Indeed anaerobic microbial community is a really complex ecosystem. The microbial community is responsible for a multitude of chemical reactions to transform the organic matter into methane.

The process implies many microbes, metabolisms and interactions intrinsically linked together. Moreover several inhibitors can be introduced or produced during the substrate degradation, technical issues can happen or the feeding composition can be modified due to the seasonal production or a modification of substrate quantity. Any small modification in the system can provoke a rupture in the fragile microbiological network and lead to a process disturbance with economic and environmental repercussions.

The first part of this manuscript presents the state of the art of waste management in France and a global overview of the existing waste treatment methods. A particular focus is given to the anaerobic digestion with a description of the process. A description of the different physico-chemical parameters and their influence on the bioprocess are described. An overview of possible strategies to optimise the bioprocess is also presented. Finally, the biomolecular and biostatistical methods available for microbial communities description are discussed.

The problematic of this thesis work and a short description of the objectives resulting from this problematic are presented.

A chapter is dedicated to the description of the material and methods used in this thesis work to address the problematic.

Abstract

Renewable energies play a crucial role in the limitation of the CO 2 emissions. Among them, anaerobic digestion allows the reduction of organic waste volume and the production of biogas, a sustainable energy. However the vulnerability of the anaerobic microbiome to the modification of operational parameters can lead to process failure and economic losses.

Increasing the knowledges about the anaerobic microbiome as a whole and its impact on the digester performances would provide the keys to limit the risk of process failure. Thanks to the development of the high throughput methodologies, the understanding of the microbiome dynamics, interactions and functioning has improved.

The aim of this thesis work was to characterise the microbiome functioning under different operating conditions and how it relates to the digester performances. To do so multiple high-throughput molecular technologies, especially 16S rRNA metabarcoding and metabolomics analyses, coupled to computational biostatistics were used.

Influence of the feedstock composition was the first parameter studied. Digesters were fed with different mixtures of substrates. The results showed that the substrate composition played a significant role on the microbial diversity and specificity which could explain the different digester performances observed between the batch reactors. An integrative analytical framework was specifically used to link the microbial activity to the substrates degradation. It allowed to identify potential degraders of specific molecules. The capacity of the microorganisms to adapt to a modification of the feedstock composition was also evaluated in semi-continuous reactors.

In a second time, the influence of ammonia accumulation on the microbial dynamics was evaluated. In semi-continuous reactors ammonia was added at different speeds. The results showed that the faster the speed of accumulation was, the more the digester performances were impacted. A longitudinal analysis was performed by carrying out a temporal sampling. This analysis allowed to evaluate the adaptation capacity of the microbes when subjected to different conditions of ammonia addition. Moreover microorganisms specific to the ammonia accumulation were identified and could be used as potential bio-indicators to forecast digester inhibition.

Finally the influence of the zeolite on the microbial interactions was studied. Zeolite is a mineral support allowing to counteract ammonia inhibition. However, the mechanisms behind this mitigation capacity remain unclear. In order to unravel them, different physical treatments were applied on the zeolite before it was added into digesters containing ammonia. In general in absence of ammonia, the zeolite did not influence the microbial composition and the digester performances, while a significant effect was observed in presence of ammonia. A discriminant analysis coupled to a sparse method allowed to highlight the impact of the zeolite on the microbial syntrophy needed for the propionate degradation. Additional data from the literature were included in order to evaluate the genericity of the results.

The results from this thesis work highlight the importance of the operational parameters, even at a small-scale, on the microbial activity. Innovative statistical frameworks for the analysis of the microbiome were proposed to go further on the microbiome description. For example, key phylotypes specifically sensitive to inhibition were identified. Some of them could be used as bio-indicators to control digester functioning. Moreover, this work highlights the value of developing the metabolomic analysis in the bioprocess fields, as it enables to monitor organic matter degradation and infer the potential role of the microorganisms in the degradation process.

Some definitions

Bio-indicators: Living organisms (microbes, plants…) or molecules (enzymes, proteins…) used to monitor an ecosystem. They provide information about the quality, stability or modification of the ecosystem in particular in case of inhibitor accumulation. [START_REF] Parmar | Bioindicators: the natural indicator of environmental pollution[END_REF] Compositional data: collection of variables with the particularity that their sum is constrained to be a constant (equal to 1 for proportions or 100 for percentages). Variables are then dependent. [START_REF] Aitchison | The statistical analysis of compositional data[END_REF] Core microbiome: Microbes shared among different conditions, essential for the good functioning of the ecosystem. [START_REF] Astudillo-García | Evaluating the core microbiota in complex communities: A systematic investigation[END_REF]Shade and Handelsman, 2012) Disturbances: Events that alter the microbial community directly or by modifying its environment. These events can occur at various frequencies, intensities, periodicities. [START_REF] Plante | Defining Disturbance for Microbial Ecology[END_REF]Shade et al., 2012) Functional elasticity: Property of microbial community to adapt to modifying conditions [START_REF] De Vrieze | Inoculum selection is crucial to ensure operational stability in anaerobic digestion[END_REF] thanks to high diversity of functions expressed by the microbes.

Functional redundancy: or functional equivalence. The fact that a variety of taxonomic communities shares similar functional properties. [START_REF] Rosenfeld | Functional redundancy in ecology and conservation[END_REF] Sensitivity/sensibility: Inverse of the resistance.

Stability:

In microbial ecology the stability can have many definitions and most of them are described by [START_REF] Grimm | Babel, or the ecological stability discussions: An inventory and analysis of terminology and a guide for avoiding confusion[END_REF]. In our case the stability referred to the resistance or resilience of the microorganisms.

Resistance: The degree of insensitivity of a microbiota facing a disturbance. (Shade et al., 2012) Resilience: The capacity rate for a microbiota to recover after a disturbance. (Shade et al., 2012) Table of content 1

2.

Comparison to previous results of mitigation of ammonia inhibition using zeolite 290

2.1. Description of the experiment from [START_REF] Poirier | Improving anaerobic digestion with support media: Mitigation of ammonia inhibition and effect on microbial communities[END_REF]

Introduction

The actual way of life of the growing population has substantially increased the pressure on the natural resources. The increasing needs of the population lead simultaneously to a massive increase of greenhouse gas emissions (GHG). For example food consumption leads to the increase of agricultural lands demand associated to deforestation. In parallel the globalisation leads to GHG emissions due to industrial processing, plastic packaging and transports around the world. Another issue of this growing consumption is the associated waste production and its non-optimal management. For a few years anthropogenic activities and their negatives impacts on the environment have begun to raise awareness. The decrease of the fossil matters availability and the increase of climatic events lead politics and researchers to investigate more sustainable options for products conception and waste management.

In this context of global warming a particular focus is given to the organic waste management. Global food wastage represented about 8% of total anthropogenic GHG emissions in 2011 and can be considered as the third largest GHG emitter in the world (FAO, 2011). For most of the countries the principal organic waste disposal solution is landfilling. However landfills occupy large valuable lands and are an important source of methane pollution. For example in France in 2015 GHG emissions from waste treatment represented 15 Mt equivalent CO 2 corresponding to 3% of the total GHG emissions of the country [START_REF] Baude | Chiffres clés du climat France[END_REF].

Organic waste are now considered as valuable resources for sustainable energy production. Anaerobic digestion together with composting appear to be promising ways to manage properly and valorise organic waste. Anaerobic digestion, or methanisation, is a microbial-based process leading to the transformation of organic waste into sustainable energies, through the biogas production, and into valuable solid matter, through the digestate. Biogas is mostly composed of methane and can be valorised in thermal and electrical energies. Digestate is a solid matter that can be valorised as a fertiliser. If this bioprocess has been known for more than 100 years, anaerobic digestion is still underutilised. Indeed anaerobic digestion contributed to only 0.2-0.4 % of worldwide electricity production in 2015 [START_REF] De Vrieze | Perspectives for microbial community composition in anaerobic digestion: from abundance and activity to connectivity[END_REF]. This can be explained by the lack of stability and flexibility of the digesters which limit their development. This is mainly due to the instability of the anaerobic microbial population facing operational modifications.

The good functioning of an anaerobic digester is based on the functional stability inside the digester. This stability is sought by the operator by the adjustment of some specific chemical parameters based on his expertise. These parameters, often pH or volatile fatty acids, are easily measurable in situ and reflect the consequences of the digester functioning. These chemical parameters are however not sufficient as indicators to prevent the process failure. They cannot provide precise or direct indications on the microbial ecosystem functioning while microbiota is at the heart of the bioprocess. Since the last decade a focus on the anaerobic microbiota has been done to better understand the bioprocess and to open new strategies for its optimisation. For example, some studies have allowed to get insights on the influence of different modifications of the microbial ecosystem. It enabled the development of mitigation or prevention strategies such as microbial acclimation. The opportunity to use microbial management to optimise bioprocesses, anaerobic digestion in particular, has now become fundamental.

The improvement of knowledges on anaerobic microbiome is enabled thanks to the development of new molecular technologies. These technologies, high-throughput and omics technologies, allow to study complex microbiota with a high resolution at a decreasing cost. In parallel computational biostatistics are developing. They enable to extract key information from big datasets provided by the high-throughput technologies. However the choice of the appropriate biostatistics methods for the ecological data interpretation is still challenging. Moreover the increase of knowledge based on these high resolution microbial data poses new complex biological questions. Further development of the statistical methodologies is required.

In this context the aim of the present thesis work is to decipher the microbial functioning of the anaerobic digestion facing operational modifications using highthroughput approaches (16S rRNA metabarcoding and metabolomics) combined to biostatistical methodologies.

Results are described in three different chapters. As the feeding composition is of particular importance for anaerobic digestion stability and performances, the influence of the anaerobic co-digestion on the microbial activity and metabolism pathways is presented in the first Chapter. Some inhibitors such as ammonia can be produced during the degradation and lead to process instability. An experimental and biostatistical strategy to identify bioindicators allowing to anticipate ammonia inhibition is described in the second Chapter. Several strategies to mitigate ammonia inhibition are available such as the use of zeolite known to improve digester performances in presence of ammonia. The influence of the presence of zeolite under a low ammonia pressure on the microbial distribution and methanogenic pathways is discussed in the third Chapter.

Finally, a general conclusion of the thesis work and the perspectives are presented.

Bibliography

Waste management 1.Definitions and regulation

The first question to be asked is: What is waste exactly? Waste, according to French regulation, is "any residue in a process of production, processing or use, any substance, material, product or, more generally, any movable property abandoned or whose owner intends to abandon" (law of the 15 th of July 1975, article L-541-1-1 of Environmental Code).

Waste management includes the production, collection, transport and treatment of the waste. France is moving to a circular economy system where the objective is to reinforce the waste management, not only by increasing the valorisation of the waste but also by controlling the entire life of the product. This means to take into account a product from its conception to its management as a waste (Ministère de l'environnement de l'énergie et de la mer, 2016).

Production and type of waste

In total in 2016 waste production corresponded to 4.6 tonnes of waste by inhabitant in France (Figure 1). The major part of this production was due to the construction activity followed by the industries. The household waste production corresponded to 12.3 % of the total waste production. In 10 years household waste production decreased by 0.3 % thanks to the awareness of the waste environmental impact (ADEME, 2018).

In France, waste are classified in terms of dangerousness, provider (industrial, city or construction) and composition (organic or inert) (Ministère de l'environnement de l'énergie et de la mer, 2017):

 Dangerous waste: presents dangerous properties  Non-dangerous waste: presents no chemical or biological dangerousness  Inert non-dangerous waste: presents no physical, chemical or biological modifications  Waste of economic activities: the producer has an economic activity and has to manage by himself or by a private partner the elimination of the waste  Household waste: the producer is a private person, the elimination is managed by the territorial communities  Household waste and assimilated waste: waste from economic activity managed by the communities with household waste The composition of the household waste was determined in 1993, 2007 and 2017 by the ADEME (Environmental and Energy Agency) during campaigns of waste characterisation (ADEME, 2019). More than 50% of the household waste is composed of degradable waste (putrescible, paper/cardboard …) (Figure 2). Since 1993 waste composition did not evolved much. However, it can be noticed an increase of the putrescible fraction from 20 to 35 % between 1993 and 2007 followed by a decrease to around 25% in 2017. This could be explained partly by the awareness about food wastage. Sanitary textiles (baby's diapers, sanitary napkin …) proportion increased from 4 to 15 % between 1993 and 2017 due to the increase of the population and the use of single use textiles. In the opposite, the content of glasses and papers/cardboards has decreased since 1993 thanks to the separated collection of these types of waste.

Waste processing

The flow of waste production is managed by four channels of elimination: thermal treatment (incineration), recycling, landfilling and biological treatments (composting and methanisation) (Figure 3).

In France, the first treatment sector is the incineration with the management of 32 % of the flow. Then sorting centres (27 %) and landfilling (24 %) are used for waste treatment. The biological treatment sector valorises 16 % of the waste.

Since the last decades, the orientation of the waste into the different channels of elimination has evolved with the increase of the recycling and biological treatments with respectively +10 and +4 % between 2007 and 2015. In opposite the landfilling decreased of 12 % (ADEME, 2018). This is due to a greater attention of the environmental issues caused by landfilling, greenhouse gas emission and use of lands, leading to a modification of the waste management policy. Moreover, as household waste is mostly composed of organic matter the interest of its use in biological treatments has increased.

The biological treatment sector is composed of two types of bioprocesses: composting and anaerobic digestion (or methanisation). The composting is an aerobic process which allows to produce compost, a stabilised fertiliser. This process will not be described further in the manuscript.

Anaerobic Digestion

A brief history and definitions

In the anaerobic digestion bioprocess a microbial community degrades the organic matter from waste into two renewable products. One of these final products is a solid matter called digestate. The digestate can be used as organic amendment for agricultural soil. The other product is the biogas mostly composed of methane (CH 4 ) and carbon dioxide (CO 2 ). This biogas can be used to replace car fuel, injected into the natural gas network or converted into electrical and thermal energy. If the anaerobic digestion is a natural process occurring in environment like wetland, where methane production was discovered by Volta in 1776, its use has been industrialised since the 19 th century only. The first applications were reported in India in 1859 and in Exeter (United Kingdom) to light the streets of the city [START_REF] Lee | Biological treatment of sewage sludge and industrial wastas[END_REF][START_REF] Meynell | Methane: Planning a digester[END_REF]. The first patent dates back to 1907 in Germany describing an early form of an industrial digester.

Implementation in France

The interest for the anaerobic digestion for the treatment of organic waste has grown steadily during the last decade. Indeed anaerobic digestion has the double advantage to reduce the volume of organic waste sent into landfills, and to produce a sustainable energy. In France in 2016, almost 616 installations of biogas plants were reported (ADEME, 2017). The major part of anaerobic digester use concerned the agricultural waste treatment for 52 %. The treatment of wastewater sludge and industrial end-products represented 16 and 13 % respectively. However, the treatment of the organic fraction of the household waste by anaerobic digestion represented only 3 % (Figure 4). This could be explained by the difficulties to collect the organic fraction of household waste.

Principle of the anaerobic digestion

The anaerobic digestion is divided into four main steps handled by a complex microbial community (Figure 5). These four steps are: hydrolysis, acidogenesis, acetogenesis and methanogenesis and are described below.

Hydrolysis

During the hydrolysis step complex biopolymers such as carbohydrates, lipids and proteins are broken down into simple organic compounds or monomers (monosaccharides, fatty acids, amino acids …) [START_REF] Batstone | The IWA Anaerobic Digestion Model No 1 (ADM1)[END_REF]Cirne et al., 2007). These reactions are catalysed by exo-enzymes [START_REF] Goel | Comparison of hydrolytic enzyme systems in pure culture and activated sludge under different electron acceptor conditions[END_REF][START_REF] Zhang | Extracellular enzyme activities during regulated hydrolysis of high-solid organic wastes[END_REF]. This step is usually considered as limiting for the entire process [START_REF] Mata-Alvarez | Anaerobic digestion of organic solid wastes. An overview of research achievements and perspectives[END_REF]. Indeed the degradation of the organic matter depends of its complexity and some substrates such as cellulose or lignin are known to be difficult to degrade [START_REF] Pavlostathis | Kinetics of anaerobic treatment: A critical review[END_REF].

Acidogenesis

The acidogenesis step consists in the fermentation of the end-products from the hydrolysis step into organic acids such as volatile fatty acids (VFAs) and alcohols. Acetate, propionate and butyrate are the mains VFAs produced during acidogenesis [START_REF] Mawson | Degradation of acetic and propionic acids in the methane fermentation[END_REF] but others VFAs and organic acids are also synthetized (valerate, succinate …). Dihydrogen (H 2 ) and CO 2 are also produced during this step.

The end-products of the acidogenesis step are important for the whole process and especially for the methane production as they can affect the efficiency and stability of the methanogenesis step (Wang et al., 2009).

Acetogenesis

The end-products of the acetogenesis step are the acetate, CO 2 and H 2 . Two metabolic pathways are reported for this step:

 Heteroacetogenic pathway which results in the production of acetate, H 2 , CO 2 and others VFAs  Homoacetogenic pathway which results in the production of acetate only. The acetate can be synthetized from organic molecules (sugars) or degradation products such as formate, CO 2 and H 2 The production of H 2 during this step can lead to the inhibition of the acetogenesis. Indeed a very low partial pressure of H 2 is necessary to make the production of acetate thermodynamically positive [START_REF] Siriwongrungson | Homoacetogenesis as the alternative pathway for H2 sink during thermophilic anaerobic degradation of butyrate under suppressed methanogenesis[END_REF]. A subsequent risk of inhibition has been identified with the dysfunction of the acetogenesis step. Indeed the accumulation of the VFAs produced during the acidogenesis step associated to a decrease in the pH will lead to the inhibition of the methanogenesis step [START_REF] Siegert | The effect of volatile fatty acid additions on the anaerobic digestion of cellulose and glucose in batch reactors[END_REF].

Methanogenesis

The final step of the anaerobic digestion, the methanogenesis, consists in methane synthesis through the conversion of the previously produced molecules (H 2 , CO 2 and acetate mostly). Two metabolic pathways lead to the methane production:

 Acetoclastic pathway which converts the acetate into methane and carbon dioxide.  Hydrogenotrophic pathway which converts carbon dioxide and dihydrogen into methane.

Other metabolic reactions

Other reactions occur in parallel to the ones previously described. In particular the syntrophic acetate oxidation (SAO) can occur in the system. This pathway allows to produce methane from the acetate after its transformation into H 2 and CO 2 . This pathway is carried out in two steps. The first step corresponds to the oxidation of the acetate to form CO 2 and H 2 . This reaction is endergonic (+104.6 kJ/mol) and requires the consumption of the H 2 to be energetically feasible. The second step corresponds to the hydrogenotrophic pathway and is exergonic (-135.6 kJ/mol). The association of both reactions leads to the production of 31.0 kJ/mol equivalent to the energy production from the acetoclastic pathway [START_REF] Hattori | Thermacetogenium phaeum gen. nov., sp nov., a strictly anaerobic, thermophilic, syntrophic acetate-oxidizing bacterium[END_REF][START_REF] Zinder | Non-Aceticlastic Methanogenesis from Acetate -Acetate Oxidation by a Thermophilic Syntrophic Coculture[END_REF].

Microbial ecology behind anaerobic digestion

Anaerobic digestion is a complex cascade of reactions generating a multitude of intermediates. To carry out these reactions a microbial consortium composed of several microbes working simultaneously is needed. If a part of the wide variety of microorganisms is ubiquitous and can handle the degradation of several molecules, some of them are more specific. In order to reach an optimal bioprocess, efficient interactions are essentials between the members of this highly complex ecosystem.

Hydrolytic population

The hydrolytic microbial population is a heterogeneous group composed of facultative anaerobic bacteria. Fungi are also able to carry out this hydrolysis step and were identified in the rumen [START_REF] Akin | Role of Rumen Fungi in Fiber Degradation[END_REF][START_REF] Nsereko | Effects of fungal enzyme preparations on hydrolysis and subsequent degradation of alfalfa hay fiber by mixed rumen microorganisms in vitro[END_REF]. The hydrolytic consortia has a fast growth rate [START_REF] Veeken | Effect of temperature on hydrolysis rates of selected biowaste components[END_REF]. A non-exhaustive list can be mentioned including Acetivibrio, Anaerovibrio, Bacillus, Bacteroides, Butyrivibrio, Cillobacterium, Clostridium, Lachnospira, Micrococcus, Macrococcus, Peptococcus, Pseudomonas, Ruminococcus, Succinomonas, Staphylococcus or Syntrophomonas (Moletta, 2015).

Acidogenic population

Acidogenic population are strictly anaerobic microbes [START_REF] Dinopoulou | Anaerobic acidogenesis of a complex wastewater: II. Kinetics of growth, inhibition, and product formation[END_REF]. Most of these microbes have a high growth rate (2 days) and are able to survive in extreme pH and temperature environments [START_REF] Bayard | Traitement biologique des déchets, Techniques[END_REF][START_REF] Demirer | Effect of retention time and organic loading rate on anaerobic acidification and biogasification of dairy manure[END_REF]. Good functioning of the anaerobic digestion process is a delicate balance between acidogenesis and methanogenesis rates. Indeed a too fast acidogenesis step leads to a rapid production of VFAs accumulation and the inhibition of methanogens [START_REF] Pavlostathis | Kinetics of anaerobic treatment: A critical review[END_REF]. The genera Clostridia, Aeromonas sharmana, Bacillus coagulans, Pseudomonas plecoglossicida, Acetobacterium, Bacillus, Bacteroides, Pelobacter and Ulyobacter as well as the family Enterobacteriaceae [START_REF] Moletta | La méthanisation. 3ème édition[END_REF] can be cited as acidogenic microbes.

Acetogenic population

The acetogenic population is constituted of bacteria with a very slow growth rate (3.6 days) and an optimal pH range of 6.6-7.6 [START_REF] Demirer | Effect of retention time and organic loading rate on anaerobic acidification and biogasification of dairy manure[END_REF]). The two metabolic pathways described previously for this step are handled by specific populations:

 The heteroacetogenic pathway is carried out by Obligate Hydrogen Producing Acetogenenic bacteria (OHPA). These bacteria need for their growth a really low partial pressure in dihydrogen (<10 -4 atm) [START_REF] Barlaz | Methane production from municipal refuse: A review of enhancement techniques and microbial dynamics[END_REF] as the reaction is thermodynamically unfavourable. A mutualistic interaction between OHPA and a dihydrogen-consumer is then essential [START_REF] Mara | Handbook of water and wastewater microbiology[END_REF]. Few OHPA were identified and belong to Syntrophomonas, Syntrophobacter or Pelotomaculum [START_REF] De Bok | The first true obligately syntrophic propionate-oxidizing bacterium, Pelotomaculum schinkii sp. nov., co-cultured with Methanospirillum hungatei, and emended description of the genus Pelotomaculum[END_REF][START_REF] Mara | Handbook of water and wastewater microbiology[END_REF].  The homoacetogenic pathway is driven by two groups of bacteria depending on the origin of the acetate production.

Methanogenic population

Methanogens belong to the archaea phylum and are strictly anaerobic. They carry out the final step of the anaerobic digestion and breakdown the acetate, dihydrogen and carbon dioxide into methane. This step is considered as a limiting step due to the very low growth rate of the archaea, similar to the acetogens. The pH range for the optimal growth of the methanogens is 6.5-8. The environment with a low buffer capacity can be prejudicial to the methanogens activity as they are really sensitive to the modification of the pH [START_REF] Demirer | Effect of retention time and organic loading rate on anaerobic acidification and biogasification of dairy manure[END_REF]).

The different methanogenic pathways described previously are managed by specific archaea:

 The hydrogenotrophic pathway is handled by hydrogenotrophic archaea in syntrophy with the hydrogen-producers (OHPA) for the methane production through the oxidation of the CO 2 and H 2 . Except Methanosaeta, all the methanogens archaea are hydrogenotrophs. They belong to the Methanosarcina, Methanoculleus, Methanobacterium, Methanobrevibacter or Methanocorpusculum genera [START_REF] Moletta | La méthanisation. 3ème édition[END_REF].  The acetoclastic pathway is managed by acetoclastic archaea to produce methane and carbon dioxide using the acetate. Two genera represent this group: Methanosaeta and Methanosarcina. Methanosaeta are usually present in environment with a low C/N ratio [START_REF] Zamanzadeh | Biogas production from food waste via co-digestion and digestion-effects on performance and microbial ecology[END_REF].  A third pathway is the methylotrophic pathway carried out by methylotrophic archaea to produce methane and carbon dioxide by converting molecules with one carbon (methanol, methylamine, …) [START_REF] Deppenmeier | Novel reactions involved in energy conservation by methanogenic archaea[END_REF]. The methylotrophs belong to the genus Methanosarcina and the orders Methanobacteriales and Methanoplasmatales [START_REF] Moletta | La méthanisation. 3ème édition[END_REF].

Specific case of the Syntrophic Acetate Oxidation

This pathway is carried out through the interaction of a bacterium to convert acetate into carbon dioxide and dihydrogen and a hydrogenotrophic archaea to produce the methane [START_REF] Hattori | Syntrophic acetate-oxidizing microbes in methanogenic environments[END_REF]. This pathway is reported to take place mostly in presence of high ammonia concentration [START_REF] Schnurer | Ammonia, a selective agent for methane production by syntrophic acetate oxidation at mesophilic temperature[END_REF][START_REF] Wang | Ammonia effect on hydrogenotrophic methanogens and syntrophic acetate-oxidizing bacteria[END_REF]Westerholm et al., 2011a). Different couples of bacteria-archaea have been identified such as Clostridium ultunense-Methanoculleus sp. [START_REF] Schnurer | Clostridium ultunense sp nov, a mesophilic bacterium oxidizing acetate in syntrophic association with a hydrogenotrophic methanogenic bacterium[END_REF], Thermacetogenium phaeum-Methanotermobacter thermautotrophicus [START_REF] Hattori | Thermacetogenium phaeum gen. nov., sp nov., a strictly anaerobic, thermophilic, syntrophic acetate-oxidizing bacterium[END_REF] or more recently Spirochaetales-Methanomicrobiales [START_REF] Lee | Evidence of syntrophic acetate oxidation by Spirochaetes during anaerobic methane production[END_REF].

Influent parameters and potential inhibitors 2.5.1. Temperature

Temperature is a major parameter in the AD process which greatly affects the microbial community and the enzymatic activity. Different ranges of temperature can be used but two different levels of temperature are most common for industrial AD: mesophilic (35°C) and thermophilic (55°C).

The choice of temperature will depend of the purpose of the treatment due to the different advantages between the two temperatures [START_REF] Kim | Comparative process stability and efficiency of anaerobic digestion_2002.pdf[END_REF]. Mesophilic digestion is considered to be more stable and resistant to inhibitors like ammonia and needs less energy to heat [START_REF] Fernández-Rodríguez | Temperature-phased anaerobic digestion of Industrial Organic Fraction of Municipal Solid Waste: A batch study[END_REF]. Thermophilic digestion is more efficient for methane production and production rate [START_REF] Lü | Metaproteomics of cellulose methanisation under thermophilic conditions reveals a surprisingly high proteolytic activity[END_REF]. The thermophilic condition allows the pathogens reduction to obtain a less biologically contaminated digestate.

pH and alkalinity

As temperature, pH affects the growth of microorganisms and enzymatic activity by modifying the acido-basis equilibrium such as for VFAs or ammonia nitrogen [START_REF] Mata-Alvarez | Anaerobic digestion of organic solid wastes. An overview of research achievements and perspectives[END_REF] or the liquid/gas equilibrium such as H 2 CO 3 /CO 2 . Taking into account the different pH ranges of the anaerobic ecosystem, the optimal range of the bioprocess is around the neutrality (6.5-8.5).

It can be noticed that the pH can be maintained stable in the digester thanks to the buffering capacity on the environment. This depends on bicarbonate, VFAs and ammonia concentrations.

Substrates composition

In theory all organic matters can be used as substrates for methane production which leads to a wide variety of potential substrates with their own composition, homogeneity and biodegradability [START_REF] Appels | Anaerobic digestion in global bio-energy production: Potential and research challenges[END_REF]. Their degradation lead to the production of a wide variety of molecules. Some of them can disturb the microbial ecosystem. The substrates can be divided into three categories depending on their molecular composition.

2.5.3.1.

Protein-rich substrates Protein-rich substrates such as food waste, algae, slaughterhouse waste, animal manure… are usually reported to cause process failure due to the release of ammonia nitrogen during their degradation [START_REF] Ács | Changes in the Archaea microbial community when the biogas fermenters are fed with protein-rich substrates[END_REF][START_REF] Akindele | The toxicity effects of ammonia on anaerobic digestion of organic fraction of municipal solid waste[END_REF][START_REF] Gallert | Effect of ammonia on the anaerobic degradation of protein by a mesophilic and thermophilic biowaste population[END_REF].

Ammonia is an essential nutrient for the microbial growth under 200 mg/L. Two major forms of ammonia are produced: the ionised NH 4 + form and free ammonia NH 3 . The total ammonia nitrogen (TAN) is the sum of these two forms of ammonia. The TAN speciation depends on the temperature and pH parameters.

For example, in a mesophilic anaerobic digester at pH 7 TAN will be composed of 1% of NH 3 , but an increase of the pH up to 8 can increase to 8% the free ammonia content. Similarly, at a similar pH, increase of the temperature from 35°C to 55°C can increase by 6 times the concentration of NH 3 [START_REF] Fernandes | Effect of ammonia on the anaerobic hydrolysis of cellulose and tributyrin[END_REF][START_REF] Rajagopal | A critical review on inhibition of anaerobic digestion process by excess ammonia[END_REF]. The link between Free Ammonia Nitrogen (FAN -NH 3 ), Total Ammonia Nitrogen (TAN), pH and temperature can be summarized [START_REF] Anthonisen | Inhibition of Nitrification by Ammonia and Nitrous Acid[END_REF]:

𝐹𝐴𝑁 = 10 𝑝𝐻 (exp ( 6344 𝑇 ) + 10 𝑝𝐻 )

𝑥 𝑇𝐴𝑁

Where T is the temperature in Kelvin. Even if ammonia is necessary for microbial growth, under certain conditions ammonia is a possible inhibitor of the anaerobic digestion. The non-ionised form (NH 3 ) is considered to be the most inhibitory form due to its high membrane-permeability [START_REF] Müller | Ammonium toxicity in bacteria[END_REF].

Little is known about the ammonia toxicity mechanisms but two mechanisms were hypothesized in pure culture. Ionised ammonium can directly affect the methane producing enzymes while free ammonia causes proton imbalance and/or potassium deficiency when passively diffused through the membrane into the cells [START_REF] Chen | Toxicants inhibiting anaerobic digestion: A review[END_REF][START_REF] Rajagopal | A critical review on inhibition of anaerobic digestion process by excess ammonia[END_REF]. A wide range of concentrations half inhibiting methane production were described in the literature, ranging from 0.05 to 1.4 gNH 3 -N/L and from 1.7 to 19 gTAN/L for respectively NH 3 and total ammonia [START_REF] Astals | Characterising and modelling free ammonia and ammonium inhibition in anaerobic systems[END_REF][START_REF] Chen | Inhibition of anaerobic digestion process: A review[END_REF]Poirier et al., 2016;[START_REF] Rajagopal | A critical review on inhibition of anaerobic digestion process by excess ammonia[END_REF]. Chen et al. (2008) attributed this dispersion to several parameters such as pH, temperature but also to the acclimation of the microbial community [START_REF] Chen | Inhibition of anaerobic digestion process: A review[END_REF].

The resistance of the microbial community to ammonia inhibition differs between the microorganisms. From bacterial community mostly Clostridiales order presents a higher resistance to ammonia inhibition than Bacteroidales. Under a low level of ammonia, below 1000 mg N/L, the methane is mostly produced from acetoclastic pathway carried out by Methanosaeta and Methanosarcina [START_REF] De Vrieze | Ammonia and temperature determine potential clustering in the anaerobic digestion microbiome[END_REF]. Under ammonia inhibition, the methane production pathway usually evolves from acetoclastic to hydrogenotrophic. Moreover a predominance of the syntrophic acetate oxidation pathway (SAO) was observed under ammonia inhibition in mesophilic condition [START_REF] Schnurer | Ammonia, a selective agent for methane production by syntrophic acetate oxidation at mesophilic temperature[END_REF]. Sulfate is also a common constituent of some protein-rich substrates. During the degradation of these substrates sulfate is reduced into sulfide by sulfate reducing bacteria (SRB). Even if sulfur is an essential element for the microbial growth, especially methanogens as the archaea cells contain more sulfur, the sulfate reduction can lead to the inhibition of the process. The inhibitory level of sulfide reported in the literature ranges from 100-800 mg/L of dissolved sulfide and 50-400 mg/L of H 2 S [START_REF] Chen | Inhibition of anaerobic digestion process: A review[END_REF]. The level of inhibition depends on different operational parameters but mostly to the pH. The pH can influence not only the proportion of non-free sulfide in the total sulfide, but also the conversion between sulfide and sulfate by affecting the activity of SRB (O'Flaherty et al.,1998). Research found that the chemical equilibrium of sulfide species was pH dependent [START_REF] Chen | Toxicants inhibiting anaerobic digestion: A review[END_REF].

Two main stages are involved in the process of inhibition (Harada et al, 1994): 1) a competition with the others anaerobes for the different substrates such as lactate or acetate; and 2) the toxicity of the produced sulfide. The H 2 S form is considered to be the most toxic form due to its diffusion into the cell membrane. The introduction of the H 2 S into the cell can lead to the modification of the intracellular pH, form disulphide cross-links between polypeptide chains and denaturate the proteins (Yuan & Zhu, 2016).

2.5.3.2.

Lipid-rich substrates Long chain fatty acids (LCFA) are the products of lipid-rich substrate hydrolysis. Many substrates contain high content of lipids such as slaughterhouse, dairy industrial effluents, oil-processing facilities, etc. Lipids have the highest methane production potential compared to carbohydrates and proteins Table 1.

The degradation of the lipids leads to the production of LCFA and glycerol. Obligate Hydrogen-Producing Acetogens (OHPA) are able to transform LCFA into acetate and H 2 , CO 2 [START_REF] Chen | Toxicants inhibiting anaerobic digestion: A review[END_REF][START_REF] Schink | Energetics of syntrophic cooperation in methanogenic degradation[END_REF]. However, a much diverse microbial community was identified in the intermediate products from the LCFA degradation thanks to the use of labelled palmitate as substrate [START_REF] Hatamoto | Diversity of anaerobic microorganisms involved in long-chain fatty acid degradation in methanogenic sludges as revealed by RNA-based stable isotope probing[END_REF].

Even if lipids have a high potential of methane production, many studies reported issues when dealing with these substrates. The fast hydrolysis of the lipids leads to a fast accumulation of LCFA and two main problems were described during their anaerobic digestion: 1) flotation and biomass washout [START_REF] Hwu | Thermophilic high-rate anaerobic treatment of wastewater containing long-chain fatty acids: Impact of reactor hydrodynamics[END_REF] and 2) acetogenic bacteria and methanogenic archaea inhibition [START_REF] Rinzema | Bactericidal effect of long chain fatty acids in anaerobic digestion[END_REF]. The principal mechanism of LCFA disturbance is their adsorption onto the cell membrane affecting the transport and/or protective functions [START_REF] Alves | Waste lipids to energy: How to optimize methane production from long-chain fatty acids (LCFA)[END_REF]. However, it is usually reported that the LCFA inhibition is reversible and mainly causes a decrease of the methane production rate.

The degree of LCFA inhibition depends of different factors such as the microbial population composition. Indeed gram+ bacteria and methanogens were described to be more sensitive to the presence of LCFA [START_REF] Sousa | Molecular assessment of complex microbial communities degrading long chain fatty acids in methanogenic bioreactors[END_REF]. Moreover the temperature and type of LCFA influence the degree of inhibition. Indeed long chain length and LCFA with more unsaturated bonds have been shown to have high inhibition effect [START_REF] Desbois | Antibacterial free fatty acids: Activities, mechanisms of action and biotechnological potential[END_REF][START_REF] Sousa | Activity and viability of methanogens in anaerobic digestion of unsaturated and saturated longchain fatty acids[END_REF].

2.5.3.3.

Carbohydrate-rich substrates Carbohydrates are provided mostly by waste from vegetables, starch or lignocellulose. Substrates constituted by different types of waste such as sewage sludge or municipal refuse can contain respectively 25-35 and 40-60% of cellulose. Hydrolysis of the cellulose is considered as the rate-limiting step in industrial anaerobic digesters [START_REF] Noike | Characteristics of carbohydrate degradation and the rate-limiting step in anaerobic digestion[END_REF]. Indeed an efficient degradation of the cellulose requires specific bacteria producing an extracellular cellulolytic complex of enzymes optimally active at pH 6.2.

It was shown that the cellulolytic degradation was more efficient in thermophilic than in mesophilic conditions and required a mixed microbial population [START_REF] Volfová | Anaerobic degradation of cellulose and formation of methane[END_REF]. Numerous cellulolytic bacteria were identified mostly from the rumen and from anaerobic sludge. As the ruminal microbial community is considered to be the most efficient cellulosedigesting ecosystem, several studies were done to improve industrial digester by using the ruminal microbiota [START_REF] Bize | Shotgun metaproteomic profiling of biomimetic anaerobic digestion processes treating sewage sludge[END_REF]Chapleur et al., 2014).

Other parameters and inhibitors

Numerous others parameters or molecules influence the efficiency of the anaerobic digester through the inhibition or the activity of specific microorganisms [START_REF] Chen | Inhibition of anaerobic digestion process: A review[END_REF].

2.5.4.1.

Ammonia nitrogen As described in details above, ammonia nitrogen is one of the most important inhibitor of the anaerobic digestion. It can be produced during the degradation of the organic nitrogen contained in several molecules such as proteins, urea. The different forms of the ammonia nitrogen (NH 4 + and NH 3 ) can also be present in the substrate. As the mechanism of the inhibition of the ammonia was already described, no further information will be given in this section.

2.5.4.2.

Oxygen The first step of the anaerobic digestion is carried out by facultative anaerobic bacteria meaning that this step can be performed in presence of oxygen. However the last steps are carried out by strictly anaerobic microbes. The presence of oxygen can have a bactericide effect on the microbial population.

2.5.4.3.

Heavy metals In anaerobic digestion many heavy metals are required for the activation or functioning of enzymes and coenzymes. Copper (Cu), zinc (Zn), lead (Pb), mercury (Hg), chromium (Cr), cadmium (Cd), iron (Fe), nickel (Ni), cobalt (Co) andmolybdenum (Mo) are the most found (Altaş, 2009). However their presence in significant concentration the heavy metals can lead to inhibition of the bioprocess. The inhibitory effect is due to the affinity of the heavy metals to the proteins and especially the thiol groups moreover heavy metals are non-biodegradable and can accumulate to potentially toxic concentrations [START_REF] Chen | Inhibition of anaerobic digestion process: A review[END_REF]. Heavy metals can be toxic at very low concentration, around 0.001 g/L (Mata-Alvarez, 2003). However, such as others inhibitors of the anaerobic digestion, the range of inhibition go from milligrams to grams per liter. This could be explained by the difficulty to determine the fraction available in the medium.

2.5.4.4.

Mineral elements At a low level the mineral elements (magnesium, sodium, calcium…) are essential for the microbial growth. For example the sodium is essential for ATP synthesis or the oxidation of the NADH, the calcium helps the aggregation of the microbes [START_REF] Yu | The roles of calcium in sludge granulation during UASB reactor start-up[END_REF])… However, when these mineral elements are present at a high level, they can cause an inhibition of the bioprocess. The sodium which can be found in agro industrial effluents can lead to the decrease of the production of exopolysaccharides from the microbes. This could decrease the formation of microbial aggregates limiting interactions between the microbes [START_REF] Lefebvre | Impact of increasing NaCl concentrations on the performance and community composition of two anaerobic reactors[END_REF]. Many others mineral elements can be cited such as magnesium, potassium, aluminium…

2.5.4.5.

Volatile fatty acids Volatile fatty acids (VFAs) are metabolic intermediates produced during the anaerobic digestion process. Beyond certain concentrations, specific to each VFA, some microorganisms are inhibited. In optimal operational conditions, VFAs degradation rate compensates the VFAs production rate. The accumulation of VFAs inside the system indicates a microbial interactions imbalance. This accumulation leads to a decrease of the pH value which would be the main reason of the inhibition due to VFAs. Indeed the acidification in the system leads to an increase of the non-ionised VFA form which is easily absorbed by the microbes through the membrane. Inside the cells the H + will be released from the VFA and modify the intracellular pH value [START_REF] Chen | Inhibition of anaerobic digestion process: A review[END_REF].

Moreover different types of VFAs are produced during the acidogenesis step. Due to thermodynamics capacity, the degradation of VFAs depends on each other. For example propionate and butyrate cannot be degraded as long as the acetate is not degraded [START_REF] Müller | Syntrophic butyrate and propionate oxidation processes: From genomes to reaction mechanisms[END_REF]. If the entire system is sensitive to the VFAs accumulation, it seems that the methanogenesis step is the most sensitive. However an inhibition of the methanogenesis leads to the accumulation of the acetate and as a consequence to the accumulation of propionate [START_REF] Li | Syntrophic propionate degradation in anaerobic digestion: A review[END_REF].

2.5.4.6.

Organic micropollutants A wide variety of organic molecules can lead to microbial disturbance. They are usually found at very low concentration in effluents used as substrates for the anaerobic digestion. They are classified in different categories such as aliphatic hydrocarbons, monocyclic aromatic compounds or polycyclic aromatic compounds.

Bioprocess optimisation ways

To increase the stability of the anaerobic digestion several strategies are developed. Actions can be applied at different levels of the bioprocess: 1) at the early beginning of the process in order to improve the performances by designing the optimal process and 2) during the biodegradation in order to stabilise the bioprocess by acting on the microorganisms.

Improving the bioprocess performances

As described in the next part, the feeding composition is the parameter influencing the most the entire process. Indeed the accessibility, the molecular composition and the products of degradation specific to each substrate lead to a bioprocess more or less fast and efficient. Different actions can be applied to optimise the quality of the feedstock.

Feeding pre-treatment

One of the objectives of the pre-treatment is to improve or replace the hydrolysis step as it is the rate-limiting step in case of complex matter. Another objective is to eliminate inhibitors or recalcitrant molecules. A wide variety of pre-treatments are available to improve the biodegradability of substrates difficult to degrade such as sewage sludge, lignocellulosic waste, animal manure…. Pre-treatments can be classified into four categories: mechanical, thermal, chemical and biological [START_REF] Jain | A comprehensive review on operating parameters and different pretreatment methodologies for anaerobic digestion of municipal solid waste[END_REF].

The main objective of the mechanical treatments is to reduce the size of solid particles in order to release cell compounds and increase specific surface area. Depending of the treatment, additional effects were observed such as solubilisation when using sonication or oxidation when using high-frequency sound waves [START_REF] Ariunbaatar | Pretreatment methods to enhance anaerobic digestion of organic solid waste[END_REF]. [START_REF] Cesaro | Sonolysis and ozonation as pretreatment for anaerobic digestion of solid organic waste[END_REF] improved by 20-40% the biogas production after sonication pre-treatment of organic fraction of municipal solid waste compared to digestion untreated [START_REF] Cesaro | Sonolysis and ozonation as pretreatment for anaerobic digestion of solid organic waste[END_REF].

Thermal treatments allow to disintegrate the vegetable cells membrane resulting in a higher solubilisation of the matter. The result of the thermal treatment depends on the type of substrate and the range of temperature [START_REF] Ariunbaatar | Pretreatment methods to enhance anaerobic digestion of organic solid waste[END_REF]. After having tested a range of temperature from 62 to 170°C and different retention times, [START_REF] Li | Upgrading of anaerobic digestion of waste activated sludge by temperature-phased process with recycle[END_REF] determined that the optimal thermal treatment was to heat at 170°C during 60 minutes for sewage sludge digestion resulting in an improvement by two times of biogas production [START_REF] Li | Upgrading of anaerobic digestion of waste activated sludge by temperature-phased process with recycle[END_REF].

Chemical treatments are applied mostly to breakdown lignin compound as they allow to destroy the organic compounds. Alkali pre-treatment is suitable in anaerobic digestion as the bioprocess needs a neutral pH. However acidic and oxidative methods lead to the improvement of biogas production as well [START_REF] Jain | A comprehensive review on operating parameters and different pretreatment methodologies for anaerobic digestion of municipal solid waste[END_REF].

Finally, biological pre-treatments can be operated either in aerobic and anaerobic conditions by using microbes or enzymes. These treatments allow to breakdown complex molecules resulting in a better solubilisation of the compounds [START_REF] Karuppiah | Biomass pretreatment for enhancement of biogas production, IntechOpen[END_REF]. For example [START_REF] Mustafa | Fungal pretreatment of rice straw with Pleurotus ostreatus and Trichoderma reesei to enhance methane production under solid-state anaerobic digestion[END_REF] succeeded to increase the methane yield from rice straw by 120% after a fungal pre-treatment (Pleurotus ostreatus and Trichoderma reesei) [START_REF] Mustafa | Fungal pretreatment of rice straw with Pleurotus ostreatus and Trichoderma reesei to enhance methane production under solid-state anaerobic digestion[END_REF]. Lipid-rich (foaming, clogging, biomass flotation)

Anaerobic co-digestion

Anaerobic digestion of unique substrate can have some disadvantages due to the physico-chemical properties of the waste (Mata-Alvarez et al., 2014). Some characteristics of current waste are summarised in the Table 2.

To overcome this bottleneck, different substrates can be mixed. This strategy is called anaerobic co-digestion. The quantity of an inhibitor produced during the substrate degradation can be diluted by the addition of another substrate which does not produce this inhibitor. For example [START_REF] Zhang | The anaerobic co-digestion of food waste and cattle manure[END_REF] improved the methane production by mixing food waste and cattle manure known to produce long chain fatty acids [START_REF] Zhang | The anaerobic co-digestion of food waste and cattle manure[END_REF]. In the same way a lack of carbon or nutriments during the degradation of one substrate, sometimes because it is difficult to degrade, can be balanced by using of highly carbonated or easily degradable substrate. [START_REF] Ahn | Enhancement of Sewage Sludge Digestion by Co-digestion with Food Waste and Swine Waste[END_REF] improved by two times the biogas production from sewage sludge, known for its low biochemical methane potential, by adding food and swine waste [START_REF] Ahn | Enhancement of Sewage Sludge Digestion by Co-digestion with Food Waste and Swine Waste[END_REF].

If this strategy has the advantage to treat several types of waste at the same time, limit the risk of inhibition and improve biogas production, further studies are needed to determine the optimal mixing ratio between the different substrates and its influence on the microbiota.

Digester configuration

The design of the digester is of great importance as well. Several types of digesters are available such as two-stage reactors. This configuration allows to separate the bioprocess with the hydrolysis and acidogenesis steps in one reactor and the acetogenesis and methanogenesis steps in the second reactor. As acidogenesis and methanogenesis take place in a separate reactor, the risk of an inhibition due to the acidification for the methanogens archaea (optimal pH value close to the neutrality) is limited (Cirne et al., 2007;[START_REF] Lehtomäki | Two-Stage Anaerobic Digestion of Energy Crops: Methane Production, Nitrogen Mineralisation and Heavy Metal Mobilisation[END_REF].

Stabilising the bioprocess functioning

The microbial community is the heart of the anaerobic digestion. Stable interactions between the microbes are extremely difficult as each community carrying the different steps of the bioprocess are dependent of each other. Several strategies were developed in order to stabilise the microbial community.

Additives

Among the different strategies to improve the biogas production, the addition of additives into the digester has been widely studied. The objectives of using additives are to stimulate the microbial growth or spatial organisation and/or to reduce the level of the inhibitor [START_REF] Romero-Güiza | The role of additives on anaerobic digestion: A review[END_REF]. Different types of additives can be found: chemical, biological or mineral support.

3.2.1.1.

Chemical additives Some ions and heavy metals are known to have an antagonist effect during anaerobic digestion. For example, nickel limits the effect of cadmium, zinc or copper. The sodium and magnesium are ammonia antagonists [START_REF] Chen | Inhibition of anaerobic digestion process: A review[END_REF]. Some studies have based their researches on the chemical interactions between magnesium and ammonia to produce struvite as a result of precipitation.

Struvite (MgNH 4 PO 4 .6H 2 O) is a valuable fertiliser [START_REF] Pastor | A pilot-scale study of struvite precipitation in a stirred tank reactor: Conditions influencing the process[END_REF][START_REF] Uysal | The determination of fertilizer quality of the formed struvite from effluent of a sewage sludge anaerobic digester[END_REF]. The production of struvite in anaerobic digestion has the double advantage to decrease the ammonia in the bioprocess and to produce a fertiliser.

Chemical additives can play a role on the biomass more directly. For example the addition of macro-or micronutrients can favour the development of microorganisms by providing optimal growth conditions [START_REF] Romero-Güiza | The role of additives on anaerobic digestion: A review[END_REF]. On the other hand biomass aggregation can be provoked by the addition of calcium or magnesium which induces the production of exopolysaccharides [START_REF] Kleyböcker | Comparison of different procedures to stabilize biogas formation after process failure in a thermophilic waste digestion system: Influence of aggregate formation on process stability[END_REF]. This aggregation allows to favour the biomass retention in the digester [START_REF] Ahn | The effect of calcium on the anaerobic digestion treating swine wastewater[END_REF] and/or increase the level of biomass resistance to an inhibitor [START_REF] Ye | The shift of the microbial community in activated sludge with calcium treatment and its implication to sludge settleability[END_REF].

3.2.1.2.

Biological additives Biological addition consists in the introduction of specific microorganisms or enzymes adapted to the inhibitor or substrate to deal with. The bio-stimulation corresponds to the introduction of enzymes, mostly lipases, proteases or cellulases, in order to help the digestion of complex substrates was studied with poor results [START_REF] Diak | Effect of enzymes on anaerobic digestion of primary sludge and septic tank performance[END_REF][START_REF] Romano | The effect of enzyme addition on anaerobic digestion of Jose Tall Wheat Grass[END_REF].

The bioaugmentation, addition of a specialised biomass, has been widely studied and allowed in several occasions to improve the digester performances. Čater et al. (2015) demonstrated the usefulness of bioaugmentation to improve the cellulosic hydrolysis by adding hydrolytic bacteria into a reactor treating brewery spent grain (Čater et al., 2015). However exogenous inoculum addition can sometimes be unsuccessful as shown by Chapleur et al. (2014) where the ruminal inoculum used for the bioaugmentation was not able to maintain in the digester (Chapleur et al., 2014).

In case of inhibition, different studies have shown the positive effect of the bioaugmentation. As some studies have shown the dominance of the syntrophic acetate oxidation during ammonia inhibition, microorganisms involved in this pathway were used to alleviate ammonia inhibition. For example [START_REF] Yang | Mitigation of ammonia inhibition through bioaugmentation with different microorganisms during anaerobic digestion: Selection of strains and reactor performance evaluation[END_REF] have shown that the coupling of the hydrogenotrophic Methanobrevibacter smithii genus and the known SAO bacteria Syntrophaceticus schinkii allowed to increase by 71% the methane production during ammonia inhibition [START_REF] Yang | Mitigation of ammonia inhibition through bioaugmentation with different microorganisms during anaerobic digestion: Selection of strains and reactor performance evaluation[END_REF].

The bioaugmentation strategy was tested also to reduce the recovery time of a digester after a period of disturbance. For example [START_REF] Tale | Bioaugmentation of overloaded anaerobic digesters restores function and archaeal community[END_REF] decreased of 100 days the delay of recovery of a digester after a too high organic loading rate [START_REF] Tale | Bioaugmentation of overloaded anaerobic digesters restores function and archaeal community[END_REF]. [START_REF] Arif | Applications of materials as additives in anaerobic digestion technology[END_REF]

Mineral supports: particular case of zeolite

To immobilise the biomass, materials can be used as a support. The use of support helps to limit the washout and increases the concentration of the active biomass inside the digester. Several materials can be used: mineral, organic, synthetic and nanomaterials. Due to their low-cost, support materials are a good alternative to improve digester performances. In addition to their microbial retention capacity, most of them have been used for their ionexchange or adsorption capacities and for their effects on microbial dynamics (Figure 6). This section will focus on zeolite.

Zeolite is a natural rock formed from ashes and volcanoes rocks. This means that a large quantity is available. Zeolite is a hydrated aluminosilicate composed of alkali and alkaline cations. It consists in a three-dimensional SiO 4 and AlO 4 tetrahedral linked by oxygen atoms. There are about 50 different types of natural zeolites depending on their Si/Al ratio [START_REF] Montalvo | Application of natural zeolites in anaerobic digestion processes: A review[END_REF].

Zeolites have already been used in many areas such as industry, agricultural technology and pollution control. In anaerobic system, zeolite have been shown to limit the inhibitory effect of molecules like ammonia [START_REF] Poirier | Improving anaerobic digestion with support media: Mitigation of ammonia inhibition and effect on microbial communities[END_REF], long chain fatty acids [START_REF] Nordell | Zeolites relieves inhibitory stress from high concentrations of long chain fatty acids[END_REF] or phenol [START_REF] Poirier | Support media can steer methanogenesis in the presence of phenol through biotic and abiotic effects[END_REF]. Zeolites are characterised by three properties: 1) absorption 2) ion-exchange capacity and 3) biomass support due to their large surface area [START_REF] Montalvo | Application of natural zeolites in anaerobic digestion processes: A review[END_REF].

The ability of absorption of the zeolite can be amplified by drying zeolite before use. The volume left by the removed water will be available for other molecules [START_REF] Kesraoui-Ouki | Natural Zeolite Utilization in Pollution Control: A Review of Applications to Metals′ Effluents[END_REF]. Specific modifications can be done on zeolite to specifically adsorb molecules based on its ion-exchange property. For example [START_REF] Zhang | Enhanced bio-methane production from ammonium-rich waste using eggshell-and lignite-modified zeolite (ELMZ) as a bio-adsorbent during anaerobic digestion[END_REF] used an eggshell and lignite modified zeolite (ELMZ) in the case of ammonium-rich waste anaerobic digestion. The ELMZ was prepared by mixing natural zeolite, starch, eggshell and lignite in ultra-pure water, then the mixture was dried at 150°C during 24h and finally heated at 600°C for couple of hours to form the porous structure. The ammonium was absorbed on the zeolite and replaced by calcium which enhanced the biomass activity [START_REF] Zhang | Enhanced bio-methane production from ammonium-rich waste using eggshell-and lignite-modified zeolite (ELMZ) as a bio-adsorbent during anaerobic digestion[END_REF]. The role of zeolite as a support for the microbial development is supported by several studies. Fernandez et al. (2007) have demonstrated the development of Methanosaeta and Methanosarcinaceae on the zeolite using fluorescence in situ hybridization [START_REF] Fernández | Real evidence about zeolite as microorganisms immobilizer in anaerobic fluidized bed reactors[END_REF]. Moreover, [START_REF] Weiß | Investigation of mircroorganisms colonising activated zeolites during anaerobic biogas production from grass silage[END_REF] used scanning electron microscopy to determine microbial structuration around zeolite particles. Using single strand conformation polymorphism, they could identify a specific microbial community which preferentially developed on the zeolite [START_REF] Weiß | Investigation of mircroorganisms colonising activated zeolites during anaerobic biogas production from grass silage[END_REF].

Acclimation

The microbial population is able to adapt to the presence of different inhibitors. This adaptation is enhanced in industrial systems as successive disturbance periods are encountered. However no consensus on a specific threshold of inhibition can be defined. Indeed inocula for experimental researches come from different industrial digesters with specific inhibitors disturbance history.

It is possible to adapt the microbial population to the presence of a specific inhibitor by increasing the level of this inhibitor. It is usually observed that a progressive increase of the inhibitor is more efficient than an abrupt increase [START_REF] Angelidaki | Thermophilic anaerobic digestion of livestock waste: the effect of ammonia[END_REF]. Yan et al. (2019) succeeded to acclimatise a microbial population in semi continuous bioreactors to high level of ammonia by increasing stepwise the level of NH 4 + from 1.1 to 9.5 g NH 4 + N/L [START_REF] Yan | Acclimatization contributes to stable anaerobic digestion of organic fraction of municipal solid waste under extreme ammonia levels: Focusing on microbial community dynamics[END_REF]. [START_REF] Lins | Development and evaluation of inocula combating high acetate concentrations during the start-up of an anaerobic digestion[END_REF] demonstrated that the duration of acclimation of each step of ammonia increase influenced the microbial resistance capacity. The longer each step of acclimation was, the more the biogas production was improved [START_REF] Lins | Development and evaluation of inocula combating high acetate concentrations during the start-up of an anaerobic digestion[END_REF].

Successive subculturing of the biomass is another technique to acclimate microbial population. [START_REF] Feijoo | Sodium inhibition in the anaerobic digestion process: Antagonism and adaptation phenomena[END_REF] and [START_REF] Abouelenien | Dry mesophilic fermentation of chicken manure for production of methane by repeated batch culture[END_REF] succeeded to adapt microbial populations respectively to 24.0 g/L of sodium [START_REF] Feijoo | Sodium inhibition in the anaerobic digestion process: Antagonism and adaptation phenomena[END_REF] and 14.0 g NH 4 + -N/L of ammonia [START_REF] Abouelenien | Dry mesophilic fermentation of chicken manure for production of methane by repeated batch culture[END_REF]. [START_REF] Lefebvre | Impact of increasing NaCl concentrations on the performance and community composition of two anaerobic reactors[END_REF] suggested that it will be easier to obtain a resistant microbial population when the acclimation is done using a simple substrate such as NaCl+ethanol than using a complex substrate such as NaCl+vinasse [START_REF] Lefebvre | Impact of increasing NaCl concentrations on the performance and community composition of two anaerobic reactors[END_REF]. They suggest that complex substrate such as distillery vinasse requires a higher number of bacteria that simple substrate (ethanol).

Through the microbial acclimation studies it can be noticed that the anaerobic microbiota presents functional elasticity allowing it to adapt to several types and concentrations of inhibitors. Playing on this ability some researchers used an acclimated microbial consortium to improve the start-up of anaerobic digesters [START_REF] Cavaleiro | Continuous high rate anaerobic treatment of oleic acid based wastewater is possible after a step feeding start-up[END_REF][START_REF] Gonçalves | Inoculum acclimation to oleate promotes the conversion of olive mill wastewater to methane[END_REF].

Different strategies were proposed to preserve this microbial functional plasticity such as regularly adding inhibitors into the feedstock [START_REF] Benjamin | Anaerobic toxicity and biodegradability of pulp mill waste constituents[END_REF], modifying the frequency of the digester feeding [START_REF] De Vrieze | Repeated pulse feeding induces functional stability in anaerobic digestion[END_REF] or modifying the substrate composition.

Technical parameters

Some technical parameters can be modified or implemented in order to control/steer the microbial development. As microorganisms need specific conditions to grow, the modification of different technical parameters leads to the development of microbes more adapted to the degradation of specific substrates or more resistant to inhibitors.

Air injection is applied to enhance the development of the facultative anaerobic population inside the digester and to improve the hydrolytic step [START_REF] Diak | Effect of micro-aeration on anaerobic digestion of primary sludge under septic tank conditions[END_REF]. This strategy is employed for the sulfuric ions (HS -) oxidation into elemental sulphur (S 0 ) and sulphates (SO 4 2-) [START_REF] Krayzelova | Microaeration for hydrogen sulfide removal during anaerobic treatment: a review[END_REF]. It can be used also in order to avoid ammonium nitrogen formation and limit the inhibition [START_REF] He | Nitrogen removal in the bioreactor landfill system with intermittent aeration at the top of landfilled waste[END_REF] by enhancing the nitrification.

Temperature and pH are both technical parameters easily adaptable. Both parameters can limit the production or the influence of inhibitors by modifying the acido-basis equilibrium. In the case of ammonia inhibition several studies have shown that a high temperature increases the risk of inhibition [START_REF] Angelidaki | Anaerobic Thermophilic Digestion of Manure At Different Ammonia Loads: effect of Temperature[END_REF]. Ammonia nitrogen also depends on the pH value. A decrease of the pH can lead to a modification of the equilibrium between the two forms of ammonia with an increase of the NH 3 form and then of the inhibition. [START_REF] Ho | Mitigating ammonia inhibition of thermophilic anaerobic treatment of digested piggery wastewater: Use of pH reduction, zeolite, biomass and humic acid[END_REF] increased by 2.4 times the methane production by decreasing the pH from 8.3 to 6.5 during ammonia inhibition [START_REF] Ho | Mitigating ammonia inhibition of thermophilic anaerobic treatment of digested piggery wastewater: Use of pH reduction, zeolite, biomass and humic acid[END_REF]. However the risk of decreasing the pH is to inhibit the archaea growth as their optimal development pH value is close to the neutrality [START_REF] Strik | A pH-based control of ammonia in biogas during anaerobic digestion of artificial pig manure and maize silage[END_REF].

Microbiology in the Era of Big Data

The industrial anaerobic digestion management is based principally on chemical analyses and empirical expert knowledges. The development of this process is limited by the fact that AD is not always enough robust and flexible to face operational modifications. This is principally due to a sensitivity of the microbiome to technical modifications. While the management of the digester functioning is based on the chemical analyses, there is still a lack of knowledge regarding the microbiome dynamics.

New methodologies for microbial community and functional activity analyses, such as high-throughput technologies, have seen a rapid development allowing to improve the knowledge about bioprocesses microbial communities.

From microbial individual to community study

Culture-dependent methods have permitted to identify key microorganisms carrying specific metabolic processes. However biases and limitations linked to these culturedependent methods did not provide exhaustive information about complex microbiome. Indeed some microbes are not cultivable or need specific conditions to grow. Information regarding interaction within the entire microbial community of a bioprocess are then missed [START_REF] Vanwonterghem | Linking microbial community structure, interactions and function in anaerobic digesters using new molecular techniques[END_REF].

Thanks to the work of Rosalind Franklin on crystallographic data (Figure 7), Watson and Crick resolved the 3D structure of the DNA in 1953 [START_REF] Watson | Molecular structure of nucleic acids[END_REF]. The development of the culture-independent methodologies and especially the ones based on the genetic material amplification through the Polymerase Chain Reaction (PCR) have allowed to go further on the identification of complex biomass present in anaerobic digesters.

However the development of analytical technics did not evolved as fast as knowledges on DNA properties. During many years, only a general community profiling of the microbial community was obtained. If the major breakthrough on DNA sequencing came from Sanger and its dideoxy technique in 1977 [START_REF] Sanger | DNA sequencing with chain-terminating inhibitors[END_REF], the generalisation of the sequencing is very recent. Many improvement were carried out recently about sequencing allowing a decrease of the cost, an increase of sequence length and number of sequences [START_REF] Heather | The sequence of sequencers: The history of sequencing DNA[END_REF][START_REF] Vanwonterghem | Linking microbial community structure, interactions and function in anaerobic digesters using new molecular techniques[END_REF] which allow to democratise the use of the sequencing technics.

Fingerprinting for a description of the global modification of the microbial community

A general community profiling of the microbial community can be obtained using several methods grouped under the term of fingerprinting. Most of these methods are based on the generation of amplicons using specific primers followed by a separation by differential electrophoresis migration. The result is a fingerprint of the community diversity and the modification of this pattern indicates a shift of the microbial composition [START_REF] Talbot | Evaluation of molecular methods used for establishing the interactions and functions of microorganisms in anaerobic bioreactors[END_REF]. Some examples of these methods can be cited:

 Denaturing Gradient Gel Electrophoresis (DGGE) [START_REF] Muyzer | Profiling of Complex Microbial Populations by Denaturing Gradient Gel Electrophoresis Analysis of Polymerase Chain Reaction-Amplified Genes Coding for 16S rRNA[END_REF]: This technology is based on the electrophoretic separation of amplicons in polyacrylamide gel containing an increasing concentration of denaturant. Amplicons with a similar length can be separated based on their base-paired differences leading to different denaturing properties. The obtained fragments can be cut and sequenced for microbial identification.  Single-Strand Conformation Polymorphism (SSCP) [START_REF] Orita | Detection of polymorphisms of human DNA by gel electrophoresis as single-strand conformation polymorphisms[END_REF]: SSCP allows to detect small modifications or single point mutations (insertion or depletion) and allows to identify genomic variants. After amplifying a region of interest of the genome and a denaturation step single-strands are separated on a non-denaturant gel.

The mobility of a single-strand depends on its length, its molecular weight and its three-dimensional state.  Automated Ribosomal Intergenic Spacer (ARISA) [START_REF] Fisher | Automated approach for ribosomal intergenic spacer analysis of microbial diversity and its application to freshwater bacterial communities[END_REF]: This technology consists in the amplification of the intergenic region between the 16S rRNA and the 23S ribosomal genes. This region varies according to its length and its sequence. Primers are designed from conserved region located on the 5' and 3' of the 16S rRNA gene coding.

Microbiota visualisation using fluorescent hybridisation

The microbial spatial conformation can be assessed through the hybridisation of fluorogenic oligonucleotides (probes) inside cells and observed using a confocal microscope [START_REF] Amann | Combination of 16s Ribosomal-Rna-Targeted Oligonucleotide Probes with Flow-Cytometry for Analyzing Mixed Microbial-Populations[END_REF]. This technic is called Fluorescent In Situ Hybridisation (FISH). Probes can target microbes at different taxonomic levels (phylum, family, genus…).

One of the advantages of the FISH is the possibility to target different types of microbes in the same hybridisation as different fluorescent molecules are available. However the use of this technic usually needs to know a priori information regarding the identity of the microorganisms present in the sample. FISH is most of the time used in combination with another method such as fingerprinting or sequencing in order to target more precisely the microbes of interest and visualise their spatial distribution.

High-throughput technologies and the omics cascade

Hight-troughput Sequencing (HTS) or Next-Generation of Sequencing (NGS) technologies are now frequently used to study the microbial structure, functional potential and activity (Figure 8). Studying deeper the microbial community thanks to the high resolution of the HTS allowed to study low abundant populations and to go further on their contribution to process performance and stability. The use of these methodologies for the comprehension of the anaerobic digestion bioprocess is increasing [START_REF] Venkiteshwaran | Correlating methane production to microbiota in anaerobic digesters fed synthetic wastewater[END_REF]. The understanding of the bioprocess at different levels of information will be of great help for the management of the bioprocess. For example determining biological indicators of an inhibition would be helpful to prevent a complete failure to occur and then to maintain a good functioning of a digester. Determining which metabolic pathways and/or microbial functions are involved in the degradation of different substrates would be useful for the choice of the optimal mix of substrates to improve digester performances.

4.1.3.1.

16S rRNA metabarcoding 16S rRNA gene (Figure 9) amplicon sequencing is the most widely used highthroughput methodology due to its low cost. It is based on the amplification of a fraction of the 16S rRNA gene followed by sequencing. The ribosomal RNA is present in all archaea and bacteria and contains hypervariable regions which allows to identify archaea from bacteria at more precise taxonomic levels [START_REF] Woese | Towards a natural system of organisms: proposal for the domains Archaea, Bacteria, and Eucarya[END_REF]. Different regions can be amplified according to the targeted microorganisms. For microbial 16S rRNA there are 9 hypervariable regions (V1 to V9). The degree of conservation of these regions leads to more or less resolution on the taxonomic level.

If 16S rRNA metabarcoding has become a standard tool for studying the microbial community some limits are still present. The resolution is not still important enough to differentiate closely related microorganisms [START_REF] Větrovský | The Variability of the 16S rRNA Gene in Bacterial Genomes and Its Consequences for Bacterial Community Analyses[END_REF]. Microbes from families such as Enterobacteriaceae, Clostridiaceae and Peptostreptococcaceae can share more than 90% of their sequences on the entire 16S rRNA gene.

The differentiation on the V4 region is then on only few nucleotides which leads to the impossibility of an identification at lower taxonomic levels than family [START_REF] Jovel | Characterization of the gut microbiome using 16S or shotgun metagenomics[END_REF]. Other gene markers can be used in order to alleviate this issue such as DNA recombinase protein (recA) or RNA polymerase subunit B (rpoB).

DNA-based technologies deliver information regarding the total diversity combining living, dormant and dead microbes which can lead to some misinterpretations about the microbial activity. Indeed DNA does not provide information on the active populations at a specific state of the bioprocess [START_REF] Lin | Temperature regulates deterministic processes and the succession of microbial interactions in anaerobic digestion process[END_REF]. Sequencing of the 16S rRNA can provide such information as RNA is produced by active microbes. However RNA molecules are difficult to manipulate as they are extremely instable and with a short life-time [START_REF] Brettar | Analysis of bacterial core communities in the central Baltic by comparative RNA-DNA-based fingerprinting provides links to structure-function relationships[END_REF].

Thanks to the increase of 16S rRNA sequence length and sequencing resolution, new predictive methods have been developed to provide insights into the metabolic potential. Methods such as Tax4Fun [START_REF] Aßhauer | Tax4Fun: Predicting functional profiles from metagenomic 16S rRNA data[END_REF], PICRUST [START_REF] Langille | Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences[END_REF] or Piphillin [START_REF] Iwai | Piphillin: Improved prediction of metagenomic content by direct inference from human microbiomes[END_REF] link the 16S rRNA gene sequences to the functional annotation of sequenced genomes to predict potential functionality. However these methods provide only information on the possible functions based on information contained on databases, which is not a real picture of the microbial activity inside the system.

4.1.3.2.

Metagenomics: What can happen Genomics is the analysis of the genome by sequencing and annotation of genes. It was first applied on cultivated organisms but it is now extended to bacterial ecosystems. Metagenomics is genomics applied on microbial community from a natural or engineering ecosystem.

Metagenomics provides information about the phylogeny but also potential functional diversity in a system [START_REF] Vanwonterghem | Genome-centric resolution of microbial diversity, metabolism and interactions in anaerobic digestion[END_REF]. Metagenomics helps to go further on the understanding of how the operational conditions of the digesters such as feedstocks composition or pre-treatments influenced the microbial diversity [START_REF] Lebuhn | Towards molecular biomarkers for biogas production from lignocellulose-rich substrates[END_REF][START_REF] Rademacher | Characterization of microbial biofilms in a thermophilic biogas system by highthroughput metagenome sequencing[END_REF][START_REF] Wong | Towards a metagenomic understanding on enhanced biomethane production from waste activated sludge after pH 10 pretreatment[END_REF].

4.1.3.3.

Metatranscriptomics: What is happening Metatranscriptomics provides information about in situ gene expression by sequencing of the reverse transcribed mRNA (messenger RNA). RNA are molecules corresponding to the transcription of the DNA and messenger RNA are molecules which contain the information to synthetize a protein. The level of complexity on the microbial analysis is decreased as metatranscriptomics focuses only on the metabolically active microbes. Several methodological challenges remain such as low recovery of RNA from environmental samples, short half-life of mRNA or bias related to complementary DNA synthesis and amplification [START_REF] Carvalhais | Application of metatranscriptomics to soil environments[END_REF][START_REF] Su | Culture-independent methods for studying environmental microorganisms: Methods, application, and perspective[END_REF].

Very few studies have been published using metatranscriptomics to decipher AD ecosystem. [START_REF] Delforno | Comparative metatranscriptomic analysis of anaerobic digesters treating anionic surfactant contaminated wastewater[END_REF] compared the active genes and the active microbial composition of three reactors to determine the surfactant biodegradation pathway [START_REF] Delforno | Comparative metatranscriptomic analysis of anaerobic digesters treating anionic surfactant contaminated wastewater[END_REF]. [START_REF] Zhao | Biological pretreatment enhances the activity of functional microorganisms and the ability of methanogenesis during anaerobic digestion[END_REF] determined the influence of biological pre-treatment on the microbial activity [START_REF] Zhao | Biological pretreatment enhances the activity of functional microorganisms and the ability of methanogenesis during anaerobic digestion[END_REF]. Others studies combined metagenomics and metatranscriptomics analyses to complement information of the global structure by studying the activity of the microbial population [START_REF] Crovadore | Metatranscriptomic and metagenomic description of the bacterial nitrogen metabolism in waste water wet oxidation effluents[END_REF][START_REF] Maus | Unraveling the microbiome of a thermophilic biogas plant by metagenome and metatranscriptome analysis complemented by characterization of bacterial and archaeal isolates[END_REF]. Indeed [START_REF] Maus | Unraveling the microbiome of a thermophilic biogas plant by metagenome and metatranscriptome analysis complemented by characterization of bacterial and archaeal isolates[END_REF] have shown that high abundance of microorganisms observed with metagenomics is not necessarily representative of its activity.

4.1.3.4.

Metaproteomics: What makes it happen Metaproteomics characterises proteins at a specific time point. Proteins are extracted, fractionated, separated using liquid chromatography and detected with tandem mass spectrometry (MS/MS) [START_REF] Langley | Proteomics: From single molecules to biological pathways[END_REF]. The identification of the proteins depends on the presence of their sequences in specific databases. The increase of the size of these databases reinforces the attractiveness of using metaproteomics. [START_REF] Patti | Metabolomics: The apogee of the omics trilogy[END_REF] Metaproteomics can be used to detect catalytic enzymes, entire metabolic pathways, novel functional proteins but also to identify the distribution of metabolic activities and how populations cooperate or compete [START_REF] Bize | Shotgun metaproteomic profiling of biomimetic anaerobic digestion processes treating sewage sludge[END_REF][START_REF] Lü | Metaproteomics of cellulose methanisation under thermophilic conditions reveals a surprisingly high proteolytic activity[END_REF]. Moreover metaproteomics can be combined to other omics methodologies such as metagenomics to complement or create databases of enzymatic or other protein functions [START_REF] Hanreich | Metagenome and metaproteome analyses of microbial communities in mesophilic biogas-producing anaerobic batch fermentations indicate concerted plant carbohydrate degradation[END_REF].

4.1.3.5.

Metabolomics: What has happened Metabolomics is the metabolites profiling. Metabolites are low-molecular-weight molecules produced during metabolism. Metabolites are usually measured using mass spectrometry or nuclear magnetic resonance spectroscopy. They provide a functional readout of cellular state and serve as direct signatures of biochemical activity. Indeed contrary to genes or proteins, metabolites are not subject to epigenetic regulation or post-translational regulations [START_REF] Patti | Metabolomics: The apogee of the omics trilogy[END_REF].

Two metabolomics approaches exist according to the information required: targeted or untargeted metabolomics as global approach (Figure 10). In the targeted metabolomics methodology, a specified list of metabolites is measured. This approach allows to answer to specific question regarding the investigation of a particular pathway. In opposite the untargeted metabolomics approach has the aim of detecting as many metabolites as possible without a priori knowledge. This approach is global in scope and outputs data related to comprehensive cellular metabolism.

Several challenges still remain with metabolomics methodologies. Indeed databases of metabolites are still scarce as the number of metabolites in biological system was underestimated. Moreover, the metabolome is not encoded in the genome such as proteins or transcripts, which would help to define metabolite chemical structure, cellular function or biochemical pathway.

In AD ecosystem, few studies have been done so far on the metabolome. Most of them based their research on intracellular untargeted metabolites to determine a fingerprint of the metabolic composition and study different conditions or time points [START_REF] Beale | An "omics" approach towards the characterisation of laboratory scale anaerobic digesters treating municipal sewage sludge[END_REF][START_REF] Sasaki | Comparison of metabolomic profiles of microbial communities between stable and deteriorated methanogenic processes[END_REF][START_REF] Yang | Metabolomics reveals stage-specific metabolic pathways of microbial communities in two-stage anaerobic fermentation of corn-stalk[END_REF].

Revealing key information from big data: the use of computational biostatistics

The fundamental question addressed by the community ecology is: "who lives with whom and why?" [START_REF] Mccune | Analysis of ecological communities[END_REF]. High-throughput and omics technologies are of great help to answer to this question thanks to the high resolution and the different level of information on microbiome (phylogeny, functions…). These technologies provide a huge amount of data and finding the key information to answer to a specific biological question is not obvious. Thankfully in parallel to the development of molecular technologies, analytical methods have been improved for the treatment of data by using bioinformatics tools on the one hand and data analysis using biostatistics methods on the other hand.

Before identifying information required for the interpretation of the biological system the data has to be pre-processed. Indeed from all the data provided by the different technologies some are of bad quality, or correspond to noise… These sequences or chromatographic peaks have to be removed from the analyses. For that purpose different tools are available such as FROGS [START_REF] Escudié | FROGS: Find, Rapidly, OTUs with Galaxy Solution[END_REF] for metabarcoding data, MG-RAST [START_REF] Glass | The Metagenomics RAST Server: A Public Resource for the Automatic Phylogenetic and Functional Analysis of Metagenomes[END_REF] for meta-omics data or XCMS [START_REF] Smith | XCMS : Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment , Matching , and Identification[END_REF] for metabolomics data. The principle and the different steps of some of these analytical softwares are described in the material and methods chapter.

The choice of the appropriate statistical method to analyse the data depends on the type of the data but most of all on the biological question to answer: investigate changes in the global diversity (diversity indices), relate species abundance in a community to environmental conditions (ordination methods), study microbial dynamics along experiment (time course modelling) or correlate data from different analysis (data integration).

Most of these methods are based on the principle of data reduction to summarise a large number of observation [START_REF] Mccune | Analysis of ecological communities[END_REF].

Data exploration: single-omics analysis

4.2.1.1.

Diversity indices Microbial diversity can be correlated to ecosystem stability. Temporal shifts in the diversity inside the bioreactor can be an indicator of process modification [START_REF] Saikaly | Use of 16S rRNA gene terminal restriction fragment analysis to assess the impact of solids retention time on the bacterial diversity of activated sludge[END_REF].

The microbial diversity within an ecosystem is called α diversity and can be measured by using divers indices. The three mainly used indices to define microbial diversity are: richness, evenness and Shannon index. They are based on the assumption that one Operational Taxonomic Unit (OTU) corresponds to one species. The richness index defines the number of OTU in the sample. The evenness is a measure of equitability of abundance meaning that a sample with multiple species at similar abundances is more diverse than a sample with predominant and sparse species. Finally Shannon index (H') is the negative sum of the proportional abundance of each OTU multiplied by the log of its proportional abundance.

𝐻 ′ = -∑ 𝑝 𝑖 𝑅 𝑖=1 𝑙𝑛𝑝 𝑖
Where R is the proportion of species in the community and p i the proportion of R is the proportion of individuals belonging to the i th species of the community. This index measures the difficulty to predict the next species that will be sampled. Shannon index is more sensitive to rare species due to the weight given to them compared to the other methods [START_REF] Mccune | Analysis of ecological communities[END_REF].

If these indices provide a first appreciation of the microbial dynamics in a digester, they do not provide further information on which species, at which moment or to which operational condition the modification was due.

4.2.1.2.

Data exploration using unsupervised multivariate methods Multivariate analyses allow the analysis of three or more variables in parallel (OTUs count matrix for example) whereas univariate analyses evaluate one single variable at a time (age). Microbial data can be analysed without a priori information by using "unsupervised" methods. Ordination methods are widely used for that purpose and allow to summarise hundreds of variables into only a few. They allow to explore the data and visualise similarities/dissimilarities of individuals within a dataset. Some of these methods are described below:

 Principal Component Analysis (PCA): PCA is widely used for data reduction and visualisation. It was first applied on ecological data by [START_REF] Goodall | Objective methods for the classification of vegetation. III. An essay in the use of factor analysis[END_REF][START_REF] Goodall | Objective methods for the classification of vegetation. III. An essay in the use of factor analysis[END_REF]. The aim of the PCA is to represent a high dimensional dataset with a smaller number of composite variables called components formed by an orthogonal linear transformation of the data. The most interesting and strongest covariation is explained by the first component, the next components explain the remaining variation. The number of components has to be chosen to get the best projection of the data which will allow to represent as much of variation as possible (Figure 11).  Principal Coordinate Analysis (PCoA): the PCoA is a variant of the PCA as the data input is a similarity/dissimilarity matrix and the distance metric used is non-Euclidean on the contrary to PCA which uses raw data and Euclidean distance metric. It is commonly applied on non-Euclidean sample-wise dissimilarity dataset or phylogenetic distances between set of taxa.  Non-Metric multiDimensional Scaling (NMDS): NMDS is increasingly used in the ecological community. This method is also based on distance matrix but contrary to PCoA, NMDS is an iterative algorithm. An ordination space is built by placing randomly the data objects in the space. The placement is refined using an algorithm until the distance between the ordinated objects and the original distance matrix get close to each other. A stress value is calculated and represents how well the ordination summarises the observation distances among the samples.

Data explanation using supervised multivariate methods

When the influence of a specific operational condition on the microbial diversity has to be studied, "supervised" multivariate methods can be used. In that case a response (Y) corresponding to the desired condition is defined. Two of these methods are commonly used in ecological data treatment:  Partial Least Square Regression (PLS): The use of PLS method in ecological field gains in importance. PLS relates two data matrices X and Y and models the structure of both matrices. In this thesis work, the PLS was used using the canonical mode. This mode allows to highlight correlations between both datasets by considering both X and Y matrices symmetrically. PLS can be used to study the links between two types of information from a same biological sample.  PLS Discriminant Analysis (PLS-DA): PLS-DA is an extension of the PLS algorithm to perform variable classification. The covariance is maximized between linear combination of the variables and the qualitative response Y. Each component is a linear combination of variables and characterises a particular source of covariation [START_REF] Lê Cao | Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems[END_REF].

Longitudinal studies

Most of the studies in anaerobic digestion field focus on the influence of different operational conditions or perturbations. In most cases the microbial community is studied at few time points to compare the microbiota between the conditions or before/after the perturbation [START_REF] Lu | Relationship between microbial community, operational factors and ammonia inhibition resilience in anaerobic digesters at low and moderate ammonia background concentrations[END_REF][START_REF] Lv | Microbial community shifts in biogas reactors upon complete or partial ammonia inhibition[END_REF][START_REF] Ziels | DNA-SIP based genome-centric metagenomics identifies key long-chain fatty acid-degrading populations in anaerobic digesters with different feeding frequencies[END_REF]. However anaerobic digestion should be considered as a long time experiment. For example Wu et al (2018) studied the dynamics of an anaerobic microbiota under stable operational conditions for two years [START_REF] Wu | Longterm successional dynamics of microbial association networks in anaerobic digestion processes[END_REF].

Appropriate methods are needed to treat this kind of longitudinal data due to their sparsity (irregular sampling), compositionality, high variability and dependencies between the variables. Time course modelling is one of the commonly used methods to model dynamics of microbes across group of samples [START_REF] Straube | DynOmics to identify delays and co-expression patterns across time course experiments[END_REF].

However if hypotheses can be drawn about potential symbiotic, interaction or competition between microbes using clustering methods [START_REF] Déjean | Clustering time-series gene expression data using smoothing spline derivatives[END_REF], time-course modelling is essentially an univariate analysis. Further developments have to be done in order to infer ecological interactions from multiple datasets.

Microbial datasets integration approaches

Thanks to the development of new technologies, as described previously, microbial communities can be studied at different levels: phylogeny, functionality… Linking together all this information would provide further insights to answer to the question: "who lives with whom and why?".

Integrating different datasets, meaning linking together data from multi-omics methods, is really challenging. Indeed each dataset has to be independently cleaned, normalised, reduced…. Some of the multivariate methods described below have been used to integrate different types of data. For example [START_REF] Guidi | Plankton networks driving carbon export in the oligotrophic ocean[END_REF] used sparse PLS [START_REF] Lê Cao | A Sparse PLS for Variable Selection when Integrating Omics Data[END_REF] to integrate environmental and metagenomics data from Tara Ocean expedition. Another integrative method was developed by Singh et al 2019 called DIABLO for "Data Integration Analysis for Biomarker discovery using Latent variable approaches for 'Omics studies". DIABLO is a supervised method which the aim is to identify co-expressed variables measured on heterogeneous datasets explaining the output (Y response) [START_REF] Singh | DIABLO: an integrative, multi-omics, multivariate method for multi-group classification[END_REF]. However these methods were mostly applied for one-time point analysis. New methodologies are being developed to take into account longitudinally studies in integrated approaches [START_REF] Bodein | A generic multivariate framework for the integration of microbiome longitudinal studies with other data types[END_REF].

Problematic

As presented in the previous chapter, anaerobic digestion bioprocess has a huge environmental potential. Indeed, AD has the double advantage to produce methane, a sustainable energy, and to decrease the volume of organic waste sent into landfills. However some difficulties still hamper its development. One of the major obstacles is the difficulty to maintain a stable and performing bioprocess. Improvements in the management of the bioprocess are needed, especially regarding the microbial community, in order to optimise the performances and stability. The management of the good functioning of the bioprocess is mostly based on chemical measurements and engineer expertise. However, as bioprocess performances depend on the microbiota functioning, another solution for a better control of the AD would be a microbial management. A microbial-based management is a management of the digester that explicitly takes advantage of the anticipated microbial response to modifications of the operating conditions. To do so, it is essential to deeper understand how technical or chemical parameters influence the microbial equilibrium and identify metabolic processes which lead to the bioprocess failure.

Significant progresses have been made on the deciphering of the anaerobic microbiota thanks to the recent development of high-throughput molecular technologies and omics approaches. Different levels of information on the microbial ecosystem are provided by these methodologies which could for example help to perform a microbial diagnosis of the digesters. It would consist in the monitoring of biological indicators, which could be enzymes, metabolites or specific microbes to determine the good functioning or the risk of failure of the digester. Microbial diagnosis would facilitate AD microbial management.

However, high-throughput molecular technologies provide a huge amount of data and extracting the key information from this data is not an easy task. Computational biostatistics can help to resolve this challenge. However, they are still not widely used. One of the difficulties lies in the choice of the appropriate method to answer a specific biological question. Moreover, new developments are still required to meet the requirements of the new generated data and go further in the interpretation of the microbial data.

Finally, while lots of information is now available about the influence of modifications of operational parameters on the microbial dynamics, some gaps remain. For example: how the feedstock composition influences the microbial dynamics? At which level the digester is influenced by the inhibitor (global performance, microbial populations, functions expressed, metabolites produced…)? Is it possible to identify early warning biological indicators of inhibition? ... In this context, the aim of my thesis work is to determine how the modification of different parameters influences the anaerobic microbiome and how it relates to the digester performances. For that purpose, multiple high-throughput molecular technologies were used especially 16S rRNA metabarcoding and metabolomics analyses. We choose to work with the 16S rRNA metabarcoding in order to target specifically active microbes which are still rarely studied. As metabolomics is one of the most recently applied omics methodologies to the environmental bioprocess field, few studies have been done, especially on anaerobic digestion. To extract the relevant information and integrate the different datasets which were generated, specific biostatistics methods were applied. They were specifically chosen to answer the targeted biological questions.

The first objective of this thesis work was to study the influence of the feedstock composition and of its modification on the microbial activity. One of the strategies to optimise the digesters performances and to counteract possible inhibitions is to carry out the co-digestion of different substrates. The choice of the feedstock composition greatly influences the digestion performances. However, no information is available regarding the link between the microbial activity and the substrate degradation. Correlations between the degradation of different substrates and the microbial activity were established through the integration of metabolomics and 16S rRNA sequencing data. This work allowed to formulate new hypotheses on potential degraders of specific molecules.

The second objective of this thesis work was to determine the influence of ammonia accumulation on the microbial activity. Ammonia is one of the major inhibitors of the methanogenesis. It can be produced during the degradation of some substrates. If some studies have identified microorganisms sensitive to the ammonia, no clear consensus has been obtained yet. In this thesis work an experiment was designed to study the resistance capacity of the microbes by monitoring the time course dynamics of their abundances. In the same way, the influence of the speed of ammonia addition on the metabolites dynamics was studied.

The last objective was to determine the underlying mechanisms of ammonia inhibition mitigation by the zeolite. Zeolite is a mineral support known to mitigate the inhibition of AD by several compounds such as ammonia or phenol. Different hypotheses on the role of the zeolite in mitigating the inhibition have been advanced: ammonia adsorption, ion exchange capacity or support for microbial growth. However, studies describing the influence of the zeolite on the microbial activity and the link with the digester performances are still scarce. In this thesis work, we compared the influence of differently pre-treated zeolites on digesters performance in presence of a low amount of ammonia. More specifically, the influence of the zeolite on the development of the microbial community was studied by comparing statistically the microbial composition when zeolite was added, in presence or not of ammonia.

Material and Methods

Experimental setup

In this thesis work different experimental setup strategies were applied. Batch reactors were used to test different experimental conditions. On the basis of the results from batch experiments specific conditions were tested in semi-continuous reactors of 5L. Semicontinuous reactors allowed being closer to industrial reactors.

Batch experimental design

Batch experiments were carried out in 1L glass bottles in mesophilic conditions, in the dark and without agitation (Figure 12). All the incubations were performed in triplicate. Batchs were sealed with a screw cap and a rubber septum. The headspaces were flushed with N 2 (purity >99.99%, Linde gas SA). A ratio substrate/inoculum of 12 gCOD / 1.2 gCOD was used.

CSTR experimental design

Six semi-continuous stirred tank reactors (Bioprocess Control) with a working volume of 5 L (7 L total) were used (Figure 13). All the experiments were carried out in mesophilic conditions obtained through a double envelop system linked to a water bath. CSTR were constantly stirred at 90 rpm using an intern agitator with two helicoid systems. A constant organic loading rate (OLR, 0.5 gCOD/L/D) and hydraulic retention time (HRT specific to the experiment) were applied. The feed was brought to the digester by a peristaltic pump system connected to a timer programmed for a feeding once a day. The feeding was prepared once a week and stored at 4°C. The substrate was mixed to the BMP buffer to reach a final volume of 2 L. Intern and extern temperature was monitored using probes linked to home-made software.

Materials

Inoculum

The inoculum used for the different experiments carried out along this work was provided by Suez and came from an anaerobic digester (Valenton) treating wastewater sludge in mesophilic conditions.

The inoculum was maintained at 35°C in a 60L reactor and regularly fed with biowaste. The feeding was stopped a few days before each experiment in order to eliminate the organic matter from the substrate in excess. Physico-chemical characteristics of the inocula are described in the Table 3.

Substrates

Different substrates were used according to the aim of the experiments. In total 7 substrates were used. Their physico-chemical characteristics are summarised in the Table 4.

The biowaste came from an industrial food waste collector used to feed an anaerobic digester (Valdis Energie, Issé). The food waste came from different origin such as schools, markets, expired products, products for quality testing. In total 200 L of biowaste were collected and stored in 1 L bottles at -20°C. Before each use biowaste was thawed and crushed using a vegetable grinder.

Two different sewage sludges were used during this thesis. The first sewage sludge came from a wastewater treatment plant in Valenton and was used for the co-digestion in batch experiment (2017 -Chapter I parts 1 and 2). As not enough sewage sludge was available to carry out the second experiment in semi-continuous digesters a new sampling was carried out. The second sewage sludge came from a wastewater treatment plant in Evry (2019 -Chapter I part 3). In both cases the sewage sludge was used directly after the sampling.

Grass was collected from the lawn of Irstea, at two different dates. The first time the collection was done in spring 2017. The grass was humidified and crushed to feed the anaerobic co-digestion batch experiments. The second time grass was collected in winter 2019 and was dried at 80°C few days and crushed before use to maintain its physicochemical properties all along the experiment.

Fish waste was collected from the fish market of Antony. Different parts of fish waste such as guts, bones and scales from several fish species, were crushed before use

Buffer

In order to limit the risk of an important drop of pH during the experiment and especially after a disturbance, a buffer was added. The buffer composition was based on the NF EN ISO 11374 standard with some modifications. The buffer was complemented with sodium hydrogenocarbonate in order to maintain the pH between the recommended values for an optimal methanogenesis. The composition is described in the Table 5.

Inhibitors

The effect of ammonia inhibition on the anaerobic digestion microbiota was studied. The experiment in semi-continuous digesters was carried out with addition of NH 4 Cl (Acros Organics, 99%). The level of inhibition was determined by estimating the IC50 (half inhibiting concentration) in order to decrease the methane production by 2. Batch experiments were carried out with different concentration of NH 4 Cl. The value of the maximal methane production rate was calculated for each condition using the Gompertz equation. The concentration partially inhibited the methane production (IC50) was estimated by plotting the methane production rate as function of the final NH 3 concentration. The curve was fitted to the Hill model by non-linear regression using Marquard's algorithm and a nonlinear regression program proposed by Duggleby as described in [START_REF] Fechner | Adapting an enzymatic toxicity test to allow comparative evaluation of natural freshwater biofilms' tolerance to metals[END_REF] to calculate IC50. As the inhibition was based on the NH 3 value the concentration was calculated at the beginning and at the end of the experiment.

To study the effect of zeolite in presence of low quantity of ammonia the (NH 4 ) 2 CO 3 (Alpha Aesar) was used in order to limit the concentration of chloride. As described previously the IC50 using (NH 4 ) 2 CO 3 was estimated.

Additives

The zeolite was used in order to study its influence on the microbial community during the mitigation of ammonia inhibition. Zeolite is a natural rock known to mitigate the inhibition of different molecules as ammonia or phenol.

The zeolite Siliz 24® was selected based on a previous study carried out by Poirier et al [START_REF] Poirier | Improving anaerobic digestion with support media: Mitigation of ammonia inhibition and effect on microbial communities[END_REF] showing a better mitigation effect compared to other mineral supports. The concentration of zeolite was based on the work of Poirier et al as well. The zeolite was sieved to get a particles size superior to 480 µm.

Different pre-treatments were carried out on zeolite in order to study different mechanisms of mitigation. A part of zeolite was heated at 500°C during 4 hours in order to remove water and volatile molecules. Another part was boiled in water at 100°C during 20 minutes. The liquid residue was filtered at 0.22 µm. Liquid and solid residues were tested as two conditions.

Sampling methodology

Every week the digestate was collected from the digesters. Approximately 6 mL and 60 mL for respectively batch and CSTR were sampled. The pH was measured on the noncentrifuged sample directly after the sampling. Tubes were centrifuged at 10000 g for 10 minutes at 4°C, supernatants and pellets were separated and snap frozen using liquid nitrogen. The supernatant was stored at -20°C for chemical analysis and the pellet at -80°C for the microbial analyses (Figure 14). During the perturbation phases of the CSTR experiments, samplings were carried out three times each week.

For the isotopic fractionation measurement 7 mL of gas was sampled in the headspace of the batch or semi-continuous reactors using a glass syringe equipped with a closing valve to avoid gas contamination. The gas was stored into vacuum serum tubes.

Chemical analysis

Biogas production and composition

Biogas production

In the case of the batch experiment the biogas accumulation in the headspace was measured regularly using a differential manometer (Digitron 2082P) at 35°C. The atmospheric pressure was also measured. The volume of the cumulated biogas was calculated using the ideal gas law.

For the analysis of the biogas composition the bottles are connected directly to the chromatograph. After the different samplings (liquid and gas) the headspace of the microcosm was depressurized and measured once again at 35°C.

In the case of CSTR experiment, the biogas production flow was followed using a µFlow system (Bioprocess Control). In this system, the biogas leaves the digester, goes through the µFlow and accumulates into a Tedlar® bag. The biogas was sampled directly from the headspace of the digester using a syringe to determine its composition and its isotopic signature.

Biogas composition

The biogas composition was measured through a gas-phase micro-chromatography (µGC, Varian CP4900). This instrument is equipped with four elution columns in parallel which allow to separate the different gases: O 2 , N 2 , CH 4 , CO 2 , N 2 O, H 2 S and NH 3. The detection is performed by thermic conductive detectors. The signal measured corresponds to the difference of thermic conductivity between the gas from the sample and the gas from the gas vector. In three of the four columns the vector gas used is helium and in the fourth one the vector gas is argon.

Data of pressure and biogas composition were used to calculate the biogas production at standard temperature and pressure. Different parameters used to quantify the methane production potential were calculated using R CRAN software and the Gompertz equation with Grofit package as described in Poirier et al. (2016):

𝑦(𝑡) = 𝐴. exp [-exp ( µ. 𝑒 𝐴 (𝜆 -𝑡) + 1)]
Were y (t) is the cumulative CH 4 production (mL), A is the ultimate CH 4 yield (mL), µ is the maximum production rate of CH 4 (mL/day), and 𝜆 is the lag phase (day).

Biogas isotopic fractionation

The methanogenic pathways (acetoclastic methanogenesis or hydrogenotrophic methanogenesis) were determined through the gas isotopic signature analysis. Periodically gas was sampled into a 7-mL vacuumed serum tube for the analysis of δ 13 CH 4 and δ 13 CO 2 . A Trace Gas Chromatograph Ultra (Thermo Scientific) attached to a Delta V Plus isotope ratio mass spectrometer via a GC combustion III (Thermo Scientific) was used to carry out the analysis.

Using an autosampler 100 µL of samples were injected (Triplus AS, Thermo Electron Corporation) into the gas chromatograph (GC) equipped with a Paraplot Q column to separate the CH 4 to the CO 2 . The parameters used for this separation were a split mode with a ratio of 20 at 150°C. The sequence for the GC was: 3 min at 35°C, increase temperature to 180°C and a stabilisation of 24 seconds at 180°C. The gas vector flux (helium) was constant at 1.8 mL/min. Then the separated CH 4 and CO 2 went through the combustion oven (940°C) to transform the gas into CO 2 . The CO 2 was ionised in the mass spectrometer electronic impact source into CO 2 + and the different ions were separated according to their mass/charge (m/z) ratio. The isotopes were detected by three different collectors on the trajectories of the isotopomeres with m/z of 44 ( ). The isotopic ratios 45/44 and 46/45 were calculated and compared to the ones of a reference CO 2 injected at the beginning and at the end of the samples analysis.

The δ 13 C expressed in ‰ was then calculated as presented in the following equation:

𝛿 13 𝐶 = [ (𝑅𝑒 -𝑅𝑠) 𝑅𝑠 ] . 1000
Where Re corresponds to the ratio 13 C/ 12 C of the sample and Rs corresponds to the ratio 13 C/ 12 C of the international standard.

As an indicator of the methanogenic pathway, the apparent isotopic factor (α app ) was calculated as presented in the following equation [START_REF] Whiticar | Biogenic methane formation in marine and freshwater environments: CO2reduction vs. acetate fermentation-Isotope evidence[END_REF]:

𝛼 𝑎𝑝𝑝 = (𝛿 13 𝐶𝑂 2 + 10 3 ) (𝛿 13 𝐶𝐻 4 + 10 3 )
It is usually assumed that if the α app is superior to 1.065 the hydrogenotrophic way is dominant. On the contrary, if the α app is inferior to 1.055 the methanogenesis is dominated by the acetoclastic way [START_REF] Conrad | Quantification of methanogenic pathways using stable carbon isotopic signatures: a review and a proposal[END_REF][START_REF] Whiticar | Biogenic methane formation in marine and freshwater environments: CO2reduction vs. acetate fermentation-Isotope evidence[END_REF].

pH

The pH was measured on the non-centrifuged samples by using a pH meter (IQ160) with 0.01 of resolution. After a two-point calibration of the probe at pH 4.01 and 7, the pH was measured in the sample.

Volatile fatty acids

Volatile Fatty Acids (VFA) concentrations were measured using ionic chromatography (ICS 5000+, Thermo Fisher Scientific) equipped with IonPAC ICE-AS1 column. The mobile phase was composed of heptafluorobutyric acid (0.4mmol/L) and tetrabutylammonium (5mmol/L). The VFA quantified were acetate, propionate, butyrate, valerate, formate, lactate and caproate.

Chemical oxygen demand

The chemical oxygen demand (COD) determines the amount of oxygen required for the chemical oxidation of organic matter. The chemical oxygen demand was measured using the micro-colorimetric method provided by Hach (kit LCK 514) on the raw samples. The range of this method goes from 100 to 2000 mgO 2 /L. The measure was carried out on a spectrometer DR-2800 (Hach). The sample is heated for 2 hours with sulfuric acid and a strong oxidizing agent, potassium dichromate. The sample's organic carbon is then oxidised with the hexavalent dichromate ion (Cr 2 O 7 2-). The oxygen transaction from Cr 2 O 7 2to CO2 reduces the Cr 2 O 7 2-into green chromic ion (Cr 3+ ). The amount of Cr3+ is then measured by using a spectrometer DR-2800 (Hach) at 610 nm.

Dissolved organic and inorganic carbons

Dissolved organic and inorganic carbons (DOC and DIC) were measured on the supernatant following the French standard NF EN 1484 using a DOC analyser TOC-L (Shimadzu). For DOC analysis, the technique used was the NPOC (Non-Purgeable Organic Carbon). After acidifying (HCl 1N) the sample at pH 2-3, a carrier gas (Air 5.0) was splashed around to remove all the inorganic carbon. The sample was then introduced into a combustion tube containing a catalyst and was heated to 680°C. The carbon contained in the sample was converted into CO 2 and transported by the carrier gas to the NDIR (Non-Dispersive Infra-Red) detector where it was analysed on a range from 1 to 20 mgC/L. For the DIC the preparation was directly performed in the injection syringe of the device. After acidifying the sample at pH 2-3, a carrier gas was splashed around and all the inorganic carbon was converted into CO 2 . The CO 2 was then transported by the carrier gas to the NDIR detector. The range of measurement was from 1 to 50 mgC/L.

Ammonia

The total ammonia nitrogen (TAN) concentration was measured using the Nessler's colorimetric method following the French standard (NF T 90-105) in spectroscopic tanks using Hach spectrometer DR2800. The link between Free Ammonia Nitrogen (FAN), Total Ammonia Nitrogen (TAN), pH and temperature can be summarized with the following equation [START_REF] Anthonisen | Inhibition of Nitrification by Ammonia and Nitrous Acid[END_REF]:

𝐹𝐴𝑁 = 10 𝑝𝐻 (exp ( 6344 𝑇 ) + 10 𝑝𝐻 ) 𝑥 𝑇𝐴𝑁
Where T is the temperature in Kelvin.

Elementary carbon and nitrogen

To analyse the carbon and nitrogen, samples were dried at 105°C. Then 10 mg of this dry matter was introduced into a combustion oven, heated at 950°C and oxidised in presence of oxygen and catalysers (VARIO EL III, Bioritech). The sample was transformed into CO 2 and NOx. The NOx were reduced into N 2 in a tube heated at 500°C in presence of copper.

The CO 2 and N 2 produced were separated through a column heated at 100°C and were detected using a catharometer.

Biomolecular analysis

Nucleic acids extraction

DNA extraction

The DNA was extracted using the commercial kit MOBIO (PowerSoil™ DNA Isolation kit) following the manufacturer's instructions. This protocol combines a bead beating and chemical lysis steps. The procedure includes also a removing step of the PCR inhibitors such as humic acid found in environmental samples. Total genomic DNA is then captured on silica membrane. After a washing step the DNA is eluted from the membrane.

RNA extraction and reverse transcription

Total RNA was extracted using the commercial kit FastRNA Pro™ Soil-Direct (MP Biomedicals) following the manufacturer's specifications. Then, DNA co-extracted was removed using TURBO™ DNase (Ambion) kit following the manufacturer's instructions. The RNA was denaturated by 2 min at 85°C in a dry bath and was then stored on ice. RNA purification was carried out using the Agencourt AMpure RNA magnetic beads purification system (Beckman Coulter) by adding 1.8 volumes of beads by volume of RNA. After mixing by pipetting and 5 min of incubation, beads were captured using a magnetic rack on one side of the tube and then washed by adding 500µL of 70% cold ethanol (diluted in DEPC-water). After incubation of the tube during 30 seconds at room temperature, the ethanol was removed. This washing step was repeated 3 times. Once ethanol finally evaporated, beads were resuspended with DEPC-water to eluted RNA from the beads. Finally beads were removed using the magnetic rack and RNA was recovered in the supernatant. The integrity and quantity of the RNA was evaluated using the High Sensitivity RNA ScreenTape and 4200 TapeStation (Agilent Technologies) following the manufacturer's protocol.

A reverse transcription PCR (RT-PCR) was carried out on the RNA using the mix iScript Reverse Transcription Supermix (Biorad) and the following thermocycler program: 5min at 25°C, 30min at 42°C and 5min at 85°C. The cDNA was quantified using Qubit 2.0 fluorometer (ssDNA assay kit, Invitrogen, Life Technologies).

16 RNA gene and cDNA sequencing

V4-V5 of the 16S rRNA gene was amplified by PCR with fusion primers 515F (5′-Ion A adapter-Barcode-GTGYCAGCMGCCGCGGTA-3′) [START_REF] Wang | Naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy[END_REF] and 928R (5′-Ion trP1 adapter-CCCCGYCAATTCMTTTRAGT-3′) [START_REF] Wang | Conservative fragments in bacterial 16S rRNA genes and primer design for 16S ribosomal DNA amplicons in metagenomic studies[END_REF], which included a barcode and sequencing adapters. The fusion PCR method used fusion primers to attach the Ion A adapter (5′-CCATCTCATCCCTGCGTGTCTCCGACTCAG-3′) linked to a barcode, and the Ion truncated P1 (trP1) adapter (5′-CCTCTCTATGGGCAGTCGGTGAT-3′) to the amplicons as they were generated during PCR.

PCR mixture contained 1X SuperFi Buffer, 0.2 mM each dNTP, 0.5 µM each primer, 1U Platinum SuperFi DNA Polymerase (Invitrogen), and 10-20 ng template DNA in a 50 µL reaction volume. Amplification was performed as follows: 30 sec at 98°C, 30 cycles of 10 s at 98°C, 10 s at 50°C, 30 sec at 72°C, followed by final extension of 5 min at 72°C. PCR products were purified using Agencourt AMPure XP magnetic beads (Beckman Coulter) according to the manufacturer's instructions, with a bead:amplicon ratio of 3:2, and were eluted in 45 µL TE Buffer (10 mM Tris-HCl pH 8.0, 1 mM EDTA). Purified amplicons were quantified using D1000 ScreenTape and 4200 TapeStation (Agilent Technologies), following the manufacturer's instructions. Then all amplicons were pre-diluted at 500 pM in molecular grade water and equimolaly pooled. The pool was then diluted at 100 pM for sequencing. To prepare template-positive Ion Sphere Particles (ISPs) containing clonally amplified DNA by emulsion PCR, the library was diluted to 26 pM and set up on the Ion OneTouch 2 Instrument (Life Technologies) using the Ion PGM Hi-Q View OT2 Kit (Life Technologies) and following the manufacturer's instructions. These templated ISPs were then purified on the Ion OneTouch ES (Life Technologies) according to the manufacturer's instructions.

Sequencing was performed on Ion Torrent Personal Genome Machine using Ion 316 Chip V2 (Life Technologies) and Ion PGM Hi-Q View Sequencing Kit (Life Technologies) according to the manufacturer's instructions (Figure 15).

Sequencing data was processed by the Torrent Suite Software. The software filtered out low quality and polyclonal sequence reads, and quality filtered data was exported as FastQ file.

Metabolomics analysis

LC-MS LTQ-ORBITRAP

Metabolomics analysis was performed on the supernatant. Samples were analysed using reverse phase liquid chromatography coupled to high resolution mass spectrometry (HPLC-ESI-HRMS) using a LTQ-Orbitrap XL instrument from Thermo Scientific (Germany) (Figure 16). Samples were diluted 1/10 in water and 10 µl of the dilution was injected into the analytical system. Chromatographic separation was performed on an accela 1250 pump at 400 µL/min with a linear gradient from 10 to 80% of mobile phase A (acetonitrile+0.05% formic acid) and 90 to 20 % of mobile phase B (water +0.05% formic acid) into a synconis C18 (50x2.1 mm, 1.7µm) column (Thermo Scientific) during 23 minutes. A stabilization phase of 5 minutes was carried out at the end of each acquisition to return at the initial condition. After chromatographic separation, the sample was ionized by electrospray ionization (ESI) on positive mode. The detection was performed in full scan over an m/z range from 50 to 500 at a resolution of 100.000. A sample of anaerobic sludge was used as a quality control (QC) and was injected every 10 samples, blanks corresponding to water were injected every 10 samples and pools corresponding to an equimolar mix of the samples were injected every 5 samples.

FTICR direct infusion

The Fourier-transform ion cyclotron resonance (FTICR) analyses were carried out in partnership with Ecole Polytechnique (Palaiseau). All samples were diluted by a factor two in ACN/FA (acetonitrile/formaldehyde) 0.1% and then centrifuged. The supernatant was used for the direct infusion analysis. A blank was prepared with H 2 O/ACN/FA (50:50:0.05 volume:volume:volume) and the quality control (QC) was an arbitrary chosen sample. Samples were analysed by direct infusion mass spectrometry using a FT-ICR SolarixXR 9.4T (Bruker Daltonik, Bremen, Germany) (Figure 17) with an electrospray source in positive mode. Spectra were recorded in the 57 -1000 m/z range with a size of 4 Mpts leading to a resolution of 300.000 at m/z 200. External calibration in positive mode was done with phosphoric acid between m/z 99 and 981 in order to be below 1 ppm of error. Ions were accumulated in collision cell for 0.2 seconds to get a total ion current similar to the one observed during calibration. 500 scans were accumulated to get a better signal sensitivity.

ESI source flow rate was set at 120 µL/h with a capillary voltage at -4500V. Nebulizer and drying gas were set respectively at 1 bar and 4 L/min and the drying gas temperature was set at 220°C. Samples were analysed in random order. Between each five samples a blank and a pool of all samples were injected. Each ten samples a QC was injected. To avoid cross contamination between each injection, syringe capillary and needle were washed by using three times 250µL of H 2 O/FA 0.05%, three times 250µL of ACN/FA 0.05% and three times 250µL of H 2 O/ACN/FA (50:50:0.05).

All spectra were then exported in metaboscape (Bruker Daltonik, Bremen, Germany) to obtain the list of all m/z ions with a Signal/Noise ratio above 3 and the corresponding intensity in all spectra.

Bioinformatic treatment

16S rRNA sequencing preprocessing

16S rRNA tags reads were imported in FROGS. FROGS (Find Rapidly OTU with Galaxy Solution) is a galaxy/CLI workflow designed to produce an OTU count matrix from high depth sequencing amplicon data [START_REF] Escudié | FROGS: Find, Rapidly, OTUs with Galaxy Solution[END_REF]. The workflow of analysis (Figure 18) is constituted by a first step of filtering. In this step all the sequences with a size between 100 to 500 bp and containing the sequence of the primers are kept. The function cutadapt allows to trim the primers. Then the sequences are clustered using the function Swarm with an aggregation distance of 3. This allows to group together the similar sequences. Then the chimera are removed (VSEARCH) with de novo UCHIME method. In each sample the chimera are detected but only those which were detected as chimera in all the samples are removed. An abundance filter is then applied before the taxonomic affiliation. Clusters with at least 10 sequences and/or present in at least 1 sample are kept. Finally the taxonomic affiliation is carried out using the silva 132_16S_pintail100 database with BLAST. On the obtained OTU count matrix a filtering step was carried out on the total sum of sequences per sample. In average the number of sequences per sample was around 13.000 and the samples with less than 10.000 sequences were removed.

Metabolomics preprocessing

In order to be able to carry out statistical analyses on the metabolomics data, the metabolomics spectra were converted into data tables. Indeed the raw data obtained from the metabolomics analysis correspond to a 3 dimensional data in the case of the LC-MS analysis with the retention time, m/z ratio and intensity results for each sample. The R package XCMS is available to analyse this kind of data [START_REF] Smith | XCMS : Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment , Matching , and Identification[END_REF]. However, according to the metabolomics analysis different functions for the peak identification have to be used in order to get the intensity table required for the statistical analysis. The objective of the preprocessing is to get a table with an intensity value for each m/z detected for each sample (Figure 19).

Peak detection

First of all the raw data were transformed into mzML format using MSConvert (ProteoWizard 3.0) in order to be analysed with XCMS. The function peak-picking (vendor function) from MSconvert was used to identify individual peaks on the chromatogram when several scan are carried out for each sample as it was the case for the LC-MS analysis.

Then using XCMS R package the peaks were detected into the chromatogram for each sample. The method centWave was used for the data acquired by the LC-MS analysis while the method mass spec wavelet was used for the data from FTICR analysis [START_REF] Du | Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching[END_REF]. This was due to the difference in the type of data. Indeed for the Direct Infusion-FTICR (DI-FTICR) acquired in direct infusion only one spectrum was acquired for each sample and no retention time value was available. The centWave method allows the detection of the peaks by combining 1) the detection of the region of interest on the m/z domain based on their intensity and 2) the detection of the chromatographic peak resolution based on the continuous wavelet transform method [START_REF] Tautenhahn | Highly sensitive feature detection for high resolution LC/MS[END_REF]. Several parameters have to be defined. The ppm value and the signal/noise ratio are both depending of the device resolution.

With the mass spec wavelet method [START_REF] Du | Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching[END_REF] the spectrum is transformed into wavelet spaces. Then ridges are detected by determining the local maxima and are defined as lines. From these ridges line the peaks are identify based on three rules: 1) as the maximum amplitude on the ridge line is proportional to the width of the peak, this amplitude as to be within a defined range; 2) the signal/noise ratio has to be considered and 3) a threshold has to be defined for the length of the ridges line to be considered as a peak.

Peak group

The detected peaks in individual samples have to be compared between each sample. This step allows the calculation of the retention time deviation between the samples. The grouping is carried out using the peak density method. Peaks are defined according to their density along the retention time axis within small m/z slice. The size of this slice has to be defined by the parameter called banwith. The selection of the peak depends also on its presence on a minimum of samples to be defined (minFraction). For example a minFraction at 0.4 means that the peak has to be detected in at least 40% of the samples.

Peak alignment

For the data from LC-MS analysis the groups of peaks have to be aligned between the samples. Indeed the elution time of the molecules through the chromatographic column can vary along the analytical sequence. The method used for the alignment, also called retention time correction, is the obiwarp method [START_REF] Prince | Chromatographic alignment of ESI-LC-MS proteomics data sets by ordered bijective interpolated warping[END_REF]. The retention time correction with this method is based on spectra similarity. Once the alignment is carried out a new grouping step has to be applied on the data. A smaller bandwidth can be used for this second grouping step due to the reduction of the slice after the retention time correction.

Filling in missing peak data

Some groups of peaks can be missing between the samples because no peak is present or because the peak cannot be detected. In order to remove these missing values the fillPeaks method is used. The method allows to extract all intensities in the mz region of the feature and to integrate the signal in the matrix instead of the missing value. If no signal can be found then no value is added.

Peak identification

Peak identification was performed by using the databases HMDB, KEGG and PubChem [START_REF] Kanehisa | KEGG for integration and interpretation of large-scale molecular data sets[END_REF][START_REF] Kim | PubChem 2019 update: improved access to chemical data[END_REF][START_REF] Wishart | HMDB 4.0: The human metabolome database for 2018[END_REF] based on the molecular mass.

Statistical analyses

In this thesis work several biostatistical methods were used. In this part the choice of the selected methods and their parameters are discussed. A summary of the different objectives of the thesis and the biostatistical methods used to answer to the specific biological question is presented in Table 6.

Variance analysis

The variance analysis was carried out in one experiment in order to statistically compare the biogas production observed for the different conditions studied. Based on the different Gompertz parameters for both CH 4 and CO 2 the variance was analysed using the ANOVA (ANalysis Of VAriance). The function granova.1w from the R package granova (version 2.1) was used for that purpose. Different parameters were estimated:

 A coefficient which corresponds to the mean of each condition minus the mean of all the samples  The mean square error between the conditions (MS-between)  The mean square error within the conditions (MS-within)  F-statistic value which is the ratio of the area of the MS-within and of the MSbetween. This value allowed to determine if there is a significant difference in the mean between the conditions  p-value  Residuals which indicate if the residuals are independent or not and confirm if the dataset meets the assumption of the ANOVA (independence, normality, homogeneity).

Data normalisation and transformation

Ecological data (metabarcoding, omics…) are defined as compositional data. Compositional data refers to proportion of a whole, their sum are constraint to be a constant (equal to 1 for proportions, 100 for percentages…). Difficulties for the analysis of these kinds of data are: 1) co-dependency of relative proportion in a constraint space 2) data distribution is not normal 3) compositional data are bounded when most of the statistical methods assume non-bounded data. Specific treatment for compositional data has to be applied before to start the statistical analyses [START_REF] Aitchison | The statistical analysis of compositional data[END_REF]. Many methods are available and the choice of the methods depends on the type of statistical analysis used to treat the data.

Sequencing data treatment

The number of sequences between the samples differs as the amount of biomass varies between the samples or due to technical variations. Data has to be normalised between the samples. The total sum scaling is applied in order to transform count data into relative abundance. Considering the dispersion in the total number of reads identified in each sample, archaeal and bacterial OTUs abundances were normalized separately. Only OTUs that exceeded 1% in at least one sample have been taken into account for the analysis.

Figure 20: Schematic representation of the data analysis by the Principal Component Analysis

In order to take into account the compositionality of the data and to sort the data from this constraint space a transformation of the data is needed. Different methods are available such as the square-root transformation or center log ratio (clr).

The square-root transformation consists to take the square root of each variable. It has the advantage to deal with zero value. As microbiome data contains many zeroes this method is usually used to transform this kind of data. The clr method transforms each variable by taking the log ratio of the count of this variable divided by the geometric mean of the count of all variables.

Metabolomics data treatment

Regarding the metabolomics data a commonly used transformation method is the log transformation. This method consists in taking the log of each variable. In this thesis work we used a log on base 10.

Batch effect correction

Batch effects can be due to different factors classified usually in three categories depending on their source: biological source (different inocula and substrates), technical source (different operators, different kits for the sequencing) and computational source (different data bioinformatics processing). Batch effects adjustment is possible in order to combine samples from different experiments.

Different methods are available to determine and to correct batch effects such as removeBatchEffect implemented in the R package LIMMA, mean-centering or singular value decomposition. The method used in this thesis work was the ComBat method implemented in the R package SVA (Leek et al., 2012). This method was developed for microarray data [START_REF] Johnson | Adjusting batch effects in microarray expression data using empirical Bayes methods[END_REF]. It assumes that the batch effect source is known and is systematic across variables. For this method a Bayesian framework is used where the additive and multiplicative batch effects are assigned to respectively a Normal and Inverse Gamma prior distributions. These estimated parameters are then used to correct the batch effect.

Principal Component Analysis

Principal Component Analysis (PCA) is an unsupervised multivariate analysis (Figure 20). This method reduces the dimensionality of dataset while retaining as much information as possible. PCA is used in first approach in order to get an overview of the differences or similarities between the samples without any a priori information and to determine the major source of variations between the samples. The PCA also allows to determine possible outliers meaning samples too different from the others, which can be due to technical issues.

An adaptation of the PCA, the sparse PCA [START_REF] Shen | Sparse principal component analysis via regularized low rank matrix approximation[END_REF] provided in the mixOmics R package allows to keep only variables explaining the more the variability. Indeed high-throughput methods provide a huge amount of data. However few of the variables (microbes or metabolites) are essential to determine the variation between the samples. The choice of the number of variables to keep is a compromise between informative variables and noisy variables and several tests are needed to determine this number.

Partial Least Square Discriminant Analysis

The Partial Least Square Discriminant Analysis (PLS-DA - (Rohart et al., 2017b)) is a supervised multivariate analysis. This linear regression method allows to classify the samples. A sparse version is also available for this method and allows to select the most discriminative variables to classify the samples according to the defined class (Figure 21). The performance of the classification can be assessed through the analysis of the classification performances based on the Balanced Error Rate (BER). The BER estimates the average proportion of misclassified samples in each category, taking into account the number of samples of each category. It is well adapted when categories are composed by a non-equal number of samples. Different parameters can be optimised in order to improve the performance of the classification such as the number of components needed to maximise the variation between the samples and the number of variables needed to explain the variation.

Data integration

Different types of datasets integration are possible: 1) integration of different datasets of analyses carried out on the same samples and 2) integration of datasets of the same analyses carried out on samples from different studies. During this thesis work both types of integration analysis were used.

The Partial Least Square (PLS) regression method allows to relates or integrate two data matrices whose the variables come from two kind of analysis (genomic, metabolomics) carried out on the same samples. In this thesis work the canonical mode was used to integrate the 16S rRNA sequencing and metabolomics data (Figure 22). The canonical mode gives a similar weight to the two matrices contrary to the classical regression mode. This mode allows to highlight the correlation between two datasets.

Time course analysis

Time evolution of the microbes and/or metabolites across an experiment was also studied in this thesis work. To do so several samples were taken across time during the experiment. Longitudinal data were analysed using a specific workflow in order to take into account temporal aspect. The analytical workflow used in this thesis work is based on the workflow developed by Straube et al [START_REF] Straube | A linear mixed model spline framework for analysing time course "Omics" data[END_REF] (Figure 23). Different steps are required in order to get the time profiles and are described below:

The first step is to remove the noise provided by the huge amount of data. To do so the variables which did not evolved at all along the experiment were removed as we were only interested by the ones evolving across time. The function lmmsDE from the R package lmms was used for that purpose. The function allows to model a curve based on the experimental time points for each variable. Then each model is compared to a null model which fits only the intercept in order to keep variables which are differentially expressed. A p-value is obtained and only variables with a p-value inferior to 5% are kept.

The second step corresponds to the modelling of a time profile based on the experimental time points for each selected variable. These fitted curved are obtained using a spline smoothing method (function spline.smooth from the R package stats). A smoothing parameter called spar has to be defined. The smoothing allows to reduce the technical analysis variation between the time points and the samples.

Then variables sharing similar pattern of response are grouped together using a clustering method. Different methods of clustering were tested such as hierarchical clustering, Clara. The KmL (k-means longitudinal data) clustering method was selected in this thesis work as it enables to provide indicators of the quality of the clustering [START_REF] Genolini | Kml : A package to cluster longitudinal data[END_REF]. However the different clustering tests gave similar results. The KmL clustering is based on the k means method but designed to cluster longitudinal data. K-means is a partitioning method based on the distance estimation to assign a point to a cluster. KmL clustering method allows to choose the distance and especially one dedicated to longitudinal data.

Once the clusters are obtained it is possible to compare the different time profiles between the different conditions in terms of shape (correlation) and time (delay) modification (Figure 24). As an example the evolution of the abundance of one specific OTU can be compared between different conditions: is the modification simultaneous? Was the modification delayed?... For that purpose the package DynOmics developed by the mixOmics team was used [START_REF] Straube | DynOmics to identify delays and co-expression patterns across time course experiments[END_REF]. Influence of the feeding composition on the microbial activity described with data integration

The substrate accessibility and composition differ from one organic matter to another. This induces the production of a large variety of molecules during the anaerobic degradation of the substrates. These molecules will be metabolised by a multitude of specific or ubiquitous microorganisms. Some of these molecules can lead to a microbial inhibition when their concentration exceeds the resistance capacity of the microorganisms. One strategy to limit the risk of inhibition and to optimise the biogas production efficiency is the anaerobic co-digestion. Mixing at least two substrates allows to improve the degradation of substrates difficult to degrade, by balancing the nutrient composition or by diluting inhibitors present in the digesters.

The wide variety of organic matter which can be used for anaerobic digestion conducted to the realisation of many studies to determine the influence of feeding composition on biogas performances and microbial community. In the case of anaerobic codigestion these studies are required to determine the best substrates ratio to optimise the biogas production. However few of them have tested different ratio of same substrate mixes to determine the optimal conditions. The influence of the feeding composition on the microbial growth has been studied for several substrates and mixes using 16S rRNA gene sequencing [START_REF] Fitamo | Microbial population dynamics in urban organic waste anaerobic co-digestion with mixed sludge during a change in feedstock composition and different hydraulic retention times[END_REF][START_REF] Hardegen | Methanogenic community shifts during the transition from sewage mono-digestion to co-digestion of grass biomass[END_REF]. However few studies have gone further in the identification of the substrate molecular composition and none of them linked their degradation to the microbial activity to explain bioprocess performances. The studies described in this chapter were developed in order to fill this lack of knowledge. This first chapter describes two experiments which were designed in order to study the influence of the feedstock composition on the microbial activity.

The first experiment was conducted in batch digesters to determine the effect of a variation in the ratio of the co-substrates composition on the digestion performances and the associated microorganisms. Three different substrates were selected in order to test three different molecular compositions. The first one was a wastewater sewage sludge known to be difficult to degrade and to lead to a low biogas production. The second one was fish waste, rich in proteins and lipids. The third one was grass, rich in proteins. The mono-digestion of these three substrates was tested in order to identify the molecules specific to each substrate.

In parallel different mixtures of sludge/fish and sludge/grass were carried out to determine the influence of different feedstock composition on the microbiome. The microbial activity was evaluated with 16S rRNA sequencing and the molecules were identified with untargeted metabolomics using HPLC-MS. In order to identify the microorganisms potentially responsible for the molecules degradation and study their dynamics in mixed substrates, an integrative data analysis workflow was developed. It is described in the second part of this chapter.

The results of this study are presented in two articles with different goals.

The first article presents the chemical performances of the mono and co-digestion. Two indicators of production were built in order to determine which mix improved the most the performances of degradation during sewage sludge co-digestion. Moreover the influence of the feeding type on the methanogenesis pathways was described using isotopic fractionation analysis, which had never been done before. The first article was published in Waste Management journal in 2019 and is entitled "Co-digestion of wastewater sludge: choosing the optimal blend".

The second article describes the statistical methodology developed in order to link the microbial activity to the molecules degradation. This article presents the results of the 16S rRNA sequencing, untargeted metabolomics and the results of the statistical analyses. The article was submitted in FEMS and is entitled "Integrative analyses to investigate the link between microbial activity and molecules degradation during anaerobic digestion".

The second experiment was carried out in semi continuous reactors, based on the results of the first experiment, to study the consequences of modification of the feeding composition across time on the active microbial community. For the second experiment only two substrates were selected based on the previous results: sewage sludge and grass. In this experiment the resistance and the resilience of the microbial community was tested by modifying the feeding composition across time. The performances, methanogenic pathways and microbial activity through the 16S rRNA sequencing were measured across the experiment.

Article 1: Co-digestion of wastewater sludge: choosing the optimal blend

Author names and affiliations Laëtitia Cardona a , Camille Levrard a , Angeline Guenne a , Olivier Chapleur a , Laurent Mazéas a a Université Paris-Saclay, INRAE, PRocédés biOtechnologiques au Service de l'Environnement, 92761, Antony, France

Highlights

 Co-digestion performances improved since 25% of fish or beyond 50% of grass added  Optimal Digestion Indicator was built to assess the optimal methane production  Mono-Digestion Indicator was built to assess the expected methane production  75% (gCOD) of fish in co-digestion increased final methane production by 1.9 times  75% (gCOD) of grass in co-digestion increased final methane production by 1.7 times

Abstract

Anaerobic co-digestion (AcoD) is a promising strategy to increase the methane production of anaerobic digestion plants treating wastewater sludge (WAS). In this work the degradability of six different mixtures of WAS with fish waste (FW) or garden-grass (GG) was evaluated and compared to the three mono-digestions. Degradation performances and methanogenic pathways, determined with the isotopic signatures of biogas, were compared across time. Fish and grass mono-digestion provided a higher final methane production than WAS mono-digestion. In co-digestion the addition of 25 % of fish was enough to increase the final methane production from WAS while 50 % of grass was necessary. To determine the optimal blend of WAS co-digestion two indicators were specifically designed, representing the maximum potential production (ODI) and the expected production in mono-digestion conditions (MDI). The comparison between these indicators and the experimental results showed that the most productive blend was composed of 75% of co-substrate, fish or grass, with WAS. Indeed, the final methane production was increased by 1.9 times with fish and by 1.7 times with grass associated to an increase of the methane production rate by 1.5 times. Even if the same succession of methanogenic pathways across time was observed for the different mixtures, their relative proportions were different. Sewage sludge degradation was mostly achieved through hydrogenotrophic pathway as confirmed by the archaeal analysis while acetoclastic archaea were identified for fish and grass degradation.

1.1.

Introduction

Anaerobic Digestion (AD) is a multistep biological process allowing to convert various types of organic waste into a renewable energy, the biogas (composed of CH 4 and CO 2 ) and digestate. This bioprocess has been used for more than a century, in particular to stabilise the wastewater sludge (WAS) obtained from wastewater treatment plants [START_REF] Astals | Balancing hygienization and anaerobic digestion of raw sewage sludge[END_REF]. It is particularly attractive as it allows to simultaneously produce energy in the form of biogas and to reduce the volume of sludge [START_REF] Luostarinen | Increased biogas production at wastewater treatment plants through co-digestion of sewage sludge with grease trap sludge from a meat processing plant[END_REF]. However, the benefit of using only WAS to produce methane by AD is limited by its low C/N ratio and low digestion efficiency, leading to low CH 4 production yield [START_REF] Astals | Anaerobic digestion of seven different sewage sludges: A biodegradability and modelling study[END_REF][START_REF] Park | Combination of different substrates to improve anaerobic digestion of sewage sludge in a wastewater treatment plant[END_REF].

A way to overcome this major drawback is to balance the low C/N ratio by mixing wastewater sludge with other substrates richer in carbon. This strategy of mixing different types of substrates, or performing anaerobic co-digestion (AcoD), has multiple advantages as: 1) improving the performances of digesters treating wastewater sludge (Mata-Alvarez et al., 2014), 2) treating several types of waste at the same time and 3) limiting the risk of inhibition that can occur during mono-digestion with the production of inhibitors for example [START_REF] Borowski | a. Co-digestion of pig slaughterhouse waste with sewage sludge[END_REF]. Almost any type of organic waste can be treated by AD but each of them has specific properties which can bring some advantages and disadvantages. For example, animal manure and slaughterhouse waste have a high organic content but usually cause process disturbances in mono-digestion because of their high proteins and/or lipids content. This can lead to the accumulation of ammonia, volatile fatty acids (VFA) and long chain fatty acids (LCFA) [START_REF] Hansen | Anaerobic digestion of swine manure: Inhibition by ammonia[END_REF][START_REF] Pitk | Co-digestion of sewage sludge and sterilized solid slaughterhouse waste: Methane production efficiency and process limitations[END_REF][START_REF] Borowski | a. Co-digestion of pig slaughterhouse waste with sewage sludge[END_REF] known to be responsible of process failure. WAS as a co-substrate for these waste allows to dilute compounds potentially leading to inhibition, such as proteins and lipids and limit the risk of inhibition by a too fast acidification thanks to the high buffer capacity of the WAS [START_REF] Prabhu | Anaerobic co-digestion of sewage sludge and food waste[END_REF].

Different waste have already been successfully tested to improve WAS anaerobic digestion. In association with slaughterhouse waste, [START_REF] Borowski | a. Co-digestion of pig slaughterhouse waste with sewage sludge[END_REF] succeeded in increasing the specific methane production by 2-fold when 50 % of slaughterhouse waste was mixed to WAS at an organic loading rate (OLR) of 4 kgVS/m 3 d.

Neither ammonia nor LCFA inhibition was observed and an effect of VFA accumulation was observed only when the OLR was superior to 4 kgVS/m 3 d. [START_REF] Wickham | Biomethane potential evaluation of codigestion of sewage sludge and organic wastes[END_REF] tested several waste such as food waste, paper pulp, fat-grease-oil (FOG) waste and dehydrated Ulva macroalgae.

Each substrate was mixed at different ratio with WAS (5, 10 and 15 % by weight). Final methane production was increased by three to six times thanks to the co-digestion compared to mono-digestion of WAS.

In this study, different mixtures of wastewater sludge (WAS) with garden-grass (GG) or fish waste (FW) as co-substrates were tested to determine the optimal blend allowing for the most efficient CH 4 production. Total fish production in the world has expanded since the last five decades from 20 million tons in 1960 to 167.2 million tons in 2014 mainly due to the increase of the aquaculture production (FAO, 2016). The amount of waste provided by the fishery industries (as canneries) is important. Some authors evaluated the possibility to use different parts of the fish (skins, viscera, bones …) [START_REF] Donoso-Bravo | Anaerobic biodegradability of fish remains: Experimental investigation and parameter estimation[END_REF], or different fish species [START_REF] Eiroa | Evaluation of the biomethane potential of solid fish waste[END_REF] for methane production with high biodegradability level. However, this type of waste can be rich in protein and/or lipids inducing ammonia and/or LCFA inhibition as observed by [START_REF] Eiroa | Evaluation of the biomethane potential of solid fish waste[END_REF]. Literature on the possibility to use fish waste as co-substrate in AD is still scarce and to the best of our knowledge co-digestion of fish waste with wastewater sludge was not studied yet.

Grass has a high potential as renewable biomass source due to its high biodegradability and biogas production potential [START_REF] Dai | Simultaneous enhancement of methane production and methane content in biogas from waste activated sludge and perennial ryegrass anaerobic co-digestion: The effects of pH and C/N ratio[END_REF][START_REF] Prochnow | Bioenergy from permanent grassland -A review: 1. Biogas[END_REF]. Using grass as a feedstock can lead to an ammonia accumulation due to the high protein content of certain types of green waste [START_REF] Prochnow | Bioenergy from permanent grassland -A review: 1. Biogas[END_REF][START_REF] Ward | Optimisation of the anaerobic digestion of agricultural resources[END_REF]. Several investigations were conducted on the anaerobic co-digestion (AcoD) of WAS and grass and showed an enhancement of methane content [START_REF] Dai | Simultaneous enhancement of methane production and methane content in biogas from waste activated sludge and perennial ryegrass anaerobic co-digestion: The effects of pH and C/N ratio[END_REF][START_REF] Hidaka | Anaerobic co-digestion of sewage sludge with shredded grass from public green spaces[END_REF]. Nonetheless further studies are needed to understand the effect of the grass addition in order to optimise the methane production.

The aim of this study is to investigate the possibility for improving wastewater sludge degradation during anaerobic co-digestion with fish waste or garden grass. Degradation performances and methanogenic pathway, determined with the isotopic signatures of biogas [START_REF] Conrad | Quantification of methanogenic pathways using stable carbon isotopic signatures: a review and a proposal[END_REF], were compared across time. Two biodegration indicators were specifically desgined to determine the mixes enabled to improve the methane production. As far we know the impact of the anaerobic co-digestion on the methanogenic pathway monitored by the isotopic analysis has not been studied yet.

Methods

Feedstock preparation and characterisation

Wastewater sludge came from an industrial wastewater treatment plant (Valenton, France). Two organic co-substrates were tested. Fish waste was collected from a fish shop and grass from the mowing of the Institute's lawn. Both waste were crushed and the solid part was stored at 4°C during two days before they were used.

The inoculum came from a mesophilic full scale anaerobic digester treating primary sludge at the Valenton (France) wastewater treatment plant. In order to degrade the residual organic matter in excess it was stored at 35°C during two weeks in anaerobic condition without feeding before being used.

All substrates and inoculum were characterised by different chemical analyses and the results are summarised in the Table 7.

Co-digestion experimental set-up

In total 27 anaerobic batch bioreactors were set-up using 1 L glass bottles (700 mL working volume). Each digester was inoculated with methanogenic sludge and fed with a mixture of a main substrate (wastewater sludge) and one co-substrate (fish waste or grass) to reach a substrate/inoculum ratio of 12 gCOD/ 1.2 gCOD. Different ratios of main substrate /co-substrate were tested (25/75, 50/50, 75/25) as detailed in the supplementary Table 9. Controls with 100 % of wastewater sludge, fish waste or grass were also carried out. All the digesters were complemented with a Biochemical Methane Potential buffer (BMP-International Standard ISO 11734 (1995)) to reach a final working volume of 700 mL. All incubations were performed in triplicate. The bioreactors were then sealed with a screw cap and a rubber septum. The headspaces were flushed with N2 (purity > 99.99 %, Linde gas SA) and the bottles were incubated at 35°C in the dark and without agitation.

Weekly, 6 mL of liquid phase were sampled through the septum using a syringe and centrifuged at 10,000 g for 10 minutes. The supernatant and the pellet were snap frozen and kept at -20°C for chemical analysis and -80°C for microbial analysis.

Biochemical Methane Potential (BMP) experimental setup

BMP tests were carried out for each substrate in mono-digestion to assess their methane maximal production under optimal parameters. The ratio substrate/inoculum used in BMP test was 0.7 gCOD/7 gCOD to limit the latency due to the microbial growth. The biochemical potential buffer previously mentioned was used to reach a final volume of 500 mL in 1L glass bottles. As for the batch experiment, bottles were sealed, flushed with N2 and incubated at 35°C in the dark without agitation. The experiment was made in triplicate. Gas production and composition were followed over time. A control containing only the inoculum was carried out in parallel and the biogas production of this control was taken into account to calculate the substrates gas productions. The mixtures details are presented in the supplementary Table 9.

Gas production and stable carbon isotope signature

The biogas accumulation in the headspace was measured using a differential manometer (Digitron 2082P). The biogas was then analysed directly in the headspace using a micro gas chromatograph (CP4900, Varian) as described in Chapleur et al. (2014). Data were used to calculate the biogas production at standard temperature and pressure. Different parameters used to quantify the methane production potential were calculated using R CRAN software and the Gompertz equation with Grofit package as described in Poirier et al. (2016):

𝑦(𝑡) = 𝐴. exp [-exp ( µ. 𝑒 𝐴 (𝜆 -𝑡) + 1)]
Were y (t) is a cumulative CH 4 production (mL), A is the ultimate CH 4 yield (mL), µ is the maximum production of CH 4 production rate (mL/day), and 𝜆 is the lag phase (day).

The methanogenic pathways during the substrates degradation (acetoclastic methanogenesis or hydrogenotrophic methanogenesis) were determined by the gas isotopic signature analysis. Periodically gas was sampled into a 7 mL vacuumed serum tubes for analysis of δ 13 CH 4 and δ 13 CO 2 . A Trace Gas Chromatograph Ultra (Thermo Scientific) attached to a Delta V Plus isotope ratio mass spectrometer via a GC combustion III (Thermo Scientific) was used to carry out the analysis. The principle of the method was described by [START_REF] Brand | High Precision Isotope Ratio Monitoring Techniques in Mass Spectrometry[END_REF][START_REF] Sugimoto | Rapid and simple measurement of carbon isotope ratio of bubble methane using GC/C/IRMS[END_REF]. The uncertainties, determined by replicate measurement, for δ 13 CH 4 and δ 13 CO 2 analysis was around 0.5‰.

As indicator of the methanogenic pathway, the apparent isotopic factor (α app ) was calculated as presented in the following equation:

𝛼 𝑎𝑝𝑝 = (𝛿 13𝐶𝑂2 + 10 3 ) / (𝛿 13𝐶𝐻4 + 10 3 )
It is usually assumed that if the α app is superior to 1.065, the hydrogenotrophic way is the most important. On the contrary if the αapp is inferior to 1.055, the methanogenesis is dominated by the acetoclastic way [START_REF] Conrad | Quantification of methanogenic pathways using stable carbon isotopic signatures: a review and a proposal[END_REF][START_REF] Whiticar | Biogenic methane formation in marine and freshwater environments: CO2reduction vs. acetate fermentation-Isotope evidence[END_REF].

Chemical analysis

Volatile Fatty Acids (VFA) concentrations were measured using ionic chromatography (ICS 5000+, Thermo Fisher Scientific) equipped with IonPAC ICE-AS1 column. The mobile phase was composed of heptafluorobutyric acid (0.4mmol/L) and tetrabutylammonium (5mmol/L). The VFA quantified were acetate, propionate, butyrate, valerate, formate, lactate and caproate. Ammonium (NH 4 + ) concentration was measured using the Nessler's colorimetric method following the French standard (NF T 90-105) in spectroscopic tanks using Hach spectrometer DR2800.

The link between Free Ammonia Nitrogen (FAN), Total Ammonia Nitrogen (TAN), pH and temperature can be summarized with the following equation [START_REF] Anthonisen | Inhibition of Nitrification by Ammonia and Nitrous Acid[END_REF]:

𝐹𝐴𝑁 = 10 𝑝𝐻 (exp ( 6344 𝑇 ) + 10 𝑝𝐻 ) 𝑥 𝑇𝐴𝑁
Where T is the temperature in Kelvin.

Dissolved organic and inorganic (DOC and DIC) carbons were measured following the French standard NF EN 1484 using a DOC analyser TOC-L Shimadzu.

Chemical oxygen demand (COD) was measured with LCK514 kit (Hach Lange) according to the manufacturer's instructions.

The carbon and nitrogen quantities contained in the substrates and inoculum were analysed on the crushed and dried sample. 10 mg of the sample was placed on sampler tin and analysed using an elementary analyser (VARIO EL III, Bioritech).

RNA extraction and 16S rRNA sequencing analysis

Based on the methane production (Figure 26), a total of 19 samples were selected. Total RNA was extracted using the commercial kit FastRNA Pro™ Soil-Direct (MP Biomedicals) following the manufacturer's specifications. Then, DNA co-extracted was removed using TURBO™ DNase (Ambion) kit following the manufacturer's instructions. The RNA was denaturated by 2 min at 85°C in a dry bath and was then stored on ice. RNA purification was carried out using the Agencourt AMpure RNA magnetic beads purification system (Beckman Coulter) by adding 1.8 volumes of beads by volume of RNA. After mixing by pipetting and 5 min of incubation, beads were captured using a magnetic rack on one side of the tube and then washed by adding 500µL of 70% cold ethanol (diluted in DEPC-water). After incubation of the tube during 30 seconds at room temperature, the ethanol was removed. This washing step was repeated 3 times. Once ethanol finally evaporated, beads were resuspended with DEPC-water to eluted RNA from the beads. Finally beads were removed using the magnetic rack and RNA was recovered in the supernatant. The integrity and quantity of the RNA was evaluated using the Hight Sensitivity RNA ScreenTape and 4200 TapeStation (Agilent Technologies) following the manufacturer's protocol.

A reverse transcription PCR (RT-PCR) was carried out on the RNA using the mix iScript Reverse Transcription Supermix (Biorad) and the following thermocycler program: 5min at 25°C, 30min at 42°C and 5min at 85°C. The cDNA was quantified using Qubit 2.0 fluorometer (ssDNA assay kit, Invitrogen, Life Technologies).

Archaeal hyper variable region V4-V5 of the 16S rRNA gene was amplified on the cDNA according to the protocol described by Madigou et al [START_REF] Madigou | Ecological consequences of abrupt temperature changes in anaerobic digesters[END_REF]. 16S rRNA tags reads were imported in FROGS. FROGS (Find Rapidly OTU with Galaxy Solution) is a galaxy/CLI workflow designed to produce an OTU count matrix from high depth sequencing amplicon data [START_REF] Escudié | FROGS: Find, Rapidly, OTUs with Galaxy Solution[END_REF]. FROGS abundance file was examined using R CRAN software (version 3.4.4). Considering the dispersion in the total number of reads identified in each sample, archaeal OTUs abundances were normalized with total sum scaling. Only OTUs that exceeded 1% in at least one sample have been taken into account for the analysis.

Results and discussion

Physico-chemical characteristics of the substrates and inoculum

The physico-chemical characteristics of the different substrates and the inoculum are summarised in Table 7. The C/N ratio of fish was lower than the C/N ratio of sludge and grass which were similar. Two key information can be drawn from these analyses. The first one is the higher amount of nitrogen (N) in fish waste (9.5 %) compared to sludge and grass (2.46 and 2.2 % respectively), explaining the low C/N ratio. This result implies a higher potential to produce ammonia during the fish degradation compared to sludge and grass. The second information is the lower quantity of dissolved organic carbon in sludge (1250 mgC/L) compared to fish (7921 mgC/L) and grass (7692 mgC/L). It suggests that even if the C/N ratio was similar between grass and sludge, the CH 4 production could start earlier in fish and grass because more readily available carbon was present.

A BMP test was carried out to determine the maximal CH 4 production potential for the different substrates. The kinetics production parameters determined after modelling of the data with Gompertz equation are presented in the Table 8. Degradation started immediately for all the substrates as expected according to the substrate/inoculum ratio used. Sludge-BMP degradation was the slowest (2.09 mgC/D/gCOD) and the less important (44.35 mgC/gCOD of CH 4 was produced). The slow degradation of the sludge was in accordance with the lower DOC initial value observed for the sludge. Fish-BMP and Grass-BMP degradation produced almost the same final amount of CH 4 (circa 119 mgC/gCOD) and at a similar rate (circa 8 mgC/D/gCOD).

Mono-digestion of the substrates

The specific methane production of wastewater sludge (S-100), fish waste (F-100) and grass (G-100) mono-digestion are presented in the Figure 25 and the kinetics parameters of production determined after modelling with Gompertz equation are presented in the Table 8. Between 30 to 50 % of the initial quantity was transformed into biogas. Wastewater sludge in the experimental batch mono-digestion produced the lowest final CH 4 quantity (46.62 mgC/gCOD) despite a similar COD amount fed to the bioreactor at the beginning of the experiment. The highest final CH 4 production was observed for fish (F-100, 87.55 mgC/gCOD, compared to respectively 68.43 and 46.62 mgC/gCOD for G-100 and S-100) while the production began earlier and was faster when grass was used as a single substrate (G-100). The faster CH 4 production rate estimated by Gompertz modelling for G-100 can be explained by the higher amount of DOC present in the grass (supplementary Figure 30-A). Because dissolved organic carbon was readily available, the CH 4 production could start earlier and be faster. It is interesting to notice that the kinetic of CH 4 production differed between fish and grass during mono-digestion experiment while they were similar during the BMP test. Because the only difference between the mono-digestion experiment and the BMP test was the Substrate/Inoculum ratio, respectively 12/1.2 and 0.7/7 gCOD, it can be hypothesised that the concentration of the methanogenic biomass at the beginning of the experiments played a non-negligible role in the kinetics of the CH 4 production. This assessment was already described in several publications [START_REF] Hobbs | Enhancing anaerobic digestion of food waste through biochemical methane potential assays at different substrate: inoculum ratios[END_REF][START_REF] Zhou | Influence of substrate-toinoculum ratio on the batch anaerobic digestion of bean curd refuse-okara under mesophilic conditions[END_REF].

Biogas productions observed in our experiment was compared to the values described in the literature for similar substrates. Abendroth et al observed a higher performance of methane production from WAS mono-digestion (250-300 mL/gCOD) [START_REF] Abendroth | From grass to gas: Microbiome dynamics of grass biomass acidification under mesophilic and thermophilic temperatures[END_REF] than in our study (95 mL/gCOD). This difference can be explained by the difference of WAS quality according to the preprocess. Indeed it was already described that industrial digesters treating WAS present different performances [START_REF] Rivière | Towards the definition of a core of microorganisms involved in anaerobic digestion of sludge[END_REF][START_REF] Sundberg | 454 Pyrosequencing Analyses of Bacterial and Archaeal Richness in 21 Full-Scale Biogas Digesters[END_REF]. In the same way grass anaerobic digestion performances will greatly depends of its type, treatment or freshness [START_REF] Prochnow | Bioenergy from permanent grassland -A review: 1. Biogas[END_REF].

Fish mono-digestion methane performances will also depends of the type and the part of fish digested [START_REF] Donoso-Bravo | Anaerobic biodegradability of fish remains: Experimental investigation and parameter estimation[END_REF]. However in our study the final methane production (198 mL/gCOD) was comparable to the methane performances obtained by Donoso-Bravo et al which was around 200-300 mL/gCOD. The concentration of acetic and propionic acids for all batchs are presented in the supplementary Figure 30-B. The acetate maximum accumulation during sludge monodigestion S-100 was the lowest (47 mgC/L/gCOD) and acetate took around 40 days to be degraded. In comparison, fish and grass mono-digestion (F-100 and G-100) degradation produced a similar level of acetate in 7 days (circa 90 mgC/L/gCOD) suggesting that organic carbon in fish and grass was more readily degradable. However the total acetate degradation occurred in 13 days for G-100 and in 43 days for F-100. These results coupled to the difference in the final methane production between G-100 and F-100 indicated a lower degradability across time of the grass compared to the fish. No butyrate was produced during the sludge degradation while butyrate accumulation was mainly observed in F-100 (maximum 30 mgC/L/gCOD compared to 10 mgC/L/gCOD in G-100). The propionate maximum accumulation was more important for F-100 (47 mgC/L/gCOD) compared to G-100 (37 mgC/L/gCOD). For G-100, the degradation of the propionate started after all the acetate had been totally degraded. In F-100 the degradation was not completed at day 72. An increase of 15 mgC/gCOD of CH 4 was observed between day 70 and the end of the experiment suggesting that a part of the 25 mgC/gCOD of propionate present at day 70, started to be degraded after day 70. Propionate is one of the most important precursors in methane production after acetate [START_REF] Lawrence | Kinetics of Methane Fermentation in Anaerobic Treatment[END_REF] but it is also reported to accumulate easily and cause process inhibition in some cases [START_REF] Gallert | Propionic acid accumulation and degradation during restart of a full-scale anaerobic biowaste digester[END_REF]Wang et al., 2009). Anaerobic oxidation of propionic acid is thermodynamically unfavorable and depends on acetate and H 2 content [START_REF] Boone | Propionate-Degrading Bacterium , Syntrophobacter wolinii sp . nov . gen . nov ., from Methanogenic Ecosystems[END_REF][START_REF] Mawson | Degradation of acetic and propionic acids in the methane fermentation[END_REF]. It is only performed by specific microorganisms. The major pathway for the anaerobic propionate degradation is a syntrophic degradation of propionate linked to H 2 transfer via a methanogen [START_REF] Ariesyady | Functional bacterial and archaeal community structures of major trophic groups in a full-scale anaerobic sludge digester[END_REF]. Delays observed in the degradation of the propionate for fish mono-digestion F-100 could be explained by the time needed by the appropriate microorganism to grow and by the syntrophy to take place.

The evolution of NH 4 + , pH and NH 3 values during the substrates degradation are presented in the supplementary Figure 30-C. F-100 produced in 7 days around 1300 mg/L of NH 4

+ compared with 350 mg/L for G-100 and S-100 corresponding to a respectively amount of 140 and 30 mg/L of NH 3 . Ammonia nitrogen is known to be an inhibitor of the AD, especially free ammonia (NH 3 ) [START_REF] Fotidis | Effect of ammonium and acetate on methanogenic pathway and methanogenic community composition[END_REF][START_REF] Rajagopal | A critical review on inhibition of anaerobic digestion process by excess ammonia[END_REF]. However a wide range of half inhibitory concentrations has been reported between 1.7 to 19 g/L of NH 4 + (Y Chen et al., 2008;Poirier et al., 2016b) and 50-1400 mgNH 3 /L [START_REF] Rajagopal | A critical review on inhibition of anaerobic digestion process by excess ammonia[END_REF] depending on multiple factors such as the microbial community, temperature... The highest free ammonia accumulation observed for F-100 was 140 mg/L at day 50. The amount of ammonia observed in F-100 was under the inhibitory values described in the literature. It cannot be excluded that the microbial community was partly inhibited, particularly the methanogens and the propionate degrading acetogenic bacteria which are known to be sensitive to free ammonia [START_REF] Calli | Effects of high free ammonia concentrations on the performances of anaerobic bioreactors[END_REF]Westerholm et al., 2011b).

Co-digestion of the substrates

Performances of wastewater sludge co-digestion with fish waste

Evolution of the cumulated CH 4 production over time for the different mixtures is presented in Figure 26 and the Table 8 details the results of the Gompertz modelling for each mixture. In all cases the addition of fish enabled to increase the final CH 4 production compared to S-100. For example, F-25, which contains 25 % of fish and 75 % of sludge, produced 65.58 mgC/gCOD of CH 4 , ie. 18 mgC/gCOD more than S-100. According to the Gompertz modelling the latency before CH 4 production start was not significantly modified between the different mixtures, but the production rate was increased from 2.31 to 3.00 mgC/D/gCOD for F-25 and F-75 respectively. Dissolved organic carbon accumulation (supplementary Figure 31-A) between days 0 to 7, representative of the solid carbon degradation during the early hydrolytic phase, increased when more than 25 % (gCOD) of fish was mixed to WAS. This could be explained by the presence of a higher quantity of easily degradable carbon or by a hydrolysis step faster when fish was present than for S-100.

Volatile fatty acids accumulation, presented in the supplementary Figure 31-B show a similar VFA pattern evolution for the mixtures F-25 and F-50 than for S-100. The acetate accumulation profile in F-75 was also similar to S-100 while the propionate maximum accumulation was closed to F-100 (45 mgC/L/gCOD). The consumption of the propionate in F-75 was completely achieved after day 60 while in F-100 the propionate degradation had not started yet. Regarding the butyrate production no significant effect of AcoD was observed. The ammonia accumulation presented in the supplementary Figure 31-C shows an increase of the NH 4 + level proportional to the amount of fish added in the feeding, indicating that ammonia production was mainly due to fish. Use wastewater sludge to dilute fish waste decreased the NH 4 + level which seemed to reduce the inhibition of the propionate degrading population observed on the fish mono-digestion. This allowed to increase the propionate degradation rate and to produce the CH 4 faster.

Performances of wastewater sludge co-digestion with garden-grass

In the case of co-digestion with grass, the final CH 4 production was increased only when more than 50 % of grass was mixed with sludge compared to sludge mono-digestion. G-25 presented an earlier CH 4 production but a final production similar to S-100. The more the proportion of grass was important the more the latency to produce CH 4 was reduced (from 4.5 days to 2.6 for G-25 and G-75 respectively).

Mixing grass and sludge increased the amount of dissolved organic carbon accumulated during the 7 first days. All the mixtures presented the same evolution of acetate accumulation than G-100 with a higher and faster accumulation than in S-100. However, propionate final accumulation was 2 times higher for the mixtures than for S-100 (20 mgC/L/gCOD) and G-100 (37 mgC/L/gCOD) but propionate degradation for all bioreactors was achieved in 30 days. For all bioreactors of grass co-digestion, the ammonia level stayed stable along the experiment closed to 350mg/L such as the level in S-100 and G-100.

Influence of the co-digestion on the methanogenic pathways

Measurement of the biogas carbon stable isotopic composition allowed to follow the modification of the methanogenic pathway across time [START_REF] Conrad | Quantification of methanogenic pathways using stable carbon isotopic signatures: a review and a proposal[END_REF]. The results are presented in the Figure 27.

The methane production in S-100 was carried out during the first 20 days mostly by the hydrogenotrophic pathway (αapp superior to 1.065). After 20 days, the methanogenic pathway changed progressively from hydrogenotrophic to acetoclastic pathway (α app inferior than 1.055 after 30 days). and 28 of the experiment (B) Samples from reactors fed with sludge and/or grass at days 14 and 21 of the experiment. Days were selected to correspond to the methane production phase. S100 stands for wastewater sludge alone, F25, F50, F75, F100 stands for respectively 25, 50, 75 or 100% of fish (F) in co-digestion with sludge, G25, G50, G75, G100 stands for respectively 25, 50, 75 or 100% of Grass (G) in co-digestion with sludge.

In the fish mono-digestion the gas production at the beginning of the experiment was dominated by the acetoclastic pathway. During the first week of the experiment, the methanogenic pathway increased from 1.04 to 1.06, namely from acetoclastic pathway to a mix of methanogenic pathways. This can be explained by a high hydrolytic activity producing H 2 and CO 2 and favouring the gas production by the hydrogenotrophic pathway. During the next 20 days the αapp decreased due to the consumption of the acetate by the acetoclastic methanogens. Finally, when all the acetate was degraded, the α app increased again due to the syntrophic oxidation of the propionate during which methane was produced by acetoclastic and hydrogenotrophic pathways. The temporal dynamics of the methanogenic pathways for the mixtures of the co-digestion with fish followed the same dynamics than F-100 but with the values of the α app corresponding to an intermediate between F-100 and S-100. Because the propionate was not degraded at the same time for the different fish mixtures, the αapp evolved differently between the different mixtures. This study shows that addition of fish waste influenced strongly the methanogenic pathway during the co-digestion.

The mono-digestion of grass presented a similar dynamics across time of the methanogenic pathway. The beginning of the experiment started in the acetoclastic pathway. After a first increase followed by a decrease of the αapp induced by the production of CO 2 then the consumption of the acetate, the αapp stabilised at 1.055 due to the propionate degradation. Contrary to the fish mixtures, the co-digestion mixtures of grass presented a distinction between two groups. The methanogenic pathway dynamics for G-25 was closer to S-100 with a beginning in the hydrogenotrophic pathway while the methanogenic pathways dynamics of G-50 and G-75 was similar to G-100. This differentiation in the methanogenic pathways for the grass mixtures can explain the difference observed in the methane production.

In support of the isotopic fractionation results the active archaeal community was analysed using 16S rRNA sequencing during the methane production phase (Figure 28). Methanosarcina genus was the most abundant archaea in all the digesters independently of the feeding composition. This archaea has a versatile methanogenesis metabolism but in regards with the isotopic results the acetoclastic pathway seemed to be dominant. Indeed, except for sludge mono-digestion, the methane was mostly produced by the acetoclastic pathway since the beginning of the experiments. Other hydrogenotrophic archaea were found in the digesters. Methanoculleus and Methanobacterium genera were found in digesters fed with sludge and/or fish. Their abundances were higher in sludge mono-digestion at the beginning of the methane production (day 14) than in fish fed digesters. This result is in accordance with the isotopic fractionation which showed that hydrogenotrophic pathway was dominant in sludge mono-digestion during the 20th first days. Methanospirullum were relatively abundant in digesters fed with grass and sludge, reaching up to 25% in G25 bioreactors. Archaea of Methanofollis and Methanocorpusculum were found specifically in digesters fed with more than 25 of grass but they remained minority.

Estimation of the optimal blend of the co-digestion using indicators

In order to evaluate the benefit of the co-digestion, the experimental values of the CH 4 production measured for the bioreactors were compared to two indicators that we built. The first one is the empirical maximum production that could be obtained under optimal conditions (determined with BMP) and called thereafter Optimal Digestion Indicator (ODI).

For the different mix, ODI was calculated as a linear combination of the BMP experimental values obtained for Sludge-BMP, Fish-BMP and Grass-BMP using the percentage of each co-substrate as coefficient. The second indicator is the empirical production that could be obtained under experimental conditions and called Mono-Digestion Indicator (MDI). MDI was calculated in the same way than ODI but using the linear combination of the mono-digestion experimental values. Figure 29 represents the comparison of the experimental CH 4 production profiles to the indicators for each mixture. The Table 8 summarises the kinetics production parameters determined after modelling the data with Gompertz equation for all the experiments and indicators.

The comparison of the experimental mono-digestion (F-100, G-100 and S-100) to the Optimal Digestion Indicator was already described in details in the section dedicated to the mono-digestion. The ODI was higher than the experimental methane production but the codigestion with fish or grass allowed to get close to the final methane production and/or the production rate of the mixtures to the ODI.

For the co-digestion with fish waste the comparison showed no significant differences between the experimental final methane production and the MDI. However the methane production rate for the F-75 (3.00 mgC/days/gCOD) was increased comparing to the MDI (2.33 mgC/days/gCOD). This result, taken with the chemical results (propionate) and the methanogenic pathway analysis, indicates that use of 75 % of fish seemed to be the optimal proportion to enhance the methane production from sewage sludge. Indeed, the propionate was consumed earlier and was associated to a faster return to the acetoclastic methanogenic pathway. On an industrial point of view if the aim is to enhance the final CH 4 production from wastewater sludge, fish waste is a good candidate even at a low quantity. The other advantage to treat fish waste in co-digestion is to limit the risk of an inhibition by the ammonia and the propionate which are accumulated during the degradation in monodigestion. The high potential of VFA production from fish was already studied [START_REF] Bermúdez-Penabad | Anaerobic digestion of tuna waste for the production of volatile fatty acids[END_REF]. Even if no such inhibition was observed in our system, the potentiality of ammonia and VFA accumulation was observed. However, in case of CH 4 production enhancement, the VFA production can inhibit the system and the co-digestion can be a solution to overcome this inhibition [START_REF] Xu | Effects of inoculum to substrate ratio and co-digestion with bagasse on biogas production of fish waste[END_REF].

Regarding the co-digestion with grass, G-25 maximal methane production was slightly lower than the one estimated by the MDI. In contrast, G-50 and G-75 allowed to increase the maximal production by 1.2 times and the CH 4 production rate from 1.2 and 1.3 respectively. Because the experimental CH 4 production was higher than the MDI prediction during all the experiment for G-50 and G-75, it can be supposed that a synergistic effect occurred between grass and wastewater sludge at these proportions.

The mixture improving the AD performances the more in term of CH 4 production is G-75. The substrate degradation started earlier, the methane production rate was improved by 1.2 times and the maximal production was increased by 1.7 times compared to the monodigestion of wastewater of sludge and 0.9 compared to the mono-digestion of grass.

Conclusion

Increasing the fish concentration in co-digestion with wastewater sludge gradually improved the final methane production up to 1.9 times when 75 % of fish was added. On the contrary grass enabled to improve the final methane production from WAS only when more than 25 % of grass was added. Adding more than 50 % of grass improved both the production rate and the final production by to 1.5 and 1.7 times respectively. Specifically built indicators showed that using 75 % of fish or grass as co-substrate with sewage sludge enabled to obtain the maximum final methane production. In nearly all the bioreactors, archaea from Methanosarcina genus accounted for more than 75% of the archaeal diversity. No significant difference in the methanogenic pathways was observed across time between fish and grass mono-digestion. It was mostly acetoclastic while wastewater sludge mono-digestion changed from hydrogenotrophic to acetoclastic methanogenesis pathway. The anaerobic co-digestion allowed to limit the variation between the methanogenesis pathway of the sludge.

Supplementary figures

Article 2: Integrative analyses to investigate the link between microbial activity and molecules degradation during anaerobic digestion

Highlights

 Data integration is powerful to decipher AD microbiota functioning  Links between microbial activity and molecule degradation were evidenced  Methanosarcina and Clostridiales correlated to the degradation of cadaverine  Lactic acid bacteria correlated to lignin compound degradation

Abstract

Anaerobic digestion (AD) is a promising biological process to convert waste into sustainable energy. However, the microbiota involved in this bioprocess is complex and additional knowledge is still needed to fully exploit its capability. High throughput methodologies open new perspectives, but innovative data integration methodologies need to be used to extract information from these rich data. We analysed the association between microbial activity and the patterns of substrate degradation during a co-digestion experiment. These parameters were longitudinally monitored with 16S rRNA sequencing and untargeted metabolomics of samples taken in a lab-scale experiment, where different digesters had been fed with 9 mixtures of fish, sludge and grass. Our objective was to identify microorganisms responsible for the degradation of molecules specific of each co-substrate. Five main groups of logically correlated features were successfully evidenced. For example the degradation of cadaverine was found to be correlated with microorganisms from the order Clostridiales and the genus Methanosarcina and the degradation of lignin compounds with cellulolytic degraders Lactobacillales. This study highlights the potential of data integration towards a comprehensive understanding of AD microbiota.

Introduction

Deciphering the microbial communities from diversified domains such as health, food safety or environment has been widely encouraged thanks to the development of the highthroughput technologies and adapted computational statistical methods. It is now possible to study the structure, activity, interaction and function of complex microbial communities using the new molecular methods such as genomic, transcriptomic, proteomic and metabolomics [START_REF] Vanwonterghem | Linking microbial community structure, interactions and function in anaerobic digesters using new molecular techniques[END_REF]. In parallel to the development of these molecular tools the development of specific statistical methods and user-friendly workflows allows to visualise, analyse and integrate the omics results [START_REF] Bouhlel | Comparison of common components analysis with principal components analysis and independent components analysis: Application to SPME-GC-MS volatolomic signatures[END_REF][START_REF] Callahan | Bioconductor Workflow for Microbiome Data Analysis: from raw reads to community analyses[END_REF]Rohart et al., 2017b;[START_REF] Singh | DIABLO: an integrative, multi-omics, multivariate method for multi-group classification[END_REF].

Anaerobic digestion (AD) is a promising bioprocess to provide sustainable energy. Indeed, drove by a complex microbial community AD allows to transform organic waste into biogas. This process involves four different steps: hydrolysis, acidogenesis, acetogenesis and methanogenesis managed by the interaction of bacteria and archaea. Nonetheless, this microbial diversity and its interaction are highly sensitive and depend on multiple parameters such as temperature [START_REF] Madigou | Ecological consequences of abrupt temperature changes in anaerobic digesters[END_REF][START_REF] Noll | Effect of temperature change on the composition of the bacterial and archaeal community potentially involved in the turnover of acetate and propionate in methanogenic rice field soil[END_REF], the presence of inhibitors such as phenol (Poirier et al., 2016a), ammonia [START_REF] Li | Pathways in bacterial and archaeal communities dictated by ammonium stress in a high solid anaerobic digester with dewatered sludge[END_REF], long chain fatty acids [START_REF] Sousa | Activity and viability of methanogens in anaerobic digestion of unsaturated and saturated longchain fatty acids[END_REF], or the feeding composition [START_REF] Zamanzadeh | Biogas production from food waste via co-digestion and digestion-effects on performance and microbial ecology[END_REF]. Several studies already have shown the utility of using omics technologies to decipher the anaerobic microbial population [START_REF] Amha | Inhibition of anaerobic digestion processes: Applications of molecular tools[END_REF][START_REF] Hassa | Metagenome, metatranscriptome, and metaproteome approaches unraveled compositions and functional relationships of microbial communities residing in biogas plants[END_REF]. The level of information differs according to the type of omics technologies used. One by one omics data analyses are routinely carried out [START_REF] Bize | Shotgun metaproteomic profiling of biomimetic anaerobic digestion processes treating sewage sludge[END_REF][START_REF] Cai | Metagenomic reconstruction of key anaerobic digestion pathways in municipal sludge and industrial wastewater biogas-producing systems[END_REF]; but the use of several omics at the same time is rarer. For example Beale et al applied metagenomic and metabolomics approaches to get new insights on the diversity and activity of the anaerobic population after a stress [START_REF] Beale | An "omics" approach towards the characterisation of laboratory scale anaerobic digesters treating municipal sewage sludge[END_REF]. However, no direct correlation was assessed between the two approaches. More generally, in the literature omics data integration methodologies are still sparse.

The aim of this study was to evaluate a methodology to correlate the microbial activity to the metabolites specific of the substrates degradation. For that purpose, 27 anaerobic bioreactors fed with different linear mixes of substrates (0-100, 25-75, 50-50, 75-25, 100-0) were carried out. Three substrates were chosen for their chemical differences potential and were mixed in order to evaluate the dynamics of substrate specific microorganisms and molecules. The active microbial community was analysed through the sequencing of the 16S rRNA. While DNA can inform us about the total diversity of a community including dead, dormant and living microorganisms, 16S rRNA sequencing give us the opportunity to characterise microorganisms active in given environment [START_REF] Lin | Temperature affects microbial abundance, activity and interactions in anaerobic digestion[END_REF] and to study metabolically active microorganisms [START_REF] De Vrieze | The active microbial community more accurately reflects the anaerobic digestion process : 16S rRNA ( gene ) sequencing as a predictive tool[END_REF]. Untargeted metabolomics using LC-MS was performed to study the pattern of the substrate degradation.

Metabolomics assays have the high potential to produce molecular fingerprints of waste degradation and to monitor the patterns of substrate degradation [START_REF] Villas-Bôas | Extracellular metabolomics: A metabolic footprinting approach to assess fiber degradation in complex media[END_REF].

To our knowledge, this is the first study of its kind that studies the correlation between microbial activity from RNA sequencing data and pattern of substrate degradation from metabolomics data. Our computational analyses identified subsets of active microorganisms highly associated to dynamics of substrate degradation across time, and posit novel hypotheses regarding the capacity of microorganisms to degrade specific molecules.

Methods

2.2.1. Feedstock preparationA sample collected in a mesophilic full scale anaerobic bioreactor was used as inoculum for the experiments. The bioreactor is an industrial digester treating primary sludge from a wastewater treatment plant (Valenton, France). The inoculum was incubated in anaerobic condition at 35°C without feeding in order to degrade the organic matter in excess before to carry out the experiments.

Substrates used in the experiments were wastewater sludge from an industrial wastewater treatment plant (Valenton, France), fish waste from a fish shop and garden grass. The fish and grass waste were crushed and stored at 4°C before the beginning of the experiments.

Bioreactors experimental set-up

Linear mixes were carried out between fish (F) and sludge (S) on the one hand and grass (G) and sludge on the other hand. The experiments were carried out in 1L glass bottles (700 mL working volume) at 35°C in the dark without agitation. In order to add the same quantity of carbon in all the digesters a similar ratio of substrate/inoculum was used to feed and inoculate the digesters (12 gCOD/1.2 gCOD) (Table 9). All the bioreactors were complemented with a biochemical potential buffer (International Standard ISO 11734 (1995)) to reach a final working volume of 700 mL. All incubations were performed in triplicate. The bioreactors were then sealed with a screw cap and a rubber septum and the headspaces were flushed with N 2 (purity >99.99%, Linde gas SA). In total, 27 anaerobic bioreactors were setup.

Weekly, 6 mL of liquid phase were sampled through the septum using a syringe and centrifuged at 10 000g for 10 minutes. The supernatant was kept at -20°C for chemical analysis and the pellet was snap frozen using liquid nitrogen and then kept at -80°C for microbial analysis.

RNA extraction and 16S rRNA sequencing

Based on the biogas production, a total of 22 samples were selected (active biogas production -supplementary Figure 36). Because of the delay in biogas production higher for the bioreactors fed with fish substrate than grass substrate, sampling points were different in order to correspond to the active biogas production phase (day 14 for grass and day 28 for fish). However a common date for all the bioreactor was selected (day 21). Total RNA was extracted using the commercial kit FastRNA Pro™ Soil-Direct (MP Biomedicals) following the manufacturer's specifications. Then, DNA co-extracted was removed using TURBO™ DNase (Ambion) kit following the manufacturer's instructions. The RNA was denaturated by 2 min at 85°C in a dry bath and was then stored on ice. RNA purification was carried out using the RNAClean XP magnetic beads purification system (Beckman Coulter) by adding 1.8 volumes of beads by volume of RNA. After mixing by pipetting and 5 min of incubation, beads were captured using a magnetic rack on one side of the tube and then washed by adding 500 µL of 70% cold ethanol (diluted in DEPC-water). After incubation of the tube during 30 seconds at room temperature, the ethanol was removed. This washing step was repeated 3 times. Once ethanol finally evaporated, beads were resuspended with DEPC-water to eluted RNA from the beads. Finally beads were removed using the magnetic rack and RNA was recovered in the supernatant. The integrity and quantity of the RNA was evaluated using the High Sensitivity RNA ScreenTape and 4200 TapeStation (Agilent Technologies) following the manufacturer's protocol. A reverse transcription PCR (RT-PCR) was carried out on the RNA using the mix iScript Reverse Transcription Supermix (Biorad) and the following thermocycler program: 5 min at 25°C, 30 min at 42°C and 5 min at 85°C. The cDNA was quantified using Qubit 2.0 fluorometer (ssDNA assay kit, Invitrogen, Life Technologies). Archaeal and bacterial hyper variable region V4-V5 of the 16S rRNA gene was amplified on the cDNA and amplicons were then sequenced according to the protocol described by Madigou et al [START_REF] Madigou | Ecological consequences of abrupt temperature changes in anaerobic digesters[END_REF].

16S rRNA tags reads were imported in FROGS. FROGS (Find Rapidly OTU with Galaxy Solution) is a galaxy/CLI workflow designed to produce an OTU count matrix from high depth sequencing amplicon data [START_REF] Escudié | FROGS: Find, Rapidly, OTUs with Galaxy Solution[END_REF]. FROGS abundance file was examined using R CRAN software (version 3.4.4). Alpha diversity was analysed using Shannon method using phyloseq R package (version 1.20.0). The raw sequences were deposited in the NCBI databases under the project accession number PRJNA562430.

Considering the dispersion in the total number of reads identified in each sample, archaeal and bacterial OTUs abundances were normalized with total sum scaling. Only OTUs that exceeded 1% in at least one sample have been taken into account for the analysis and the OTUs selected were transformed using square-root transformation.

Metabolomics analysis

Metabolomics analysis was performed on the supernatant. Samples were analysed using reverse phase liquid chromatography coupled to high resolution mass spectrometry (HPLC-ESI-HRMS) using a LTQ-Orbitrap XL instrument (Thermo Scientific). Samples were diluted at 1/10 in water and 10 µL of the dilution was injected into the analytical system. Chromatographic separation was performed on Accela 1250 pump at 400 µL/min with a linear gradient from 10 to 80% of mobile phase A (acetonitrile + 0.05% formic acid) and 90 to 20 % of mobile phase B (water + 0.05% formic acid) into a syncronis C18 column (50x2.1 mm, 1.7µm , Thermo Scientific) during 23 minutes. A stabilization phase of 5 minutes was performed at the end of each acquisition to return at the initial condition. After chromatographic separation, the sample was ionized by electrospray ionization (ESI) on positive mode. The detection was performed in full scan over an m/z range from 50 to 500 at a resolution of 100 000. A sample of anaerobic sludge was used as a quality control (QC) and was injected each 10 experiment samples, blanks corresponding to water were injected every 10 samples and pools corresponding to an equimolar mix of the samples were injected every each 5 samples.

The raw data obtained from the LC-MS analysis were transformed into mzXML format using MSConvert (ProteoWizard 3.0). The XCMS R package (version 1.52.0) was used to process the data [START_REF] Smith | XCMS : Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment , Matching , and Identification[END_REF]. The method centWave was used to determine peaks on chromatogram with a ppm error of 10 and a peakwidth with a minimum of 20 and a maximum of 50. The same identified peaks in each sample were grouped using the group method with a bandwidth of 30. Then the time retention between the samples was corrected using the orbitrap method and according to the peak groups. A second grouping was carried out with a higher accuracy of 25. Finally the missing peaks between samples were filled using the fillPeaks method. Peak identification was performed by using the databases HMDB, KEGG and PubChem [START_REF] Kanehisa | KEGG for integration and interpretation of large-scale molecular data sets[END_REF][START_REF] Kim | PubChem 2019 update: improved access to chemical data[END_REF][START_REF] Wishart | HMDB 4.0: The human metabolome database for 2018[END_REF] based on the molecular mass.

Statistical analysis

Statistical methods from mixOmics R package (Rohart et al., 2017b) were used to highlight the relationship between the key microorganisms and the molecules degradation pattern.

Firstly, in order to highlight the most active microorganisms involved in the substrate degradation independently of the mixture composition, the method sparse Principal Component Analysis (sPCA, [START_REF] Shen | Sparse principal component analysis via regularized low rank matrix approximation[END_REF]) was used on the microbial dataset. This method allows to select the OTUs that highly contributed to explain the main source of variance in the data.

To study the dynamics of the metabolites between the different feeding types, we used sparse Partial Least Squares Discriminant Analysis (sPLS-DA, [START_REF] Lê Cao | Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems[END_REF]) on the mono-digestion samples (S100, F100, G100) at day 0 to select the most highly discriminative molecules. This heatmap shows the abundances of the most discriminant microorganisms selected by the sPCA in the different feeding composition at days 21-28 for bioreactors containing fish, 14-21 for bioreactors containing grass and 14-21-28 for bioreactors containing sludge only. Duplicates were carried out on the bioreactors containing only fish, grass or sludge at day 21. Taxonomy is indicated at the genus level for archaea (pink) and order level for bacteria (black) completed by the OTU number. Heatmap color goes from blue to red in accordance to the abundance increase.

This analysis enabled to identify the molecules specific of each substrate at the beginning of the experiment. These most discriminant molecules were selected based on the error rate of classification obtained for each component when adding progressively the number of molecules.

The patterns of abundance of the selected OTUs and molecules within the mixtures and across time were observed with heatmaps (heatmap.2 function from gplots R package, version 3.0.1) using the Ward method and Manhattan Euclidean distance for respectively OTUs and molecules abundances.

Secondly, to study the correlation between microbial activities and molecules degradation across time, the selected molecules data were transformed using a ratio between the molecules abundances at Day 0 and days 14, 21 or 28. The two sets of data (selected microorganisms and molecules in step 1) were statistically integrated using a PLS canonical mode [START_REF] Lê Cao | Sparse canonical methods for biological data integration: application to a cross-platform study[END_REF] and their variabilities through the different mixtures were studied. Microorganisms and molecules with a similar pattern of dynamics were grouped together using a hierarchical clustering method to identify the microorganisms involved in the degradation of the molecules.

Results and discussion

Influence of the feeding composition on the microbial community

The alpha diversity using Shannon index (supplementary Figure 37) was calculated on archaeal and bacterial communities in order to evaluate the influence of the feeding composition on the microbial diversity. We observed a decrease in archaeal and bacterial diversity with increasing amount of fish in the feed. The low microbial diversity induced by the presence of fish substrate might be explained by a lower molecules diversity or functional redundancy of the microorganisms to degrade the fish substrate than grass or sewage sludge.

The sPCA sample plot highlighted the influence of the feeding composition on the microorganisms abundance (Figure 32). We identified 43 OTUs representing 65 to 85% of the total microbial community that explained most of the variability in the 16S rRNA data. We observed groups of samples corresponding to days 21-28 for fish and 14-21 for grass, indicating a low time variability within the conditions and a stable microbial community across time.

The individual abundances of the selected microorganisms were compared between the different mixtures (Figure 33). The active archaeal community was different according to the feeding type. Fish mono-digestion (F100) was mostly drove by the archaea Methanosarcina OTU_6 and Methanoculleus OTU_18; grass mono-digestion (G100) by Methanospirullum OTU_24, Methanosarcina OTU_1, Methanofollis OTU_64 and sludge mono-digestion (S100) by Methanosarcina OTU_1, Methanobacterium OTU_63 and 204 and two OTUs of Methanoculleus (18 and 50). Except Methanosarcina which has a versatile methanogenesis metabolism, all the identified archaea use the hydrogenotrophic pathway to produce methane. Fish substrate induced the activity of a specific OTU of Methanosarcina not found for the other substrate.

The diversity in bacteria community also differed between feeding types. As observed in the alpha diversity the fish substrate induced a lower microorganism's diversity than grass or sludge. In fish mono-digestion the order Clostridiales represented more than 90% of the community. In grass mono-digestion the abundance of several cellulose degraders Spirochaetales, Fibrobacterales, Lactobacillales and Enterobacteriales was higher than in fish and sludge mono-digestion. Their ability to degrade cellulolytic substrates as grass can explain their abundances in G100. Bacterial community of sludge mono-digestion was mostly composed by Cloacimonadales, Synergistales, Anaerolineales, Hydrogenedentiales, Erysipelotrichales and Coprothermobateriales. Members from the orders Synergistales and Anaerolineales are known or suspected to be able of syntrophic interaction with hydrogenotrophic methanogens [START_REF] Ito | Identification of a novel acetate-utilizing bacterium belonging to Synergistes group 4 in anaerobic digester sludge[END_REF][START_REF] Sekiguchi | In Situ Detection, Isolation, and Physiological Properties of a Thin Filamentous Microorganism Abundant in Methanogenic Granular Sludges: a Novel Isolate Affiliated with a Clone Cluster, the Green Non-Sulfur Bacteria, Subdivision I[END_REF]. In sludge the abundances of potential syntrophic partners in presence with hydrogenotrophic methanogens suggests a methane production mostly from hydrogenotrophic methanogenesis, in line with the archaeal community described in this incubation.

In light of the literature the substrate used to feed the digesters lead to the development of adapted microbial degraders [START_REF] De Francisci | Microbial diversity and dynamicity of biogas reactors due to radical changes of feedstock composition[END_REF][START_REF] Lee | Microbial communities underpinning mesophilic anaerobic digesters treating food wastewater or sewage sludge: A full-scale study[END_REF]. More particularly, in addition to a low diversity fish substrate induced the growth of a more specific community than grass or sludge. Indeed only 5 to 9% of the OTUs were shared between fish-sludge or fish-grass respectively while 17% of the OTUs were common between grass-sludge.

The proportion of the selected microorganisms within the mixtures evolved according to the feeding composition, but was not linearly proportional. For example, the relative abundances of the active OTUs in the mix 75:25 of fish-sludge (F75) was relatively similar to the abundance of active microorganisms in the fish mono-digestion. As sludge was more abundant in the feeding, more microorganisms characteristic of sludge were recovered. On the contrary the grass active community remained dominant up to 25% of grass in mix with 75% of sludge (G25). In most cases the microbial community in the bioreactors fed with two substrates corresponded to a non-linear mix of the microorganisms characteristics of each substrate. However some microorganisms were only found in specific conditions such as Methanocorpusculum and specific OTUs of Clostridiales in the mixes 50:50 and 75:25 of grass:sludge.

Biogas production reflected the active microbial composition, suggesting an association between microorganisms' dynamics and the production performances (supplementary Figure 36). For example the similarity of the microbial composition between F100 and F75 or G25 and S100 induced a similar biogas production between these digesters. The bioreactors performances will depend on the presence of the adapted microorganisms able to transform the organic matter into biogas. This heatmap shows the dynamics within the samples and across time of the intensity of the most discriminant molecules selected by the sPLS-DA in the different feeding type fish, grass and sludge at day 0. Analyses were carried out at days 21-28 for bioreactors containing fish, 14-21 for bioreactors containing grass and 14-21-28 for bioreactors containing sludge. For each waste mixture and date, triplicates bioreactors called 1, 2 and 3 were analysed. Heatmap color goes from blue to red in accordance with the abundance increase.

Substrates degradation dynamics

The dynamics of the substrate degradation for each bioreactor was studied within each mixture and across time. For that purpose, the fingerprints of molecules degradation was analysed using a HPLC-ESI-HRMS methodology. A total of 267 molecules were detected. To identify molecules that were initially present in the different waste and were specific to each substrate, sPLS-DA was performed on the mono-digestion bioreactors (F100, G100 and S100) at day 0. A total of 70 molecules were determined to be specific of the 3 substrates.

The pattern of degradation of the selected molecules within the mixtures and across time was evaluated by comparing their intensities (Figure 34). The molecules were grouped according to their initial intensities at day 0. Four major groups can be distinguished from this clustering with 3 groups where the intensity of the molecules was high in sludge (S), grass (G) or fish (F) specifically. One group (FG) was formed where intensity of the molecules was high in both fish and grass but low in sludge.

At day 0 the intensity of the molecules representative of the different feeding type differed within the samples. This result was expected due to the chemical differences between the substrates. As expected, the intensity of the molecules representative of the sludge decreased when fish or grass was mixed to sludge, whereas the intensity of some molecules representative of fish or grass was similar within all the digesters containing fish or grass.

As expected, the intensity of the molecules at day 0 decreased across time but we also identified molecules whose intensity either increased or remained stable during the experiment. These molecules with stable intensity across time may not be easily degraded, or be a product of degradation of the other molecules.

The putative identification of the degraded molecules based on the comparison of their theoretical formula and molecular weight to databases is described in the supplementary Table 10. Diethylthiophosphate (X340) and 6-Methylquinoline (X218 and X270) respectively a metabolite of organophosphorus and a flavouring ingredient found in tea were identified in the bioreactors fed with sludge. These metabolites could be found in urine so their presence in wastewater sludge seemed logical. In grass, several molecules from plant constituents such as betaine (X245), or coming from lignin compounds degradation (X388 and X365 respectively trans-ferulic acid and p-coumaric acid) were identified. Moreover metabolites from sugar metabolism (galactitol X329) were found in grass bioreactors. In fish bioreactors mostly metabolites from organic compound degradation (cadaverine X132) and from amino acids degradation (histamine X150) were identified. The identification of all these metabolites differently linked to each substrate was relevant. S100 stands for wastewater sludge alone, F25, F50, F75, F100 stands for respectively 25, 50, 75 or 100% of fish (F) in co-digestion with sludge, G25, G50, G75, G100 stands for respectively 25, 50, 75 or 100% of Grass (G) in co-digestion with sludge.

Correlation between microbial activity and substrates degradation pattern

A PLS analysis was performed to integrate the microorganism activity with the molecules degradation. The rate of molecules degradation was estimated by calculating the ratio of the molecules intensity at the days of interest and their initial intensity in the bioreactors at day 0.

The ordination plots from PLS (supplementary Figure 38-C) shows similar patterns between the two datasets, suggesting that a correlation structure exists. The correlation circle plot (supplementary Figure 38-D) visualises correlated groups of active microorganisms and molecules with high degradation rate. To further identify microorganisms potentially responsible of the molecules degradation, hierarchical clustering was performed. Figure 35 depicts the mean values of activity/degradation rate according to the feeding types. Five groups of correlated microorganisms/molecules were identified according to their substrate specificity (supplementary Table 11). Group 1 included microorganisms and molecules with a high activity/degradation rate related to sludge and/or grass. Groups 2-4 included microorganisms and molecules characterising specifically sludge, grass or fish respectively while group 5 included microorganisms/molecules that were either active or degraded irrespective of fish and sludge.

Group 1 included two genera of archaea, Methanosarcina and Methanospirullum, and the molecules diethylthiophosphate and n-hexenal. These molecules are respectively biomarkers of urine and plants compound and are highly relevant in the context of this study. One hypothesis explaining the correlation of these archaea and molecules could be due to an indirect role of the archaea in the molecules degradation through a syntrophic interaction with bacteria. However no further explanation can be done at this stage regarding their correlation. Such association has not been reported and further investigations are required.

Group 2 correlated 14 OTUs from the orders Cloacimonadales, Clostridiales, Anaerolineales, Synergistales, Bacteroidales, Hydrogenedentiales and Coprothermobacterales to 8 molecules resulting from tryptophan degradation (tryptophanol X217 and tryptamine X269), 6-methylquinoline and thioxoacetic acid. As example of relevant correlation, Anaerolineales and Synergistales are known for their ability to degrade amino acids [START_REF] Swiatczak | Microbiota of anaerobic digesters in a full-scale wastewater treatment plant[END_REF]. Their correlation with tryptophanol and tryptamine was then highly relevant. Surprisingly no methanogen was clustered to the sludge degradation group even though they were expected to be highly correlated to these syntrophic bacteria. One reason could that methanogens involved were not specific partners of these bacteria. Indeed most of the methanogens were found ubiquitously in bioreactors fed partly either with sludge or grass as shown in the Figure 33.

In group 3, 12 OTUs mostly from the orders Clostridiales, Lactobacillales, Bacteroidales and Spirochaetales and the archaea Methanofollis were correlated to 7 molecules resulting from sugar, lignin compound, plant component or protein degradation. The correlation between lactic acid bacteria such as Lactobacillales to the degradation of lignin compounds such as trans-ferulic acid (X388) and p-coumaric acid (X365) was highly relevant. Indeed such microorganisms were identified as lignin degraders [START_REF] Fessard | Why Are Weissella spp. Not Used as Commercial Starter Cultures for Food Fermentation?[END_REF][START_REF] Filannino | Hydroxycinnamic Acids Used as External Acceptors of Electrons : an Energetic Advantage for Strictly Heterofermentative Lactic[END_REF].

In group 4 Methanosarcina and 2 OTUs from the order Clostridiales were correlated to molecules resulting to amino acids degradation as cadaverine (X132) and 5aminopentanoic acid (X208) from lysine degradation, histamine (X150) from histidine degradation and phenylpyruvic acid (X300) from phenylalanine degradation. A role of these microorganisms in the cadaverine degradation is highly relevant and can be supported by a previous study. Roeder and Schink described a new strain close to Clostridium aminobutyricum, able to degrade cadaverine, in co-culture with the archaea Methanospirullum [START_REF] Roeder | Syntrophic degradation of cadaverine by a defined methanogenic coculture[END_REF]. Moreover some Clostridium were identified to be involved in the histamine degradation [START_REF] Pugin | A wide diversity of bacteria from the human gut produces and degrades biogenic amines[END_REF].

In Group 5 no further information about the molecules identification could be provided yet. Methanoculleus and Methanobacterium were correlated to the genus Syntrophomonas. Some species of this bacterium are known to growth in syntrophy with H 2consumer as methanogens [START_REF] Mcinerney | Syntrophomonas wolfei gen. nov. sp. nov., an Anaerobic, Syntrophic, Fatty Acid-Oxidizing Bacterium[END_REF].

In the different groups, there were molecules that are still lacking full identifications, due to their highly diverse structure. The potential presence of one or more adducts or massto charge error specific to each instrument [START_REF] Longnecker | Environmental metabolomics : Databases and tools for data analysis[END_REF], or the lack of databases for molecules specific to complex environment reached to a difficulty of molecule identification.

The statistical method developed in this study allows to posit hypotheses on the degradation of molecules by different microorganisms. These hypotheses were relevant in regards with the literature. However in order to go further in the interpretation complementary analyses have to be performed. Firstly the molecules identification has to be reinforced by using MS/MS fragmentation and by comparing spectra to pure molecules. Secondly, the proof of the degradation of the molecules by the identified microorganisms can be obtained by performing culture on these molecules, or performing experiment with labelled molecules (stable isotope probing, (Chapleur et al., 2016b)). Moreover a global but non-exhaustive overview of the metabolites was achieved in this study due to the molecules affinity to the chromatographic column. Indeed, in our study we focus mostly on organic compounds (organic acids, fatty acids) by using hydrophobic chromatographic column but other types of metabolites can be detected by changing technic or column, depending on the research expectations.

Conclusion

This study demonstrates the presence of association between anaerobic digester feeding composition and microbiota development. Our method focused on extracting correlated patterns of microorganisms' activity and degradation of molecules, characteristic of each substrate. We identified a subset of active microorganisms that were highly correlated with molecules degradation patterns and were biologically relevant. The development of omics methodologies and associated databases will open new ways of microbial systems biology approaches to study and improve bioprocesses functioning mechanisms. Shannon method was used to determine the archaea (A) and bacteria (B) alpha diversity for the different feeding composition at days 21-28 for bioreactors containing fish, 14-21 for bioreactors containing grass and 14-21-28 for bioreactors containing sludge only. Duplicates analyses were carried out in the bioreactors containing only fish, grass or sludge at day 21. Phase 1 corresponds to the acclimation phase; Phase 2 corresponds to the modification of the feeding composition (all the reactors were fed with a ratio of sludge:grass at 50:50); Phase 3 corresponds to the return to the initial feeding composition (S: sludge only and G: sludge:grass 90:10)

Supplementary figures

Influence of the modification of the substrate composition on the anaerobic microbiome

The previous pages present the results about the influence of the substrate composition on the digestion performances in one part and on the microbial activity in another part. Thanks to the statistical workflow developed in the second study it was possible to associate microorganisms to the degradation of molecules specific to the different substrates used to feed the digesters.

In order to go further in the understanding of the influence of the feeding composition on the microbial activity a new experiment was designed. The objectives of this second experiment were to 1) evaluate the adaptation capacity of a microbial community to a modification of the feeding after an acclimation phase or without an acclimation phase 2) determine the resilience capacity of the community when the feeding composition is set back to the initial one. In order to be closer to industrial systems this experiment was carried out in semi continuous reactors.

Two feeding conditions were tested in triplicate. One triplicate of reactors was initially fed with a mono substrate (sewage sludge) and the other triplicate of digesters was fed with a mix of two substrates (10:90 grass:sewage sludge). A modification of the feeding composition was then operated to get a ratio of 50:50 of grass:sewage sludge in all the digesters. Finally the feeding composition was reset to the initial composition for both triplicates. The experimental design is summarised in the Figure 39. Both substrates were chosen based on the results of the batch experiments. Indeed due to a low production of NH 4 + during the degradation of grass, grass substrate was chosen instead of fish waste in order to focus on the influence of the substrate composition and not on the ammonia inhibition. Biogas production and chemical analyses were performed as described in the first article and 16S rRNA sequencing was performed on several time points along the experiment.

Material and methods

Experimental setup

Six semi-continuous stirred tank reactors (CSTR, Bioprocess Control) with a working volume of 5 L (7 L total) were operated in mesophilic conditions during 146 days under a constant agitation (90 rpm). A constant organic loading rate (OLR, 0.5 gCOD/L/Day) and hydraulic retention time (HRT, 21 days) were applied (feeding once a day). The feedstock was prepared once a week and stored at 4°C. Three different feedings were used in this experiment: F1 is composed of 320 g of sewage sludge (ratio 0:100 of grass:sewage) diluted in a biochemical potential buffer (International Standard ISO 11734 (1995)) to reach a final working volume of 2 L; F2 is composed of 3 g of grass and 288 g of sludge (ratio 10:90 of grass:sewage sludge) and F3 is composed of 15 g of grass and 160 g of sludge (ratio 50:50 grass:sewage).

The duration of the first step of acclimation was 63 days during which a triplicate of digesters was fed with F1 (called S series) and another triplicate of digesters was fed with F2 (called G series). Once the carbon consumption and methane production had stabilised, the feeding composition (F1 or F2) of both triplicate was changed to F3 for 21 days in order to simulate an intermittent addition of grass. Finally the feeding composition was reset to the initial values, F1 for S series and F2 for G series and maintained for 63 days.

Weekly, 60 mL of liquid phase was sampled and centrifuged at 10 000g for 10 minutes. During the modification of the feeding composition, sampling frequency was increased and samplings were carried out twice a week. The supernatant was kept at -20°C for chemical analyses and the pellet was kept at -80°C for microbial analysis. Both parts were snap frozen using liquid nitrogen before storage.

Biogas and chemical analyses

The biogas production flow was measured using a µFlow gas counter (Bioprocess Control). The biogas composition was analysed directly from the headspace of the bioreactors using a micro gas chromatograph (CP4900, Varian) as described in part 1.2.4. Different chemical analyses were performed such as biogas isotopic fractionation, NH 4 + , NH 3 , pH, DOC, DIC, and different VFA (acetate, propionate and butyrate). The protocols of analyses were described previously in part 1.2.5.

Microbial and statistical analyses

The microbial activity was followed across the experiment using 16S rRNA sequencing as described in part 1.2.6.

As the sequencing results were obtained at the end of the thesis, only a global description of the community dynamics will be provided in this manuscript. Principal Component Analysis was carried out in order to describe the dynamics of the community.

Preliminary Results

These results were obtained as part of an internship carried out by Alexandra Claudin from April to August 2019. She followed the semi-continuous reactors and carried out the sampling. These preliminary results add deeper information regarding the microbial dynamics induced by the modification of the feeding composition and complement the results from the co-digestion batch experiments.

Influence of the feeding modification on the digester performances

The results of the different physico-chemical parameters are presented in the Figure 40.

3.2.1.1.

Phase 1: Stabilisation The stabilisation phase lasted 63 days corresponding to 3 retention times. One triplicate of digesters, called S series, was fed with 100% of sludge and the other triplicate of digesters (G series) was fed with 10% of grass and 90% of sludge. At the beginning of the experiment the biogas production increased from 100 to 350 ml/day for the S series and 400 mL/day for the G series. An important difference within the digesters from the G series can be observed. This could be explained by a possible difference in the grass degradation within the triplicates. The biogas was composed of 67% and 75% of CH 4 for respectively S and G series. During the first 30 days the CH 4 amount decreased for the G series until 70%. For the S series the CH 4 amount fluctuated between 65 to 70% before stabilising at 70%. In parallel the CO 2 amount stabilised at 30% before the end of the stabilisation phase. The isotopic fractionation indicator (α app ) was under 1.055 during the 63 days for both conditions meaning that the CH 4 was produced mostly by the acetoclastic methanogenesis pathway.

The initial pH value was 8 and decreased at 7.85 with a variation between 7.7 and 7.9 in all the digesters. The initial NH 4 + was around 350 mg/L and decreased to 300 mg/L at the end of the stabilisation phase. The NH 3 concentration was estimated using the pH, NH 4 + and temperature values. It stabilised at 20 mg/L in all the digesters. No ammonia was produced during the stabilisation phase which was expected in regards with the results obtained in batch experiments. Ammonia values were low compared to the inhibitory values reported in the literature, 1.7-19 gTAN/L and 0.05-1.4 gNH 3 /L [START_REF] Rajagopal | A critical review on inhibition of anaerobic digestion process by excess ammonia[END_REF][START_REF] Yenigün | Ammonia inhibition in anaerobic digestion: A review[END_REF], suggesting that no ammonia inhibition occurred in both conditions.

The DIC concentration decreased from 2200 mgC/L at the beginning of the experiment to stabilise around 1900 mgC/L in all the digesters at the end of the stabilisation phase. For the DOC the results were different between the triplicates. Indeed in G1, G3 and S1 no accumulation was observed meaning that no dissolved intermediates accumulated in these digesters. However in S2 and S3 an accumulation of DOC was observed, with a peak at day 42 with a maximum of 400 and 500 mgC/L respectively. DOC were totally consumed in both reactors at day 55. In the digester G2 DOC value decreased to reach a concentration around 160 mgC/L. This value was reached by all the other digesters at the end of the stabilisation phase.. In parallel to the DOC accumulation observed in some digesters, VFAs also accumulated. However in all the digesters VFAs did not exceed 250 mgC/L, remaining under the inhibitory values listed in the literature and estimated above 1000 mgC/L [START_REF] Appels | Principles and potential of the anaerobic digestion of waste-activated sludge[END_REF][START_REF] Siegert | The effect of volatile fatty acid additions on the anaerobic digestion of cellulose and glucose in batch reactors[END_REF]. During the stabilisation phase within both triplicates a fluctuation in the carbon content was observed as described below. One of the explanations for these fluctuations could be a technical issue during the pumping of the substrate (sewage sludge) which was thick. However at the end of the stabilisation phase all the digesters reached a similar value of DOC and VFAs.

Regarding the physico-chemical results from the two triplicates of digesters, the presence of grass (G series) seemed to enhance biogas production compared to reactors fed with sludge only (S series). However the biogas composition and the other parameters were not different between the two series. This is in line with the results observed in the previous batch experiments. Indeed the presence of 25% of grass in the feeding composition did not led to a modification of the final CH 4 production compared to the sludge mono-digestion.

Because all the chemical results were stable for more than 10 days (from day 50 to 63) it was decided to carry on with the modification of the feeding composition.

3.2.1.2.

Phase 2: Feedstock composition modification The feeding composition was modified for all the digesters to reach a ratio of 50:50 sludge:grass and was maintained during 21 days. This duration corresponds to one HRT and was selected to simulate a punctual modification of the feeding composition.

For the pH, NH 4 + and NH 3 parameters, no significant differences were observed after the modification of the feeding composition. As observed in the batch experiments a low quantity of ammonia was produced during the degradation of the grass and sludge.

During the phase 2, technical issues limited the measurement of the biogas yield. Part of the data are missing. The biogas yield reached 450 mL/day for the G series while it reached 350 mL/day for the S series. The methane decreased from 70 to 55% for the G series depending on the replicate and from 70% to 65% for the S series. These observations are in line with the literature. Indeed Fitamo and colleagues (2016) modified the feedstock composition from mono-digestion of sewage sludge to the co-digestion of 10% sludge and 90% of a mix of food waste and garden grass. The biogas productivity was improved by 3 and was associated to a decrease from 69 to 60% in the methane content [START_REF] Fitamo | Co-digestion of food and garden waste with mixed sludge from wastewater treatment in continuously stirred tank reactors[END_REF].

The isotopic fractionation of the methane did not evolved after the modification of the feeding composition no matter the initial feeding. This means that the metabolism for the methane production was not influenced by the feeding modification. The acetoclastic pathway was the major pathway for methane production. Regarding the DOC concentration a low accumulation up to 150 mgC/L was observed in all the digesters except S2 and G2 where the DOC accumulated respectively up to 400 and 250 mgC/L. This accumulation was associated to an increase of the acetate concentration.

The addition or the increase of grass into the feedstock composition led to a slight modification of the biogas production and composition. This could be explained by the way of the grass addition. The Figure 41 represents the quantity of grass into the digesters for both S and G series if the grass was not degraded. It can be observed that the grass accumulated slowly to reach a maximum of 35% of the digester. The microbial population would probably have time to adapt to the modification of the feedstock composition.

3.2.1.3.

Phase 3: Return to the initial feedstock composition After one HRT the feeding composition was reset to the initial one. Digesters from the S series were fed with 100% of sludge and the digesters from G series were fed with a ratio sludge:grass of 90:10.

In both series S and G, a lower biogas production can be observed during the phase 3 than during the phase 2. After the modification of the feedstock composition in phase 3; the biogas production was reproducible between the triplicates of the S series and seemed to be maintained around 250 mL/day. For the G series, the results were not so reproducible. The CH 4 content increased in less than 10 days to 70% after the return of the initial feeding composition for all the digesters. This can be associated to a decreased of the DOC concentration from 150 to 100 mgC/L, which was the minimal value during the stabilisation phase. Even if an evolution of the CH 4 content was observed no modification of the isotopic fractionation was observed. The methane was produced all along the experiment through the acetoclastic pathway mostly. All the others parameters did not evolved after the modification of the feeding composition.

The return to the initial feeding composition did not lead to a modification of the chemical parameters. Similarly, no differences were observed between the digesters from the S and G series. As for the phase 2 the modification of the feeding composition was slow and could explain the absence of strong evolution of chemical parameters. initially with a ratio 10:90 of grass:sludge). S1-S2-S3 represent the replicate of the S series (fed initially with a ratio 0:100 of grass:sludge). The solid line represents the first modification of the feeding composition to a ratio of 50:50 of grass:sludge. The dashed line represents the second modification of the feeding composition.

Microbial community dynamics

The modification of the feedstock composition led to a moderate modification of the physico-chemical parameters. Moreover no significant differences were observed between the series initially fed differently. However we checked if the microbial composition was modified across the experiment and between the conditions. Indeed as shown by the previous results from batch experiments, the microbes evolved between the conditions of sludge mono-digestion (S100) and grass:sludge co-digestion at 25% of grass (G25) while the CH 4 final production was similar between the conditions. In order to study the microbial activity 16S rRNA sequencing was carried out at different time points. In total 54 samples were selected, corresponding to 9 time points per digesters.

A general observation of the microbial composition was carried out using a bar plot representation (Figure 42 and Figure 43). Regarding the archaea active community the genera were similar between the digesters from the series S and G. Mostly Methanosaeta and Methanoculleus genera were represented with respectively more than 30% and 10% of the total microbial community. An exception was observed. For the digester G2 the archaeal community differed from the replicates G1 and G3. This difference is linked to the performances results and especially high DOC concentration observed in this digester. In this digester the genera Methanosarcina and Methanosaeta represented both 10 % of the total microbial. The high abundance of Methanosaeta was in line with the methane isotopic fractionation. The α app results indicated that the methane was produced through the acetoclastic pathway. Methanosaeta produces methane only through the acetoclastic pathway and Methanosarcina is an ubiquitous archaea able to produce methane through the different pathways.

The modification of the feedstock composition did not influence highly the archaea community in both series S and G. An exception can be done for the digesters of the G series. It can be noticed that the abundance of the genus Methanomethylovorans (less than 5% of the total microbial community) increased when the feeding composition was modified from a ratio of 10:90 to 50:50 of grass:sludge. However the abundance of this genus was the highest when the feeding composition was returned to the initial composition (phase 3).

The activity of these microbes when the grass quantity was increased in the feedstock composition can corroborate one of the hypotheses that some microbes need a specific ratio of grass:sludge to be active.

Regarding the bacterial community no obvious differences can be observed between the two series S and G and when modifying the feeding composition as well. The bacterial community was much more diversified than the archaeal community with 34 different genera. The bar plot represents the abundances of the bacteria at the order level. It can be noticed that the Bacteroidales order was the most abundant in all the digesters. The abundance of the orders Synergistales and Syntrophobacterales seemed to differ between the digesters from the S and G series with a higher abundance for the samples from the S series. In order to go further in the analysis of the microbial community and be able to determinate more precisely the differences between the digesters from the two series multivariate analysis was used. In first approach Principal Component Analysis (PCA) was carried out in order to visualise the temporal dynamics of the microbial community, both archaea and bacteria, in relationship with the feeding condition (Figure 44). The first axis of the PCA represents the dynamics of the active microbial communities for G and S series along the experiment (22% of the explained variance).

During the stabilisation phase (Phase 1) the microbiota differed between the digesters fed with sludge only (S) and a ratio 90:10 of sludge:grass (G). This means that the presence of grass led to a modification of the microbial community even at a low quantity.

When the feeding composition was modified (Phase 2) to a ratio of 50:50 of sludge:grass in all the digesters, the microbial community in both series evolved. Regarding the S series in phase 2 the microbial population get close to the one from the G series in phase 1. Moreover the PCA result showed that the modification of the quantity of grass in the feedstock led to the modification of the microbial community regarding the G series. Indeed samples from the Phase 2 of the G series differed from the samples from the Phase 1. This result confirmed the observation made in the batch experiments. Indeed the microbial population greatly evolved between batchs containing 25 and 50% of grass.

The return of the feeding composition to the initial one (Phase 3) led to a new modification of the microbial communities. For both S and G series the microbial population did not return to the initial one.

The result from the bar plot representation showed few differences of the microbial activity between the conditions. However the PCA result indicated an modification of the microbial activity. This result indicates that the modification of the feedstock composition in our experiment influenced mostly the minor microorganisms, with low abundances.

Conclusion on the experiment conducted in CSTR

In this study the use of two different feedstocks led to the development of two microbial populations. This result is in line with the literature which shows the great influence of the feedstock composition on the microbial development. However no differences were observable in the performances between digesters fed differently with sludge only or with a ratio 10:90 of grass:sludge. This indicates that different microbial communities adapted to the degradation of the substrate leading to a similar bioprocess performance. By modifying the substrate composition regularly a selection of the microbes more able to deal with the substrate are expected to be selected. The analysis of the microbial community needs to be completed in order to determine which microbes are specific to the different feedstock composition. Moreover the microbial identification should be associated to functional analyses using for example metabolomics and metatranscriptomics. Indeed these analyses would provide further information to explain the similarity of the performances between the digesters and answer questions such as: is it due to a functional redundancy, a similar metabolism…?

The addition of a new substrate in the feedstock, without increasing the organic loading rate, led to a slight decrease in the methane production. This decrease can be related to the modification of the microbial community. The interesting result is that the microbial population which was not adapted to the new substrate tend to be similar to the population adapted to the degradation of this substrate. Finally the return of the feeding composition to the one used to adapt the microbial population initially induced a new modification of the microbial population. An evolution in the biogas production rate can be observed in parallel.

In order to complete the analysis of the microbial community another strategy of statistical analysis can be envisaged. Indeed in this study 9 time points covering all the different phases of the feedstock modification were carried out. It would be interesting and provide further information to study the temporal dynamics of the different microbes across the experiment and compare these modifications between the digester. Due to the lack of time to analyse the data when writing the manuscript this will not be described here. However a strategy of time course analysis is described in the next chapter as we obtained similar type of data in another experiment.

General conclusions of the chapter

The objective of the studies presented in this chapter was to determine the influence of the feeding composition on the microbial activity and composition. In our case the influence of the co-digestion on the microbial activity and methanogenic pathways were studied. For that purpose two approaches were applied. The first experiment was designed in order to determine the effect of linear mixes of chemically different substrates. The second experiment focused on the influence of the modification of the feeding composition across time. This last experiment was carried out in semi continuous reactors to be closer to an industrial digester. Key information can be drawn from these two experiments. Regarding the influence of the feeding composition on the digester performances and particularly on biogas production it can be noticed that different substrates lead to different performances. This has already been shown by many studies. However this study demonstrates that biogas production is not linearly related to the composition of the feedstock. The production will greatly depend on the chemical composition of the substrate added in the mix but also on its quantity. The digester performances depend on the microbial capacity to adapt to the addition of a new substrate.

The analysis of the microbial community confirms the influence of the feedstock composition on the microbiome. Indeed the results showed that the specificity and the diversity of a microbiome depend on the chemical composition of the substrate. For example, the alpha diversity measured during the fish degradation was lower than during the degradation of grass and sludge and the active microorganisms were more specific. Moreover the results of the microbiome analysis show the influence of the quantity of a substrate in the feedstock composition on the activity of specific microbes. For example, during the grass digestion, in both batch and semi-continuous reactors, some microbes were active only when a certain quantity of grass was present in the feedstock.

As the substrates have a high influence on the microbial community development it is essential to better understand the link between the molecular composition of the substrate and the microbial activity. For that purpose in parallel to the microbiota analysis, the metabolome was analysed using untargeted metabolomics. This analysis was performed for the moment only for the batch experiment but will be carried out also for the semi continuous reactors.

Perspectives

The results presented in this chapter confirm the necessity in the anaerobic digestion field to go further in the determination of the molecular composition of the substrates which is intrinsically linked to the microbial ecosystem and the biogas production. In particular, it could be interesting to study the microbial functionality more precisely by carrying out metaomics analyses. Metatranscriptomics analysis will be performed on samples taken during each phase of the semi continuous experiment. This will provide more information regarding the functional redundancy and/or the microbial plasticity between acclimated and nonacclimated digesters.

The omics analysis and their integration with the others data will provide further information on the metabolic pathways affected by the modification of the feeding composition, how they are affected (redundancy or specific pathway) and how this can influence the biogas performances. This information will be useful in more or less long term to build decision-making tool for the choice of substrates to feed digesters, especially in the case of co-digestion, and increase their efficiency and stability.

Influence of ammonia accumulation speed on the microbiome

Different sources of perturbation can lead to a bioprocess disturbance such as the modification of technical parameters (temperature, pH, stirring), the presence of inhibitors or the feedstock composition [START_REF] Chen | Inhibition of anaerobic digestion process: A review[END_REF]. As seen in the previous chapter the substrate degradation can lead to the production of inhibitors such as ammonia.

Ammonia nitrogen is one of the major inhibitors of the anaerobic digestion. In addition to its production during the degradation of protein-rich substrates, ammonia can be originally present in the feedstock. If the inhibition by the ammonia is well studied, many questions remain about the inhibition mechanisms, the influence of ammonia on the microbial dynamics or the range of inhibition. Indeed many studies report contradictory results about the influence of the ammonia on the microbial composition. Moreover most of the studies analysed the influence of different ammonia concentrations on the microbial composition. Few of them studied the influence of accumulation speed on the microbial activity. However it should be interesting to go further on this question as the inhibition of an industrial plant can be due to a progressive accumulation.

In this thesis work we focused on two descriptors of the microbiome activity: microbial activity (16S rRNA sequencing) and metabolomics dynamics (untargeted metabolomics). The identification of microbes or metabolites sensitive to the ammonia, no matter its accumulation speed, would be helpful to identify bio-indicators of the bioprocess functioning.

The objectives of this present study were to:

 Evaluate the effect of the ammonia accumulation speed on the bioprocess performances and methanogenic pathways. For that purpose semicontinuous digesters were fed with an increasing quantity of ammonia to reach a final concentration of 183 mg-NH 3 /L at different dates. One digester was kept as a control without ammonia addition. Different physico-chemical parameters were measured to evaluate the performances. The biogas isotopic composition was followed to determine the methanogenic pathways.  Characterise the temporal dynamics of different descriptors of the microbiome associated to the disturbance. To do so, 16S rRNA sequencing was carried out all along the experiment to follow the microbial activity.

Untargeted metabolomics was carried out on the same time period to follow the metabolic composition. A longitudinal statistical analysis including a timecourse modelling coupled to a clustering method was carried out on these datasets separately to highlight groups of descriptors with similar time course dynamics after the disturbance.  Investigate the acclimation capacity of the microbiome when subjected to increasing ammonia concentration and identify potential bio-indicators.

The microbiome dynamics was studied by comparing the temporal dynamics of the microbes and the metabolomics composition between the different conditions. The untargeted metabolomics analysis was carried out in partnership with Ecole Polytechnique (Palaiseau). An article is being drafted in order to describe the results obtained from this study.

Anaerobic microbial ecosystem dynamics facing to different ammonia accumulation speeds

Abstract

Ammonia is a well-known inhibitor of the anaerobic digestion. A longitudinal analysis was carried out to determine the influence of the ammonia accumulation speed on the anaerobic digestion performances and the microbiome dynamics. Experiments were carried out in six semi-continuous 5 litres lab digesters. One digester was kept as a control without ammonia addition, and the five others were subjected to different ammonia accumulation speeds to reach a final concentration of 183 mg-NH 3 /L. Untargeted metabolomics and 16S rRNA sequencing analyses were carried out all along the experiment to evaluate the microbiome capacity to adapt to the ammonia accumulation and to identify potential bio-indicators of the good or bad functioning. A longitudinal and clustering analysis of the 16S rRNA sequencing dataset allowed to identify three potential bio-indicators (Smithella, Petrotogaceae and Leptospiraceae) and to evaluate the adaptation capacity of the different microbes. The metabolomics dynamics analysis allowed to highlight metabolites which increased specifically in presence of ammonia. Moreover the temporal intensity of some metabolites differed according to the ammonia accumulation speed, increasing instead of decreasing.

Introduction

In a global warming context, the control of greenhouse gas emission (GHG) and the need for sustainable energy are of primary importance. Organic waste disposal has a nonnegligible impact on the GHG emission and a proper waste management must be employed. Since the last decades anaerobic digestion bioprocess has been widely employed and studied for the treatment of organic waste. Anaerobic digestion (AD) is a microbial-based process where microorganisms transform the organic matter into renewable energy in the form of methane-rich biogas. If AD has been studied for more than 100 years, the electricity produced through biogas remains minor, contributing to 0.2-0.4% of global electricity production [START_REF] De Vrieze | Perspectives for microbial community composition in anaerobic digestion: from abundance and activity to connectivity[END_REF]. This can be explained by technical challenges encountered to maintain an optimal and stable process. Indeed AD is composed of a cascade of reactions catalysed by a highly complex consortium of microorganisms making the process sensitive to the accumulation of potential inhibitors [START_REF] Chen | Inhibition of anaerobic digestion process: A review[END_REF]. It is crucial to better understand the impact of these inhibitors on the microbiota, heart of the AD bioprocess, and improve the microbial management of digesters to optimise AD functioning.

One of the major inhibitors encountered in the AD bioprocess is the ammonia nitrogen provided partly by the degradation of nitrogen-rich organic matter such as proteins or urea (Mata-Alvarez et al., 2014). It is considered that below 200 mg/L of Total Ammonia Nitrogen (TAN), ammonia is an essential nutrient for microbial growth. However at higher concentrations the risk of inhibition increases. More specifically the free ammonia nitrogen (FAN -NH 3 ) form is usually recognised as the most toxic one due to an easiest diffusion through the cells membranes than NH 4 + [START_REF] Anthonisen | Inhibition of Nitrification by Ammonia and Nitrous Acid[END_REF][START_REF] Astals | Characterising and modelling free ammonia and ammonium inhibition in anaerobic systems[END_REF].

Studies on the ammonia inhibition in AD bioprocess mostly tested different levels of ammonia concentration (low, moderate, high) by adding NH 4 Cl or substrate known to produce ammonia [START_REF] Chen | Effects of Ammonia on Anaerobic Digestion of Food Waste: Process Performance and Microbial Community[END_REF][START_REF] Lv | Microbial community shifts in biogas reactors upon complete or partial ammonia inhibition[END_REF][START_REF] Lv | Ammonium Chloride vs Urea-Induced Ammonia Inhibition of the Biogas Process Assessed by Stable Isotope Analysis[END_REF]Poirier et al., 2016). The objectives of these studies were to determine the threshold of ammonia inhibition based on the bioprocess performances and its influence on the microbial community development. However as the inhibition depends on multiple parameters such as operational parameters or microbial adaptation, no consensus has been found for a specific threshold of inhibition or microbial sensitivity [START_REF] Rajagopal | A critical review on inhibition of anaerobic digestion process by excess ammonia[END_REF][START_REF] Yenigün | Ammonia inhibition in anaerobic digestion: A review[END_REF]. Indeed a wide range of concentration where methane production was half inhibited were described in the literature, from 0.05 to 1.4 gFAN.L-1 and from 1.7 to 19 gTAN.L-1 [START_REF] Astals | Characterising and modelling free ammonia and ammonium inhibition in anaerobic systems[END_REF][START_REF] Chen | Inhibition of anaerobic digestion process: A review[END_REF]Poirier et al., 2016;[START_REF] Rajagopal | A critical review on inhibition of anaerobic digestion process by excess ammonia[END_REF]. Regarding the microbiota it is usually assumed that Clostridiales order and hydrogenotrophic archaea present a higher resistance to ammonia than Bacteroidales [START_REF] Lv | Ammonium Chloride vs Urea-Induced Ammonia Inhibition of the Biogas Process Assessed by Stable Isotope Analysis[END_REF] and acetoclastic archaea [START_REF] De Vrieze | Ammonia and temperature determine potential clustering in the anaerobic digestion microbiome[END_REF]. This leads to the enhancement of the hydrogenotrophic pathways for methane production [START_REF] Li | Pathways in bacterial and archaeal communities dictated by ammonium stress in a high solid anaerobic digester with dewatered sludge[END_REF]. Moreover a predominance of the syntrophic acetate oxidation pathway (SAO) was observed under ammonia inhibition in mesophilic condition [START_REF] Fotidis | Effect of ammonium and acetate on methanogenic pathway and methanogenic community composition[END_REF][START_REF] Schnurer | Ammonia, a selective agent for methane production by syntrophic acetate oxidation at mesophilic temperature[END_REF]. However contradictory results were obtained by other authors [START_REF] Calli | Effects of high free ammonia concentrations on the performances of anaerobic bioreactors[END_REF]. The aim of our work is to determine the influence of the speed of ammonia accumulation on the microbiome dynamics and investigate the adaptation capacity of the microbiome. For that purpose ammonia was added in six semi-continuous digesters at different speeds to reach the same specific FAN concentration. Modifications of the microbial activity and metabolites composition were studied. To do so, respectively 16S rRNA sequencing and untargeted direct infusion mass spectrometry (FTICR) were carried out at different time points across the experiment.

Methods

Feedstock preparation

Food biowaste coming from an industrial food deconditioning plant was used as a substrate. It was stored at -20°C for the duration of the experiment. Enough quantity was thawed and grounded prior to use. The inoculum came from a mesophilic full scale anaerobic bioreactor (Valenton, France). The bioreactor is an industrial digester treating primary sludge from a wastewater treatment plant.

Determination of the half inhibiting ammonia concentration

In order to determine the concentration of ammonia which partly inhibits the bioprocess, batch experiments were carried out. Five concentrations were tested (0, 2, 5, 7 and 17 g-NH 4

+ /L) based on previous results obtained in the laboratory by Poirier et al and the values found in the literature (Poirier et al., 2016b). All the incubations were carried out in triplicate in 1L glass bottle, in mesophilic condition and without agitation. A ratio of substrate/inoculum of 12 gCOD / 1.2 gCOD was used. Only the biogas composition was followed all along the experiment and the ammonia concentration was measured at two time points, first and final days.

The parameters of production were estimated using Gompertz equation as described in the Material and Method part. Based on the methane production rate (µ) value and the NH 3 final concentration, the half inhibiting ammonia concentration (IC50) was determined using a Hill model as described in (Chapleur et al., 2016a). The result of the IC50 is presented in the Figure 45.

Experimental set-up

Six semi-continuous stirred tank reactors (Bioprocess Control) with a working volume of 5 L (7 L total) were operated in mesophilic condition during 252 days under a constant agitation (90 rpm) (Figure 46). A constant organic loading rate (OLR, 0.5 gCOD/L/Day) and hydraulic retention time (HRT, 25 days) were applied with a feeding once a day and drawoff. The feeding was prepared once a week and stored at 4°C with the following composition: 87g of biowaste diluted in a biochemical potential buffer (International Standard ISO 11734 (1995)) to reach a final working volume of 2 L.

A first step of stabilisation was performed during 98 days where all the reactors were fed in the same way. Once the carbon consumption and methane production was stabilised, NH 4 Cl (Arcos Organic, 99%) was added in the feeding in order to reach a final NH 3 concentration of 183 mg/L estimated to be partially inhibiting. This concentration was reached at different dates between the reactors. One digester was not perturbed as a control (P0) and 5 different speeds of perturbation were tested in the last 5 digesters. P1 reached the final NH 3 concentration directly (2 days), P2 reached it after 0.5 HRT (14 days), P3 after 1 HRT (25 days), P4 after 2 HRT (50 days) and P5 after 3 HRT (75 days). For each bioreactor, once the defined NH 3 concentration was reached the stress was maintained at constant value during 3 HRT meaning until days 182, 189, 203, 232 and 252 for the digesters P1 to P5.

Weekly, 60 mL of liquid phase were sampled and centrifuged at 10 000g for 10 minutes. After the beginning of the perturbation and during two weeks two samples per week were taken. The supernatant was kept at -20°C for chemical analysis and the pellet at -80°C for microbial analysis. Both parts were snapped frozen before storage using liquid nitrogen.

Chemical analyses

The biogas production flow was measured using a µFlow (Bioprocess Control). The biogas was collected into a Tedlar® bag and the composition was analysed directly from the bag of the bioreactors using a micro gas chromatograph (CP4900, Varian) as described in Chapleur et al. (2014).

The methanogenic pathways during the digestion were determined by the gas isotopic signature analysis as described by Cardona et al (2019). As indicator of the methanogenic pathway, the apparent isotopic factor (α app ) was calculated as presented in the following equation [START_REF] Whiticar | Biogenic methane formation in marine and freshwater environments: CO2reduction vs. acetate fermentation-Isotope evidence[END_REF]: α app = (δ 13 CO 2 + 10 3 ) / (δ 13 CH 4 + 10 3 ) It is usually assumed that if the α app is superior to 1.065, the hydrogenotrophic way is the most important. On the contrary if the α app is inferior to 1.055, the methanogenesis is dominated by the acetoclastic way [START_REF] Conrad | Quantification of methanogenic pathways using stable carbon isotopic signatures: a review and a proposal[END_REF][START_REF] Whiticar | Biogenic methane formation in marine and freshwater environments: CO2reduction vs. acetate fermentation-Isotope evidence[END_REF].

The concentration of the ammonia perturbation was estimated based on the free ammonia nitrogen concentration (FAN or NH 3 ). For that purpose the ammonium (NH 4 + ) concentration was measured using the Nessler's colorimetric method following the French standard (NF T 90-105) in spectroscopic tanks using Hach spectrometer DR2800.

The NH 3 value was determined using the equation from [START_REF] Anthonisen | Inhibition of Nitrification by Ammonia and Nitrous Acid[END_REF] and already presented in the previous chapter.

Volatile Fatty Acids (VFA) concentrations were measured using ionic chromatography (ICS 5000+, Thermo Fisher Scientific) as described in Cardona et al (2019). The quantified VFA were acetate, propionate and butyrate.

RNA extraction and 16S rRNA sequencing

In total 60 samples were sequenced corresponding to 10 time points for each bioreactor. The time period covered by the sampling ranged from day 70 to day 154 corresponding to two weeks before ammonia accumulation until the end of this accumulation.

The protocol for the RNA extraction and the cDNA sequencing were described in the material and methods section, respectively part 91.2 and 9.2.

16S rRNA reads were imported in FROGS. FROGS (Find Rapidly OTU with Galaxy Solution) is a galaxy/CLI workflow designed to produce an OTU count matrix from high depth sequencing amplicon data [START_REF] Escudié | FROGS: Find, Rapidly, OTUs with Galaxy Solution[END_REF]. FROGS abundance file was examined using R CRAN software (version 3.4.4). Only OTUs that exceeded 1% in at least one sample have been taken into account for the analysis and the OTUs selected were transformed using center log ratio transformation (clr). The raw sequences were deposit on the NCBI database under the project number PPRJNA631885. Samples are referred under the accession numbers from SAMN14896080 to SAMN14896214.

Metabolomics FTICR

For the analysis the supernatant was diluted by a factor two in a mix of acetonitrile/formic acid 0.1% (ACN/FA 0.1%). The dilution was centrifuged and the supernatant was used for the direct infusion analysis. Samples were analysed by direct infusion mass spectrometry using a FT-ICR SolarixXR 9.4T (Bruker Daltonik, Bremen, Germany) with an electrospray source in positive mode. Spectra were recorded in a 57 -1000 m/z range with a size of 4Mpts leading to a resolution of 300.000 at a mass/charge (m/z) ratio of 200. External calibration in positive mode was done with phosphoric acid between m/z 99 and 981 in order to be below 1ppm of error. Ions were accumulated in collision cell for 0.2 s to get a total ion current similar to the one observed during calibration. 500 scans were accumulated to get a better signal sensitivity. ESI source flow rate was set at 120 µL/h with a capillary voltage at -4500V. Nebulizer and drying gas were set respectively at 1 bar and 4 L/min and the drying gas temperature was set at 220°C. A blank sample was prepared with a mix of H 2 O/ACN/FA (50:50:0.05 volume:volume). Samples were analysed in a random order. Every five samples, blank and pool were injected. Every ten samples a sample defined as quality control (QC) was injected. This QC corresponded to a sample chosen randomly. To avoid cross contamination between each injection, syringe capillary and needle were washed three times by using 250µL of H 2 O/FA 0.05%, three times 250µL of ACN/FA 0.05% and three times 250µL of H 2 O/ACN/FA (50:50:0.05).

All spectra were exported in metaboscape (Bruker Daltonik, Bremen, Germany) to obtain the list of all m/z ions with a signal/noise ratio above 3 and the corresponding intensity in all spectra. The XCMS R package (version 3.4.4) was used to process the data [START_REF] Smith | XCMS : Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment , Matching , and Identification[END_REF]. The chromatographic peaks region of interests (ROIs) were detected using the method mass spec wavelet [START_REF] Du | Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching[END_REF] with a signal/noise ratio of 3. The ROIs found in different samples were grouped using the MZclust method. Finally, missing ROIs in the samples were filled using the FillChromPeaks method.

Statistical analysis of the microbiome

Firstly for each microbe and metabolite the temporal dynamics was determined. The framework of analysis was based on the linear mixed model spline analysis described by Straube et al [START_REF] Straube | A linear mixed model spline framework for analysing time course "Omics" data[END_REF] and was carried out on the OTUs and ROIs datasets individually. The Figure 47 summarises the different steps of the analytical workflow. A first step of filtering was carried out on each dataset. The microbes and ROIs that did not evolve across time were removed from the datasets. To do so the lmmsDE function from the R package lmms (v1.3.3) was used with the cubic p-spline method. The variables (microbes or ROIs) whose abundance increased or decreased with a significant p-value under 5%, were kept for the analysis. Then for each kept variables the time profile was modelled using the smooth.spline function from the package stats (smoothing parameter of 0.5).

Secondly, to group together the variables sharing a similar temporal dynamics, a clustering was carried out on the first derivatives of the predicted fitted curves [START_REF] Déjean | Clustering time-series gene expression data using smoothing spline derivatives[END_REF]. The kml method (k-means for longitudinal data, [START_REF] Genolini | Kml : A package to cluster longitudinal data[END_REF]) was applied for the clustering.

The clustering method was carried out on the microbial and metabolomics data separately for the digester P1 which was inhibited faster. A particular focus was given to this digester because its performances were highly impacted by the ammonia accumulation. This digester was then considered as a reference of perturbation for the next analysis. In order to keep only the variables which were influenced by the ammonia accumulation, the time profiles of each variables were compared between the digester P1 and the digester control P0 (no ammonia accumulation).

The variables whose time profiles evolved in the same way in both digesters were removed. Indeed, if the variables evolved in the control and in P1 similarly, their dynamics cannot be explained by the ammonia accumulation but must be due to another factor.

The final step of this analysis consisted in comparing the time profiles of each variable between the digester P1 and the 4 others digesters. The comparison of the time profiles between the digesters revealed information about the capacity of adaptation of the microbes in one part and on the metabolites dynamics in another part. This comparison was carried out using the R package DynOmics [START_REF] Straube | DynOmics to identify delays and co-expression patterns across time course experiments[END_REF].

Briefly the time trajectory of each variable was compared between P1 (defined as a reference) and another bioreactor (defined as a query). The method based on the fast Fourier transform allows to determine 1) the delay of the modification initiation between the trajectories which allows to determine the variables able to adapt or not to the ammonia accumulation, and 2) the correlation between the shape of each variable curves which allows to determine the variables which react in the same way between the different digesters.

Results and discussion

Influence of ammonia accumulation speed on bioreactor performances

Figure 48 shows the chemical performances results of biogas, methane, FAN, pH and VFAs (acetate and propionate) production for the different bioreactors during 250 days.

1.3.1.1.

Stabilisation phase The stabilisation phase was carried out during 98 days. The biogas yield stabilised around 400 mL/Day at day 98 except for the digester P3 where the biogas yield was only 350 mL/Day. It was explained by gas leakage from the digester. However all the other chemical parameters measured during the stabilisation phase were comparable between all the digesters.

The biogas was composed of 85% of methane in all the digesters. The isotopic fractionation index (α app ) which allows to determine the methanogenic pathway was inferior to 1.055. This result indicates that the methane was mostly produced through the acetoclastic methanogenesis pathway.

The NH 3 (FAN) value stabilised around 20 mg/L. According to the literature, the FAN inhibiting range is comprised between 80 and 200 mg-NH 3 /L in mesophilic condition [START_REF] Westerholm | Biogas production through syntrophic acetate oxidation and deliberate operating strategies for improved digester performance[END_REF]. During the stabilisation phase no inhibition seemed to happen in the digesters. This was confirmed by the absence of VFAs accumulation and no pH evolution.

As the performances results were not evolving, the digesters were considered to be stable and the perturbation phase was initiated at day 98.

Perturbation of the digesters performances

In order to evaluate the effect of different speeds of ammonia accumulation on the digester performances, ammonium chloride was added into 5 of the 6 digesters to reach a final concentration of 183 mg-NH 3 /L. The NH 3 concentration was reached at different dates in the different digesters (P1: at day 2, P2: day 14, P3: day 25, P4: day 50 and P5: day 75). One digester (P0) was used as a control of good functioning and no ammonium was added. Once the final NH 3 concentration was reached, the stress was maintained at 183 mg-NH 3 /L during 3 HRT corresponding to 75 days. These periods corresponded to days 105-182 for P1, 119-189 for P2, 126-203 for P3, 140-232 for P4 and 168-252 for P5.

All along the experiment the different chemical parameters in P0 did not evolved reflecting the stability of the bioprocess.

A decrease of the biogas yield was observed in the digesters P1 to P5 alongside with the NH 3 accumulation. However the biogas composition did not evolved in all the digesters. Indeed in the digesters P1 and P2, corresponding to a fast ammonia accumulation, an evolution in the methane percentage can be observed while no significant evolution was observed in the bioreactors P3, P4 and P5. In P1 and P2, for a short period following the ammonia addition the methane level maintained at 85%. During this period the methanogenic pathway changed from acetoclastic to hydrogenotrophic as indicated by the α app value. Then the CH 4 decreased from 85% to respectively 50 and 75% during the perturbation phase in P1 and P2. Associated to this decrease the α app evolved to a value inferior to 1.055, representative to the acetoclastic pathway. Ruiz-Sanchez et al ( 2019) showed a similar modification of the biogas isotopic fractionation when the ammonia accumulated from 1 to 3.5 and 6 gN-TAN/L [START_REF] Ruiz-Sánchez | Functional biodiversity and plasticity of methanogenic biomass from a full-scale mesophilic anaerobic digester treating nitrogen-rich agricultural wastes[END_REF]. Our results are relevant with this study. In our digesters ammonia accumulated from 0.2 to 4 gN/L in all the digester. However our study showed that only a fast accumulation led to a modification of the methanogenic pathways.

It can be deduced from the biogas isotopic composition results that the acetoclastic archaea population was more sensitive to a fast ammonia accumulation than the hydrogenotrophic population. Decreasing the speed of ammonia accumulation decreased the impact on the microbial population explaining a lower modification of the biogas composition and the methanogenic pathways. This could probably be due to the acclimation of the microbial community to the ammonia.

The addition of NH 4 Cl to increase the NH 3 concentration led to a decrease of the pH value mainly in the P1 and P2 from 8 to respectively 7.4 and 7.6. The pH remained stable at these values due to the accumulation of the acetate and propionate in the digesters. In all the digesters an accumulation of the VFAs was observed. The accumulation was most abundant in P1 (1012 mgC/L for acetate and 240 mgC/L for propionate). The beginning of the VFAs accumulation was delayed alongside the NH 3 accumulation between the digesters. This result indicates that even a low ammonia accumulation can lead to a perturbation of the bioprocess but at a lower level than a fast accumulation. This could be explained by a progressive acclimation of the microbial community in the digesters P3 to P5.

Recovery of the digester performances

In all the digesters the biogas performances recovered. Moreover in P1 and P2 the methane percentage increased to 85% such as at the beginning of the experiment.

For the digesters P1 and P2 it took around 40 days after the beginning of the ammonia accumulation for the methane performances to recover. The increase in the CH 4 percentage occurred after the α app decreased to 1.055. This decrease indicates a production of the methane through the acetoclastic methanogenesis pathway such as during the stabilisation phase. This means that the acetoclastic archaeal population was able to recover from the inhibition. It can be due to a modification or a resilience of the acetoclastic archaeal population. The modification in the archaeal community led to the decrease of the VFAs concentration, especially for the digesters P2 to P5. However the VFAs concentration in P1 did not return to its initial value at the end of the experiment. The recovery of the VFAs degradation seemed to be lower after a fast ammonia inhibition than after a slow one.

Influence of ammonia accumulation speed on the microbial active community

The influence of the ammonia accumulation speed on the microbial active community was evaluated through the dynamics of the 16S rRNA abundances across time. A Principal Component Analysis (PCA) was used in order to get an overview of this dynamics between the digesters (Figure 49).

The first axis explained 29% of the variation between the samples mostly due to the time dynamics. The analysis shows that the temporal dynamics of the active microbial community was limited to the ammonia accumulation speed. Indeed while the different time points are grouped together for the P0 digester, meaning stability in the microbial community, more and more time points belonging to the perturbation phase were dispersed when the speed of ammonia accumulation increased. The second axis, representing 10% of the variance, highlights the difference of the microbial active community between the digesters.

As multiple conditions were carried out, it was decided to focus first on the identification of the microbes influenced by a fast ammonia accumulation. A particular focus was done on the study of the digester P1 considered as a reference of inhibition. Indeed in this digester the accumulation of ammonia had a remarkable effect on the performances. Then the dynamics of the identified microbes was compared to the other digesters in order to evaluate the influence of the ammonia accumulation speed.

Microbial dynamics during a fast accumulation of ammonia

To evaluate the temporal dynamics of the microbes from the digester P1, a specific statistical approach was used to take into account a longitudinal analysis. Based on the abundances of the experimental sample points, a time profile was modelled for each OTU. A first step consisted into the filtering of the OTUs that did not evolved across time. Indeed the aim of this work was to focus on the active microbes which were inhibited or favoured by the ammonia accumulation. In total, 57% of the microbial community (92 OTUs) presented an modification of their time profiles. Then the selected microbes sharing a similar time profile were clustered together and 8 different clusters of trajectories were obtained.

The temporal dynamics of the microbes selected in P1 were compared to their temporal dynamics in the digester P0. This comparison was carried out in order to eliminate the microbes whose modifications were probably not due to the ammonia accumulation. The supplemental Figure 54 represents the comparison between P1 and P0 by clusters. In the different clusters some of the OTUs shared a similar time profile in both digesters P0 and P1 meaning that the modification of these OTUs in P1 cannot be attributed specifically to the accumulation of ammonia. These OTUs were then removed. After the filtering, 60 OTUs were kept. The Figure 50 represents the temporal dynamics of these selected OTUs in the digester P1. The taxonomic affiliation of the microbes in each cluster is indicated in the supplementary Table 12.

Clusters 1, 2, 3: Decrease of the OTUs abundances

The clusters 1, 2 and 3 grouped together the OTUs which were inhibited by the presence of ammonia. Indeed their abundances decreased over time after the beginning of the ammonia addition. However the inhibition of these microbes was delayed between the clusters. Indeed the activity decreased just after the ammonia addition in the cluster 1, while in the clusters 2 and 3 the activity decreased after around 10 and 25 days respectively. These delays can be representative of a capacity of the microbes to resist to the presence of ammonia for a certain period. It can be also linked to the inhibition of partners more sensible to the presence of ammonia and inducing the decrease of activity of others microbes.

From these clusters, 5 archaea were identified: two Methanospirillum genus members, Methanomassiliicoccaceae family member, Methanobrevibacter genus member, Methanomicrobiales order member and Bathyarchaeia class member. These archaea are mostly hydrogenotrophic archaea. As seen with the performances results and particularly the isotopic fractionation, after the ammonia addition there was a change in the methanogenic pathway from acetoclastic to hydrogenotrophic. The decrease in the activity of Methanospirullum and Methanobrevibacter can be due to a higher sensitivity to the ammonia than another hydrogenotrophic archaea or to the inhibition of their bacterial partners by the ammonia. [START_REF] Jarrell | Inhibition of methanogenesis in pure cultures by ammonia, fatty acids, and heavy metals, and protection against heavy metal toxicity by sewage sludge[END_REF] demonstrated in pure culture that Methanospirullum hungatei was more sensitive than Methanosarcina barkeri, Methanobacterium thermoautotrophicum and M. formicicum [START_REF] Jarrell | Inhibition of methanogenesis in pure cultures by ammonia, fatty acids, and heavy metals, and protection against heavy metal toxicity by sewage sludge[END_REF].

Members of Methanomassiliicoccaceae family (Thermoplasmata class) were identified from anaerobic digested sludge, rumen or human faeces. They are obligatory hydrogenotrophic archaea and produce methane from methanol and hydrogen [START_REF] Dridi | Methanomassiliicoccus luminyensis gen. nov., sp. nov., a methanogenic archaeon isolated from human faeces[END_REF][START_REF] Iino | Candidatus methanogranum caenicola: A novel methanogen from the anaerobic digested sludge, and proposal of Methanomassiliicoccaceae fam. nov. and Methanomassiliicoccales ord. nov., for a methanogenic lineage of the class Thermoplasmata[END_REF]. Bathyarchaeia from the Crenarchaeota phylum can be retrieved in anaerobic digesters [START_REF] Collins | Distribution, localization, and phylogeny of abundant populations of Crenarchaeota in anaerobic granular sludge[END_REF]. The role of the Crenarchaeota in anaerobic digester is still unclear. However it was demonstrated that they are heterotrophs and are able to degrade many compounds [START_REF] Seyler | Crenarchaeal heterotrophy in salt marsh sediments[END_REF]. [START_REF] Chouari | Co-occurence of Crenarchaeota, Thermoplasmata and methanogens in anaerobic sludge digesters[END_REF] highlight the cooccurrence of the Crenarchaeota phylum and Thermoplasmata class in 22 anaerobic digesters. They hypothesised a possible interaction through syntrophy or competition, between these microbes [START_REF] Chouari | Co-occurence of Crenarchaeota, Thermoplasmata and methanogens in anaerobic sludge digesters[END_REF]. In our study these two groups are found in the same cluster indicating that they share a similar temporal dynamics. This result would indicate a possible syntrophy between these microbes. The inhibition of one or the two microbes by the ammonia led to the decrease of their abundances.

Fourteen different bacterial families were identified in these clusters. From these bacteria two genera are identified as propionate degraders: Smithella (cluster 1) and Syntrophobacter (cluster 2) [START_REF] Mcinerney | Physiology, ecology, phylogeny, and genomics of microorganisms capable of syntrophic metabolism[END_REF]. The abundance of Syntrophobacter decreased 3 days after Smithella. This result indicates that the sensibility of Syntrophobacter to the ammonia accumulation was higher than the one of Smithella. This result is in line with the literature [START_REF] Wang | Response of Propionate-Degrading Methanogenic Microbial Communities to Inhibitory Conditions[END_REF]. Smithella genus was identified to be able to degrade the propionate in co-culture with Methanospirillum [START_REF] De Bok | Pathway of Propionate Oxidation by a Syntrophic Culture of Smithella propionica and Methanospirillum hungatei[END_REF]. [START_REF] Zhang | Inhibitory effects of ammonia on syntrophic propionate oxidation in anaerobic digester sludge[END_REF] demonstrated that Smithella in syntrophy with Methanosaetaceae and Methanospirillaceae were moderately and severely inhibited respectively at 3 and 7-10 g-NH 4 + /L [START_REF] Zhang | Inhibitory effects of ammonia on syntrophic propionate oxidation in anaerobic digester sludge[END_REF].

In the digester P1, the NH 4 + concentration was around 6 g-NH 4 + /L, which is in line with the results obtained by Zhang et al. As a consequence of the inhibition of the propionate degrader, the propionate accumulated into the digester. A long chain fatty acids degrader was identified also in these clusters: Syntrophomonas genus. Different amino-acids degraders and hydrolytic microbes were also identified to be inhibited by ammonia in our experiment such as Ruminococcaceae, Fibrobacteraceae, and Rikenellaceae.

Cluster 4: Low decrease of the OTUs abundances In the cluster 4, 4 microbes were grouped. Their abundance decreased slightly after the beginning of the ammonia accumulation. These microbes belonged to the families Marinilabiliaceae, Clostridiaceae, Lachnospiraceae and Christensenellaceae. The influence of the ammonia on these microbes is less obvious in regards with the low temporal modification.

Cluster 5: Inhibition of the increase of the OTUs abundances

In the cluster 5 the microbial activity increased during the stabilisation phase. This result seemed to indicate that these microbes were not stable during the first phase of the experiment. This could be due to an adaptation of these microbes to the biowaste substrate used to feed the digesters. However the activity of these microbes was stable in the control digester during the stabilisation phase which contradicts the adaptation to the substrate. These microbes belong to the families Methanobacteriaceae, Dysgonomonadaceae, Spirochaetaceae, Synergistaceae and Enterococcaceae. After the ammonia addition their abundances stopped to increase indicating a possible influence of the ammonia on their activities. Members of these families were found in other clusters with a different pattern of dynamics. This indicates that within a same family, different members can react in a different way facing a similar inhibitor. The analysis of the microbes at the OTU level allows to provide this more precise information which could be missed when studying the microbes at higher taxonomic level.

Clusters 6, 7, 8: Increase of the OTUs abundances

The clusters 6, 7 and 8 grouped together the OTUs which were favoured by the presence of ammonia. Their abundances increased over time after the beginning of ammonia addition. This can be due of the inhibition of some other microbes. As observed for the clusters 1, 2 and 3, delays can be observed in the increase of activity between the clusters. These delays can be explained by the time needed for their activities, or the need of syntrophy with another microbe or in the contrary the need of the disappearance of a specific microbe before being active.

In the cluster 7 the genus Methanobacterium was identified. This archaea produce methane through the hydrogenotrophic pathway. The abundance of this genus increased from 1 to 10% across the experiment. In the same cluster the genus Pelotomaculum was identified. This microbe is a propionate degrader such as Syntrophobacter and Smithella described previously [START_REF] Mcinerney | Physiology, ecology, phylogeny, and genomics of microorganisms capable of syntrophic metabolism[END_REF]. Our temporal analysis result seemed to indicate that from the propionate degrader microbes, Pelotomaculum is the less sensible to the ammonia accumulation. It could be also due to the modification of the hydrogenotrophic archaea community with the decrease of Methanospirullum and the increase of Methanobacterium.

In these different clusters 16 bacterial families were identified, mostly from the Clostridia class. This result is in line with the literature where members of the Clostridiales order were determined as more resistant to the presence of ammonia [START_REF] Fischer | Long-term investigation of microbial community composition and transcription patterns in a biogas plant undergoing ammonia crisis[END_REF].

Members of the families Ruminococcaceae and Syntrophomonadaceae were found in this cluster. Other OTUs from these two families were identified also in the cluster 2. In a similar way, members of families Dysgonomonadaceae, Spirochaetaceae, Synergistaceae and Enterococcaceae were found in previous clusters.

Acclimation capacity of the microbes influenced by the ammonia accumulation speed

In order to determine the acclimation capacity to a progressive ammonia accumulation, the temporal dynamics of the different microbes were compared between the digesters. As all the digesters were operated in the same way during the stabilisation phase the digesters can be considered as replicates during this phase.

Two parameters (profile correlation and time delay) were estimated. The temporal dynamics of the microbes in P1 was considered to be a reference and the estimation of the parameters was defined by carrying out pairwise correlation between each digester and P1. The correlation between the time profiles of each OTU between the different digesters was estimated in order to determine the similarity of the time profile. If the correlation was higher than 0.5 for an OTU between (at least) one digester and the reference P1, the OTU was kept. Two time profiles can be similar and a delay can be observed in the modification. The estimation of the delay allows to keep the microbes which the activity evolved alongside the ammonia accumulation. Finally the time profiles of these selected OTUs were checked manually and are represented in the Figure 51.

The temporal comparison of the OTUs between the digesters allowed to determine the influence of ammonia addition speed on the dynamics of the microbes. The Figure 51 shows the abundances of the OTUs that were modified in all or just some of the digesters but also the differences in their time dynamics. For example the OTU_134 (Enterobacteriaceae order) was favoured by the fast accumulation of ammonia and its activity maintained in P1. However in the digesters P2 and P3 the increase of the activity of this OTU was transient. Moreover in the digesters P4 and P5, where the accumulation was slower than in P1 to P3, the abundance of this OTU did not evolved at all.

Temporal dynamics of the archaea

A focus was given to the archaea due to the modification of the methanogenic pathways during the ammonia accumulation observed in the digesters P1 and P2. The influence of the ammonia accumulation speed on the archaeal activity is unclear. Indeed for the archaea Methanospirillum (OTU_234) and Methanobacterium (OTU_654), a temporal modification can be observed in P0 too. Even if these modifications were different between P0 and P1 at least, no conclusion could be drawn from this result. For the other OTUs of Methanospirillum and the Methanomicrobiales family member (OTU_180 and 173 respectively) no temporal modification was observed in P0.

Microbes with a temporal modification in P1 only

Five OTUs evolved only in the digester P1 following the ammonia addition. These microbes, Rikenellaceae, Cloacimonadales, Pedosphaeraceae, Clostridiaceae, Enterobacteraceae genus Vagococcus and Erysipelotrichaceae genus Erysipelothrix (respectively OTU_24, 55, 67, 206, 216 and 479), seemed to be sensitive to a fast ammonia accumulation but were able to resist when the ammonia was introduced more progressively in the digester. Possible interactions such as competition/syntrophy between these microbes can be suspected as their dynamics was observed in one digester only. It is possible that the inhibition of the members of the families Rikenellaceae, Cloacimonadales and Pedosphaeraceae allowed the development of the other microbes.

Microbes with a temporal modification in all the digesters

Only three OTUs evolved in all the digesters with a similar temporal dynamics. These OTUs, Smithella, Petrotogaceae and Leptospiraceae (respectively OTU_62, 92 and 176) had a similar profile with a time delay between the different digesters.

As seen previously Smithella is a propionate degrader acting in syntrophy with a hydrogenotrophic archaea. Two OTUs were actually identified in the digesters: OTU_62 and OTU_68. The OTU_62 decreased in all the digesters with a delay alongside the ammonia accumulation. The OTU_68 decreased also but not in the digester P5. However the delay of the modification of abundance of this OTU was more important than the OTU_62. In that case, in the time range we analysed, we cannot be sure of this absence of modification. Further time points after the day 154 should be carried out to provide such information.

Petrotogaceae (OTU_92) family members were shown to be active mostly during the degradation of highly complex carbohydrates substrates in thermophilic condition [START_REF] Buettner | Pseudomonas spp. are key players in agricultural biogas substrate degradation[END_REF]. From our results it seemed that some Petrotogaceae were sensitive to the ammonia accumulation in mesophilic condition. Some members of this family, Defluviitoga tunisiensis producer of acetate, H 2 and CO 2 , were usually observed associated with a hydrogenotrophic archaea such as Methanoculleus and Methanosarcina (Šafarič et al., 2018).

Smithella, Petrotogaceae and Leptospiraceae could be of a great interest as bioindicators of the ammonia inhibition. Indeed no matter the speed of the ammonia accumulation, these microbes were influenced and their activities were modified. This could be interesting especially in the case of ammonia accumulation at low speed. Indeed the performances results show that a low ammonia accumulation did not influence that much the different chemical parameters except the biogas yield which decreased. The decrease in the activity of these microbes which evolved in presence of ammonia could compensate the absence of clear chemical modification for the management of the digester and can be used as a warning of potential ammonia accumulation.

Microbes with a temporal modification in some digesters Some microbes, 12 OTUs, were able to acclimatise to the progressive ammonia accumulation. Depending on the microbes there were no temporal modifications in the digesters P3 to P5. Moreover for some of them the shapes of the temporal dynamics was different between the digesters such as the OTU_134 described previously. This microbe presented a transient modification in the digesters P2 and P3 but not in P4 and P5.

From this cluster some microbes were inhibited by the addition of ammonia: two OTUs of Synergistaceae (Smithella), Syntrophomonadaceae, Fibrobacteraceae and Syntrophaceae. Members of the families Syntrophomonadaceae (OTU_30) and Syntrophaceae (OTU_68) are known to be long chain fatty acid degraders [START_REF] Nakasaki | Characterizing the microbial community involved in anaerobic digestion of lipid-rich wastewater to produce methane gas[END_REF][START_REF] Sousa | Ecophysiology of syntrophic communities that degrade saturated and unsaturated long-chain fatty acids[END_REF]. The genus Fibrobacter (OTU_66) is a cellulolytic degrader identified in rumen [START_REF] Rahman | A phylogenomic analysis of the bacterial phylum fibrobacteres[END_REF][START_REF] Raut | Deciphering the unique cellulose degradation mechanism of the ruminal bacterium Fibrobacter succinogenes S85[END_REF]. The role of Smithella was already discussed previously.

On the contrary other microbes were favoured by the ammonia addition: Phycisphaeraceae, two OTUs of Clostridia, Dysgonomonadaceae genus Dysgonomonas, Lachnospiraceae, Enterobacteriaceae, and Peptococcaceae genus Pelotomaculum (respectively OTU_41,75 and 174,82,122,134,139). Planctomycetes (OTU_41) is a heterotrophic aerobic phylum. However a special group of this phylum was already identified to be able of ammonia oxidation under anaerobic conditions [START_REF] Kartal | Candidatus "Anammoxoglobus propionicus" a new propionate oxidizing species of anaerobic ammonium oxidizing bacteria[END_REF][START_REF] Schmid | Candidatus "Scalindua brodae", sp. nov., Candidatus "Scalindua wagneri", sp. nov., Two New Species of Anaerobic Ammonium Oxidizing Bacteria[END_REF][START_REF] Schmid | Molecular evidence for genus level diversity of bacteria capable of catalyzing anaerobic ammonium oxidation[END_REF]. This can explain that their abundances increased when ammonia was added. Pelotomaculum is a propionate degrader acting with a hydrogenotrophic archaea.

The differentiation of the time profiles of these microbes can be explained by an adaptation capacity to the ammonia accumulation when the speed of addition is slow. It can also be explained by the interaction between the microbes. From the sensibility of an important partner will depend the activity of one of these microbes. Principal Component Analysis (PCA) was carried out on all the samples simultaneously, but for the sake of clarity, the samples corresponding to the different digesters were presented separately (P0: control without ammonia addition; P5: accumulation of ammonia during 75 days; P4: accumulation of ammonia during 50 days; P3: accumulation of ammonia during 25 days; P2: accumulation of ammonia during 14 days; P1: accumulation of ammonia during 2 days). Numbers correspond to the sampling date. Green colour corresponds to the stabilisation phase (days 70, 77, 84, 92 and 98) and black colour corresponds to the ammonia accumulation phase (days 101, 105, 108, 112, 119, 126, 133, 140, 147 and 154).

Influence of ammonia accumulation speed on the metabolites composition

As for the analysis of the active microbial community a longitudinal analysis was carried out on the data from the untargeted metabolomics analysis. The similar temporal period was covered by the analysis from day 70 to day 154 (13 time points). The untargeted metabolomics analyses were performed using a Fourier-transform ion cyclotron resonance (FTICR) in direct infusion, meaning without chromatography before the analysis in the mass spectrometer. This avoided the selection of particular metabolites due to the characteristics of the chromatographic column. The metabolomics analysis was carried out by Edith Nicol from Ecole polytechnique thanks to a partnership developed with our team.

Firstly the influence of the ammonia accumulation speed on the metabolic composition was studied with a PCA (Figure 52). The result shows for all the digesters except for the control P0 without ammonia addition, a temporal modification of the metabolic profil. Depending on the ammonia accumulation speed, the dispersion of the samples along the first axis (75% of the explained variance) was more or less regular. Indeed the samples from the digester P1 were clustered into two distinct groups.

Secondly a similar longitudinal workflow than described previously was used for the analysis: 1) on the digester P1 the time profiles of each metabolite were modelled from the experimental time points, 2) the metabolites sharing a similar time profiles were grouped together using the Kml clustering method, 3) the different clustered time profiles were compared between the digesters. This comparison is represented in the Figure 53.

Metabolites temporal dynamics between the different digesters

The temporal dynamics of the metabolic composition was studied. Four different time profiles were obtained after the longitudinal statistical analysis. The clustering method allowed to compare these profiles between them and between the digesters. The correlation between the profiles of each metabolite between the digester and the digester P2 to P5 allowed to determine the difference in the dynamics within each cluster.

Clusters 1 and 2: Decreasing intensity

In the clusters 1 and 2 the intensity of the metabolites decreased after the addition of ammonia. Between these two clusters the decrease started later in the cluster 2 than in the cluster 1. In all the digesters except P0 the intensity of these metabolites decreased. This is relevant with the results observed from the PCA where no temporal modification of the metabolites was observed in the digester P0. Compared to the digesters P2 to P5, a delay was observed before the time modification following the ammonia accumulation speed. Moreover the slope of the curve decreased between the digesters.

The decrease observed for these metabolites can be explained by a disequilibrium between the production and consumption of these metabolites. This can be associated to the modification of the active microbial population. The microorganisms responsible of the production of the metabolites can be inhibited by the ammonia accumulation and/or the microorganisms responsible to their degradation were favoured by the ammonia. Cluster 3: Differentiation of the time profiles according to the speed of the ammonia accumulation

In the cluster 3 the intensity of the metabolites decreased for the digester P1. As observed in cluster 1 no temporal modification was observed in the digester P0. However it can be observed that 40% of the metabolites from this cluster presented in the digesters P2 to P5 an increase of the intensity of these metabolites instead of a decrease. This could be explained by the influence of the ammonia which was different on the active microbial community according to the accumulation speed. Indeed, some microbes did not evolved in a similar way between the different digesters as we saw in the Figure 51. The absence of inhibition or the inhibition of some microbes more or less sensitive to the ammonia accumulation speed can explain the modifications of the temporal dynamics of these metabolites.

To go further on this assumption, it would be interesting to correlate both datasets of active microbial community and metabolomics to identify potential degraders as it was carried out in the Chapter I.

Cluster 4: Increasing intensity

In the cluster 4 the metabolites were not present in the digester P0 all along the experiment. However in the digester with ammonia, the intensity of some of these metabolites increased alongside the ammonia accumulation. Some metabolites however evolved in the digesters with a fast accumulation but not in P5 where a low accumulation was observed.

Two explanations can be given for the absence of these metabolites in P0. These metabolites were consumed all along the experiment. Their increase in the digesters with ammonia can be explained by the inhibition of the microbes responsible of their degradation, or these metabolites were not produced at all in P0. The increase in P1 to P5 can be correlated to the activity of microbes favoured by the ammonia accumulation.

Conclusion and perspectives

In all cases the ammonia accumulation led to the perturbation of the bioprocess with a decrease of the biogas production. This was associated to VFAs accumulation, mostly acetate and propionate. However the impact of the ammonia accumulation on the performances was more important when the accumulation was fast (digesters P1 and P2, respectively 2 and 15 days of accumulation). A modification of the methanogenic pathways from acetoclastic to hydrogenotrophic was observed in these digesters.

The active microbial community and the metabolomics composition were studied using a longitudinal statistical analysis. This analysis allowed to determine the characteristics of the temporal dynamics (delays, correlation…) of each variables (microbes or metabolites) between the digesters. Thanks to this analytical workflow and this experimental design different hypothesis could be drawn regarding the:  interactions, syntrophy or competition, between the microbes according to their temporal dynamics. As an example Methanobacterium-Pelotomaculum are potential syntrophs involved in the degradation of the propionate. This couple seemed to be less sensitive to the presence of ammonia. On the contrary the activity of Methanospirullum, Smithella and Syntrophobacter decreased after the ammonia inhibition.  adaptation capacity of the microbes by comparing the time profiles between the digesters. For example, the activity of Syntrophomonadaceae and Syntrophaceae, two LCFA degraders, was decreased by a fast ammonia accumulation. However their activity did not evolved when a low ammonia accumulation was applied in the digesters.  identification of possible bio-indicators of the bioprocess functioning. For example microbes or metabolites evolving specifically in all the digesters containing ammonia no matter the speed of accumulation could be considered as bio-indicators. Regarding the microbial community three OTUs could be considered as bio-indicators. It would be interesting to study also the microbes evolving in digesters where the accumulation of ammonia was low. Indeed these microbes could be used as indicators of a early ammonia inhibition.

Firstly the identification of the metabolites has to be completed. To do so a first analysis consists into the determination of the raw formula based on the different ratio mass/charge (m/z) selected. From these raw formulae the comparison of their elemental composition and the ratio oxygen to carbon or hydrogen to carbon can be studied using Van Krevelen analysis. It would be interesting to determine if the clustering obtained through the statistical analysis corresponded to different families of molecules (sugar, proteins…). A second analysis consists in the identification of the metabolites. This can be achieved by MS/MS analysis. These identifications would provide further information regarding the metabolic pathways involved in the bioprocess and the influence of the speed of the ammonia accumulation on it.

Secondly further analyses should be performed to determine if the potential bioindicators identified in this study could be considered as real bio-indicators. To do so it would be interesting to look at these microbes in different bioprocesses subjected to ammonia inhibition. Combining results from different studies operating digesters in different conditions (feedstock composition, operational conditions…) would be of great interest to identified specifically real bio-indicators. Statistical tools were developed in order to integrate samples from different studies such as MINT (Multivariate INTegrative method) (Rohart et al., 2017a).

This study can be supplemented by the integration of the microbial and metabolomics datasets. A similar workflow of statistical analysis than described in the Chapter I could be used in order to link the degradation of the metabolites to potential microbial degraders. However in this present study a temporal analysis was carried out which was not the case in the previous chapter. Another workflow should be more appropriated for the analysis of longitudinal data such as the workflow presented in [START_REF] Bodein | A generic multivariate framework for the integration of microbiome longitudinal studies with other data types[END_REF].

Underlying mechanisms of action of the zeolite on microbial development in presence of ammonia

As we have seen in the previous chapters multitude of parameters, nutrient imbalance or inhibitors for example, can lead to the breakdown of the digester. Depending on these parameters different mitigation strategies can be applied in order to limit the inhibition. We have seen in the Chapter I that for the nutrient imbalance the anaerobic co-digestion can be of great help by mixing substrates with complementary physico-chemical properties.

In the case of the inhibitors several strategies have been studied and depend on the mitigation strategy targeted. For example the risk of inhibitor production can be decreased using co-digestion and by adjusting the C/N ratio [START_REF] Prabhu | Anaerobic co-digestion of sewage sludge and food waste[END_REF]. The level of the inhibition can be modified by using air stripping [START_REF] Lei | Pretreatment of anaerobic digestion effluent with ammonia stripping and biogas purification[END_REF]. An adapted microbial community can be obtained using bio-augmentation [START_REF] Fotidis | Enriched ammonia-tolerant methanogenic cultures as bioaugmentation inocula in continuous biomethanation processes[END_REF] or by adding specific nutrient elements [START_REF] Westerholm | Trace element and temperature effects on microbial communities and links to biogas digester performance at high ammonia levels[END_REF]. Another strategy is to add in the digester mineral support media which can act at different levels of the inhibition [START_REF] Arif | Applications of materials as additives in anaerobic digestion technology[END_REF][START_REF] Romero-Güiza | The role of additives on anaerobic digestion: A review[END_REF].

In this thesis work we focused on ammonia inhibition and studied the underlying mechanisms of mitigation by using zeolite support. The mineral support strategy was chosen in regards with the results obtained with the ammonia inhibition study described in the Chapter II. After a fast ammonia addition, a modification of the microbial community and associated methane production pathway was observed. More precisely the methane production through the hydrogenotrophic pathway was favoured after the inhibition of the acetoclastic archaea. Moreover some studies have shown that mineral supports are beneficial for microorganism's interaction [START_REF] Fernández | Real evidence about zeolite as microorganisms immobilizer in anaerobic fluidized bed reactors[END_REF][START_REF] Weiß | Investigation of mircroorganisms colonising activated zeolites during anaerobic biogas production from grass silage[END_REF], the choice of this strategy to study the mitigation of the ammonia inhibition was then relevant. The choice of the zeolite as support was made in regards with a previous thesis work done by Simon Poirier (2016) and described in [START_REF] Poirier | Improving anaerobic digestion with support media: Mitigation of ammonia inhibition and effect on microbial communities[END_REF] In this work different types of mineral supports were tested under ammonia inhibition. One of the results obtained is the higher mitigation effect of the zeolite compared to another mineral support such as activated carbon or chitosan. However the mechanism of mitigation was not explained in this work.

The beneficial effect of the zeolite to counteract ammonia inhibition was demonstrated by other authors [START_REF] Alonso-Vicario | Purification and upgrading of biogas by pressure swing adsorption on synthetic and natural zeolites[END_REF][START_REF] Borja | Effect of Natural Zeolite Support on the Kinetics of Cow Manure Anaerobic-Digestion[END_REF][START_REF] Ho | Mitigating ammonia inhibition of thermophilic anaerobic treatment of digested piggery wastewater: Use of pH reduction, zeolite, biomass and humic acid[END_REF][START_REF] Ziganshina | Effect of the Organic Loading Rate Increase and the Presence of Zeolite on Microbial Community Composition and Process Stability During Anaerobic Digestion of Chicken Wastes[END_REF]. However the mechanism behind the mitigation and its influence on the microbial activity remains unclear.

Different assumptions about the mechanisms of action of the zeolite during ammonia inhibition were studied. However these studies focused mostly on the influence on the digester performances or on the microbial development at a high level of ammonia inhibition. Few studies have investigated the link between the microbial development due to the presence of zeolite and the performances. In this chapter we will describe the strategy used to explain the mode of action of the zeolite and its influence on the microbial development and methanogenic pathways. This study was done under a low inhibitory pressure (low level of ammonia) in order to understand the influence of the zeolite on the microbial activity at the beginning of a potential inhibition. The experimental design and the results are described in an article submitted to Chemical Engineering Journal in 2019 and called "Zeolite favours propionate syntrophic degradation during anaerobic digestion under low ammonia stress". Briefly, two series of batch experiments were carried out. They differed by the addition, or no, of a low quantity of ammonia. For each series five conditions were carried out by adding zeolite treated differently. One condition was a control without zeolite and the four others contained (1) untreated zeolite, (2) heated zeolite, (3) liquid or (4) solid residue of boiled zeolite in water. The influence of the different zeolite treatments on the digester performances and on the microbial community distribution using 16S rRNA gene sequencing was compared between the conditions. On the microbial data pairwise comparisons between samples of the different conditions were carried out in order to identify the microorganisms the most influenced by the presence of ammonia and/or zeolite. The PLS-DA (Partial Least Square Discriminant Analysis) was used to carry out these pairwise comparisons as we wanted to identify microbes allowing the classification between the groups of samples.

Abstract

Zeolite addition has been widely suggested for its ability to overcome ammonia stress occurring during anaerobic digestion. However little is known regarding the underlying mechanisms of mitigation and especially the influence of zeolite on the microbial structuration in particular at an early stage of ammonia accumulation. The aim of this study was to bring new contributions on the effect of surface availability and dissolved material addition by the zeolite on the microbial activity under a low ammonia stress. Replicated batch experiments with different types of pre-treated zeolites (raw, boiled, heated) were conducted. The microbial population was characterised with 16S rRNA sequencing. Methanogenic pathways were identified with methane isotopic fractionation. No differential effect between the treatments was observed. In presence of ammonia, zeolite mitigated similarly the decrease of biogas production rate in all cases. Zeolite induced the development of Izimaplamatales order and preserved Peptococcaceae family members, known as propionate degraders. Moreover methane isotopic fractionation showed that hydrogenotrophic methanogenesis was maintained in presence of ammonia and zeolite. Our results put forward the benefit of zeolite to improve the bacteria-archaea syntrophy needed for propionate degradation and methane production under a low ammonia concentration.

Introduction

In a context of sustainable energy development, anaerobic digestion (AD) is recognised worldwide as a promising bioenergy technology. AD has the double advantage to reduce the volume of organic waste and produce methane-rich biogas, usable in both heat and power generation. AD is a multi-steps bioprocess managed by a complex microbial community sensitive to the modifications of the environmental conditions. Ammonia nitrogen is one of the most important parameters influencing the digestion performances. It is formed during the degradation of nitrogen-rich substrates such as animal manure, slaughterhouse waste or grass (Mata-Alvarez et al., 2014b;[START_REF] Munk | Influence of nitrogen-rich substrates on biogas production and on the methanogenic community under mesophilic and thermophilic conditions[END_REF]. Even if ammonia is an essential nutrient for microorganisms growth, a concentration beyond 200 mg/L TAN (Total Ammonia Nitrogen) can be harmful [START_REF] Chen | Inhibition of anaerobic digestion process: A review[END_REF]. A wide range of concentration has been reported for the AD inhibition by ammonia: 1.7-19 gTAN/L and 0.05-1.4 gNH 3 /L (FAN, Free Ammonia Nitrogen). The importance of the inhibition depends on multiple parameters such as temperature, pH or microbial adaptation [START_REF] Rajagopal | A critical review on inhibition of anaerobic digestion process by excess ammonia[END_REF][START_REF] Yenigün | Ammonia inhibition in anaerobic digestion: A review[END_REF]. However effects on performances are often observed as soon as ammonia starts to accumulate. For example [START_REF] Chen | Effects of Ammonia on Anaerobic Digestion of Food Waste: Process Performance and Microbial Community[END_REF] have tested the effect of low to moderate ammonia concentrations on the performances of biogas production and on the microbial composition. They have shown a decrease in methane content from 2 gNH 4 + -N/L in semi continuous reactors. It was associated to a modification of the microbial population [START_REF] Chen | Effects of Ammonia on Anaerobic Digestion of Food Waste: Process Performance and Microbial Community[END_REF]. Moreover concentration as low as 1 gNH 4 + -N/L was reported by Poirier et al (2016) to modify the microbial composition and decrease degradation performances in batch experiments (Poirier et al., 2016b).

One of the consequences of ammonia presence is the modification of the methanogenic pathway. Acetoclastic archaea are usually considered as the most sensitive microorganisms [START_REF] Yenigün | Ammonia inhibition in anaerobic digestion: A review[END_REF] and a modification of the methanogenic pathway from acetoclastic to hydrogenotrophic methanogenesis is typically observed when ammonia is present at a threshold of 0.14-0.28 gNH 3 /L (3.0-3.3 gNH 4 + -N/L) [START_REF] Westerholm | Biogas production through syntrophic acetate oxidation and deliberate operating strategies for improved digester performance[END_REF]. Indeed ammonia can induced the production of CH 4 through the Syntrophic Acetate Oxidation (SAO) [START_REF] Schnurer | Ammonia, a selective agent for methane production by syntrophic acetate oxidation at mesophilic temperature[END_REF]. In this pathway a bacteria in syntrophy with a hydrogenotrophic archaea oxidises the acetate into CH 4 through H 2 and CO 2 .

Different strategies were studied to mitigate the ammonia stress at different steps of the process. Anaerobic co-digestion allows to balance the C/N ratio by mixing at least two substrates, one with a low protein content [START_REF] Prabhu | Anaerobic co-digestion of sewage sludge and food waste[END_REF], to reduce the concentration of ammonia formed in the digesters. Stripping allows to capture ammonia in gas bubbles to decrease its concentration [START_REF] Bousek | Influence of the gas composition on the efficiency of ammonia stripping of biogas digestate[END_REF]. Bioaugmentation allows to enhance the resistant microorganisms by the addition of specific cultures [START_REF] Tian | Bioaugmentation strategy for overcoming ammonia inhibition during biomethanation of a protein-rich substrate[END_REF][START_REF] Yang | Mitigation of ammonia inhibition through bioaugmentation with different microorganisms during anaerobic digestion: Selection of strains and reactor performance evaluation[END_REF]. Several studies demonstrated the success of using mineral supports such as natural zeolite to mitigate the ammonia inhibition and improve digester performances. Mitigation of the inhibition was observed for ammonia concentrations from 2.5 to 19 gNH 4 + -N/L [START_REF] Montalvo | Effect of particle size and doses of zeolite addition on anaerobic digestion processes of synthetic and piggery wastes[END_REF][START_REF] Poirier | Improving anaerobic digestion with support media: Mitigation of ammonia inhibition and effect on microbial communities[END_REF][START_REF] Tada | Effect of natural zeolite on methane production for anaerobic digestion of ammonium rich organic sludge[END_REF].

However, exact role of zeolite in the mitigation of ammonia stress is still unknown. Zeolite presents natural ion-exchange properties, absorptive capacity and can be a support for the biomass [START_REF] Montalvo | Application of natural zeolites in anaerobic digestion processes: A review[END_REF]. In particular, zeolite naturally contains Na + , K + , Ca 2+ and Mg 2+ which could stimulate the microbial growth or serve as antagonist to NH 4 + [START_REF] Chen | Inhibition of anaerobic digestion process: A review[END_REF][START_REF] Krakat | Methods of ammonia removal in anaerobic digestion: A review[END_REF]. A potential role of zeolite in the immobilisation of the biomass was also evidenced by Fernandez et al [START_REF] Fernández | Real evidence about zeolite as microorganisms immobilizer in anaerobic fluidized bed reactors[END_REF] using electronic microscopy.

Most of the studies investigated the increase of performances due to zeolite addition under ammonia inhibition [START_REF] Fotidis | Inoculum and zeolite synergistic effect on anaerobic digestion of poultry manure[END_REF][START_REF] Tada | Effect of natural zeolite on methane production for anaerobic digestion of ammonium rich organic sludge[END_REF]. Other authors focused on the microbial colonisation [START_REF] Weiß | Activated zeolite -Suitable carriers for microorganisms in anaerobic digestion processes?[END_REF]. However, only a few of them studied the influence of the zeolite on the structuration of the microbial community in presence of ammonia. Previous studies demonstrated that under non inhibiting conditions, zeolite did not influence the degradation performances [START_REF] Poirier | Improving anaerobic digestion with support media: Mitigation of ammonia inhibition and effect on microbial communities[END_REF]. Moreover Poirier et al. showed that the microbial structuration was not modified by the zeolite in absence of inhibition while it was influenced under ammonia inhibition [START_REF] Poirier | Improving anaerobic digestion with support media: Mitigation of ammonia inhibition and effect on microbial communities[END_REF][START_REF] Su | Application of biochar in a CIC reactor to relieve ammonia nitrogen stress and promote microbial community during food waste treatment[END_REF]. However no further information on the underlying mechanisms of action of the zeolite on the structuration of the microbial community and its methanogenic activity was drawn. In particular no further information is available regarding the effect of zeolite on microbial activity at the early stage of ammonia accumulation.

The aim of this work was to investigate the mode of action of the zeolite on the anaerobic microbial community in presence of low concentration of ammonia. For that purpose different physico-chemical treatments were carried out on the zeolite to test different hypothesis concerning the mechanisms of action. The ion-exchange capacity of the zeolite was first tested. For that purpose the ionic composition of the zeolite was modified by realising directly soluble ions of the zeolite into water. Effects of both the modified zeolite and the ionic solution obtained were tested. Secondly, the increase of the surface available for microbial growth was tested by pre-treating thermically the zeolite to free sites. Lab digesters were set-up to evaluate the influence of the zeolite treated these ways on microbial communities during AD in presence of ammonia. Based on the ammonia inhibition range, a concentration of 2 g-NH 4

+ /L was selected. The objective was to induce a low stress and simulate a bioprocess at an early stage of ammonia accumulation. A control experiment, without ammonia was also set-up.

Methods

Feedstock preparation

The inoculum came from a mesophilic full scale anaerobic digester treating biowaste. Two weeks before the experiment, the excess of residual organic matter was removed by stopping the feeding of the inoculum. The inoculum was centrifuged at 10,000g during 10 minutes before use.

The biowaste came from came from an industrial food waste collector used to feed an anaerobic digester (Valdis Energie, Issé). The food waste came from different origin such as schools, markets, expired products, products for quality testing. The biowaste was crushed before use.

The physico-chemical characteristics of the inoculum and biowaste are presented in the supplementary Table 15.

Zeolite preparation

Zeolite was obtained from Somez society (France). The zeolite was sieved to obtain a homogeneous size between 0.48-0.50 mm. To release easily soluble ions a part of the zeolite was boiled at 100°C in water during 20 minutes. The liquid residue, containing the easily soluble ions, was filtered at 0.22 µm before use and residual solid part was dried before use. To increase the surface availability for microbial colonisation and/or modify the ionexchange capacity another part of the zeolite was heated in oven at 400°C during 4 hours.

Experimental set-up

Table 13 summarises the composition of the different digesters. In total 30 anaerobic batch bioreactors were set-up in 1L glass bottles (700mL working volume). Each digester was inoculated with methanogenic sludge and fed with biowaste to reach a substrate/inoculum ratio of 12g COD/1,2g COD. Zeolites previously prepared were added at a concentration of 15g/L measured before pre-treatment. In parallel a control without zeolite was carried out. In total 5 conditions were tested in triplicate. Ammonium carbonate (Alfa Aesar) was added to reach a concentration of NH 4 + of 2g/L. In parallel of the series with ammonia (N series) a control series without addition of ammonium carbonate (C series) was carried out. All the digesters were complemented with a biochemical potential buffer (International Standard ISO 11734 (1995)) to reach a final working volume of 700mL. To obtain an equal quantity of carbonate in the different digesters, sodium carbonate was added into the BMP for the control C series. The bioreactors were then sealed with a screw cap and a rubber septum. The headspaces were flushed with N 2 (purity >99.99%, Linde gas SA) and the bottles were incubated at 35°C in the dark and without agitation.

Weekly, 6mL of liquid phase were sampled through the septum using a syringe and centrifuged at 10,000g for 10 minutes. Supernatant and pellet were snap frozen using liquid nitrogen and kept at -20°C and -80°C respectively.

Gas production and stable carbon isotope signature

The different parameters such as biogas composition, NH 4 + /NH 3 , volatiles fatty acids were measured as described in Cardona et al (2019).

Production parameters were calculated using R CRAN software and the Gompertz equation with R Grofit package. Where y(t) is a cumulative CH 4 production (mgC), A is the ultimate CH 4 yield (mgC), µ is the maximum production of CH 4 production rate (mgC/day), and 𝜆 is the lag phase (day).

The link between Free Ammonia Nitrogen (FAN), Total Ammonia Nitrogen (TAN), pH and temperature can be summarized with the following equation [START_REF] Anthonisen | Inhibition of Nitrification by Ammonia and Nitrous Acid[END_REF]:

𝐹𝐴𝑁 = 10 𝑝𝐻 (exp ( 6344 𝑇 ) + 10 𝑝𝐻 ) 𝑥 𝑇𝐴𝑁
Where T is the temperature in Kelvin.

Isotopic fractionation

As indicator of the methanogenic pathway the isotopic fractionation of the CH 4 (δ 13 C-CH 4 ) was measured using a Trace Gas Chromatograph Ultra (Thermo Scientific) attached to a Delta V Plus isotope ratio mass spectrometer via a GC combustion III (Thermo Scientific). Periodically gas was sampled into a 7-mL vacuumed serum tubes for the analysis. It is assumed that a value of δ 13 C-CH 4 inferior to -60‰ indicates a CH 4 production through the hydrogenotrophic pathway and superior to -60‰ the acetoclastic pathway [START_REF] Whiticar | Biogenic methane formation in marine and freshwater environments: CO2reduction vs. acetate fermentation-Isotope evidence[END_REF].

RNA extraction and 16S rRNA sequencing analysis

Total DNA was extracted from sample's pellet using DNeasy PowerSoil kit (QIAGEN) following the manufacturer instructions. The DNA quantity was measured by Qubit (dsDNA HS assay kit, Invitrogen).

Archaeal and bacterial hyper variable region V4-V5 of the 16S rRNA gene was amplified by PCR with fusion primers 515F (5′-Ion A adapter-Barcode-GTGYCAGCMGCCGCGGTA-3′) and 928R (5′-Ion trP1 adapter-CCCCGYCAATTCMTTTRAGT-3′), which include a barcode and sequencing adapters, and then sequenced according to the protocol described by [START_REF] Madigou | Ecological consequences of abrupt temperature changes in anaerobic digesters[END_REF] with some modifications. V4-V5 of the 16S rRNA gene was amplified by PCR using the following mix: 1X SuperFi Buffer, 0.2 mM each dNTP, 0.5 µM each primer, 1U Platinum SuperFi DNA Polymerase (Invitrogen), and 10-20 ng template DNA in a 50 µL reaction volume. Amplification was performed as follows: 30 sec at 98°C, 30 cycles of 10 s at 98°C, 10 s at 50°C, 30 sec at 72°C, followed by final extension of 5 min at 72°C. PCR products were purified using Agencourt AMPure XP magnetic beads (Beckman Coulter) according to the manufacturer's instructions, with a bead:amplicon ratio of 1.2, and were eluted in 45 µL TE Buffer (10 mM Tris-HCl pH 8.0, 1 mM EDTA). Purified amplicons were quantified using 4200 TapeStation or DNA 1000 Kit and 2100 Bioanalyzer (Agilent Technologies), following the manufacturer's instructions. Then all amplicons were prediluted at 500 pM in molecular grade water and equimolarly pooled. The pool was then diluted at 100 pM for sequencing. Briefly, to prepare template-positive Ion Sphere Particules (ISPs) containing clonally amplified DNA by emulsion PCR, the library was diluted to 26 pM and set up on the Ion OneTouch 2 Instrument (Life Technologies) using the Ion PGM Hi-Q View OT2 Kit (Life Technologies) and following the manufacturer's instructions. These templated ISPs were then purified on the Ion OneTouch ES (Life Technologies) according to the manufacturer's instructions. Sequencing was performed on Ion Torrent Personal Genome Machine using Ion 316 Chip V2 (Life Technologies) and Ion PGM Hi-Q View Sequencing Kit (Life Technologies) according to the manufacturer's instructions. Sequencing data was processed by the Torrent Suite Software. The software filtered out low quality and polyclonal sequence reads, and quality filtered data was exported as FastQ file.

16S rRNA reads were imported in FROGS. FROGS (Find Rapidly OTU with Galaxy Solution) is a galaxy/CLI workflow designed to produce an OTU count matrix from high depth sequencing amplicon data [START_REF] Escudié | FROGS: Find, Rapidly, OTUs with Galaxy Solution[END_REF]. FROGS abundance file was examined using R CRAN software (version 3.4.4). Only OTUs that exceeded 1% in at least one sample have been taken into account for the analysis and the OTUs selected were transformed using center log ratio transformation (clr). The raw sequences were deposit on the NCBI database under the project number PPRJNA632440. Samples are referred under the accession numbers from SAMN14909551 to SAMN14909584.

Data analyses

The variance between the biogas production observed under the different conditions was estimated by comparing the Gompertz parameters for both CH 4 and CO 2 with analysis of variance ANOVA. The function granova.1w from the R package granova (version 2.1) was used for that purpose. This package provides a graphical representation of the ANOVA results. The obtained graphic represents the mean of each condition (C0, C1, C2, C3, C4, N0, N1, N2, N3, and N4) classified on the x-axis according to the estimated contrast coefficients. This coefficient is the group mean (mean of each condition) minus the grand mean (mean of all the samples) and taking into account the number of samples per group (here 3 for all groups). These conditions are ordered on the horizontal axis according to the value of this coefficient from the smallest to the largest and to visualise at the same time the variation within and between the groups. In addition the graphic represents the mean square error between the conditions (MS-between) and the mean square error within the conditions (MSwithin) as an area of squares. The comparison between the group mean and the area of the MS-within allows to determine which conditions are significantly different form the others. The F-statistic value is also indicated on the graph. This value is the ratio of the area of the MS-within and of the MS-between. Combined to the p-value (p<0.05), the F-statistic value (F>1) allows to determine if there is a significant difference in the mean between the conditions for the different Gompertz parameters. Finally the residuals are also represented.

The repartition of the residuals indicates if the residuals are independent or not and confirm if the dataset meets the assumption of the ANOVA (independence, normality, homogeneity).

An OTU count matrix was designed using FROGS (Find Rapidly OTU with Galaxy Solution), a galaxy/CLI workflow [START_REF] Escudié | FROGS: Find, Rapidly, OTUs with Galaxy Solution[END_REF]. The OTUs abundances were examined through statistical analyses using R CRAN software (version 3.5.1). Low abundant OTUs were filtered: OTUs present above 1% in at least one sample were kept. Considering the dispersion in the total number of reads identified in each sample, archaeal and bacterial OTUs abundances were then scaled with total sum scaling. Finally they were transformed with centered log ratio (CLR) [START_REF] Lê Cao | MixMC: A multivariate statistical framework to gain insight into microbial communities[END_REF]. Multivariate analyses were computed using mixOmics R package (version 6.6.1) (Rohart et al., 2017b). Firstly, Principal Component Analysis (PCA) was performed to highlight the relationship between samples from the different conditions (ammonia/zeolite).

Secondly, sparse Partial Least Square Discriminant Analysis was carried out to compare the different conditions and to characterise the microorganisms influenced by the presence of ammonia and/or zeolite. Three pairwise sPLS-DA were carried out respectively between the control C series and N series without zeolite (N0-N1); between N series without and with zeolite (N1-N3-N4) and between C series and N series with zeolite. The abundances of the most discriminant microorganisms selected by the three sPLS-DAs were summarised in a heatmap using Euclidean distance and ward clustering method.

Results and discussion

Effect of zeolite treatments on the anaerobic digestion performances

Based on the biogas production represented on the supplemental Figure 60 differences on CH 4 and CO 2 production between the N series conditions can be observed. To compare them more accurately, each gas production curve were modelled with Gompertz equation and the variance between the estimated Gompertz parameters were compared using ANOVA. The Figure 1 compares the Gompertz parameters estimated for CH 4 and CO 2 kinetics for each condition according to the ANOVA result. For all the parameters the p-value and F-statistic values are significant (p-value<0.05 and F>1) which means that one or several conditions are significantly different from the others. In addition the residuals results for all the parameters are randomly distributed meaning that the ANOVA results can be trusted. The Gompertz values are summarised in the Table 14.

Effect of a low stress of ammonia without addition of zeolite

The use of a low concentration of ammonia (2 g-NH 4 + /L) was carried out in order to simulate the early stage of an ammonia accumulation.

Regarding the results of the CO 2 production (Figure 55-A-B-C), the presence of ammonia independently of zeolite addition (Nseries) resulted in a decrease of the maximal production (from 499 to 270 mgC for the extreme values) and of the production rate (from 19.8 to 9.6 mgC/day for the extreme values). In addition the lag phase was increased from 0 to 5.8 days between the samples from the C and N series. Regarding the CH 4 production (Figure 55-D-E-F), the presence of ammonia led to an increase of the maximal production from 895 to 1005 mgC for the extreme values of the C and N series. However the lag phase increased from 5.8 to 9 days from the extreme value of the C series and the conditions N0 and N2. The values of the production rate were not so differentiated between the samples from the C and N series. The mean square error between the conditions was close to the mean square error within the conditions meaning that the variation in the production rate values between the conditions was not significant. The comparison of the CH 4 and CO 2 production values indicate that the presence of a low concentration of ammonia slowed down the process. However no strong inhibition resulted from the presence of ammonia. This was expected as the objective of the study was to investigate the effect of the zeolite during the early stage of ammonia accumulation. The operational performances were even improved by an enrichment of the biogas in CH 4 in presence of ammonia. It can be supposed that the presence of ammonia led to a modification of the methanogenic pathway from acetoclastic to hydrogenotrophic pathway leading to the use of CO 2 to produce CH 4 .

1.3.1.2.

Effect of zeolite in presence of ammonia Based on the lag phase and production rate results of the CH 4 and CO 2 production, the digesters could be grouped in two different groups. A first group was composed by N0 (no zeolite) and N2 (liquid residue). A second group was composed by the digesters with solid zeolite, no matter the treatment (N1-N3-N4).

The presence of zeolite, no matter the treatment (N1-N3-N4), allowed to decrease the lag phase by 2 days for both CO 2 and CH 4 and to increase the production rate by 1.2 times for the CO 2 compared to N0-N2.

The different treatments, boiled and heated (N3 and N4), applied on the zeolite did not modify importantly the performances compared to the raw zeolite (N1) as shown in the Table 14. Boiling the zeolite (N2 and N3) was done in order to modify the ionic composition of the zeolite by realising a part of the ions from the zeolite into the water. The ion exchange capacity did not seem to be the mechanism behind the ammonia mitigation effect or in the condition of our experiment direct ion release was not important enough. Indeed, no difference was observed between N0 and N2, and effect of N3 was not different from N1.

Heating the zeolite (N4) was done in order to remove water and organic residues which could have released free sites to improve molecules adsorption or microbial colonisation. Increasing the surface on the zeolite did not improve the performances. This could be due to a limited growth of microorganisms in the time of the experiment which did not saturate the surface of the zeolite in N1 and did not benefit from the increased surface in N4.

In all cases zeolite may have acted as a support for the microbial growth and/or improved the microbial interaction by decreasing the spatial distance between microorganisms engaged for example in a syntrophy.

1.3.1.3.

Effect of zeolite without ammonia Regarding all the digesters without ammonia addition (C series) biogas performances were not significantly modified by the treatments in regard with the difference observed in presence of ammonia. Indeed the CH 4 and CO 2 maximal production were respectively close to 925 ±27.8 and 487 ±8.9 mgC for all the digesters. The CH 4 production rate was slightly reduced in presence of solid zeolite (C1-C3-C4) and liquid residue (C2), from 47 mgC/day for the control C0 to 42-46 mgC/day for solid zeolite and 38 mgC/day for liquid residue. However the lag phase was not modified between the reactors. This result is in accordance with a previous study [START_REF] Poirier | Improving anaerobic digestion with support media: Mitigation of ammonia inhibition and effect on microbial communities[END_REF] showing that the presence of the zeolite in absence of ammonia had a low influence on the digesters performances.

Effect of zeolite treatments on different chemical parameters

The Figure 56 summarises the evolution of the acetate, propionate and butyrate concentrations, pH, Free Ammonia Nitrogen (FAN) and Total Ammonia Nitrogen (TAN) concentration for the different conditions.

1.3.2.1.

Volatile fatty acids evolution All the digesters in absence of ammonia (C series) presented the same VFA evolution. The maximum of accumulation was around 750 mgC/L, 450 mgC/L and 400 mgC/L for respectively acetate, propionate and butyrate. No effect of zeolite was observed in that case.

Under low ammonia stress, the VFA results confirmed the grouping of the different conditions observed with the gas production (N0-N2 and N1-N3-N4).

In presence of ammonia and without zeolite (N0-N2) higher acetate (1050 mgC/L) coupled to lower propionate (200 mgC/L) and butyrate (150 mgC/L) accumulations than in digesters from C series was observed. The acetate can be degraded by acetoclastic archaea or bacteria in syntrophy with hydrogenotrophic archaea. Increased acetate accumulation could be due to the stress of the acetoclastic archaea by the ammonia, as observed in previous studies [START_REF] Wang | Ammonia effect on hydrogenotrophic methanogens and syntrophic acetate-oxidizing bacteria[END_REF]. However both propionate and butyrate require syntrophy for their degradation [START_REF] Müller | Syntrophic butyrate and propionate oxidation processes: From genomes to reaction mechanisms[END_REF]. Their lower accumulation in presence of ammonia suggested that syntrophic archaea were not inhibited. This was contradictory with several studies that evidenced that ammonia could inhibit the syntrophic propionate degrader bacteria (SPOB) and/or methanogens leading to an accumulation of the propionate [START_REF] Calli | Effects of high free ammonia concentrations on the performances of anaerobic bioreactors[END_REF][START_REF] Zhang | Inhibitory effects of ammonia on syntrophic propionate oxidation in anaerobic digester sludge[END_REF]. However in our study only a small amount of NH 3 was present leading to a low ammonia stress. As suggested by the biogas composition and the accumulation of acetate, the acetoclastic methanogens seemed to be delayed in favour of the hydrogenotrophic ones. This succession of events may have also favoured the syntrophic degradation of the butyrate and propionate in presence of ammonia.

In presence of ammonia and zeolite (N1-N3-N4) the pattern of evolution of the butyrate was similar to the one of N0-N2. However acetate and propionate evolutions were similar to the C series except that the propionate was fully consumed earlier than in the C series. It can be hypothesized that the zeolite limited the perturbation of the acetoclastic archaea by NH 3 from the beginning of the experiment. The preservation of the activity of the acetoclastic methanogens allowed to consume the acetate. This explains the pattern of acetate degradation similar than the conditions from the C series. Regarding the influence of the zeolite on the propionate and butyrate patterns of degradation it can be hypothesised a rearrangement of the butyrate/propionate bacteria degraders in favour of the butyrate degraders which would explain the low butyrate accumulation; or a competition between the propionate and butyrate degraders for the syntrophy with the hydrogenotrophic archaea. For both hypotheses the propionate degraders and/or their syntrophy with the hydrogenotrophic archaea was favoured by the zeolite in presence of ammonia as the degradation of the propionate occurred faster than in C series.

1.3.2.2.

Ammonia evolution In the digesters of N series, the NH 4 + concentration remained stable around 2000 mg/L all along the experiment. No additional ammonia was produced during the degradation of biowaste. This also confirmed that the adsorption of ammonia by the zeolite remained limited in our experiment. More precisely, the free ammonia nitrogen (FAN) stabilised at 200 mg-NH 3 /L after 7 days at 600 mg-NH 3 /L. It was due to a decrease of pH when the VFAs started to accumulate, leading to a decrease of the FAN. It is usually assumed that the FAN is the most toxic form of ammonia for the methanogens community, between 50 to 1400 mg-NH 3 /L [START_REF] Astals | Characterising and modelling free ammonia and ammonium inhibition in anaerobic systems[END_REF][START_REF] Rajagopal | A critical review on inhibition of anaerobic digestion process by excess ammonia[END_REF]. In our case, the first week at a high concentration of FAN was probably the most stressful for the microorganisms. This can explain the delay observed on the biogas production in presence of ammonia. However, after a few days, decrease of pH resulted in the expected low ammonia stress in the digesters of N series. In the digesters without ammonia (C series) the NH 4 + remained also stable but at a very low concentration (200 mg-NH 4 + /L and 15 mg-NH 3 /L).

Effect of the ammonia and zeolite on the methanogenic pathways

The methanogenic pathways for the different conditions were monitored using the isotopic fractionation of the CH 4 (δ 13 C-CH 4 ) and the results are summarized in the Figure 57. Values of δ 13 C-CH 4 under -60 ‰ indicate that the methane was principally produced by the hydrogenotrophic pathway while values beyond -60 ‰ indicate that CH 4 was produced through the acetoclastic pathway [START_REF] Qu | Combined monitoring of changes in delta(CH4)-C-13 and archaeal community structure during mesophilic methanization of municipal solid waste[END_REF].

In absence of ammonia (C series) in all experiments the δ 13 C-CH 4 increased from -55 ‰ to -35 ‰ during the degradation of acetate (day 0 to day 30). This progressive increase indicates that the acetate degradation was mainly due to acetoclastic archaea. Finally during propionate degradation the value decreased to -55 ‰ and stabilised at this level until the end of the experiment. As the propionate degradation implied hydrogenotrophic archaea, the CH 4 produced by their activity led to the dilution of CH 4 produced by acetoclastic archaea.

In presence of ammonia (N series) with or without zeolite the δ 13 C-CH 4 was lower at the beginning of the degradation. The δ 13 C-CH 4 then increased from -70 ‰ to -45 ‰ and -35 ‰ respectively for the digesters N1-N3-N4 (with zeolite) and N0-N2 (without solid zeolite) during acetate degradation. This indicates that the acetoclastic pathway was minored in favour of the hydrogenotrophic pathway at the beginning of the experiment. This result is in line with the low CO 2 production observed, as CO 2 was used by the hydrogenotrophic archaea for CH 4 production.

After 15 days two types of patterns were observed grouping the digesters N0-N2 on one side and N1-N3-N4 on the other side, as observed with the other data.

In absence of zeolite when ammonia was present (N0-N2) the δ 13 C-CH 4 reached the same value than the control without ammonia (-35 ‰) at day 33. The propionate degradation then led to the decrease of the δ 13 C-CH 4 until -50 ‰. In contrast with the stability in the isotopic composition observed for C series once all the propionate degradation was achieved, a further decrease in the isotopic composition for the N0-N2 digesters was observed until -60 ‰. This result suggests that a mix of both methanogenic pathways seemed to occur under ammonia inhibition without zeolite. This result is in line with a previous study. The author observed a mix of the methanogenotrophic pathways at an ammonia level of 1.9 and 2 g-TAN/L [START_REF] Ruiz-Sánchez | Effect of ammonia on the active microbiome and metagenome from stable full-scale digesters[END_REF].

In presence of zeolite and ammonia (N1-N3-N4) the δ 13 C-CH 4 increased until -35 ‰ at day 25. The propionate degradation led to a decrease of the δ 13 C-CH 4 to -60 ‰ and then the value continued to decrease to reach a final value of -65 ‰. The lower increase of the δ 13 C-CH 4 compared to the digesters without zeolite N0-N2 indicated that an important part of the CH 4 was produced by the hydrogenotrophic pathway even if acetoclastic archaea were active.

In both cases in presence of ammonia, even at a low concentration, and with or without zeolite the CH 4 isotopic composition indicates that the acetoclastic archaea were sensitive to the presence of NH 3 . The presence of zeolite enabled to maintain an important activity of the hydrogenotrophs leading to a faster degradation of the VFAs and an enrichment of CH 4 in the biogas. The presence of a solid support seemed to influence anaerobic metabolisms as differences between no support (N0-N2) and with support (N1-N3-N4) was observed either on the isotopic fractionation and VFAs patterns of degradation.

Microbial community description -general trends

The microbial community was analysed for each triplicate (30 samples) at one time point (day 22) corresponding to the peak of CH 4 production in all the digesters (supplementary Figure A.1). The samples distribution was studied using a PCA analysis (supplementary Figure A.2) and the microbial composition is described in the supplemental Figure 61.

The PCA showed that the microbial community differed between the C and N series (supplementary Figure 61-A) which is in accordance with the chemical results. The first axis of the PCA divided the samples according to the presence of ammonia (67% of explained variance). Other PCA analyses were carried out on the subsets of samples corresponding to C and N series (supplementary Figure 61-B and C respectively) to focus on the influence of the zeolite treatments. For both PCA on C or N series the first axis divided the samples from the conditions without zeolite (0) or with liquid residue (2) from the samples of the conditions with zeolite (1: raw; 3: solid residue; 4: heated). However the separation of these two groups was stronger in presence of ammonia (34% of explained variance) than in absence of ammonia (19%). Moreover in the case of N series, within each condition, the samples of the triplicate reactors were closer to each other than with the other conditions, showing a good reproducibility and a stronger impact of the presence of zeolite. A first microbial composition description was done using bar plot (supplementary Figure 62). In total 89 OTUs were identified after filtering corresponding to 13 archaea (6 genera) and 76 bacteria (26 families).

Between the conditions C and N at the genus level the archaeal community differed mostly by the abundance of the genus RumEn M2. Its abundance in the corresponding digesters was of respectively 6 and 2%. However in the two series and between the different zeolite conditions, the archaeal community was mostly represented at similar proportion by Methanosarcina, Methanobacterium and Methanoculleus. The development of these archaea in the digesters without ammonia can explain the absence of modification in the archaeal community when ammonia was present. Indeed, Methanosarcina and Methanoculleus are known to be resistant to the presence of ammonia (Poirier et al., 2016b;[START_REF] Tian | Bioaugmentation strategy for overcoming ammonia inhibition during biomethanation of a protein-rich substrate[END_REF]. Methanoculleus is a hydrogenotrophic archaea while Methanosarcina is a versatile archaea able to use both acetoclastic and hydrogenotrophic pathways. The bacterial community was different between the digesters with or without ammonia. The abundances of the Bacteroidales and Synergistales decreased while the Clostridiales and Spirochaetales abundances increased in proportion when ammonia was present. Cloacimonadales were observed only in the digesters without ammonia while Lactobacillales and MBA03 were only found in the digesters with ammonia. Lactobacillales were already identified in AD systems containing ammonia [START_REF] Su | Application of biochar in a CIC reactor to relieve ammonia nitrogen stress and promote microbial community during food waste treatment[END_REF] their presence in the digesters with ammonia was then relevant. Order Izimaplasmatales was found only in the digesters with ammonia and in presence of zeolite.

Due to the complexity of the microbial composition and in order to highlight the influence of the zeolite on the arrangement of the microbiota, statistical analyses were carried out to decipher this important data.

Identification of the key phylotypes of the different conditions

Microorganisms specifically influenced by the presence of ammonia and zeolite with or without ammonia were characterised using sPLS-DA. This method allows the classification of the samples into groups and allows the identification of the key microorganisms discriminating these groups. To evidence respectively samples discriminant of digesters inhibited by ammonia and not inhibited, of digester containing zeolite or not in presence of ammonia, of digesters without ammonia and digesters with ammonia but containing zeolite, three pairwise sPLS-DA were carried out between the following groups A (samples from the C series), B (samples from N0-N2) and C (samples from N1-N3-N4) in order to increase the accuracy of the analysis. The samples were classified into the different groups with a mean error rate inferior to 4%. Abundances of the key microorganisms characteristic of the different groups and selected through the different sPLS-DA are summarised in a heatmap (Figure 58). Their corresponding taxonomic affiliation is reported in the supplementary Table 16. The microorganisms can be grouped in 5 clusters according to the influence of the zeolite and/or the presence of ammonia.

Microorganisms inhibited by the presence of ammonia

The cluster 1 grouped together the microorganisms present in the group A, without ammonia, and absent in presence of ammonia no matter the presence or not of zeolite. 7 OTUs belonging to the families Lachnospiraceae, Rikenellaceae, Bacillaceae, Clostridiaceae and to the order Bacteroidales (GZKB124) were identified. Lachnospiraceae family is recognised as cellulolytic degrader and is known to produce large amounts of acetate and CO 2 [START_REF] Florentino | Overcoming ammonia inhibition in anaerobic blackwater treatment with granular activated carbon: The role of electroactive microorganisms[END_REF]. Rikenellaceae family is known to use lactate fermentation where acetate and propionate are the end-products [START_REF] Yi | Effect of increasing total solids contents on anaerobic digestion of food waste under mesophilic conditions: Performance and microbial characteristics analysis[END_REF]. Clostridiaceae and Bacteroidales are usually reported to be the main phyla in the anaerobic digesters and are known to be cellulolytic microbes [START_REF] Hassa | Metagenome, metatranscriptome, and metaproteome approaches unraveled compositions and functional relationships of microbial communities residing in biogas plants[END_REF].

Microorganisms preserved by the presence of zeolite under ammonia low stress

The cluster 2 grouped the microorganisms present in group A, inhibited in group B by the ammonia but preserved thanks to the presence of zeolite (group C). 9 OTUs were found and belonged to the families Peptococcaceae, Spirochaetaceae, Lachnospiraceae, Cloacimonadaceae, Izimaplasmatales order and members of the genus Methanoculleus. Cloacimonadaceae are known to be acetogens [START_REF] Lee | Microbial communities underpinning mesophilic anaerobic digesters treating food wastewater or sewage sludge: A full-scale study[END_REF]. Members of the family Peptococcaceae are syntrophic obligate propionate oxidizers [START_REF] Ziganshin | Bacteria and archaea involved in anaerobic digestion of distillers grains with solubles[END_REF]. The presence at day 22 of this microbe was in accordance with the degradation pathway of the propionate. Indeed in the digesters without ammonia and with ammonia and zeolite (group A and C) the propionate degradation occurred earlier than in digesters with ammonia and no zeolite (group B). OTU from the family Spirochaetaceae, genus Treponema, was identified. Some members of this genus are known to degrade cellulose and some others to be homoacetogens and reduce H 2 and CO 2 into acetate [START_REF] Lee | Distribution and abundance of Spirochaetes in full-scale anaerobic digesters[END_REF]. Finally Izimaplasmatales order was also identified in the group C. Izimaplasmatales from the Tenericutes phylum was described recently by Skennerton et al [START_REF] Skennerton | Phylogenomic analysis of Candidatus "Izimaplasma" species: Free-living representatives from a Tenericutes clade found in methane seeps[END_REF]. It was identified from methane seep sediment samples. The members of Izimaplasmatales order are able to ferment anaerobically simple sugars. To the best of our knowledge this study is the first one reporting the presence of Izimaplasmatales order in anaerobic digesters. Further analyses should be proceeded in order to fully understand the role of these microorganisms in the anaerobic digestion.

Microorganisms favoured by the presence of ammonia

The cluster 3 grouped the microorganisms which were absent in the group A but present in the groups B and C, meaning that their development was favoured by the presence of ammonia, independently of the presence of zeolite. The microorganisms from this cluster, represented by 9 OTUs, belonged to the same families than the one identified in the previously described clusters: Lachnospiraceae, Peptococcaceae, Clostridiaceae, Izimaplasmatales and Syntrophomonadaceae. Syntrophomonadaceae family degrades long chain fatty acids into acetate and glycerol [START_REF] Sousa | Ecophysiology of syntrophic communities that degrade saturated and unsaturated long-chain fatty acids[END_REF] but members of the Syntrophomonas genus, identified in this system, are butyrate degraders [START_REF] Narihiro | Draft Genome Sequence of Syntrophomonas wolfei subsp . methylbutyratica Strain 4J5 T ( JCM 14075 ), a Mesophilic Butyrate-and[END_REF][START_REF] Zou | Characteristics of an Anaerobic, Syntrophic, Butyrate-degrading Bacterium in Paddy Field Soil[END_REF]. The development of this Syntrophomonas could explain the faster butyrate degradation observed in the groups with ammonia no matter the presence of zeolite (groups B and C) than in control without ammonia (group A). Microorganisms slightly inhibited by the presence of ammonia The cluster 4 grouped the microorganisms more abundant in group A than groups B and C. 7 OTUs were identified in this cluster and belonged to the families Dysgonomonadaceae, Synergistaceae and Syntrophomonadaceae. Dysgonomonadaceae family is composed by three genera, Fermentimonas, Petrimonas and Proteiniphilum, which have fermentative activities producing acetate [START_REF] Hahnke | Description of Proteiniphilum saccharofermentans sp. nov., Petrimonas mucosa sp. nov. and Fermentimonas caenicola gen. nov., sp. nov., isolated from mesophilic laboratory-scale biogas reactors, and emended description of the genus Proteiniphilum[END_REF]. Synergistaceae family is composed of amino acid degraders [START_REF] Rivière | Towards the definition of a core of microorganisms involved in anaerobic digestion of sludge[END_REF]. All the Syntrophomonadaceae present in this cluster belonged to the genus Syntrophomonas. Members of this genus seemed to be differently influenced by the presence of ammonia as in cluster 3 another OTU of this genus was favoured by the presence of ammonia.

1.3.5.5.

Microorganisms enhanced by the presence of ammonia The cluster 5 grouped microorganisms more abundant in groups C and B than in group A. These microorganisms developed in absence of ammonia but their growth was enhanced by its presence. Mostly archaea were identified in this cluster with three bacteria, Marinilabiliaceae, Paludibacteraceae families and member of the order Clostridiales. Members of Paludibacteraceae use various sugars to produce acetate and propionate as major end-products [START_REF] Ueki | Paludibacter propionicigenes gen. nov., sp. nov., a novel strictly anaerobic, Gram-negative, propionate-producing bacterium isolated from plant residue in irrigated rice-field soil in Japan[END_REF]. Identified archaea belonged to the genera Methanoculleus, Methanobacterium and Methanosarcina. As seen with the bar plot these genera were present in the groups A, B and C. The heatmap highlights that the archaeal population seemed to be influenced mostly at the species level. OTUs of Methanobacterium_41, Methanoculleus_2 and Methanosarcina_1 were favoured by the presence of ammonia. Unfortunately no additional information at the species level was available from the sequencing results.

Biological pathway reconstruction

Based on the conclusion drawn from the chemical and microbial data, we summarized the characteristics of AD in the different conditions tested, to evidence the effect of zeolite addition and in presence of ammonia stress (Figure 59). In a normal condition without ammonia and no matter the zeolite treatments (C series) the biogas was composed by 66% of CH 4 and 34% of CO 2 . The principal pathway for the CH 4 production was the acetoclastic one. It was confirmed by the isotopic fractionation and the VFAs pattern of degradation. Specific microorganisms were identified in this condition as Cloacimonadales and Rikenellaceae. These microorganisms were responsible to the production of the VFAs.

In presence of ammonia and without zeolite (N0-N2) the modification occurred mostly on the methanogenic pathway. It had consequences on the pattern of VFAs degradation. In line with a part of the literature, the acetoclastic methanogenesis archaea were stressed by the presence of NH 3 [START_REF] Yenigün | Ammonia inhibition in anaerobic digestion: A review[END_REF] at the beginning of the experiment. This led to a CH 4 production mainly through the hydrogenotrophic pathway. However after one week the NH 3 concentration decreased, the acetoclastic archaea were able to be active again and the CH 4 was then produced by a mix of hydrogenotrophic and acetoclastic pathways. The growth of specific OTUs of Methanosarcina, Methanoculleus and Methanobacterium, all able of hydrogenotrophy, was however enhanced by the presence of ammonia.

The microbial analysis showed a rearrangement of the acetogenic community mostly at the OTU level. Additionally bacterium such as Paludibacteraceae specifically developed in presence of ammonia.

In presence of ammonia and with zeolite (N1-N3-N4) the hydrogenotrophic pathway seemed to be preserved more importantly than without zeolite. The presence of zeolite seemed to preserve the hydrogenotrophic archaea and their partners leading to a faster degradation of the VFAs than in control and an enrichment of the biogas in CH 4 . This could be explained by the preservation of specific OTUs as Peptococcaceae, a propionate degrader, and the archaea mentioned previously. Izimaplasmatales, identified only in presence of zeolite could play a role in the performance modification; however we could not put forward any hypothesis due to the lack of knowledge on this microorganism.

These results indicate that in our study the presence of ammonia led to an important dynamics of obligate syntrophic bacteria and hydrogenotrophic archaea coupled to a limitation of the perturbation of the acetoclastic methanogens. Zeolite could act as a support for the microbial growth which improved the proximity and exchanges between the microorganisms and protected them. [START_REF] Poirier | Improving anaerobic digestion with support media: Mitigation of ammonia inhibition and effect on microbial communities[END_REF] showed that different supports mitigated the ammonia inhibition differently and the mitigation was associated to changes of the microbial community. Each type of support has specific counteracting capacity which led to a non-negligible effect on the microbial community as for example the conductive material improving electron transfer between microorganisms [START_REF] Florentino | Overcoming ammonia inhibition in anaerobic blackwater treatment with granular activated carbon: The role of electroactive microorganisms[END_REF].

Conclusion

Zeolite particularly acted on the microbial distribution when ammonia was present at a low concentration and allowed the growth of microorganisms like Izimaplasmatales, Peptococcaceae or Methanobacterium. The rearrangement of the community allowed to modify the pattern of VFAs degradation especially the syntrophic butyrate and propionate degradation between bacteria and hydrogenotrophic archaea. The reasons for this rearrangement cannot be explained by the pre-treatments tested in our study as no strong modification between raw and modified zeolites could be observed. The effect seemed to be mostly due to the presence of a solid support. The mechanism behind the mitigation could be due to microbial colonisation improving microorganism's proximity. It would be interesting to investigate the influence of zeolite on the syntrophic microbial metabolism. In addition we showed that despite no strong inhibition the presence of a zeolite led to a microbial restructuration. The effect of higher level of ammonia on the microbiota in presence of zeolite could also be investigated.

Comparison to previous results of mitigation of ammonia inhibition using zeolite

In order to go further on the evaluation of the microbial response facing to ammonia pressure in presence of zeolite we extended our analysis with the results from another experiment carried out by Simon Poirier in 2016. By mixing both datasets we wanted to determine if the improvement of the syntrophic association bacteria-archaea observed in the previous study was a general trend of the mitigation by zeolite.

Description of the experiment from Poirier et al (2017)

Poirier et al ( 2017) studied the influence of the zeolite addition and other supports media on the digesters performances and on the microbial development under ammonia inhibition (Figure 63). The experimental design was similar to the one described in the previous part of this chapter. The experiments were carried out in batch in mesophilic condition. One series was a control without ammonia (C series) and the other series contained ammonia at 19 g-TAN/L (N series). In both series different conditions were tested by adding or not 10 g/L of a mineral support.

In order to compare both experiments, samples from digesters containing zeolite with or without ammonia were selected. In Poirier et al different analytical time points were available for the 16S rRNA metabarcoding. In order to be comparable with our analysis where the sampling was realised before the consumption of the VFAs, the time points with similar pattern of VFAs were kept from the experiment by Poirier et al. From all the mineral support conditions tested in both studies only the conditions with the zeolite Siliz24® without treatment were kept. Samples are described in the Table 17.

Overview of the results

In order to evaluate the possibility to combine both datasets a PCA was carried out on the overall data (Figure 64). The first two axes explained 58% of the variability between the samples. From this variability 38% on the first axis is due to the origin of the samples: LC (Laëtitia Cardona) or SP (Simon Poirier). This variation is considered as batch effect as it is unrelated to any biological variation of interest.

In our case the batch effect is known and corresponds to the origin of the samples between the two studies. Before the analysis and in order to limit the computational source of variation the sequences from both experiments were processed in FROGS together. The taxonomic data were aggregated to the genera level to limit the differences. Then the ComBat method (Leek et al., 2012) was applied on the data. The quality of the batch effect removal was evaluated by comparing the samples distribution before and after the batch effect correction using a PLS-DA (Figure 65). The discriminant analysis on the corrected data was carried out because the objective was to identify the microorganisms influenced by the presence of ammonia and/or zeolite without taking into account the origin of the samples. Using the PLS-DA we wanted to classify the samples between 3 groups: without ammonia, with ammonia only and with ammonia and zeolite. Based on the results obtained with our previous experiment, the addition of the zeolite when no ammonia is present did not influence the microbial community. It was for this reason that the samples without ammonia and with or without zeolite were grouped together.

The results of the PLS-DA (Figure 65) show that before the batch effect correction samples from LC and SP experiments where easily distinguishable. This confirmed the results of the PCA and the influence of the origin of the samples on the microbial community. After the batch effect correction 3 distinct groups were observed: control without ammonia no matter the presence of zeolite (C series), with ammonia without zeolite (N0 condition) and with ammonia and zeolite (N1 condition). The difference between the samples with or without ammonia represented 39% of the explained variability on the first axis of the PLS-DA. In order to identify the microbes influenced by the different conditions, pairwise sparse PLS-DA were carried out.

Identification of the key phylotypes influenced by the different conditions

In order to evidence microbes which discriminate samples of digesters with ammonia from control without ammonia, of digesters with ammonia and zeolite from digesters with ammonia but without zeolite, pairwise sparse PLS-DA were carried out as described in the first part of this study. The results are summarised in a heatmap (Figure 66) which represents the abundance of the microbes selected by the two sPLS-DA analyses. Their corresponding taxonomic affiliation is reported in the supplementary Table 18. The microorganisms can be grouped in 7 clusters according to the influence of the zeolite and/or the presence of ammonia.

Core microbiome between the conditions

Cluster 7 grouped together the microbes which were abundant in all samples no matter the study or the experimental conditions. This cluster is composed by microbes involved mostly in the hydrolysis and fermentative steps of the anaerobic digestion such as Lachnospiraceae, Ruminococcaceae, Synergistaceae or Dysgonomonadaceae already described in the previous part. The order Christensenellaceae is a saccharolytic degrader [START_REF] Oosterkamp | Identification of methanogenesis and syntrophy as important microbial metabolic processes for optimal thermophilic anaerobic digestion of energy cane thin stillage[END_REF]. Little is known regarding the order Limnochordaceae. It is a moderate thermophilic microbe able to grow both in absence and presence of oxygen [START_REF] Watanabe | Limnochorda pilosa gen. Nov, sp. Nov, a moderately thermophilic, facultatively anaerobic, pleomorphic bacterium and proposal of Limnochordaceae fam. Nov, Limnochordales ord. Nov. And Limnochordia classis nov. In the phylum Firmicutes[END_REF]. These microbes are present in both studies and are not influenced by the presence of the ammonia and/or zeolite.

Microorganisms inhibited by the presence of ammonia

Cluster 4 grouped together microbes more abundant in digesters without ammonia than in digesters containing ammonia no matter the presence of zeolite. Different members from the orders Ruminococcaceae (5 different genera) and Lachnospiraceae (3 different genera) were found in this cluster. Moreover others microbes were identified such as Rikenellaceae already described as acetate and propionate producer, Paenibacillaceae involved in the degradation of lignin [START_REF] Jacob | Selective digestion of industrial potato wastes for efficient biomethanation: a sustainable solution for safe environmental disposal[END_REF], 2 members of the phylum Lentisphaerae, Deferribacteraceae, Veillonellaceae involved in the VFAs degradation [START_REF] Shida | Performance and composition of bacterial communities in anaerobic fluidized bed reactors for hydrogen production: Effects of organic loading rate and alkalinity[END_REF], Desulfovibrionaceae a lactate degrader in syntrophy with hydrogenotrophic archaea [START_REF] Detman | Methane-yielding microbial communities processing lactate-rich substrates: A piece of the anaerobic digestion puzzle[END_REF] and Clostridiaceae. These microbes were inhibited by the presence of ammonia in both studies.

Microorganisms favoured by the presence of ammonia

Clusters 3 and 6 grouped together the microbes favoured by the presence of ammonia no matter the presence or not of zeolite in both studies. In the cluster 3 microbes were not present in the system while in cluster 6 microbes were lowly abundant. Once again microbes from the families Lachnospiraceae, Dysgonomonadaceae and Ruminococcaceae were found. These families seemed to be composed by different microbes able to develop in the different conditions used in this work. More specific microbes were found such as families Peptostreptococcaceae.

Members of this family were identified as H 2 producer potentially during butyrate production [START_REF] Mockaitis | Acidic and thermal pretreatments for anaerobic digestion inoculum to improve hydrogen and volatile fatty acid production using xylose as the substrate[END_REF][START_REF] Na Ayudthaya | Organic acid production from potato starch waste fermentation by rumen microbial communities from Dutch and Thai dairy cows[END_REF], Defluviitalaceae identified as an acid-producing bacteria [START_REF] He | Investigation of foaming causes in three mesophilic food waste digesters: Reactor performance and microbial analysis[END_REF], Enterococcaceae and Caldicoprobacteraceae which ferment xylan and simple sugars to lactate, acetate, H 2 , CO 2 [START_REF] Zamanzadeh | Biogas production from food waste via co-digestion and digestion-effects on performance and microbial ecology[END_REF].

Microorganisms maintained by the zeolite in presence of ammonia

Cluster 2 group together microbes that maintained in presence of ammonia thanks to the addition of zeolite. Two of these microbes were found only in the LC study while the two others were found in both LC and SP studies. The genus Pelotomaculum (Peptococcaceae order) and the order Burkholderiaceae were found in both studies. Peptococcaceae order was described in the previous part as a syntrophic propionate degrader. Its presence was maintained by the zeolite. The genus Parapusillimonas from the family Burkholderiaceae was isolated from brownich-black granules from a wastewater-treatment bioreactor by Kim et al in 2010 [START_REF] Kim | Parapusillimonas granuli gen. nov., sp. nov., isolated from granules from a wastewater-treatment bioreactor[END_REF].

Microorganisms specific to the study from Cardona et al

If there is a core microbial community existing between both studies, microbes specific to one or the other study or with a different dynamics between the studies can be found. This could be explained by the different levels of ammonia used in the two studies and or the different inoculum.

Cluster 1 grouped together the microbes which were favoured by the presence of ammonia but only in the samples from the LC study. These microbes were absent in the samples from the SP study. Lachnospiraceae and Ruminococcaceae orders were found in this cluster. These two orders were found in almost all the clusters, represented by different genera.

Cluster 5 grouped together the microbes which were inhibited by the presence of ammonia but only in the samples from the LC study. These microbes were present in SP study no matter the presence of ammonia and/or zeolite. Two microbes from this cluster were maintained by the presence of zeolite. These microbes belong to the orders Lachnospiraceae and Cloacimonadaceae. Members of the order Cloacimonadaceae such as genus Candidatus Cloacimonas acidaminovorans, are suspected to be involved in the syntrophic degradation of the formate producing H 2 and CO 2 [START_REF] Solli | A metagenomic study of the microbial communities in four parallel biogas reactors[END_REF]. This result is in line with the assumption that the zeolite acted on the equilibrium of the VFAs by helping the syntrophy between the VFAs degraders. Not much information can be found regarding the genus Tyzzerella from the order Lachnospiraceae.

Conclusion

Working on both studies with two different levels of ammonia concentration has allowed to confirm the influence of the zeolite in presence of ammonia on the development of Peptococcaceae. Indeed even if ammonia concentration was different between the two experiments, one at 2 g N/L to simulate an early beginning of ammonia accumulation and the other at 19 g N/L to half inhibit the system, the Peptococcaceae order was favoured by the presence of zeolite. Furthermore 3 others microbes were identified to be favoured by the presence of zeolite in presence of ammonia: Burkholderiaceae order, Tyzzerella and Candidatus Cloacimonas genera.

Extending the analyses of a microbial community using datasets coming from the literature can help to get more insights on the understanding of the influence of different technical parameters on the development of the microbiota. This can allow also to study the genericity of the results especially when different inocula or technical parameters are used. As example this study shows that 84 % of the selected microbes through the PLS-DA is similar between both studies. However others microbes are really specific to one or the other study or did not evolved in the same way. It would be interesting to add further data from the literature in order to increase the number of samples used for the statistical analysis. Indeed only 19 samples in total were used, which can be limiting for the statistical interpretation. Moreover this would allow to increase the range of ammonia concentration studied. We showed that the zeolite modifes the microbial composition only in presence of ammonia even at low concentration. Further studies have to be performed in order to determine from which concentration this modification happens and if its influence is similar at different concentrations of ammonia.

Conclusion and perspectives of the chapter

Several questions remain regarding the mechanisms of action of the zeolite on the microbial ecosystem. Why zeolite influences the microbial growth only in presence of ammonia while the community does not evolve in its absence? In presence of zeolite, which concentration of ammonia leads to the modification of the microbial ecosystem? Which level of the microbial expression is influenced by the zeolite: growth, activity or metabolism? … Determining the minimal concentration of ammonia in presence of zeolite which leads to the modification of the microbial population can be interesting to study the possibility of using zeolite as a protective agent in addition to its well documented mitigating effect. This can be determined by carrying out a series of batches containing zeolite with different concentration of ammonia. As done in this thesis work the digester performances and the microbial community through the 16S rRNA gene metabarcoding should be studied.

In order to go further in the understanding of the influence of the zeolite onto the microbial ecosystem, it is necessary to carry out further analyses. Indeed in our work we studied the microbial development through the DNA metabarcoding. No information regarding microbes activities and metabolisms are provided except for the archaea through the biogas isotopic fractionation. What we show in this thesis work is that in presence of ammonia the zeolite allows to improve the syntrophy between bacteria and archaea, especially for the degradation of the propionate. Microbial syntrophy is a transfer of electron between microbe A (bacteria) and microbe B (archaea). This transfer can be done through two ways: indirect (IIET) and direct (DIET) interspecies transfer. In the IIET the bacteria produces H 2 during the reduction of organics and methanogens use this H 2 to reduce the CO 2 and produce CH 4 [START_REF] Xu | Enhancing direct interspecies electron transfer in syntrophic-methanogenic associations with (semi)conductive iron oxides: Effects and mechanisms[END_REF]. In the DIET the electron transfer is not mediated by diffusive agent such as H 2 but directly via conductive material such as pili or conductive mineral supports (Park et al., 2018a) (Figure 67). Recently many studies described the role of conductive or semi-conductive supports in the syntrophy improvement through DIET (Xu et al., 2019). Some of them show the influence on the propionate syntrophic degradation of such materials [START_REF] Viggi | Magnetite particles triggering a faster and more robust syntrophic pathway of methanogenic propionate degradation[END_REF][START_REF] Xu | Enhancing direct interspecies electron transfer in syntrophic-methanogenic associations with (semi)conductive iron oxides: Effects and mechanisms[END_REF]. Moreover Peptococcaceae known as propionate degraders have been shown to be involved in DIET with a methanogens [START_REF] Wu | Evaluating the effect of biochar on mesophilic anaerobic digestion of waste activated sludge and microbial diversity[END_REF].

It would be interesting to continue the analyses on the microbial community involved in our experimental system by carrying out metatranscriptomics analyses in order to determine the functions active in the system and especially during the propionate degradation phase. This would provide further information on the mechanism involved in the syntrophy but also to get an overview of the active microbes and their functions in the system. We saw in our study that the microbial population was different depending on ammonia and zeolite addition. Transcriptomic analysis would allow to determine if the microbial community modification induces metabolism modifications or if there is a functional redundancy between the microorganisms. Moreover more information regarding Izimaplasmatales would allow to determine its role in our system as poor information is available on this family. Metabolomics analyses would complement these data by providing information on substrate degradation pathways.

Different choices were made during this thesis work regarding the experimental design as well as the analytical and statistical methodologies used. All these choices influenced somehow the results and the interpretation made based on it. The different choices taken along this thesis work are explained beyond.

Discussion about the experimental methods

Batch and semi-continuous digesters

In this thesis work two types of experimental set up were used: batch and semi continuous digesters (CSTR). Both were used for specific purposes.

Batch experiments were carried out upstream to prepare the CSTR experiments. Indeed batch digesters are easier to handle and have a low cost which facilitate their use to test multiple conditions at the same time. In this thesis work batch digesters were used to determine the concentration of inhibitors to add in the digester in order to partially inhibit the bioprocess. Batch digesters were used also to study the influence of different mixes of substrates and evaluate the most appropriate ratio for a second experiment in CSTR.

Semi-continuous digesters were used based on the results obtained from the batch experiments. CSTR allows to be closer to an industrial bioprocess with a bigger volume than the batch and a regular feeding. The management of a CSTR requires different instruments (water bath, peristaltic pump, gas meter…) which increase the cost of the experiment but also increase the risk of failure. CSTR experiments were used in this thesis work to evaluate the influence of specific parameters such as the speed of an inhibitor accumulation and the modification of the feedstock composition. However the differences in the experimental management and the higher microbial dynamics in CSTR than in batch due to the regular feeding, lead to difficulties to compare the results obtained from both experimental devices.

Inoculum

During this thesis work it was decided to use an active inoculum to inoculate the different experiments. The inoculum came from a mesophilic digester treating wastewater sewage sludge. It was decided to maintain this inoculum in a bioreactor in our lab in order to control the different parameters which could influence the microbial activity.

The inoculum was maintained in mesophilic condition and fed with biowaste (sampled once and preserved at -20°C). The feedstock was changed from sewage sludge to biowaste as this last substrate is more diverse in term of chemical composition.

The use of a fresh inoculum allows to work with an active and diversified microbial community. This allows to be closer to industrial parameters by using a complex microbiota and to decrease the lag phase due to the reactivation of the microbial community when working on defrosted inocula.

Ammonia inhibition

A part of this thesis work consisted in studying the influence of the ammonia inhibition on the microbial ecosystem and its mitigation. To do so chemical products were used to simulate the ammonia accumulation instead of using protein-rich substrates. This was done in order to control more precisely the ammonia concentration. The difficulty during this work was to maintain a stable level of inhibition with free ammonia nitrogen (FAN -NH 3 ) instead of the total ammonia (TAN). The FAN is considered usually as the most inhibiting form of the ammonia [START_REF] Astals | Characterising and modelling free ammonia and ammonium inhibition in anaerobic systems[END_REF]. However the NH 3 concentration is very sensitive to pH and temperature values. pH was changing across time, despite the buffering capacity of the BMP solution. It required a constant adaptation of the ammonia quantity added into the digester to obtain the desired FAN value in the digesters especially during the ammonia inhibition study (Chapter II).

Two chemical compounds were used during this thesis work: NH 4 Cl and (NH 4 ) 2 HCO 3 . The first experiment was realised with NH 4 Cl, but for the other experiments we decided to use (NH 4 ) 2 HCO 3 instead to limit the potential risk of chloride inhibition [START_REF] Chen | Inhibition of anaerobic digestion process: A review[END_REF]. Both compounds had a different influence on the pH and the NH 3 values. Indeed the NH 4 Cl induced a decrease of the pH and consequently of the NH 3 concentration while the (NH 4 ) 2 HCO 3 , due to the presence of carbonate, increased the pH and NH 3 concentration.

It should be considered also that the use of different sources of ammonia, NH4Cl, urea or another protein-rich substrate, can lead to different results due to a different influence on the microbial community as observed by [START_REF] Lv | Ammonium Chloride vs Urea-Induced Ammonia Inhibition of the Biogas Process Assessed by Stable Isotope Analysis[END_REF].

Discussion about the analytical methods

Untargeted metabolomics analysis

During this thesis work untargeted analyses were carried out to follow the substrate degradation using two analytical technics. Metabolomics analysis enabled the identification of the metabolome. The metabolites, composing the metabolome, bring information regarding the metabolism pathways.

As the objective during this thesis work was to study the metabolome as a whole, untargeted methodologies were used to determine the metabolites linked to the substrate degradation. Liquid-chromatography mass spectrometry (LTQ-orbitrap) and Fouriertransform ion cyclotron resonance (FTICR) in direct infusion were used to analysis the supernatant, meaning dissolved extracellular metabolites. The use of a chromatographic column allows to provide an information about the retention time which is useful for the identification of the metabolites. However due to the physico-chemical characteristics of the different columns only some compounds will be eluted by the column and will be analysed. The use of a direct infusion limits this selection. Due to its higher resolution the FTICR allows a better mass precision than the Orbitrap.

Metabolomics analysis is still maturing especially in the bioprocesses field. The assignation of a feature (spectral peak) to a metabolite name is challenging compared to the 16S rRNA sequencing or proteomics data. Indeed the metabolomics databases are incomplete as the metabolomics methodology is a new born in the omics methodologies. As a consequence each raw formula can often be assigned to different metabolites. The identification of the metabolites is then difficult and time consuming and further analyses such as MS/MS fragmentation have to be performed to complete the identification.

However metabolomics analysis has a great potential in the understanding of the bioprocesses microbiome. Metabolomics analysis can provide further information and complement the study of the microbial functioning. As we saw in this thesis work, the molecular composition of the substrates and the temporal dynamics of these metabolites can be correlated to the microbial community. However further efforts have to be done on the development of the databases especially for the investigation of the anaerobic bioprocesses as for now metabolomics analysis is used mostly as a fingerprinting methodology. Differences between two conditions are determined by comparing the profiles of the spectra obtained from these conditions.

Statistical analyses

Metabolomics and 16S rRNA sequencing provide a huge amount of data. The extraction of the useful information requires appropriate analytical workflows of statistics.

The first challenge to handle with is the type of the biological data which are compositional. Data contains many missing values and most of all they are constrained (the abundance of one variable depends of the abundance of the other variables) while computational methods assumed the data are not bounded. This leads to the use of normalisation and transformation before applying the statistical methods. The choice of the transformation method can lead to different results and should not be underestimated. The methods used as part of this thesis work, square-root (sqrt), centre log ratio (CLR) and log ratio (log) are commonly used for the sequencing and metabolomics data analysis.

The second challenge is to define the most appropriate statistical methods to use as different methods exist. Two elements have to be taken into account: 1) the type of data (compositional, normally distributed, number of samples…) and 2) at which biological question we want to answer. This last point is crucial and determines not only the statistical method to use but also the experimental design which should help to answer to the question. In this thesis work different questions have been addressed which induced the use and development of specific statistical workflow. For example a specific workflow was design in order to correlate the microbial activity to the substrate degradation, or another one was used to carry out the analysis of samples coming from different studies. The methods used were most of them issue to the R package mixOmics. A partnership was developed between Olivier Chapleur and the team developing this package (Kim Anh Lê Cao from Melbourne University and Sébastien Déjean Toulouse University) which allowed a close interaction between this team of statisticians and myself.

Discussion about the results

Influence of operational parameters on the microbiome

One of the objectives of this thesis work was to assess the influence of the feeding composition and its modification on the microbiome dynamics. The feeding composition is known to influence the microbiota development. However information regarding the link between the variation of the feedstock composition and the microbial dynamics is still missing. In order to fill this lack, two experiments were carried out in batch and in semicontinuous digesters.

The first experiment consisted in the evaluation of the microbiome dynamics facing to different ratio of substrates mixing. This experiment was carried out in batch in order to test different conditions. A statistical workflow was specifically developed in order to link the dynamics of substrate degradation to the microbial activity in order to determine potential degraders of the different molecules. This workflow implied classification methods for the determination of the molecules coming from each substrate used in the study, but also a correlation analysis to link both metabolomics and 16S rRNA sequencing datasets.

If the statistical analysis allowed to formulate hypotheses on potential degraders further experiments have to be done to confirm the results. Different methods can be applied. Directly from the samples of this study metatranscriptomics could be performed to determine the active functions. Otherwise cultivation of the different microbes identified as potential degrader of a molecule can be performed to determine in real the capacity of the microbe to degrade the molecule.

The second experiment was carried out in CSTR. The influence of the modification of the feeding composition across time on the microbiome dynamics was studied. The choice of the substrates and the ratio to choose was based on the batch results. For example the choice of using the grass instead of the fish was based on the fact that very little ammonia accumulated during the degradation of the grass. The performances results obtained from the CSTR partly correspond to our expectations based on the batch results. Even if the inoculum and the grass were not the same between both experiments the performances results at the beginning of the CSTR experiment were similar to the ones from the batch: no high ammonia accumulation, no significant differences of the performances between the mono-digestion of sludge and the co-digestion of 10% grass and 90% sludge. The second step of the CSTR experiment, modification of the ratio between grass:sludge to 50:50, led to different results in the performances between batch and CSTR. However the experimental conditions were different as in the CSTR the feedstock composition was changed across time contrary to the batch experiment.

As the CSTR experiment is a longitudinal study an adapted analytical workflow was used. Indeed the statistical workflow developed based on the batch experiment did not take into account a time series. Moreover the workflow focused on the degradation of the metabolites specific to each substrate. It would be interesting to study the metabolomics dynamics as a whole including degradation, production and equilibrium between production/degradation. An integrative methodology taking into account time-series like the one developed by [START_REF] Bodein | A generic multivariate framework for the integration of microbiome longitudinal studies with other data types[END_REF] could help to bring this objective. The difficulty is that it is not possible to determine if a metabolite is degraded or produced due to the absence of a microbe or the activity to another one. For example if the intensity of a metabolite decreases across time it can be due to the inhibition of its microbial producer or the development of a microbial degrader. Both correlated and anti-correlated active microbes have to be considered.

If the analysis of the correlation between the active microbial activity and the metabolites dynamics allows to posit only hypotheses of degradation/production, it permits to carry out a screening from a complex microbiome. This allows selecting and orientating research which can imply long and tedious experiments.

A better knowledge of the chemical composition of the feedstock used in anaerobic bioprocess will be helpful for a better management of the digester performances. Indeed the chemical analysis of substrate associates to the microbial analysis of the inoculum could be carried out upstream of an industrial plant installation, such as BMP carried out to determine the optimal biogas production. It could also allow the identification of microbes able to degrade specific molecules, toxic or difficult to degrade molecules, or identify microbes able to produce molecules of interest.

Identification of bio-indicators of the bioprocess functioning

The second objective of this thesis work was to study the microbiome dynamics alongside the ammonia accumulation speed. Ammonia nitrogen is a well-known inhibitor of the bioprocess. However the link between the ammonia accumulation and the microbial dynamics is still not understood and contradictory results can be found. In our experiment we compared the effect of different speeds of ammonia addition in semi-continuous bioreactors.

The impact on the digesters performances decreased together with the decrease of the speed of ammonia accumulation. Modelling the time dynamics of the microbial and metabolomics dynamics and comparing these dynamics between the different speeds allowed to determine the adaptation microbial capacity on the one hand and to identify potential bioindicators on the other hand.

Bio-indicators can be biological processes (enzymes, proteins…) or living microbes which can be used to screen the health of an ecosystem such as natural environment or bioprocesses [START_REF] Parmar | Bioindicators: the natural indicator of environmental pollution[END_REF]. There are some characteristics to consider a species or metabolites as a bio-indicator. They should be abundant and common between the bioprocess.

In addition to the identification of potential bio-indicators, this longitudinal study comparing the temporal dynamics of microbes and metabolites confirmed the usefulness of the acclimation strategies. Indeed we saw that the dynamics of the descriptors depends on the ammonia accumulation speeds related to a decrease of the performances modification.

Prevention and remediation of the bioprocess

The third and last objective of this thesis work was to go further on the mechanisms of the zeolite mitigation and its influence on the microbiome dynamics. In our experiment different treatments were applied on zeolite to change its physico-chemical properties. These different zeolites were added into batch reactors with or without ammonia. Previous results from [START_REF] Poirier | Improving anaerobic digestion with support media: Mitigation of ammonia inhibition and effect on microbial communities[END_REF] have shown that the presence of zeolite induced a modification on the microbial population only during ammonia inhibition.

Based on this result our study focused on the evaluation of the zeolite activity at very low ammonia stress, simulating a beginning of ammonia accumulation.

During that work we did not observe differences on the digester performances and on the microbiota structuration between the conditions containing solid zeolite independently of the treatment (raw, heated or boiled). However the presence of zeolite under a low ammonia stress induced a modification of the microbial population compared to the conditions without ammonia and containing zeolite, as observed in Poirier et al study.

A classification method was used to determine more precisely the influence of the presence of ammonia and/or zeolite on the microbiota. The results showed that the addition of zeolite in presence of ammonia led to the modification of the microbial community especially of microbes involved in a syntrophy for the volatile organic acids degradation. This resulted in the decrease of the inhibition. Because this results were noticeably similar than the one obtained by Poirier et al. and in order to determine if a general trend on the influence of zeolite on the microbiota can be draw, it was decided to extend the analysis by combining the datasets from both studies. Statistical analyses were developed in order to carry out the integration of data provided by different studies. Some concerns have to be taken into account. Indeed as we said before an important heterogeneity on the anaerobic microbiome is due to the operational parameters (feedstock composition, inoculum, type of digesters…) and can drown the relevant information, here the zeolite effect.

Different methods to evaluate and remove this batch effect are available. These methods have to be used carefully and appropriately in order to avoid misinterpretation from the modified data. In our comparison few sample points were kept. In order to gain in accuracy and be able to draw a general interpretation it would be necessary to increase the number of samples in the model.

Conclusion

Maintaining optimal anaerobic bioprocess performances and stability is a huge challenge. Indeed the anaerobic microbiota is highly complex and easily disturbed by the modification of operational parameters. The development of new strategies of management of the microbial ecosystem would complement the panel of tools already available to follow the bioprocess functioning. It requires an improved knowledge of AD microbiome. The aim of my thesis work was to determine how the modification of different parameters influences the anaerobic microbiome and how it relates to the digester performances. To do so high throughput analytical methodologies coupled with specific computational statistical methods were used to assess the dynamics of microbiome descriptors.

The results from this thesis work allowed to evaluate the influence of the modification of different parameters such as feeding composition and inhibitor accumulation on the microbial activity, methanogenic metabolism and metabolites composition. Longitudinal experiments were used to study the microbial adaptation and the identification of potential bio-indicators. Integrative analyses were applied in order to link molecules to potential degraders but also extend the analysis to data from other studies in order to determine a general trend of the microbial functioning. This work highlights 1) the great potential of the metabolomics analysis in the field of anaerobic digestion and the need to go further on its development, and 2) the usefulness of developing statistical methodologies and analytical workflows to meet the questions of the microbial ecologists in bioprocesses field: how to understand the whole functioning of a microbiome and how to use these information to manage the bioprocess as best we can.

To complement this thesis work and answer these questions further analyses should be performed. Deeper information regarding the functional pathways and how they are affected by the operational parameters could provide a better description of the microbiome. To do so it would be interesting to apply multiple omics methodologies at the same time to investigate the microbiome functioning. Integrative analysis of multiple omics data would help to determine more precisely at which level of the microbiota functioning the inhibitor is acting. Correlating the data from the different omics using integrative statistical analyses would allow to evidence the cause of: 1) the population rearrangement, 2) the functional redundancy, 3) the metabolic pathways modification…. This thesis work was valorised in different congresses (2 oral presentations and 3 posters sessions) and 1 publication was published. I hope to valorise the completed work in further publications (2 articles were submitted and 2 others are in preparation). Des progrès significatifs ont été réalisés ces dernières années dans la compréhension du microbiote anaérobique grâce au récent développement des techniques d'analyse moléculaire à haut débit et des approches omiques. Parmi ces technologies il peut être cité le séquençage du gène codant pour l'ARN 16S. Ce gène est présent chez tous les microorganismes. Il contient des régions hypervariables permettant l'identification des différents microorganismes en comparant les séquences obtenues à des bases de données. L'analyse non ciblée du matériel génétique d'un échantillon complexe se nomme la métagénomique et permet l'étude globale du microbiome dans un échantillon à un instant t. D'autres méthodologies omiques existent, telles que la métatranscriptomique ou encore la métaprotéomique. Différents niveaux d'information concernant l'écosystème microbien sont fournis par ces techniques ce qui pourrait permettre, par exemple, de réaliser un diagnostic microbien du digesteur. Cela consisterait en un suivi d'indicateurs biologiques, qui pourraient être des enzymes, métabolites ou des microorganismes spécifiques, pour déterminer le bon fonctionnement ou le risque d'une défaillance du digesteur.

Les technologies moléculaires à haut débit fournissent une quantité importante de données et extraire l'information clé de ces jeux de données n'est pas chose aisée. Les analyses biostatistiques computationnelles peuvent aider à identifier ces informations importantes. L'une des difficultés dans l'analyse statistiques est le choix de la méthode à employer parmi toutes celles existantes et qui permettrait de répondre de façon pertinente à la question biologique que l'on se pose. Cependant, de nouveaux développements sont encore nécessaires pour permettre l'analyse des données issues technologies d'analyse du microbiome et pour aller plus loin dans l'interprétation des données microbiennes obtenues. En effet, même si un grand nombre d'information est maintenant disponible sur la modification de la communauté microbienne par des paramètres opérationnels, des questions demeurent. Par exemple : comment la composition de l'alimentation d'un digesteur influence la dynamique microbienne ? A quel niveau du bioprocédé un inhibiteur influence un digesteur (performances globales, population microbienne, fonctions exprimées, métabolites produits …) ? Est-il possible d'identifier des bio-indicateurs précoces d'une inhibition …? Dans ce contexte, l'objectif de mon travail de thèse est de déterminer comment la modification de différents paramètres influence le microbiome est comment cela impact les performances du digesteur. Pour cela, différentes technologies moléculaires à haut débit ont été utilisées, plus spécifiquement l'analyse de l'ARN 16S et la métabolomique. Nous avons choisi de travailler sur l'ARN 16S directement car cette technique permet de cibler spécifiquement les microorganismes actifs qui sont encore peu souvent étudiés. La métabolomique permet l'étude des métabolites qui sont des molécules de faibles poids moléculaire, dont l'étude permet de déterminer les voies métabolique utilisées lors de la dégradation d'un substrat. Cette méthodologie est particulièrement récente en comparaison des autres méthodologies omiques et est encore peu utilisée dans le domaine de la digestion anaérobie. Pour extraire les informations essentielles et intégrer les différentes données obtenues, des méthodes spécifiques de biostatistiques ont été utilisées. Ces méthodes ont été choisies pour leur pertinence à répondre aux questions posées durant ce travail de thèse.

Le premier objectif de ce travail de thèse a été d'étudier l'influence de la composition et de sa modification sur l'activité microbienne. Une des stratégies pour l'optimisation des performances des digesteurs et contrer de possibles inhibitions, est de réaliser la digestion commune de différents déchets. Le choix de la composition de l'alimentation influence grandement les performances de production. Cependant, peu d'informations existent concernant le lien entre l'activité microbienne et la dégradation des substrats constituant l'alimentation. Dans un premier temps l'influence de la composition de l'alimentation a été étudiée. Les digesteurs ont été alimentés avec différents mélanges de substrats. Les résultats montrent que la composition en substrat joue un rôle sur la diversité et la spécificité du microbiome. Ceci expliquerait les différences de performances de production de biogaz entre les réacteurs. Dans un deuxième temps, des corrélations entre la dégradation de différents substrats et l'activité microbienne ont été établies grâce à l'intégration des données de l'analyse de l'ARN 16S et métabolomique. Ce travail de thèse a permis d'établir de nouveaux potentiels dégradeurs de molécules spécifiques de différents substrats.

Le second objectif de ce travail de thèse a été de déterminer l'influence de l'accumulation de l'azote ammoniacal sur l'activité microbienne. L'azote ammoniacal est un inhibiteur majeur de la digestion anaérobie et particulièrement de l'étape de méthanogénèse. Il peut être produit lors de la dégradation de substrats riches en protéines. Si certaines études ont permis d'identifier des microorganismes sensibles à l'azote ammoniacal, aucun consensus n'a pu être établi pour le moment. Dans ce travail une expérience a été réalisée en ajoutant de l'azote ammoniacal à différentes vitesses. Les résultats montrent qu'une accumulation rapide entraine des modifications importantes sur les performances des digesteurs. La capacité de résistance des microorganismes a été étudiée en réalisant une étude longitudinale de l'ARN 16S tout au long de l'expérience. Cette analyse a permis d'évaluer la capacité d'adaptation des microbes face à des conditions d'accumulation de l'azote ammoniacal différentes. De plus, des microorganismes spécifiquement

  .2.5. Chemical analysis .................................................................................. 1.2.6. RNA extraction and 16S rRNA sequencing analysis ............................ 1.3. Results and discussion .................................................................................. 1.3.1. Physico-chemical characteristics of the substrates and inoculum ......... 1.3.2. Mono-digestion of the substrates........................................................... 1.3.3. Co-digestion of the substrates ............................................................... 1.3.4. Influence of the co-digestion on the methanogenic pathways .............. 1.3.5. Estimation of the optimal blend of the co-digestion using indicators ... 1.4. Conclusion .................................................................................................... 1.5. Supplementary figures.................................................................................. 2. Article 2: Integrative analyses to investigate the link between microbial activity and molecules degradation during anaerobic digestion ......................................................... 2.1. Introduction .................................................................................................. 2.2. Methods ........................................................................................................ 2.2.1. Feedstock preparation ............................................................................ 2.2.2. Bioreactors experimental set-up ............................................................ 2.2.3. RNA extraction and 16S rRNA sequencing .......................................... 2.2.4. Metabolomics analysis .......................................................................... 2.2.5. Statistical analysis ................................................................................. 2.3. Results and discussion .................................................................................. 2.3.1. Influence of the feeding composition on the microbial community ..... 2.3.2. Substrates degradation dynamics .......................................................... 2.3.3. Correlation between microbial activity and substrates degradation pattern 174 2.4. Conclusion .................................................................................................... 2.5. Supplementary figures.................................................................................. 3. Influence of the modification of the substrate composition on the anaerobic microbiome 186 3.1. Material and methods ................................................................................... 3.1.1. Experimental setup ................................................................................ 3.1.2. Biogas and chemical analyses ............................................................... 3.1.3. Microbial and statistical analyses .......................................................... 3.2. Preliminary Results ...................................................................................... 3.2.1. Influence of the feeding modification on the digester performances .... 3.2.2. Microbial community dynamics ............................................................ 1.2.6. RNA extraction and 16S rRNA sequencing analysis ............................ 1.2.7. Data analyses ......................................................................................... 1.3. Results and discussion .................................................................................. 1.3.1. Effect of zeolite treatments on the anaerobic digestion performances .. 1.3.2. Effect of zeolite treatments on different chemical parameters .............. 1.3.3. Effect of the ammonia and zeolite on the methanogenic pathways ...... 1.3.4. Microbial community descriptiongeneral trends ............................... 1.3.5. Identification of the key phylotypes of the different conditions ........... 1.3.6. Biological pathway reconstruction ........................................................ 1.4. Conclusion .................................................................................................... Supplementary materials ....................................................................................

2. 3

 3 .5. Microorganisms specific to the study from Cardona et al ..................... 2.4. Conclusion ....................................................................................................3.Conclusion and perspectives of the chapter ..................................................... Supplementary materials .................................................................................... General discussion of this thesis work ....................................................................... 1. Discussion about the experimental methods .................................................... 1.1. Batch and semi-continuous digesters ........................................................... 1.2. Inoculum....................................................................................................... 1.3. Ammonia inhibition ..................................................................................... 2. Discussion about the analytical methods ......................................................... 2.1. Untargeted metabolomics analysis ............................................................... 2.2. Statistical analyses........................................................................................ 3. Discussion about the results ............................................................................. 3.1. Influence of operational parameters on the microbiome .............................. 3.2. Identification of bio-indicators of the bioprocess functioning .....................

Figure 1 :

 1 Figure 1: Details of waste production by inhabitant in France in 2016 (modified from (ADEME, 2018))

Figure 2 :Figure 3 :

 23 Figure 2: Evolution in percentage of the household waste composition in France since 1993 (modified from (ADEME, 2019))

Figure 4 :

 4 Figure 4: Origins of the waste treated by the methanisation in France in 2016 (modified from ((ADEME, 2017))

Figure 5 :

 5 Figure 5: Anaerobic digestion main steps. OHPA: Obligate Hydrogen Producing-Acetate bacteria; SAO: Syntrophic Acetate Oxidation; HAPA: HomoAcetogenic Producing-Acetate bacteria

Figure 6 :

 6 Figure 6: Contributions of support materials in anaerobic digestion[START_REF] Arif | Applications of materials as additives in anaerobic digestion technology[END_REF]

Figure 7 :

 7 Figure 7: 'Photo 51' from Rosalind Franklin representing a fuzzy X-ray of a strand DNA (http://edu.mnhn.fr/mod/page/view.php?id=1893)

Figure 8 :Figure 9 :

 89 Figure 8: Omics methods to determine phylogenetic and functional diversity of a microbial community. Adapted from[START_REF] Vanwonterghem | Linking microbial community structure, interactions and function in anaerobic digesters using new molecular techniques[END_REF]

Figure 10 :

 10 Figure 10: The targeted (a) and untargeted (b) workflow for LC/MS-based metabolomics[START_REF] Patti | Metabolomics: The apogee of the omics trilogy[END_REF]

Figure 11 :

 11 Figure 11: Projection of high-dimensional data using Principal Component Analysis (PCA). The aim of the PCA is to maximise the covariance between individuals to get the best projection and explain the variation within a dataset.

Figure 12 :

 12 Figure 12: Picture of the batch reactor

Figure 14 :

 14 Figure 14: Scheme of the sampling strategy

Figure 15 :

 15 Figure 15: Principle of the sequencing using a Personal Genome Machine (Ion Torrent)

Figure 17 :

 17 Figure 17: General principle of the metabolomics analysis using FTICR (Bruker) modified from https://www.semanticscholar.org/paper/Negative-Ion-Electron-Capture-Dissociation-(niECD)%3A-Wang/d26bbee5ffc3042880cca222d75625741e29f7e1/figure/2

Figure 18 :

 18 Figure 18: FROGS analytic workflow description for the sequencing data preprocessing (http://frogs.toulouse.inra.fr/)

Figure 19 :

 19 Figure 19: XCMS analytic workflow for the metabolomics data preprocessing (Smith et al., 2006)

Figure 21 :Figure 22 :

 2122 Figure 21: Schematic representation of the data analysis by the Partial Least Square Discriminant Analysis

Figure 23 :

 23 Figure 23: Time course analysis framework (Straube et al., 2015). A first step consists in the filtering of the variables where no temporal modification can be observed. Then based on the experimental samples a time profile curve is modelled. The third step consists in clustering the variables sharing a similar time profile. P-values are estimated to determine the differences between the variables time modification, the defined conditions or considering both time and group.

Figure 24 :

 24 Figure 24: Relationship between a condition 1 trajectory (reference) and a condition 2 trajectory (query) (Straube et al., 2017). a) the trajectories are positively correlated with no delay; b) positively correlated with negative delay as the query evolves after the reference; c) negatively correlated with positive delay as the query evolves before the reference; d) negatively correlated with no delay; e) negatively correlated with negative delay and f) positively correlated with positive delay

Figure 25 :

 25 Figure 25: Cumulated CH 4 production (mgC/gCOD) over time (days) for the different substrates in mono-digestion experiments. Mean values of the triplicate bioreactors for CH 4 productions, error bars represent standard deviation within triplicates. S, F and G stand for Sludge, Fish and Grass respectively.

Figure 26 :

 26 Figure 26: Cumulated CH 4 production (mgC/gCOD) over time (days) for Fish and Grass used as co-substrates in co-digestion with wastewater sludge. Mean values of the triplicate bioreactors, error bars represent standard deviation within triplicates. S-100 stands for wastewater sludge alone, F-25, F-50, F-75, F-100 stands for respectively 25, 50, 75 or 100% of fish (F) in co-digestion with sludge, G-25, G-50, G-75, G-100 stands for respectively 25, 50, 75 or 100% of Grass (G) in co-digestion with sludge.

Figure 27 :

 27 Figure 27: Apparent isotope fractionation over time (days) for Fish and Grass used as co-substrates in co-digestion with wastewater sludge. Mean values of the triplicate bioreactors, error bars represent standard deviation within triplicates. S-100 stands for wastewater sludge alone, F-25, F-50, F-75, F-100 stands for respectively 25, 50, 75 or 100% of fish (F) in codigestion with sludge, G-25, G-50, G-75, G-100 stands for respectively 25, 50, 75 or 100% of Grass (G) in co-digestion with sludge.

Figure 28 :

 28 Figure 28: Taxonomic composition at genus level based on the 16S rRNA archaeaspecific amplicon sequences. (A) Samples from reactors fed with sludge and/or fish at days 21

Figure 29 :

 29 Figure 29: Experimental Batch methane production (mgC/gCOD) compared to the Maximal Digestion Indicator and the Optimal Digestion Indicator over time (days) for fish and grass substrates in co-digestion with wastewater sludge. Mean values of the triplicate bioreactors for CH 4 productions, error bars represent standard deviation within triplicates. S-100 stands for wastewater sludge alone, F-25, F-50, F-75, F-100 stands for respectively 25, 50, 75 or 100% of fish (F) in co-digestion with sludge, G-25, G-50, G-75, G-100 stands for respectively 25, 50, 75 or 100% of Grass (G) in co-digestion with sludge.

Figure 30 :

 30 Figure 30: Physico-chemical results of the mono-digestion reactors. (A) Dissolved Organic and Inorganic Carbon (mgC/L/gCOD), (B) Volatile Fatty Acids (mgC/L/gCOD) and (C) Ammonia evolution over time (days) for the different substrates in mono-digestion. Mean values of the triplicate bioreactors, error bars represent standard deviation within triplicates. S, F and G stand for Sludge, Fish and Grass respectively.

Figure 31 :

 31 Figure 31: Physico-chemical results of the co-digestion reactors (A) Dissolved Organic and Inorganic Carbon, (B) Volatile Fatty Acids and (C) Ammonia evolution (mgC/L/gCOD) over time (Days) for the both substrates Fish and Grass waste in co-digestion with wastewater sludge at different ratios. Mean values of the triplicate bioreactors, error bars represent standard deviation within triplicates. S-100 stands for wastewater sludge alone, F-25, F-50, F-75, F-100 stands for respectively 25, 50, 75 or 100% of fish (F) in co-digestion with sludge, G-25, G-50, G-75, G-100 stand for respectively 25, 50, 75 or 100% of Grass (G) in codigestion with sludge.

Figure 32 :

 32 Figure 32: Microbial dynamics across time and with respect to the different feeding compositions. Sample plot from the sPCA from the 16S rRNA dataset. Digesters are represented by colours and number of days by symbols.

Figure 33 :

 33 Figure 33: Most discriminant active microbial community composition and dynamics.

Figure 34 :

 34 Figure 34: Molecules dynamics within bioreactors and across time. This heatmap shows the

Figure 35 :

 35 Figure 35: Dynamics of the active microorganisms (solid blue line) correlated to the molecules degradation (dashed red line) within the samples at day 21. Duplicates were carried out on the bioreactors containing only fish, grass or sludge. Mean values of the different microbial activity or molecules degradation rates by clusters, trace represents standard deviation. S100 stands for wastewater sludge alone, F25, F50, F75, F100 stands for respectively 25, 50, 75 or 100% of fish (F) in co-digestion with sludge, G25, G50, G75, G100 stands for respectively 25, 50, 75 or 100% of Grass (G) in co-digestion with sludge.

Figure 36 :

 36 Figure 36: Cumulated biogas production (mgC/gCOD) over time (Days) for the different bioreactors. Mean values of the triplicate bioreactors, error bars represent standard deviation within triplicates. S100 stands for wastewater sludge alone, F25, F50, F75, F100 stands for respectively 25, 50, 75 or 100% of fish (F) in co-digestion with sludge, G25, G50, G75, G100 stands for respectively 25, 50, 75 or 100% of Grass (G) in co-digestion with sludge. Red solid lines correspond to the 16S rRNA sequencing points and metabolomics points. Purple dashed line corresponds to the point where only metabolomics analysis was carried out.

Figure 37 :

 37 Figure 37: Alpha diversity of the total active microbial community within the mixtures.

Figure 38 :

 38 Figure 38: PLS data integration of the microbial activity and molecules degradation rate. Sample ordination plots according to the microbial activity (A) and molecules degradation dataset (B) indicates a similar influence of the feeding composition on the microorganisms as on the degradation of the molecules. In C, each arrow links matches samples from the two superimposed ordination plots. Short arrows represent a strong similarity in the information contained in both datasets. The correlation circle plot (D) shows the microorganisms and the molecules which had a similar dynamics across the different samples.

Figure 39 :

 39 Figure 39: Experimental design of anaerobic co-digestion in semi continuous reactors. S series corresponds to the reactors initially fed with only sludge. G series corresponds to the reactors initially fed with a ratio sludge:grass of 90:10. Numbers (1, 2, 3) correspond to the replicates.Phase 1 corresponds to the acclimation phase; Phase 2 corresponds to the modification of the feeding composition (all the reactors were fed with a ratio of sludge:grass at 50:50); Phase 3 corresponds to the return to the initial feeding composition (S: sludge only and G: sludge:grass 90:10)

Figure 40 :

 40 Figure 40: Physico-chemical parameters results for the G series (initially fed with 10:90 grass:sludge) and S series (initially fed with 0:100 grass:sludge) for each triplicate and across time. The solid line indicates the modification of the feedstock composition to reach a ratio 50:50 of grass:sludge in all the digesters. The dashed line indicates the return to the initial feedstock composition by triplicates (G and S series).

Figure 41 :

 41 Figure 41: Estimation of grass accumulation in the digesters across time if no grass was degraded. S series corresponds to the triplicate initially fed with sludge only and G series to the triplicate fed with a ratio grass:sludge of 10:90. Numbers of days in x axis correspond to the sequencing sampling date.

Figure 42 :

 42 Figure 42: Taxonomic composition at genus level based on the 16S rRNA archaeaspecific amplicon sequences by replicate. G1-G2-G3 represent the replicate of the G series (fedinitially with a ratio 10:90 of grass:sludge). S1-S2-S3 represent the replicate of the S series (fed initially with a ratio 0:100 of grass:sludge). The solid line represents the first modification of the feeding composition to a ratio of 50:50 of grass:sludge. The dashed line represents the second modification of the feeding composition.

Figure 43 :

 43 Figure 43: Taxonomic composition at order level based on the 16S rRNA bacteriaspecific amplicon sequences by replicate. G1-G2-G3 represent the replicate of the G series (fed

Figure 44 :

 44 Figure 44: Principal component analysis (PCA) representing the microbiota dynamics across time for the different feeding condition. S: digesters initially fed with only sludge; G: digesters initially fed with a mix of 10:90 grass:sludge; Phase 1: stabilisation phase; Phase 2: modification of the feedstock composition to 50:50 grass:sludge; Phase 3: return to the initial feeding composition.

Figure 45 :

 45 Figure 45: Estimation of the half inhibiting ammonia concentration using a Hill model based on the CH 4 production rate. Batch experiments with different ammonia concentration carried out before to the semi-continuous digesters experiment.

Figure 46 :

 46 Figure 46: Scheme of the experimental design and sampling dates indication for the 16S rRNA sequencing and untargeted metabolomics analyses

Figure 47 :

 47 Figure 47: Analytical workflow used for the analysis of the temporal dynamics of the OTUs between the digesters inhibited at different ammonia accumulation speeds.

Figure 48 :

 48 Figure 48: Performance data for biogas and methane (CH 4 ) production, isotopic fractionation index (α app ), NH 3 , pH and volatile fatty acids (acetate, propionate). Vertical red line corresponds to the beginning of the ammonia addition. Vertical black dashed line corresponds to the day when the targeted NH 3 concentration was reached in the different digesters. Horizontal black lines correspond to the α app values to determine the methanogenic pathways (acetoclastic <1.055 and hydrogenotrophic > 1.065).

Figure 49 :

 49 Figure 49: Microbial community dynamics (16S rRNA metabarcoding) in the different digesters and across time. A single principal Component Analysis (PCA) was carried out on allthe samples simultaneously, but for the sake of clarity, the samples corresponding to the different digesters were presented separately P0: control without ammonia addition; P5: accumulation of ammonia during 75 days; P4: accumulation of ammonia during 50 days; P3: accumulation of ammonia during 25 days; P2: accumulation of ammonia during 14 days; P1: accumulation of ammonia during 2 days. Numbers correspond to the sampling date. Green colour corresponds to the stabilisation phase (days 70, 84 and 98) and black colour corresponds to the ammonia accumulation phase(days 101, 105, 108, 112, 119, 133 and 154).

Figure 50 :

 50 Figure 50: Time profile dynamics of the OTUs in the digester P1. Smooth spline curves of the OTUs distributed in 8 clusters after kml clustering on the P1 data. Vertical red line corresponds to the beginning of the ammonia accumulation. Vertical black dashed line corresponds to the day when the targeted NH 3 concentration was reached in the different digesters.

Figure 51 :

 51 Figure 51: Time course profiles in the different digesters of selected microbes. Vertical red line corresponds to the beginning of the ammonia accumulation. Vertical black dashed line corresponds to the day when the targeted NH 3 concentration was reached in the different digesters.

Figure 52 :

 52 Figure 52: Metabolites composition dynamics (untargeted FTICR) in the different digesters and across time. Principal Component Analysis (PCA) was carried out on all the

Figure 53 :

 53 Figure 53: Metabolites time profiles dynamics in the different digesters. Vertical red line corresponds to the beginning of the ammonia accumulation. Vertical black dashed line corresponds to the day when the targeted NH 3 concentration was reached in the different digesters.

Figure 55 :

 55 Figure 55: Variance test comparison of the Gombertz parameters (maximal production, production rate and lag phase) for both CO 2 and CH 4 . Each black point represents one digester. Red triangles represent the mean for each group. Green point represents the mean of all samples (Grand Mean). The different conditions are labelled on the top. Digesters with ammonia are named N and digesters without ammonia are named C. The number correspond to the different conditions of zeolite 0: no zeolite, 1: raw zeolite, 2: liquid residue, 3: solid residue, 4: heated zeolite. Conditions are ordered according to the contrast coefficient represented in the x-axis. The range of the parameter value is represented on the left in addition to the value of the Grand Mean and its standard deviation. The values of the group means are represented on the right. The blue square represents the area of the mean square root error within the conditions. The red square represents the area of the mean square root error between the conditions. The F-statistic and p-value is indicated. F-statistic value is the ratio of the area MS-within and of the MS-between. The residuals are represented as a blue line reported on the right of the y-axis. A-ANOVA results for the CO 2 maximal production parameter. B-ANOVA results for the CO 2 production rate parameter.C-ANOVA results for the CO 2 lag phase parameter. D-ANOVA results for the CH 4 maximal production parameter. E-ANOVA results for the CH 4 production rate parameter.F-ANOVA results for the CH 4 lag phase parameter.

Figure 56 :

 56 Figure 56: Evolution of the chemical parameters. The data are the mean values for the triplicate bioreactors, standard deviations are indicated.

Figure 57 :

 57 Figure 57: Methanogenic pathway measured through the isotopic fractionation of the CH 4 (δ 13 C-CH 4 ). The data are the mean values for the triplicate bioreactors, standard deviations are indicated.

Figure 58 :

 58 Figure 58: Abundances of the most relevant microorganisms in the digesters under ammonia inhibition. This heatmap represents the OTUs (column) selected after 3 pair-wise sparse Partial Least Square Discriminant Analysis (sPLS-DA) on the samples (row) to discriminate the different types of digesters. The colour of the heatmap goes from white to blue in light with the abundances of the OTUs after center log ratio transformation. Taxonomic affiliation is presented at the family level for the bacteria and genus level for the archaea. Side colours of the graph represent the groups of samples used to compute the sPLS-DA. Arrows indicate remarkable OTUs.

Figure 59 :

 59 Figure 59: Summary of the hypothetical pathways of degradation for the different conditions. Here are highlighted in blue the molecules which accumulated and the methanogenesis pathway involved during the degradation. Specific microorganisms identified by the sparse PLS-DA are represented inside a rectangle and situated in the pathway where we supposed they were active. The role of the microorganisms between question marks is unknown. Genus Mbac: Methanobacterium; Orders Spi: Spirochaetaceae; Cloa: Cloacimonadaceae; Rik: Rikenellaceae; Pal: Paludibacteraceae; Pep: Peptococcaceae; Mari: Marinilabiliaceae; Family Izi: Izimaplasmatales

Figure 61 :

 61 Figure 61: Comparison of microbial community diversity (16S rRNA metabarcoding) in the different digesters/reactors. A) Principal component analysis of all the samples of digesterswithout (C in blue) and with ammonia (N in red). B) Principal component analysis of all the samples of digesters without ammonia (C series). C) Principal component analysis of all the samples of digesters with ammonia (N series). Samples are grouped according to the type of zeolite added (0: no zeolite; 1: untreated zeolite; 2: liquid residue after boiling zeolite; 3: boiled zeolite and 4: heated zeolite).

Figure 62 :

 62 Figure 62: Microbial community composition at day 22 during the maximal CH 4 production phase. Relative abundances of the archaeal (A) and bacterial (B) community at day 22 in the different batch conditions and for each triplicate. Taxonomic affiliation is presented at the genus level for archaea and order level for bacteria.

Figure 63 :

 63 Figure 63: Graphical abstract of the experiment done by Simon Poirier to study the influence of different mineral supports on the digester performances and microbial ecosystem under ammonia inhibition (Poirier et al., 2017)

Figure 64 :Figure 65 :

 6465 Figure 64: Comparison of microbial community diversity (16S rRNA metabarcoding) in the different digesters from both studies. Principal component analysis of all the samples from digesters without (C) and with ammonia (N). Samples colours represent the type of zeolite added (0: no zeolite; 1: untreated zeolite). Samples markers represent the origin of the samples (LC: Cardona thesis; SP: Poirier thesis)

Figure 66 :

 66 Figure 66: Abundance of the most relevant microorganisms in the digesters under ammonia inhibition. This heatmap represents the OTUs (column) selected after 2 pairwise sparse Partial Least Square Discriminant Analysis (sPLS-DA) on the samples (row) to discriminate the different types of digesters. The colour of the heatmap goes from white to blue in light with the abundances of the OTUs after center log ratio transformation. Taxonomic affiliation is presented at the family level for the bacteria and genus level for the archaea. Side colours of the graph represent the groups of samples used to compute the sPLS-DA. Red rectangles indicate remarkable OTUs.

Figure 67 :

 67 Figure 67: Schematic representation of three mechanisms of direct interspecies electron transfer (DIET) between bacteria and archaea. (A) DIET via conductive pili. (B) DIET via membrane-bound electron transport proteins. (C) DIET via abiotic conductive materials.(Park et al., 2018b)

Titre:

  Faire progresser les biotechnologies environnementales par le biais d'approches d'écologie moléculaire: de la description à la gestion Mots-clés : digestion anaérobie ; séquençage haut débit ; métabolomique non ciblée ; biostatistiques ; perturbation ; zéoliteRésumé :L'utilisation des énergies renouvelables joue un rôle crucial dans la réduction des émissions de gaz à effet de serre. Dans ce contexte de préservation de l'environnement, la digestion anaérobie (DA) est un bioprocédé permettant la réduction du volume de déchets organiques et la production de biogaz, une énergie verte. Cependant, son développement est limité du fait de nombreuses difficultés. Un de ces obstacles est la difficulté de maintenir un procédé stable et performant. Des améliorations dans le suivi de ce bioprocédé est nécessaire, spécialement concernant la communauté microbienne. Pour le moment le suivi du bon fonctionnement du bioprocédé se base sur des analyses chimiques et l'expertise de l'exploitant. Hors le bon fonctionnement dépend principalement du fonctionnement de la communauté microbienne au sein du digesteur. En effet, la vulnérabilité du microbiome face à des modifications de paramètres opératoires peut entrainer la défaillance des exploitations, la pollution due au non traitement des déchets ainsi que des pertes économiques. La caractérisation fine de la composition du microbiome et de son fonctionnement pourrait fournir des clés pour limiter le risque d'un dysfonctionnement du bioprocédé. Pour ce faire, il est essentiel de comprendre précisément comment les paramètres techniques et chimiques du bioprocédé influencent l'équilibre microbien et identifier les procédés métaboliques qui conduisent à la défaillance du bioprocédé.

  The first group (group I) consumes only carbon substrates to produce acetate and is represented by Butyribacterium and Peptococcus genera for example.

				The second group
	(group II) uses CO 2 and H 2 through the acetyl-CoA pathway (2CO 2 + 4H 2 ->
	CH 3 COOH + 2H 2 O). They belong to genera Acetoanaerobacterium,
	Acetobacterium,	Acetofilamentum,	Acetohalobium,	Acetomicrobium,
	Acetothermus, Acetotitomaculum, Clostridium, Eubacterium, Sporomusa or
	Thermoanaerobacter.

Table 1 : Potential biogas production from lipids, carbohydrates and proteins

 1

	(Alves et

Table 2 : Example of physico-chemical properties of different types of organic waste

 2

	(Mata-Alvarez et al., 2014)
	Waste	Characteristics
	Sewage sludge	Low organic load, high buffer
	Animal manure	Low organic load, protein-rich (ammonia)
	Organic municipal solid waste Variable composition (seasonality), improper material
	Agro-industrial	Variable composition, may lack N
	Slaughterhouse	Protein-rich, lipid-rich (long chain fatty acids)
	FOG (Fat-Oil-Grease)

Table 3 : Physico-chemical characteristics of the inoculum Inoculum COD (gO 2 /L)

 3

	12.92

Table 4 : Physico-chemical characteristics of the different substrates Biowaste Fish Sewage sludge batch Spring grass Oil Sewage sludge CSTR Winter grass COD (gO 2 /L)

 4

			287	310	104	95	3418	65	681.5
	MS (%)	21	24	5	11	0	8.1	98.02
	MV (%)	92	78.5	81.5	84	100	49.23	74.35
	C (%)	50.05	43.67	41.58	42.55	ND	38.3	41.7
	N (%)	3.44	9.5	2.46	2.2	ND	2.91	2.54
	C/N		14.53	4.6	16.89	19.37	ND	13.16	16.42
	DOC (mgC/L)	2704	7921.6 1250.4	7692	199.2	1520	7692
	DIC (mgC/L)	32	346.4	99.2	424.8	36.8	104	36
	NH 4	+ (mg/L)	56.42	899.21 299.41	438.2 591.87 273.66	343.1

Table 5: Buffer composition Name Formula Concentration (g/L) Dipotassium phosphate

		K 2 HPO 4	0.27
	Disodium phosphate dodecahydrate	Na 2 HPO 4 , 12H 2 O	1.12
	Calcium chloride dihydrate	CaCl 2 , 2H 2 O	0.08
	Magnesium chloride tetrahydrate	MgCl 2 , 4H 2 O	0.1
	Ferrous chloride tetrahydrate	FeCl 2 , 4H 2 O	0.02
	Sodium sulfide nonahydrate	Na 2 S, 9H 2 O	0.1
	Sodium hydrogenocarbonate	NaHCO 3	11.76

  12 C 16 O 2 ), 45 ( 13 C 16 O 2 and 12 C 17 O 16 O) and 46 ( 12 C 16 O 18 O,

13 C 16 O 17 O, 12 C 17 O 17 O

Table 6 : Summary of the experiments carried out in the thesis work Objective Influence of the feeding composition: case of the anaerobic co-digestion Identification of bio- molecular indicators of ammonia inhibition Influence of the zeolite under low ammonia pressure Sub- objectives

 6

				Influence of modifications
		Determine the optimal blend	Correlation between active microbes and molecules degradation rate	of the feeding composition on an acclimated or not acclimated microbial	Influence of the ammonia accumulation speed on the microbiome	Determination of the underlying mechanisms of the zeolite
				community
	Thesis chapter	Chapter I part 1	Chapter I part 2	Chapter I part 3	Chapter II	Chapter III
	Experimental design	Batch experiment	CSTR experiment	CSTR experiment	Batch experiment
			16 RNA sequencing		16 RNA sequencing
	Analyses	chemical analysis	untargeted metabolomics (LC-	16S rRNA sequencing	untargeted metabolomics	16S rRNA gene sequencing
			MS LTQ orbitrap)		(FTICR in direct infusion)
			Determination of the			Identification of the microbes
			molecules specific of each			influenced by the presence of
	Statistical analyses	Gompertz modelling on biogas production	substrate: sparse PLS-DA Correlation between active microbes and molecules degradation: PLS in canonical mode	Visualisation of the sample distribution according to the feeding modification across time: PCA	Determination of the dynamics of the different variables (microbes or metabolites): time course analyses	ammonia and/or the zeolite: sparse PLS-DA Comparison to data from the literature with a correction of batch effect and sPLS-DA
	Publication	accepted in Waste	submitted to FEMS	In preparation	In preparation	submitted to Chemical

Table 7 : Characteristics of substrates and inoculum

 7

	Parameters	Wastewater Sludge	Fish	Grass	Inoculum
	NH 4	+ (mgN/L)	299	899	438	628
	DOC (mgC/L)	1250	7921	7692	149
	DIC(mgC/L)	99	346	424	753
	COD (gO2/L)	103	310	95	13
	C (%)	41.58	43.67	42.55	22.58
	N (%)	2.46	9.50	2.20	2.19
	C/ N	16.89	4.60	19.37	10.29
	Dry matter (DM) (%)	5	24	11	1
	Volatile matter (VM) (%)	81	79	84	61
	Lactate (mgC/L)	0.00	0.00	398.80	23.40
	Formate (mgC/L)	0.00	138.52	0.00	0.00
	Acetate (mgC/L)	537.08	62.88	11.08	2.08
	Propionate (mgC/L)	441.83	0.00	0.00	0.00
	Butyrate (mgC/L)	199.20	0.00	0.00	0.00
	Valerate (mgC/L)	43.88	0.00	0.00	0.00

Table 8 : Kinetics parameters for CH 4 production using Gompertz model for the different mixtures of substrates, BMP tests and the biodegradation indicators.

 8 The data are the mean values for the triplicate bioreactors, standard deviations are indicated. 𝜇 correspond to the CH 4 production rate, 𝜆 to the latency and A to the maximum production.

			Name	𝜇 (mgC/day/gCOD) 𝜆 (day) A (mgC/gCOD)
			F-100	2.35 (±0.57)	10.1 (±0.8) 87.55 (±13.00)
	Batchs Experimental	F-75 F-50 F-25 S-100 G-25 G-50 G-75	3.00 (±0.11) 2.49 (±0.35) 2.31 (±0.10) 2.26 (±0.26) 1.93 (±0.10) 2.97 (±0.21) 3.45 (±0.46)	10.8 (±0.6) 86.31 (±4.93) 7.7 (±0.8) 72.03 (±4.49) 9.4 (±0.5) 64.65 (±1.46) 12.8 (±1.3) 46.62 (±0.8) 4.5 (±0.6) 42.75 (±5.41) 3.2 (±1.1) 70.80 (±4.72) 2.6 (±0.7) 79.30 (±6.99)
			G-100	2.81 (±0.12)	3.1 (±0.2) 68.43 (±0.97)
	Mono-Digestion	Indicator	F75-MDI F50-MDI F25-MDI G25-MDI G50-MDI G75-MDI	2.33 (±0.49) 2.31 (±0.43) 2.28 (±0.35) 2.40 (±0.24) 2.54 (±0.20) 2.67 (±0.16)	10.8 (±0.9) 77.32 (±9.95) 11.5 (±1.1) 67.09 (±6.90) 12.1 (±0.5) 56.85 (±3.85) 10.4 (±1.0) 52.07 (±0.84) 8.0 (±0.8) 57.53 (±0.89) 5.5 (±0.5) 62.98 (±0.93)
	test	Fish-BMP	8.18 (±0.24)	0 119.35 (±15.28)
	BMP	Sludge-BMP Grass-BMP	2.09 (±0.17) 8.47 (±3.67)	0 0 118.33 (±12.9) 44.35 (±18.3)
	Digestion Optimal	Indicator	F75-ODI F50-ODI F25-ODI G25-ODI G50-ODI G75-ODI	6.66 (±0.22) 5.14 (±0.21) 3.61 (±0.19) 6.88 (±2.80) 5.28 (±1.92) 3.69 (±1.05)	0 100.60 (±16.04) 0 81.85 (±16.79) 0 63.10 (±17.55) 0 99.84 (±14.25) 0 81.34 (±15.60) 0 62.85 (±16.95)

Table 9 : Mixture details for the batch experiment and the BMP test.

 9

	Reactor	Substrate ratios (%) Sludge (g) Fish (g) Grass (g) Inoculum (g)
	F-100	Fish	0	39	0
	F-75	sludge:fish 25:75	29	29	0
	F-50	sludge:fish 50:50	58	19	0
	F-25	sludge:fish 75:25	87	10	0
	S-100	Sludge	116	0	0
	G-25	sludge:grass 75:25	87	0	32
	G-50	sludge:grass 50:50	58	0	63
	G-75	sludge:grass 25:75	29	0	95
	G-100	Grass	0	0	126
	Sludge-BMP	Sludge	6.74	0	0
	Fish-BMP	Fish	0	2.26	0
	Grass-BMP	Grass	0	0	7.37

Table 10 : Putative identification of the molecules in each cluster

 10

	Cluster	Substrate specificty	Feature	Measured mass (in Da)	Elemental composition	Calculated mass (in Da)	Error (in ppm)	Ion assignment	MS/MS Fragments	Molecule name	HMDB	Databases Code PubChem	LipidMaps
	1	Sludge or Grass		214.98765 116.1069	C 4 H 10 O 3 PSNa 2 214.98782 C 6 H 14 NO 116.10699	-0.7684 -0.7264 [M+NH 4 ] + 1 n/a [M+2Na-1 n/a H] +	Diethylthiophosphate N-(3-Methylbutyl)acetamide	HMDB0001460 655 HMDB0031651 98643
				218.91951	C 4 H 4 O 4 S 2 K	218.91881	3.2144	[2M+K] +	1 n/a	Thioxoacetic acid		18612521
				144.08073	C 10 H 10 N	144.08132	-4.1567 [M+H] +	1 n/a	6-Methylquinoline	HMDB0033115 7059
				161.1072	C 10 H 13 N 2	161.10787	-4.1705 [M+NH 4 ] + 1 n/a	6-Methylquinoline	HMDB0033115 7059
				161.10723	C 10 H 13 N 2	161.10787	-3.983	[M+H] +	144.0804	Tryptamine	HMDB0000303 1150
	2	Sludge		144.08073	C 10 H 10 N	144.08132	-4.1567	[M+H-H 2 O] +	1 n/a	L-Tryptophanol	HMDB0003447 6951149
				436.88109					1 n/a
				430.91425					1 n/a
				234.89398					1 n/a
				240.96739	C 9 H 7 O 3 K 2	240.96694	1.8974	[M+2K-H] + 1 n/a	p-Coumaric acid	HMDB0002035 637542
				156.04210	C 5 H 11 NO 2 K	156.04269	-3.7528 [M+K] +	112.0520	Betaine	HMDB0000043 247
				270.97801	C 10 H 9 O 4 K 2	270.97805	-0.138	[M+2K-H] 1 n/a	Trans-ferulic acid	HMDB0000954 445858
	3	Grass		141.06586	C 6 H 9 N 2 O 2	141.06585	0.0226	[M+H] +	123.0549	Imidazolepropionic acid HMDB0002271 70630
				205.06843	C 6 H 14 O 6 Na	205.06881	-1.8535 [M+Na] +	1 n/a	Galactitol	HMDB0000107 11850
				105.11022					1 n/
				130.12247					1 n/a

1 n/a: MS/MS spectrum not acquired for the given ion.

Table 11 : Active microorganisms identified in each cluster

 11

	Cluster	Feeding type	Microorganisms identification	Order and genus taxonomy information
	1	Sludge and Grass	Methanosarcina_1 Methanospirillum_24	Methanosarcinaceae -Methanosarcina Methanospirillaceae -Methanospirillum
				Cloacimonadales_12	Cloacimonadaceae -W5
				Cloacimonadales_13	Cloacimonadaceae -W5
				Clostridiales_3	Clostridiaceae 1 -Clostridium sensu stricto 1
				Anaerolineales_8	Anaerolineaceae -Flexilinea
				Synergistales_11	Synergistaceae -unknown genus
				Anaerolineales_20	Anaerolineaceae -unknown genus
	2	Sludge		Clostridiales_14 unknown order_25	Peptostreptococcaceae -Multi-affiliation BRC1 -unknown genus
				Clostridiales_39	Clostridiaceae 1 -unknown genus
				Bacteroidales_60	Marinilabiliaceae -unknown genus
				Hydrogenedentiales_31	Hydrogenedensaceae -unknown genus
				Erysipelotrichales_30	Erysipelotrichaceae -Turicibacter
				Coprothermobacterales_43	Coprothermobacteraceae -Coprothermobacter
				Clostridiales_26	Peptostreptococcaceae -Intestinibacter
				Clostridiales_10	Lachnospiraceae -unknown genus
				Methanofollis_64	Methanomicrobiaceae -Methanofollis
				Clostridiales_17	Syntrophomonadaceae -Syntrophomonas
				Clostridiales_42	Peptococcaceae -Pelotomaculum
				Clostridiales_77	Clostridiaceae 1 -Clostridium sensu stricto 1
	3	Grass		Bacteroidales_67 Lactobacillales_87	Rikenellaceae -DMER64 Enterococcaceae -Enterococcus
				Lactobacillales_52	Leuconostocaceae -Weissella
				Bacteroidales_76	M2PB4-65 termite group -unknown genus
				Spirochaetales_79	Spirochaetaceae -Treponema 2
				Clostridiales_80	Clostridiaceae 1 -Clostridium sensu stricto 12
				Enterobacteriales_104	Enterobacteriaceae -unknown genus
				Methanosarcina_6	Methanosarcinaceae -Methanosarcina
	4	Fish		Clostridiales_2	Clostridiaceae 1 -Clostridium sensu stricto 11
				Clostridiales_5	Clostridiaceae 1 -Clostridium sensu stricto 13
				Methanoculleus_18	Methanomicrobiaceae -Methanoculleus
				Clostridiales_15	Syntrophomonadaceae -Syntrophomonas
	5	Fish Sludge	and	Methanoculleus_50	Methanomicrobiaceae -Methanoculleus
				Methanobacterium_63	Methanobacteriaceae -Methanobacterium
				Methanobacterium_204	Methanobacteriaceae -Methanobacterium

Table 12 : Taxonomic affiliation of the OTUs grouped in the corresponding clusters

 12

	Cluster	OTU	Order	Family	Genus
		OTU_234 Methanomicrobiales	Methanospirillaceae	Methanospirillum
		OTU_324 Methanomassiliicoccales Methanomassiliicoccaceae
		OTU_20
		OTU_22 Sphingobacteriales	Lentimicrobiaceae	Lentimicrobium
		OTU_24 Bacteroidales	Rikenellaceae	Blvii28 wastewater-
					sludge group
		OTU_32 Bacteroidales	Rikenellaceae	Rikenellaceae RC9
					gut group
	1	OTU_62 Syntrophobacterales	Syntrophaceae	Smithella
		OTU_64
		OTU_66 Fibrobacterales	Fibrobacteraceae	Fibrobacter
		OTU_68 Syntrophobacterales	Syntrophaceae	Smithella
		OTU_79
		OTU_92 Petrotogales	Petrotogaceae	AUTHM297
		OTU_111 Pirellulales	Pirellulaceae
		OTU_176 Leptospirales	Leptospiraceae	RBG-16-49-21
		OTU_225
		OTU_173 Methanomicrobiales
		OTU_180 Methanomicrobiales	Methanospirillaceae	Methanospirillum
		OTU_31 Clostridiales	Syntrophomonadaceae	Syntrophomonas
	2	OTU_61 Clostridiales	Syntrophomonadaceae	Syntrophomonas
		OTU_67 Pedosphaerales	Pedosphaeraceae
		OTU_85 Clostridiales	Ruminococcaceae
		OTU_104 Syntrophobacterales	Syntrophobacteraceae	Syntrophobacter
		OTU_95
	3	OTU_13 Synergistales OTU_30 Synergistales	Synergistaceae Synergistaceae	Multi-affiliation Syner-01
		OTU_55 Cloacimonadales	Cloacimonadaceae	W5
		OTU_4	Clostridiales	Clostridiaceae 1	Multi-affiliation
		OTU_8	Bacteroidales	Marinilabiliaceae
	4	OTU_48 Clostridiales	Lachnospiraceae	Epulopiscium
		OTU_69 Clostridiales	Christensenellaceae	Christensenellaceae
					R-7 group
		OTU_470 Methanobacteriales	Methanobacteriaceae	Methanobrevibacter
		OTU_16 Bacteroidales	Dysgonomonadaceae	Petrimonas
	5	OTU_33 Spirochaetales	Spirochaetaceae	Sphaerochaeta
		OTU_84 Synergistales	Synergistaceae	Aminobacterium
		OTU_395 Lactobacillales	Enterococcaceae	Vagococcus
		OTU_36 Spirochaetales	Spirochaetaceae	Treponema 2
		OTU_46 Bacteroidales	Dysgonomonadaceae	Fermentimonas
		OTU_63 Lactobacillales	Enterococcaceae	Vagococcus
	6	OTU_82 Bacteroidales OTU_102 Bacillales	Dysgonomonadaceae Bacillaceae	Dysgonomonas Bacillus
		OTU_122 Clostridiales	Lachnospiraceae
		OTU_264 Bacillales	Bacillaceae
		OTU_271 Deferribacterales	Deferribacteraceae	Geovibrio

1. Article 1: Zeolite favours propionate syntrophic degradation during anaerobic digestion under low ammonia stress Author names and affiliations

  Zeolite preserved propionate degrader Peptococcaceae sensitive to ammonia  Zeolite preserved activity of hydrogenotrophic methanogens  Izimaplasmatales developed only in presence of zeolite and ammonia

	Cardona Laëtitia a , Mazéas Laurent a , Chapleur Olivier a
	a Université Paris-Saclay, INRAE, PRocédés biOtechnologiques au Service de
	l'Environnement, 92761, Antony, France
	Highlights
	 Zeolite increased archaea-bacteria syntrophy
	

Table 13 : Experimental set-up

 13

	Ammonia Reactor	Zeolite type	Sludge (g)	Biowaste (g)	(NH 4 ) 2 CO 3 (g/L)	Zeolite (g/L)
		N0	No zeolite	4	29		0
		N1	Untreated	4	29		15
	+ NH 4	N2	Liquid residue after boiling zeolite at	4	29		15
	g/L		100°C, filtered 0.22µm
	2	N3	Solid residue after boiling zeolite at 100°C	4	29		15
		N4	Heated 400°C	4	29		15
		C0	No zeolite	4	29		0
		C1	Untreated	4	29		15
	+ NH 4	C2	Liquid residue after boiling zeolite at	4	29		15
	g/L		100°C, filtered 0.22µm
	0	C3	Solid residue after boiling zeolite at 100°C	4	29		15
		C4	Heated 400°C	4	29		15

Table 14 : Kinetics parameters for CH 4 and CO 2 production using Gompertz model for the different zeolite conditions.

 14 The data are the mean values for the triplicate bioreactors, standard deviations are indicated. µ corresponds to the CH 4 production rate, λ to the latency and A to the maximum of production.

			Without ammonia				With ammonia
			A (mgC)	µ	λ (day)		A (mgC)	µ	λ (day)
				(mgC/day)					(mgC/day)
			C 931.28	47.46	6.5		N 1004.19	43.92	8.9
		0	(±6.55)	(±1.46)	(±0.2)	0	(±34.23)	(±2.24)	(±0.8)
			C 897.40	44.10	6.9		N	958.42	49.60	5.3
		1	(±11.91)	(±1.74)	(±0.1)	1	(±35.28)	(±3.38)	(±0.5)
	CH 4	2	C 895.13 (±38.83)	38.42 (±5.73)	6.0 (±0.6)	2	N (±39.59) 990.15	44.49 (±0.81)	6.4 (±0.2)
			C 937.47	46.74	6.8		N	994.72	49.03	9.0
		3	(±19.95)	(±2.09)	(±0.5)	3	(±26.77)	(±0.33)	(±0.1)
			C 960.97	42.62	5.8		N 1005.48	49.18	7.1
		4	(±37.45)	(±2.53)	(±0.3)	4	(±15.62)	(±1.04)	(±0.2)
			C 494.36	19.78	0.0		N	289.67	9.99	6.3
		0	(±6.16)	(±0.55)		0		(±6.52)	(±0.27)	(±0.8)
			C 499.22	18.56	0.0		N	301.94	12.78	3.5
		1	(±5.96)	(±0.20)		1	(±14.59)	(±1.62)	(±0.4)
	CO 2	2	C 476.07 (±7.92)	17.49 (±0.67)	0.0	2	N	270.06 (±9.18)	9.62 (±0.37)	5.8 (±0.1)
			C 484.55	18.45	0.0		N	300.71	11.87	3.8
		3	(±46.77)	(±0.87)		3	(±15.98)	(±0.29)	(±0.9)
			C 487.19	17.72	0.0		N	301.00	12.27	3.9
		4	(±19.22)	(±0.56)		4		(±2.98)	(±0.19)	(±0.2)

Table 16 : Taxonomic affiliation of the selected microorganisms.

 16 OTUs are grouped by the cluster identify on the heatmap after the sparse Partial Least Square Discriminant Analysis (sPLS-DA)

	Cluster	OTU number	Affiliation (Family and Genus level)
			GZKB124
			Clostridiaceae 1	Clostridium sensu stricto 1
			Lachnospiraceae
	1a		Bacillaceae	Bacillus
			Rikenellaceae
			Lachnospiraceae
			Rikenellaceae
			Izimaplasmatales
			Methanomicrobiaceae	Methanoculleus
			Lachnospiraceae
			Methanomicrobiaceae	Methanoculleus
	1b		Spirochaetaceae	Treponema
			Spirochaetaceae	Treponema
			Cloacimonadaceae	Candidatus Cloacimonas
			Peptococcaceae
			Peptococcaceae
			Clostridiaceae	Family XI
			Lachnospiraceae
			Peptococcaceae
			Clostridiaceae	Natronincola
	2		Izimaplasmatales order
			Clostridiaceae	Family XI
			Clostridiaceae	Family XI
			Lachnospiraceae	Herbinix
			Syntrophomonadaceae Syntrophomonas
			Syntrophomonadaceae Syntrophomonas
			Synergistaceae	Aminobacterium
			Syntrophomonadaceae Syntrophomonas
	3a		Dysgonomonadaceae	Fermentimonas
			Dysgonomonadaceae	Petrimonas
			Syntrophomonadaceae Syntrophomonas
			Syntrophomonadaceae Syntrophomonas
			Clostridiaceae	Family XI
			Methanomicrobia Class
			Methanomicrobiacaea	Methanobacterium
	3b		Paludibacteraceae Methanomicrobiacaea	Methanobacterium
			Marinilabiliaceae
			Methanosarcinaceae	Methanosarcina
			Methanomicrobiaceae	Methanoculleus

Table 17 : Description of the samples selected for the combined analysis of the experiments from Laëtitia Cardona and Simon Poirier Study Batch Name

 17

			Level of TAN (g/L)	Ammonia Zeolite	Time point (days)
		C0_SP
	al
	et Poirier	C1_SP N0_SP N1_SP	19	x x	x x	16 -43 -60
	al	C0_LC
	et Cardona	C1_LC N0_SP N1_SP	2	x x	x x	22

Supplementary materials

Supplementary materials

Supplementary materials

General discussion of this thesis work

Title: Advancing environmental biotechnologies through molecular ecology approaches: from description to management Keywords: anaerobic digestion; high throughput sequencing; untargeted metabolomics; biostatistics; disturbance; zeolite

Abstract:

Renewable energies play a crucial role in the limitation of the CO 2 emissions. Among them, anaerobic digestion allows the reduction of organic waste volume and the production of biogas, a sustainable energy. However the vulnerability of the anaerobic microbiome to the modification of operational parameters can lead to process failure and economic losses. Increasing the knowledges about the anaerobic microbiome as a whole and its impact on the digester performances would provide the keys to limit the risk of process failure. Thanks to the development of the high throughput methodologies, the understanding of the microbiome dynamics, interactions and functioning has improved.

The aim of this thesis work was to characterise the microbiome functioning under different operating conditions and how it relates to the digester performances. To do so multiple highthroughput molecular technologies, especially 16S rRNA metabarcoding and metabolomics analyses, coupled to computational biostatistics were used.

Influence of the feedstock composition was the first parameter studied. Digesters were fed with different mixtures of substrates. The results showed that the substrate composition played a significant role on the microbial diversity and specificity which could explain the different digester performances observed between the batch reactors. An integrative analytical framework was specifically used to link the microbial activity to the substrates degradation. It allowed to identify potential degraders of specific molecules. The capacity of the microorganisms to adapt to an modification of the feedstock composition was also evaluated in semi-continuous reactors.

In a second time, the influence of ammonia accumulation on the microbial dynamics was evaluated. In semi-continuous reactors ammonia was added at different speeds. The results showed that the faster the speed of accumulation was, the more the digester performances were impacted. A longitudinal analysis was performed by carrying out a temporal sampling. This analysis allowed to evaluate the adaptation capacity of the microbes when subjected to different conditions of ammonia addition. Moreover microorganisms specific to the ammonia accumulation were identified and could be used as potential bio-indicators to forecast digester inhibition.

Finally the influence of the zeolite on the microbial interactions was studied. Zeolite is a mineral support allowing to counteract ammonia inhibition. However, the mechanisms behind this mitigation capacity remain unclear. In order to unravel them, different physical treatments were applied on the zeolite before it was added into digesters containing ammonia. In general in absence of ammonia, the zeolite did not influence the microbial composition and the digester performances, while a significant effect was observed in presence of ammonia. A discriminant analysis coupled to a sparse method allowed to highlight the impact of the zeolite on the microbial syntrophy needed for the propionate degradation. Additional data from the literature were included in order to evaluate the genericity of the results.

The results from this thesis work highlight the importance of the operational parameters, even at a small-scale, on the microbial activity. Innovative statistical frameworks for the analysis of the microbiome were proposed to go further on the microbiome description. For example, key phylotypes specifically sensitive to inhibition were identified. Some of them could be used as bioindicators to control digester functioning. Moreover, this work highlights the value of developing the metabolomic analysis in the bioprocess fields, as it enables to monitor organic matter degradation and infer the potential role of the microorganisms in the degradation process.

inhibés par l'accumulation de l'azote ammoniacal ont été identifiés et pourraient être utilisés comme de bio-indicateurs pour signaler une inhibition du digesteur. De la même façon, l'évolution temporelle des métabolites a été analysée.

Le troisième et dernier objectif de ce travail de thèse a été de déterminer le mécanisme permettant à la zéolite de limiter l'inhibition par l'azote ammoniacal. La zéolite est un support minéral connue pour limiter les inhibitions pouvant avoir lieu durant le bioprocédé est dues à différents composés comme l'azote ammoniacal ou le phénol. Différentes hypothèses sur le rôle de la zéolite pour limiter l'inhibition ont été avancées : adsorption de l'azote, capacité d'échange d'ions ou le rôle de support pour la croissance microbienne. Cependant, les études décrivant l'influence de la zéolite sur l'activité microbienne et le lien avec les performances des digesteurs sont encore peu nombreuses. Dans ce travail, nous avons comparé l'influence de la zéolite sur les performances de production en présence d'une faible quantité d'azote ammoniacal. De façon générale, la zéolite n'influence pas la composition microbienne et les performances des digesteurs en absence d'azote ammoniacal, alors qu'un effet significatif a été observé en présence d'azote. Une analyse discriminante a permis de mettre en évidence l'impact de la zéolite sur la syntrophie microbienne nécessaire à la dégradation du propionate. Des données de la littérature ont été inclues à l'analyse afin d'évaluer la généricité des résultats.

Les résultats de ces travaux de thèse mettent en évidence l'importance des paramètres opérationnels sur l'activité microbienne. Des analyses statistiques innovantes ont été proposées pour décrire plus en profondeur le microbiome. Par exemple, des phylotypes clés sensibles à des inhibitions spécifiques ont été identifiés. Certains pourraient être utilisés comme bio-indicateurs pour le suivi du fonctionnement des digesteurs. De plus, ce travail met en évidence la plus-value de la métabolomique dans le domaine des bioprocédés, puisqu'elle permet de suivre la dégradation de la matière organique et de déduire le rôle potentiel des microbes dans le procédé de dégradation.