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Philippe Savarin

Le Professeur Christian

Frédéric Docteur

Docteur Flamant

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Karine Gauthier

Tony Palama

Docteur Aurélien Scalabre

Sylvie Guibert

Diane Anne Guido

Tanguy, Pierrick, Cécile, Dorothea James Olivier

Audrey

David

Baptiste

Damblon pour avoir accepté d'évaluer ce travail de thèse, ainsi que le Docteur Laetitia Shintu et le Docteur Patrice Rodien pour avoir accepté d'être membres du jury.

Ce manuscrit de thèse est l'aboutissement de trois années de travail et de nombreux défis. Je souhaiterais donc remercier les personnes talentueuses avec

Résumé

La métabolomique par résonance magnétique nucléaire (RMN) permet d'étudier la réponse métabolique globale d'un système biologique à un stimulus ou un événement physiopathologique (maladie, manipulation génétique, etc.). Cette discipline connaît un essor important dans la recherche clinique et biologique, et constitue ainsi un outil à fort potentiel pour la découverte de biomarqueurs de maladies, et l'étude de la fonction des gènes.

Cette thèse est dédiée à l'application de la métabolomique par RMN à hauts champs pour l'étude des pathologies associées à la signalisation thyroïdienne chez la souris. L'objectif global est d'identifier des biomarqueurs spécifiques liés aux différentes maladies hormonales : l'hypothyroïdie et la maladie génétique émergente résistance à l'hormone thyroïdienne due à une mutation au niveau du récepteur TRα1 (RTHα). Cette dernière est particulièrement difficile à diagnostiquer à cause du manque de marqueurs biochimiques et de symptômes spécifiques à cette maladie. De plus, elle présente des similitudes avec l'hypothyroïdie au niveau symptomatique. Des modèles murins de RTHα et de l'hypothyroïdie ont été analysés, et l'investigation a été menée sur l'urine et le plasma sanguin dans le but de différencier métaboliquement ces maladies et d'identifier des biomarqueurs spécifiques à RTHα. Des signatures métaboliques liées à chaque maladie ont été identifiées dans l'urine et le plasma sanguin. Cinq métabolites qui varient de façon significative ont été identifiés dans l'urine comme étant liés à la maladie RTHα : trimethylamine, dimethylamine, isovalerylglycine, N-acetylglucosamine et la choline. Dans le sang, ce sont les lipides insaturés qui varient de façon significative chez les souris mimant la maladie RTHα.

Abstract

Metabolomics by nuclear magnetic resonance (NMR) allows studying the metabolic response of a global biological system to a stimuli or a physiopathological even (diseases, genetic modifications, etc.). This discipline is growing especially in the clinical and biological fields, and represents a strong potential tool to identify biomarkers related to diseases, and study the function of genes.

This thesis is dedicated to the application of metabolomics by high field NMR to study thyroid signalisation pathologies in mice. The main goal is to identify biomarkers related to the emerging genetic disease called resistance to thyroid hormone due to a mutation in thyroid hormone receptor TRα1 (RTHα). This disease is particularly difficult to diagnose because of the lack of biochemical markers and specific symptoms. In addition, it presents common features with hypothyroidism in term of symptoms. Mice models of RTHα and hypothyroidism were analysed, and the investigation were driven on urine and blood plasma in order to differentiate metabolically theses diseases and identify biomarkers related to RTHα. Metabolic fingerprints related to each disease were identified in both urine and blood plasma. Five metabolites vary significantly in the urine of RTHα mice: trimethylamine, dimethylamine, isovalerylglycine, N-acetylglucosamine and choline. Unsaturated lipids vary significantly in the blood plasma of RTHα mice.

The impact of thyroid hormones (TH) and the thyroid hormone receptor TRβ on the liver metabolism were also studied in the present manuscript through NMR-based metabolomics. A mouse model, with a specific knock-out of TRβ gene in hepatocytes (LTRβ-KO), were used to study this question. To understand the function of TH mediated by TRβ, the liver metabolic response to TH, obtained from liver aqueous extracts and intact liver tissues, TRβKO and wild-type mice were compared. The results suggest the presence of direct and indirect effects of thyroid hormones on the liver metabolism.

Table of abbreviations

Introduction

The relation between the variation in the composition and the aspect of biofluids or tissues and pathologies was identified hundreds of years back 1 . This concept exists from the Middle age, in a "urine charts", the colors, smells and tastes were related to specific medical conditions (Figure 1). These variations come, of course, from the metabolism, which is influenced by certain conditions. Nowadays, modern-day metabolomics uses the same principle to analyze biological samples, but with modern techniques. The big challenge in biological and clinical studies is to discover biological markers, which lead to diagnose easily and at early stage of a disease to improve human health. The goal is to take care of patients as soon as possible to curb the disease. Biomarkers are "biological characteristics that are measured and evaluated as indicators of normal or pathological processes, and pharmacologic responses to a therapeutic intervention" [START_REF] Mamas | The role of metabolites and metabolomics in clinically applicable biomarkers of disease[END_REF] . A good biomarker must be easily measurable, and help for the diagnosis. High throughput "-omics" methods (genomics, transcriptomics, proteomics, metabolomics, etc.) lead to a new diagnostic approach and to targeted therapies.

In practical terms, metabolomics consists in the measurement of the small molecules, named metabolites, involved in biochemical processes present in a biological system (tissue, cells, biofluids, etc.). Metabolites are the final products of interactions between genes, proteins and the impact of environment. Thus, each individual has his own metabolic state at a specific time, which is also defined by the metabotype [START_REF] Holmes | Metabolic phenotyping in health and disease[END_REF] . Analyzing metabolites provides information that cannot be obtained only from the genome or the proteome. With this approach, the overall response of a biological system to a physiological or pathological event (disease, genetic manipulation, drugs, nutrition, lifestyle, etc.) can be defined.

Metabolomics was applied to different biological and clinical conditions like cancers, cardiovascular diseases, diabetes, inborn errors of metabolism, etc [START_REF] Jacob | A targeted metabolomics approach for clinical diagnosis of inborn errors of metabolism[END_REF][START_REF] Coene | Next-generation metabolic screening: targeted and untargeted metabolomics for the diagnosis of inborn errors of metabolism in individual patients[END_REF][START_REF] Ikegami | Metabolomic analysis in heart failure[END_REF][START_REF] Ruiz Canela | Comprehensive metabolomic profiling and incident cardiovascular disease: A Systematic Review[END_REF][START_REF] Markley | The future of NMR-based metabolomics[END_REF][START_REF] Sethi | Metabolomics: From Fundamentals to Clinical Applications[END_REF][START_REF] Newgard | Metabolomics and Metabolic Diseases: Where Do We Stand?[END_REF][START_REF] Jobard | A serum metabolomic fingerprint of bevacizumab and temsirolimus combination as first-line treatment of metastatic renal cell carcinoma[END_REF][START_REF] Wang | Proteomics, metabolomics and metagenomics for type 2 diabetes and its complications[END_REF] . Metabolomics studies in this field aim to understand the impact of diseases on the organism, and to discover biomarkers that predict disease incidence, severity, and progression and detect abnormalities.

In addition to its application in the discovery of biomarkers, metabolomics is often used to understand biological processes of diseases and genetic mutations [START_REF] Holmes | Metabolic phenotyping in health and disease[END_REF][START_REF] Newgard | Metabolomics and Metabolic Diseases: Where Do We Stand?[END_REF][START_REF] Oresic | Metabolomics, a novel tool for studies of nutrition, metabolism and lipid dysfunction[END_REF][START_REF] Struja | Metabolomics for Prediction of Relapse in Graves' Disease: Observational Pilot Study[END_REF][START_REF] Armitage | Metabolomics in cancer biomarker discovery: Current trends and future perspectives[END_REF] . Metabolomics is now developing as an important functional genomics tool to evaluate the impact of gene change [START_REF] Bino | Potential of metabolomics as a functional genomics tool[END_REF] . This thesis is dedicated to the application of NMR-based metabolomics for the investigation of thyroid signalisation pathologies in mice. We prospectively explored urine and blood plasma of murine models to obtain metabolic fingerprints of hypothyroidism, excess of thyroid hormone and the emerging genetic disease called resistance to thyroid hormone due to a mutation in thyroid hormone receptor TRα1 (RTHα). RTHα is particularly difficult to diagnose because of the lack of biochemical markers and specific symptoms.

Nowadays, only few researchers were carried out to understand this disease, and try to identify biomarkers to facilitate its diagnosis. RTHα patients present common features with hypothyroid patients with near normal thyroid function tests. The study of hypothyroidism here serves as a base to understand RTHα. We investigate in the end the impact of thyroid hormones and the role of thyroid hormone nuclear receptor β in the liver, by using liver samples.

The first chapter presents the metabolomics approach and its application as a diagnostic tool in the medical field. We set then in the second chapter the background of the present study. We describe thyroid hormone functions, molecular actions, their receptors and the different hormonal diseases. Materials and methods are detailed in the chapter 3. The different steps of our metabolomics studies are described.

The fourth chapter is dedicated to the investigation by using non-targeted 1 H-NMRbased metabolomics of two biofluids (urine and blood plasma) issue from mice to identify non-hormonal biomarkers related to hypothyroidism. It was crucial for us to study this disease at first to understand then the specific metabolic changes caused by RTHα. Here, murine models for hypothyroidism were compared to a control group to obtain metabolic fingerprints related to each disease in both urine and plasma. Clear discriminations between murine model for hypothyroidism and the control group were observed and specific metabolic signatures of the hypothyroid condition were identified.

The fifth chapter concerns the study of the new emerging genetic disease called resistance to thyroid hormone due to a mutation in thyroid hormone receptor TRα (RTHα), which its symptoms are close to those of hypothyroidism. Patients present a high variability in clinical features (skeletal dysplasia, growth retardation, intellectual disability, etc.), and the absence of reliable biochemical markers make the diagnosis of this disease difficult. The main goal of the study is to find, by the use of NMR based-metabolomics approach, a metabolic signature related to RTHα in mice, identify specific biomarkers and differentiate it from hypothyroidism. Five mice models carrying a mutation in Thra gene were studied, and the investigation were driven on urine and blood. Five metabolites vary significantly in the urine of RTHα mice: trimethylamine, dimethylamine, isovalerylglycine, N-acetylglucosamine and choline. Unsaturated lipids vary significantly in RTHα mice blood plasma.

In the sixth chapter, the impact of thyroid hormones (TH) and the thyroid hormone receptor TRβ on the liver metabolism were studied through NMR-based metabolomics. A mouse model, with a specific knockout of TRβ gene in hepatocytes (LTRβ-KO), was generated to study this question. To understand the function of TH mediated by TRβ, the liver metabolic response to TH, obtained from liver aqueous extracts and intact liver tissues, LTRβ-KO and wild-type mice were compared. The results suggest the presence of direct and indirect effects of thyroid hormones on the liver metabolism.

Metabolomics: from fundamentals to clinical applications

1.1. Metabolomics approach 1.1.1. Definitions Metabolites are low molecular weights molecules (less than 1.5kDa) that are involved in metabolic pathways. They are the reactants, intermediates and end products of complex multiple cascade, which is initiated by the genome followed by the transcriptome and proteome. They can be endogenous (from the organism) or exogenous (i.e. nutrition, medication, microbiota, etc.). They include polysaccharides, lipids, steroids, organic acids, amino acids, ketones and peptides, or else. They fulfill diverse functions, ranging from signal transduction to energy fuels [START_REF] Psychogios | The Human serum metabolome[END_REF][START_REF] Bouatra | The human urine metabolome[END_REF] . Each type of biological sample (fluid, tissue, cells, organism)

or each subtype (type of cell, type of fluid, etc.) has his own characteristic set of metabolites [START_REF] Holmes | Metabolic phenotyping in health and disease[END_REF] .

The Metabolome represents the entire set of metabolites present in a biological system [START_REF] Fiehn | Combining genomics, metabolome analysis, and biochemical modelling to understand metabolic networks[END_REF] . The human metabolome is estimated to contain about 150 000 metabolites (endogenous and exogenous) or more [START_REF] Wilkins | Application of metabolomics in Alzheimer's disease[END_REF] . Environmental factors (diet, gut microbial activity, disease, medication, stress level, physical activity, etc.) have an effect on the metabolism, which lead to changes in the concentrations of metabolites, and thus in the metabolome.

Metabolomics [START_REF] Fiehn | Combining genomics, metabolome analysis, and biochemical modelling to understand metabolic networks[END_REF][START_REF] Nicholson | Metabonomics': understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data[END_REF] corresponds to the quantification and identification as many as possible of the metabolites present in a biological system (biological fluid, cell, tissue, organism, etc.). Analyzing metabolites in a biological system provides information on the metabolic phenotype or metabotype of individuals or populations [START_REF] Holmes | Metabolic phenotyping in health and disease[END_REF] . Thanks to technological advances in the identification, quantification and analysis of metabolites, metabolomics has grown significantly in different fields 1,[START_REF] Wishart | Metabolomics: applications to food science and nutrition research[END_REF] . Metabolomic analyses are often built as casecontrol studies, which aim to compare two groups of samples coming from two different physio-pathological states. By the use of bioinformatics and biostatistics tools, the difference between the two groups in term of molecules can be identified [START_REF] Madsen | Chemometrics in metabolomics-A review in human disease diagnosis[END_REF] .

The application of metabolomics in different fields has increased strongly over the past years (Figure 1.1.1). The research was performed in the Web of science (WOS) database, using metabolomics or metabonomics as keywords. The data was collected up to 2017.

From genomics to metabolomics

Omics strategies represent an important step in sciences area, and they are widely used nowadays. The principle of these approaches is to identify and quantify the entire set of biomolecules (genes, transcripts, proteins, metabolites, etc.) present in a given biological matrix (tissues, cells, organisms, biofluids), and to determine the relationship between their variations and specific pathophysiological states or external perturbations. Studying only genes, transcripts or proteins is insufficient to understand the complete phenotypic changes induced by these factors [START_REF] Dunn | Systems level studies of mammalian metabolomes: the roles of mass spectrometry and nuclear magnetic resonance spectroscopy[END_REF] . The number of metabolites is smaller than the estimated number of genes (25.000), transcripts (100.000) and proteins (1.000.000) found in humans [START_REF] Newgard | Metabolomics and Metabolic Diseases: Where Do We Stand?[END_REF] . Metabolites represent a more sensitive level [START_REF] Kell | Metabolic footprinting and systems biology: the medium is the message[END_REF] than the other biomolecules, and they are the result of the interaction between theses biomolecules and the environment. In addition to the interactions between metabolites and the other biomolecules, metabolites are also the building blocks of proteins (amino-acids), genes and transcripts (nucleotides). Metabolic pathways reflect the genome and proteome states with up to 10.000-fold increase in metabolite concentration resulting from a single amino acid change in a protein or base change in a gene [START_REF] Wishart | Metabolomics: applications to food science and nutrition research[END_REF][START_REF] Heude | Metabolomics: From Fundamentals to Clinical Applications[END_REF] . Metabolic changes are detectable within minutes of a biological event and thus provide an almost 'realtime' feedback [START_REF] Wishart | Metabolomics: applications to food science and nutrition research[END_REF] . Now, metabolomics is considered as a strong phenotyping tool, which provides molecular information that complement data obtained from genomics, transcriptomics and proteomics (Figure 1.1.2). Omics strategies (Genomics, Transcriptomics, Proteomics and Metabolomics) aim at identifying all the biomolecules present in biological system (genes, RNA, proteins, metabolites).

Platforms for metabolomics

The two main technologies that are amply employed in metabolomics studies are NMR spectroscopy and mass spectrometry [START_REF] Dunn | Systems level studies of mammalian metabolomes: the roles of mass spectrometry and nuclear magnetic resonance spectroscopy[END_REF][START_REF] Bollard | NMR-based metabonomic approaches for evaluating physiological influences on biofluid composition[END_REF][START_REF] Woo | Mass spectrometry based metabolomic approaches in urinary biomarker study of women's cancers[END_REF] . NMR spectroscopy is used in metabolomics studies to detect hydrogen atoms in metabolites, and identify compounds with identical masses 8 . Each metabolite, with concentrations above the detection limit, gives a 1 H NMR spectrum. Thus, a 1 H NMR spectrum of a complex mixture (biofluids, cells/tissue extracts) corresponds to the superposition of the spectra of all the present metabolites, which means

GENOMICS TRANSCRIPTOMICS PROTEOMICS METABOLOMICS

DNA RNA Proteins

Traduction Transcription

Metabolites

Genotype Phenotype

Enzymatic reactions

Amino acids

Cabohydrates

Nucleic acids

Lipids that NMR provides a direct fingerprint of all observable metabolites in a short time. NMR has several advantages like a minimal sample preparation, low cost per sample, excellent data acquisition and reproducibility [START_REF] Zhang | Advances in NMR-based biofluid analysis and metabolite profiling[END_REF][START_REF] Izquierdo-Garcã | Descriptive review of current NMR-based metabolomic data analysis packages[END_REF] . However, NMR has a low sensitivity and limited detection range (micromolar) compared to MS (nanomolar). NMR is not destructive; samples can be reused for other analyses, and has also the ability to analyze intact tissues like biopsies.

In the other hand, MS [START_REF] Dettmer | Mass spectrometry-based metabolomics[END_REF] is highly sensitive and has the capability to detect a broader range of metabolites. But samples are usually separated before analysis using chromatography (gas chromatography (GC), liquid chromatography (LC), ultraperformance (UP), high pressure (HP)) or electrophoresis (capillary electrophoresis/electrospray ionization).

NMR and MS are thus highly complementary [START_REF] Psychogios | The Human serum metabolome[END_REF][START_REF] Bouatra | The human urine metabolome[END_REF] , and their use is often necessary for full molecular characterization [START_REF] Lindon | The handbook of metabonomics and metabolomics[END_REF] .

Whatever the technique used, the result of the detection and quantification of metabolites is called a spectrum.

Nuclear magnetic resonance

NMR was first described in 1938, and become rapidly a powerful and interdisciplinary method [START_REF] Rabi | A new method of measuring nuclear magnetic moment[END_REF] . Demonstration of the use of protons ( 1 H) NMR and the development of Fourier Transformation (FT) were developed in the followed few years [START_REF] Lindon | The handbook of metabonomics and metabolomics[END_REF] . The first use of 1 H NMR for metabolic studies was described in 1977 [START_REF] Brown | Human erythrocyte metabolism studies by 1H spin echo NMR[END_REF] , where some metabolites were identified in a suspension of red blood cells. From here, 1 H NMR was recognized to have a promising role in the investigation of diseases, especially since the development of modern spectrometers and software.

The principle of NMR

Atom is composed of a nucleus and electrons. The nucleus, itself, is composed of protons and neutrons. All these particles possess an intrinsic property called spin. NMR is a nuclei specific spectroscopy, which detects only nuclei with a non-zero nuclear spin because they act as elementary magnet contrary to nuclei with zero nuclear spin. The isotopes of particular interest and use to organic chemists are: 1 H, [START_REF] Oresic | Metabolomics, a novel tool for studies of nutrition, metabolism and lipid dysfunction[END_REF] C, [START_REF] Armitage | Metabolomics in cancer biomarker discovery: Current trends and future perspectives[END_REF] N, [START_REF] Fiehn | Combining genomics, metabolome analysis, and biochemical modelling to understand metabolic networks[END_REF] F and 31 P. 1 H NMR remain the best described and the most used in metabolomics, because approximately all the metabolites present in a biological sample contain hydrogens [START_REF] Heude | Metabolomics: From Fundamentals to Clinical Applications[END_REF][START_REF] Bollard | NMR-based metabonomic approaches for evaluating physiological influences on biofluid composition[END_REF] .

In practical terms, when the sample is placed in a strong magnetic field, an electromagnetic radiation (radiofrequency pulses) is used to excite the hydrogens. After their excitation, the protons returns to equilibrium, and the energy is recorded as an oscillating electromagnetic signal, named the free induction decay (FID). A mathematical algorithm, Fourier transformation (FT), is applied to the FID to produce 1 H NMR spectra of intensity versus chemical shift (δ) using the computer. The magnitude or intensity of NMR resonance signals is displayed along the vertical axis of a spectrum, and is proportional to the molar concentration of the different metabolites present in the sample. NMR can calculate metabolites concentration by using an internal or external standard with a known concentration.

Spectrum

in Hz) corresponds to the measure of the interaction between a pair of protons is also often used in the identification of metabolites.

In order to standardize the NMR scale it is necessary so set a 0 reference point to which all protons can then be compared. This association with the reference signal is called the chemical shift. This shift is measured in parts per million (ppm). Other biological samples like cells, biopsies or small organisms (Caenorhabditis elegans, Daphnia, etc.) are used to understand the cellular metabolisms of physiological and pathological processes. This kind of sample is considered as semi-solid samples. They are heterogeneous, and a different NMR technique is used in this case. The use of liquid NMR state experiments here gives spectra with very poor resolution.

The development of a technique called high-resolution 1 H magic angle spinning (MAS) NMR spectroscopy has enabled to acquire high-resolution NMR data on small pieces of intact tissues with no pretreatment [START_REF] Haukaas | Metabolic portraits of breast cancer by HR MAS MR spectroscopy of Intact tissue samples[END_REF][START_REF] Kumar | High-resolution NMR spectroscopy of human body fluids and tissues in relation to prostate cancer[END_REF][START_REF] Moestue | MAS MR spectroscopy in metabolic characterization of cancer[END_REF][START_REF] Sitter | High-resolution magic angle spinning (HR MAS) MR spectroscopy in metabolic characterization of human cancer[END_REF] . The sample heterogeneity causes line broadening. This method is based on the fact that the sample is spinning at an angle (so-called magic angle) of 54.7°, which reduces the loss of information caused by line broadening effect [START_REF] Mcdonald | Free-induction decays in a rotating solid[END_REF] . The limited of the 1D spectra is the presence of overlapped signals; a given pic can hide two different metabolites. To facilitate metabolite identification, two-dimensional (2D) 1 H NMR spectra are highly recommended to provide additional information about metabolite content. Total Correlation SpectroscopY (TOCSY) [START_REF] Braunschweiler | Coherence transfer by isotopic mixing: application to proton correlation spectroscopy[END_REF] and J-resolved [START_REF] Rodriguez-Martinez | J-Resolved 1H NMR 1D-projections for large-scale metabolic phenotyping studies: application to blood plasma analysis[END_REF] experiments reveal molecular connectivities and the multiplicity of resonances, respectively. 1 H- [START_REF] Oresic | Metabolomics, a novel tool for studies of nutrition, metabolism and lipid dysfunction[END_REF] C HSQC (Heteronuclear Single Quantum Correlation), which investigate heteronuclear coupling, is also used. 2D NMR spectroscopy is useful for increasing signal dispersion and to clarify the connectivities between signals. They help to identify metabolites. ). To answer to a specific biological or clinical issue, it is primordial to choose the more appropriate type of sample. In clinical studies, the priority is given to biofluids because there is a real need to non-invasive methods to understand and diagnose diseases. For biological studies, tissues and cells in addition to biofluids are used to understand mechanisms in a specific organ. In this case, animal models are investigated because it is ethically non acceptable to take biopsies from healthy individuals.

Biofluids are easily obtained, and non-invasive compared to biopsies [START_REF] Psychogios | The Human serum metabolome[END_REF][START_REF] Bouatra | The human urine metabolome[END_REF][START_REF] Dunn | Systems level studies of mammalian metabolomes: the roles of mass spectrometry and nuclear magnetic resonance spectroscopy[END_REF] . Urine, one of the most widely studied biofluids in metabolomics, reflects a more short-term state of the organism. Urine contains endogenous and exogenous (like drugs, food) metabolites, which made it very interesting in disease biomarker discovery 2 and toxicology [START_REF] Zhang | Advances in NMR-based biofluid analysis and metabolite profiling[END_REF] .

About 3000 urine metabolites were identified [START_REF] Bouatra | The human urine metabolome[END_REF] . For metabolomics studies, it is important to collect the first morning voids, which are following by an overnight fast to reduce the impact of food or mediation [START_REF] Saude | Variation of metabolites in normal human urine[END_REF] . Fasting plasma and serum changes reflect more chronic and longterm snapshots of the system [START_REF] Chetwynd | Metabolomics: From fundamentals to clinical applications[END_REF] . However, it is very important to consider external factors related to ethnicity, diet, diurnal rhythm, etc., for human samples before drawing conclusions.

Analyzing cerebrospinal fluid (CSF) offers great understanding of neurological disorders.

Saliva is a good source of biomarkers related to pathologies [START_REF] Lubes | GC-MS based metabolomics used for the identification of cancer volatile organic compounds as biomarkers[END_REF] . Sweat and breast milk is used in metabolomics to improve the infants nutrition.

Metabolomic study on the whole organism does not provide information about a specific cell type or organ. Investigate specific cells and organs is more appropriate to understand the local mechanisms. These two approaches can be combined to provide complementary information.

Standardization of protocols has enabled to work in the same conditions and make the data comparable [START_REF] Griffiths | Mass spectrometry: from proteomics to metabolomics and lipidomics[END_REF][START_REF] Beckonert | Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts[END_REF] .

Targeted and untargeted analyses

A targeted metabolomics consists in looking for specific metabolites in a biological system. In practical terms, it is the quantification (concentrations are determined) or semiquantification (intensities are determined) of a set of metabolites that might be associated to the common classes, to study specific metabolic pathways for example. Lipidomics 9 , a subtype of metabolomics, which aim to study only lipids, can be considered as targeted metabolomics. This discipline can be also considered as untargeted metabolomics because there are different types of lipids. An extraction step for wanted metabolites is often needed in this approach.

In the other hand, untargeted metabolomics is the quantification or semi-quantification of all the detectable metabolites present in a biological sample. Sample preparation is usually minimal.

Metabolomics and biomarkers

Metabolomics to study pathologies

Clinical metabolomics is nowadays an area of intense investigation. This use was born from the need to diagnose diseases, understand their mechanisms, identify new drugs targets, and monitor therapeutic outcomes. Specific investigations are carried to identify potential biomarkers related to pathologies [START_REF] Newgard | Metabolomics and Metabolic Diseases: Where Do We Stand?[END_REF][START_REF] Oresic | Metabolomics, a novel tool for studies of nutrition, metabolism and lipid dysfunction[END_REF][START_REF] Spratlin | Clinical applications of metabolomics in oncology: A review[END_REF] . Metabolic changes as a disease symptom have already been recognized in ancient medicine 1 . The challenge of biological and clinical researches is to develop fast and reliable methods for diagnosing the disease non-invasively.

Since the start of metabolomics, the number of studies in biological and clinical researches has grown quickly. The study design consists often in the comparison between control (healthy) and case subjects, and the same thing in terms of cells, tissues and biofluids, in human and/or animal models. We can find also epidemiologic studies, which aim to determine the causes of diseases outcomes.

Several pathologies were highly investigated through metabolomics especially cancer including lung, colorectal, breast, prostate, bladder, gastric and thyroid [START_REF] Woo | Mass spectrometry based metabolomic approaches in urinary biomarker study of women's cancers[END_REF][START_REF] Sitter | High-resolution magic angle spinning (HR MAS) MR spectroscopy in metabolic characterization of human cancer[END_REF][START_REF] Srivastava | Taurine -a possible fingerprint biomarker in non-muscle invasive bladder cancer: A pilot study by 1 H NMR spectroscopy[END_REF][START_REF] Assi | A statistical framework to model the meeting-in-the-middle principle using metabolomic data: application to hepatocellular carcinoma in the EPIC study[END_REF][START_REF] Sreekumar | Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression[END_REF][START_REF] Monleón | Metabolite profiling of faecal water extracts from human colorectal cancer[END_REF][START_REF] Tan | Early prediction of lung cancer based on the combination of trace element analysis in urine and an Adaboost algorithm[END_REF][START_REF] Hirayama | Quantitative metabolome profiling of colon and stomach cancer microenvironment by capillary electrophoresis time-of-flight mass spectrometry[END_REF][START_REF] Wen | A new NMR-based metabolomics approach for the diagnosis of biliary tract cancer[END_REF][START_REF] Navas-Carrillo | High-resolution proteomics and metabolomics in thyroid cancer: Deciphering novel biomarkers[END_REF] . It was discovered that blood acetate is associated with biliary tract cancer [START_REF] Wen | A new NMR-based metabolomics approach for the diagnosis of biliary tract cancer[END_REF] , urine taurine are associated to bladder cancer [START_REF] Srivastava | Taurine -a possible fingerprint biomarker in non-muscle invasive bladder cancer: A pilot study by 1 H NMR spectroscopy[END_REF] . Investigation of cerebrospinal fluid revels a deregulation in cholesterol and phospholipids metabolisms in brain cancer [START_REF] Monleón | Benign and atypical meningioma metabolic signatures by high-resolution magic-angle spinning molecular profiling[END_REF] .

For Cardiovascular diseases, amino-acid metabolism were found altered, and an increase of methylated arginine species and lipids (specially fatty acids) were noticed [START_REF] Ikegami | Metabolomic analysis in heart failure[END_REF][START_REF] Ruiz Canela | Comprehensive metabolomic profiling and incident cardiovascular disease: A Systematic Review[END_REF][START_REF] Wang | Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease[END_REF] .. In neurological field, Alzheimer and depression are highly studied [START_REF] Wilkins | Application of metabolomics in Alzheimer's disease[END_REF][START_REF] Cai | Metabolomic analysis of biochemical changes in the plasma and urine of first-episode neuroleptic-naïve schizophrenia patients after treatment with risperidone[END_REF] . Other diseases like infectious diseases [START_REF] Koriem | A lipidomic concept in infectious diseases[END_REF][START_REF] Eoh | Metabolomics: A window into the adaptive physiology of Mycobacterium tuberculosis[END_REF] and diabetes [START_REF] Wang | Proteomics, metabolomics and metagenomics for type 2 diabetes and its complications[END_REF] and others were also investigated.

Metabolomics and genetic diseases

The association of genetic variation with metabolite levels is well documented [START_REF] Kettunen | Genome-wide association study identifies multiple loci influencing human serum metabolite levels[END_REF][START_REF] Gieger | Genetics meets metabolomics: A genome-wide association study of metabolite profiles in human serum[END_REF] . A number of studies have highlighted the influence of genetics on the metabolites levels [START_REF] Shin | An atlas of genetic influences on human blood metabolites[END_REF][START_REF] Illig | A genome-wide perspective of genetic variation in human metabolism[END_REF] . It was discovered more than 150 genetic loci that associate with blood levels of more than 300 distinct metabolites [START_REF] Raffler | Genome-wide association study with targeted and non-targeted NMR metabolomics identifies 15 novel loci of urinary human metabolic individuality[END_REF][START_REF] Tsepilov | Nonadditive effects of genes in human metabolomics[END_REF] . For example, metabolomics approach was used to explore several genetic diseases like Inborn errors metabolism, which is actually implemented in clinical routines [START_REF] Sandlers | The future perspective: metabolomics in laboratory medicine for inborn errors of metabolism[END_REF] . Mitochondrial diseases, a group of disorders that can result from abnormalities in the mitochondrial and nuclear genomes, could be also recognized by metabolomics [START_REF] Legault | A Metabolic signature of mitochondrial dysfunction revealed through a monogenic form of leigh syndrome[END_REF] .

Conclusion

The application of metabolomic approach in medical research is a dynamic field, and has a high potential in disease diagnosis. These discoveries make metabolomics a promising diagnostic tool, with has the advantage to be non-invasive and highly reproducible.

Metabolomic technologies offer a sensitive means to search human biofluids for metabolite profiles potentially usable as biomarkers for diseases. In this thesis, we will use a 1 H NMR based metabolomics to investigate murine models for thyroid hormone pathologies.

2 Thyroid hormone system and diseases 2.1 Thyroid hormone system

Thyroid hormone functions

Thyroid hormones (TH), thyroxine (T4) and 3,5,3'-triiodothyronine (T3), the main secretion products of the thyroid gland, are essential for normal growth, development and metabolism regulation [START_REF] Brent | Mechanisms of thyroid hormone action[END_REF][START_REF] Cheng | Molecular aspects of thyroid hormone actions[END_REF][START_REF] Yen | Physiological and molecular basis of thyroid hormone action[END_REF] . T4 is more abundant in the blood, T3 is considered as the major active hormone due to its high affinity for nuclear receptors and intracellularly generated from T4.

TH play significant roles during embryogenesis and childhood. They are involved in nearly all tissues, with major effects on the basal metabolic rate and oxygen consumption [START_REF] Louzada | Similarities and differences in the peripheral actions of thyroid hormones and their metabolites[END_REF][START_REF] Van Der Spek | Increased circulating interleukin-8 in patients with resistance to thyroid hormone receptor α[END_REF] .

TH are known to maintain heart rate, myocardial contractility and vascular function [START_REF] Cokkinos | Thyroid hormones and cardiac remodeling[END_REF] . They are also key regulators of thermogenesis, which allows the maintenance of body internal temperature [START_REF] Martínez-Sánchez | Thyroid hormones induce browning of white fat[END_REF][START_REF] Silva | Thyroid hormone and the energetic cost of keeping body temperature[END_REF] . They are involved in body weight regulation, in the development and maintenance of adult bone mass and strength [START_REF] Williams | Thyroid diseases and bone health[END_REF] . It is well established that changes in TH level, compared to the reference range, is associated with body weight change in both men and women [START_REF] Bjergved | Thyroid Function and Body Weight: A community-based longitudinal study[END_REF] . Skeletal muscle is an important TH target for contractile function and regeneration [START_REF] Visser | Physiological Thyroid Hormone Levels Regulate Numerous Skeletal Muscle Transcripts[END_REF] . TH regulate lipids metabolism such as: cholesterol synthesis and efflux, bile acid synthesis, fatty acid metabolism. Carbohydrates are also regulated by thyroid hormones (Figure 2.1.1). HPT axis regulates and maintains thyroid hormones homeostasis. Hypothalamus secretes TRH in response to low concentration of circulating T4/T3. This secretion stimulates the pituitary gland, which secretes TSH in turn. This leads to T4/T3 release in the blood. T3 negative feedback on TRH and TSH levels keeps the T3/T4 ration constant in circulation. Thyroid hormones have a central role in development, growth and metabolism regulation. Thyroid hormones exert their effects via thyroid hormone receptors (TRs), member of the superfamily of hormone-responsive nuclear transcription factors. These receptors share a similar structure and mechanism of action [START_REF] Flamant | Thyroid hormone receptors: the challenge of elucidating isotype-specific functions and cell-specific response[END_REF] . THRA and THRB encode the two major TR isoforms: TRα1 and TRβ (β1 and β2), which are distributed differentially in tissues. TRα1 is predominantly expressed in brain (central nervous system), heart (myocardium), skeletal muscle, adipose tissue and gastrointestinal tract. TRα2 and TRα3 are variants of TRs, which are non TH-binding proteins, and their function is not understood [START_REF] Zhang | The mechanism of action of thyroid hormones[END_REF] . TRβ is expressed in the sensory tissue (the inner ear and retina), the kidney, the liver and cardiac ventricles [START_REF] Gauthier | Different functions for the thyroid hormone receptors TRα and TRβ in the control of thyroid hormone production and post-natal development[END_REF] .

Thyroid hormones receptors

As described in figure 2.1.2, all these receptors contain three conserved domains: a Nterminal domain, a DNA-binding domain (DBD) and a ligand-binding domain (LBD). The α and β receptors have a similar DBD and LBD, but differ in their N-terminal domain. The three receptors (TRα1, TRβ1, TRβ2) bind the same ligand due to these structural homologies [START_REF] Brent | Mechanisms of thyroid hormone action[END_REF] .

TRs are considered as transcription factors because they regulate target gene expression directly through DNA response element (TRE) [START_REF] Evans | Nuclear receptors, RXR, and the big bang[END_REF] .

Non-genomic actions of thyroid hormone were also reported and are little studied [START_REF] Leonard | Non-genomic actions of thyroid hormone in brain development[END_REF] . They have extranuclear actions, which are not TRE-mediated.

Mechanisms of action

The thyroid gland secretes T4 and T3 hormones, which are transported into the cells by membrane transporters like the monocarboxylate transporter (MCT) family [START_REF] Visser | Thyroid hormone transporters: the knowns and the unknowns[END_REF] , and more precisely MCT8. T3 is secreted by the thyroid gland, but also converted from T4 under the action of type 1 or type 2 of 5' deiodinase, which are tissue-dependent. Unliganded TR in the nucleus forms a heterodimer with the retinoid X receptor (RXR), which then binds the thyroid hormone response element (TRE) in the regulatory sequences of TH-responsive gene. This results in the activation or repression of TH target genes transcription.

In the absence of T3, corepressor proteins are associated to the TR-RXR complex and prevent genes transcription [START_REF] Stanya | New insights into the functions and regulation of the transcriptional corepressors SMRT and N-CoR[END_REF] . However, in the presence of T3, corepressor proteins leave the complex, and coactivator proteins are recruited, which lead to target genes transcription 87 (Figure 2.1.3). Membrane transporters transport T4 and T3, after their secretion by thyroid gland, into the cell. A part of T4 is converted to T3 under the action of type 1 and 2 5' deiodinase (D1 and D2). Unliganded TR heterodimerizes with RXR, and then the complex recognizes TRE. In the absence of T3, corepressor proteins bind the complex to repress target gene expression. In the presence of T3, corepressor proteins are dropped and coactivator proteins are recruited, which lead to target gene transcription.

Regulation

TH regulates a large amount of genes, which are involved in growth, metabolic balance and thermogenesis [START_REF] Muller | Thyroid hormone regulation of metabolism[END_REF] . TH synthesis and secretion are regulated by a negative-feedback system that involves the hypothalamus, pituitary, and thyroid gland (hypothalamic/pituitary/thyroid (HPT) axis) [START_REF] López | Energy balance regulation by thyroid hormones at central level[END_REF] . Hypothalamus secretes TRH. Thyrotropes, endocrine cells from pituitary, secrete then Thyroid-Stimulating hormone (TSH). TSH acts on the thyroid gland to induce thyroxine (T4) and 3,5,3'-triiodothronine (T3) hormones production, which then act on body growth and development and metabolism regulation. High levels of T4 and T3 in turn feed back lead to TSH level secretion diminution in the pituitary, which then regulate T4 and T3 levels.

Alterations in thyroid hormone system

TH action requires (i) the availability of TH (production/secretion, conversion of T4 to T3) (ii) intact and adapted membrane transport (iii) cytosolic and nuclear processing (iv) association with intact receptors (v) interaction with co-regulators (co-activator or corepressor proteins). Alteration in one of these steps can lead to a heavy metabolic disorder [START_REF] Hannoush | Defects of thyroid hormone synthesis and action[END_REF] .

Associated diseases are the most common endocrine disorders worldwide [START_REF] Oetting | New insights into thyroid hormone action[END_REF] . In addition to clinical symptoms, biochemical tests like determination of TSH and free T4 levels in blood are used to suspect thyroid disorder. Hypothyroidism and hyperthyroidism lead to alterations in metabolism (lipids, carbohydrates), growth and energy homeostasis [START_REF] Taylor | Global epidemiology of hyperthyroidism and hypothyroidism[END_REF] . Hypothyroidism, results from low levels of TH, presents a low metabolic rate, and leads to cardiovascular diseases, whereas hyperthyroidism, which is a catabolic syndrome, is associated with a high metabolic rate, and leads to tachycardia and loss of body mass. These diseases are related to the secretion and production of the thyroid hormones by the organism.

Other diseases are known to lead to hypothyroid or hyperthyroid phenotypes, without alteration in TH production, but caused by genetic mutations. Thyroid hormone cell transport defects (THCTD) caused by a mutation in MCT8 gene lead to production of defective celltransport proteins, which reduce hormone transport and causes reduced levels of intracellular TH [START_REF] Hannoush | Defects of thyroid hormone synthesis and action[END_REF] . Thyroid hormone metabolism defect (THMD) is caused by a mutation in selenocysteine-binding protein 2, which interferes with conversion of T4 to T3, resulting in a low T3 and high T4 [START_REF] Fu | Thyroid hormone metabolism defects in a mouse model of SBP2 deficiency[END_REF] .

Other diseases known under the name of resistance to thyroid hormone (RTH) [START_REF] Dumitrescu | The syndromes of reduced sensitivity to thyroid hormone[END_REF] were discovered. The first described cause was a mutation in TRβ gene in 1989, which is characterized by high serum concentration of free T4 and T3, and normal or slightly elevated TSH concentration. This disease is called RTHβ. Twenty-four years later, the first mutation in TRα gene was discovered, and the disease has been named RTHα.

Resistance to thyroid hormones α

Resistance to thyroid hormone (RTH) was first described as a clinical entity in 1967 [START_REF] Refetoff | Familial syndrome combining deaf-mutism, stippled epiphyses, goiter and abnormally high PBI: possible target organ refractoriness to thyroid hormone[END_REF] .

Patients are hyposensitive to TH and they present reduced clinical and biochemical manifestations of TH action relative to the circulating hormone levels. The molecular explication of this syndrome was determined in 1989 when the first case of RTH caused by a mutation in the THRB gene was discovered. This disorder was named resistance to thyroid hormone due to a mutation in the thyroid hormone receptor β, abbreviated by RTHβ [START_REF] Yen | Effects of ligand and thyroid hormone receptor isoforms on hepatic gene expression profiles of thyroid hormone receptor knockout mice[END_REF][START_REF] Refetoff | Syndromes of reduced sensitivity to thyroid hormone: genetic defects in hormone receptors, cell transporters and deiodination[END_REF] .

RTHβ is characterized by the impairment of the HPT axis: elevated levels of thyroid hormone, normal or elevated levels of TSH. Patients present with different degree of goiter, hearing abnormalities, tachycardia, mental retardation, attention-deficit, and delayed bone growth and maturation [START_REF] Dumitrescu | The syndromes of reduced sensitivity to thyroid hormone[END_REF][START_REF] Refetoff | Syndromes of reduced sensitivity to thyroid hormone: genetic defects in hormone receptors, cell transporters and deiodination[END_REF][START_REF] Brucker-Davis | genetic and clinical features of 42 kindreds with resistance to thyroid-hormone -the nationalinstitutes-of-health prospective-study[END_REF] . They present features of hypothyroidism and hyperthyroidism, which is explained by variable resistance in different tissues. The incidence of RTHβ is actually estimated to be 1 in 40.000 with 160 different mutations approximately [START_REF] Lafranchi | Follow-up of newborns with elevated screening T4 concentrations[END_REF]100 . Patients with RTHβ are currently found in more than 400 families 101 . This syndrome can be suspected in patients with these features and can easily be recognized by physicians.

The first cases

Patients with mutations in THRA were not identified until 2012. The first case of RTHα was discovered, after a whole-exome sequencing, in a little 6-year-old girl, which presents classical features of hypothyroidism (e.g. growth retardation, developmental retardation, skeletal dysplasia, low heart rate, and severe constipation) and nearly normal thyroid function biochemical tests 102 .

Other cases, female and male of different ages, were discovered with different mutations in TRα1 100,103-112 . The phenotype of the identified patients consists of varying degrees of growth impairment, mental and motor development, delayed bone, constipation, and nearnormal thyroid function tests. Some cases have specific health issues like autism 106 , chronic anaemia 104,110,113 , epilepsy 114 , etc. (Appendix 1).

RTHα is different from RTHβ

RTHα is clinically distinct from RTHβ. TRα and TRβ, are differentially expressed during development and are differentially distributed in adult tissues, which explains the difference between the two diseases. TRβ has an important role in inner ear, cerebellar, and retinal development, TSH regulation, and mediating the metabolic actions of T3 in the liver [START_REF] Yen | Physiological and molecular basis of thyroid hormone action[END_REF] . TRα has specific roles in the heart, brain, intestine and in mediating adaptive thermogenesis in brown adipose tissue (BAT) [START_REF] Yen | Physiological and molecular basis of thyroid hormone action[END_REF]115 . RTHα is thus characterized by a tissue-selective hypothyroidism (e.g. skeleton, gastrointestinal tract, myocardium, etc.) and near-normal thyroid function biochemical tests. In figure 3.2.1, organs that present a dominance of TRα (bones, heart, intestine, brain and particularly the central nervous system, and muscles) are resistant to TH. While, liver that presents more than 80% of TRβ is resistant to TH. The pituitary-thyroid axis in RTHα is not dysregulated as noticed in RTHβ patients.

A high variability in the clinical features

The high variability of clinical features and the absence of specific traits and reliable biochemical markers make the diagnosis of this disease difficult. In addition, identified RTHα patients present some common features with hypothyroid patients, which may delay the diagnosis in some cases. Since its first description, 45 cases of RTHα have been reported

worldwide, corresponding to 25 different mutations of TRα1. In 2015, the sequence of 60 000 anonymous exomes were released by the Exac database (http://exac.broadinstitute.org), revealing the existence of 68 THRA missense or frameshift, which almost certainly alter TRα1 function. It is thus thought that the incidence of RTHα may be similar to RTHβ incidence.

Heart Intestine Muscle

Central nervous system

Locomotor The inability to quickly recognize patients with RTHα is regrettable because the developmental consequences of the disease can be greatly reduced by an early therapeutic intervention. Motor skills and body growth have been restored in a young patient treated with an excess of thyroxine to overcome tissue resistance 108,109 . Two adult patients with normal adult stature received thyroxine treatment during childhood by chance 105 . Authors suggest here that the neurocognitive abnormalities are may be less severe because of this treatment 105 .

Another study on a murine model (Arg384cys) showed also that increased concentrations of thyroid hormones can reverse neurological abnormalities 116 .

It is thus primordial to find a reliable way to identify these patients, and to manage them at early stage to minimize the consequences of this disease.

Molecular aspect

All the identified patients are heterozygous for THRA mutation, which generate a mutant receptor that inhibits wild-type receptor function in a dominant negative manner 106,108,112 . All the known mutations are present in the carboxyterminus, the ligand-binding domain (LBD) of the receptor (Figure 3.2.2), to which the helix 12 belongs. These mutations lead to the destabilization of this helix 102 . Two types of mutations were identified in RTHα patients: frameshift 103,104,107,114 and missense mutations 102,105-108 . Amino acid substitutions (D211G, H361Q, R384H, A263V) reduce the affinity of TRα1 for T3. While, c-terminal substitutions (P398R and E403K), truncations (C392X and E403X), and frameshift mutations (C380fs387X, A382PfsX7, F397fs406X, and F401S) alter ligand binding, by modifying or eliminating the helix 12, and prevent thus coactivator recruitment 117 . This kind of mutation is more deleterious and present severe loss of function.

Thyroid hormones regulate target genes transcription via nuclear receptors TRα and TRβ. These receptors are composed of 3 distinct regions: N-terminal domain, DNA binding domain (DBD) and ligand binding domain (LBD). All the known RTHα mutations are located in the LBD (A). Mutations in TRα1 disturb target gene transcriptional regulation. Two functional alterations were observed in the presence of T3: the dissociation of corepressor proteins is prevented (B), or affinity between TRα1

and T3 is reduced (C).

Relation between genotype and phenotype?

There are thus different mutations with different degrees of severity of the disease. Two patients (6-year-old and 15-year-old girls), which were found with the same mutation (E403X) 102,118 present similar features. Two other patients were found with TRα1 mutation in the same position (A263V and A263S), but, as noticed, the original amino acid alanine was substituted by valine in the first case and by serine in the other case. The functional analysis of the A263V mutant indicates a greater defect in transcriptional activity of TRα1 than that induced by the A263S mutation. It was also noticed that a more up-stream mutation lead to a more severe phenotype, like the mutation C392X 118 . All these observations suggest that the variability in the symptoms is maybe related to the type of the mutation and its position. The relationship between genotype and phenotype need to be further discerned. To understand this relationship and to study the impact of RTHα disease on the whole organism, different murine models were developed.

Mouse models to study RTHα

Mouse Thra and human THRA genes present sequence similarities. The two TRα1 amino acid sequences differ at three positions only (AA34, 37, and 170), which makes mouse lines with Thra mutations highly relevant animal models for RTHα disease. Prior to the identification of human RTHα cases, several researchers were done using TRα knockout and knockin mouse models to predict the phenotype of human disorder [START_REF] Lafranchi | Follow-up of newborns with elevated screening T4 concentrations[END_REF]115,[119][120][121][122][123][124] . The first mouse model of RTHα was a knockout of Thra gene, in which TRα1 and TRα2 isoforms were inactivated 124 . Mice failed to survive 5 weeks after birth, and features of hypothyroidism and serious delayed maturation in the small intestine and bones were observed in these mice 124 . It was reported also in another study that a mutation in TRα1 exhibit several distinct neurological abnormalities: extreme anxiety, reduced recognition memory, and locomotor dysfunction 116 . Another model was developed before its discovery in a human patient 107,121 .

Other researchers showed the significant implication of TRα1 on metabolic homeostasis 119 .

The phenotype of mice models is often close to the clinical features of RTHα patients.

These models allowed the good understanding of the function of THRA gene and the TRα receptor, and facilitate the comprehension of RTHα disease and its impact on the organism.

For the metabolomic study presented in this thesis, we have used mouse models that From a global literature screening, about a hundred of research studies have been conducted to study the thyroid system by metabolomics approaches. About 50% of these studies address about thyroid cancer, which is explained by its increased incidence worldwide. The main medical treatment in the case of thyroid cancer is the total thyroidectomy (removal of the thyroid), even if the tumour is benign. A number of studies, with different methods, were performed to distinguish between cancer patients and healthy individuals, and between benign and malignant thyroid nodules [START_REF] Navas-Carrillo | High-resolution proteomics and metabolomics in thyroid cancer: Deciphering novel biomarkers[END_REF]125 to improve thyroid cancer diagnosis. Several toxicological studies, which highlight the harmful impact of certain chemicals (mainly glyphosphate-based herbicide and decabromodiphenyl) on the thyroid hormone system 126,127 . A few studies, also aimed providing deeper understanding of thyroid disease mechanism, such Graves' disease [START_REF] Struja | Metabolomics for Prediction of Relapse in Graves' Disease: Observational Pilot Study[END_REF] , hypothyroidism 128 , hyperthyroidism 129 , depression, low T3 syndrome, type 2 diabetes 12 and selenium deficiency. Others correspond to population-based studies seeking for linear relationship between either TSH or free T4 (FT4) and metabolite levels [START_REF] Navas-Carrillo | High-resolution proteomics and metabolomics in thyroid cancer: Deciphering novel biomarkers[END_REF]129 . Other works focused on the direct effect of thyroid hormones status on the brain 130 or hepatic lipids, to provide deeper understanding of the mechanism behind the biological phenomenon.

At this point, metabolomics investigations of genetic diseases that disturb thyroid system such as RTH (α and β) or THCTD or THMD, have never been reported in the literature.

We notice overall that the thyroid system has little been explored by metabolomics approaches, and more precisely genetic diseases. A number of studies have highlighted the influence of genetics on the metabolites levels [START_REF] Shin | An atlas of genetic influences on human blood metabolites[END_REF][START_REF] Illig | A genome-wide perspective of genetic variation in human metabolism[END_REF] . The association of genetic variation with metabolite levels is well documented [START_REF] Kettunen | Genome-wide association study identifies multiple loci influencing human serum metabolite levels[END_REF] . For example, metabolomics approach was used to explore several genetic diseases like Inborn errors metabolism, which is actually implemented in clinical routines [START_REF] Sandlers | The future perspective: metabolomics in laboratory medicine for inborn errors of metabolism[END_REF] . Mitochondrial diseases, a group of disorders that can result from abnormalities in the mitochondrial and nuclear genomes, could be also recognized by metabolomics [START_REF] Legault | A Metabolic signature of mitochondrial dysfunction revealed through a monogenic form of leigh syndrome[END_REF] .

In the work described in the following chapters of this thesis, we study different pathologies related to the thyroid hormone system. We exploit the metabolic fingerprint of hypothyroidism. We investigate the same series of mouse models of RTHα used by Markossian et al. 117 to assess the capacity of 1 H NMR analysis of body fluids to recognize the presence of Thra mutations. And in the end, we study the impact of thyroid hormone on the hepatic metabolism and the specific role of the thyroid hormone receptor β in this mediation.

3 Materials, methods and analytical workflow

Introduction

The NMR metabolomic global workflow contains several steps: sample preparation, NMR data acquisition, data processing, statistical analysis, metabolite identification, and interpretation. Two different approaches can be used: untargeted approach (with no a priori hypothesis) and targeted approach (applied on a predefined set of metabolites). The different steps are detailed in the following section (Figure 3.1). • Urine Time of collection for urine samples can make a quantitative and qualitative difference in the urine metabolome. The first morning void is the preferred type, because it is collected after several hours of fasting, which minimizes the impact of food or medication on the urine metabolome. It is important to respect the same time of collection between subjects, and between time points in the case of longitudinal studies. For human subjects, urine collection is easy contrary to animals. Animal houses need to be clean before their first urine, which are around 7 am for the majority. In addition, in some cases, we find urine and feces in the same place, which can alter considerably the composition of urine. The volume of mouse urine needed for classical metabolomics studies is 200µl. This quantity is sometimes not reached, and we need to wait for a second sample, in fasting condition also, to obtain the needed volume. Urine is then quickly stored at -80°C after collection, before NMR analysis.

• Blood

The main difference between blood serum and plasma is the presence or absence of clotting. For plasma, the whole blood is collected into tubes with anti-coagulant and then a centrifugation step lead to separate the liquid state from blood cells. For serum, whole blood is collected into tubes and is allowed to clot for a specified time and temperature before centrifugation to pellet the clot and cells. Different studies showed that there is not a difference between these two types in term of metabolite composition. In our study, we collected mice blood from the abdominal aortic, and we investigated the plasma. The timing of blood collection is also important. The volume collected from mice is also limited given the quantity of their whole blood, which is around 1ml. After the coagulation step, we obtain about 200-300 µl of plasma from each mouse. Samples are stored at -80°C after their collection.

• Liver biopsies Tissues are metabolically active and therefore require rapid metabolic quenching when they have been collected. Liver used for the work described in chapter 6 of this thesis, were flash-frozen in liquid nitrogen just after their collection, and then stored at -80°C. We collected about 100-150mg of liver for each sample, which provides good coverage of the tissue metabolome 131 .

Preparation and handling

• Urine

The samples were prepared according to standard protocol (Bruker Biospin GmbH, Rheinstetten, Germany). Urine samples were thawed at room temperature, and then centrifuged for 5 min at 6000 rpm. 200 µl of the supernatant were mixed with 300 µl of water and 50 µl of buffer solution (1.5M KH 2 PO 4 , 2mM sodium azide (NaN 3 ), 0.1% trimethylsilyl propionate (TSP) pH7.4 in D2O).

Variations in chemical shift of the same metabolite signals can be observed due to small differences in pH between samples. It is crucial to homogenise the PH in the samples, where the use of a buffer solution here. NaN 3 is used to prevent microbial contamination, and TSP as an internal standard. 550 µl of the mixture were then transferred into 5mm NMR tubes.

• Plasma Plasma sample were thawed at room temperature, coagulated particles were removed by centrifugation at 6000 rpm during 5 minutes. It was difficult to have 300µl of plasma from all mice. We decided thus to dilute the samples and then normalize data before statistical analysis performing. The liquid phase (200µl) was mixed with 100µl of water and 300µl of buffer (0.142M Na2HPO4, 0.1%NaN3, 0.1% TSP pH7.4 in 20% D2O). Samples were kept at 4°C until analysis.

• Liver tissue 30µL disposable Kel-F® inserts with sealing caps for 4 mm NMR rotors were filled with intact liver fragments and 5µL of D2O. Inserts were stored at -80 °C before highresolution magic angle spinning nuclear magnetic resonance ( 1 H HR-MAS NMR) analyses.

Extractions of polar and lipophilic metabolites was performed as described in Beckonert protocol [START_REF] Beckonert | Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts[END_REF] . A liver fragment (0.3 g) was homogenized in 1.2 mL 80% methanol at 4°C. The homogenates were sonicated twice in an ice-cold water bath for 10 s. Homogenates were then transferred into a glass tube and 3 mL of chloroform were added. Tubes were vortexed again and diluted adding by adding 1.2 mL of chloroform and 1.2 mL of water.

Tubes were vortexed, kept on ice for 15 min, and the phases were separated by centrifugation (1500 g, 20 min, 4°C). Each phase (aqueous / lipophilic) was collected and vacuum. The dry residue was dissolved in 600 µL of deuterated phosphate buffer (pH = 7.2, 100 % D2O) [START_REF] Beckonert | Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts[END_REF] .

An additional series of quality control (QC) samples for each type of samples was included to evaluate the quality and the reproducibility of the NMR data acquisition. 2D NMR experiments ( 1 H- [START_REF] Oresic | Metabolomics, a novel tool for studies of nutrition, metabolism and lipid dysfunction[END_REF] C HSQC, 1 H-1 H TOCSY and J-Resolved) were recorded on a subset of samples (urine, plasma and liver aqueous extracts) to achieve structural assignment of the metabolic signals.

NMR-based metabolomics

NMR data processing

• Baseline correction and spectra calibration NMR spectra are processed by baseline and phase corrections, and then calibrated. Spectral data are converted to a mathematical matrix, where each row relates to a given sample and each column corresponds to a single measurement in that experiment.

• Alignment Some variations in the chemical shift of certain peaks signals can be noticed, and this is mainly due to differences in pH or osmolarity between samples. Spectra need to be perfectly stackable to ensure that each column of the X matrix corresponds to the same metabolite before statistical analysis. Icoshift 132 is a very effective tool for the alignment of NMR spectra. This method was employed as described in figure 3.2.2. Other alignment tool exist [START_REF] Zhang | Advances in NMR-based biofluid analysis and metabolite profiling[END_REF][START_REF] Izquierdo-Garcã | Descriptive review of current NMR-based metabolomic data analysis packages[END_REF] .

V 1 V 2 V 3 V 4 V n V n-1 V n-2 ... x 1,1 x 1,2 x 1,3 x 1,4 x 2,1 x 2,2 x 2,3 x 2,4
x 3,1 x 3,2 x 3,3 x 3,4 x 4,1 x 4,2 x 4,3 x 4,4

x 5,1 x 5,2 x 5,3 x 5,4

x 6,1 x 6,2 x 6,3 x 6,4 • Normalization NMR samples preparation is straightforward and the protocols are standardized and highly reproducible. However, sample handling and dilution can introduce variations in signal intensities, which are not related to the metabolism. These variations can interfere with multivariate statistical analysis. The goal of normalization 133 is thus to remove or minimize these variations to make the data from all samples directly comparable with each other.

x 1,n-2 x 1,n-1 x 1,n x 2,n-2 x 2,n-1 x 2,n x 3,n-2 x 3,n-1 x 3,n x 4,n-2 x 4,n-1 x 4,n
Normalization is essential for urine samples because urine concentration is highly related to the amount of ingested water, drugs, toxins and treatments. The most appropriate normalization in this case is PQN (Probabilistic Quotient Normalization) 134 , which looks for the most probable coefficient of dilution between each spectrum and a reference spectrum to normalize spectra.

For blood plasma, metabolite concentrations are highly regulated by homeostasis, and changes caused by a physiological situation are generally small but significant. It is not necessary to normalize in this case, unless the volume differs slightly from one sample to another. Normalization on total intensity, where each bucket is divided by the total intensity of the spectrum, is used in this case to eliminate the effects of variable concentration.

• Scaling Variables (metabolite concentrations) often have different numerical ranges. A variable with a large range has a large variance, whereas a variable with a small range has a small variance. The scaling methods aim to adjust the variance of the different metabolites 135 .

There is several ways to scale the data. The most known is Auto scaling or Unit Variance (UV), data are mean-centered and then each value is divided by the standard deviation. Pareto scaling is also widely used, it aims to reduce the relative importance of large values, and keep data structure partially intact. Here, data are mean-centered, and then divided by the square root of the standard deviation. Both methods make the metabolites equally important.

Multivariate analysis of metabolic profiles

In biological studies, several variables (intensities at a specific NMR chemical shift, age, height, weight, sex…) are used to characterize samples (observations). These data can be arranged in a table, where each row represents an observation and the columns represent the different variables.

The high number of variables generated in metabolomics studies makes the analyses difficult. It is complicated to overview and summarizes the data. The development of chemometrical tools constitutes an important step in sciences, which help to extract information out of data. This discipline is defined as " the chemical discipline that uses mathematical, statistical, and other methods employing formal logic, to design or select optimal measurement procedures and experiments, and to provide maximum relevant chemical information by analyzing chemical data" 136 . Contrary to statistics, Chemometrics is based on computing intensive methods. They are in general multivariate. The main objective of NMR-based metabolomics is to classify a spectrum based on its chemical composition, and then identify which spectrum areas or metabolites are responsible for this classification. With other words, the goal is to interpret chemical and biological changes related to class differences.

Multivariate statistical analyses based on projection methods can be classified in two complementary approaches for modeling data: unsupervised and supervised statistical approaches. Unsupervised analysis like principal component analysis (PCA), which is considered as the basis of for other multivariate analysis, is widely used in metabolomics studies as an exploratory tool in the beginning of any analysis 137 . Peaks intensities in the spectra are used as coordinates in multidimensional plots of metabolic activity. PCA helps to detect trends, identify outlier samples (resulting from error in sampling, preparation or spectrum acquisition), and delineate classes. This approach is only based on the spectra and no information about samples is provided. Other unsupervised methods can be used like Hierarchical Clustering Analysis (HCA) 138 and self-organizing maps.

In supervised analyses, samples are characterized in classes and they are associated to an outcome y i value. Projection to latent structures (PLS) 139,140 and its extension orthogonal partial least squares (OPLS) are based on linear regression method, which aim to find a relationship between a descriptor matrix X (areas from the spectra) and a response matrix Y (e.g. a specific disease). PLS and OPLS can be used for discrimination in the form of PLS- After multivariate analysis, univariate analyses are performed to identify significant changes in metabolite concentrations. Statistical recoupling of variables (SRV) [START_REF] Assi | A statistical framework to model the meeting-in-the-middle principle using metabolomic data: application to hepatocellular carcinoma in the EPIC study[END_REF] is a homemade tool, which aims to extract the significant differences in metabolite levels between groups defining a metabolic signature. Delayed treatment of neonatal hypothyroidism may result in profound neurodevelopmental delay 150 . As described in the previous sections, RTHα patients present some common features with hypothyroid patients. This chapter is dedicated to the study of metabolic changes caused by hypothyroidism, in order to distinguish them from those caused by THRA mutations.

• Models validation

In the present study, we carried out a metabolomic study investigating urine and blood plasma to obtain a reference metabolic signature of hypothyroidism to study RTHα disease.

Different anti-thyroid drugs and techniques (thyroidectomy), with their advantages and disadvantages, exist to control TH synthesis 128,151 . Propylthiouracil (PTU) is often used to induce hypothyroidism in mice and rats models to study this disease 128,152 . The inconvenience is that this treatment was associated with liver toxicity 151,153 . The prevalence of this toxicity is extremely low, but should be suspected in research studies. For this metabolomic study, we used PTU-induced hypothyroidism mice models to find biomarkers related to hypothyroidism. The metabolic signature of this condition reflects thus the metabolic changes caused by the hypothyroid condition, and probably the presence of a toxic effect of the PTU treatment. To distinguish between hypothyroid effects from PTU toxic effect, we used another group, treated with PTU followed by T3 treatment during 4 days. The goal is to assess the reversibility of PTU-induced hypothyroidism. A quick reversibility could reflect a direct effect of TH and the absence of secondary effects of PTU. For this group, a longitudinal study was performed to characterize the kinetic of the metabolic changes. Here we will call this group of mice "TH treated" group.

Study design

In this study, 30 adult male mice divided in 3 different groups of 10 mice each one were used. The first group is a control group without any treatment. A hypothyroid phenotype was induced in mice by two weeks propylthiouracil (PTU) treatment containing diet 154 . PTU inhibits the enzyme thyroperoxidase, which has an action in thyroid hormone synthesis 155 .

In the third group, mice were treated during 14 days with PTU, and underwent three injections of T3 the last 4 days. This group has an excess of thyroid hormones compared to control group (Figure 4 correspond to 1 day, 2 days and 3 days after the beginning of T3 injection in TH treated group. At D 14 , urine was collected from the 3 groups, and mice were then anesthetized in order to collect blood. Mice were killed after this step because the blood volume taken is important and can be lethal. After blood collection, we proceeded to plasma preparation.

Urine and plasma samples were then stored at -80°C until NMR analysis. Forty-five metabolites were identified in urine and 33 in blood plasma (Figure 4.2.2 and appendices 2 and 3).

Results

Quality of the 1 H NMR data

A principal component analysis (PCA) was performed for urine and plasma samples separately. The goal is to assess the quality and the reproducibility of the 1 H NMR data sets in on the hand, and clean the data sets by identifying and then removing outliers, on the other hand. We can also identify trends in this step.

Here, the good stability and reproducibility is proven by the clustering of the QC samples in both urine and plasma data sets ( The PCA and the OPLS-DA analyses score plots (Figure 4 NMR investigation of urine and plasma allowed also distinguishing between hypothyroid and control groups. A metabolic signature associated with this disease was identified in each biofluid. Hypothyroidism is characterized by a hypo-metabolic state leading to reduced energy expenditure, increased cholesterol levels, reduced lipolysis and gluconeogenesis. This explains the increased level of lipids, and decreased levels of glucose. It was reported that HDL levels are normal, elevated or in some cases decreased in hypothyroidism 156 . Here we notice a significant decrease of these lipoproteins in the plasma.

The high level of urinary creatinine in hypothyroid group could be explained by renal abnormalities that occur because of the deficiency of thyroid TH reduces the cardiac output leading to generalize hypo-dynamic state of the circulatory system. The vital substances of Krebs cycle like succinate, oxoglutarate and citrate decrease in hypothyroid mice compared to control mice. This can be explained by the reduced biogenesis and respiratory capacity in free mitochondria and neuronal oxygen consumption in the cerebral cortex in developing rats induced by hypothyroidism 157 . Our results are quite agreed with the results of previous studies about thyroid disorder 152,157 . Metabolites related to gut microbiota (hippurate and indoxylsulfate) are present in high concentrations compared to the control group, suggesting probably a modification in the intestine. Variation in the urinary concentration of phenylalanine suggests a perturbation in phenylalanine metabolism. Wu et al. found showed that some metabolites related to phenylalanine metabolism vary in hypothyroid rats.

Impact of thyroid hormones on the metabolism

Unsupervised and supervised multivariate analyses were performed in order to study the evolution of TH treated group over time. Supervised analysis OPLS-DA was applied to remove information unrelated to T3 treatment. A clear separation between samples at different time points is noticed in both PCA and OPLS-DA ( Here, we were able to distinguish between control and TH treated populations using 1 H NMR metabolomic profile for urine samples. NMR investigation of urine and plasma here allowed distinguishing between TH treated and control populations. A metabolic signature associated with TH response in the organism was identified in each biofluid. TH seems to modify rapidly the metabolism. This point was also noticed in a metabolomics study performed by Pietzner et al. 158 . The metabolic signature of TH treated mice is close to the hyperthyroidism-induced metabolic changes described in the literature. Alanine level increases in both urine and plasma of TH treated mice. It is known that hyperthyroidism is associated with elevated alanine amino-transferase, which increases serum alanine concentration, and this reflects a deregulation in liver metabolism 159 . The increased levels of glucose and decreased level of lipids in TH treated mice blood reflect a hyper-metabolic state. Hyperthyroidism is characterized by increased resting energy expenditure, reduced cholesterol levels, increased lipolysis and gluconeogenesis 160,161 . Indoxylsulfate and hippurate, which are produced from the tryptophan, depend on the gut microbiota activity 162 . Their variation reflects a perturbation in the The metabolic signature related to TH treatment is different from that related to PTUinduced hypothyroidism, which suggests that TH reversed quickly the impact of PTU on the metabolism.

Conclusion

The present study, using a 1 H NMR-based approach, shows that the metabolomics profile is deeply influenced by the TH levels. The three different groups (euthyroid, hypothyroid and "TH treated") present different metabolic profiles. A global urine and blood metabolomic analysis based on 1 H NMR was applied to provide comprehensive and complementary insights into hypothyroidism and the impact of TH at the metabolic level. Specific metabolic signatures were identified for each condition in each biofluid.

Determination of TSH and free thyroxine (FT4) levels represent the gold standard in evaluation of thyroid function. Despite the huge amount of publications studying the action of thyroid hormones on metabolism, so far only the tip of the iceberg have been discovered, with an unexplored "metabolic world" under the surface. The need to a new method for the discovery of new markers of thyroid function and to detect novel pathways of thyroid hormone action is important. Through this study, we showed that NMR-based metabolomics could be an important tool to investigate hypothyroidism, and eventually hyperthyroidism.

The hypothyroid signature described in this chapter in urine and plasma is further exploited in the chapter 5 to evaluate the specificity of metabolic markers for the emerging genetic disease resistance to thyroid hormone α (RTHα).

NMR as a putative diagnostic tool for

the presence of thyroid hormone receptor alpha 1 mutations

Introduction

Resistance to thyroid hormone (RTHα) is an emerging genetic disease due to mutations in the THRA gene, which encodes thyroid hormone receptor alpha (TRα1). Since its first description in 2012, 45 cases of RTHα have been reported worldwide, corresponding to 25 different mutations of TRα1 100,105,109,110,112,163 . The high variability of clinical features and the absence of reliable biochemical markers make the diagnosis of this disease difficult. In addition, identified RTHα patients present some common features with hypothyroid patients, which may delay the diagnosis in some cases 108 . Some of these mutations have been recently modelled in mice. In our study, we used five germline mutations (four frameshift and the N359Y missense mutation) in the mouse Thra gene, closely modelling the mutations found in RTHα patients 117 . Two of the four frameshift mutations are very close to human mutations, while the two others do not exist in human patients. N359Y missense mutation used here was found in a French patient, with very different symptoms compared to RTHα patients. This patient suffers from extreme skeletal malformations, macrocytic anaemia, chronic diarrhea, and hypercalcemia 110 .

We characterized the metabolic phenotypes of urine and plasma samples collected from the five animal models using an untargeted Nuclear Magnetic Resonance (NMR)-based metabolomics approach. Multivariate statistical analysis of the metabolomic profiles shows that biofluids of mouse models that carry human-like mutations can be discriminated from controls. Metabolic signatures associated with Thra mutations in urine and plasma are stable over time and clearly differ from the metabolic fingerprint of hypothyroidism in the mouse.

Our results provide a proof-of-principle that easily accessible NMR metabolic fingerprints of biofluids could be used to diagnose RTHα in humans. This chapter describes in details results that were submitted recently for publication 164 . . .

Study design

The research project was approved by a local ethic committee and subsequently authorized by Four mice models for RTHα were generated in order to understand their impact on the metabolism.

Thra S1 and Thra S2 mutations are short receptors and are closed to mutations found in human patients, and Thra L1 and Thra L2 are long receptors and are different from those found in human patients.

95 mice (12 Thra S1/+ , 10 Thra 2/+ , 10 Thra L1/+ , 12 Thra L2/+ , 7 N359Y and 44 wild-type)

were followed during 6 months for this study. Littermates without mutation were used as controls. Urine and plasma samples were collected at two time points: urine samples were collected at 3 and 6 months of age in order to study the RTHα fingerprint stability over time, and plasma samples were collected in anti-coagulant tubes at 6 months (Figure 5.2.2). Mutants and wild-type mice were followed during 6 months. Urine samples were collected from different mice groups at 3 and 6 months old and blood at 6 months old. NMR analysis was performed for all urine and blood plasma samples.

N359Y

The steps of metabolomic analyses and multivariate statistical analysis were described in the chapter 3.

Results

Quality of the 1 H NMR data

A principal component analysis (PCA) was performed for each set of samples, urine and blood plasma, to assess the reproducibility of the 1 H NMR data sets. Here, the good stability and reproducibility is proven by the clustering of the QC samples in each models (Figure 5.3.1). We considered the different groups of mice models as a single group, with the hypothesis that these mutations have the same impact on the metabolome. A PCA and an OPLS-DA of the metabolic profiles were performed to compare mutants mice group to wildtype group, in both urine and blood plasma. No discrimination between the two groups was noticed in both biofluids (Figure 5.3.2).

Discrimination between the group of mutants and wild-type group

Discrimination between each mutant group and wild-type group

We then asked whether each mutant group could be discriminated from wild-type group.

The goal was to understand the specific impact of each mutation on the metabolism. We performed an OPLS-DA for each comparison (mutant vs. wild-type), in urine (at different ages) and plasma. As described in Tables 5.1 and table 5.2, in urine, only Thra S1/+ group can be discriminated from wild-type group. We notice also here that there is a conservation of the metabotype overtime, because we obtain the same results at 3 and 6 months old.

- In plasma, both Thra S1/+ and Thra S2/+ groups can be discriminated from wild-type group (Table 5.3). We notice also that the discrimination is slightly better in the blood plasma, and here an additional group can be discriminated from wild-type group. These results suggest that we cannot consider the set of mutants group as a homogenous group, theses mutations do not impact the metabolism with the same manner. Thra S1/+ and probably Thra S2/+ have a bigger effect than the other mutations. Our results are in line with the findings of Markossian et al 117 . They noticed that Thra S1/+ and Thra S2/+ display very similar phenotypes, reproducing several developmental traits present in RTHα patients, while Thra L1/+ and Thra L2/+ mice have an almost normal phenotype 117 . Discrimination between N359Y group and wild-type group were not possible. It was reported that the patient carrying this mutation has also other mutations in her genome (Appendix 1), and the biological study done by Markossian et al 117 show that N359Y mice do not have the same features that the N359Y patient. We decided to focus only on the four frameshift mutations for the next sections.

Metabolomic analysis segregates two types of frameshift mutations

We addressed whether metabolic profiling could differentiate Thra S1/+ , Thra S2/+ , Thra L1/+ and Thra L2/+ mice. A four-classes orthogonal partial least square discriminant analysis (OPLS-DA) of the metabolic profiles was performed to evaluate the impact of the different Thra mutations on urine metabolome. Although the 4 groups were not separated, we noted that mutants with short receptors (Thra S1/+ and Thra S2/+ ) were clustered in this statistical model as are mutants with long receptors (Thra L1/+ and Thra L2/+ ), and that the two groups tend to separate (Figure 5.3.3). Within each group, a new OPLS-DA analysis was unable to discriminate between the two mutations (Thra L1/+ from Thra L2/+ mice, and Thra S1/+ from Thra S2/+ mice, in both urine and plasma; data not shown). As no clear discrimination was observed, we pooled the mutant mice according to the type of frameshift mutation that they carry (Thra L/+ group and Thra S/+ group), and repeated OPLS-DA analysis. This improved the statistical power of the analysis and led to a clear distinction between these two groups. Statistical significance of these models was assessed by high values of goodness-of-fit parameters R 2 and Q 2 (which explain the variance and predictive power of the model, respectively) and CV-ANOVA p-values < 0.05 and model resampling under the null hypothesis. Altogether, these results show a clear distinction between Thra L/+ and Thra S/+ mice, as expected from previous phenotyping.

T pred 2

Discrimination between Thra S/+ mice with human-like mutations and wild-type mice

We then asked whether the group of Thra S/+ mice, which carry mutations relevant to human cases, could be recognized from a group of wild-type littermates. We performed three comparisons, using either urine (3 or 6 months) or plasma. OPLS-DA of the metabolic profiles was performed to drive robust statistical models based on the discrimination between Thra S/+ and wild-type mice. We observed a significant discrimination between Thra S/+ samples and their associated wild-type controls in all three comparisons (Figure 5.3.5). By contrast similar comparisons were unable to discriminate the group of Thra L/+ mice from the wild-type group, as expected from the very limited consequences of these mutations on phenotype (data not shown). x 10

Determination of metabolic signature of Thra mutation in urine and blood plasma

The most significant variations of individual metabolites discriminating the Thra S/+ group from wild-type were identified with univariate analysis of spectral variables from urine and plasma NMR profiles. Urine metabolic signatures of Thra S/+ mice were characterized by increased levels of hippurate, formate, fumarate, and decreased levels of Krebs cycle metabolites (acetate, succinate, citrate and cis-aconitate), urea, Concentrations of dimethylamine, trimethylamine, isovalerylglycine, choline and Nacetylglucosamine were shown to be significantly lower in urine of Thra S/+ mice, while polyunsaturated lipids were lower in the blood. Overall, this study provides metabolic signatures associated with RTHα in urine and plasma that present common and complementary information.

Comparison between the metabolic signatures of urine of 3 and 6 months old mice provides useful information on the temporal stability of the observed metabolic changes.

Overall, a large similarity in metabolites composition and variations was observed, 13 metabolites varying in the same way at both ages (Figure 5.3.6). Few differences were noticed, notably for formate and fumarate, which differential concentration only appear in 6 months old mice. Variation in oxoglutarate content was also amplified in 6 months old mice.

Dimethylamine, trimethylamine and choline remained present in both signatures but became statistically significant, in univariate analysis, only after 6 months. This shows that the urine metabolic signature associated with RTHα is globally stable over time, while variations become more visible with age for some metabolites.

RTHα signature is different from hypothyroidism signature

RTHα patients share symptoms with hypothyroid patients. We thus investigated whether the metabolic signatures found for Thra mutations can be distinguished from a signature of hypothyroidism. We compared thus these results with the results detailed in the chapter 2, where wild-type mice were made hypothyroid with propyl-thio-uracyl treatment, and 1 H-NMR analysis was performed to obtain a signature related to hypothyroidism in urine and plasma. We noticed that there is a strong discrimination was observed between hypothyroid and control groups. We noticed also that 24 and 17 metabolites vary in hypothyroid condition with respect to the non-treated group.

When the Thra S/+ and hypothyroidism metabolic signatures were compared, clear differences were obvious. Only 6 common metabolites variations were found in urine: increased level of hippurate, and decreased levels of acetate, citrate, succinate, dimethylamine and trimethylamine (Figure 5.3.7 A). In plasma, only 7 metabolites were shared between RTHα and hypothyroidism metabolic signatures. Both were characterized by a decrease in low density lipoprotein (LDL), very low density lipoprotein (VLDL), acetate, citrate, pyruvate, choline, phosphocholine and glucose (Figure 5.3.7 B).

These results show that RTHα and hypothyroidism metabolic signatures present few similarities. The metabolic profiles recorded in urine and plasma could therefore potentially contribute to the specific diagnosis of RTHα, which is a rare disease, much less frequent than hypothyroidism. ↑ : present in higher level compared to control group. ↓: lower ever compared to control group.

Metabolites in red vary significantly in Thra S/+ condition.

Discussion

The main objective of our study was to establish that metabolomic fingerprinting can be used as a diagnostic tool for a rare genetic disease, RTHα. Multivariate analysis of urine spectra provides a metabolic fingerprint that enables to recognize mice carrying a pathological mutation in the Thra gene, relevant to the human genetic disease. By contrast, the method did not identify the presence of a weak allele, which has little consequences on the development and physiology, and has never been reported in humans. Also the method did not identify a single metabolite, which presence in urine or plasma would be the hallmark of RTHα. Its statistical power rather relies on the capacity to evaluate the concentrations of a large number of metabolites simultaneously.

Considering that mouse Thra and human THRA genes display extensive sequence similarities, mouse lines with Thra mutations are highly relevant animal models for RTHα.

We thus expect that 1 H NMR can be used for RTHα diagnostic in humans. However, clinical implementation of this method would imply to address additional issues: first, the environment and genetic background of lab animals is highly controlled, and the body fluid composition is certainly more variable in human populations. Therefore, the predictive capacity of the method remains to be tested on a human cohort. Second, the interpretation of the metabolic signatures is not straightforward, and we thus cannot ascertain at this point that the underlying biochemical mechanisms are conserved in humans. In the following, we thus attempt to give hypothetical interpretations of the observed changes.

One encouraging observation is that, although 1 H NMR is instrumental to recognize hypothyroidism, privation of thyroid hormone provides a very different signature than Thra mutations. Our analyses show that, while Thra mutations and hypothyroidism may have a similar impact on glucose metabolism, they impact differently the plasma content in lipids and cholesterol. In particular, variations in the plasma content in fatty acids are in opposite direction. This is expected if one considers that liver, which secretes a large fraction of the lipids found in serum and urine, expresses Thrb at much higher level than Thra. Liver is thus expected to be more sensitive to changes in TH status than to Thra mutations. Studying both urine and plasma is also useful because they provide common and complementary information. For example, coordinated variations suggest that changes in urine reflect variations initially present in plasma, for creatinine, citrate and acetate and not an alteration of the renal function.

Additional features of the metabolic signature can be tentatively interpreted in biochemical terms. Isovalerylglycine is a product of leucine degradation to acetyl-CoA, which is an important feeder into the Krebs cycle 165 . A significant decrease in urinary level of isovalerylglycine, and the decrease in the levels of citrate, succinate and acetate may thus indicate a change in the degradation of leucine.

Finally, the lower concentration of dimethylamine, trimethylamine, choline and hippurate observed in the urine of mutant mice and hypothyroid mice may reflect intestinal alterations.

Gut microbial metabolism of choline, present in diet, leads to the production of trimethylamine, which, once absorbed, is metabolized to generate dimethylamine. Hippurate, is also synthesized by the gut microbiota and its decreased concentration in urine is a marker of bowel inflammation 166 . It is tempting to speculate that changes for these metabolites are direct or indirect consequences of the defect in the intestinal epithelium, reported in hypothyroid 167 and Thra mutant mice 117,168 . Also providing a snapshot of only a fraction of metabolome, 1 H NMR analysis of Thra mutant mice may thus be revealing changes in metabolic pathways and physiology, which are expected to be conserved in humans.

Functional

genomics by metabonomics to study the thyroid hormone receptor β

The previous chapters were dedicated to the application of metabolomics for biofluids analysis in order to discover biomarkers related to hormonal diseases (hypothyroidism, hyperthyroidism and RTHα). In this chapter, we propose to use metabolomics to address a specific biological question. Metabolomics has the potential to get insight into molecular mechanisms and metabolic pathways and can be used by biological studies as a strategy to read-out metabolism. Among model organisms, genetically modified organisms using knocking-in or knocking-out (KO) technologies can be viewed as functional genomic tools for which the study of the resulting altered phenotype can serve to elucidate the role of the protein products of the targeted genes. In this chapter, we provide a description of the impact of the regulation by thyroid hormone of liver metabolism using a mouse model that is characterized by a specific deletion of the gene encoding the thyroid hormone receptor β (TRβ) in hepatocytes, i.e. LTRβ-KO mice 169 . This project was started few years ago with another PhD student (A. Fages). The obtained results were very interesting, but a supplementary experiment done by another student from the laboratory then challenged one of the results. My work, in this project, was to repeat the same experiment to validate it, and extend the analysis to extract deeper information. This chapter contains a combination of the previous results and the new analysis. To improve the statistical analysis, larger groups for the liquid NMR analysis were used. This chapter describes in details results that were submitted recently for publication 170 . HR-MAS NMR experiments and their analysis were done exclusively by A. Fages.

Introduction

TH regulates metabolism in a global manner, acting in the brain, white fat, brown fat, skeletal muscle, pancreas and liver. In the liver, the major TH receptor is TRβ1. Individual organs also respond to TH in an indirect manner, as TH can trigger various humoral responses, change the sensitivity of an organ to exogenous signals, and exert an important influence on the autonomous nervous system [START_REF] Muller | Thyroid hormone regulation of metabolism[END_REF]157 . In vivo analysis of the metabolic function of TH is thus challenging, due to the intricate combination of multiple levels of regulation.

Here we used metabolomics analyses of the hepatic metabolome by nuclear magnetic resonance (NMR) to gain a deeper understanding of the effect of TH on the mouse liver metabolism. This method proved to be well suited to analyze the hydrophilic fraction of the metabolome and identified novel liver metabolites sensitive to hypothyroidism and/or subsequent TH replacement. We also determined the metabolome modifications induced by the elimination of TRβ1 receptors selectively in mice hepatocytes using a Cre/loxP recombination strategy. Both intact tissues and hydrophilic fraction were investigated by NMR to characterize metabolic fingerprints for these TRβ1 KO mice with respect to wildtype. By selectively eliminating TRβ1 from hepatocytes we were able to identify a subset of metabolites that are related to the direct influence of TH on this cell type.

Study design

Thrβlox/lox mice 171 were crossed to AflpCre mice 172 to selectively eliminate TRβ1 from hepatocytes. The resulting mice are designated in the following as HepTRβ-KO mice.

Littermates without Cre were kept as control. All mice were fed ad libitum and housed under recommended standard conditions for 3 to 5 months. TH deficiency in adult animals was induced by 14 days of propyl-thio-uracyl (PTU) treatment as described 173 . For TH replacement, T3 treatment was performed for the last 4 days of PTU treatment using daily intraperitoneal injections (Figure 6.2.1). Liver biopsies were immediately flash-frozen after dissection in liquid nitrogen and subsequently stored at -80°C. Biopsies of the right lobe were used for metabolomics studies.

Results

Untargeted NMR Metabolomics analysis of the liver response to thyroid hormone

We attempted to gain a broad view of the liver metabolome for the four groups of mice (WT-PTU, WT-PTU+TH, TRβ-KO-PTU, and TRβ-KO-PTU+TH) by using 1 H NMR spectroscopy by following two distinct experimental approaches. We used HR-MAS analysis From these results, we were able to discriminate hypothyroid phenotype from hyperthyroid phenotype. Metabolic fingerprints associated to this condition, which reflects the impact of TH on the liver metabolism, were identified in both aqueous liver extracts and intact liver tissue.

Impact of TRβ on liver metabolism

To understand the metabolic function of TRβ in the liver, we studied the impact of TH on mice having a specific KO of TRβ in the liver (LTRβ-KO mice). This leads also to identify the indirect effect of TH on the liver metabolism. The supervised statistical analysis of the 1 H HR-MAS NMR spectra highlighted systematic variation between PTU and PTU-TH mice (Figure 6.3.3.A). While no significant variation identified from multiple univariate testing (after Benjamini-Hochberg correction), the global metabolic signature shows that TH treatment is associated with a relative decrease of glucose content and an increase of lipid content in the liver (Figure 6.3.3.B).

These results are in agreement with the results obtained from the analysis of the aqueous extracts that also display a relative decrease of glucose in the PTU-TH group.

↑ ↑ Inosine ↑ ↑ Deoxyuridine ↑ / Glucose ↑ ↓ ↓ ↓ Taurine ↑ ↑ Succinate ↑ ↑ Alanine ↑ ↑ ↓ ↑ Lactate ↑ ↑ Leucine ↑ ↓ AMP ↓ ↓ ADP ↓ ↓ NAD/NADP+ ↓ ↓ UDP-Galactose/UDP- Glucose ↓ / Glutathione ↓ ↑ Phosphocholine, choline ↓ ↓ ↑ ↓ Isocitrate ↓ ↓ Hydroxybutyrate ↓ ↑ Isoleucine ↓ ↓ Valine ↓ ↓ Acetate ↓ ↑ ↓ Guanidoacetate ↓ ↑ Methylhistidine ↓ ↓ Glutamine ↓ ↓ Glycine ↓ ↓ N-acetylglucosamine ↓ ↓ Tyrosine ↓ ↓ Maltose ↑ ↑ Betaine ↓ ↓ Mannose ↑ / Acetoacetate ↓ Glutamate / ↑ Anserine ↓ Glycogen ↓ Methionine ↓ Lipids ↑ ↑

Conclusion

In the present study we used 1 H HR-MAS of unprocessed liver biopsies and NMR of liver aqueous extracts for a global and unbiased analysis of the metabolomic consequences of eliminating TRβ1 from hepatocytes.

We expected that the elimination of TRβ1 from the hepatocytes would abrogate the liver response to T3. Surprisingly, although the metabolic signature to T3 was modified and the changes induced by T3 of generally of lesser amplitude as compared to controls, the response remained significant. The residual response of HepTRβ-KO mice could have different sources. The first possible explanation would be that it reflects the response of nonhepatocyte cell types, which represent 20% of the liver cell mass, and in which both TRα1 and TRβ1 could be present. The second possibility would be that hepatocytes have enough TRα1 to mediate a significant response. Thra gene expression is very low in adult mice liver, and previous transcriptome analyses concluded that Thrb knock-out, but not Thra knock-out, alters the liver response to T3 [START_REF] Yen | Effects of ligand and thyroid hormone receptor isoforms on hepatic gene expression profiles of thyroid hormone receptor knockout mice[END_REF]174,175 . More targeted approaches also concluded that only TRβ1 is required for the T3 response of hepatocytes 176,177 . However, the combination of both Thra and Thrb mutations seems to aggravate the liver phenotype, leaving open the possibility that TRα1 can partially compensate for the absence of TRβ1 after Thrb knock-out [START_REF] Yen | Effects of ligand and thyroid hormone receptor isoforms on hepatic gene expression profiles of thyroid hormone receptor knockout mice[END_REF] . While these mutations are not restricted to hepatocytes, and might thus affect liver function in a very indirect manner, Cre/loxP recombination has already been used to restrict to hepatocytes the expression of a Thra mutant encoding the dominant-negative TRα1L400R receptor. This selective alteration appeared sufficient to reduce the expression of the genes encoding pyruvate kinase and phosphoenolpyruvate carboxykinase, which should notably impact hepatic neoglucogenesis 178 . Overall, the possibility exists that TRα1 fulfills a specific function in hepatocytes. Finally the residual metabolomics response of the HepTRβ-KO liver might reflect a T3 response-taking place outside of the liver. One important pathway has been identified, which involves a T3-dependent sympathetic stimulation of the liver while stimulates hepatic glucose production [START_REF] Martínez-Sánchez | Thyroid hormones induce browning of white fat[END_REF] . It can be hypothesized that this pathway is important to control blood glucose and triglyceride concentration. As TH normally controls several aspects of glucose and glycogen metabolism, a combination of cell-autonomous and distant regulations could explain why the glucose content of liver is decreased by hypothyroidism in control mice and paradoxically increased in HepTRβ-KO mice.

Our metabolomic analysis provides a static view of liver metabolism. Therefore interpreting our data in terms of metabolism should thus be made with caution. It also provides a partial of the metabolome, as lipids, which metabolism is highly sensitive to T3 cannot be precisely analyzed. For example, we observed significant changes in acetate and hydroxybutyrate content, two molecules that are key elements of fatty acids metabolism, and these changes should probably correlate with changes in fatty acid composition. Only few of the changes in liver metabolites content caused by the thyroid hormone status are clearly altered in HepTRβ-KO mice and thus reflect the cell-autonomous influence of TRβ1. This allows to distinguish two categories of metabolites: for choline, glutamine, glutathione, glycine, guanidoacetate the liver content differ between HepTRβ-KO and control liver only when mice are made hypothyroid. By contrast for in leucine, isoleucine, succinate and valine, only the TH response of hypothyroid HepTRβ-KO mice is clearly blunted. This distinction between metabolites parallels the conclusion of transcriptome studies, which showed that some metabolic genes are more sensitive to the negative regulation exerted by the unliganded TRβ1, while others are not sensitive to hypothyroidism but quickly upregulated after T3 treatment [START_REF] Yen | Effects of ligand and thyroid hormone receptor isoforms on hepatic gene expression profiles of thyroid hormone receptor knockout mice[END_REF] .

The decrease of glutathione observed in response to T3 has been reported before 179 . It is likely to reflect a T3-induced oxidative stress {Mancini, 2016 #11488}, and increased glutathione consumption 180 . The presence of AMP and succinate in the metabolic signature of T3 response is also compatible with an increase in metabolic rate, succinate and ATP consumption reported before 181 . The presence of branched chain amino acids (leucine, isoleucine, valine) among these metabolites is a novel information which interpretation will require deeper investigation. It is noticeable that a recent metabolomic study in which an excess of TH was given to healthy subjects also identified an increase of leucine and isoleucine content in the serum 158 . A urine metabolome analysis also found a positive correlation between the circulating level of T4 and the urinary content in glycine and alanine in euthyroid subjects 182 . A unifying hypothesis would be that the release of branched aminoacids reflects an increase in protein catabolism.

Conclusion and perspectives

Metabolomic is a comprehensive technique that allows capturing an instant metabolic picture of an organism, reflecting molecular and pathophysiological states. Metabolic profiles are sensitive to environmental factors, genetic, diseases lifestyle and pathophysiological stimuli, making metabolomics as a promising tool to identify disease biomarkers and to get insight into metabolism to address specific fundamental biological questions.

In this thesis, untargeted 1 H NMR-based metabolomics was applied to study the emerging genetic disease RTHα, for which absence of biochemical markers and specific symptoms make it hard to diagnose. RTHα features present common features with hypothyroidism. By studying both urine and plasma collected from dedicated mice models for RTHα and hypothyroidism, we were able to identify metabolic fingerprint related to hypothyroidism and RTHα, and differentiate them. Our results suggest that 1 H NMR of body fluids might be able to recognize a metabolic signature of RTHα in humans. These findings could be the starting point for further investigations aimed at translating these results into clinical practice with carefully designed and controlled prospective studies. Metabolomics could be used in the near future as a systematic, cheap, and automatic analysis of the urinary metabolome at birth, which would detect many common or rare diseases including RTHα.

Studying both urine and blood plasma is highly useful because they provide common and complementary information. The difference between the two biofluids is that blood analysis provides information about lipids, lipoproteins and glucose metabolisms. While, urine analysis gives an idea about kidney function and gut microbial state, for example. A number of issues should be addressed before clinical implementation of 1 H NMR for RTHα diagnostic: first of all, the plasma and urine metabolome are downstream readouts of complex physiological processes, which are different in humans and rodents. Second, the environment and genetic background of lab animals is highly controlled, and the body fluid metabolomes are certainly more variable in human populations. Performing this study on patients is primordial to complete and validate our findings. However, the majority of RTHα patients are treated with thyroid hormone, which improve their condition, and this can skew the study.

These different factors must be taken into account for the further and additional studies in humans. Pr. K. Chatterjee, director of Cambridge clinical centres, who discovered the first RTHα case, owns urine samples of RTHα patients. Collaboration between the two laboratories is foreseen in the near future to carry out new investigations on RTHα human samples.

We also used NMR-based metabolomics to investigate mouse model to gain a deeper understanding of impact of thyroid hormones and TRβ nuclear receptor loss of function on the liver metabolism. Both intact liver tissues and hydrophilic liver fraction were investigated.

We were able here to identify a subset of metabolites that are related to the influence of TH on the liver metabolism. We would have liked to perform a lipidomic study to understand the roles of lipids in our biological question, but our technical tools limited us because it needs specific methodologies. The new advances in analytical technologies, in particular, the development of new MS, chromatographic and of course NMR tools 183 for the characterization and quantification of the wide array of diverse lipid species in the cellular lipidome [START_REF] Griffiths | Mass spectrometry: from proteomics to metabolomics and lipidomics[END_REF]184 are nowadays very interesting.

Metabolomics is certainly a promising tool to understand biological processes, and identify diseases biomarkers, but bias can be easily inserted in the different steps (study design, sample collection and preparation, statistical analysis, data interpretation, etc.). On the other hand, metabolites assignment is a very delicate step because we need to be sure that a specific peak is referred to a specific metabolite. The situation is further complicated by peak shifts and overlaps typical in the spectra. Identification is time consuming because it requires the use of 1D and 2D spectra and different reference databases to assign peaks. In addition, in reference databases, many NMR peaks are not assigned to metabolites. This is an active research area, where improvements in metabolite identification, in particular its automation, are underway, and they will considerably reduce the needed time for this fastidious task.

Most of the untargeted studies show the ability of metabolomics to distinguish between disease and healthy control cohorts. However, only few studies follow up to replicate and validate their discoveries in accordance with clinical performance standards. Biomarker identification represents only the starting point in the translation from discovery phase to clinical diagnostics. The interpretation of the obtained metabolic signature is a very delicate step. Combination of genomics, transcriptomics, proteomics and metabolomics and other disciplines can be a formidable technological resource to further expand our knowledge of the complexities of human diseases.

This thesis allows a deeper understanding of three different diseases (hypothyroidism, hyperthyroidism and RTHα) and the impact of thyroid hormone on the global system and on liver metabolism. It also contributes to identify a specific metabolic signature of the emerging genetic disease resistance to thyroid hormone receptor RTHα, which is a first step to facilitate its diagnosis in humans.

L

  'impact des hormones thyroïdiennes (HT) et le récepteur TRβ sur le métabolisme hépatique a été également étudié dans ce présent manuscrit. Un modèle murin présentant une inactivation du récepteur TRβ au niveau des hépatocytes (LTRβ-KO) a été généré pour étudier cette question. Pour comprendre la fonction des HT médiée par le récepteur TRβ, les réponses métaboliques hépatiques à HT, obtenues sur des extraits hépatiques aqueux et tissus hépatiques intacts, des souris TRβ-KO et des souris sauvages ont été comparées. Les résultats obtenus suggèrent la présence d'un effet direct (par le récepteur TRβ) et un effet indirect des hormones thyroïdiennes sur le métabolisme hépatique.

Figure 1 :

 1 Figure 1: "Urine charts". Ulrich Pinder published this illustration in 1506, in his book "Epiphanie Medicorum". This chart connects colors, smells and tastes of urine to diseases.

Figure 1 . 1 . 1 :

 111 Figure 1.1.1: Number of metabolomics publications per year.

Figure 1 . 1 . 2 :

 112 Figure 1.1.2: Correlation between omics sciences.

Figure 1 . 2 . 1 :

 121 Figure 1.2.1: Diagram of NMR spectrometer.A supraconducting magnet is present in the heart of the spectrometer.5 mm glass tube is oriented between the poles of a powerful magnet. Radio frequency (RF) radiation of appropriate energy is broadcast into the sample from an antenna coil. A receiver coil surrounds the sample tube, and dedicated electronic devices and a computer monitor emission of absorbed RF energy. A free induction decay (FID) is acquired and then converted to NMR spectrum by Fourier transformation(FT) algorithm.

Figure 1 . 2 . 2 :

 122 Figure 1.2.2: Schematic representation of NMR parameters.

Figure 1 . 2 . 3 : 1 H

 1231 Figure 1.2.3: Example of a 600 MHz 1 H-NMR urine spectrum.

Figure 1 . 2 . 4 :

 124 Figure 1.2.4: Typical NMR spectra. (A) Typical spectrum of liver extract acquired at 700MHz. (B) Typical spectrum of intact liver tissue acquired at 700MHz with HR-MAS technique.

1

 1 Biological samples explored by metabolomicsDifferent sample types can be used in metabolomics studies: biofluids (whole blood, plasma, serum, urine, saliva, amniotic fluid, faecal water, etc.), intact cells and tissues (cardiac, liver, kidney, etc.

Figure 2 . 1 . 1 :

 211 Figure 2.1.1: The hypothalamus-pituitary-thyroid (HPT) axis and thyroid hormones (TH) actions.

Figure 2 . 1 . 2 :

 212 Figure 2.1.2: Schematic alignment of thyroid hormone receptors: TRβ1, TRβ2 and TRα1.TH receptors are composed of three domains: N-terminal domain (activation function), DNA-binding domain (DBD) and Ligand-binding domain (LBD). DBD and LBD are highly conserved domainsbetween the three TH receptors, contrary to the N-terminal domain.

Figure 2 . 1 . 3 :

 213 Figure 2.1.3: Mechanisms of action of thyroid hormones.

Figure 2 . 2 . 1 :

 221 Figure 2.2.1: The sensitivity of organs to thyroid hormones depends on the type of thyroid receptor present.

  hormones α >> β : Brain, muscle, intestine, heart, bone β >> α : Liver

Figure 2 . 2 . 2 :

 222 Figure 2.2.2: THRA mutations consequences.

  were previously generated by Markossian et al117 to understand the relationship between specific THRA mutations and phenotype. Five novel germline mutations (four frameshift: E395fs401X, E395fs485X, E395fs406X, and K389fs479X; and one missense N359Y), which are closely modeling the mutations found in RTHα patients, were introduced in mouse Thra gene. Markossian et al have investigated skeleton, blood, heart, cerebellum and intestinal epithelium. They concluded that like human patients, mutant mice displayed a hypothyroid-like phenotype, with altered development. Phenotype severity varied between the different mouse models 117 .

Figure 3 . 1 :

 31 Figure 3.1: Analytical workflow for NMR-based metabolomics studies

  The calibration is performed on a metabolite signals with stable chemical shift (uninfluenced by PH) like α-glucose anomeric proton (δ = 5.23 ppm) for blood plasma, and with an internal standard like TSP (3-trimethylsilylpropionic acid) introduced in samples for urine. HR-MAS spectra were calibrated on the CH3 alanine doublet at δ = 1.48 ppm and spectra of aqueous extracts were referenced to the anomeric glucose doublet at δ = 5.23 ppm.• BucketingBefore performing statistical analysis, spectral data are converted to a mathematical matrix (X matrix) by a simple binning approach (Figure3.2.1). Each row corresponds to a spectrum (a sample) and each column corresponds to regions of individual NMR spectra denoted NMR buckets. After a bucketing with a resolution of 0.001 ppm and a spectral width between 0 and 10 ppm, 10 000 variables are generated for each spectrum. These variables reflect spectral peak intensities or metabolite concentrations at a given point of the spectra.Biological samples contain a large amount of water, and its residual signal is important. The residual water signal is excluded in this step. Other signals can be excluded like molecules coming from contaminations (e.g. ethanol used to decontaminate animals) or solvents used during extraction. Regions of the spectrum containing only noise (in the beginning and the end of the spectrum) can be removed.

Figure 3 . 2 . 1 :

 321 Figure 3.2.1: Bucketing step.

Figure 3 . 2 . 2 :

 322 Figure 3.2.2: Overview of the Icoshift algorithm results.

  DA141 and OPLS-DA142 . Discrimination means the separation between two classes and then find the cause (e.g. biomarkers related to a specific disease) of this separation. OPLS-DA, contrary to PLS-DA, has the ability to separate predictive from non-predictive (orthogonal) variation, which facilitate the interpretation. It is actually the method of choice for discrimination and classification. Data can be visualized as score plot, which each point represents a sample, and as loading plot, which shows the contribution of variables (metabolite) involved in the discrimination (Figure3.2.3).

Figure 3 . 2 . 3 :

 323 Figure 3.2.3: Visualization of data from an OPLS-DA analysis.5A) This score plot shows a clear discrimination between the group 1 and the group 2. Each point represents a sample. 5B) This loading plot shows the contribution of each metabolite involved in thedifference between the two groups. Here, we see that acetate is associated with group 2 and lipids with group 1.

  Several methods are used to ensure the robustness, to validate the performance of the discrimination. R 2 and Q 2 parameters correspond to the explained variance and the predictive power of the model. They aim to assess the quality of the model. To validate PLS and OPLS models, CV-ANOVA 143 and permutation test 144 are generally used. The values in Y matrix are randomly permuted a thousand of times, and for each Y matrix permuted, R 2 and Q 2 are calculated and compared to the original values. If the original values are higher than the new calculated values, this indicates the good quality of the model by rejecting the null hypothesis (Figure 3.2.4).

Figure 3 . 2 . 4 : 2 4. 1 H4. 1 Introduction

 324211 Figure 3.2.4: Resampling under the null hypothesis to check model robustness.

Figure 4 . 2 . 1 :

 421 Figure 4.2.1: Study design. Wild-type mice underwent 14 days of PTU treatment to obtain hypothyroid phenotype. For the control and the hypothyroid groups, urine was collected at D 11 and D 14 , and blood was collected at D 14 . To study the response of hypothyroid group to TH, wild-type mice underwent 14 days of PTU

Figure 4 . 2 . 2 :

 422 Figure 4.2.2: Mean 600MHz 1 H NMR spectra from mice urine (A) and plasma (B), with metabolites annotations.

  Figure 4.3.1). In urine PCA, 4 samples are identified as outliers (Figure 4.3.1.A). Three of them are issue from the same mice. These samples were removed from the data set. No outliers were identified in plasma PCA.The two PCA show a clear separation between the different groups, which mean that they have a different chemical composition. It is so strong that we see it in this unsupervised statistical analysis.

Figure 4 . 3 . 1 : 2

 4312 Figure 4.3.1: Quality of the model and outliers identification in urine and plasma samples.(A) Principal component analysis (score plot) based on 1 H NMR spectra of QC samples and all urine samples from all times points. A= 12, N= 96, R 2 X= 0.851 (B) PCA score plot based on 1 H NMR spectra of QC samples and all plasma samples from all time points. A= 9, N= 33, R 2 X= 0.959. The QC samples clustering (in red) show the good stability and reproducibility of the experiments.

Figure 4 . 3 . 2 :

 432 Figure 4.3.2: Discrimination between control, hypothyroid and TH treated groups at D 11 and D 14 in urine. At D 11 , hypothyroid and TH treated groups had received the same PTU treatment. (A) PCA score plot of urine samples at D 11 shows a clear separation between control group and hypothyroid-TH treated group. A= 4, N= 27, R 2 X= 0.68 (B) PCA score plot of urine samples at D 14 shows a clear separation between control, hypothyroid and TH treated groups. A= 2, N= 28, R 2 X= 0.68.

Figure 4 . 3 . 3 : 63 The

 43363 Figure 4.3.3: Discrimination between hypothyroid phenotype and control groups in urine. (A) PCA score plot. A= 2, N= 18, R 2 X= 0.65. (B) OPLS-DA score plot. A= 1+1, N= 18, R 2 Y= 0.98, Q 2 = 0.96, p-value=4.76.10 -9 . (C) OPLS-DA model validation by resampling 1000 times under the null hypothesis. (D) OPLS-DA multivariate metabolic signature discriminating control group from hypothyroid phenotype group.

Figure 4 . 3 . 4 :

 434 Figure 4.3.4: Discrimination between hypothyroid phenotype and control groups in plasma. (A) PCA score plot. A= 5, N= 19, R 2 X= 0.9. (B) OPLS-DA score plot. A= 1+7, N= 19, R 2 Y= 0.99, Q 2 = 0.56, p-value= 0.0015. (C) OPLS-DA model validation by resampling 1000 times under the null hypothesis. (D) OPLS-DA multivariate metabolic signature discriminating control group from hypothyroid phenotype group.

Figure 4 . 3 . 5 A

 435 and B). The OPLS-DA model shows a strong discrimination of the 4 classes, reflected by the explained variance (R 2 Y= 0.8), the prediction power (Q 2 = 0.55), CV-ANOVA p-value (p-value= 1.5.10 - 5 ). In addition, the robustness of the OPLS-DA model was validated using permutations (1000) under the null hypothesis (Figure 4.3.5.C).We noticed an important evolution of samples metabolome from D 11 to D 12 , and from D 12 to D 13 , compared to D 13 to D 14 , which reflect the rate of reaction of the metabolism in response to TH over time(Figure 4.3.5 B). This suggests that changes related to TH administration are important in the beginning, and then the metabolome stagnates gradually.

Figure 4 . 3 . 5 : 4 . 3 . 5 66•

 435435 Figure 4.3.5: Metabolomic urine profiles showing the evolution of hyperthyroid phenotype group

Figure 4 . 3 . 5 :•

 435 Figure 4.3.5: Discrimination between control and TH treated groups in urine. (A) PCA score plot. A= 2, N= 19, R 2 X= 0.72. (B) OPLS-DA score plot. A= 1+2, N= 19, R 2 Y= 0.995, Q 2 = 0.97, p-value= 1.9.10 -8 . (C) OPLS-DA model validation by resampling 1000 times under the null hypothesis. (D) OPLS-DA multivariate metabolic signature discriminating control group from TH treated group. The signature is colored according to the correlation between NMR variables and case-control status after significance to univariate ANOVA testing followed by Benjamini-Hochberg multiple corrections. NMR variables that do not achieve the FDR threshold of 0.05 are represented in grey. DMA (dimethylamine), DMG (dimethylglycine), OG (oxoglutarate), TMA (trimethylamine).

Figure 4 . 3 . 6 :

 436 Figure 4.3.6: Discrimination between TH treated and control groups in plasma. (A) PCA score plot. A= 9, N= 19, R 2 X= 0.97. (B) OPLS-DA score plot. A= 1+1, N= 19, R 2 Y= 0.88, Q 2 = 0.8, p-value= 8.7.10 -5 . (C) OPLS-DA model validation by resampling 1000 times under the null hypothesis. (D) OPLS-DA multivariate metabolic signature discriminating control group from TH treated group.

  which is probably related to a modification in the intestine. A relation between these metabolites and the TH status was already reported in a mass spectrometrybased metabolomics study 152 .

the

  French Ministry of Research (licence #6711), and carried out in accordance with the European Community Council Directives of September 22, 2010 (2010/63/EU) regarding the protection of animals. Mice with Thra were all with a major contribution of the C57Bl/6 genetic background. We studied 5 mutations of Thra, which introduce a translation frameshift, eliminating the C-terminal helix, which normally interacts with transcription coactivators. E395fs401X and E395fs406X mutations are +1 frameshifts, introducing stop codons few nucleotides downstream to the mutated codon, and shortening the protein (Thra S1/+ and Thra S2/+ mice; S for small) as in some severe cases of RTHα found in patients.This results in a complete loss of transactivation capacity and a marked dominant-negative activity in heterozygous mice, leading to altered development. The two other mutations, E395fs485X and K389fs479X are +2 frameshifts, which have no known equivalent in human patients, and result in the production of an amphigoric protein of high molecular weight (Thra L1/+ and Thra L2/+ mice; L for large) which has little dominant-negative activity.Regarding N359Y mutation, there is no frameshift in this model. Therefore, apart from the N359Y amino acid substitution, the C-terminal part of TRα1, including helix 12, is intact.The N359Y amino acid substitution reduces the affinity of TRα1 for T3, but does not alter the interface required for coactivator or corepressor recruitment (Figure5.2.1).

Figure 5 . 2 . 1 :

 521 Figure 5.2.1: Mice models

Figure 5 . 2 . 2 :

 522 Figure 5.2.2: Study design.

Figure 5 . 3 . 1 :

 531 Figure 5.3.1: PCA based on 1 H NMR spectra of QC and samples (urine or plasma). The good stability of the NMR setup and reproducibility of the experiment is attested by the grouped set of QC samples on the PCA score plots. (A) PCA based on urine samples of 3 months old mice (A= 9, N= 95, R 2 = 0,792). (B) PCA based on urine samples of 6 months old mice (A= 13, N= 101, R 2 = 0,811). (C) PCA based on plasma samples of 6 months old mice (A= 29, N= 102, R 2 = 0,985).

Figure 5 . 3 . 2 :

 532 Figure 5.3.2: Discrimination between wild-type and mutants groups. (A) PCA score plot for 3 months old mice urine (A= 14, N= 91, R 2 = 0.86). (B) PCA score plot for 6 months old mice urine (A= 15, N= 88, R 2 = 0.84). (C) PCA score plot for 6 months old mice plasma (A= 18, N= 95, R 2 = 0.97).

Figure 5 . 3 . 3 :

 533 Figure 5.3.3: Discrimination between Thra S1/+ , Thra S2/+ , Thra L1/+ and Thra L2/+ . (A) OPLS-DA score plot for 3 months old mice urine, discriminating Thra S1/+ , Thra S2/+ , Thra L1/+ and Thra L2/+ (N = 40, 3+2 components, R 2 Y = 0.661, Q 2 = 0.355, ANOVA of the cross-validated residuals (CV-ANOVA) p-value = 0.005). (B) The OPLS-DA model validation by resampling 1000 times under the null hypothesis.

Figure 5 . 3 . 4 :

 534 Figure 5.3.4: Discrimination between the two type of frameshift mutations: Thra S/+ and Thra L/+ . (A) OPLS-DA model for 3 months old mice urine (N = 41, 1+3 components, R 2 Y = 0.908, Q 2 = 0.669, CV-ANOVA p-value = 6.6.10 -6 ). (B) Corresponding OPLS-DA model validation by resampling 1000 times under the null hypothesis. (C) OPLS-DA model for 6 months old mice urine (N = 43, 1+4 components, R 2 Y = 0.924, Q 2 = 0.57, CV-ANOVA p-value = 8.10 -5 ). (D) Corresponding OPLS-DA model validation by resampling 1000 times under the null hypothesis. (E) OPLS-DA model for 6 months old mice blood plasma (N = 44, 1+5 components, R 2 Y = 0.829, Q 2 = 0.458, CV-ANOVA pvalue = 0.04). (F) Corresponding OPLS-DA model validation by resampling 1000 times under the null hypothesis.

Figure 5 . 3 . 5 :

 535 Figure 5.3.5: Significant discrimination between human-like mutations Thra S/+ and wild-type groups. (A) OPLS-DA model for 3 months old mice urine, discriminating Thra S/+ and wild-type (N = 62, 1+3 components, R 2 Y = 0.744, Q 2 = 0.453, CV-ANOVA p-value = 4.5.10 -5 ). (B) The OPLS-DA model validation by resampling 1000 times under the null hypothesis. (C) OPLS-DA model for 6 months old

  discriminating Thra S/+ and wild-type (N = 62, 1+1 components, R 2 Y = 0.457, Q 2 = 0.217, CV-ANOVA p-value = 7.10 -3 ). (D) Corresponding OPLS-DA model validation by resampling 1000 times under the null hypothesis. (E) OPLS-DA model for 6 months old mice plasma, discriminating Thra S/+ and wild-type (N = 66, 1+4 components, R 2 Y = 0.768, Q 2 = 0.591, CV-ANOVA p-value = 9.24.10 -8 ). (F) Corresponding OPLS-DA model validation by resampling 1000 times under the null hypothesis.

  taurine and oxoglutarate with respect to wild-type animals (Figure5.3.6 A-B). Meanwhile, plasma metabolic signature was characterized by higher levels of formate, phenylalanine, valine, isoleucine, allantoin and lactate, and lower levels of Krebs cycle metabolites (acetate, succinate, ketobutyrate), lipids, lipoproteins (VLDL, LDL), phosphocholine, glucose, creatine, threonine, glutamine and pyruvate when compared to wild-type mice (Figure5.3.6 C). The metabolic signatures of Thra S/+ obtained from urine and plasma metabolic profiles share similarities in the composition and variations of 4 metabolites: formate, creatinine, acetate and citrate.

Figure 5 . 3 . 6 :

 536 Figure 5.3.6: Determination of a metabolic signature of Thra S/+ mutation in urine and blood plasma.

83 (

 83 A) OPLS-DA loading plot for 3 months old mice urine, discriminating Thra S/+ and wild-type, after significance to ANOVA tests followed by Benjamini-Hochberg multiple correction. The coloured spectral regions correspond to the statistically significant signals. Non-significant NMR variables are colored in grey. (B) OPLS-DA loading plot for 6 months old mice urine, discriminating Thra S/+ and wild-type. (C) OPLS-DA loading plot for 6 months old mice plasma, discriminating Thra S/+ and wildtype.

Figure 5 . 3 . 7 :

 537 Figure 5.3.7: Summary of key metabolites associated with Thra S/+ and hypothyroid phenotypes.

Figure 6 . 2 . 1 :

 621 Figure 6.2.1: Representation of the protocol. Wild-type mice and LTRβ-KO mice underwent ten days of PTU treatment prior to four days of PTU+TH or PTU only in order to induce hyperthyroid phenotype (PTU-TH) and hypothyroid treatment (PTU), respectively.

  of intact tissues, a direct method associated with minimal sample processing, as well as solution NMR investigation of aqueous liver extracts. HR-MAS NMR analysis enabled us to detect a range of lipophilic and hydrophilic low molecular weight metabolites, while the solution NMR data provided a high-resolution, though more indirect, picture of water-soluble metabolite extracted from liver tissues, allowing us to detect up to 40 hydrophilic metabolites.The extraction process has removed proteins and lipids. Representative metabolic profiles obtained using the two techniques are illustrated in figure6.3.1.

Figure 6 . 3 . 1 : 91 (

 63191 Figure 6.3.1: Metabolites assignment

Figure 6 . 3 . 2 :

 632 Figure 6.3.2: Discrimination between PTU and PTU+TH groups in aqueous liver extracts and intact liver. (A) 1 H HR-MAS spectra of liver tissue from wild-type mice. O-PLS-DA model discriminating PTU treatment from PTU-TH treatment with (N = 41, R 2 Y = 0.76, Q 2 = 0.58). (B) The O-OPLS loading plot after SRV analysis and Benjamini-Hochberg multiple testing correction. The coloured spectral regions correspond to the statistically significant signals. (C) OPLS-DA model of the liver extracts of wild-type mice, discriminating PTU treatment from PTU-TH treatment with (N = 32, 1+4 components, R 2 Y = 0.89, Q 2 = 0.61, CV-ANOVA p-value = 0.01). (D) The corresponding OPLS-DA loading plot. Acetate (Ace), acetoacetate (Aca), alanine (Ala), anserine (Ans), choline (Cho), deoxyuridine (Deo), glutamate (Glu), glutamine (Gln), glycine (Gly), guanidoacetate (Gua), inosine (Ino), isocitrate (Iso), isoleucine (Ile), lactate (Lac), leucine (Leu), maltose (Mal), mannose (Man), methionine (Met), niacinamide (Nia), succinate (Suc), taurine (Tau), tyrosine (Tyr), UDP-glucose (UDP-Glc), valine (Val), α-glucose (α-Glc), β-glucose (β-Glc), N-acetylglucosamine (N-aga), 3hydroxybutyrate (3-hb).

Figure 6 .

 6 3.3.C presents the O-PLS analysis of the liver extract. The explained variance and the predicted variance of the model are enhanced to 0.77 and 0.65, respectively. The metabolic signature also shows a relative increase of choline and AMP and a relative decrease of phosphocholine for mice that had undergone TH treatment compared to PTU treatment (Figure 6.3.3.D).

Figure 4 . 4 . 3 :

 443 Figure 4.4.3: The indirect effect of thyroid hormones on the liver metabolism. (A) 1 H HR-MAS spectra of liver tissue from LTRβ-KO mice. O-PLS-DA model discriminating PTU treatment from PTU-TH treatment with (N = 42, R 2 Y = 0.74, Q 2 = 0.5). (B) The corresponding OPLS-DA loading plot. (C) OPLS-DA model of the liver extracts of LTRβ-KO mice, discriminating PTU treatment from PTU-TH treatment with (N = 33, 1+1 components, R 2 Y = 0.773, Q 2 = 0.651, CV-ANOVA p-value = 4.07.10 -6 ). (D) The corresponding OPLS-DA loading plot. All these models are validated by resampling 1000 times. Acetate (Ace), acetoacetate (Aca), alanine (Ala), anserine (Ans), choline (Cho), deoxyuridine (Deo), glutamate (Glu), glutamine (Gln), glycine (Gly), guanidoacetate (Gua), inosine (Ino), isocitrate (Iso), isoleucine (Ile), lactate (Lac), leucine (Leu), maltose (Mal),

3.2 Biological samples preparation

	3.2.1 Collection and storage
	NMR liquid samples preparation is straightforward and the protocols 46 are
	standardized and highly reproducible. This step can be automated by using a robotic liquid
	handling technology. It is very important to collect the biofluids with the same manner, and to
	handle properly the samples. Because, in some cases, errors related to samples handling (time
	of collection, contaminations) can introduce variations in signal intensity that are not from
	metabolism.

  set to 1.5s with relaxation delay of 2s. The noesy mixing time was set to 10ms. All FIDs were multiplied by an exponential function corresponding to a 0.3Hz line-broadening factor, prior Fourier transformation. set to 3.5 kHz. 1D 1 H NOESY NMR experiments with water presaturation were performed on each sample to derive metabolic profiles. 256 free induction decays (FIDs) were co-added, with a 12 ppm spectral width and an acquisition time of 1.36 s, corresponding to 22856 data points, with a relaxation delay of 2 s, for a total experimental time of 15 minutes per spectrum. The NOESY mixing time was set to 100 ms. The 1 H 90° hard pulse length was calibrated at 6.5µs.

	• Plasma
	All NMR experiments were performed on a Bruker Avance III spectrometer operating
	at 600.55 MHz (proton resonance frequency), with a 5mm standard TCI cryoprobe, and an
	automated sample changer with sample cooling (4°C). The temperature was controlled at
	310K for plasma samples. Standard one-dimensional 1 H NMR spectra were acquired with the
	'noesygppr1d' and 'cpmgpr1d.be' pulse sequences for each sample. A total of 128 transient
	3.3.1 Data acquisition	2D NMR
	experiments, including 1 H-1 H TOCSY and 1 H-13 C HSQC experiments, were carried out on a
	subset of selected samples to characterize structural connectivity between nuclei and refine
	• Urine metabolite identification. A simple 1D Bloch decay 1 H NMR experiment was performed on
	All NMR experiments were performed on a Bruker Avance III spectrometer operating each lipid extract sample.
	at 600.55 MHz (proton resonance frequency), with a 5mm standard TCI cryoprobe, and an
	automated sample changer with sample cooling (4°C). The temperature was controlled at

300K for urine. Standard one-dimensional

1

H NMR spectra were acquired with the 'noesygppr1d' pulse sequence for each sample. A total of 128 transient free induction decays (FID) were collected for each experiment with a spectral width of 20 ppm. The acquisition time was free induction decays (FID) were collected for each experiment with a spectral width of 20 ppm. The acquisition time was set to 1.5s with relaxation delay of 2 s. The noesy mixing time was set to 10ms. All FIDs were multiplied by an exponential function corresponding to a 0.3Hz line-broadening factor, prior Fourier transformation.

• Liver extracts

Experiments were carried out on a Bruker Avance III spectrometer, operating at 600.55 MHz ( 1 H resonance frequency), equipped with a TCI cryoprobe and an automated sample changer with cooling capacity. The experiment temperature was controlled at 300°K. 1D 1 H NOESY NMR experiments with water presaturation and gradients were performed on each aqueous extract sample to derive metabolic profiles. 256 FIDs were co-added, with a 20 ppm spectral width and an acquisition time of 1.36 s, corresponding to 32 k data points, with a relaxation delay of 2 s, for a total experimental time of 15 minutes per spectrum. The NOESY mixing time was set to 10 ms and the delay for gradient recovery was set to 200 µs. The 1 H 90° hard pulse length was automatically calibrated at around 13 µs for each sample.

• Intact liver tissue

Experiments were carried out on a Bruker Avance II spectrometer, operating at 700.09 MHz ( 1 H resonance frequency), equipped with a 4 mm HR-MAS double resonance ( 1 H-13 C) probe. Temperature was controlled at 10°C throughout the experiments and the magic-angle spinning frequency was

  .2.1). Urine was collected at different time points: at D 11 and D 14 for control and hypothyroid groups, and from D 11 to D 14 for TH treated group. Mice from hypothyroid group and TH treated group have the same status at D 11 . D 12 , D 13 and D 14

Table 5 .1: Discrimination between each mutant group and wild-type group in 3-month-old mice urine.

 5

	A		Wild-type		Mutants	Wild-type BC Mutants	Wild-type		Mutants
		4	Urine				Urine					Plasma
												0,4
							2
		2
												0,2
	PC2	0				PC2	0				PC2	0
		-2					-2					-0,2
		-4
						3 months old	-4				6 months old	0,4				6 months old
		-8	-4	0	4	8	-4	-2	0	2	4	-0,4	-0,2	0	0,2	0,4
				PC1					PC1					PC1

Table 5 .2: Discrimination between each mutant group and wild-type group in 6-month-old mice urine.

 5

Table 3 .4: Discrimination between each mutant group and wild-type group in 6-month-old mice plasma.

 3

	Statistical models	A	N	R 2	Q 2	p-value
	Thra S1/+ vs. WT	1+6	55	0.832	0.422	0.034
	Thra S2/+ vs. WT	1+4	54	0.745	0.44	0.002
	Thra L1/+ vs. WT	0	53	-	-	-
	Thra L2/+ vs. WT	0	54	-	-	-
	N359Y vs. WT	0	51	-	-	-

Table 6 .

 6

1: Summary of metabolite variation in the different condition. 1 H

liquid NMR of liver extracts 1 H HR-MAS of intact liver tissue

	Metabolites	T3 response wild-	T3 response	T3 response wild-	T3 response
		type	LTRβ-KO	type	LTRβ-KO
	Niacinamide

Metabolites identified from 1D and 2D NMR profiles of mice blood plasma.

	Appendix 3 Appendix 4	Citrate Phenylalanine	• Mild scoliosis 2,51 3,10 7,87	This amino acid change is 48,39 d 39,62 q s
		Isoleucine Creatine Lactate	• Severe defects in 2,67 3,25 0,92 growth, mental, and 3,03 7,34 1 motor development 3,92 7,35 1,32	almost certainly pathogenic Mice model were 48,30 d 38,45 dd t 39,93 s 132,04 d d 56,83 s 130,33 m 22,7 d
	N359Y C380fs387X A263S ID 1 1 2 3 2 4 5 3 6	investigated TR1 and the non-receptor isoform TRα2 affected This case differs from previous reported cases of RTHα. This phenotype is not only the result of TRα mutation. Other mutations were found in her genome. Her parents are not One of his two older 33,16 s 131,81 m 13 C [ppm] • Congenital macrocytic anemia • Severe bone malformation • Growth retardation • Dwarfism • Clavicular agenesis • Abnormalities of the fingers and toes • Elbow joints • Chronic motor diarrhea • Hypercalcemia • Lower TSH • Higher FT3 levels Female child First evaluation at the age of A 27-year-old female 7 patients from the same family First evaluation at the age of 13 months • Because of frequent infections. • Low FT4 • Normal FT3 and TSH • Delay in tooth eruption • Constipation Second examination at the age Creatinine 3,03 7,42 Metabolite 1 H [ppm] 4,1 71 q Multiplicit sisters is affected (mild growth retardation) Two of his three cousins are affected Constipation is the common feature between 1-Methylhistidine 3,03 30,85 3,18 31,01 m 3,69 36,24 s 3,94 56,58 dd 7,00 122,70 s 7,67 144,85 4,04 59,26 s Dimethylamine 2,71 37,59 s Dimethylglycine 2,91 46,51 s 3,71 62,81 s Formate 8,45 151,19 s Fumarate 6,51 138,00 s Phosphocholine 3,21 56,94 y Leucine 0,94 d s 3,60 68,43 3-Hydroxybutyrate 1,19 24,3 d 0,96 d m 4,14 60,05 2,29 q 1,71 m m Succinate 2,39 37,05 2,4 q Lipids (mainly LDL) 0,84 16,4 m s Taurine 3,25 50,57 4,15 m Lipids (mainly VLDL) 0,86 23,4 m t 3,41 38,38 Acetate 1,91 26,1 1,25 34,3 m s t Acetoacetate 2,27 32,2 s 1,55 27,35 m these family members. 1-methylicotinamide 4,47 51,48 s Glutamine 2,12 m Trigonelline 8,89 146,93 3,43 56,2 s Lipids 0,93 27,3 m of 2 years and 7 months • Mild growth retardation • Mild clinical features • Mildly increased head Low/borderline low 8,18 131,27 m 2,46 m 9,11 s Alanine 1,47 18,89 d 1,26 25 m hemoglobin level in six of 8,96 150,37 m 3,76 t Trimethylamine 2,85 45,66 s 3,78 53,1 q 1,7 29 m the seven affected subjects but in none of the 9,27 148,13 m Glycine 3,55 44,57 s Tryptophan 3,48 31,73 dd Allantoin 5,39 66,27 s 2,22 36,3 m circumference unaffected relatives 2-oxoglutarate 2,44 33,67 t Glycolate 3,95 63,27 s 4,04 57,14 dd Betaine 3,25 56,29 s 2,7 27,8 m • TSH levels were normal • Serum free T3 ranged 3,00 38,54 t Guanidoacetate 3,79 47,58 s 7,49 115,04 d 3,89 s Methionine 2,12 s	110 107
	4 7 8 5 9 10 11 12 6 13 7 8 9 13 11 12 15 10 14	16 months: • Can not control her head until the age of 8 months (normal < 2 months) • Not able to sit at 16 months (normal < 8 months) • No teeth eruption (normal at 13months) • Constipation • Horse sounding cry • Coarse facies • Macroglossia from high-normal to 7,19 3,96 7,19 3,19 O-Phosphocholine 3-Indoxyl sulfate Hippurate Choline 3,19 high and serum free T4 7,27 7,54 7,26 4,4 Phenylalanine 3,12 and rT3 from normal to 7,35 7,63 Tyrosine 3,05 Citrate 2,53 7,32 low Male child At birth • Macroglossia First evaluated at the age of 8 months 7,54 7,69 3-hydroxybutyrate 1,18 2,30 2,39 4,14 7,82 8,55 Isovalerylglycine 0,92 2,03 2,17 3,75 3,19 2,68 7,37 3,94 Creatine 3,03 7,42 6,88 3,92 Pyruvate 2,37 7,18 Creatinine 3,04 Succinate 2,39 Urea 5,8 4,05 Threonine 1,325 Valine 0,98 Formate 8,45 3,61 Fumarate 6,51 4,24 • High FT3, low FT4, and 3-hydroxyisovalerate 1,26 7,96 1,03 β-glucose 3,24 Trimethylamine 2,88 a normocytic normochromic anemia 2,35 Lactate 1,32 2,26 3,4 Tyrosine 6,89 • Umbilical hernia were detected Acetate 1,91 4,10 3,6 3,46 7,19 • Normocytic anemia • Normal TSH • Low FT4 • High T3 • High head Acetoacetate 2,28 Leucine 0,92 3,49 Urea 5,73 circumference 3,46 0,94 3,9 Valine 0,98 • Bone age corresponded to 6 months Alanine 1,48 1,66 4,64 1,035 • Normal TSH response At the age of 2 years: • Treatment with thyroxine • Sitting occurred at 26 months • Resolution of umbilical hernia at 3 years moderately impaired • Serum free T3 ranged from high-normal to high and serum free T4 and rT3 from normal to low 35-year-old mother In adulthood Choline 3,19 2,06 2,43 Betaine 3,25 3,89 Butyrate 0,90 1,55 2,14 Methylamine 2,59 Methylguanidine 2,82 N-acetylcholine 2,14 3,21 N-acetylglucosamine 2,05 Glutamine 2,11 5,23 3,85 3,82 3,76 3,72 severely impaired 6,05 4,29 3,71 • Motor development Allantoin 5,38 2,66 3,54 to TRH • Cognitive ability 3,78 Malate 2,36 α-glucose 3,4 3,6	affected 46,67 122,56 42 125,04 131,87 125,31 56,5 119,26 135,22 38,01 48,7 114,82 120,44 24,60 68,54 130,02 23,51 28,76 47,72 46,27 48,71 59,65 118,79 133,70 19,5 138 30,83 20,76 76,78 52,28 22,80 31,9 72,2 26,19 71,64 63,34 78,37 The child and his mother m d s m m m m q s m dd d m d d d dd dd m dd s d m d d d m dd s m q s s d s s d s d s d s d m s s s d dd s s d m dd m s q d m m 32,20 s 23,51 d 78,4 t are affected 55,91 s 23,99 d 63,3 dd 19,2 d 19,05 d 27,24 98,5 d 20,5 d m affected s 33,5 m s Her parents were not 56,17 s s t m t 30,17 25,05 29,5 m s 94,5 d s 63,1 m s 74 ddd s m s 63,4 dd s dd 75,3 t 66,28 s dd 74 dd 53,61 q dd 72,2 q 63,2 d
	14 16 R384H 17	Cis-aconitate N-acetylglycine Glycine Histidine	At the age of 12, 7 years • Severely handicapped • Not able to walk • Constipation • Slow speech • Borderline intellectual functioning 3,51 8,08 2,46 4,06 8,18 3,77 3,10 2,03 3,54 • Impaired executive 5,68 3,77 7,05	70,57 A mice model were d m 58,89 m d 57 t developed (Results: impairment of 46,36 s 24,95 44,5 s postnatal development 126,82 d 46,07 s
					118 119 121 122

Metabolites identified from 1D and 2D NMR profiles of mice liver aqueous extracts.

	35	Valine	8,1 1,71 0,98	137,4 29 19,4	s m d
		Choline	3,19 1,89 1,03	56,7 32,6 20,75	s m d
			3,5 3,02 2,26	70,15 42 31,9	dd t m
			4 3,74 3,6	58,5 57,45 63,35	ddd t d
	36	Creatine Methionine N-Methylhistidine	3,02 2,16 7,92	56,43 32,7 141,14	s m s
	ID	Metabolite	1 H [ppm] 3,92 2,63 7,05	13 C [ppm] 39,5 31,58 127,2	Multiplicity s t s
	1	3-Hydroxybutyrate Glucose	1,2 3,23 3,85 3,93	24,45 77 56,8 56,2	d dd dd dd
		N-Acetylcysteine	2,314 3,39 3,7	49,23 72,34 34,5	m m s
		N-Acetylglutamate	2,414 3,45 4,1 3,24	49,23 78,57 58,14 27,6	m m m m
			4,16 3,53 2,22	65,5 74,2 36,8	m dd t
	2	Acetate	1,91 3,72 2,05	25,97 75,63	s m m
	3	Acetoacetate	2,27 3,82 2,02	32,26 74,14 24,75	s m s
			3,43 3,89 1,87	56 63,47 31,2	s dd m
	4	ADP NAD+	4,15 4,63	83 98,7	m d
		Niacinamide	4,16 5,22 7,58	65,4 94,93 126,8	m d dd
		Glutamate	4,57 2,04 8,24	73,7 29,8 139,3	m m dd
			5,94 2,12 8,7	87 154,5	m m dd
			8,29 2,34 8,92	148,38 36,35 150,4	s m s
		O-Acetylcarnitine	8,54 3,75 2,13	140,4 57,64 23,2	s dd s
	5	L-Alanine Glutamine	1,48 2,12 2,48	19 29,28 42,8	d m dd
			3,76 2,44 2,61	53,5 33,9 42,8	q m dd
	6	AMP	4,01 3,76 3,18	66,2 57,23 56,4	dd t s
		Glutathione	4,36 4,2 3,61	87,14 58	dd q d
			4,5 3,78 3,82	73,3 57/46 70,7	dd m dd
			6,12 2,97 5,57	28,32 69,5	d dd q
		O-Phosphocholine	8,23 2,54 3,21	155 34	s m s
		O-Phosphoethanolamine	8,58 2,15 3,24	142,6 29 43,35	s m td
	7	Aspartate Histidine	2,66 3,16 4,01	39 30 63	dd dd t
		Phenylalanine	2,8 3,23 3,19	39,5 30	dd dd m
			3,89 3,98 3,98	55 59	dd dd dd
	8	ATP	4,21 7,09 7,32	67,7 132,12	m d d
			4,28 7,9 7,36	67,78 130,42	m d m
		Isoleucine	4,39 0,93 7,42	86,6 13,9 132,8	m t m
		Sarcosine	4,5 0,99 2,73	73 17,36 35,6	m d s
			4,619 1,24 3,6	76,9 27 53,5	t m s
		Succinate	6,14 1,45 2,39	89,27 27 36,8	d m s
		Taurine	8,239 1,98 3,25	155,36 38,68 50,37	s m t
			8,528 3,66 3,42	142,5 62,5 38,3	s d t
	9	Carnosine Lactate Trimethylamine N-oxide	2,68 1,32 3,25	34,8 22,9 62,2	dt d s
		Tyrosine Leucine Lysine	3,03 3,19 4,1 3,02 3,24 0,95 3,17 4,48 1,7 3,92 7,09 3,72 6,87 1,46 7,17	30,8 30,8 71,3 38,27 38,57 24 38,27 57,57 42.5/26.7 59 119,87 56,2 118,9 24 133,5	dd q dd dd t dd dt m dd m m m s m m
					123 124 125

H Chemical Shift (ppm)1 H Chemical Shift (ppm)

Acknowledgements

Appendix 2: Metabolites identified from 1D and 2D NMR profiles of mice urine Appendix 3: Metabolites identified from 1D and 2D NMR profiles of mice plasma Appendix 4: Metabolites identified from 1D and 2D NMR profiles of mice liver aqueous extracts

Metabolite identification

In addition, 1D 1 H-NMR and 2D NMR experiments ( 1 H- [START_REF] Oresic | Metabolomics, a novel tool for studies of nutrition, metabolism and lipid dysfunction[END_REF] C HSQC, 1 H-1 H TOCSY and 1 H J-resolved experiments) were recorded on a subset of samples to achieve structural assignment of the metabolite signals. Metabolite identification was performed using academic spectral databases (HMDB 145 and MMMDB 146,147 ), as well as proprietary databases (NMR Suite v. 7.1, Chenomx Inc., Edmonton, Canada; AMIX SpectraBase v. 1.1.2, Bruker GmbH). mannose (Man), methionine (Met), niacinamide (Nia), succinate (Suc), taurine (Tau), tyrosine (Tyr), UDP-glucose (UDP-Glc), valine (Val), α-glucose (α-Glc), β-glucose (β-Glc), N-acetylglucosamine (Naga), 3-hydroxybutyrate (3-hb).

Summary

If we compare the composition of the TH signature obtained from aqueous liver extracts in both wild type and LTRβ-KO mice, we notice that there are 20 common metabolites that vary in the same way (Table 6.1). This suggests that the activity of TH is maintained despite the absence of TRβ. So, there is an indirect effect of TH on liver metabolism.

We notice a relative increase of the lipid content within hyperthyroid mice compared to hypothyroid mice. Besides the increase of energy expenditure associated with hyperthyroidism, liver of LTRβ-KO mice is no longer able to provide fuel through lipolysis or cholesterol release. This result strengthens the idea that in normal condition, the effect of lipolysis is stronger than the effect of lipogenesis. We also notice a relative decrease of glucose content within hyperthyroid mice. The action of TH on glucose metabolism in the liver is complex and includes stimulation by a direct action of TH on the liver but also probably by an indirect effect of TH through the sympathetic nervous system.

Appendices