,80E+00 1,60E+00 4,10E+01 1,70E+00 Inc (%) - 3 10 - - 21 - - - - 13

Cultivation and sampling for proteotyping

Liquid cultures of 6 mL were initiated with samples from Pool 1 or Pool 2. The cultures were incubated at 26°C for Pool 1 under vigorous shaking (200 rpm). After one week of cultivation, the 6 mL cultures were transferred into a 30 mL culture from the corresponding medium. These cultures were then sampled after 45 days, 137 days, 173 days, and 227 days. For the cultures initiated with the first sampling in Pool 2 in 2015, the temperature of incubation was 28°C and the kinetic points were: 82 days, 153 days, and 207 days.

These kinetic points were chosen to have enough material to be analyzed using phylopeptidomics. Samples from Pool 1 presented a faster growth than Pool 2. After the second point of kinetic from Pool 1 (day 137), it had been realized that microbial material needed for phylopeptidomics analysis could be reduced.

Starting from day 173 in Pool 1 and day 153 in Pool 2 the kinetic points were closer (sampled every months and a half for Pool 1 and two months for Pool 2). Each sample was preserved at -80°C in GSB.

For each kinetic point, 2 mL of culture were sampled and centrifuged at 20000 g for 5 minutes at room temperature. The supernatant was removed and the resulting wet pellet weighed. The tubes were then flash-frozen in liquid nitrogen and stored at -80°C before being analyzed by metaproteomics. The protocol used is presented in Scheme 2.1.

These preliminary allowed us to select liquid media and isolate interesting organisms from Pool 2 after sample A direct metagenetics analysis (cf. Chapter III.4).

Scheme 2.1: Preliminary experiments protocol schematized

Water cultivation from Pool 2 in 2017

In 2017, 600 mL of samplings were centrifuged for 20 min at 16000 g at 10°C. Microbial pellet was resuspended in about 3-4 mL of supernatant. 16 to 150 µL of the mix were cultivated in each culture (depending on the number of cultures and volume of recovered mix).The water was cultivated in 5 mL of LB diluted twelve times and Reasoner´s 2A (R2A) diluted three times (HiMedia Laboratories Pvt. Ltd, USA).

Dilutions were performed with sterile milliQ water. These media were chosen according to the preliminary results and in order to isolate Variovorax present in more than 95% in the sample (cf. Chapter III.4). Each kinetic point was individual to avoid any contamination.

For the second sampling in Pool 2, performed in 2017, the kinetic points were: 3 days, 7 days, 14 days, and 21 days. Each culture was performed in triplicate. For each kinetic point per liquid medium, three cultures were analyzed by metaproteomics, and one culture was preserved at -80°C in four aliquots.

For each kinetic point, 5 mL of culture were sampled and centrifuged at 20000 g for 5 minutes at room temperature. The supernatant was removed and the resulting wet pellet weighed. The tubes were then flash-frozen in liquid nitrogen and stored at -80°C before being analyzed by metaproteomics.

The protocol used is presented in Scheme 2.2.

2015: Preliminary experiments

Metaproteomics analysis

The metaproteomics analysis was done as previously described (cf. Taxonomical composition of a biofilm sampled from a nuclear fuel pool assessed by metaproteomics). Briefly, samples were thawed on ice, and a volume of 59 µL of Laemmli buffer LDS1X (Invitrogen) was added per mg of wet bacterial pellet as described by Hartmann et al [295]. After denaturation for 5 min at 99°C, the protein sample was loaded onto a 4-12% gradient 10-well NuPAGE gel (Invitrogen). The proteins were subjected to electrophoresis for 5 min and then stained with Coomassie Blue Safe staining (Invitrogen) as recommended by the supplier.

The polyacrylamide band corresponding to the whole proteome was sliced, then unstained with ultra-pure water, reduced with dithiothreitol, and treated with iodoacetamide. It was then subjected to proteolysis with Trypsin Gold Mass Spectrometry Grade (Promega) in the presence of 0.01% ProteaseMAX surfactant (Promega), as previously described [322]. The resulting peptides were analyzed with an ESI-Q Exactive HF mass spectrometer (Thermo) equipped with an ultra-high field Orbitrap analyser and coupled to an Ultimate 3000 176 RSL Nano LC System (ThermoFisher). A volume of 10 µL of peptides was injected onto a reverse phase Acclaim PepMap 100 C18 column (3 µm, 100 Å, 75 µm id x 500 mm) and resolved at a flow rate of 0.2 µL/min with a 60 min gradient of CH3CN in presence of 0.1% formic acid. The Q-Exactive HF instrument was operated with a Top20 strategy with only ions with potential charge states of 2+ and 3+ selected for MS/MS and with a dynamic exclusion of 10 sec. The proteotyping interpretation of MS/MS spectra was done with the National Center for Biotechnology Information non-redundant fasta file (NCBInr) database with the Mascot search engine (Matrix science). Peptide -to-MS/MS spectrum assignation was done with full trypsin specificity, maximum of one missed cleavage, mass tolerances of 5 ppm on the parent ion and 0.02 Da on the MS/MS, static modification of carboxyamidomethylated cysteine (+57.0215), and oxidized methionine (+15.9949) as dynamic modification. Post-processing of Mascot dat files was done as previously indicated (reference public biofilm).

Results

Preliminary results

Phylopeptidomics analysis of the water from Pool 1 campaign

Pool 1 is used to store 60 Co radioactive sources to perform irradiation experiments on materials. We first sampled water from the middle of the pool (sample E), inoculated LB diluted media (1/6 and 1/12), and monitor the diversity of organisms by phylopeptidomics. As shown in Table 2.10, this sample contained 2x10 6 cells per liter as estimated by microscopy with a Malassez cell. Therefore, the quantity of microbial cells in the 50 µL aliquot of sample that was used for inoculating the medium is really low: 100 cells. After 45 days of culture, an aliquot of each culture was sampled and the proteins present were extracted, proteolyzed by trypsin, and the resulting peptides were analyzed by high resolution tandem mass spectrometry.

Additional samples were taken from the two cultures after 137, 173 and 227 days without renewing the media. Phylopeptidomics analysis was performed in the same conditions on all these samples. As reported in Table 2.13, the number of families detected for the LB 1/6 and LB 1/12 cultures is 20 and 16, and the number of genus detected is 27 and 20, respectively. Even after a long incubation period, no drastic loss of biodiversity could be observed, nor the preponderance of one genus over the others.

Mainly genera belonging to the Proteobacteria phylum (and more specifically a-Proteobacteria) were identified in both kinetics. The main genus present in Sample E during both kinetics was Caulobacter (between 17 to 70% in LB 1/6 and 13 to 52% in LB 1/12). 2 to 9% of the samples were composed by Bacillus.

The Xanthobacter genus was also present in both kinetics and represented up to 13% of the samples. One eukaryotic phylum was identified in LB 1/12 with the presence of the Fusarium fungus at day 173 (Tables 2 .11 & 2.12).

Location A B C D E Total cell count (10 6 cells/L) 2.0 ± 1.2 2.0 ± 1.6 4.5 ± 0.5 2.0 ± 0.9 2.0 ± 0.8
Chapter II -Methods development and optimization 94 We sampled additional water samples after scrubbing the walls to detach biofilms. Four samples could be harvested (Samples A to D). In this case, the water contained small biofilm particles that were collected by centrifugation. As shown in Table 2.10, the total cell counts were relatively similar for all the samples (2.0x10 6 cells per liter), except the Sample C which was shown to contain 4.5x10 6 cells per liter. Cultures in LB 1/6 and LB 1/12 were inoculated with these four water samples. The samples harvested near the western wall presented the most diversity when in culture. Between 49 and 50 genera were identified from the top location sample while between 33 and 49 genera were identified at the bottom location. The northern wall presented a smaller diversity with only 32 to 34 identified genera (Table 2.13). The phylopeptidomics analysis of the resulting kinetics is shown in Tables 2.14 to 2.17.

In Sample A, Caulobacter, Sphingomonas, and Sphingopyxis were the main identified genera (Table 2.14).

They were present in all kinetic points at both dilutions. Caulobacter represented between 7 to 43% of the samples. 6 to 19% of the biomass belonged to Sphingomonas while 3 to 10% belonged to Sphingopyxis. The genera Pseudomonas and Bacillus were also present during these kinetics (0 to 6% and 0 to 3% respectively).

The genus Fusarium appeared at D 137 in LB 1/12 represented almost 7% of the identified peptides in LB 1/12 at D 173 (Table 2 .14).

In Sample B one of the main identified genera was Streptomyces (4 to 5% for both dilutions) (Table 2

Middle of the pool Northern wall

Western wall 97 represented between 2 to 5% (Rhizobium) and 2 to 7% (Azorhizobium) of the identified bacteria. At D 173 in LB 1/6, genus Fusarium represented 10% of the sample (Table 2.17).

Chapter II -Methods development and optimization 98 A stability of the community in liquid culture over time could be observed in all media, starting from two and a half months of cultivation.

BHI medium (at both dilutions) presented a smaller diversity compared to the other media used. 26 genera were common to all media. These genera were among the main genera identified.

Phylopeptidomics analysis of the water from Pool 2 sampled during the 2017 campaign

In another campaign, we had the opportunity to sample once again Pool2 but at three different locations and initiated cultures in two liquid media: LB 1/12 and R2A 1/3. The latest medium was chosen on the basis that Variovorax was the main bacteria identified using metagenetics in Pool 2 during the 2015 campaign (cf. Chapter III.2). Based on the previous results, we choose to follow the culture in a short-term mode and sampled the cultures at 3, 7, 14, and 21 days. Only Sample H was analyzed at 3, 7, 14, and 21 days. Samples G and I were only analyzed at days 7 and 28.

Kinetic from Sample G: microbial composition in the bottom of the pool in 2017

In the 2017 LB kinetic from Sample G, the main identified genera were Sphingomonas (2 to 65%), followed by Sphingobium (0 to 10%), Pelomonas (1 to 11%), Rhizobium (0 to 6.5%), and Pseudomonas (2 to 4%).

Streptomyces genus was present in important proportion (about 32%) for only 1 triplicate at D7 and D28.

In the other duplicates this genus represented between 2 and 4% of the sample. From this kinetic it can be observed that, if the triplicates were not similar within each other, a certain consistency can be seen between the 2 kinetic points. The first triplicate in D7 presented an important proportion of Sphingomonas (65%) when the second triplicate in D28 contained 49% of the same genus. Similarly Pelomonas represented about 4.7% of the sample at D7 and 3% at D28. The third triplicate at D7 and D28 also presented some similarity in microbial composition and proportion. Streptomyces represented at both kinetic points about 32% of the sample, while Ralstonia and Burkholderia were present at about 44% and 10% respectively (Fig.

2.9; Appendix 4, Table 6.4). In the R2A kinetic, the genus Pelomonas was also present in important quantity (1 to 15%). The genera Bradyrhizobium and Rhizobium were also present in the kinetic at a proportion from 1.5 to 4.5% and 0 to 6% respectively (Fig. 2.10; Appendix 4, Table 6.5). In this kinetic the evenness of each genus was greater than in the other kinetics. Indeed the greater observed proportion in Sample G R2A kinetic was 15% for Pelomonas while in the other kinetics, Sphingomonas could represent up to 65% of the sample, and

Ralstonia up to 40%.

Figure 2.10: Kinetic of the liquid culture of Sample G from Pool 2 in 2017 at the genus level in R2A

Diversity was thus present at both kinetic points in both media (Fig. 2.9 & 2.10). A stability in the diversity could also be observed despite the visible growth of the microbial community (Fig. 2.11). During the 2017 kinetic, the main genera found in Sample H in LB and R2A liquid cultures were the same:

Streptomyces, Bradyrhizobium, Rhizobium, Burkholderia, Acidovorax, Pelomonas, Methylibium, Rhizobacter, Rubrivivax, and Pseudomonas (Appendix 4, Tables 6.6 & 6.7). As in the 2015 kinetic, some diversity could be observed in the cultures, starting from day 7 (Fig. 2.12 & 2.13; Appendix 4, Tables 6.6 &6.7). The kinetic from Sample H were not especially even between the genera repartition.

In the LB kinetic, at D14, the Sphingomonas genus represented 72% of the identified and attributed peptides. Despite an observable similarity between the triplicates at D7, the triplicate appeared different at D14. For example triplicate 3 was composed of about 73% of Sphingomonas while the others contained 3 to 4% of this genus. Similarly triplicate 2 presented 24% of Caulobacter cells while it was only present in one other triplicate with a 1% proportion. At D28 the first two triplicates were similar both in composition and proportion. Triplicate 3 possessed more Rhizobiales genera than the other two. The proportion of phyla others than Proteobacteria was similar between each triplicate at D28 (Fig. 2.12; Appendix 4, Table 6.6).

B.

16%) compared to triplicates 1 and 2 (6 and 10% respectively) and Variovorax (about 4%), Rhizobacter (about 6%), and Rubrivivax (about 4%) were only present in triplicate 3. At D28 a major difference can be observed in triplicate 1: about 40% of the sample was composed by Actinobacteria, while the other triplicates were composed of about 5% of this phylum (Appendix 4, Table 6.7).

Figure 2.13: Kinetic of the liquid culture of Sample H from Pool 2 in 2017 at the genus level in R2A

A microbial diversity was identified in Sample H in both kinetics. A stability in the diversity could also be observed despite the visible growth of the microbial community (Fig. 2.14). Without addition of liquid media microorganisms were able to develop and grow even 3 months after insemination in a 6 mL nutritive media. The stability could also be observed in Sample I between 7 and 28 days of liquid culture (Fig. 2. 15 & 2.16).

During the LB kinetic triplicates were different a D7. Triplicates 1 and 3 were similar while triplicate 2 was A stability in the diversity could also be observed despite the visible growth of the microbial community (Fig. 2.17). In all kinetics (location and media), the genus Methylobacterium was absent or present in very low quantity.

The phylum Ascomycota (fungi) was also present in all kinetics from Pool 2, from the 2015 and the 2017 kinetics. However the fungi were identified in small proportion, less than 1% except for D7 in LB in Sample G (2%). In the 2015 kinetic the fungi represented between 3.5 to 9% of the identified peptides. In the latter the genus Trichoderma was highly represented (3.5 to 7%).

Discussion

In the present study, we have shown that inoculating a culture with a low volume of water sampled from a pool allows in some days to obtain enough biological material to identify the main microbial players in the culture. We have observed that the microbial diversity of the culture is assessable when enough biological material is obtained and a certain stability of this microbial diversity settled over time in several diluted rich liquid media. The taxonomical identification at the genera taxonomical rank using phylopeptidomics, a novel metaproteomic approach, allows visualizing the presence of different Life kingdoms as prokaryotes and eukaryotes are distinguishable in the same analysis, and their respective biomass contributions.

For Pool 1, water diversity in the middle of the pool was lower (20 to 27 genera) than the other locations (32 to 50 genera). Samples A to D were collected after rubbing the walls to resuspend biofilms. It is known that biofilms are predominant in aquatic environments [START_REF] Leclerc | Relationships between common water bacteria and pathogens in drinking-water. Heterotrophic Plate Counts and Drinking-water Safety[END_REF][START_REF] Geesey | Sessile bacteria: An important component of the microbial population in small mountain streams[END_REF][START_REF] Costerton | Biofilms, the Customized Microniche[END_REF][START_REF] Costerton | Bacterial biofilms in nature and disease[END_REF]. The resuspension of microorganisms from biofilms enriched the water sampling and could thus lead to a greater diversity. The western wall (39 to 55 genera) presented the most diversity after cultivation than the northern wall (32 to 34 genera). This diversity could be explained by the configuration of the pool. Cobalt sources, when present at the bottom of the northern wall, are a few centimeters away of the wall and a dose rate of circa 100 Gy/h can be measured. Near the western wall, sources are more often present but at a greater distance from the wall.

Pool 2 is used to cool the 235 U radioactive nuclear reactor and produce radionuclides for medicine. From this very extreme environment, water samples have been used to inoculate cultures in rich media diluted in water. These cultures exhibited some extended microbial diversity after one week of cultivation, and

were not populate only with a few major players. Thus, this culture approach can help maintaining and amplifying microbial consortia by specific culture conditions. The main genera identified during this shortterm kinetic experiment were present in all tested media during the 2017 kinetic of Pool 2. Some genera present in small proportion were only identified in one liquid medium, thus highlighting the interest of culturomics. Twenty-eight and 35 genera were identified only in LB or R2A respectively, while 98 genera were common to both media (Appendix 4, Table 6.10). The R2A medium thus exhibits a slightly higher Some strains present in these pools belonged to genera that had never been described in such environments before. Among the genera mainly present in the kinetics, Xanthobacter, Caulobacter, Erythrobacter, Acidovorax, Methylibium, Streptomyces and Trichoderma have never been described in SNFP. Some of these genera can however exhibit resistance to ionizing radiations. The isolation of these strains could help understanding the mechanisms implemented by the cell to resist the harsh condition prevailing in both pools by studying them in presence of the different stresses prevailing in both pools.

A majority of Xanthobacter strains can produce slime. It was proved that slime producer strains grow better than non-producer at a high oxygen concentration [315]. The fact that this peculiar genus was present in this pool could be explained by its ability to produce slime and its potential protective effect against radiation. Members of the Erythrobacter genus isolated in the Mediterranean Sea and the Pacific Ocean

Conclusion

To study the diversity present in an aquatic environment and observe its evolution in liquid medium throughout time, a kinetic can be performed in diluted rich liquid media. By increasing the number of media and dilution some diversity can be observed, however the use of two or three media and two different dilutions could be enough to identify the main genera present in the water of constraint environments. As seen above, enough biological material could be obtained after day 7 in order to perform phylopeptidomics and assess the microbial diversity which persisted over time, up to several months. The use of a kinetic in diluted but rich media with phylopeptidomics monitoring allows exploring new perspectives for microbiology experiments. For example, it can be helpful to amplify the biological material and orient further agar-plate isolation of the most interesting microorganisms. Indeed several genera identified in both pools had never been described in similar environments. The main genera, such as Sphingomonas, Ralstonia or even Methylobacterium and Bacillus were also found in Spent Nuclear Fuel Pools all over the globe.

Moreover the main genera found in both Pool 1 and Pool 2 were common even though the pools are different.

A majority of Proteobacteria were identified in both pools after culture. This result is in accordance with a previous description of similar environment (SNFPs) where, surprisingly, mainly Gram negative bacteria had been isolated [185]. The presence large presence of Gram negative bacteria, unable to sporulate, can highlight the potential radioresistance of these organisms under a metabolically active form. Another explanation could be that these bacteria entered a viable but not culturable state in the pools and the liquid culture allowed them to become metabolically active again. However the selected media were favorable to Gram negative growth. In terms of population, Pools 1 and 2 from the 2017 kinetic were more similar than This study highlighted the presence of a great diversity present in pools from nuclear facilities although the samples that could be used were in low amounts due to low cell density in the water and the inability to sample and treat more than 30 L. The study of two different pools, including one pool with a change in the living condition, brought to light the differences in microbiota. Each pool thus owned its own conditiondependent microbiota.

Chapter II -Methods development and optimization 116

Chapter conclusion

In this chapter, it has been shown that the sequencing of the V4V5 regions allowed a better taxonomic identification when compared to the V4 sequencing. Moreover a 0.016% cut-off should be used when analyzing metagenetics data obtained after an Illumina MiSeq sequencing and OCToPUS analysis. Different methods can be used to harvest microorganisms and extract DNA. The different methods all have their strengths and weaknesses. However two harvesting methods should be used: filtration and centrifugation.

Only one DNA extraction protocol can be used, especially if a phenol-chloroform-isoamyl alcohol based method is used.

It was also proved that using a new metaproteomic approach (phylopeptidomics) could enhance the knowledge of the microbial community present in extreme environments. The use of Spontex sponge to recover biofilms from a radioactive storage pool was proved to be adapted to the sampling conditions.

Furthermore, even though the sensitivity of metaproteomics was lower than metagenetics, several genera were identified in the recovered biofilm. With more material, as demonstrated in 2.2 after a cultivation step, a numerous number of different genera from several phyla were identified using metaproteomics.

The taxonomical identification with metagenetics and metaproteomics could only be certain until the genus taxonomic level and not the species one. Using meta-analysis, particularly metagenetics, the depth of the analysis and the size of the sequenced region of the 16S rDNA were indeed too low and small to enable identification at the genus level. Moreover some genera can be phylogenetically extremely close and the sequencing of only a fragment of 16S rDNA cannot discriminate at the genus level. For example, after the V4V5 sequencing of the ZymoBIOMICS DNA standard the OCToPUS software could not discriminate whether one OTU belonged to the Escherichia genus or the Shigella one. The combination of both metagenetics and metaproteomics can not only provide more information about the diversity present in water from nuclear fuel pools but could present complementary results in microbiota analysis. Finally the use of a liquid culture in several diluted rich media enabled the identification of a large diversity of genera although the water sample was really low (50 µL). This method also presented the advantage that the great diversity is stable over time.

Chapter III:

Deciphering the microbial composition of the cooling pools of nuclear facilities

Chapter III -Deciphering the microbial composition of the cooling pools of nuclear facilities 118

Deciphering the microbial composition of the cooling pools of nuclear facilities

It is now known that pools from nuclear facilities can harbor life. Few studies to study microbial diversity have been performed in nuclear pools. Different genera have been isolated in the spent nuclear fuel pools (SNFPs) of different facilities, in Spain, Hungary or in the USA [152,177,181,[183][184][185][186][187][188]. The microorganisms discovered in these pools were identified after a cultivation step. Thus they only represented a small part of the microbial diversity of microorganisms present in those environments. One recent study identified the bacterial population present in a spent nuclear fuel pool [193]. Using a metagenomic approach consisting in sequencing the amplicon of the 16S rRNA gene of the microbiota found in this storage basin, the authors found different phyla, including Acidobacteria, Actinobacteria, Planctomycetes, Proteobacteria, Nitrospirae and Deinococcus-Thermus [193]. In this peculiar SNFP, the majority of the sequences corresponded to the families Burkholderiaceae (23%), Nitrospiraceae (23%), Hyphomicrobiaceae (17%), and

Comamonadaceae (6%). The evenness of the different families was quite important, due to the presence of some selection pressures (nutritive stress, presence of radiations and radionuclides). Depending on the pool's conditions the microbiota can be different when compared to another SNFP. Indeed, in the SNFP of Cofrentes, a majority of Proteobacteria and Firmicutes, as well as some Actinobacteria were identified while in a SNFP in Sweden, the main genus was Meiothermus [186,190]. The physico-chemical composition of each pool may influence the microbiota composition as well as the hydrodynamics and maintenance procedures.

Till now no investigation have been performed on the pool containing a working nuclear reactor. Knowing the nature of the microbiota present in nuclear facilities might be important for several reasons, the first one being the maintenance of the reactor structure integrity. Some organisms can indeed modified the water composition, form biofilms and lead to corrosion. Secondly the radioactive risk is well controlled in nuclear facilities but the biological one is absent of all control. The safety of the staff present in nuclear facilities could not only depend on the radiation but also should take into consideration any possible biological contamination. Studying these organisms will open interesting industrial perspectives as the isolated strains could be implemented in a new nuclear effluents decontamination process and fundamental perspectives as their resistance and radionuclides accumulation pathways could be deciphered. In this chapter, the microbiota in two different pools was assessed at different locations and time points. It was thus possible to follow the microbiota stability or not depending on the sampling location and the pools' condition. The microbiota present in these pools were analyzed using meta-analysis.

Meta-analysis based on 16S rRNA amplicon sequencing has established itself as a powerful tool in various fields including medical and environmental applications to get insight into microbial community composition [198,230,285]. Despite some bias occurring from DNA extraction, PCR, the choice of the 16S rRNA region, the size of the fragment to sequence, and the quantity of microorganisms and DNA [236,[330][331][332], it offers unsurpassed insight into the identity and the diversity of the taxa present in a given sample.

However, owing to the above-mentioned bias in DNA extraction, the variable number of copies of the 16S rRNA gene, and the variable number of copies of the chromosomes depending on the microorganism and on its physiological state, it is far from being quantitative [235]. Over the last 10 years, metaproteomics has been used to assess functional aspects of communities inside their environment. This method also allows a more quantitative approach compared to metagenetics [301]. In the first presented study, we explored for the first time the microbial communities present in the water of the cooling pool of an operating nuclear reactor's core. Microbial communities present in this pool were also investigated during reactor's shutdown. In contrast to most previous studies conducted on spent nuclear fuel storage pools, we performed here non-culture selective high throughput meta-multi-omics approaches combining i) analysis of 16SrDNA gene amplification and sequencing, and ii) direct metaproteomics analysis.

In nature, many microorganisms can form biofilms as a mean to colonize a specific environment and facilitate their long-term fate. For this, different microorganisms stick generally onto a surface, are embedded within a self-produced matrix of extracellular polymeric substances and may further colonize their surroundings. This specific growth pattern is indeed more protective than the planktonic form (cf.

Chapter I.3.1). It has been estimated that biofilms are predominant in aquatic ecosystems and represent more than 99.9% of the total microbial community [START_REF] Leclerc | Relationships between common water bacteria and pathogens in drinking-water. Heterotrophic Plate Counts and Drinking-water Safety[END_REF]. Inside a biofilm, the pH can be appropriately modified, and nutrients and organic material may be more easily recycled, creating more favourable conditions for microorganism survival and growth [START_REF] Costerton | Microbial Biofilms[END_REF]284]. As a consequence, microbial biofilms have been detected in very inhospitable places such as used nuclear fuel pools [176,181] or acid mine drainage [244,256]. Biofilms can lead to corrosion and thus pose a threat to the integrity of the pool's walls. It has indeed been proved that Microbial Influenced Corrosion (MIC) can occur in these environments due to the presence of microorganisms [183]. Knowing the biofilm composition is important to get clues on how to prevent their development. In previous studies, microbial colonization of stainless steel and titanium coupons placed in a bioreactor connected with the water of a Spanish SNFP have been studied [184,187].

Over a year the bioreactor stayed in the pool and pumped the water with an "on-off" cycle (pump was on for 2 h and off for 1 h). The metals were then recovered and the developed biofilms were analyzed after cultivation on solid media at 30°C [184,187]. This biofilm concentrates some radionuclides: 60 Co (about 80%), 59 Fe (about 25%), 95 Nb (about 13%), 65 Zn (2-5%), and 54 Mn (5-10%) after 174 days of immersion.

Mainly Gram negative bacteria (more than 60% of Proteobacteria identified in all studies) were identified in SNFPs in all previous studies performed [152,177,181,[184][185][186][187][188][191][192][193]. The goal of the second study was to study the diversity present in the biofilms collected in the cooling pool of a nuclear reactor. To do so eight different biofilms were recovered from 6 different locations in the pool and analyzed using a metagenetic analysis.

Finally, the study of the organisms living in a pool used to store radioactive 60 Co sources has never been performed to our knowledge. Pools used to store radioactive cobalt sources do not contain radionuclides in their water. The only stresses present in these pool are ionizing radiation and nutritive stress. Studying the microbiota present in such environments, with only a radiative and nutritive stress can be of great interest to identify radioresistant organisms. Moreover these stresses are less stringent than the ones present in SNFPs, with lower dose rates and the use of tap water instead of demineralized water. A higher microbial diversity should thus be expected. In the third presented work the microbiota of a 60 Co sources storage pool located in Saclay was studied. The presence of natural light in this infrastructure, placed in a hall with windows, could favor the development of photosynthetic organisms. Indeed, the presence of algae in presence of ionizing radiation has already been reported since a green micro-algae belonging to the Coccomyxa genus has been identified in a SNFP and can resist -radiation up to 20 kGy [176]. The filtration system of this pool had been modified in July 2016. The nature of the organisms present in this pool was studied at three different time points and at 6 different locations. Both water and biofilms were recovered for analysis. Moreover each previously studied pool seemed to possess its own microbiota despite the presence of common genera. Here, the aim of this study was to identify microorganisms present in a 60 Co sources storage pool, establishing a space and time comprehensive microbial inventory.

Chapter III -Deciphering the microbial composition of the cooling pools of nuclear facilities 120

3.1 Microbial composition of a nuclear reactor cooling pool during operation and at shutdown deciphered using multi meta-analysis (article in preparation)

Abstract

Water pools present inside nuclear facilities such as the ones used to store spent nuclear fuel constitute harsh environments where radiative, nutritive, and metallic stress prevail. Very few studies have investigated the microbial composition of such pools owing to the constraining handling conditions. This work presents the first complete inventory of microorganisms present in the core's cooling pool of a nuclear reactor. The microbial composition of the pool's water was investigated both during operation and at shutdown. To get the most exhaustive view of the microbiota composition, two direct and complementary meta-omics approaches were used, namely metagenetics and metaproteomics. They were complemented by the cultivation of the water samples in diluted media which has proven to be a valuable approach to identify the main microbial species when initially present in low concentration (article in preparation).

Despite highly stringent conditions (presence of radiations and radionuclides at high concentration, lack of nutrients), 27 different OTUs were identified in the pool during operation, and 96 OTUs were identified at shutdown. A change in the microbiota composition occurred between the samples collected during operation and the ones collected after the shutdown. The main genus during operation was Variovorax, a genus which might use dihydrogen produced by water radiolysis as energy source. It completely disappeared at shutdown to the profit of Methylobacterium.

Materials and Methods

Water sampling in the cooling pool of the nuclear reactor's core and characterization

The While the reactor was in operation, a 2 L water volume was collected at Loc. A on December 2015 using a permanently installed pipe extensively purged before sampling. When the reactor was shut down, a total volume of 8 L was collected on March 2017 at Loc. A using the same device, and at Loc. B and Loc. C using a sterile sampling vial attached to the lower end of a pole and opened at the sampling point (Wildco®).

While in operation, the pH 7.0 pool's demineralized water (conductivity 0.5 µS/cm), maintained at 35°C, contained the β-emitter 3 H (2.94x10 8 Bq/m 3 ) and the -emitter radionuclides 24 Na (2.9x10 9 Bq/m 3 ), 7 Be (4.0x10 7 Bq/m 3 ), 27 Mg, 41 Ar, 56 Mn, 99 mTc, 122 Sb, 187 W, 188 Re, 197 mHg yielding a total  activity of 3.1 10 9

Bq/m 3 . During the reactor shutdown, the dose rates were below the threshold of 0. Bq/m 3 ), 124 Sb (2.8x10 5 Bq/m 3 ), 58 Co (2.5x10 5 Bq/m 3 ), 99m Tc (2.5x10 5 Bq/m 3 ), 122 Sb (1.7x10 5 Bq/m 3 ), 99 Mo (1.7x10 5 Bq/m 3 ), 60 Co (1.3x10 5 Bq/m 3 ), and 54 Mn (3.6x10 4 Bq/m 3 ), yielding a total γ activity of 3.25x10 6 Bq/m 3 .

121 Whatever the operating mode, the pool water is continuously filtered at a 500 m 3 /h flow rate, and is purified on ion exchange resins at 30 m 3 /h, meaning a turnover of the water of this 536 m 3 pool in 18 h.

The initial microbial cell density in water sampled during the reactor shutdown was assessed using a Malassez cell and light microscopy after microorganisms' concentration by centrifugation of 50 mL of water. Chapter II.1.2). After centrifugation Samples collected during reactor operation were stored for 3 days at 4°C before handling to reduce their activity by radioactive decay of 24 Na (half-life 15 hours) to 24 µS/h in contact with water, enabling its manipulation in controlled conditions. They were centrifuged and filtered as described above prior to DNA extraction for metagenetic analysis.

Water sample treatment and microbial communities harvesting

DNA extraction, Illumina sequencing, and sequence data analysis for meta-amplicomic analyses

Three methods were used for the treatment of samples harvested prior to amplicon sequencing, namely, centrifugation of 1 L of water followed by phenol-chloroform-isoamyl alcohol DNA extraction, filtration of 1 L of water followed by phenol-chloroform-isoamyl alcohol extraction, and filtration of 1 L of water followed by the DNeasy® PowerWater® kit (Qiagen, Germany) kit extraction.

DNA from the microbial communities of half the filters was extracted with DNeasy® PowerWater® kit from Qiagen (Germany) according to the manufacturer's instructions. The centrifuged water and the other half of the filters were extracted using a phenol-chloroform method, which was adapted from Vilchez-Vargas et al. [276]. The liquid sample was centrifuged 20 minutes at 20,000 g at room temperature and the supernatant discarded. One milliliter of lysis buffer (100 mM Tris pH 8; 100 mM EDTA pH 8; 100 mM NaCl;

1% polyvinylpyrrolidone; 2% SDS) was added and the pellet resuspend. The mixture was then transferred to a glass bead (0.1 mm) tube from Mobio. The tube was disrupted three times at 30 Hz for 60 seconds with 30 seconds break between each run with a disruptor MM 300 from Retsch/Qiagen. The tube was then centrifuged 5 minutes at 20,000 g at room temperature and the supernatant removed and transferred into a new tube. One milliliter of phenol-chloroform-isoamyl alcohol (25:24:1) was added. The sample was then vortexed and vigorously mixed by inverting the tube. The supernatant was recovered and transferred into a new tube after a centrifugation step of 1 minute at 17,000 g at 4°C. Seven hundred microliters of chloroform were added to the sample. The sample was then vortexed and vigorously mixed by inverting the tube. The supernatant was recovered after a centrifugation step of 1 minute at 17,000 g at 4°C. The supernatant was divided into two tubes (around 450 µl in each) and 45 µl of sodium acetate 3 M and 500 µL of isopropyl alcohol were added into each tube. The samples were then mixed by inverting the tubes and put overnight at -20°C. The tubes were centrifuged 30 minutes at 17,000 g at 4°C and the supernatant Chapter III -Deciphering the microbial composition of the cooling pools of nuclear facilities 122 removed. Two steps of washing were performed by adding 750 µL of 75% ethanol and a centrifugation step of 5 minutes at 17,000 g at 4°C. The supernatant was removed and the pellet dried with a heating block at 55°C. Sixty microliters of sterile milliQ water were added to dissolve the pellet. To have a more pure DNA and remove any trace of alcohol, the sample was put on a 0.05 µm filter disposed on sterile water for 15 minutes before being transferred into a new tube. The different methods were called method 1, method 2, and method 3 for centrifugation followed by phenol-chloroform-isoamyl alcohol DNA extraction, filtration followed by phenol-chloroform-isoamyl alcohol DNA extraction, and filtration followed by the DNeasy® PowerWater® kit respectively.

The sequencing of genomic DNA on 16S rDNA was performed by LGC Genomics, UK. The sequencing was conducted as followed. The PCRs included about 1-10 ng of DNA extract (total volume 1 μL), 15 pmol of each forward primer and reverse primer (in 20 μL volume of 1 x MyTaq buffer containing 1.5 units MyTaq DNA polymerase (Bioline) and 2 μL of BioStabII PCR Enhancer (Sigma) and additionally 0.2 μl of DNase (Artic Zymes) for the 16S PCR. For each sample, the forward and reverse primers had the same 10-nt barcode sequence. Each PCRs were carried out for 20 cycles, dependent on the primer pair, using the following parameters: 1 min 96°C pre-denaturation; 96°C for 15 s, 50°C for 30 s, 70°C for 90 s and primers without inline barcodes have been used (341F/1061R). For the second round 1 μL PCR product from the first round was used and the PCR conditions were the same as before. For this reaction barcoded primers have been added to amplify the V4V5 regions (515YF-926R: 5'-GTGYCAGCMGCCGCGGTAA-3' and 5'-CCGYCAATTYMTTTRAGTTT-3' respectively). The primers used to amplify V4 region were 515F/806R (5'-GTGCCAGCMGCCGCGGTAA-3' and 5'-GGACTACHVGGGTWTCTAAT-3' respectively).

DNA concentration of amplicons of interest was determined by gel electrophoresis. About 20 ng amplicon DNA of each sample were pooled for up to 48 samples carrying different barcodes. The amplicon pools were purified with one volume AMPure XP beads (Agencourt) to remove primer dimer and other small mispriming products, followed by an additional purification on MiniElute columns (Qiagen). About 100 ng of each purified amplicon pool DNA was used to construct Illumina libraries using the Ovation Rapid DR Multiplex System 1-96 (NuGEN). Illumina libraries were pooled and size selected by preparative gel electrophoresis. Sequencing was done on an Illumina MiSeq using V3 Chemistry (Illumina).

Sequencing data were analyzed through the OCToPUS pipeline [210] for contig assembly, quality filtering, de-noising, chimera removal, and OTU clustering. The OTUs were then taxonomically assigned at 97% identity against the SILVA database [333-335] using the mothur software [213]. Based on the analysis of a mock sample, the cut-off threshold was set at 0.016%. The results of the different methods were compared in (article in preparation, cf. Chapter II.1.2). Sequencing results at each location were pooled and are presented here, using subsampling at 28977 reads. The main OTUs unidentified at the genus level were manually blasted against Nucleotide BLAST from NCBI.

Protein extraction, peptide analysis using nanoLC/MS/MS, and taxonomic analysis by metaproteomics

Water samples (4.5 L per sampling location) dedicated to metaproteomic analysis were filtered and stored at 4°C prior to their pretreatment which was carried out in a controlled laboratory. Filters were shook into 15 mL of sterile MilliQ water to resuspend the microorganisms. The water was centrifuged for 20 minutes at 20,000 g at 10°C. The supernatant was removed and the pellet resuspended in 720 µL of supernatant.

123

The metaproteomics analysis was done as previously described (cf. Taxonomical composition of a biofilm sampled from a nuclear fuel pool assessed by metaproteomics). Briefly, samples were thawed on ice, and a volume of 50 µL of Laemmli buffer LDS1X (Invitrogen) was added as described by Hartmann et al [295].

After denaturation for 5 min at 99°C, the protein sample was loaded onto a 4-12% gradient 10-well NuPAGE gel (Invitrogen). The proteins were subjected to electrophoresis for 5 min and then stained with Coomassie Blue Safe staining (Invitrogen) as recommended by the supplier. The polyacrylamide band corresponding to the whole proteome was sliced, then unstained with ultra-pure water, reduced with dithiothreitol, and treated with iodoacetamide. It was then subjected to proteolysis with Trypsin Gold Mass Spectrometry Grade (Promega) in the presence of 0.01% ProteaseMAX surfactant (Promega), as previously described

Results

Configuration of the pool

The pool contains the uranium sources which consists of a stack of four casings used to position the fuel elements and to channel the cooling water. It includes the base with notably the water inlet, the core vessel with the fuel elements, the water outlet casing, and the chimney which limits the mixture between water leaving the core and the water in the reactor pool as described in Figure 3.1. In this configuration, water could only be sampled at Location A owing to the huge dose rate prevailing in the core unit. Pool water is highly radioactive, with a tritium activity of 2.4±0.2x10 8 Bq/m 3 whatever the functioning mode, and a gamma activity reaching 2. The initial microbial cell density in water sampled during the reactor shutdown was assessed at 2.7±0.6x10 3 cells/mL, 1.4±0.2 x10 3 cells/mL, and 0.5±0.25 x10 3 cells/mL at Loc. A, Loc. B, and Loc. C, respectively. These values are in the range of the densities reported for microbial density in spent nuclear fuel pools [152,184,189,193]. Unlike some pools where the water can be considered as stagnant, the continuous filtering and cleaning at a high flow rate of the water in the facility studied here enabled the renewal of the pool volume in 18 h and maintained microbial density at a low level.

Direct meta-analysis of communities present in the reactor core's cooling pool water

Microbiota present during reactor's operation (2015 samplings)

The 2015 water sample had to be stored for three days to reduce its radioactivity at a level enabling its handling in controlled conditions via the deactivation of the short-lived radionuclide 24 Na, whose activity decreases by half in 15 hours.

The evenness of the samples was below 1 (0.96 with method 1 and 0.68 with method 2) (Table 3.1). This can indicate the presence of one OTU dominating the sample.

Figure 3.2: Microbial diversity at the genus level present at location A (bottom of the pool) from the 2015 samplings.

Metagenomics results after a V4V5 sequencing and a 0.016% cut-off of all methods. The phyla (or class for the Proteobacteria) is indicated for each genus. A majority of Variovorax (about 75%) can be observed at this location. The second main genus was Sphingomonas (about 18%).

In this pool a majority of Proteobacteria (about 95%), and more generally Gram negative bacteria (about 99%), have been identified, which was also observed by Bagwell et al. in the metagenetic analysis of the Savanah River Site SNFP [193].

16S rRNA amplicon sequencing

The evenness of the samples was quantified using the Shannon index. This index combines species richness and abundance into a single value of evenness [339]. When this index is close to 0 a single species dominates the sample. A greater evenness could be observed at Loc. C (Shannon index of 0.8±0.5) (Table 3.2) which was submitted to the highest dose rate of 25 Gy/h than at Loc. B (Shannon index of 0.5±0.4) where the dose rate was nearly 10 6 lower, as well as at Loc. A in 2017 (Shannon index of 0.2±0.04) where the radiation dose rate was below the detection limit. A maximum of 38 OTUs was identified in these samples. A year after the reactor definitive shutdown the microbiota present at Location A has been analyzed (Fig.

3.3)

. A total of 36 genera, belonging to 9 phyla were identified. In this sampling a majority of Methylobacterium was detected, representing about 97% of the sequences. No other genera represented more than 1% of the sample. Among the other genera, one genus (Burkholderia) represented more 0.45% of the sequences, while OTUs belonging to the Chlamydiales order represented about 0.5% of the analyzed sequences. OTUs not identified at the phylum level represented about 0.8% of the sequences. Similarly to the 2015 sampling, a majority of Proteobacteria (98%) was identified in this sample, and more generally Gram negative bacteria (99%). In this sample one genus (and more specifically, one OTU) dominated the identified sequences.

Sample Raw reads Read number OTUs number Shannon index

Comparison between the 2015 and 2017 samplings at Location A

A comparison between the two sampling dates at Location A was performed (Fig. 3.4). It can be observed that, while a majority of Variovorax was identified during the reactor's operation, no Variovorax was detected after the definitive shutdown of the reactor. Another genus, Methylobacterium, took over

Variovorax as the main genus present in the water. The genus Methylobacterium was already present during the reactor's operation but at about 0.2%. A change in the microbiota can thus be observed.

Moreover the number of sequences attributed to Sphingomonas went from 18% during operation to less than 0.1% after the reactor's shutdown. Finally, genera belonging to the Actinobacteria (Corynebacterium, Brachybacterium, Kocuria, and Nocardioides), the Bacteroidetes (Heliimonas and Mucilaginibacter) and the -Proteobacteria (Serratia, Marinomonas, Acinetobacter, Enhydrobacter, and Pseudomonas) were not detected during the reactor's shutdown while they were present in small quantities during operation (about 3% and 1% for Mucilaginibacter and Heliimonas respectively, below 1% for the others). Similarly some Planctomycetes, belonging to Schlesneria and Zavarzinella genera were identified in 2017 (during shutdown) at 0.03 and 0.01% respectively.

4: Microbial communities present in the cooling pool of the reactor's core assessed during the reactor operation (2015) and at shutdown (2017). Meta-amplicomics 16S rRNA analysis at the family taxonomic level. Analysis from Loc. A samplings.

A change in proportion can be observed between the two samplings. Indeed, the genus Methylobacterium represented less than 1% of the sample harvested in operation, while the genus Variovorax represented about 75%. At shutdown, Methylobacterium contributed to around 95% of the sample while while Variovorax has not been identified in the in the sampling performed at shutdown.

Diversity at Locations B and C

A total of 57 genera were identified in Location B (Fig. 3.5). As in Figure 3.5, a majority of Methylobacterium can be detected in the samples (about 87% of the sample). Another genera was present in proportions higher than 1%. Sphingomonas genus was detected in the samples and represented about 9% of the sequences. Among the detected genera, only 5 were above 0.1%, including Methylobacterium and Sphingomonas. Clostridium sensu stricto, Burkholderia, and Pelomonas each represented about 0.2% of the sequences. Several OTUs were only identified at the order level and two were above 0.1%:

"Chlamydiales_unclassified" (0.4%) and "Rhizobiales_unclassified" (0.2%). The genus Meiothermus was detected in the samples but represented about 0.01% of the sequences. Nine phyla were detected at low abundance, including Acidobacteria (0.06%), Actinobacteria (0.1%), Bacteroidetes (0.3%), Chlamydiae (0.5%), Cyanobacteria-Chloroplast, with the presence of "Streptophyta_unclassified" (0.04%), Deinococcus-Thermus (0.01%), Firmicutes (0.3%), Planctomycetes (0.1%), and Verrucomicrobia (0.01%).

5: Microbial diversity at the genus level present at location B (above the chimney) from the 2017 samplings.

Metagenomics results after a V4V5 sequencing and a 0.016% cut-off of all methods. The phyla (or class for the Proteobacteria) is indicated for each genus. A majority of Methylobacterium (about 87%) can be observed at this location. The second main genus was Sphingomonas (about 9%).

Similarly to the 2015 and 2017 samplings at Location A, a majority of Proteobacteria (98%) was identified in this sample, and more generally Gram negative bacteria (99%). In this sample too one genus (and more specifically, one OTU) dominated the identified sequences.

A total of 38 genera belonging to 8 phyla were identified at location C in 2017. Figure 3.6 represents the proportion of each identified genus from the analyzed sequences. While this proportion can give some indications about the main genera present, it does not represent the proportion of each genus in the water sampled at location C. A majority of Mehtylobacterium (about 80%) was identified in this sample, followed by Sphingomonas (about 8%) and Clostridium sensu stricto (about 7%). Apart from these three genera, 35 genera in proportion lower than 1% were also identified. The genus Meiothermus was detected in very small quantity (less than 0.02%). At low abundance and low diversity level, bacteria belonging to the Acidobacteria (0.04%), Actinobacteria (0.8%), Bacteroidetes (0.1%), Chlamydiae (0.2%), Deinococcus-Thermus (0.01%) and Planctomycetes (0.03%) phyla have been identified.

6: Microbial diversity at the genus level present at location C (inside the chimney) from the 2017 samplings.

Metagenomics results after a V4V5 sequencing and a 0.016% cut-off of all methods. The phyla (or class for the Proteobacteria) is indicated for each genus. A majority of Methylobacterium (about 80%) can be observed at this location. The second main genus was Sphingomonas (about 8%), followed by Clostridium (about 7%).

Unlike the two other 2017 samplings, Proteobacteria phylum represented 90% of the sequences while Firmicutes represented about 8%. Gram negative bacteria represented about 91% of the sample.

One OTU dominated all samples from the 2017 samplings and belonged to the Methylobacterium genus.

For each sampling, it could be noticed that only one OTU dominated the sample. In 2015, this OTU (OTU n°2) belonged to Variovorax genus and in 2017, this OTU (OTU n°1) belonged to Methylobacterium genus.

Direct proteotyping with metaproteomics

A direct metaproteomic approach was attempted after filtration of 4.5 L of water on the 2017 samplings.

While the number of microorganisms present is relatively low for this approach (12.2±2.7x10 6 cells/L, 6.3±0.9x10 6 cells/L, and 2.3±1.1x10 6 cells/L for locations A, B, and C respectively), enough signal could be recorded highlighting the presence of four bacterial phyla.

Table 3.3: Proportion of each genus present in the nuclear pool during shutdown analyzed by metaproteomics

The Proteobacteria phylum was present in all samples and represented 64%, 60%, and 48% of the peptide signal at locations A, B, and C respectively. The second main detected phylum was Actinobacteria, with 35%, 39% and 51% of the peptide signal at locations A, B, and C respectively (Table 3 133 When comparing the three different sampling locations from the 2017 campaign, it can be noticed that most of the genera were common to all locations using both analysis methods. Indeed, 15 genera, representing 97.5% of the sequences were common to the three locations, including Methylobacterium and Sphingomonas, the two main genera identified in the samples. While no genus was solely identified in Loc.

Figure 3.7: Microbial communities present in the cooling pool of the reactor's core assessed during the reactor shutdown, analyzed by metaproteomics at A) the family taxonomic level, B) the genus taxonomic level.

A, 11 genera (0.6% of the sequences) were only identified in Loc. B. Finally, 10 genera (1.8% of the sequences) were only identified in Loc. C (Appendix 6, Fig. 6.3).

Using metaproteomics, 5 genera were common to all locations, representing 84.4% of the peptide signal identified, including Streptomyces, Methylobacterium, and Pelomonas. No genus was solely found in locations A and B, but 4 genera were only identified in Loc. C, representing 32% of the peptide signal of this sample (Appendix 6, Fig. 6.4).

The comparison of the 16S rRNA amplicon sequencing and metaproteomic approaches to investigate the microorganisms present in the water of the core's cooling pool revealed that the most abundant genera were detected by both method (Appendix 6, Fig. 6.5). 55.9% of the peptide signal and 96.8% of the sequences in the water from Loc. A belonged to the same genera, while about 53% of the peptide signal and 87.5% of the sequences belonged to the same genera at Loc. B. At Loc. C, the common genera identified using metaproteomics and metagenetics represented about 38.5% of the peptide signal and 79.7% of the sequences. Methylobacterium was detected by both methods. However, while Streptomyces represented between 26 and 41% of the identified peptide signal, it was not detected using metagenetics.

Discussion

This work presented the first analysis of the microbiota present in the cooling pool of a nuclear reactor, both during operation and at shutdown. The environment in this pool was hostile due to the presence of high ionizing radiation, toxic radioactive metals and few nutrients. Despite these different stresses this pool hosted some bacterial diversity (27 OTUs during operation, 96 OTUs during shutdown). A majority of Proteobacteria were detected using metagenetics (all samples from all locations) and metaproteomics (from the 2017 samplings of locations A and B). Firmicutes were also brought to the fore by amplicon sequencing, and Actinobacteria by metaproteomics.

Despite the conditions prevailing in the pool during operation (presence of radiations, radionuclides and lack of nutrients), different bacteria have been identified using metagenetics. A total of 27 different OTUs were detected in the samples after the OCToPUS analysis, which is much lower than the 4,000 OTUs Bacteria isolated from SNFPs in Spain, Argentina, and Slovakia after a cultivation step mainly belonged to the Proteobacteria, the Firmicutes, and the Actinobacteria [152,181,[184][185][186][187]192]. A recent study inventorying the population of a SNFP in the USA using amplicon sequencing also highlighted a majority of Proteobacteria (83%). However, Methylobacterium represented only 2% of the reads [193]. The main phyla found in this pool were similar to the ones detected in our study, including Acidobacteria, Planctomycetes,

Chapter III -Deciphering the microbial composition of the cooling pools of nuclear facilities 134 Actinobacteria, Bacteroidetes, and Deinococcus-Thermus. All are environmental bacteria, which may have contaminated the pool owing to the traffic in and out of the reactor.

During the core's operation some microbial diversity was detected despite the presence of high radiation and radionuclides concentration in the water. To be able to analyze the water the decrease of radioactivity was mandatory before manipulation. The presence of one species dominating the sample, belonging to the Variovorax genus, could be a consequence of the harsh conditions present in the pool. This species was more suited to resist the prevailing conditions. At shutdown the dominant species was different meaning that Variovorax was no longer the best fitted organism. One Methylobacterium species was able to occupy the pool once the water radiolysis was over. At shutdown, it was possible to screen the population from different spots presenting an irradiation dose rate varying of a 10 8 factor (from the 0. All the genera detected in the core's cooling pool are environmental bacteria, which may have contaminated the pool owing to the traffic in and out of the reactor. They might either have been selected

and/or have adapted to the specific conditions of the pool. Among the main genera present in this pool, members of the Methylobacterium genus are ubiquitous in nature, being found in freshwater and aquatic sediments [343]. They constitute common airborne organisms which are found in industrial environments.

A Methylobacterium strain has recently been detected using metagenetics in a spent nuclear fuel storage basin at the Savannah River Site in the USA [193]. Some strains have been shown to exhibit resistance to gamma irradiation 10-40 times higher than that withstood by many other Gram-negative bacteria [315,344,345]. M. extorquens and M. radiotolerans presented a D10 of 4.5 and 3. The microbial diversity in the cooling pool of a nuclear reactor is thus important, with a domination of the genera Variovorax during operation and Methylobacterium during shutdown. This diversity was even greater than the one described in this work. It was indeed shown in Chapter II.2 that a microbial diversity was observed during the liquid culture kinetics. A total of 181 different genera were indeed identified during the kinetics.

The identification of the genus Variovorax in the pool dominating the microbiota present during a working cycle is an interesting discovery. The given hypothesis linking the high abundance of this genus and the metabolism of hydrogen produced by the reactor should however be confirmed with specific experiments.

Studying the microorganisms present in the cooling pool of a working nuclear reactor will be needed to

Chapter III -Deciphering the microbial composition of the cooling pools of nuclear facilities 136 confirm Variovorax predominance or other hydrogen-metabolism active microorganisms. Finally, a deeper metaproteomics analysis of water samples could be done in order to identify the possible metabolism pathways.

Unravelling the microbial community of biofilms present in a nuclear reactor pool (article in preparation)

Abstract

It is known that life can be found in spent nuclear fuel pools and nuclear reactor cooling pools. The aim of this study was to analyze microbial diversity present in biofilms collected from the cooling pool of a nuclear reactor The use of metagenetics revealed that the main dominant genus in all biofilms but one was Clostridium, as well as a high bacterial diversity. The identification of anaerobic bacteria in these samples raises microbial influenced corrosion concerns. Microbial monitoring of nuclear cooling pools and spent nuclear fuel pools is thus highly recommended.

Material and Methods

Samplings

Eight different scrubs were performed in a nuclear reactor cooling pool to recover biofilms. These samplings were harvested in 2017, one year after the definitive shutdown of the reactor. The location of the scrubs in relation to the reactor position in this pool is schematized in Figure 3.8. The scrub number 5 was the furthest to the reactor while Scrub 2 was the closest. Scrub 1 was used to recover the biofilm present on the footbridge under a spotlight at a depth of 4.5 meters (Fig. 3.9-1). The radioactivity level was below the detection limits (< 1 µGy/h) in this location (Table 3.4). Scrub 2 was made on an extinguished spot at a depth of 8 meters. Scrubs 3 and 7 were performed on the cofferdam, on the pool side at a depth of 1.5 meters while scrubs 4 and 8 were sampled on the same cofferdam but at a depth of 4 meters (Fig. 3.9-2). The biofilm recovered by scrub 5 was located on the opposite wall from the nuclear fuels in the pool at a 1 m depth. Due to the presence of visible deposit on the lid of a spent nuclear fuel at a 4 m depth, a scrub was performed (Scrub 6, Fig. 3.9-3). The recovered biofilms were highly radioactive (Table 3.4) and contained 137 Cs and 60 Co. The scrubs were composed of cellulose and are used by the staff of nuclear facilities to determine the radiation level present on an object. The scrubs were either attached to a metal pole for an underwater sampling or directly used by the handler when the support could be brought out of the water (Fig. 3.9). The recovered scrubs were immediately placed inside sterile hermetic plastic bags to avoid contact with ambient air. In a sterile environment the scrubs were introduced into 50 mL sterile Falcon tubes with 15 mL of sterile milliQ water and shook to resuspend the recovered organisms. After the resuspension step, the presence of visible particles containing 60 Co and the emission of more than 800 counts per second measured with the radiagem 2000 (CANBERRA, USA) forced us to let the Falcons decant overnight at RT. The 5 top mL were recovered after a 1 min centrifugation at 100 g. 800 µL were used to extract DNA and 500 µL were cryopreserved in Glycerol Strain Buffer at -80°C preservation (65% glycerol v/v, 0.1 M MgSO4, 0.025 M Tris-HCl pH 8). The gamma dose rate of each sample was determined using a -spectrometry (composed by a germanium detector EGC-10-180R and an ORION analyzer, the results were treated with the InterWinner software).

A) B)

Biofilm recovered

B) C) A) Samplings location A) B)
1.

2.

3.

139 Due to the activity of the samples, the DNA extraction was performed in the radioactive room of the PCV laboratory in CEA -Grenoble. While this environment is not sterile, the fume hood was thoroughly cleaned prior manipulation.

DNA extraction

The DNA was extracted with a phenol-chloroform method, adapted from Vilchez-Vargas et al. [276]. The samples were centrifuged 20 minutes at 20,000 g at room temperature and the supernatant discarded. One milliliter of lysis buffer (100 mM Tris pH 8; 100 mM EDTA pH 8; 100 mM NaCl; 1% polyvinylpyrrolidone; 2% SDS) was added and the pellet resuspend. The mixture was then transferred into a glass bead (0.1 mm) tube from Mobio. The tube was disrupted three times at 30 Hz for 60 seconds with 30 seconds break between each run with a disruptor MM 300 from Retsch/Qiagen. The tube was then centrifuged 5 minutes at 20,000 g at room temperature and the supernatant removed and transferred into a new tube. One milliliter of phenol-chloroform-isoamyl alcohol (25:24:1) was added. The sample was then vortexed and vigorously mixed by inverting the tube. The supernatant was recovered and transferred into a new tube after a centrifugation step of 1 minute at 17,000 g at 4°C. Seven hundred microliters of chloroform were added to the sample. The sample was then vortexed and vigorously mixed by inverting the tube. The supernatant was recovered after a centrifugation step for 1 minute at 17000 g at 4°C. The supernatant was divided into two tubes (around 450 µL in each) and 45 µL of sodium acetate 3M and 500 µL of isopropyl alcohol were added into each tube. The samples were then mixed by inverting the tubes and put overnight at -20°C. The tubes were centrifuged 30 minutes at 17,000 g at 4°C and the supernatant removed. Two steps of washing were performed by adding 750 µL of 75% ethanol and a centrifugation step of 5 minutes at 17,000 g at 4°C.

The supernatant was removed and the pellet dried with a heating block at 55°C. Sixty microliters of sterile milliQ water were added to dissolve the pellet. To have a more pure DNA and remove any trace of alcohol, the sample was put on a 0.05 µm filter disposed on sterile water for 15 minutes before being transferred into a new tube.

Sequencing and data analysis

The sequencing of the genomic DNA on 16S rDNA was performed by LGC Genomics, UK. The sequencing was conducted as followed.

The PCRs included about 1-10 ng of DNA extract (total volume 1 μL), 15 pmol of each forward primer and reverse primer (in 20 μL volume of 1 x MyTaq buffer containing 1.5 units MyTaq DNA polymerase (Bioline) and 2 μL of BioStabII PCR Enhancer (Sigma) and additionally 0.2 μl of DNase (Artic Zymes) for the 16S PCR.

For each sample, the forward and reverse primers had the same 10-nt barcode sequence. Each PCRs were carried out for 20 cycles, dependent on the primer pair, using the following parameters: 1 min 96°C predenaturation; 96°C for 15 s, 50°C for 30 s, 70°C for 90 s and primers without inline barcodes have been used

(341F/1061R). For the second round 1 μL PCR product from the first round was used and the PCR conditions were the same as before. For this reaction barcoded primers have been added to amplify the V4V5 regions (515YF-926R: 5'-GTGYCAGCMGCCGCGGTAA-3' and 5'-CCGYCAATTYMTTTRAGTTT-3' respectively).

DNA concentration of amplicons of interest was determined by gel electrophoresis. About 20 ng amplicon DNA of each sample were pooled for up to 48 samples carrying different barcodes. The amplicon pools were purified with one volume AMPure XP beads (Agencourt) to remove primer dimer and other small

Chapter III -Deciphering the microbial composition of the cooling pools of nuclear facilities 140 mispriming products, followed by an additional purification on MiniElute columns (Qiagen). About 100 ng of each purified amplicon pool DNA was used to construct Illumina libraries using the Ovation Rapid DR

Multiplex . Illumina libraries were pooled and size selected by preparative gel electrophoresis. Sequencing was done on an Illumina MiSeq using V3 Chemistry (Illumina).

Sequencing data were analyzed through the OCToPUS pipeline [210] for contig assembly, quality filtering, de-noising, chimera removal, and OTU clustering. The OTUs were then taxonomically assigned at 97% identity against the SILVA database [333-335] using the mothur software [213].

Results

Radioactivity of the samples

The radioactivity of the biofilms was measured (Table 3.4). Scrubs 3 and 4, collected on the cofferdam, presented the highest gamma dose of 70 and 80 Bq respectively. Scrubs 1, 2 and 5 presented a gamma dose below 10 Bq. Scrub 5, presenting the lowest gamma dose of the samples (below 4 Bq) was collected at the opposite wall from the nuclear fuels.

Table 3.4: Gamma dose of each sample measured using a -spectrometry (germanium detector EGC-10-180R and an ORION

analyzer, the results were treated with the InterWinner software).

Metagenetics analysis of the samples

In this analysis the rarefaction curves presented a reached plateau for each scrub, meaning that a comprehensive analysis was obtained with these data with only 5000 reads (Fig. 3.10). Diversity in scrubs 1, 2, 4, 5, 6, 7, and 8 was achieved at about 10-17 OTUs while it was achieved at about 45 OTUs in Scrub 3.

Scrub 3 was thus more diverse in terms of microbial diversity than the other scrubs. However the diversity in these samples is far to be huge compared to most soil samples, air, or marine water [193,231]. The evenness of the samples was quantified using the Shannon index. The evenness indicates whether the diversity is well divided between the different OTUs or if one OTU dominates the sample. This index combines species richness and abundance into a single value of evenness [339]. When this index is close to 0 a single species dominates the sample. The evenness of the different scrubs was important for each sample. The Shannon indexes were between 1.56 and 3.07 (Table 3 .5). Using this index no sample was dominated by one genus, except for scrubs 5 and 6 since they possessed the lowest Shannon indexes (1.56 and 1.59 respectively). However the Clostridiaceae family (Firmicutes) was the main family present in these scrubs (more than 50% of the sequences were identified as Clostridiceae) (Fig. 3.11). After a metagenetic analysis, the majority of the scrubs were composed of 11 to 18 OTUs, except for Scrub 3 which consisted of 48 OTUs (including 14

Scrub Gamma dose (

OTUs identified as "Bacteria_unclassified" representing a total of 33% of the analyzed sequences from Scrub

3) (Appendix 7, Table 6.11). Two OTUs (OTU 8 and OTU 56) were identified by OCToPUS as "Enterobacteriaceae_unclassified". After comparison of the sequence of these two OTUs it appears that only a few nucleotides were different (Appendix 7,Fig. 6.6). When blasting the sequences on the nucleotide BLAST tool from NCBI, OTU 56 presented a great score with Serratia while the OTU 8 was identified as

Salmonella. Both sequences presented a 100% coverage and identity with the corresponding organism.

Three phyla were identified in Scrub 1: Actinobacteria (5%), Firmicutes (57%), and Proteobacteria (38%).

The sample was dominated by the genus Clostridium sensu stricto with about 51% of the identified sequences (Firmicutes), followed by Proteus with 15% of the sequences (-Proteobacteria), and

Ochrobactrum with about 10% of the sequences (a-Proteobacteria). The genus Mycetocola (Actinobacteria) represented about 5% of the sequences, the genus Pelomonas (a-Proteobacteria) represented about 3% of the sequences, and the genus Melissococcus (Firmicutes) represented about 3% of the sequences. About 9% of the sequences were attributed to OTU 8 (corresponding to Salmonella) (Fig. 3.11; Appendix 7, Table 6.11).

Four phyla were identified in Scrub 2: Actinobacteria (6%), Bacteroidetes (0.1%), Firmicutes (56%), and Proteobacteria (39%). In this scrub too, a majority of Clostridium sensu stricto (45%) was identified. The second main genus was Sphingomonas, with 14% of the sequences. Melissococcus (10%), Ochrobactrum (9%), Mycetocola (6%), Pelomonas (5%), Proteus (5%), and OTU 8 (Salmonella) (5%) constituted the main genera identified (Fig. 3.11; Appendix 7, Table 6.11).

Scrub 3 represented the greater diversity and evenness among the different scrubs (Fig. 3.10, Table 3 .8).

This can be confirmed by the diversity present in the sample. Bacteria belonging to seven phyla were identified in this sample: Actinobacteria (1%), Bacteroidetes (3%), Chloroflexi (4%), Deinococcus-Thermus (3%), Firmicutes (11%), Planctomycetes (1%), and Proteobacteria (44%). In this sample, the genus Clostridium sensu stricto represented only 4% of the sequences. The main identified genera were Sphingomonas (7%), Pelomonas (7%), and Methylobacterium (7%), all belonging to the Proteobacteria. The genus Sediminibacterium represented 3% of the sequences and belongs to the Bacteroidetes phylum.

Several OTUs, not identified at the genus level, were also present in important proportion. OTU 11, belonging to the Comamonadaceae family, represented 10% of the analyzed sequences. OTUs 12,[START_REF] Jolivet | Thermococcus gammatolerans sp. nov., a hyperthermophilic archaeon from a deep-sea hydrothermal vent that resists ionizing radiation[END_REF][START_REF] Costerton | Microbial Biofilms[END_REF], not identified at the phylum level, represented 9%, 9%, and 7% of the sample respectively. OTU 14, belonging to the Clostridia class, represented about 7% of the sequences. Four OTUs belonging to the -Proteobacteria class represented about 8% of the sequences, with one OTU representing more than 6% of the sequences. Finally, 3% of the sequences were attributed to OTU 22, which has been associated to the Chloroflexi phylum. Chloroflexi and Deinococcus-Thermus phyla were only identified in Scrub 3 (Fig. 3.11;Appendix 7,Table 6.11).

Five phyla were identified in Scrub 4: Actinobacteria (3%), Bacteroidetes (5%), Firmicutes (34%), Planctomycetes (0.3%), and Proteobacteria (58%). Planctomycetes phylum was only identified in scrubs 3

B)

Chapter III -Deciphering the microbial composition of the cooling pools of nuclear facilities 146 Several "Bacteria_unclassified" have been identified in Scrub 3. When manually aligning these OTUs' sequences with the nucleotide BLAST tool from NCBI, they were aligned with undescribed bacteria or with bacteria belonging to two different phyla. For example, OTU 16 presented a 100% coverage and 98-99% identity (with an E-value of 0.0) with one bacterium belonging to the Acidobacteria phylum and one bacterium belonging to the Firmicutes phylum (Fig. 3.12).

Discussion

In all scrubs but Scrub 3, the genus Clostridium sensu stricto was the main genus identified (31 to 64%).

Among the second main genera, Sphingomonas, Curvibacter, Proteus, and Mycetocola were identified. Some Methylobacterium were also identified in Scrub 3

The Mycetocola genus belongs to the Actinobacteria phylum. It is usually found on food (fruit, vegetables, cheese) [347]. To our knowledge it has never been described in similar environment. The Sphingomonas genus was identified in several SNFPs in Spain and in the USA after a cultivation step [185,188]. One strain, isolated in the Antarctic Dry Valleys, exhibited in its genome the presence of oxidative stress resistance genes [306]. Another strain exhibited a high resistance to UV radiation with a 10% survival at a 1500 J.m -2 UV dose [307]. The Methylobacterium genus is ubiquitous and can be found in aquatic environments [315]. Some strains have been shown to exhibit resistance to gamma irradiation 10-40 times higher than that exhibited by many other Gram-negative bacteria [315,344,345]. Several species can be radioresistant, such as M. extorquens and M. radiotolerans with a D10 of 4.5 kGy and 3.5 kGy respectively [346]. This genus was found in SNFPs, in the water and the biofilms [184][185][186]. Pelomonas genus was identified via metagenetics in an American SNFP after a metagenetics analysis [193]. The genus Curvibacter was first identified in 2004 from well water [351]. A strain closely related to the Curvibacter genus was isolated from a SNFP in England after cultivation of the water in agar medium complemented with CoCl2 and could resist cobalt toxicity up to 3 mM [352], while E. coli presented almost no growth at doses higher than 100 µM [353,354]. This genus was found in scrubs 3 to 5 and scrubs 7 and 8. To our knowledge this is the first time this genus was found in a highly radioactive environment. The biofilms activity was in range of 4 to 80 Bq (Table 3.4) and was mainly due to the presence of 60 Co. This bacterium can not only tolerate cobalt but can also survive high radiation levels. Proteus bacteria are facultative anaerobic. They can also produce an extracellular polysaccharide matrix. It was shown that the mean lethal dose (37% survival) of ionizing radiation was circa 100 Gy but some growth could be observed after a 2.4 kGy irradiation [355]. A Proteus strain could also resist the presence of 32 P in its culture up to a 8 µCi dose (2.96x10 5 Bq) [356]. The ability of P. vulgaris to adsorb cobalt ions on its membrane was pointed out in 1952 [357]. Clostridium are anaerobic Firmicutes but some species can tolerate oxygen [358]. C. subterminale and C. celerecrescens had been found in a SNFP in the USA [188]. The other studies from other SNFPs were performed aerobically, explaining the absence of this genus in their studies. Clostridium spores were proved to resist a dose up to 8.5 kGy, and for some strain, up to 14 kGy [359,360]. Clostridium cells can grow under a biofilm shape [361]. The radioresistance of spores may indicate that Clostridium cells were under spore shape in the biofilm. However Clostridum cells may have been under vegetative and spore shape in the biofilm, with spores being present in higher quantity than the vegetative cells. The high number of spores could explain the inability to detect Clostridium (and Firmicutes) using metaproteomics. It was shown that some Clostridium strains were able to reduce uranium [362].

Growth under a biofilm form is known to be advantageous to microorganisms. The extracellular matrix could have protected the cells from radiation and radionuclides. Several identified bacteria might have been able to survive the hostile conditions of the pool because of the presence of biofilms.

The presence of biofilm composed by anaerobic microbial genera can pose some concerns concerning MIC in nuclear installations [START_REF] Borenstein | Microbiologically Influenced Corrosion Handbook[END_REF]363,364]. Corrosion on spent fuel bundles can lead to a leak of the cooling element and thus change the characteristics of the water. Moreover corrosion on the pool' walls might lead at long term to a leak if untreated and unnoticed. The detection of biofilms and anaerobic bacteria is thus an important information concerning the monitoring of the pool's integrity and controlled conditions maintaining.

The composition of biofilms was highly different from the water composition observed in this pool in both 2015 and 2017 (cf. Microbial composition of a nuclear reactor cooling pool during operation and at shutdown deciphered using multi meta-analysis). The water was mainly composed by Methylobacterium genus at the time of the biofilms' sampling, while scrubs were mainly composed by Clostridium genus and genera belonging to the Proteobacteria phylum. This study highlighted the microbiota differences observed in the pool depending on their living form (vegetative or spores and biofilms). However the low number of reads from the biofilms' samples (between 1959 and 4747 reads for scrubs 1, 2, 4, 5, 6, and 7, and 38984 reads for Scrub 3) compared to the number of reads in the water samples (3696 for one sample and 28977 to 127024 reads for the others) could explain the observed difference.

Conclusion

This work completes the previous description of biofilms present in SNFPs all over the world. Most of the previous studies were performed after culture [152,177,181,[183][184][185][186][187][188]192] and only one used a metagenetic approach [193]. However it is the first time that biofilms from the cooling pool of a nuclear reactor were sampled and analyzed. Despite the stresses present in such environment (lack of nutrient, presence of radionuclides and radioactivity) microorganisms were able to grow. The presence of radioactive cobalt particles in the biofilm did not prevent microbial growth. One main difference between these results and the previous studies was the abundant presence of Clostridium (Firmicutes). The Firmicutes phylum was present from 11% to 72% in the biofilms (metagenetics). This phylum did not represent as much in the previous studies, whether they were after cultivation [152,181,[183][184][185][186][187] or using a metagenetic approach [193]. It was only in Scrub 4 that Proteobacteria was present for more than half of the sample (metagenetics results).

Chapter III -Deciphering the microbial composition of the cooling pools of nuclear facilities 148The presence of biofilms in this pool highlighted the presence of microbial diversity present in the cooling water of a nuclear reactor. The microorganisms found in these biofilms were similar between each other with the notable exception of Scrub 3. All scrubs were radioactive due to the presence of cobalt particles. Despite this radioactivity life was able to thrive in this pool under biofilms shape. Regardless of the potentiality of radioresistance research on these strains, the presence of biofilms and anaerobic bacteria could be a major concern for the pool's integrity and the physico-chemical changes possible. A deep cleaning of the pool or a monitoring of the cell density should thus be regularly performed in every nuclear pool.

Acknowledgements

The authors warmly thank M.

Direct meta-analyses reveal microbial diversity over space and time in a nuclear pool (article in preparation)

Abstract

Several studies exist about life found in spent nuclear fuel pools. However no data concerning the microbial diversity of the cooling pool used to store radioactive cobalt sources have been yet obtained. This work presents the first description of microbial diversity present in such environment. Using complementary methods (metagenetics and direct phylopeptidomics) several microorganisms have been identified in Pool 1, including bacteria, fungi, and micro-algae. The study over time of this pool highlighted a major microbial change when the pool's filtration system had been modified. A majority of Cyanobacteria was previously identified, replaced afterwards by Limnobacter. Different depths as well as different sampling locations have been studied for obtaining a comprehensive view of this specific infrastructure.

Material and Methods

Sampling

The pool is located in CEA-Saclay and is used to store 60 The water was centrifuged at 15,000 g for 20 minutes at room temperature (RT). The supernatant was discarded and the microbial pellet resuspended in 1 mL of supernatant (in 2017). In 2015 800 mL were filtered through a 0.22 µm sterile hydrophilic polyethersulfone filters (MicroFunnelTM Supor®, Pall Corporation, USA) for DNA extraction. In 2017, 5 L were used for the metaproteomic analysis and 1 L for DNA extraction. In 2017, 5 L were centrifuged to be directly analyzed via metaproteomics and the liter left was centrifuged for DNA extraction. In June 2016, 2 L were sampled in the middle of the pool, near the surface (at a 1 m depth) while 2 L were recovered for the samples collected near the northern and western walls, near the surface (at a 1 m depth) and near the bottom of the pool (at a 4 m depth). The 2 L were centrifuged and directly analyzed using metaproteomics.

In 2015 and 2016, the water was sampled in five different locations, four near the walls after rubbing the walls with a metallic broom to resuspend any possible biofilm present, and one at the middle of the pool: the bottom and top of the northern wall (samples A and B respectively) and the bottom and the top of the western wall (samples C and D respectively) and water collected from the middle of the pool near the surface (sample E). The top samplings were performed at a 1 meter depth while the bottom samplings were performed at a 5 m 50 depth. The sample from the middle of the pool was performed at a depth of 1 meter.

The mix water plus particles was recovered and analyzed using the same procedure than water samplings. A, B, C, andD) were also performed using sterile Spontex® sponges in 2016.

Scrubs of the walls (

In 2017 the water was sampled in 6 different locations (A, B, C, D, E, and F corresponding to the sampling performed at the bottom of the middle of the pool) without rubbing of the walls. Scrubs on location A, B, C, and D, and the top and bottom corner between the western and the northern wall were performed in duplicate with sterile Spontex® sponges.

Chapter III -Deciphering the microbial composition of the cooling pools of nuclear facilities 150

DNA extraction

The DNA was extracted with a phenol-chloroform method, adapted from . The samples were centrifuged 20 minutes at 20,000 g at room temperature and the supernatant discarded. One milliliter of lysis buffer (100 mM Tris pH 8; 100 mM EDTA pH 8; 100 mM NaCl; 1% polyvinylpyrrolidone; 2% SDS) was added and the pellet resuspend. The mixture was then transferred to a glass bead (0.1 mm) tube from Mobio (USA). The tube was disrupted three times at 30 Hz for 60 seconds with 30 seconds break between each run with a disruptor MM 300 from Retsch/Qiagen. The tube was then centrifuged 5 minutes at 20,000 g at room temperature and the supernatant removed and transferred into a new tube. One milliliter of phenol-chloroform-isoamyl alcohol (25:24:1) was added. The sample was then vortexed and vigorously mixed by inverting the tube. The supernatant was recovered and transferred into a new tube after a centrifugation step of 1 minute at 17,000 g at 4°C. Seven hundred microliters of chloroform were added to the sample. The sample was then vortexed and vigorously mixed by inverting the tube. The supernatant was recovered after a centrifugation step of 1 minute at 17,000 g at 4°C. The supernatant was divided into two tubes (around 450 µL in each) and 45 µL of sodium acetate 3M and 500 µL of isopropyl alcohol were added into each tube. The samples were then mixed by inverting the tubes and put overnight at -20°C. The tubes were centrifuged 30 minutes at 17,000 g at 4°C and the supernatant removed. Two steps of washing were performed by adding 750 µL of 75% ethanol and a centrifugation step of 5 minutes at 17,000 g at 4°C. The supernatant was removed and the pellet dried with a heating block at 55°C. Sixty microliters of sterile milliQ water were added to dissolve the pellet. To have a more pure DNA and remove any trace of alcohol, the sample was put on a 0.05 µm filter disposed on sterile water for 15 minutes before being transferred into a new tube.

Sequencing and data analysis

The sequencing of the genomic DNA on 16S rDNA was performed by LGC Genomics. The sequencing was conducted as followed. The PCRs included about 1-10 ng of DNA extract (total volume 1 μL), 15 pmol of each forward primer and reverse primer (in 20 μL volume of 1 x MyTaq buffer containing 1.5 units MyTaq DNA polymerase (Bioline) and 2 μL of BioStabII PCR Enhancer (Sigma) and additionally 0.2 μl of DNase (Artic Zymes) for the 16S PCR. For each sample, the forward and reverse primers had the same 10-nt barcode sequence. Each PCRs were carried out for 20 cycles, dependent on the primer pair, using the following parameters: 1 min 96°C pre-denaturation; 96°C for 15 s, 50°C for 30 s, 70°C for 90 s and primers without inline barcodes have been used (341F/1061R). For the second round 1 μL PCR product from the first round was used and the PCR conditions were the same as before. For this reaction barcoded primers have been added to amplify the V4V5 regions (515YF-926R: 5'-GTGYCAGCMGCCGCGGTAA-3' and 5'-CCGYCAATTYMTTTRAGTTT-3' respectively). DNA concentration of amplicons of interest was determined by gel electrophoresis. About 20 ng amplicon DNA of each sample were pooled for up to 48 samples carrying different barcodes. The amplicon pools were purified with one volume AMPure XP beads (Agencourt) to remove primer dimer and other small mispriming products, followed by an additional purification on MiniElute columns (Qiagen). About 100 ng of each purified amplicon pool DNA was used to construct Illumina libraries using the Ovation Rapid DR Multiplex . Illumina libraries were pooled and size selected by preparative gel electrophoresis. Sequencing was done on an Illumina MiSeq using V3 Chemistry (Illumina). The raw data obtained after sequencing were analyzed using the OCToPUS software [210]. This software eliminated any singleton sequences, chimeras, clustered according to a threshold similar sequences in OTUs, and assigned each OTU to a taxonomical level using the RDP database (Ribosomal Database Project).

Direct metaproteomics: sample preparation and analysis

The metaproteomics analysis was done as previously described (cf. Taxonomical composition of a biofilm sampled from a nuclear fuel pool assessed by metaproteomics). Briefly, samples were thawed on ice, and a volume of 57 µL of Laemmli buffer LDS1X (Invitrogen) was added per mg of wet bacterial pellet as described by Hartmann et al [295]. After denaturation for 5 min at 99°C, the protein sample was loaded onto a 4-12% gradient 10-well NuPAGE gel (Invitrogen). The proteins were subjected to electrophoresis for 5 min and then stained with Coomassie Blue Safe staining (Invitrogen) as recommended by the supplier.

The polyacrylamide band corresponding to the whole proteome was sliced, then unstained with ultra-pure water, reduced with dithiothreitol, and treated with iodoacetamide. It was then subjected to proteolysis with Trypsin Gold Mass Spectrometry Grade (Promega) in the presence of 0.01% ProteaseMAX surfactant (Promega), as previously described [322]. The resulting peptides were analyzed with an ESI-Q Exactive HF mass spectrometer (Thermo) equipped with an ultra-high field Orbitrap analyser and coupled to an Ultimate 3000 176 RSL Nano LC System (ThermoFisher). A volume of 10 µL of peptides was injected onto a reverse phase Acclaim PepMap 100 C18 column (3 µm, 100 Å, 75 µm id x 500 mm) and resolved at a flow rate of 0.2 µL/min with a 60 min gradient of CH3CN in presence of 0.1% formic acid. The Q-Exactive HF instrument was operated with a Top20 strategy with only ions with potential charge states of 2+ and 3+ selected for MS/MS and with a dynamic exclusion of 10 sec. The proteotyping interpretation of MS/MS spectra was done with the National Center for Biotechnology Information non-redundant fasta file (NCBInr) database with the Mascot search engine (Matrix science). Peptide -to-MS/MS spectrum assignation was done with full trypsin specificity, maximum of one missed cleavage, mass tolerances of 5 ppm on the parent ion and 0.02 Da on the MS/MS, static modification of carboxyamidomethylated cysteine (+57.0215), and oxidized methionine (+15.9949) as dynamic modification. Post-processing of Mascot dat files was done as previously indicated (cf. Taxonomical composition of a biofilm sampled from a nuclear fuel pool assessed by metaproteomics).

Results

Metagenetic analysis of water and biofilm samples during the 2015 sampling campaign

The 2015 samplings were performed on Location E and on locations A, B, C, and D after rubbing the walls.

The analyzed samples thus contained the microbiota present in the water and the one present on the walls, except for Location E.

The evenness of the samples was quantified using the Shannon index. The evenness indicates whether the diversity is well divided between the different OTUs or if one OTU dominates the sample. This index combines species richness and abundance into a single value of evenness [339]. When this index is close to 0 a single species dominates the sample. The samples presenting the greater evenness between the different genera were sample E and sample D filter (2.58 and 2.67 respectively). The samples collected at the bottom of the western wall as well as the scrub from the bottom northern wall presented the smaller evenness (0.47 to 0.63) (Table 3 .5). A total of 12 phyla were identified in the water and the scrubs in 2015.

Chapter III -Deciphering the microbial composition of the cooling pools of nuclear facilities 152 The diversity was reached quickly for all samples, except for sample B filter, which exhibited a greater diversity and reached a plateau above 250000 reads and at about 350 OTUs. The other samples reached a plateau faster, below 50000 reads. For sample D filter and sample D scrub 2 the plateau was reached at about 100 OTUs while the other samples reached the plateau below 50 OTUs (cf. Appendix 10,Fig. 6.14).

This indicates that a comprehensive analysis was obtained with these data.

In the sample collected from the middle of the pool some bacterial diversity can be observed (Fig. 3.13). A majority of Cyanobacteria belonging mostly to the Bacillariophyta and also to Streptophyta families was identified (49.4% of the identified sequences; Appendix 8,Table 6.13). The evenness between the families and genera was greater than the one observed in 3.2-Microbial composition of a nuclear reactor cooling pool during operation and at shutdown deciphered using multi meta-analysis. Bacteria belonging to 7 phyla were identified (Actinobacteria, Bacteroidetes, Cyanobacteria, Firmicutes, Nitrospirae, Planctomycetes, and Proteobacteria). In this sample the Proteobacteria phylum does not represent the majority of the microbiota. proportion in the sample and Sphingobacteriaceae (with the genus Mucilaginibacter) was present at 2.4% (Fig. 3.14; Appendix 8, Table 6. 13 and Fig. 6.8).

Sample Shannon index

The water recovered after rubbing Location B was more diverse than the scrubs performed at the same location (47 families and 57 genera for the water vs. 14-21 families and 16-24 genera for the scrubs; Appendix 8, Table 6.13). In the water 59.4% of the analyzed sequences belonged to the Bacillariophyta family while only 2.9 and 3.4% were attributed to this family in scrub 1 and 2 respectively. The evenness in the water was smaller than in the scrubs from the same location. The other main families and genera in the water were Sphingomonadaceae (6% with 2.5% of Blastomonas), Saprospiraceae (4.7%), Nitrospiraceae (with the genus Nitrospira, 3.5%), Rhodobacteraceae (3.5% with 3.2% of Rhodobacter) and Planctomycetaceae (2.2%) (Fig. 3.14; Appendix 8, Table 3. 13 and Fig. 6.8).

The biofilms recovered through scrubs at Location B were not similar in composition. In Scrub 1 a majority of Sphingomonadaceae (49.9% with 47.8% of Sphingomonas) was observed while only 12.6% of this family respectively. However while this family only includes the Microbacterium genus in Scrub 2, 16.6% could not be identified at the genus level (after nucleotide BLAST, several genera were possible for this OTU) in Scrub 1. The 5% left were associated with the Microbacterium genus. The Methylobacteriaceae family, with the Methylobacterium genus, represented 18.8 and 14.9% of scrubs 1 and 2 respectively. Finally the family

Bradyrhizobiaceae was identified at 7.3 and 1.2% with the genus Afipia constituting 7.3 and 1.1% of scrubs 1 and 2 respectively (Fig. 3.14; Appendix 8, Table 6. 13 and Fig. 6.8).

A total of nine phyla were identified at the northern wall, all depths and sampling method taken together. At the bottom of the western wall, the diversity was smaller compared to the other location of the pool.

Only 28 genera were identified at Location C. The diversity of the scrub was lower than the scrub from the bottom northern wall (6 genera for Sample C biofilm vs. 18 genera for Sample A biofilm) (Appendix 8, Table 6.13). On both samples from the bottom western wall the evenness was low, with the Sphingomonadaceae family representing 1.7% of the filtered water and 85.5% of the scrub. In the filtered water a majority of Commamonadaceae was identified (87.6%) while no sequence belonging to this family was identified in the scrub (Fig. 3.15; Appendix 8, Table 6.13). Interestingly, the genus from the Commamonadaceae family was not identified but after alignment it appeared to be exactly the same sequence as OTU 2 from Pool 2 in 2015 (Fig. 3.16). In the water 7.6% of Caulobacteraceae (namely Brevundimonas), 2% of Methylobacteriaceae (Methylobacterium), and 1.7% Sphingomonadaceae (Blastomonas) were identified.

In the scrub, the genus identified belonging to the Sphingomonadaceae was not Blastomonas but Sphingomonas (Appendix 8, Table 6. 13 and Fig. 6.9). The other main genus from the scrub was Stenotrophomonas (belonging to the Xanthomonadaceae) and represented up to 13.4% of the scrub. A 0.016% cut-off was used.

Despite differences observed between the water composition in the middle of the pool, near the walls after rubbing of the walls and the scrubs, several families and genera were found in almost all samples, such as Microbacterium (only absent from sample E), Sphingomonas (only present at 0.003% in the sample D filter and beyond the cut-off), and the phylum Cyanobacteria with the Bacillariophyta and Streptophyta families.

Metagenetic analysis of water and biofilm samples during the 2017 sampling campaign

A total of 55 OTUs were identified in 2017 in sample A. In Sample B, 20 OTUs were identified while 58, 43, 15, and 13 OTUs were present in samples C, D, E, and F respectively. The evenness in the 2017 water samples was important for all samples but one. Sample F had indeed a Shannon index of 0.69 (Table 3.7).

This sample, containing 13 OTUs (cf. Appendix 9), was thus dominated by one or two species. In 2017, after a change in the water filtration and conditions, six samplings were performed. Some differences between the top and the bottom samplings can be observed for all locations in the pool (Fig. 3.18 to 3.20; Appendix 9, Table 6. [START_REF] Anderson | Studies on a radio-resistant micrococcus. I. Isolation, morphology, cultural characteristics, and resistance to gamma radiation[END_REF]Fig. 6.11 to 3.13).

Sample Shannon index

In the water collected from the middle of the pool, 20 genera were identified, belonging to 3 different phyla Pseudomonadaceae (Pseudomonas genus) families (4 and 2.3% respectively). The water collected in the middle of the pool presented a low diversity (20 genera) (Fig. 3.18; Appendix 9, Table 6. [START_REF] Anderson | Studies on a radio-resistant micrococcus. I. Isolation, morphology, cultural characteristics, and resistance to gamma radiation[END_REF]Fig. 6.11). The water collected near the northern wall presented a higher diversity than the water from the middle of the pool (38 genera). A majority of Burkholderiaceae was identified in the sample B (76.5% of Limnobacter) while this family (and genus) represented only 21.3% of sample A. The families Caulobacteraceae (10.9 and 5.3% for samples A and B respectively) and Sinobacteraceae (5.7 and 2.4% for samples A and B respectively)

were also represented in both depths with the genera Brevundimonas (10.9 and 5.3% for samples A and B respectively) and Nevskia (2.1 and 2.4% for samples A and B respectively). At the bottom northern wall the family Erythrobacteraceae (with the genus Porphyrobacter) represented 22.1% of the sample while this genus was present at only 1.3% in sample B. At the sample B location the family Bacillariophyta (2%) was identified (Fig. 3.19; Appendix 9, Table 6.14, Fig. 6.12). 3.20;Appendix 9,Table 6.14,Fig. 6.13). Fewer differences between the two depths at the western location were observed compared with the other two locations studied (northern wall and middle of the pool). A total of 9 phyla were identified in the water of this pool in the 2017 samplings. The Cyanobacteria phylum was not identified in the middle of the pool (both depth) and the bottom of the western wall.

The comparison of the two sampling dates after a metagenetics analysis and a subsampling at 21570 sequences is presented in Appendix 11. A total of 47 genera were common between the 2015 and 2017 samplings, all locations taken together. Three phyla were not common to both sampling date: Chloroflexi (only present in 2015), Hydrogenedentes (only present in 2015), and "Candidatus division WPS-1" (only present in 2017). The 2015 sampling exhibited more diversity than the 2017 sampling (106 genera vs. 65 genera respectively), due to the fact that more samples were recovered and analyzed in 2015 as well as the fact that the pool was cleaned 3 months before the 2017 sampling (for the 2015 sampling, the pool was cleaned a year before).

Direct metaproteomics analysis of water and biofilms during the 2016 campaign

After the 2016 water sampling, the cell density was assessed for the five location samplings. The water recovered near the northern and western walls at both depths was recovered after rubbing the walls to resuspend potential biofilms. The location with the more cell density was at the bottom of the western wall (Table 3.8). Scrubs of the walls were also performed and analyzed using direct metaproteomics. The composition of the recovered biofilms was similar between all locations and depths. The Sphingomonadaceae and Caulobacteraceae families were found in each scrub. The Sphingomonadaceae was the main family detected for each sample (80.4 to 95.6%). The other family present in all samples was Caulobacteraceae (4.4 to 11%). The Comamondaceae family was not detected at the bottom of the western wall but was present in every other location (2.8 to 8.6%) (Fig. 3.22;Appendix 12,Table 6.16). Strangely, while the Comamonadaceae family (Acidovorax genus) was detected in the samples A, B, and D scrubs, it was only detected in the water plus particles mix collected near the western wall at both depths and was not identified near the northern wall using direct metaproteomics.

Direct metaproteomics analysis of water and biofilms during the 2017 campaign

During the 2017 analysis only the water was recovered, unlike 2016 when water and particles obtained after rubbing the walls were recovered. Low cell density was observed at locations A, B, C, D, and E (less than 2 million cells per L) (Table 3.9). Between 2.5 to 20 million cells were analyzed via metaproteomics ( 5L for the analysis).

Table 3.9: Cell count at the six different water sampling location from the 2017 sampling

The use of direct metaproteomics to analyze the water highlighted the diversity present in the water.

(A) (B)

Chapter III -Deciphering the microbial composition of the cooling pools of nuclear facilities 164 Some differences in the microbiota composition depending on the depth and location can be observed in the water after a direct metaproteomic analysis. The Pseudomonadaceae family (and genus Pseudomonas)

were present in all samples, with a higher proportion in water sample F and water sample C (100%) when it was present at 20.2% in water sample D, 9.8 and 9.7% for water samples E and A respectively. The microbial composition between the water samples E and A seemed similar at the family level but not at the genus level. Several genera were present in sample E but not in sample A. The Cytophagaceae family (with Hymenobacter) represented 0.6% of the identified proteins in sample E while Salmonella was also present in the Enterobacteraceae family along with Escherichia. Paenibacillus was present in sample E (4.4%) and

Streptomyces was present in sample A (6.6%). Those two samples appeared to be the more diverse after a metaproteomic analysis. The genera Achromobacter was also present in sample D (79.8%) and Bacillus was present in sample B (30.4%). A total of 4 different phyla was identified in these samples (Fig. 3.23; Appendix 12, Table 6.17). Unlike the 2016 samples, no Cyanobacteria nor algae were detected in 2017. The biofilms recovery was performed in duplicates and analyzed using direct metaproteomics. As observed in Figure 3.24, the duplicates from all locations do not correspond between each other. This absence of similarity between the duplicates might be because of the poor cell density and maybe sign heterogeneity on each part of the same wall due to too early microbial colonization. The Spontex® sponges were indeed rubbed against the walls to recover biofilm. A small quantity of biofilm might have been recovered in this case. Moreover the cleaning of the pool, 3 months before, consisted in rubbing the walls to eliminate limescale, biofilms, etc. The new biofilms might have not grown sufficiently to be analyzed using direct metaproteomics. The diversity of these samples was limited after the metaproteomic analysis. Between . The location at the bottom of the corner between the western and northern walls was the more diverse with the presence of Bradyrhizobium, Selenomonas, Fusobacterium, and Neisseria. A total of 4 different phyla were identified in these samples (Fig. 3.24;

Appendix 12, Table 6.18).

Figure3.24: Proportion of each genus from the scrubs collected in 2017 after the change of filtration analyzed without cultivation step by metaproteomics. (A) Family level;

(B) Genus level.

Discussion

Differences can be observed between the 2015 and 2017 samplings. In 2015, 239 OTUs were identified in both water and biofilms samples (205 OTUs were found in the water samples) while 106 OTUs were identified in the water in 2017. In 2015, 12 different phyla were identified in the water samples and in the biofilms using metagenetics: Acidobacter, Actinobacteria, Bacteroidetes, Chloroflexi, Cyanobacteria, Firmicutes, Gemmatimonadetes, Hydrogenedentes, Nitrospirae, Planctomycetes, Proteobacteria, and Verrucomicrobia. In 2017, 9 phyla were identified in the water samples: Acidobacter, Actinobacteria, Bacteroidetes, Cyanobacteria, Firmicutes, Nitrospirae, Planctomycetes, Proteobacteria, and Verrucomicrobia.

In 2015 a high proportion of Cyanobacteria and more specifically the Bacillariophyta family was observed in several samples, up to 59.4%. In 2017 this family was present in only 3 samples at 2% maximum. Similarly the Chloroflexi phylum was only detected in 2015. This change in microbiota can also be observed when . In 2016, Acidovorax, Sphingomonas and Caulobacter were present in the biofilms but in 2017 none of these genera were detected using the same method (direct metaproteomics). The diversity in the biofilms was lower in 2017 than in 2016 (between 0 to 3 genera detected in one sample from the 6 sampling locations in duplicate in 2017 while 2 to 3 genera were found in 2016 in all 4 samples).

Another interesting point is the fact that in 2016 the biofilms were similar within each other but the composition was quite different in 2017, except for the top samples from the western and northern walls.

It is unclear whether the Bacillariophyta family belongs to the Cyanobacteria phylum or is associated with the 16S rRNA gene present in algae chloroplast. Nonetheless this family was found in Chinese intertidal sediment [365]. The two OTUs present in this pool in 2015 and associated with Bacillariophyta were closely related with the chloroplastic DNA of different algae, including Phaeodactylum and Thalassiosira species (Fig. 3.25). These two algae genera were found after the metaproteomic analysis of the water and the water plus particles mix.

Chapter III -Deciphering the microbial composition of the cooling pools of nuclear facilities 168 The 16S rDNA sequences were obtained from NCBI. The evolutionary history was inferred by using the Maximum Likelihood method based on the . The bootstrap consensus tree inferred from 500 replicates [2] is taken to represent the evolutionary history of the taxa analyzed [2]. Branches corresponding to partitions reproduced in less than 50% bootstrap replicates are collapsed. The percentage of replicate trees in which the associated taxa clustered together in the bootstrap test (500 replicates) are shown next to the branches [367]. Initial tree(s) for the heuristic search were obtained by applying the Neighbor-Joining method to a matrix of pairwise distances estimated using the Maximum Composite Likelihood (MCL) approach. A discrete Gamma distribution was used to model evolutionary rate differences among sites (5 categories (+G, parameter = 0.7011)). The rate variation model allowed for some sites to be evolutionarily invariable ([+I], 12.2640% sites). The analysis involved 29 nucleotide sequences. All positions with less than 0% site coverage were eliminated. That is, fewer than 100% alignment gaps, missing data, and ambiguous bases were allowed at any position. There were a total of 349 positions in the final dataset. Evolutionary analyses were conducted in MEGA5 [368].

Amongst the diverse microorganisms found here, some can be related to extreme environment from previous works. For example, strains from the genus Scytonema, from the Cyanobacteria phylum, have been proved to resist radiation with a D10 between 4 and 12 kGy and an LD50 circa 8 kGy [369]. Scytonema bacteria produce scytonemin, an antioxidant pigment, allowing them to survive high levels of radiation [START_REF] Rastogi | Cyanobacterial Sunscreen Scytonemin: Role in Photoprotection and Biomedical Research[END_REF]370]. The cyanobacteria Chroococcidiopsis was studied for its ability to survive oxidative stress and its potential ability to survive in space and under Martian conditions [171,172]. Several members of the Chroococcidiopsis genus were irradiated at several doses. Depending on the strains, the survival to X-ray at an exposure of 2.5 kGy was between 35 to 80%. All the tested strains survived after a 15 kGy exposure while no survival has been detected at 20 kGy [173].

We also detected the presence of Blastomonas, which can be found in the environment and possess carotenoids [315]. However no data were available about potential radioresistance. This genus was not found in the different SNFPs analysis over the world. The genus Limnobacter was highly abundant in the 2017 sampling. This genus was not present in the different SNFPs analyzed. Strains closely related to the Limnobacter genus were identified in an acid mine drainage contaminated with several heavy metals [371].

The Microbacterium belong to the Actinobacteria. Several strains of this genus are UV resistant [372,373].

Moreover, this genus was already found in the Cofrentes SNFP [184,185], and one strain has been isolated from Chernobyl and shown by proteomics to overproduced specific proteins in response to uranium contamination [141]. Nitrite oxidation is the main energy source of Nitrospira bacteria. This bacterial genus can be found in aquatic environment, such as freshwater and activated sludge [165]. It is the dominant nitrite oxidizing bacteria genus in several aquatic habitats, as well as wastewater treatment systems and bioreactors [374]. Bacteria closely related to the Nitrospira genus were identified in a radon hot spring in Australia (radon levels in the water between 2000 and 5000 Bq/L) [375]. The Brevundimonas genus (a-Proteobacteria) can be found in water and soil [315]. One bacterial strain identified as a Brevundimonas was isolated from a hot spring containing high levels of radon. This strain was also proved to biosorb and desorb cadmium [376]. Another strain, isolated in the Antarctic Cry Valley, exhibited a D10 of 1.5 kGy after a gamma-irradiation at -79°C and some viability was observed after a 7 kGy irradiation [377]. The Sphingomonas genus was identified in several SNFPs in Spain and in the USA after a cultivation step [185,188]. One strain, isolated in the Antarctic Dry Valleys, exhibited in its genome the presence of oxidative stress resistance genes [306]. Some strains of the Acidovorax genus can use the oxidation of H2 as an energy source [315]. This genus can be found in soil, water, and activated sludge for example. A species of Acidovorax, A. facilis, can accumulate uranium and is able to survive a concentration of 500 µM [378]. This genus has also been found in the SNFP of Savanah River Site [193]. Bacteria identified as Stenotrophomonas sp. have been isolated from Andean lakes and exhibited a great resistance to UV radiation [379,380].

Nevskia bacteria can be found on the surface of freshwater environments. It can form a pellicle on water surface and create slime [381]. N. ramosa has an effective photorepair mechanism when exposing cells to light after a UV irradiation [382]. It has however never been described in SNFP or radioactive environment.

Blastocatella fastidiosa was the first described species of Acidobacteria subdivision 4 and was isolated from a semiarid savanna soil [383]. Blastocatella genus was found in a heavy metal contaminated land in China [384]. This genus was isolated recently and little is known about it. Considering the environments these genera can be found, it appears that they possess performant defenses against oxidative stress induced by ionizing radiation, metallic stress, and desiccation.

Conclusion

In this pool a great diversity was observed. Prokaryotes and eukaryotes have been found living in this constraint environment thanks to a novel proteotyping methodology based on high-resolution tandem mass spectrometry. In some samples, the biomass contributions of prokaryotes and photosynthetic eukaryotes were found almost similar, highlighting the importance of microbial equilibrium as recently

Chapter conclusions

In this chapter it has been observed that an unexpected diversity could be seen in the water of nuclear storage pools. This diversity was assessed using direct analysis methods (metagenetics and direct metaproteomics) and liquid cultures (kinetic in different media). The combine use of all three methods allowed a deep inventory of the microbial diversity present in these pools. The microbiota present in both pools (analyzed with and without culture) were not similar to the already described microorganisms in similar environments.

Both pools presented different physico-chemical conditions between in each but also within each other. A change in the microbiota has been observed in both pools when the conditions were changed. In Pool 1 the main change was the filtration system while in Pool 2 the definitive shutdown of the reactor deeply modified the pool's conditions. Differences were observed in Pool 2 in the microbiota composition as expressed by the non-metric multidimensional scaling (NMDS) presented in Figure 3.26. Three groups could be observed: one group containing the analysis from the 2015 water sampling, one group with the 2017 water analysis and the final one containing the scrubs from 2017. Only one sample (Scrub 3) was apart from the other scrubs, probably because of its richness and evenness. The survival mechanisms implemented by the organisms in Pool 2 were most likely different between the bacteria identified in the water and the ones identified in the biofilms. Using direct metaproteomics could help identify these mechanisms. As specified above the microbial composition of both pools was different despite the presence of common genera mainly belonging to the Proteobacteria phylum after metagenetics analysis (cf. Appendix 11). During the kinetic the same tendency could be observed, with 60 genera common to both pools (out of 215 genera) (cf. Chapter II, Appendix 5). This difference can be easily explained by the different conditions of the pools.

Moreover Pool 1 is less stringent than Pool 2 in terms of nutrients and ionizing radiation. Furthermore no radionuclides were found in the water of Pool 1. Pool 1 was thus useful to develop methods to sample and analyze the microbial diversity present in nuclear environments.

The diversity observed in Pool 1 was greater than the one found in Pool 2, whether the conditions changed or not. Photosynthetic organisms were found in Pool 1 (Cyanobacteria and algae) in high proportion while less than 0.3% of Cyanobacteria were found in Pool 2 (during operation and at shutdown). The evenness in the microbial diversity was greater in Pool 1 than in the water of Pool 2. The biofilms collected in Pool 2 presented an evenness similar to the one found in Pool 1 (water and scrubs).

Several microorganisms including several radioresistant bacteria have been isolated from the water and biofilms sampled in Pool 2. No unknown bacteria at the genus level were isolated in this pool. Several bacteria were similar to unnamed already identified species (i.e. 100% hit with a bacteria identified at the genus level but not species one: for example, a hit with a Bacillus sp.). The ability to accumulate and survive different radionuclides was studied for the radioresistant bacteria isolated from Pool 2.

Chapter IV:

Radioresistance and bioaccumulation of uranium and cobalt by bacteria isolated from a nuclear reactor cooling pool 4. Radioresistance and bioaccumulation of uranium and cobalt by bacteria isolated from a nuclear reactor cooling pool

Introduction

In France, there are currently 58 nuclear reactors, distributed in 19 facilities, to produce electricity.

Moreover 8 nuclear reactors are used for research in France. All these installations produce radioactive wastes that are treated accordingly to their nature (cf. Chapter I, Appendix 1). Once treated the wastes are stored in deposits. Radioactive wastes management is an issue concerning the environment and public health and is taken into account when building a nuclear power plant. Nuclear energy is the third source of electric production in the world. In France, 77% of electricity is produced using nuclear fission (EDF). The volume of radioactive wastes will increase due to nuclear exploitation. The actual solution is waste underground burial but it is of limited space. One solution to reduce wastes volume could be changing the effluents decontamination methods. Nuclear effluents are currently mainly decontaminated via resins and chemical products. The use of biotechnology to reduce the volume of wastes produced by nuclear effluents is currently under consideration [112,152,386,387]. Large volume of wastes with low radionuclides concentration could indeed be reduced using biotechnologies.

The advantages of radionuclides accumulating microorganisms can be numerous. They can in fact concentrate radionuclides inside their cells and thus reduce the volume of wastes to store. Microorganisms able to change the toxicity and solubility of radionuclides can be used in the environment, either to immobilize radionuclides or to accelerate their removal by changing their chemical form (e.g. reduction)

[115]. Several studies have been performed in situ with the natural microbiota present in contaminated soils [153][154][155]. Among the microorganisms able to accumulate toxic metals and radionuclides, several fungi, bacteria and algae exhibited a great accumulation ability [113,134,135,146,147,149,150,152,195,[388][389][390][391][392][393]. A laboratory-scale process has been developed using the micro-algae Coccomyxa actinabiotis to depollute nuclear effluent [112]. Throughout time one evidence has come to light: organisms can be used to decontaminate low concentration of large volume of wastes [143,176]. Microorganisms able to accumulate or reduce radionuclides are more likely to be found in an environment presenting this stress.

To accumulate radionuclides and survive this accumulation microorganisms need to resist ionizing radiations. Nuclear pools provide an environment containing ionizing radiations and radionuclides. In these environments microorganisms able to accumulate radionuclide should be more easily discovered than in any other environments (with the exception of Chernobyl and Fukushima surroundings).

Since two decades we now know that life can be found in Spent Nuclear Fuel Pools (SNFP) [152,[181][182][183][184][185][186][187][191][192][193]. The Spanish SNFP Cofrentes was deeply studied [181,[184][185][186][187]. Several microorganisms, mainly bacteria, have been isolated from these pools. To be able to survive the hostile conditions present in such environment (ionizing radiation, presence of radionuclides, lack of nutrients) the organisms must have developed mechanisms to resist the different stresses. They include biofilms' formation, sporulation or Viable But Non-Culturable state (VBNC) for example. Other mechanisms, independent from physiological shape, can be implemented to resist radiations and metallic stress. Microorganisms can resist metallic stress using different mechanisms. They can either i) accumulate the atom and detoxify it to potentially eliminate it or ii) prevent its penetration in the cell.

In this study the radioresistance of bacteria isolated from the cooling pool of a nuclear reactor (during operation and at shutdown) has been assessed. Their ability to accumulate cobalt and uranium was also tested. The radionuclides 238 U and 60 Co were chosen since they are the most interesting in terms of bioremediation and bio-decontamination in the case of a nuclear incident or just to decontaminate nuclear liquid effluents.

Uranium is used as a fuel and uranium can be released in nature during nuclear accidents. Agriculture is however the main uranium contamination in soils (cf. Chapter I. 4.2.1.3). Cobalt 60 can be produced in nuclear reactor when particles from the pool's structure and elements are activated by the reactor's neutronic flow. It usually represents about 47% of total gamma activity in liquid effluents in EDF power plants (IRSN website: https://www.irsn.fr/FR/Pages/Home.aspx). During treatment cobalt is dissolved into liquid effluents. In 1999 the 60 Co activity was estimated at about 3.21x10 11 Bq in the SNFP of La Hague. In England, at the Sellafield nuclear fuel reprocessing and nuclear decommissioning site, the activity of cobalt in effluents was estimated at 1.5x10 12 Bq in 1997 (IRSN website). Cobalt can be toxic for organisms and the environment [394][395][396][397][398]. It is thus necessary to depollute any cobalt contamination.

To decontaminate these radionuclides, several methods exist (cf. I.4.2.3 for uranium biodecontamination).

Cobalt can be removed from waste using chemical precipitation, ion exchange, solvent extraction and evaporation. Chemical precipitation can be performed using a polyurethane foam or EDTA and iron. This method allowed cobalt extraction but was not the best method since about 50% of cobalt was recovered

Material and Methods

Microbial strains isolation

The basin studied is the cooling pool containing water in direct contact with the core of a nuclear reactor located on the CEA-Saclay center. It is 11 m deep, 7.5 m long, and 6.5 m wide. While the reactor was in operation, a 2 L water volume was collected on December 2015 using a permanently installed pipe extensively purged before sampling. When the reactor was shut down, a total volume of 8 L was collected on March 2017 using the permanently installed pipe, as well as a sterile sampling vial (Wildco, USA) attached to the lower end of a pole and opened at the samplings points.

During reactor shutdown 0.5 L of the collected water were used for cultivation and cryopreservation.

Microorganisms were concentrated and harvested by centrifugation at 16,000 g for 20 min at 10°C. The pellet was resuspended in 1 mL of supernatant. This milliliter was dispatched and cultivated in presence of 6 ml of liquid media over a kinetic culture. Two liquid media were used: Luria-Bertani (LB) diluted 1/12 (Roth) and Reasoner's 2A (R2A) diluted 1/3 (HiMedia Laboratories Pvt. Ltd.). Four kinetic points were

Chapter IV -Radioresistance and bioaccumulation of uranium and cobalt by bacteria isolated from a nuclear reactor cooling pool 176 analyzed: 3 days, 7 days, 14 days and 21 days. The cultures were incubated at 28°C under shaking. To isolate strains, 100 µL of each kinetic point were plated at different dilution on the corresponding agar medium, namely LB diluted ten times and R2A diluted twice. Plates were incubated at room temperature until microbial growth was detected and then kept at 4°C. The microorganisms were isolated from these plates.

For anaerobic cultures, 100 µL of the liquid cultures from day 28 of the kinetic from each location in LB medium were diluted in 1.9 mL of sterile water. One milliliter from this solution was introduced in BSR or BTR liquid media kit from Labège-CFG Services (Orléans, France). The cultures were incubated 2 months at room temperature without agitation. The isolation of microorganisms was performed on R2A diluted 1/2 agar plate introduced in anaerobic jar (GENbox anaer, Biomérieux, France). The bacteria isolated from the kinetic possessed the code-name "SKH".

Biofilms present in this pool in 2017 were recovered via cellulosic scrubs. After sampling the scrubs were placed on sterile hermetic plastic bags to be treated in a sterile environment. In a sterile environment the scrubs were introduced into 50 mL sterile Falcon tubes with 15 mL of sterile milliQ water to resuspend the recovered organisms. After the resuspension step, the presence of visible cobalt particles forced us to let the Falcons decant overnight at RT to eliminate as much radioactivity as possible to transport the samples outside of the radioactive zone. The 5 top milliliters were recovered the next day. Ten microliters were plated in different media for microbial isolation. The media used were LB (Roth, Germany), Tryptic Soy Agar (TSA), TSA diluted ½, Nutrient Agar (NA), NA diluted ½, (5 g tryptone, 2.5 g yeast extract, 1 g glucose, and 15 g agar per L), TCA diluted ½, Brain Heart Infusion (BHI), and BHI diluted 1/10 (Sigma, Germany).

Samples collected during reactor operation were stored for 3 days at 4°C before handling to reduce their activity by radioactive decay of 24 Na (half-life of 15 hours), enabling its manipulation in controlled conditions. To isolate microbial strains harvested during reactor operation, 0.5 L water sample was centrifuged at 15,000 g for 20 min. The supernatant was discarded except for the last 4.5 mL which were used to resuspend the microbial pellet. Aliquots of 500 µL were plated on different agar media, namely LB (Roth, Germany), TSA, TSA diluted ½, NA, NA diluted ½, TCA, TCA diluted ½, BHI, and BHI diluted 1/10 (Sigma, Germany). Another 0.5 L water sample were filtered. Filters were stamped onto agar plates containing the same media. Plates were incubated at 28°C until microbial growth was detected and microorganisms were isolated by successive plating.

Strain identification

16S rDNA and ITS sequencing

Once the microorganisms were isolated, a colony PCR was performed. Amplification of 16S rDNA was performed to identify bacteria and ITS amplification was performed to identify fungi [START_REF] Selbmann | Fungi at the edge of life: cryptoendolithic black fungi from Antarctic desert[END_REF]247,403]. Once isolated, microorganisms were identified by 16S rDNA sequencing after PCR performed on a colony. Amplification of 16S rDNA was carried out using universal primers 27F (5'-AGAGTTTGATCMTGGCTCAG-3') and 1492R (5'-TACGGYTACCTTGTTACGACTT-3') at an annealing temperature of 61°C. Amplification of ITS was carried out using universal primers ITS1 (5'-TCCGTAGGTGAACCTGCGG-3') and ITS4 (5'-TCCTCCGCTTATTGATATGC-3') at an annealing temperature of 55°C. The elongation for both amplifications was performed by the DNA Phusion polymerase (Thermo Fisher, USA) at 72°C for 45 s and 60 s for the 16S rDNA and the ITS, respectively. PCR products obtained after 35 cycles were gel purified (NucleoSpin® Gel and PCR Clean-up, Macherey-Nagel, Germany). The DNA was sequenced by GATC (Germany) with the Suprem Run method. The obtained sequences were aligned against NCBI GenBank database using nucleotide BLAST.

Phylopeptidomics

Each bacteria isolated from the 2017 kinetic were identified using phylopeptidomics. The metaproteomics analysis was done as previously described (cf. Taxonomical composition of a biofilm sampled from a nuclear fuel pool assessed by metaproteomics).

Briefly, microbial colonies were recovered and a volume of X µL of Laemmli buffer LDS1X (Invitrogen) was added per mg of wet bacterial pellet as described by Hartmann et al [295]. After denaturation for 5 min at 99°C, the protein sample was loaded onto a 4-12% gradient 10-well NuPAGE gel (Invitrogen). The proteins were subjected to electrophoresis for 5 min and then stained with Coomassie Blue Safe staining (Invitrogen)

as recommended by the supplier. The polyacrylamide band corresponding to the whole proteome was sliced, then unstained with ultra-pure water, reduced with dithiothreitol, and treated with iodoacetamide.

It was then subjected to proteolysis with Trypsin Gold Mass Spectrometry Grade (Promega) in the presence of 0.01% ProteaseMAX surfactant (Promega), as previously described

Characterization of radioresistance

Each isolated microorganisms was cultivated in liquid LB medium and subcultured twice. When the stationary phase was reached, 2 mL of each culture were centrifuged at 20,000 g for 5 min at room temperature. The supernatant was discarded. The pellet was washed twice with sterile NaCl 0.9% and resuspended in 1 mL of sterile NaCl 0.9%. Bacteria were then counted using a Malassez cell. 10 6 cells were introduced into 200 µL of sterile liquid LB medium, in one single row of a 96-well plate. Bacteria were prepped the day before irradiation and the sealed plates were stored overnight at +4°C. They were irradiated at a dose rate of 2,200 Gy/hr using 60 Co sources at room temperature (ARC-Nucléart, CEA -Grenoble), integrating total doses of 200, 500, 1000, and 1500 Gy. A control plate (0 Gy) was handled in the same conditions. After irradiation, the bacteria were incubated at 28°C on LB agar plate at two different dilutions corresponding to 100 and 1000 cells per plate. Experiments were performed in duplicate. When bacterial growth appeared on the control plate, after at least one week, the colonies were counted and

Chapter IV -Radioresistance and bioaccumulation of uranium and cobalt by bacteria isolated from a nuclear reactor cooling pool 178 compared to the control to assess the percentage of survival. Escherichia coli strain DH5a and Deinococcus radiodurans were also tested in the same conditions as non radioresistant and highly radioresistant control species, respectively.

The bacterial irradiation was also performed in NaCl 0.9% instead of LB. The procedure was the same as the one detailed above.

Radionuclides accumulation studies

Determining the screening conditions

To determine the incubation time, the metal concentrations to use and to assess the impact of uranium on cell viability, a preliminary experiment was performed on six bacteria among the most radioresistant. The accumulation was assessed after 1 and 24 hours of contact with uranium. Direct viability was also performed at both time points. Briefly, 800 µL were centrifuged 15 min at 20,000 g at RT. The supernatant was recovered and the pellet was washed once with 1 mL of sterile NaCl 0.9% and a second time with Na2CO3 10 mM to eliminate most uranium adsorption on the cell walls. The two different washes were pooled into one same tube. Direct cell viability was performed on the 200 µL culture left. The tubes were centrifuged 15 min at 20,000 g at RT and the supernatant discarded. The pellet was resuspended in 99.5 µL of NaCl 0.9%. 0.2 µL of propidium iodide (1 mg/mL) and 0.3 µL of fluorescein diacetate (2.5 mg/mL) were added. After 5 min incubation in the dark the tubes were centrifuged 5 min at 20,000 g at RT and the supernatant discarded. The pellet was washed twice with 100 µL of NaCl 0.9% and resuspended in 100 µL of NaCl 0.9%. Direct cell viability was observed using a microscope (Zeiss Axio Scope A1) and an epifluorescent lamp (HXP 120C). GFP filter (excitation: 457-487 nm; emission: 502-538 nm) was used to highlight living cells while FITC filter (excitation: 467-498 nm; emission: 513-556 nm) was used to highlight dead cells. Cell viability was analyzed using ImageJ (v. 1.46r) by assessing the surface of living and dead cells.

Screening experiment

Each isolated microorganisms (11 strains) was cultivated in liquid LB medium and subcultured twice. When the stationary phase was reached, 2 mL of each culture were centrifuged at 20,000 g for 5 min at room temperature. The supernatant was discarded. The pellet was washed twice with sterile NaCl 0.9% and resuspended in 1 mL of sterile NaCl 0.9%. Bacteria were then counted using a Malassez cell. 1.40x10 8 cells were introduced in sterile NaCl 0.9% pH 5.5. Different metals were tested: UO 2 (NO 3 ) 2 6H 2 O and CoCl 2 at three different concentration in duplicate: 5 -50 and 500 µM. The final volume of the experiment was 1 mL of NaCl 0.9%. The tubes were incubated at 28°C with agitation (200 rpm). Control tubes with 5-50-500 µM of uranium or cobalt were done to measure the metal concentration really introduced. After 24h of incubation the tubes were centrifuged 15 min at 20,238 g at RT. 475 µL of supernatant were recovered and the rest of supernatant was discarded. The pellet was washed once (5 µM) or twice with 1 mL of sterile NaCl 0.9%. After centrifugation 475 µL of supernatant were recovered and the rest of supernatant was discarded.

The two different washes were pooled into one same tube. Nitric acid (HNO3) was added to the different collected fractions to a final concentration of 0.5%. The pellet was preserved at -20°C without addition of acid prior to mineralization.

The radionuclides 238 U and 60 Co were chosen since they are the most interesting in terms of bioremediation and bio-decontamination in the case of a nuclear incident or just to decontaminate nuclear liquid effluents.

Normal saline and a pH at 5.5 were chosen to avoid any possible speciation between the liquid media and the radionuclides.

Metal analysis

Five hundred microliters of pure HNO3 were added to the bacterial pellet. The mixture was placed into 15 mL mineralization tubes (Courtage Analyses Services, France). The tubes were placed in a HOT BLOCK Environmental Express for 2h30 at 90°C. At the end of the mineralization 4.5 mL of milliQ water were added to the mixture.

The three recovered fraction (pellet, supernatant, wash) were diluted in HNO3 0.5% as presented in Table 4.1. The concentration of metal was measured using an ICP-MS (Inductively Coupled Plasma Mass Spectrometry) iCAP RQ (Thermo Scientific, USA). The results were recovered and analyzed using the Qtegra software (Thermo Scientific, USA). Microorganisms have been isolated from both campaigns. These microorganisms originated either directly from the samples or from the culture kinetics of the communities. Five fungi were identified in 2015 as well as 22 bacteria. In 2015 the identification was conducted using 16S rDNA and ITS sequencing for bacteria and fungi respectively. No fungi was isolated in 2017 but 24 bacteria were identified. Among these bacteria, the ones isolated during the kinetics were identified via phylopeptidomics and the ones isolated from biofilms were identified via 16S rDNA sequencing. Isolated strains belonged to different phyla and genera (Table 4.2). Only Actinobacteria, Firmicutes and Proteobacteria were isolated among the bacteria. A majority of Firmicutes and Actinobacteria were isolated during reactor's operation while a majority of Proteobacteria was isolated during shutdown.

Bacteria and fungi isolated from the 2015 sampling and the biofilms recovered in 2017 were coded "CEA-NOB-XXX" and "CEA-NOF-XXX" respectively. Bacteria isolated from the 2017 kinetic were coded "SKH XXX".

Among the isolated bacteria, only Pantoea sp. and Rothia sp. had never been described in SNFPs. The genus Leifsonia was found in an Argentinian SNFP [192], Nocardia genus and Gordonia bronchialis were found in a Spanish SNFP [181,184,185]. The species Ralstonia pickettii was found in several SNFPs in France and Spain [184,186,189], while this genus was also found in the Cofrentes SNFP [181,185]. Many Bacillus species have been isolated in Spain and Argentina [184,186,192]. Staphylococcus epidermidis was found in Cofrentes [184,186] and several Staphylococcus species were isolated from French and Spanish SNFPs [181,185,189]. S. epidermidis was also isolated from feathers of birds living in the Chernobyl exclusion area where the received dose was 0.

U Co

Chapter IV -Radioresistance and bioaccumulation of uranium and cobalt by bacteria isolated from a nuclear reactor cooling pool 180 species were found in France, Argentina and USA [177,189,192]. The genera Mycobacterium, Cellulomonas, Bradyrhizobium, and Afipia were all found in Cofrentes [186] [184,185]. Sphingomonas genus was isolated from SNFPs in Spain and USA [185,188]. Acinetobacter genus was found in French and

Argentinian SNFPs [189,192]. Kocuria genus was identified in a Slovak SNFP [152]. Pelomonas and Mycobacterium genera were identified using metagenetics in an American SNFP [193]. Concerning the fungi, only Aspergillus genus was isolated from the Cofrentes SNFP [181] (Table 4.2).

Several bacteria were identified using phylopeptidomics. For some of the tested colonies, several organisms were detected and identified. This phenomenon proved the corresponding bacterial culture was not pure.

Interestingly the inability to identify some bacteria to the species level and the attribution of several taxon ID may indicate that these bacteria might be unknown. However the lack of exhaustiveness of protein databases might explain this phenomenon. Many isolated bacteria were matched to the same strain (e.g.

Ralstonia pickettii with the R. pickettii strain ID 329) (Table 4.3). Among these bacteria, the genera Ideonella, Aquincola, Acidovorax, and Methylibium had never been described in SNFPs after cultivation. The analysis method might explain the inability to detect these genera in the previously described SNFPs. These bacteria were identified in impure cultures. Phylopeptidomics allows indeed the identification of several genera when simple 16S rDNA sequencing requires pure bacterial cultures. The identification of Ideonella with metagenetics in an American SNFP [193] could confirm this hypothesis.

Even though several bacteria were isolated from this pool in 2015 and 2017, an important proportion of the strains were similar and belonged to the same species or genera (e.g. Bacillus sp., Ralstonia pickettii, Sphingomonas sp., Pelomonas sp.). The chosen media might have not selected original strains that could need more exotic medium. A greater diversity could be discovered use the use of several different and specific media.

Radioresistance screening

The ability to survive radiations has been assessed on the isolated bacteria (from both campaigns) which were able to grow in liquid media (27 bacteria tested). Two controls were used to assess the experimental procedure: one radiosensitive (E. coli) and the other highly radioresistant (D. radiodurans). Bacteria radioresistance was assessed using two different conditions: an irradiation in NaCl 0.9% to prevent important metabolic activity and an irradiation in liquid LB to allow a metabolic activity. Only bacteria presenting a growth on control plates (0 Gy) were analyzed. Some of the isolated bacteria did not present any growth during the liquid culture and some irradiated bacteria did not present any growth on the agar plate, even the non-irradiated ones (7 bacteria). This explained the number of tested bacteria, lower than the number of isolated bacteria. Only 20 bacteria were thus studied.

Some bacteria which did not exhibit growth on agar control plate (0 Gy) after irradiation in NaCl exhibited bacterial growth when irradiated in liquid medium. The opposite was also true.

Irradiation in NaCl

After an irradiation in NaCl 0.9% Deinococcus radiodurans (positive control) presented low lethality after a 1 kGy irradiation. Escherichia coli (negative control) presented no survival after 500 Gy irradiation (Fig. 4.1).

Both controls thus confirmed the chosen irradiation conditions. Four bacterial groups could be observed after the irradiation in NaCl. The first group, containing two Bacillus sp. and Micrococcus luteus, could well resist a 500 Gy irradiation (more than 40% survival) and presented a great viability after 1 kGy (between 10 to 27.1%). The second group presented a great viability after a 200 Gy irradiation but presented a survivability between 7.9 and 16.1% at 500 Gy. After a 1 kGy irradiation the survivability of the strains was between 0.14 and 1.8%. Bacteria in this group were Pantoea vagans, Brevibacillus agri, two Leifsonia sp., and Gordonia bronchialis. The third group was composed of the two Nocardia niigatensis, Kocuria koreensis and Pantoea sp. This group presented between 24.2 to 34.3% survival after a 200 Gy irradiation but exhibited a viability smaller than 10% at 500 Gy (4.5 to 9.9% viability). At 1 kGy less than 2% of the cells were alive. The last group was composed of two B. thuringiensis, Sphingomonas sp., and Ralstonia pickettii.

These bacteria presented no survival after a 500 Gy irradiation. The last group presented a similar survivability profile than E. coli and should thus not be considered as radioresistant (Fig. 4.1; Appendix 13, Table 6.21).

Irradiation in LB medium

Several bacteria could withstand a dose of 200 Gy while a smaller number could resist a 1 kGy irradiation (Fig. 4.2). From our results, four groups could be seen. The first one, a highly resistant one, which contained Pelomonas sp., which exhibited a similar survival than E. coli and can thus be considered as radiosensitive (Fig. 4.2; Appendix 13, Table 6.22). It should be noted that, in the tested condition, while E. coli did not resist a dose higher than 200 Gy and presents high mortality at 200 Gy (less than 1% survival), D. radiodurans did not present a complete survival started from 200 Gy. This bacterium is known to survive up to 5 kGy irradiation without any mutation nor lethal effect when put in the optimum conditions [START_REF] Slade | Oxidative stress resistance in Deinococcus radiodurans[END_REF]. However it was the bacterium that could best withstand the 1.5 kGy irradiation. These results showed that in these screening conditions the chosen protocol was adapted for a screening, but did not present the optimum conditions of survival for the strains.

It is possible that, in the optimum conditions, the ability to resist radiations could be higher. Moreover this experiment only showed the survival after an acute irradiation. For a chronic irradiation it is possible that the dose received could be higher than 1.5 kGy and cells could survive this dose.

The irradiation in LB or NaCl did not change the survivability of E. coli. The irradiation of D. radiodurans was more lethal when it occurred in LB media than in NaCl. Several bacteria were influenced by the irradiation medium (liquid LB or NaCl). Seven strains presented a different survivability depending on the condition.

M. luteus and K. koreensis had their resistance increased when the irradiation occurred in LB. The five other strains (Leifsonia sp., G. bronchialis, B. agri, and two Bacillus sp.) all presented a higher radioresistance after a NaCl irradiation (Appendix 13). It is possible that an irradiation in liquid media allowed the immediate induction of DNA repair and ROS scavenging mechanisms for M. luteus and K. koreensis. Having an active metabolism for these strains during irradiation might thus be an advantage. Concerning the five other strains the irradiation in LB was more lethal. One possible explanation could be the presence of an active metabolism due to the presence of nutritive environments, potentially enhancing the damages. In NaCl an almost inactive metabolism could have prevented damages, making them more repairable after cultivation. This phenomenon was observed by Hu et al. in 2005. Vibrio parahaemolyticus was irradiated in phosphatebuffered saline (PBS) or in oyster, where it can naturally be found. Its LD50 (dose needed to eliminate 50% of a population) was slightly lower in oyster (0.57 kGy) than in PBS (0.60 kGy) [405]. This result is in contradiction with the study performed by [START_REF] Farag | Radiation Sensitivity of Listeria monocytogenes in Phosphate Buffer, Trypticase Soy Broth, and Poultry Feed[END_REF]. A Listeria monocytogenes strain was irradiated in TSB and phosphate buffer. D10 for this strain was about 0.18 kGy in phosphate buffer and 0.21 kGy in TSB [406]. Both studies could explain the differences observed between the irradiation of our strains in NaCl or LB. They could also prove that each strain can react differently depending on the irradiation conditions. One strain could exhibit better survivability in NaCl or PBS when another strain will prefer irradiation in liquid broth.

No data concerning ionizing radiations of the species Brevibacillus agri and the genus Brevibacillus were previously found. In the literature, M. luteus was shown to survive a 1 kGy irradiation [177], Kocuria koreensis could resist an UV irradiation of 1 kGy [407], the genus Leifsonia was already found in similar environment (spent nuclear fuel pool) [192], while Bacillus possesses species that can be UV resistant [379].

The two tested Bacillus exhibited no cell viability a 500 Gy but presented some bacterial growth after a 1 kGy irradiation in NaCl. It might have been caused by the presence of resistant bacterial spores during irradiation [408][409][410]. Pantoea genus also possesses species (P. agglomerans) able to resist to a gammaradiations [411], while the Nocardia genus contains species found in spent nuclear fuel pool [181,184,185].

This genus can also survive up to a 3 kGy irradiation, maybe due to its ability to sporulate [170]. The species Gordonia bronchialis was also found in a spent nuclear fuel pool in Spain [181,184].

Bioaccumulation screening

Bacteria exhibiting radioresistance greater than E. coli (radiosentive control) were tested for radionuclides accumulation. Among them, highly radioresistant strains as well as strains never described for U or Co accumulation were selected. 4.3.3.1 Uranium accumulation 4.3.3.1.1 Conditions screening The uranium accumulation of the different strains was higher for all tested strains after 24 hours than 1 hour (Table 4.4). Some of the tested bacteria appeared to present a great accumulation capacity (e.g.

Leifsonia sp. with a maximum accumulation at 457±50 nmol/10 9 cells, Bacillus sp. 448 at 247±55 nmol/10 9 cells, and Kocuria koreensis at 211±20 nmol/10 9 cells). Some duplicate presented a huge discrepancy between each other. For example, after 1 hour in presence of 200 µM of uranium, M. luteus presented a 10 fold difference between the two duplicates. For K. koreensis at 200 µM one duplicate accumulated 81.0 nmol for 10 9 cells while the other one accumulated 20.7 nmol for 10 9 cells. When looking at the uranium recovered in each fraction it can be noticed that some samples presented a dosed uranium higher than the uranium introduced while others exhibited some uranium loss compared to the quantity of uranium introduced and measured by ICP-MS (Appendix 14, Tables 6. 24 & 6.25). Due to technical ICP-MS issues not all data could have been re-analyzed. The samples to re-analyze are highlighted in green in Appendices 14 and 15. At 20 and 200 µM cellular death was observed when compared with negative control and 5 µM (Appendix 14, Table 6.23). In light of the results obtained with this screening conditions, a 24 hours accumulation was selected along with the 5 -50 and 500 µM uranium concentrations. Direct cell viability was not tested.

Uranium accumulation

Considering results obtained with the screening experiment, the incubation time with uranium was set at 24 hours. Concerning the accumulation of uranium, not all bacteria presented the same profile. Some bacteria presented indeed a high accumulation profile while the others accumulated less. For most bacteria the accumulation was higher at 500 µM. N. niigatensis 030, the two Pantoea strains and Gordonia bronchialis did not present a higher accumulation at 500 µM. Bacillus sp. 448 and Brevibacillus agri 032 both accumulated more than 650 nmol for 10 9 cells at 500 µM. These two bacteria were the most accumulating bacteria at 500 µM. In presence of 1000 µM of uranium B. agri 032 was the only strain able to accumulate more than 200 nmol of U for 10 9 cells. All bacteria presented a decrease in uranium accumulation at 1000 µM, suggesting this dose was too high and killed most of the cells (Fig. 4.3). The two Bacillus strains did not accumulate uranium with a same profile. Bacillus sp. 448 was the bacteria which accumulated the most uranium while Bacillus sp. 033 accumulated similarly than other strains such as Micrococcus luteus 002 or Kocuria koreensis 006. Pantoea vagans 450 exhibited an accumulation plateau below 100 nmol for 10 9 cells at 500 and 1000 µM. This strain did thus not well survive a uranium concentration higher than 50 µM (Fig. 4.3). In the preliminary results accumulation of circa 100 nmol for 10 9 cells was detected at 200 µM for this strain. At this dose Pantoea vagans 450 seemed to have lost cell viability since the accumulation was close to the ones found at 500 and 1000 µM. This observation was confirmed with direct cell viability for which only 23% of the bacteria were alive at 200 µM (Table 4.4; Appendix 14, Table 6.23).

Micrococcus luteus 002

Kocuria

The less accumulating strains were Pantoea sp. 497, Gordonia bronchialis 012, Leifsonia sp. 010, and both Nocardia niigatensis. Despite the low accumulation profile of the latter strains they can be considered as uranium accumulating strains. All strains, except for N. niigatensis 030, were indeed able to accumulate more than 100 nmol of uranium for 10 9 cells (Fig. 4.3;Appendix 14,Table 6.27).

Beyond 500 µM of uranium the accumulation of K. koreensis 006 seemed to reach a plateau since more uranium were recovered after the washes (Appendix 14, Table 6.27).

A decrease in uranium accumulation can be observed at 1000 µM. This decrease can be explained by a cell mortality. It was indeed observed that a decrease in cell viability occurred at 20 and 200 µM during the preliminary screening (Appendix 14, Table 6.23). No data is available in the literature concerning the interactions between Gordonia genus and uranium.

Genus Gordonia is widely distributed in aquatic and terrestrial habitats [347]. G. bronchialis can be an opportunistic pathogen for human [412].

The genus Brevibacillus was found in a uranium contaminated site in the USA and was shown to reduce uranium [413]. No other information are available concerning this genus with uranium.

No data concerning the accumulation of uranium by Kocuria koreensis was found in the literature. This species was isolated in 2010 which could explain the absence of study. However the species Kocuria erythromyxa could adsorb 68% of a 0.1 µM 235 U solution. This strain was also able to adsorb plutonium [414,415]. For these studies, no washes were performed. No data concerning uranium accumulation of Nocardia niigatensis is available in the literature. However N. erythopolis was able to adsorb 1.56 mmol/g dry weight [416].

The species M. luteus was proven to adsorb uranium. It was shown that it could adsorb up to 2.60 mmol/g dry weight [416]. In another study about 163 µmol/g of cells were adsorbed by M. luteus. It was also shown that a saturation occurred after 30 min at about 150 µmol/g of cells [389]. In all studies concerning the uranium accumulation by M. luteus, no washes were performed. Moreover the authors described the adsorption of uranium and not its absorption or accumulation. It is then highly probable that uranium is adsorb by the cell wall. This hypothesis can be confirmed by the presence of significant uranium in the washes of our M. luteus 002 strain (Appendix 14, Table 6.27).

Different species and strains of Bacillus sp. were able to adsorb high amounts of uranium [417,418]. Both Members of the Pantoea genus were able to accumulate uranium. P. agglomerans was proved to biosorb 81% of a 0.1 µM 235 U solution as well as other radionuclides [415]. However no wash was performed in this study and adsorption on the cell surface was probable. An important adsorption could be observed in our study where uranium was recovered in the washes (Appendix 14, Table 6.27). In a study, after 24 hours of contact, about 80 mg/g of cells was accumulated by Pantoea sp. TW18. This accumulation was obtained after three washes of the cells with distilled water [422].

One strain of Leifsonia was shown to remove about 68% of a solution of uranium at different concentration (8.4 to 126.05 µM). This strain was coated with biochar to enhance the adsorption of uranium with this strain. With this coating the bacterium was able to remove 99.82% of a 42 µM uranium solution [423].

Cobalt accumulation

After 24 hours of exposure to three different concentrations of cobalt, its quantity in each fraction (pellet, supernatant, washes) was assessed (Appendix 15, Table 6.29). Unlike uranium, the tested strains did not accumulate cobalt to large extents. Three strains did not accumulate any cobalt at any dose. These strains were Bacillus sp. 448 and 033, and Brevibacillus agri. Kocuria koreensis was the most accumulating strain with about 11 nmol of cobalt accumulated by 10 9 cells. These strains could accumulate between 450 and 750 nmol of uranium for 10 9 cells. The strains Gordonia bronchialis, Nocardia niigatensis 020, Pantoea vagans, and Micrococcus luteus accumulated cobalt in smaller proportion than K. koreensis. The latter strains accumulated cobalt at all tested concentrations. The strain Pantoea sp. 497 accumulated small quantities of cobalt at 30 and 300 µM (less than 0.5 nmol for 10 9 cells). The strain Leifsonia sp. 010

presented no accumulation at 3 and 30 µM while about 0.5 nmol of cobalt were accumulated by 10 9 cells at 300 µM. The strain N. niigatensis 030 presented an accumulation of about 0.8 nmol for 10 9 cells only at 300 µM (Fig. 4.5; Appendix 15, Table 6.29).

The fraction containing the higher quantity of cobalt was the supernatant. A few was recovered in the washes, mainly at 300 µM (Appendix 15,Table 4.29). This indicated that not only the cobalt was not accumulated by the bacteria but it was not adsorbed or weakly fixed on the cells' surface. Cobalt is involved in metabolism as an enzyme cofactor [394,424]. It is thus naturally present in small quantity in bacterial cells and regulated. Due to cobalt toxicity at high levels transports to eliminate cobalt exists in order to maintain cobalt homeostasis [394,425,426]. The cobalt role in cell metabolism could explain the absence of cobalt accumulation in the tested strains. Furthermore bacteria low metabolism induced by NaCl could have prevent cobalt to enter the cell. No data is available in literature concerning the interactions between the genera Pantoea, Brevibacillus, Nocardia, Leifsonia, and Gordonia with cobalt. Chapter IV -Radioresistance and bioaccumulation of uranium and cobalt by bacteria isolated from a nuclear reactor cooling pool 191 isolated from drain waters of a German uranium waste pile were able to partially accumulate cobalt. The tested vegetative cells were able to sorb up to 40% of a 0.7 µM cobalt solution when spores of the same strains were able to sorb up to 20% of the same solution. An extraction with 0.01 M EDTA-Tris was performed to assess the accumulation and adsorption proportion. In the vegetative cells, more than 50% of the sorbed cobalt was extracted while none was extracted in the spores [428]. These Bacillus strains were thus able to slightly accumulate cobalt but could adsorb it on their surface. Several Bacillus strains isolated from Indian uranium mine wastes were able to accumulate cobalt up to 150 µmol/g dry weight. However some of the Bacillus tested strains did not present any accumulation [429]. B. lichenformis was able to adsorb cobalt up to 20% of a 0.5 mM cobalt solution. Electron microscopic showed that cobalt was accumulated at the surface wall [430]. Our two Bacillus strains did not present any cobalt accumulation.

Conclusion

Many bacteria and some fungi were isolated from Pool 2 at shutdown and during operation. Most of the tested bacteria presented a radioresistance ability at 200 Gy. Some of them could even resist 1 kGy and beyond. The bacteria isolated during the reactor operation were among the most radioresistant ones. The different irradiation conditions slightly changed the bacterial ability to resist radiations during the screening. It should thus be remembered that these screenings did not represent the optimal irradiation conditions but allowed to quickly select resistant strains and eliminate sensitive strains. In LB irradiation, M. luteus 002 D10 (dose needed to eliminate 90% of a population) was above 1.5 kGy. SKH 450 (Pantoea vagans) presented a D10 between 1 and 1.5 kGy when Pantoea sp. 497 was between 500 and 1000 Gy. Both

Nocardia niigatensis strains had a D10 close to 500 Gy. K. koreensis had a D10 between 500 and 1000 Gy. The other strains D10 was between 200 and 500 Gy, except for Pelomonas sp. where the D10 was below 200 Gy.

These bacteria were not only able to resist radiations but could also highly accumulate uranium. Some of them were able to almost entirely remove uranium for the solution at 5 µM (Micrococcus luteus 002, Kocuria koreensis 006, Brevibacillus agri 032, Bacillus sp. 033, Bacillus sp. 448 and Pantoea sp. 497) while only K. koreensis and Bacillus sp. 448 present presented almost no uranium in supernatant and washes at 50 µM. However at higher concentrations the ability to remove uranium from the medium decreased.

Bacillus sp. 448 was the strain presenting the higher uranium accumulation.

Some bacteria isolated from this pool are described for the first time in such environment, however several isolated bacteria from this pool had already been described in several SNFPs around the world [152, 181, 184-186, 188, 189, 192].

A deeper study of Micrococcus luteus 002, Kocuria koreensis 006, Brevibacillus agri 032, Bacillus sp. 033, Bacillus sp. 448 and Pantoea sp. 497 could be considered to highlight accumulation mechanisms.

Concerning cobalt accumulation, those strains were not adapted to solely decontaminate SNFPs effluent.

They could be used as an additional process but other microorganisms were proved to be more efficient.

Several genera studied in this work had never been described concerning their ability to resist radiations or accumulate radionuclides. This work was the first description of uranium and cobalt accumulation for one and five genera respectively.

Chapter V:

General Discussion, Conclusions, and Perspectives To be able to identify microorganisms from a water sample, metagenetics and direct metaproteomics were used. In Chapter II, it was shown that the use of direct phylopeptidomics was possible to identify the main microbial genera present in a sample. The analysis of a biofilm recovered using sterile Spontex® enabled the identification of three main genera, all bacterial. With the identification of peptides related to one micro-algae (Phaeodactylum) along with peptides associated to two bacterial genera (Acidovorax and Xanthobacter) in a water sample, it was possible to identity in the same run prokaryotes and eukaryotes (Chapter III). This technique presented thus an advantage compared to metagenetics for which it is indeed possible to identify both prokaryotes and eukaryotes (with 16S rDNA, and 18S rDNA plus ITS sequencing)

during the same run, but it is complicated and results were not guaranteed (personal information from LGC Genomics). However phylopeptidomics exhibits some disadvantages: first of all the sensitivity of mass spectrometers are lower than sequencing technologies; secondly the protein databases are less exhaustive than genetic databases. This innovative approach is thus promising to identify microorganisms present in a sample. When exploring biodiversity present in extreme environment using metagenetics, the information obtained via sequencing should be of high confidence. Unreliable sequences should thus be removed. It is known that biases can occur during DNA extraction, PCR and sequencing [236]. To be able to trust the results, i.e. lowering the false-positive rate, a cut-off should be proposed. This cut-off should be the perfect equilibrium between removing all sequences absent from the original sample (false-positives) but not removing sequences present in small quantities (false negatives). Despite extensive metagenetics published works, most of them present the metagenetics results without any cut-off after bioinformatics analysis using mothur or MG-RAST for example [193,231,296]. In one study, to be sure to eliminate false-positive, only taxa with an abundance greater than 2% were retained and presented [253]. However when setting a cut-off as high as 2%, the risk of removing true positives, i.e. interesting sequences obtained from the sample, is important. Mohd Shaufi et al. decided to remove rare OTUs with less than five sequences in all tested samples [431]. The issue of a cut-off to be able to trust results obtained from gut microbiota of clinical samples was assessed by White et al. [432]. To do so, they developed a statistical method to compare clinical metagenomic samples from two treatment populations on the basis of count data obtained via sequencing. However this method relies on p-values and does not give a cut-off if only one sample was sequenced [432]. In Chapter II a 0.016% cut-off was determined after an Illumina MiSeq sequencing of the regions V4V5 of the 16S rDNA from a DNA standard and an OCToPUS analysis. This cutoff was low enough to keep all genera present in the DNA standard but eliminated sequences belonging to bacteria not present in the DNA standard. With this cut-off we can be confident about the results presented in this work: no bacteria that were not present in the water samples could have been identified. However, another standard should be used for such analysis but does not yet exist. This standard should cover a large extent of dynamic range in terms of concentrations of microorganisms. Here, the standard used is a commercial standard that does not encompass sufficient diversity and lacks an important dynamic range.

For example, no bacteria are present in very low concentrations near the established cut-off. Thus our results deserve to be further complemented with additional experiments.

We have also checked whether microbial cultures in liquid diluted media could be used for enrichment of some microorganisms for further isolation. We have monitored the biodiversity over a liquid kinetic and have shown that the diversity is relatively large and even, while a microbial stability settled over time. While only the fraction of cultivable microorganisms can then be assessed, the data are worth to consider as biotechnological relevant microorganisms should be cultivable for being used.

All three methods highlighted the presence of unique genera that are in small quantities (e.g. Ideonella, Mehtylibium or Rhizobacter genera for the consortia cultivation; the Acidobacteria and Verrucomicrobia phyla, Nitrospira, Vampirovibrio genera for metagenetics; Sporolactobacillus and Scytonema genera for direct phylopeptidomics). These three methods were thus complementary for the microbial water analysis.

Microbial diversity in nuclear cooling pools

Two different pools were studied over time during this PhD. Pool 1 is used to store radioactive cobalt sources while Pool 2 is the cooling pool of a nuclear reactor. Several samplings were realized in these pools.

The prevailing conditions in the pools had changed between two samplings. For Pool 1, the filtration system changed and the pool was cleaned a few months before the third sampling. For Pool 2 the first sampling was performed during the last operating cycle while the second sampling was realized a year after the reactor's definitive shutdown. Several observations were possible from these results.

From the different campaigns, between 0.5 to 4.0 million cells were used for DNA extraction to analyze water samples. No cell count could have been performed on the biofilms due to the small amount of biomass collected and the radioactivity in the biofilms collected in Pool 2. However, when looking at the saturation curves of all samples, it can be observed that a plateau was reached for each sample, indicating that the sequencing allowed a good inventory of the bacterial species present in the water.

-Identification of a large extent of microbial diversity Using all three methods previously described and multiplying the samples of biofilms and waters, some diversity was observed in both pools. While a majority of bacteria was identified, several fungi and algae were also found. A total of 379 genera was identified in both pools. Compared to previous studies in which microorganisms have been isolated after a cultivation step (up to 56 isolated bacteria in Cofrentes) [152,177,181,[183][184][185][186][187][188]192], this number can be judged as really important. However in a recent study performed in an American SNFP (Savanah River Site) using direct metagenetics, about 4000 OTUs were identified. This difference can be explained by the cell density in the water. Between 1.8x10 6 to 2.8x10 8 cells per liter had been identified in the USA SNFP [193], while 2 to 4.5x10 6 cells per liter had been identified in Pool 1, and 0.75 to 2.7x10 6 cells per liter had been identified in Pool 2. In our studies a maximum of 4 L were used to perform the metagenetics analysis. At the Savanah River Site SNFP no indication was given concerning the volume sampled. It was however specified that a peristaltic pump had been used. It is possible that more than 4 L had been sampled for metagenetics analysis.

A majority of Proteobacteria (205 genera), and more generally a majority of Gram negative bacteria were identified (about 283 genera) in both pools (136 and 163 genera belonging to the Proteobacteria for Pool 1 and Pool 2 respectively; 169 and 189 Gram negative bacteria in Pool 1 and Pool 2 respectively). This result is in accordance with a previous description of similar environment (SNFPs) where, mainly Gram negative bacteria had been isolated [185]. Similarly about 83% of Proteobacteria and 10% of Acidobacteria (Gram negative) were identified at the Savanah River Site SNFP [193].

Only 19 genera were always identified in both pools at both time point (2015 and 2017). This could imply that microorganisms can be metabolically active under these conditions and these pools are not just a place where airborne bacteria fall. Among these genera a majority of Proteobacteria was identified (14 genera), while 3 and 2 genera belonged to Firmicutes and Actinobacteria phyla respectively (Appendix 16). If both pools were to be compared, 88 genera would only be found in Pool 1 (no matter the sampling date) and 169 would only be found in Pool 2. 123 genera were common to both pools. The majority of the identified Cyanobacteria were found in Pool 1. At least two algae were also present in this pool, identified using direct metaproteomics. The "Bacillariophyta_unclassified" designation obtained using metagenetics could highlight the presence of algae, detected through their chloroplastic DNA [433]. It has indeed been hypothesized that chloroplast were Cyanobacteria after endocytosis [336,337]. The preservation of 16S rDNA in photosynthetic eukaryotes could explain the presence of the Bacillariophyta family in Pool 1, dominating almost all samples in the 2015 sampling. Moreover proteins closely related to two microalgae (Phaeodactylum and Thalassiosira) were identified in this pool in the 2016 sampling before the filtration system change whereas from the 2015 water sampling one green microalgae, Eustigmatos vischeri, was isolated. Phaeodactylum triconatum exhibited a great resistance to a 0.16 W.m -2 UV-B irradiation at the long term (after 7 days) and an enhanced UV-absorbing compounds and carotenoid production was observed (2.5 fold and 1.5 fold for UV-absorbing compounds and carotenoid respectively) [434].

Thalassiosira pseudonana presented some antioxidant response in presence of UVB with an increase of superoxide dismutase activity, involved in reactive oxygen species scavenging [435]. It could thus be possible that algal strains belonging to Phaeodactylum or Thalassiosira genera were present in Pool 1, despite the presence of ionizing radiations.

The majority of identified fungi were present during Pool 2 kinetics of 2015 and 2017. Fungi are able to form spores and can thus survive in hostile environment until better conditions are found. All Chlamydiae bacteria were identified in Pool 2 in 2017. Planctomycetes and Chloroflexi phyla were identified in both pools. However only the Planctomycetes phylum had already been described in a SNFP [193]. The conditions in Pool 2 in 2017 were less stringent than in 2015. The 2015 sampling was performed during a working nuclear cycle while the sampling in 2017 was performed more than a year after the complete shutdown of the reactor. The radioactivity was thus lower and less radionuclides were present in the water in 2017.

Despite the presence of great diversity, direct analysis highlighted the lack of evenness mainly in Pool 2 water. One OTU indeed represented at least 90% of the sample both in 2015 (Variovorax) and 2017 (Methylobacterium). In Pool 2 scrubs, the evenness was greater despite a lower diversity. For the 2017 sampling in Pool 1 the water was dominated by Limnobacter (21.1 to 76.6%). The stringent conditions prevailing in both pools might explain this observed domination.

All three genera belong to the Proteobacteria phylum. Methylobacterium is an a-Proteobacteria while Variovorax and Limnobacter belong to the b-Proteobacteria, and more specifically to the Burkholderiales order.

The Methylobacterium genus is ubiquitous in nature, and can be found in freshwater and aquatic sediments [343]. This genus constitutes common airborne organisms which are found in industrial environments.

Methylobacterium species are aerobic, chemoorganotrophic, and mesophilic bacteria [315]. A Methylobacterium strain has recently been detected in a spent nuclear fuel storage basin at the Savannah River Site in the USA [193]. The ability of this genus to resist a certain degree of desiccation, leading to oxidative stress, contributes to their survival in hostile environments [315]. Several mechanisms involved in desiccation and gamma radiations resistance are indeed common. Some strains have been shown to exhibit resistance to gamma irradiation 10-40 times higher than that withstood by many other Gramnegative bacteria [315,344,345]. One species (Methylobacterium sp. strain rad) was even isolated after irradiation of 20 kGy [346], and M. extorquens and M. radiotolerans presented a D10 of 4.5 and 3.5 kGy,respectively [346]. M. extorquens produces carotenoids [436], which are involved in oxidative stress defense [START_REF] Ordenes-Aenishanslins | Pigments from UV-resistant Antarctic bacteria as photosensitizers in Dye Sensitized Solar Cells[END_REF][START_REF] Tian | Evaluation of the antioxidant effects of carotenoids from Deinococcus radiodurans through targeted mutagenesis, chemiluminescence, and DNA damage analyses[END_REF][START_REF] Tian | Effects of carotenoids from Deinococcus radiodurans on protein oxidation[END_REF]. The domination of this genus (between 60 and 97% of the samples) in Pool 2 in 2017 is thus not a surprise considering its ability to resist radiations and its presence in an American SNFP.

The genus Variovorax is aerobic and can be found in soils and water. It can contain carotenoid pigments, which are known to be antioxidant molecules (cf. Chapter I.4.1.2.3). This genus was predominant during the reactor operation (65 to 85% of the samples). It was also identified in the SNFP of Cofrentes after a cultivation step [184], but no Variovorax was identified in the Savanah River Site SNFP [193]. The genus Variovorax has also been identified in a uranium contaminated soil in China [437]. The ability of some Variovorax strains (biotype I) to make use of hydrogen oxidation as an energy source [315,350] might explains the prevalence of this genus during operation. High amounts of H2 are indeed formed due to water radiolysis when a nuclear reactor is operating.

The genus Limnobacter was first described in 2001 by Spring et al. with the identification of L. thiooxidans, a thiosulfate oxidizing bacteria [438]. Limnobacter species have been isolated from lake sediments, a volcanic deposit, Baltic Sea and soils [438][439][440][441]. Up to this day, only aerobic species have been described in this genus. This genus has never been described in the different SNFPs analyzed, whether it was with cultivation step or using metagenetics. Strains closely related to the Limnobacter genus were identified in an acid mine drainage contaminated with several heavy metals [371]. After a genome sequencing of Limnobacter sp. MED105, a putative protein involved in the bacterial oxidative stress defense system was potentially identified (protein A6GUX4) (https://www.uniprot.org/uniprot/A6GUX4). The ability to oxidize thiosulfate associated with the presence of a putative antioxidant molecule, and the identification of this genus in an acid mine drainage contaminated with heavy metals could be indications that the Limnobacter genus possesses performant mechanisms involved in resistance to oxidative stress.

The microbial composition of Pool 1 was different from the microbiota of other SNFPs and from Pool 2. The presence of Bacillariophyta (up to 60%) was unique and never described in similar environment. Moreover no Cyanobacteria were found in pools from nuclear facilities over the world. However this pool does not contain radionuclides unlike the SNFPs studied over the world. These results indicated a dependence of the microbiota on the conditions prevailing in the pool. Each pool has its unique microbiota and a pool can present a change in the microbiota when one parameter is changed.

Several OTUs identified with metagenetics were not described to the genus taxonomic level (142 OTUs and 71 OTUs for pools 1 and 2 respectively with a 0.016% cut-off) and several "Bacteria_unclassified" were present in the pools (25 OTUs and 7 OTUs for pools 1 and 2 respectively with a 0.016% cut-off). The diversity could thus be even greater.

-Differences observed in a same pool Despite differences observed between Pool 1 and Pool 2, differences in the microbiota were observed between two sampling date. In Pool 1, between the 2015 and 2016 samplings and the 2017 sampling, the pool was deeply cleaned and a change in the filtration condition occurred. Before the change, several Chapter V -General discussion, conclusions, and perspectives 197 Cyanobacteria were present and represented most of the samples. After the change a majority of Proteobacteria was observed and Cyanobacteria represented less than 2% of the samples. Few differences were observed between the sampling depths and locations.

Among the main bacteria identified in Pool 2 biofilms, Clostridium bacteria are able to form biofilms [442,443]. It is known that microorganisms present in biofilms can benefit from each other, leading to a cell community in which each bacteria can help another one [START_REF] Costerton | Microbial Biofilms[END_REF]. The interactions and advantages of bacteria to grow as biofilms is thus known and have been described in Chapter I.3.1. The advantage of life in a biofilm can be numerous compared to planktonic life: both aerobic and anaerobic bacteria can be found in biofilms, it can concentrate nutrients and locally change the pH [START_REF] Costerton | Biofilms, the Customized Microniche[END_REF]. The extracellular matrix can also provide a protection against physical and chemical stresses [START_REF] Costerton | Microbial Biofilms[END_REF][START_REF] Stewart | Antibiotic resistance of bacteria in biofilms[END_REF][START_REF] Costerton | Bacterial biofilms in nature and disease[END_REF]444,445].

It is known that some Variovorax strains are able to use H2 oxidation as an energy source [315,350]. Water radiolysis leads to the formation, among others, of H2. When the reactor was operating, H2 was abundantly present in water due to radiolysis. In 2015 the dominance of Variovorax might have been possible thanks to H 2 and this bacterium ability to use H 2 as an energy source. When the reactor was definitively shutdown, the radiation diminished and thus the level of H2 decreased too. Other genera, not competitive enough during reactor functioning, might have been better suited to the new conditions present in the pool during shutdown. This could explain the completely loss of Variovorax in 2017 to the profit of Methylobacterium.

This change in the microbiota was not only noticed via direct analysis (metagenetics) but also via the liquid culture kinetics of the water. Indeed, while b-Proteobacteria was the main observed class during the 2017 culture kinetic, a-Proteobacteria dominated the 2015 kinetic. This could indicate that not only the main genera were different but also the minor genera, present in quantities too low to be detected by metagenetics. A deep change in the microbiota was thus influenced by the shutdown of the nuclear reactor.

However the presence of the same Firmicutes and Actinobacteria genera (Bacillus, Clostridium, Streptomyces, Nocardia, etc.) may indicate the survivability of these organisms in a radiative environment, maybe present in the water in spore shape.

In Pool 2 the microbiota was completely different if the sample originated from a biofilm or a water sampling. Differences were also observed between the two sampling dates (2015: operating reactor, or 2017: shutdown of the reactor for more than a year). These differences can be easily observed in Figure 5.1, representing the non-metric multidimensional scaling (NMDS) of the different samplings from Pool 2. Three groups can be observed in this NMDS: the biofilms (in blue), the 2015 water sampling (in orange) and the 2017 water sampling (in grey). The biofilms were mainly composed of Clostridium bacteria. In 2015 water samples were dominated by Variovorax while in 2017 water was dominated by Methylobacterium.

Clostridium are anaerobic Firmicutes but some species can tolerate oxygen [358]. C. subterminale and C. celerecrescens had been found in a SNFP in the USA [188]. Clostridium spores were proved to resist a dose up to 8.5 kGy, and for some strain, up to 14 kGy [359,360]. It was shown also that some Clostridium strains were able to reduce uranium [362]. The genus Clostridium is usually chemoorganotrophic, but some species can be chemoautotrophic [358]. For example, it was shown that a strain of C. difficile was able to grow with CO2 and H2 as energy source [446]. C. perfringens was proved to possess an adaptive response to oxidative stress (induced by H2O2), both in aerobic and anaerobic conditions [447]. Clostridium genus can thus resist oxidative stress, induced by H2O2 or by ionizing radiations. Moreover strains able to use CO2 and H2 as energy source can confer this genus an advantage in nuclear pool. Oxygen diffusion is limited in a biofilm and is rapidly used by aerobic species, which can lead to an increase of facultative and obligate anaerobic bacteria [448]. The presence of Clostridium [449,450], and more generally anaerobic and sulfate-reducing bacteria (SRB) [448], in the biofilms could lead to Microbial Induced Corrosion (MIC) [START_REF] Borenstein | Microbiologically Influenced Corrosion Handbook[END_REF]363,364]. Sulfate-reducing bacteria (e.g. Desulfovibrio genus) can produce sulfide, a corrosive compound. Acid-producing bacteria (e.g. Thiobacillus spp. can also provoke corrosion.

Acids produced by microorganisms able to produce an exopolysaccharide matrix are concentrated at the metal surface, thus leading to corrosion of the metal [363]. It was also shown that sulfate-reducing bacteria can use acids produced by acid-producing bacteria as nutrients. SRB can thus proliferate at sites of corrosion due to the acid production of acid-producing bacteria [451]. Among the bacteria isolated from a corroding metal, Sphingomonas capsulate and Methylobacterium sp. have been identified [452]. In the scrubs recovered from Pool 2, two OTUs belonging to Sphingomonas genus and two OTUs belonging to Methylobacterium genus have been identified after metagenetics analysis in scrubs 2 to 5 and 8 for Sphingomonas, and scrubs 3 and 7 for Methylobacterium. The identification of genera described in MIC should be a concern since it can corrupt the walls' integrity and change the pool's conditions if an old fuel bundle is pierced, freeing radionuclides such as uranium or transuranium elements. This study highlighted the need of nuclear installation operators to regularly monitor the microbial community from their installations and regularly clean the pools. MIC has also been evaluated in a French nuclear waste underground disposal [453]. In this study, it was shown that high corrosion rates could be caused by Chapter V -General discussion, conclusions, and perspectives 199 biooxidation of pyrites under aerobic conditions. Under anaerobic conditions, bacteria may lead to a localized corrosion at a longer term [453].

-Several microorganisms never described in similar environment The use of three methods to identify microorganisms present in nuclear cooling pools enabled the identification of hundreds of genera, several of them never described in similar environments (e.g. , Caulobacter, Erythrobacter, Methylibium, Acidovorax, Blastomonas, Mycetocola and Streptomyces genera). The identification of Chloroflexi in both pools might be the first description of this phylum in a radiative environment.

Xanthobacter

The isolation of these strains should be interesting and could help understanding the mechanisms implemented by the cell to resist the harsh conditions prevailing in both pools. Among the isolated microorganisms, no unknown genera were identified. All bacteria were identified at the genus or species level. Several organisms, after a 16S rDNA nucleotide identification using the BLAST tool from NCBI, were matched to undescribed but referenced bacteria.

Radioresistance and radionuclides accumulation of bacteria isolated from Pool 2

To identify bacteria able to accumulate radionuclides and thus resist radiations, only bacteria isolated in Pool 2 were studied. Pool 2 is indeed a nuclear reactor cooling pool (ionizing radiations and radionuclides) while Pool 1 is used to store radioactive cobalt sources (ionizing radiations near the sources and no radionuclides). The probability of finding bacteria able to accumulate radionuclides is thus higher in Pool 2 than in Pool 1.

Many bacteria and some fungi were isolated from Pool 2 at shutdown and during operation. Most of the tested bacteria presented a radioresistance ability at 200 Gy. Some of them could even resist 1 kGy and beyond (e.g. Micrococcus luteus CEA-NOB-002, Kocuria koreensis CEA-NOB-006, Leifsonia sp. CEA-NOB-010, Pantoea vagans SKH 497). The bacteria isolated during the reactor operation were among the most radioresistant ones. The different irradiation conditions slightly changed the bacterial ability to resist radiations during the screening. It should thus be remembered that these screenings did not represent the optimal irradiation conditions but allowed to quickly select resistant strains and eliminate sensitive strains.

In LB irradiation, M. luteus 002 D10 was above 1.5 kGy. SKH 450 (Pantoea vagans) presented a D10 between 1 and 1.5 kGy when Pantoea sp. 497 was between 500 and 1000 Gy. Both Nocardia niigatensis strains had a D10 close to 500 Gy. K. koreensis had a D10 between 500 and 1000 Gy. The other strains D10 was between 200 and 500 Gy, except for Pelomonas sp. where the D10 was below 200 Gy. Bacteria isolated from Pool 2 were not only able to resist radiations but could also highly accumulate uranium. Some of them were able to almost entirely remove uranium for the solution at 5 µM (Micrococcus luteus 002, Kocuria koreensis 006, Brevibacillus agri 032, Bacillus sp. 033, Bacillus sp. 448 and Pantoea sp. 497) while only K. koreensis and Bacillus sp. 448 presented almost no uranium in supernatant and washes at 10 and 50 µM. However at higher concentrations the ability to remove uranium from the medium decreased. Bacillus sp. 448 was the strain presenting the higher uranium accumulation. The seven strains able to remove almost all uranium from a 5 µM solution and the two strains able to remove uranium from a 50 µM solution could be used in biotechnologies. Their ability is indeed of great interest to decontaminate diluted uranium from a large volume of effluent. These strains did not accumulate cobalt. The cobalt concentrations might have been too important and killed the strains. However the chosen concentrations were used for other bacterial strains which exhibited accumulation and thus survival [152,388,430,454]. Another possibility could be changing the incubation time. To be able to determine whether these strains could accumulate cobalt or not a kinetic with several time points (e.g. 1 hour, 12 hours, 24 hours) should be tested.

Radionuclides accumulating bacteria have previously been described in a Slovak SNFP: Kocuria palustris, Micrococcus luteus, and Ochrobactrum sp. were proved to accumulate Cs and Co after 48 and 24h of incubation respectively [152]. The Slovak isolated M. luteus strain was able to accumulate cobalt (up to 12.2 µmol.g -1 dry weight) while the M. luteus strain isolated from Pool 2 did not present cobalt accumulation. This implies that the cobalt accumulation is not a characteristic of all M. luteus strains, unlike radioresistance. The latter characteristic was indeed identified in a M. luteus strain isolated in an American SNFP by Bruhn et al. and presented a D10 above 1 kGy [177] which is comparable with the D10 of the M. luteus isolated from Pool 2 (about 1.5 kGy).

In another study by Sarro et al., radionuclides accumulation properties were tested. It was proved that most of 60 Co was biosorbed and not bioaccumulate by the biofilms [184]. To develop a biotechnological process in order to decontaminate nuclear effluent, the sorption of radionuclides on biofilm matrix should be considered and could be used.

Our isolated strains allowed a great uranium decontamination at low uranium concentration after 24 hours.

A deeper study of Micrococcus luteus 002, Kocuria koreensis 006, Brevibacillus agri 032, Bacillus sp. 033, Bacillus sp. 448 and Pantoea sp. 497 could be considered to highlight accumulation mechanisms.

Concerning cobalt accumulation those strains were not adapted to solely decontaminate SNFPs effluent.

They could be used as an additional process but other microorganisms were proved to be more efficient.

Several genera studied in this work had never been described concerning their ability to resist radiations or accumulate radionuclides. This work was the first description of uranium and cobalt accumulation for one (Gordonia) and five genera respectively (Pantoea, Brevibacillus, Nocardia, Leifsonia, and Gordonia).

General conclusions

Great diversity can be found in SNPFs and the cooling pool of nuclear reactors, both at shutdown and in operation. This PhD highlighted the presence of microbial diversity in aquatic nuclear environments. Several microorganisms identified in both pools (Pool 1 and Pool 2) were not identified in previous studies concerning microorganisms present in different SNFPs around the world. It was shown that each pool possesses its own microbiota depending on the physico-chemical conditions. However some microbial Chapter V -General discussion, conclusions, and perspectives 201 families and genera were common to both pools and every SNFPs, such as Bacillus or Sphingomonas. The use of direct methods (phylopeptidomics and metagenetics) emphasized the presence of different phyla undescribed in such environments before. As previously noticed [152,177,[181][182][183][184][185][186][187][188][191][192][193] a majority of Proteobacteria and more generally a majority of Gram negative bacteria were identified in the studied pool.

The conditions prevailing in Pool 1 (no radionuclides, natural light) enabled the growth of photosynthetic organisms. This diversity was detected using several methods. The combine use of three different complementing analysis methods allowed a complete overview of microbial diversity present in an environmental sample. Direct use of phylopeptidomics enabled eukaryotes and prokaryotes identification in a same run. Despite a lower sensitivity compared to metagenetics, phylopeptidomics was able to identify organisms (e.g. Streptomyces) which were not detected using metagenetics. We were also able to isolate and identify different bacteria and fungi. The isolation of radioresistant bacteria as well as uranium accumulating bacteria in Pool 2 proved that the implemented protocol can be used to identify and isolate radioresistant and radionuclides accumulating organisms, confirmed with the previous study of Tišáková et al. in which bacteria isolated from a SNFP were able to accumulate cobalt and cesium [152].

Perspectives

Despite the identification of different radioresistant bacteria able to accumulate uranium, none of them exhibited great cobalt accumulation abilities. Several methods could be employed to isolate more radioresistant and cobalt accumulating strains. First of all a radiative selection pressure could be used to isolate more radioresistant organisms. Using this approach some unknown organisms could be discovered.

Secondly a cobalt selection pressure (e.g. cultivation in medium containing bioavailable cobalt) could be applied to water and biofilm samples. This method could be used to isolate resistant microorganisms to several radionuclides of industrial interest.

With the identification of hyperaccumulating strains it would be possible to develop a nuclear effluent treatment process to biodecontaminate radionuclides. A deep study of the optimum conditions to accumulate radionuclides as well as acquire knowledge about the cells behavior. If no strain exhibit the wanted properties (different radionuclides accumulation), a co-culture with different strains able to abundantly accumulate one or two different radionuclides could be implemented.

Finally, the identification of radioresistant and accumulating strains could be useful to highlight new resistance mechanisms implemented by strains. A complete genome sequencing and annotation should allow us to identify proteins and pathways involved in radioresistance and radionuclides accumulation. As seen in I.4.1.2 different pathways exist to counter the negative effect of ROS production due to ionizing radiations. Hence, by identifying genes involved in radioresistance and/or radionuclides accumulation, some strains could be bioengineered to develop an optimized biodecontamination process. To do so a deeper strain study should be performed to identify the optimum cultures conditions. Molecular tools should exist or be implemented to bioengineer these strains.

The main discovery of this PhD thesis is related to the identification of Variovorax in the pool of a functioning nuclear reactor. Our hypothesis linking the high abundance of this genus and the metabolism of hydrogen produced by the reactor should be explored and confirmed with specific experiments. Moreover, a deeper metaproteomics analysis of these samples could be done in order to explore the possible metabolism pathways. This discovery calls for novel investigation of microorganisms in functioning 202 nuclear facilities and confirmation of the predominance of Variovorax or other hydrogen-metabolism active microorganisms in such facilities. Finally, interesting analysis of the metabolic interactions between microorganisms could be done to identify the corresponding molecular mechanisms and understand the development of consortia and biofilm formation in such extreme environments. Acidobacteria_unclassified Acidobacteria_unclassified Acidobacteria_unclassified Acidobacteria_unclassified Otu273 0,000 0,008 0,000 0,000 0,000 0,000 0,000 0,000 0,020 0,000 Acidobacteria_Gp3 Acidobacteria_Gp3_unclassified Acidobacteria_Gp3_unclassified Acidobacteria_Gp3_unclassified Otu163 0,000 0,020 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu428 0,000 0,002 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Gp3 Gp3_unclassified Gp3_unclassified Otu77 0,000 0,123 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Acidobacteria_Gp4 Acidobacteria_Gp4_unclassified Acidobacteria_Gp4_unclassified Acidobacteria_Gp4_unclassified Otu222 0,000 0,011 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Gp4 Gp4_unclassified Gp4_unclassified Otu127 0,000 0,044 0,000 0,000 0,000 0,000 0,151 0,000 0,000 0,000 Otu78 0,000 0,112 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu249 0,000 0,000 0,000 0,000 0,020 0,000 0,000 0,000 0,000 0,000 Otu352 0,000 0,013 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu282 0,000 0,007 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Aridibacter Aridibacter_unclassified Otu55 0,000 0,228 0,000 0,000 0,000 0,000 0,058 0,000 0,002 0,000 Otu235 0,000 0,011 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu256 0,000 0,009 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu53 0,000 0,003 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu359 0,000 0,002 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu429 0,000 0,002 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Blastocatella Blastocatella_unclassified Otu33 0,000 0,496 0,000 0,000 0,031 0,000 4,097 0,000 0,508 0,000 Acidobacteria_Gp6 Gp6 Gp6_unclassified Gp6_unclassified Otu44 0,000 0,402 0,049 0,000 0,000 0,000 0,281 0,000 0,079 0,000 Otu153 0,000 0,024 0,000 0,000 0,000 0,000 0,052 0,000 0,011 0,000 Otu390 0,000 0,003 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu421 0,000 0,002 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Actinobacteria Actinobacteria Acidimicrobiales Acidimicrobiales_unclassified Acidimicrobiales_unclassified Otu158 0,000 0,021 0,000 0,000 0,000 0,000 0,000 0,000 0,007 0,000 Otu332 0,000 0,000 0,000 0,000 0,000 0,014 0,000 0,000 0,000 0,000 Otu315 0,000 0,006 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Actinomycetales Actinomycetales_unclassified Actinomycetales_unclassified Otu221 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,784 Otu288 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,017 0,000 0,000 Actinomycetaceae Actinomyces Otu330 0,000 0,000 0,000 0,000 0,017 0,000 0,000 0,000 0,000 0,000 Otu437 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,005 0,000 Brevibacteriaceae Brevibacterium Otu241 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,025 0,000 Corynebacteriaceae Corynebacterium Otu103 0,107 0,002 0,000 0,000 0,005 0,000 0,045 0,000 0,000 4,422 Otu204 0,142 0,000 0,000 0,000 0,000 0,000 0,037 0,000 0,000 0,000 Otu194 0,000 0,000 0,000 0,000 0,000 0,000 0,103 0,000 0,000 0,000 Otu303 0,000 0,000 0,000 0,000 0,020 0,000 0,000 0,000 0,000 0,000 Dermabacteraceae Brachybacterium Otu166 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,014 0,000 Microbacteriaceae Microbacteriaceae_unclassified Otu12 0,000 0,000 16,626 0,000 0,000 0,000 0,006 0,000 0,000 2,482 Otu226 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,765 Otu484 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,075 Microbacterium Otu17 4,616 1,190 5,044 17,907 0,025 0,281 0,031 1,735 3,558 0,000 Micrococcaceae Micrococcus Otu146 0,273 0,002 0,000 0,000 0,000 0,000 0,000 0,005 0,001 0,000 Rothia Otu170 0,202 0,002 0,000 0,000 0,020 0,000 0,035 0,000 0,000 0,000 Mycobacteriaceae Mycobacterium Otu28 0,000 0,028 0,000 0,223 0,000 0,000 0,056 0,000 2,036 0,000 Otu178 0,000 0,021 0,000 0,038 0,000 0,000 0,054 0,043 0,308 0,000 Propionibacteriaceae Propionibacteriaceae_unclassified Otu371 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,261 Propionibacterium Otu105 0,688 0,005 0,012 0,187 0,042 0,002 0,144 0,006 0,029 0,970 Pseudonocardiaceae Pseudonocardia Otu366 0,000 0,004 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Bifidobacteriales Bifidobacteriaceae Bifidobacterium Otu403 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,187 Gaiellales Gaiellaceae Gaiella Otu295 0,000 0,006 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Rubrobacterales Rubrobacteraceae Rubrobacter Otu362 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,280 Armatimonadetes Chthonomonadetes Chthonomonadales Chthonomonadaceae Chthonomonas/Armatimonadetes_gp3 Otu233 0,000 0,011 0,000 0,000 0,000 0,000 0,004 0,000 0,000 0,000 Bacteria_unclassified Bacteria_unclassified Bacteria_unclassified Bacteria_unclassified Bacteria_unclassified Otu25 0,000 0,000 0,000 0,807 0,026 0,000 0,000 0,000 2,936 0,000 Otu31 0,000 0,804 0,094 0,000 0,000 0,000 0,408 0,084 0,774 0,000 Otu51 0,000 0,251 0,000 0,000 0,000 0,000 0,926 0,000 0,108 0,765 Otu29 0,000 0,799 0,000 0,000 0,000 0,000 0,128 0,000 0,187 0,000 Otu39 0,000 0,455 0,000 0,000 0,000 0,000 0,146 0,000 0,106 0,000 Otu54 0,000 0,266 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu65 0,000 0,173 0,073 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu59 0,000 0,201 0,007 0,000 0,000 0,000 0,016 0,000 0,000 0,000 Otu64 0,000 0,179 0,040 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu230 0,122 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,018 0,000 Otu205 0,000 0,002 0,000 0,000 0,000 0,000 0,087 0,000 0,035 0,000 Otu287 0,000 0,002 0,011 0,041 0,000 0,000 0,045 0,000 0,004 0,000 Otu286 0,000 0,007 0,000 0,000 0,000 0,000 0,045 0,015 0,000 0,000 Otu100 0,000 0,065 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu106 0,000 0,061 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu348 0,050 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu134 0,000 0,043 0,000 0,003 0,000 0,000 0,004 0,000 0,000 0,000 Otu212 0,000 0,004 0,000 0,000 0,000 0,000 0,000 0,000 0,030 0,000 Otu223 0,000 0,004 0,000 0,000 0,000 0,000 0,000 0,000 0,028 0,000 Otu323 0,000 0,002 0,000 0,000 0,000 0,000 0,000 0,013 0,013 0,000 Otu145 0,000 0,026 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu151 0,000 0,024 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu209 0,000 0,019 0,000 0,000 0,000 0,000 0,000 0,000 0,002 0,000 Otu164 0,000 0,019 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu169 0,000 0,018 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu306 0,000 0,002 0,000 0,000 0,000 0,000 0,000 0,015 0,000 0,000 Otu232 0,000 0,011 0,000 0,000 0,000 0,000 0,000 0,000 0,006 0,000 Otu181 0,000 0,016 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu184 0,000 0,015 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu186 0,000 0,015 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu336 0,000 0,003 0,000 0,000 0,000 0,000 0,000 0,000 0,010 0,000 Otu418 0,000 0,000 0,000 0,000 0,006 0,007 0,000 0,000 0,000 0,000 Otu203 0,000 0,013 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu216 0,000 0,012 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu229 0,000 0,012 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu215 0,000 0,012 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu239 0,000 0,010 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu244 0,000 0,010 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu260 0,000 0,009 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu272 0,000 0,009 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu255 0,000 0,009 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu254 0,000 0,009 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu274 0,000 0,009 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu320 0,000 0,008 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu389 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,008 0,000 0,000 Otu284 0,000 0,007 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu406 0,000 0,003 0,000 0,000 0,000 0,000 0,004 0,000 0,000 0,000 Otu307 0,000 0,006 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu328 0,000 0,005 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu337 0,000 0,005 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu373 0,000 0,004 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu408 0,000 0,004 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu410 0,000 0,004 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu383 0,000 0,003 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu395 0,000 0,003 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu399 0,000 0,003 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu398 0,000 0,003 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu409 0,000 0,003 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu411 0,000 0,003 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu427 0,000 0,002 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu432 0,000 0,002 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu435 0,000 0,002 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu442 0,000 0,002 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu445 0,000 0,002 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu455 0,000 0,002 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu464 0,000 0,001 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu489 0,000 0,001 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu504 0,000 0,001 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu508 0,000 0,001 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu507 0,000 0,001 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu536 0,000 0,001 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu537 0,000 0,001 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu574 0,000 0,001 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu599 0,000 0,001 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu602 0,000 0,001 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Bacteroidetes Bacteroidetes_unclassified Bacteroidetes_unclassified Bacteroidetes_unclassified Bacteroidetes_unclassified Otu129 0,000 0,018 0,000 0,000 0,000 0,000 0,039 0,000 0,000 2,612 Otu68 0,000 0,000 0,000 0,000 0,000 0,000 0,103 0,000 0,000 0,000 Otu110 0,000 0,059 0,000 0,000 0,000 0,000 0,019 0,000 0,000 0,000 Otu107 0,000 0,060 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu117 0,000 0,050 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu228 0,000 0,012 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Cytophagia Cytophagales Cytophagales_unclassified Cytophagales_unclassified Otu49 0,000 0,269 0,000 0,000 0,000 0,000 0,212 0,000 0,190 0,000 Otu140 0,000 0,009 0,000 0,000 0,000 0,000 0,202 0,000 0,000 0,000 Otu142 0,000 0,028 0,000 0,000 0,000 0,000 0,101 0,000 0,000 0,000 Otu135 0,000 0,032 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu360 0,000 0,000 0,000 0,000 0,000 0,000 0,029 0,000 0,000 0,000 Otu291 0,000 0,007 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Cytophagaceae Spirosoma Otu283 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,018 0,000 Flavobacteriia Flavobacteriales Flavobacteriaceae Chryseobacterium Otu311 0,000 0,000 0,000 0,000 0,019 0,000 0,000 0,000 0,000 0,000 Cloacibacterium Otu118 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 3,247 Flavobacterium Otu174 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 1,232 Otu522 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,001 0,000 0,000 Sphingobacteriia Sphingobacteriales Sphingobacteriales_unclassified Sphingobacteriales_unclassified Otu545 0,000 0,001 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Chitinophagaceae Chitinophagaceae_unclassified Otu245 0,000 0,000 0,000 0,000 0,000 0,000 0,070 0,000 0,000 0,000 Lacibacter Otu119 0,000 0,045 0,000 0,000 0,000 0,000 0,093 0,000 0,019 0,000 Parasegetibacter Otu407 0,000 0,003 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Sediminibacterium Otu248 0,000 0,011 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Saprospiraceae Saprospiraceae_unclassified Otu10 0,000 4,719 0,068 0,000 0,010 0,000 3,357 0,000 1,800 0,000 Haliscomenobacter Otu85 0,000 0,095 0,000 0,000 0,000 0,000 0,052 0,026 0,020 0,000 Sphingobacteriaceae Sphingobacteriaceae_unclassified Otu130 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 2,519 Mucilaginibacter Otu41 2,379 0,000 0,000 0,000 0,000 0,008 0,000 0,000 0,000 0,000 Pedobacter Otu349 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,317 Chlamydiae Chlamydiia Chlamydiales Chlamydiales_unclassified Chlamydiales_unclassified Otu426 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,006 0,000 Otu447 0,000 0,002 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu480 0,000 0,001 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu503 0,000 0,001 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu509 0,000 0,001 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Parachlamydiaceae Parachlamydiaceae_unclassified Otu481 0,000 0,001 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Neochlamydia Otu530 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,001 0,000 0,000 Chloroflexi Caldilineae Caldilineales Caldilineaceae Litorilinea Otu308 0,000 0,006 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Chloroflexia Chloroflexales Oscillochloridaceae Oscillochloris Otu448 0,000 0,002 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Thermomicrobia Sphaerobacterales Sphaerobacteraceae Sphaerobacteraceae_unclassified Otu262 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,021 0,000 0,000 Nitrolancea Otu176 0,000 0,019 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Cyanobacteria/Chloroplast Chloroplast Chloroplast Bacillariophyta Bacillariophyta_unclassified Otu1 0,000 59,421 2,896 3,362 0,087 0,000 30,624 0,007 36,520 43,142 Otu549 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,037 Otu341 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,012 0,000 Otu531 0,000 0,000 0,000 0,000 0,000 0,000 0,004 0,000 0,000 0,000 Otu479 0,000 0,001 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu513 0,000 0,001 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu519 0,000 0,001 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu520 0,000 0,001 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu538 0,000 0,001 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu540 0,000 0,001 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu543 0,000 0,001 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu544 0,000 0,001 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu572 0,000 0,001 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu573 0,000 0,001 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu594 0,000 0,001 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu598 0,000 0,001 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu600 0,000 0,001 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu595 0,000 0,001 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu597 0,000 0,001 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu596 0,000 0,001 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Streptophyta Streptophyta_unclassified Otu70 1,602 0,013 0,138 0,268 0,047 0,075 0,179 0,042 0,115 6,177 Cyanobacteria Cyanobacteria_unclassified Cyanobacteria_unclassified Cyanobacteria_unclassified Otu99 0,000 0,068 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu299 0,000 0,006 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Deinococcus-Thermus Deinococci Thermales Thermaceae Thermus Otu401 0,000 0,000 0,000 0,000 0,010 0,000 0,000 0,000 0,000 0,000 Firmicutes Bacilli Bacillales Bacillales_unclassified Bacillales_unclassified Otu382 0,000 0,003 0,000 0,000 0,012 0,000 0,000 0,000 0,000 0,000 Otu300 0,000 0,007 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Bacillales_Incertae_Sedis_XI Gemella Otu155 0,000 0,023 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Bacillaceae Anoxybacillus Otu329 0,000 0,000 0,000 0,000 0,017 0,000 0,000 0,000 0,000 0,000 Bacillus Otu381 0,000 0,003 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Listeriaceae Brochothrix Otu343 0,000 0,000 0,018 0,000 0,000 0,000 0,000 0,000 0,014 0,000 Staphylococcaceae Staphylococcus Otu90 0,282 0,006 0,000 0,000 0,059 0,000 0,569 0,037 0,000 2,575 Thermoactinomycetaceae_2 Planifilum Otu264 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,578 Lactobacillales Carnobacteriaceae Granulicatella Otu219 0,000 0,000 0,046 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Enterococcaceae Enterococcus Otu112 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 1,586 Streptococcaceae Lactococcus Otu361 0,000 0,000 0,000 0,000 0,000 0,000 0,029 0,000 0,000 0,000 Streptococcus Otu115 0,000 0,055 0,000 0,000 0,000 0,000 0,000 0,009 0,000 0,000 Otu319 0,000 0,003 0,000 0,000 0,000 0,000 0,041 0,000 0,000 0,000 Leuconostocaceae Leuconostoc Otu470 0,000 0,000 0,000 0,000 0,000 0,003 0,000 0,000 0,000 0,000 Clostridia Clostridiales Lachnospiraceae Lachnospiraceae_unclassified Otu301 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,429 Peptoniphilaceae Peptoniphilus Otu259 0,098 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Ruminococcaceae Ethanoligenens Otu175 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 1,232 Clostridiales_Incertae_Sedis_XI Anaerococcus Otu201 0,145 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Finegoldia Otu446 0,000 0,000 0,000 0,000 0,000 0,000 0,014 0,000 0,000 0,000 Negativicutes Selenomonadales Veillonellaceae Veillonella Otu160 0,231 0,014 0,000 0,000 0,000 0,000 0,080 0,000 0,000 0,933 Gemmatimonadetes Gemmatimonadetes Gemmatimonadales Gemmatimonadaceae Gemmatimonas Otu91 0,000 0,028 0,000 0,000 0,007 0,000 0,557 0,000 0,129 0,000 Otu154 0,000 0,024 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu380 0,000 0,003 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Hydrogenedentes Candidatus_Hydrogenedens Candidatus_Hydrogenedens_unclassified Candidatus_Hydrogenedens_unclassified Candidatus_Hydrogenedens_unclassified Otu269 0,000 0,000 0,000 0,000 0,000 0,000 0,056 0,000 0,000 0,000 Nitrospirae Nitrospira Nitrospirales Nitrospiraceae Nitrospira Otu13 0,000 3,482 0,000 0,000 0,017 0,000 0,136 0,000 0,000 1,344 Otu603 0,000 0,001 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Planctomycetes Planctomycetia Planctomycetales Planctomycetaceae Planctomycetaceae_unclassified Otu120 0,000 0,005 0,049 0,000 0,000 0,000 0,301 0,000 0,113 1,120 Otu38 0,000 0,666 0,044 0,000 0,000 0,000 0,017 0,000 0,019 0,000 Otu147 0,000 0,025 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,634 Otu34 0,000 0,587 0,066 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu86 0,000 0,090 0,015 0,000 0,000 0,000 0,411 0,015 0,089 0,000 Otu43 0,000 0,414 0,055 0,000 0,000 0,000 0,041 0,000 0,091 0,000 Otu191 0,000 0,015 0,051 0,000 0,000 0,000 0,113 0,000 0,022 0,000 Otu111 0,000 0,002 0,000 0,000 0,023 0,000 0,016 0,000 0,143 0,000 Otu124 0,000 0,046 0,000 0,000 0,000 0,000 0,023 0,000 0,104 0,000 Otu104 0,000 0,076 0,028 0,000 0,000 0,000 0,021 0,000 0,000 0,000 Otu84 0,000 0,095 0,021 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu136 0,000 0,018 0,000 0,000 0,000 0,000 0,014 0,009 0,075 0,000 Otu81 0,000 0,110 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu250 0,000 0,005 0,000 0,000 0,000 0,000 0,043 0,000 0,024 0,000 Otu101 0,000 0,065 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu268 0,000 0,004 0,032 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu214 0,000 0,017 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu188 0,000 0,015 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu195 0,000 0,014 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu227 0,000 0,011 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu237 0,000 0,011 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu276 0,000 0,010 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu246 0,000 0,009 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu265 0,000 0,009 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu289 0,000 0,008 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu285 0,000 0,007 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu325 0,000 0,005 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu434 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,005 0,000 Otu376 0,000 0,004 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu377 0,000 0,004 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu392 0,000 0,003 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu420 0,000 0,002 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu443 0,000 0,002 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu444 0,000 0,002 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu463 0,000 0,001 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu477 0,000 0,001 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Gemmata Otu88 0,000 0,086 0,019 0,000 0,000 0,000 0,675 0,000 0,069 0,000 Otu83 0,000 0,099 0,014 0,000 0,009 0,000 0,276 0,000 0,071 0,000 Otu526 0,000 0,000 0,000 0,000 0,000 0,000 0,060 0,000 0,000 0,000 Otu335 0,000 0,005 0,000 0,000 0,012 0,000 0,000 0,000 0,000 0,000 Otu207 0,000 0,008 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu356 0,000 0,004 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Pirellula Otu217 0,000 0,012 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Schlesneria Otu27 0,000 0,914 0,000 0,000 0,000 0,000 0,041 0,000 0,196 0,000 Otu132 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,047 0,089 0,000 Singulisphaera Otu298 0,000 0,006 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Zavarzinella Otu152 0,000 0,013 0,000 0,000 0,000 0,000 0,000 0,000 0,059 0,000 Otu423 0,000 0,002 0,000 0,000 0,000 0,000 0,010 0,000 0,000 0,000 Otu267 0,000 0,008 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 249 Proteobacteria Proteobacteria_unclassified Proteobacteria_unclassified Proteobacteria_unclassified Proteobacteria_unclassified Otu108 0,000 0,006 0,000 0,000 0,000 0,000 0,031 0,000 0,147 0,000 Otu190 0,000 0,017 0,000 0,000 0,000 0,000 0,058 0,000 0,033 0,000 Otu114 0,000 0,053 0,023 0,000 0,000 0,000 0,000 0,000 0,020 0,000 Otu182 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,042 0,000 Otu193 0,000 0,019 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu405 0,000 0,002 0,000 0,000 0,000 0,000 0,006 0,000 0,007 0,000 Otu198 0,000 0,014 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu379 0,000 0,000 0,014 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu202 0,000 0,013 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu266 0,000 0,008 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu439 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,005 0,000 Otu346 0,000 0,005 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu369 0,000 0,004 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu386 0,000 0,003 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu413 0,000 0,003 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu441 0,000 0,002 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu452 0,000 0,002 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Alphaproteobacteria Alphaproteobacteria_unclassified Alphaproteobacteria_unclassified Alphaproteobacteria_unclassified Otu42 0,000 0,171 0,007 0,000 0,121 0,000 2,915 0,000 0,820 0,000 Otu20 0,000 1,860 0,010 0,000 0,000 0,000 0,231 0,000 0,240 0,000 Otu71 0,000 0,059 0,000 0,000 0,000 0,000 1,013 0,000 0,077 0,000 Otu57 0,000 0,326 0,011 0,000 0,000 0,000 0,182 0,000 0,400 0,000 Otu45 0,000 0,415 0,026 0,000 0,000 0,000 0,000 0,000 0,075 0,149 Otu87 0,000 0,087 0,000 0,000 0,000 0,000 0,213 0,000 0,030 0,000 Otu122 0,000 0,007 0,000 0,000 0,000 0,000 0,140 0,000 0,114 0,000 Otu82 0,000 0,105 0,000 0,000 0,000 0,000 0,146 0,000 0,007 0,000 Otu67 0,000 0,164 0,000 0,000 0,000 0,000 0,000 0,000 0,015 0,000 Otu97 0,000 0,070 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu251 0,000 0,008 0,000 0,000 0,012 0,000 0,000 0,000 0,024 0,000 Otu270 0,000 0,009 0,000 0,008 0,000 0,000 0,000 0,000 0,020 0,000 Otu263 0,000 0,000 0,000 0,000 0,028 0,000 0,000 0,000 0,000 0,000 Otu302 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,018 0,000 Otu344 0,000 0,006 0,000 0,000 0,000 0,000 0,000 0,000 0,009 0,000 Otu247 0,000 0,009 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu422 0,000 0,003 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu475 0,000 0,001 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Caulobacterales Caulobacteraceae Brevundimonas Otu6 0,000 0,004 0,019 49,090 7,565 0,000 0,000 0,000 9,779 0,000 Otu23 0,000 0,343 0,000 0,000 0,018 0,000 2,389 0,030 1,275 0,000 Otu510 0,000 0,000 0,000 0,005 0,000 0,000 0,000 0,000 0,000 0,000 Otu569 0,000 0,000 0,000 0,003 0,000 0,000 0,000 0,000 0,000 0,000 Otu424 0,000 0,002 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu506 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,002 0,000 Phenylobacterium Otu322 0,000 0,005 0,000 0,000 0,012 0,000 0,031 0,000 0,000 0,000 Otu278 0,000 0,008 0,000 0,000 0,000 0,000 0,000 0,000 0,004 0,000 Rhizobiales Rhizobiales_unclassified Rhizobiales_unclassified Otu339 0,000 0,009 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu425 0,000 0,002 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu535 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,001 0,000 0,000 Otu534 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,001 0,000 0,000 Rhizobiales_incertae_sedis Phreatobacter Otu261 0,000 0,009 0,029 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Beijerinckiaceae Beijerinckiaceae_unclassified Otu271 0,000 0,000 0,000 0,000 0,026 0,000 0,000 0,000 0,000 0,000 Bradyrhizobiaceae Afipia Otu19 0,000 0,083 7,328 1,109 0,000 0,601 0,068 0,000 0,063 0,000 Otu532 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,157 0,000 Bosea Otu46 0,000 0,018 0,008 0,000 0,000 0,000 0,087 0,000 0,096 0,000 Bradyrhizobium Otu208 0,000 0,000 0,000 0,121 0,000 0,000 0,000 0,000 0,000 0,000 Brucellaceae Ochrobactrum Otu141 0,000 0,000 0,008 0,000 0,000 0,000 0,000 0,000 0,006 0,056 Hyphomicrobiaceae Hyphomicrobiaceae_unclassified Otu210 0,000 0,054 0,000 0,000 0,000 0,000 0,085 0,000 0,023 0,000 Otu79 0,000 0,108 0,005 0,000 0,000 0,000 0,008 0,000 0,019 0,000 Otu171 0,000 0,018 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Hyphomonas Otu113 0,000 0,030 0,000 0,000 0,000 0,000 0,386 0,000 0,000 0,000 Pedomicrobium Otu60 0,000 0,203 0,000 0,000 0,000 0,000 0,047 0,000 0,000 0,000 Methylobacteriaceae Methylobacterium Otu4 0,166 0,001 4,601 13,920 1,976 0,065 0,000 46,859 1,178 1,325 Otu7 0,000 0,005 14,170 0,000 0,041 0,000 0,000 1,655 16,999 0,000 Otu157 0,249 0,001 0,000 0,000 0,000 0,000 0,083 0,000 0,000 0,000 Otu391 0,000 0,003 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu511 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,002 0,000 0,000 Otu533 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,001 0,000 Rhizobiaceae Rhizobiaceae_unclassified Otu279 0,000 0,000 0,000 0,000 0,000 0,000 0,052 0,000 0,000 0,000 Rhizobium Otu197 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,970 Rhodobiaceae Rhodobiaceae_unclassified Otu74 0,000 0,129 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Xanthobacteraceae Xanthobacter Otu150 0,000 0,024 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Rhodobacterales Rhodobacteraceae Rhodobacteraceae_unclassified Otu26 0,000 0,275 0,033 0,000 0,014 0,000 3,866 0,000 2,499 0,000 Otu231 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,765 Otu125 0,000 0,045 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Rhodobacter Otu16 0,000 3,212 0,000 0,000 0,000 0,000 3,107 0,000 0,371 0,000 Rubellimicrobium Otu402 0,000 0,000 0,000 0,000 0,010 0,000 0,000 0,000 0,000 0,000 Rhodospirillales Acetobacteraceae Acetobacteraceae_unclassified Otu61 0,000 0,195 0,042 0,000 0,000 0,000 0,080 0,000 0,061 0,000 Otu93 0,000 0,075 0,000 0,000 0,000 0,000 0,062 0,000 0,038 0,000 Otu148 0,000 0,024 0,000 0,000 0,000 0,000 0,023 0,000 0,000 0,000 Otu220 0,000 0,008 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Reyranella Reyranella_unclassified Otu133 0,000 0,040 0,011 0,000 0,000 0,000 0,000 0,000 0,019 0,000 Otu156 0,000 0,023 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Rhodospirillaceae Rhodospirillaceae_unclassified Otu98 0,000 0,070 0,000 0,000 0,000 0,000 0,000 0,000 0,052 0,000 Otu168 0,000 0,019 0,000 0,000 0,000 0,000 0,000 0,000 0,021 0,000 Otu351 0,000 0,004 0,000 0,000 0,000 0,000 0,000 0,000 0,011 0,000 Otu384 0,000 0,003 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Rhodospirillales_unclassified Rhodospirillales_unclassified Otu350 0,000 0,005 0,000 0,000 0,000 0,000 0,000 0,000 0,007 0,000 Otu277 0,000 0,008 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu416 0,000 0,004 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Rickettsiales Rickettsiaceae Rickettsiaceae_unclassified Otu385 0,000 0,003 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu417 0,000 0,002 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Sphingomonadales Erythrobacteraceae Porphyrobacter Otu14 0,000 0,330 0,015 0,000 0,000 0,000 0,755 0,000 0,361 0,000 Sphingomonadaceae Blastomonas Otu18 0,000 2,472 0,000 0,000 1,692 0,000 2,556 0,000 0,025 4,478 Hephaestia Otu514 0,000 0,000 0,000 0,000 0,000 0,002 0,000 0,000 0,000 0,000 Otu521 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,001 0,000 0,000 Novosphingobium Otu374 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,009 0,952 Sphingobium Otu275 0,000 0,008 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Sphingomonadaceae_unclassified Otu22 0,000 1,800 0,061 0,000 0,000 0,002 1,370 0,000 1,550 0,000 Otu47 0,000 0,064 0,017 0,000 0,000 0,000 0,611 0,000 0,219 0,000 Otu566 0,006 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu564 0,006 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu565 0,006 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu567 0,006 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu451 0,000 0,000 0,000 0,000 0,000 0,005 0,000 0,000 0,000 0,000 Otu353 0,000 0,004 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu462 0,000 0,000 0,000 0,000 0,000 0,004 0,000 0,000 0,000 0,000 Otu515 0,000 0,000 0,000 0,000 0,000 0,002 0,000 0,000 0,000 0,000 Otu568 0,000 0,000 0,002 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu571 0,000 0,000 0,002 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu576 0,000 0,000 0,000 0,000 0,000 0,002 0,000 0,000 0,000 0,000 Otu578 0,000 0,000 0,000 0,000 0,000 0,002 0,000 0,000 0,000 0,000 Otu582 0,000 0,000 0,000 0,000 0,000 0,002 0,000 0,000 0,000 0,000 Otu523 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,001 0,000 0,000 Otu524 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,001 0,000 0,000 Otu529 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,001 0,000 0,000 Sphingomonas Otu2 0,000 0,004 0,628 0,000 0,012 0,000 0,000 0,000 0,000 0,000 Sphingopyxis Otu69 0,000 0,022 0,000 0,000 0,000 0,000 0,184 0,000 0,188 0,000 Otu318 0,000 0,006 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu400 0,000 0,003 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 SAR11 Candidatus_Pelagibacter Candidatus_Pelagibacter_unclassified Otu577 0,000 0,000 0,000 0,000 0,002 0,000 0,000 0,000 0,000 0,000 Betaproteobacteria Betaproteobacteria_unclassified Betaproteobacteria_unclassified Betaproteobacteria_unclassified Otu30 0,000 0,729 0,000 0,000 0,000 0,000 0,000 0,000 0,050 0,000 Otu165 0,000 0,019 0,000 0,000 0,000 0,000 0,045 0,000 0,000 0,000 Otu206 0,000 0,011 0,000 0,000 0,000 0,000 0,000 0,000 0,032 0,000 Otu338 0,000 0,000 0,000 0,000 0,000 0,000 0,035 0,000 0,000 0,000 Otu305 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,015 0,000 Otu334 0,000 0,007 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu461 0,000 0,001 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Burkholderiales Burkholderiales_unclassified Burkholderiales_unclassified Otu15 0,000 0,454 0,061 0,000 0,004 0,000 22,630 0,032 2,017 0,000 Otu40 0,000 0,563 0,000 0,000 0,166 0,000 0,452 0,000 0,283 1,325 Otu601 0,000 0,172 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Burkholderiales_incertae_sedis Burkholderiales_incertae_sedis_unclassified Otu581 0,000 0,000 0,000 0,000 0,000 0,000 0,004 0,000 0,000 0,000 Burkholderiaceae Burkholderia Otu367 0,000 0,000 0,000 0,000 0,000 0,000 0,027 0,000 0,000 0,000 Burkholderia Otu469 0,000 0,000 0,000 0,000 0,000 0,003 0,000 0,000 0,000 0,000 Limnobacter Otu5 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,423 0,000 Ralstonia Otu72 0,000 0,000 0,012 0,000 0,124 0,000 0,000 0,000 0,000 0,000 Otu516 0,000 0,000 0,000 0,000 0,003 0,000 0,000 0,000 0,000 0,000 Comamonadaceae Comamonadaceae_unclassified Otu3 0,000 0,001 0,000 0,000 87,363 0,000 0,000 0,002 0,002 0,000 Otu8 0,000 0,009 0,008 0,000 0,188 0,000 0,004 0,001 0,118 0,168 Otu453 0,000 0,000 0,000 0,000 0,005 0,000 0,000 0,000 0,000 0,000 Otu472 0,000 0,000 0,000 0,000 0,004 0,000 0,000 0,000 0,000 0,000 Otu473 0,000 0,000 0,000 0,000 0,004 0,000 0,000 0,000 0,000 0,000 Otu518 0,000 0,000 0,000 0,000 0,003 0,000 0,000 0,000 0,000 0,000 Otu517 0,000 0,000 0,000 0,000 0,003 0,000 0,000 0,000 0,000 0,000 Otu590 0,000 0,000 0,000 0,000 0,002 0,000 0,000 0,000 0,000 0,000 Otu589 0,000 0,000 0,000 0,000 0,002 0,000 0,000 0,000 0,000 0,000 Oxalobacteraceae Massilia Otu37 0,000 0,000 0,000 0,000 0,000 0,000 0,019 0,000 0,000 0,000 Gallionellales Gallionellaceae Sideroxydans Otu131 0,000 0,000 0,000 0,000 0,000 0,000 0,095 0,000 0,000 0,000 Hydrogenophilales Hydrogenophilaceae Hydrogenophilus Otu388 0,000 0,000 0,000 0,020 0,000 0,000 0,000 0,000 0,000 0,000 Methylophilales Methylophilaceae Methylophilus Otu327 0,000 0,005 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Neisseriales Neisseriaceae Snodgrassella Otu144 0,314 0,000 0,000 0,116 0,000 0,000 0,000 0,003 0,000 0,000 Rhodocyclales Rhodocyclaceae Rhodocyclaceae_unclassified Otu258 0,000 0,003 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Gammaproteobacteria Gammaproteobacteria_unclassified Gammaproteobacteria_unclassified Gammaproteobacteria_unclassified Otu32 0,000 0,636 0,000 0,000 0,012 0,000 2,676 0,000 0,863 1,717 Otu35 0,000 0,562 0,000 0,000 0,000 0,000 0,058 0,000 0,097 0,000 Otu75 0,000 0,150 0,000 0,000 0,000 0,000 0,000 0,000 0,133 0,000 Otu128 0,000 0,036 0,000 0,000 0,000 0,000 0,085 0,000 0,098 0,000 Otu126 0,000 0,045 0,000 0,000 0,000 0,000 0,000 0,000 0,016 0,000 Otu290 0,000 0,004 0,000 0,000 0,000 0,000 0,049 0,000 0,000 0,000 Otu161 0,000 0,021 0,000 0,000 0,000 0,000 0,000 0,000 0,011 0,000 Otu280 0,000 0,000 0,000 0,000 0,024 0,000 0,000 0,000 0,000 0,000 Otu185 0,000 0,016 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu368 0,000 0,001 0,015 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu211 0,000 0,013 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu225 0,000 0,011 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu253 0,000 0,009 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu321 0,000 0,005 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu375 0,000 0,004 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu393 0,000 0,004 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu419 0,000 0,002 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu488 0,000 0,001 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu505 0,000 0,001 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu575 0,000 0,001 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Chromatiales Chromatiaceae Rheinheimera Otu172 0,000 0,001 0,000 0,000 0,000 0,000 0,000 0,000 0,045 0,000 Enterobacteriales Enterobacteriaceae Enterobacter Otu238 0,113 0,000 0,000 0,000 0,000 0,000 0,029 0,000 0,000 0,000 Escherichia/Shigella Otu89 0,000 0,000 0,014 0,062 0,000 0,000 0,000 0,000 0,000 0,000 Legionellales Coxiellaceae Aquicella Otu109 0,000 0,066 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu326 0,000 0,005 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu440 0,000 0,002 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Legionellaceae Legionella Otu121 0,000 0,034 0,000 0,000 0,000 0,000 0,320 0,000 0,085 0,000 Otu466 0,000 0,000 0,000 0,000 0,000 0,000 0,008 0,000 0,039 0,000 Otu137 0,000 0,030 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu378 0,000 0,003 0,000 0,000 0,000 0,000 0,025 0,000 0,000 0,000 Otu200 0,000 0,014 0,000 0,000 0,000 0,000 0,000 0,000 0,003 0,000 Oceanospirillales Oceanospirillaceae Marinomonas Otu149 0,267 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Pasteurellales Pasteurellaceae Pasteurellaceae_unclassified Otu364 0,000 0,001 0,000 0,000 0,000 0,000 0,029 0,000 0,000 0,000 Haemophilus Otu187 0,000 0,018 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Pseudomonadales Moraxellaceae Acinetobacter Otu102 0,000 0,000 0,000 0,000 0,000 0,015 0,167 0,000 0,026 1,344 Otu236 0,000 0,000 0,002 0,033 0,026 0,000 0,033 0,026 0,011 0,000 Enhydrobacter Otu243 0,000 0,000 0,011 0,028 0,000 0,008 0,000 0,010 0,018 0,000 Moraxellaceae_unclassified Otu296 0,000 0,000 0,025 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Psychrobacter Otu179 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,042 0,000 0,000 Pseudomonadaceae Pseudomonas Otu196 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,970 Otu138 0,139 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,034 0,000 Otu478 0,000 0,001 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Xanthomonadales Sinobacteraceae Nevskia Otu11 0,000 0,002 0,000 0,000 0,000 0,000 0,000 0,000 0,012 0,000 Xanthomonadaceae Stenotrophomonas Otu9 0,000 0,000 0,002 0,000 0,000 13,390 0,000 0,000 0,031 2,743 Otu579 0,000 0,000 0,000 0,000 0,000 0,002 0,000 0,000 0,000 0,000 Otu580 0,000 0,000 0,000 0,000 0,000 0,002 0,000 0,000 0,000 0,000 Deltaproteobacteria Deltaproteobacteria_unclassified Deltaproteobacteria_unclassified Deltaproteobacteria_unclassified Otu66 0,000 0,174 0,000 0,000 0,000 0,000 0,180 0,000 0,143 0,000 Otu50 0,000 0,268 0,000 0,000 0,000 0,000 0,074 0,000 0,052 0,000 Otu76 0,000 0,125 0,000 0,000 0,000 0,000 0,000 0,000 0,017 0,000 Otu292 0,000 0,006 0,000 0,000 0,000 0,000 0,033 0,000 0,000 0,000 Otu281 0,000 0,007 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu316 0,000 0,006 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu345 0,000 0,005 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu397 0,000 0,003 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu414 0,000 0,002 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu449 0,000 0,002 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Bdellovibrionales Bdellovibrionaceae Bdellovibrio Otu358 0,000 0,004 0,000 0,000 0,000 0,000 0,029 0,000 0,000 0,000 Otu467 0,000 0,000 0,000 0,000 0,000 0,000 0,008 0,000 0,000 0,000 Otu342 0,000 0,005 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu347 0,000 0,005 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu357 0,000 0,004 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Vampirovibrio Otu139 0,000 0,028 0,000 0,000 0,000 0,000 0,062 0,000 0,081 0,205 Otu95 0,000 0,072 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu234 0,000 0,011 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu242 0,000 0,010 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu340 0,000 0,005 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu355 0,000 0,004 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu365 0,000 0,004 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu396 0,000 0,003 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu542 0,000 0,001 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Myxococcales Myxococcales_unclassified Myxococcales_unclassified Otu92 0,000 0,096 0,000 0,000 0,000 0,000 0,083 0,000 0,000 0,000 Otu96 0,000 0,070 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu293 0,000 0,006 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu314 0,000 0,006 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu317 0,000 0,006 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu412 0,000 0,003 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu415 0,000 0,002 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Otu476 0,000 0,001 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Labilitrichaceae Labilithrix Otu294 0,000 0,008 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Myxococcaceae Aggregicoccus Otu370 0,000 0,004 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Sandaracinaceae Sandaracinus Otu177 0,000 0,018 0,000 0,000 0,000 0,000 0,083 0,000 0,000 0,616 Otu240 0,000 0,010 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Verrucomicrobia Spartobacteria Spartobacteria_unclassified Spartobacteria_unclassified Spartobacteria_unclassified Otu24 0,000 1,468 0,061 0,000 0,000 0,000 1,194 0,000 0,457 0,000 Subdivision3 Subdivision3_unclassified Subdivision3_unclassified Subdivision3_unclassified Otu48 0,000 0,290 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 Verrucomicrobiae Verrucomicrobiales Verrucomicrobiaceae Prosthecobacter Otu80 0,000 0,107 0,000 0,000 0,000 0,000 0,693 0,000 0,028 0,000 Archaea-Thaumarchaeota Nitrosopumilales Nitrosopumilaceae Nitrosopumilus Nitrosopumilus_unclassified Otu485 0,000 0,000 0,000 0,007 0,000 0,000 0,000 0,000 0,000 0,000 unknown_unclassified unknown_unclassified unknown_unclassified unknown_unclassified unknown_unclassified Otu539 0,000 0,001 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 253 Acidobacteria_Gp3_unclassified Acidobacteria_Gp3_unclassified Acidobacteria_Gp3_unclassified Otu163 0,014 0,000 0,000 0,000 0,000 0,000 Gp3 Gp3_unclassified Gp3_unclassified Otu77 0,008 0,000 0,000 0,000 0,000 0,000 Acidobacteria_Gp4 Acidobacteria_Gp4_unclassified Acidobacteria_Gp4_unclassified Acidobacteria_Gp4_unclassified Otu222 0,000 0,000 0,503 0,000 0,000 0,000 Gp4 Gp4_unclassified Gp4_unclassified Otu249 0,000 0,000 0,044 0,000 0,000 0,000 Aridibacter Aridibacter_unclassified Otu55 0,054 0,000 0,094 0,107 0,000 0,000 Otu53 0,008 0,004 0,993 0,000 0,000 0,005 Otu359 0,000 0,000 0,017 0,000 0,000 0,002 Blastocatella Blastocatella_unclassified Otu33 0,036 0,000 0,048 0,000 0,000 0,000 Acidobacteria_Gp6 Gp6 Gp6_unclassified Gp6_unclassified Otu44 0,014 0,000 0,000 0,000 0,000 0,000 Otu390 0,008 0,000 0,000 0,000 0,000 0,000 Otu153 0,000 0,004 0,000 0,000 0,000 0,000 Actinobacteria Actinobacteria Actinomycetales Corynebacteriaceae Corynebacterium Otu194 0,000 0,000 0,000 0,000 0,147 0,000 Microbacteriaceae Microbacterium Otu17 0,010 0,000 0,006 0,000 0,000 0,000 Micrococcaceae Micrococcus Otu146 0,000 0,007 0,000 0,000 0,000 0,000 Mycobacteriaceae Mycobacterium Otu178 0,030 0,006 0,009 0,000 0,000 0,000 Bacteria_unclassified Bacteria_unclassified Bacteria_unclassified Bacteria_unclassified Bacteria_unclassified Otu52 0,010 0,000 1,001 0,199 0,000 0,011 Otu39 0,000 0,019 0,050 0,032 0,777 0,000 Otu51 0,216 0,000 0,000 0,172 0,000 0,000 Otu29 0,032 0,000 0,000 0,107 0,000 0,000 Otu54 0,139 0,000 0,009 0,000 0,000 0,000 Otu145 0,040 0,000 0,000 0,000 0,000 0,000 Otu336 0,000 0,000 0,034 0,000 0,000 0,000 Otu304 0,000 0,000 0,027 0,000 0,000 0,005 Otu297 0,000 0,000 0,028 0,000 0,000 0,000 Otu239 0,000 0,017 0,000 0,000 0,000 0,000 Otu244 0,012 0,000 0,008 0,000 0,000 0,000 Otu459 0,010 0,000 0,000 0,000 0,000 0,000 Otu465 0,000 0,000 0,008 0,000 0,000 0,000 Otu205 0,008 0,000 0,000 0,000 0,000 0,000 Otu64 0,000 0,007 0,000 0,000 0,000 0,000 Otu458 0,000 0,000 0,007 0,000 0,000 0,000 Otu460 0,000 0,000 0,000 0,000 0,000 0,006 Otu223 0,006 0,000 0,000 0,000 0,000 0,000 Otu501 0,006 0,000 0,000 0,000 0,000 0,000 Otu493 0,000 0,006 0,000 0,000 0,000 0,000 Otu557 0,004 0,000 0,000 0,000 0,000 0,000 Otu255 0,000 0,004 0,000 0,000 0,000 0,000 Otu550 0,000 0,004 0,000 0,000 0,000 0,000 Otu497 0,000 0,000 0,000 0,000 0,000 0,004 Otu499 0,000 0,000 0,000 0,000 0,000 0,004 Otu512 0,000 0,000 0,003 0,000 0,000 0,000 Otu230 0,000 0,000 0,000 0,000 0,000 0,002 Otu556 0,000 0,000 0,000 0,000 0,000 0,002 Otu586 0,000 0,000 0,002 0,000 0,000 0,000 Otu587 0,000 0,000 0,002 0,000 0,000 0,000 Otu588 0,000 0,000 0,002 0,000 0,000 0,000 Otu592 0,000 0,000 0,002 0,000 0,000 0,000 257 Bacteroidetes Bacteroidetes_unclassified Bacteroidetes_unclassified Bacteroidetes_unclassified Bacteroidetes_unclassified Otu68 0,422 0,000 0,675 0,000 0,000 0,000 Otu159 0,036 0,000 0,091 0,000 0,000 0,006 Otu110 0,077 0,004 0,000 0,000 0,000 0,000 Otu129 0,042 0,000 0,000 0,000 0,000 0,000 Otu107 0,000 0,000 0,012 0,000 0,000 0,000 Otu117 0,010 0,000 0,000 0,000 0,000 0,000 Cytophagia Cytophagales Cytophagales_unclassified Cytophagales_unclassified Otu49 0,210 0,000 0,026 1,938 0,000 0,000 Otu140 0,089 0,000 0,000 0,000 0,000 0,000 Otu324 0,046 0,000 0,000 0,000 0,000 0,000 Otu372 0,022 0,000 0,015 0,000 0,000 0,000 Otu142 0,020 0,000 0,000 0,000 0,000 0,000 Cytophagaceae Hymenobacter Otu213 0,000 0,017 0,051 0,000 0,000 0,000 Sphingobacteriia Sphingobacteriales Chitinophagaceae Lacibacter Otu119 0,238 0,000 0,000 0,000 0,000 0,000 Saprospiraceae Haliscomenobacter Otu85 0,585 0,000 0,041 0,000 0,000 0,000 Saprospiraceae_unclassified Otu10 0,073 0,000 0,000 0,473 0,000 0,000 Chlamydiae Chlamydiia Chlamydiales Chlamydiales_unclassified Chlamydiales_unclassified Otu482 0,000 0,007 0,000 0,000 0,000 0,000 Otu471 0,000 0,000 0,005 0,000 0,000 0,000 Parachlamydiaceae Parachlamydiaceae_unclassified Otu559 0,000 0,004 0,000 0,000 0,000 0,000 Otu585 0,000 0,000 0,002 0,000 0,000 0,000 Chloroflexi Thermomicrobia Sphaerobacterales Sphaerobacteraceae Sphaerobacteraceae_unclassified Otu262 0,014 0,000 0,000 0,000 0,000 0,000 Cyanobacteria Chloroplast Chloroplast Bacillariophyta Bacillariophyta_unclassified Otu1 1,993 0,006 0,030 0,380 0,000 0,000 Firmicutes Bacilli Bacillales Listeriaceae Listeria Otu491 0,000 0,006 0,000 0,000 0,000 0,000 Staphylococcaceae Staphylococcus Otu90 0,000 0,004 0,000 0,000 2,353 0,000 Lactobacillales Enterococcaceae Enterococcus Otu112 0,000 0,000 0,000 0,000 4,391 0,000 Clostridia Clostridiales Clostridiaceae Clostridium_IV Otu551 0,000 0,004 0,000 0,000 0,000 0,000 Clostridium_sensu_stricto Otu58 0,063 0,037 0,054 3,023 15,735 0,010 Otu143 0,004 0,000 0,003 0,297 2,143 0,000 Otu167 0,000 0,000 0,000 0,125 1,450 0,002 Negativicutes Selenomonadales Acidaminococcaceae Phascolarctobacterium Otu192 0,000 0,000 0,000 0,246 0,000 0,000 Deinococcus-Thermus Deinococci Deinococcales Trueperaceae Truepera Otu436 0,000 0,000 0,008 0,000 0,000 0,000 Gemmatimonadetes Gemmatimonadetes Gemmatimonadales Gemmatimonadaceae Gemmatimonas Otu431 0,016 0,000 0,000 0,000 0,000 0,000 Nitrospirae Nitrospira Nitrospirales Nitrospiraceae Nitrospira Otu13 0,079 0,000 0,000 0,923 0,000 0,000 Planctomycetes Planctomycetia Planctomycetales Planctomycetaceae Planctomycetaceae_unclassified Otu162 0,000 0,011 0,014 0,380 0,000 0,000 Otu214 0,091 0,000 0,000 0,000 0,000 0,000 Otu120 0,081 0,000 0,000 0,000 0,000 0,002 Otu101 0,058 0,000 0,000 0,000 0,000 0,000 Otu331 0,038 0,000 0,000 0,000 0,000 0,006 Otu136 0,000 0,000 0,020 0,000 0,000 0,000 Otu86 0,000 0,015 0,000 0,000 0,000 0,000 Otu454 0,012 0,000 0,000 0,000 0,000 0,000 Otu111 0,000 0,000 0,007 0,000 0,000 0,000 Otu34 0,006 0,000 0,000 0,000 0,000 0,000 Otu463 0,000 0,000 0,005 0,000 0,000 0,000 Otu43 0,004 0,000 0,000 0,000 0,000 0,000 Gemmata Otu83 0,000 0,045 0,033 0,612 0,000 0,015 Otu207 0,000 0,000 0,055 0,000 0,000 0,000 Schlesneria Otu27 0,014 0,000 0,000 0,000 0,000 0,000 Proteobacteria Proteobacteria_unclassified Proteobacteria_unclassified Proteobacteria_unclassified Proteobacteria_unclassified Otu313 0,000 0,000 0,024 0,000 0,000 0,000 Otu438 0,000 0,000 0,000 0,000 0,000 0,008 Otu457 0,000 0,000 0,007 0,000 0,000 0,000 Alphaproteobacteria Alphaproteobacteria_unclassified Alphaproteobacteria_unclassified Alphaproteobacteria_unclassified Otu42 0,085 0,004 0,073 0,250 0,000 0,007 Otu57 0,056 0,000 0,000 0,000 0,000 0,000 Otu71 0,032 0,000 0,019 0,000 0,000 0,000 Otu20 0,040 0,000 0,000 0,000 0,000 0,000 Otu251 0,006 0,000 0,000 0,000 0,000 0,000 Caulobacterales Caulobacteraceae Brevundimonas Otu23 5,270 10,880 3,695 1,896 0,000 0,134 Otu424 0,012 0,000 0,000 0,000 0,000 0,000 Otu6 0,000 0,004 0,000 0,000 0,000 0,000 Caulobacter Otu310 0,010 0,000 0,024 0,000 0,000 0,000 Phenylobacterium Otu322 0,004 0,009 0,019 0,000 0,000 0,005 Otu278 0,008 0,000 0,000 0,000 0,000 0,000 Rhizobiales Bradyrhizobiaceae Afipia Otu19 0,018 0,000 0,000 0,380 0,000 0,000 Otu532 0,000 0,000 0,002 0,000 0,000 0,000 Bosea Otu46 0,528 0,700 1,524 0,682 0,000 0,245 Bradyrhizobium Otu208 0,000 0,000 0,000 0,000 0,987 0,000 Brucellaceae Brucellaceae_unclassified Otu199 0,000 0,000 0,000 0,232 0,000 0,000 Ochrobactrum Otu141 0,000 0,000 0,000 0,478 0,000 0,000 Hyphomicrobiaceae Hyphomicrobiaceae_unclassified Otu210 0,016 0,000 0,000 0,473 0,000 0,000 Pedomicrobium Otu60 0,004 0,000 0,000 0,000 0,000 0,000 Methylobacteriaceae Methylobacterium Otu404 0,000 0,006 0,012 0,000 0,000 0,002 Phyllobacteriaceae Mesorhizobium Otu218 0,014 0,000 0,050 0,000 0,000 0,019 Rhizobiaceae Rhizobium Otu94 0,530 0,000 0,000 0,000 0,000 0,000 Xanthobacteraceae Xanthobacteraceae_unclassified Otu123 0,127 0,021 0,275 0,264 0,000 0,042 Rhodobacterales Rhodobacteraceae Rhodobacteraceae_unclassified Otu26 0,645 0,000 1,507 2,244 0,000 0,005 Otu125 0,024 0,000 0,000 0,000 0,000 0,000 Rhodobacter Otu16 0,488 0,000 0,000 0,352 0,000 0,000 Rhodospirillales Acetobacteraceae Acetobacteraceae_unclassified Otu220 0,000 0,000 0,000 0,195 0,000 0,000 Otu61 0,016 0,000 0,000 0,000 0,000 0,000 Otu93 0,010 0,000 0,000 0,000 0,000 0,000 Roseomonas Otu593 0,000 0,000 0,002 0,000 0,000 0,000 Reyranella Reyranella_unclassified Otu474 0,008 0,000 0,000 0,000 0,000 0,000 Otu133 0,004 0,000 0,000 0,000 0,000 0,002 Rhodospirillaceae Niveispirillum Otu387 0,000 0,000 0,014 0,000 0,000 0,000 Skermanella Otu224 0,000 0,000 0,049 0,000 0,000 0,002 Sphingomonadales Erythrobacteraceae Erythrobacteraceae_unclassified Otu312 0,000 0,000 0,024 0,000 0,000 0,000 Porphyrobacter Otu14 1,265 22,108 7,019 6,574 1,408 1,521 Otu552 0,000 0,004 0,000 0,000 0,000 0,000 Sphingomonadaceae Blastomonas Otu18 0,000 0,011 0,014 0,000 0,000 0,000 Sphingomonadaceae_unclassified Otu47 2,515 1,503 1,138 1,887 0,000 0,086 Otu22 1,244 0,007 0,000 0,000 0,000 0,000 Otu353 0,012 0,000 0,000 0,000 0,000 0,000 Sphingomonas Otu2 1,000 2,620 0,052 0,000 1,029 0,220 Otu56 0,054 0,131 0,952 0,172 0,000 0,011 Otu73 0,155 0,501 0,614 0,000 0,000 0,041 Sphingopyxis Otu69 0,014 0,009 0,665 0,000 0,000 0,000 Betaproteobacteria Betaproteobacteria_unclassified Betaproteobacteria_unclassified Betaproteobacteria_unclassified Otu490 0,000 0,006 0,003 0,000 0,000 0,000 Otu334 0,008 0,000 0,000 0,000 0,000 0,000 Otu486 0,000 0,007 0,000 0,000 0,000 0,000 Otu584 0,000 0,000 0,002 0,000 0,000 0,000 Burkholderiales Alcaligenaceae Alcaligenes Otu183 0,000 0,000 0,007 0,000 0,000 0,000 Burkholderiales_unclassified Burkholderiales_unclassified Otu40 0,171 0,000 0,279 0,134 0,000 0,030 Otu15 0,591 0,000 0,830 0,153 0,000 0,005 Otu601 0,129 0,000 0,321 0,121 0,000 0,000 Otu546 0,000 0,004 0,000 0,000 0,000 0,000 Otu553 0,000 0,000 0,000 0,000 0,000 0,002 Burkholderiaceae Burkholderia Otu469 0,000 0,000 0,005 0,000 0,000 0,000 Otu555 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,025 Otu554 0,000 0,000 0,002 0,000 0,000 0,002 Otu570 0,000 0,000 0,002 0,000 0,000 0,000 Otu583 0,000 0,000 0,002 0,000 0,000 0,000 Ralstonia Otu72 0,000 0,000 0,000 0,023 0,000 0,000 Comamonadaceae Comamonadaceae_unclassified Otu8 0,121 33,989 0,085 0,705 2,731 0,466 Otu548 0,000 0,009 0,000 0,000 0,000 0,000 Oxalobacteraceae Massilia Otu37 0,000 0,000 2,202 0,000 0,000 0,002 Gallionellales Gallionellaceae Sideroxydans Otu131 0,006 0,017 0,154 0,000 0,000 0,000 Rhodocyclales Rhodocyclaceae Methyloversatilis Otu252 0,000 0,000 0,040 0,000 0,000 0,000 Rhodocyclaceae_unclassified Otu258 0,000 0,015 0,038 0,000 0,000 0,000 259 Gammaproteobacteria Gammaproteobacteria_unclassified Gammaproteobacteria_unclassified Gammaproteobacteria_unclassified Otu32 0,065 0,032 0,164 0,250 0,378 0,000 Otu75 0,016 0,000 0,000 0,000 0,000 0,000 Otu161 0,000 0,009 0,000 0,000 0,000 0,000 Otu560 0,000 0,004 0,000 0,000 0,000 0,000 Chromatiales Chromatiaceae Rheinheimera Otu172 0,018 0,000 0,000 0,144 0,000 0,000 Ectothiorhodospiraceae Ectothiorhodospiraceae_unclassified Otu354 0,000 0,000 0,021 0,000 0,000 0,000 Enterobacteriales Enterobacteriaceae Enterobacter Otu238 0,000 0,000 0,000 0,088 0,000 0,000 Escherichia/Shigella Otu89 0,583 0,004 0,002 0,797 0,000 0,000 Morganella Otu547 0,000 0,004 0,000 0,172 0,000 0,000 Proteus Otu116 0,000 0,013 0,015 0,501 3,971 0,000 Legionellales Coxiellaceae Aquicella Otu433 0,000 0,000 0,000 0,037 0,000 0,000 Otu109 0,000 0,011 0,010 0,000 0,000 0,000 Otu326 0,000 0,000 0,005 0,000 0,000 0,000 Coxiella Otu483 0,000 0,007 0,000 0,000 0,000 0,000 Legionellaceae Legionella Otu137 0,000 0,000 0,003 0,000 0,000 0,002 Pseudomonadales Moraxellaceae Moraxellaceae_unclassified Otu180 0,000 0,000 0,072 0,000 0,000 0,000 Perlucidibaca Otu21 0,000 0,166 7,987 0,000 0,000 0,002 Pseudomonadaceae Pseudomonas Otu138 0,000 0,000 0,000 0,000 2,290 0,000 Xanthomonadales Sinobacteraceae Nevskia Otu11 2,436 2,067 0,614 3,959 0,000 20,529 Otu309 0,000 0,000 0,026 0,000 0,000 0,000 Sinobacteraceae_unclassified Otu36 0,004 3,595 0,051 0,000 0,000 0,007 Deltaproteobacteria Deltaproteobacteria_unclassified Deltaproteobacteria_unclassified Deltaproteobacteria_unclassified Otu292 0,000 0,000 0,027 0,000 0,000 0,000 Bdellovibrionales Bacteriovoracaceae Peredibacter Otu257 0,000 0,000 0,038 0,000 0,000 0,000 Otu468 0,000 0,000 0,005 0,000 0,000 0,000 Otu591 0,000 0,000 0,002 0,000 0,000 0,000 Bdellovibrionaceae Bdellovibrio Otu502 0,006 0,000 0,000 0,000 0,000 0,000 Vampirovibrio Otu95 0,000 0,009 0,016 0,000 0,000 0,000 Otu139 0,008 0,000 0,000 0,000 0,000 0,000 Otu242 0,000 0,000 0,000 0,000 0,000 0,005 Myxococcales Myxococcales_unclassified Myxococcales_unclassified Otu92 0,018 0,000 0,000 0,000 0,000 0,000 Verrucomicrobia Opitutae Opitutales Opitutaceae Opitutus Otu541 0,004 0,000 0,000 0,000 0,000 0,000 Spartobacteria Spartobacteria_unclassified Spartobacteria_unclassified Spartobacteria_unclassified Otu450 0,000 0,000 0,000 0,000 0,000 0,007 Verrucomicrobiae Verrucomicrobiales Verrucomicrobiaceae Prosthecobacter Otu80 0,125 0,000 0,059 0,000 0,000 0,000 candidate_division_WPS-1 candidate_division_WPS-1_unclassified candidate_division_WPS-1_unclassified candidate_division_WPS-1_unclassified candidate_division_WPS-1_unclassified Otu394 0,000 0,000 0,013 0,000 0,000 0,000 unknown_unclassified unknown_unclassified unknown_unclassified unknown_unclassified unknown_unclassified Otu500 0,000 0,000 0,000 0,014 0,000 0,000 Appendix 13: Bacterial viability after irradiation 78 56,47 35,12 34,88 22,17 41,20 49,02 59,86 15,16 43,82 29,48 36,74 33,94 79,95 38,13 49,20 81,54 ND 33,60 63,10 91,43 90,16 70,[START_REF] Dadachova | Ionizing radiation: how fungi cope, adapt, and exploit with the help of melanin[END_REF]88 Std 12,55 11,90 16,19 1,82 3,17 6,61 21,36 12,42 21,26 19,66 25,20 6,13 ND 24,72 11,11 1,68 11,62 6,56 Mean 78,90 67,43 38,52 67,13 24,00 55,54 42,94 38,07 61,70 48,81 28,90 20,68 17,82 41,95 30,76 71,64 82,61 71,17 45,98 31,42 76,50 85,23 27,47 22

T F A F M A -V C F A -V L M A -V L H A

Finally 3 .

 3 , I want to thank Michel DELAGE, Eric LATIL, Frédéric COULON, Jean-Baptiste BAUGE, Daniel DO COSTA, and the entire Pool 1 and Pool 2 staff for their help, availability, and allowing us to study their nuclear pool. I also want to thank the Programme Transverse de Toxicology from the CEA who funded this work. Deciphering the microbial composition of the cooling pools of nuclear facilities ..................................3.1Microbial composition of a nuclear reactor cooling pool during operation and at shutdown deciphered using multi meta-analysis (article in preparation) ..................................................................... 3.1.1 Materials and Methods ............................................................................................................... 3.1.1.1 Water sampling in the cooling pool of the nuclear reactor's core and characterization ...... 3.1.1.2 Water sample treatment and microbial communities harvesting ........................................ 3.1.1.3 DNA extraction, Illumina sequencing, and sequence data analysis for meta-amplicomic analyses 121 3.1.1.4 Protein extraction, peptide analysis using nanoLC/MS/MS, and taxonomic analysis by metaproteomics ................................................................................................................................. 3.1.2 Results ........................................................................................................................................ 3.1.2.1 Configuration of the pool .................................................................................................... 3.1.2.2 Direct meta-analysis of communities present in the reactor core's cooling pool water ....... 3.1.3 Discussion ...................................................................................................................................

  . If life could have arisen in such harsh conditions, it is not a surprise to find microorganisms in a multitude of metabolic niches.

(

  former Micrococcus radiodurans) (Fig. 1.2) [14]. The LD50 within 30 days for human is around 4 Gy and a dose around 40 or 50 Gy compulsorily leads to the death of the person, according to the IRSN (Institut de Radioprotection et de Sûreté Nucléaire -French Radioprotection and Nuclear Safety Institute).

Figure 1 . 2 :

 12 Figure 1.2: Deinococcus radiodurans observed using a photon microscopy (x40)

Figure 1 . 3 :

 13 Figure 1.3: Biofilm leading to biocorrosion (adapted from[START_REF] Borenstein | Microbiologically Influenced Corrosion Handbook[END_REF]). The cells first attached to the surface and a thin anaerobic biofilm is formed. This biofilm grows and matures until it changes the physical and chemical conditions of the surface. These changes can lead to the corrosion of the surface.

Figure 1 . 4 :

 14 Figure 1.4: Bacterial sporulation steps (adapted from[START_REF] Leggett | Bacterial spore structures and their protective role in biocide resistance[END_REF]). A vegetative cell will divide asymmetrically. One cell will lead to the formation of the spore while the mother cell will go under a programmed cell death to release the mature spore.

Figure 1 . 5 :

 15 Figure 1.5: Ionizing and Non-ionizing radiations. Radiations are electromagnetic waves, consisting of oscillating electric and magnetic fields traveling through space. Depending on the frequency (in Hz) of the wave, the radiation can be visible (visible light) or invisible to the human eye and ionizing or not.

1. 4 . 1 . 3 . 2

 4132 Enzymatic pathways: DNA repair Depending on the kingdom living organisms have developed different pathways to repair damaged DNA, i.e. eukaryotes or prokaryotes. Prokaryotes can possess the same chromosome in several copies in their cells, presenting the advantage of providing an intact copy for DSBs repair.

Figure 1 . 6 :

 16 Figure 1.6:The four main pathways used to repair double strand breaks in DNA (adapted from[START_REF] Confalonieri | Bacterial and archaeal resistance to ionizing radiation[END_REF]). Homologous Recombination (HR) is the main pathway in DNA double-strand break repair in bacteria. It uses an intact homologous DNA to restore the correct DNA sequence at damaged sites. Single-Strand Annealing (SSA) and Synthesis-Dependent Strand Annealing (SDSA) are the main DNA repair pathways found in D. radiodurans: the ends of the DSB are resected to produce a long 3'ended single stranded DNA (ssDNA). Non-Homologous End-Joining (NHEJ) is the main pathway in DNA double-strand break repair in eukaryotes. This pathway is based on the binding of double-strand DNA carrying strand breaks nucleoids. Proteins ligates the end of the break together.

Figure 1 . 7 :

 17 Figure 1.7: Nuclear chain reaction occurring in a nuclear reactor

Figure 1 . 8 :

 18 Figure 1.8: Nuclear fallout after the Fukushima nuclear accident (NOAA)

1. 9 )

 9 were discovered in 1773 and can be found in very diverse environments all over the world. They are small invertebrates (up to 2.1 mm in length). Currently, circa 1200 species are known. These animals are unusual for several reasons. They can be found in various environments, from the bottom of the oceans to the top of a mountain, in freshwater, marine and terrestrial environments. They can resist extreme temperatures (from -272.8 to 150°C) and pressures (from 200 hPa to 7500 MPa; the atmospheric pressure being around 1023 hPa). The terrestrial species need a thin film of water around them to stay active. They can enter into a cryoptobiotic state which allows them to resist anaerobic environments, cold temperature, excess salinity and desiccation. During the cryoptobiotic state, their metabolism decreases drastically to very low level and can occur several times in every stage of their life, from youth to adult form. Their ability to resist such harsh conditions makes them interesting model to study the effect of space on the organisms [160]. Tardigrades exhibits resistance to high doses of radiation. Depending on the species, this resistance can change. The survival of Milnesium tardigradum after -irradiation was studied, at short-and long-term. The LD 50 2 hours after irradiation was around 5.5 kGy and dropped to 5 kGy 48 hours after exposition. This species could only laid eggs under a 2 kGy irradiation, while Richtersius coronifer could laid eggs after a 5 kGy irradiation.

Figure 1

 1 Figure 1.9: Tardigrade (SEM)

Figure 1 . 10 :

 110 Figure 1.10: Deinococcus radiodurans and Deinococcus deserti under a microscope (CEA -Genoscope)

Figure 1 . 11 :

 111 Figure 1.11: Chroococcidiopsis sp. under a microscope (Universität Rostock)

Figure 1 . 12 :

 112 Figure 1.12: Section analysis of a steel coupon after removal of a biofilm (adapted from [183]). The curve represents the surface of a metal coupon after removal of a biofilm and observation via an Atomic Force Microscope. Corrosion are indicatedwith arrows. Two gaps can be observed, measuring less than 2 µm with a depth around 100 nm.

Figure 1 . 13 :

 113 Figure 1.13: Coccomyxa actinabiotis observed using a microscope (x40) (C. Rivasseau -CEA)

1 .

 1 15 presents the rRNA 16S gene of Escherichia coli with the nine variable regions (in orange) and the Chapter I -Bibliographical introduction 44 conserved regions (in white). For the 16S rDNA, a very used universal primer pair is the 27F/1492R (5'-AGAGTTTGATCMTGGCTCAG-3' and 5'-TACGGYTACCTTGTTACGACTT-3' respectively) providing a fragment of 1465 pb

Figure 1 . 15 :

 115 Figure 1.15: 16S rRNA gene of Escherichia coli showing the nine variable regions and the position of the 27F/1492R primers (adapted from [209])

Figure 1 . 16 :

 116 Figure 1.16: Comparison of the 16S rDNA of different bacteria and archaea (red: different nucleotides; blue: same nucleotide) (adapted from [210])

Figure 1 . 17 :

 117 Figure 1.17: Organization of the bacterial 16S rDNA [200]. The variable regions present a smaller steric hindrance, making them more sensitive to mutations.

  (e.g. Uchime, CATCh). The sequences are then clustered into OTUs depending on their identity with a 0.10 distance cut-off. To identify the clustered OTUs a database (e.g. SILVA, Ribosomal Database Project) must be downloaded. OCToPUS presents the advantage of cleaning the sequences(chimeras, singleton, etc.) without entering the control command. It does automatically the same steps than mothur. A cut-off of 0.03 was chosen to cluster the OTUs[210]. The use of 16S Classifier does not need informatic skills and is freely available. It uses an internet interface in which the raw metagenetics results are loaded. It presented a better performance on the identification of 16S rDNA region compared to the use of RDP Classifier[214].MG-RAST is an open-submission data portal for processing and analyzing metagenomic datasets. It can be used for a metagenetic purpose (identification of microorganisms) but also for metagenomics analysis on several genes present in a microbiota. It is mainly use for meta-data analysis[215]. Among the three presented software analysis, the easier to use is 16S Classifier.

Figure 1 . 18 :

 118 Figure 1.18:Experimental design of metagenomics [216]

1. 7

 7 PhD project 1.7.1 Framework and objectives of the PhD project This PhD was carried out in the framework of the INFERNUS project leaded by Corinne RIVASSEAU and funded by the Programme Transversal de Toxicologie from the CEA. The goal of this project was to analyze the microorganisms present in French spent nuclear fuel pools. Their ability to resist ionizing radiation and to concentrate specific radionuclides were determined after isolation and identification. This project was divided in two parts: first the inventory of the microbial genera present in French SNFPs using direct molecular approaches, then the identification of the main mechanisms involved in radioresistance and radionuclides resistance and accumulation.

Figure 1 . 19 :

 119 Figure 1.19: Scheme of the metaproteomics identification method [248]. Interpretation of MS/MS spectra, against protein sequence database or de novo. Protein sequence databases are derived from genomics and/or RNAseq or draft genome data.In this latter case, unannotated nucleic acid molecules are translated into open reading frames (ORFs), either along the six or three possible frames. Further proteogenomics approaches may be used to improve the protein sequence database. Tandem mass spectrometry data, acquired on peptides from a given organism or microbiome, are assigned against the most appropriate protein sequence database, giving peptide-to-spectrum matches. These are then interpreted in order to infer which proteins are present and to quantify these items.

Figure 1

 1 Figure 1.20: Overall view of Pool 1

Figure 1 . 21 :

 121 Figure 1.21: Pool 1 containing the 60 Co radioactive sources, between the western and the northern wall

  around 70 MWth [250]. The water use to cool the core was thus highly radioactive and still contains Radioactive sources pH s at 25°c (µS/cm) COD (mg/L) MIS (mg/L) F -(mg/L) Cl -(mg/L) Br -(mg/L

Figure 1 . 22 :

 122 Figure 1.22: Schematic view of Pool 2 reactor (red light: position of the reactor)

  At three different locations (Loc. A, B, and C) in a shutdown nuclear reactor pool, 8 liters of water were recovered and stored into sterile plastic container. Among these 8 liters, only 3 were used to extract DNA.One liter was centrifuged at 16000 g for 20 min at 10°C to harvest the microbial community. The two other liters were filtered on two different 0.22 µm hydrophilic polyethersulfone filters (MicroFunnelTM Supor®, Pall Corporation, USA). The filters were introduced into sterile 50 mL Falcon tubes for transportation. The initial microbial cell density in water sampled was assessed with a Malassez cell at 2.7x10 6 cell/L, 1.4x10 6 cell/L, and below 0.75x10 6 cell/L at Loc. A, Loc. B, and Loc. C, respectively, using light microscopy after microorganism concentration by centrifugation. The filters and the pellet were preserved at + 4°C prior to DNA extraction. The radiations dose at sampling location A (bottom of the pool) was below detection limits (< 0.1 µGy/h), about 15 µGy/h at sampling location B (above the chimney), and about 25 Gy/h at sampling location C (inside the chimney).

Figure 2 . 2 :

 22 Figure 2.2: Mock analysis after a V4 metagenetics sequencing. Comparison between the theoretical composition of the mock sample (red) and the experimentally established composition (blue) identified after a V4 metagenomics sequencing and OCToPUS analysis. Green curve: number of sequences identified for each OTUs

  e rim e n ta l c o m p o s itio n T h e o r e tic a l c o m p o s itio n N u m b e r o f s e q u e n c e s Chapter II -Methods development and optimization 68

Figure

  Figure 2.3: Reads removal on the mock sample after a V4 or V4V5 metagenetic sequencing.In blue: % of V4V5 reads obtained after each treatment, in red: % of V4 reads obtained after each treatment.

Figure 2 . 4 :

 24 Figure 2.4: Venn diagram after a V4V5 metagenetics sequencing at A) Loc. A, B) Loc. B, C) Loc. C. Only the OTUs above the 0.016% threshold were selected. In brackets: proportion of the OTUs in the sample.

Figure 2 .

 2 5 shows the pool surface and the strategy for protein extraction, peptide analysis by tandem mass spectrometry and metaproteomics analysis of the data. A first biofilm (Biofilm A) was collected at 4 m below the surface water on a wall intermittently irradiated by gamma radiations at a dose rate of 100 Gy/h. Another biofilm (Biofilm B) was collected near the surface water of this wall (1 m deep). Two other biofilms were collected at 4 m below the surface (Biofilm C) and near the surface (Biofilm D) of the western wall.

Figure 2 . 6 :

 26 Figure 2.6: Proportion of each genus in the four biofilms after a direct phylopeptidomics analysis. The biofilms were recovered from four locations (Biofilms D, C, B, and A). The ratios of microorganisms were established either by Order-specific TSMs or Genus-specific TSMs. Sphingomonas, Caulobacter, and Acidovorax are shown in Blue, Red, and Yellow, respectively.

  pH s at 25°c (µS/cm) COD (mg/L) MIS (mg/L) F -(mg/L) Cl -(mg/L) Br -(mg/Lmg/L) Na + (mg/L) K + (mg/L) Ca 2+ (mg/L) Mg 2+ (mg/L)

Figure 2 . 7 :

 27 Figure 2.7: Schematic view of Pool 1. Samples A, B, and E can be highlighted by natural light during day time while samples C and D are always in the shadow. The location of the cobalt sources are indicated when they are present. The sources near the western wall are present more often than the ones near the northern wall.

Figure 2 . 8 :

 28 Figure 2.8: Sampling locations of the different samples in Pool 2 (simplified and adapted from [250])

  Scheme 2.2: Pool 2 2017 samplings' treatment protocol schematized

Figure 2 . 9 :EBPPMMMR

 29 Figure 2.9: Kinetic of the liquid culture of Sample G from Pool 2 in 2017 at the genus level in LB

Figure 2 . 11 : 2

 2112 Figure 2.11: Weight of Sample G pellet at each 2017 kinetic point

Figure 2 . 12 :

 212 Figure 2.12: Kinetic of the liquid culture of Sample H from Pool 2 in 2017 at the genus level in LB

Figure 2 . 14 :

 214 Figure 2.14: Weight of Sample H pellet at each 2017 kinetic point

R

  r A c tin o b a S tr e p to m y c e s B a c te ro id e te s P a e n ib a c illu s C lo s trid iu m O th e r C lo s trid i O th e r R h iz o b ia B r a d y r h iz o b ia c H y p h o m ic r o b ia M e s o rh iz o b iu m R h iz o b iu m R h o d o b a c te ra l R h o d o s p irilla le S p h in g o m o n a s A lc a lig e n a c e a e A q u a b a c te riu m A q u in c o la B u rk h o ld e ria R a ls to n ia A c id o v o ra x C a ld im o n a s C o m a m o n a s P e lo m o n a s P s e u d o rh o d o fe V a rio v o ra x Id e o n e lla M e th y lib iu m H e rb a s p irillu m J a n th in o b a c te r M a s s ilia P o ly a n g iu m b r R h iz o b a c te r R u b riv iv a x S p h a e ro tilu s T h io m o n a s N e is s e ria T h a u e ra D e lta p ro te o b a c O th e r G a m m a p E n te r o b a c te ria P s e u d o m o n a s X a n th o m o n a d a M y c o p la s m a A s c o m y c o ta B a c illu s B re v u n d im o n a M e th y lo b a c te ri r A c tin o b a c te r ia S tr e p to m y c e s B a c te ro id e te s P a e n ib a c illu s C lo s trid iu m O th e r C lo s trid ia le s O th e r R h iz o b ia le s B r a d y r h iz o b ia c e a e H y p h o m ic r o b ia c e a e M e s o rh iz o b iu m R h iz o b iu m R h o d o b a c te ra le s R h o d o s p irilla le s S p h in g o m o n a s A lc a lig e n a c e a e A q u a b a c te riu m A q u in c o la B u rk h o ld e ria R a ls to n ia A c id o v o ra x C a ld im o n a s C o m a m o n a s P e lo m o n a s P s e u d o rh o d o fe ra x V a rio v o ra x Id e o n e lla M e th y lib iu m H e rb a s p irillu m J a n th in o b a c te riu m M a s s ilia P o ly a n g iu m b r a c h y s p o r u m _ g e n u s R h iz o b a c te r R u b riv iv a x S p h a e ro tilu s T h io m o n a s N e is s e ria T h a u e ra D e lta p ro te o b a c te ria O th e r G a m m a p ro te o b a c te ria E n te r o b a c te ria c e a e P s e u d o m o n a s X a n th o m o n a d a c e a e M y c o p la s m a A s c o m y c o ta B a c illu s B re v u n d im o n a s M e th y lo b a c te ria c e a e r A c tin o b a c te r ia S tr e p to m y c e s B a c te ro id e te s P a e n ib a c illu s C lo s trid iu m O th e r C lo s trid ia le s O th e r R h iz o b ia le s B r a d y r h iz o b ia c e a e H y p h o m ic r o b ia c e a e M e s o rh iz o b iu m R h iz o b iu m R h o d o b a c te ra le s R h o d o s p irilla le s S p h in g o m o n a s A lc a lig e n a c e a e A q u a b a c te riu m A q u in c o la B u rk h o ld e ria R a ls to n ia A c id o v o ra x C a ld im o n a s C o m a m o n a s P e lo m o n a s P s e u d o rh o d o fe ra x V a rio v o ra x Id e o n e lla M e th y lib iu m H e rb a s p irillu m J a n th in o b a c te riu m M a s s ilia P o ly a n g iu m b r a c h y s p o r u m _ g e n u s R h iz o b a c te r R u b riv iv a x S p h a e ro tilu s T h io m o n a s N e is s e ria T h a u e ra D e lta p ro te o b a c te ria O th e r G a m m a p ro te o b a c te ria E n te r o b a c te ria c e a e P s e u d o m o n a s X a n th o m o n a d a c e a e M y c o p la s m a A s c o m y c o ta B a c illu s B re v u n d im o n a s M e th y lo b a c te ria c e a e r A c tin o b a c te r ia S tr e p to m y c e s B a c te ro id e te s P a e n ib a c illu s C lo s trid iu m O th e r C lo s trid ia le s O th e r R h iz o b ia le s B r a d y r h iz o b ia c e a e H y p h o m ic r o b ia c e a e M e s o rh iz o b iu m R h iz o b iu m R h o d o b a c te ra le s R h o d o s p irilla le s S p h in g o m o n a s A lc a lig e n a c e a e A q u a b a c te riu m A q u in c o la B u rk h o ld e ria R a ls to n ia A c id o v o ra x C a ld im o n a s C o m a m o n a s P e lo m o n a s P s e u d o rh o d o fe ra x V a rio v o ra x Id e o n e lla M e th y lib iu m H e rb a s p irillu m J a n th in o b a c te riu m M a s s ilia P o ly a n g iu m b r a c h y s p o r u m _ g e n u s R h iz o b a c te r R u b riv iv a x S p h a e ro tilu s T h io m o n a s N e is s e ria T h a u e ra D e lta p ro te o b a c te ria O th e r G a m m a p ro te o b a c te ria E n te r o b a c te ria c e a e P s e u d o m o n a s X a n th o m o n a d a c e a e M y c o p la s m a A s c o m y c o ta B a c illu s B re v u n d im o n a s M e th y lo b a c te ria c e a e r A c tin o b a c te r ia S tr e p to m y c e s B a c te ro id e te s P a e n ib a c illu s C lo s trid iu m O th e r C lo s trid ia le s O th e r R h iz o b ia le s B r a d y r h iz o b ia c e a e H y p h o m ic r o b ia c e a e M e s o rh iz o b iu m R h iz o b iu m R h o d o b a c te ra le s R h o d o s p irilla le s S p h in g o m o n a s A lc a lig e n a c e a e A q u a b a c te riu m A q u in c o la B u rk h o ld e ria R a ls to n ia A c id o v o ra x C a ld im o n a s C o m a m o n a s P e lo m o n a s P s e u d o rh o d o fe ra x V a rio v o ra x Id e o n e lla M e th y lib iu m H e rb a s p irillu m J a n th in o b a c te riu m M a s s ilia P o ly a n g iu m b r a c h y s p o r u m _ g e n u s R h iz o b a c te r R u b riv iv a x S p h a e ro tilu s T h io m o n a s N e is s e ria T h a u e ra D e lta p ro te o b a c te ria O th e r G a m m a p ro te o b a c te ria E n te r o b a c te ria c e a e P s e u d o m o n a s X a n th o m o n a d a c e a e M y c o p la s m a A s c o m y c o ta B a c illu s B re v u n d im o n a s M e th y lo b a c te ria c e a e r A c tin o b a c te r ia S tr e p to m y c e s B a c te ro id e te s P a e n ib a c illu s C lo s trid iu m O th e r C lo s trid ia le s O th e r R h iz o b ia le s B r a d y r h iz o b ia c e a e H y p h o m ic r o b ia c e a e M e s o rh iz o b iu m R h iz o b iu m R h o d o b a c te ra le s R h o d o s p irilla le s S p h in g o m o n a s A lc a lig e n a c e a e A q u a b a c te riu m A q u in c o la B u rk h o ld e ria R a ls to n ia A c id o v o ra x C a ld im o n a s C o m a m o n a s P e lo m o n a s P s e u d o rh o d o fe ra x V a rio v o ra x Id e o n e lla M e th y lib iu m H e rb a s p irillu m J a n th in o b a c te riu m M a s s ilia P o ly a n g iu m b r a c h y s p o r u m _ g e n u s R h iz o b a c te r R u b riv iv a x S p h a e ro tilu s T h io m o n a s N e is s e ria T h a u e ra D e lta p ro te o b a c te ria O th e r G a m m a p ro te o b a c te ria E n te r o b a c te ria c e a e P s e u d o m o n a s X a n th o m o n a d a c e a e M y c o p la s m a A s c o m y c o ta B a c illu s B re v u n d im o n a s M e th y lo b a c te ria c e a e e r A c tin o b a c te r ia S tr e p to m y c e s B a c te ro id e te s P a e n ib a c illu s C lo s trid iu m O th e r C lo s trid ia le s O th e r R h iz o b ia le s B r a d y r h iz o b ia c e a e H y p h o m ic r o b ia c e a e M e s o rh iz o b iu m R h iz o b iu m R h o d o b a c te ra le s R h o d o s p irilla le s S p h in g o m o n a s A lc a lig e n a c e a e A q u a b a c te riu m A q u in c o la B u rk h o ld e ria R a ls to n ia A c id o v o ra x C a ld im o n a s C o m a m o n a s P e lo m o n a s P s e u d o rh o d o fe ra x V a rio v o ra x Id e o n e lla M e th y lib iu m H e rb a s p irillu m J a n th in o b a c te riu m M a s s ilia P o ly a n g iu m b r a c h y s p o r u m _ g e n u s R h iz o b a c te r R u b riv iv a x S p h a e ro tilu s T h io m o n a s N e is s e ria T h a u e ra D e lta p ro te o b a c te ria O th e r G a m m a p ro te o b a c te ria E n te r o b a c te ria c e a e P s e u d o m o n a s X a n th o m o n a d a c e a e M y c o p la s m a A s c o m y c o ta B a c illu s B re v u n d im o n a s M e th y lo b a c te ria c e a e r A c tin o b a c te r ia S tr e p to m y c e s B a c te ro id e te s P a e n ib a c illu s C lo s trid iu m O th e r C lo s trid ia le s O th e r R h iz o b ia le s B r a d y r h iz o b ia c e a e H y p h o m ic r o b ia c e a e M e s o rh iz o b iu m R h iz o b iu m R h o d o b a c te ra le s R h o d o s p irilla le s S p h in g o m o n a s A lc a lig e n a c e a e A q u a b a c te riu m A q u in c o la B u rk h o ld e ria R a ls to n ia A c id o v o ra x C a ld im o n a s C o m a m o n a s P e lo m o n a s P s e u d o rh o d o fe ra x V a rio v o ra x Id e o n e lla M e th y lib iu m H e rb a s p irillu m J a n th in o b a c te riu m M a s s ilia P o ly a n g iu m b r a c h y s p o r u m _ g e n u s R h iz o b a c te r R u b riv iv a x S p h a e ro tilu s T h io m o n a s N e is s e ria T h a u e ra D e lta p ro te o b a c te ria O th e r G a m m a p ro te o b a c te ria E n te r o b a c te ria c e a e P s e u d o m o n a s X a n th o m o n a d a c e a e M y c o p la s m a A s c o m y c o ta B a c illu s B re v u n d im o n a s M e th y lo b a c te ria c e a e r A c tin o b a c te r ia S tr e p to m y c e s B a c te ro id e te s P a e n ib a c illu s C lo s trid iu m O th e r C lo s trid ia le s O th e r R h iz o b ia le s B r a d y r h iz o b ia c e a e H y p h o m ic r o b ia c e a e M e s o rh iz o b iu m R h iz o b iu m R h o d o b a c te ra le s R h o d o s p irilla le s S p h in g o m o n a s A lc a lig e n a c e a e A q u a b a c te riu m A q u in c o la B u rk h o ld e ria R a ls to n ia A c id o v o ra x C a ld im o n a s C o m a m o n a s P e lo m o n a s P s e u d o rh o d o fe ra x V a rio v o ra x Id e o n e lla M e th y lib iu m H e rb a s p irillu m J a n th in o b a c te riu m M a s s ilia P o ly a n g iu m b r a c h y s p o r u m _ g e n u s R h iz o b a c te r R u b riv iv a x S p h a e ro tilu s T h io m o n a s N e is s e ria T h a u e ra D e lta p ro te o b a c te ria O th e r G a m m a p ro te o b a c te ria E n te r o b a c te ria c e a e P s e u d o m o n a s X a n th o m o n a d a c e a e M y c o p la s m a A s c o m y c o ta B a c illu s B re v u n d im o n a s M e th y lo b a c te ria c e a e e r A c tin o b a c te r ia S tr e p to m y c e s B a c te ro id e te s P a e n ib a c illu s C lo s trid iu m O th e r C lo s trid ia le s O th e r R h iz o b ia le s B r a d y r h iz o b ia c e a e H y p h o m ic r o b ia c e a e M e s o rh iz o b iu m R h iz o b iu m R h o d o b a c te ra le s R h o d o s p irilla le s S p h in g o m o n a s A lc a lig e n a c e a e A q u a b a c te riu m A q u in c o la B u rk h o ld e ria R a ls to n ia A c id o v o ra x C a ld im o n a s C o m a m o n a s P e lo m o n a s P s e u d o rh o d o fe ra x V a rio v o ra x Id e o n e lla M e th y lib iu m H e rb a s p irillu m J a n th in o b a c te riu m M a s s ilia P o ly a n g iu m b r a c h y s p o r u m _ g e n u s R h iz o b a c te r R u b riv iv a x S p h a e ro tilu s T h io m o n a s N e is s e ria T h a u e ra D e lta p ro te o b a c te ria O th e r G a m m a p ro te o b a c te ria E n te r o b a c te ria c e a e P s e u d o m o n a s X a n th o m o n a d a c e a e M y c o p la s m a A s c o m y c o ta B a c illu s B re v u n d im o n a s M e th y lo b a c te ria c e a e e r A c tin o b a c te r ia S tr e p to m y c e s B a c te ro id e te s P a e n ib a c illu s C lo s trid iu m O th e r C lo s trid ia le s O th e r R h iz o b ia le s B r a d y r h iz o b ia c e a e H y p h o m ic r o b ia c e a e M e s o rh iz o b iu m R h iz o b iu m R h o d o b a c te ra le s R h o d o s p irilla le s S p h in g o m o n a s A lc a lig e n a c e a e A q u a b a c te riu m A q u in c o la B u rk h o ld e ria R a ls to n ia A c id o v o ra x C a ld im o n a s C o m a m o n a s P e lo m o n a s P s e u d o rh o d o fe ra x V a rio v o ra x Id e o n e lla M e th y lib iu m H e rb a s p irillu m J a n th in o b a c te riu m M a s s ilia P o ly a n g iu m b r a c h y s p o r u m _ g e n u s R h iz o b a c te r R u b riv iv a x S p h a e ro tilu s T h io m o n a s N e is s e ria T h a u e ra D e lta p ro te o b a c te ria O th e r G a m m a p ro te o b a c te ria E n te r o b a c te ria c e a e P s e u d o m o n a s X a n th o m o n a d a c e a e M y c o p la s m a A s c o m y c o ta B a c illu s B re v u n d im o n a s M e th y lo b a c te ria c e a e r A c tin o b a c te r ia S tr e p to m y c e s B a c te ro id e te s P a e n ib a c illu s C lo s trid iu m O th e r C lo s trid ia le s O th e r R h iz o b ia le s B r a d y r h iz o b ia c e a e H y p h o m ic r o b ia c e a e M e s o rh iz o b iu m R h iz o b iu m R h o d o b a c te ra le s R h o d o s p irilla le s S p h in g o m o n a s A lc a lig e n a c e a e A q u a b a c te riu m A q u in c o la B u rk h o ld e ria R a ls to n ia A c id o v o ra x C a ld im o n a s C o m a m o n a s P e lo m o n a s P s e u d o rh o d o fe ra x V a rio v o ra x Id e o n e lla M e th y lib iu m H e rb a s p irillu m J a n th in o b a c te riu m M a s s ilia P o ly a n g iu m b r a c h y s p o r u m _ g e n u s R h iz o b a c te r R u b riv iv a x S p h a e ro tilu s T h io m o n a s N e is s e ria T h a u e ra D e lta p ro te o b a c te ria O th e r G a m m a p ro te o b a c te ria E n te r o b a c te ria c e a e P s e u d o m o n a s X a n th o m o n a d a c e a e M y c o p la s m a A s c o m y c o ta B a c illu s B re v u n d im o n a s M e th y lo b a c te ria c e a e r A c tin o b a c te r ia S tr e p to m y c e s B a c te ro id e te s P a e n ib a c illu s C lo s trid iu m O th e r C lo s trid ia le s O th e r R h iz o b ia le s B r a d y r h iz o b ia c e a e H y p h o m ic r o b ia c e a e M e s o rh iz o b iu m R h iz o b iu m R h o d o b a c te ra le s R h o d o s p irilla le s S p h in g o m o n a s A lc a lig e n a c e a e A q u a b a c te riu m A q u in c o la B u rk h o ld e ria R a ls to n ia A c id o v o ra x C a ld im o n a s C o m a m o n a s P e lo m o n a s P s e u d o rh o d o fe ra x V a rio v o ra x Id e o n e lla M e th y lib iu m H e rb a s p irillu m J a n th in o b a c te riu m M a s s ilia P o ly a n g iu m b r a c h y s p o r u m _ g e n u s R h iz o b a c te r R u b riv iv a x S p h a e ro tilu s T h io m o n a s N e is s e ria T h a u e ra D e lta p ro te o b a c te ria O th e r G a m m a p ro te o b a c te ria E n te r o b a c te ria c e a e P s e u d o m o n a s X a n th o m o n a d a c e a e M y c o p la s m a A s c o m y c o ta B a c illu s B re v u n d im o n a s M e th y lo b a c te ria c e a e r A c tin o b a c te r ia S tr e p to m y c e s B a c te ro id e te s P a e n ib a c illu s C lo s trid iu m O th e r C lo s trid ia le s O th e r R h iz o b ia le s B r a d y r h iz o b ia c e a e H y p h o m ic r o b ia c e a e M e s o rh iz o b iu m R h iz o b iu m R h o d o b a c te ra le s R h o d o s p irilla le s S p h in g o m o n a s A lc a lig e n a c e a e A q u a b a c te riu m A q u in c o la B u rk h o ld e ria R a ls to n ia A c id o v o ra x C a ld im o n a s C o m a m o n a s P e lo m o n a s P s e u d o rh o d o fe ra x V a rio v o ra x Id e o n e lla M e th y lib iu m H e rb a s p irillu m J a n th in o b a c te riu m M a s s ilia P o ly a n g iu m b r a c h y s p o r u m _ g e n u s R h iz o b a c te r R u b riv iv a x S p h a e ro tilu s T h io m o n a s N e is s e ria T h a u e ra D e lta p ro te o b a c te ria O th e r G a m m a p ro te o b a c te ria E n te r o b a c te ria c e a e P s e u d o m o n a s X a n th o m o n a d a c e a e M y c o p la s m a A s c o m y c o ta B a c illu s B re v u n d im o n a s M e th y lo b a c te ria c e a e r A c tin o b a c te r ia S tr e p to m y c e s B a c te ro id e te s P a e n ib a c illu s C lo s trid iu m O th e r C lo s trid ia le s O th e r R h iz o b ia le s B r a d y r h iz o b ia c e a e H y p h o m ic r o b ia c e a e M e s o rh iz o b iu m R h iz o b iu m R h o d o b a c te ra le s R h o d o s p irilla le s S p h in g o m o n a s A lc a lig e n a c e a e A q u a b a c te riu m A q u in c o la B u rk h o ld e ria R a ls to n ia A c id o v o ra x C a ld im o n a s C o m a m o n a s P e lo m o n a s P s e u d o rh o d o fe ra x V a rio v o ra x Id e o n e lla M e th y lib iu m H e rb a s p irillu m J a n th in o b a c te riu m M a s s ilia P o ly a n g iu m b r a c h y s p o r u m _ g e n u s R h iz o b a c te r R u b riv iv a x S p h a e ro tilu s T h io m o n a s N e is s e ria T h a u e ra D e lta p ro te o b a c te ria O th e r G a m m a p ro te o b a c te ria E n te r o b a c te ria c e a e P s e u d o m o n a s X a n th o m o n a d a c e a e M y c o p la s m a A s c o m y c o ta B a c illu s B re v u n d im o n a s M e th y lo b a c te ria c e a e r A c tin o b a c te r ia S tr e p to m y c e s B a c te ro id e te s P a e n ib a c illu s C lo s trid iu m O th e r C lo s trid ia le s O th e r R h iz o b ia le s B r a d y r h iz o b ia c e a e H y p h o m ic r o b ia c e a e M e s o rh iz o b iu m R h iz o b iu m R h o d o b a c te ra le s R h o d o s p irilla le s S p h in g o m o n a s A lc a lig e n a c e a e A q u a b a c te riu m A q u in c o la B u rk h o ld e ria R a ls to n ia A c id o v o ra x C a ld im o n a s C o m a m o n a s P e lo m o n a s P s e u d o rh o d o fe ra x V a rio v o ra x Id e o n e lla M e th y lib iu m H e rb a s p irillu m J a n th in o b a c te riu m M a s s ilia P o ly a n g iu m b r a c h y s p o r u m _ g e n u s R h iz o b a c te r R u b riv iv a x S p h a e ro tilu s T h io m o n a s N e is s e ria T h a u e ra D e lta p ro te o b a c te ria O th e r G a m m a p ro te o b a c te ria E n te r o b a c te ria c e a e P s e u d o m o n a s X a n th o m o n a d a c e a e M y c o p la s m a A s c o m y c o ta B a c illu s B re v u n d im o n a s M e th y lo b a c te ria c e a e r A c tin o b a c te r ia S tr e p to m y c e s B a c te ro id e te s P a e n ib a c illu s C lo s trid iu m O th e r C lo s trid ia le s O th e r R h iz o b ia le s B r a d y r h iz o b ia c e a e H y p h o m ic r o b ia c e a e M e s o rh iz o b iu m R h iz o b iu m R h o d o b a c te ra le s R h o d o s p irilla le s S p h in g o m o n a s A lc a lig e n a c e a e A q u a b a c te riu m A q u in c o la B u rk h o ld e ria R a ls to n ia A c id o v o ra x C a ld im o n a s C o m a m o n a s P e lo m o n a s P s e u d o rh o d o fe ra x V a rio v o ra x Id e o n e lla M e th y lib iu m H e rb a s p irillu m J a n th in o b a c te riu m M a s s ilia P o ly a n g iu m b r a c h y s p o r u m _ g e n u s R h iz o b a c te r R u b riv iv a x S p h a e ro tilu s T h io m o n a s N e is s e ria T h a u e ra D e lta p ro te o b a c te ria O th e r G a m m a p ro te o b a c te ria E n te r o b a c te ria c e a e P s e u d o m o n a s X a n th o m o n a d a c e a e M y c o p la s m a A s c o m y c o ta B a c illu s B re v u n d im o n a s M e th y lo b a c te ria c e a e r A c tin o b a c te r ia S tr e p to m y c e s B a c te ro id e te s P a e n ib a c illu s C lo s trid iu m O th e r C lo s trid ia le s O th e r R h iz o b ia le s B r a d y r h iz o b ia c e a e H y p h o m ic r o b ia c e a e M e s o rh iz o b iu m R h iz o b iu m R h o d o b a c te ra le s R h o d o s p irilla le s S p h in g o m o n a s A lc a lig e n a c e a e A q u a b a c te riu m A q u in c o la B u rk h o ld e ria R a ls to n ia A c id o v o ra x C a ld im o n a s C o m a m o n a s P e lo m o n a s P s e u d o rh o d o fe ra x V a rio v o ra x Id e o n e lla M e th y lib iu m H e rb a s p irillu m J a n th in o b a c te riu m M a s s ilia P o ly a n g iu m b r a c h y s p o r u m _ g e n u s R h iz o b a c te r R u b riv iv a x S p h a e ro tilu s T h io m o n a s N e is s e ria T h a u e ra D e lta p ro te o b a c te ria O th e r G a m m a p ro te o b a c te ria E n te r o b a c te ria c e a e P s e u d o m o n a s X a n th o m o n a d a c e a e M y c o p la s m a A s c o m y c o ta B a c illu s B re v u n d im o n a s M e th y lo b a c te ria c e a e r A c tin o b a c te r ia S tr e p to m y c e s B a c te ro id e te s P a e n ib a c illu s C lo s trid iu m O th e r C lo s trid ia le s O th e r R h iz o b ia le s B r a d y r h iz o b ia c e a e H y p h o m ic r o b ia c e a e M e s o rh iz o b iu m R h iz o b iu m R h o d o b a c te ra le s R h o d o s p irilla le s S p h in g o m o n a s A lc a lig e n a c e a e A q u a b a c te riu m A q u in c o la B u rk h o ld e ria R a ls to n ia A c id o v o ra x C a ld im o n a s C o m a m o n a s P e lo m o n a s P s e u d o rh o d o fe ra x V a rio v o ra x Id e o n e lla M e th y lib iu m H e rb a s p irillu m J a n th in o b a c te riu m M a s s ilia P o ly a n g iu m b r a c h y s p o r u m _ g e n u s R h iz o b a c te r R u b riv iv a x S p h a e ro tilu s T h io m o n a s N e is s e ria T h a u e ra D e lta p ro te o b a c te ria O th e r G a m m a p ro te o b a c te ria E n te r o b a c te ria c e a e P s e u d o m o n a s X a n th o m o n a d a c e a e M y c o p la s m a A s c o m y c o ta B a c illu s B re v u n d im o n a s M e th y lo b a c te ria c e a e r A c tin o b a c te r ia S tr e p to m y c e s B a c te ro id e te s P a e n ib a c illu s C lo s trid iu m O th e r C lo s trid ia le s O th e r R h iz o b ia le s B r a d y r h iz o b ia c e a e H y p h o m ic r o b ia c e a e M e s o rh iz o b iu m R h iz o b iu m R h o d o b a c te ra le s R h o d o s p irilla le s S p h in g o m o n a s A lc a lig e n a c e a e A q u a b a c te riu m A q u in c o la B u rk h o ld e ria R a ls to n ia A c id o v o ra x C a ld im o n a s C o m a m o n a s P e lo m o n a s P s e u d o rh o d o fe ra x V a rio v o ra x Id e o n e lla M e th y lib iu m H e rb a s p irillu m J a n th in o b a c te riu m M a s s ilia P o ly a n g iu m b r a c h y s p o r u m _ g e n u s R h iz o b a c te r R u b riv iv a x S p h a e ro tilu s T h io m o n a s N e is s e ria T h a u e ra D e lta p ro te o b a c te ria O th e r G a m m a p ro te o b a c te ria E n te r o b a c te ria c e a e P s e u d o m o n a s X a n th o m o n a d a c e a e M y c o p la s m a A s c o m y c o ta B a c illu s B re v u n d im o n a s M e th y lo b a c te ria c e a e r A c tin o b a c te r ia S tr e p to m y c e s B a c te ro id e te s P a e n ib a c illu s C lo s trid iu m O th e r C lo s trid ia le s O th e r R h iz o b ia le s B r a d y r h iz o b ia c e a e H y p h o m ic r o b ia c e a e M e s o rh iz o b iu m R h iz o b iu m R h o d o b a c te ra le s R h o d o s p irilla le s S p h in g o m o n a s A lc a lig e n a c e a e A q u a b a c te riu m A q u in c o la B u rk h o ld e ria R a ls to n ia A c id o v o ra x C a ld im o n a s C o m a m o n a s P e lo m o n a s P s e u d o rh o d o fe ra x V a rio v o ra x Id e o n e lla M e th y lib iu m H e rb a s p irillu m J a n th in o b a c te riu m M a s s ilia P o ly a n g iu m b r a c h y s p o r u m _ g e n u s R h iz o b a c te r R u b riv iv a x S p h a e ro tilu s T h io m o n a s N e is s e ria T h a u e ra D e lta p ro te o b a c te ria O th e r G a m m a p ro te o b a c te ria E n te r o b a c te ria c e a e P s e u d o m o n a s X a n th o m o n a d a c e a e M y c o p la s m a A s c o m y c o ta B a c illu s B re v u n d im o n a s M e th y lo b a c te ria c e a e r A c tin o b a c te r ia S tr e p to m y c e s B a c te ro id e te s P a e n ib a c illu s C lo s trid iu m O th e r C lo s trid ia le s O th e r R h iz o b ia le s B r a d y r h iz o b ia c e a e H y p h o m ic r o b ia c e a e M e s o rh iz o b iu m R h iz o b iu m R h o d o b a c te ra le s R h o d o s p irilla le s S p h in g o m o n a s A lc a lig e n a c e a e A q u a b a c te riu m A q u in c o la B u rk h o ld e ria R a ls to n ia A c id o v o ra x C a ld im o n a s C o m a m o n a s P e lo m o n a s P s e u d o rh o d o fe ra x V a rio v o ra x Id e o n e lla M e th y lib iu m H e rb a s p irillu m J a n th in o b a c te riu m M a s s ilia P o ly a n g iu m b r a c h y s p o r u m _ g e n u s R h iz o b a c te r R u b riv iv a x S p h a e ro tilu s T h io m o n a s N e is s e ria T h a u e ra D e lta p ro te o b a c te ria O th e r G a m m a p ro te o b a c te ria E n te r o b a c te ria c e a e P s e u d o m o n a s X a n th o m o n a d a c e a e M y c o p la s m a A s c o m y c o ta B a c illu s B re v u n d im o n a s M e th y lo b a c te ria c e a e r A c tin o b a c te r ia S tr e p to m y c e s B a c te ro id e te s P a e n ib a c illu s C lo s trid iu m O th e r C lo s trid ia le s O th e r R h iz o b ia le s B r a d y r h iz o b ia c e a e H y p h o m ic r o b ia c e a e M e s o rh iz o b iu m R h iz o b iu m R h o d o b a c te ra le s R h o d o s p irilla le s S p h in g o m o n a s A lc a lig e n a c e a e A q u a b a c te riu m A q u in c o la B u rk h o ld e ria R a ls to n ia A c id o v o ra x C a ld im o n a s C o m a m o n a s P e lo m o n a s P s e u d o rh o d o fe ra x V a rio v o ra x Id e o n e lla M e th y lib iu m H e rb a s p irillu m J a n th in o b a c te riu m M a s s ilia P o ly a n g iu m b r a c h y s p o r u m _ g e n u s R h iz o b a c te r R u b riv iv a x S p h a e ro tilu s T h io m o n a s N e is s e ria T h a u e ra D e lta p ro te o b a c te ria O th e r G a m m a p ro te o b a c te ria E n te r o b a c te ria c e a e P s e u d o m o n a s X a n th o m o n a d a c e a e M y c o p la s m a A s c o m y c o ta B a c illu s B re v u n d im o n a s M e th y lo b a c te ria c e a e r A c tin o b a c te r ia S tr e p to m y c e s B a c te ro id e te s P a e n ib a c illu s C lo s trid iu m O th e r C lo s trid ia le s O th e r R h iz o b ia le s B r a d y r h iz o b ia c e a e H y p h o m ic r o b ia c e a e M e s o rh iz o b iu m R h iz o b iu m R h o d o b a c te ra le s R h o d o s p irilla le s S p h in g o m o n a s A lc a lig e n a c e a e A q u a b a c te riu m A q u in c o la B u rk h o ld e ria R a ls to n ia A c id o v o ra x C a ld im o n a s C o m a m o n a s P e lo m o n a s P s e u d o rh o d o fe ra x V a rio v o ra x Id e o n e lla M e th y lib iu m H e rb a s p irillu m J a n th in o b a c te riu m M a s s ilia P o ly a n g iu m b r a c h y s p o r u m _ g e n u s R h iz o b a c te r R u b riv iv a x S p h a e ro tilu s T h io m o n a s N e is s e ria T h a u e ra D e lta p ro te o b a c te ria O th e r G a m m a p ro te o b a c te ria E n te r o b a c te ria c e a e P s e u d o m o n a s X a n th o m o n a d a c e a e M y c o p la s m a A s c o m y c o ta B a c illu s B re v u n d im o n a s M e th y lo b a c te ria c e a e r A c tin o b a c te r ia S tr e p to m y c e s B a c te ro id e te s P a e n ib a c illu s C lo s trid iu m O th e r C lo s trid ia le s O th e r R h iz o b ia le s B r a d y r h iz o b ia c e a e H y p h o m ic r o b ia c e a e M e s o rh iz o b iu m R h iz o b iu m R h o d o b a c te ra le s R h o d o s p irilla le s S p h in g o m o n a s A lc a lig e n a c e a e A q u a b a c te riu m A q u in c o la B u rk h o ld e ria R a ls to n ia A c id o v o ra x C a ld im o n a s C o m a m o n a s P e lo m o n a s P s e u d o rh o d o fe ra x V a rio v o ra x Id e o n e lla M e th y lib iu m H e rb a s p irillu m J a n th in o b a c te riu m M a s s ilia P o ly a n g iu m b r a c h y s p o r u m _ g e n u s R h iz o b a c te r R u b riv iv a x S p h a e ro tilu s T h io m o n a s N e is s e ria T h a u e ra D e lta p ro te o b a c te ria O th e r G a m m a p ro te o b a c te ria E n te r o b a c te ria c e a e P s e u d o m o n a s X a n th o m o n a d a c e a e M y c o p la s m a A s c o m y c o ta B a c illu s B re v u n d im o n a s M e th y lo b a c te ria c e a e r A c tin o b a c te r ia S tr e p to m y c e s B a c te ro id e te s P a e n ib a c illu s C lo s trid iu m O th e r C lo s trid ia le s O th e r R h iz o b ia le s B r a d y r h iz o b ia c e a e H y p h o m ic r o b ia c e a e M e s o rh iz o b iu m R h iz o b iu m R h o d o b a c te ra le s R h o d o s p irilla le s S p h in g o m o n a s A lc a lig e n a c e a e A q u a b a c te riu m A q u in c o la B u rk h o ld e ria R a ls to n ia A c id o v o ra x C a ld im o n a s C o m a m o n a s P e lo m o n a s P s e u d o rh o d o fe ra x V a rio v o ra x Id e o n e lla M e th y lib iu m H e rb a s p irillu m J a n th in o b a c te riu m M a s s ilia P o ly a n g iu m b r a c h y s p o r u m _ g e n u s R h iz o b a c te r R u b riv iv a x S p h a e ro tilu s T h io m o n a s N e is s e ria T h a u e ra D e lta p ro te o b a c te ria O th e r G a m m a p ro te o b a c te ria E n te r o b a c te ria c e a e P s e u d o m o n a s X a n th o m o n a d a c e a e M y c o p la s m a A s c o m y c o ta B a c illu s B re v u n d im o n a s M e th y lo b a c te ria c e a e r A c tin o b a c te r ia S tr e p to m y c e s B a c te ro id e te s P a e n ib a c illu s C lo s trid iu m O th e r C lo s trid ia le s O th e r R h iz o b ia le s B r a d y r h iz o b ia c e a e H y p h o m ic r o b ia c e a e M e s o rh iz o b iu m R h iz o b iu m R h o d o b a c te ra le s R h o d o s p irilla le s S p h in g o m o n a s A lc a lig e n a c e a e A q u a b a c te riu m A q u in c o la B u rk h o ld e ria R a ls to n ia C a ld im o n a s C o m a m o n a s P e lo m o n a s P s e u d o rh o d o fe ra x V a rio v o ra x Id e o n e lla M e th y lib iu m H e rb a s p irillu m J a n th in o b a c te riu m M a s s ilia P o ly a n g iu m b r a c h y s p o r u m _ g e n u s R h iz o b a c te r R u b riv iv a x S p h a e ro tilu s T h io m o n a s N e is s e ria T h a u e ra D e lta p ro te o b a c te ria O th e r G a m m a p ro te o b a c te ria P s e u d o m o n a s X a n th o m o n a d a c e a e M y c o p la s m a B a c illu s B re v u n d im o n a s M e th y lo b a c te ria c e a e r A c tin o b a c te r ia P a e n ib a c illu s C lo s trid iu m O th e r C lo s trid ia le s O th e r R h iz o b ia le s B r a d y r h iz o b ia c e a e H y p h o m ic r o b ia c e a e M e s o rh iz o b iu m R h iz o b iu m R h o d o b a c te ra le s R h o d o s p irilla le s S p h in g o m o n a s A lc a lig e n a c e a e A q u a b a c te riu m A q u in c o la B u rk h o ld e ria R a ls to n ia C o m a m o n a s P s e u d o rh o d o fe ra x V a rio v o ra x Id e o n e lla M e th y lib iu m H e rb a s p irillu m J a n th in o b a c te riu m M a s s ilia P o ly a n g iu m b r a c h y s p o r u m _ g e n u s R h iz o b a c te r R u b riv iv a x S p h a e ro tilu s T h io m o n a s T h a u e ra D e lta p ro te o b a c te ria O th e r G a m m a p ro te o b a c te ria P s e u d o m o n a s X a n th o m o n a d a c e a e M y c o p la s m a B a c illu s B re v u n d im o n a s M e th y lo b a c te ria c e a e r A c tin o b a c te r ia S tr e p to m y c e s P a e n ib a c illu s C lo s trid iu m O th e r C lo s trid ia le s O th e r R h iz o b ia le s B r a d y r h iz o b ia c e a e H y p h o m ic r o b ia c e a e M e s o rh iz o b iu m R h iz o b iu m R h o d o b a c te ra le s R h o d o s p irilla le s S p h in g o m o n a s A lc a lig e n a c e a e A q u a b a c te riu m A q u in c o la B u rk h o ld e ria R a ls to n ia C o m a m o n a s P e lo m o n a s P s e u d o rh o d o fe ra x V a rio v o ra x Id e o n e lla M e th y lib iu m H e rb a s p irillu m J a n th in o b a c te riu m M a s s ilia P o ly a n g iu m b r a c h y s p o r u m _ g e n u s R h iz o b a c te r R u b riv iv a x S p h a e ro tilu s T h io m o n a s T h a u e ra D e lta p ro te o b a c te ria O th e r G a m m a p ro te o b a c te ria P s e u d o m o n a s X a n th o m o n a d a c e a e M y c o p la s m a B a c illu s B re v u n d im o n a s M e th y lo b a c te ria c e a e r A c tin o b a c te r ia P a e n ib a c illu s C lo s trid iu m O th e r C lo s trid ia le s O th e r R h iz o b ia le s B r a d y r h iz o b ia c e a e H y p h o m ic r o b ia c e a e M e s o rh iz o b iu m R h iz o b iu m R h o d o b a c te ra le s R h o d o s p irilla le s S p h in g o m o n a s A lc a lig e n a c e a e A q u a b a c te riu m A q u in c o la B u rk h o ld e ria R a ls to n ia C o m a m o n a s P e lo m o n a s P s e u d o rh o d o fe ra x V a rio v o ra x Id e o n e lla M e th y lib iu m H e rb a s p irillu m J a n th in o b a c te riu m M a s s ilia P o ly a n g iu m b r a c h y s p o r u m _ g e n u s R h iz o b a c te r R u b riv iv a x S p h a e ro tilu s T h io m o n a s T h a u e ra D e lta p ro te o b a c te ria O th e r G a m m a p ro te o b a c te ria P s e u d o m o n a s X a n th o m o n a d a c e a e M y c o p la s m a B a c illu s B re v u n d im o n a s M e th y lo b a c te ria c e a e n ib a c illu s C lo s trid iu m O th e r C lo s trid ia le s O th e r R h iz o b ia le s B r a d y r h iz o b ia c e a e H y p h o m ic r o b ia c e a e M e s o rh iz o b iu m R h iz o b iu m R h o d o b a c te ra le s R h o d o s p irilla le s S p h in g o m o n a s A lc a lig e n a c e a e A q u in c o la R a ls to n ia C o m a m o n a s P s e u d o rh o d o fe ra x Id e o n e lla M e th y lib iu m H e rb a s p irillu m J a n th in o b a c te riu m M a s s ilia P o ly a n g iu m b r a c h y s p o r u m _ g e n u s R h iz o b a c te r S p h a e ro tilu s T h io m o n a s T h a u e ra D e lta p ro te o b a c te ria O th e r G a m m a p ro te o b a c te ria P s e u d o m o n a s M y c o p la s m a B a c illu s B re v u n d im o n a s M e th y lo b a c te ria c e a e n ib a c illu s C lo s trid iu m O th e r C lo s trid ia le s O th e r R h iz o b ia le s H y p h o m ic r o b ia c e a e M e s o rh iz o b iu m R h iz o b iu m R h o d o b a c te ra le s R h o d o s p irilla le s S p h in g o m o n a s A lc a lig e n a c e a e A q u in c o la R a ls to n ia C o m a m o n a s P s e u d o rh o d o fe ra x Id e o n e lla H e rb a s p irillu m J a n th in o b a c te riu m M a s s ilia P o ly a n g iu m b r a c h y s p o r u m _ g e n u s R h iz o b a c te r S p h a e ro tilu s T h io m o n a s T h a u e ra D e lta p ro te o b a c te ria P s e u d o m o n a s M y c o p la s m a B a c illu s B re v u n d im o n a s M e th y lo b a c te ria c e a e r A c tin o b a c te r ia S tr e p to m y c e s B a c te ro id e te s P a e n ib a c illu s C lo s trid iu m O th e r C lo s trid ia le s O th e r R h iz o b ia le s B r a d y r h iz o b ia c e a e H y p h o m ic r o b ia c e a e M e s o rh iz o b iu m R h iz o b iu m R h o d o b a c te ra le s R h o d o s p irilla le s S p h in g o m o n a s A lc a lig e n a c e a e A q u a b a c te riu m A q u in c o la B u rk h o ld e ria R a ls to n ia A c id o v o ra x C a ld im o n a s C o m a m o n a s P e lo m o n a s P s e u d o rh o d o fe ra x V a rio v o ra x Id e o n e lla M e th y lib iu m H e rb a s p irillu m J a n th in o b a c te riu m M a s s ilia P o ly a n g iu m b r a c h y s p o r u m _ g e n u s

PSFigure 2 . 16 :

 216 Figure 2.16: Kinetic of the liquid culture of Sample I from Pool 2 in 2017 at the genus level in R2A

Figure 2 . 17 :CCC

 217 Figure 2.17: Weight of Sample I pellet at each 2017 kinetic point

Figure 2 .

 2 Figure 2.18 presents the microbial diversity comparison between all samples. Among the identified genera 46 were common to all three sampling's locations. Samples G and H were more similar within each other (30 genera in common) than with Sample I (5 common with Sample G, and 11 common with Sample H). Sample H presented the highest diversity. This diversity can be explained by the number of kinetic points (4 for Sample H vs. 2 for samples G and I).

Figure 2 . 18 :

 218 Figure 2.18: Venn diagram of the kinetics from Samples G, H, and I represented the number of genera common to all samples, two samples or only one sample.

  propensity for maintaining diversity compared to the LB medium. Between the 2015 and 2017 kinetics from Pool 2, 20 genera were only found in 2015 while 108 genera were solely found in 2017. Among the 53 genera common to both kinetics, 5 genera belong to the Actinobacteria (Nocardia, Cellulomonas, Arthrobacter, Microbacterium, and Streptomyces), 4 genera belong to the Bacteroidetes (Bacteroides, Porphyromonas, Prevotella, and Flavobacterium), 4 genera belong to the Firmicutes (Bacillus, Paenibacillus, Clostridium, and Butyrivibrio), 4 genera belong to the Ascomycota (Zygosaccharomyces, Metarhizium, Trichoderma, and Fusarium). Finally 36 genera belonging to the Proteobacteria were common (27 a-Proteobacteria, 3 b-Proteobacteria, 5 -Proteobacteria, and one d-Proteobacteria). The sample used for the 2015 kinetics originated from the cooling pool of a working nuclear reactor, and the microbiota was analyzed after at least 2.5 months of culture. The samples used for the 2017 kinetics originated from the same pool, but a year after the definitive shutdown of the pool. Moreover the microbiota was after 3, 7, 14 and 28 days of culture. A deep comparison between the results from the 2015 and 2017 kinetics is thus not applicable here.

  were found to be UV-B resistant[324]. The Methylibium genus was identified in 2006[325]. In one study, Chapter II -Methods development and optimization 114 the frozen permafrost of Arctic was irradiated at 100 kGy and one strain of Methylibium survived this irradiation[326]. The genus Streptomyces, belonging to the Actinobacteria was also identified. This genus can form spores and is abundantly found in soils and can be found in aquatic environments. Streptomyces rimosus was found to resist gamma-radiation with a D10 of 1 kGy[179]. A strain of S. canarius, isolated in Egypt, was shown to resist up to 25 kGy -irradiation[327]. Most of the isolated Streptomyces from this study exhibited a resistance to -radiation and only a few species did not survive doses higher than 5 kGy[327].One genus of the Ascomycota phylum was particularly present in the kinetic from Pool 1: the Trichoderma fungus. It is commonly found in forest and in soil. In a study, spores from T. viride exhibited a D10 between 1 and 1.5 kGy[328].Bosea, Bradyrhizobium, Rhizobium, Sphingomonas, Methylobacterium, Ralstonia, Brukholderia, Pelomonas, Pseudomonas, Leifsonia, and Bacillus genera, identified during the different kinetics, have already been identified in the water ofSNFPs [152, 177, 181,[183][184][185][186][187][188][189] 329].A few differences in the microbial diversity could be observed between the kinetics obtained in Pool when compared to the ones obtained in Pool 2 in 2015 and in 2017 (Appendix 5, Fig.6.2). Interestingly only 2 genera were common to both pools in 2015. The common genera were Xanthobacter and Methylocystis, belonging to the a-Proteobacteria. The physical and chemical conditions of the pools were highly different during the two 2015 samplings. The main stresses in Pool 1 were nutritive stress and the presence of radiation near the radioactive sources. In Pool 2 the stresses were not only the lack of nutrients but also the presence of highly active radionuclides, such as24 Na or60 Co, but also the presence of high radiation levels. As explained above some Xanthobacter species can produce slime which could protect them from radiation. The Methylocystis genus is methanotrophic and can resist desiccation, thus could have all the enzymatic arsenal to resist to high radiations that are known to cause the same DNA damages[315]. The kinetic from Pool 2 in 2017 presented the most unique genera with a total of 79 genera compared to the other two kinetics (34 and 18 genera for Pool 1 and Pool 2 2015, respectively). It can be explained by the number of phylopeptidomics analysis performed on these samples, higher than the other two kinetics. One point worth noticing was the absence of Cyanobacteria during the 2015 kinetic from Pool 2. All Cyanobacteria genera were indeed identified in Pool 1 in 2015 or in Pool 2 from the 2017 kinetic.

Pool 2

 2 during the 2015 kinetic. The conditions in Pool 2 in 2017 were less stringent than in 2015. The 2015 sampling was performed during a working nuclear cycle while the sampling in 2017 was performed more than a year after the complete shutdown of the reactor. The radioactivity was thus lower and less radionuclides were present in the water in 2017.

  basin studied is the cooling pool containing light water in direct contact with the 58 MW core of a nuclear reactor. It is 11 m deep, 7.5 m long, and 6.5 m wide. Water samples were collected at three different points of interest of the basin, called location A (Loc. A), location B (Loc. B), and location C (Loc. C). Loc. A corresponds to the bottom of the pool 2 m from the core at a depth of 9 m, Loc. B to the top of the chimney containing the nuclear core, and Loc. C to the inside of the chimney.

  Samples harvested during reactor shutdown were processed for microbial community assessment immediately after collection. Each sample was divided into 3 x 1 L for direct meta-analysis by amplicon sequencing, and 4.5 L for direct metaproteomic analysis. Microorganisms were concentrated and harvested either by centrifugation at 16,000 g for 20 min at 10°C (1 L) or by filtration on 0.22 µm hydrophilic polyethersulfone filters (MicroFunnelTM Supor®, Pall Corporation, USA) (2 x 1 L) (see article Comparison of two 16S rDNA regions metagenetics sequencing to identify microbial diversity in an aquatic environment complemented with microbial harvesting and DNA extraction optimization in preparation for details: cf.

[

  322]. The resulting peptides were analyzed with an ESI-Q Exactive HF mass spectrometer (Thermo) equipped with an ultra-high field Orbitrap analyser and coupled to an Ultimate 3000 176 RSL Nano LC System (ThermoFisher). A volume of 10 µL of peptides was injected onto a reverse phase Acclaim PepMap 100 C18 column (3 µm, 100 Å, 75 µm id x 500 mm) and resolved at a flow rate of 0.2 µL/min with a 60 min gradient of CH3CN in presence of 0.1% formic acid. The Q-Exactive HF instrument was operated with a Top20 strategy with only ions with potential charge states of 2+ and 3+ selected for MS/MS and with a dynamic exclusion of 10 sec. The proteotyping interpretation of MS/MS spectra was done with the National Center for Biotechnology Information non-redundant fasta file (NCBI-nr) database with the Mascot search engine (Matrix science). Peptide -to-MS/MS spectrum assignation was done with full trypsin specificity, maximum of one missed cleavage, mass tolerances of 5 ppm on the parent ion and 0.02 Da on the MS/MS, static modification of carboxyamidomethylated cysteine (+57.0215), and oxidized methionine (+15.9949) as dynamic modification. Post-processing of Mascot dat files was done as previously indicated (cf. Chapter II.1.2).

Figure 3 . 1 :

 31 Figure 3.1: Sampling locations of the different samples in Pool 2 (simplified and adapted from [250])

Figure 3 .

 3 Figure 3.4: Microbial communities present in the cooling pool of the reactor's core assessed during the reactor operation (2015) and at shutdown (2017). Meta-amplicomics 16S rRNA analysis at the family taxonomic level. Analysis from Loc. A samplings.A change in proportion can be observed between the two samplings. Indeed, the genus Methylobacterium represented less than 1% of the sample harvested in operation, while the genus Variovorax represented about 75%. At shutdown, Methylobacterium contributed to around 95% of the sample while while Variovorax has not been identified in the in the sampling performed at shutdown.

  Figure 3.5: Microbial diversity at the genus level present at location B (above the chimney) from the 2017 samplings.Metagenomics results after a V4V5 sequencing and a 0.016% cut-off of all methods. The phyla (or class for the Proteobacteria) is indicated for each genus. A majority of Methylobacterium (about 87%) can be observed at this location. The second main genus was Sphingomonas (about 9%).

Figure 3 .

 3 Figure 3.6: Microbial diversity at the genus level present at location C (inside the chimney) from the 2017 samplings.Metagenomics results after a V4V5 sequencing and a 0.016% cut-off of all methods. The phyla (or class for the Proteobacteria) is indicated for each genus. A majority of Methylobacterium (about 80%) can be observed at this location. The second main genus was Sphingomonas (about 8%), followed by Clostridium (about 7%).

  identified byBagwell et al. in the Savanah River Site SNFP [193]. One genus can possess several OTUs, which can give an indication about the species number contained per genus. The evenness of the samples collected during reactor shutdown was quite low, with a Shannon index in the 0.2 to 1.2 range compared to other environmental samples[193]. In the results represented by Bagwell et al., the Shannon indexes of the samples collected in the SNFP were at about5 [193].

  1 µGy/h at Loc. A to the 25 Gy/h at Loc. C). The high levels of radiation present at Loc. C could explain the lower cell density.Moreover the water present in Loc. C was more stagnant due to the pool's configuration.Metaproteomics highlighted the presence of genera such as Streptomyces, Asanoa, Bradyrhizobium, and Mycoplasma, which were not detected by amplicon sequencing and represented about 36% of the relative protein abundance. The 16S rDNA gene amplification approach actually suffers from non-quantitative representativeness particularly for Gram-positive bacteria. It has been shown that most of the classical DNA extraction protocols considerably underestimate the Gram-positive bacteria fraction, probably owing to the higher mechanical resistance of their cell walls[272]. Several DNA extraction protocols advise the use of 0.1 mm beads associated with a mechanical lysis via the use of a Precellys® for example[272, 276, 340, 341], while another study proposes a 2h incubation of the sample in presence of lysis buffer at 65°C[342].Metaproteomics actually made it possible to get away from metagenomics biases and to highlight the presence of two families of Gam-positive bacteria of the Actinobacteria phylum which accounted for 30% of the consortia composition, based on proteins. Even though biases also exist due to protein extraction, to the size of the cells, and to the less extensive databases, metaproteomics probably assesses more accurately the quantitative composition of the consortia[301].

  functioning, might have been better fitted to the new conditions present in the pool during shutdown. This could explain the completely loss of Variovorax in 2017 to the profit of Methylobacterium.The change of temperature and pH between operation (temperature of 35°C and pH 7.0) and shutdown (pH 5.4) could also partly explain the change in the microbiota, such as the presence of Acidobacteria during shutdown. However since no indication was obtained concerning the species taxonomical level of the bacteria, these explanations can only be hypotheses. No Variovorax nor Methylobacterium have been isolated from both campaigns, preventing the determination of the main physical or chemical factor responsible for this change.The description "Streptophyta_unclassified" could have two different meaning in this sample: it can i) highlight the presence of a micro-algae or ii) highlight the presence of Cyanobacteria. It has been hypothesized that chloroplast were Cyanobacteria after endocytosis[336, 337]. The Ribosomal Database Project associates this "Streptophyta" classification to the Cyanobacteria phylum (one Cyanobacteria-Chloroplast phylum)[335]. The presence of a micro-algae can be possible since a green micro-algae, Coccomyxa actinabiotis, has been isolated from a French SNFP[123, 338].

Figure 3 . 8 :Figure 3 . 9 :

 3839 Figure 3.8: Scheme of the scrubs location compared to the position of the reactor (reactor: box with the blue light)

Figure 3 . 10 :

 310 Figure 3.10: Rarefaction curve of the scrubs (obtained from Past software [278]). The diversity was reached quickly for all scrubs, except for scrub 3, which exhibited a greater diversity and reached a plateau above 5000 reads and at about 45 OTUs. The other scrubs reached a plateau faster, below 2000 reads and at about 10-17 OTUs.

Figure 3 . 11 :

 311 Figure 3.11: Proportion of each OTU in the scrubs samples at the taxonomical level (A) family, and (B) genus.A 0.016% cut-off was used.

  n e lla c e a e _ u n c la s s if ie d C lo s t r id ia le s _ u n c la s s if ie d C lo s t r id ia c e a e _ 1 _ u n c la s s if ie d C lo s t r id iu m _ IV C lo s t r id iu m _ s e n s u _ s t r ic t

Figure 3 . 12 :

 312 Figure 3.12: Results of the BLAST query of OTU 16. Nucleotide BLAST tool from NCBI was used for this alignment.

  Delage and C. Dubois (CEA, DEN/DDCC/UADS/SEROS, Saclay) for providing access to the facility, F. Coulon and E. Latil (CEA, DRF/PSAC/USPS/SPRE/SRP, Saclay) for completing the gamma spectrometry measurements, the radiological controls, and the transportation documents, F. Ventroux (CEA, DEN/DDCC/UADS/SEROS) for the photo authorization and the photographs, S. Echivard (CEA, DEN/DDCC/UADS/SEROS) for the scrubbing, and J. Martin-Laffon (DRF/BIG/PCV) for her help in bibliographic research. The authors gratefully acknowledge the Nuclear Toxicology Program of the CEA for financial support via the INFERNUS project.

  Co sources. It is annually cleaned in December. A sampling bottle (Wildco®) was used to sample 1 L or 6 L of water (in November 2015 and March 2017 respectively). The recovered water was transferred in sterile bottles from VWR (PETG Media Bottle Sterile).

Figure 3 . 13 :

 313 Figure 3.13: Diversity analyzed at the family level from the water sample E after filtration using metagenetics. A 0.016% cut-off was used.

  Deciphering the microbial composition of the cooling pools of nuclear facilities 154 (and genus) were identified in Scrub 2. Likewise 49.1% of bacteria belonging to the Caulobacteraceae (all identified as Brevundimonas) were found in Scrub 2 when only 0.02% of this family (and genus) were detected in Scrub 1. The Microbacteriaceae family represented 21.7 and 17.9% of scrubs 1 and 2

Figure 3 . 14 :

 314 Figure 3.14: Diversity analyzed at the family level from the samples collected at the northern wall using metagenetics. A 0.016% cut-off was used.

Figure 3 . 15 :

 315 Figure 3.15: Diversity analyzed at the family level from the samples collected at the bottom western wall using metagenetics. A 0.016% cut-off was used.

Figure 3 . 16 :Figure 3 . 17 :

 316317 Figure 3.16: Sequence homology between OTU 3 from Pool 1 in 2015 and OTU 2 from Pool 2 in 2015. Alignment realized using MultAlin, INRA.

Figure 3 . 18 :

 318 Figure 3.18: Diversity observed at the family level from the samples collected at the middle of the pool using metagenetics in 2017. A 0.016% cut-off was used.

Figure 3 . 19 :

 319 Figure 3.19: Diversity observed at the family level from the samples collected at the northern wall using metagenetics in 2017. A 0.016% cut-off was used.

Figure 3 . 20 :

 320 Figure 3.20: Diversity observed at the family level from the samples collected at the western wall using metagenetics in 2017.A 0.016% cut-off was used.

8 :Figure 3 . 21 :

 8321 Figure 3.21: Proportion of each genus from the water collected in 2016 before the change of filtration analyzed without cultivation step by metaproteomics. For sample A, B, C, and D the water was recovered after rubbing the wall to resuspend potential biofilms. (A) Family level; (B) Genus level.

Figure 3 . 22 :

 322 Figure 3.22: Proportion of each genus from the scrubs collected in 2016 before the change of filtration analyzed without cultivation step by metaproteomics. (A) Family level; (B) Genus level.

Figure 3 . 23 :BBC

 323 Figure 3.23: Proportion of each genus from the water collected in 2017 after the change of filtration analyzed without cultivation step by metaproteomics. (A) Family level; (B) Genus level.

  one and three families and genera were observed per location. The presence of Chroococcidiopsis (Cyanobacteria) is worth noticing at sample C scrub and in the high corner of the walls. The 2 other genera found in more than one sample were Pseudomonas (sample D scrub 1 and sample B scrub 1) and Rhizobium (sample D scrub 2 and sample B scrub 2)

  u d o m o n a d a c e a e M o ra x e lla c e a e A lte ro m o n a d a c e a e N e is s e ria c e a e Id e o n e lla _ fa m ily B u r k h o le d e ria c e a e R h iz o b ia c e a e B r a d y r h iz o b ia c e a e F u s o b a c te ria c e a e V e illo n e lla c e a e C h r o o c o c c id io p s id a c e a e d y r h iz o b iu m F u s o b a c te r iu m S e le n o m o n a s C h r o o c o c c id io p s is between the 2015 and 2017 metagenetics results (cf. Appendix 11). In 2015 a majority of Cyanobacteria was observed while Limnobacter was the dominant genus in 2017. The detection of algae in the 2016 sampling may indicate the presence of a rich and diverse microbiota in this pool compared to Pool 2. Moreover the biofilm composition was different in 2016 (before the filtration system change) and in 2017 (after the change)

Figure 3 . 25 :

 325 Figure 3.25: Molecular Phylogenetic analysis of OTUs 1, 70, 99, and 549 (Cyanobacteria phylum) by Maximum Likelihood method. The 16S rDNA sequences were obtained from NCBI. The evolutionary history was inferred by using the Maximum Likelihood method based on the. The bootstrap consensus tree inferred from 500 replicates[2] is taken to represent the evolutionary history of the taxa analyzed[2]. Branches corresponding to partitions reproduced in less than 50% bootstrap replicates are collapsed. The percentage of replicate trees in which the associated taxa clustered together in the bootstrap test (500 replicates) are shown next to the branches[367]. Initial tree(s) for the heuristic search were obtained by applying the Neighbor-Joining method to a matrix of pairwise distances estimated using the Maximum Composite Likelihood (MCL) approach. A discrete Gamma distribution was used to model evolutionary rate differences among sites(5 categories (+G, parameter = 0.7011)). The rate variation model allowed for some sites to be evolutionarily invariable ([+I], 12.2640% sites). The analysis involved 29 nucleotide sequences. All positions with less than 0% site coverage were eliminated. That is, fewer than 100% alignment gaps, missing data, and ambiguous bases were allowed at any position. There were a total of 349 positions in the final dataset. Evolutionary analyses were conducted in MEGA5[368].

Figure 3 . 26 :

 326 Figure 3.26: Non-metric multidimensional scaling of the different samplings from Pool 2. The results from the scrubs (blue)and the water analysis from 2015 (orange) and 2017 (grey) were obtained using the metagenetics results.

[

  399]. It can be improved using amine-functionalized silica nanoparticles[400]. This addition allowed a stable foam on a longer time. Bioengineering could allow a great radionuclides accumulation by microbial strains. An Escherichia coli strain as well as Deinococcus radiodurans R1 were transformed with Ni/Co transporter (gene from Rhodopseudomonas palustris CGA009 and Novosphingobium aromaticivorans F-199) to bioremove cobalt from spent decontamination solutions of nuclear power reactors. The modified E. coli was able to remove up to 85% of dissolved Co (of a 8 nM solution) in two cycles while modified D. radiodurans could remove 90% (of a 8.5 nM solution). These experiments revealed the ability to bioengineer bacterial strains to accumulate Co. However D. radiodurans exhibited a better accumulation than E. coli. Finally to remove Co present in the studied solutions, only 2 kg of modified bacteria were needed, which was less than the 50 kg estimated using a wild Co accumulating strain or the 8000 kg of ionexchange resin existing[401, 402].

  [322]. The resulting peptides were analyzed with an ESI-Q Exactive HF mass spectrometer (Thermo) equipped with an ultra-high field Orbitrap analyser and coupled to an Ultimate 3000 176 RSL Nano LC System (ThermoFisher). A volume of 10 µL of peptides was injected onto a reverse phase Acclaim PepMap 100 C18 column (3 µm, 100 Å, 75 µm id x 500 mm) and resolved at a flow rate of 0.2 µL/min with a 60 min gradient of CH3CN in presence of 0.1% formic acid. The Q-Exactive HF instrument was operated with a Top20 strategy with only ions with potential charge states of 2+ and 3+ selected for MS/MS and with a dynamic exclusion of 10 sec. The proteotyping interpretation of MS/MS spectra was done with the National Center for Biotechnology Information nonredundant fasta file (NCBI-nr) database with the Mascot search engine (Matrix science). Peptide -to-MS/MS spectrum assignation was done with full trypsin specificity, maximum of one missed cleavage, mass tolerances of 5 ppm on the parent ion and 0.02 Da on the MS/MS, static modification of carboxyamidomethylated cysteine (+57.0215), and oxidized methionine (+15.9949) as dynamic modification. Post-processing of Mascot dat files was done as previously indicated (reference public biofilm).

Figure 4 . 1 :

 41 Figure 4.1: Heat-map of the radioresistance of the isolated species in NaCl. % of survival of the strains when compared to the control plate. In dark grey: no data available.

  Micrococcus luteus, Kocuria koreensis and Pantoea vagans could well survive a 500 Gy irradiation (49.5 to 92.7%) and presented survival after a 1 kGy irradiation (3.7 to 37.2%) and beyond for M. luteus. The second group contained bacteria that could withstand a 500 Gy irradiation (2.1 to 14.4%) but presented no survival beyond this dose. The concerning bacteria were Leifsonia sp., Kocuria koreensis, two Nocardia niigatensis, Brevibacillus agri, and Pantoea sp. (two different strains). The third group survived well the 200 Gy irradiation (32.0 to 53.7%) but presented no growth after a 500 Gy irradiation and contained Leifsonia sp., Godornia bronchialis, Bacillus pumilus, and Bacillus sp. (two strains). Finally the last group contained

Figure 4 . 2 :

 42 Figure 4.2: Heat-map of the radioresistance of the isolated species in LB. % of survival of the strains when compared to the control plate.

Figure 4 . 3 :

 43 Figure 4.3: Uranium accumulation at different concentration of 11 bacterial strains isolated from a nuclear reactor pool.

  vegetative and spore forms were able to adsorb uranium (up to 200-225 mg/g dry cells for vegetative form and 275-300 mg/g dry cells for spores). At least half of the uranium was recovered when washing the cells with EDTA/TRIS 0.01 M. The uranium was thus mainly present on the cell walls[418]. B. subtilis was able to adsorb up to 90% of uranium in a 1000 ppm (1 g/L or about 4 mM) in a pH 4.9 solution [419]. Moreover three Bacillus species (B. licheniformis, B. megaterium, and B. subtilis) could adsorb uranium from 159 to 220 µmol/g of cells[389]. In our study the two Bacillus strains were able to highly accumulate uranium, or, at least, strongly bind uranium on the cell's walls. This hypothesis is consistent with the analysis of uranium complexes formed at the surface of two different Bacillus species performed byHening et al. (2011). The bacteria were washed three times with NaCl 0.9% and their uranium complexes were studied with an extended X-ray absorption fine structure spectroscopy. The authors discovered that uranium was strongly bound to phosphoryl residues on the cell surface [420]. B. vallismortis was able to biosorb (no wash performed) up to 100% of a 20 µM uranium solution[421].

Figure 4 . 4 :

 44 Figure 4.4: Cobalt accumulation at different concentration of 11 bacterial strains isolated from a nuclear reactor pool.

1

 1 The use of different analysis methods to highlight microbial diversity

Figure 5 . 1 :

 51 Figure 5.1 : Non-metric multidimensional scaling of the different samplings from Pool 2. The results from the scrubs (blue) and the water analysis from 2015 (orange) and 2017 (grey) were obtained using the metagenetics results.

  Different radioresistant bacteria have been isolated in the SNFP of the Idaho National Engineering and Environmental Laboratory. All isolated bacteria resisted a 50 Gy irradiation dose while only Corynebacterium aquaticum, Pseudomonas putida, Comamonas acidovorans, Gluconobacter cerinus, Micrococcus diversus, Rhodococcus rhodochrous could resist a 600 Gy irradiation [177]. Radioresistant bacteria have also been isolated from the Cofrentes SNFP (Spain). Strains belonging to the Stenotrophomonas, Mycobacterium, Nocardia, Burkholderia, Ralstonia, and Staphylococcus genera presented D 10 values of about 1.4 kGy [184].

Figure 6 . 1 :

 61 Figure 6.1: Repartition of the French nuclear waste at the end of 2013

Figure 6 . 7 :Figure 6 . 8 :Figure 6 . 9 :

 676869 Figure 6.7: Diversity at the genus level from the water sample E water after filtration analyzed using metagenetics. A 0.016% cut-off was used

Figure 6 . 10 :M

 610 Figure 6.10: Diversity at the family level from the samples collected at the top western wall analyzed using metagenetics.A 0.016% cut-off was used.

Figure 6 . 11 :

 611 Figure 6.11: Diversity observed at the family level from the samples collected at the middle of the pool using metagenetics.A 0.016% cut-off was used.

Figure 6 . 12 :Figure 6 . 13 :Figure 6 . 15 :

 612613615 Figure 6.12: Diversity observed at the family level from the samples collected at the northern wall using metagenetics. A 0.016% cut-off was used.

0 µM 5

 5 µM 20 µM 200 µM 0 µM 5 µM 20 µM 200 µM 0 µM 5 µM 20 µM 200 µM 0 µM 5 µM 20 µM 200 µM 0 µM 5 µM 20 µM 200 µM 0 µM 5 µM 20 µM 200 µM Mean 55,

  3.1.4 Acknowledgements ....................................................................................................................3.2 Unravelling the microbial community of biofilms present in a nuclear reactor pool (article in preparation)................................................................................................................................................ 3.2.1 Material and Methods ................................................................................................................ FIGURE 3.4: MICROBIAL COMMUNITIES PRESENT IN THE COOLING POOL OF THE REACTOR'S CORE ASSESSED DURING THE REACTOR OPERATION (2015) AND AT SHUTDOWN (2017). META-AMPLICOMICS 16S RRNA ANALYSIS AT THE FAMILY TAXONOMIC LEVEL. ANALYSIS FROM LOC. A SAMPLINGS. A CHANGE IN PROPORTION CAN BE OBSERVED BETWEEN THE TWO SAMPLINGS. INDEED, THE GENUS METHYLOBACTERIUM REPRESENTED LESS THAN 1% OF THE SAMPLE HARVESTED IN OPERATION, WHILE THE GENUS VARIOVORAX REPRESENTED ABOUT 75%. AT SHUTDOWN, METHYLOBACTERIUM CONTRIBUTED TO AROUND 95% OF THE SAMPLE WHILE WHILE VARIOVORAX HAS NOT BEEN IDENTIFIED IN THE IN THE SAMPLING PERFORMED AT SHUTDOWN. ......... FIGURE 3.5: MICROBIAL DIVERSITY AT THE GENUS LEVEL PRESENT AT LOCATION B (ABOVE THE CHIMNEY) FROM THE 2017 SAMPLINGS.

3.2.1.1 Samplings ........................................................................................................................... 3.2.1.2 DNA extraction ................................................................................................................... 3.2.1.3 Sequencing and data analysis ............................................................................................. 3.2.2 Results ........................................................................................................................................ 3.2.2.1 Radioactivity of the samples ............................................................................................... 3.2.2.2 Metagenetics analysis of the samples ................................................................................. 3.2.3 Discussion ................................................................................................................................... 3.2.4 Conclusion .................................................................................................................................. 3.2.5 Acknowledgements .................................................................................................................... 3.3 Direct meta-analyses reveal microbial diversity over space and time in a nuclear pool (article in preparation)................................................................................................................................................

+H • + • OH + H 2 Formation of free radicals H • , • OH , H 2 O 2 , H 2 , OH -, H + , O 2 •-, e -…Table 1 .1: Different forms of ROS

 1 .1.

	H 2 O
	H 2 O + H 2 O •+ + e -	H 2 O*
	H 2 O + H • + H 3 O + + • OH + H 2 O* + H 2 + OH -+ e -	H 2 O

Table 1 .2: D10 or LD50 in kGy of different microorganisms

 1

	Microorganisms	Radioresistance (D 10 ) (kGy)
	Escherichia coli	0.1 to 0.2
	Aspergillus fumigatus	0.63
	Methylobacterium radiotolerans	1
	Micrococcus luteus	1
	Chroococcidiopsis spp.	4
	Deinococcus deserti	>7.5
	Deinococcus radiodurans	10
	Coccomyxa actinabiotis	LD 50 : 10
	Thermococcus gammatolerans	30

. E. coli is not considered as radioresistant. D10: indicates the dose needed to eliminate 90% of the population

([47, 159, 176-179])

Table 1 .3: Comparison of the main sequencing technologies [222-227]

 1

			Sequencing chemistry Read length	Accuracy	Running time	Reagent Cost / Mb	Advantages	Disadvantages
			Dideoxy chain terminator	≈ 750 bp	99.999%	NA	NA	Low error rate	Expensive
								Read lenght (750 bp)	Slow
								High quality	Cloning needed
	PacBio		Real-time	≈ 1000 bp	< 90%	15 min -4 hours	7 -38 $ Long reads (up to 30+ kb with a mean size of 4,2 to 8,5 kb)	High error rate for the Standard Long reads
								Near random distribition of errors
								(increase the coverage to correct the	Still in development
								errors)
								No PCR bias	Expensive
								Capability of directly detecting modified
								nucleotides
								Fast
	Roche 454 GS Junior Titanium Pyrosequencing	≈ 400 bp	99%	10 hours	22 $	Multiplexing (multiple samples at the same time)	Expensive
		FLX Titanium		400 -600 bp	99.99%	10 hours	12 $	Long read length	High error rate
		FLX+		1000 bp	99.99%	23 hours	7 $	Very long read lenght	Reagent and upgrade issues
	Illumina	MiSeq	Reversible terminator	150 x 2 bp	> 99.9%	4 -39 hours	1 $	Low error rate	Short read length (but the best from
									Illumina)
		HiSeq 2000	Sequencing by synthesis	50 -100 bp	98%	3 -10 days	0.02 $	Cheap	Slow
								Fast	Few reads
								Large volume
								High throughput
	Ion Torrent PGM	Ion semiconductor	≈ 200 bp	≈ 99%	2 hours	0.01 -0.09 $ Fast	Higher error rates than Illumina
								Low error rates
								Cheap
								Easy to use
								No optics used
	ABI SOLiD	3730 xl	Dideoxy chain terminator	400 -900 bp	99.999%	20 min -3 hours	2308 $ Low cost for very small studies	Expensive for large amount of data
								Accuracy	Short read length
		5500 xl		60 x 2 bp	NA	6 days	< 0.07 $ Cheap	Slow
		SOLiD v4		50 x 2 bp	99.94%	7 -14 days	0.13 $	Accuracy
		MinIon	Real-time	up to or > 50 kb 97% for the 2 nd	≈ 9 kb/hour	0.01 -0.04 $ Fast and possibility to stop and restart	High error rate (frequently above 10% but
					generation	150 Mb/hour		the sequencing whenever needed	improving with the new generations)
						no time limitation		Long read
								Cheap

Table 1 .6: Radionuclides composition of the water in Pool 2

 1

  To sequence region of the 16S rDNA, a first step of PCR is necessary. During this amplification step, biases can occur, especially if the region is GC rich. It has been observed that there were large discrepancies in the proportion of gram negative bacteria established using next generation sequencing when compared to the results of direct microscopy, and culture-based methods. The proportion These biases can lead to a change in proportion of eachOTU [236] or the absence of some microorganisms for which DNA was not completely extracted and not properly amplified[236]. Finally the use of the DNA extraction protocol can influence the obtained results. In 2016, a study showed the results of 21 different DNA extraction protocol. They found out that the use of a phenol-chloroform-isoamyl alcohol and a protocol using the Qiagen-based lysis extraction presented the most reliable results. Furthermore

	these two extraction protocols were reproducible between different laboratories. However this study was
	performed on human feces. No harvesting of the microorganisms was thus necessary [272]. Human feces
	have a high bacterial density (about 0.9x10 11 bacterial/g fresh feces [273]) compared to water (about 3x10 4
	bacteria/mL in seawater [274], 5.8x10 6 bacteria/mL in lakes [275] or about 10 6 cells/L in spent nuclear fuel
	pool water [cf. Chapter III]

of gram negative bacteria was underestimated using NGS [270]. Moreover, even though metagenetics can indeed identify the microorganisms present in a population the DNA extraction should be optimized to recover DNA from every cells [271]. Biases cannot only occur during DNA extraction and PCR steps, but also Chapter II -Methods development and optimization 62 sequencing.

Table 2 .1: Theoretical proportion of each bacterium in the ZymoBIOMICS TM DNA standard and the proportions obtained after a V4 and a V4V5 sequencing with Illumina MiSeq ZymoBIOMICS TM DNA standard theoretical composition Proportion (%) Number of sequences Proportion (%) Number of sequences Proportion (%)

 2

	Bacillus	15,7	17027	18,6	28673	21,5
	Lactobacillus	18,8	16283	17,8	9895	7,4
	Staphylococcus	13,3	13483	14,7	25039	18,8
	Listeria	15,9	12338	13,5	20457	15,3
	Salmonella	11,3	11141	12,1	14919	11,2
	Escherichia/Shigella	10	9043	9,9	16100	12,1
	Enterococcus	10,4	7311	8,0	11936	9,0
	Pseudomonas	4,6	5055	5,5	6173	4,6
	Bacteria_unclassified		14	0,0
	Porphyrobacter		4	0,0
	Limnobacter		2	0,0	8	0,0
	Methylobacterium		2	0,0	3	0,0
	Lactobacillales_unclassified		2	0,0
	Enterobacteriaceae_unclassified				33	0,0
	Bacilli_unclassified				58	0,0
	Bacillales_unclassified				21	0,0
	Clostridiaceae_1_unclassified				2	0,0
	Acinetobacter				2	0,0

V4 sequencing V4V5 sequencing Figure 2.1: Mock analysis after a V4V5 metagenetics sequencing. Comparison between the

	theoretical composition of the
	mock sample (red) and the experimentally established composition (blue) identified after a V4V5 metagenomics sequencing
	and OCToPUS analysis. Green curve: number of sequences identified for each OTUs

Table 2 .2: Standard deviation of each OTU after a V4 and V4V5 sequencing compared to the theoretical mock

 2

2.3: Reads removal on the mock sample after a V4 or V4V5 metagenetic sequencing. In blue

		V4V5 sequencing V4 sequencing
	Bacillus	2,03	4,11
	Lactobacillus	0,74	8,05
	Staphylococcus	0,99	3,88
	Listeria	1,73	0,39
	Salmonella	0,60	0,08
	Escherichia/Shigella	0,10	1,47
	Enterococcus	1,72	1,02
	Pseudomonas	0,65	0,02
			: % of V4V5 reads
	obtained after each treatment, in red: % of V4 reads obtained after each treatment.

Standard deviation with the theoretical composition Number of sequences Loss (%) Number of sequences Loss (%)

		V4V5		V4
	Kingdom level	91705		133319
	Phylum level	91691	0,015	133319	0
	Class level	91691	0	133319	0
	Order level	91691	0	133261	0,044
	Family level	91689	0,002	133240	0,016
	Genus level	82646	9,861	117105	12,103

Table 2 .4: Sequencing error rates determined after OCToPUS treatment on the ZymoBIOMICS TM mock sample after a V4 and V4V5 sequencing

 2

Table 2 .5: Number of sequences obtained after a V4V5 Illumina MiSeq sequencing and after the OCToPUS analysis

 2

		V4	V4V5
	Error rate (%)	0.01	0.02

Number of sequences Loc. A -meth. 1 71157 Loc. A -meth. 2 99970 Loc. A -meth. 3 127024 Loc. B -meth. 1 100394 Loc. B -meth. 2 45166 Loc. B -meth. 3 105307 Loc. C -meth. 1 3696 Loc. C -meth. 2 28977 Loc. C -meth. 3 95759

Table 2 .6: ANOVA analysis of the three different methods. Mann

 2

-Whitney pairwise with raw p values and uncorrected significance (obtained from Past software

[278]

). In bold: significant difference obtained when p-value<0.05.

Table 2 .7: MS/MS spectra acquired per LC-MS/MS run and their interpretation.

 2

		Biofilm A	Biofilm B	Biofilm C	Biofilm D
	# MS/MS spectra	14677	4657	25777	16052
	# TSMs All taxa	2095	728	2645	1744
	# peptide sequences	1387	594	1931	1325

Table 2 .8: Number of taxa-to-spectrum matching (TSMs) and taxon-specific peptides (spePEPs) detected at the different taxonomical ranks in the four biofilms.

 2

	Taxonomy	Biofilm A	Biofilm B	Biofilm C	Biofilm D
	Rank	Taxa	# TSMs	# spePEPs	# TSMs	# spePEPs	# TSMs	# spePEPs	# TSMs	# spePEPs
	Superkingdom	Bacteria	1168	724	577	427	1313	913	676	484
	Phylum	Proteobacteria	1136	580	572	326	1264	743	634	367
	Class	Alphaproteobacteria	1011	493	547	303	1205	703	551	316
		Betaproteobacteria	171	55	42	9	-	-	99	24
	Order	Sphingomonadales	903	393	523	264	1142	589	484	1134
		Caulobacterales	148	39	51	14	87	10	70	1464
		Burkholderiales	160	47	33	9	-	-	90	1178
	Family	Sphingomonadaceae	903	383	523	259	1140	568	484	255
		Caulobacteraceae	148	39	51	14	87	10	70	22
		Comamonadaceae	119	29	20	3	-	-	52	13
	Genus	Sphingomonas	893	340	521	226	1131	497	478	214
		Caulobacter	122	33	35	9	52	7	53	18
		Acidovorax	96	16	16	3	-	-	40	8

Biofilm C stands out from the othersTable 2 .

 2 8 presents also the phylopeptidomics data for the three other biofilms. For the Biofilm B biofilm, a Sphingomonadales and Caulobacterales, were contributing to this signal. The genera identified were:

	low quantity of material was noted from the number of MS/MS spectra recorded (4,657 compared to
	14,677 for Biofilm A). Although this decrease is important (a third of Biofilm A), the phylopeptidics signal
	could be assigned to the genus level as previously described for Biofilm A. Here, the presence of three
	orders could be validated: Sphingomonadales, Caulobacterales, and Burkholderiales, with 393, 39 and 47
	unique specific peptides, respectively. At the Family taxonomical rank, enough signal could certified for the
	presence of Sphingomonadaceae (259 taxon-specific peptides) and Caulobacteraceae (14 taxon-specific
	peptides), but not for Comamonadaceae (only 3 taxon-specific peptides). Logically, the same occurs at the
	Genus taxonomical rank, with 226, 9 and 3 taxon-specific peptides for the presence of Sphingomonas,
	Caulobacter, and Acidovorax, respectively. Regarding Biofilm D, a large quantity of MS/MS spectra was
	recorded. In this case, a rather low level of TSMs was assigned to the superkingdom Bacteria (676)
	compared to Biofilm A (1168). Anyway, the three genera present in this sample are unambiguously
	identified by phylopeptidomics: Sphingomonas, Caulobacter, and Acidovorax, certified by 214, 18 and 8
	taxon-specific peptides, respectively. The latest biofilm sample, Biofilm C, was the largest MS/MS spectra
	dataset, i.e. corresponding to the most material rich sample. In this case, only one class is identified
	(Alphaproteobacteria) and with a large number of taxon-specific peptides (703). Two orders,

Table 2 .10: Cell count at the five different water sampling location using a Malassez cell.

 2

Table 2 .11: Proportion of each genus during the 2015 kinetic of Sample E in LB 1/6

 2

	D 45	D 137	D 173	D 227

Table 2 .12: Proportion of each genus during the 2015 kinetic of Sample E in LB 1/12

 2

	D 45	D 137	D 173

Table 2 .13: Number of identified families and genera during the kinetic for each sample

 2

2.2.3.1.2 Phylopeptidomics analysis of samples from Pool 1 after wall scrubbing

6 Sample A LB 1/12 Sample B LB 1/6 Sample B LB 1/12 Sample C LB 1/6 Sample C LB 1/12 Sample D 1/LB 6 Sample D LB 1/12 Sample E LB 1/6 Sample E LB 1/12 Number of

	families	23	20	25	23	22	33	34	33	20	16
	Number of genera	33	32	34	32	33	49	49	50	27	20

.15)

. This genus was present in all kinetic point at a 1/12 dilution and only present at the first two kinetic points (D 45 and 137) at a 1/6 dilution. The genus Caulobacter was present in greater proportion in LB 1/6 than LB 1/12

(14 to 27% and 3 to 22% respectively)

. This change in proportion observed between both dilutions could imply an important nutrient need of Caulobacter cells. Sphingomonas represented between 6 to 29% of the samples and were present in similar proportion in both dilutions

(6 to 23% in LB 1/6 and 11 to 29% in LB 1/12)

. The Pseudomonas genus was present in similar proportion in both kinetics, and represented about 4% of the total biomass. Acidovorax was mainly detected in LB 1/12 (3 to 29%). In LB 1/6 this genus represented 0 to 4% of the sample (detection in LB 1/6 started at D 173). Acidovorax appeared to have a smaller growth rate than other bacteria and is not competitive enough in a rich medium. At greater dilution this bacterium seemed to thrive in a poor medium. The genus Fusarium represented almost 7% of the identified peptides in LB 1/12 at D 227 (Table

2

.15).

In Sample C, Sphingomonas was the main genus (2 to 28%) (Table

2

.16)

. In this location (bottom western wall) the Caulobacter genus present at only one kinetic point per dilution and represented 2% (LB 1/6) and 1% (LB 1/12) of the identified genera. Hydrogenophaga (b-Proteobacteria) was present in all kinetic points from both dilutions (between 1 to 8% in LB 1/6 and 5 to 6% in LB 1/12). Pseudomonas and Bacillus represented about 3 and 2% of the samples respectively (at both dilutions) (Table

2

.16).

In Sample D, the main genus was Caulobacter in both kinetics (8 to 32%) (Table

2

.17). Sphingomonas was also present in important proportion at all kinetic points in both dilutions (6 to 15%). Both Rhizobium and Azorhizobium genera were present in all kinetic points in LB 1/6 and appeared at D 137 in LB 1/12. They Sample A LB 1/

Table 2 .14: Proportion of each genus during the 2015 kinetic of Sample A in LB at both dilutions

 2

	D 45	D 137	D 173	D 227	D 45	D 137	D 173	D 227

Table 2 .15: Proportion of each genus during the 2015 kinetic of Sample B in LB at both dilutions

 2

	D 45	D 137	D 173	D 227	D 45	D 137	D 173	D 227

Table 2 .16: Proportion of each genus during the 2015 kinetic of Sample C in LB at both dilutions

 2

	D 45	D 137	D 173	D 227	D 45	D 137	D 173	D 227

Table 2 .17: Proportion of each genus during the 2015 kinetic of Sample D in LB at both dilutions

 2 In the kinetic cultures, the main genus for Samples A, B, and D was Caulobacter, which represents 3%-70% in terms of biomass contribution. In Sample C this genus was only present at D 137 (2%) and D 173 (1%) in LB 1/6 and LB 1/12 respectively. The genus Xanthobacter was also present in high proportion in all samples of the kinetic, mainly starting after 4 months of cultivation. The third main genus found in all samples belonged to the Sphingomonadaceae family. The main genera found in all samples in average proportion were Sphingomonas and Sphingopyxis. Two other genera, Sphingobium and Novosphingobium were also present, but in smaller proportion and not in all samples.2.2.3.1.3 Phylopeptidomics analysis of the water from Pool 2 sampled during the 2015 campaignFor the Pool 2 samples, different media were used for the cultures: BHI and BHI diluted 10 times, LB and LB diluted twice, TSB and TSB diluted twice, and NB and NB diluted twice in water. The eight resulting kinetics were analyzed at days82, 153 and 207.

	D 45	D 137	D 173	D 227	D 45	D 137	D 173	D 227

Table 2 .

 2

[START_REF] Leclerc | Relationships between common water bacteria and pathogens in drinking-water. Heterotrophic Plate Counts and Drinking-water Safety[END_REF]

presents the phylopeptidomics results for the eight cultures. The main genera identified during the 2015 kinetic were Leifsonia (about 4%), Bosea (about 3%), Bradyrhizobium (22 to 36%), Rhodopseudomonas (3 to 4%), Prosthecomicrobium (about 4%), Methylobacterium (about 3%), Mesorhizobium (about 4%), Rhizobium (about 4%), and the Trichoderma fungi (4 to 7%). A majority of Proteobacteria were identified and more specifically a-Proteobacteria during the 2015 kinetic.

Table 2 .18: Proportion of each genus during the 2015 kinetic of Sample F.

 2 Results from all different media were mixed. The mean of each genus in each medium is presented in this table.To analyze microbial community in Pool 2 water in 2017 only two media were chosen: one general such as LB, and R2A, optimized for water samples[323].

	D 82	D 153	D 207

,

  Streptomyces was present at 20 to 30% in the sample when it was only present at about 2% in triplicate 2. Similarly, Ralstonia was detected in both triplicate 1 and 3(40 and 26% respectively) while it was completely absent from triplicate 2. At D28 the triplicates appeared to be highly similar. In all triplicates, Streptomyces represented about 3% of the sample, Bradyrhizobium and Methylobacterium were present at about 2-3%, Novosphingobium represented between 10-12%, Sphingomonas represented between 10-11%, Burkholderia represented about 2% of the sample, and Pseudomonas represented about 3% (Fig.2.15; Appendix 4, Table6.8).

	Figure 2.

15: Kinetic of the liquid culture of Sample I from Pool 2 in 2017 at the genus level in LB During

  Steptomyces represented about 18% of triplicate 3 when it was only present at about 3% in triplicates 1 and 2. Moreover Ralstonia was absent from triplicates 1 and 2 but represented 23% of triplicate 3. Several Rhizobiales, present in similar proportion in triplicates 1 and 2 were absent from triplicate 3 (e.g. Bradyrhizobium and Mesorhizobium at about 2% each, Methylobacterium at about 2%,…) (Fig.2.16; Appendix 4, Table6.9).

the R2A kinetic the triplicate from D7 were similar. Streptomyces and Bradyrhizobium both represented about 2% of the samples, Altererythrobacter was present at about 6%, Erythrobacter was present at about 7% of the sample, and Sphingomonas represented about 12% of the sample for example.

This similarity observed at D7 was not detected at D28 between the triplicates. If triplicates 1 and 2 were similar, triplicate 3 was different.

Table 3 .1: 16S rRNA gene amplicon sequencing analysis from the 2015 samplings.

 3 The read number represents the reads attributed to OTUs, and the OTUs number represents the number of OTUs above the 0.016% cut-off. 26 genera belonging to five phyla were identified in the pool during a working cycle (Fig.3.2).While this proportion can give some indications about the main genera present, it does not represent the proportion of each genus in the water sampled at this location. A majority of Variovorax (about 75%) was noticed in this pool, followed by the genus Sphingomonas (about 18%). The identified genera belonged mainly to the Proteobacteria phylum(16 genera). The other genera were divided between the

	0 m
				42°C	Heavy water
		B		39°C
	5 m
					Chanel 1
	7 m	47°C C		Heat exchangers
					Deactivation basin
	11 m 10 m	33°C	36°C	A

Chloroplast phylum)

[335]

. The presence of a micro-algae can be possible since a green micro-algae, Coccomyxa actinabiotis, has been isolated from a French SNFP

[123, 338]

.

Table 3 .2: 16S rRNA gene amplicon sequencing analysis from the 2017 samplings.

 3 The read number represents the reads attributed to OTUs, and the OTUs number represents the number of OTUs above the 0.016% cut-off.

  Most of the isolated Streptomyces from this study exhibited a resistance to -radiation and only a few species did not survive doses higher than 5 kGy [327]. To our knowledge, this is the first time the genus Streptomyces was identified in a nuclear pool.Variovorax, predominant during the reactor operation, is an environmental genus[315]. One Variovorax paradoxus strain was also identified in the SNFP of Cofrentes after a cultivation step[184]. The ability of some Variovorax strains to make use of hydrogen oxidation as an energy source[315, 350] might explains the prevalence of this genus during operation. H2 is indeed formed through the radiolysis of water, which occurs when a reactor is operating. When the reactor is shut down, the level of H2 drops. The advantage conferring by using H 2 as an energy source could explain Variovorax prevalence during the reactor's operation on other genera enable to use this energy. Other genera, not competitive enough during reactor

	The genus Sphingomonas is also widely distributed in aquatic environments [315]. One strain isolated from
	the Antarctic Dry Valleys combined the ability to withstand UV radiations, hyper aridity, and low
	temperatures encountered in Antarctica. This strain contained in its genome numerous stress response
	genes (116 in total), including oxidative stress resistance genes [306]. Another strain (Sphingomonas sp.
	strain RB2256) exhibited a high resistance to UV radiation, with a D10 of about 175. J.m -² [307]. Members of
	this genus were also identified in the Cofrentes SNFP in Spain and in a SNFP of the Idaho Nuclear Technology
	and Engineering Center in the USA, after a cultivation step [177, 185].
	5 kGy, respectively [346], and
	one species (Methylobacterium sp. strain rad) was even isolated after irradiation of 20 kGy [346].
	Streptomyces bacteria are aerobic, spore-forming, and environmental organisms [347]. Streptomyces can
	produce eumelanin, which is an antioxidant pigment, helping prevent the oxidative damages from ionizing

radiation and ROS formation

[START_REF] Gao | Microbial ultraviolet sunscreens[END_REF]

. This genus was also identified in a contaminated soil sample from Chernobyl

[348]

. Streptomyces radiopugnans was isolated from a radiation-polluted soil in China and presented great survival after a 5 kGy irradiation and could resist up to

15 kGy [349]

. S. rimosus was found to resist gamma-radiation with a D10 of 1 kGy

[179]

. A strain of S. canarius, isolated in Egypt, was shown to resist up to 25 kGy -irradiation

[327]

.

Table 3 .5: Shannon index of the scrubs.

 3 When this index is close to 0, one species dominates the sample.Seven phyla were identified in the different scrubs. The majority of the identified genera and families belonged to the Proteobacteria phylum (34 OTUs identified as Proteobacteria, and 17 OTUs identified as Actinobacteria, Bacteroidetes, Chloroflexi, Deinococcus-Thermus, Firmicutes, and Planctomycetes).

	Sample Shannon index
	Scrub 1	1.76
	Scrub 2	1.77
	Scrub 3	3.07
	Scrub 4	2.06
	Scrub 5	1.56
	Scrub 6	1.59
	Scrub 7	1.85
	Scrub 8	1.95

Table 3 .6: Shannon index of the 2015 samplings.

 3 When this index is close to 0, one species dominates the sample.21 OTUs were identified in the scrub from Sample A. 125, 36, and 18 OTUs were identified in samples B filter, scrub 1 and scrub 2 respectively. Only 19 and 6 OTUs were identified in sample C filter and scrub respectively, while 106, 16, and 99 OTUs were identified in samples D filter, scrub 1 and scrub 2 respectively. Finally, 43 OTUs were present in sample E filter.

Table 3 .7: Shannon index of the 2017 samplings

 3

. When this index is close to 0, one species dominates the sample.

  the diversity in the samples was performed using rarefaction curves (cf.Appendix 10). The maximum diversity plateau was reached quickly for all samples, except for sample B filter 2015, which exhibited a greater diversity and reached a plateau above 250000 reads and at about 350 OTUs. The other samples reached a plateau faster, below 50000 reads. Both samples E (filtration from 2015 and centrifugation from 2017) started to reach a plateau below 2000 reads and between 15 to 40 OTUs.

	A comparison of
	Sample A	1.75
	Sample B	1.25
	Sample C	1.60
	Sample D	1.58
	Sample E	1.5
	Sample F	0.69

(

  Actinobacteria, Firmicutes and Proteobacteria). At both depths, the major family was Burkholdericeae at 76.5 and 60.2% for samples F and E respectively. The genus Limnobacter was the only genus from this family present in these samples. From Sample F the other main family present was Sinobacteraceae (and the genus Nevskia) at 20.5%. The last genus present at more than 1% in this sample was Porphyrobacter, from the Erythrobacteraceae family (1.5%). The evenness from sample E was greater(1.38 vs. 0.67 for sample F; Appendix 9 Table 6.14). The genus Limnobacter was present at 60.2% while bacteria belonging to Clostridiaceae represented 19.3% of the microbiota. Only the genus Clostridium was identified in this family.

Two other Firmicutes were identified: Staphylococcus and Enterococcus (2.4 and 4.4% respectively). Finally two -Proteobacteria were also identified, belonging to the Enterobacteriaceae (Proteus genus) and

Table 4 .1: Dilution factors used to analyze metal concentration via the ICP-MS

 4

	4.3 Results and discussions
	4.3.1 Microbial identification

Table 4 .3: Identification of microorganisms isolated in the pool from the 2017 (shutdown reactor) samplings.

 4 This identification was performed with phylopeptidomics. Taxon ID represent the name of the closest identified strain in NCBI. The secondary organisms indicate the presence of several strains in the tested colony.

	Code	Growing temperature	Medium	Kinetic point isolation	Main oraganism	Taxon ID Secondary organisms Taxon ID Secondary organisms	Taxon ID Secondary organisms Taxon ID
	SKH448	RT	R2A 1/2 3 months Bacillus sp.	1581712 or 1579346
	SKH451	37°C	R2A 1/2 3 months Bacillus sp.	1581712 or 1579346	Pantoea sp.	1347370 or 470934
	SKH444	RT	BTR	28 days	Cellulomonas sp.	/
	SKH434	RT	BSR	28 days	Cellulomonas sp.	1346710
	SKH497	37°C	LB 1/10 3 months Pantoea sp.	1526743
	SKH446	37°C	LB 1/10 3 months Pantoea sp.	470934 Pantoea sp.	1526743
	SKH450	37°C	R2A 1/2 3 months Pantoea vagans	470934
	SKH85	RT	LB 1/10	3 days	Pelomonas sp.	1736464 Pelomonas sp.	1736434 Ideonella sp.	1547922
	SKH113	RT	LB 1/10	14 days	Pelomonas sp.	1736464 Pelomonas sp.	1736430 Methylibium sp.	1437443 or 1430884
	SKH112	RT	LB 1/10	14 days	Pelomonas sp.	1736464 Pelomonas sp.	1736430 Sphingomonas sp.	59803
	SKH111	RT	LB 1/10	14 days	Pelomonas sp.	1736464
	SKH107	RT	LB 1/10	14 days	Pelomonas sp.	1736464 Pelomonas sp.	1736430 Acidovorax sp.	1144317
	SKH104	RT	LB 1/10	14 days	Pelomonas sp.	1736464
	SKH118	RT	R2A 1/2	14 days	Pelomonas sp.	1736464 Pelomonas sp.	1736434 Ideonella sp.	1547922
	SKH158	RT	R2A 1/2	28 days	Pelomonas sp.	1736464 Pelomonas sp.	1736434 Ideonella sp.	1547922 Ralstonia pickettii	329
	SKH153	RT	LB 1/10	28 days	Pelomonas sp.	1736464 Pelomonas sp.	1736430 Ideonella sp.	1547922
	SKH152	RT	LB 1/10	28 days	Pelomonas sp.	1736464 Pelomonas sp.	1736434 Ideonella sp.	1547922
	SKH151	RT	LB 1/10	28 days	Pelomonas sp.	1736464 Pelomonas sp.	1736434 Ideonella sp.	1547922
	SKH150	RT	LB 1/10	28 days	Pelomonas sp.	1736464 Pelomonas sp.	1736430 Ideonella sp.	1547922
	SKH91	37°C	R2A 1/2	7 days	Ralstonia pickettii	329
	SKH110	RT	R2A 1/2	14 days	Ralstonia pickettii	329
	SKH109	RT	R2A 1/2	14 days	Ralstonia pickettii	329	Pelomonas sp.	1736464 Ideonella sp.	1547922
	SKH117	RT	R2A 1/2	14 days	Ralstonia pickettii	329	Pelomonas sp.	1736430 Aquincola tertiaricarbonis	391953
	SKH116	RT	R2A 1/2	14 days	Ralstonia pickettii	329	Pelomonas sp.	1736430
	SKH115	RT	R2A 1/2	14 days	Ralstonia pickettii	329	Pelomonas sp.	1736430
	SKH174	RT	R2A 1/2	28 days	Ralstonia pickettii	329
	SKH171	RT	R2A 1/2	28 days	Ralstonia pickettii	329	Pelomonas sp.	1736464
	SKH167	RT	R2A 1/2	28 days	Ralstonia pickettii	329	Pelomonas sp.	1736430
	SKH163	RT	R2A 1/2	28 days	Ralstonia pickettii	329
	SKH160	RT	R2A 1/2	28 days	Ralstonia pickettii	329
	SKH87	RT	LB 1/10	3 days	Sphingomonas sp.	59803
	SKH106	RT	LB 1/10	14 days	Sphingomonas echinoides	59803	Sphingomonas sp.	1564113 Pelomonas sp.	1736529 or 1736580
	SKH105	RT	LB 1/10	14 days	Sphingomonas echinoides	59803	Sphingomonas sp.	1564113 Pelomonas sp.	1736529 or 1736580
	SKH156	RT	LB 1/10	28 days	Sphingomonas elodea	179878 Sphingomonas sp.	194867 Sphingomonas sp.	194870
	SKH155	RT	LB 1/10	28 days	Sphingomonas elodea	179878 Sphingomonas sp.	194867 Sphingomonas sp.	194870
	SKH154	RT	LB 1/10	28 days	Sphingomonas sp.

Table 4 .4: Uranium accumulation for 10 9 cells in nmol.

 4 Preliminary results on the six tested bacteria.

koreensis 006 Leifsonia sp. 010 Bacillus sp. 033 Bacillus sp. 448

  After 24 hours of exposure to three different concentrations of uranium, its quantity in each fraction (pellet, supernatant, washes) was assessed (Appendix 14, Table6.27). Almost no uranium was found in the supernatant and in the washes for the lower concentrations for some bacteria. Kocuria koreensis presented almost no uranium in the supernatant nor washes for the 5 -10 and 50 µM concentrations. SKH 448 (Bacillus sp.) presented this pattern too. At the 5 µM concentration six bacteria almost entirely accumulated the uranium present in the medium (Micrococcus luteus 002, Kocuria koreensis 006, Brevibacillus agri 032, Bacillus sp. 033, Bacillus sp. 448 and Pantoea sp. 497) (Appendix 14, Table6.27).

								Pantoea
								vagans 450
		5 µM -1	8,64	14,52	25,76	10,56	1,82	4,31
		5 µM -2	9,41	18,50	38,62	11,87	4,94	4,56
	1h00	20 µM -1 20 µM -2	33,90 26,16	18,32 23,88	38,11 94,74	12,55 36,50	49,88 43,21	18,06 20,34
		200 µM -1	38,13	80,98	72,60	55,66	81,93	96,33
		200 µM -2	3,65	20,67	84,39	60,16	84,27	25,89
		5 µM -1	17,61	25,88	15,33	21,09	25,15	12,85
		5 µM -2	13,72	29,91	12,93	30,73	19,44	12,72
		20 µM -1	4,80	252,60	375,55	82,87	85,14	73,90
		20 µM -2	5,08	116,26	301,89	83,60	78,65	77,94
		200 µM -1	16,08	191,80	506,88	147,55	191,83	113,18
		200 µM -2	50,21	231,87	407,76	133,17	303,11	93,98

  The only found information concerning cobalt accumulation of the genus Kocuria concerned K. palustris. This species was able to accumulate cobalt at about 17 µmol/g Bacillus pumilus was proved to be able to accumulate or adsorb cobalt up to 40 mg/g dry weight while B. cereus, in the same conditions, could only accumulate 6.5 mg/g dry weight[388]. Several strains of Bacillus

	dry weight [152].
	A M. luteus strain isolated in a Slovak SNFP was able to accumulate cobalt at about 90 µmol/g dry weight
	[152]. Another M. luteus strain, isolated from an Algerian wastewater, could accumulate 12.8% (pH 5) to
	79.4% (pH 8.5) of a 1.69 µM cobalt solution. At pH 5.5 this strain could accumulate 18.22% of the same
	solution [427]. Another Micrococcus species, M. lylae, was able to accumulate or adsorb up to 23 mg/g dry
	weight [388].

Table 6 .5: Proportion of each genus during the 2017 kinetic in R2A (sample G)

 6

	1	2	3	1	2	3

Table 6 .6: Proportion of each genus during the 2017 kinetic in LB (sample H)

 6

	1	2	3	1	2	3	1	2	3	1	2	3

Table 6 .7: Proportion of each genus during the 2017 kinetic in R2A (sample H)

 6

	1	2	3	1	2	3	1	2	3	1	2	3

Table 6 .8: Proportion of each genus during the 2017 kinetic in LB (sample I)

 6

	1	2	3	1	2	3

Table 6 .9: Proportion of each genus during the 2017 kinetic in R2A (sample I)

 6

	1	2	3	1	2	3

Table 6 .10: Sum up of the different genera found in the 2017 kinetic from Pool 2.

 6 Red: only found in LB(28 genera), blue: only found in R2A(35 genera), green: found in both media(98 genera)

	Phylum	Class	Order	Family	Genus
	Actinobacteria	Actinobacteria	Actinomycetales	Actinomycetaceae	Actinomyces
			Bifidobacteriales	Bifidobacteriaceae	Bifidobacterium
			Corynebacteriales	Corynebacteriaceae	Corynebacterium
				Gordoniaceae	Gordonia
				Mycobacteriaceae	Mycobacterium
				Nocardiaceae	Nocardia
					Rhodococcus
			Micrococcales	Cellulomonadaceae	Actinotalea
					Cellulomonas
				Intrasporangiaceae	Janibacter
				Microbacteriaceae	Microbacterium
				Micrococcaceae	Arthrobacter
				Promicromonosporaceae	Promicromonosporaceae bacterium W15_genus
			Propionibacteriales	Nocardioidaceae	Mumia
					Nocardioides
			Pseudonocardiales	Pseudonocardiaceae	Amycolatopsis
					Lentzea
			Streptomycetales	Streptomycetaceae	Streptomyces
	Bacteroidetes	Bacteroidia	Bacteroidales	Bacteroidaceae	Bacteroides
				Prevotellaceae	Prevotella
				Porphyromonadaceae	Porphyromonas
		Chitinophagia	Chitinophagales	Chitinophagaceae	Sediminibacterium
		Cytophagia	Cytophagales	Cytophagaceae	Rhodonellum
		Flavobacteriia	Flavobacteriales	Flavobacteriaceae	Chryseobacterium
					Flavobacterium
		Sphingobacteriia	Sphingobacteriales	Sphingobacteriaceae	Pedobacter
					Sphingobacterium
	bacterium YEK0313_phylum bacterium YEK0313_class bacterium YEK0313_order bacterium YEK0313_family	bacterium YEK0313_genus
	Cyanobacteria	Cyanobacteria	Nostocales	Tolypothrichaceae	Tolypothrix
			Synechococcales	Leptolyngbyaceae	Leptolyngbya
				Synechococcaceae	Synechococcus
	Firmicutes	Bacilli	Bacillales	Bacillaceae	Bacillus
				Paenibacillaceae	Paenibacillus
			Lactobacillales	Lactobacillaceae	Lactobacillus
				Streptococcaceae	Streptococcus
		Clostridia	Clostridiales	Clostridiaceae	Clostridium
				Lachnospiraceae	Blautia
					Butyrivibrio
					Dorea
					Lachnoclostridium
					Oribacterium
				Peptococcaceae	Desulfosporosinus
		Negativicutes	Selenomonadales	Veillonellaceae	Pelosinus
	Proteobacteria	Alphaproteobacteria	Caulobacterales	Caulobacteraceae	Asticcacaulis
					Brevundimonas
					Caulobacter
					Phenylobacterium
			Rhizobiales	Aurantimonadaceae	Aureimonas
					Aurantimonas
				Bartonellaceae	Bartonella
				Beijerinckiaceae	Beijerinckia
				Bradyrhizobiaceae	Afipia
					Bosea
					Bradyrhizobium
					Rhodopseudomonas
				Chelatococcaceae	Chelatococcus
				Hyphomicrobiaceae	Devosia
					Hyphomicrobium
					Prosthecomicrobium
				Methylobacteriaceae	Methylobacterium
					Microvirga
				Methylocystaceae	Pleomorphomonas
				Phyllobacteriaceae	Aminobacter
					Mesorhizobium
				Rhizobiaceae	Agrobacterium
					Kaistia
					Rhizobium
				Rhodobiaceae	Amorphus
				Xanthobacteraceae	Azorhizobium

5: Comparison of metaproteomic and metagenetic analysis of microbial communities present in the cooling pool of the reactor's core at shutdown, at genus level, at the different sampling locations

	C Proteobacteria	Metaproteomics Alphaproteobacteria Alphaproteobacteria_unclassified Rhizobiales	Alphaproteobacteria_unclassified Bradyrhizobiaceae Metagenetics Brucellaceae	Alphaproteobacteria_unclassified Bradyrhizobiaceae_unclassified Brucellaceae_unclassified	Otu35 Otu80 0,204 Otu79		0,988 0,044	1,450 3,071
						Ochrobactrum	Otu47 10,516 8,857
					Gp3_unclassified (0.03%) Bacteria_unclassified (0.19%) Methylobacteriaceae	Methylobacterium Acidobacteria	Otu1 Otu54		6,305 0,244	1,428
	Actinobacteria Firmicutes a-Proteobacteria -Proteobacteria Tenericutes	Asanoa (27.3%) Streptomyces (26.3%) Clostridium (1.0%) Rhizobium (3.1%) Escherichia (0.6%) Pseudomonas (2.7%) Mycoplasma (0.5%) Betaproteobacteria	Rhodobacterales 38.5% Sphingomonadales 61.5% (7) -79.7% (4) Betaproteobacteria_unclassified 20.1% (27) Burkholderiales	Bacteroidetes_unclassified (0.06%) Chitinophagaceae_unclassified (0.02%) Rhodobacteraceae Rhodobacteraceae_unclassified Bacteroidetes Sediminibacterium (0.03%) Chlamydiales_unclassified (0.19%) Atopostipes (0.50%) Enterococcus (0.09%) Streptococcus (0.07%) Firmicutes Sphingomonadaceae Sphingomonas Chlamydiae Sphingomonadaceae_unclassified Betaproteobacteria_unclassified Betaproteobacteria_unclassified Clostridium_IV (0.19%) Clostridium_sensu_stricto (7.58%) Alcaligenaceae Alcaligenes	Otu36 Otu71 Otu6 Otu39 Otu57 Otu98 Otu87	14,172	0,213 7,170 19,320 2,338 2,465 0,097	7,887 1,736 1,208	6,938
					Pelosinus (0.02%) Burkholderiaceae	Ralstonia	Otu28		0,031 0,097	0,088 0,114
					Planctomycetaceae_unclassified (0.02%) Wautersia Ochrobactrum (0.55%) Comamonadaceae Comamonadaceae_unclassified Planctomycetes Pedomicrobium (0.02%) Novosphingobium (0.21%) a-Proteobacteria Curvibacter	Otu70 Otu11 Otu104		0,084 5,651 0,964 9,320 13,166 9,653	25,989 16,219
	Actinobacteria a-Proteobacteria b-Proteobacteria	Microbacterium (0.5% -0.81%) Bradyrhizobium (3.3% -0.02%) Methylobacterium (34.0% -78.82%) Comamonas (0.7% -0.05%) Rhodocyclales	Sphingomonas (8.71%) Burkholderiales_unclassified (0.04%) Curvibacter (0.39%) Diaphorobacter b-Proteobacteria Pelomonas Oxalobacteraceae Herminiimonas Pelomonas (0.04%) Gammaproteobacteria_unclassified (0.11%) Rhodocyclaceae Rhodocyclaceae_unclassified	Otu49 0,102 Otu17 3,063 5,004 Otu58 Otu127 0,709	8,883 0,139 5,631 1,095 6,964 2,064	8,512	4,026 0,549 1,342
		Proteus (0.65%) Gammaproteobacteria Gammaproteobacteria_unclassified Gammaproteobacteria_unclassified Gammaproteobacteria_unclassified Otu18 Salmonella (0.07%) -Proteobacteria Otu42 Serratia (0.16%) Legionella (0.08%) Otu122		6,097 0,711 0,523	1,406
					Haemophilus (0.05%)		Otu77		0,177
				Enterobacterales	Phaselicystis (0.03%) Enterobacteriaceae	d-Proteobacteria Enterobacteriaceae_unclassified	Otu8 8,627 4,606	1,553 3,602 4,721 0,769 1,365
							Otu56			1,142
					Morganellaceae	Morganella	Otu76		0,164	1,475	6,642
						Proteus	Otu33 15,212 5,182	1,077 12,184 8,363 6,509 4,240 5,710
				Legionellales	Coxiellaceae	Aquicella	Otu37		0,905
				Pseudomonadales	Pseudomonadaceae	Pseudomonadaceae_unclassified	Otu10		1,311
		Deltaproteobacteria	Myxococcales	Myxococcales_unclassified	Myxococcales_unclassified	Otu29		1,203
							Otu116		0,056
		Hydrogenophilalia	Hydrogenophilales	Hydrogenophilaceae	Hydrogenophilus	Otu63			2,229

: (A) Loc. A, (B) Loc. B, and (C) Loc. C.

Abundance, in %, and number of genera, in brackets. Bold numbers refer respectively to metagenetic analysis.

Table 6 .12: OTUs number obtained for each scrub after Illumina MiSeq V4V5 sequencing and OCToPUS analysis. Figure 6.6: Sequence homology between OTUs 8 and 56, both identified as

 6

	Scrub OTUs number
	1	12
	2	11
	3	48
	4	15
	5	13
	6	11
	7	18
	8	15

"Enterobacteriacea_unclassified". Homology obtained using MultAlin (http://multalin.toulouse.inra.fr/multalin/multalin.html)

Table 6 .13: Diversity present in Pool 1 in the different analyzed samples from 2015.

 6 In red: OTUs with a proportion higher than the 0.016% cut-off

	Phylum	Class	Order	Family	Genus	OTU	Sample A

scrub Sample B filter Sample B scrub 1 Sample B scrub 2 Sample C filter Sample C scrub Sample D filter Sample D scrub 1 Sample D scrub 2 Sample E filter Acidobacteria

Table 6 .21: Cell viability after irradiation in NaCl (in %).

 6 The viability was calculated after counting colonies and comparing it to the control plates. (-): ND

Table 6 .22: Cell viability after irradiation in liquid LB (in %).

 6 The viability was calculated after counting colonies and comparing it to the control plates. (-): ND

Negative control Positive control CEA-NOB-002 CEA-NOB-006 CEA-NOB-010 CEA-NOB-012 CEA-NOB-014 CEA-NOB-020 CEA-NOB-030 CEA-NOB-031 CEA-NOB-032 CEA-NOB-033 CEA-NOB-034 SKH 154 SKH 163 SKH 446 SKH 448 SKH 450

	Dose (Gy) Escherichia coli	Deinococcus radiodurans	Micrococcus luteus	Kocuria koreensis	Leifsonia sp.	Gordonia bronchialis	Leifsonia sp.	Nocardia niigatensis	Nocardia niigatensis	Bacillus thuringiensis	Brevibacillus agri	Bacillus sp.	Bacillus thuringiensis	Sphingomonas sp.	Ralstonia pickettii	Pantoea sp.	Bacillus sp.	Pantoea vagans
	0	100	100	100,00	100,00	100,00	100,00	100,00	100,00	100,00	100,00	100,00	100,00	100,00	100,00	100,00	100,00	100,00	100,00
	200	-	100	106,14	24,19	72,44	50,60	100,00	29,37	35,26	-	77,78	-	-	-	-	-	110,56	95,90
	500	0	100	42,06	5,60	10,48	12,84	7,86	9,90	4,50	0,00	11,40	43,33	0,00	0,03	0,00	7,00	50,80	16,10
	1000	0	90	17,48	0,07	0,28	0,18	0,14	1,80	0,78	1,50	1,45	10,00	2,00	0,00	0,00	0,73	27,07	1,84

Negative control Positive control CEA-NOB-002 CEA-NOB-003 CEA-NOB-006 CEA-NOB-008 CEA-NOB-010 CEA-NOB-012 CEA-NOB-014 CEA-NOB-020 CEA-NOB-030 CEA-NOB-032 CEA-NOB-033 SKH 111

																SKH 448	SKH 450	SKH 497
	Dose (Gy) Escherichia coli	Deinococcus radiodurans	Micrococcus luteus	Bacillus sp.	Kocuria koreensis	Bacillus pumilus	Leifsonia sp.	Gordonia bronchialis	Leifsonia sp.	Nocardia niigatensis	Nocardia niigatensis	Brevibacillus agri	Bacillus sp.	Pelomonas sp.	Bacillus sp.	Pantoea vagans	Pantoea sp.
	0	100,00	100,00	100,00	100,00	100,00	100,00	100,00	100,00	100,00	100,00	100,00	100,00	100,00	100,00	100,00	100,00	100,00
	200	6,64	51,50	80,08	32,04	92,72	33,65	58,31	12,55	92,02	36,70	55,23	34,15	37,34	4,77	9,88	49,50	19,28
	500	0,00	40,14	46,63	3,27	34,93	1,42	2,12	1,40	6,11	14,37	11,57	6,50	1,94	0,00	0,00	20,45	15,16
	1000	0,00	24,77	37,19	0,00	3,69	0,00	0,02	0,11	0,00	0,93	0,76	0,00	0,00	0,00	0,15	14,07	2,42
	1500	0,00	22,65	17,72	0,00	0,64	0,00	0,00	0,09	0,00	0,53	0,23	1,22	0,00	0,00	0,00	3,23	0,43

SKH 448 CEA-NOB-033 CEA-NOB-010 Bacillus sp. Bacillus sp. Leifsonia sp. CEA-NOB-002 CEA-NOB-006 SKH 450 Micrococcus luteus Kocuria koreensis Pantoea vagansTable 6 .25: Preliminary results -Uranium recovered per fraction (in nmol) after 24 hours and the recovery % compared to the introduced uranium. Some data were inconsistent with the introduced uranium

 6 (e.g. CEA-NOB-002 at 200 µM or CEA-NOB-006 at 20 µM, duplicate 1).

			CEA-NOB-002		CEA-NOB-006		CEA-NOB-010		CEA-NOB-033	SKH 448			SKH 450
	+ 1h
	+ 24h	Std	2,10 20,00 24,06	4,99 8,20 22,06		2,41 2,85 19,07		12,19 0,69 17,03	0,19 3,97	6,68		,76 4,04 0,98 21,26
	5-1	1,48	1,75	0,73	3,96	83,84	2,07	1,33	1,22	4,63	98,02	0,62	2,70	0,57	3,89	82,32
	5-2	1,61	2,11	0,52	4,24	89,84	2,64	1,23	0,57	4,44	94,00	0,65	2,33	0,64	3,62	76,80
	20-1	5,81	8,77	12,68	27,26	127,26	2,62	11,07	4,95	18,64	87,00	2,58	20,54	4,77	27,89	130,19
	20-2	4,48	12,09	32,81	49,39	230,56	3,41	9,36	4,96	17,73	82,78	2,91	11,65	4,73	19,29	90,02
	200-1	6,54	311,47	26,97	344,97	171,80	11,57	160,29	10,61	182,47	90,87	13,76	232,95	6,44	253,15	126,07
	200-2	0,63	274,55	12,34	287,52	143,18	2,95	108,29	7,84	119,09	59,31	3,70	131,70	7,85	143,25	71,34
		Pellet (nmol) Supernatant (nmol) Wash (nmol) Total (nmol) % recovered Pellet (nmol) Supernatant (nmol) Wash (nmol) Total (nmol) % recovered Pellet (nmol) Supernatant (nmol) Wash (nmol) Total (nmol) % recovered
	5-1	0,39	2,03	2,97	5,38	114,06	1,51	2,51	0,46	4,48	94,82	2,21	1,59	1,90	5,69	120,59
	5-2	1,06	1,96	1,35	4,37	92,54	1,70	2,21	0,39	4,30	91,04	3,31	1,55	0,72	5,58	118,28
	20-1	10,69	9,37	10,55	30,61	142,88	1,79	11,93	6,72	20,44	95,43	3,27	10,63	10,48	24,38	113,79
	20-2	9,26	7,76	11,53	28,55	133,28	5,21	11,29	8,89	25,39	118,50	8,12	9,20	18,70	36,02	168,13
	200-1	17,56	166,08	12,53	196,16	97,69	7,95	275,25	13,33	296,53	147,67	6,22	567,59	42,87	616,68	307,11
	200-2	18,06	108,68	9,81	136,55	68,00	8,59	281,69	16,34	306,62	152,70	7,23	180,61	24,97	212,81	105,98

Pellet (nmol) Supernatant (nmol) Wash (nmol) Total (nmol) % recovered Pellet (nmol) Supernatant (nmol) Wash (nmol) Total (nmol) % recovered Pellet (nmol) Supernatant (nmol) Wash (nmol) Total (nmol) % recovered

Table 6 .26: Uranium measured in the controlled tubes.

 6 Represents the uranium actually introduced in the bacteria.Pellet (nmol) Supernatant (nmol) Wash (nmol) Total (nmol) % recovered Pellet (nmol) Supernatant (nmol) Wash (nmol) Total (nmol) % recovered Pellet (nmol) Supernatant (nmol) Wash (nmol) Total (nmol) % recovered

			CEA-NOB-002				CEA-NOB-006				SKH 450
			Micrococcus luteus				Kocuria koreensis				Pantoea vagans
	Pellet (nmol) Supernatant (nmol) Wash (nmol) Total (nmol) % recovered Pellet (nmol) Supernatant (nmol) Wash (nmol) Total (nmol) % recovered Pellet (nmol) Supernatant (nmol) Wash (nmol) Total (nmol) % recovered
	5-1	3,02	1,23	1,19	5,44	115,17	3,70	1,56	0,58	5,83	123,51	1,84	2,35	0,63	4,81	101,89
	5-2	2,35	0,90	2,75	6,00	127,06	4,27	0,89	0,66	5,82	123,40	1,82	2,77	0,59	5,17	109,63
	20-1	0,82	2,76	12,67	16,26	75,89	36,09	11,36	10,60	58,05	270,97	10,56	12,79	5,17	28,51	133,08
	20-2	0,87	3,05	14,19	18,11	84,54	16,61	6,90	7,43	30,94	144,43	11,13	13,27	7,65	32,05	149,62
	200-1	2,76	59,63	23,24	85,63	42,64	27,40	156,09	20,83	204,32	101,75	16,17	168,24	15,13	199,54	99,37
	200-2	8,61	75,62	38,77	123,00	61,25	33,12	157,27	19,85	210,25	104,70	13,43	178,42	0,01	191,86	95,55
			SKH 448					CEA-NOB-033				CEA-NOB-010
			Bacillus sp.					Bacillus sp.					Leifsonia sp.
	5-1	5,39	0,82	0,42	6,63	140,49	3,01	1,76	0,48	5,26	111,39	1,31	0,99	0,38	2,69	56,99
	5-2	4,17	1,11	0,52	5,80	122,90	4,39	1,74	0,47	6,60	139,86	1,11	1,28	0,83	3,22	68,26
	20-1	18,25	8,13	11,02	37,40	174,59	11,84	11,46	9,76	33,06	154,31	32,19	9,48	7,84	49,51	231,10
	20-2	16,85	6,63	9,45	32,94	153,74	11,94	8,39	6,36	26,69	124,58	25,88	7,56	7,40	40,83	190,60
	200-1	41,11	121,59	19,36	182,05	90,66	21,08	148,51	13,19	182,77	91,02	43,45	174,35	15,68	233,48	116,28
	200-2	64,95	191,88	46,55	303,38	151,09	19,02	153,36	11,20	183,59	91,43	34,95	157,42	16,90	209,28	104,22
	Theoretical	Uranium
	uranium (µM)	measured (µM)
	5 µM		5,18
	10 µM		10,33
	50 µM		53,88
	500 µM		533,18
	1000 µM		982,51

Table 6 .27: Uranium recovered per fraction (in nmol) after 24 hours and the recovery % compared to the introduced uranium.

 6 In grey: negative results measured with the ICP-MS, no uranium was detected in the fraction (replaced by 0 for the balance), in green: results to be re-analyzed.Pellet (nmol) Supernatant (nmol) Wash (nmol) Total (nmol) % recovered Pellet (nmol) Supernatant (nmol) Wash (nmol) Total (nmol) % recovered Pellet (nmol) Supernatant (nmol) Wash (nmol) Total (nmol) % recovered Pellet (nmol) Supernatant (nmol) Wash (nmol) Total (nmol) % recovered Pellet (nmol) Supernatant (nmol) Wash (nmol) Total (nmol) % recovered Pellet (nmol) Supernatant (nmol) Wash (nmol) Total (nmol) % recovered Pellet (nmol) Supernatant (nmol) Wash (nmol) Total (nmol) % recovered Pellet (nmol) Supernatant (nmol) Wash (nmol) Total (nmol) % recovered Pellet (nmol) Supernatant (nmol) Wash (nmol) Total (nmol) % recovered Pellet (nmol) Supernatant (nmol) Wash (nmol) Total (nmol) % recovered Pellet (nmol) Supernatant (nmol) Wash (nmol) Total (nmol) % recovered Pellet (nmol) Supernatant (nmol) Wash (nmol) Total (nmol) % recovered

			CEA-NOB-002				CEA-NOB-006				SKH 450					SKH 448
			Micrococcus luteus				Kocuria koreensis				Pantoea vagans				Bacillus sp.
	5-1	3,64	0,67	0,10	4,41	85,20	3,41	0,32	0,02	3,75	72,48	5,96	1,27	0,44	7,67	148,22	4,62	0,11	0,01	4,73	91,49
	5-2	3,67	0,61	0,11	4,39	84,74	3,89	0,35	0,02	4,26	82,31	4,90	2,78	0,74	8,43	162,80	4,04	0,11	0,05	4,20	81,12
	10-1	4,92	1,18	0,66	6,75	65,40	7,49	0,65	0,29	8,43	81,58	5,96	1,27	0,44	7,67	74,27	5,36	1,16	0,63	7,16	69,28
	10-2	6,49	0,96	0,72	8,17	79,11	9,30	0,38	0,24	9,92	96,03	4,90	2,78	0,74	8,43	81,57	5,23	1,04	0,51	6,78	65,64
	20-1	0,82	2,76	12,67	16,26	75,89	36,09	11,36	10,60	58,05	270,97	10,56	12,79	5,17	28,51	133,08	18,25	8,13	11,02	37,40	174,59
	20-2	0,87	3,05	14,19	18,11	84,54	16,61	6,90	7,43	30,94	144,43	11,13	13,27	7,65	32,05	149,62	16,85	6,63	9,45	32,94	153,74
	50-1	41,86	1,86	1,65	45,37	84,20	50,00	-1,32	0,98	49,66	92,16	51,05	43,90	5,10	100,05	185,67	48,11	-2,52	0,33	45,91	85,21
	50-2	43,55	18,65	1,47	63,67	118,16	40,23	-1,05	0,94	40,12	74,46	47,27	51,38	4,67	103,31	191,73	56,68	-0,38	0,24	56,54	104,92
	200-1	2,76	59,63	23,24	85,63	42,64	27,40	156,09	20,83	204,32	101,75	16,17	168,24	15,13	199,54	99,37	41,11	121,59	19,36	182,05	90,66
	200-2	8,61	75,62	38,77	123,00	61,25	33,12	157,27	19,85	210,25	104,70	13,43	178,42	0,01	191,86	95,55	64,95	191,88	46,55	303,38	151,09
	500-1	62,40	454,35	15,12	531,87	99,75	68,69	460,14	9,40	538,22	100,94	11,03	1124,37	12,30	1147,70	215,25	97,80	390,50	15,14	503,44	94,42
	500-2	62,41	451,77	16,33	530,50	99,50	71,88	487,20	9,50	568,59	106,64	10,46	1085,66	6,19	1102,31	206,74	119,65	389,44	16,50	525,59	98,58
	1000-1	14,12	1071,13	86,34	1171,59	119,25	14,14	2809,17	10,70	2834,01	288,45	8,78	1124,37	12,30	1145,45	116,58	8,44	1073,23	14,95	1096,62	111,61
	1000-2	14,23	995,55	117,63	1127,41	114,75	17,30	2893,83	14,82	2925,94	297,80	8,75	1085,66	6,19	1100,60	112,02	11,98	1092,30	7,61	1111,88	113,17
			SKH 497					CEA-NOB-032				CEA-NOB-033
			Pantoea sp.				Brevibacillus agri				Bacillus sp.
	5-1	4,07	0,45	0,13	4,65	89,87	4,24	0,11	0,01	4,36	84,27	4,86	0,04	0,28	5,18	99,99
	5-2	4,13	0,46	0,12	4,71	91,08	4,60	0,10	0,01	4,72	91,12	4,83	0,04	0,31	5,18	100,07
	10-1	2,38	7,38	0,97	10,73	103,93	8,52	0,80	2,90	12,22	118,27	0,00	3,24	4,61	7,85	76,03
	10-2	2,66	6,60	0,97	10,22	98,97	7,49	0,80	99,92	108,21	1047,69	2,47	2,27	4,46	9,21	89,15
	50-1	30,01	13,67	1,84	45,52	84,48	51,03	10,16	1,26	62,45	115,90	39,22	-0,40	2,32	41,14	76,34
	50-2	34,78	16,22	1,83	52,83	98,04	50,19	0,10	1,34	51,62	95,80	38,72	9,05	1,29	49,05	91,04
	500-1	30,84	510,66	6,97	548,47	102,87	98,29	437,31	12,91	548,50	102,87	62,61	482,69	7,18	552,49	103,62
	500-2	32,52	502,57	4,64	539,74	101,23	97,10	445,42	10,73	553,25	103,76	60,94	472,73	8,10	541,77	101,61
	1000-1	3,51	1413,80	5,00	1422,31	144,76	25,44	1098,78	38,43	1162,66	118,34	17,69	1109,18	10,97	1137,84	115,81
	1000-2	3,23	1120,11	13,22	1136,57	115,68	37,49	1098,86	13,58	1149,93	117,04	8,50	1045,24	10,08	1063,82	108,28
			CEA-NOB-010				CEA-NOB-020				CEA-NOB-030				CEA-NOB-012
			Leifsonia sp.				Nocardia niigatensis				Nocardia niigatensis				Gordonia bronchialis
	5-1	1,41	2,81	0,43	4,65	89,90	2,38	1,47	0,26	4,12	79,51	1,26	3,39	0,36	5,01	96,75	2,16	2,86	0,01	5,04	97,34
	5-2	1,89	3,56	0,46	5,91	114,13	2,52	1,71	0,36	4,59	88,78	1,25	3,82	0,37	5,44	105,11	1,54	3,49	0,03	5,06	97,82
	50-1	11,16	42,66	1,74	55,55	103,10	17,19	28,28	2,83	48,30	89,64	5,17	51,81	2,46	59,45	110,33	5,13	48,25	2,57	55,95	103,83
	50-2	11,12	40,83	1,84	53,78	99,81	16,75	33,51	2,92	53,19	98,70	7,70	589,65	1,40	598,75	1111,17	11,27	47,46	2,46	61,19	113,56
	500-1	25,82	549,63	7,78	583,22	109,39	20,97	540,63	5,08	566,69	106,28	3,82	582,71	7,67	594,20	111,44	3,09	539,90	13,11	556,10	104,30
	500-2	21,66	537,55	4,02	563,24	105,64	21,56	540,15	6,42	568,13	106,56	3,65	36,84	5,56	46,04	8,64	2,90	604,34	15,08	622,32	116,72

Pellet (nmol) Supernatant (nmol) Wash (nmol) Total (nmol) % recovered Pellet (nmol) Supernatant (nmol) Wash (nmol) Total (nmol) % recovered Pellet (nmol) Supernatant (nmol) Wash (nmol) Total (nmol) % recovered

----------

Acknowledgements

Acknowledgements The authors gratefully thank M. Delage (CEA/DEN/DDCC/UADS/SEROS) and D. Duval (CEA/DEN/DANS/DPC/SECR/LABRA) for providing access to the facility, J.-B. Baugé, D. do Couto and P. Le Tutour (CEA/DEN/DANS/DPC/SECR/LABRA) for help with biofilm sampling, J. Malardé and C. Branquet (CEA/DRF/PSAC/USPS/SPRE/SRL) for the radioactivity control of the samples, and J. Martin-Laffon for her help in bibliographic research, as well as J.-C. Gaillard and G. Miotello (CEA/DRF/Joliot/DMTS/SPI/Li2D) for their help with tandem mass spectrometry calibration. The Nuclear Toxicology Program of the CEA is greatly acknowledged for financial support (INFERNUS project grant).

Acknowledgements

The authors gratefully thank M. Delage and C. Dubois (CEA, DEN/DDCC/UADS/SEROS, Saclay) for providing access to the facility, F. Coulon and E. Latil (CEA, DRF/PSAC/USPS/SPRE/SRP, Saclay) for completing the gamma spectrometry measurements, the radiological controls, and the transportation documents, S.

Echivard (CEA, DEN/DDCC/UADS/SEROS) for the water sampling, P. Servan (CEA, Fontenay-aux Roses), and J. Timmins (IBS, Grenoble) for providing the D. radiodurans strain and discussion, and J. Martin-Laffon (DRF/BIG/PCV) for her help in bibliographic research. The authors gratefully acknowledge the Nuclear Toxicology Program of the CEA for financial support (INFERNUS project grant).

and 4. In this sample, Clostridium sensu stricto was the main genus identified (31%), followed by Sphingomonas (19%) and Proteus (12%). The genera Curvibacter (9%), Diaphrobacter (9%), Herminiimonas (6%), and Sediminibacterium (3%) were among the main genera identified (Fig. 3.11; Appendix 7, Table 6.11).

Bacteria belonging to three different phyla were identified in Scrub 5: Actinobacteria (3%), Firmicutes (62%), and Proteobacteria (35%). In this scrub, Clostridium sensu stricto represented 60% of the analyzed sequences, followed by Curvibacter (13%). The other main genera included Proteus (8%), OTU 8 (Salmonella) (4%), Sphingomonas (2%) (Sphingomonadaceae family represented 5% of the sample), Pelomonas (2%), and OTU 43, belonging to the Actinomycetales order (3%) (Fig. 3.11; Appendix 7, Table 6.11).

The same three phyla were also identified in Scrub 6: Actinobacteria (2%), Firmicutes (72%), and Proteobacteria (25%). In this sample too Clostridium sensu stricto was the main genus (64%), followed by Diaphrobacter (9%), Proteus (7%), Wautersia (6%), OTU 8 (Salmonella) (5%), and Exiguobacterium (4%). The genus Mycetocola represented about 1% of the sequences (Fig. 3.11; Appendix 7, Table 6.11).

Four phyla were identified in Scrub 7: Actinobacteria (5%), Bacteroidetes (2%), Firmicutes (46%), and Proteobacteria (48%). The main genus identified in this sample was Clostridium sensu stricto (41%), followed by Curvibacter (26%). The other main genera identified were Mycetocola (5%), Proteus (4%), Sediminibacterium (2%), Methylobacterium (1%), Alcaligenes (1%), OTU 56 (Serratia) (1%), or belonged to the Rhodobacteraceae family (OTU 36 -8% and OTU 71 -2%) (Fig. 3.11; Appendix 7, Table 6.11).

Finally, Scrub 8 was composed of bacteria belonging to three phyla: Actinobacteria (1%), Firmicutes (49%), and Proteobacteria (48%). Similarly to all scrubs but Scrub 3, Clostridium sensu stricto was the main genus identified (44%). Curvibacter (16%), Sphingomonas (7%), Morganella (7%), Proteus (6%), Pelomonas (4%), Hydrogenophilus (2%), and Herminiimonas (1%) have also been identified (Fig. 3.11; Appendix 7, Table 6.11).

The last location analyzed was the top western wall. In this analysis an important diversity was observed in the scrubs and in the filtered water (15 genera in Scrub 1, 51 genera in Scrub 2, and 64 genera in the water) (Appendix 8,Table 6.13). A total of 8 bacterial phyla were identified at this location.

In the water the phylum Cyanobacteria represented 30.8% of the analyzed sequences. 30.6% of the analyzed sequences were attributed to the Bacillariophyta family. The 0.2% left were attributed to the Streptophyta family (Fig. 3.17; Appendix 8,Table 6.13). The genera belonging to these two families were not identified (Appendix 8, Table 6. [START_REF] Jolivet | Thermococcus gammatolerans sp. nov., a hyperthermophilic archaeon from a deep-sea hydrothermal vent that resists ionizing radiation[END_REF]Fig. 6.10). In the water sample 22.6% of the sequences were identified as belonging to the Burkholderiales order but neither the family nor the genus were identified (Fig. 3.17; Appendix 8,Table 6.13). More than 4% of the sequences identified from the sample D filtered water were associated with the Blastocatella family (Acidobacteria phylum). In this sample the Rhodobacteraceae family represented almost 7% of the sequences. In this family, 3.1% belonged to the Rhodobacter and 3.9% were not identified at the genus level but belonged to the same OTU (Fig. 3.17; Appendix 8, Table 6. [START_REF] Jolivet | Thermococcus gammatolerans sp. nov., a hyperthermophilic archaeon from a deep-sea hydrothermal vent that resists ionizing radiation[END_REF]Fig. 6.10). The genera Blastomonas and Sphingomonas were also present at a proportion of 2.6 and 4.4% respectively.

The scrubs 1 and 2 were, as scrubs 1 and 2 from the sample B location, not similar in composition. In Scrub 1, 48.5% of the analyzed sequences were attributed to the Methylobacteriaceae family and more specifically to Methylobacterium, while this genus represented 18.2% of Scrub 2. The other main family of Scrub 1 was Sphingomonadaceae and more specifically Sphingomonas (49.2%) when this family (and genus) represented only 6.7% of Scrub 2. More diversity was observed in Scrub 2. The family Bacillariophyta displayed 36.5% of the sequences. The family Caulobacteraceae (composed by the genus Brevundimonas) represented 11.1% of the sample. In this scrub 8.7% of the OTU were attributed to the Sphingomonadaceae family, composed at 6.7% by the genus Sphingomonas. Finally, Microbacterium represented 3.6% of the sequences in this scrub (Fig. 3.17; Appendix 8, Table 6. [START_REF] Jolivet | Thermococcus gammatolerans sp. nov., a hyperthermophilic archaeon from a deep-sea hydrothermal vent that resists ionizing radiation[END_REF]Fig. 6.10).

observed [385]. Indeed, photosynthetic eukaryotes required for long-term stability that nutrient recycling by diverse heterotrophic bacteria takes place. The heterotrophes remineralize the inevitably leaked organic matter from photosynthetic organisms, making nutrients circulate in a mutualistic system. The combined use of metaproteomics and metagenetics allowed a deep analysis of the microbial diversity present in this pool. Some differences could be observed between the location and depth of samplings. However the main differences were noticed between the two different pool conditions. After the cleaning and change of filtration system the microbiota was indeed extremely different. The drastic decrease of Bacillariophyta and the increase of Limnobacter were the main changes observed.

The presence of algae in this pool could be interesting for several reasons. First of all, if we assume that the Bacillariophyta family indicated, in fact, the presence of algae (due to a chloroplastic DNA sequencing), this pool would have been composed of photosynthetic eukaryotes in high proportion in 2015. The isolation of such organisms could help discover new resistance mechanisms. Secondly it could indicate the presence of a complex microbiota in a constrained environment.

The biofilm composition also changed between 2015 and 2017. The presence of Sphingomonas in biofilms analyzed in 2015 (metagenetics) and 2016 (metaproteomics) in large proportion with its severe decrease in the 2017 biofilms could indicate an influence of the filtration and condition changes in the pool in opposite with the annual cleaning process.

While the use of metaproteomics was less sensitive than metagenetics it can be useful for quick analysis and monitoring of the pool's microbiota. A monitoring of the microbiota of SNFPs or radioactive sources storage pools should be performed annually to help prevent microbial influenced corrosion.

The microbial composition of this pool was different from the microbiota of SNFPs. The presence of Bacillariophyta was unique and never described in similar environment. Moreover no Cyanobacteria were found in the studied SNFPs over the world. However this pool does not contain radionuclides unlike the SNFPs studied over the world. These results indicates a dependence of the microbiota on the conditions prevailing in the pool. Each pool has its unique microbiota and a pool can present a change in the microbiota when one of the numerous parameters has been changed.

The protocols developed from this pool were used to analyze the microbiota present in the water and on biofilms in the cooling pool of a nuclear reactor. These methods could be used to unveil the microbial diversity present in constrained environments.

Acknowledgments

The authors thank J.-B. Baugé, D. Do Couto, D. Duval, D. Le Tutour, and M. Delage (CEA, Saclay). This work was founded by the Transverse Toxicology Program of the CEA.

Table 4.2: Identification of microorganisms isolated in the pool from the 2015 (operating reactor) and 2017 (shutdown reactor) samplings.

In 2015, all strains were identified via sequencing. In 2017 the "SKH XXX" bacteria were isolated from the kinetic and identified using phylopeptidomics while the "CEA-NOB-XXX" were isolated from scrubs and identified using 16S rDNA sequencing. Treatment: the VTC are then eliminated through the usual waste management ways according to their physical, chemical and biological characteristics.

Low activity wastes (TFA): wastes from the nuclear industry and are solid wastes (concrete, rubble…). It represents less than 30% of the radioactive wastes (Fig. 6.1).

Treatment: the TFA are stocked in "big bags" or in metal boxes in the department of Aude. An extremely low proportion of wastes are recycled in the nuclear industry. Lead can for example be melted to create protection against radiation in a nuclear installation.

Low and medium activity with short-life wastes (FMA-VC): mainly short-life radionuclides, such as 60 Co or 137 Cs. They represents around 60% of the total waste volume (Fig. 6.1).

Treatment: the FMA-VC are stocked in the department of Aube. Before being stocked, the wastes are compacted or solidified and placed into a metal container or in concrete. Some of the waste can be incinerated. A "package" of FMA-VC waste is composed of 15-20% of radioactive waste and 80-85% of coating.

Low activity and long-life wastes (FA-VL): mainly graphite wastes from old reactors. This category represents around 7% of the total waste volume (Fig. 6.1).

Treatment: there is currently no existing treatment for those wastes. The producers of the wastes hoar them in the production site or specific installations until a solution is found.

Medium activity and long-life wastes (MA-VL): solid or liquid wastes from nuclear reactors. The radionuclides present have half-lives greater than 31 years. This category represent 3% if the total waste volume (Fig. 6.1).

Treatment: the MA-VL are compacted or placed in concrete and stocked by the producers or by Areva in La

Hague. There is currently a project to burry those wastes at a Cigéo center (Centre industriel de stockage géologique) in Bure, near the surface.

High activity and long-life wastes (HA-VL): this category represents less than 0.2% of the total waste volume (Fig. 6.1) but produce more than 96% of the radioactivity produced by the wastes. They come from the treatment of the fuels used in a nuclear facility.

Treatment: these wastes are vitrified, so the radioactive radiation are blocked by the glass. The vitrified wastes are then introduced inside stainless steel containers and stocked by their producers, in the CEA Marcoule or by Areva in its site of La Hague and in the Gard. They are planned to be stored deeply underground by Cigéo in Bure.

Appendix 2: Microorganisms found in nuclear facilities The [DNA] was extremely low and can be explained by the low cell quantity. The DNA quality was also extremely poor and can be explained by the fact that the nanodrop's sensitivity for DNA is 50 ng/µL as well as the low cell quantity. Other DNA quantity method was impossible due to the lack of DNA and sample.

[DNA] (ng/µL) A 260nm/280nm A 260nm/230nm Loc. A -meth.

Abstract

Biotechnology with microorganisms able to survive high radiation doses and concentrate radionuclides is an attractive technology to decontaminate nuclear effluents with low waste volume. By using two direct and complementary analysis methods, metagenetics and metaproteomics, the complete inventories of microorganisms present in a French nuclear reactor cooling pool (during operation and at shutdown) and in a radioactive 60 Co sources storage pool have been performed. The microbiota from the two pools were extremely different, with a majority of Cyanobacteria in the sources storage pool, and Proteobacteria in the reactor pool. Depending on the pools' conditions a change in the microbiota had been observed. Variovorax was the main genus identified during the reactor's operation. Some strains of Variovorax are able to use H2 as an energy source, conferring them an advantage in nuclear pools. Several microorganisms have been isolated from the reactor pool. Among them 46 bacteria were identified. Their abilities to resist ionizing radiations and to accumulate specific radionuclides were determined. Several genera studied in this work had never been described concerning their ability to resist radiations or accumulate radionuclides. Six strains could entirely remove uranium from a 5 µM solution and up to 50 µM for two strains. This work presents the first complete inventory of a working nuclear reactor cooling pool. This PhD work opens interesting industrial perspectives as the isolated strains could be implemented in a new nuclear effluents decontamination process and fundamental perspectives as their resistance and radionuclides accumulation pathways could be deciphered.

Résumé

Les biotechnologies basées sur des microorganismes capables de survivre à de fortes doses de rayonnement